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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0f3658ef326902c5347a0f758c47e22adbd01c01
| 128
|
py
|
Python
|
src/pytree/binarytree/node/__init__.py
|
RESPULTE/PyTree
|
fea511d1414f536a1a523b8b01645ae763083027
|
[
"MIT"
] | null | null | null |
src/pytree/binarytree/node/__init__.py
|
RESPULTE/PyTree
|
fea511d1414f536a1a523b8b01645ae763083027
|
[
"MIT"
] | null | null | null |
src/pytree/binarytree/node/__init__.py
|
RESPULTE/PyTree
|
fea511d1414f536a1a523b8b01645ae763083027
|
[
"MIT"
] | null | null | null |
from .rbt_node import RBT_Node
from .splay_node import Splay_Node
from .avl_node import AVL_Node
from .bst_node import BST_Node
| 25.6
| 34
| 0.84375
| 24
| 128
| 4.166667
| 0.291667
| 0.4
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.125
| 128
| 4
| 35
| 32
| 0.892857
| 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
| 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
| 6
|
0f38c4bca4a03270776e43d257c76c0ff64f7d61
| 2,628
|
py
|
Python
|
test/Base/test_base.py
|
rustam-azimov/CFPQ_PyAlgo
|
1f40c300a2dfeded5297ca48d0ddde26cfa8887c
|
[
"Apache-2.0"
] | 11
|
2020-08-16T15:29:32.000Z
|
2022-01-26T12:45:39.000Z
|
test/Base/test_base.py
|
rustam-azimov/CFPQ_PyAlgo
|
1f40c300a2dfeded5297ca48d0ddde26cfa8887c
|
[
"Apache-2.0"
] | 4
|
2021-02-10T13:35:54.000Z
|
2021-06-04T07:14:32.000Z
|
test/Base/test_base.py
|
rustam-azimov/CFPQ_PyAlgo
|
1f40c300a2dfeded5297ca48d0ddde26cfa8887c
|
[
"Apache-2.0"
] | 3
|
2021-02-23T16:08:38.000Z
|
2021-12-10T12:47:06.000Z
|
import pytest
from cfpq_data import cfg_from_txt
from src.graph.graph import Graph
from src.problems.Base.Base import BaseProblem
from src.utils.useful_paths import LOCAL_CFPQ_DATA
from src.problems.utils import ResultAlgo
@pytest.mark.CI
def test_binary_tree(algo):
test_data_path = LOCAL_CFPQ_DATA.joinpath('binary_tree')
base_algo: BaseProblem = algo()
graph = Graph.from_txt(test_data_path.joinpath('Graphs/graph_1.txt'))
grammar = cfg_from_txt(test_data_path.joinpath('Grammars/g.cfg'))
base_algo.prepare(graph, grammar)
result: ResultAlgo = base_algo.solve()
assert result.matrix_S.nvals == 20
@pytest.mark.CI
def test_cycle(algo):
test_data_path = LOCAL_CFPQ_DATA.joinpath('cycle')
base_algo: BaseProblem = algo()
graph = Graph.from_txt(test_data_path.joinpath('Graphs/graph_1.txt'))
grammar = cfg_from_txt(test_data_path.joinpath('Grammars/g.cfg'))
base_algo.prepare(graph, grammar)
result: ResultAlgo = base_algo.solve()
assert result.matrix_S.nvals == 9
@pytest.mark.CI
def test_line(algo):
test_data_path = LOCAL_CFPQ_DATA.joinpath('line')
base_algo: BaseProblem = algo()
graph = Graph.from_txt(test_data_path.joinpath('Graphs/graph_1.txt'))
grammar = cfg_from_txt(test_data_path.joinpath('Grammars/g.cfg'))
base_algo.prepare(graph, grammar)
result: ResultAlgo = base_algo.solve()
assert result.matrix_S.nvals == 2
@pytest.mark.CI
def test_loop(algo):
test_data_path = LOCAL_CFPQ_DATA.joinpath('loop')
base_algo: BaseProblem = algo()
graph = Graph.from_txt(test_data_path.joinpath('Graphs/graph_1.txt'))
grammar = cfg_from_txt(test_data_path.joinpath('Grammars/g.cfg'))
base_algo.prepare(graph, grammar)
result: ResultAlgo = base_algo.solve()
assert result.matrix_S.nvals == 1
@pytest.mark.CI
def test_two_cycles(algo):
test_data_path = LOCAL_CFPQ_DATA.joinpath('two_cycles')
base_algo: BaseProblem = algo()
graph = Graph.from_txt(test_data_path.joinpath('Graphs/graph_1.txt'))
grammar = cfg_from_txt(test_data_path.joinpath('Grammars/g.cfg'))
base_algo.prepare(graph, grammar)
result: ResultAlgo = base_algo.solve()
assert result.matrix_S.nvals == 6
@pytest.mark.CI
def test_two_nonterm(algo):
test_data_path = LOCAL_CFPQ_DATA.joinpath('two_nonterm')
base_algo: BaseProblem = algo()
graph = Graph.from_txt(test_data_path.joinpath('Graphs/graph_1.txt'))
grammar = cfg_from_txt(test_data_path.joinpath('Grammars/g.cfg'))
base_algo.prepare(graph, grammar)
result: ResultAlgo = base_algo.solve()
assert result.matrix_S.nvals == 156
| 32.444444
| 73
| 0.743151
| 391
| 2,628
| 4.703325
| 0.12532
| 0.078303
| 0.117455
| 0.097879
| 0.854812
| 0.813486
| 0.78956
| 0.78956
| 0.709081
| 0.665579
| 0
| 0.006631
| 0.139269
| 2,628
| 80
| 74
| 32.85
| 0.806366
| 0
| 0
| 0.6
| 0
| 0
| 0.090183
| 0
| 0
| 0
| 0
| 0
| 0.1
| 1
| 0.1
| false
| 0
| 0.1
| 0
| 0.2
| 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
| 0
| 0
|
0
| 6
|
7e46a1228d30bac81a6359b793082174c76af16a
| 5,029
|
py
|
Python
|
optimization/utility/layers.py
|
langestefan/5LIL0_lab1_keyword
|
395d8ba1c44f35883b8127f2c37b1efceb15e50d
|
[
"MIT"
] | null | null | null |
optimization/utility/layers.py
|
langestefan/5LIL0_lab1_keyword
|
395d8ba1c44f35883b8127f2c37b1efceb15e50d
|
[
"MIT"
] | null | null | null |
optimization/utility/layers.py
|
langestefan/5LIL0_lab1_keyword
|
395d8ba1c44f35883b8127f2c37b1efceb15e50d
|
[
"MIT"
] | null | null | null |
import os
import sys
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from .quantizers import quantize, wraparound, masking
class QConv2d(nn.Conv2d):
"""
QConv2d simulates the reduced-precision forward pass of nn.Conv2d by quantizing
inputs, weights and outputs. Also the weights are extended with masks to allow pruning.
Biases are not pruned...
"""
def __init__(self, in_channels, out_channels, kernel_size,
stride=1, padding=0, dilation=1, groups=1, bias=True,
verbose=False, **kwargs):
# assign keyword attributes to self.__dict__
for attr,val in kwargs.items():
setattr(self, attr, kwargs.get(attr, val))
# set quantization parameters to default value (0)
for attr in ['bits_inputs', 'bits_weights', 'bits_accumulator', 'bits_outputs',
'frac_len_inputs', 'frac_len_weights', 'frac_len_outputs']:
setattr(self, attr, kwargs.get(attr, 0))
self.fp_groups = ['inputs', 'weights', 'accumulator', 'outputs']
self.quantized_list = [g for g in self.fp_groups if getattr(self, 'bits_' + g) > 0]
if verbose and self.quantized_list:
print("{}: {{{}}} are quantized!"\
.format(self.name, ", ".join(self.quantized_list)))
super().__init__(in_channels, out_channels, kernel_size,
stride, padding, dilation, groups, bias)
# weight mask. mask is needed to disable weights during inference and training
self.register_buffer('mask', torch.ones(out_channels))
def forward(self, x):
q_input = quantize(x, self.bits_inputs, self.frac_len_inputs)
q_weights = quantize(masking(self.weight, self.mask), self.bits_weights, self.frac_len_weights)
q_bias = quantize(self.bias, self.bits_accumulator, self.frac_len_inputs + self.frac_len_weights)
acc = F.conv2d(q_input, q_weights, q_bias, self.stride, self.padding, self.dilation, self.groups)
out = wraparound(acc, self.bits_accumulator, self.frac_len_inputs + self.frac_len_weights)
return quantize(out, self.bits_outputs, self.frac_len_outputs, inplace=True)
def __repr__(self):
s = "{layer_type} ({in_channels}, {out_channels}"
s += ", kernel_size={kernel_size}"
s += ", stride={stride}"
s += ", padding={padding}"
s += ", bits_{quantized_list}={quantized_bits})"
return s.format(layer_type=self.__class__.__name__,
quantized_bits=[int(getattr(self,'bits_'+g)) for g in self.fp_groups \
if getattr(self,'bits_'+g) > 0], **self.__dict__)
class QLinear(nn.Linear):
"""
QLinear simulates the reduced-precision forward pass of nn.Linear by quantizing
inputs, weights and outputs. Also the weights are extended with masks to allow pruning.
Biases are not pruned...
"""
def __init__(self, in_features, out_features, bias=True,
verbose=False, **kwargs):
# assign keyword attributes to self.__dict__
for attr,val in kwargs.items():
setattr(self, attr, kwargs.get(attr, val))
# set quantization parameters to default value (0)
for attr in ['bits_inputs', 'bits_weights', 'bits_accumulator', 'bits_outputs',
'frac_len_inputs', 'frac_len_weights', 'frac_len_outputs']:
setattr(self, attr, kwargs.get(attr, 0))
self.fp_groups = ['inputs', 'weights', 'accumulator', 'outputs']
self.quantized_list = [g for g in self.fp_groups if getattr(self, 'bits_' + g) > 0]
if verbose and self.quantized_list:
print("{}: {{{}}} are quantized!"\
.format(self.name, ", ".join(self.quantized_list)))
super().__init__(in_features, out_features, bias)
# weight mask. mask is needed to disable weights during inference and training
self.register_buffer('mask', torch.ones(out_features))
def forward(self, x):
q_input = quantize(x, self.bits_inputs, self.frac_len_inputs)
q_weights = quantize(masking(self.weight, self.mask), self.bits_weights, self.frac_len_weights)
q_bias = quantize(self.bias, self.bits_accumulator, self.frac_len_inputs + self.frac_len_weights)
acc = F.linear(q_input, q_weights, q_bias)
out = wraparound(acc, self.bits_accumulator, self.frac_len_inputs + self.frac_len_weights)
return quantize(out, self.bits_outputs, self.frac_len_outputs, inplace=True)
def __repr__(self):
s = "{layer_type} ({in_features}, {out_features}"
s += ", bits_{quantized_list}={quantized_bits})"
return s.format(layer_type=self.__class__.__name__,
quantized_bits=[int(getattr(self,'bits_'+g)) for g in self.fp_groups \
if getattr(self,'bits_'+g) > 0], **self.__dict__)
| 48.355769
| 105
| 0.635514
| 642
| 5,029
| 4.70405
| 0.179128
| 0.046358
| 0.050993
| 0.031788
| 0.868874
| 0.856291
| 0.843709
| 0.82053
| 0.792053
| 0.792053
| 0
| 0.0045
| 0.248757
| 5,029
| 103
| 106
| 48.825243
| 0.794865
| 0.145158
| 0
| 0.647059
| 0
| 0
| 0.137125
| 0.02431
| 0
| 0
| 0
| 0
| 0
| 1
| 0.088235
| false
| 0
| 0.102941
| 0
| 0.279412
| 0.029412
| 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
| 0
|
0
| 6
|
7e76e9d3dc6b59e077db6dcfbb944b1e10c21aa0
| 2,435
|
py
|
Python
|
electric_slide/scripts/algorithm.py
|
markreynoso/electric_slide
|
90c34ac4a6f16e9b7eb6fa2ad1112226dcf4769b
|
[
"MIT"
] | null | null | null |
electric_slide/scripts/algorithm.py
|
markreynoso/electric_slide
|
90c34ac4a6f16e9b7eb6fa2ad1112226dcf4769b
|
[
"MIT"
] | 14
|
2017-11-11T02:04:17.000Z
|
2020-01-06T18:51:47.000Z
|
electric_slide/scripts/algorithm.py
|
markreynoso/electric_slide
|
90c34ac4a6f16e9b7eb6fa2ad1112226dcf4769b
|
[
"MIT"
] | 1
|
2020-09-06T13:57:25.000Z
|
2020-09-06T13:57:25.000Z
|
"""Algorithms for the AI of solving a sliding puzzle."""
from electric_slide.scripts.priority_q import Node, PriorityQ
def manhattan_distance(board_state, size=3):
"""Sum up the manhattan distance of all numbers in the board."""
diff = 0
for y in range(size):
for x in range(size):
idx = board_state[y][x] - 1
if idx != 8:
diff += abs(idx % size - x) + abs(idx // size - y)
return diff
def a_star(starting_state, heuristic=manhattan_distance):
"""Find a solution path by exploring possible boards.
Based on heuristic value and path length.
"""
available = PriorityQ()
curr = Node(starting_state, None, None)
visited = [curr]
while heuristic(curr.state): # heuristic is non-zero when unsolved
for move in curr.legal_moves():
move_state = curr.board.practice_slide(move)
node = Node(move_state, move, curr)
value = heuristic(node.state) + len(node.path())
if available.priority(node) is None and node not in visited:
available.push(node, value)
elif node not in visited:
p = available.priority(node)
if p > value:
available.remove(node, p)
available.push(node, value)
curr = available.pop()
visited.append(curr)
return list(reversed(curr.path())) # heuristic is zero, i.e. solved
def greedy_pure_search(starting_state, heuristic=manhattan_distance):
"""Find solution path by exploring possible boards.
Based on heuristic value only.
"""
available = PriorityQ()
curr = Node(starting_state, None, None)
visited = [curr]
while heuristic(curr.state): # heuristic is non-zero when unsolved
for move in curr.legal_moves():
move_state = curr.board.practice_slide(move)
node = Node(move_state, move, curr)
value = heuristic(node.state)
if available.priority(node) is None and node not in visited:
available.push(node, value)
elif node not in visited:
p = available.priority(node)
if p > value:
available.remove(node, p)
available.push(node, value)
curr = available.pop()
visited.append(curr)
return list(reversed(curr.path())) # heuristic is zero, i.e. solved
| 37.461538
| 72
| 0.603696
| 305
| 2,435
| 4.747541
| 0.285246
| 0.046961
| 0.058011
| 0.044199
| 0.776243
| 0.776243
| 0.716851
| 0.716851
| 0.716851
| 0.716851
| 0
| 0.002347
| 0.300205
| 2,435
| 64
| 73
| 38.046875
| 0.847418
| 0.172074
| 0
| 0.723404
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.06383
| false
| 0
| 0.021277
| 0
| 0.148936
| 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
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
7e87832ca4666495527f5e3d6fefde9aafc8f940
| 66
|
py
|
Python
|
Inheritance/Exercises/02. zoo/project/snake.py
|
geodimitrov/PythonOOP_SoftUni
|
f1c6718c878b618b3ab3f174cd4d187bd178940b
|
[
"MIT"
] | 1
|
2021-06-30T11:53:44.000Z
|
2021-06-30T11:53:44.000Z
|
Inheritance/Exercises/02. zoo/project/snake.py
|
geodimitrov/PythonOOP_SoftUni
|
f1c6718c878b618b3ab3f174cd4d187bd178940b
|
[
"MIT"
] | null | null | null |
Inheritance/Exercises/02. zoo/project/snake.py
|
geodimitrov/PythonOOP_SoftUni
|
f1c6718c878b618b3ab3f174cd4d187bd178940b
|
[
"MIT"
] | null | null | null |
from project.reptile import Reptile
class Snake(Reptile):
pass
| 22
| 35
| 0.787879
| 9
| 66
| 5.777778
| 0.777778
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.151515
| 66
| 3
| 36
| 22
| 0.928571
| 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
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
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| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 6
|
0e44449338cf3ff3bb3d124ef22c8f7bbca760a5
| 19
|
py
|
Python
|
tests/mod_ext2/__init__.py
|
fossabot/extendable
|
c01f8ffa452df6ce5222244e76f9a6615b7dfc0b
|
[
"MIT"
] | 137
|
2017-06-08T07:58:53.000Z
|
2022-01-20T06:29:51.000Z
|
src/extractor/__init__.py
|
WhiteRobe/iscopy
|
41b660dda2765db96e3ae19864610f9773e5eadf
|
[
"MIT"
] | 27
|
2021-02-05T20:56:05.000Z
|
2022-03-07T04:11:57.000Z
|
src/extractor/__init__.py
|
WhiteRobe/iscopy
|
41b660dda2765db96e3ae19864610f9773e5eadf
|
[
"MIT"
] | 24
|
2017-10-12T03:25:00.000Z
|
2022-01-20T06:29:58.000Z
|
from . import base
| 9.5
| 18
| 0.736842
| 3
| 19
| 4.666667
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.210526
| 19
| 1
| 19
| 19
| 0.933333
| 0
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| 0
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| 0
| 0
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| 0
| true
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| 1
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| 0
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| 0
| 0
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| 0
| 0
| 0
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| null | 0
| 0
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| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
0e8a7823a31dc54a79b7c4f7e93d976a20c6655b
| 9,976
|
py
|
Python
|
PythonLearn/opencv/findcontours.py
|
OKKyu/PythonLearn
|
48dc4cc2a1a34d99b09f8d37a5566d448dcf987c
|
[
"MIT"
] | null | null | null |
PythonLearn/opencv/findcontours.py
|
OKKyu/PythonLearn
|
48dc4cc2a1a34d99b09f8d37a5566d448dcf987c
|
[
"MIT"
] | null | null | null |
PythonLearn/opencv/findcontours.py
|
OKKyu/PythonLearn
|
48dc4cc2a1a34d99b09f8d37a5566d448dcf987c
|
[
"MIT"
] | null | null | null |
#! python3
# -*- coding:utf-8 -*-
import cv2
import sys, traceback
import numpy as np
import matplotlib.pyplot as plt
def findContours(imgName, drawMode, minArea):
try:
img = cv2.imread(imgName,cv2.IMREAD_COLOR)
if img is None:
print("no file reading...")
sys.exit(1)
dst = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
adjustImShow("to gray",dst,800,800)
ret, dst = cv2.threshold(dst, 50, 255, cv2.THRESH_BINARY)
adjustImShow("threshold 1",dst,800,800)
contours, hierarcy = cv2.findContours(dst,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
if drawMode == 1:
afContours = []
for contour in contours:
if cv2.contourArea(contour) > int(minArea):
print("contour size: " + str(cv2.contourArea(contour)))
afContours.append(contour)
img = cv2.drawContours(img,afContours, -1, (0,255,0), 1)
else:
for contour in contours:
if cv2.contourArea(contour) > int(minArea):
print("contour size: " + str(cv2.contourArea(contour)))
x,y,w,h = cv2.boundingRect(contour)
cv2.rectangle(img,(x,y), (x+w,y+h), (0,255,0),5)
adjustImShow("result",img,800,800)
cv2.waitKey(0)
cv2.destroyAllWindows()
except Exception as ex:
print("Error:", sys.exc_info()[0])
print(sys.exc_info()[1])
def findContoursAdaptive(imgName, drawMode, minArea):
try:
img = cv2.imread(imgName,cv2.IMREAD_COLOR)
if img is None:
print("no file reading...")
sys.exit(1)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
adjustImShow("gray",gray,800,800)
dst = cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,3,3)
adjustImShow("dst",dst,800,800)
contours, hierarcy = cv2.findContours(dst,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
if drawMode == 1:
afContours = []
for contour in contours:
if cv2.contourArea(contour) > int(minArea):
print("contour size: " + str(cv2.contourArea(contour)))
afContours.append(contour)
img = cv2.drawContours(img,afContours, -1, (0,255,0), 1)
else:
for contour in contours:
if cv2.contourArea(contour) > int(minArea):
print("contour size: " + str(cv2.contourArea(contour)))
x,y,w,h = cv2.boundingRect(contour)
cv2.rectangle(img,(x,y), (x+w,y+h), (0,255,0),5)
adjustImShow("result",img,800,800)
cv2.waitKey(0)
cv2.destroyAllWindows()
except Exception as ex:
print("Error:", sys.exc_info()[0])
print(sys.exc_info()[1])
def findContoursInRange(imgName, drawMode, minArea):
try:
img = cv2.imread(imgName,cv2.IMREAD_COLOR)
if img is None:
print("no file reading...")
sys.exit(1)
dst = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
adjustImShow("to gray",dst,800,800)
dst2 = cv2.inRange(dst,0,40)
adjustImShow("in range row",dst2,800,800)
dst3 = cv2.inRange(dst,210,240)
adjustImShow("in range high",dst3,800,800)
mix = cv2.bitwise_or(dst2,dst3)
adjustImShow("mix",mix,800,800)
cv2.imwrite("mix.png",mix)
ret, dst = cv2.threshold(dst, 50, 255, cv2.THRESH_BINARY)
adjustImShow("threshold 1",dst,800,800)
contours, hierarcy = cv2.findContours(dst,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
if drawMode == 1:
afContours = []
for contour in contours:
if cv2.contourArea(contour) > int(minArea):
print("contour size: " + str(cv2.contourArea(contour)))
afContours.append(contour)
img = cv2.drawContours(img,afContours, -1, (0,255,0), 1)
else:
for contour in contours:
if cv2.contourArea(contour) > int(minArea):
print("contour size: " + str(cv2.contourArea(contour)))
x,y,w,h = cv2.boundingRect(contour)
cv2.rectangle(img,(x,y), (x+w,y+h), (0,255,0),5)
adjustImShow("result",img,800,800)
cv2.waitKey(0)
cv2.destroyAllWindows()
except Exception as ex:
print("Error:", sys.exc_info()[0])
print(sys.exc_info()[1])
def findContoursHue(imgName, drawMode, minArea, frHue, toHue):
try:
img = cv2.imread(imgName,cv2.IMREAD_COLOR)
if img is None:
print("no file reading...")
sys.exit(1)
#conv bgr to hsv
hsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)
hsv = cv2.GaussianBlur(hsv, (5, 5), 3)
hue,Saturation,Value = cv2.split(hsv)
# binary transformation (only use H channnel)
_ret, img_H1 = cv2.threshold(hue, frHue / 360 * 179, 255, cv2.THRESH_BINARY_INV)
_ret, img_H2 = cv2.threshold(hue, toHue / 360 * 179, 255, cv2.THRESH_BINARY_INV)
dst = 255 - (img_H2 - img_H1)
adjustImShow("dst",dst,800,800)
dst = cv2.bitwise_not(dst)
adjustImShow("dst",dst,800,800)
contours, hierarcy = cv2.findContours(dst,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
if drawMode == 1:
afContours = []
for contour in contours:
if cv2.contourArea(contour) > int(minArea):
print("contour size: " + str(cv2.contourArea(contour)))
afContours.append(contour)
img = cv2.drawContours(img,afContours, -1, (0,255,0), 1)
else:
for contour in contours:
if cv2.contourArea(contour) > int(minArea):
print("contour size: " + str(cv2.contourArea(contour)))
x,y,w,h = cv2.boundingRect(contour)
cv2.rectangle(img,(x,y), (x+w,y+h), (0,255,0),5)
adjustImShow("result",img,800,800)
cv2.waitKey(0)
cv2.destroyAllWindows()
except Exception as ex:
print("Error:", sys.exc_info()[0])
print(sys.exc_info()[1])
def findContoursHSV(imgName, drawMode, minArea, frHue, toHue, frSa, toSa, frVal, toVal):
try:
img = cv2.imread(imgName,cv2.IMREAD_COLOR)
if img is None:
print("no file reading...")
sys.exit(1)
#conv bgr to hsv
hsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)
#hsv = cv2.GaussianBlur(hsv, (5, 5), 3)
hsv = cv2.medianBlur(hsv,33)
adjustImShow("hsv blur",hsv,800,800)
hue,Saturation,Value = cv2.split(hsv)
# binary transformation (only use H channnel)
_ret, img_H1 = cv2.threshold(hue, frHue / 360 * 179, 255, cv2.THRESH_BINARY_INV)
_ret, img_H2 = cv2.threshold(hue, toHue / 360 * 179, 255, cv2.THRESH_BINARY_INV)
fHue = 255 - (img_H2 - img_H1)
fHue = cv2.bitwise_not(fHue)
adjustImShow("hue filter",fHue,800,800)
_ret, img_S1 = cv2.threshold(Saturation, frSa , 255, cv2.THRESH_BINARY_INV)
_ret, img_S2 = cv2.threshold(Saturation, toSa , 255, cv2.THRESH_BINARY_INV)
fSa = 255 - (img_S2 - img_S1)
fSa = cv2.bitwise_not(fSa)
adjustImShow("satulation filter",fSa,800,800)
_ret, img_V1 = cv2.threshold(Value, frVal , 255, cv2.THRESH_BINARY_INV)
_ret, img_V2 = cv2.threshold(Value, toVal , 255, cv2.THRESH_BINARY_INV)
fV = 255 - (img_V2 - img_V1)
fV = cv2.bitwise_not(fV)
adjustImShow("Value filter",fV,800,800)
dst1 = cv2.bitwise_and(fHue,fSa)
dst2 = cv2.bitwise_and(fHue,fV)
dst = cv2.bitwise_and(dst1,dst2)
adjustImShow("dst",dst,800,800)
contours, hierarcy = cv2.findContours(dst,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
if drawMode == 1:
afContours = []
for contour in contours:
if cv2.contourArea(contour) > int(minArea):
print("contour size: " + str(cv2.contourArea(contour)))
afContours.append(contour)
img = cv2.drawContours(img,afContours, -1, (0,255,0), 1)
else:
for contour in contours:
if cv2.contourArea(contour) > int(minArea):
print("contour size: " + str(cv2.contourArea(contour)))
x,y,w,h = cv2.boundingRect(contour)
cv2.rectangle(img,(x,y), (x+w,y+h), (0,255,0),5)
adjustImShow("result",img,800,800)
cv2.waitKey(0)
cv2.destroyAllWindows()
except Exception as ex:
print("Error:", sys.exc_info()[0])
print(sys.exc_info()[1])
def adjustImShow(wname,img,height,width):
cv2.namedWindow(wname, cv2.WINDOW_NORMAL)
cv2.resizeWindow(wname,height,width)
cv2.imshow(wname,img)
#findContoursOrigin(sys.argv[1],sys.argv[2],sys.argv[3])
#gray scale
#findContoursGray(sys.argv[1],sys.argv[2],sys.argv[3])
#findContoursAdaptive(sys.argv[1],sys.argv[2],sys.argv[3])
#green 50 ~ 150
#findContoursHue(sys.argv[1],sys.argv[2],sys.argv[3],50,150)
#red 0 ~ 60, 300 ~ 360
#findContoursHue(sys.argv[1],sys.argv[2],sys.argv[3],0,60)
#findContoursHue(sys.argv[1],sys.argv[2],sys.argv[3],300,360)
#brown 20 ~ 50
#findContoursHue(sys.argv[1],sys.argv[2],sys.argv[3],30,70)
#findContoursHSV(sys.argv[1],sys.argv[2],sys.argv[3], 50,150, 0,250, 0,255)
| 36.408759
| 98
| 0.556034
| 1,221
| 9,976
| 4.469287
| 0.134316
| 0.030786
| 0.076965
| 0.032985
| 0.790178
| 0.761591
| 0.761591
| 0.743999
| 0.743999
| 0.730804
| 0
| 0.078855
| 0.313553
| 9,976
| 273
| 99
| 36.542125
| 0.71802
| 0.071872
| 0
| 0.767196
| 0
| 0
| 0.045892
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.031746
| false
| 0
| 0.021164
| 0
| 0.05291
| 0.132275
| 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
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
7ec368e9537b26ecd3b5d610321b400312fc1097
| 22
|
py
|
Python
|
dag_executor/Step/__init__.py
|
GennadiiTurutin/dag_executor
|
ddc7eab1e0e98753309e245247ac00e465e52ec1
|
[
"MIT"
] | null | null | null |
dag_executor/Step/__init__.py
|
GennadiiTurutin/dag_executor
|
ddc7eab1e0e98753309e245247ac00e465e52ec1
|
[
"MIT"
] | null | null | null |
dag_executor/Step/__init__.py
|
GennadiiTurutin/dag_executor
|
ddc7eab1e0e98753309e245247ac00e465e52ec1
|
[
"MIT"
] | null | null | null |
from .step import Step
| 22
| 22
| 0.818182
| 4
| 22
| 4.5
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.136364
| 22
| 1
| 22
| 22
| 0.947368
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
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| 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
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
7ef2137eda770d8fbe72cf7150d091a786f54dd4
| 1,445
|
py
|
Python
|
2017/day05.py
|
gcalmettes/AdventOfCode2017
|
374347c981b603981b7d0b21dad3fc594b126c82
|
[
"MIT"
] | 1
|
2021-12-12T22:59:49.000Z
|
2021-12-12T22:59:49.000Z
|
2017/day05.py
|
gcalmettes/AdventOfCode2017
|
374347c981b603981b7d0b21dad3fc594b126c82
|
[
"MIT"
] | null | null | null |
2017/day05.py
|
gcalmettes/AdventOfCode2017
|
374347c981b603981b7d0b21dad3fc594b126c82
|
[
"MIT"
] | 1
|
2019-12-03T05:37:49.000Z
|
2019-12-03T05:37:49.000Z
|
"""
http://adventofcode.com/2017/day/5
"""
def escapeMaze(numList):
count = 1
instructions = numList[:]
currentPos = 0
currentJump = instructions[currentPos]
nextPos = currentPos+currentJump
while ((currentPos>=0) & (currentPos<len(numList))):
instructions[currentPos]+=1 #update jump instruction before leaving
currentPos = nextPos #go to next pos
if ((currentPos<0) | (currentPos>=len(numList))):
break
currentJump = instructions[currentPos]
nextPos = currentPos+currentJump
count+=1
return count
# part 2
def escapeMaze2(numList):
count = 1
instructions = numList[:]
currentPos = 0
currentJump = instructions[currentPos]
nextPos = currentPos+currentJump
while ((currentPos>=0) & (currentPos<len(numList))):
if currentJump >=3:
offset = (-1)
else:
offset = 1
instructions[currentPos]+=offset #update jump instruction before leaving
currentPos = nextPos #go to next pos
if ((currentPos<0) | (currentPos>=len(numList))):
break
currentJump = instructions[currentPos]
nextPos = currentPos+currentJump
count+=1
return count
if __name__ == "__main__":
with open("day05_input.txt", "r") as f:
INPUT = [int(line) for line in f]
# part 1
print(escapeMaze(INPUT))
# part 2
print(escapeMaze2(INPUT))
| 25.803571
| 80
| 0.619377
| 148
| 1,445
| 5.986486
| 0.344595
| 0.074492
| 0.148984
| 0.180587
| 0.717833
| 0.717833
| 0.717833
| 0.717833
| 0.717833
| 0.717833
| 0
| 0.024645
| 0.269896
| 1,445
| 55
| 81
| 26.272727
| 0.815166
| 0.110727
| 0
| 0.666667
| 0
| 0
| 0.018883
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.051282
| false
| 0
| 0
| 0
| 0.102564
| 0.051282
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 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
| 6
|
7d2ef170359ccd2d83638d183234b1e8ae50eb54
| 198
|
py
|
Python
|
src/optimizers.py
|
krypto-i9/static-asl-interpreter
|
4cfbb92627757273cc0fd6456410a21bee8c1e80
|
[
"BSD-3-Clause"
] | null | null | null |
src/optimizers.py
|
krypto-i9/static-asl-interpreter
|
4cfbb92627757273cc0fd6456410a21bee8c1e80
|
[
"BSD-3-Clause"
] | null | null | null |
src/optimizers.py
|
krypto-i9/static-asl-interpreter
|
4cfbb92627757273cc0fd6456410a21bee8c1e80
|
[
"BSD-3-Clause"
] | null | null | null |
import torch.optim as optim
class Optimizer:
def adamW(net, LR):
return optim.AdamW(net.parameters(), lr=LR)
def adam(net, LR):
return optim.Adam(net.parameters(), lr=LR)
| 19.8
| 51
| 0.641414
| 29
| 198
| 4.37931
| 0.448276
| 0.125984
| 0.173228
| 0.251969
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.227273
| 198
| 9
| 52
| 22
| 0.830065
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0.166667
| 0.333333
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 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
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 6
|
7d388ac4381ddd05a8fb406efaadf6388d5f7618
| 179
|
py
|
Python
|
complexnet/__init__.py
|
chuanjiangcui/urus-mri-recon
|
120f8fe000b9c39318dbdede91494a6b42a5edc3
|
[
"MIT"
] | 15
|
2020-04-08T07:00:31.000Z
|
2021-12-08T12:01:29.000Z
|
complexnet/__init__.py
|
chuanjiangcui/urus-mri-recon
|
120f8fe000b9c39318dbdede91494a6b42a5edc3
|
[
"MIT"
] | null | null | null |
complexnet/__init__.py
|
chuanjiangcui/urus-mri-recon
|
120f8fe000b9c39318dbdede91494a6b42a5edc3
|
[
"MIT"
] | 7
|
2020-01-14T02:18:51.000Z
|
2021-10-16T14:26:51.000Z
|
from .cmplxconv import *
from .cmplxdropout import *
from .cmplxfc import *
from .cmplxmodrelu import *
from .cmplxupsample import *
from .radialbn2 import *
from .zrelu import *
| 22.375
| 28
| 0.765363
| 21
| 179
| 6.52381
| 0.428571
| 0.437956
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.006623
| 0.156425
| 179
| 7
| 29
| 25.571429
| 0.900662
| 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
| 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
| 6
|
add11e41faeb057e52f3f2a4a87b144a35d78788
| 5,445
|
py
|
Python
|
tests/test_keys.py
|
paulskinau/python-xmlsec
|
73f74466495969a50ceb1266cef1cc55c8cefe89
|
[
"MIT"
] | null | null | null |
tests/test_keys.py
|
paulskinau/python-xmlsec
|
73f74466495969a50ceb1266cef1cc55c8cefe89
|
[
"MIT"
] | null | null | null |
tests/test_keys.py
|
paulskinau/python-xmlsec
|
73f74466495969a50ceb1266cef1cc55c8cefe89
|
[
"MIT"
] | 1
|
2020-01-22T12:37:56.000Z
|
2020-01-22T12:37:56.000Z
|
from tests import base
import copy
import xmlsec
consts = xmlsec.constants
class TestKeys(base.TestMemoryLeaks):
def test_key_from_memory(self):
key = xmlsec.Key.from_memory(self.load("rsakey.pem"), format=consts.KeyDataFormatPem)
self.assertIsNotNone(key)
def test_key_from_memory_with_bad_args(self):
with self.assertRaises(TypeError):
xmlsec.Key.from_memory(1, format="")
def test_key_from_file(self):
key = xmlsec.Key.from_file(self.path("rsakey.pem"), format=consts.KeyDataFormatPem)
self.assertIsNotNone(key)
def test_key_from_file_with_bad_args(self):
with self.assertRaises(TypeError):
xmlsec.Key.from_file(1, format="")
def test_key_from_fileobj(self):
with open(self.path("rsakey.pem"), "rb") as fobj:
key = xmlsec.Key.from_file(fobj, format=consts.KeyDataFormatPem)
self.assertIsNotNone(key)
def test_generate(self):
key = xmlsec.Key.generate(klass=consts.KeyDataAes, size=256, type=consts.KeyDataTypeSession)
self.assertIsNotNone(key)
def test_generate_with_bad_args(self):
with self.assertRaises(TypeError):
xmlsec.Key.generate(klass="", size="", type="")
def test_from_binary_file(self):
key = xmlsec.Key.from_binary_file(klass=consts.KeyDataDes, filename=self.path("deskey.bin"))
self.assertIsNotNone(key)
def test_from_binary_file_with_bad_args(self):
with self.assertRaises(TypeError):
xmlsec.Key.from_binary_file(klass="", filename=1)
def test_from_binary_data(self):
key = xmlsec.Key.from_binary_data(klass=consts.KeyDataDes, data=self.load("deskey.bin"))
self.assertIsNotNone(key)
def test_from_binary_data_with_bad_args(self):
with self.assertRaises(TypeError):
xmlsec.Key.from_binary_data(klass="", data=1)
def test_load_cert_from_file(self):
key = xmlsec.Key.from_file(self.path("rsakey.pem"), format=consts.KeyDataFormatPem)
self.assertIsNotNone(key)
key.load_cert_from_file(self.path("rsacert.pem"), format=consts.KeyDataFormatPem)
def test_load_cert_from_file_with_bad_args(self):
key = xmlsec.Key.from_file(self.path("rsakey.pem"), format=consts.KeyDataFormatPem)
self.assertIsNotNone(key)
with self.assertRaises(TypeError):
key.load_cert_from_file(1, format="")
def test_load_cert_from_fileobj(self):
key = xmlsec.Key.from_file(self.path("rsakey.pem"), format=consts.KeyDataFormatPem)
self.assertIsNotNone(key)
with open(self.path("rsacert.pem"), "rb") as fobj:
key.load_cert_from_file(fobj, format=consts.KeyDataFormatPem)
def test_load_cert_from_memory(self):
key = xmlsec.Key.from_file(self.path("rsakey.pem"), format=consts.KeyDataFormatPem)
self.assertIsNotNone(key)
key.load_cert_from_memory(self.load("rsacert.pem"), format=consts.KeyDataFormatPem)
def test_load_cert_from_memory_with_bad_args(self):
key = xmlsec.Key.from_file(self.path("rsakey.pem"), format=consts.KeyDataFormatPem)
self.assertIsNotNone(key)
with self.assertRaises(TypeError):
key.load_cert_from_memory(1, format="")
def test_name(self):
key = xmlsec.Key.from_file(self.path("rsakey.pem"), format=consts.KeyDataFormatPem)
self.assertIsNone(key.name)
key.name = "rsakey"
self.assertEqual("rsakey", key.name)
def test_copy(self):
key = xmlsec.Key.from_file(self.path("rsakey.pem"), format=consts.KeyDataFormatPem)
key2 = copy.copy(key)
del key
key2.load_cert_from_file(self.path("rsacert.pem"), format=consts.KeyDataFormatPem)
class TestKeysManager(base.TestMemoryLeaks):
def test_add_key(self):
key = xmlsec.Key.from_file(self.path("rsakey.pem"), format=consts.KeyDataFormatPem)
mngr = xmlsec.KeysManager()
mngr.add_key(key)
def test_add_key_with_bad_args(self):
mngr = xmlsec.KeysManager()
with self.assertRaises(TypeError):
mngr.add_key("")
def test_load_cert(self):
mngr = xmlsec.KeysManager()
mngr.add_key(xmlsec.Key.from_file(self.path("rsakey.pem"), format=consts.KeyDataFormatPem))
mngr.load_cert(self.path("rsacert.pem"), format=consts.KeyDataFormatPem, type=consts.KeyDataTypeTrusted)
def test_load_cert_with_bad_args(self):
mngr = xmlsec.KeysManager()
mngr.add_key(xmlsec.Key.from_file(self.path("rsakey.pem"), format=consts.KeyDataFormatPem))
with self.assertRaises(TypeError):
mngr.load_cert(1, format="", type="")
def test_load_cert_from_memory(self):
mngr = xmlsec.KeysManager()
mngr.add_key(xmlsec.Key.from_file(self.path("rsakey.pem"), format=consts.KeyDataFormatPem))
mngr.load_cert_from_memory(self.load("rsacert.pem"), format=consts.KeyDataFormatPem, type=consts.KeyDataTypeTrusted)
def test_load_cert_from_memory_with_bad_args(self):
mngr = xmlsec.KeysManager()
mngr.add_key(xmlsec.Key.from_file(self.path("rsakey.pem"), format=consts.KeyDataFormatPem))
with self.assertRaises(TypeError):
mngr.load_cert_from_memory(1, format="", type="")
def test_load_invalid_key(self):
mngr = xmlsec.KeysManager()
with self.assertRaises(ValueError):
mngr.add_key(xmlsec.Key())
| 40.333333
| 124
| 0.695133
| 700
| 5,445
| 5.181429
| 0.088571
| 0.050179
| 0.075269
| 0.162393
| 0.848084
| 0.809209
| 0.714916
| 0.669699
| 0.653984
| 0.612903
| 0
| 0.002926
| 0.184022
| 5,445
| 134
| 125
| 40.634328
| 0.813414
| 0
| 0
| 0.436893
| 0
| 0
| 0.046281
| 0
| 0
| 0
| 0
| 0
| 0.23301
| 1
| 0.242718
| false
| 0
| 0.029126
| 0
| 0.291262
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 1
| 1
| 1
| 0
| 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
| 0
| 0
|
0
| 6
|
bc36742cc2b9937c14f5192d9caa87c5dc94e7b8
| 47
|
py
|
Python
|
components/wifi_provisioning/python/security/__init__.py
|
chenqy2018/esp-va-sdk
|
2196a16586ded4186161fefba2113b6cacf7eaf7
|
[
"MIT-0"
] | null | null | null |
components/wifi_provisioning/python/security/__init__.py
|
chenqy2018/esp-va-sdk
|
2196a16586ded4186161fefba2113b6cacf7eaf7
|
[
"MIT-0"
] | null | null | null |
components/wifi_provisioning/python/security/__init__.py
|
chenqy2018/esp-va-sdk
|
2196a16586ded4186161fefba2113b6cacf7eaf7
|
[
"MIT-0"
] | null | null | null |
from security1 import*
from security0 import*
| 11.75
| 22
| 0.808511
| 6
| 47
| 6.333333
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.05
| 0.148936
| 47
| 3
| 23
| 15.666667
| 0.9
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 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
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
bc374a288c5ea00dcd7d8cec0773aa960caa84b9
| 49
|
py
|
Python
|
movieknight/models/__init__.py
|
nfearnley/MovieKnight
|
cc88050b03c1e759580e3ded3c27e3efbf4925e2
|
[
"MIT"
] | null | null | null |
movieknight/models/__init__.py
|
nfearnley/MovieKnight
|
cc88050b03c1e759580e3ded3c27e3efbf4925e2
|
[
"MIT"
] | null | null | null |
movieknight/models/__init__.py
|
nfearnley/MovieKnight
|
cc88050b03c1e759580e3ded3c27e3efbf4925e2
|
[
"MIT"
] | null | null | null |
from .base import Base
from .movies import Movie
| 16.333333
| 25
| 0.795918
| 8
| 49
| 4.875
| 0.625
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.163265
| 49
| 2
| 26
| 24.5
| 0.95122
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 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
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
70b9974d571374d6c2afa99505427daa8bece968
| 34
|
py
|
Python
|
CrashCourse/cOOP/hellom/some_file.py
|
atabaksahraei/Python-for-Developer
|
6972b7c9a500312ce6a359817feb5f8461391078
|
[
"MIT"
] | null | null | null |
CrashCourse/cOOP/hellom/some_file.py
|
atabaksahraei/Python-for-Developer
|
6972b7c9a500312ce6a359817feb5f8461391078
|
[
"MIT"
] | null | null | null |
CrashCourse/cOOP/hellom/some_file.py
|
atabaksahraei/Python-for-Developer
|
6972b7c9a500312ce6a359817feb5f8461391078
|
[
"MIT"
] | null | null | null |
def f():
print("Function f()")
| 17
| 25
| 0.529412
| 5
| 34
| 3.6
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.205882
| 34
| 2
| 25
| 17
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0.342857
| 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
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 6
|
cb3294d55ab8fbc69ce8a5e0e842d89f85e36e54
| 37
|
py
|
Python
|
EditSQL/error_detector.py
|
Deliangus/MISP
|
8632b5ea120f8385825a08eb930232d3ea74c426
|
[
"MIT"
] | 54
|
2019-10-07T03:36:25.000Z
|
2021-12-27T02:11:11.000Z
|
EditSQL/error_detector.py
|
Deliangus/MISP
|
8632b5ea120f8385825a08eb930232d3ea74c426
|
[
"MIT"
] | 1
|
2021-08-13T07:48:15.000Z
|
2021-08-31T01:30:12.000Z
|
EditSQL/error_detector.py
|
Deliangus/MISP
|
8632b5ea120f8385825a08eb930232d3ea74c426
|
[
"MIT"
] | 4
|
2020-01-29T17:38:28.000Z
|
2021-12-10T19:09:37.000Z
|
from MISP_SQL.error_detector import *
| 37
| 37
| 0.864865
| 6
| 37
| 5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.081081
| 37
| 1
| 37
| 37
| 0.882353
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 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
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
cb3b7bae2cca4101addd6ae05591bf5c287803ae
| 191
|
py
|
Python
|
tests/net/hostname.py
|
dem4ply/chibi
|
1f13db8200e8e60bbb839436d4c995d6b6220957
|
[
"WTFPL"
] | null | null | null |
tests/net/hostname.py
|
dem4ply/chibi
|
1f13db8200e8e60bbb839436d4c995d6b6220957
|
[
"WTFPL"
] | null | null | null |
tests/net/hostname.py
|
dem4ply/chibi
|
1f13db8200e8e60bbb839436d4c995d6b6220957
|
[
"WTFPL"
] | null | null | null |
from unittest import TestCase
from chibi.net.hostname import get_hostname
class Test_get_hostname( TestCase ):
def test_get_hostname( self ):
self.assertTrue( get_hostname() )
| 21.222222
| 43
| 0.753927
| 25
| 191
| 5.52
| 0.52
| 0.318841
| 0.217391
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.17801
| 191
| 8
| 44
| 23.875
| 0.878981
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.2
| 1
| 0.2
| false
| 0
| 0.4
| 0
| 0.8
| 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
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
cb3e5a12fe5c42944b24233700cbdb1bc282e359
| 37,349
|
py
|
Python
|
scripts/feature_ablation_study.py
|
rpo19/BLINK
|
a9b62d08aa3ce4958e9bec62888c67c4eee18533
|
[
"MIT"
] | null | null | null |
scripts/feature_ablation_study.py
|
rpo19/BLINK
|
a9b62d08aa3ce4958e9bec62888c67c4eee18533
|
[
"MIT"
] | null | null | null |
scripts/feature_ablation_study.py
|
rpo19/BLINK
|
a9b62d08aa3ce4958e9bec62888c67c4eee18533
|
[
"MIT"
] | null | null | null |
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.metrics import f1_score
import pickle
from sklearn.metrics import classification_report, roc_curve
import os
import matplotlib.pyplot as plt
import seaborn as sns
import sys
only = None
if len(sys.argv) >= 2:
only = sys.argv[1].split(',')
print('Only', only)
sns.set(style='ticks', palette='Set2')
sns.despine()
def myplot(y, y_pred, y_pred_round, title, outpath):
plt.figure()
df = pd.DataFrame({
'y': y,
'y_pred': y_pred,
'y_pred_rnd': y_pred_round
})
# kde
df[['y', 'y_pred']].plot(kind='kde', title=title+'_kde')
plt.tight_layout(pad=0.5)
plt.savefig(os.path.join(outpath, title+'_kde.png'))
plt.close()
# roc curve
fpr, tpr, thresholds = roc_curve(df['y'], df['y_pred'])
plt.figure()
pd.DataFrame({
'tpr': tpr,
'fpr': fpr,
'thresholds': thresholds
}).plot(x='fpr', y='tpr', title=title+'_roc')
plt.tight_layout(pad=0.5)
plt.savefig(os.path.join(outpath, title+'_roc.png'))
plt.close()
# correct
correct = df.query('y == y_pred_rnd')['y_pred'].rename('correct')
plt.figure()
correct.plot(kind='kde', title=title+'_kde', legend=True)
plt.tight_layout(pad=0.5)
# plt.savefig(os.path.join(outpath, title+'_kde_correct.png'))
# plt.close()
# errors
errors = df.query('y != y_pred_rnd')['y_pred'].rename('errors')
# plt.figure()
# errors.plot(kind='density', title=title+'errors kde')
errors.plot(kind='density', title=title+'kde', legend=True)
plt.tight_layout(pad=0.5)
# plt.savefig(os.path.join(outpath, title+'_kde_errors.png'))
plt.savefig(os.path.join(outpath, title+'_kde_correct_errors.png'))
plt.close()
plt.close('all')
outpath = 'output/feature_ablation_study'
os.makedirs(outpath, exist_ok=True)
print('loading dataset...')
dataset = pd.read_pickle('./data/nil_dataset.pickle')
print('loaded...')
tasks = [
{
'name': 'aida_under_cross_max',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'cross_stats_10_max',
]
},
{
'name': 'aida_under_all_max',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'cross_stats_10_max',
'bi_stats_10_max',
]
},
{
'name': 'aida_all_max',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'no',
'features': [
'cross_stats_10_max',
'bi_stats_10_max',
]
},
{
'name': 'aida_under_all_max_levenshtein',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'cross_stats_10_max',
'bi_stats_10_max',
'cross_levenshtein'
# no bi levenshtein
]
},
{
'name': 'aida_under_all_max_jaccard',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'cross_stats_10_max',
'bi_stats_10_max',
'cross_jaccard'
# no bi levenshtein
]
},
{
'name': 'aida_under_cross_max_jaccard',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'cross_stats_10_max',
'cross_jaccard'
# no bi levenshtein
]
},
{
'name': 'aida_under_all_max_stdev4',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'cross_stats_10_max',
'cross_stats_4_stdev',
'bi_stats_10_max',
'bi_stats_4_stdev',
]
},
{
'name': 'aida_under_all_max_stdev10',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'cross_stats_10_max',
'cross_stats_10_stdev',
'bi_stats_10_max',
'bi_stats_10_stdev',
]
},
{
'name': 'aida_under_all_max_stats10',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'cross_stats_10_max',
'cross_stats_10_mean',
'cross_stats_10_median',
'cross_stats_10_stdev',
'bi_stats_10_max',
'bi_stats_10_mean',
'bi_stats_10_median',
'bi_stats_10_stdev',
]
},
{
'name': 'aida_under_all_max_ner_wiki',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'cross_stats_10_max',
'bi_stats_10_max',
'ner_per',
'ner_loc',
'ner_org',
'ner_misc',
'wiki_per_cross',
'wiki_loc_cross',
'wiki_org_cross',
'wiki_misc_cross',
]
},
{
'name': 'aida_under_all_max_ner_wiki_stdev4',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'cross_stats_10_max',
'cross_stats_4_stdev',
'bi_stats_10_max',
'bi_stats_4_stdev',
'ner_per',
'ner_loc',
'ner_org',
'ner_misc',
'wiki_per_cross',
'wiki_loc_cross',
'wiki_org_cross',
'wiki_misc_cross',
]
},
{
'name': 'aida_under_all_max_ner_wiki_stdev4_levenshtein',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'cross_stats_10_max',
'cross_stats_4_stdev',
'bi_stats_10_max',
'bi_stats_4_stdev',
'cross_levenshtein',
'ner_per',
'ner_loc',
'ner_org',
'ner_misc',
'wiki_per_cross',
'wiki_loc_cross',
'wiki_org_cross',
'wiki_misc_cross',
]
},
{
'name': 'aida_under_all_max_ner_wiki_levenshtein',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'cross_stats_10_max',
'bi_stats_10_max',
'cross_levenshtein',
'ner_per',
'ner_loc',
'ner_org',
'ner_misc',
'wiki_per_cross',
'wiki_loc_cross',
'wiki_org_cross',
'wiki_misc_cross',
]
},
{
'name': 'aida_under_all_max_ner_wiki_jaccard',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'cross_stats_10_max',
'bi_stats_10_max',
'cross_jaccard',
'ner_per',
'ner_loc',
'ner_org',
'ner_misc',
'wiki_per_cross',
'wiki_loc_cross',
'wiki_org_cross',
'wiki_misc_cross',
]
},
{
'name': 'aida_under_all_max_ner_wiki_levenshtein_jaccard',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'cross_stats_10_max',
'bi_stats_10_max',
'cross_levenshtein',
'cross_jaccard',
'ner_per',
'ner_loc',
'ner_org',
'ner_misc',
'wiki_per_cross',
'wiki_loc_cross',
'wiki_org_cross',
'wiki_misc_cross',
]
},
{
'name': 'aida_under_all_max_levenshtein_jaccard',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'cross_stats_10_max',
'bi_stats_10_max',
'cross_levenshtein',
'cross_jaccard'
# no bi levenshtein
]
},
{
'name': 'aida_under_all_max_stdev4_levenshtein',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'cross_stats_10_max',
'cross_stats_4_stdev',
'bi_stats_10_max',
'bi_stats_4_stdev',
'cross_levenshtein'
]
},
{
'name': 'aida_under_all_max_stdev4_jaccard',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'cross_stats_10_max',
'cross_stats_4_stdev',
'bi_stats_10_max',
'bi_stats_4_stdev',
'cross_jaccard'
]
},
{
'name': 'aida_under_all_max_stdev4_levenshtein_jaccard',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'cross_stats_10_max',
'cross_stats_4_stdev',
'bi_stats_10_max',
'bi_stats_4_stdev',
'cross_levenshtein',
'cross_jaccard'
]
},
{
'name': 'aida_under_all_max_stats10_levenshtein',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'cross_stats_10_max',
'cross_stats_10_mean',
'cross_stats_10_median',
'cross_stats_10_stdev',
'bi_stats_10_max',
'bi_stats_10_mean',
'bi_stats_10_median',
'bi_stats_10_stdev',
'cross_levenshtein'
]
},
{
'name': 'aida_under_all_max_stats10_jaccard',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'cross_stats_10_max',
'cross_stats_10_mean',
'cross_stats_10_median',
'cross_stats_10_stdev',
'bi_stats_10_max',
'bi_stats_10_mean',
'bi_stats_10_median',
'bi_stats_10_stdev',
'cross_jaccard'
]
},
{
'name': 'aida_under_all_max_stats10_levenshtein_jaccard',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'cross_stats_10_max',
'cross_stats_10_mean',
'cross_stats_10_median',
'cross_stats_10_stdev',
'bi_stats_10_max',
'bi_stats_10_mean',
'bi_stats_10_median',
'bi_stats_10_stdev',
'cross_levenshtein',
'cross_jaccard'
]
},
{
'name': 'aida_under_all_max_stats10_levenshtein_ner_wiki',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'cross_stats_10_max',
'cross_stats_10_mean',
'cross_stats_10_median',
'cross_stats_10_stdev',
'bi_stats_10_max',
'bi_stats_10_mean',
'bi_stats_10_median',
'bi_stats_10_stdev',
'cross_levenshtein',
'ner_per',
'ner_loc',
'ner_org',
'ner_misc',
'wiki_per_cross',
'wiki_loc_cross',
'wiki_org_cross',
'wiki_misc_cross',
]
},
{
'name': 'aida_under_all_max_stats10_ner_wiki',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'cross_stats_10_max',
'cross_stats_10_mean',
'cross_stats_10_median',
'cross_stats_10_stdev',
'bi_stats_10_max',
'bi_stats_10_mean',
'bi_stats_10_median',
'bi_stats_10_stdev',
'ner_per',
'ner_loc',
'ner_org',
'ner_misc',
'wiki_per_cross',
'wiki_loc_cross',
'wiki_org_cross',
'wiki_misc_cross',
]
},
{
'name': 'aida_all_max_stats10_ner_wiki',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'no',
'features': [
'cross_stats_10_max',
'cross_stats_10_mean',
'cross_stats_10_median',
'cross_stats_10_stdev',
'bi_stats_10_max',
'bi_stats_10_mean',
'bi_stats_10_median',
'bi_stats_10_stdev',
'ner_per',
'ner_loc',
'ner_org',
'ner_misc',
'wiki_per_cross',
'wiki_loc_cross',
'wiki_org_cross',
'wiki_misc_cross',
]
},
{
'name': 'aida_under_cross_max_stats10_ner_wiki',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'cross_stats_10_max',
'cross_stats_10_mean',
'cross_stats_10_median',
'cross_stats_10_stdev',
'ner_per',
'ner_loc',
'ner_org',
'ner_misc',
'wiki_per_cross',
'wiki_loc_cross',
'wiki_org_cross',
'wiki_misc_cross',
]
},
{
'name': 'aida_under_all_max_stats10_ner_wiki_jaccard',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'cross_stats_10_max',
'cross_stats_10_mean',
'cross_stats_10_median',
'cross_stats_10_stdev',
'bi_stats_10_max',
'bi_stats_10_mean',
'bi_stats_10_median',
'bi_stats_10_stdev',
'cross_jaccard',
'ner_per',
'ner_loc',
'ner_org',
'ner_misc',
'wiki_per_cross',
'wiki_loc_cross',
'wiki_org_cross',
'wiki_misc_cross',
]
},
################## bi
{
'name': 'aida_under_bi_max',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'bi_stats_10_max',
],
'y': 'y_bi',
},
{
'name': 'aida_under_bi_max_levenshtein',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'bi_stats_10_max',
'bi_levenshtein'
# no cross levenshtein
],
'y': 'y_bi',
},
{
'name': 'aida_under_bi_max_jaccard',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'bi_stats_10_max',
'bi_jaccard'
# no cross levenshtein
],
'y': 'y_bi',
},
{
'name': 'aida_under_bi_max_stdev4',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'bi_stats_10_max',
'bi_stats_4_stdev',
# no cross levenshtein
],
'y': 'y_bi',
},
{
'name': 'aida_under_bi_max_stdev10',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'bi_stats_10_max',
'bi_stats_10_stdev',
# no cross levenshtein
],
'y': 'y_bi',
},
{
'name': 'aida_under_bi_max_stats10',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'bi_stats_10_max',
'bi_stats_10_mean',
'bi_stats_10_median',
'bi_stats_10_stdev',
# no cross levenshtein
],
'y': 'y_bi',
},
{
'name': 'aida_under_bi_max_ner_wiki',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'bi_stats_10_max',
'ner_per',
'ner_loc',
'ner_org',
'ner_misc',
'wiki_per_bi',
'wiki_loc_bi',
'wiki_org_bi',
'wiki_misc_bi',
# no cross levenshtein
],
'y': 'y_bi',
},
{
'name': 'aida_under_bi_max_ner_wiki_stdev4',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'bi_stats_10_max',
'bi_stats_4_stdev',
'ner_per',
'ner_loc',
'ner_org',
'ner_misc',
'wiki_per_bi',
'wiki_loc_bi',
'wiki_org_bi',
'wiki_misc_bi',
# no cross levenshtein
],
'y': 'y_bi',
},
{
'name': 'aida_under_bi_max_ner_wiki_levenshtein',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'bi_stats_10_max',
'bi_levenshtein',
'ner_per',
'ner_loc',
'ner_org',
'ner_misc',
'wiki_per_bi',
'wiki_loc_bi',
'wiki_org_bi',
'wiki_misc_bi',
# no cross levenshtein
],
'y': 'y_bi',
},
{
'name': 'aida_under_bi_max_ner_wiki_jaccard',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'bi_stats_10_max',
'bi_jaccard',
'ner_per',
'ner_loc',
'ner_org',
'ner_misc',
'wiki_per_bi',
'wiki_loc_bi',
'wiki_org_bi',
'wiki_misc_bi',
# no cross levenshtein
],
'y': 'y_bi',
},
{
'name': 'aida_under_bi_max_ner_wiki_levenshtein_jaccard',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'bi_stats_10_max',
'bi_levenshtein',
'bi_jaccard',
'ner_per',
'ner_loc',
'ner_org',
'ner_misc',
'wiki_per_bi',
'wiki_loc_bi',
'wiki_org_bi',
'wiki_misc_bi',
# no cross levenshtein
],
'y': 'y_bi',
},
{
'name': 'aida_under_bi_max_levenshtein_jaccard',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'bi_stats_10_max',
'bi_levenshtein',
'bi_jaccard',
# no cross levenshtein
],
'y': 'y_bi',
},
{
'name': 'aida_under_bi_max_stdev4_levenshtein',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'bi_stats_10_max',
'bi_stats_4_stdev',
'bi_levenshtein',
# no cross levenshtein
],
'y': 'y_bi',
},
{
'name': 'aida_under_bi_max_stdev4_jaccard',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'bi_stats_10_max',
'bi_stats_4_stdev',
'bi_jaccard',
# no cross levenshtein
],
'y': 'y_bi',
},
{
'name': 'aida_under_bi_max_stdev4_levenshtein_jaccard',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'bi_stats_10_max',
'bi_stats_4_stdev',
'bi_levenshtein',
'bi_jaccard'
# no cross levenshtein
],
'y': 'y_bi',
},
{
'name': 'aida_under_bi_max_stats10_levenshtein',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'bi_stats_10_max',
'bi_stats_10_mean',
'bi_stats_10_median',
'bi_stats_10_stdev',
'bi_levenshtein'
# no cross levenshtein
],
'y': 'y_bi',
},
{
'name': 'aida_under_bi_max_stats10_jaccard',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'bi_stats_10_max',
'bi_stats_10_mean',
'bi_stats_10_median',
'bi_stats_10_stdev',
'bi_jaccard'
# no cross levenshtein
],
'y': 'y_bi',
},
{
'name': 'aida_under_bi_max_stats10_levenshtein_jaccard',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'bi_stats_10_max',
'bi_stats_10_mean',
'bi_stats_10_median',
'bi_stats_10_stdev',
'bi_levenshtein',
'bi_jaccard'
# no cross levenshtein
],
'y': 'y_bi',
},
{
'name': 'aida_under_bi_max_stats10_levenshtein_ner_wiki',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'bi_stats_10_max',
'bi_stats_10_mean',
'bi_stats_10_median',
'bi_stats_10_stdev',
'bi_levenshtein',
'ner_per',
'ner_loc',
'ner_org',
'ner_misc',
'wiki_per_bi',
'wiki_loc_bi',
'wiki_org_bi',
'wiki_misc_bi',
# no cross levenshtein
],
'y': 'y_bi',
},
{
'name': 'aida_under_bi_max_stats10_jaccard_ner_wiki',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'bi_stats_10_max',
'bi_stats_10_mean',
'bi_stats_10_median',
'bi_stats_10_stdev',
'bi_jaccard',
'ner_per',
'ner_loc',
'ner_org',
'ner_misc',
'wiki_per_bi',
'wiki_loc_bi',
'wiki_org_bi',
'wiki_misc_bi',
# no cross levenshtein
],
'y': 'y_bi',
},
{
'name': 'aida_under_bi_max_stdev4_levenshtein_ner_wiki',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'bi_stats_10_max',
'bi_stats_4_stdev',
'bi_levenshtein',
'ner_per',
'ner_loc',
'ner_org',
'ner_misc',
'wiki_per_bi',
'wiki_loc_bi',
'wiki_org_bi',
'wiki_misc_bi',
# no cross levenshtein
],
'y': 'y_bi',
},
{
'name': 'aida_under_bi_max_stats10_ner_wiki',
'train': ['AIDA-YAGO2_train_ner'],
'test': ['AIDA-YAGO2_testa_ner', 'AIDA-YAGO2_testb_ner'],
'sampling': 'undersample',
'features': [
'bi_stats_10_max',
'bi_stats_10_mean',
'bi_stats_10_median',
'bi_stats_10_stdev',
'ner_per',
'ner_loc',
'ner_org',
'ner_misc',
'wiki_per_bi',
'wiki_loc_bi',
'wiki_org_bi',
'wiki_misc_bi',
# no cross levenshtein
],
'y': 'y_bi',
},
]
# assert no duplicates
vc = pd.DataFrame([task['name'] for task in tasks]).value_counts()
if not (vc <= 1).all():
print('!' * 30)
print('Duplicates:')
print('!' * 30)
print(vc[vc > 1])
raise Exception('duplicate task!')
csv_report = pd.DataFrame()
if only is not None:
tasks = [t for t in tasks if t['name'] in only]
current_report = None
if os.path.isfile(os.path.join(outpath, 'feature_ablation_summary.csv')):
current_report = pd.read_csv(os.path.join(outpath, 'feature_ablation_summary.csv'), index_col=0)
for task in tasks:
print('-'*30)
print(task['name'])
if current_report is not None and task['name'] in current_report.index:
print('skipping yeah....')
continue
y_whom = 'y_cross'
if 'y' in task:
y_whom = task['y']
train_df = dataset[dataset['src'].isin(task['train'])]
if isinstance(task['test'], list):
test_df = dataset[dataset['src'].isin(task['test'])]
elif isinstance(task['test'], float):
train_df, test_df = train_test_split(train_df, test_size = task['test'], random_state = 1234)
else:
raise Exception()
train_df_shape_original = train_df.shape[0]
test_df_shape_original = test_df.shape[0]
train_df = train_df[train_df[task['features']].notna().all(axis=1)]
test_df = test_df[test_df[task['features']].notna().all(axis=1)]
train_df_shape_notna = train_df.shape[0]
test_df_shape_notna = test_df.shape[0]
if task['sampling'] == 'undersample':
print('undersampling...')
train_df_0 = train_df.query(f'{y_whom} == 0')
train_df_1 = train_df.query(f'{y_whom} == 1')
train_df_1 = train_df_1.sample(frac=1).iloc[:train_df_0.shape[0]]
train_df = pd.concat([train_df_0, train_df_1]).sample(frac=1)
elif task['sampling'] == 'no':
pass
else:
raise Exception()
train_df_shape_actual = train_df.shape[0]
test_df_shape_actual = test_df.shape[0]
df_size_report = pd.DataFrame({
'train': [train_df_shape_original, train_df_shape_notna, train_df_shape_actual],
'test': [test_df_shape_original, test_df_shape_notna, test_df_shape_actual]
}, index=['original', 'notna', 'actual']).to_markdown()
print(df_size_report)
print(pd.DataFrame(train_df[y_whom].value_counts()).to_markdown())
X_train = train_df[task['features']].values
y_train = train_df[y_whom].values
X_test = test_df[task['features']].values
y_test = test_df[y_whom].values
# model
clf = make_pipeline(
StandardScaler(),
LogisticRegression(random_state=1234, max_iter=200)
)
clf.fit(X_train, y_train)
y_pred = np.array(list(map(lambda x: x[1], clf.predict_proba(X_test))))
y_pred_round = np.round(y_pred)
test_df['y_pred_round'] = y_pred_round
test_df['y_pred'] = y_pred
bi_baseline = test_df.query('bi_labels == bi_best_candidate or Wikipedia_title == bi_best_candidate_title').shape[0]
cross_baseline = test_df.query('cross_labels == cross_best_candidate or Wikipedia_title == cross_best_candidate_title').shape[0]
bi_acc = test_df.query('(y_pred_round == 1 and (bi_labels == bi_best_candidate or Wikipedia_title == bi_best_candidate_title)) or (bi_labels == -1 and y_pred_round == 0)').shape[0]
cross_acc = test_df.query('(y_pred_round == 1 and (cross_labels == cross_best_candidate or Wikipedia_title == cross_best_candidate_title)) or (cross_labels == -1 and y_pred_round == 0)').shape[0]
bi_acc_correcting_nel = test_df.query(
'(y_pred_round == 1 and (bi_labels == bi_best_candidate or Wikipedia_title == bi_best_candidate_title))'
' or (bi_labels != bi_best_candidate and y_pred_round == 0)').shape[0]
cross_acc_correcting_nel = test_df.query(
'(y_pred_round == 1 and '
'(cross_labels == cross_best_candidate or Wikipedia_title == cross_best_candidate_title))'
' or (cross_labels != cross_best_candidate and y_pred_round == 0)').shape[0]
_classification_report = classification_report(y_test, y_pred_round)
# oracle corrects in [0.25, 0.75]
# TODO maybe look for a better way to get them (e.g. correct-error kde intersections ?)
tl = 0.25
th = 0.75
oracle_df = pd.DataFrame({
'y_test': y_test,
'y_pred': y_pred,
'y_pred_round': y_pred_round
})
oracle_original_shape = oracle_df.shape[0]
oracle_df = oracle_df.query(f'y_pred <= {tl} or y_pred >= {th}')
_classification_report_oracle = classification_report(oracle_df['y_test'], oracle_df['y_pred_round'])
test_df_oracle = test_df.query(f'y_pred <= {tl} or y_pred >= {th}')
bi_acc_oracle = test_df_oracle.query('(y_pred_round == 1 and (bi_labels == bi_best_candidate or Wikipedia_title == bi_best_candidate_title)) or (bi_labels == -1 and y_pred_round == 0)').shape[0]
cross_acc_oracle = test_df_oracle.query('(y_pred_round == 1 and (cross_labels == cross_best_candidate or Wikipedia_title == cross_best_candidate_title)) or (cross_labels == -1 and y_pred_round == 0)').shape[0]
_f1_0 = f1_score(y_test, y_pred_round, pos_label=0)
_f1_1 = f1_score(y_test, y_pred_round, pos_label=1)
_macro_avg_f1 = (_f1_0 + _f1_1) / 2
_f1_0_oracle = f1_score(oracle_df['y_test'], oracle_df['y_pred_round'], pos_label=0)
_f1_1_oracle = f1_score(oracle_df['y_test'], oracle_df['y_pred_round'], pos_label=1)
_macro_avg_f1_oracle = (_f1_0_oracle + _f1_1_oracle) / 2
csv_report = csv_report.append({
'name': task['name'],
'bi_baseline': bi_baseline / test_df_shape_actual,
'cross_baseline': cross_baseline / test_df_shape_actual,
'bi_acc': bi_acc / test_df_shape_actual,
'cross_acc': cross_acc / test_df_shape_actual,
'bi_acc_adjusted': bi_acc / test_df_shape_original,
'cross_acc_adjusted': cross_acc / test_df_shape_original,
'bi_acc_correcting_nel': bi_acc_correcting_nel / test_df_shape_actual,
'cross_acc_correcting_nel': cross_acc_correcting_nel / test_df_shape_actual,
'0-f1': _f1_0,
'1-f1': _f1_1,
'macro-avg-f1': _macro_avg_f1,
'oracle_ratio': 1 - (oracle_df.shape[0] / oracle_original_shape),
'bi_acc_oracle': bi_acc_oracle / test_df_oracle.shape[0],
'cross_acc_oracle': cross_acc_oracle / test_df_oracle.shape[0],
'0-f1-oracle': _f1_0_oracle,
'1-f1-oracle': _f1_1_oracle,
'macro-avg-f1-oracle': _macro_avg_f1_oracle,
}, ignore_index=True)
print(_classification_report)
print('-- Performances over test set:', task['test'], '--')
print('Bi baseline:', bi_baseline / test_df_shape_actual)
print('Cross baseline:', cross_baseline / test_df_shape_actual)
print('Bi acc:', bi_acc / test_df_shape_actual)
print('Cross acc:', cross_acc / test_df_shape_actual)
print('Bi acc adjusted:', bi_acc / test_df_shape_original)
print('Cross acc adjusted:', cross_acc / test_df_shape_original)
print(f'-- Oracle HITL evaluation when y_pred in [{tl}, {th}]')
print('Ratio to human validator:', 1 - (oracle_df.shape[0] / oracle_original_shape))
print(_classification_report_oracle)
print('Bi acc oracle:', bi_acc_oracle / test_df_oracle.shape[0])
print('Cross acc oracle:', cross_acc_oracle / test_df_oracle.shape[0])
with open(os.path.join(outpath, task['name']+'_report.txt'), 'w') as fd:
print(pd.DataFrame(train_df[y_whom].value_counts()).to_markdown(), file=fd)
print(df_size_report, file=fd)
print(_classification_report, file=fd)
print('-- Performances over test set:', task['test'], '--', file=fd)
print('Bi baseline:', bi_baseline / test_df_shape_actual, file=fd)
print('Cross baseline:', cross_baseline / test_df_shape_actual, file=fd)
print('Bi acc:', bi_acc / test_df_shape_actual, file=fd)
print('Cross acc:', cross_acc / test_df_shape_actual, file=fd)
print('Bi acc adjusted:', bi_acc / test_df_shape_original, file=fd)
print('Cross acc adjusted:', cross_acc / test_df_shape_original, file=fd)
print(f'-- Oracle HITL evaluation when y_pred in [{tl}, {th}]', file=fd)
print('Ratio to human validator:', oracle_df.shape[0] / oracle_original_shape, file=fd)
print(_classification_report_oracle, file=fd)
print('Bi acc oracle:', bi_acc_oracle / test_df_oracle.shape[0], file=fd)
print('Cross acc oracle:', cross_acc_oracle / test_df_oracle.shape[0], file=fd)
with open(os.path.join(outpath, task['name']+'_model.pickle'), 'wb') as fd:
pickle.dump(clf, fd)
myplot(y_test, y_pred, y_pred_round, task['name'], outpath)
print('-'*30)
# if only is not None:
# csv_report_old = pd.read_csv(os.path.join(outpath, 'feature_ablation_summary.csv'), index_col=0)
# csv_report_old = csv_report_old[~csv_report_old['name'].isin(csv_report['name'].unique())]
# csv_report = pd.concat([csv_report_old, csv_report])
csv_report = csv_report.set_index('name')
csv_report = (csv_report*100).round(decimals=1)
if current_report is not None:
current_report = current_report[~current_report.index.isin(csv_report.index)]
csv_report = pd.concat([current_report, csv_report])
csv_report.to_csv(os.path.join(outpath, 'feature_ablation_summary.csv'))
| 33.708484
| 213
| 0.52909
| 4,221
| 37,349
| 4.222459
| 0.057569
| 0.058127
| 0.046962
| 0.052236
| 0.8475
| 0.820513
| 0.793357
| 0.767323
| 0.741794
| 0.695169
| 0
| 0.026896
| 0.338001
| 37,349
| 1,108
| 214
| 33.708484
| 0.693953
| 0.031139
| 0
| 0.632056
| 0
| 0.004032
| 0.39293
| 0.060621
| 0
| 0
| 0
| 0.000903
| 0
| 1
| 0.001008
| false
| 0.001008
| 0.013105
| 0
| 0.014113
| 0.042339
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| null | 0
| 0
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| 0
| 0
| 0
| 0
|
0
| 6
|
cb615634591b320aff95a2e6ed6041e1dcb67015
| 30
|
py
|
Python
|
src/pycalc/__init__.py
|
Tigxy/PyCalc
|
7ab71a8d249d3afd864b88809ab6ab62c4b23433
|
[
"MIT"
] | null | null | null |
src/pycalc/__init__.py
|
Tigxy/PyCalc
|
7ab71a8d249d3afd864b88809ab6ab62c4b23433
|
[
"MIT"
] | null | null | null |
src/pycalc/__init__.py
|
Tigxy/PyCalc
|
7ab71a8d249d3afd864b88809ab6ab62c4b23433
|
[
"MIT"
] | null | null | null |
from .calculator import solve
| 15
| 29
| 0.833333
| 4
| 30
| 6.25
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.133333
| 30
| 1
| 30
| 30
| 0.961538
| 0
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| 0
| 1
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| true
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| null | 0
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| null | 0
| 0
| 0
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| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
cb7e21bfc5129d64c7519f807b219b82ff3890bc
| 102
|
py
|
Python
|
pbrl/competitive/__init__.py
|
jjccero/rliccd
|
748bf92a1d9e401172a0d9c435ea75a8e37c6538
|
[
"MIT"
] | null | null | null |
pbrl/competitive/__init__.py
|
jjccero/rliccd
|
748bf92a1d9e401172a0d9c435ea75a8e37c6538
|
[
"MIT"
] | null | null | null |
pbrl/competitive/__init__.py
|
jjccero/rliccd
|
748bf92a1d9e401172a0d9c435ea75a8e37c6538
|
[
"MIT"
] | 1
|
2021-10-12T12:43:58.000Z
|
2021-10-12T12:43:58.000Z
|
from pbrl.competitive.pbt import CompetitivePBT
from pbrl.competitive.runner import CompetitiveRunner
| 34
| 53
| 0.882353
| 12
| 102
| 7.5
| 0.666667
| 0.177778
| 0.422222
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.078431
| 102
| 2
| 54
| 51
| 0.957447
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
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| null | 0
| 1
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| 0
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| null | 0
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| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
cb97bc74be98498be7ac86c52f9401ec9855c145
| 112
|
py
|
Python
|
python/gigasecond/gigasecond.py
|
Victor-Chinewubeze/algorithms-exercism
|
34669348762eef69b68a2f43260ab10ac1c4eb2a
|
[
"MIT"
] | 1
|
2020-04-16T23:06:33.000Z
|
2020-04-16T23:06:33.000Z
|
python/gigasecond/gigasecond.py
|
Victor-Chinewubeze/algorithms-exercism
|
34669348762eef69b68a2f43260ab10ac1c4eb2a
|
[
"MIT"
] | 7
|
2021-05-08T11:46:15.000Z
|
2021-05-10T19:31:11.000Z
|
python/gigasecond/gigasecond.py
|
Victor-Chinewubeze/algorithms-exercism
|
34669348762eef69b68a2f43260ab10ac1c4eb2a
|
[
"MIT"
] | 1
|
2020-01-09T16:33:39.000Z
|
2020-01-09T16:33:39.000Z
|
from datetime import datetime, timedelta
def add(moment):
return moment + timedelta(seconds = 1000000000)
| 18.666667
| 51
| 0.758929
| 13
| 112
| 6.538462
| 0.769231
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.107527
| 0.169643
| 112
| 5
| 52
| 22.4
| 0.806452
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0.333333
| 0.333333
| 1
| 0
| 1
| 0
| 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
| 1
| 0
| 0
| 1
| 1
| 1
| 0
|
0
| 6
|
1dc56470c9c49eef8392ccb12c51ff261a0603bc
| 126
|
py
|
Python
|
icbd/type_analyzer/tests/imports1.py
|
kmod/icbd
|
9636564eb3993afa07c6220d589bbd1991923d74
|
[
"MIT"
] | 7
|
2015-04-06T15:17:13.000Z
|
2020-10-21T04:57:00.000Z
|
icbd/type_analyzer/tests/imports1.py
|
kmod/icbd
|
9636564eb3993afa07c6220d589bbd1991923d74
|
[
"MIT"
] | null | null | null |
icbd/type_analyzer/tests/imports1.py
|
kmod/icbd
|
9636564eb3993afa07c6220d589bbd1991923d74
|
[
"MIT"
] | 4
|
2016-05-16T17:53:08.000Z
|
2020-11-28T17:18:50.000Z
|
import import_test
import_test.a # e 0
import_test.b # 12 module 'b'
import_test.c # 12 module 'c'
import_test.dup # 12 int
| 15.75
| 29
| 0.730159
| 25
| 126
| 3.48
| 0.44
| 0.574713
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.067308
| 0.174603
| 126
| 7
| 30
| 18
| 0.769231
| 0.301587
| 0
| 0
| 0
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| 0
| 0
| 0
| 1
| 0
| true
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| 0
| null | 1
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| 0
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| 1
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| 0
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
1df8718f96d0ca3b6b867f2d868d79a8c995ba6b
| 205
|
py
|
Python
|
main.py
|
mineshpatel1/wordle
|
14f24adad80078c6caee7cab6920b642ca481e7c
|
[
"MIT"
] | null | null | null |
main.py
|
mineshpatel1/wordle
|
14f24adad80078c6caee7cab6920b642ca481e7c
|
[
"MIT"
] | null | null | null |
main.py
|
mineshpatel1/wordle
|
14f24adad80078c6caee7cab6920b642ca481e7c
|
[
"MIT"
] | null | null | null |
import time
from utils import log
from web_drivers.nyt_web_driver import NYTWebDriver
from web_drivers.quordle_web_driver import QuordleWebDriver
if __name__ == '__main__':
QuordleWebDriver().play()
| 22.777778
| 59
| 0.819512
| 27
| 205
| 5.703704
| 0.592593
| 0.090909
| 0.181818
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.121951
| 205
| 8
| 60
| 25.625
| 0.855556
| 0
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| 0
| 0
| 0.039024
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.666667
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 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
| 6
|
381c695acdb2521c19baf4a4f777fdf5976dba96
| 94
|
py
|
Python
|
autumn/plots/streamlit/__init__.py
|
malanchak/AuTuMN
|
0cbd006d1f15da414d02eed44e48bb5c06f0802e
|
[
"BSD-2-Clause-FreeBSD"
] | null | null | null |
autumn/plots/streamlit/__init__.py
|
malanchak/AuTuMN
|
0cbd006d1f15da414d02eed44e48bb5c06f0802e
|
[
"BSD-2-Clause-FreeBSD"
] | null | null | null |
autumn/plots/streamlit/__init__.py
|
malanchak/AuTuMN
|
0cbd006d1f15da414d02eed44e48bb5c06f0802e
|
[
"BSD-2-Clause-FreeBSD"
] | null | null | null |
from .run_mcmc_plots import run_mcmc_plots
from .run_scenario_plots import run_scenario_plots
| 31.333333
| 50
| 0.893617
| 16
| 94
| 4.75
| 0.375
| 0.184211
| 0.315789
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.085106
| 94
| 2
| 51
| 47
| 0.883721
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
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| 1
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| 1
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| 0
| null | 0
| 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
| 6
|
69e01bf2e1b274002c1889f9ebba67740b23e011
| 219
|
py
|
Python
|
test/data/__init__.py
|
davemarr621/interval_tree_1
|
1624413449e57b7fb5ae8a2de72d2fbd460f19e0
|
[
"Apache-2.0"
] | 488
|
2015-01-12T18:43:32.000Z
|
2022-03-30T02:19:50.000Z
|
test/data/__init__.py
|
davemarr621/interval_tree_1
|
1624413449e57b7fb5ae8a2de72d2fbd460f19e0
|
[
"Apache-2.0"
] | 81
|
2015-01-01T03:13:38.000Z
|
2022-03-21T22:45:16.000Z
|
test/data/__init__.py
|
davemarr621/interval_tree_1
|
1624413449e57b7fb5ae8a2de72d2fbd460f19e0
|
[
"Apache-2.0"
] | 93
|
2015-03-09T15:45:30.000Z
|
2022-03-05T07:27:02.000Z
|
from . import issue4
from . import issue4_result
from . import issue25_orig
from . import issue41_orig
from . import ivs0
from . import ivs1
from . import ivs1_float_copy_structure
from . import ivs2
from . import ivs3
| 21.9
| 39
| 0.794521
| 33
| 219
| 5.090909
| 0.424242
| 0.535714
| 0.190476
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.060109
| 0.164384
| 219
| 9
| 40
| 24.333333
| 0.857924
| 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
| 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
| 6
|
0e15c25639b491a29344473ef417f628072d3f3b
| 188
|
py
|
Python
|
skeleton/blueprints/default/default.py
|
jrdiniz/flask-skeleton
|
9d6c21d4089a0e07757647342c8d6dc55410a2f1
|
[
"Apache-2.0"
] | 1
|
2020-04-25T03:44:36.000Z
|
2020-04-25T03:44:36.000Z
|
skeleton/blueprints/default/default.py
|
jrdiniz/flask-skeleton
|
9d6c21d4089a0e07757647342c8d6dc55410a2f1
|
[
"Apache-2.0"
] | null | null | null |
skeleton/blueprints/default/default.py
|
jrdiniz/flask-skeleton
|
9d6c21d4089a0e07757647342c8d6dc55410a2f1
|
[
"Apache-2.0"
] | null | null | null |
from flask import render_template
from flask import current_app
def index():
current_app.logger.info('Logging message from blueprint default')
return render_template('index.html')
| 31.333333
| 69
| 0.792553
| 26
| 188
| 5.576923
| 0.653846
| 0.124138
| 0.206897
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.132979
| 188
| 6
| 70
| 31.333333
| 0.889571
| 0
| 0
| 0
| 0
| 0
| 0.253968
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.2
| true
| 0
| 0.4
| 0
| 0.8
| 0.2
| 1
| 0
| 0
| null | 0
| 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
| 6
|
389ebe19dd94fa68ef2e7515b19f60c15072cb88
| 172
|
py
|
Python
|
checksit/rules/processors.py
|
cedadev/checksit
|
e2ef9c7287c2b3bf19cdad6c73ba0c368bb7bf60
|
[
"BSD-3-Clause"
] | null | null | null |
checksit/rules/processors.py
|
cedadev/checksit
|
e2ef9c7287c2b3bf19cdad6c73ba0c368bb7bf60
|
[
"BSD-3-Clause"
] | 4
|
2022-03-31T22:36:01.000Z
|
2022-03-31T22:41:24.000Z
|
checksit/rules/processors.py
|
cedadev/checksit
|
e2ef9c7287c2b3bf19cdad6c73ba0c368bb7bf60
|
[
"BSD-3-Clause"
] | null | null | null |
import os
def lowercase(value):
return value.lower()
def uppercase(value):
return value.upper()
def no_extension(value):
return os.path.splitext(value)[0]
| 13.230769
| 37
| 0.697674
| 24
| 172
| 4.958333
| 0.583333
| 0.277311
| 0.268908
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.007092
| 0.180233
| 172
| 13
| 37
| 13.230769
| 0.836879
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.428571
| false
| 0
| 0.142857
| 0.428571
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 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
| 1
| 1
| 0
|
0
| 6
|
38a9d315033f1ff28d9d143724798764d52b7d63
| 167
|
py
|
Python
|
vitlab/trainer/resnet/base.py
|
tor4z/ViTLab
|
7e3d12df71f64a4376eef36e580882c7614f44ad
|
[
"MIT"
] | null | null | null |
vitlab/trainer/resnet/base.py
|
tor4z/ViTLab
|
7e3d12df71f64a4376eef36e580882c7614f44ad
|
[
"MIT"
] | null | null | null |
vitlab/trainer/resnet/base.py
|
tor4z/ViTLab
|
7e3d12df71f64a4376eef36e580882c7614f44ad
|
[
"MIT"
] | null | null | null |
from ..base import BaseTrainer
class ResNetBaseTrainer(BaseTrainer):
def __init__(self, opt, device_id=None):
super().__init__(opt, device_id=device_id)
| 23.857143
| 50
| 0.736527
| 21
| 167
| 5.333333
| 0.666667
| 0.214286
| 0.196429
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.155689
| 167
| 6
| 51
| 27.833333
| 0.794326
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0.25
| 0
| 0.75
| 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
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 6
|
38ae7fd7611632279c027dab1eafdf35b2559414
| 424
|
py
|
Python
|
bitmovin_api_sdk/encoding/configurations/audio/dolby_digital_plus/__init__.py
|
bitmovin/bitmovin-api-sdk-python
|
5a85147669c84b8ca411cf2d4dbdddc92d85bbe7
|
[
"MIT"
] | 11
|
2019-07-03T10:41:16.000Z
|
2022-02-25T21:48:06.000Z
|
bitmovin_api_sdk/encoding/configurations/audio/dolby_digital_plus/__init__.py
|
bitmovin/bitmovin-api-sdk-python
|
5a85147669c84b8ca411cf2d4dbdddc92d85bbe7
|
[
"MIT"
] | 8
|
2019-11-23T00:01:25.000Z
|
2021-04-29T12:30:31.000Z
|
bitmovin_api_sdk/encoding/configurations/audio/dolby_digital_plus/__init__.py
|
bitmovin/bitmovin-api-sdk-python
|
5a85147669c84b8ca411cf2d4dbdddc92d85bbe7
|
[
"MIT"
] | 13
|
2020-01-02T14:58:18.000Z
|
2022-03-26T12:10:30.000Z
|
from bitmovin_api_sdk.encoding.configurations.audio.dolby_digital_plus.dolby_digital_plus_api import DolbyDigitalPlusApi
from bitmovin_api_sdk.encoding.configurations.audio.dolby_digital_plus.customdata.customdata_api import CustomdataApi
from bitmovin_api_sdk.encoding.configurations.audio.dolby_digital_plus.dolby_digital_plus_audio_configuration_list_query_params import DolbyDigitalPlusAudioConfigurationListQueryParams
| 106
| 184
| 0.933962
| 51
| 424
| 7.313725
| 0.372549
| 0.160858
| 0.214477
| 0.144772
| 0.576408
| 0.576408
| 0.576408
| 0.576408
| 0.576408
| 0.576408
| 0
| 0
| 0.028302
| 424
| 3
| 185
| 141.333333
| 0.90534
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 1
| 0
| 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
| 6
|
38b66f0464504f6f9a7c17905d2954cc86a9411f
| 3,549
|
py
|
Python
|
tests/test_api/test_infra_downstream.py
|
mowangdk/huskar
|
7692fbc5672a5ae6e2a33616c493466a7137f8cd
|
[
"MIT"
] | 59
|
2019-10-31T10:50:10.000Z
|
2021-11-26T04:32:25.000Z
|
tests/test_api/test_infra_downstream.py
|
mowangdk/huskar
|
7692fbc5672a5ae6e2a33616c493466a7137f8cd
|
[
"MIT"
] | 5
|
2019-10-31T10:37:30.000Z
|
2020-03-02T06:45:46.000Z
|
tests/test_api/test_infra_downstream.py
|
mowangdk/huskar
|
7692fbc5672a5ae6e2a33616c493466a7137f8cd
|
[
"MIT"
] | 9
|
2019-10-31T10:35:00.000Z
|
2019-12-01T14:13:58.000Z
|
from __future__ import absolute_import
from huskar_api.models.infra import InfraDownstream
from ..utils import assert_response_ok
def test_get_infra_downstream(client, test_token):
InfraDownstream.bindmany() \
.bind('base.foo', 'redis', 'cache-1', 'idcs', 'alta1', 'url',
'redis.100010') \
.bind('base.foo', 'redis', 'cache-1', 'idcs', 'altb1', 'url',
'redis.100010') \
.bind('base.bar', 'redis', 'cache-1', 'idcs', 'alta1', 'url',
'redis.100010') \
.bind('base.bar', 'redis', 'cache-2', 'idcs', 'alta1', 'url',
'redis.100011') \
.commit()
r = client.get('/api/infra-config-downstream/redis.100010', headers={
'Authorization': test_token})
assert_response_ok(r)
downstream = r.json['data']['downstream']
assert len(downstream) == 3
assert downstream[0]['user_application_name'] == 'base.foo'
assert downstream[0]['user_infra_type'] == 'redis'
assert downstream[0]['user_infra_name'] == 'cache-1'
assert downstream[0]['user_scope_pair'] == {
'type': 'idcs', 'name': 'alta1'}
assert downstream[0]['user_field_name'] == 'url'
assert downstream[1]['user_application_name'] == 'base.foo'
assert downstream[1]['user_infra_type'] == 'redis'
assert downstream[1]['user_infra_name'] == 'cache-1'
assert downstream[1]['user_scope_pair'] == {
'type': 'idcs', 'name': 'altb1'}
assert downstream[1]['user_field_name'] == 'url'
assert downstream[2]['user_application_name'] == 'base.bar'
assert downstream[2]['user_infra_type'] == 'redis'
assert downstream[2]['user_infra_name'] == 'cache-1'
assert downstream[2]['user_scope_pair'] == {
'type': 'idcs', 'name': 'alta1'}
assert downstream[2]['user_field_name'] == 'url'
r = client.get('/api/infra-config-downstream/redis.100011', headers={
'Authorization': test_token})
assert_response_ok(r)
downstream = r.json['data']['downstream']
assert len(downstream) == 1
assert downstream[0]['user_application_name'] == 'base.bar'
assert downstream[0]['user_infra_type'] == 'redis'
assert downstream[0]['user_infra_name'] == 'cache-2'
assert downstream[0]['user_scope_pair'] == {
'type': 'idcs', 'name': 'alta1'}
assert downstream[0]['user_field_name'] == 'url'
InfraDownstream.bind(
'base.baz', 'redis', 'cache-1', 'idcs', 'alta1', 'url', 'redis.100011')
InfraDownstream.unbind(
'base.bar', 'redis', 'cache-2', 'idcs', 'alta1', 'url')
# Stale data
r = client.get('/api/infra-config-downstream/redis.100011', headers={
'Authorization': test_token})
assert_response_ok(r)
downstream = r.json['data']['downstream']
assert len(downstream) == 1
assert downstream[0]['user_application_name'] == 'base.bar'
assert downstream[0]['user_infra_name'] == 'cache-2'
assert downstream[0]['user_scope_pair'] == {
'type': 'idcs', 'name': 'alta1'}
assert downstream[0]['user_field_name'] == 'url'
# Fresh data
r = client.post('/api/infra-config-downstream/redis.100011', headers={
'Authorization': test_token})
assert_response_ok(r)
downstream = r.json['data']['downstream']
assert len(downstream) == 1
assert downstream[0]['user_application_name'] == 'base.baz'
assert downstream[0]['user_infra_name'] == 'cache-1'
assert downstream[0]['user_scope_pair'] == {
'type': 'idcs', 'name': 'alta1'}
assert downstream[0]['user_field_name'] == 'url'
| 42.759036
| 79
| 0.625247
| 429
| 3,549
| 4.986014
| 0.130536
| 0.209444
| 0.143058
| 0.176718
| 0.85554
| 0.845722
| 0.779804
| 0.661057
| 0.604956
| 0.58345
| 0
| 0.038062
| 0.185686
| 3,549
| 82
| 80
| 43.280488
| 0.702076
| 0.005917
| 0
| 0.569444
| 0
| 0
| 0.343262
| 0.08227
| 0
| 0
| 0
| 0
| 0.513889
| 1
| 0.013889
| false
| 0
| 0.041667
| 0
| 0.055556
| 0
| 0
| 0
| 0
| null | 1
| 0
| 1
| 1
| 1
| 1
| 0
| 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
| 6
|
38cd7c28ccbd20d03ddfd14ff358b1df1162784b
| 26
|
py
|
Python
|
services/ecp/__init__.py
|
thegreathippo/calamity
|
a58a42a7dd828159f5730c9b49aa474bce6ee51a
|
[
"MIT"
] | null | null | null |
services/ecp/__init__.py
|
thegreathippo/calamity
|
a58a42a7dd828159f5730c9b49aa474bce6ee51a
|
[
"MIT"
] | null | null | null |
services/ecp/__init__.py
|
thegreathippo/calamity
|
a58a42a7dd828159f5730c9b49aa474bce6ee51a
|
[
"MIT"
] | null | null | null |
from .core import Cycle
| 6.5
| 23
| 0.730769
| 4
| 26
| 4.75
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.230769
| 26
| 3
| 24
| 8.666667
| 0.95
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 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
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
2a07c1166ba9f247be5c82c8448f45e09a57316b
| 27
|
py
|
Python
|
LPBv2/vision/__init__.py
|
TierynnB/LeaguePyBot
|
2e96230b9dc24d185ddc0c6086d79f7d01e7a643
|
[
"MIT"
] | 45
|
2020-11-28T04:45:45.000Z
|
2022-03-31T05:53:37.000Z
|
LPBv2/vision/__init__.py
|
TierynnB/LeaguePyBot
|
2e96230b9dc24d185ddc0c6086d79f7d01e7a643
|
[
"MIT"
] | 13
|
2021-01-15T00:50:10.000Z
|
2022-02-02T15:16:49.000Z
|
vision/__init__.py
|
JackGoldsworth/Vision
|
084330bec340596167944b623bc7b8d7d9c26b01
|
[
"MIT"
] | 14
|
2020-12-21T10:03:31.000Z
|
2021-11-22T04:03:03.000Z
|
from .vision import Vision
| 13.5
| 26
| 0.814815
| 4
| 27
| 5.5
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.148148
| 27
| 1
| 27
| 27
| 0.956522
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 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
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
2a4d905ab1b857b9a0e09f7299b54848c3f98f21
| 42
|
py
|
Python
|
snowplow_tracker/celery/__init__.py
|
snowplow/snowplow-python-tracker
|
67642d247cefc8019a3584e89ae9159cc09b4844
|
[
"Apache-2.0"
] | 33
|
2015-02-07T12:25:32.000Z
|
2022-03-08T14:54:51.000Z
|
snowplow_tracker/celery/__init__.py
|
snowplow/snowplow-python-tracker
|
67642d247cefc8019a3584e89ae9159cc09b4844
|
[
"Apache-2.0"
] | 145
|
2015-01-02T19:15:40.000Z
|
2022-03-04T16:12:21.000Z
|
snowplow_tracker/celery/__init__.py
|
snowplow/snowplow-python-tracker
|
67642d247cefc8019a3584e89ae9159cc09b4844
|
[
"Apache-2.0"
] | 56
|
2015-01-02T19:04:20.000Z
|
2021-11-29T14:29:54.000Z
|
from .celery_emitter import CeleryEmitter
| 21
| 41
| 0.880952
| 5
| 42
| 7.2
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.095238
| 42
| 1
| 42
| 42
| 0.947368
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 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
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
2a5c9e1ea8946830797f9c254bc01f3466041a84
| 37
|
py
|
Python
|
kalasearch/__init__.py
|
oeddyo/kalasearch-python-sdk
|
bbb0cd44bd19d23ee4f1a3ea0aa5599e3e1fd38b
|
[
"MIT"
] | 9
|
2020-08-19T06:48:25.000Z
|
2022-02-05T07:24:30.000Z
|
kalasearch/__init__.py
|
oeddyo/kalasearch-python-sdk
|
bbb0cd44bd19d23ee4f1a3ea0aa5599e3e1fd38b
|
[
"MIT"
] | 1
|
2020-08-24T00:55:32.000Z
|
2020-08-24T00:55:32.000Z
|
kalasearch/__init__.py
|
oeddyo/kalasearch-python-sdk
|
bbb0cd44bd19d23ee4f1a3ea0aa5599e3e1fd38b
|
[
"MIT"
] | 4
|
2021-11-09T20:41:13.000Z
|
2022-03-22T09:13:54.000Z
|
from kalasearch.client import Client
| 18.5
| 36
| 0.864865
| 5
| 37
| 6.4
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.108108
| 37
| 1
| 37
| 37
| 0.969697
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 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
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
aad8b890f6718df7645a99de7c2debc1cf66c34a
| 2,861
|
py
|
Python
|
test/common/test_print.py
|
thatch/BitSwanPump
|
98a5b8d09f9b59d5361611cee0bd45e7b4c69e3f
|
[
"BSD-3-Clause"
] | 17
|
2019-02-14T09:26:03.000Z
|
2022-03-11T09:23:52.000Z
|
test/common/test_print.py
|
thatch/BitSwanPump
|
98a5b8d09f9b59d5361611cee0bd45e7b4c69e3f
|
[
"BSD-3-Clause"
] | 91
|
2019-05-06T18:59:02.000Z
|
2022-01-11T06:22:32.000Z
|
test/common/test_print.py
|
thatch/BitSwanPump
|
98a5b8d09f9b59d5361611cee0bd45e7b4c69e3f
|
[
"BSD-3-Clause"
] | 10
|
2019-04-23T08:48:58.000Z
|
2022-02-13T14:24:28.000Z
|
import sys
from unittest.mock import patch, call
import bspump.common
import bspump.unittest
class TestPrintSink(bspump.unittest.ProcessorTestCase):
@patch('builtins.print')
def test_print_sink(self, mocked_print):
events = [
(None, "Please, print this to me"),
]
self.set_up_processor(bspump.common.PrintSink)
output = self.execute(
events
)
self.assertEqual(
mocked_print.mock_calls[1],
call("Please, print this to me", file=sys.stdout)
)
self.assertEqual(
[event for context, event in output],
[]
)
class TestPPrintSink(bspump.unittest.ProcessorTestCase):
@patch('pprint.PrettyPrinter.pprint')
def test_pprint_sink(self, mocked_print):
event = {"list": ["a", "b"], "dict": {"a": "b"}}
events = [
(None, event),
]
self.set_up_processor(bspump.common.PPrintSink)
output = self.execute(
events
)
mocked_print.assert_called_with(event)
self.assertEqual(
[event for context, event in output],
[]
)
class TestPrintProcessor(bspump.unittest.ProcessorTestCase):
@patch('builtins.print')
def test_print_processor(self, mocked_print):
event = "Please, print this to me"
events = [
(None, event),
]
self.set_up_processor(bspump.common.PrintProcessor)
output = self.execute(
events
)
self.assertEqual(
mocked_print.mock_calls[1],
call("Please, print this to me", file=sys.stdout)
)
self.assertEqual(
[event for context, event in output],
[event]
)
class TestPPrintProcessor(bspump.unittest.ProcessorTestCase):
@patch('pprint.PrettyPrinter.pprint')
def test_pprint_processor(self, mocked_print):
event = {"list": ["a", "b"], "dict": {"a": "b"}}
events = [
(None, event),
]
self.set_up_processor(bspump.common.PPrintProcessor)
output = self.execute(
events
)
mocked_print.assert_called_with(event)
self.assertEqual(
[event for context, event in output],
[event]
)
class TestPrintContextProcessor(bspump.unittest.ProcessorTestCase):
@patch('builtins.print')
def test_print_context_processor(self, mocked_print):
context = {"context": "dict"}
events = [
(context, None),
]
self.set_up_processor(bspump.common.PrintContextProcessor)
output = self.execute(
events
)
self.assertEqual(
mocked_print.mock_calls[1],
call(context, file=sys.stdout)
)
self.assertEqual(
[event for context, event in output],
[]
)
class TestPPrintContextProcessor(bspump.unittest.ProcessorTestCase):
@patch('pprint.PrettyPrinter.pprint')
def test_pprint_context_processor(self, mocked_print):
context = {"context": "dict"}
events = [
(context, None),
]
self.set_up_processor(bspump.common.PPrintContextProcessor)
output = self.execute(
events
)
mocked_print.assert_called_with(context)
self.assertEqual(
[event for context, event in output],
[]
)
| 19.201342
| 68
| 0.700454
| 335
| 2,861
| 5.841791
| 0.176119
| 0.06745
| 0.095043
| 0.110373
| 0.828309
| 0.789474
| 0.774144
| 0.774144
| 0.752172
| 0.607563
| 0
| 0.001264
| 0.17057
| 2,861
| 148
| 69
| 19.331081
| 0.82343
| 0
| 0
| 0.564815
| 0
| 0
| 0.092625
| 0.028312
| 0
| 0
| 0
| 0
| 0.111111
| 1
| 0.055556
| false
| 0
| 0.037037
| 0
| 0.148148
| 0.203704
| 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
| 0
|
0
| 6
|
2aea2020577d9626a47302cc418970d241d60219
| 612
|
py
|
Python
|
erasure/contracts.py
|
ankitchiplunkar/erasure.py
|
9b0e24883dbb3db8fe9cf2b4c4fb8cce583442a3
|
[
"MIT"
] | 4
|
2019-12-17T02:27:44.000Z
|
2021-05-07T12:26:03.000Z
|
erasure/contracts.py
|
ankitchiplunkar/erasure.py
|
9b0e24883dbb3db8fe9cf2b4c4fb8cce583442a3
|
[
"MIT"
] | 9
|
2019-12-12T18:47:19.000Z
|
2019-12-29T21:49:59.000Z
|
erasure/contracts.py
|
ankitchiplunkar/erasure.py
|
9b0e24883dbb3db8fe9cf2b4c4fb8cce583442a3
|
[
"MIT"
] | null | null | null |
CONTRACTS = {
'mainnet': {
"v1.2.0": {
"FeedFactory": "0xEF078E8330f99186079BE1d2ee6b4a5d6f23E8F1",
"ErasureUsers": "0x789D0082B20A929D6fB64EB4c10c68e827AAB7aB"
}
},
'rinkeby': {
"v1.2.0": {
"FeedFactory": "0xDE19C478b2eD51668e36704b2341b81DEBFe2c40",
"ErasureUsers": "0xbF7339e68b81a1261FDF46FDBe916cd88f3609c0"
}
},
'test': {
"v1.2.0": {
"FeedFactory": "0x67B5656d60a809915323Bf2C40A8bEF15A152e3e",
"ErasureUsers": "0xe78A0F7E598Cc8b0Bb87894B0F60dD2a88d6a8Ab"
}
}
}
| 29.142857
| 72
| 0.588235
| 25
| 612
| 14.4
| 0.6
| 0.025
| 0.033333
| 0.125
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.373272
| 0.29085
| 612
| 20
| 73
| 30.6
| 0.456221
| 0
| 0
| 0.15
| 0
| 0
| 0.583333
| 0.411765
| 0
| 0
| 0.411765
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| null | 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
2af668a233fc8794e7f2220b009ecfc3ad64218d
| 514
|
py
|
Python
|
temboo/core/Library/Zendesk/Macros/__init__.py
|
jordanemedlock/psychtruths
|
52e09033ade9608bd5143129f8a1bfac22d634dd
|
[
"Apache-2.0"
] | 7
|
2016-03-07T02:07:21.000Z
|
2022-01-21T02:22:41.000Z
|
temboo/core/Library/Zendesk/Macros/__init__.py
|
jordanemedlock/psychtruths
|
52e09033ade9608bd5143129f8a1bfac22d634dd
|
[
"Apache-2.0"
] | null | null | null |
temboo/core/Library/Zendesk/Macros/__init__.py
|
jordanemedlock/psychtruths
|
52e09033ade9608bd5143129f8a1bfac22d634dd
|
[
"Apache-2.0"
] | 8
|
2016-06-14T06:01:11.000Z
|
2020-04-22T09:21:44.000Z
|
from temboo.Library.Zendesk.Macros.ApplyMacroToAllTickets import ApplyMacroToAllTickets, ApplyMacroToAllTicketsInputSet, ApplyMacroToAllTicketsResultSet, ApplyMacroToAllTicketsChoreographyExecution
from temboo.Library.Zendesk.Macros.ApplyMacroToTicket import ApplyMacroToTicket, ApplyMacroToTicketInputSet, ApplyMacroToTicketResultSet, ApplyMacroToTicketChoreographyExecution
from temboo.Library.Zendesk.Macros.ListMacros import ListMacros, ListMacrosInputSet, ListMacrosResultSet, ListMacrosChoreographyExecution
| 128.5
| 197
| 0.918288
| 33
| 514
| 14.30303
| 0.545455
| 0.063559
| 0.108051
| 0.152542
| 0.190678
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.040856
| 514
| 3
| 198
| 171.333333
| 0.957404
| 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
| 0
| 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
| 6
|
2d731ad6536ad67493f492915555d1ec1d48ddb7
| 3,618
|
py
|
Python
|
openprocurement/tender/core/tests/tender.py
|
raccoongang/openprocurement.tender.core
|
9497d4a9f7c0cd4a0c95efad6425d224d5634ddb
|
[
"Apache-2.0"
] | 1
|
2021-11-18T16:34:33.000Z
|
2021-11-18T16:34:33.000Z
|
openprocurement/tender/core/tests/tender.py
|
raccoongang/openprocurement.tender.core
|
9497d4a9f7c0cd4a0c95efad6425d224d5634ddb
|
[
"Apache-2.0"
] | 30
|
2017-03-22T12:16:17.000Z
|
2018-08-08T04:27:28.000Z
|
openprocurement/tender/core/tests/tender.py
|
raccoongang/openprocurement.tender.core
|
9497d4a9f7c0cd4a0c95efad6425d224d5634ddb
|
[
"Apache-2.0"
] | 13
|
2017-02-22T15:59:17.000Z
|
2018-05-11T06:17:28.000Z
|
# -*- coding: utf-8 -*-
import os
import unittest
from openprocurement.tender.core.tests.base import BaseWebTest
class TenderResourceTest(BaseWebTest):
relative_to = os.path.dirname(__file__)
def test_empty_listing(self):
response = self.app.get('/tenders')
self.assertEqual(response.status, '200 OK')
self.assertEqual(response.content_type, 'application/json')
self.assertEqual(response.json['data'], [])
self.assertNotIn('{\n "', response.body)
self.assertNotIn('callback({', response.body)
self.assertEqual(response.json['next_page']['offset'], '')
self.assertNotIn('prev_page', response.json)
response = self.app.get('/tenders?opt_jsonp=callback')
self.assertEqual(response.status, '200 OK')
self.assertEqual(response.content_type, 'application/javascript')
self.assertNotIn('{\n "', response.body)
self.assertIn('callback({', response.body)
response = self.app.get('/tenders?opt_pretty=1')
self.assertEqual(response.status, '200 OK')
self.assertEqual(response.content_type, 'application/json')
self.assertIn('{\n "', response.body)
self.assertNotIn('callback({', response.body)
response = self.app.get('/tenders?opt_jsonp=callback&opt_pretty=1')
self.assertEqual(response.status, '200 OK')
self.assertEqual(response.content_type, 'application/javascript')
self.assertIn('{\n "', response.body)
self.assertIn('callback({', response.body)
response = self.app.get('/tenders?offset=2015-01-01T00:00:00+02:00&descending=1&limit=10')
self.assertEqual(response.status, '200 OK')
self.assertEqual(response.content_type, 'application/json')
self.assertEqual(response.json['data'], [])
self.assertIn('descending=1', response.json['next_page']['uri'])
self.assertIn('limit=10', response.json['next_page']['uri'])
self.assertNotIn('descending=1', response.json['prev_page']['uri'])
self.assertIn('limit=10', response.json['prev_page']['uri'])
response = self.app.get('/tenders?feed=changes')
self.assertEqual(response.status, '200 OK')
self.assertEqual(response.content_type, 'application/json')
self.assertEqual(response.json['data'], [])
self.assertEqual(response.json['next_page']['offset'], '')
self.assertNotIn('prev_page', response.json)
response = self.app.get('/tenders?feed=changes&offset=0', status=404)
self.assertEqual(response.status, '404 Not Found')
self.assertEqual(response.content_type, 'application/json')
self.assertEqual(response.json['status'], 'error')
self.assertEqual(response.json['errors'], [
{u'description': u'Offset expired/invalid', u'location': u'params', u'name': u'offset'}
])
response = self.app.get('/tenders?feed=changes&descending=1&limit=10')
self.assertEqual(response.status, '200 OK')
self.assertEqual(response.content_type, 'application/json')
self.assertEqual(response.json['data'], [])
self.assertIn('descending=1', response.json['next_page']['uri'])
self.assertIn('limit=10', response.json['next_page']['uri'])
self.assertNotIn('descending=1', response.json['prev_page']['uri'])
self.assertIn('limit=10', response.json['prev_page']['uri'])
def suite():
suite = unittest.TestSuite()
suite.addTest(unittest.makeSuite(TenderResourceTest))
return suite
if __name__ == '__main__':
unittest.main(defaultTest='suite')
| 45.797468
| 99
| 0.65948
| 414
| 3,618
| 5.669082
| 0.202899
| 0.153387
| 0.235194
| 0.061355
| 0.794205
| 0.783553
| 0.767363
| 0.731998
| 0.68726
| 0.666809
| 0
| 0.022438
| 0.174682
| 3,618
| 79
| 100
| 45.797468
| 0.763563
| 0.005804
| 0
| 0.609375
| 0
| 0.015625
| 0.235539
| 0.080367
| 0
| 0
| 0
| 0
| 0.65625
| 1
| 0.03125
| false
| 0
| 0.046875
| 0
| 0.125
| 0
| 0
| 0
| 0
| null | 0
| 1
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 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
| 6
|
2d9145d844f8158bb50dcb034584013c92cf4868
| 319
|
py
|
Python
|
carsus/io/cmfgen/__init__.py
|
parikshit14/carsus
|
3f67e8068829829361d7b1da9020e1fde9dcac2e
|
[
"BSD-3-Clause"
] | null | null | null |
carsus/io/cmfgen/__init__.py
|
parikshit14/carsus
|
3f67e8068829829361d7b1da9020e1fde9dcac2e
|
[
"BSD-3-Clause"
] | null | null | null |
carsus/io/cmfgen/__init__.py
|
parikshit14/carsus
|
3f67e8068829829361d7b1da9020e1fde9dcac2e
|
[
"BSD-3-Clause"
] | null | null | null |
from carsus.io.cmfgen.base import (CMFGENOscillatorStrengthsParser,
CMFGENEnergyLevelsParser,
CMFGENCollisionalStrengthsParser,
CMFGENPhotoionizationCrossSectionParser)
from carsus.io.cmfgen.hdfgen import hdf_dump
| 63.8
| 75
| 0.601881
| 18
| 319
| 10.611111
| 0.722222
| 0.104712
| 0.125654
| 0.188482
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.363636
| 319
| 5
| 76
| 63.8
| 0.940887
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.4
| 0
| 0.4
| 0
| 1
| 0
| 1
| null | 0
| 0
| 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
| 0
| 0
|
0
| 6
|
2d9dbbfde8913b3deb37d7bafb11c2b532ddadc7
| 156,531
|
py
|
Python
|
gluoncv/model_zoo/quantized/quantized.py
|
ptrendx/gluon-cv
|
5017de97bffcdf7fd90a0be5cdd3201dc7af769e
|
[
"Apache-2.0"
] | 69
|
2021-01-22T18:09:15.000Z
|
2022-03-29T09:38:03.000Z
|
gluoncv/model_zoo/quantized/quantized.py
|
ptrendx/gluon-cv
|
5017de97bffcdf7fd90a0be5cdd3201dc7af769e
|
[
"Apache-2.0"
] | 3
|
2019-09-03T01:35:15.000Z
|
2019-11-13T06:28:00.000Z
|
gluoncv/model_zoo/quantized/quantized.py
|
ptrendx/gluon-cv
|
5017de97bffcdf7fd90a0be5cdd3201dc7af769e
|
[
"Apache-2.0"
] | 3
|
2020-01-10T16:50:08.000Z
|
2020-11-13T06:59:13.000Z
|
# pylint: skip-file
# pylint: disable=line-too-long
"""Create quantized model from JSON files..."""
import os
import warnings
import mxnet as mx
from mxnet.context import cpu
from mxnet.gluon import SymbolBlock
from gluoncv.utils.compress_json import get_compressed_model
__all__ = ['mobilenet1_0_int8', 'resnet50_v1_int8',
'ssd_300_vgg16_atrous_voc_int8', 'ssd_512_mobilenet1_0_voc_int8',
'ssd_512_resnet50_v1_voc_int8', 'ssd_512_vgg16_atrous_voc_int8',
'fcn_resnet101_voc_int8', 'fcn_resnet101_coco_int8',
'psp_resnet101_voc_int8', 'psp_resnet101_coco_int8',
'deeplab_resnet101_voc_int8', 'deeplab_resnet101_coco_int8']
_compressed_int8_json = {
'fcn_resnet101_voc_int8': b'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',
'fcn_resnet101_coco_int8': 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',
'psp_resnet101_voc_int8': 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',
'deeplab_resnet101_voc_int8': 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GT5i4wzV1gOq+2tNsIfEOz0iatEUubCM3icjEPz8X85riYeL0uyhvBoqaXj+G2rgUu5pvhYjOGIeFi6zKc0Usm5U2LgUgQr9dkvWgsCQJE6sgoQKSKjAJEriKnAJFKQEQNy1gEIi64ZE7uswCBSOSkg7+RxPAbMcNdYIa9kUQZw4FI5EAkIgKRA93ChSEBHoYE8XrlQ+7K6xWwYUiAhiGhCRgSmoEhM4YhgSHrwpDRSyYwZDEMieL1mqwXjSURBoasbQRZKCMKDGlcRjQYshE5MWDI2mLW8XrxQo/JXi8brU0nyVXrXi9T9FmZ0ku7lCn6wkzRF2by+sJ8byrtZTUtvZad+7eMLXm9WB1Lby+y10srXIwT4TFO3FxNi3i9Lsp2waKml0Xitq4FjBObwTgzhiHBOLf3el2xfLS610s36PUaDQPxerGvu/F6JfF6TdaLppQEQ/PWrntYKCMKzWtcRjSatxE5MWje2mJWKG0yF5O8XT6sOne9VZvqlDZZftdteQGT5S4wy11gli9jbfky1pYvY20hl7EmNIvLxRI8F0ub42Li9boobwSLml4+htu6FrhYaoaLzRiGhIuty3BGL5mUNy0GIqoTs9dkwWgw2akmSKSOjsJE6ugoUOQ6egoVqURF7AwqEr2LDnwFHMvfaW75O80tt4JZbgVzHQMhTnEq4jkV8YhU5IC4cIkIZTS4SGTXRLF80SF3Zfk6Zo5ggdPPyICb1wIWOc0foLnInLFIwMi6YGT8mgkZWU5GlDi/JguWhxMFQ0bW9oUs1RGFjLSuIxoZ2YqeGGRkbTUrub/csJTlmhHfqeu9+nwN95crOq+cKe4tOsVc0Snm8orDfG8o7WVVLr2WnTu6nC65v1hlS28vtPvLAjMdhc901ObqXMT/dVnOCxY4/VwSuHlNMB3VDtOZMRYJ07m5CeyadaXVTWCmQRPYeByIC4x93YELzByC8LY1T3p/99GoC8zkSQWn5GkvaMMyonC9xmVEw3obkROD6q0tZoV6Jz+H6FkTgj35VHD1To5XNTle1eT5Xbnn/jDPF7j2fIFrxxe4dpALXBOiReRjpwkNIh47tnBrdGw3RAkdm543gkVNLx/DbR0sGnste8AjYyctFTDWTLGTOUAcqXU6ad9VgMhtS50OQOTwv/ZMYGwswal0WvtuaaGMAkSqyChA5CpyChCpBETCDCASlFIG3ADm+YvOPX/RuefWMM+tYT5yIJL4hmVAxFtEIHKgW7gwBLlW6NjCrdm/KsCQ7dq/znNGsKjp5WK4rWsBhoCXCZ20VGBIYzCkekGLwJCz2VnfdkWcTtvvNKTp8hANQ0MOijasIwoOaV1HNB6yFT0xgEhTar77+MfzGqWHL5NLc7WHL3IBVh422OkACw/bVlGmlWuoKZNKBcyu07CGRcSuY3DdKGL/c/HAy2zEQ6r06goDJ+eBk/Nwchgn5/FkUTWpK5wJtM5zFSyQztu3tZrC/fizlKNvsaiwnBBCBQxmQeFg9oLeRjB+ftZOoecV6DnbuglpfpUyn5PwSZS50vov8eLMTb1VXqvT566tr/8SimuvBFXcW1wrJhTXign5LUR8ryvtjeW/VlzTJXSl9V/2Zzg/Nirw9V8CVh5aeiSDlYmetlBy0XzIlnPR1558gYVN74ESbusAM9Lhhx9IOWmhpZKVoqz8MvkuAnkZGHO6DEzX1DIwL0UzozEhq8Cwr7tZBcbc3vT08jy2pUexZxOMgXkYu7YlYqGMKE9jG5cR7XHsRuTEeB67tpjLXU+mG5ax+EjWRe30SbaN53qK/JXfkb/yO3I/VOR+qJiXYd5tBL6h2ePaqJEf10ZcTGbgMZnZHCaTZWAuyhvBoqaXj+G2rgVKZpqhZDOGIaFk60Kc0UsmzqfFRMTKMjCT9aKxxAoQqSOjAJEqMgoQuYqcAkQqARE1LGMZiARv7emnwgMivDA98nVxE78RS3xd3MTXxU18XdzI69cjZP36gW7hwhALD0PsIhiyh2QbXAbm9Y+1IRhisWGIhYYhtgkYYpuBITOGIYEh68KQ0UsmMGQxDHGyDMx0wWgwcTA0ZHUv70IdUXBI6zqi8ZCt6IkBRFZXs47/i69xe8HbgpRNJ2lR6/6vVHyndyq+0zv54t5Q3JuX2+V7U2lvLPrKki15upIt+r+K7wqnqpZj0oLGchQuy3HwLMdtrrBF/F+XpbxgYdPLJHFb1wLMcc3AnBnjkMCc27/5+4pFpNUtX6pBy9doGIjli33djeXLi+VrumA0p3gYprd2+cNCGVGQXuMyohG9jciJAfTWFrNChZOZwfKcUz5cjeVVqXDSHbvt1p3iG5pvGL6RV17abTi+kVdUetnAq3AiOItLxjw8GfObI2Ni+boobwSLml4+htu6FsCYbwaMzRiGBIytC3FGL5lUOS0mIkEsX5P1orEkCBCpI6MAkSoyChC5ipwCRCoBETssYxGIWBeDDyefCg+IeE46At+IfIOZwfT3Rx4vDEQpDkQ8ByIeEYgc6BYuDAnwMCSI5SsfcleWr4ANQwI0DAlNwJDQDAyZMQwJDFkXhoxeMoEhi2FIFMvXdMFoMIkwNGR1M8hCHVFwSOs6ovGQreiJAURWV7OS5csNa1l85Venuy6c2DIat3xpVbJbaVV6jZdWJXuYViV7mFZ5rWG+N5T2ssKWXsvObFyHvWeWL17M0tsLbfmyuCwnwrOcuLnCFrF8XZbygoVNL5PEbV0LMCc2A3NmjEMCc25u+bpmEWl1y5du0PI1GgZi+WJfd2P5SmL5mi4YzSkJhumtXf6wUEYUpNe4jGhEbyNyYgC9tcWsUOHkh2UsVjh5ZXx3UjyBV+GkeB2T4nVMmpvBNDeDabaotdZsUevD2ajCSUEuak1wFpeMJXgyljZHxsTydVHeCBY1vXwMt3UtgLHUDBibMQwJGFsX4oxeMqlyWkxEVCeer8mC0WCyU02QSB0dhYnU0VGgyHX0FCpSiYqEYRnLC+FYHaw5+VRwVERbjjsc3+COMM0dYTpyKpL4hmVURFtEKnJAXLhEhDIaXCSya6I4v+iQu3J+HTNHsMDpZ2TAzWsBi5zmD9BcZM5YJGBkXTAyfs2EjCwnI0oMYNMVy+OJgkEjq3tDlgqJwkaaFxINjmxGUAw6srqclUxg8WJEsmu1St7Gk0/WuAnMFA1YRhX3Fg1jpmgYM3n1Yb7XlfayUpdey86NXaYrmcBYeUtvL7QJLACDHYUPdtTmil3EBnZh4gsWOf18Erh5TZAd1Q7ZmTEYCdm5vRfsitWl1b1gpkEv2HgciBmMfd2NGUxpcYNNVyxPKxoG8K1dFbFURxS+17qOaHhvK3pi0L211axQ+5SGZSw7wrrkor4W2KtT+2R4hZPhFU6Ge8UM94pZvua15WteG77mtYFc85pILTAm0/iYTG8Ok4kn7LLsESxw+lkZcPOaoGS6HUo2YywSSrYy0Rm9ZlL/tByPGHGGTRYsDydG6EglHYWO1NFR6Mh19BQ6UoeO2G5YxrIzLNiQwNfLsfxF6Ja/CN1yz5jlnjHrOR0JfEMzOmI1Ih05oC5gMmLwyYgRZ1g+5L6cYQacjBhsMmLaICOmHTIyYywSMrIyGRm9ZkJGlpMRK86w6Yrl8cTCoJHVDSNLhURhI80LiQZHNiMoBh1ZXc46zjCrhrUsOsNU9NGcJEetO8Ns0ZVlU2mvK7rIXNFF5vIixXyvKe1lJS+9lp27vWwsOcNYmUtvL7IzzHbAYMfigx27uZIXcYZdmPiCRU4/nwRuXhNkx7ZDdmYMRkJ2bu8Mu2KVaXVnmG3RGTYaB+IMY1/34wxz4gybrlieVhwM4Fu7KmKpjih8r3Ud0fDeVvTEoHtrq1mh9kkPy1iufUrRpZM8HK/2yfEKJ8crnBz3jDnuGXN8VWzHV8V2fFVsB7kqNpFaYEzm8DGZ2xwmE2fYZdkjWOD0szLg5jVByVw7lGzGWCSUbGWiM3rNpP5pOR7x4gybLFgeTrzQkUo6Ch2po6PQkevoKXSkEh0xwzKW6YjWXQwnnwqOjnh256U9f1+6554xzz1j3jIg4h3f6Bgd8R0iHTmgLmAy4vHJiBdnWD7kvpxhHpyMeGwy4tsgI74dMjJjLBIysjIZGb1mQkaWk5EgzrDpiuXxJMCgkdUNI0uFRGEjzQuJBkc2IygGHVldzkrOMDusZdEZplNw+mqvVV/DGeaLriwfinuLLjJfdJGFvFgx36tKe1nJS69l524v70vOMFbm0tsL7QwzwGAn4IOdsLmSF3GGXZj4gkVOP58Ebl4TZCe0Q3ZmDEZCdm7uDLtmlWl1Z5hr0Rk2GgfiDGNf9+MMi+IMm65YnlYiDOBbuypiqY4ofK91HdHw3lb0xKB7a6tZofbJDctYrH0KVrsOfFXswCucAq9wCtwzFrhnLPBVsQNfFTvwVbED5KrYRGqBMVnEx2Rxc5hMnGGXZY9ggdPPyoCb1wQli+1QshljkVCylYnO6DWT+qfleCSJM2yyYHk4SUJHKukodKSOjkJHrqOn0JFKdMQPy1imIz52PoLTEf7e9MDfmx65Zyxyz1jUDIhEw+lI5HQkItKRA+oCJiMJn4wkcYblQ+7LGZbAyUjCJiOpDTKS2iEjM8YiISMrk5HRayZkZDEZ0Z04w6YrRuPJTjYUNLK6YWSpkChspHkh0eDIZgTFoCOry1nJGRaGtSw6wzplQzzxbrTuDItFV1Z0xb1FF1ksushiXqyY702lvazkpdeyc7dXtCVnGCtz6e2FdoZ5XLBDCRku2Nk1cWslL+IMuzDxBYucfj4J3LwWyM5p7gNNduYMRkJ2bu8Mu2KVaXVnmG/QGTYeB+IMY1934wzTSpxh0xXL04qCAXxrV0Us1RGF77WuIxre24qeGHRvbTUr1D7FYRmLtU/JxRROcmO82qfEK5wSr3BK3DOWuGcs8VWxE18VO/FVsRPkqthEaoExmcLHZGpzmEycYZdlj2CB08/KgJvXBCVT7VCyGWORULKVic7oNZP6p+V4RIszbLJgeTjRQkcq6Sh0pI6OQkeuo6fQkUp0JA3LWKQjsVNB6ZNPhUdH+HvTE39veuKescQ8Y6bLy1fvNhSnI57TEY9IRw6oC5iMaHwyosUZlg+5K2fYMXMEC5x+RgbcvCbIiG6HjMwYi4SMrExGRq+ZkJHlZMSIM2y6Ynk8MTBoZHXDyFIhUdhI80KiwZHNCIpBR1aXs44zzHXDWr7iDNOdOsmzGneGma7kyjKdKe4tuchMV3KRmS4vVsz3hsJeXvLSa9mZ2+uw99wZxspcenuhnWEJGOwYfLBjNlfyIs6wCxNfsMjp55PAzWuC7Jh2yM6MwUjIzu2dYVesMq3uDAstOsNG40CcYezrfpxhVpxh0xXL04qFAXxrV0Us1RGF77WuIxre24qeGHRvbTWX1z45NSxj2RkWUorYzjDTsQon0/EKJ9XxDcU32KrYRrFVsQ9nU3Q2yFWxidQCYzKLj8ns5jCZOMMuyx7BAqeflQE3rwlKZtuhZDPGIqFkKxOd0Wsm9U/L8YgTZ9hkwfJw4oSOVNJR6EgdHYWOXEdPoSOV6IgelrHsDLMxanA6oizHHo5veL4R+EbkdCTxDcvoiLKIdOSAuoDJiMMnI06cYfmQ+3KGOXAy4rDJiGuDjLh2yMiMsUjIyMpkZPSaCRlZTka8OMOmK5bHEw+DRlY3jCwVEoWNNC8kGhzZjKAYdGR1OSs5w8wMRJKS1vFqr1Vfwxmmi64srYp7iy4yXXSR6bxYMd/rSntZyUuvZefOMN0VnGG8zKW3F9kZ5jQw2PH4YMdvruRFnGEXJr5gkdPPJ4Gb1wTZ8e2QnRmDkZCdmzvDrlllWt0ZFlt0ho3GgTjD2Nf9OMOCOMOmK5anlQAD+NauiliqIwrfa11HNLy3FT0x6N7aalaofbLDMhbBnjPRpqst+VSn9knzCifNK5w094xp7hkzfFVsw1bFPpyNap805KrYRGqBMVnAx2Rhc5hMnGGXZY9ggdPPyoCb1wQlC+1QshljkVCylYnO6DWT+qfleCSKM2yyYHk4iUJHKukodKSOjkJHrqOn0JFKdMQNy1imI12yCdwZZnhaZQzf4J4xwz1jxnM6EviGZnTEaEQ6ckBdwGQk4pORKM6wfMh9OcMiOBmJ2GQktkFGYjtkZMZYJGRkZTIyes2EjCwnI0mcYdMVy+NJgkEjqxtGlgqJwkaaFxINjmxGUAw6srqclZxhfljLsjPMK2dOErHWnWGm6MoyqbTXFl1ktugis3mxYr7XlPaykpdey87dXiaWnGGszKW3F9oZ5oDBTsIHO2lzJS/iDLsw8QWLnH4+Cdy8JshOaofszBiMhOzc3hl2xSrT6s6w1KIzbDQOxBnGvu7GGbb7gTjDJitG08rupyiAb+2qiKU6ovC91nVEw3tb0ROD7q2tZoXapzAD7PnOJAvuDLO8wsnyCifLPWOWe8YsXxXb8lWxLV8V20Kuik2kFheTUVqDi8l2P9gaJhNn2GXZI1jg9LMy4Oa1QMlOcwhoSjZnLBJKti7RGb9mUv+0HI8ocYZNFiwPJ0roSCUdhY7U0VHoyHX0FDpSiY7EYRlfcYap0OmTTwVHRxy78zKOvzfd8YTLcc+YswyIOMc3OkZHXIdIRw6oC5iMKHwyosQZlg+5K2fYMXMEC5x+RgbcvCbIiGqHjMwYi4SMrExGRq+ZkJHlZESLM2y6Ynk80TBoZHXDyFIhUdhI80KiwZHNCIpBR1aXs5IzLA1rWXaGueh76KNxZ5grurJcKO4tushc0UXm82LFfK8q7WUlL72Wnbu9nC85w1iZS28vtDMsAoMdjQ929OZKXsQZdmHiCxY5/XwSuHlNkB3dDtmZMRgJ2bm9M+yKVabVnWGqa9AaNh4IYg1jX/djDTNiDZuuWJ5XDAzhW7ssYqmOKICvdR3R+N5W9MTAe2urubz4yXfDMhbJnk3OGXMtslen+Mnz/NrzEifPTWOem8Y8Xxbb82WxPV8W20Mui02oFpiTGXxOZjbHycQadln2CBY4/awMuHlNYDLTDiabMRYJJluZ6IxeMymAWo5HrFjDJguWhxMrdKSSjkJH6ugodOQ6egodqURH1LCMZTriUnLg1jDPX5zu+YvTAzeNBW4aC5oBkWA4HYmcjkREOnJAXcBkxOKTESvWsHzIfVnDLDgZsdhkxLZBRmw7ZGTGWCRkZGUyMnrNhIwsJyNOrGHTFcvjiYNBI6s7RpYKicJGmhcSDY5sRlAMOrK6nHWsYV4Pa/maNSxeb/WcNaxhoWjLCq64t2gjC0UbWcirFfO9qbSXlbz0WnZu9wq2ZA1jZS69vcjWMK+AwY7DBztucyUvYg27MPEFi5x+PgncvCbIjmuH7MwYjITs3N4adsUq0/rWMNWiNWw0EMQaxr7uxxrmxRo2XbE8r3gYwrd2WcRSHVEAX+s6ovG9reiJgffWVrNC8ZMZlrFI9kIXVAwnnwqu+CnyEqfIS5wiz7wjN41Fvi525OtiR74udoRcF5tQLTAn8/iczG+Ok4k17LLsESxw+lkZcPOawGS+HUw2YywSTLYy0Rm9ZlIAtRyPBLGGTRYsDydB6EglHYWO1NFR6Mh19BQ6UomO2GEZy3QkWGPQ6Qh/c3rkb06P3DQWuWksdQyIJMXpiOd0xCPSkQPqAiYjAZ+MBLGG5UPuyxoWwMlIwCYjoQ0yEtohIzPGIiEjK5OR0WsmZGQ5GYliDZuuWB5PIgwaWd0xslRIFDbSvJBocGQzgmLQkdXlrGQNc8Nalq1h1nlzkhy1bg1LRVtWMsW9RRtZKtrIUl6tmO8Npb2s5KXXsnO7V9Ilaxgrc+nthbaGWWCwE/HBTtxcyYtYwy5MfMEip59PAjevCbIT2yE7MwYjITs3t4Zds8q0vjVMt2gNGw0EsYaxr/uxhiWxhk1XLM8rCYbwrV0WsVRHFMDXuo5ofG8remLgvbXVrFD85IdlLJI958MVyV6d4qfES5wSK3GyXcc3FN9g62Lbjq+Lnfi62AlyXWxCtcCcLOFzsrQ5TibWsMuyR7DA6WdlwM1rApOldjDZjLFIMNnKRGf0mkkB1GI8Yjuxhk0WjIaTnWpCR+roKHSkjo5CR66jp9CRSnQkDMtYtoZZb/zpp0KjI7azHHs4vuH5RuAbkdORxDfywtQvG3h05IC6cMkIZTS4ZGTXRLGG0SF3ZQ07Zo5ggdPPyICb1wIZOc0foMnInLFIyMi6ZGT8mgkZWU5GlFjDpiuWxxMFg0ZWd4wsFRKFjTQvJBoc2YygGHRkdTkrWcPiDESStLU6nnyytq1hVuUbNr5XFfeWbGRWlWxkVuXVivleV9jLS156LTuzex32nlvDWJlLby+0NSwAgx2FD3bU5kpexBp2YeILFjn9fBK4eU2QHdUO2ZkxGAnZub017IpVpvWtYaZBa9h4IIg1jH3djTXMarGGTVcszysahvCtXRaxVEcUwNe6jmh8byt6YuC9tdWsUPyUhmUskj2vdYrm5FPBFT8pXuKkeImTinyDm8Y0WxfbarYu9uFsVPykINfFJlQLzMk0PifTm+NkYg27LHsEC5x+VgbcvCYwmW4Hk80YiwSTrUx0Rq+ZFEAtxyNGrGGTBcvDiRE6UklHoSN1dBQ6ch09hY7UoSOhG5axXPfUJR9P8i88OqI1xx48sdLcNKa5aUx7TkcC39CMjmiNSEcOqAuYjBh8MmLEGpYPuS9rmAEnIwabjJg2yIhph4zMGIuEjKxMRkavmZCR5WTEijVsumJ5PLEwaGR1x8hSIVHYSPNCosGRzQiKQUdWl7OONSyoYS1fsYa5ZK/2YvU1rGG6aMvSqbTXFG1kpmgjM3m1Yr7XlPaykpdey86tYToWrGG8zKW3F9kaFjpgsGPxwY7dXMmLWMMuTHzBIqefTwI3rwmyY9shOzMGIyE7t7eGXbHKtL41zLZoDRsNBLGGsa/7sYY5sYZNVyzPKw6G8K1dFrFURxTA17qOaHxvK3pi4L211axQ/KRnkD1nbOr0yaeCK34yvMTJ8BInw01jhpvGDF8X2/B1sQ1fF9tArotNqBaYkzl8TuY2x8nEGnZZ9ggWOP2sDLh5TWAy1w4mmzEWCSZbmeiMXjMpgFqOR7xYwyYLlocTL3Skko5CR+roKHTkOnoKHalER8ywjGU6Eo224AvnWHbnZS1/c7rlpjHLUy5rGRCxjm90jI7YDpGOHFAXMBnx+GTEizUsH3Jf1jAPTkY8NhnxbZAR3w4ZmTEWCRlZmYyMXjMhI8vJSBBr2HTF8ngSYNDI6o6RpUKisJHmhUSDI5sRFIOOrC5nJWuYHdaygEjUW+2TMif3Xq1bw2zRlmVDcW/RRmaLNjKXVyvme1VpLyt56bXs3O5lfckaxspcenuhrWEGGOwEfLATNlfyItawCxNfsMjp55PAzWuC7IR2yM6MwUjIzs2tYdesMq1vDXMtWsNGA0GsYezrfqxhUaxh0xXL80qEIXxrl0Us1REF8LWuIxrf24qeGHhvbTUrFD+5YRnLiz4Zk9DXxXa8xMnxDNtx05jjpjHH18V2fF1sx9fFdpDrYhOqBeZkEZ+Txc1xMrGGXZY9ggVOPysDbl4TmCy2g8lmjEWCyVYmOqPXTAqgluORJNawyYLl4SQJHamko9CROjoKHbmOnkJHKtERPyxj2RqmjXHg1jDH35zu+JvTPTeNeW4a85oBEW84HYmcjkREOnJAXcBkJOGTkSTWsHzIfVnDEjgZSdhkJLVBRlI7ZGTGWCRkZGUyMnrNhIwsJiOuE2vYdMVoPNnJhoJGVneMLBUShY00LyQaHNmMoBh0ZHU5K1nDwrCWRWuYUUqrkzyrdWuYL9qyvCvuLdrIfNFG5vNqxXxvKu1lJS+9lp3bvbwtWcNYmUtvL7Q1zOOCHUrIcMHOrolbK3kRa9iFiS9Y5PTzSeDmtUB2TnMfaLIzZzASsnN7a9gVq0zrW8N8g9aw8UAQaxj7uhtrmFNiDZuuWJ5XFAzhW7ssYqmOKICvdR3R+N5W9MTAe2urWaH4KV5M9nZ5sY/JglvDAi9xCrzEKXDTWOC5d+DrYge+Lnbg62IHyHWxCdUCczKFz8nU5jiZWMMuyx7BAqeflQE3rwlMptrBZDPGIsFkKxOd0WsmBVDL8YgWa9hkwfJwooWOVNJR6EgdHYWOXEdPoSOV6EgalrFsDbPKmZPKBEA6wt+cHvib0wM3jQVuGosdAyJRcTriOR3xiHTkgLqAyYjGJyNarGH5kLuyhh0zR7DA6WdkwM1rgozodsjIjLFIyMjKZGT0mgkZWU5GjFjDpiuWxxMDg0ZWd4wsFRKFjTQvJBoc2YygGHRkdTnrWMNiN6xl0RpmU3AqnHyyxq1hsWjLiqa4t2gji0UbWcyrFfO9obSXlbz0WnZu94q6ZA1jZS69vdDWsAQMdgw+2DGbK3kRa9iFiS9Y5PTzSeDmNUF2TDtkZ8ZgJGTn9tawK1aZ1reGhRatYaOBINYw9nU/1jAr1rDpiuV5xcIQvrXLIpbqiAL4WtcRje9tRU8MvLe2msuLn/jLvyYXP4UQojmpqsArfoq8xCnyEqfETWOJm8YSXxc78XWxI18XO0Kui02oFpiTWXxOZjfHycQadln2CBY4/awMuHlNYDLbDiabMRYJJluZ6IxeMymAWo5HnFjDJguWhxMndKSSjkJH6ugodOQ6egodqURH9Aw64o2GpyOJvzk98TenJ24aS9w0liKnI4lvWEZHkkWkIwfUBUxGHD4ZcWINy4fclzXMgZMRh01GXBtkxLVDRmaMRUJGViYjo9dMyMhyMuLFGjZdsTyeeBg0srpjZKmQKGykeSHR4MhmBMWgI6vLWckaZoa1LFrDXNdFd/rJ2raG7T5QwZblOlXcW7KRua5kI3NdXq2Y73WFvbzkpdeyM7vXYe+5NYyVufT2IlvDogYGOx4f7PjNlbyINezCxBcscvr5JHDzmiA7vh2yM2MwErJzc2vYNatM61vDYovWsNFAEGsY+7ofa1gQa9h0xfK8EmAI39plEUt1RAF8reuIxve2oicG3ltbzQrFT3ZYxiLZU8HacLVFn6oUP7mOlTi5LvCNyDeYacwpti62U2xd7MPZFP0O5LrYhGqBOVnA52Rhc5xMrGGXZY9ggdPPyoCb1wQmC+1gshljkWCylYnO6DWTAqjleCSKNWyyYHk4iUJHKukodKSOjkJHrqOn0JFKdMQNy/jKwjlaeex3qjulOfYwfIOnVsrxDc/pSOAbmtERpRHpyAF1AZORiE9GoljD8iH3ZQ2L4GQkYpOR2AYZie2QkRljkZCRlcnI6DUTMrKcjCSxhk1XLI8nCQaNrO4YWSokChtpXkg0OLIZQTHoyOpyVrKG+WEty28NCzGok+SodWuYKtqyVCrt1UUbmS7ayHRerZjvNaW9rOSl17Jza5iKBWsYL3Pp7YW2hjlgsJPwwU7aXMmLWMMuTHzBIqefTwI3rwmyk9ohOzMGIyE7t7eGXbHKtL41LLVoDRsNBLGGsa+7sYb5Tqxh0xWjeWUnGwrhW7ssYqmOKICvdR3R+N5W9MTAe2urWaH4KQzLWCZ7vutivBbZq1P8pHkmrXmJk+amMc1NY5qti+104htsXezDBl7xE6FaXE5GaQ0uJ9s1cWucTKxhl2WPYIHTz8qAm9cCJjvNIaAx2ZyxSDDZukRn/JpJAdRyPKLEGjZZsDycKKEjlXQUOlJHR6Ej19FT6EglOhKHZSxbw1yK6Wp1T3XoiGF3Xs4ovsFNY4abxoxlQMQ4vtExOmI6RDpyQF3AZEThkxEl1rB8yF1Zw46ZI1jg9DMy4OY1QUZUO2RkxlgkZGRlMjJ6zYSMLCcjWqxh0xXL44mGQSOrO0aWConCRpoXEg2ObEZQDDqyupyVrGFpWMsCItFvO2VV2JQ1zBRtWSYU9xZtZKZoI7N5tWK+V5X2spKXXsvO7V7Gl6xhrMyltxfaGhaBwY7GBzt6cyUvYg27MPEFi5x+PgncvCbIjm6H7MwYjITs3N4adsUq0+rWMN01aA0bDwSxhrGv+7GGGbGGTVcszysGhvCtXRaxVEcUwNe6jmh8byt6YuC9tdVcXvyUumEZi8VPLngTwK1hlpc4WV7iZHmObblpzPJ1sS1fF9vydbEt5LrYhGqBOZnB52Rmc5xMrGGXZY9ggdPPyoCb1wQmM+1gshljkWCylYnO6DWTAqjleMSKNWyyYHk4sUJHKukodKSOjkJHrqOn0JFKdEQNy1hcOMdrrRX4O9Utf3O65W9Od9w05rhpzGkGRJzhdCRyOhIR6cgBdQGTEYtPRqxYw/Ih92UNs+BkxGKTEdsGGbHtkJEZY5GQkZXJyOg1EzKynIw4sYZNVyyPJw4GjazuGFkqJAobaV5INDiyGUEx6MjqctaxhiU9A5FEa2PQJ5+scWuYK9qynCvuLdrIXNFG5vJqxXxvKu1lJS+9lp3bvZwtWcNYmUtvL7I1LClgsOPwwY7bXMmLWMMuTHzBIqefTwI3rwmy49ohOzMGIyE7t7eGXbHKtL41TLVoDRsNBLGGsa/7sYZ5sYZNVyzPKx6G8K1dFrFURxTA17qOaHxvK3pi4L211axQ/GRmkD2donYneTZe8ZPnJU6elzh5bhrz3DTm+brYnq+L7fm62B5yXWxCtcCczONzMr85TibWsMuyR7DA6WdlwM1rApP5djDZjLFIMNnKRGf0mkkB1HI8EsQaNlmwPJwEoSOVdBQ6UkdHoSPX0VPoSCU6YodlLNKRFJz24AvneP7mdM/fnO65acxz01joGBAJitMRz+mIR6QjB9QFTEYCPhkJYg3Lh9yXNSyAk5GATUZCG2QktENGZoxFQkZWJiOj10zIyHIyEsUaNl2xPJ5EGDSyumNkqZAobKR5IdHgyGYExaAjq8tZyRrmhrUsF5A4bezJvVfr1rBQtGUFU9xbtJGFoo0s5NWK+d5Q2stKXnotO7d7BV2yhrEyl95eaGuYBQY7ER/sxM2VvIg17MLEFyxy+vkkcPOaIDuxHbIzYzASsnNza9g1q0zrW8N0i9aw0UAQaxj7ugNrmD1EYXdTwOf2qrTqDLN5Wulg+N5e0YZlRKF7jcuIxvY2IicG2VtbzAp1T35YxuILw2KwzoC7wgKvbgq8uilyv1jkfrHIl8SOfEnswJfEDpBLYhOlRURkpxkNIiE7tnBrgGw3RAkfm543gkVNLx/DbR0sHHste8BjYyctFTTWTNGTPVCc0UsmNU+LiYi6PRE5/K89MxgbS5QAkToyChCpIqMAkavIKUCkEhAJwzIWgUgIoUMHIpGnTpG/Jz1yi1jkFrEYORBJfMMyIBItIhA50C1cGKLgYYjanA2sAgzZrg3sPGcEi5peLobbuhZgiGoGhswYhgSGrAtDRi+ZwJDFMETfdmWcbv8i2JcikZbLQzQMDTko2rCOKDikdR3ReMhW9MQAIk2p+e7jH89r1B6+TC7tFR++6AVYethgrwOsPGxbRZlXrqGmzCoVOHtKwxoWncQm+qhHF1v7c/HAy6zEQ6r0CgsTR+eJo/N0chhD5747WV1NCgtnEq3zZAWLpPP2ba2ocD/+LAXpW6wqLCeEUAGDWVE4mL2gtxEMoJ+1U/B5BXzOtm6Cml/FzOcofBJmrrQGTLw4czNvuy5oa8cyt5bWgEnF9VeSKu4trheTiuvFpPxaIr7XFfb6rvzXiuu6pK6wBszhDGfHHvZCrwETsPLQ0jMZrEz0tIWSi+ZDtpyLvvboCyxsek+UcFsHmJEOP/1AykkLLZWsFGXxl8l3EcgrwdjTlWC6phaCeamaGY0JWQeGfd3NOjDm9q6n7w9kG3oUezbBGJiHsWt7IhbKiPI0tnEZ0R7HbkROjOexa4u53PakOHib/Ey26zrbneTIcL4n37E023eGb1i+wZPtLq/FvNsIfCOvsfyygfu8NuJyMgPPyczmOJksBHNR4ggWNb2EDLd1LWAy0wwmmzEMCSZbl+KMXjLxPi1GIlYWgpmsF40lVohIHRmFiFSRUYjIVeQUIlKLiKhhHYsrwaRo/emNFiARiRx1sKVxver4huIbbGlcrwwnIpETEcgK9he+hYtDLDwOsYtwyC6RtBtcCub1j7UhHGKxcYiFxiG2CRxim8EhM4YhwSHr4pDRSyY4ZDEOcbIUzHTBaDBxMDxkdTvvQh1RgEjrOqIRka3oiYFEVlezjgVMdXpYzAIX0W+NSr039TbuAfOq9G5vr0rv9vbKF/eG4t685i7fm0p7u5K37NCyc1+XskUPWOmd4bmw5Zi1wNEchUtzHDzNcZsrbhET2GVJL1jY9HJJ3Na1gHNcMzhnxjgkOOf2bwC/YiFpdd+XatD3NRoG4vtiX3fj+/Li+5ouGM0pHobqrV0CsVBGFKjXuIxoTG8jcmIgvbXFrFHlZC6meeqtj9qpq9G8OlVOmtcyaV7LpLkjTHNHmM7rL+02HN/oWJWT7hCrnDKfxWVjHp6N+c2xMTF+XZQ5gkVNLyPDbV0LaMw3g8ZmDEOCxtbFOKOXTCqdFjORIMavyXrRWBIEidSRUZBIFRkFiVxFTkEitZCIHdaxWOCklPYdOhLxnHUEvsEtYZpbwkzHKIhRHIl4jkQ8IhJ5AVy4OCTA45Agxq98yF0ZvwI2DgnQOCQ0gUNCMzhkxjAkOGRdHDJ6yQSHLMYhUYxf0wWjwSTC8JDVLSELdUQBIq3riEZEtqInBhJZXc1axi83LGbx5V/OeW1OcrDWjV+maLoypRd6eVM0iZmiSczkRYf53lDay4pbei07N3MZXTJ+sYKW3l5s45fFpTkRnubEzRW3iPHrsqQXLGx6uSRu61rAObEZnDNjHBKcc3Pj11UrSas7v3SDzq/ROBDnF/vaoPPr4fv3+7/VPfz6nz/t8o0vT//xbTcIvX/80B1CsqsN+QbvVZ1vxwY2ph57e+ENid+YvJvR9Ib07240XYEE3p22N6OCyMrWqJPyF/PA7m1MyoTTjwVXJ2V4NZTh1VCWm8osN5VZvkC25QtkG75AtoFcIDsTXgS6dklShMDaprV3c+TN+VePujvwdmkmCh1fvRSvlbbCILp5Kcn6wO6Cdgu+g63GGruKe4o0egGlNisnyrvpezcb/bTT9ZiAFpLlV1Pk8q/xbNPy19Bay5JFe1yy86r3SGpVyoTvrBtTTyjTVTUVylRfU6FM19dWKFMlyhRmUCaruuTCycfCpkyWW/Mst+bZk8PyMuB/8e7EmieUqeZdmmqMMqkalGlfPqjR/HoVONPQB9sWaTrPRqFjrB3S9Gr61FqbGyBNSkjTBkiTEtJ0U9IUxkmT4w8s9xuKb1yfNGkhTYvUE9J0VU2FNNXXVEjT9bUV0lSJNMVhGYukyRkdzEkqCU6aHF/w2vFnT44nki67EXcb4UchTZe1amZShE+adAXStF9nXcOtDFWBNA19sG2RpvNsFDrG2iFNr6ZPrbW5AdKkhTRtgDRpIU03JU1xAmk6yTh5wujiLUiTEdK0SD0hTVfVVEhTfU2FNF1fWyFNlUhTGpbxlZom08WrraR1BdLkuVnO82dPnr+BzXOznOdvYBPSVPUuzTRGmkwF0rRfVM34DZKmoQ+2LdJ0no1Cx1g7pOnV9Km1NjdAmoyQpg2QJiOk6aakKY2TJs97yn5D8Y1x0kQNzEn7p48fP+yGlcHEfnKXsYfT9bpMKef/HlX7o0uZxKDTfyBLed1s/2b/p6ibPv7+y9kP92Pr/oLsP8nHwy3AX3vD+KupxpT7g1fS+2ve1VpZUWuResIFr6qpcMH6mgoXvL62wgXrcEH++SZzQaVM1CfvNMPjgp47Gv0JJOTLawVODAN/2WDgy2vtz5bTvLA6F5yTDOLjQtsYLrQ1LJCy0NbpAW2hwvMEFTq+2kGFr2ZUrbW5AVRoBRVuABVaQYW3RIWqG0eFgTsf9huKb1yACv/p/Yf3z0+Pn//P05fdX9P/csn91MPjl0+fvsO13bD//UyfX870biT///UFW+3O708T+T/ev/v66/f9w5l4sCdX55WPukvNfn78esnneun/O4V+/rb7NM9fD8ue7s4x8onevf/tbLQ6ZDa7fnsYMdzI57EnphVeV+D4nYXn66sEN0WDMzL88Luuosj31P+h0F2n3vOZ2EU3bxXl3R/P0Pfb149j/cVP0WopVXArcVmlY9c+l3XCZa+rqXDZ+poKl72+tsJlK3FZNSxjcY72PoZ48nJCPC4bOJcNnMsGnj1FzmUj57KRc9nAuWyA57Kvp2bAVMM1RmWdUNn+IfdGZc/TU+j4aofKvppPtdbmBqisEyq7ASrrhMrelMqqcSobOZWNnMrGSVT2Xz5//LSf3hfdO717OcmEMs3Hvx0u2pt/OL2l+fnb/sN//OtfixH65tNLSI7BzDgJztYBk699/Ap0csGdz4V0Mk4iuZfeW69UI7r/X2vWcQGPFRUUzLhUQYGKtZUUhFgJIephGQsTqX7bBW01+OKCkT9ZjZwnRs4TI+eJib+5Ip3wRM8TQI+OEEeSKKD7cGxqeCNG2J7N+/WP1TYjBCaCuPwPn/a1wfaE5LVH8oTb0Y9vwO30OLdLvNZuv6H4xlW43Y0onRrJeJP58QaUriqTW3QrcSGTS5Mg5hwmp27K5NR3JNdadeCLUodUFYCAqFZv3JUyF4eZMm87a5L2Y3H25/KR5zftI6HG5+bEb8QTvxFPfFmM5HBvqvFvodX1bqGXNWsf75j39aqd2p/XE/LejX05+8es/dl9qyvkufqS9PbVbNbcMJvdjeAT0lgOKRNfkSZNWpFmpilotvHHxlecP/sfjMwWqVJi9tefn8sQdK1Hp6rbv3ijgWenA8rJk9Sr6SnPVevqKU9Zr6urPHNddutGGMvOACU+xhQC9DNXx50aoVN8Q/MNwzfYytqhc3njcLZj1nPYgF1mO2Gssj0x/4G4HRttqzx0pUM2+ND1goQTNqagHsheniG11F6Mh7XT2iyPbnEf3Q5cQXmQSz++wYNcO0rAQud5lsjq7w4btR7kDvQIjMe6oTvtRPUe60786BUe8i64d7nsIW/o4hSxlrNEeeQ7Vzd5ALyQIijlLg5BZd9G56wdfU3Xn8tHXvgAOHSsqjqojm+cUIHER/W0+h3+/NERNpnGeTg8aWRogU3Ig2N2iDw4vuWDYzeeNitOWhWrfzxs3OzB8cjKRDUeHAfVK3j80/Gyn17uww+776fQ3/813/+13/913//13/+N3/9N3/9VxxPsJ7CXb46nUsdzqONJ1PEsKhy/OZ5PH39dH39dH5uij+fRx/Po469r+vVje8yxPeZ4QnM8jzmexxzPY47tMccTmuMJzfGE9nhCezyhPbbQHs9jj+exx/PY43ns8Tzu+Ovu+Ovu2DBHYh9P6I7nccfzuON5/LE9/nhCfzyPP57H01U7NswfT+iP5wnH84TjecKxYeF4wnA8TzieJxzPE44NC8cTxuN54vE88XieeGxYPJ4wHs8Tj+eJx/OkY8PS8YTpeJ50PE86nicdG5aOJ0zUQ3MXpT7aUSftqJd21Ds76p4d9c8u0nd0vtzVc19Xhr6jbp/7fe7v1OGVovNpap/OQUTno+6vqP8rTS2lUFAUAopiQFEQKEMtpXhQFAeKAkFRJCgKBUWxoCgGlM1RTuez1FKKDEURoSgkFMWEctRSCg9FYaFcHkDofI5aSjGiKDYUBYei6FCeWkqBonwekeh8FCLKU0spWhRFiaIwURQnKlBLKWRUyIMdnZmiRlHYqEhtpnhRFDCKIkZFOl/MoyedhaJGUdioRC2lCFIUOYpCR1HsKAoeRdGjKWY0xYzuaGzuaHCmONIUPZqiR1P0aJoxNMWRpujRFD2aokfTtKEpjjRFj6bo0WwOoZbmWSTPHhQ9Ok8keSahONIUPdrkSYnOR9OJpjjSFD2aokdT9GiaSjTFkbZ5lqPzUfRomlg0xZGm6NEUPZqiR9PsoimONEWPpujRFD2aphhNcaQpejRFj6bo0TTPaIojTdGjKXo0RY+myUZTHGmKHk3Ro0Oe4amlFD2aYkZTzGiadTRNO5riSFP0aIoeHXPyQC2lONIUPZqiR1P0aJqANMWRpujRKWcjOR2hfITiyFD0GIoeQ9FjaBYyFEeGoseonN/Q+WgWMhRHhqLHUPQYih5Ds5ChODIUPYaix1D0GJqFDMWRoegxFD0mJ2I5E2OpGJ0vJ2M5G8vpWM7HKHoMRY+h6DE0Cxmbszw6H0WPoegxNAsZiiND0WMoegxFj3E5baQzU/QYih5D0WNoFjIUR4aix1D0GIoeQ7OQoTgyFD2GosdQ9BiahQzFkaHoMSEntnQ+mnsMxZGh6DEUPYaix9AsZCiODEWPoegxFD2GZiFDcWQoegxFj6HoMTQLGYojQ9FjKXosRY+lWchSHFmKHkvRYyl6LM1CluLIUvRYih5L0WNpFrIUR5aix1L0WIoeS7OQpTiyFD2WosdS9FiahSzFkaXosRQ9lqLH0ixkKY4sRY+l6LEUPZZmIZvvZ/J9TL6RoeixNAvZfHeT72rybQ1Fj6VZyFIcWYoeS9FjKXoszUKW4shS9FiKHkvRY2kWshRHlqLHUvRYih5Ls5ClOLIUPZaix1L0WJqFLMWRpeixFD2WosfSLGQpjixFj6XosRQ9lmYhS3FkKXosRY+l6LE0C1mKI0vRYyl6LEWPTfnWk+49KXocRY+j6HE0CzmKI0fR4yh6HEWPo1nIURw5ih5H0eMoehzNQo7iyFH0OIoeR9HjaBZyFEeOosdR9DiKHkezkKM4chQ9zuT7bjofzUKO4shR9DiKHkfR42gWchRHjqLHUfQ4ih5Hs5CjOHKZClD0uAwIMiHIiCCjAYoelylBxgQUR46ix1H0OIoeR7OQozhyFD2OosdR9DiahRzFkaPocRQ9jqLH0SzkKI4cRY+j6HEUPY5mIUdx5Ch6HEWPo+hxNAs5iiNH0eMoehxFj6NZyFEcOYojR3HkKI4cxZGn+chT9HiKHk/R42kW8hRHnqLHU/R4ih5Ps5CnOPIUPZ6ix1P0eJqFPMWRp+jxFD2eosfTLOQpjjxFj6fo8RQ9nmYhT3HkKXo8RY+n6PE0C3mKI0/R4yl6PEWPp1nIUxx5ihlPMeNp7vEUPZ6ix1OkeJpxPMWMp5jxFDM+g7RM0ihSfIZqmapRVHiKCk9R4SkqPM0unmYNT/3eU7/31O899XtPPdtTz/bUsz3NEJ76uKe+66nvBhrvA433gXpsoB4bqMcG6pNBqT8RL+6z/8MRRBq+//saKibE9/3fzLuO32RydPzmdVKcb/MvIsX5Ruf4DSW6U7jwFAqcc6fjNwMUOE+cx2+m4OA8kNJoQsFFUUFdnHopdT7qSXSRj9+cM1/qHOfwl1Dv8a9PYr4Xod7jeWYTXupT1MsnE94ZXJfON0Jz6XxFrpsZLrVlBs2l8w1zXerKY+SWfrfEcBmvpbZMJreX81o63wilpfMVee1t2WxmqcNEls5XZLOZw9IAOJnIXs5h6Xwj9DU/titx2MxcqS1T6esM5krnm0FfM3PNfJV+dzJpvZyv0vlGqCqdr8RXGUultkymqpezVDrfCEGl8xVZauam1JbJBPVybpqn/2FaSucrctPMSKktk2np5Yw0JyjDZJTOV2SkObuhtkwlozN4KJ1vhILS+Yo8NLNPastkCno5+6TzjRBPOl+JfTLOSW2ZTDwv55yUYo7QzZyuljhnZprEjybTzcuZZmaQwySTzldkmplfUlsmk8zL+WUu8RimlnS+Ir/MrJLaMpVazmCVuQhlmFDS+YqsMt+7UFsmE8rLuSTdDo3QSLoBKnFJxiDpZmoyjbycQdL5Rsgjna/IIDNvpLZMJo+X88bMB4cpY771LPHGzBapLZMp4+Vskc43QhTpfEW2mDkitWUqUVzEEUvMkJgNje1ljpjv8okBTaaHlzNDOt8IKaTzlZgh44PUlsmk8HI+SOebzAfpfNTvR/ggje0+194xZljig5m80Pkye8n0sEgK6Xw0tnvq9576/XSOSOejfj+HLdL5qI+XeKOnsd1Tvw/U70fII43tgfp9oH4fqI8H6uOBxvZAY3ugfh+o3wfq44H6eKCxPdDYHqjfB+r3gfp4oD4eaGwPNLYH6veB+n2gPh6ojwca2wON7YH6faB+H6iPB+rjgcb2QGN7oH4fqN8H6uOB+nigsT3Q2B6o3wfq94H6eKA+HmhsDzS2B+r3gfp9oD4eMmDMdaa50DRTx4wdM2VkNaZ0vlxlSv0+UL8P1McD9fFAY3ugsT1Qvw/U7wP18UB9PNDYHmhsD9TvA/X7kDl7yufLbJTgKI33kfp9pD4eqY9HGtsjje2R+n2kfh+pj0fq45HG9khje6R+H6nfR+rjkfp4pLE90tgeqd9H6veR+nikPh5pbI80tkfq95H6faQ+HqmPRxrbI43tkfp9pH4fqY9H6uORxvZIY3ukfh+p30fq45H6eKSxPdLYHqnfR+r3kfp4pD4eaWyPNLZH6veR+n2kfh+pj0ca2yON7ZH6faR+H6nfR+r3MRP2jNgzY8+QPVdWU7+PmbzT2B6p30fq45H6eKQ+HlP+3YztM7cnTE9je6I+nmhsT9THE/XxRH08UR9P1McTje2J+niiPp6oZycavRP17EQ9O1HPTtSzE/XsRKN3op6dqD8n6rvJ5N+gz0F9N1HfTdR3E/XdRGN2ojE7UX9O1J8T9d1EfTfRSJ2oxyaXf4M0oFE5UY9N3v6Jnt5lN85hMs7uF1rx+eW4bOP/7W/PT18ffn/6/GXvCtv95pv3z3svr+p2t2r/9vc//f3/A2ZsxEE=',
'psp_resnet101_coco_int8': b'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',
'deeplab_resnet101_coco_int8': b'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'
}
def _not_impl(*args, **kwargs):
raise NotImplementedError("Not yet implemented for quantized models")
def _create_quantized_models(name, sym_prefix):
def func(pretrained=False, tag=None, root='~/.mxnet/models', ctx=cpu(0), **kwargs):
r"""Quantized model.
Parameters
----------
pretrained : bool or str
Boolean value controls whether to load the default pretrained weights for model.
String value represents the hashtag for a certain version of pretrained weights.
tag : str, default is None
Optional length-8 sha1sum of parameter file. If `None`, best parameter file
will be used.
ctx : Context, default CPU
The context in which to load the pretrained weights.
root : str, default $MXNET_HOME/models
Location for keeping the model parameters.
"""
from ..model_zoo import get_model
from ..model_store import get_model_file
curr_dir = os.path.abspath(os.path.dirname(__file__))
model_name = name.replace('mobilenet1_', 'mobilenet1.')
model_name = model_name.replace('mobilenet0_', 'mobilenet0.')
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
import tempfile
if 'fcn' in model_name or 'psp' in model_name or 'deeplab' in model_name:
model_json = get_compressed_model(model_name, _compressed_int8_json)
fd, path = tempfile.mkstemp(dir=curr_dir, suffix='.json', text=True)
with open(path, 'w') as f:
f.write(model_json)
sym_net = SymbolBlock.imports(path, ['data'], None, ctx=ctx)
os.close(fd)
try:
os.remove(path)
except:
pass
else:
json_file = os.path.join(curr_dir, '{}-symbol.json'.format(model_name))
sym_net = SymbolBlock.imports(json_file, ['data'], None, ctx=ctx)
base_name = '_'.join(model_name.split('_')[:-1])
param_file = get_model_file(base_name, tag=tag, root=root) if pretrained else None
kw = {}
if 'deeplab' not in base_name:
kw.update({'prefix': sym_prefix})
net = get_model('_'.join(model_name.split('_')[:-1]), **kw)
classes = getattr(net, 'classes', [])
if param_file:
# directly imports weights saved by save_parameters is not applicable
# so we hack it by load and export once to a temporary params file
net.load_params(param_file)
net.hybridize()
if '512' in base_name:
net(mx.nd.zeros((1, 3, 512, 512)))
elif '300' in base_name:
net(mx.nd.zeros((1, 3, 300, 300)))
elif 'psp' in base_name or 'deeplab' in base_name:
net(mx.nd.zeros((1, 3, 480, 480)))
else:
net(mx.nd.zeros((1, 3, 224, 224)))
with tempfile.TemporaryDirectory() as tmpdirname:
prefix = os.path.join(tmpdirname, 'tmp')
net.export(prefix, epoch=0)
param_prefix = prefix + '-0000.params'
sym_net.collect_params().load(param_prefix)
sym_net.classes = classes
sym_net.reset_class = _not_impl
sym_net.set_nms = _not_impl
return sym_net
func.__name__ = name
globals()[name] = func
_create_quantized_models('mobilenet1_0_int8', 'mobilenet0_')
_create_quantized_models('resnet50_v1_int8', 'resnetv10_')
_create_quantized_models('ssd_300_vgg16_atrous_voc_int8', 'ssd0_')
_create_quantized_models('ssd_512_mobilenet1_0_voc_int8', 'ssd0_')
_create_quantized_models('ssd_512_resnet50_v1_voc_int8', 'ssd0_')
_create_quantized_models('ssd_512_vgg16_atrous_voc_int8', 'ssd0_')
_create_quantized_models('fcn_resnet101_voc_int8', 'fcn0_')
_create_quantized_models('fcn_resnet101_coco_int8', 'fcn0_')
_create_quantized_models('psp_resnet101_voc_int8', 'pspnet0_')
_create_quantized_models('psp_resnet101_coco_int8', 'pspnet0_')
_create_quantized_models('deeplab_resnet101_voc_int8', 'deeplabv30_')
_create_quantized_models('deeplab_resnet101_coco_int8', 'deeplabv30_')
| 1,385.230088
| 25,874
| 0.961286
| 4,515
| 156,531
| 33.273533
| 0.832337
| 0.000792
| 0.001817
| 0.000479
| 0.016402
| 0.013679
| 0.011649
| 0.008267
| 0.007149
| 0.00667
| 0
| 0.145803
| 0.008708
| 156,531
| 112
| 25,875
| 1,397.598214
| 0.822373
| 0.004811
| 0
| 0.022727
| 0
| 0.068182
| 0.978132
| 0.975845
| 0
| 1
| 0
| 0
| 0
| 1
| 0.034091
| false
| 0.011364
| 0.125
| 0
| 0.170455
| 0
| 0
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
2df74c1ebc0e2d8b66eb99098eed8a6aaaf9621c
| 46
|
py
|
Python
|
Game15/modules/__init__.py
|
ttkaixin1998/pikachupythongames
|
609a3a5a2be3f5a187c332c7980bb5bb14548f02
|
[
"MIT"
] | 4,013
|
2018-06-16T08:00:02.000Z
|
2022-03-30T11:48:14.000Z
|
Game15/modules/__init__.py
|
pigbearcat/Games
|
b8c47ef1bcce9a9db3f3730c162e6e8e08b508a2
|
[
"MIT"
] | 22
|
2018-10-18T00:15:50.000Z
|
2022-01-13T08:16:15.000Z
|
Game15/modules/__init__.py
|
pigbearcat/Games
|
b8c47ef1bcce9a9db3f3730c162e6e8e08b508a2
|
[
"MIT"
] | 2,172
|
2018-07-20T04:03:14.000Z
|
2022-03-31T14:18:29.000Z
|
'''初始化'''
from .game import gemSprite, gemGame
| 23
| 36
| 0.717391
| 6
| 46
| 5.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.108696
| 46
| 2
| 36
| 23
| 0.804878
| 0.065217
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 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
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
2dff5eae56e8c8203237ce687e2f607befb44a5b
| 28
|
py
|
Python
|
algo/daac/train.py
|
xlnwel/grl
|
7d42bb2e78bc3e7b7c3ebbcf356a4d1cf12abebf
|
[
"Apache-2.0"
] | 5
|
2021-09-04T14:50:39.000Z
|
2022-03-13T09:53:09.000Z
|
algo/ppo2/train.py
|
xlnwel/d2rl
|
7d42bb2e78bc3e7b7c3ebbcf356a4d1cf12abebf
|
[
"Apache-2.0"
] | null | null | null |
algo/ppo2/train.py
|
xlnwel/d2rl
|
7d42bb2e78bc3e7b7c3ebbcf356a4d1cf12abebf
|
[
"Apache-2.0"
] | 2
|
2022-01-25T09:32:01.000Z
|
2022-03-13T09:53:14.000Z
|
from algo.ppo.train import *
| 28
| 28
| 0.785714
| 5
| 28
| 4.4
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.107143
| 28
| 1
| 28
| 28
| 0.88
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 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
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
9301e05dee685490e7e2d5ce0e7aad3f661915c8
| 30
|
py
|
Python
|
astroaugmentations/utils/__init__.py
|
mb010/AstroAugmentations
|
ce62b0e8a10eece1972b884e86f072236c104400
|
[
"MIT"
] | 1
|
2022-03-17T10:16:21.000Z
|
2022-03-17T10:16:21.000Z
|
astroaugmentations/utils/__init__.py
|
mb010/AstroAugmentations
|
ce62b0e8a10eece1972b884e86f072236c104400
|
[
"MIT"
] | 4
|
2022-03-07T14:33:00.000Z
|
2022-03-18T16:12:03.000Z
|
astroaugmentations/utils/__init__.py
|
mb010/AstroAugmentations
|
ce62b0e8a10eece1972b884e86f072236c104400
|
[
"MIT"
] | null | null | null |
from . import kernel_creation
| 15
| 29
| 0.833333
| 4
| 30
| 6
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.133333
| 30
| 1
| 30
| 30
| 0.923077
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 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
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
934f5d2721ee72e9ab69cdcd969c87c896289334
| 33
|
py
|
Python
|
zerodha/zerodha/cron.py
|
adit-negi/zerodha-web-task
|
cd18df546d6dde1fd2f756f5acd9bdf6bc54326c
|
[
"MIT"
] | null | null | null |
zerodha/zerodha/cron.py
|
adit-negi/zerodha-web-task
|
cd18df546d6dde1fd2f756f5acd9bdf6bc54326c
|
[
"MIT"
] | null | null | null |
zerodha/zerodha/cron.py
|
adit-negi/zerodha-web-task
|
cd18df546d6dde1fd2f756f5acd9bdf6bc54326c
|
[
"MIT"
] | 2
|
2021-05-09T15:45:23.000Z
|
2021-08-19T10:44:10.000Z
|
def my_cron_job():
print(2+2)
| 16.5
| 18
| 0.636364
| 7
| 33
| 2.714286
| 0.857143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.074074
| 0.181818
| 33
| 2
| 19
| 16.5
| 0.62963
| 0
| 0
| 0
| 0
| 0
| 0
| 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
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 6
|
935dc1b838afe10d6a5f600fbc71ca35c60b5e3b
| 11,869
|
py
|
Python
|
tests/python/cuda/test_comm.py
|
jakeKonrad/torch-quiver
|
16e01b8b61459ae41b7386b6a57ef9d20dfb6606
|
[
"Apache-2.0"
] | 196
|
2021-10-30T23:40:27.000Z
|
2022-03-28T03:43:18.000Z
|
tests/python/cuda/test_comm.py
|
jakeKonrad/torch-quiver
|
16e01b8b61459ae41b7386b6a57ef9d20dfb6606
|
[
"Apache-2.0"
] | 32
|
2021-11-03T15:07:50.000Z
|
2022-03-07T09:03:33.000Z
|
tests/python/cuda/test_comm.py
|
jakeKonrad/torch-quiver
|
16e01b8b61459ae41b7386b6a57ef9d20dfb6606
|
[
"Apache-2.0"
] | 24
|
2021-10-31T12:28:34.000Z
|
2022-03-19T03:03:13.000Z
|
import torch
import torch.multiprocessing as mp
import torch_quiver as torch_qv
import quiver
import torch.distributed as dist
import os
import time
LOCAL_ADDR = '192.168.0.78'
MASTER_ADDR = '192.168.0.78'
MASTER_PORT = 12355
def child_sendrecv_proc(rank, ws, id):
torch.cuda.set_device(rank)
comm = torch_qv.NcclComm(rank, ws, id)
print(f"{rank} ready")
if rank == 0:
a = torch.zeros(10, device=0)
comm.send(a, 1)
else:
a = torch.ones(10, device=1)
comm.recv(a, 0)
print(f"{rank} tensor {a}")
num = 10000
size = 1024 * 1024
large = torch.zeros(size, device=rank)
for i in range(5):
if rank == 0:
comm.send(large, 1)
else:
comm.recv(large, 0)
torch.cuda.current_stream().synchronize()
t0 = time.time()
for i in range(num):
if rank == 0:
comm.send(large, 1)
else:
comm.recv(large, 0)
torch.cuda.current_stream().synchronize()
t1 = time.time()
if rank == 1:
print(f"Latency {(t1 - t0) / num}")
print(f"Throughput {num * size * 4 / 1024 / 1024 / 1024 / (t1 - t0)}")
def child_sendrecv_proc_pair(rank, ws, id):
torch.cuda.set_device(rank // 2)
comm = torch_qv.NcclComm(rank, ws, id)
print(f"{rank} ready")
if rank % 2 == 0:
a = torch.zeros(10, device=rank // 2)
comm.send(a, rank + 1)
else:
a = torch.ones(10, device=rank // 2)
comm.recv(a, rank - 1)
torch.cuda.current_stream().synchronize()
print(f"{rank} tensor {a}")
num = 10000
size = 1024 * 1024
large = torch.zeros(size, device=rank // 2)
for i in range(5):
if rank % 2 == 0:
comm.send(large, rank + 1)
else:
comm.recv(large, rank - 1)
torch.cuda.current_stream().synchronize()
t0 = time.time()
for i in range(num):
if rank % 2 == 0:
comm.send(large, rank + 1)
else:
comm.recv(large, rank - 1)
torch.cuda.current_stream().synchronize()
t1 = time.time()
if rank % 2 == 1:
print(f"Latency {(t1 - t0) / num}")
print(f"Throughput {num * size * 4 / 1024 / 1024 / 1024 / (t1 - t0)}")
def child_sendrecv_proc_pair_bidirect(rank, ws, id):
torch.cuda.set_device(rank // 2)
comm = torch_qv.NcclComm(rank, ws, id)
print(f"{rank} ready")
if rank % 2 == 0:
a = torch.zeros(10, device=rank // 2)
comm.send(a, rank + 1)
else:
a = torch.ones(10, device=rank // 2)
comm.recv(a, rank - 1)
torch.cuda.current_stream().synchronize()
print(f"{rank} tensor {a}")
num = 10000
size = 1024 * 1024
large = torch.zeros(size, device=rank // 2)
large_inverse = torch.zeros(size, device=rank // 2)
stream = torch.cuda.Stream()
stream_inverse = torch.cuda.Stream()
for i in range(5):
if rank // 2 == 0:
if rank % 2 == 0:
comm.send(large, rank + 1)
else:
comm.recv(large, rank - 1)
else:
if rank % 2 == 0:
comm.recv(large, rank + 1)
else:
comm.send(large, rank - 1)
torch.cuda.current_stream().synchronize()
t0 = time.time()
for i in range(num):
if rank // 2 == 0:
if rank % 2 == 0:
comm.send(large, rank + 1)
else:
comm.recv(large, rank - 1)
else:
if rank % 2 == 0:
comm.recv(large_inverse, rank + 1)
else:
comm.send(large_inverse, rank - 1)
torch.cuda.current_stream().synchronize()
t1 = time.time()
if rank % 2 == 1:
print(f"Latency {(t1 - t0) / num}")
print(f"Throughput {num * size * 4 / 1024 / 1024 / 1024 / (t1 - t0)}")
def child_allreduce_proc(rank, ws, id):
torch.cuda.set_device(rank)
comm = torch_qv.NcclComm(rank, ws, id)
print(f"{rank} ready")
if rank == 0:
a = torch.zeros(10, device=0)
else:
a = torch.ones(10, device=1)
comm.allreduce(a)
print(f"{rank} tensor {a}")
num = 100
size = 1024 * 1024
large = torch.zeros(size, device=rank)
for i in range(5):
comm.allreduce(large)
torch.cuda.current_stream().synchronize()
t0 = time.time()
for i in range(num):
comm.allreduce(large)
torch.cuda.current_stream().synchronize()
t1 = time.time()
if rank == 1:
print(f"Latency {(t1 - t0) / num}")
print(f"Throughput {num * size * 4 / 1024 / 1024 / 1024 / (t1 - t0)}")
def child_allreduce_proc_pair(rank, ws, id):
torch.cuda.set_device(rank // 2)
comm = torch_qv.NcclComm(rank, ws, id)
print(f"{rank} ready")
if rank % 2 == 0:
a = torch.zeros(10, device=rank // 2)
else:
a = torch.ones(10, device=rank // 2)
comm.allreduce(a)
torch.cuda.current_stream().synchronize()
print(f"{rank} tensor {a}")
num = 10
size = 1024 * 1024 * 1024
large = torch.zeros(size, device=rank // 2)
for i in range(5):
comm.allreduce(large)
torch.cuda.current_stream().synchronize()
t0 = time.time()
for i in range(num):
comm.allreduce(large)
torch.cuda.current_stream().synchronize()
t1 = time.time()
if rank == 1:
print(f"Latency {(t1 - t0) / num}")
print(f"Throughput {num * size * 4 / 1024 / 1024 / 1024 / (t1 - t0)}")
def test_local():
id = quiver.comm.getNcclId()
ws = 2
procs = []
for i in range(ws):
proc = mp.Process(target=child_sendrecv_proc, args=(i, ws, id))
proc.start()
procs.append(proc)
for proc in procs:
proc.join()
def test_dist(rank):
ws = 2
store = dist.TCPStore(MASTER_ADDR, MASTER_PORT, ws,
MASTER_ADDR == LOCAL_ADDR)
if rank == 0:
id = quiver.comm.getNcclId()
store.set("id", id)
else:
id = store.get("id")
print(f"{rank} init store {id}")
child_sendrecv_proc(rank, ws, id)
def test_dist_pair(rank):
local_size = 2
store = dist.TCPStore(MASTER_ADDR, MASTER_PORT, 2,
MASTER_ADDR == LOCAL_ADDR)
if rank == 0:
id = quiver.comm.getNcclId()
store.set("id", id)
else:
id = store.get("id")
print(f"{rank} init store {id}")
procs = []
for i in range(local_size):
proc = mp.Process(target=child_sendrecv_proc_pair,
args=(i * 2 + rank, 2 * local_size, id))
proc.start()
procs.append(proc)
for proc in procs:
proc.join()
def test_dist_pair_bidirect(rank):
local_size = 2
store = dist.TCPStore(MASTER_ADDR, MASTER_PORT, 2,
MASTER_ADDR == LOCAL_ADDR)
if rank == 0:
id = quiver.comm.getNcclId()
store.set("id", id)
else:
id = store.get("id")
print(f"{rank} init store {id}")
procs = []
for i in range(local_size):
proc = mp.Process(target=child_sendrecv_proc_pair_bidirect,
args=(i * 2 + rank, 2 * local_size, id))
proc.start()
procs.append(proc)
for proc in procs:
proc.join()
def test_nccl_allreduce(rank):
ws = 2
store = dist.TCPStore(MASTER_ADDR, MASTER_PORT, ws,
MASTER_ADDR == LOCAL_ADDR)
if rank == 0:
id = quiver.comm.getNcclId()
store.set("id", id)
else:
id = store.get("id")
print(f"{rank} init store {id}")
child_allreduce_proc(rank, ws, id)
def test_nccl_allreduce_pair(rank):
local_size = 2
store = dist.TCPStore(MASTER_ADDR, MASTER_PORT, 2,
MASTER_ADDR == LOCAL_ADDR)
if rank == 0:
id = quiver.comm.getNcclId()
store.set("id", id)
else:
id = store.get("id")
print(f"{rank} init store {id}")
procs = []
for i in range(local_size):
proc = mp.Process(target=child_allreduce_proc_pair,
args=(i * 2 + rank, 2 * local_size, id))
proc.start()
procs.append(proc)
for proc in procs:
proc.join()
def child_feat_partition(rank, ws, id, device, host, hosts, global2host):
torch.cuda.set_device(device)
dim = 5
info = quiver.feature.PartitionInfo(device, host, hosts, global2host)
size = 10
comm = quiver.comm.NcclComm(rank, ws, id, hosts, 1)
feat = torch.ones(
(size, dim), device=device, dtype=torch.float) * (1 + rank) * size
for i in range(size):
feat[i] += i
print(f"{rank} hold {feat}")
dist_feat = quiver.feature.DistFeature(feat, info, comm)
ids = torch.randint(high=size,
size=(20, ),
dtype=torch.int64,
device=device)
print(f"{rank} request global {ids}")
host2ids, _ = dist_feat.info.dispatch(ids)
print(f"{rank} request local {host2ids}")
host2feats = dist_feat.comm.exchange(host2ids, feat)
print(f"{rank} receive {host2feats}")
def child_feat_partition_pair(rank, ws, id, device, host, hosts, global2host):
torch.cuda.set_device(device)
dim = 400
info = quiver.feature.PartitionInfo(device, host, hosts, global2host)
nodes = 10000000
comm = quiver.comm.NcclComm(rank, ws, id, hosts, 2)
feat = torch.ones(
(nodes, dim), device=device, dtype=torch.float) * (1 + rank)
dist_feat = quiver.feature.DistFeature(feat, info, comm)
size = 1000000
ids = torch.randint(high=nodes,
size=(size, ),
dtype=torch.int64,
device=device)
print('ready')
host2ids, _ = dist_feat.info.dispatch(ids)
for h, ids in enumerate(host2ids):
print(f"{h} size {ids.size(0)}")
host2feats = dist_feat.comm.exchange(host2ids, feat)
num = 100
print('test once')
t0 = time.time()
for i in range(num):
print(i)
beg = time.time()
host2ids, _ = dist_feat.info.dispatch(ids)
mid = time.time()
host2feats = dist_feat.comm.exchange(host2ids, feat)
end = time.time()
print(f"dispatch {mid - beg}")
print(f"exchange {end - mid}")
t1 = time.time()
if rank == 0:
print(f"Latency {(t1 - t0) / num}")
print(
f"Throughput {num * size * dim * 4 / 1024 / 1024 / 1024 / (t1 - t0)}"
)
def test_feat_partition():
id = quiver.comm.getNcclId()
ws = 2
size = 10
global2host = torch.randint(high=ws, size=(size, ), dtype=torch.int64)
print(f"g2h {global2host}")
procs = []
for i in range(ws):
proc = mp.Process(target=child_feat_partition,
args=(i, ws, id, i, i, ws, global2host))
proc.start()
procs.append(proc)
for proc in procs:
proc.join()
def test_feat_partition_pair(rank):
local_size = 2
ws = 6
store = dist.TCPStore(MASTER_ADDR, MASTER_PORT, 3,
MASTER_ADDR == LOCAL_ADDR)
if rank == 0:
id = quiver.comm.getNcclId()
store.set("id", id)
else:
id = store.get("id")
print(f"{rank} init store {id}")
size = 10000000
global2host = torch.randint(high=3, size=(size, ), dtype=torch.int64)
procs = []
for i in range(local_size):
proc = mp.Process(target=child_feat_partition_pair,
args=(i + rank * local_size, ws, id, i, rank, 3,
global2host))
proc.start()
procs.append(proc)
for proc in procs:
proc.join()
if __name__ == "__main__":
mp.set_start_method('spawn')
# test_local()
# test_dist_pair_bidirect(0)
# test_nccl_allreduce_pair(0)
test_feat_partition()
# test_feat_partition_pair(0)
| 30.278061
| 81
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| 391
| 82
| 30.355499
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0
| 6
|
fa86c538be4945c82a6d26fd5cc6743f442637ac
| 16,208
|
py
|
Python
|
cottonformation/res/datapipeline.py
|
MacHu-GWU/cottonformation-project
|
23e28c08cfb5a7cc0db6dbfdb1d7e1585c773f3b
|
[
"BSD-2-Clause"
] | 5
|
2021-07-22T03:45:59.000Z
|
2021-12-17T21:07:14.000Z
|
cottonformation/res/datapipeline.py
|
MacHu-GWU/cottonformation-project
|
23e28c08cfb5a7cc0db6dbfdb1d7e1585c773f3b
|
[
"BSD-2-Clause"
] | 1
|
2021-06-25T18:01:31.000Z
|
2021-06-25T18:01:31.000Z
|
cottonformation/res/datapipeline.py
|
MacHu-GWU/cottonformation-project
|
23e28c08cfb5a7cc0db6dbfdb1d7e1585c773f3b
|
[
"BSD-2-Clause"
] | 2
|
2021-06-27T03:08:21.000Z
|
2021-06-28T22:15:51.000Z
|
# -*- coding: utf-8 -*-
"""
This module
"""
import attr
import typing
from ..core.model import (
Property, Resource, Tag, GetAtt, TypeHint, TypeCheck,
)
from ..core.constant import AttrMeta
#--- Property declaration ---
@attr.s
class PropPipelineParameterAttribute(Property):
"""
AWS Object Type = "AWS::DataPipeline::Pipeline.ParameterAttribute"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-datapipeline-pipeline-parameterobjects-attributes.html
Property Document:
- ``rp_Key``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-datapipeline-pipeline-parameterobjects-attributes.html#cfn-datapipeline-pipeline-parameterobjects-attribtues-key
- ``rp_StringValue``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-datapipeline-pipeline-parameterobjects-attributes.html#cfn-datapipeline-pipeline-parameterobjects-attribtues-stringvalue
"""
AWS_OBJECT_TYPE = "AWS::DataPipeline::Pipeline.ParameterAttribute"
rp_Key: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type),
metadata={AttrMeta.PROPERTY_NAME: "Key"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-datapipeline-pipeline-parameterobjects-attributes.html#cfn-datapipeline-pipeline-parameterobjects-attribtues-key"""
rp_StringValue: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type),
metadata={AttrMeta.PROPERTY_NAME: "StringValue"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-datapipeline-pipeline-parameterobjects-attributes.html#cfn-datapipeline-pipeline-parameterobjects-attribtues-stringvalue"""
@attr.s
class PropPipelinePipelineTag(Property):
"""
AWS Object Type = "AWS::DataPipeline::Pipeline.PipelineTag"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-datapipeline-pipeline-pipelinetags.html
Property Document:
- ``rp_Key``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-datapipeline-pipeline-pipelinetags.html#cfn-datapipeline-pipeline-pipelinetags-key
- ``rp_Value``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-datapipeline-pipeline-pipelinetags.html#cfn-datapipeline-pipeline-pipelinetags-value
"""
AWS_OBJECT_TYPE = "AWS::DataPipeline::Pipeline.PipelineTag"
rp_Key: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type),
metadata={AttrMeta.PROPERTY_NAME: "Key"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-datapipeline-pipeline-pipelinetags.html#cfn-datapipeline-pipeline-pipelinetags-key"""
rp_Value: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type),
metadata={AttrMeta.PROPERTY_NAME: "Value"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-datapipeline-pipeline-pipelinetags.html#cfn-datapipeline-pipeline-pipelinetags-value"""
@attr.s
class PropPipelineParameterObject(Property):
"""
AWS Object Type = "AWS::DataPipeline::Pipeline.ParameterObject"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-datapipeline-pipeline-parameterobjects.html
Property Document:
- ``rp_Attributes``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-datapipeline-pipeline-parameterobjects.html#cfn-datapipeline-pipeline-parameterobjects-attributes
- ``rp_Id``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-datapipeline-pipeline-parameterobjects.html#cfn-datapipeline-pipeline-parameterobjects-id
"""
AWS_OBJECT_TYPE = "AWS::DataPipeline::Pipeline.ParameterObject"
rp_Attributes: typing.List[typing.Union['PropPipelineParameterAttribute', dict]] = attr.ib(
default=None,
converter=PropPipelineParameterAttribute.from_list,
validator=attr.validators.deep_iterable(member_validator=attr.validators.instance_of(PropPipelineParameterAttribute), iterable_validator=attr.validators.instance_of(list)),
metadata={AttrMeta.PROPERTY_NAME: "Attributes"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-datapipeline-pipeline-parameterobjects.html#cfn-datapipeline-pipeline-parameterobjects-attributes"""
rp_Id: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type),
metadata={AttrMeta.PROPERTY_NAME: "Id"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-datapipeline-pipeline-parameterobjects.html#cfn-datapipeline-pipeline-parameterobjects-id"""
@attr.s
class PropPipelineParameterValue(Property):
"""
AWS Object Type = "AWS::DataPipeline::Pipeline.ParameterValue"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-datapipeline-pipeline-parametervalues.html
Property Document:
- ``rp_Id``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-datapipeline-pipeline-parametervalues.html#cfn-datapipeline-pipeline-parametervalues-id
- ``rp_StringValue``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-datapipeline-pipeline-parametervalues.html#cfn-datapipeline-pipeline-parametervalues-stringvalue
"""
AWS_OBJECT_TYPE = "AWS::DataPipeline::Pipeline.ParameterValue"
rp_Id: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type),
metadata={AttrMeta.PROPERTY_NAME: "Id"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-datapipeline-pipeline-parametervalues.html#cfn-datapipeline-pipeline-parametervalues-id"""
rp_StringValue: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type),
metadata={AttrMeta.PROPERTY_NAME: "StringValue"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-datapipeline-pipeline-parametervalues.html#cfn-datapipeline-pipeline-parametervalues-stringvalue"""
@attr.s
class PropPipelineField(Property):
"""
AWS Object Type = "AWS::DataPipeline::Pipeline.Field"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-datapipeline-pipeline-pipelineobjects-fields.html
Property Document:
- ``rp_Key``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-datapipeline-pipeline-pipelineobjects-fields.html#cfn-datapipeline-pipeline-pipelineobjects-fields-key
- ``p_RefValue``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-datapipeline-pipeline-pipelineobjects-fields.html#cfn-datapipeline-pipeline-pipelineobjects-fields-refvalue
- ``p_StringValue``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-datapipeline-pipeline-pipelineobjects-fields.html#cfn-datapipeline-pipeline-pipelineobjects-fields-stringvalue
"""
AWS_OBJECT_TYPE = "AWS::DataPipeline::Pipeline.Field"
rp_Key: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type),
metadata={AttrMeta.PROPERTY_NAME: "Key"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-datapipeline-pipeline-pipelineobjects-fields.html#cfn-datapipeline-pipeline-pipelineobjects-fields-key"""
p_RefValue: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "RefValue"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-datapipeline-pipeline-pipelineobjects-fields.html#cfn-datapipeline-pipeline-pipelineobjects-fields-refvalue"""
p_StringValue: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "StringValue"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-datapipeline-pipeline-pipelineobjects-fields.html#cfn-datapipeline-pipeline-pipelineobjects-fields-stringvalue"""
@attr.s
class PropPipelinePipelineObject(Property):
"""
AWS Object Type = "AWS::DataPipeline::Pipeline.PipelineObject"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-datapipeline-pipeline-pipelineobjects.html
Property Document:
- ``rp_Fields``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-datapipeline-pipeline-pipelineobjects.html#cfn-datapipeline-pipeline-pipelineobjects-fields
- ``rp_Id``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-datapipeline-pipeline-pipelineobjects.html#cfn-datapipeline-pipeline-pipelineobjects-id
- ``rp_Name``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-datapipeline-pipeline-pipelineobjects.html#cfn-datapipeline-pipeline-pipelineobjects-name
"""
AWS_OBJECT_TYPE = "AWS::DataPipeline::Pipeline.PipelineObject"
rp_Fields: typing.List[typing.Union['PropPipelineField', dict]] = attr.ib(
default=None,
converter=PropPipelineField.from_list,
validator=attr.validators.deep_iterable(member_validator=attr.validators.instance_of(PropPipelineField), iterable_validator=attr.validators.instance_of(list)),
metadata={AttrMeta.PROPERTY_NAME: "Fields"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-datapipeline-pipeline-pipelineobjects.html#cfn-datapipeline-pipeline-pipelineobjects-fields"""
rp_Id: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type),
metadata={AttrMeta.PROPERTY_NAME: "Id"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-datapipeline-pipeline-pipelineobjects.html#cfn-datapipeline-pipeline-pipelineobjects-id"""
rp_Name: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type),
metadata={AttrMeta.PROPERTY_NAME: "Name"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-datapipeline-pipeline-pipelineobjects.html#cfn-datapipeline-pipeline-pipelineobjects-name"""
#--- Resource declaration ---
@attr.s
class Pipeline(Resource):
"""
AWS Object Type = "AWS::DataPipeline::Pipeline"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-datapipeline-pipeline.html
Property Document:
- ``rp_Name``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-datapipeline-pipeline.html#cfn-datapipeline-pipeline-name
- ``rp_ParameterObjects``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-datapipeline-pipeline.html#cfn-datapipeline-pipeline-parameterobjects
- ``p_Activate``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-datapipeline-pipeline.html#cfn-datapipeline-pipeline-activate
- ``p_Description``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-datapipeline-pipeline.html#cfn-datapipeline-pipeline-description
- ``p_ParameterValues``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-datapipeline-pipeline.html#cfn-datapipeline-pipeline-parametervalues
- ``p_PipelineObjects``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-datapipeline-pipeline.html#cfn-datapipeline-pipeline-pipelineobjects
- ``p_PipelineTags``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-datapipeline-pipeline.html#cfn-datapipeline-pipeline-pipelinetags
"""
AWS_OBJECT_TYPE = "AWS::DataPipeline::Pipeline"
rp_Name: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type),
metadata={AttrMeta.PROPERTY_NAME: "Name"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-datapipeline-pipeline.html#cfn-datapipeline-pipeline-name"""
rp_ParameterObjects: typing.List[typing.Union['PropPipelineParameterObject', dict]] = attr.ib(
default=None,
converter=PropPipelineParameterObject.from_list,
validator=attr.validators.deep_iterable(member_validator=attr.validators.instance_of(PropPipelineParameterObject), iterable_validator=attr.validators.instance_of(list)),
metadata={AttrMeta.PROPERTY_NAME: "ParameterObjects"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-datapipeline-pipeline.html#cfn-datapipeline-pipeline-parameterobjects"""
p_Activate: bool = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(bool)),
metadata={AttrMeta.PROPERTY_NAME: "Activate"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-datapipeline-pipeline.html#cfn-datapipeline-pipeline-activate"""
p_Description: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "Description"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-datapipeline-pipeline.html#cfn-datapipeline-pipeline-description"""
p_ParameterValues: typing.List[typing.Union['PropPipelineParameterValue', dict]] = attr.ib(
default=None,
converter=PropPipelineParameterValue.from_list,
validator=attr.validators.optional(attr.validators.deep_iterable(member_validator=attr.validators.instance_of(PropPipelineParameterValue), iterable_validator=attr.validators.instance_of(list))),
metadata={AttrMeta.PROPERTY_NAME: "ParameterValues"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-datapipeline-pipeline.html#cfn-datapipeline-pipeline-parametervalues"""
p_PipelineObjects: typing.List[typing.Union['PropPipelinePipelineObject', dict]] = attr.ib(
default=None,
converter=PropPipelinePipelineObject.from_list,
validator=attr.validators.optional(attr.validators.deep_iterable(member_validator=attr.validators.instance_of(PropPipelinePipelineObject), iterable_validator=attr.validators.instance_of(list))),
metadata={AttrMeta.PROPERTY_NAME: "PipelineObjects"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-datapipeline-pipeline.html#cfn-datapipeline-pipeline-pipelineobjects"""
p_PipelineTags: typing.List[typing.Union['PropPipelinePipelineTag', dict]] = attr.ib(
default=None,
converter=PropPipelinePipelineTag.from_list,
validator=attr.validators.optional(attr.validators.deep_iterable(member_validator=attr.validators.instance_of(PropPipelinePipelineTag), iterable_validator=attr.validators.instance_of(list))),
metadata={AttrMeta.PROPERTY_NAME: "PipelineTags"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-datapipeline-pipeline.html#cfn-datapipeline-pipeline-pipelinetags"""
| 60.932331
| 223
| 0.76999
| 1,740
| 16,208
| 7.074713
| 0.05
| 0.170593
| 0.043786
| 0.067669
| 0.900487
| 0.900487
| 0.880016
| 0.828513
| 0.828513
| 0.828513
| 0
| 0.000069
| 0.101061
| 16,208
| 265
| 224
| 61.162264
| 0.844818
| 0.336254
| 0
| 0.456522
| 0
| 0
| 0.085284
| 0.059099
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.028986
| 0
| 0.282609
| 0
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| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
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| 0
| 0
| 0
| 0
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| 0
| 0
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| null | 0
| 0
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| 0
| 0
|
0
| 6
|
fa985e13f0c199e0cf54854d04461a0c7a40dec4
| 69
|
py
|
Python
|
scheduletweet/twitter/adminviews.py
|
singh1114/schedule-tweet
|
b55ee35b0ee51c3843543cfa4cd4b3e63d1dcea1
|
[
"CC0-1.0"
] | 1
|
2020-10-25T19:23:57.000Z
|
2020-10-25T19:23:57.000Z
|
scheduletweet/twitter/adminviews.py
|
singh1114/schedule-tweet
|
b55ee35b0ee51c3843543cfa4cd4b3e63d1dcea1
|
[
"CC0-1.0"
] | null | null | null |
scheduletweet/twitter/adminviews.py
|
singh1114/schedule-tweet
|
b55ee35b0ee51c3843543cfa4cd4b3e63d1dcea1
|
[
"CC0-1.0"
] | null | null | null |
from django.views import View
class ScheduleTwitter(View):
pass
| 13.8
| 29
| 0.768116
| 9
| 69
| 5.888889
| 0.888889
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.173913
| 69
| 5
| 30
| 13.8
| 0.929825
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.333333
| 0.333333
| 0
| 0.666667
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| 1
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 6
|
faa3442caf0697529eefc025f315a367a4cb06f8
| 200
|
py
|
Python
|
src/graphmb/__init__.py
|
MicrobialDarkMatter/GraphMB
|
04d777953bb7e5e23ec445e3d956c11c120feaa1
|
[
"MIT"
] | 5
|
2022-02-28T16:06:59.000Z
|
2022-03-16T01:04:31.000Z
|
src/graphmb/__init__.py
|
AndreLamurias/GraphMB
|
9b861ba5522bfe8262c833b3bab1903c0a65a4b5
|
[
"MIT"
] | 7
|
2022-02-21T13:21:07.000Z
|
2022-03-28T08:07:46.000Z
|
src/graphmb/__init__.py
|
MicrobialDarkMatter/GraphMB
|
04d777953bb7e5e23ec445e3d956c11c120feaa1
|
[
"MIT"
] | null | null | null |
import os
os.environ["DGLBACKEND"] = "pytorch"
import dgl
from . import contigsdataset
from . import graph_functions
from . import evaluate
from . import graphsage_unsupervised
from . import version
| 20
| 36
| 0.795
| 25
| 200
| 6.28
| 0.56
| 0.318471
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.14
| 200
| 9
| 37
| 22.222222
| 0.912791
| 0
| 0
| 0
| 0
| 0
| 0.085
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.875
| 0
| 0.875
| 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
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
facd0c068d2ca1b5ab4aac73ed30d7740eef0822
| 1,168
|
py
|
Python
|
terrascript/scaleway/d.py
|
mjuenema/python-terrascript
|
6d8bb0273a14bfeb8ff8e950fe36f97f7c6e7b1d
|
[
"BSD-2-Clause"
] | 507
|
2017-07-26T02:58:38.000Z
|
2022-01-21T12:35:13.000Z
|
terrascript/scaleway/d.py
|
mjuenema/python-terrascript
|
6d8bb0273a14bfeb8ff8e950fe36f97f7c6e7b1d
|
[
"BSD-2-Clause"
] | 135
|
2017-07-20T12:01:59.000Z
|
2021-10-04T22:25:40.000Z
|
terrascript/scaleway/d.py
|
mjuenema/python-terrascript
|
6d8bb0273a14bfeb8ff8e950fe36f97f7c6e7b1d
|
[
"BSD-2-Clause"
] | 81
|
2018-02-20T17:55:28.000Z
|
2022-01-31T07:08:40.000Z
|
# terrascript/scaleway/d.py
# Automatically generated by tools/makecode.py ()
import warnings
warnings.warn(
"using the 'legacy layout' is deprecated", DeprecationWarning, stacklevel=2
)
import terrascript
class scaleway_account_ssh_key(terrascript.Data):
pass
class scaleway_baremetal_offer(terrascript.Data):
pass
class scaleway_instance_image(terrascript.Data):
pass
class scaleway_instance_security_group(terrascript.Data):
pass
class scaleway_instance_server(terrascript.Data):
pass
class scaleway_instance_volume(terrascript.Data):
pass
class scaleway_k8s_cluster(terrascript.Data):
pass
class scaleway_k8s_pool(terrascript.Data):
pass
class scaleway_lb_ip(terrascript.Data):
pass
class scaleway_marketplace_image(terrascript.Data):
pass
class scaleway_rdb_acl(terrascript.Data):
pass
class scaleway_rdb_database(terrascript.Data):
pass
class scaleway_rdb_instance(terrascript.Data):
pass
class scaleway_registry_image(terrascript.Data):
pass
class scaleway_registry_namespace(terrascript.Data):
pass
class scaleway_vpc_private_network(terrascript.Data):
pass
| 15.783784
| 79
| 0.781678
| 140
| 1,168
| 6.271429
| 0.35
| 0.236902
| 0.346241
| 0.410023
| 0.635535
| 0.489749
| 0
| 0
| 0
| 0
| 0
| 0.003012
| 0.14726
| 1,168
| 73
| 80
| 16
| 0.878514
| 0.0625
| 0
| 0.432432
| 1
| 0
| 0.035714
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.432432
| 0.054054
| 0
| 0.486486
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 6
|
fae379f80084aa9fe3666eea20b584c74cd1ca23
| 4,171
|
py
|
Python
|
tests/unit_tests/test_tethys_apps/test_models/test_CustomSetting.py
|
quyendong/tethys
|
99bcb524d5b2021b88d5fa15b7ed6b8acb460997
|
[
"BSD-2-Clause"
] | 1
|
2020-10-08T20:38:33.000Z
|
2020-10-08T20:38:33.000Z
|
tests/unit_tests/test_tethys_apps/test_models/test_CustomSetting.py
|
quyendong/tethys
|
99bcb524d5b2021b88d5fa15b7ed6b8acb460997
|
[
"BSD-2-Clause"
] | 1
|
2018-04-14T19:40:54.000Z
|
2018-04-14T19:40:54.000Z
|
tests/unit_tests/test_tethys_apps/test_models/test_CustomSetting.py
|
quyendong/tethys
|
99bcb524d5b2021b88d5fa15b7ed6b8acb460997
|
[
"BSD-2-Clause"
] | 1
|
2021-09-07T14:47:11.000Z
|
2021-09-07T14:47:11.000Z
|
from tethys_sdk.testing import TethysTestCase
from tethys_apps.models import TethysApp, CustomSetting
from django.core.exceptions import ValidationError
class CustomSettingTests(TethysTestCase):
def set_up(self):
self.test_app = TethysApp.objects.get(package='test_app')
pass
def tear_down(self):
pass
def test_clean_empty_validation_error(self):
custom_setting = self.test_app.settings_set.select_subclasses().get(name='default_name')
custom_setting.value = ''
custom_setting.save()
# Check ValidationError
ret = CustomSetting.objects.get(name='default_name')
self.assertRaises(ValidationError, ret.clean)
def test_clean_int_validation_error(self):
custom_setting = self.test_app.settings_set.select_subclasses().get(name='default_name')
custom_setting.value = 'test'
custom_setting.type = 'INTEGER'
custom_setting.save()
# Check ValidationError
ret = CustomSetting.objects.get(name='default_name')
self.assertRaises(ValidationError, ret.clean)
def test_clean_float_validation_error(self):
custom_setting = self.test_app.settings_set.select_subclasses().get(name='default_name')
custom_setting.value = 'test'
custom_setting.type = 'FLOAT'
custom_setting.save()
# Check ValidationError
ret = CustomSetting.objects.get(name='default_name')
self.assertRaises(ValidationError, ret.clean)
def test_clean_bool_validation_error(self):
custom_setting = self.test_app.settings_set.select_subclasses().get(name='default_name')
custom_setting.value = 'test'
custom_setting.type = 'BOOLEAN'
custom_setting.save()
# Check ValidationError
ret = CustomSetting.objects.get(name='default_name')
self.assertRaises(ValidationError, ret.clean)
def test_get_value_empty(self):
custom_setting = self.test_app.settings_set.select_subclasses().get(name='default_name')
custom_setting.value = ''
custom_setting.save()
self.assertIsNone(CustomSetting.objects.get(name='default_name').get_value())
def test_get_value_string(self):
custom_setting = self.test_app.settings_set.select_subclasses().get(name='default_name')
custom_setting.value = 'test_string'
custom_setting.type = 'STRING'
custom_setting.save()
ret = CustomSetting.objects.get(name='default_name').get_value()
self.assertEqual('test_string', ret)
def test_get_value_float(self):
custom_setting = self.test_app.settings_set.select_subclasses().get(name='default_name')
custom_setting.value = '3.14'
custom_setting.type = 'FLOAT'
custom_setting.save()
ret = CustomSetting.objects.get(name='default_name').get_value()
self.assertEqual(3.14, ret)
def test_get_value_integer(self):
custom_setting = self.test_app.settings_set.select_subclasses().get(name='default_name')
custom_setting.value = '3'
custom_setting.type = 'INTEGER'
custom_setting.save()
ret = CustomSetting.objects.get(name='default_name').get_value()
self.assertEqual(3, ret)
def test_get_value_boolean_true(self):
test_cases = ['true', 'yes', 't', 'y', '1']
for test in test_cases:
custom_setting = self.test_app.settings_set.select_subclasses().get(name='default_name')
custom_setting.value = test
custom_setting.type = 'BOOLEAN'
custom_setting.save()
ret = CustomSetting.objects.get(name='default_name').get_value()
self.assertTrue(ret)
def test_get_value_boolean_false(self):
test_cases = ['false', 'no', 'f', 'n', '0']
for test in test_cases:
custom_setting = self.test_app.settings_set.select_subclasses().get(name='default_name')
custom_setting.value = test
custom_setting.type = 'BOOLEAN'
custom_setting.save()
ret = CustomSetting.objects.get(name='default_name').get_value()
self.assertFalse(ret)
| 37.918182
| 100
| 0.680173
| 494
| 4,171
| 5.455466
| 0.1417
| 0.183302
| 0.103896
| 0.133581
| 0.828571
| 0.815213
| 0.79666
| 0.775139
| 0.758071
| 0.758071
| 0
| 0.00304
| 0.21122
| 4,171
| 109
| 101
| 38.266055
| 0.816109
| 0.020858
| 0
| 0.6375
| 0
| 0
| 0.087767
| 0
| 0
| 0
| 0
| 0
| 0.125
| 1
| 0.15
| false
| 0.025
| 0.0375
| 0
| 0.2
| 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
| 0
| 0
|
0
| 6
|
877e232a8cd8a9acf37d1c52fab8d43ac931e77b
| 44
|
py
|
Python
|
news_getter/__init__.py
|
isevitt/news-getter
|
8fb65aed15c82f39a1cb26f5ed1a7b2404e6fbd9
|
[
"MIT"
] | null | null | null |
news_getter/__init__.py
|
isevitt/news-getter
|
8fb65aed15c82f39a1cb26f5ed1a7b2404e6fbd9
|
[
"MIT"
] | null | null | null |
news_getter/__init__.py
|
isevitt/news-getter
|
8fb65aed15c82f39a1cb26f5ed1a7b2404e6fbd9
|
[
"MIT"
] | null | null | null |
from news_getter.news_get import NewsGetter
| 22
| 43
| 0.886364
| 7
| 44
| 5.285714
| 0.857143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.090909
| 44
| 1
| 44
| 44
| 0.925
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 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
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
879e74c68efd03bbe47b96b2d00c9b8137f35cf6
| 4,124
|
py
|
Python
|
web/gatekeeping/api/position.py
|
gabegm/Headcount-Planning-Management-System
|
6946509e6d8c530b9f0e51b68047c4cb01dedd2e
|
[
"MIT"
] | 2
|
2020-03-17T10:55:39.000Z
|
2022-03-21T00:04:38.000Z
|
web/gatekeeping/api/position.py
|
gabegm/Headcount-Planning-Management-System
|
6946509e6d8c530b9f0e51b68047c4cb01dedd2e
|
[
"MIT"
] | null | null | null |
web/gatekeeping/api/position.py
|
gabegm/Headcount-Planning-Management-System
|
6946509e6d8c530b9f0e51b68047c4cb01dedd2e
|
[
"MIT"
] | null | null | null |
from flask import (g, abort, session)
from gatekeeping.db import get_db
from gatekeeping.api.user import get_user_function
def get_position(id, check_submitter=True):
position = get_db().execute(
'SELECT p.id, ps.name as status, rs.name as recruitment_status, number, pl.name as pillar, c.name as company, d.name as department,'
'f.id as function_id, f.name as function, title, functional_reporting_line, disciplinary_reporting_line, holder,'
'hours, start_date, end_date, salary, social_security_contribution, fringe_benefit, performance_bonus, super_bonus, management_bonus'
' FROM position p'
' JOIN company c ON p.company_id = c.id'
' JOIN pillar pl ON p.pillar_id = pl.id'
' JOIN department d ON p.department_id = d.id'
' JOIN function f ON p.function_id = f.id'
' JOIN position_status ps ON p.status_id = ps.id'
' JOIN recruitment_status rs ON p.recruitment_status_id = rs.id'
' WHERE p.number = ? AND p.isBudget = 0',
(id,)
).fetchone()
if position is None:
abort(404, "Position id {0} doesn't exist.".format(id))
user_functions = get_user_function(session['user_id'])
function_ids = [function['function_id'] for function in user_functions]
if check_submitter and g.user['type'] != 'ADMIN' and position['function_id'] not in function_ids:
abort(403)
return position
def get_position_by_id(id, check_submitter=True):
position = get_db().execute(
'SELECT p.id, ps.name as status, rs.name as recruitment_status, number, pl.name as pillar, c.name as company, d.name as department,'
'f.id as function_id, f.name as function, title, functional_reporting_line, disciplinary_reporting_line, holder,'
'hours, start_date, end_date, salary, social_security_contribution, fringe_benefit, performance_bonus, super_bonus, management_bonus'
' FROM position p'
' JOIN company c ON p.company_id = c.id'
' JOIN pillar pl ON p.pillar_id = pl.id'
' JOIN department d ON p.department_id = d.id'
' JOIN function f ON p.function_id = f.id'
' JOIN position_status ps ON p.status_id = ps.id'
' JOIN recruitment_status rs ON p.recruitment_status_id = rs.id'
' WHERE p.id = ? AND p.isBudget = 0',
(id,)
).fetchone()
if position is None:
abort(404, "Position id {0} doesn't exist.".format(id))
user_functions = get_user_function(session['user_id'])
function_ids = [function['function_id'] for function in user_functions]
if check_submitter and g.user['type'] != 'ADMIN' and position['function_id'] not in function_ids:
abort(403)
return position
def get_positions(check_submitter=True):
query = (
'SELECT p.id, ps.name as status, rs.name as recruitment_status, number, pl.name as pillar, c.name as company, er.rate as rate, d.name as department,'
'f.name as function, title, functional_reporting_line, disciplinary_reporting_line, holder,'
'hours, start_date, end_date, salary, fringe_benefit, social_security_contribution, performance_bonus, super_bonus, management_bonus'
' FROM position p'
' JOIN company c ON p.company_id = c.id'
' JOIN exchange_rate er on c.exchange_rate_id = er.id'
' JOIN pillar pl ON p.pillar_id = pl.id'
' JOIN department d ON p.department_id = d.id'
' JOIN function f ON p.function_id = f.id'
' JOIN position_status ps ON p.status_id = ps.id'
' JOIN recruitment_status rs ON p.recruitment_status_id = rs.id'
' WHERE isBudget = 0'
)
if check_submitter and g.user['type'] != 'ADMIN':
user_functions = get_user_function(session['user_id'])
if user_functions:
function_ids = [function['function_id'] for function in user_functions]
query += ' AND f.id IN (?' + ', ?' * (len(function_ids)-1) + ')'
return get_db().execute(query, tuple(function_ids)).fetchall()
abort(403, 'You are not assigned to any functions')
return get_db().execute(query).fetchall()
| 46.337079
| 157
| 0.67386
| 605
| 4,124
| 4.4
| 0.155372
| 0.040571
| 0.020661
| 0.012397
| 0.860255
| 0.836589
| 0.836589
| 0.836589
| 0.80879
| 0.80879
| 0
| 0.006544
| 0.221872
| 4,124
| 89
| 158
| 46.337079
| 0.822998
| 0
| 0
| 0.7
| 0
| 0.042857
| 0.564606
| 0.077091
| 0.014286
| 0
| 0
| 0
| 0
| 1
| 0.042857
| false
| 0
| 0.042857
| 0
| 0.142857
| 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
| 0
| 0
|
0
| 6
|
354ce7a5fccba75ebe10ba5320b4828e8ff84d6c
| 134
|
py
|
Python
|
tests/test_roomacoustics.py
|
ben-kas/pyrato
|
5880c53588f807a0fdd5271003aa72b5a3e2330e
|
[
"MIT"
] | 3
|
2021-01-05T10:43:58.000Z
|
2022-02-16T07:57:11.000Z
|
tests/test_roomacoustics.py
|
ben-kas/pyrato
|
5880c53588f807a0fdd5271003aa72b5a3e2330e
|
[
"MIT"
] | 1
|
2022-02-17T07:43:01.000Z
|
2022-02-17T07:43:01.000Z
|
tests/test_roomacoustics.py
|
ben-kas/pyrato
|
5880c53588f807a0fdd5271003aa72b5a3e2330e
|
[
"MIT"
] | 1
|
2020-12-14T16:11:42.000Z
|
2020-12-14T16:11:42.000Z
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Tests for `roomacoustics` package."""
def test_import():
import roomacoustics
| 14.888889
| 40
| 0.641791
| 16
| 134
| 5.3125
| 0.875
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.008929
| 0.164179
| 134
| 8
| 41
| 16.75
| 0.75
| 0.574627
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| true
| 0
| 1
| 0
| 1.5
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 1
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
35862b6c6a5a4ddb830c5a0f01205bc0be751a53
| 96
|
py
|
Python
|
venv/lib/python3.8/site-packages/pyflakes/__main__.py
|
Retraces/UkraineBot
|
3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71
|
[
"MIT"
] | 2
|
2022-03-13T01:58:52.000Z
|
2022-03-31T06:07:54.000Z
|
venv/lib/python3.8/site-packages/pyflakes/__main__.py
|
DesmoSearch/Desmobot
|
b70b45df3485351f471080deb5c785c4bc5c4beb
|
[
"MIT"
] | 19
|
2021-11-20T04:09:18.000Z
|
2022-03-23T15:05:55.000Z
|
venv/lib/python3.8/site-packages/pyflakes/__main__.py
|
DesmoSearch/Desmobot
|
b70b45df3485351f471080deb5c785c4bc5c4beb
|
[
"MIT"
] | null | null | null |
/home/runner/.cache/pip/pool/f6/19/ad/4eaa38a8043077bd5fd9e91630881be777ebc4657011a6c55861bf5a21
| 96
| 96
| 0.895833
| 9
| 96
| 9.555556
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.4375
| 0
| 96
| 1
| 96
| 96
| 0.458333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 0
| 1
| 0
| 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 | 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
359544b911a99f0c5f8b34ab22463fc2a8c9cbfb
| 2,864
|
py
|
Python
|
core/models/utils.py
|
benoitmarteau/facies_classification_benchmark
|
25650361780191b1c624510728a156fa30b16829
|
[
"MIT"
] | 60
|
2019-01-12T21:37:33.000Z
|
2022-03-23T02:17:43.000Z
|
core/models/utils.py
|
benoitmarteau/facies_classification_benchmark
|
25650361780191b1c624510728a156fa30b16829
|
[
"MIT"
] | 6
|
2019-10-09T03:33:58.000Z
|
2021-12-24T17:34:34.000Z
|
core/models/utils.py
|
benoitmarteau/facies_classification_benchmark
|
25650361780191b1c624510728a156fa30b16829
|
[
"MIT"
] | 25
|
2019-01-26T13:04:02.000Z
|
2021-11-26T08:20:26.000Z
|
import torch
import torch.nn as nn
import torch.nn.functional as F
class conv2DBatchNorm(nn.Module):
def __init__(self, in_channels, n_filters, k_size, stride, padding, bias=True, dilation=1):
super(conv2DBatchNorm, self).__init__()
if dilation > 1:
conv_mod = nn.Conv2d(int(in_channels), int(n_filters), kernel_size=k_size,
padding=padding, stride=stride, bias=bias, dilation=dilation)
else:
conv_mod = nn.Conv2d(int(in_channels), int(n_filters), kernel_size=k_size,
padding=padding, stride=stride, bias=bias, dilation=1)
self.cb_unit = nn.Sequential(conv_mod,
nn.BatchNorm2d(int(n_filters)),)
def forward(self, inputs):
outputs = self.cb_unit(inputs)
return outputs
class deconv2DBatchNorm(nn.Module):
def __init__(self, in_channels, n_filters, k_size, stride, padding, bias=True):
super(deconv2DBatchNorm, self).__init__()
self.dcb_unit = nn.Sequential(nn.ConvTranspose2d(int(in_channels), int(n_filters), kernel_size=k_size,
padding=padding, stride=stride, bias=bias),
nn.BatchNorm2d(int(n_filters)),)
def forward(self, inputs):
outputs = self.dcb_unit(inputs)
return outputs
class conv2DBatchNormRelu(nn.Module):
def __init__(self, in_channels, n_filters, k_size, stride, padding, bias=True, dilation=1):
super(conv2DBatchNormRelu, self).__init__()
if dilation > 1:
conv_mod = nn.Conv2d(int(in_channels), int(n_filters), kernel_size=k_size,
padding=padding, stride=stride, bias=bias, dilation=dilation)
else:
conv_mod = nn.Conv2d(int(in_channels), int(n_filters), kernel_size=k_size,
padding=padding, stride=stride, bias=bias, dilation=1)
self.cbr_unit = nn.Sequential(conv_mod,
nn.BatchNorm2d(int(n_filters)),
nn.ReLU(inplace=True),)
def forward(self, inputs):
outputs = self.cbr_unit(inputs)
return outputs
class deconv2DBatchNormRelu(nn.Module):
def __init__(self, in_channels, n_filters, k_size, stride, padding, bias=True):
super(deconv2DBatchNormRelu, self).__init__()
self.dcbr_unit = nn.Sequential(nn.ConvTranspose2d(int(in_channels), int(n_filters), kernel_size=k_size,
padding=padding, stride=stride, bias=bias),
nn.BatchNorm2d(int(n_filters)),
nn.ReLU(inplace=True),)
def forward(self, inputs):
outputs = self.dcbr_unit(inputs)
return outputs
| 39.232877
| 111
| 0.594274
| 329
| 2,864
| 4.911854
| 0.148936
| 0.069307
| 0.068069
| 0.059406
| 0.830446
| 0.778465
| 0.778465
| 0.778465
| 0.778465
| 0.778465
| 0
| 0.012042
| 0.30412
| 2,864
| 72
| 112
| 39.777778
| 0.798796
| 0
| 0
| 0.627451
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.156863
| false
| 0
| 0.058824
| 0
| 0.372549
| 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
| 0
| 0
|
0
| 6
|
35a909edaa08d5749b27af0ca66eb65fb69ba90f
| 25
|
py
|
Python
|
different/__init__.py
|
amatuni/different
|
efc9c5881be6b2d05e6799928e22deb085a8a4a7
|
[
"MIT"
] | null | null | null |
different/__init__.py
|
amatuni/different
|
efc9c5881be6b2d05e6799928e22deb085a8a4a7
|
[
"MIT"
] | null | null | null |
different/__init__.py
|
amatuni/different
|
efc9c5881be6b2d05e6799928e22deb085a8a4a7
|
[
"MIT"
] | null | null | null |
from .different import *
| 12.5
| 24
| 0.76
| 3
| 25
| 6.333333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.16
| 25
| 1
| 25
| 25
| 0.904762
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 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
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
35be9629bca17875a8e96abc6004217494ee829c
| 3,027
|
py
|
Python
|
main.py
|
Sakurai07/Google.py
|
d849ced11de8e8c7731c8da2a5d8c2288917d7c3
|
[
"MIT"
] | null | null | null |
main.py
|
Sakurai07/Google.py
|
d849ced11de8e8c7731c8da2a5d8c2288917d7c3
|
[
"MIT"
] | null | null | null |
main.py
|
Sakurai07/Google.py
|
d849ced11de8e8c7731c8da2a5d8c2288917d7c3
|
[
"MIT"
] | null | null | null |
print("Made by Sakurai07")
print("https://github.com/sakurai07/")
from googleapi import google
import os
import time
import asyncio
dots = 1
num_page = 1
dp = 1
def ser(q, num_page):
dots = 1
search_results = google.search(q, num_page)
while search_results == []:
c()
dots += 1
if dots == 0:
print("(|)loading")
elif dots == 1:
print("(/)loading.")
elif dots == 2:
print("(-)loading..")
elif dots == 3:
print("(\)loading...")
dots = 0
search_results = google.search(search, num_page)
c()
for result in search_results:
print()
print(result.name)
print()
print("description:",result.description)
print()
print("link:",result.link)
print()
print("page",num_page)
def c():
os.system("clear")
while True:
num_page = 1
search = input("search google.py: ")
print("querying...")
search_results = google.search(search, num_page)
while search_results == []:
c()
dots += 1
if dots == 0:
print("(|)loading")
elif dots == 1:
print("(/)loading.")
elif dots == 2:
print("(-)loading..")
elif dots == 3:
print("(\)loading...")
dots = 0
search_results = google.search(search, num_page)
c()
for result in search_results:
print()
print(result.name)
print()
print("description:",result.description)
print()
print("link:",result.link)
print()
print("page:",num_page)
while True:
print("type n for new tab, np for next page and lp for last page")
men2 = input(">")
if men2 == "n":
num_page = 1
search = input("search google.py: ")
print("querying...")
search_results = google.search(search, num_page)
while search_results == []:
c()
dots += 1
if dots == 0:
print("(|)loading")
elif dots == 1:
print("(/)loading.")
elif dots == 2:
print("(-)loading..")
elif dots == 3:
print("(\)loading...")
dots = 0
search_results = google.search(search, num_page)
c()
for result in search_results:
print(result.name)
print()
print("description:",result.description)
print()
print("link:",result.link)
print()
print("page number:",num_page)
elif men2 == "np":
dp += 1
ser(search, dp)
elif men2 == "lp":
if dp != 0:
dp -= 1
ser(search,dp)
else:
dp = dp
ser(search, dp)
| 27.770642
| 74
| 0.451933
| 308
| 3,027
| 4.36039
| 0.181818
| 0.067759
| 0.107223
| 0.134028
| 0.760983
| 0.740134
| 0.740134
| 0.740134
| 0.740134
| 0.740134
| 0
| 0.019763
| 0.414932
| 3,027
| 109
| 75
| 27.770642
| 0.738566
| 0
| 0
| 0.771429
| 0
| 0
| 0.126156
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.019048
| false
| 0
| 0.038095
| 0
| 0.057143
| 0.380952
| 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
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
35e43354ac608991c840c28cb5d56f0d56507a66
| 22
|
py
|
Python
|
ampere/__init__.py
|
nealde/Ampere
|
75fa9c34940a71ef865eb98b551b4a4a27da96c3
|
[
"MIT"
] | 19
|
2019-03-25T09:49:47.000Z
|
2022-02-20T07:40:18.000Z
|
ampere/__init__.py
|
nealde/battery
|
75fa9c34940a71ef865eb98b551b4a4a27da96c3
|
[
"MIT"
] | 6
|
2018-09-28T19:27:25.000Z
|
2019-01-07T16:00:09.000Z
|
ampere/__init__.py
|
nealde/battery
|
75fa9c34940a71ef865eb98b551b4a4a27da96c3
|
[
"MIT"
] | 6
|
2019-01-25T16:50:56.000Z
|
2021-01-29T21:36:32.000Z
|
from .ampere import *
| 11
| 21
| 0.727273
| 3
| 22
| 5.333333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.181818
| 22
| 1
| 22
| 22
| 0.888889
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 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
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
35e6933f2cd85e4b19bcac70124a6811ba23f30e
| 8,974
|
py
|
Python
|
tests/components/homekit_controller/test_cover.py
|
MrDelik/core
|
93a66cc357b226389967668441000498a10453bb
|
[
"Apache-2.0"
] | 30,023
|
2016-04-13T10:17:53.000Z
|
2020-03-02T12:56:31.000Z
|
tests/components/homekit_controller/test_cover.py
|
MrDelik/core
|
93a66cc357b226389967668441000498a10453bb
|
[
"Apache-2.0"
] | 24,710
|
2016-04-13T08:27:26.000Z
|
2020-03-02T12:59:13.000Z
|
tests/components/homekit_controller/test_cover.py
|
MrDelik/core
|
93a66cc357b226389967668441000498a10453bb
|
[
"Apache-2.0"
] | 11,956
|
2016-04-13T18:42:31.000Z
|
2020-03-02T09:32:12.000Z
|
"""Basic checks for HomeKitalarm_control_panel."""
from aiohomekit.model.characteristics import CharacteristicsTypes
from aiohomekit.model.services import ServicesTypes
from tests.components.homekit_controller.common import setup_test_component
def create_window_covering_service(accessory):
"""Define a window-covering characteristics as per page 219 of HAP spec."""
service = accessory.add_service(ServicesTypes.WINDOW_COVERING)
cur_state = service.add_char(CharacteristicsTypes.POSITION_CURRENT)
cur_state.value = 0
targ_state = service.add_char(CharacteristicsTypes.POSITION_TARGET)
targ_state.value = 0
position_state = service.add_char(CharacteristicsTypes.POSITION_STATE)
position_state.value = 0
position_hold = service.add_char(CharacteristicsTypes.POSITION_HOLD)
position_hold.value = 0
obstruction = service.add_char(CharacteristicsTypes.OBSTRUCTION_DETECTED)
obstruction.value = False
name = service.add_char(CharacteristicsTypes.NAME)
name.value = "testdevice"
return service
def create_window_covering_service_with_h_tilt(accessory):
"""Define a window-covering characteristics as per page 219 of HAP spec."""
service = create_window_covering_service(accessory)
tilt_current = service.add_char(CharacteristicsTypes.HORIZONTAL_TILT_CURRENT)
tilt_current.value = 0
tilt_target = service.add_char(CharacteristicsTypes.HORIZONTAL_TILT_TARGET)
tilt_target.value = 0
def create_window_covering_service_with_v_tilt(accessory):
"""Define a window-covering characteristics as per page 219 of HAP spec."""
service = create_window_covering_service(accessory)
tilt_current = service.add_char(CharacteristicsTypes.VERTICAL_TILT_CURRENT)
tilt_current.value = 0
tilt_target = service.add_char(CharacteristicsTypes.VERTICAL_TILT_TARGET)
tilt_target.value = 0
async def test_change_window_cover_state(hass, utcnow):
"""Test that we can turn a HomeKit alarm on and off again."""
helper = await setup_test_component(hass, create_window_covering_service)
await hass.services.async_call(
"cover", "open_cover", {"entity_id": helper.entity_id}, blocking=True
)
helper.async_assert_service_values(
ServicesTypes.WINDOW_COVERING,
{
CharacteristicsTypes.POSITION_TARGET: 100,
},
)
await hass.services.async_call(
"cover", "close_cover", {"entity_id": helper.entity_id}, blocking=True
)
helper.async_assert_service_values(
ServicesTypes.WINDOW_COVERING,
{
CharacteristicsTypes.POSITION_TARGET: 0,
},
)
async def test_read_window_cover_state(hass, utcnow):
"""Test that we can read the state of a HomeKit alarm accessory."""
helper = await setup_test_component(hass, create_window_covering_service)
await helper.async_update(
ServicesTypes.WINDOW_COVERING,
{CharacteristicsTypes.POSITION_STATE: 0},
)
state = await helper.poll_and_get_state()
assert state.state == "closing"
await helper.async_update(
ServicesTypes.WINDOW_COVERING,
{CharacteristicsTypes.POSITION_STATE: 1},
)
state = await helper.poll_and_get_state()
assert state.state == "opening"
await helper.async_update(
ServicesTypes.WINDOW_COVERING,
{CharacteristicsTypes.POSITION_STATE: 2},
)
state = await helper.poll_and_get_state()
assert state.state == "closed"
await helper.async_update(
ServicesTypes.WINDOW_COVERING,
{CharacteristicsTypes.OBSTRUCTION_DETECTED: True},
)
state = await helper.poll_and_get_state()
assert state.attributes["obstruction-detected"] is True
async def test_read_window_cover_tilt_horizontal(hass, utcnow):
"""Test that horizontal tilt is handled correctly."""
helper = await setup_test_component(
hass, create_window_covering_service_with_h_tilt
)
await helper.async_update(
ServicesTypes.WINDOW_COVERING,
{CharacteristicsTypes.HORIZONTAL_TILT_CURRENT: 75},
)
state = await helper.poll_and_get_state()
assert state.attributes["current_tilt_position"] == 75
async def test_read_window_cover_tilt_vertical(hass, utcnow):
"""Test that vertical tilt is handled correctly."""
helper = await setup_test_component(
hass, create_window_covering_service_with_v_tilt
)
await helper.async_update(
ServicesTypes.WINDOW_COVERING,
{CharacteristicsTypes.VERTICAL_TILT_CURRENT: 75},
)
state = await helper.poll_and_get_state()
assert state.attributes["current_tilt_position"] == 75
async def test_write_window_cover_tilt_horizontal(hass, utcnow):
"""Test that horizontal tilt is written correctly."""
helper = await setup_test_component(
hass, create_window_covering_service_with_h_tilt
)
await hass.services.async_call(
"cover",
"set_cover_tilt_position",
{"entity_id": helper.entity_id, "tilt_position": 90},
blocking=True,
)
helper.async_assert_service_values(
ServicesTypes.WINDOW_COVERING,
{
CharacteristicsTypes.HORIZONTAL_TILT_TARGET: 90,
},
)
async def test_write_window_cover_tilt_vertical(hass, utcnow):
"""Test that vertical tilt is written correctly."""
helper = await setup_test_component(
hass, create_window_covering_service_with_v_tilt
)
await hass.services.async_call(
"cover",
"set_cover_tilt_position",
{"entity_id": helper.entity_id, "tilt_position": 90},
blocking=True,
)
helper.async_assert_service_values(
ServicesTypes.WINDOW_COVERING,
{
CharacteristicsTypes.VERTICAL_TILT_TARGET: 90,
},
)
async def test_window_cover_stop(hass, utcnow):
"""Test that vertical tilt is written correctly."""
helper = await setup_test_component(
hass, create_window_covering_service_with_v_tilt
)
await hass.services.async_call(
"cover", "stop_cover", {"entity_id": helper.entity_id}, blocking=True
)
helper.async_assert_service_values(
ServicesTypes.WINDOW_COVERING,
{
CharacteristicsTypes.POSITION_HOLD: True,
},
)
def create_garage_door_opener_service(accessory):
"""Define a garage-door-opener chars as per page 217 of HAP spec."""
service = accessory.add_service(ServicesTypes.GARAGE_DOOR_OPENER)
cur_state = service.add_char(CharacteristicsTypes.DOOR_STATE_CURRENT)
cur_state.value = 0
cur_state = service.add_char(CharacteristicsTypes.DOOR_STATE_TARGET)
cur_state.value = 0
obstruction = service.add_char(CharacteristicsTypes.OBSTRUCTION_DETECTED)
obstruction.value = False
name = service.add_char(CharacteristicsTypes.NAME)
name.value = "testdevice"
return service
async def test_change_door_state(hass, utcnow):
"""Test that we can turn open and close a HomeKit garage door."""
helper = await setup_test_component(hass, create_garage_door_opener_service)
await hass.services.async_call(
"cover", "open_cover", {"entity_id": helper.entity_id}, blocking=True
)
helper.async_assert_service_values(
ServicesTypes.GARAGE_DOOR_OPENER,
{
CharacteristicsTypes.DOOR_STATE_TARGET: 0,
},
)
await hass.services.async_call(
"cover", "close_cover", {"entity_id": helper.entity_id}, blocking=True
)
helper.async_assert_service_values(
ServicesTypes.GARAGE_DOOR_OPENER,
{
CharacteristicsTypes.DOOR_STATE_TARGET: 1,
},
)
async def test_read_door_state(hass, utcnow):
"""Test that we can read the state of a HomeKit garage door."""
helper = await setup_test_component(hass, create_garage_door_opener_service)
await helper.async_update(
ServicesTypes.GARAGE_DOOR_OPENER,
{CharacteristicsTypes.DOOR_STATE_CURRENT: 0},
)
state = await helper.poll_and_get_state()
assert state.state == "open"
await helper.async_update(
ServicesTypes.GARAGE_DOOR_OPENER,
{CharacteristicsTypes.DOOR_STATE_CURRENT: 1},
)
state = await helper.poll_and_get_state()
assert state.state == "closed"
await helper.async_update(
ServicesTypes.GARAGE_DOOR_OPENER,
{CharacteristicsTypes.DOOR_STATE_CURRENT: 2},
)
state = await helper.poll_and_get_state()
assert state.state == "opening"
await helper.async_update(
ServicesTypes.GARAGE_DOOR_OPENER,
{CharacteristicsTypes.DOOR_STATE_CURRENT: 3},
)
state = await helper.poll_and_get_state()
assert state.state == "closing"
await helper.async_update(
ServicesTypes.GARAGE_DOOR_OPENER,
{CharacteristicsTypes.OBSTRUCTION_DETECTED: True},
)
state = await helper.poll_and_get_state()
assert state.attributes["obstruction-detected"] is True
| 32.05
| 81
| 0.720303
| 1,045
| 8,974
| 5.858373
| 0.107177
| 0.061745
| 0.032016
| 0.077752
| 0.912937
| 0.889579
| 0.836328
| 0.819013
| 0.779811
| 0.748122
| 0
| 0.007079
| 0.197236
| 8,974
| 279
| 82
| 32.164875
| 0.842726
| 0.035324
| 0
| 0.544118
| 0
| 0
| 0.045343
| 0.010843
| 0
| 0
| 0
| 0
| 0.088235
| 1
| 0.019608
| false
| 0
| 0.014706
| 0
| 0.044118
| 0
| 0
| 0
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0
| 6
|
ea24ee23463f2e6fbe7c9346efd64b147c438492
| 10,450
|
py
|
Python
|
panel/tests/view_tests.py
|
freejooo/vigilio
|
d21bf4f9d39e5dcde5d7c21476d8650e914c3c66
|
[
"MIT"
] | 137
|
2021-03-26T18:19:45.000Z
|
2022-03-06T07:48:23.000Z
|
panel/tests/view_tests.py
|
rrosajp/vigilio
|
d21bf4f9d39e5dcde5d7c21476d8650e914c3c66
|
[
"MIT"
] | 11
|
2021-03-28T00:07:00.000Z
|
2021-05-04T12:54:58.000Z
|
panel/tests/view_tests.py
|
rrosajp/vigilio
|
d21bf4f9d39e5dcde5d7c21476d8650e914c3c66
|
[
"MIT"
] | 16
|
2021-03-27T23:58:53.000Z
|
2022-03-20T14:52:13.000Z
|
from typing import Dict, List
import pytest
from django.test import Client
from django.urls import reverse
from pytest_mock import MockerFixture
from stream.models import Movie
from stream.tests.factories import MovieFactory
@pytest.fixture
def ignore_setup(mocker: MockerFixture) -> None:
mocker.patch("panel.decorators.is_panel_ready")
@pytest.fixture
def bad_setup(mocker: MockerFixture) -> None:
_setup = mocker.patch("panel.decorators.is_panel_ready")
_setup.return_value = False
@pytest.fixture
def ignore_settings(mocker: MockerFixture) -> None:
mocker.patch("panel.decorators.are_settings_filled")
@pytest.fixture
def bad_settings(mocker: MockerFixture) -> None:
_settings = mocker.patch("panel.decorators.are_settings_filled")
_settings.return_value = False
@pytest.mark.usefixtures("db")
class TestSetupPanel:
def test_blocks_unauthorized(self, client: Client) -> None:
response = client.get(reverse("panel:setup_panel"))
assert response.status_code == 302
assert response.url == f"{reverse('login')}?next={reverse('panel:setup_panel')}"
def test_redirects_to_initial_setup(
self, user_client: Client, ignore_setup, bad_settings
) -> None:
response = user_client.get("/panel/setup-panel/")
assert response.status_code == 302
assert (
response.url
== f"{reverse('panel:initial_setup')}?next={reverse('panel:setup_panel')}"
)
def test_renders_context_correctly(
self, user_client: Client, mocker: MockerFixture, ignore_settings, ignore_setup
) -> None:
commands: Dict[str, List[str]] = {"ffmpeg": ["test1"], "ffprobe": ["test2"]}
status = mocker.patch("panel.views.get_installation_status")
status.return_value = commands
response = user_client.get("/panel/setup-panel/", HTTP_REFERER="/categories")
assert response.status_code == 200
context: str = response.content.decode("UTF-8")
for key, value in commands.items():
assert key in context
assert value[0] in context
@pytest.mark.usefixtures("db")
class TestIndex:
def test_blocks_unauthorized(self, client: Client) -> None:
response = client.get(reverse("panel:index"))
assert response.status_code == 302
assert response.url == f"{reverse('login')}?next={reverse('panel:index')}"
def test_redirects_to_setup(self, user_client: Client, bad_setup) -> None:
response = user_client.get(reverse("panel:index"))
assert response.status_code == 302
assert response.url == "/panel/setup-panel/"
def test_redirects_to_initial_setup(
self, user_client: Client, ignore_setup, bad_settings
) -> None:
response = user_client.get("/panel/")
assert response.status_code == 302
assert (
response.url
== f"{reverse('panel:initial_setup')}?next={reverse('panel:index')}"
)
def test_responds_correctly(
self, user_client: Client, ignore_settings, ignore_setup
) -> None:
movie: Movie = MovieFactory()
response = user_client.get("/panel/")
assert response.status_code == 200
assert movie.title in response.content.decode("UTF-8")
@pytest.mark.usefixtures("db")
class TestAddMovie:
def test_blocks_unauthorized(self, client: Client) -> None:
response = client.get(reverse("panel:add_movie"))
assert response.status_code == 302
assert response.url == f"{reverse('login')}?next={reverse('panel:add_movie')}"
def test_redirects_to_setup(self, user_client: Client, bad_setup) -> None:
response = user_client.get("/panel/add-movie/")
assert response.status_code == 302
assert response.url == "/panel/setup-panel/"
def test_redirects_when_daemons_are_not_running(
self, user_client: Client, mocker: MockerFixture, ignore_setup
) -> None:
background = mocker.patch("panel.decorators.is_background_running")
background.return_value = False
response = user_client.get("/panel/add-movie/")
assert response.status_code == 302
assert (
response.url
== f"{reverse('panel:background_processes')}?next={reverse('panel:add_movie')}"
)
def test_redirects_to_initial_setup(
self, user_client: Client, mocker: MockerFixture, ignore_setup, bad_settings
) -> None:
mocker.patch("panel.decorators.is_background_running")
response = user_client.get("/panel/add-movie/")
assert response.status_code == 302
assert (
response.url
== f"{reverse('panel:initial_setup')}?next={reverse('panel:add_movie')}"
)
def test_responds_correctly(
self, user_client: Client, mocker: MockerFixture, ignore_settings, ignore_setup
) -> None:
mocker.patch("panel.decorators.is_background_running")
response = user_client.get("/panel/add-movie/")
assert response.status_code == 200
assert response.context[-1].template_name == "panel/add_movie.html"
@pytest.mark.usefixtures("db")
class TestBackgroundManagement:
def test_blocks_unauthorized(self, client: Client) -> None:
response = client.get(reverse("panel:background_management"))
assert response.status_code == 302
assert (
response.url
== f"{reverse('login')}?next={reverse('panel:background_management')}"
)
def test_redirects_to_setup(self, user_client: Client, bad_setup) -> None:
response = user_client.get("/panel/background-management/")
assert response.status_code == 302
assert response.url == "/panel/setup-panel/"
def test_redirects_to_initial_setup(
self, user_client: Client, ignore_setup, bad_settings
) -> None:
response = user_client.get("/panel/background-management/")
assert response.status_code == 302
assert (
response.url
== f"{reverse('panel:initial_setup')}?next={reverse('panel:background_management')}"
)
def test_responds_correctly(
self, user_client: Client, ignore_settings, ignore_setup
) -> None:
response = user_client.get("/panel/background-management/")
assert response.status_code == 200
assert response.context[-1].template_name == "panel/background_management.html"
@pytest.mark.usefixtures("db")
class TestBackgroundProcess:
def test_blocks_unauthorized(self, client: Client) -> None:
response = client.get(reverse("panel:background_processes"))
assert response.status_code == 302
assert (
response.url
== f"{reverse('login')}?next={reverse('panel:background_processes')}"
)
def test_redirects_to_setup(self, user_client: Client, bad_setup) -> None:
response = user_client.get("/panel/background-processes/")
assert response.status_code == 302
assert response.url == "/panel/setup-panel/"
def test_redirects_to_initial_setup(
self, user_client: Client, ignore_setup, bad_settings
) -> None:
response = user_client.get("/panel/background-processes/")
assert response.status_code == 302
assert (
response.url
== f"{reverse('panel:initial_setup')}?next={reverse('panel:background_processes')}"
)
def test_responds_correctly(
self, user_client: Client, mocker: MockerFixture, ignore_settings, ignore_setup
) -> None:
mocker.patch("panel.views.is_redis_online")
mocker.patch("panel.views.is_celery_running")
mocker.patch("panel.views.is_qbittorrent_running")
response = user_client.get("/panel/background-processes/")
assert response.status_code == 200
assert response.context[-1].template_name == "panel/background_process.html"
@pytest.mark.usefixtures("db")
class TestMovieDetail:
def test_blocks_unauthorized(self, client: Client) -> None:
response = client.get(reverse("panel:movie_detail", kwargs={"movie_id": 1}))
assert response.status_code == 302
assert (
response.url
== f"{reverse('login')}?next={reverse('panel:movie_detail', kwargs={'movie_id': 1})}"
)
def test_redirects_to_setup(self, user_client: Client, bad_setup) -> None:
movie: Movie = MovieFactory()
response = user_client.get(f"/panel/movie-detail/{movie.id}")
assert response.status_code == 302
assert response.url == "/panel/setup-panel/"
def test_redirects_to_initial_setup(
self, user_client: Client, ignore_setup, bad_settings
) -> None:
movie: Movie = MovieFactory()
response = user_client.get(f"/panel/movie-detail/{movie.id}")
assert response.status_code == 302
assert (
response.url
== f"{reverse('panel:initial_setup')}?next={reverse('panel:movie_detail', kwargs={'movie_id': 1})}"
)
def test_renders_movies(
self, user_client: Client, ignore_settings, ignore_setup
) -> None:
movie: Movie = MovieFactory()
response = user_client.get(f"/panel/movie-detail/{movie.id}")
assert response.status_code == 200
assert response.context[-1].template_name == "panel/movie_detail.html"
assert f'mid="{movie.id}"' in response.content.decode("UTF-8")
@pytest.mark.usefixtures("db")
class TestUserHistory:
def test_blocks_unauthorized(self, client: Client) -> None:
response = client.get(reverse("panel:user_history"))
assert response.status_code == 302
assert (
response.url == f"{reverse('login')}?next={reverse('panel:user_history')}"
)
def test_redirects_to_initial_setup(
self, user_client: Client, ignore_setup, bad_settings
) -> None:
response = user_client.get("/panel/history/")
assert response.status_code == 302
assert (
response.url
== f"{reverse('panel:initial_setup')}?next={reverse('panel:user_history')}"
)
def test_responds_correctly(self, user_client: Client, ignore_settings) -> None:
response = user_client.get("/panel/history/")
assert response.status_code == 200
assert response.context[-1].template_name == "panel/user_history.html"
| 35.067114
| 111
| 0.660478
| 1,203
| 10,450
| 5.526185
| 0.095594
| 0.109507
| 0.081227
| 0.097473
| 0.855897
| 0.830024
| 0.811673
| 0.755415
| 0.739621
| 0.730445
| 0
| 0.011585
| 0.215311
| 10,450
| 297
| 112
| 35.185185
| 0.799146
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| 0.616071
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| 0.004464
| 0.214067
| 0.166986
| 0
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| 0.25
| 1
| 0.138393
| false
| 0
| 0.03125
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| null | 0
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| null | 0
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| 0
|
0
| 6
|
ea57fec66f53af30e47e4cae0c5c43b28af3d25f
| 10,825
|
py
|
Python
|
CPDNN_base.py
|
xuzhiqin1990/MSDNN2ellipticPDEs
|
ddaee034474c18bc23b51824fb6a00539c07d52c
|
[
"MIT"
] | 1
|
2021-12-23T07:40:04.000Z
|
2021-12-23T07:40:04.000Z
|
CPDNN_base.py
|
xuzhiqin1990/MSDNN2ellipticPDEs
|
ddaee034474c18bc23b51824fb6a00539c07d52c
|
[
"MIT"
] | null | null | null |
CPDNN_base.py
|
xuzhiqin1990/MSDNN2ellipticPDEs
|
ddaee034474c18bc23b51824fb6a00539c07d52c
|
[
"MIT"
] | 1
|
2021-12-31T10:57:17.000Z
|
2021-12-31T10:57:17.000Z
|
# -*- coding: utf-8 -*-
"""
Modify on 2020年4月1日
@author: LXG
Benchmark Code of Coupled PhaseDNN for ODE.
"""
import tensorflow as tf
import numpy as np
# ---------------------------------------------- my activations -----------------------------------------------
def srelu(x):
return tf.nn.relu(1-x)*tf.nn.relu(x)
def sin_srelu(x):
return tf.nn.relu(1-x)*tf.nn.relu(x)*tf.sin(2*np.pi*x)
def sin2_srelu(x):
return 2.0*tf.nn.relu(1-x)*tf.nn.relu(x)*tf.sin(4*np.pi*x)*tf.sin(2*np.pi*x)
def slrelu(x):
return tf.nn.leaky_relu(1-x)*tf.nn.leaky_relu(x)
def pow2relu(x):
return tf.nn.relu(1-x)*tf.nn.relu(x)*tf.nn.relu(x)
def selu(x):
return tf.nn.elu(1-x)*tf.nn.elu(x)
def wave(x):
return tf.nn.relu(x) - 2*tf.nn.relu(x-1/4) + \
2*tf.nn.relu(x-3/4) - tf.nn.relu(x-1)
def phi(x):
return tf.nn.relu(x) * tf.nn.relu(x)-3*tf.nn.relu(x-1)*tf.nn.relu(x-1) + 3*tf.nn.relu(x-2)*tf.nn.relu(x-2) \
- tf.nn.relu(x-3)*tf.nn.relu(x-3)*tf.nn.relu(x-3)
# 生成DNN的权重和偏置
# layers indicates the number of HIDDEN LAYERS, without input and output layers
# tf.random_normal(): 用于从服从指定正太分布的数值中取出随机数
# tf.random_normal(shape,mean=0.0,stddev=1.0,dtype=tf.float32,seed=None,name=None)
# hape: 输出张量的形状，必选.--- mean: 正态分布的均值，默认为0.----stddev: 正态分布的标准差，默认为1.0
# dtype: 输出的类型，默认为tf.float32 ----seed: 随机数种子，是一个整数，当设置之后，每次生成的随机数都一样---name: 操作的名称
def Initial_DNN(in_size, out_size, hidden_index, variateFlag):
layers = len(hidden_index)
Weights = [] # 权重列表，用于存储隐藏层的权重
Biases = [] # 偏置列表，用于存储隐藏层的偏置
# 第一层的权重和偏置，对输入数据做变换
W = tf.Variable(0.1 * tf.random.normal([in_size, hidden_index[0]]), dtype='float32',
name='W-transInput' + str(variateFlag))
B = tf.Variable(0.1 * tf.random.uniform([1, hidden_index[0]]), dtype='float32',
name='B-transInput' + str(variateFlag))
Weights.append(W)
Biases.append(B)
# 隐藏层：第二至倒数第二层的权重和偏置
for i_layer in range(layers - 1):
W = tf.Variable(0.1 * tf.random.normal([hidden_index[i_layer], hidden_index[i_layer+1]]), dtype='float32',
name='W-hidden' + str(i_layer + 1) + str(variateFlag))
B = tf.Variable(0.1 * tf.random.uniform([1, hidden_index[i_layer+1]]), dtype='float32',
name='B-hidden' + str(i_layer + 1) + str(variateFlag))
Weights.append(W)
Biases.append(B)
# 最后一层的权重和偏置。将最后的结果变换到输出维度
W = tf.Variable(0.1 * tf.random.normal([hidden_index[-1], out_size]), dtype='float32',
name='W-outTrans' + str(variateFlag))
B = tf.Variable(0.1 * tf.random.uniform([1, out_size]), dtype='float32',
name='B-outTrans' + str(variateFlag))
Weights.append(W)
Biases.append(B)
return Weights, Biases
# 这里可不可以利用attention 呢？关联 input 和 output。得到 attention 系数后，然后作用到input上
# 但是如何让input 和 output attention 关联起来呢 ？
def multilayer_DNN_residual(variable_input, Weights, Biases, CPDNN_activation=tf.nn.relu):
layers = len(Weights) # 得到输入到输出的层数，即隐藏层层数
H = variable_input # 代表输入数据，即输入层
beta = tf.constant(0.1)
H_pre = variable_input
W_pre = Weights[0]
dims_pre = tf.shape(W_pre)
for k in range(layers-1):
W = Weights[k]
B = Biases[k]
H = CPDNN_activation(tf.add(tf.matmul(H, W), B)) * CPDNN_activation(1 - tf.add(tf.matmul(H, W), B))
dim_post = tf.shape(W)
if dim_post[-1] == dims_pre[-1]:
H = H + beta*H_pre
H_pre = H
W_pre = W
dims_pre = tf.shape(W_pre)
W_out = Weights[-1]
B_out = Biases[-1]
output = tf.add(tf.matmul(H, W_out), B_out)
# dim_post = tf.shape(W_out)
# if dim_post[-1] == dims_pre[-1]:
# output = output + beta * H
output = tf.nn.tanh(output)
return output
# 这里可不可以利用attention 呢？关联 input 和 output。得到 attention 系数后，然后作用到input上
# 但是如何让input 和 output attention 关联起来呢 ？
def multilayer_DNN_attention(variable_input, Weights, Biases, CPDNN_activation=tf.nn.relu):
layers = len(Weights) # 得到输入到输出的层数，即隐藏层层数
H = variable_input # 代表输入数据，即输入层
for k in range(layers-1):
W = Weights[k]
B = Biases[k]
# H = CPDNN_activation(tf.add(tf.matmul(H, W), B))
H = CPDNN_activation(tf.add(tf.matmul(H, W), B)) * CPDNN_activation(1 - tf.add(tf.matmul(H, W), B))
W_out = Weights[-1]
B_out = Biases[-1]
output = tf.add(tf.matmul(H, W_out), B_out)
# 下面这个是输出层
output = tf.nn.tanh(output)
return output
def multilayer_DNN(variable_input, Weights, Biases, CPDNN_activation=tf.nn.relu):
layers = len(Weights) # 得到输入到输出的层数，即隐藏层层数
H = variable_input # 代表输入数据，即输入层
for k in range(layers-1):
W = Weights[k]
B = Biases[k]
# H = CPDNN_activation(tf.add(tf.matmul(H, W), B))
H = CPDNN_activation(tf.add(tf.matmul(H, W), B)) * CPDNN_activation(1 - tf.add(tf.matmul(H, W), B))
W_out = Weights[-1]
B_out = Biases[-1]
output = tf.add(tf.matmul(H, W_out), B_out)
# 下面这个是输出层
output = tf.nn.tanh(output)
return output
def CPS_DNN(input_x, freqs, Weights0, Biases0, Weights_COS, Biases_COS, Weights_SIN, Biases_SIN,
activate_name=tf.nn.relu):
if activate_name == 'relu':
CPDNN_activation = tf.nn.relu
elif activate_name == 'leaky_relu':
CPDNN_activation = tf.nn.leaky_relu(0.2)
elif activate_name == 'elu':
CPDNN_activation = tf.nn.elu
elif activate_name == 'srelu':
CPDNN_activation = srelu
elif activate_name == 'sin_srelu':
CPDNN_activation = sin_srelu
elif activate_name == 'sin2_srelu':
CPDNN_activation = sin2_srelu
elif activate_name == 'slrelu':
CPDNN_activation = slrelu
elif activate_name == 'selu':
CPDNN_activation = selu
elif activate_name == 'phi':
CPDNN_activation = phi
# 计算 m=0 时的拟合
Real = multilayer_DNN(input_x, Weights0, Biases0, CPDNN_activation)
# # 计算 m=1,2,3.... 时的拟合,其中{1,2,3....}是频率数目
for k in range(len(freqs)):
temp_multilayer1 = multilayer_DNN(freqs[k] * input_x, Weights_COS[k], Biases_COS[k], CPDNN_activation) * tf.cos(freqs[k] * input_x)
temp_multilayer1 = 2 * temp_multilayer1
temp2 = multilayer_DNN(input_x, Weights_SIN[k], Biases_SIN[k], CPDNN_activation) * tf.sin(freqs[k]*input_x)
temp2 = 2 * temp2
Real = Real + multilayer_DNN(input_x, Weights_COS[k], Biases_COS[k], CPDNN_activation) * tf.cos(freqs[k]*input_x)\
+ multilayer_DNN(input_x, Weights_SIN[k], Biases_SIN[k], CPDNN_activation) * tf.sin(freqs[k]*input_x)
return Real
def CPS_DNN_scale(input_x, freqs, Weights0, Biases0, Weights_COS, Biases_COS, Weights_SIN, Biases_SIN,
activate_name=tf.nn.relu):
if activate_name == 'relu':
CPDNN_activation = tf.nn.relu
elif activate_name == 'leaky_relu':
CPDNN_activation = tf.nn.leaky_relu(0.2)
elif activate_name == 'elu':
CPDNN_activation = tf.nn.elu
elif activate_name == 'srelu':
CPDNN_activation = srelu
elif activate_name == 'sin_srelu':
CPDNN_activation = sin_srelu
elif activate_name == 'sin2_srelu':
CPDNN_activation = sin2_srelu
elif activate_name == 'slrelu':
CPDNN_activation = slrelu
elif activate_name == 'selu':
CPDNN_activation = selu
elif activate_name == 'phi':
CPDNN_activation = phi
# 计算 m=0 时的拟合
Real = multilayer_DNN(input_x, Weights0, Biases0, CPDNN_activation)
# # 计算 m=1,2,3.... 时的拟合
for k in range(len(freqs)):
temp_multilayer = multilayer_DNN(freqs[k]*input_x, Weights_COS[k], Biases_COS[k], CPDNN_activation) * tf.cos(freqs[k]*input_x)
Real = Real + multilayer_DNN(freqs[k]*input_x, Weights_COS[k], Biases_COS[k], CPDNN_activation) * tf.cos(freqs[k]*input_x) \
+ multilayer_DNN(freqs[k]*input_x, Weights_SIN[k], Biases_SIN[k], CPDNN_activation) * tf.sin(freqs[k]*input_x)
return Real
def CPS_DNN_residual(input_x, freqs, Weights0, Biases0, Weights_COS, Biases_COS, Weights_SIN, Biases_SIN,
activate_name=tf.nn.relu):
if activate_name == 'relu':
CPDNN_activation = tf.nn.relu
elif activate_name == 'leaky_relu':
CPDNN_activation = tf.nn.leaky_relu(0.2)
elif activate_name == 'elu':
CPDNN_activation = tf.nn.elu
elif activate_name == 'srelu':
CPDNN_activation = srelu
elif activate_name == 'sin_srelu':
CPDNN_activation = sin_srelu
elif activate_name == 'sin2_srelu':
CPDNN_activation = sin2_srelu
elif activate_name == 'slrelu':
CPDNN_activation = slrelu
elif activate_name == 'selu':
CPDNN_activation = selu
elif activate_name == 'phi':
CPDNN_activation = phi
# 计算 m=0 时的拟合
Real = multilayer_DNN_residual(input_x, Weights0, Biases0, CPDNN_activation)
# # 计算 m=1,2,3.... 时的拟合
for k in range(len(freqs)):
Real = Real + multilayer_DNN_residual(input_x, Weights_COS[k], Biases_COS[k], CPDNN_activation) * tf.cos(freqs[k]*input_x) \
+ multilayer_DNN_residual(input_x, Weights_SIN[k], Biases_SIN[k], CPDNN_activation) * tf.sin(freqs[k]*input_x)
return Real
# L1正则化参数
def regular_weights_L1(weights0, weights_cos, weights_sin):
layers1 = len(weights0)
freq_num2sin_cos = len(weights_cos)
layers2sin_cos = len(weights_cos[0])
regular_w = 0
for i_layer1 in range(layers1):
regular_w = regular_w + tf.reduce_mean(tf.abs(weights0[i_layer1]), keep_dims=False)
for i_freq in range(freq_num2sin_cos):
for i_layer_sin_cos in range(layers2sin_cos):
regular_w = regular_w + tf.reduce_mean(tf.abs(weights_cos[i_freq][i_layer_sin_cos]), keep_dims=False) + \
tf.reduce_mean(tf.abs(weights_sin[i_freq][i_layer_sin_cos]), keep_dims=False)
return regular_w
# L2正则化参数
def regular_weights_L2(weights0, weights_cos, weights_sin):
layers1 = len(weights0)
freq_num2sin_cos = len(weights_cos)
layers2sin_cos = len(weights_cos[0])
regular_w = 0
for i_layer1 in range(layers1):
regular_w = regular_w + tf.norm(weights0[i_layer1])
for i_freq in range(freq_num2sin_cos):
for i_layer_sin_cos in range(layers2sin_cos):
regular_w = regular_w + tf.norm(weights_cos[i_freq][i_layer_sin_cos]) + \
tf.norm(weights_sin[i_freq][i_layer_sin_cos])
return regular_w
| 39.079422
| 140
| 0.618291
| 1,568
| 10,825
| 4.066964
| 0.110332
| 0.11761
| 0.03889
| 0.025404
| 0.824212
| 0.809472
| 0.774188
| 0.753332
| 0.702995
| 0.679787
| 0
| 0.021747
| 0.24388
| 10,825
| 276
| 141
| 39.221014
| 0.757361
| 0.120831
| 0
| 0.628866
| 0
| 0
| 0.028718
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.087629
| false
| 0
| 0.010309
| 0.041237
| 0.185567
| 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
| 0
| 0
|
0
| 6
|
57631a3429ee691ebbe9b1b03059e22c3b2abd26
| 1,457
|
py
|
Python
|
server/openapi_server/models/__init__.py
|
NCATS-Tangerine/kba-reasoner
|
19ea293fa02693b2df5ecfe1d91856b34e73a3e1
|
[
"MIT"
] | null | null | null |
server/openapi_server/models/__init__.py
|
NCATS-Tangerine/kba-reasoner
|
19ea293fa02693b2df5ecfe1d91856b34e73a3e1
|
[
"MIT"
] | null | null | null |
server/openapi_server/models/__init__.py
|
NCATS-Tangerine/kba-reasoner
|
19ea293fa02693b2df5ecfe1d91856b34e73a3e1
|
[
"MIT"
] | null | null | null |
# coding: utf-8
# flake8: noqa
from __future__ import absolute_import
# import models into model package
from openapi_server.models.credentials import Credentials
from openapi_server.models.edge import Edge
from openapi_server.models.edge_attribute import EdgeAttribute
from openapi_server.models.expertise_level import ExpertiseLevel
from openapi_server.models.expertise_levels import ExpertiseLevels
from openapi_server.models.feedback import Feedback
from openapi_server.models.feedback_response import FeedbackResponse
from openapi_server.models.knowledge_graph import KnowledgeGraph
from openapi_server.models.message import Message
from openapi_server.models.message_feedback import MessageFeedback
from openapi_server.models.message_terms import MessageTerms
from openapi_server.models.node import Node
from openapi_server.models.node_attribute import NodeAttribute
from openapi_server.models.previous_message_processing_plan import PreviousMessageProcessingPlan
from openapi_server.models.q_edge import QEdge
from openapi_server.models.q_node import QNode
from openapi_server.models.query import Query
from openapi_server.models.query_graph import QueryGraph
from openapi_server.models.rating import Rating
from openapi_server.models.ratings import Ratings
from openapi_server.models.remote_knowledge_graph import RemoteKnowledgeGraph
from openapi_server.models.result import Result
from openapi_server.models.result_feedback import ResultFeedback
| 50.241379
| 96
| 0.889499
| 193
| 1,457
| 6.481865
| 0.264249
| 0.202238
| 0.31255
| 0.422862
| 0.388489
| 0
| 0
| 0
| 0
| 0
| 0
| 0.001484
| 0.074811
| 1,457
| 28
| 97
| 52.035714
| 0.926558
| 0.040494
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
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| 1
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| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
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| 0
| 0
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| 0
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
576b2b3948aeaf7a364d5f3299beb9f8f6eb5d14
| 129
|
py
|
Python
|
middleware/__init__.py
|
liveonnet/p3_server
|
2dab6eab6e98b3ef0d26093eb461c635f5bc07b4
|
[
"Apache-2.0"
] | null | null | null |
middleware/__init__.py
|
liveonnet/p3_server
|
2dab6eab6e98b3ef0d26093eb461c635f5bc07b4
|
[
"Apache-2.0"
] | null | null | null |
middleware/__init__.py
|
liveonnet/p3_server
|
2dab6eab6e98b3ef0d26093eb461c635f5bc07b4
|
[
"Apache-2.0"
] | null | null | null |
from .log import logger_factory
from .nurse import nurse_handler_factory
l_middleware = [logger_factory, nurse_handler_factory]
| 25.8
| 54
| 0.852713
| 18
| 129
| 5.722222
| 0.5
| 0.252427
| 0.368932
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.100775
| 129
| 4
| 55
| 32.25
| 0.887931
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.666667
| 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
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| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
579946fe2fb85154771b93ea1c6d330770f41752
| 179
|
py
|
Python
|
rasa/core/frames/__init__.py
|
msamogh/rasa
|
f8b84bd3e36c8336294484174c2fce04d7b07e75
|
[
"Apache-2.0"
] | 4
|
2020-04-17T17:08:59.000Z
|
2021-07-21T16:25:00.000Z
|
rasa/core/frames/__init__.py
|
msamogh/rasa
|
f8b84bd3e36c8336294484174c2fce04d7b07e75
|
[
"Apache-2.0"
] | 5
|
2020-04-10T09:08:46.000Z
|
2021-08-25T14:40:03.000Z
|
rasa/core/frames/__init__.py
|
msamogh/rasa
|
f8b84bd3e36c8336294484174c2fce04d7b07e75
|
[
"Apache-2.0"
] | 1
|
2020-05-02T16:00:20.000Z
|
2020-05-02T16:00:20.000Z
|
from rasa.core.frames.frame import Frame, FrameSet
from rasa.core.frames.frame_policy import FramePolicy
from rasa.core.frames.rule_based_frame_policy import RuleBasedFramePolicy
| 44.75
| 73
| 0.871508
| 26
| 179
| 5.846154
| 0.461538
| 0.157895
| 0.236842
| 0.355263
| 0.302632
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.072626
| 179
| 3
| 74
| 59.666667
| 0.915663
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 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
| 0
| 0
|
0
| 6
|
57af2b0a56f304480a46a8e275f7500734150c8d
| 1,553
|
py
|
Python
|
backend/blog/views.py
|
jadames03/food-talk
|
c2b8a4fecb603b72309ae2bc6bcf47dc3cd22448
|
[
"MIT"
] | 4
|
2022-01-11T03:46:17.000Z
|
2022-01-13T01:12:01.000Z
|
backend/blog/views.py
|
jadames03/food-talk
|
c2b8a4fecb603b72309ae2bc6bcf47dc3cd22448
|
[
"MIT"
] | 2
|
2022-01-10T17:00:54.000Z
|
2022-01-13T03:01:35.000Z
|
backend/blog/views.py
|
jadames03/food-talk
|
c2b8a4fecb603b72309ae2bc6bcf47dc3cd22448
|
[
"MIT"
] | 19
|
2022-01-08T02:29:16.000Z
|
2022-02-01T00:41:09.000Z
|
from django.shortcuts import render
from django.http import HttpResponse
# Create your views here.
def index(request):
return render(request, 'blog/index.html')
def signin(request):
return render(request, 'blog/signin.html')
def signup(request):
return render(request, 'blog/signup.html')
def rasberry(request):
return render(request, 'blog/raspberry.html')
def burger(request):
return render(request, 'blog/burger.html')
def caldereta(request):
return render(request, 'blog/caldereta.html')
def honey(request):
return render(request, 'blog/honey.html')
def macaron(request):
return render(request, 'blog/macaron.html')
def mango(request):
return render(request, 'blog/mango.html')
def pastelon(request):
return render(request, 'blog/pastelon.html')
def sisig(request):
return render(request, 'blog/sisig.html')
def motehingar(request):
return render(request, 'blog/motehingar.html')
def scrambled_eggs(request):
return render(request, 'blog/scrambled_eggs.html')
def lasagna(request):
return render(request, 'blog/lasagna.html')
def protein_pancakes(request):
return render(request, 'blog/protein_pancakes.html')
def pastelon(request):
return render(request, 'blog/pastelon.html')
def spaghetti(request):
return render(request, 'blog/spaghetti.html')
def mushroom(request):
return render(request, 'blog/mushroom.html')
def chicken(request):
return render(request, 'blog/chicken.html')
def coconut(request):
return render(request, 'blog/coconut.html')
| 23.892308
| 56
| 0.730844
| 198
| 1,553
| 5.712121
| 0.191919
| 0.229885
| 0.335986
| 0.45977
| 0.583554
| 0.106101
| 0.106101
| 0.106101
| 0.106101
| 0.106101
| 0
| 0
| 0.137798
| 1,553
| 64
| 57
| 24.265625
| 0.84466
| 0.01481
| 0
| 0.095238
| 0
| 0
| 0.233639
| 0.032723
| 0
| 0
| 0
| 0
| 0
| 1
| 0.47619
| false
| 0
| 0.047619
| 0.47619
| 1
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 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
| 1
| 1
| 0
|
0
| 6
|
17b4775bff2a050573de841692361db025b60b28
| 94,300
|
py
|
Python
|
saas/aiops/api/aiops-server/ai_lib/time_series_prediction/algorithm/preprocess/simpleRobustSTL/RobustSTLcode.py
|
iuskye/SREWorks
|
a2a7446767d97ec5f6d15bd00189c42150d6c894
|
[
"Apache-2.0"
] | 407
|
2022-03-16T08:09:38.000Z
|
2022-03-31T12:27:10.000Z
|
saas/aiops/api/aiops-server/ai_lib/time_series_prediction/algorithm/preprocess/simpleRobustSTL/RobustSTLcode.py
|
Kwafoor/SREWorks
|
37a64a0a84b29c65cf6b77424bd2acd0c7b42e2b
|
[
"Apache-2.0"
] | 25
|
2022-03-22T04:27:31.000Z
|
2022-03-30T08:47:28.000Z
|
saas/aiops/api/aiops-server/ai_lib/time_series_prediction/algorithm/preprocess/simpleRobustSTL/RobustSTLcode.py
|
Kwafoor/SREWorks
|
37a64a0a84b29c65cf6b77424bd2acd0c7b42e2b
|
[
"Apache-2.0"
] | 109
|
2022-03-21T17:30:44.000Z
|
2022-03-31T09:36:28.000Z
|
"""Our Proposed Robust STL based on
Spare Model and Non-local Season bilateral filtering
Input: original_data - T x 1
Output: decomposed_data - T x 3 (trend, season, irregular)
Author: Qingsong <qingsong.wen@alibaba-inc.com>
"""
from numpy.linalg import norm, cholesky
import scipy.sparse as sparse
import time
import numbers
# import logging
from numbers import Number
# import scipy.sparse as sp
# from scipy.sparse.linalg import lsqr
# from scipy.sparse import csc_matrix
from sklearn.preprocessing import StandardScaler
from cvxopt import matrix
import pandas as pd
import numpy as np
from scipy.linalg import circulant
from .fast_stl_utils import DetailA, PDHG, gADMMSolver
"""two functions to get trend and season:
Sparse and robust method to get trend
Bilateral season adjust to get season
Author: Qingsong <qingsong.wen@alibaba-inc.com>
"""
def gaussian_expfun(x, sigma):
minimal_sigma_n = 1e-6
sigma = minimal_sigma_n if sigma < minimal_sigma_n else sigma
return np.exp(-(x * x) / (2.0 * sigma * sigma))
def gaussian_kernel(sigma, radius):
"""Computes 1D Gaussian kernel weights
The length of weights is 2*radius + 1
"""
if sigma <= 0:
raise ValueError('sigma must be larger than zero.')
tmp_x = np.arange(-radius, radius + 1)
phi_x = gaussian_expfun(tmp_x, sigma)
phi_x /= phi_x.sum()
return phi_x
def calcu_noise_sigma(ts_data):
"""Calculate the eatimated noise's Standard Deviation
This method here is based on the paper:
Theo Gasser, Lothar Sroka, and Christine Jennen-Stienmetz.
Residual variance and residual pattern in nonlinear regression.
"""
ts_data_len = len(ts_data)
if ts_data_len < 3:
raise Exception('ts_data_len should be at least 3')
y_ip1 = ts_data[2:]
y_i = ts_data[1:-1]
y_im1 = ts_data[:-2]
sigma_n2 = (2.0 / (3 * len(y_ip1))) * sum(
(0.5 * y_im1 - y_i + 0.5 * y_ip1)**2)
sigma_n = np.sqrt(sigma_n2)
# sigma_n = 1e-5 if sigma_n < 1e-5 else sigma_n
minimal_sigma_n = 1e-6
sigma_n = minimal_sigma_n if sigma_n < minimal_sigma_n else sigma_n
return sigma_n
def data_validity_check(ts_data):
# old version-->if isinstance(ts_data, int) or isinstance(ts_data, float):
if isinstance(ts_data, numbers.Number):
ts_data = np.array([ts_data])
if not (isinstance(ts_data, list) or isinstance(ts_data, np.ndarray)
or isinstance(ts_data, pd.DataFrame)):
raise ValueError('For denoising, input data must be list, np.ndarray,'
'or pd.DataFrame (for online, input can be single'
'point with int/flaot data type)!')
# check pandas
if isinstance(ts_data, pd.DataFrame):
if ts_data.shape[1] != 1:
raise ValueError('For denoising, if input data is pandas dataframe'
', it must be 1D data!')
# check array or list
if isinstance(ts_data, list):
ts_data = np.array(ts_data)
if isinstance(ts_data, np.ndarray):
if ts_data.ndim > 2:
raise ValueError('For denoising, input data cannot be 3D or higher'
'dimentional array or list!')
if ts_data.ndim == 2:
if not (ts_data.shape[0] == 1 or ts_data.shape[1] == 1):
raise ValueError('For denoising, if input data is 2D array or'
'list, one of the dim must be 1!')
def bilateral_weighted_data(input_data,
sigma_i=4,
leftmost_dist_w=0.8,
sigma_d=None,
refer_value=None,
normalize_toggle=True):
# check leftmost_dist_w
if leftmost_dist_w <= 0 or leftmost_dist_w >= 1.0:
raise ValueError('leftmost_dist_w should 0 < leftmost_dist_w < 1')
# Ini
ts_data_ori = to_2dnp_array(input_data)
data_len = len(ts_data_ori)
# normalize
if normalize_toggle:
scaler = StandardScaler()
ts_data = scaler.fit_transform(ts_data_ori)
else:
ts_data = ts_data_ori.copy()
if refer_value is None:
refer_value = ts_data[-1]
# distance_weights
refer_idx = -1 # default, the last point for distance_refer_idx
leftmost_idx = data_len + refer_idx
dx = np.arange(-leftmost_idx, -refer_idx).reshape(-1, 1)
if sigma_d is None:
sigma_d = np.sqrt(-0.5 * leftmost_idx * leftmost_idx /
np.log(leftmost_dist_w))
distance_weights = gaussian_expfun(dx, sigma_d)
# intensity_weights
sigma_n = np.std(ts_data)
sig_I = sigma_i * sigma_n
dy = ts_data - refer_value
intensity_weights = gaussian_expfun(dy, sig_I)
# final bilateral_weights
bilateral_weights = distance_weights * intensity_weights
weighted_data = ts_data_ori * bilateral_weights
return weighted_data, distance_weights,\
intensity_weights, bilateral_weights
def to_2dnp_array(X):
"""Convert array-like data (list/numpy array/pandas) to 2D
numpy array.
"""
if isinstance(X, np.ndarray):
if X.ndim == 1:
return X.reshape((-1, 1))
if X.ndim == 2:
return X
if isinstance(X, Number):
X = [X]
X = np.array(X)
X = X.reshape([-1, np.prod(X.shape) // X.shape[0]])
return X
"""Two Bilateral filter classes for denoising time series.
Input: ts_data - T x 1
Output: filterd_ts_data - T x 1
"""
class BilateralFilter():
"""Basic Bilateral Filter for denoising time series
It is used to as a baseline for denoising time signals under different
Bilateral Filters.
The filtering width is around 2*(truncate*sigma) + 1
Parameters
----------
sigma_i : double, optional
[default=2.5]
The standard deviation for intensity used in bilateral gaussian kernel
sigma_d : double, optional
[default=2.5]
The standard deviation for distance used in bilateral gaussian kernel
truncate: double, optional
[default=6.0]
Used for the radius of the filter, which equals to truncate times
sigma_d
pad_mode: str, 'symmetric' or 'reflect'
[default='symmetric']
The scheme used for padding edge data points
Suppose we have data (a b c d ...... e f g h):
For the edge data, if they are extended using 'symmetric' about edge of
the last pixel, we have (c b a | a b c d ...... e f g h | h g f)
For the edge data, if they are extended using 'reflect' about the edge
of the last pixel, we have (d c b | a b c d ...... e f g h | g f e)
"""
def __init__(self,
sigma_i=2.0,
sigma_d=2.5,
truncate=8.0,
pad_mode='symmetric'):
self.sigma_i = sigma_i
self.sigma_d = sigma_d
self.truncate = truncate
self.pad_mode = pad_mode
def fit_transform(self, ts_data, override=True):
"""
This method to do denoising filtering process for time sereis.
Parameters
----------
ts_data : array-like, shape = (n_timelength, 1) or (n_timelength,)
The input data, 1D data: can be numpy array, list,
or pandas dataframe.
override : boolean
To be used for online case, ignore for now.
Returns
-------
filterd_out : array-like, shape = (n_timelength, 1)
The output of denoised time series
"""
sigma_i = self.sigma_i
sigma_d = self.sigma_d
truncate = self.truncate
pad_mode = self.pad_mode
data_validity_check(ts_data)
ts_data = np.array(ts_data).flatten()
radius = int(truncate * sigma_d + 0.5)
dx = np.arange(-radius, radius + 1)
sig_D = sigma_d
exp_D = gaussian_expfun(dx, sig_D)
# calculate the eatimated noise variance
sigma_n = calcu_noise_sigma(ts_data)
sig_I = sigma_i * sigma_n # note sigma_i is just a multiplicative para
if (pad_mode == 'symmetric') or (pad_mode == 'reflect'):
ts_data_pad = np.pad(ts_data, radius, pad_mode)
else:
raise RuntimeError(
'The pad_mode is supported; only support symmetric or reflect')
data_len = len(ts_data)
filterd_out = np.zeros(data_len)
for idx in range(data_len):
idx_pad = idx + radius
data_window = ts_data_pad[(idx_pad - radius):(idx_pad + radius +
1)]
dy = data_window - ts_data_pad[idx_pad]
exp_I = gaussian_expfun(dy, sig_I)
bilateral_weights = exp_I * exp_D
bilateral_weights /= bilateral_weights.sum()
filterd_out[idx] = sum(data_window * bilateral_weights)
filterd_out = filterd_out.reshape(-1, 1)
return filterd_out
class CausalBilateralFilter():
"""Causal Bilateral Filter for denoising time series
It is used in our real situations, and there is no pad_mode compared with
BilateralFilter, since we only use previous points to filter noise
of current point.
The filtering width is around 2*(truncate*sigma) + 1
Parameters
----------
sigma_i : double, optional
[default=2.5]
The standard deviation for intensity used in bilateral gaussian kernel
sigma_d : double, optional
[default=2.5]
The standard deviation for distance used in bilateral gaussian kernel
truncate: double, optional
[default=6.0]
Used for the radius of the filter, which equals to truncate
times sigma_d
"""
def __init__(self, sigma_i=2.0, sigma_d=2.5, truncate=8.0):
self.sigma_i = sigma_i
self.sigma_d = sigma_d
self.truncate = truncate
self.ts_data = None
self.filtered_ts_data = None
def fit_transform(self, ts_data, override=True):
"""
This method generates the latent variables on seasonal data
using T statistics and frequency.
Parameters
----------
X : array-like, shape = (n_timelength, 1)
The input data, only support 1D data currently.
override : boolean
To be used for online case, ignore for now.
Returns
-------
X_t : array-like, shape = (n_timelength, n_t_dim)
The output latent variable data, could be high dimensional
latent representation
debug: dict
Store the intermediate variables for debugging purpose
"""
sigma_i = self.sigma_i
sigma_d = self.sigma_d
truncate = self.truncate
data_validity_check(ts_data)
ts_data = np.array(ts_data).flatten()
radius = int(truncate * sigma_d + 0.5)
dx = np.arange(-radius, 1)
sig_D = sigma_d
exp_D = gaussian_expfun(dx, sig_D)
# calculate the eatimated noise variance
sigma_n = calcu_noise_sigma(ts_data)
sig_I = sigma_i * sigma_n # note sigma_i is just a multiplicative para
filterd_out = np.copy(ts_data)
data_len = len(ts_data)
for idx in range(radius, data_len):
data_window = ts_data[(idx - radius):(idx + 1)]
dy = data_window - ts_data[idx]
exp_I = gaussian_expfun(dy, sig_I)
bilateral_weights = exp_I * exp_D
bilateral_weights /= bilateral_weights.sum()
filterd_out[idx] = sum(data_window * bilateral_weights)
filterd_out = filterd_out.reshape(-1, 1)
self.radius = radius
self.exp_D = exp_D
self.ts_data = ts_data
self.filterd_out = filterd_out
return filterd_out
def fit_transform_online(self, new_ts_minibatch):
"""Update the model with newX to transform the data
Online version to do the fit transform with much faster speed.
Parameters
---------
new_ts_minibatch : array-like, shape = (n_minibatch, 1)
or (n_minibatch,)
Single time series of length mini_batch_timelength.
Returns
-------
X_t : array-like, shape = (n_timelength,)
A transformed time series of the same time length as the
original ts_data fed into the model. Will need to maintain the
state of the old data in order to return the transformed
time series with the correct timelength.
"""
if self.ts_data is None:
raise ValueError("fit_transform_online can not be called in the\
very first time, should followed fit_transform.")
radius = self.radius
exp_D = self.exp_D
ts_data = self.ts_data
filterd_out = self.filterd_out
sigma_i = self.sigma_i
# check new_ts_minibatch and change format to 1d np.array
data_validity_check(new_ts_minibatch)
new_ts_minibatch = np.array(new_ts_minibatch).flatten()
# update ts_data
mini_batch_len = len(new_ts_minibatch)
ts_data = np.concatenate([ts_data[mini_batch_len:], new_ts_minibatch])
# calculate the eatimated noise variance
sigma_n = calcu_noise_sigma(ts_data)
sig_I = sigma_i * sigma_n # note sigma_i is just a multiplicative para
new_filterd_out = np.copy(new_ts_minibatch)
data_len = len(ts_data)
for idx in range((data_len - mini_batch_len), data_len):
data_window = ts_data[(idx - radius):(idx + 1)]
dy = data_window - ts_data[idx]
exp_I = gaussian_expfun(dy, sig_I)
bilateral_weights = exp_I * exp_D
bilateral_weights /= bilateral_weights.sum()
new_filterd_out[idx - (data_len - mini_batch_len)] = sum(
data_window * bilateral_weights)
# update filterd_out
new_filterd_out = new_filterd_out.reshape(-1, 1)
filterd_out = np.concatenate(
[filterd_out[mini_batch_len:], new_filterd_out])
filterd_out = filterd_out.reshape(-1, 1)
self.ts_data = ts_data
self.filterd_out = filterd_out
return filterd_out
def _get_prev_transform(self):
# debug purpose
return self.filterd_out
class BilateralSeasonTstat():
"""
This method generates the latent variables on seasonal data
using T statistics and frequency with the idea of Bilateral Filter.
Parameters
----------
num_period : int
The number of previous history periods data.
num_period can be 0,1,2,3...,
when num_period=0, it only uses local data
period_len : int
The length of one period in terms of data points.
When num_period=0, this para is not used
neighbour_wdw_size : int
The half length of the window used in historical period data
sigma_i : double, optional
[default=2.5]
The standard deviation for intensity used in bilateral gaussian kernel
sigma_d : double, optional
[default=2.5]
The standard deviation for distance used in bilateral gaussian kernel
adj_win_open : Boolean, [default=False]
A toggle to control if we will use adjacent data (local data, not
period data).
adj_win_size : int
The length of the window used in local adjacent data
adj_win_weight : float
the weight used to control the contribution from local adjacent data
lamb: float, [0.,1.], optional
[default=0]
The float number to control the ratio of local/global variance.
Default 0 means we only use the local variance from seasonal data.
Set the value to 0.5 means we are using the geometric mean of
local/global variance. Set to 1 to use only global variance.
div_var_toggle: boolean
[default=False]
The toggle that determines whether to divide (x - _mean) by the
harmonic mean of sigma_global and sigma when calculating the latent
representation. Default False means that it will not divide.
"""
def __init__(self,
num_period=2,
period_len=1440,
neighbour_wdw_size=10,
sigma_i=1.0,
sigma_d=1e10,
adj_win_open=False,
adj_win_size=10,
adj_win_weight=0.001,
lamb=0,
div_var_toggle=False,
fit_transform_len_spec=None):
self.period_len = period_len
self.num_period = num_period
self.sigma_i = sigma_i
self.sigma_d = sigma_d
self.neighbour_wdw_size = neighbour_wdw_size
self.adj_win_open = adj_win_open
self.adj_win_size = adj_win_size
self.adj_win_weight = adj_win_weight
self.lamb = lamb
self.div_var_toggle = div_var_toggle
self.ts_data = None
self.filtered_out = None
self.fit_transform_len_spec = fit_transform_len_spec
self._required_len = neighbour_wdw_size + period_len * num_period
def fit_transform(self, ts_data, override=True):
"""
This method to do denoising filtering process for time sereis.
Parameters
----------
ts_data : array-like, shape = (n_timelength, 1) or (n_timelength,)
The input data, 1D data: can be numpy array, list,
or pandas dataframe.
override : boolean
To be used for online case, ignore for now.
Returns
-------
filtered_out : array-like, shape = (n_timelength, 1)
The output of denoised time series
"""
period_len = self.period_len
num_period = self.num_period
sigma_i = self.sigma_i
sigma_d = self.sigma_d
adj_win_open = self.adj_win_open
adj_win_weight = self.adj_win_weight
adj_win_size = self.adj_win_size
# truncate = self.truncate
neighbour_wdw_size = self.neighbour_wdw_size
lamb = self.lamb
div_var_toggle = self.div_var_toggle
fit_transform_len_spec = self.fit_transform_len_spec
data_validity_check(ts_data)
ts_data = np.array(ts_data).flatten()
# radius = int(truncate * sigma_d + 0.5)
adj_radius = adj_win_size
radius = neighbour_wdw_size
sig_D = sigma_d
dx = np.arange(-adj_radius, 0)
exp_D = gaussian_expfun(dx, sig_D)
dx_2s = np.arange(-radius, radius + 1)
exp_D_2s = gaussian_expfun(dx_2s, sig_D)
# calculate the eatimated noise variance
# sigma_n = calcu_noise_sigma(ts_data)
sigma_n = np.std(ts_data)
# minimal_sigma_n = 1e-2
# sigma_n = minimal_sigma_n if sigma_n < minimal_sigma_n else sigma_n
sig_I = sigma_i * sigma_n # note sigma_i is just a multiplicative para
# filtered_out = np.copy(ts_data)
filtered_out = np.zeros(ts_data.shape)
data_len = len(ts_data)
if data_len <= radius + period_len * num_period:
raise Exception('data length is not right')
# specific fit_transform_len if necessary
if fit_transform_len_spec is None:
start_point = radius + period_len * 1
else:
start_point = data_len - fit_transform_len_spec
# do filtering
for idx in range(start_point, data_len):
# this part indicates using local neiborhood data (diff)
data_window_combined = np.array([])
bilateral_weights_combined = np.array([])
if adj_win_open:
data_window = ts_data[(idx - adj_radius):idx]
dy = data_window - ts_data[idx]
exp_I = gaussian_expfun(dy, sig_I)
bilateral_weights = adj_win_weight * exp_I * exp_D
# concatenate data and weights
data_window_combined = np.concatenate(
(data_window_combined, data_window))
bilateral_weights_combined = np.concatenate(
(bilateral_weights_combined, bilateral_weights))
# this part indicates using non-local neiborhood data
# from previous period signals
actual_num_period = 0
for idx_period in range(1, num_period + 1):
if idx - radius - period_len * idx_period >= 0:
actual_num_period += 1
data_window = ts_data[(idx - radius -
period_len * idx_period):(
idx + radius + 1 -
period_len * idx_period)]
dy = data_window - ts_data[idx]
exp_I = gaussian_expfun(dy, sig_I)
bilateral_weights = exp_I * exp_D_2s
# concatenate data and weights
data_window_combined = np.concatenate(
(data_window_combined, data_window))
bilateral_weights_combined = np.concatenate(
(bilateral_weights_combined, bilateral_weights))
weights_sum = bilateral_weights_combined.sum()
if weights_sum == 0:
bilateral_weights_combined = 1.0 / len(
bilateral_weights_combined)
else:
bilateral_weights_combined /= weights_sum
_mean = np.sum(data_window_combined * bilateral_weights_combined)
# If div_var_toggle is True, calculate the necessary statistic
# and divide (x - _mean) by the harmonic mean of 2 var
if div_var_toggle:
# adjusted local var
_local_diff_square = np.square(data_window_combined - _mean)
_local_var = np.sum(_local_diff_square *
bilateral_weights_combined)
_local_var = 1e-10 if _local_var < 1e-10 else _local_var
# adjusted global var
_data_global = ts_data[(idx - radius -
period_len * actual_num_period):idx]
_global_var = np.var(_data_global)
_global_var = 1e-10 if _global_var < 1e-10 else _global_var
# harmonic mean of sigma_local and sigma_global
_harmonic_std_inv = np.sqrt(lamb / _global_var +
(1 - lamb) / _local_var)
# multipy it by _harmonic_std_inv
filtered_out[idx] = (ts_data[idx] - _mean) * _harmonic_std_inv
# Otherwise, only use (x - _mean)
else:
filtered_out[idx] = ts_data[idx] - _mean
filtered_out = filtered_out.reshape(-1, 1)
self.radius = radius
self.adj_radius = adj_radius
self.exp_D = exp_D
self.exp_D_2s = exp_D_2s
self.ts_data = ts_data
self.filtered_out = filtered_out
return filtered_out
def fit_transform_online(self, new_ts_minibatch):
"""Update the model with newX to transform the data
Online version to do the fit transform with much faster speed.
Parameters
---------
new_ts_minibatch : array-like, shape = (n_minibatch, 1)
or (n_minibatch,)
Single time series of length mini_batch_timelength.
Returns
-------
X_t : array-like, shape = (n_timelength,)
A transformed time series of the same time length as the
original ts_data fed into the model. Will need to maintain the
state of the old data in order to return the transformed
time series with the correct timelength.
"""
if self.ts_data is None:
raise ValueError("fit_transform_online can not be called in the\
very first time, should followed fit_transform.")
adj_radius = self.adj_radius
radius = self.radius
exp_D = self.exp_D
exp_D_2s = self.exp_D_2s
ts_data = self.ts_data
filtered_out = self.filtered_out
sigma_i = self.sigma_i
period_len = self.period_len
num_period = self.num_period
adj_win_open = self.adj_win_open
adj_win_weight = self.adj_win_weight
lamb = self.lamb
div_var_toggle = self.div_var_toggle
# check new_ts_minibatch and change format to 1d np.array
data_validity_check(new_ts_minibatch)
new_ts_minibatch = np.array(new_ts_minibatch).flatten()
# update ts_data
mini_batch_len = len(new_ts_minibatch)
ts_data = np.concatenate([ts_data[mini_batch_len:], new_ts_minibatch])
# calculate the eatimated noise variance
# sigma_n = calcu_noise_sigma(ts_data)
sigma_n = np.std(ts_data)
# minimal_sigma_n = 1e-2
# sigma_n = minimal_sigma_n if sigma_n < minimal_sigma_n else sigma_n
sig_I = sigma_i * sigma_n # note sigma_i is just a multiplicative para
new_filtered_out = np.copy(new_ts_minibatch)
data_len = len(ts_data)
for idx in range((data_len - mini_batch_len), data_len):
# # this part indicates using local neiborhood data
data_window_combined = np.array([])
bilateral_weights_combined = np.array([])
if adj_win_open:
data_window = ts_data[(idx - adj_radius):idx]
dy = data_window - ts_data[idx]
exp_I = gaussian_expfun(dy, sig_I)
bilateral_weights = adj_win_weight * exp_I * exp_D
# concatenate data and weights
data_window_combined = np.concatenate(
(data_window_combined, data_window))
bilateral_weights_combined = np.concatenate(
(bilateral_weights_combined, bilateral_weights))
# this part indicates using non-local neiborhood data
# from previous period signals
actual_num_period = 0
for idx_period in range(1, num_period + 1):
if idx - radius - period_len * idx_period >= 0:
actual_num_period += 1
data_window = ts_data[(idx - radius -
period_len * idx_period):(
idx + radius + 1 -
period_len * idx_period)]
dy = data_window - ts_data[idx]
exp_I = gaussian_expfun(dy, sig_I)
bilateral_weights = exp_I * exp_D_2s
# concatenate data and weights
data_window_combined = np.concatenate(
(data_window_combined, data_window))
bilateral_weights_combined = np.concatenate(
(bilateral_weights_combined, bilateral_weights))
weights_sum = bilateral_weights_combined.sum()
if weights_sum == 0:
bilateral_weights_combined = 1.0 / len(
bilateral_weights_combined)
else:
bilateral_weights_combined /= weights_sum
_mean = np.sum(data_window_combined * bilateral_weights_combined)
# If div_var_toggle is True, calculate the necessary statistic
# and divide (x - _mean) by the harmonic mean of 2 var
if div_var_toggle:
# adjusted local var
_local_diff_square = np.square(data_window_combined - _mean)
_local_var = np.sum(_local_diff_square *
bilateral_weights_combined)
_local_var = 1e-10 if _local_var < 1e-10 else _local_var
# adjusted global var
_data_global = ts_data[(idx - radius -
period_len * actual_num_period):idx]
_global_var = np.var(_data_global)
_global_var = 1e-10 if _global_var < 1e-10 else _global_var
# harmonic mean of sigma_local and sigma_global
_harmonic_std_inv = np.sqrt(lamb / _global_var +
(1 - lamb) / _local_var)
# multipy it by _harmonic_std_inv
new_filtered_out[idx - (data_len - mini_batch_len)] = (
ts_data[idx] - _mean) * _harmonic_std_inv
# Otherwise, only use (x - _mean)
else:
new_filtered_out[idx - (data_len -
mini_batch_len)] = ts_data[idx] - _mean
# update filtered_out
new_filtered_out = new_filtered_out.reshape(-1, 1)
filtered_out = np.concatenate(
[filtered_out[mini_batch_len:], new_filtered_out])
filtered_out = filtered_out.reshape(-1, 1)
self.ts_data = ts_data
self.filtered_out = filtered_out
return filtered_out
def fit_transform_inverse(self, X_t):
"""Inverse transformation of data from X_t back to origin X.
This should not change the old data maintained in the transformer.
Parameters
---------
X_t : array-like, shape = (mini_batch_timelength, n_transformed_dim)
Transformed time series of length mini_batch_timelength.
The single ts could be of high dimension.
Returns
-------
X_o : array-like, shape = (mini_batch_timelength, n_dim)
Origin time series of the same time length as X_t.
"""
X_t = to_2dnp_array(X_t)
mini_batch_size, n_dim = X_t.shape
X_before = self.ts_data[-mini_batch_size:]
X_after = self._get_prev_transform()[-mini_batch_size:]
X_o = X_t - X_after + X_before
return X_o
def _get_prev_transform(self):
# debug purpose
return self.filtered_out
def get_required_len(self):
return self._required_len
"""
Matrix inverse is used in this version
This is an ADMM solver for the generalized lasso problem:
min_x 1/2 * ||Ax - b||_2^2 + lambda ||Fx - p||_1
Many prolems in our pipeline can be transformed into the form of the
genearlized lasso defined above.
In using this solver, the user must explicitly give the defintion of
A, b, F, p, reg_lambda (the lambda in original formulation)
=== Input ===
A: m*n matrix
b: m vector
F: l*n matrix
p: l vector
reg_lambda: a nonnegative scalar
Some other important parameters:
rho: the penalized parameter in ADMM
max_iter: the maximum number of iterations
tol_abs: the absolute tolerance parameter
tol_rel: the relative tolerance parameter
Optional parameters:
x0: the initial value of x for warm start
Quite: True/False, to enable the display or intermediate results or not
=== Output ===
We return both the optimial solution and the whole history in the solving
process (we call history)
1. The solution is returned in the vector x
2. The history is returned in the dictionary h containing the objective value,
the primal and dual residual norms, and the tolerances for the primal and dual
residual norms at each iteration.
Notes:
1. Over relaxation is supported in the API, but currently not fully
implemented. We will implement it in the near future.
rel_par is the over-relaxation parameter (typical values for rel_par
are between 1.0 and 1.8).
"""
# from scipy.sparse.linalg import spsolve
# from scipy.linalg import solve_triangular
def g_lasso_admm_solver(A,
b,
F,
p,
reg_lambda,
x0=None,
rho=1.0,
MAX_ITER=5000,
rel_par=1.,
QUIET=True,
tol_abs=1e-5,
tol_rel=1e-2,
S_inv=None,
warm_start_toggle=False):
# Parameter checking
if reg_lambda < 0:
reg_lambda = 0
if rel_par < 1:
rel_par = 1.0
if rel_par > 2:
rel_par = 2.0
if rho < 0:
rho = 1.0
if MAX_ITER < 0:
MAX_ITER = 50
if rel_par < 1:
rel_par = 1.0
if QUIET is None:
QUIET = True
if tol_abs < 0:
tol_abs = 1e-3
if tol_rel < 0:
tol_rel = 1e-2
if not QUIET:
tic = time.time()
# Data preprocessing
m, n = A.shape
ell = F.shape[0]
b = b.ravel()
p = p.ravel()
if x0 is not None:
x0 = x0.ravel()
# Initialize
x = np.zeros(n)
z = np.zeros(ell)
u = np.zeros(ell)
if not QUIET:
print('\n%3s\t%10s\t%10s\t%10s\t%10s\t%10s' %
('iter', 'r norm', 'eps pri', 's norm', 'eps dual', 'objective'))
# Saving state
h = {}
h['obj_val'] = np.zeros(MAX_ITER)
h['r_norm'] = np.zeros(MAX_ITER)
h['s_norm'] = np.zeros(MAX_ITER)
h['eps_pri'] = np.zeros(MAX_ITER)
h['eps_dual'] = np.zeros(MAX_ITER)
# Cache some results
Atb = A.T.dot(b)
# Cache the Cholesky decomposition
# L, U = Cholesky_decomp_sp(A, F, rho)
if S_inv is None:
S = A.T.dot(A) + rho * F.T.dot(F)
S_inv = np.linalg.inv(S)
# L = cholesky(S)
# U = L.T
for k in range(MAX_ITER):
# x-update
if warm_start_toggle and k == 0 and x0 is not None:
x = x0
else:
# x = spsolve(U, spsolve(L, q))
q = Atb + rho * F.T.dot(z - u + p)
x = S_inv.dot(q)
# solve_triangular(L, q, lower=True, overwrite_b=True)
# solve_triangular(U, q, lower=False, overwrite_b=True)
# x = q
Fx = F.dot(x)
# z-update
zold = np.copy(z)
v = Fx + u - p
z = soft_thresholding(v, reg_lambda * 1. / rho)
# u-update
u = u + Fx - z - p
# Diagnostics, reporting, termination checks
h['obj_val'][k] = f_objective(A, b, reg_lambda, x, z)
h['r_norm'][k] = norm(Fx - z - p)
h['s_norm'][k] = norm(-rho * F.T.dot(z - zold))
h['eps_pri'][k] = np.sqrt(ell) * tol_abs + tol_rel * np.array(
[norm(Fx), norm(z), norm(p)]).max()
h['eps_dual'][k] = np.sqrt(n) * tol_abs + tol_rel * norm(
rho * F.T.dot(u))
if not QUIET:
# h['obj_val'][k] = f_objective(A, b, reg_lambda, x, z)
print('%4d\t%10.4f\t%10.4f\t%10.4f\t%10.4f\t%10.2f' %
(k + 1, h['r_norm'][k], h['eps_pri'][k], h['s_norm'][k],
h['eps_dual'][k], h['obj_val'][k]))
if (h['r_norm'][k] < h['eps_pri'][k]) and \
(h['s_norm'][k] < h['eps_dual'][k]):
break
if not QUIET:
toc = time.time() - tic
print("\nElapsed time is %.2f seconds" % toc)
return x.ravel(), h
# Supporting functions for the genearlized Lasso solver
def f_objective(A, b, reg_lambda, x, z):
return 0.5 * np.square(A.dot(x) - b).sum() + reg_lambda * norm(z, 1)
def soft_thresholding(x, alpha):
return np.maximum(0., x - alpha) - np.maximum(0., -x - alpha)
def Cholesky_decomp_sp(A, F, rho):
S = A.T.dot(A) + rho * F.T.dot(F)
L = cholesky(S)
L = sparse.csc_matrix(L)
U = sparse.csc_matrix(L.T)
return L, U
# def lad_trend(denoised_data, data_T, vlambda, vlambda_diff, solver_method,
# trend_solver_admm_rho, maxiters, show_progress, S_inv,
# warm_start_toggle, warm_start_iniValue):
# # normalize input data
# scaler = StandardScaler()
# denoised_data_scaled = scaler.fit_transform(denoised_data)
# T = data_T
# N = len(denoised_data_scaled)
# # season diff
# pd_denoised_data = pd.DataFrame(denoised_data_scaled)
# g_t = pd_denoised_data.diff(periods=T)[T:].values.flatten().reshape(-1, 1)
# # reformulate 3 L1 to 1 L1 (extended L1)
# extended_g_t = np.vstack((g_t, np.zeros((N - 1, 1)), np.zeros((N - 2, 1))))
# row = np.hstack((np.ones((1, T)), np.zeros((1, N - 1 - T))))
# mat_delta_t = circulant(row).T[:(N - T), :]
# eye_tmp = vlambda * np.eye(N - 1)
# row_tmp = np.hstack(
# (np.array([1, -1]).reshape(1, 2), np.zeros((1, N - 2 - 1))))
# Diff_1st_tmp = vlambda_diff * circulant(row_tmp).T[:(N - 2), :]
# extended_mat = np.vstack((mat_delta_t, eye_tmp, Diff_1st_tmp))
# # method based on LP in CVXOPT
# if solver_method == 'cvxopt_default':
# from .L1_LPsolver import l1blas
# P = matrix(extended_mat)
# q = matrix(extended_g_t)
# # delta_t = l1(P, q)
# delta_t = l1blas(P, q, maxiters, show_progress) # faster
# next_warm_start_value = np.asarray(delta_t).reshape(-1, )
# elif solver_method == 'cvxopt_mosek':
# from .L1_LPsolver import l1mosek2
# P = matrix(extended_mat)
# q = matrix(extended_g_t)
# # delta_t = l1mosek(P,q)
# delta_t = l1mosek2(P, q, maxiters, show_progress) # faster
# next_warm_start_value = np.asarray(delta_t).reshape(-1, )
# elif solver_method == 'g_lasso_admm':
# l_admm, n_admm = extended_mat.shape
# m_admm = 1
# A_admm = np.zeros((m_admm, n_admm))
# b_admm = np.zeros(m_admm)
# F_admm = extended_mat
# p_admm = extended_g_t
# p_admm = p_admm.ravel()
# reg_lambda = 1.0
# R_admm = g_lasso_admm_solver(A_admm,
# b_admm,
# F_admm,
# p_admm,
# reg_lambda,
# x0=warm_start_iniValue,
# rho=trend_solver_admm_rho,
# MAX_ITER=maxiters,
# rel_par=1.,
# QUIET=(not show_progress),
# tol_abs=1e-4,
# S_inv=S_inv,
# warm_start_toggle=warm_start_toggle)
# delta_t = R_admm[0].reshape(-1, 1)
# next_warm_start_value = R_admm[0]
# # add [0] as the first element to keep data length
# delta_t_out = np.vstack(([0], np.asarray(delta_t)))
# ini_trend_data_scaled = np.cumsum(delta_t_out).reshape(-1, 1)
# # de-normalize data
# ini_trend_data = scaler.inverse_transform(ini_trend_data_scaled) - \
# float(scaler.mean_)
# return ini_trend_data, next_warm_start_value
def lad_trend(denoised_data, data_T, vlambda, vlambda_diff, solver_method,
trend_solver_admm_rho, trend_down_sample, maxiters,
show_progress, S_inv, warm_start_toggle, warm_start_iniValue,
update_fast_trend, fast_trend_record):
# trend down_sample
d_a = trend_down_sample
ori_len = len(denoised_data)
denoised_data = denoised_data.copy()[::d_a]
T = int(data_T/d_a)
# normalize input data
scaler = StandardScaler()
denoised_data_scaled = scaler.fit_transform(denoised_data)
# T = data_T
N = len(denoised_data_scaled)
# season diff
pd_denoised_data = pd.DataFrame(denoised_data_scaled)
g_t = pd_denoised_data.diff(periods=T)[T:].values.flatten().reshape(-1, 1)
# reformulate 3 L1 to 1 L1 (extended L1)
extended_g_t = np.vstack((g_t, np.zeros((N - 1, 1)), np.zeros((N - 2, 1))))
if solver_method == 'GADMM' or solver_method == 'PDHG':
pass
else:
row = np.hstack((np.ones((1, T)), np.zeros((1, N - 1 - T))))
mat_delta_t = circulant(row).T[:(N - T), :]
eye_tmp = vlambda * np.eye(N - 1)
row_tmp = np.hstack((np.array([1, -1]).reshape(1, 2),
np.zeros((1, N - 2 - 1))))
Diff_1st_tmp = vlambda_diff * circulant(row_tmp).T[:(N - 2), :]
extended_mat = np.vstack((mat_delta_t, eye_tmp, Diff_1st_tmp))
# method based on LP in CVXOPT
gadmm_pdhg_solver = None
if solver_method == 'cvxopt_default':
from .L1_LPsolver import l1blas
P = matrix(extended_mat)
q = matrix(extended_g_t)
# delta_t = l1(P, q)
delta_t = l1blas(P, q, maxiters, show_progress) # faster
next_warm_start_value = np.asarray(delta_t).reshape(-1,)
elif solver_method == 'cvxopt_mosek':
from .L1_LPsolver import l1mosek2
P = matrix(extended_mat)
q = matrix(extended_g_t)
# delta_t = l1mosek(P,q)
delta_t = l1mosek2(P, q, maxiters, show_progress) # faster
next_warm_start_value = np.asarray(delta_t).reshape(-1,)
elif solver_method == 'g_lasso_admm':
l_admm, n_admm = extended_mat.shape
m_admm = 1
A_admm = np.zeros((m_admm, n_admm))
b_admm = np.zeros(m_admm)
F_admm = extended_mat
p_admm = extended_g_t
p_admm = p_admm.ravel()
reg_lambda = 1.0
R_admm = g_lasso_admm_solver(A_admm, b_admm, F_admm, p_admm,
reg_lambda, x0=warm_start_iniValue,
rho=trend_solver_admm_rho,
MAX_ITER=maxiters, rel_par=1.,
QUIET=(not show_progress),
tol_abs=1e-4, S_inv=S_inv,
warm_start_toggle=warm_start_toggle)
delta_t = R_admm[0].reshape(-1, 1)
next_warm_start_value = R_admm[0]
elif solver_method == 'GADMM' or solver_method == 'PDHG':
if solver_method == 'GADMM':
solver_cls = gADMMSolver
else:
solver_cls = PDHG
b_vec = extended_g_t
if update_fast_trend:
# A_mat = extended_mat
A_operator = DetailA(N, T, vlambda, vlambda_diff)
A_norm = max(T, vlambda, vlambda_diff)
gadmm_pdhg_solver = \
solver_cls(A_operator=A_operator, A_norm=A_norm)
gadmm_pdhg_solver.paramTuning(b_vec=b_vec, maxIter=40)
# gc.collect()
# time.sleep(.1)
else:
gadmm_pdhg_solver = fast_trend_record
(g_soln, g_obj, g_obj_history, g_time_history) = \
gadmm_pdhg_solver.solve(b_vec=b_vec, max_iter=maxiters)
delta_t = g_soln
next_warm_start_value = np.asarray(delta_t).reshape(-1,)
# add [0] as the first element to keep data length
delta_t_out = np.vstack(([0], np.asarray(delta_t)))
ini_trend_data_scaled = np.cumsum(delta_t_out).reshape(-1, 1)
# de-normalize data
ini_trend_data = scaler.inverse_transform(ini_trend_data_scaled) - \
float(scaler.mean_)
# trend up_sample
ini_trend_data = np.repeat(ini_trend_data, d_a)[:ori_len].reshape(-1, 1)
next_warm_start_value = np.repeat(next_warm_start_value, d_a)[:ori_len]
return ini_trend_data, next_warm_start_value, gadmm_pdhg_solver
def bilateral_season(coarse_season,
data_T,
bilateral_period_num,
neighbour_wdw_size,
sigma_i,
sigma_d,
fit_transform_len_spec=None):
# ref: coarse_season = denoised_data - ini_trend_data
b_season_tstat = BilateralSeasonTstat(
num_period=bilateral_period_num,
period_len=data_T,
neighbour_wdw_size=neighbour_wdw_size,
sigma_i=sigma_i,
sigma_d=sigma_d,
fit_transform_len_spec=fit_transform_len_spec)
irregular_season = b_season_tstat.fit_transform(coarse_season)
ini_season_data = coarse_season - irregular_season
return ini_season_data, irregular_season
# class RobustSTL():
# """Robust STL for decomposing time series
# Parameters
# ----------
# data_T : int
# The length of one period in terms of data points.
# The value must be larger than 1
# noise_toggle: boolean
# [default=True]
# The toggle that determines whether to denoise signal.
# noise_sigma_i : double
# [default=2.0]
# The standard deviation for intensity used in bilateral gaussian kernel
# noise_sigma_d : double
# [default=2.5]
# The standard deviation for distance used in bilateral gaussian kernel
# truncate: double
# [default=8.0]
# Used for the radius of the filter, which equals to truncate times
# sigma_d
# trend_toggle: boolean
# [default=True]
# The toggle that determines whether to detrend signal.
# trend_vlambda : double
# [default=40]
# The lambda to control first difference of trend in L1 reg
# trend_vlambda_diff : double
# [default=20]
# The lambda to control twice difference of trend in L1 reg
# trend_solver_method: str, 'g_lasso_admm','cvxopt_default', 'cvxopt_mosek'
# [default='g_lasso_admm']
# The solver to solve the L1 problem. cvxopt_mosek is much
# faster but need license.
# trend_solver_maxiters : int
# [default=15]
# Set the iteration number in the optimization progress in trend filter
# trend_solver_show_progress: boolean
# [default=False]
# Show the optimization progress in trend filter
# trend_solver_admm_rho: float
# The para rho used in the admm solver in trend filter
# trend_solver_warm_start: boolean
# [default=True]
# speed up in the online mode by using warm_start in the admm solver
# season_toggle: boolean
# [default=True]
# The toggle that determines whether to deseasonize signal.
# season_bilateral_period_num : int
# The number of previous periods used in the deseason.
# num_period can be 1,2,3...,
# season_neighbour_wdw_size : int
# [default=20]
# The half length of the window used in historical period data
# season_sigma_i : double
# [default=2.5]
# The standard deviation for intensity used in bilateral gaussian kernel
# season_sigma_d : double
# [default=2.5]
# The standard deviation for distance used in bilateral gaussian kernel
# online_history_length : int
# The length of history data used in online mode for RobustSTL
# online_trend_update_freq : int
# [default=5]
# speed up in the online mode by only calculating trend every
# online_trend_update_freq times
# online_update_mode : string
# [default='only_mini_batch']
# The online update mode, can be 'only_mini_batch' or
# 'entire_online_history', default is 'only_mini_batch'
# """
# def __init__(self,
# data_T=288,
# noise_toggle=True,
# noise_sigma_i=2.0,
# noise_sigma_d=2.5,
# noise_truncate=8.0,
# trend_toggle=True,
# trend_vlambda=40,
# trend_vlambda_diff=20,
# trend_solver_method='g_lasso_admm',
# trend_solver_maxiters=15,
# trend_solver_show_progress=False,
# trend_solver_admm_rho=1.0,
# trend_solver_warm_start=True,
# season_toggle=True,
# season_bilateral_period_num=2,
# season_neighbour_wdw_size=20,
# season_sigma_i=2,
# season_sigma_d=10,
# online_history_length=288 * 3,
# online_trend_update_freq=5,
# online_update_mode='only_mini_batch'):
# self.data_T = data_T
# if data_T <= 1:
# raise ValueError("The data_T (period) must be larger than 1")
# self.noise_toggle = noise_toggle
# self.noise_sigma_i = noise_sigma_i
# self.noise_sigma_d = noise_sigma_d
# self.noise_truncate = noise_truncate
# self.trend_toggle = trend_toggle
# self.trend_vlambda = trend_vlambda
# self.trend_vlambda_diff = trend_vlambda_diff
# self.trend_solver_method = trend_solver_method
# self.trend_solver_maxiters = trend_solver_maxiters
# self.trend_solver_show_progress = trend_solver_show_progress
# self.trend_solver_admm_rho = trend_solver_admm_rho
# self.trend_solver_warm_start = trend_solver_warm_start
# self.season_toggle = season_toggle
# self.season_bilateral_period_num = season_bilateral_period_num
# if season_bilateral_period_num < 1:
# raise ValueError("The season_bilateral_period_num at least 1")
# self.season_neighbour_wdw_size = season_neighbour_wdw_size
# self.season_sigma_i = season_sigma_i
# self.season_sigma_d = season_sigma_d
# self.online_history_length = online_history_length
# self.online_trend_update_freq = online_trend_update_freq
# self.online_update_mode = online_update_mode
# self.online_count = 0
# # calcu S_inv for fit_transform_online only
# N = online_history_length
# T = data_T
# vlambda = trend_vlambda
# vlambda_diff = trend_vlambda_diff
# row = np.hstack((np.ones((1, T)), np.zeros((1, N - 1 - T))))
# mat_delta_t = circulant(row).T[:(N - T), :]
# eye_tmp = vlambda * np.eye(N - 1)
# row_tmp = np.hstack(
# (np.array([1, -1]).reshape(1, 2), np.zeros((1, N - 2 - 1))))
# Diff_1st_tmp = vlambda_diff * circulant(row_tmp).T[:(N - 2), :]
# extended_mat = np.vstack((mat_delta_t, eye_tmp, Diff_1st_tmp))
# F_admm = extended_mat
# rho = trend_solver_admm_rho
# S = rho * F_admm.T.dot(F_admm)
# self.S_inv = np.linalg.inv(S)
# def fit_transform(self,
# ts_data,
# online_mode=False,
# S_inv=None,
# warm_start_iniValue=None,
# online_trend_update_toggle=True,
# mini_batch_len=None,
# override=True):
# """
# This method to do robust STL filtering process for time sereis.
# Parameters
# ----------
# ts_data : array-like, shape = (n_timelength, 1) or (n_timelength,)
# The input data, 1D data: can be numpy array, list,
# or pandas dataframe.
# online_mode: boolean
# [default=False]
# In batch mode, this is False. Used in online mode to speed up.
# S_inv : matrix
# To be used for online case only to speed up
# warm_start_iniValue: array-like, shape = (n_timelength, 1)
# Used in online mode to speed up the trend solver.
# online_trend_update_toggle: boolean
# [default=True]
# In batch mode, this is True, which decides when to upate trend.
# mini_batch_len: int
# the length of mini_batch
# override : boolean
# To be used for online case, ignore for now.
# Returns
# -------
# decomposed_data : array-like, shape = (n_timelength, 3)
# The output of decomposed signal: [trend, season, irregular]
# """
# data_T = self.data_T
# noise_toggle = self.noise_toggle
# noise_sigma_i = self.noise_sigma_i
# noise_sigma_d = self.noise_sigma_d
# noise_truncate = self.noise_truncate
# trend_toggle = self.trend_toggle
# trend_vlambda = self.trend_vlambda
# trend_vlambda_diff = self.trend_vlambda_diff
# trend_solver_method = self.trend_solver_method
# trend_solver_maxiters = self.trend_solver_maxiters
# trend_solver_show_progress = self.trend_solver_show_progress
# trend_solver_admm_rho = self.trend_solver_admm_rho
# trend_solver_warm_start = self.trend_solver_warm_start
# season_toggle = self.season_toggle
# season_bilateral_period_num = self.season_bilateral_period_num
# season_neighbour_wdw_size = self.season_neighbour_wdw_size
# season_sigma_i = self.season_sigma_i
# season_sigma_d = self.season_sigma_d
# online_history_length = self.online_history_length
# data_validity_check(ts_data)
# ts_data = np.array(ts_data).flatten().reshape(-1, 1)
# original_data = ts_data.copy()
# # 1st step: denoising
# if noise_toggle:
# if not online_mode: # batch mode
# self.denoising_filter = CausalBilateralFilter(
# sigma_i=noise_sigma_i,
# sigma_d=noise_sigma_d,
# truncate=noise_truncate)
# denoised_data = self.denoising_filter.fit_transform(
# original_data)
# else: # online mode
# new_ts_minibatch = ts_data[-mini_batch_len:]
# denoised_data = self.denoising_filter.fit_transform_online(
# new_ts_minibatch)
# denoised_data = denoised_data[-online_history_length:]
# else:
# denoised_data = original_data # no denoising
# noise_data = original_data - denoised_data
# # 2nd step: detrending
# if trend_toggle:
# # online_trend_update_toggle == True by default,
# # since in batch mode we must do this
# if online_trend_update_toggle:
# ini_trend_data, next_warm_start_value = lad_trend(
# denoised_data=denoised_data,
# data_T=data_T,
# vlambda=trend_vlambda,
# vlambda_diff=trend_vlambda_diff,
# solver_method=trend_solver_method,
# trend_solver_admm_rho=trend_solver_admm_rho,
# maxiters=trend_solver_maxiters,
# show_progress=trend_solver_show_progress,
# S_inv=S_inv,
# warm_start_toggle=trend_solver_warm_start,
# warm_start_iniValue=warm_start_iniValue)
# else:
# next_warm_start_value = np.hstack(
# (self.next_warm_start_value[mini_batch_len:],
# np.tile(0.0, mini_batch_len)))
# # ini_trend_data, can also do interplate or prediction]
# ini_trend_data_tmp = self.ini_trend_data.reshape(-1, )
# ini_trend_data = np.hstack(
# (ini_trend_data_tmp[mini_batch_len:],
# np.tile(ini_trend_data_tmp[-1], mini_batch_len)))
# ini_trend_data = ini_trend_data.reshape(-1, 1)
# # summary
# coarse_season = denoised_data - ini_trend_data
# self.ini_trend_data = ini_trend_data
# self.next_warm_start_value = next_warm_start_value
# else:
# ini_trend_data = np.zeros_like(denoised_data)
# coarse_season = denoised_data
# self.next_warm_start_value = np.zeros(online_history_length)
# # 3rd step: de-season
# if season_toggle:
# # # version 1 with later evel_adj = np.mean(ini_season_data)
# # ini_season_data, irregular_season = bilateral_season(
# # coarse_season=coarse_season,
# # data_T=data_T,
# # bilateral_period_num=season_bilateral_period_num,
# # neighbour_wdw_size=season_neighbour_wdw_size,
# # sigma_i=season_sigma_i,
# # sigma_d=season_sigma_d)
# # # version 2 is the same as above when set level_adj=0 later
# # # much better speedup with level_adj = np.mean(coarse_season)
# if not online_mode: # batch mode
# ini_season_data, irregular_season = bilateral_season(
# coarse_season=coarse_season,
# data_T=data_T,
# bilateral_period_num=season_bilateral_period_num,
# neighbour_wdw_size=season_neighbour_wdw_size,
# sigma_i=season_sigma_i,
# sigma_d=season_sigma_d,
# fit_transform_len_spec=None)
# self.ini_season_data = ini_season_data[-online_history_length:]
# else: # online mode:
# ini_season_data, irregular_season = bilateral_season(
# coarse_season=coarse_season,
# data_T=data_T,
# bilateral_period_num=season_bilateral_period_num,
# neighbour_wdw_size=season_neighbour_wdw_size,
# sigma_i=season_sigma_i,
# sigma_d=season_sigma_d,
# fit_transform_len_spec=mini_batch_len)
# ini_season_data = np.vstack(
# (self.ini_season_data[mini_batch_len:],
# ini_season_data[-mini_batch_len:]))
# self.ini_season_data = ini_season_data
# else:
# coarse_season_mean = np.mean(coarse_season)
# irregular_season = coarse_season - coarse_season_mean
# ini_season_data = coarse_season_mean * \
# np.ones_like(coarse_season)
# # 4nd step: adjust trend, season, irregular_season.
# # make mean(adjusted_season) = 0
# # level_adj = np.mean(ini_season_data)
# # adopt mean(coarse_season) to approx mean(ini_season_data)
# # in the version 1 in step_3
# level_adj = np.mean(coarse_season)
# adjusted_season = ini_season_data - level_adj
# if trend_toggle:
# adjusted_trend = ini_trend_data + level_adj
# irregular_data = irregular_season + noise_data
# else:
# adjusted_trend = ini_trend_data
# irregular_data = irregular_season + noise_data + level_adj
# decomposed_data = np.hstack(
# (adjusted_trend, adjusted_season, irregular_data))
# self.ts_data = ts_data
# self.adjusted_trend = adjusted_trend
# self.adjusted_season = adjusted_season
# self.irregular_data = irregular_data
# self.decomposed_data = decomposed_data
# return decomposed_data
# def fit_transform_online(self, new_ts_minibatch):
# """
# The online version of RobustSTL to do the fit transform with
# much faster speed.
# Note:
# 1, online mode only uses the most recent online_history_length data
# (including the newest new_ts_minibatch) to do decomposition.
# 2, the output would be the same as the output of fit_transform (i.e.,
# the length is len(ts_data)), but only the last len(new_ts_minibatch)
# data is computed from the fit_transform_online.
# Parameters
# ---------
# new_ts_minibatch : array-like, shape = (n_minibatch, 1)
# or (n_minibatch,)
# Single time series of length mini_batch_timelength.
# Returns
# -------
# decomposed_data : numpy array, shape = (n_timelength, 3)
# The output of decomposed signal: [trend, season, irregular]
# """
# S_inv = self.S_inv
# next_warm_start_value = self.next_warm_start_value
# online_history_length = self.online_history_length
# online_update_mode = self.online_update_mode
# online_trend_update_freq = self.online_trend_update_freq
# ts_data = self.ts_data
# adjusted_trend = self.adjusted_trend
# adjusted_season = self.adjusted_season
# irregular_data = self.irregular_data
# decomposed_data = self.decomposed_data
# mini_batch_len = len(new_ts_minibatch)
# if self.ts_data is None:
# raise ValueError("fit_transform_online can not be called in the\
# very first time, should followed fit_transform.")
# if mini_batch_len > online_history_length:
# raise ValueError("for online detrend, the length of mini_batch\
# should not larger than the online_history_length")
# if len(ts_data) < online_history_length:
# raise ValueError("for online detrend, the online_history_length\
# should not larger than the initial len(ts_data)")
# # check new_ts_minibatch and change format to 1d np.array
# data_validity_check(new_ts_minibatch)
# new_ts_minibatch = np.array(new_ts_minibatch).flatten()
# new_ts_minibatch = new_ts_minibatch.reshape(-1, 1)
# # update ts_data
# ts_data = np.concatenate([ts_data[mini_batch_len:], new_ts_minibatch])
# # detrend for ts_data_online_part
# ts_data_online = ts_data[-online_history_length:]
# next_warm_start_value = \
# next_warm_start_value[-(online_history_length-1):]
# # warm_start_ini = np.hstack(
# # (next_warm_start_value[mini_batch_len:],
# # np.tile(next_warm_start_value[-1], mini_batch_len)))
# warm_start_ini = np.hstack((next_warm_start_value[mini_batch_len:],
# np.tile(0, mini_batch_len)))
# # count the No. of online ops to decide if do online_trend_update
# if self.online_count == 0:
# online_trend_update_toggle = True
# else:
# online_trend_update_toggle = False
# # update self.online_count for next use
# self.online_count = (self.online_count + 1) % online_trend_update_freq
# # do fit_transform
# online_mode = True
# self.fit_transform(ts_data_online, online_mode, S_inv, warm_start_ini,
# online_trend_update_toggle, mini_batch_len)
# adjusted_trend_online = self.adjusted_trend
# adjusted_season_online = self.adjusted_season
# irregular_data_online = self.irregular_data
# # decomposed_data_online = self.decomposed_data
# # # update trend_signal residual_signal
# if online_update_mode == 'only_mini_batch':
# adjusted_trend = np.vstack(
# (adjusted_trend[mini_batch_len:],
# adjusted_trend_online[-mini_batch_len:]))
# adjusted_season = np.vstack(
# (adjusted_season[mini_batch_len:],
# adjusted_season_online[-mini_batch_len:]))
# irregular_data = np.vstack(
# (irregular_data[mini_batch_len:],
# irregular_data_online[-mini_batch_len:]))
# elif online_update_mode == 'entire_online_history':
# update_data_len = online_history_length
# adjusted_trend = np.vstack(
# (adjusted_trend[mini_batch_len:(-update_data_len +
# mini_batch_len)],
# adjusted_trend_online))
# adjusted_season = np.vstack(
# (adjusted_season[mini_batch_len:(-update_data_len +
# mini_batch_len)],
# adjusted_season_online))
# irregular_data = np.vstack(
# (irregular_data[mini_batch_len:(-update_data_len +
# mini_batch_len)],
# irregular_data_online))
# else:
# raise ValueError("input para of online_update_mode is not right")
# decomposed_data = np.hstack(
# (adjusted_trend, adjusted_season, irregular_data))
# self.ts_data = ts_data
# self.adjusted_trend = adjusted_trend
# self.adjusted_season = adjusted_season
# self.irregular_data = irregular_data
# self.decomposed_data = decomposed_data
# return decomposed_data
# def _get_prev_transform(self):
# # debug purpose --> should be n*3 numpy.array
# return self.decomposed_data
class RobustSTL():
"""Robust STL for decomposing time series
Parameters
----------
data_T : int
The length of one period in terms of data points.
The value must be larger than 1
noise_toggle: boolean
[default=True]
The toggle that determines whether to denoise signal.
noise_sigma_i : double
[default=2.0]
The standard deviation for intensity used in bilateral gaussian kernel
noise_sigma_d : double
[default=2.5]
The standard deviation for distance used in bilateral gaussian kernel
truncate: double
[default=8.0]
Used for the radius of the filter, which equals to truncate times
sigma_d
trend_toggle: boolean
[default=True]
The toggle that determines whether to detrend signal.
trend_vlambda : double
[default=40]
The lambda to control first difference of trend in L1 reg
trend_vlambda_diff : double
[default=20]
The lambda to control twice difference of trend in L1 reg
trend_solver_method: str, 'g_lasso_admm','cvxopt_default', 'cvxopt_mosek'
[default='g_lasso_admm']
The solver to solve the L1 problem. cvxopt_mosek is much
faster but need license.
trend_solver_maxiters : int
[default=15]
Set the iteration number in the optimization progress in trend filter
trend_solver_show_progress: boolean
[default=False]
Show the optimization progress in trend filter
trend_solver_admm_rho: float
The para rho used in the admm solver in trend filter
trend_solver_warm_start: boolean
[default=True]
speed up in the online mode by using warm_start in the admm solver
season_toggle: boolean
[default=True]
The toggle that determines whether to deseasonize signal.
season_bilateral_period_num : int
The number of previous periods used in the deseason.
num_period can be 1,2,3...,
season_neighbour_wdw_size : int
[default=20]
The half length of the window used in historical period data
season_sigma_i : double
[default=2.5]
The standard deviation for intensity used in bilateral gaussian kernel
season_sigma_d : double
[default=2.5]
The standard deviation for distance used in bilateral gaussian kernel
online_history_length : int
The length of history data used in online mode for RobustSTL
online_trend_update_freq : int
[default=5]
speed up in the online mode by only calculating trend every
online_trend_update_freq times
online_update_mode : string
[default='only_mini_batch']
The online update mode, can be 'only_mini_batch' or
'entire_online_history', default is 'only_mini_batch'
"""
def __init__(self,
data_T=288,
noise_toggle=True,
noise_sigma_i=2.0,
noise_sigma_d=2.5,
noise_truncate=8.0,
trend_toggle=True,
trend_vlambda=40,
trend_vlambda_diff=20,
trend_solver_method='g_lasso_admm',
trend_solver_maxiters=15,
trend_solver_show_progress=False,
trend_solver_admm_rho=1.0,
trend_solver_warm_start=True,
trend_down_sample=1,
season_toggle=True,
season_bilateral_period_num=2,
season_neighbour_wdw_size=20,
season_sigma_i=2,
season_sigma_d=10,
fastbatch_length=None,
online_history_length=None,
online_trend_update_freq=5,
online_update_mode='only_mini_batch'):
self.data_T = data_T
if data_T <= 1:
raise ValueError("The data_T (period) must be larger than 1")
self.noise_toggle = noise_toggle
self.noise_sigma_i = noise_sigma_i
self.noise_sigma_d = noise_sigma_d
self.noise_truncate = noise_truncate
self.trend_toggle = trend_toggle
self.trend_vlambda = trend_vlambda
self.trend_vlambda_diff = trend_vlambda_diff
self.trend_solver_method = trend_solver_method
self.trend_solver_maxiters = trend_solver_maxiters
self.trend_solver_show_progress = trend_solver_show_progress
self.trend_solver_admm_rho = trend_solver_admm_rho
self.trend_solver_warm_start = trend_solver_warm_start
self.trend_down_sample = trend_down_sample
self.season_toggle = season_toggle
self.season_bilateral_period_num = season_bilateral_period_num
if season_bilateral_period_num < 1:
raise ValueError("The season_bilateral_period_num at least 1")
self.season_neighbour_wdw_size = season_neighbour_wdw_size
self.season_sigma_i = season_sigma_i
self.season_sigma_d = season_sigma_d
self.fastbatch_length = fastbatch_length
if online_history_length is None:
self.online_history_length = 3 * data_T
else:
self.online_history_length = online_history_length
self.online_trend_update_freq = online_trend_update_freq
self.online_update_mode = online_update_mode
self.online_count = 0
# calcu S_inv for fit_transform_online only
if trend_solver_method == 'GADMM' or trend_solver_method == 'PDHG':
self.S_inv = None
else:
N = self.online_history_length
T = self.data_T
vlambda = trend_vlambda
vlambda_diff = trend_vlambda_diff
row = np.hstack((np.ones((1, T)), np.zeros((1, N - 1 - T))))
mat_delta_t = circulant(row).T[:(N - T), :]
eye_tmp = vlambda * np.eye(N - 1)
row_tmp = np.hstack(
(np.array([1, -1]).reshape(1, 2), np.zeros((1, N - 2 - 1))))
Diff_1st_tmp = vlambda_diff * circulant(row_tmp).T[:(N - 2), :]
extended_mat = np.vstack((mat_delta_t, eye_tmp, Diff_1st_tmp))
F_admm = extended_mat
rho = trend_solver_admm_rho
S = rho * F_admm.T.dot(F_admm)
self.S_inv = np.linalg.inv(S)
# Fast-RobustSTL code extension: based on GADMM or PDHG
# temp for fast-stl, if ts_data_length is changed, need update
# for the first time we need update, so ini value=0 here
self.ts_data_length = 0
self.fast_trend_record = None
def fit_transform_fastbatch(self, ts_data):
data_T = self.data_T
fastbatch_length = self.fastbatch_length
if fastbatch_length is None:
fastbatch_length = 4 * data_T
# decide how many times to call fit_transform
data_validity_check(ts_data)
ts_data = np.array(ts_data).flatten().reshape(-1, 1)
total_len = len(ts_data)
segments_num = int(total_len / fastbatch_length)
if segments_num <= 1:
decomposed_data = self.fit_transform(ts_data)
return decomposed_data
else:
adjusted_trend = np.array([])
adjusted_season = np.array([])
irregular_data = np.array([])
# for loop and contact, the last one is longer.
for seg_idx in range(segments_num):
print("seg_idx")
print(seg_idx)
if seg_idx == segments_num - 1:
seg_data = ts_data[seg_idx * fastbatch_length:]
else:
seg_data = ts_data[seg_idx *
fastbatch_length:(seg_idx + 1) *
fastbatch_length]
self.fit_transform(seg_data)
adjusted_trend_tmp = self.adjusted_trend
adjusted_season_tmp = self.adjusted_season
irregular_data_tmp = self.irregular_data
adjusted_trend = np.append(adjusted_trend, adjusted_trend_tmp)
adjusted_season = np.append(adjusted_season,
adjusted_season_tmp)
irregular_data = np.append(irregular_data, irregular_data_tmp)
adjusted_trend = adjusted_trend.reshape(-1, 1)
adjusted_season = adjusted_season.reshape(-1, 1)
irregular_data = irregular_data.reshape(-1, 1)
decomposed_data = np.hstack(
(adjusted_trend, adjusted_season, irregular_data))
self.adjusted_trend = adjusted_trend
self.adjusted_season = adjusted_season
self.irregular_data = irregular_data
self.decomposed_data = decomposed_data
return decomposed_data
def fit_transform(self,
ts_data,
online_mode=False,
S_inv=None,
warm_start_iniValue=None,
online_trend_update_toggle=True,
mini_batch_len=None,
override=True):
"""
This method to do robust STL filtering process for time sereis.
Parameters
----------
ts_data : array-like, shape = (n_timelength, 1) or (n_timelength,)
The input data, 1D data: can be numpy array, list,
or pandas dataframe.
online_mode: boolean
[default=False]
In batch mode, this is False. Used in online mode to speed up.
S_inv : matrix
To be used for online case only to speed up
warm_start_iniValue: array-like, shape = (n_timelength, 1)
Used in online mode to speed up the trend solver.
online_trend_update_toggle: boolean
[default=True]
In batch mode, this is True, which decides when to upate trend.
mini_batch_len: int
the length of mini_batch
override : boolean
To be used for online case, ignore for now.
Returns
-------
decomposed_data : array-like, shape = (n_timelength, 3)
The output of decomposed signal: [trend, season, irregular]
"""
data_T = self.data_T
noise_toggle = self.noise_toggle
noise_sigma_i = self.noise_sigma_i
noise_sigma_d = self.noise_sigma_d
noise_truncate = self.noise_truncate
trend_toggle = self.trend_toggle
trend_vlambda = self.trend_vlambda
trend_vlambda_diff = self.trend_vlambda_diff
trend_solver_method = self.trend_solver_method
trend_solver_maxiters = self.trend_solver_maxiters
trend_solver_show_progress = self.trend_solver_show_progress
trend_solver_admm_rho = self.trend_solver_admm_rho
trend_solver_warm_start = self.trend_solver_warm_start
trend_down_sample = self.trend_down_sample
season_toggle = self.season_toggle
season_bilateral_period_num = self.season_bilateral_period_num
season_neighbour_wdw_size = self.season_neighbour_wdw_size
season_sigma_i = self.season_sigma_i
season_sigma_d = self.season_sigma_d
online_history_length = self.online_history_length
data_validity_check(ts_data)
ts_data = np.array(ts_data).flatten().reshape(-1, 1)
original_data = ts_data.copy()
# 1st step: denoising
if noise_toggle:
if not online_mode: # batch mode
self.denoising_filter = CausalBilateralFilter(
sigma_i=noise_sigma_i,
sigma_d=noise_sigma_d,
truncate=noise_truncate)
denoised_data = self.denoising_filter.fit_transform(
original_data)
else: # online mode
new_ts_minibatch = ts_data[-mini_batch_len:]
denoised_data = self.denoising_filter.fit_transform_online(
new_ts_minibatch)
denoised_data = denoised_data[-online_history_length:]
else:
denoised_data = original_data # no denoising
noise_data = original_data - denoised_data
# 2nd step: detrending
if trend_toggle:
# online_trend_update_toggle == True by default,
# since in batch mode we must do this
if online_trend_update_toggle:
# suppose operate as (1) batch, online, online,...
# or (2) batch, batch, batch, ....
# if input ts_data_length, fast-stl needs update
cur_ts_data_length = len(ts_data)
if self.ts_data_length != cur_ts_data_length:
self.ts_data_length = cur_ts_data_length
update_fast_trend = True
else:
update_fast_trend = False
ini_trend_data, next_warm_start_value, fast_trend_solver = \
lad_trend(
denoised_data=denoised_data,
data_T=data_T,
vlambda=trend_vlambda,
vlambda_diff=trend_vlambda_diff,
solver_method=trend_solver_method,
trend_solver_admm_rho=trend_solver_admm_rho,
trend_down_sample=trend_down_sample,
maxiters=trend_solver_maxiters,
show_progress=trend_solver_show_progress,
S_inv=S_inv,
warm_start_toggle=trend_solver_warm_start,
warm_start_iniValue=warm_start_iniValue,
update_fast_trend=update_fast_trend,
fast_trend_record=self.fast_trend_record)
self.fast_trend_record = fast_trend_solver
else:
next_warm_start_value = np.hstack(
(self.next_warm_start_value[mini_batch_len:],
np.tile(0.0, mini_batch_len)))
# ini_trend_data, can also do interplate or prediction]
ini_trend_data_tmp = self.ini_trend_data.reshape(-1, )
ini_trend_data = np.hstack(
(ini_trend_data_tmp[mini_batch_len:],
np.tile(ini_trend_data_tmp[-1], mini_batch_len)))
ini_trend_data = ini_trend_data.reshape(-1, 1)
# summary
coarse_season = denoised_data - ini_trend_data
self.ini_trend_data = ini_trend_data
self.next_warm_start_value = next_warm_start_value
else:
ini_trend_data = np.zeros_like(denoised_data)
coarse_season = denoised_data
self.next_warm_start_value = np.zeros(online_history_length)
# 3rd step: de-season
if season_toggle:
# # version 1 with later evel_adj = np.mean(ini_season_data)
# ini_season_data, irregular_season = bilateral_season(
# coarse_season=coarse_season,
# data_T=data_T,
# bilateral_period_num=season_bilateral_period_num,
# neighbour_wdw_size=season_neighbour_wdw_size,
# sigma_i=season_sigma_i,
# sigma_d=season_sigma_d)
# # version 2 is the same as above when set level_adj=0 later
# # much better speedup with level_adj = np.mean(coarse_season)
if not online_mode: # batch mode
ini_season_data, irregular_season = bilateral_season(
coarse_season=coarse_season,
data_T=data_T,
bilateral_period_num=season_bilateral_period_num,
neighbour_wdw_size=season_neighbour_wdw_size,
sigma_i=season_sigma_i,
sigma_d=season_sigma_d,
fit_transform_len_spec=None)
self.ini_season_data = ini_season_data[-online_history_length:]
else: # online mode:
ini_season_data, irregular_season = bilateral_season(
coarse_season=coarse_season,
data_T=data_T,
bilateral_period_num=season_bilateral_period_num,
neighbour_wdw_size=season_neighbour_wdw_size,
sigma_i=season_sigma_i,
sigma_d=season_sigma_d,
fit_transform_len_spec=mini_batch_len)
ini_season_data = np.vstack(
(self.ini_season_data[mini_batch_len:],
ini_season_data[-mini_batch_len:]))
self.ini_season_data = ini_season_data
else:
coarse_season_mean = np.mean(coarse_season)
irregular_season = coarse_season - coarse_season_mean
ini_season_data = coarse_season_mean * \
np.ones_like(coarse_season)
# 4nd step: adjust trend, season, irregular_season.
# make mean(adjusted_season) = 0
# level_adj = np.mean(ini_season_data)
# adopt mean(coarse_season) to approx mean(ini_season_data)
# in the version 1 in step_3
level_adj = np.mean(coarse_season)
adjusted_season = ini_season_data - level_adj
if trend_toggle:
adjusted_trend = ini_trend_data + level_adj
irregular_data = irregular_season + noise_data
else:
adjusted_trend = ini_trend_data
irregular_data = irregular_season + noise_data + level_adj
decomposed_data = np.hstack(
(adjusted_trend, adjusted_season, irregular_data))
self.ts_data = ts_data
self.adjusted_trend = adjusted_trend
self.adjusted_season = adjusted_season
self.irregular_data = irregular_data
self.decomposed_data = decomposed_data
return decomposed_data
def fit_transform_online(self, new_ts_minibatch):
"""
The online version of RobustSTL to do the fit transform with
much faster speed.
Note:
1, online mode only uses the most recent online_history_length data
(including the newest new_ts_minibatch) to do decomposition.
2, the output would be the same as the output of fit_transform (i.e.,
the length is len(ts_data)), but only the last len(new_ts_minibatch)
data is computed from the fit_transform_online.
Parameters
---------
new_ts_minibatch : array-like, shape = (n_minibatch, 1)
or (n_minibatch,)
Single time series of length mini_batch_timelength.
Returns
-------
decomposed_data : numpy array, shape = (n_timelength, 3)
The output of decomposed signal: [trend, season, irregular]
"""
S_inv = self.S_inv
next_warm_start_value = self.next_warm_start_value
online_history_length = self.online_history_length
online_update_mode = self.online_update_mode
online_trend_update_freq = self.online_trend_update_freq
ts_data = self.ts_data
adjusted_trend = self.adjusted_trend
adjusted_season = self.adjusted_season
irregular_data = self.irregular_data
decomposed_data = self.decomposed_data
mini_batch_len = len(new_ts_minibatch)
if self.ts_data is None:
raise ValueError("fit_transform_online can not be called in the\
very first time, should followed fit_transform.")
if mini_batch_len > online_history_length:
raise ValueError("for online detrend, the length of mini_batch\
should not larger than the online_history_length")
if len(ts_data) < online_history_length:
raise ValueError("for online detrend, the online_history_length\
should not larger than the initial len(ts_data)")
# check new_ts_minibatch and change format to 1d np.array
data_validity_check(new_ts_minibatch)
new_ts_minibatch = np.array(new_ts_minibatch).flatten()
new_ts_minibatch = new_ts_minibatch.reshape(-1, 1)
# update ts_data
ts_data = np.concatenate([ts_data[mini_batch_len:], new_ts_minibatch])
# detrend for ts_data_online_part
ts_data_online = ts_data[-online_history_length:]
next_warm_start_value = \
next_warm_start_value[-(online_history_length-1):]
# warm_start_ini = np.hstack(
# (next_warm_start_value[mini_batch_len:],
# np.tile(next_warm_start_value[-1], mini_batch_len)))
warm_start_ini = np.hstack((next_warm_start_value[mini_batch_len:],
np.tile(0, mini_batch_len)))
# count the No. of online ops to decide if do online_trend_update
if self.online_count == 0:
online_trend_update_toggle = True
else:
online_trend_update_toggle = False
# update self.online_count for next use
self.online_count = (self.online_count + 1) % online_trend_update_freq
# do fit_transform
online_mode = True
self.fit_transform(ts_data_online, online_mode, S_inv, warm_start_ini,
online_trend_update_toggle, mini_batch_len)
adjusted_trend_online = self.adjusted_trend
adjusted_season_online = self.adjusted_season
irregular_data_online = self.irregular_data
# decomposed_data_online = self.decomposed_data
# # update trend_signal residual_signal
if online_update_mode == 'only_mini_batch':
adjusted_trend = np.vstack(
(adjusted_trend[mini_batch_len:],
adjusted_trend_online[-mini_batch_len:]))
adjusted_season = np.vstack(
(adjusted_season[mini_batch_len:],
adjusted_season_online[-mini_batch_len:]))
irregular_data = np.vstack(
(irregular_data[mini_batch_len:],
irregular_data_online[-mini_batch_len:]))
elif online_update_mode == 'entire_online_history':
update_data_len = online_history_length
adjusted_trend = np.vstack(
(adjusted_trend[mini_batch_len:(-update_data_len +
mini_batch_len)],
adjusted_trend_online))
adjusted_season = np.vstack(
(adjusted_season[mini_batch_len:(-update_data_len +
mini_batch_len)],
adjusted_season_online))
irregular_data = np.vstack(
(irregular_data[mini_batch_len:(-update_data_len +
mini_batch_len)],
irregular_data_online))
else:
raise ValueError("input para of online_update_mode is not right")
decomposed_data = np.hstack(
(adjusted_trend, adjusted_season, irregular_data))
self.ts_data = ts_data
self.adjusted_trend = adjusted_trend
self.adjusted_season = adjusted_season
self.irregular_data = irregular_data
self.decomposed_data = decomposed_data
return decomposed_data
def _get_prev_transform(self):
# debug purpose --> should be n*3 numpy.array
return self.decomposed_data
class RobustSTL_partialComponent(RobustSTL):
"""Robust STL for decomposing time series, only output partial component
Parameters
----------
partial_component: str
[default='trend']
The partial component of the RobustSTL output, can be 'trend',
'season', 'residual', 'deseasonalized', and 'detrended'.
'deseasonalized' indicates 'trend' + 'residual'; while
'detrended' indicates 'season' + 'residual'.
data_T : int
The length of one period in terms of data points.
The value must be larger than 1
noise_toggle: boolean
[default=True]
The toggle that determines whether to denoise signal.
noise_sigma_i : double
[default=2.0]
The standard deviation for intensity used in bilateral gaussian kernel
noise_sigma_d : double
[default=2.5]
The standard deviation for distance used in bilateral gaussian kernel
truncate: double
[default=8.0]
Used for the radius of the filter, which equals to truncate times
sigma_d
trend_toggle: boolean
[default=True]
The toggle that determines whether to detrend signal.
trend_vlambda : double
[default=200]
The lambda to control first difference of trend in L1 reg
trend_vlambda_diff : double
[default=200]
The lambda to control twice difference of trend in L1 reg
trend_solver_method: str, 'g_lasso_admm', 'cvxopt_default', 'cvxopt_mosek'
[default='g_lasso_admm']
The solver to solve the L1 problem. cvxopt_mosek is much
faster but need license.
trend_solver_maxiters : int
[default=200]
Set the iteration number in the optimization progress in trend filter
trend_solver_show_progress: boolean
[default=False]
Show the optimization progress in trend filter
season_toggle: boolean
[default=True]
The toggle that determines whether to deseasonize signal.
season_bilateral_period_num : int
The number of previous periods used in the deseason.
num_period can be 1,2,3...,
season_neighbour_wdw_size : int
[default=20]
The half length of the window used in historical period data
season_sigma_i : double
[default=2.5]
The standard deviation for intensity used in bilateral gaussian kernel
season_sigma_d : double
[default=2.5]
The standard deviation for distance used in bilateral gaussian kernel
online_history_length : int
The length of history data used in online mode for RobustSTL
online_update_mode : string
[default='only_mini_batch']
The online update mode, can be 'only_mini_batch' or
'entire_online_history', default is 'only_mini_batch'
"""
def __init__(self,
partial_component='trend',
data_T=288,
noise_toggle=True,
noise_sigma_i=2.0,
noise_sigma_d=2.5,
noise_truncate=8.0,
trend_toggle=True,
trend_vlambda=40,
trend_vlambda_diff=20,
trend_solver_method='g_lasso_admm',
trend_solver_maxiters=15,
trend_solver_show_progress=False,
trend_solver_admm_rho=1.0,
trend_solver_warm_start=True,
season_toggle=True,
season_bilateral_period_num=2,
season_neighbour_wdw_size=20,
season_sigma_i=2,
season_sigma_d=10,
online_history_length=288 * 3,
online_trend_update_freq=5,
online_update_mode='only_mini_batch'):
super().__init__(data_T, noise_toggle, noise_sigma_i, noise_sigma_d,
noise_truncate, trend_toggle, trend_vlambda,
trend_vlambda_diff, trend_solver_method,
trend_solver_maxiters, trend_solver_show_progress,
trend_solver_admm_rho, trend_solver_warm_start,
season_toggle, season_bilateral_period_num,
season_neighbour_wdw_size, season_sigma_i,
season_sigma_d, online_history_length,
online_trend_update_freq, online_update_mode)
self.partial_component = partial_component
def _return_partial_component(self, partial_component, decomposed_data):
if partial_component == 'trend':
decomposed_out = decomposed_data[:, 0].reshape(-1, 1)
elif partial_component == 'season':
decomposed_out = decomposed_data[:, 1].reshape(-1, 1)
elif partial_component == 'residual':
decomposed_out = decomposed_data[:, 2].reshape(-1, 1)
elif partial_component == 'deseasonalized':
decomposed_out = decomposed_data[:, 0].reshape(-1, 1) + \
decomposed_data[:, 2].reshape(-1, 1)
elif partial_component == 'detrended':
decomposed_out = decomposed_data[:, 1].reshape(-1, 1) + \
decomposed_data[:, 2].reshape(-1, 1)
else:
raise ValueError("the parameter partial_component must one of the\
'trend', 'season', 'residual', 'deseasonalized', 'detrended'")
return decomposed_out
def fit_transform(self,
ts_data,
online_mode=False,
S_inv=None,
warm_start_iniValue=None,
online_trend_update_toggle=True,
mini_batch_len=None,
override=True):
partial_component = self.partial_component
decomposed_data = super().fit_transform(ts_data, online_mode, S_inv,
warm_start_iniValue,
online_trend_update_toggle,
mini_batch_len, override)
return self._return_partial_component(partial_component,
decomposed_data)
def fit_transform_online(self, new_ts_minibatch):
partial_component = self.partial_component
decomposed_data = super().fit_transform_online(new_ts_minibatch)
return self._return_partial_component(partial_component,
decomposed_data)
def _get_prev_transform(self):
partial_component = self.partial_component
decomposed_data = self.decomposed_data
return self._return_partial_component(partial_component,
decomposed_data)
| 41.017834
| 81
| 0.602185
| 11,959
| 94,300
| 4.430638
| 0.055021
| 0.020722
| 0.016533
| 0.012569
| 0.801363
| 0.781018
| 0.760824
| 0.741781
| 0.724474
| 0.713207
| 0
| 0.01167
| 0.322132
| 94,300
| 2,298
| 82
| 41.035683
| 0.817233
| 0.43596
| 0
| 0.418559
| 0
| 0.001974
| 0.025131
| 0.002581
| 0
| 0
| 0
| 0
| 0
| 1
| 0.033564
| false
| 0.000987
| 0.012833
| 0.005923
| 0.081935
| 0.004936
| 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
| 0
|
0
| 6
|
17ee13f9bc0b0c5139e69b291f86ec2aa00e5ea7
| 4,096
|
py
|
Python
|
py/hyper_dk_series.py
|
kazuibasou/hyper-dk-series
|
f47709d06285b1788cbbf7cd29480f87a2f2ccbc
|
[
"MIT"
] | null | null | null |
py/hyper_dk_series.py
|
kazuibasou/hyper-dk-series
|
f47709d06285b1788cbbf7cd29480f87a2f2ccbc
|
[
"MIT"
] | null | null | null |
py/hyper_dk_series.py
|
kazuibasou/hyper-dk-series
|
f47709d06285b1788cbbf7cd29480f87a2f2ccbc
|
[
"MIT"
] | null | null | null |
import hypergraph
import rewiring
import random
def randomizing_d_v_zero_d_e_zero(G):
# Given a hypergraph, return a randomized hypergraph with (d_v, d_e) = (0, 0).
B_M = 0
for v in G.V:
B_M += len(G.elist[v])
randG = hypergraph.HyperGraph()
randG.V = list(G.V)
randG.E = [[] for e_i in range(0, len(G.E))]
for v in randG.V:
randG.elist[v] = []
for i in range(0, B_M):
v = random.choice(randG.V)
e_i = random.randrange(0, len(randG.E))
randG.add_node_to_hyperedge(v, e_i)
print("Successfully generated a randomized hypergraph with (d_v, d_e) = (0, 0).\n")
return randG
def randomizing_d_v_one_d_e_zero(G):
# Given a hypergraph, return a randomized hypergraph with (d_v, d_e) = (1, 0).
stublist = []
for v in G.V:
k = len(G.elist[v])
for i in range(0, k):
stublist.append(v)
random.shuffle(stublist)
randG = hypergraph.HyperGraph()
randG.V = list(G.V)
randG.E = [[] for e_i in range(0, len(G.E))]
for v in randG.V:
randG.elist[v] = []
while len(stublist) > 0:
v = stublist.pop()
e_i = random.randrange(0, len(randG.E))
randG.add_node_to_hyperedge(v, e_i)
print("Successfully generated a randomized hypergraph with (d_v, d_e) = (1, 0).\n")
return randG
def randomizing_d_v_two_d_e_zero(G):
# Given a hypergraph, return a randomized hypergraph with (d_v, d_e) = (2, 0).
randG = hypergraph.HyperGraph()
randG = randomizing_d_v_one_d_e_zero(G)
randG = rewiring.targeting_rewiring_d_v_two(G, randG)
print("Successfully generated a randomized hypergraph with (d_v, d_e) = (2, 0).\n")
return randG
def randomizing_d_v_two_five_d_e_zero(G):
# Given a hypergraph, return a randomized hypergraph with (d_v, d_e) = (2.5, 0).
randG = hypergraph.HyperGraph()
randG = randomizing_d_v_two_d_e_zero(G)
randG = rewiring.targeting_rewiring_d_v_two_five(G, randG)
print("Successfully generated a randomized hypergraph with (d_v, d_e) = (2.5, 0).\n")
return randG
def randomizing_d_v_zero_d_e_one(G):
# Given a hypergraph, return a randomized hypergraph with (d_v, d_e) = (0, 1).
stublist = []
for e_i in range(0, len(G.E)):
s = len(G.E[e_i])
for i in range(0, s):
stublist.append(e_i)
random.shuffle(stublist)
randG = hypergraph.HyperGraph()
randG.V = list(G.V)
randG.E = [[] for e_i in range(0, len(G.E))]
for v in randG.V:
randG.elist[v] = []
while len(stublist) > 0:
v = random.choice(randG.V)
e_i = stublist.pop()
randG.add_node_to_hyperedge(v, e_i)
print("Successfully generated a randomized hypergraph with (d_v, d_e) = (0, 1).\n")
return randG
def randomizing_d_v_one_d_e_one(G):
# Given a hypergraph, return a randomized hypergraph with (d_v, d_e) = (1, 1).
node_stublist = []
for v in G.V:
k = len(G.elist[v])
for i in range(0, k):
node_stublist.append(v)
random.shuffle(node_stublist)
hyperedge_stublist = []
for e_i in range(0, len(G.E)):
s = len(G.E[e_i])
for i in range(0, s):
hyperedge_stublist.append(e_i)
random.shuffle(hyperedge_stublist)
randG = hypergraph.HyperGraph()
randG.V = list(G.V)
randG.E = [[] for e_i in range(0, len(G.E))]
for v in randG.V:
randG.elist[v] = []
while len(node_stublist) > 0 and len(hyperedge_stublist) > 0:
v = node_stublist.pop()
e_i = hyperedge_stublist.pop()
randG.add_node_to_hyperedge(v, e_i)
print("Successfully generated a randomized hypergraph with (d_v, d_e) = (1, 1).\n")
return randG
def randomizing_d_v_two_d_e_one(G):
# Given a hypergraph, return a randomized hypergraph with (d_v, d_e) = (2, 1).
randG = hypergraph.HyperGraph()
randG = randomizing_d_v_one_d_e_one(G)
randG = rewiring.targeting_rewiring_d_v_two(G, randG)
print("Successfully generated a randomized hypergraph with (d_v, d_e) = (2, 1).\n")
return randG
def randomizing_d_v_two_five_d_e_one(G):
# Given a hypergraph, return a randomized hypergraph with (d_v, d_e) = (2.5, 1).
randG = hypergraph.HyperGraph()
randG = randomizing_d_v_two_d_e_one(G)
randG = rewiring.targeting_rewiring_d_v_two_five(G, randG)
print("Successfully generated a randomized hypergraph with (d_v, d_e) = (2.5, 1).\n")
return randG
| 25.128834
| 86
| 0.697021
| 741
| 4,096
| 3.62753
| 0.070175
| 0.02381
| 0.125
| 0.14881
| 0.928199
| 0.909226
| 0.892485
| 0.864583
| 0.854167
| 0.854167
| 0
| 0.015826
| 0.166992
| 4,096
| 163
| 87
| 25.128834
| 0.771981
| 0.151123
| 0
| 0.617647
| 0
| 0
| 0.171807
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.078431
| false
| 0
| 0.029412
| 0
| 0.186275
| 0.078431
| 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
| 0
|
0
| 6
|
17f16335a161822fc33f58a19a47a085b9c2d421
| 150
|
py
|
Python
|
regex/Introduction/Matching Word & Non-Word Character.py
|
patwadeepak/hackerrank-practice-problems
|
fa29269b543816bb73ec1ea1e339cae30e9764cd
|
[
"Apache-2.0"
] | null | null | null |
regex/Introduction/Matching Word & Non-Word Character.py
|
patwadeepak/hackerrank-practice-problems
|
fa29269b543816bb73ec1ea1e339cae30e9764cd
|
[
"Apache-2.0"
] | null | null | null |
regex/Introduction/Matching Word & Non-Word Character.py
|
patwadeepak/hackerrank-practice-problems
|
fa29269b543816bb73ec1ea1e339cae30e9764cd
|
[
"Apache-2.0"
] | null | null | null |
# Author: Deepak Kumar
Regex_Pattern = r"\w\w\w\W\w\w\w\w\w\w\w\w\w\w\W\w\w\w"
import re
print(str(bool(re.search(Regex_Pattern, input()))).lower())
| 25
| 59
| 0.666667
| 35
| 150
| 2.8
| 0.428571
| 0.346939
| 0.489796
| 0.612245
| 0.183673
| 0.183673
| 0.183673
| 0.183673
| 0.183673
| 0.183673
| 0
| 0
| 0.08
| 150
| 6
| 59
| 25
| 0.710145
| 0.133333
| 0
| 0
| 0
| 0.333333
| 0.27907
| 0.27907
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.333333
| 0
| 0.333333
| 0.333333
| 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
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
aa26f8dd5ee9a367ac110dc3fd111c072b1be57e
| 41
|
py
|
Python
|
tetris/__init__.py
|
AndrejHafner/tetris-reinforcement-learning
|
52db5d8ce7f9162b15575456a0effc69dd7fb2bf
|
[
"MIT"
] | null | null | null |
tetris/__init__.py
|
AndrejHafner/tetris-reinforcement-learning
|
52db5d8ce7f9162b15575456a0effc69dd7fb2bf
|
[
"MIT"
] | null | null | null |
tetris/__init__.py
|
AndrejHafner/tetris-reinforcement-learning
|
52db5d8ce7f9162b15575456a0effc69dd7fb2bf
|
[
"MIT"
] | null | null | null |
from tetris import *
from block import *
| 13.666667
| 20
| 0.756098
| 6
| 41
| 5.166667
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.195122
| 41
| 2
| 21
| 20.5
| 0.939394
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 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
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
aa36919f75e56ab26ed3310aebfc87ae676cbbb1
| 93
|
py
|
Python
|
app_routes/threads/applications/application_id/__init__.py
|
kskarbinski/threads-api
|
c144c1cb51422095922310d278f80e4996c10ea0
|
[
"MIT"
] | null | null | null |
app_routes/threads/applications/application_id/__init__.py
|
kskarbinski/threads-api
|
c144c1cb51422095922310d278f80e4996c10ea0
|
[
"MIT"
] | null | null | null |
app_routes/threads/applications/application_id/__init__.py
|
kskarbinski/threads-api
|
c144c1cb51422095922310d278f80e4996c10ea0
|
[
"MIT"
] | null | null | null |
from .threads_applications_application_id_route import ThreadsApplicationsApplicationIdRoute
| 46.5
| 92
| 0.946237
| 8
| 93
| 10.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.043011
| 93
| 1
| 93
| 93
| 0.94382
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 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
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
aa43e9ebc0154242e5fbad59c31b18d5ce2f6b44
| 107
|
py
|
Python
|
tfquery/__init__.py
|
mazen160/tfquery
|
ddc145d7bf04f002c8ea49079fbc688106fc1485
|
[
"MIT"
] | 262
|
2021-04-28T11:38:53.000Z
|
2022-03-16T12:31:14.000Z
|
tfquery/__init__.py
|
mazen160/tfquery
|
ddc145d7bf04f002c8ea49079fbc688106fc1485
|
[
"MIT"
] | 2
|
2021-05-12T07:30:25.000Z
|
2021-09-19T11:29:22.000Z
|
tfquery/__init__.py
|
mazen160/tfquery
|
ddc145d7bf04f002c8ea49079fbc688106fc1485
|
[
"MIT"
] | 12
|
2021-04-28T17:15:08.000Z
|
2022-02-12T10:30:45.000Z
|
import tfquery.tfstate
import tfquery.sql_handler
import tfquery.tfstate_v3_migration
import tfquery.utils
| 21.4
| 35
| 0.88785
| 15
| 107
| 6.133333
| 0.533333
| 0.565217
| 0.434783
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.010101
| 0.074766
| 107
| 4
| 36
| 26.75
| 0.919192
| 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
| 0
| 0
|
0
| 6
|
a4ab1c633e82fd238750d2b31a8147783e37d999
| 149
|
py
|
Python
|
src/Diagnostics.AIProjects/TrainingAPI/__app__/TrainingModule/Exceptions.py
|
prbajrac/Azure-AppServices-Diagnostics
|
ba66a7f40b6ec1f4ec388e7b07820607944a998d
|
[
"MIT"
] | null | null | null |
src/Diagnostics.AIProjects/TrainingAPI/__app__/TrainingModule/Exceptions.py
|
prbajrac/Azure-AppServices-Diagnostics
|
ba66a7f40b6ec1f4ec388e7b07820607944a998d
|
[
"MIT"
] | null | null | null |
src/Diagnostics.AIProjects/TrainingAPI/__app__/TrainingModule/Exceptions.py
|
prbajrac/Azure-AppServices-Diagnostics
|
ba66a7f40b6ec1f4ec388e7b07820607944a998d
|
[
"MIT"
] | 1
|
2019-12-16T07:08:12.000Z
|
2019-12-16T07:08:12.000Z
|
class TrainingException(Exception):
pass
class PublishingException(Exception):
pass
class ResourceConfigDownloadFailed(Exception):
pass
| 18.625
| 46
| 0.791946
| 12
| 149
| 9.833333
| 0.5
| 0.330508
| 0.305085
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.147651
| 149
| 8
| 47
| 18.625
| 0.929134
| 0
| 0
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.5
| 0
| 0
| 0.5
| 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
| 0
| 0
| 0
| 0
|
0
| 6
|
a4fd0b3e5ce2d5d894105faa5edf53ac5c033337
| 3,755
|
py
|
Python
|
tests/time_series_image_test.py
|
AeRabelais/DeepBench
|
ddb95b394eae381f60b031572cb79fac6418376f
|
[
"Apache-2.0"
] | null | null | null |
tests/time_series_image_test.py
|
AeRabelais/DeepBench
|
ddb95b394eae381f60b031572cb79fac6418376f
|
[
"Apache-2.0"
] | 1
|
2022-03-17T19:09:41.000Z
|
2022-03-17T19:09:41.000Z
|
tests/time_series_image_test.py
|
AeRabelais/DeepBenchmark
|
ddb95b394eae381f60b031572cb79fac6418376f
|
[
"Apache-2.0"
] | null | null | null |
from unittest import TestCase
from src.deepbench.image.time_series_image import TimeSeriesImage
class TestTimeSeriesImage(TestCase):
def test_1d_init(self):
with self.assertRaises(AssertionError):
image_shape = (12,)
object_params = [{"object_type": "test_object"}]
TimeSeriesImage(object_params, image_shape)
def test_2d_init(self):
image_shape = (12, 12)
object_params = [{"object_type": "test_object"}]
time_series = TimeSeriesImage(object_params, image_shape)
self.assertEqual(image_shape, time_series.image_shape)
def test_3d_init(self):
image_shape = (12, 12, 3)
object_params = [{"object_type": "test_object"}]
time_series = TimeSeriesImage(object_params, image_shape)
self.assertEqual(image_shape, time_series.image_shape)
def test_0d_init(self):
with self.assertRaises(AssertionError):
image_shape = ()
object_params = [{"object_type": "test_object"}]
TimeSeriesImage(object_params, image_shape)
def test_no_object_init(self):
with self.assertRaises(AssertionError):
image_shape = (12, 12)
object_params = []
TimeSeriesImage(object_params, image_shape)
def test_one_object_init(self):
image_shape = (12, 12)
object_params = [{"object_type": "test_object"}]
time_series = TimeSeriesImage(object_params, image_shape)
self.assertEqual(image_shape, time_series.image_shape)
self.assertEqual(object_params, time_series.objects)
def test_two_object_init(self):
with self.assertRaises(AssertionError):
image_shape = (12, 12)
object_params = [
{"object_type": "test_object"},
[{"object_type": "test_object"}],
]
TimeSeriesImage(object_params, image_shape)
def test_combine_one_object(self):
image_shape = (12, 12)
object_params = [{"object_type": "test_object"}]
time_series = TimeSeriesImage(object_params, image_shape)
time_series.combine_objects()
self.assertEqual(image_shape, time_series.image.shape)
def test_generate_gaussian_noise(self):
object_params = [{"object_type": "test_object"}]
image_shape = (14, 14)
one_image_sky = TimeSeriesImage(object_params, image_shape)
one_image_sky.combine_objects()
one_image_sky.generate_noise("gaussian")
self.assertIsNotNone(one_image_sky.image)
self.assertEqual(image_shape, one_image_sky.image.shape)
def test_generate_poisson_noise(self):
object_params = [{"object_type": "test_object"}]
image_shape = (14, 14)
one_image_sky = TimeSeriesImage(object_params, image_shape)
one_image_sky.combine_objects()
one_image_sky.generate_noise("poisson")
self.assertIsNotNone(one_image_sky.image)
self.assertEqual(image_shape, one_image_sky.image.shape)
def test_add_fake_noise(self):
with self.assertRaises(NotImplementedError):
object_params = [{"object_type": "test_object"}]
image_shape = (14, 14)
one_image_sky = TimeSeriesImage(object_params, image_shape)
one_image_sky.combine_objects()
one_image_sky.generate_noise("Fake Noise")
def test_image_not_made(self):
with self.assertRaises(AssertionError):
object_params = [{"object_type": "test_object"}]
image_shape = (14, 14)
one_image_sky = TimeSeriesImage(object_params, image_shape)
##Go straight to noise instead of making objects first
one_image_sky.generate_noise("gaussian")
| 37.929293
| 71
| 0.664447
| 428
| 3,755
| 5.446262
| 0.126168
| 0.15444
| 0.070785
| 0.10296
| 0.834835
| 0.811669
| 0.785071
| 0.766195
| 0.743887
| 0.720721
| 0
| 0.016451
| 0.239148
| 3,755
| 98
| 72
| 38.316327
| 0.79944
| 0.013848
| 0
| 0.623377
| 0
| 0
| 0.080249
| 0
| 0
| 0
| 0
| 0
| 0.194805
| 1
| 0.155844
| false
| 0
| 0.025974
| 0
| 0.194805
| 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
| 0
| 0
|
0
| 6
|
352daed1b4556a28e943fc7c3ffe93fb18304c40
| 147
|
py
|
Python
|
teacher/metrics/__init__.py
|
Kaysera/fuzzy-lore
|
128131e0f41f480d509b63c5e75d0ce58f07bae4
|
[
"MIT"
] | 3
|
2022-03-09T16:54:02.000Z
|
2022-03-10T13:28:31.000Z
|
teacher/metrics/__init__.py
|
Kaysera/fuzzy-lore
|
128131e0f41f480d509b63c5e75d0ce58f07bae4
|
[
"MIT"
] | 1
|
2022-03-17T16:30:02.000Z
|
2022-03-24T17:54:08.000Z
|
teacher/metrics/__init__.py
|
Kaysera/fuzzy-lore
|
128131e0f41f480d509b63c5e75d0ce58f07bae4
|
[
"MIT"
] | null | null | null |
from .rule import coverage, precision, fidelity, rule_fidelity
__all__ = [
"coverage",
"precision",
"fidelity",
"rule_fidelity"
]
| 16.333333
| 62
| 0.659864
| 14
| 147
| 6.5
| 0.5
| 0.373626
| 0.549451
| 0.637363
| 0.813187
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.217687
| 147
| 8
| 63
| 18.375
| 0.791304
| 0
| 0
| 0
| 0
| 0
| 0.258503
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.142857
| 0
| 0.142857
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
1020223877cb8dabf05806c36d92071551102f3f
| 105
|
py
|
Python
|
fs/__init__.py
|
Stift007/python-fs
|
aac31ab4dbcd659bb1451ec8ea2132254585a3fb
|
[
"MIT"
] | null | null | null |
fs/__init__.py
|
Stift007/python-fs
|
aac31ab4dbcd659bb1451ec8ea2132254585a3fb
|
[
"MIT"
] | null | null | null |
fs/__init__.py
|
Stift007/python-fs
|
aac31ab4dbcd659bb1451ec8ea2132254585a3fb
|
[
"MIT"
] | null | null | null |
from fs import readFile,writeFile,readFileSync,writeFileSync,readDir,readDirSync,readDirString,move,copy
| 52.5
| 104
| 0.885714
| 12
| 105
| 7.75
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.038095
| 105
| 1
| 105
| 105
| 0.920792
| 0
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| 0
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| 0
| true
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
102ad9e69f291b3b78691c8b5f28d8da71dae4a1
| 26,838
|
py
|
Python
|
chunair/kicad-footprint-generator-master/KicadModTree/tests/moduletests/test_kicad5_padshapes.py
|
speedypotato/chuni-lite
|
c8dda8428723f8c4f99075e7cbaa22a44cbc187d
|
[
"CC-BY-4.0"
] | 2
|
2022-03-18T23:42:51.000Z
|
2022-03-19T15:31:34.000Z
|
chunair/kicad-footprint-generator-master/KicadModTree/tests/moduletests/test_kicad5_padshapes.py
|
speedypotato/chuni-lite
|
c8dda8428723f8c4f99075e7cbaa22a44cbc187d
|
[
"CC-BY-4.0"
] | null | null | null |
chunair/kicad-footprint-generator-master/KicadModTree/tests/moduletests/test_kicad5_padshapes.py
|
speedypotato/chuni-lite
|
c8dda8428723f8c4f99075e7cbaa22a44cbc187d
|
[
"CC-BY-4.0"
] | null | null | null |
# KicadModTree is free software: you can redistribute it and/or
# modify it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# KicadModTree is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with kicad-footprint-generator. If not, see < http://www.gnu.org/licenses/ >.
#
# (C) 2018 by Thomas Pointhuber, <thomas.pointhuber@gmx.at>
# (C) 2018 by Rene Poeschl, github @poeschlr
from __future__ import division
import unittest
from KicadModTree import *
RESULT_ROUNDRECT_FP = """(module round_rect_test (layer F.Cu) (tedit 0)
(descr "A example footprint")
(tags example)
(fp_text reference REF** (at 0 0) (layer F.SilkS)
(effects (font (size 1 1) (thickness 0.15)))
)
(fp_text value round_rect_test (at 0 0) (layer F.Fab)
(effects (font (size 1 1) (thickness 0.15)))
)
(pad 3 smd roundrect (at 5 0 45) (size 1 1) (layers F.Cu F.Mask F.Paste) (roundrect_rratio 0.1))
(pad 2 smd roundrect (at -5 0) (size 1 1) (layers F.Cu F.Mask F.Paste) (roundrect_rratio 0.5))
(pad 1 smd rect (at 0 0) (size 1 1) (layers F.Cu F.Mask F.Paste))
)"""
RESULT_ROUNDRECT_FP2 = """(module round_rect_test (layer F.Cu) (tedit 0)
(descr "A example footprint")
(tags example)
(fp_text reference REF** (at 0 0) (layer F.SilkS)
(effects (font (size 1 1) (thickness 0.15)))
)
(fp_text value round_rect_test (at 0 0) (layer F.Fab)
(effects (font (size 1 1) (thickness 0.15)))
)
(pad 3 smd roundrect (at 5 0 45) (size 1 1) (layers F.Cu F.Mask F.Paste) (roundrect_rratio 0.25))
(pad 2 smd roundrect (at -5 0) (size 1 2) (layers F.Cu F.Mask F.Paste) (roundrect_rratio 0.25))
(pad 1 smd roundrect (at 0 0) (size 2 4) (layers F.Cu F.Mask F.Paste) (roundrect_rratio 0.125))
)"""
RESULT_SIMPLE_POLYGON_PAD = """(module round_rect_test (layer F.Cu) (tedit 0)
(descr "A example footprint")
(tags example)
(fp_text reference REF** (at 0 0) (layer F.SilkS)
(effects (font (size 1 1) (thickness 0.15)))
)
(fp_text value round_rect_test (at 0 0) (layer F.Fab)
(effects (font (size 1 1) (thickness 0.15)))
)
(pad 1 smd custom (at 0 0) (size 1 1) (layers F.Cu F.Mask F.Paste)
(options (clearance outline) (anchor circle))
(primitives
(gr_poly (pts
(xy -1 -1) (xy 2 -1) (xy 1 1) (xy -1 2)) (width 0))
))
)"""
RESULT_SIMPLE_OTHER_CUSTOM_PAD = """(module round_rect_test (layer F.Cu) (tedit 0)
(descr "A example footprint")
(tags example)
(fp_text reference REF** (at 0 0) (layer F.SilkS)
(effects (font (size 1 1) (thickness 0.15)))
)
(fp_text value round_rect_test (at 0 0) (layer F.Fab)
(effects (font (size 1 1) (thickness 0.15)))
)
(pad 1 smd custom (at 0 0) (size 1 1) (layers F.Cu F.Mask F.Paste)
(options (clearance outline) (anchor circle))
(primitives
(gr_arc (start -1 0) (end -1 -0.5) (angle -180) (width 0.15))
(gr_line (start -1 -0.5) (end 1.25 -0.5) (width 0.15))
(gr_line (start 1.25 -0.5) (end 1.25 0.5) (width 0.15))
(gr_line (start 1.25 0.5) (end -1 0.5) (width 0.15))
))
(pad 2 smd custom (at 0 3) (size 1 1) (layers F.Cu F.Mask F.Paste)
(options (clearance outline) (anchor circle))
(primitives
(gr_arc (start -1 0) (end -1 -0.5) (angle -180) (width 0.15))
(gr_line (start -1 -0.5) (end 1.25 -0.5) (width 0.15))
(gr_line (start 1.25 -0.5) (end 1.25 0.5) (width 0.15))
(gr_line (start 1.25 0.5) (end -1 0.5) (width 0.15))
))
(pad 3 smd custom (at 0 -3) (size 1 1) (layers F.Cu F.Mask F.Paste)
(options (clearance outline) (anchor circle))
(primitives
(gr_circle (center 0.5 0.5) (end 1 0.5) (width 0.15))
))
)"""
RESULT_CUT_POLYGON = """(module round_rect_test (layer F.Cu) (tedit 0)
(descr "A example footprint")
(tags example)
(fp_text reference REF** (at 0 0) (layer F.SilkS)
(effects (font (size 1 1) (thickness 0.15)))
)
(fp_text value round_rect_test (at 0 0) (layer F.Fab)
(effects (font (size 1 1) (thickness 0.15)))
)
(pad 1 smd custom (at 0 0) (size 0.5 0.5) (layers F.Cu F.Mask F.Paste)
(options (clearance outline) (anchor circle))
(primitives
(gr_poly (pts
(xy -2 -2) (xy 2 -2) (xy 2 2) (xy 1 1)
(xy 1 0) (xy 0 0) (xy 0 1) (xy 1 1)
(xy 2 2) (xy -2 2)) (width 0))
))
)"""
RESULT_CHAMFERED_PAD = """(module chamfered_pad (layer F.Cu) (tedit 0)
(descr "A example footprint")
(tags example)
(fp_text reference REF** (at 0 0) (layer F.SilkS)
(effects (font (size 1 1) (thickness 0.15)))
)
(fp_text value chamfered_pad (at 0 0) (layer F.Fab)
(effects (font (size 1 1) (thickness 0.15)))
)
(pad 1 smd custom (at 0 0) (size 0.764298 0.764298) (layers F.Cu F.Mask F.Paste)
(options (clearance outline) (anchor circle))
(primitives
(gr_poly (pts
(xy -0.5 -0.166667) (xy -0.166667 -0.5) (xy 0.166667 -0.5) (xy 0.5 -0.166667)
(xy 0.5 0.166667) (xy 0.166667 0.5) (xy -0.166667 0.5) (xy -0.5 0.166667)) (width 0))
))
(pad 1 smd custom (at 2 2) (size 1.357538 1.357538) (layers F.Cu F.Mask F.Paste)
(options (clearance outline) (anchor circle))
(primitives
(gr_poly (pts
(xy -1.05 -0.5) (xy -0.55 -1.55) (xy 0.55 -1.55) (xy 1.05 -0.5)
(xy 1.05 0.5) (xy 0.55 1.55) (xy -0.55 1.55) (xy -1.05 0.5)) (width 0))
))
)"""
RESULT_CHAMFERED_PAD_AVOID_CIRCLE = """(module test_avoid_circle (layer F.Cu) (tedit 0)
(descr "A example footprint")
(tags example)
(fp_text reference REF** (at 0 0) (layer F.SilkS)
(effects (font (size 1 1) (thickness 0.15)))
)
(fp_text value test_avoid_circle (at 0 0) (layer F.Fab)
(effects (font (size 1 1) (thickness 0.15)))
)
(fp_circle (center 3 3.5) (end 3.3 3.5) (layer F.SilkS) (width 0.01))
(pad 1 smd custom (at 2 2.5) (size 1.445 1.445) (layers F.Cu F.Mask F.Paste)
(options (clearance outline) (anchor circle))
(primitives
(gr_poly (pts
(xy -0.875 -0.693665) (xy -0.443665 -1.125) (xy 0.443665 -1.125) (xy 0.875 -0.693665)
(xy 0.875 0.693665) (xy 0.443665 1.125) (xy -0.443665 1.125) (xy -0.875 0.693665)) (width 0))
))
)"""
RESULT_CHAMFERED_PAD_GRID = """(module test_chamfered_grid (layer F.Cu) (tedit 0)
(descr "A example footprint")
(tags example)
(fp_text reference REF** (at 0 0) (layer F.SilkS)
(effects (font (size 1 1) (thickness 0.15)))
)
(fp_text value test_chamfered_grid (at 0 0) (layer F.Fab)
(effects (font (size 1 1) (thickness 0.15)))
)
(pad 1 smd custom (at 0 -1.25) (size 0.823223 0.823223) (layers F.Cu F.Mask F.Paste)
(options (clearance outline) (anchor circle))
(primitives
(gr_poly (pts
(xy -0.5 -0.75) (xy -0.25 -1) (xy 0.5 -1) (xy 0.5 1)
(xy -0.25 1) (xy -0.5 0.75)) (width 0))
))
(pad 1 smd custom (at 0 1.25) (size 0.823223 0.823223) (layers F.Cu F.Mask F.Paste)
(options (clearance outline) (anchor circle))
(primitives
(gr_poly (pts
(xy -0.5 -0.75) (xy -0.25 -1) (xy 0.5 -1) (xy 0.5 1)
(xy -0.25 1) (xy -0.5 0.75)) (width 0))
))
(pad 1 smd custom (at 0 3.75) (size 0.823223 0.823223) (layers F.Cu F.Mask F.Paste)
(options (clearance outline) (anchor circle))
(primitives
(gr_poly (pts
(xy -0.5 -0.75) (xy -0.25 -1) (xy 0.5 -1) (xy 0.5 1)
(xy -0.25 1) (xy -0.5 0.75)) (width 0))
))
(pad 1 smd custom (at 0 6.25) (size 0.823223 0.823223) (layers F.Cu F.Mask F.Paste)
(options (clearance outline) (anchor circle))
(primitives
(gr_poly (pts
(xy -0.5 -0.75) (xy -0.25 -1) (xy 0.5 -1) (xy 0.5 1)
(xy -0.25 1) (xy -0.5 0.75)) (width 0))
))
(pad 1 smd custom (at 1.5 -1.25) (size 0.823223 0.823223) (layers F.Cu F.Mask F.Paste)
(options (clearance outline) (anchor circle))
(primitives
(gr_poly (pts
(xy -0.5 -0.75) (xy -0.25 -1) (xy 0.25 -1) (xy 0.5 -0.75)
(xy 0.5 1) (xy -0.5 1)) (width 0))
))
(pad 1 smd rect (at 1.5 1.25) (size 1 2) (layers F.Cu F.Mask F.Paste))
(pad 1 smd rect (at 1.5 3.75) (size 1 2) (layers F.Cu F.Mask F.Paste))
(pad 1 smd custom (at 1.5 6.25) (size 0.823223 0.823223) (layers F.Cu F.Mask F.Paste)
(options (clearance outline) (anchor circle))
(primitives
(gr_poly (pts
(xy -0.5 -1) (xy 0.5 -1) (xy 0.5 0.75) (xy 0.25 1)
(xy -0.25 1) (xy -0.5 0.75)) (width 0))
))
(pad 1 smd custom (at 3 -1.25) (size 0.823223 0.823223) (layers F.Cu F.Mask F.Paste)
(options (clearance outline) (anchor circle))
(primitives
(gr_poly (pts
(xy -0.5 -1) (xy 0.25 -1) (xy 0.5 -0.75) (xy 0.5 0.75)
(xy 0.25 1) (xy -0.5 1)) (width 0))
))
(pad 1 smd custom (at 3 1.25) (size 0.823223 0.823223) (layers F.Cu F.Mask F.Paste)
(options (clearance outline) (anchor circle))
(primitives
(gr_poly (pts
(xy -0.5 -1) (xy 0.25 -1) (xy 0.5 -0.75) (xy 0.5 0.75)
(xy 0.25 1) (xy -0.5 1)) (width 0))
))
(pad 1 smd custom (at 3 3.75) (size 0.823223 0.823223) (layers F.Cu F.Mask F.Paste)
(options (clearance outline) (anchor circle))
(primitives
(gr_poly (pts
(xy -0.5 -1) (xy 0.25 -1) (xy 0.5 -0.75) (xy 0.5 0.75)
(xy 0.25 1) (xy -0.5 1)) (width 0))
))
(pad 1 smd custom (at 3 6.25) (size 0.823223 0.823223) (layers F.Cu F.Mask F.Paste)
(options (clearance outline) (anchor circle))
(primitives
(gr_poly (pts
(xy -0.5 -1) (xy 0.25 -1) (xy 0.5 -0.75) (xy 0.5 0.75)
(xy 0.25 1) (xy -0.5 1)) (width 0))
))
)"""
RESULT_CHAMFERED_PAD_GRID_AVOID_CIRCLE = """(module test_chamfered_grid (layer F.Cu) (tedit 0)
(descr "A example footprint")
(tags example)
(fp_text reference REF** (at 0 0) (layer F.SilkS)
(effects (font (size 1 1) (thickness 0.15)))
)
(fp_text value test_chamfered_grid (at 0 0) (layer F.Fab)
(effects (font (size 1 1) (thickness 0.15)))
)
(fp_circle (center 2 2.5) (end 2.2 2.5) (layer F.SilkS) (width 0.01))
(pad 1 smd custom (at -1.4 -2.1) (size 0.795 0.795) (layers F.Cu F.Mask F.Paste)
(options (clearance outline) (anchor circle))
(primitives
(gr_poly (pts
(xy -0.5 -0.210086) (xy -0.210086 -0.5) (xy 0.5 -0.5) (xy 0.5 0.5)
(xy -0.5 0.5)) (width 0))
))
(pad 1 smd rect (at -1.4 -0.7) (size 1 1) (layers F.Cu F.Mask F.Paste))
(pad 1 smd rect (at -1.4 0.7) (size 1 1) (layers F.Cu F.Mask F.Paste))
(pad 1 smd custom (at -1.4 2.1) (size 0.795 0.795) (layers F.Cu F.Mask F.Paste)
(options (clearance outline) (anchor circle))
(primitives
(gr_poly (pts
(xy -0.5 -0.5) (xy 0.5 -0.5) (xy 0.5 0.5) (xy -0.210086 0.5)
(xy -0.5 0.210086)) (width 0))
))
(pad 1 smd rect (at 0 -2.1) (size 1 1) (layers F.Cu F.Mask F.Paste))
(pad 1 smd rect (at 0 -0.7) (size 1 1) (layers F.Cu F.Mask F.Paste))
(pad 1 smd rect (at 0 0.7) (size 1 1) (layers F.Cu F.Mask F.Paste))
(pad 1 smd rect (at 0 2.1) (size 1 1) (layers F.Cu F.Mask F.Paste))
(pad 1 smd custom (at 1.4 -2.1) (size 0.795 0.795) (layers F.Cu F.Mask F.Paste)
(options (clearance outline) (anchor circle))
(primitives
(gr_poly (pts
(xy -0.5 -0.5) (xy 0.210086 -0.5) (xy 0.5 -0.210086) (xy 0.5 0.5)
(xy -0.5 0.5)) (width 0))
))
(pad 1 smd rect (at 1.4 -0.7) (size 1 1) (layers F.Cu F.Mask F.Paste))
(pad 1 smd rect (at 1.4 0.7) (size 1 1) (layers F.Cu F.Mask F.Paste))
(pad 1 smd custom (at 1.4 2.1) (size 0.795 0.795) (layers F.Cu F.Mask F.Paste)
(options (clearance outline) (anchor circle))
(primitives
(gr_poly (pts
(xy -0.5 -0.5) (xy 0.5 -0.5) (xy 0.5 0.210086) (xy 0.210086 0.5)
(xy -0.5 0.5)) (width 0))
))
)"""
RESULT_CHAMFERED_ROUNDED_PAD = """(module chamfered_pad (layer F.Cu) (tedit 0)
(descr "A example footprint")
(tags example)
(fp_text reference REF** (at 0 0) (layer F.SilkS)
(effects (font (size 1 1) (thickness 0.15)))
)
(fp_text value chamfered_pad (at 0 0) (layer F.Fab)
(effects (font (size 1 1) (thickness 0.15)))
)
(pad 1 smd custom (at 0 0) (size 3.646447 3.646447) (layers F.Cu F.Mask F.Paste)
(options (clearance outline) (anchor circle))
(primitives
(gr_poly (pts
(xy -2 -1.5) (xy -1.5 -2) (xy 1.5 -2) (xy 2 -1.5)
(xy 2 1.5) (xy 1.5 2) (xy -1.5 2) (xy -2 1.5)) (width 0))
))
(pad 1 smd roundrect (at 0 0) (size 4 4) (layers B.Cu) (roundrect_rratio 0.25))
(pad 1 smd custom (at 0 5) (size 2.292893 2.292893) (layers F.Cu F.Mask F.Paste)
(options (clearance outline) (anchor circle))
(primitives
(gr_poly (pts
(xy -2 -0.5) (xy -1 -1.5) (xy 1 -1.5) (xy 2 -0.5)
(xy 2 0.5) (xy 1 1.5) (xy -1 1.5) (xy -2 0.5)) (width 0))
))
(pad 1 smd custom (at 0 5) (size 2.292893 2.292893) (layers B.Cu)
(options (clearance outline) (anchor circle))
(primitives
(gr_poly (pts
(xy -1.292893 -0.207107) (xy -0.707107 -0.792893) (xy 0.707107 -0.792893) (xy 1.292893 -0.207107)
(xy 1.292893 0.207107) (xy 0.707107 0.792893) (xy -0.707107 0.792893) (xy -1.292893 0.207107)) (width 1.414214))
))
(pad 1 smd custom (at 5 0) (size 2.292893 2.292893) (layers F.Cu F.Mask F.Paste)
(options (clearance outline) (anchor circle))
(primitives
(gr_poly (pts
(xy -2 -0.5) (xy -1 -1.5) (xy 2 -1.5) (xy 2 0.5)
(xy 1 1.5) (xy -2 1.5)) (width 0))
))
(pad 1 smd custom (at 5 0) (size 2.292893 2.292893) (layers B.Cu)
(options (clearance outline) (anchor circle))
(primitives
(gr_poly (pts
(xy -1.292893 -0.207107) (xy -0.707107 -0.792893) (xy 1.292893 -0.792893) (xy 1.292893 0.207107)
(xy 0.707107 0.792893) (xy -1.292893 0.792893)) (width 1.414214))
))
)""" # NOQA: E501
class Kicad5PadsTests(unittest.TestCase):
def testRoundRectPad(self):
kicad_mod = Footprint("round_rect_test")
kicad_mod.setDescription("A example footprint")
kicad_mod.setTags("example")
kicad_mod.append(Text(type='reference', text='REF**', at=[0, 0], layer='F.SilkS'))
kicad_mod.append(Text(type='value', text="round_rect_test", at=[0, 0], layer='F.Fab'))
kicad_mod.append(Pad(number=3, type=Pad.TYPE_SMT, shape=Pad.SHAPE_ROUNDRECT,
at=[5, 0], rotation=45, size=[1, 1], layers=Pad.LAYERS_SMT,
radius_ratio=0.1))
kicad_mod.append(Pad(number=2, type=Pad.TYPE_SMT, shape=Pad.SHAPE_ROUNDRECT,
at=[-5, 0], size=[1, 1], layers=Pad.LAYERS_SMT,
radius_ratio=0.5))
kicad_mod.append(Pad(number=1, type=Pad.TYPE_SMT, shape=Pad.SHAPE_ROUNDRECT,
at=[0, 0], size=[1, 1], layers=Pad.LAYERS_SMT,
radius_ratio=0))
file_handler = KicadFileHandler(kicad_mod)
result = file_handler.serialize(timestamp=0)
# file_handler.writeFile('test.kicad_mod')
self.assertEqual(result, RESULT_ROUNDRECT_FP)
def testRoundRectPad2(self):
kicad_mod = Footprint("round_rect_test")
kicad_mod.setDescription("A example footprint")
kicad_mod.setTags("example")
kicad_mod.append(Text(type='reference', text='REF**', at=[0, 0], layer='F.SilkS'))
kicad_mod.append(Text(type='value', text="round_rect_test", at=[0, 0], layer='F.Fab'))
kicad_mod.append(Pad(number=3, type=Pad.TYPE_SMT, shape=Pad.SHAPE_ROUNDRECT,
at=[5, 0], rotation=45, size=[1, 1], layers=Pad.LAYERS_SMT,
radius_ratio=0.25, maximum_radius=0.25))
kicad_mod.append(Pad(number=2, type=Pad.TYPE_SMT, shape=Pad.SHAPE_ROUNDRECT,
at=[-5, 0], size=[1, 2], layers=Pad.LAYERS_SMT,
radius_ratio=0.25, maximum_radius=0.25))
kicad_mod.append(Pad(number=1, type=Pad.TYPE_SMT, shape=Pad.SHAPE_ROUNDRECT,
at=[0, 0], size=[2, 4], layers=Pad.LAYERS_SMT,
radius_ratio=0.25, maximum_radius=0.25))
file_handler = KicadFileHandler(kicad_mod)
result = file_handler.serialize(timestamp=0)
# file_handler.writeFile('test_max_radius.kicad_mod')
self.assertEqual(result, RESULT_ROUNDRECT_FP2)
def testPolygonPad(self):
kicad_mod = Footprint("round_rect_test")
kicad_mod.setDescription("A example footprint")
kicad_mod.setTags("example")
kicad_mod.append(Text(type='reference', text='REF**', at=[0, 0], layer='F.SilkS'))
kicad_mod.append(Text(type='value', text="round_rect_test", at=[0, 0], layer='F.Fab'))
kicad_mod.append(Pad(number=1, type=Pad.TYPE_SMT, shape=Pad.SHAPE_CUSTOM,
at=[0, 0], size=[1, 1], layers=Pad.LAYERS_SMT,
primitives=[Polygon(nodes=[(-1, -1), (2, -1), (1, 1), (-1, 2)])]
))
file_handler = KicadFileHandler(kicad_mod)
result = file_handler.serialize(timestamp=0)
# file_handler.writeFile('test.kicad_mod')
self.assertEqual(result, RESULT_SIMPLE_POLYGON_PAD)
def testCustomPadOtherPrimitives(self):
kicad_mod = Footprint("round_rect_test")
kicad_mod.setDescription("A example footprint")
kicad_mod.setTags("example")
kicad_mod.append(Text(type='reference', text='REF**', at=[0, 0], layer='F.SilkS'))
kicad_mod.append(Text(type='value', text="round_rect_test", at=[0, 0], layer='F.Fab'))
kicad_mod.append(
Pad(number=1, type=Pad.TYPE_SMT, shape=Pad.SHAPE_CUSTOM,
at=[0, 0], size=[1, 1], layers=Pad.LAYERS_SMT,
primitives=[
Arc(center=(-1, 0), start=(-1, -0.5), angle=-180, width=0.15),
Line(start=(-1, -0.5), end=(1.25, -0.5), width=0.15),
Line(start=(1.25, -0.5), end=(1.25, 0.5), width=0.15),
Line(start=(1.25, 0.5), end=(-1, 0.5), width=0.15)
]
))
kicad_mod.append(
Pad(number=2, type=Pad.TYPE_SMT, shape=Pad.SHAPE_CUSTOM,
at=[0, 3], size=[1, 1], layers=Pad.LAYERS_SMT,
primitives=[
Arc(center=(-1, 0), start=(-1, -0.5), angle=-180, width=0.15),
PolygoneLine(nodes=[(-1, -0.5), (1.25, -0.5), (1.25, 0.5), (-1, 0.5)], width=0.15)
]
))
kicad_mod.append(
Pad(number=3, type=Pad.TYPE_SMT, shape=Pad.SHAPE_CUSTOM,
at=[0, -3], size=[1, 1], layers=Pad.LAYERS_SMT,
primitives=[
Circle(center=(0.5, 0.5), radius=0.5, width=0.15)
]
))
file_handler = KicadFileHandler(kicad_mod)
result = file_handler.serialize(timestamp=0)
# file_handler.writeFile('test.kicad_mod')
self.assertEqual(result, RESULT_SIMPLE_OTHER_CUSTOM_PAD)
def testCutPolygon(self):
kicad_mod = Footprint("round_rect_test")
kicad_mod.setDescription("A example footprint")
kicad_mod.setTags("example")
kicad_mod.append(Text(type='reference', text='REF**', at=[0, 0], layer='F.SilkS'))
kicad_mod.append(Text(type='value', text="round_rect_test", at=[0, 0], layer='F.Fab'))
p1 = Polygon(nodes=[(0, 0), (1, 0), (1, 1), (0, 1)])
p2 = Polygon(nodes=[(-2, -2), (2, -2), (2, 2), (-2, 2)])
p2.cut(p1)
kicad_mod.append(Pad(number=1, type=Pad.TYPE_SMT, shape=Pad.SHAPE_CUSTOM,
at=[0, 0], size=[0.5, 0.5], layers=Pad.LAYERS_SMT,
primitives=[p2]
))
file_handler = KicadFileHandler(kicad_mod)
result = file_handler.serialize(timestamp=0)
# file_handler.writeFile('test.kicad_mod')
self.assertEqual(result, RESULT_CUT_POLYGON)
def testChamferedPad(self):
kicad_mod = Footprint("chamfered_pad")
kicad_mod.setDescription("A example footprint")
kicad_mod.setTags("example")
kicad_mod.append(Text(type='reference', text='REF**', at=[0, 0], layer='F.SilkS'))
kicad_mod.append(Text(type='value', text="chamfered_pad", at=[0, 0], layer='F.Fab'))
kicad_mod.append(
ChamferedPad(number=1, type=Pad.TYPE_SMT,
at=[0, 0], size=[1, 1], layers=Pad.LAYERS_SMT, chamfer_size=[1/3, 1/3],
corner_selection=[1, 1, 1, 1]
))
kicad_mod.append(
ChamferedPad(number=1, type=Pad.TYPE_SMT,
at=[2, 2], size=[2.1, 3.1], layers=Pad.LAYERS_SMT, chamfer_size=[0.5, 1.05],
corner_selection=[1, 1, 1, 1]
))
file_handler = KicadFileHandler(kicad_mod)
result = file_handler.serialize(timestamp=0)
# file_handler.writeFile('test_cp.kicad_mod')
self.assertEqual(result, RESULT_CHAMFERED_PAD)
def testChamferedPadAvoidCircle(self):
kicad_mod = Footprint("test_avoid_circle")
kicad_mod.setDescription("A example footprint")
kicad_mod.setTags("example")
kicad_mod.append(Text(type='reference', text='REF**', at=[0, 0], layer='F.SilkS'))
kicad_mod.append(Text(type='value', text="test_avoid_circle", at=[0, 0], layer='F.Fab'))
pad = ChamferedPad(
number=1, type=Pad.TYPE_SMT, at=[2, 2.5],
size=[1.75, 2.25], layers=Pad.LAYERS_SMT, chamfer_size=[0.25, 0.25],
corner_selection=[1, 1, 1, 1]
)
c = [3, 3.5]
d = 0.6
kicad_mod.append(Circle(center=c, radius=d/2, width=0.01))
pad.chamferAvoidCircle(center=c, diameter=d, clearance=0.005)
kicad_mod.append(pad)
file_handler = KicadFileHandler(kicad_mod)
result = file_handler.serialize(timestamp=0)
# file_handler.writeFile('test_avoid_circle.kicad_mod')
self.assertEqual(result, RESULT_CHAMFERED_PAD_AVOID_CIRCLE)
def testChamferedPadGrid(self):
kicad_mod = Footprint("test_chamfered_grid")
kicad_mod.setDescription("A example footprint")
kicad_mod.setTags("example")
kicad_mod.append(Text(type='reference', text='REF**', at=[0, 0], layer='F.SilkS'))
kicad_mod.append(Text(type='value', text="test_chamfered_grid", at=[0, 0], layer='F.Fab'))
kicad_mod.append(
ChamferedPadGrid(
number=1, type=Pad.TYPE_SMT,
center=[1.5, 2.5], size=[1, 2], layers=Pad.LAYERS_SMT,
chamfer_size=[0.25, 0.25], chamfer_selection=1,
pincount=[3, 4], grid=[1.5, 2.5]
))
file_handler = KicadFileHandler(kicad_mod)
result = file_handler.serialize(timestamp=0)
# file_handler.writeFile('test_chamfered_grid.kicad_mod')
self.assertEqual(result, RESULT_CHAMFERED_PAD_GRID)
def testChamferedPadGridCornerOnly(self):
kicad_mod = Footprint("test_chamfered_grid")
kicad_mod.setDescription("A example footprint")
kicad_mod.setTags("example")
kicad_mod.append(Text(type='reference', text='REF**', at=[0, 0], layer='F.SilkS'))
kicad_mod.append(Text(type='value', text="test_chamfered_grid", at=[0, 0], layer='F.Fab'))
chamfer_select = ChamferSelPadGrid(0)
chamfer_select.setCorners()
pad = ChamferedPadGrid(
number=1, type=Pad.TYPE_SMT,
center=[0, 0], size=[1, 1], layers=Pad.LAYERS_SMT,
chamfer_size=[0.25, 0.25], chamfer_selection=chamfer_select,
pincount=[3, 4], grid=[1.4, 1.4]
)
c = [2.0, 2.5]
d = 0.4
kicad_mod.append(Circle(center=c, radius=d/2, width=0.01))
pad.chamferAvoidCircle(center=c, diameter=d, clearance=0.005)
kicad_mod.append(pad)
file_handler = KicadFileHandler(kicad_mod)
result = file_handler.serialize(timestamp=0)
# file_handler.writeFile('test_chamfered_grid.kicad_mod')
self.assertEqual(result, RESULT_CHAMFERED_PAD_GRID_AVOID_CIRCLE)
def testChamferedRoundedPad(self):
kicad_mod = Footprint("chamfered_pad")
kicad_mod.setDescription("A example footprint")
kicad_mod.setTags("example")
kicad_mod.append(Text(type='reference', text='REF**', at=[0, 0], layer='F.SilkS'))
kicad_mod.append(Text(type='value', text="chamfered_pad", at=[0, 0], layer='F.Fab'))
kicad_mod.append(
ChamferedPad(number=1, type=Pad.TYPE_SMT,
at=[0, 0], size=[4, 4], layers=Pad.LAYERS_SMT, chamfer_size=[0.5, 0.5],
corner_selection=[1, 1, 1, 1]
))
kicad_mod.append(
ChamferedPad(number=1, type=Pad.TYPE_SMT,
at=[0, 0], size=[4, 4], layers=["B.Cu"], chamfer_size=[0.5, 0.5],
corner_selection=[1, 1, 1, 1], radius_ratio=0.25
))
kicad_mod.append(
ChamferedPad(number=1, type=Pad.TYPE_SMT,
at=[0, 5], size=[4, 3], layers=Pad.LAYERS_SMT, chamfer_size=[1, 1],
corner_selection=[1, 1, 1, 1]
))
kicad_mod.append(
ChamferedPad(number=1, type=Pad.TYPE_SMT,
at=[0, 5], size=[4, 3], layers=["B.Cu"], chamfer_size=[1, 1],
corner_selection=[1, 1, 1, 1], radius_ratio=0.25
))
kicad_mod.append(
ChamferedPad(number=1, type=Pad.TYPE_SMT,
at=[5, 0], size=[4, 3], layers=Pad.LAYERS_SMT, chamfer_size=[1, 1],
corner_selection=[1, 0, 1, 0]
))
kicad_mod.append(
ChamferedPad(number=1, type=Pad.TYPE_SMT,
at=[5, 0], size=[4, 3], layers=["B.Cu"], chamfer_size=[1, 1],
corner_selection=[1, 0, 1, 0], radius_ratio=0.25
))
file_handler = KicadFileHandler(kicad_mod)
result = file_handler.serialize(timestamp=0)
# file_handler.writeFile('test_cp.kicad_mod')
self.assertEqual(result, RESULT_CHAMFERED_ROUNDED_PAD)
| 42.264567
| 121
| 0.576533
| 4,289
| 26,838
| 3.509676
| 0.052693
| 0.018202
| 0.015944
| 0.027237
| 0.912841
| 0.896831
| 0.879891
| 0.869528
| 0.854381
| 0.844549
| 0
| 0.108668
| 0.264513
| 26,838
| 634
| 122
| 42.33123
| 0.653934
| 0.044899
| 0
| 0.65019
| 0
| 0.173004
| 0.536593
| 0
| 0
| 0
| 0
| 0
| 0.019011
| 1
| 0.019011
| false
| 0
| 0.005703
| 0
| 0.026616
| 0.057034
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
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| null | 0
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| 0
| 0
| 0
| 0
|
0
| 6
|
1057f1cfeeeeaf5980f0ba235f3ed7dfa0ac48cf
| 38
|
py
|
Python
|
data/__init__.py
|
nilesh2797/perceiver-lm
|
2431e7bb68fbd118273f7feb35559de47eb341a2
|
[
"MIT"
] | null | null | null |
data/__init__.py
|
nilesh2797/perceiver-lm
|
2431e7bb68fbd118273f7feb35559de47eb341a2
|
[
"MIT"
] | null | null | null |
data/__init__.py
|
nilesh2797/perceiver-lm
|
2431e7bb68fbd118273f7feb35559de47eb341a2
|
[
"MIT"
] | null | null | null |
from data.mnist import MNISTDataModule
| 38
| 38
| 0.894737
| 5
| 38
| 6.8
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.078947
| 38
| 1
| 38
| 38
| 0.971429
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 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
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
107ad86525db42d105b828cdc6f1f58cdb61aab6
| 48
|
py
|
Python
|
flask_gdrive/__init__.py
|
Shubby98/Flask-GDrive
|
b6876fa224b24f4d00ff9382a46a7bc573331198
|
[
"MIT"
] | 8
|
2019-10-09T19:42:56.000Z
|
2020-02-22T02:14:09.000Z
|
flask_gdrive/__init__.py
|
Shubby98/Flask-GDrive
|
b6876fa224b24f4d00ff9382a46a7bc573331198
|
[
"MIT"
] | 9
|
2019-10-22T16:32:23.000Z
|
2019-12-26T17:57:32.000Z
|
flask_gdrive/__init__.py
|
Shubby98/Flask-GDrive
|
b6876fa224b24f4d00ff9382a46a7bc573331198
|
[
"MIT"
] | 7
|
2019-10-22T20:29:54.000Z
|
2021-01-27T19:42:00.000Z
|
from .flask_gdrive import GDriveDB, GDriveStatic
| 48
| 48
| 0.875
| 6
| 48
| 6.833333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.083333
| 48
| 1
| 48
| 48
| 0.931818
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 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
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
108fa568c8c68bc0dd73a4e1b35ad8ae6c4fffd7
| 16
|
py
|
Python
|
python/20181228/Code/p3/c1.py
|
Realize0917/career
|
b5d02ac53cfc3ce3a2ca38d11480c51560283e67
|
[
"MIT"
] | 3
|
2019-01-17T05:50:51.000Z
|
2019-03-15T10:10:07.000Z
|
python/20181228/Code/p3/c1.py
|
Realize0917/career
|
b5d02ac53cfc3ce3a2ca38d11480c51560283e67
|
[
"MIT"
] | 10
|
2019-01-17T06:07:03.000Z
|
2019-02-19T05:55:25.000Z
|
python/20181228/Code/p3/c1.py
|
Realize0917/career
|
b5d02ac53cfc3ce3a2ca38d11480c51560283e67
|
[
"MIT"
] | 4
|
2018-12-22T07:32:55.000Z
|
2019-03-06T09:13:48.000Z
|
from . import c2
| 16
| 16
| 0.75
| 3
| 16
| 4
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.076923
| 0.1875
| 16
| 1
| 16
| 16
| 0.846154
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 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
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
52e04bbe3b8482d94899bd6269df5fecbcf95a06
| 88
|
py
|
Python
|
publichealth/home/models/__init__.py
|
pcoder/public-health-ch
|
cebc4849653560c54238b67814074353ff7c01f3
|
[
"MIT"
] | 2
|
2020-10-29T16:27:21.000Z
|
2021-06-07T12:47:46.000Z
|
publichealth/home/models/__init__.py
|
pcoder/public-health-ch
|
cebc4849653560c54238b67814074353ff7c01f3
|
[
"MIT"
] | 11
|
2017-05-09T10:50:28.000Z
|
2021-12-15T17:01:23.000Z
|
publichealth/home/models/__init__.py
|
pcoder/public-health-ch
|
cebc4849653560c54238b67814074353ff7c01f3
|
[
"MIT"
] | 4
|
2017-04-24T13:06:55.000Z
|
2021-06-04T02:18:32.000Z
|
from .forms import *
from .models import *
from .snippets import *
from .admin import *
| 17.6
| 23
| 0.727273
| 12
| 88
| 5.333333
| 0.5
| 0.46875
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.181818
| 88
| 4
| 24
| 22
| 0.888889
| 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
| 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
| 6
|
52fe4b7dc792f6d4a01c74ad7832281ff390be3b
| 66
|
py
|
Python
|
doorknob/__init__.py
|
escofresco/tinydoor
|
a8810cf7ca302bb91f5089f1ccdc37c30ec81cf6
|
[
"MIT"
] | 4
|
2020-06-17T13:45:57.000Z
|
2020-06-25T17:10:18.000Z
|
doorknob/__init__.py
|
escofresco/tinydoor
|
a8810cf7ca302bb91f5089f1ccdc37c30ec81cf6
|
[
"MIT"
] | 80
|
2020-06-16T18:53:42.000Z
|
2022-03-12T00:44:37.000Z
|
doorknob/__init__.py
|
escofresco/tinydoor
|
a8810cf7ca302bb91f5089f1ccdc37c30ec81cf6
|
[
"MIT"
] | null | null | null |
from .detect import *
from .foyer import *
from .helpers import *
| 16.5
| 22
| 0.727273
| 9
| 66
| 5.333333
| 0.555556
| 0.416667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.181818
| 66
| 3
| 23
| 22
| 0.888889
| 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
| 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
| 6
|
5e0cb7f52572d0a94fba5847fd8189821152e621
| 20
|
py
|
Python
|
perceptual/alignment/ewert/__init__.py
|
LIMUNIMI/PerceptualEvaluation
|
6e1fcdf65ae5cb86997443607bb2050163b64720
|
[
"MIT"
] | 6
|
2021-09-18T08:36:26.000Z
|
2022-03-25T16:37:04.000Z
|
perceptual/alignment/ewert/__init__.py
|
LIMUNIMI/PerceptualEvaluation
|
6e1fcdf65ae5cb86997443607bb2050163b64720
|
[
"MIT"
] | 1
|
2021-08-24T11:18:25.000Z
|
2021-12-18T19:46:05.000Z
|
perceptual/alignment/ewert/__init__.py
|
LIMUNIMI/PerceptualEvaluation
|
6e1fcdf65ae5cb86997443607bb2050163b64720
|
[
"MIT"
] | 3
|
2021-07-13T15:11:38.000Z
|
2021-11-26T07:38:00.000Z
|
from . import align
| 10
| 19
| 0.75
| 3
| 20
| 5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.2
| 20
| 1
| 20
| 20
| 0.9375
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 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
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
eabc2d596d684adf815d34a4c8002b761aab2c78
| 21,923
|
py
|
Python
|
nova/tests/unit/api/openstack/compute/test_extended_server_attributes.py
|
bopopescu/nova-token
|
ec98f69dea7b3e2b9013b27fd55a2c1a1ac6bfb2
|
[
"Apache-2.0"
] | null | null | null |
nova/tests/unit/api/openstack/compute/test_extended_server_attributes.py
|
bopopescu/nova-token
|
ec98f69dea7b3e2b9013b27fd55a2c1a1ac6bfb2
|
[
"Apache-2.0"
] | null | null | null |
nova/tests/unit/api/openstack/compute/test_extended_server_attributes.py
|
bopopescu/nova-token
|
ec98f69dea7b3e2b9013b27fd55a2c1a1ac6bfb2
|
[
"Apache-2.0"
] | 2
|
2017-07-20T17:31:34.000Z
|
2020-07-24T02:42:19.000Z
|
begin_unit
comment|'# Copyright 2011 OpenStack Foundation'
nl|'\n'
comment|'# All Rights Reserved.'
nl|'\n'
comment|'#'
nl|'\n'
comment|'# Licensed under the Apache License, Version 2.0 (the "License"); you may'
nl|'\n'
comment|'# not use this file except in compliance with the License. You may obtain'
nl|'\n'
comment|'# a copy of the License at'
nl|'\n'
comment|'#'
nl|'\n'
comment|'# http://www.apache.org/licenses/LICENSE-2.0'
nl|'\n'
comment|'#'
nl|'\n'
comment|'# Unless required by applicable law or agreed to in writing, software'
nl|'\n'
comment|'# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT'
nl|'\n'
comment|'# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the'
nl|'\n'
comment|'# License for the specific language governing permissions and limitations'
nl|'\n'
comment|'# under the License.'
nl|'\n'
nl|'\n'
name|'from'
name|'oslo_config'
name|'import'
name|'cfg'
newline|'\n'
name|'from'
name|'oslo_serialization'
name|'import'
name|'jsonutils'
newline|'\n'
name|'import'
name|'webob'
newline|'\n'
nl|'\n'
name|'from'
name|'nova'
op|'.'
name|'api'
op|'.'
name|'openstack'
name|'import'
name|'wsgi'
name|'as'
name|'os_wsgi'
newline|'\n'
name|'from'
name|'nova'
name|'import'
name|'compute'
newline|'\n'
name|'from'
name|'nova'
name|'import'
name|'exception'
newline|'\n'
name|'from'
name|'nova'
name|'import'
name|'objects'
newline|'\n'
name|'from'
name|'nova'
name|'import'
name|'test'
newline|'\n'
name|'from'
name|'nova'
op|'.'
name|'tests'
op|'.'
name|'unit'
op|'.'
name|'api'
op|'.'
name|'openstack'
name|'import'
name|'fakes'
newline|'\n'
nl|'\n'
nl|'\n'
DECL|variable|NAME_FMT
name|'NAME_FMT'
op|'='
name|'cfg'
op|'.'
name|'CONF'
op|'.'
name|'instance_name_template'
newline|'\n'
DECL|variable|UUID1
name|'UUID1'
op|'='
string|"'00000000-0000-0000-0000-000000000001'"
newline|'\n'
DECL|variable|UUID2
name|'UUID2'
op|'='
string|"'00000000-0000-0000-0000-000000000002'"
newline|'\n'
DECL|variable|UUID3
name|'UUID3'
op|'='
string|"'00000000-0000-0000-0000-000000000003'"
newline|'\n'
DECL|variable|UUID4
name|'UUID4'
op|'='
string|"'00000000-0000-0000-0000-000000000004'"
newline|'\n'
DECL|variable|UUID5
name|'UUID5'
op|'='
string|"'00000000-0000-0000-0000-000000000005'"
newline|'\n'
nl|'\n'
nl|'\n'
DECL|function|fake_services
name|'def'
name|'fake_services'
op|'('
name|'host'
op|')'
op|':'
newline|'\n'
indent|' '
name|'service_list'
op|'='
op|'['
name|'objects'
op|'.'
name|'Service'
op|'('
name|'id'
op|'='
number|'0'
op|','
name|'host'
op|'='
name|'host'
op|','
name|'forced_down'
op|'='
name|'True'
op|','
nl|'\n'
name|'binary'
op|'='
string|"'nova-compute'"
op|')'
op|']'
newline|'\n'
name|'return'
name|'objects'
op|'.'
name|'ServiceList'
op|'('
name|'objects'
op|'='
name|'service_list'
op|')'
newline|'\n'
nl|'\n'
nl|'\n'
DECL|function|fake_compute_get
dedent|''
name|'def'
name|'fake_compute_get'
op|'('
op|'*'
name|'args'
op|','
op|'**'
name|'kwargs'
op|')'
op|':'
newline|'\n'
indent|' '
name|'return'
name|'fakes'
op|'.'
name|'stub_instance_obj'
op|'('
nl|'\n'
name|'None'
op|','
number|'1'
op|','
name|'uuid'
op|'='
name|'UUID3'
op|','
name|'host'
op|'='
string|'"host-fake"'
op|','
nl|'\n'
name|'node'
op|'='
string|'"node-fake"'
op|','
nl|'\n'
name|'reservation_id'
op|'='
string|'"r-1"'
op|','
name|'launch_index'
op|'='
number|'0'
op|','
nl|'\n'
name|'kernel_id'
op|'='
name|'UUID4'
op|','
name|'ramdisk_id'
op|'='
name|'UUID5'
op|','
nl|'\n'
name|'display_name'
op|'='
string|'"hostname-1"'
op|','
nl|'\n'
name|'root_device_name'
op|'='
string|'"/dev/vda"'
op|','
nl|'\n'
name|'user_data'
op|'='
string|'"userdata"'
op|','
nl|'\n'
name|'services'
op|'='
name|'fake_services'
op|'('
string|'"host-fake"'
op|')'
op|')'
newline|'\n'
nl|'\n'
nl|'\n'
DECL|function|fake_compute_get_all
dedent|''
name|'def'
name|'fake_compute_get_all'
op|'('
op|'*'
name|'args'
op|','
op|'**'
name|'kwargs'
op|')'
op|':'
newline|'\n'
indent|' '
name|'inst_list'
op|'='
op|'['
nl|'\n'
name|'fakes'
op|'.'
name|'stub_instance_obj'
op|'('
nl|'\n'
name|'None'
op|','
number|'1'
op|','
name|'uuid'
op|'='
name|'UUID1'
op|','
name|'host'
op|'='
string|'"host-1"'
op|','
name|'node'
op|'='
string|'"node-1"'
op|','
nl|'\n'
name|'reservation_id'
op|'='
string|'"r-1"'
op|','
name|'launch_index'
op|'='
number|'0'
op|','
nl|'\n'
name|'kernel_id'
op|'='
name|'UUID4'
op|','
name|'ramdisk_id'
op|'='
name|'UUID5'
op|','
nl|'\n'
name|'display_name'
op|'='
string|'"hostname-1"'
op|','
nl|'\n'
name|'root_device_name'
op|'='
string|'"/dev/vda"'
op|','
nl|'\n'
name|'user_data'
op|'='
string|'"userdata"'
op|','
nl|'\n'
name|'services'
op|'='
name|'fake_services'
op|'('
string|'"host-1"'
op|')'
op|')'
op|','
nl|'\n'
name|'fakes'
op|'.'
name|'stub_instance_obj'
op|'('
nl|'\n'
name|'None'
op|','
number|'2'
op|','
name|'uuid'
op|'='
name|'UUID2'
op|','
name|'host'
op|'='
string|'"host-2"'
op|','
name|'node'
op|'='
string|'"node-2"'
op|','
nl|'\n'
name|'reservation_id'
op|'='
string|'"r-2"'
op|','
name|'launch_index'
op|'='
number|'1'
op|','
nl|'\n'
name|'kernel_id'
op|'='
name|'UUID4'
op|','
name|'ramdisk_id'
op|'='
name|'UUID5'
op|','
nl|'\n'
name|'display_name'
op|'='
string|'"hostname-2"'
op|','
nl|'\n'
name|'root_device_name'
op|'='
string|'"/dev/vda"'
op|','
nl|'\n'
name|'user_data'
op|'='
string|'"userdata"'
op|','
nl|'\n'
name|'services'
op|'='
name|'fake_services'
op|'('
string|'"host-2"'
op|')'
op|')'
op|','
nl|'\n'
op|']'
newline|'\n'
name|'return'
name|'objects'
op|'.'
name|'InstanceList'
op|'('
name|'objects'
op|'='
name|'inst_list'
op|')'
newline|'\n'
nl|'\n'
nl|'\n'
DECL|class|ExtendedServerAttributesTestV21
dedent|''
name|'class'
name|'ExtendedServerAttributesTestV21'
op|'('
name|'test'
op|'.'
name|'TestCase'
op|')'
op|':'
newline|'\n'
DECL|variable|content_type
indent|' '
name|'content_type'
op|'='
string|"'application/json'"
newline|'\n'
DECL|variable|prefix
name|'prefix'
op|'='
string|"'OS-EXT-SRV-ATTR:'"
newline|'\n'
DECL|variable|fake_url
name|'fake_url'
op|'='
string|"'/v2/fake'"
newline|'\n'
DECL|variable|wsgi_api_version
name|'wsgi_api_version'
op|'='
name|'os_wsgi'
op|'.'
name|'DEFAULT_API_VERSION'
newline|'\n'
nl|'\n'
DECL|member|setUp
name|'def'
name|'setUp'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'super'
op|'('
name|'ExtendedServerAttributesTestV21'
op|','
name|'self'
op|')'
op|'.'
name|'setUp'
op|'('
op|')'
newline|'\n'
name|'fakes'
op|'.'
name|'stub_out_nw_api'
op|'('
name|'self'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'stubs'
op|'.'
name|'Set'
op|'('
name|'compute'
op|'.'
name|'api'
op|'.'
name|'API'
op|','
string|"'get'"
op|','
name|'fake_compute_get'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'stubs'
op|'.'
name|'Set'
op|'('
name|'compute'
op|'.'
name|'api'
op|'.'
name|'API'
op|','
string|"'get_all'"
op|','
name|'fake_compute_get_all'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'stub_out'
op|'('
string|"'nova.db.instance_get_by_uuid'"
op|','
name|'fake_compute_get'
op|')'
newline|'\n'
nl|'\n'
DECL|member|_make_request
dedent|''
name|'def'
name|'_make_request'
op|'('
name|'self'
op|','
name|'url'
op|')'
op|':'
newline|'\n'
indent|' '
name|'req'
op|'='
name|'fakes'
op|'.'
name|'HTTPRequest'
op|'.'
name|'blank'
op|'('
name|'url'
op|')'
newline|'\n'
name|'req'
op|'.'
name|'headers'
op|'['
string|"'Accept'"
op|']'
op|'='
name|'self'
op|'.'
name|'content_type'
newline|'\n'
name|'req'
op|'.'
name|'headers'
op|'='
op|'{'
name|'os_wsgi'
op|'.'
name|'API_VERSION_REQUEST_HEADER'
op|':'
nl|'\n'
name|'self'
op|'.'
name|'wsgi_api_version'
op|'}'
newline|'\n'
name|'res'
op|'='
name|'req'
op|'.'
name|'get_response'
op|'('
nl|'\n'
name|'fakes'
op|'.'
name|'wsgi_app_v21'
op|'('
name|'init_only'
op|'='
op|'('
string|"'servers'"
op|','
nl|'\n'
string|"'os-extended-server-attributes'"
op|')'
op|')'
op|')'
newline|'\n'
name|'return'
name|'res'
newline|'\n'
nl|'\n'
DECL|member|_get_server
dedent|''
name|'def'
name|'_get_server'
op|'('
name|'self'
op|','
name|'body'
op|')'
op|':'
newline|'\n'
indent|' '
name|'return'
name|'jsonutils'
op|'.'
name|'loads'
op|'('
name|'body'
op|')'
op|'.'
name|'get'
op|'('
string|"'server'"
op|')'
newline|'\n'
nl|'\n'
DECL|member|_get_servers
dedent|''
name|'def'
name|'_get_servers'
op|'('
name|'self'
op|','
name|'body'
op|')'
op|':'
newline|'\n'
indent|' '
name|'return'
name|'jsonutils'
op|'.'
name|'loads'
op|'('
name|'body'
op|')'
op|'.'
name|'get'
op|'('
string|"'servers'"
op|')'
newline|'\n'
nl|'\n'
DECL|member|assertServerAttributes
dedent|''
name|'def'
name|'assertServerAttributes'
op|'('
name|'self'
op|','
name|'server'
op|','
name|'host'
op|','
name|'node'
op|','
name|'instance_name'
op|')'
op|':'
newline|'\n'
indent|' '
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'server'
op|'.'
name|'get'
op|'('
string|"'%shost'"
op|'%'
name|'self'
op|'.'
name|'prefix'
op|')'
op|','
name|'host'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'server'
op|'.'
name|'get'
op|'('
string|"'%sinstance_name'"
op|'%'
name|'self'
op|'.'
name|'prefix'
op|')'
op|','
nl|'\n'
name|'instance_name'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'server'
op|'.'
name|'get'
op|'('
string|"'%shypervisor_hostname'"
op|'%'
name|'self'
op|'.'
name|'prefix'
op|')'
op|','
nl|'\n'
name|'node'
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_show
dedent|''
name|'def'
name|'test_show'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'url'
op|'='
name|'self'
op|'.'
name|'fake_url'
op|'+'
string|"'/servers/%s'"
op|'%'
name|'UUID3'
newline|'\n'
name|'res'
op|'='
name|'self'
op|'.'
name|'_make_request'
op|'('
name|'url'
op|')'
newline|'\n'
nl|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'res'
op|'.'
name|'status_int'
op|','
number|'200'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertServerAttributes'
op|'('
name|'self'
op|'.'
name|'_get_server'
op|'('
name|'res'
op|'.'
name|'body'
op|')'
op|','
nl|'\n'
name|'host'
op|'='
string|"'host-fake'"
op|','
nl|'\n'
name|'node'
op|'='
string|"'node-fake'"
op|','
nl|'\n'
name|'instance_name'
op|'='
name|'NAME_FMT'
op|'%'
number|'1'
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_detail
dedent|''
name|'def'
name|'test_detail'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'url'
op|'='
name|'self'
op|'.'
name|'fake_url'
op|'+'
string|"'/servers/detail'"
newline|'\n'
name|'res'
op|'='
name|'self'
op|'.'
name|'_make_request'
op|'('
name|'url'
op|')'
newline|'\n'
nl|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'res'
op|'.'
name|'status_int'
op|','
number|'200'
op|')'
newline|'\n'
name|'for'
name|'i'
op|','
name|'server'
name|'in'
name|'enumerate'
op|'('
name|'self'
op|'.'
name|'_get_servers'
op|'('
name|'res'
op|'.'
name|'body'
op|')'
op|')'
op|':'
newline|'\n'
indent|' '
name|'self'
op|'.'
name|'assertServerAttributes'
op|'('
name|'server'
op|','
nl|'\n'
name|'host'
op|'='
string|"'host-%s'"
op|'%'
op|'('
name|'i'
op|'+'
number|'1'
op|')'
op|','
nl|'\n'
name|'node'
op|'='
string|"'node-%s'"
op|'%'
op|'('
name|'i'
op|'+'
number|'1'
op|')'
op|','
nl|'\n'
name|'instance_name'
op|'='
name|'NAME_FMT'
op|'%'
op|'('
name|'i'
op|'+'
number|'1'
op|')'
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_no_instance_passthrough_404
dedent|''
dedent|''
name|'def'
name|'test_no_instance_passthrough_404'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
nl|'\n'
DECL|function|fake_compute_get
indent|' '
name|'def'
name|'fake_compute_get'
op|'('
op|'*'
name|'args'
op|','
op|'**'
name|'kwargs'
op|')'
op|':'
newline|'\n'
indent|' '
name|'raise'
name|'exception'
op|'.'
name|'InstanceNotFound'
op|'('
name|'instance_id'
op|'='
string|"'fake'"
op|')'
newline|'\n'
nl|'\n'
dedent|''
name|'self'
op|'.'
name|'stubs'
op|'.'
name|'Set'
op|'('
name|'compute'
op|'.'
name|'api'
op|'.'
name|'API'
op|','
string|"'get'"
op|','
name|'fake_compute_get'
op|')'
newline|'\n'
name|'url'
op|'='
name|'self'
op|'.'
name|'fake_url'
op|'+'
string|"'/servers/70f6db34-de8d-4fbd-aafb-4065bdfa6115'"
newline|'\n'
name|'res'
op|'='
name|'self'
op|'.'
name|'_make_request'
op|'('
name|'url'
op|')'
newline|'\n'
nl|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'res'
op|'.'
name|'status_int'
op|','
number|'404'
op|')'
newline|'\n'
nl|'\n'
nl|'\n'
DECL|class|ExtendedServerAttributesTestV2
dedent|''
dedent|''
name|'class'
name|'ExtendedServerAttributesTestV2'
op|'('
name|'ExtendedServerAttributesTestV21'
op|')'
op|':'
newline|'\n'
nl|'\n'
DECL|member|setUp
indent|' '
name|'def'
name|'setUp'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'super'
op|'('
name|'ExtendedServerAttributesTestV2'
op|','
name|'self'
op|')'
op|'.'
name|'setUp'
op|'('
op|')'
newline|'\n'
name|'self'
op|'.'
name|'flags'
op|'('
nl|'\n'
name|'osapi_compute_extension'
op|'='
op|'['
nl|'\n'
string|"'nova.api.openstack.compute.contrib.select_extensions'"
op|']'
op|','
nl|'\n'
name|'osapi_compute_ext_list'
op|'='
op|'['
string|"'Extended_server_attributes'"
op|']'
op|')'
newline|'\n'
nl|'\n'
DECL|member|_make_request
dedent|''
name|'def'
name|'_make_request'
op|'('
name|'self'
op|','
name|'url'
op|')'
op|':'
newline|'\n'
indent|' '
name|'req'
op|'='
name|'webob'
op|'.'
name|'Request'
op|'.'
name|'blank'
op|'('
name|'url'
op|')'
newline|'\n'
name|'req'
op|'.'
name|'headers'
op|'['
string|"'Accept'"
op|']'
op|'='
name|'self'
op|'.'
name|'content_type'
newline|'\n'
name|'res'
op|'='
name|'req'
op|'.'
name|'get_response'
op|'('
name|'fakes'
op|'.'
name|'wsgi_app'
op|'('
name|'init_only'
op|'='
op|'('
string|"'servers'"
op|','
op|')'
op|')'
op|')'
newline|'\n'
name|'return'
name|'res'
newline|'\n'
nl|'\n'
nl|'\n'
DECL|class|ExtendedServerAttributesTestV23
dedent|''
dedent|''
name|'class'
name|'ExtendedServerAttributesTestV23'
op|'('
name|'ExtendedServerAttributesTestV21'
op|')'
op|':'
newline|'\n'
DECL|variable|wsgi_api_version
indent|' '
name|'wsgi_api_version'
op|'='
string|"'2.3'"
newline|'\n'
nl|'\n'
DECL|member|assertServerAttributes
name|'def'
name|'assertServerAttributes'
op|'('
name|'self'
op|','
name|'server'
op|','
name|'host'
op|','
name|'node'
op|','
name|'instance_name'
op|','
nl|'\n'
name|'reservation_id'
op|','
name|'launch_index'
op|','
name|'kernel_id'
op|','
nl|'\n'
name|'ramdisk_id'
op|','
name|'hostname'
op|','
name|'root_device_name'
op|','
nl|'\n'
name|'user_data'
op|')'
op|':'
newline|'\n'
indent|' '
name|'super'
op|'('
name|'ExtendedServerAttributesTestV23'
op|','
name|'self'
op|')'
op|'.'
name|'assertServerAttributes'
op|'('
nl|'\n'
name|'server'
op|','
name|'host'
op|','
name|'node'
op|','
name|'instance_name'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'server'
op|'.'
name|'get'
op|'('
string|"'%sreservation_id'"
op|'%'
name|'self'
op|'.'
name|'prefix'
op|')'
op|','
nl|'\n'
name|'reservation_id'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'server'
op|'.'
name|'get'
op|'('
string|"'%slaunch_index'"
op|'%'
name|'self'
op|'.'
name|'prefix'
op|')'
op|','
nl|'\n'
name|'launch_index'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'server'
op|'.'
name|'get'
op|'('
string|"'%skernel_id'"
op|'%'
name|'self'
op|'.'
name|'prefix'
op|')'
op|','
nl|'\n'
name|'kernel_id'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'server'
op|'.'
name|'get'
op|'('
string|"'%sramdisk_id'"
op|'%'
name|'self'
op|'.'
name|'prefix'
op|')'
op|','
nl|'\n'
name|'ramdisk_id'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'server'
op|'.'
name|'get'
op|'('
string|"'%shostname'"
op|'%'
name|'self'
op|'.'
name|'prefix'
op|')'
op|','
nl|'\n'
name|'hostname'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'server'
op|'.'
name|'get'
op|'('
string|"'%sroot_device_name'"
op|'%'
name|'self'
op|'.'
name|'prefix'
op|')'
op|','
nl|'\n'
name|'root_device_name'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'server'
op|'.'
name|'get'
op|'('
string|"'%suser_data'"
op|'%'
name|'self'
op|'.'
name|'prefix'
op|')'
op|','
nl|'\n'
name|'user_data'
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_show
dedent|''
name|'def'
name|'test_show'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'url'
op|'='
name|'self'
op|'.'
name|'fake_url'
op|'+'
string|"'/servers/%s'"
op|'%'
name|'UUID3'
newline|'\n'
name|'res'
op|'='
name|'self'
op|'.'
name|'_make_request'
op|'('
name|'url'
op|')'
newline|'\n'
nl|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'res'
op|'.'
name|'status_int'
op|','
number|'200'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertServerAttributes'
op|'('
name|'self'
op|'.'
name|'_get_server'
op|'('
name|'res'
op|'.'
name|'body'
op|')'
op|','
nl|'\n'
name|'host'
op|'='
string|"'host-fake'"
op|','
nl|'\n'
name|'node'
op|'='
string|"'node-fake'"
op|','
nl|'\n'
name|'instance_name'
op|'='
name|'NAME_FMT'
op|'%'
number|'1'
op|','
nl|'\n'
name|'reservation_id'
op|'='
string|'"r-1"'
op|','
nl|'\n'
name|'launch_index'
op|'='
number|'0'
op|','
nl|'\n'
name|'kernel_id'
op|'='
name|'UUID4'
op|','
nl|'\n'
name|'ramdisk_id'
op|'='
name|'UUID5'
op|','
nl|'\n'
name|'hostname'
op|'='
string|'"hostname-1"'
op|','
nl|'\n'
name|'root_device_name'
op|'='
string|'"/dev/vda"'
op|','
nl|'\n'
name|'user_data'
op|'='
string|'"userdata"'
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_detail
dedent|''
name|'def'
name|'test_detail'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'url'
op|'='
name|'self'
op|'.'
name|'fake_url'
op|'+'
string|"'/servers/detail'"
newline|'\n'
name|'res'
op|'='
name|'self'
op|'.'
name|'_make_request'
op|'('
name|'url'
op|')'
newline|'\n'
nl|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'res'
op|'.'
name|'status_int'
op|','
number|'200'
op|')'
newline|'\n'
name|'for'
name|'i'
op|','
name|'server'
name|'in'
name|'enumerate'
op|'('
name|'self'
op|'.'
name|'_get_servers'
op|'('
name|'res'
op|'.'
name|'body'
op|')'
op|')'
op|':'
newline|'\n'
indent|' '
name|'self'
op|'.'
name|'assertServerAttributes'
op|'('
name|'server'
op|','
nl|'\n'
name|'host'
op|'='
string|"'host-%s'"
op|'%'
op|'('
name|'i'
op|'+'
number|'1'
op|')'
op|','
nl|'\n'
name|'node'
op|'='
string|"'node-%s'"
op|'%'
op|'('
name|'i'
op|'+'
number|'1'
op|')'
op|','
nl|'\n'
name|'instance_name'
op|'='
name|'NAME_FMT'
op|'%'
op|'('
name|'i'
op|'+'
number|'1'
op|')'
op|','
nl|'\n'
name|'reservation_id'
op|'='
string|'"r-%s"'
op|'%'
op|'('
name|'i'
op|'+'
number|'1'
op|')'
op|','
nl|'\n'
name|'launch_index'
op|'='
name|'i'
op|','
nl|'\n'
name|'kernel_id'
op|'='
name|'UUID4'
op|','
nl|'\n'
name|'ramdisk_id'
op|'='
name|'UUID5'
op|','
nl|'\n'
name|'hostname'
op|'='
string|'"hostname-%s"'
op|'%'
op|'('
name|'i'
op|'+'
number|'1'
op|')'
op|','
nl|'\n'
name|'root_device_name'
op|'='
string|'"/dev/vda"'
op|','
nl|'\n'
name|'user_data'
op|'='
string|'"userdata"'
op|')'
newline|'\n'
nl|'\n'
nl|'\n'
DECL|class|ExtendedServerAttributesTestV216
dedent|''
dedent|''
dedent|''
name|'class'
name|'ExtendedServerAttributesTestV216'
op|'('
name|'ExtendedServerAttributesTestV21'
op|')'
op|':'
newline|'\n'
DECL|variable|wsgi_api_version
indent|' '
name|'wsgi_api_version'
op|'='
string|"'2.16'"
newline|'\n'
nl|'\n'
DECL|member|assertServerAttributes
name|'def'
name|'assertServerAttributes'
op|'('
name|'self'
op|','
name|'server'
op|','
name|'host'
op|','
name|'node'
op|','
name|'instance_name'
op|','
nl|'\n'
name|'host_status'
op|')'
op|':'
newline|'\n'
indent|' '
name|'super'
op|'('
name|'ExtendedServerAttributesTestV216'
op|','
name|'self'
op|')'
op|'.'
name|'assertServerAttributes'
op|'('
nl|'\n'
name|'server'
op|','
name|'host'
op|','
name|'node'
op|','
name|'instance_name'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'server'
op|'.'
name|'get'
op|'('
string|"'host_status'"
op|')'
op|','
name|'host_status'
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_show
dedent|''
name|'def'
name|'test_show'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'url'
op|'='
name|'self'
op|'.'
name|'fake_url'
op|'+'
string|"'/servers/%s'"
op|'%'
name|'UUID3'
newline|'\n'
name|'res'
op|'='
name|'self'
op|'.'
name|'_make_request'
op|'('
name|'url'
op|')'
newline|'\n'
nl|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'res'
op|'.'
name|'status_int'
op|','
number|'200'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertServerAttributes'
op|'('
name|'self'
op|'.'
name|'_get_server'
op|'('
name|'res'
op|'.'
name|'body'
op|')'
op|','
nl|'\n'
name|'host'
op|'='
string|"'host-fake'"
op|','
nl|'\n'
name|'node'
op|'='
string|"'node-fake'"
op|','
nl|'\n'
name|'instance_name'
op|'='
name|'NAME_FMT'
op|'%'
number|'1'
op|','
nl|'\n'
name|'host_status'
op|'='
string|'"DOWN"'
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_detail
dedent|''
name|'def'
name|'test_detail'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'url'
op|'='
name|'self'
op|'.'
name|'fake_url'
op|'+'
string|"'/servers/detail'"
newline|'\n'
name|'res'
op|'='
name|'self'
op|'.'
name|'_make_request'
op|'('
name|'url'
op|')'
newline|'\n'
nl|'\n'
name|'self'
op|'.'
name|'assertEqual'
op|'('
name|'res'
op|'.'
name|'status_int'
op|','
number|'200'
op|')'
newline|'\n'
name|'for'
name|'i'
op|','
name|'server'
name|'in'
name|'enumerate'
op|'('
name|'self'
op|'.'
name|'_get_servers'
op|'('
name|'res'
op|'.'
name|'body'
op|')'
op|')'
op|':'
newline|'\n'
indent|' '
name|'self'
op|'.'
name|'assertServerAttributes'
op|'('
name|'server'
op|','
nl|'\n'
name|'host'
op|'='
string|"'host-%s'"
op|'%'
op|'('
name|'i'
op|'+'
number|'1'
op|')'
op|','
nl|'\n'
name|'node'
op|'='
string|"'node-%s'"
op|'%'
op|'('
name|'i'
op|'+'
number|'1'
op|')'
op|','
nl|'\n'
name|'instance_name'
op|'='
name|'NAME_FMT'
op|'%'
op|'('
name|'i'
op|'+'
number|'1'
op|')'
op|','
nl|'\n'
name|'host_status'
op|'='
string|'"DOWN"'
op|')'
newline|'\n'
dedent|''
dedent|''
dedent|''
endmarker|''
end_unit
| 12.125553
| 88
| 0.597774
| 3,269
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| 0.699284
| 0.681924
| 0.634205
| 0
| 0.015661
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| 89
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0
| 6
|
eac05de33ce6171f7a6b4c2473c0c4605f5814d7
| 134
|
py
|
Python
|
semiconductor_photophysics/old/_semiconductor.py
|
wright-group/semiconductor-photophysics
|
e4c03aace7828a9b69b77dcc7a4fdd1de3f3fc20
|
[
"MIT"
] | null | null | null |
semiconductor_photophysics/old/_semiconductor.py
|
wright-group/semiconductor-photophysics
|
e4c03aace7828a9b69b77dcc7a4fdd1de3f3fc20
|
[
"MIT"
] | null | null | null |
semiconductor_photophysics/old/_semiconductor.py
|
wright-group/semiconductor-photophysics
|
e4c03aace7828a9b69b77dcc7a4fdd1de3f3fc20
|
[
"MIT"
] | null | null | null |
"""
"""
import numpy as np
def beta_mu():
"""
pg. 96, eq. 6.37 of Haug and Koch
eq. 14 of 10.1063/1.340957
"""
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0
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d800a109eaaaa7b619651dfd9eb4c99865844e37
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py
|
Python
|
obliquestrategies/__init__.py
|
FdelMazo/obliquestrategies
|
f2db3ef378c642b2cecd55945c68c03ab9cc4d9a
|
[
"MIT"
] | 3
|
2019-01-09T02:04:16.000Z
|
2021-03-02T07:05:51.000Z
|
obliquestrategies/__init__.py
|
FdelMazo/obliquestrategies
|
f2db3ef378c642b2cecd55945c68c03ab9cc4d9a
|
[
"MIT"
] | null | null | null |
obliquestrategies/__init__.py
|
FdelMazo/obliquestrategies
|
f2db3ef378c642b2cecd55945c68c03ab9cc4d9a
|
[
"MIT"
] | null | null | null |
from .obliquestrategies import *
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| 32
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| 9
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| 0.931034
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0
| 6
|
dc33488317528d08ed53ed4e99476b80f3f60b4c
| 117
|
py
|
Python
|
hong/__init__.py
|
SherylHYX/Ro-SOS-Metric-Expression-Network-MEnet-for-Robust-Salient-Object-Segmentation
|
d774372e474161aae4dd26a033ae3858ed7255cf
|
[
"MIT"
] | 2
|
2021-12-21T03:20:28.000Z
|
2022-02-08T12:35:34.000Z
|
hong/__init__.py
|
SherylHYX/Ro-SOS-Metric-Expression-Network-MEnet-for-Robust-Salient-Object-Segmentation
|
d774372e474161aae4dd26a033ae3858ed7255cf
|
[
"MIT"
] | null | null | null |
hong/__init__.py
|
SherylHYX/Ro-SOS-Metric-Expression-Network-MEnet-for-Robust-Salient-Object-Segmentation
|
d774372e474161aae4dd26a033ae3858ed7255cf
|
[
"MIT"
] | null | null | null |
from hong.data1 import InputLayer
from hong.resize import ResizeToSameSize
from hong.pyloss import EuclideanLossLayer
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| 42
| 0.880342
| 15
| 117
| 6.866667
| 0.6
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| 0.094017
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| 42
| 39
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0
| 6
|
dc751d3809e8f1074c83664ce6cc404544cf99f6
| 26
|
py
|
Python
|
src/models/__init__.py
|
behavioral-data/multiverse
|
82b7265de0aa3e9d229ce9f3f86b8b48435ca365
|
[
"MIT"
] | null | null | null |
src/models/__init__.py
|
behavioral-data/multiverse
|
82b7265de0aa3e9d229ce9f3f86b8b48435ca365
|
[
"MIT"
] | null | null | null |
src/models/__init__.py
|
behavioral-data/multiverse
|
82b7265de0aa3e9d229ce9f3f86b8b48435ca365
|
[
"MIT"
] | 1
|
2021-08-19T15:21:50.000Z
|
2021-08-19T15:21:50.000Z
|
from .CORAL_BART import *
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0
| 6
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dc833882a25782f379d9aa6590147f30ee0bcd66
| 128
|
py
|
Python
|
tests/test_version.py
|
fabaff/hrepr
|
f6de915f1d34c47ceab11f5f70e433a30e6de174
|
[
"MIT"
] | null | null | null |
tests/test_version.py
|
fabaff/hrepr
|
f6de915f1d34c47ceab11f5f70e433a30e6de174
|
[
"MIT"
] | null | null | null |
tests/test_version.py
|
fabaff/hrepr
|
f6de915f1d34c47ceab11f5f70e433a30e6de174
|
[
"MIT"
] | null | null | null |
def test_version():
from hrepr.version import version
assert isinstance(version, str) and len(version.split(".")) == 3
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| 68
| 0.695313
| 17
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| 5.176471
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0
| 6
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dc95d59392d1e99430ee3e518e0e55d2a3d7c921
| 24,243
|
py
|
Python
|
db/data/2008/11/get_gzs.py
|
nataliemcmullen/WikiMiney
|
562177a74179cb28469604f4fb9d021cebeed0a6
|
[
"MIT"
] | 1
|
2020-11-18T19:20:58.000Z
|
2020-11-18T19:20:58.000Z
|
db/data/2008/11/get_gzs.py
|
nataliemcmullen/WikiMiney
|
562177a74179cb28469604f4fb9d021cebeed0a6
|
[
"MIT"
] | null | null | null |
db/data/2008/11/get_gzs.py
|
nataliemcmullen/WikiMiney
|
562177a74179cb28469604f4fb9d021cebeed0a6
|
[
"MIT"
] | null | null | null |
urls = [
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]
import os
base = "http://dumps.wikimedia.org/other/pagecounts-raw/"
tail = "2008/2008-11/"
i = 0
for url in urls:
i = i + 1
one = "en-" + url[:-3]
two = url[:-3]
three = url
if not (os.path.isfile(one) or os.path.isfile(two) or os.path.isfile(three)):
#os.system("curl --silent -O %s >> /dev/null" % (base + tail + url))
os.system("curl -O %s" % (base + tail + url))
print "%d completeted of %d total. %d remaining" % (i, len(urls), len(urls) - i)
| 32.849593
| 84
| 0.783731
| 2,962
| 24,243
| 6.414585
| 0.037812
| 0.454105
| 0.028421
| 0.001474
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.430962
| 0.033865
| 24,243
| 737
| 85
| 32.894166
| 0.380241
| 0.002764
| 0
| 0
| 0
| 0
| 0.868418
| 0.863702
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0.001362
| null | null | 0.001362
| 0
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
dca12c8a4e5b0341db34ca48db5bc1ae4957ddd0
| 10,241
|
py
|
Python
|
ocrsite/ocrlab/migrations/0001_initial.py
|
mikesname/python-ocrlab
|
435cc2548b38d92f8ffdc4bee8845f5a58d655ce
|
[
"MIT"
] | 4
|
2016-01-04T09:36:05.000Z
|
2020-10-18T01:33:39.000Z
|
ocrsite/ocrlab/migrations/0001_initial.py
|
mikesname/python-ocrlab
|
435cc2548b38d92f8ffdc4bee8845f5a58d655ce
|
[
"MIT"
] | null | null | null |
ocrsite/ocrlab/migrations/0001_initial.py
|
mikesname/python-ocrlab
|
435cc2548b38d92f8ffdc4bee8845f5a58d655ce
|
[
"MIT"
] | 3
|
2017-05-04T08:46:45.000Z
|
2021-10-06T19:25:11.000Z
|
# encoding: utf-8
import datetime
from south.db import db
from south.v2 import SchemaMigration
from django.db import models
class Migration(SchemaMigration):
def forwards(self, orm):
# Adding model 'Preset'
db.create_table('ocrlab_preset', (
('id', self.gf('django.db.models.fields.AutoField')(primary_key=True)),
('created_on', self.gf('django.db.models.fields.DateField')()),
('updated_on', self.gf('django.db.models.fields.DateField')(null=True, blank=True)),
('name', self.gf('django.db.models.fields.CharField')(unique=True, max_length=100)),
('slug', self.gf('autoslug.fields.AutoSlugField')(unique=True, max_length=50, populate_from=None, unique_with=(), db_index=True)),
('description', self.gf('django.db.models.fields.TextField')(blank=True)),
('public', self.gf('django.db.models.fields.BooleanField')(default=True)),
('data', self.gf('ocrlab.models.jsonfield.JSONTextField')(name='data')),
('profile', self.gf('django.db.models.fields.related.ForeignKey')(blank=True, related_name='presets', null=True, to=orm['ocrlab.Profile'])),
))
db.send_create_signal('ocrlab', ['Preset'])
# Adding model 'Profile'
db.create_table('ocrlab_profile', (
('id', self.gf('django.db.models.fields.AutoField')(primary_key=True)),
('created_on', self.gf('django.db.models.fields.DateField')()),
('updated_on', self.gf('django.db.models.fields.DateField')(null=True, blank=True)),
('name', self.gf('django.db.models.fields.CharField')(unique=True, max_length=100)),
('slug', self.gf('autoslug.fields.AutoSlugField')(unique=True, max_length=50, populate_from=None, unique_with=(), db_index=True)),
('description', self.gf('django.db.models.fields.TextField')(blank=True)),
('data', self.gf('ocrlab.models.jsonfield.JSONTextField')(name='data')),
))
db.send_create_signal('ocrlab', ['Profile'])
# Adding model 'HelperFileApp'
db.create_table('ocrlab_helperfileapp', (
('id', self.gf('django.db.models.fields.AutoField')(primary_key=True)),
('name', self.gf('django.db.models.fields.CharField')(unique=True, max_length=100)),
('slug', self.gf('autoslug.fields.AutoSlugField')(unique=True, max_length=50, populate_from=None, unique_with=(), db_index=True)),
('description', self.gf('django.db.models.fields.TextField')(blank=True)),
))
db.send_create_signal('ocrlab', ['HelperFileApp'])
# Adding model 'HelperFileType'
db.create_table('ocrlab_helperfiletype', (
('id', self.gf('django.db.models.fields.AutoField')(primary_key=True)),
('name', self.gf('django.db.models.fields.CharField')(unique=True, max_length=100)),
('slug', self.gf('autoslug.fields.AutoSlugField')(unique=True, max_length=50, populate_from=None, unique_with=(), db_index=True)),
('description', self.gf('django.db.models.fields.TextField')(blank=True)),
))
db.send_create_signal('ocrlab', ['HelperFileType'])
# Adding model 'HelperFile'
db.create_table('ocrlab_helperfile', (
('id', self.gf('django.db.models.fields.AutoField')(primary_key=True)),
('created_on', self.gf('django.db.models.fields.DateField')()),
('updated_on', self.gf('django.db.models.fields.DateField')(null=True, blank=True)),
('name', self.gf('django.db.models.fields.CharField')(unique=True, max_length=100)),
('slug', self.gf('autoslug.fields.AutoSlugField')(unique=True, max_length=50, populate_from=None, unique_with=(), db_index=True)),
('description', self.gf('django.db.models.fields.TextField')(blank=True)),
('file', self.gf('django.db.models.fields.files.FileField')(max_length=100)),
('type', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['ocrlab.HelperFileType'])),
('app', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['ocrlab.HelperFileApp'])),
))
db.send_create_signal('ocrlab', ['HelperFile'])
def backwards(self, orm):
# Deleting model 'Preset'
db.delete_table('ocrlab_preset')
# Deleting model 'Profile'
db.delete_table('ocrlab_profile')
# Deleting model 'HelperFileApp'
db.delete_table('ocrlab_helperfileapp')
# Deleting model 'HelperFileType'
db.delete_table('ocrlab_helperfiletype')
# Deleting model 'HelperFile'
db.delete_table('ocrlab_helperfile')
models = {
'contenttypes.contenttype': {
'Meta': {'ordering': "('name',)", 'unique_together': "(('app_label', 'model'),)", 'object_name': 'ContentType', 'db_table': "'django_content_type'"},
'app_label': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'model': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '100'})
},
'ocrlab.helperfile': {
'Meta': {'object_name': 'HelperFile'},
'app': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['ocrlab.HelperFileApp']"}),
'created_on': ('django.db.models.fields.DateField', [], {}),
'description': ('django.db.models.fields.TextField', [], {'blank': 'True'}),
'file': ('django.db.models.fields.files.FileField', [], {'max_length': '100'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '100'}),
'slug': ('autoslug.fields.AutoSlugField', [], {'unique': 'True', 'max_length': '50', 'populate_from': 'None', 'unique_with': '()', 'db_index': 'True'}),
'type': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['ocrlab.HelperFileType']"}),
'updated_on': ('django.db.models.fields.DateField', [], {'null': 'True', 'blank': 'True'})
},
'ocrlab.helperfileapp': {
'Meta': {'object_name': 'HelperFileApp'},
'description': ('django.db.models.fields.TextField', [], {'blank': 'True'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '100'}),
'slug': ('autoslug.fields.AutoSlugField', [], {'unique': 'True', 'max_length': '50', 'populate_from': 'None', 'unique_with': '()', 'db_index': 'True'})
},
'ocrlab.helperfiletype': {
'Meta': {'object_name': 'HelperFileType'},
'description': ('django.db.models.fields.TextField', [], {'blank': 'True'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '100'}),
'slug': ('autoslug.fields.AutoSlugField', [], {'unique': 'True', 'max_length': '50', 'populate_from': 'None', 'unique_with': '()', 'db_index': 'True'})
},
'ocrlab.preset': {
'Meta': {'object_name': 'Preset'},
'created_on': ('django.db.models.fields.DateField', [], {}),
'data': ('ocrlab.models.jsonfield.JSONTextField', [], {'name': "'data'"}),
'description': ('django.db.models.fields.TextField', [], {'blank': 'True'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '100'}),
'profile': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'presets'", 'null': 'True', 'to': "orm['ocrlab.Profile']"}),
'public': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'slug': ('autoslug.fields.AutoSlugField', [], {'unique': 'True', 'max_length': '50', 'populate_from': 'None', 'unique_with': '()', 'db_index': 'True'}),
'updated_on': ('django.db.models.fields.DateField', [], {'null': 'True', 'blank': 'True'})
},
'ocrlab.profile': {
'Meta': {'object_name': 'Profile'},
'created_on': ('django.db.models.fields.DateField', [], {}),
'data': ('ocrlab.models.jsonfield.JSONTextField', [], {'name': "'data'"}),
'description': ('django.db.models.fields.TextField', [], {'blank': 'True'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '100'}),
'slug': ('autoslug.fields.AutoSlugField', [], {'unique': 'True', 'max_length': '50', 'populate_from': 'None', 'unique_with': '()', 'db_index': 'True'}),
'updated_on': ('django.db.models.fields.DateField', [], {'null': 'True', 'blank': 'True'})
},
'taggit.tag': {
'Meta': {'object_name': 'Tag'},
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'slug': ('django.db.models.fields.SlugField', [], {'unique': 'True', 'max_length': '100', 'db_index': 'True'})
},
'taggit.taggeditem': {
'Meta': {'object_name': 'TaggedItem'},
'content_type': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'taggit_taggeditem_tagged_items'", 'to': "orm['contenttypes.ContentType']"}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'object_id': ('django.db.models.fields.IntegerField', [], {'db_index': 'True'}),
'tag': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'taggit_taggeditem_items'", 'to': "orm['taggit.Tag']"})
}
}
complete_apps = ['ocrlab']
| 64.00625
| 174
| 0.585587
| 1,095
| 10,241
| 5.348858
| 0.085845
| 0.087417
| 0.150589
| 0.215127
| 0.779068
| 0.759262
| 0.757214
| 0.735359
| 0.712652
| 0.681578
| 0
| 0.008839
| 0.193536
| 10,241
| 159
| 175
| 64.408805
| 0.700327
| 0.027829
| 0
| 0.48855
| 0
| 0
| 0.487631
| 0.291935
| 0
| 0
| 0
| 0
| 0
| 1
| 0.015267
| false
| 0
| 0.030534
| 0
| 0.068702
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 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
| 6
|
dcaf01bcebe9e95a012850cce9fe142ee7956fb5
| 1,241
|
py
|
Python
|
scripts.old/diff_blend.py
|
nangtani/blender-import-lwo
|
0bb9856642e04a44e081f1af4089c842d9b405cb
|
[
"MIT"
] | 33
|
2020-04-14T07:48:20.000Z
|
2022-03-30T14:59:33.000Z
|
scripts.old/diff_blend.py
|
nangtani/blender-import-lwo
|
0bb9856642e04a44e081f1af4089c842d9b405cb
|
[
"MIT"
] | 15
|
2020-04-16T23:27:00.000Z
|
2022-03-19T16:18:01.000Z
|
scripts.old/diff_blend.py
|
nangtani/blender-import-lwo
|
0bb9856642e04a44e081f1af4089c842d9b405cb
|
[
"MIT"
] | 4
|
2020-04-22T17:40:11.000Z
|
2022-02-04T16:37:47.000Z
|
import os
import sys
sys.path.append(os.environ["LOCAL_PYTHONPATH"])
from lwo_helper import diff_files, delete_everything
def main():
# # outfile0 = "tests/basic/ref_blend/2.80/cycles/LWO2/ngon/ngon0.lwo.blend"
# # outfile1 = "tests/basic/dst_blend/2.80/cycles/LWO2/ngon/ngon0.lwo.blend"
# # outfile0 = "tests/basic/ref_blend/2.80/cycles/LWO2/box/box5-ngon.lwo.blend"
# # outfile1 = "tests/basic/dst_blend/2.80/cycles/LWO2/box/box5-ngon.lwo.blend"
# outfile0 = "tests/basic/ref_blend/2.79/blender_render/LWO2/box/box5-ngon.lwo.blend"
# outfile1 = "tests/basic/dst_blend/2.79/blender_render/LWO2/box/box5-ngon.lwo.blend"
#
# diff_files(outfile0, outfile1)
#
# delete_everything()
outfile0 = "tests/basic/ref_blend/2.80/cycles/LWO2/ngon/ngon0.lwo.blend"
outfile1 = "tests/basic/dst_blend/2.80/cycles/LWO2/ngon/ngon0.lwo.blend"
diff_files(outfile0, outfile1)
# delete_everything()
# outfile0 = "tests/basic/ref_blend/2.80/cycles/LWO2/box/box5-ngon.lwo.blend"
# outfile1 = "tests/basic/dst_blend/2.80/cycles/LWO2/box/box5-ngon.lwo.blend"
# diff_files(outfile0, outfile1)
#
# delete_everything()
if __name__ == "__main__":
main()
| 35.457143
| 93
| 0.691378
| 181
| 1,241
| 4.574586
| 0.20442
| 0.120773
| 0.077295
| 0.135266
| 0.85628
| 0.85628
| 0.85628
| 0.85628
| 0.830918
| 0.830918
| 0
| 0.062917
| 0.154714
| 1,241
| 34
| 94
| 36.5
| 0.726406
| 0.636583
| 0
| 0
| 0
| 0.2
| 0.334118
| 0.277647
| 0
| 0
| 0
| 0
| 0
| 1
| 0.1
| false
| 0
| 0.3
| 0
| 0.4
| 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
| 0
| 0
|
0
| 6
|
f4b01973aab9cd5ae90ff9b7672b6fb5cf74f590
| 160
|
py
|
Python
|
bot/config/logger.py
|
guibperes/botnet
|
4f41f94774fa45f04290668c007bde8b1ae7e948
|
[
"MIT"
] | null | null | null |
bot/config/logger.py
|
guibperes/botnet
|
4f41f94774fa45f04290668c007bde8b1ae7e948
|
[
"MIT"
] | null | null | null |
bot/config/logger.py
|
guibperes/botnet
|
4f41f94774fa45f04290668c007bde8b1ae7e948
|
[
"MIT"
] | null | null | null |
import logging
def logger_init():
logging.basicConfig(format='%(process)s [%(levelname)s] (%(asctime)s) %(threadName)s - %(message)s', level=logging.INFO)
| 32
| 124
| 0.7
| 21
| 160
| 5.285714
| 0.714286
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.1
| 160
| 4
| 125
| 40
| 0.770833
| 0
| 0
| 0
| 0
| 0.333333
| 0.4375
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| true
| 0
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
f4b358b0ecdcbaee8dcaf28ad910d28e89f14e32
| 101
|
py
|
Python
|
tests/test_day23.py
|
runfalk/advent-of-code-2019-py
|
992ab28fc03a9944796688e96536d22b4541bf3f
|
[
"MIT"
] | null | null | null |
tests/test_day23.py
|
runfalk/advent-of-code-2019-py
|
992ab28fc03a9944796688e96536d22b4541bf3f
|
[
"MIT"
] | null | null | null |
tests/test_day23.py
|
runfalk/advent-of-code-2019-py
|
992ab28fc03a9944796688e96536d22b4541bf3f
|
[
"MIT"
] | null | null | null |
from aoc.day23 import solve
def test_solve():
assert solve("data/day23.txt") == (17740, 12567)
| 16.833333
| 52
| 0.683168
| 15
| 101
| 4.533333
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.166667
| 0.168317
| 101
| 5
| 53
| 20.2
| 0.642857
| 0
| 0
| 0
| 0
| 0
| 0.138614
| 0
| 0
| 0
| 0
| 0
| 0.333333
| 1
| 0.333333
| true
| 0
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 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
| 1
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
f4bc0aa8e5f93035dccd23b6580f6f4f55633390
| 9,077
|
py
|
Python
|
tests/test_base_api.py
|
asampat3090/pymeerkat
|
94ab92535bf88a43300eb3dc8fb70cc3ad83f9a3
|
[
"MIT"
] | null | null | null |
tests/test_base_api.py
|
asampat3090/pymeerkat
|
94ab92535bf88a43300eb3dc8fb70cc3ad83f9a3
|
[
"MIT"
] | 3
|
2015-07-16T06:26:47.000Z
|
2015-07-16T21:38:28.000Z
|
tests/test_base_api.py
|
asampat3090/pymeerkat
|
94ab92535bf88a43300eb3dc8fb70cc3ad83f9a3
|
[
"MIT"
] | null | null | null |
"""
Test cases for the base_api.py file
"""
# Import generic libraries
import unittest
# Add relevant dirs to the sys path
import env
import os
# Import library to test
import base_api
API_VERSION = '1.0'
# Read API_KEY from 'my_api_key' in 'tests/' directory OR
if os.path.isfile('my_api_key'):
with open('my_api_key', 'rb') as f:
API_KEY = f.readline()
else:
API_KEY = os.environ['MEERKAT_API_KEY']
MEERKAT_API = base_api.MeerkatAPI(API_KEY)
# Global test vars
TEST_HEADER = {'Authorization': API_KEY}
TEST_CODE = 200
class MeerkatAPITests(unittest.TestCase):
"""
Test class with test cases for the MeerkatAPI class
"""
# Test methods that use the HTTP requests library
def test_get_leaderboard(self):
"""
Tests get_leaderboard() function
"""
# Set url for this function
test_url = MEERKAT_API.leaderboard_url + '?v=%s' % API_VERSION
# Execute the actual code and get status code
called_header = MEERKAT_API.headers
called_code, called_url, _ = MEERKAT_API.get_leaderboard(print_flag=False)
# verify the results
self.assertEqual(called_header, TEST_HEADER)
self.assertEqual(called_code, TEST_CODE)
self.assertEqual(called_url, test_url)
def test_get_live_broadcasts(self):
"""
Test for get_live_broadcasts()
"""
# Set url for this function
test_url = MEERKAT_API.live_now_url + '?v=%s' % API_VERSION
# Execute the actual code and get status code
called_header = MEERKAT_API.headers
called_code, called_url, _ = MEERKAT_API.get_live_broadcasts(print_flag=False)
# verify the results
self.assertEqual(called_header, TEST_HEADER)
self.assertEqual(called_code, TEST_CODE)
self.assertEqual(called_url, test_url)
def test_get_scheduled_broadcasts(self):
"""
Test for get_scheduled_broadcasts()
"""
# Set url for this function
test_url = MEERKAT_API.scheduled_streams_url + '?v=%s' % API_VERSION
# Execute the actual code and get status code
called_header = MEERKAT_API.headers
called_code, called_url, _ = MEERKAT_API.get_scheduled_broadcasts(print_flag=False)
# verify the results
self.assertEqual(called_header, TEST_HEADER)
self.assertEqual(called_code, TEST_CODE)
self.assertEqual(called_url, test_url)
def test_get_broadcast_summary(self):
"""
Test for get_broadcast_summary(broadcast_id)
"""
# Get the first broadcast id from most recent live broadcast
broadcast_id = MEERKAT_API.get_live_broadcasts(print_flag=False)[2][0]['id']
# Set url for this function
test_url = MEERKAT_API.stream_summary_template_url.replace(
'{broadcastId}',
str(broadcast_id)) + '?v=%s' % API_VERSION
# Execute the actual code and get status code
called_header = MEERKAT_API.headers
called_code, called_url, _ = MEERKAT_API.get_broadcast_summary(
broadcast_id,
print_flag=False)
# verify the results
self.assertEqual(called_header, TEST_HEADER)
self.assertEqual(called_code, TEST_CODE)
self.assertEqual(called_url, test_url)
def test_get_broadcast_watchers(self):
"""
Test for get_broadcast_watchers(broadcast_id)
"""
# Get the first broadcast id from most recent live broadcast
broadcast_id = MEERKAT_API.get_live_broadcasts(print_flag=False)[2][0]['id']
# Set url for this function
test_url = MEERKAT_API.broadcast_watchers_url.replace(
'{broadcastId}',
str(broadcast_id)) + '?v=%s' % API_VERSION
# Execute the actual code and get status code
called_header = MEERKAT_API.headers
called_code, called_url, _ = MEERKAT_API.get_broadcast_watchers(
broadcast_id,
print_flag=False)
# verify the results
self.assertEqual(called_header, TEST_HEADER)
self.assertEqual(called_code, TEST_CODE)
self.assertEqual(called_url, test_url)
def test_get_broadcast_restreams(self):
"""
Test for get_broadcast_restreams(broadcast_id)
"""
# Get the first broadcast id from most recent live broadcast
broadcast_id = MEERKAT_API.get_live_broadcasts(print_flag=False)[2][0]['id']
# Set url for this function
test_url = MEERKAT_API.broadcast_restreams_url.replace('{broadcastId}',str(broadcast_id)) + '?v=%s' % API_VERSION
# Execute the actual code and get status code
called_header = MEERKAT_API.headers
called_code, called_url, _ = MEERKAT_API.get_broadcast_restreams(broadcast_id, print_flag=False)
# verify the results
self.assertEqual(called_header, TEST_HEADER)
self.assertEqual(called_code, TEST_CODE)
self.assertEqual(called_url, test_url)
def test_get_broadcast_likes(self):
"""
Test for get_broadcast_likes(broadcast_id)
"""
# Get the first broadcast id from most recent live broadcast
broadcast_id = MEERKAT_API.get_live_broadcasts(print_flag=False)[2][0]['id']
# Set url for this function
test_url = MEERKAT_API.broadcast_likes_url.replace(
'{broadcastId}',
str(broadcast_id)) + '?v=%s' % API_VERSION
# Execute the actual code and get status code
called_header = MEERKAT_API.headers
called_code, called_url, _ = MEERKAT_API.get_broadcast_likes(broadcast_id, print_flag=False)
# verify the results
self.assertEqual(called_header, TEST_HEADER)
self.assertEqual(called_code, TEST_CODE)
self.assertEqual(called_url, test_url)
def test_get_broadcast_comments(self):
"""
Test for get_broadcast_comments(broadcast_id)
"""
# Get the first broadcast id from most recent live broadcast
broadcast_id = MEERKAT_API.get_live_broadcasts(print_flag=False)[2][0]['id']
# Set url for this function
test_url = MEERKAT_API.broadcast_comments_url.replace(
'{broadcastId}',
str(broadcast_id)) + '?v=%s' % API_VERSION
# Execute the actual code and get status code
called_header = MEERKAT_API.headers
called_code, called_url, _ = MEERKAT_API.get_broadcast_comments(
broadcast_id,
print_flag=False)
# verify the results
self.assertEqual(called_header, TEST_HEADER)
self.assertEqual(called_code, TEST_CODE)
self.assertEqual(called_url, test_url)
def test_get_broadcast_activities(self):
"""
Test for get_broadcast_activities(broadcast_id)
"""
# Get the first broadcast id from most recent live broadcast
broadcast_id = MEERKAT_API.get_live_broadcasts(print_flag=False)[2][0]['id']
# Set url for this function
test_url = MEERKAT_API.broadcast_activities_url.replace(
'{broadcastId}',
str(broadcast_id)) + '?v=%s' % API_VERSION
# Execute the actual code and get status code
called_header = MEERKAT_API.headers
called_code, called_url, _ = MEERKAT_API.get_broadcast_activities(
broadcast_id,
print_flag=False)
# verify the results
self.assertEqual(called_header, TEST_HEADER)
self.assertEqual(called_code, TEST_CODE)
self.assertEqual(called_url, test_url)
def test_get_broadcast_stream_link(self):
"""
Test for get_broadcast_stream_link(broadcast_id)
"""
# Get the first broadcast id from most recent live broadcast
broadcast_id = MEERKAT_API.get_live_broadcasts(print_flag=False)[2][0]['id']
# test url
test_link = 'http://cdn.meerkatapp.co/broadcast/' + str(broadcast_id) + '/live.m3u8'
# Execute code to get link
called_link = MEERKAT_API.get_broadcast_stream_link(broadcast_id)
self.assertEqual(called_link, test_link)
def test_get_user_profile(self):
"""
Test for get_user_profile(user_id)
"""
# Get the first user id from leaderboard
user_id = MEERKAT_API.get_leaderboard(print_flag=False)[2][0]['id']
# Set url for this function
test_url = MEERKAT_API.profile_url.replace('{userId}', str(user_id)) + '?v=%s' % API_VERSION
# Execute the actual code and get status code
called_header = MEERKAT_API.headers
called_code, called_url, _ = MEERKAT_API.get_user_profile(user_id, print_flag=False)
# verify the results
self.assertEqual(called_header, TEST_HEADER)
self.assertEqual(called_code, TEST_CODE)
self.assertEqual(called_url, test_url)
# # Test method that saves the live stream as images.
# def test_save_live_stream():
# # check number of files in the directory
# pass
if __name__ == '__main__':
unittest.main()
| 35.73622
| 121
| 0.66652
| 1,158
| 9,077
| 4.917962
| 0.104491
| 0.071993
| 0.114311
| 0.022827
| 0.809658
| 0.752414
| 0.742757
| 0.736611
| 0.735909
| 0.729236
| 0
| 0.00338
| 0.250303
| 9,077
| 253
| 122
| 35.87747
| 0.833505
| 0.24821
| 0
| 0.560345
| 0
| 0
| 0.039711
| 0
| 0
| 0
| 0
| 0
| 0.267241
| 1
| 0.094828
| false
| 0
| 0.034483
| 0
| 0.137931
| 0.155172
| 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
| 0
|
0
| 6
|
52010d535b2e970b837548d198e51bcfe4f505e7
| 59
|
py
|
Python
|
elliot/recommender/NN/user_knn/__init__.py
|
swapUniba/Elliot_refactor-tesi-Ventrella
|
3ddffc041696c90a6f6d3e8906c212fc4f55f842
|
[
"Apache-2.0"
] | null | null | null |
elliot/recommender/NN/user_knn/__init__.py
|
swapUniba/Elliot_refactor-tesi-Ventrella
|
3ddffc041696c90a6f6d3e8906c212fc4f55f842
|
[
"Apache-2.0"
] | null | null | null |
elliot/recommender/NN/user_knn/__init__.py
|
swapUniba/Elliot_refactor-tesi-Ventrella
|
3ddffc041696c90a6f6d3e8906c212fc4f55f842
|
[
"Apache-2.0"
] | 1
|
2021-06-02T06:57:07.000Z
|
2021-06-02T06:57:07.000Z
|
from elliot.recommender.NN.user_knn.user_knn import UserKNN
| 59
| 59
| 0.881356
| 10
| 59
| 5
| 0.8
| 0.28
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.050847
| 59
| 1
| 59
| 59
| 0.892857
| 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
| 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
| 6
|
523c863e99c11f3053687e0105d0e8af145350a0
| 170
|
py
|
Python
|
tpu_star/__init__.py
|
shonenkov/TPU-Star
|
184ca912e4c3e6300af0156213ed792997d1fcc4
|
[
"Apache-2.0"
] | 8
|
2020-10-28T07:03:20.000Z
|
2021-12-22T09:10:40.000Z
|
tpu_star/__init__.py
|
shonenkov/TPU-Star
|
184ca912e4c3e6300af0156213ed792997d1fcc4
|
[
"Apache-2.0"
] | 8
|
2020-10-21T10:00:22.000Z
|
2021-10-05T18:45:04.000Z
|
tpu_star/__init__.py
|
shonenkov/TPU-Star
|
184ca912e4c3e6300af0156213ed792997d1fcc4
|
[
"Apache-2.0"
] | null | null | null |
# -*- coding: utf-8 -*-
from . import datasets, experiment, colab_utils, utils
__version__ = '0.0.1-rc10'
__all__ = ['datasets', 'experiment', 'colab_utils', 'utils']
| 21.25
| 60
| 0.664706
| 21
| 170
| 4.904762
| 0.666667
| 0.349515
| 0.446602
| 0.543689
| 0.640777
| 0
| 0
| 0
| 0
| 0
| 0
| 0.041096
| 0.141176
| 170
| 7
| 61
| 24.285714
| 0.664384
| 0.123529
| 0
| 0
| 0
| 0
| 0.29932
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.333333
| 0
| 0.333333
| 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
| 0
| 0
|
0
| 6
|
5292eefe114d53fff74bd33058d4916a0aa620fe
| 8,176
|
py
|
Python
|
build/ARM/python/m5/internal/CreditLink_vector.py
|
Jakgn/gem5_test
|
0ba7cc5213cf513cf205af7fc995cf679ebc1a3f
|
[
"BSD-3-Clause"
] | null | null | null |
build/ARM/python/m5/internal/CreditLink_vector.py
|
Jakgn/gem5_test
|
0ba7cc5213cf513cf205af7fc995cf679ebc1a3f
|
[
"BSD-3-Clause"
] | null | null | null |
build/ARM/python/m5/internal/CreditLink_vector.py
|
Jakgn/gem5_test
|
0ba7cc5213cf513cf205af7fc995cf679ebc1a3f
|
[
"BSD-3-Clause"
] | null | null | null |
# This file was automatically generated by SWIG (http://www.swig.org).
# Version 2.0.11
#
# Do not make changes to this file unless you know what you are doing--modify
# the SWIG interface file instead.
from sys import version_info
if version_info >= (2,6,0):
def swig_import_helper():
from os.path import dirname
import imp
fp = None
try:
fp, pathname, description = imp.find_module('_CreditLink_vector', [dirname(__file__)])
except ImportError:
import _CreditLink_vector
return _CreditLink_vector
if fp is not None:
try:
_mod = imp.load_module('_CreditLink_vector', fp, pathname, description)
finally:
fp.close()
return _mod
_CreditLink_vector = swig_import_helper()
del swig_import_helper
else:
import _CreditLink_vector
del version_info
try:
_swig_property = property
except NameError:
pass # Python < 2.2 doesn't have 'property'.
def _swig_setattr_nondynamic(self,class_type,name,value,static=1):
if (name == "thisown"): return self.this.own(value)
if (name == "this"):
if type(value).__name__ == 'SwigPyObject':
self.__dict__[name] = value
return
method = class_type.__swig_setmethods__.get(name,None)
if method: return method(self,value)
if (not static):
self.__dict__[name] = value
else:
raise AttributeError("You cannot add attributes to %s" % self)
def _swig_setattr(self,class_type,name,value):
return _swig_setattr_nondynamic(self,class_type,name,value,0)
def _swig_getattr(self,class_type,name):
if (name == "thisown"): return self.this.own()
method = class_type.__swig_getmethods__.get(name,None)
if method: return method(self)
raise AttributeError(name)
def _swig_repr(self):
try: strthis = "proxy of " + self.this.__repr__()
except: strthis = ""
return "<%s.%s; %s >" % (self.__class__.__module__, self.__class__.__name__, strthis,)
try:
_object = object
_newclass = 1
except AttributeError:
class _object : pass
_newclass = 0
def _swig_setattr_nondynamic_method(set):
def set_attr(self,name,value):
if (name == "thisown"): return self.this.own(value)
if hasattr(self,name) or (name == "this"):
set(self,name,value)
else:
raise AttributeError("You cannot add attributes to %s" % self)
return set_attr
class SwigPyIterator(object):
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined - class is abstract")
__repr__ = _swig_repr
__swig_destroy__ = _CreditLink_vector.delete_SwigPyIterator
__del__ = lambda self : None;
def value(self): return _CreditLink_vector.SwigPyIterator_value(self)
def incr(self, n=1): return _CreditLink_vector.SwigPyIterator_incr(self, n)
def decr(self, n=1): return _CreditLink_vector.SwigPyIterator_decr(self, n)
def distance(self, *args): return _CreditLink_vector.SwigPyIterator_distance(self, *args)
def equal(self, *args): return _CreditLink_vector.SwigPyIterator_equal(self, *args)
def copy(self): return _CreditLink_vector.SwigPyIterator_copy(self)
def next(self): return _CreditLink_vector.SwigPyIterator_next(self)
def __next__(self): return _CreditLink_vector.SwigPyIterator___next__(self)
def previous(self): return _CreditLink_vector.SwigPyIterator_previous(self)
def advance(self, *args): return _CreditLink_vector.SwigPyIterator_advance(self, *args)
def __eq__(self, *args): return _CreditLink_vector.SwigPyIterator___eq__(self, *args)
def __ne__(self, *args): return _CreditLink_vector.SwigPyIterator___ne__(self, *args)
def __iadd__(self, *args): return _CreditLink_vector.SwigPyIterator___iadd__(self, *args)
def __isub__(self, *args): return _CreditLink_vector.SwigPyIterator___isub__(self, *args)
def __add__(self, *args): return _CreditLink_vector.SwigPyIterator___add__(self, *args)
def __sub__(self, *args): return _CreditLink_vector.SwigPyIterator___sub__(self, *args)
def __iter__(self): return self
SwigPyIterator_swigregister = _CreditLink_vector.SwigPyIterator_swigregister
SwigPyIterator_swigregister(SwigPyIterator)
import m5.internal.param_CreditLink
import m5.internal.param_NetworkLink
import m5.internal.param_ClockedObject
import m5.internal.param_ClockDomain
import m5.internal.param_SimObject
import m5.internal.drain
import m5.internal.serialize
import m5.internal.enum_PwrState
import m5.internal.param_PowerModel
import m5.internal.PowerModelState_vector
import m5.internal.param_PowerModelState
import m5.internal.param_SubSystem
import m5.internal.param_ThermalDomain
class vector_CreditLink(object):
thisown = _swig_property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
__repr__ = _swig_repr
def iterator(self): return _CreditLink_vector.vector_CreditLink_iterator(self)
def __iter__(self): return self.iterator()
def __nonzero__(self): return _CreditLink_vector.vector_CreditLink___nonzero__(self)
def __bool__(self): return _CreditLink_vector.vector_CreditLink___bool__(self)
def __len__(self): return _CreditLink_vector.vector_CreditLink___len__(self)
def pop(self): return _CreditLink_vector.vector_CreditLink_pop(self)
def __getslice__(self, *args): return _CreditLink_vector.vector_CreditLink___getslice__(self, *args)
def __setslice__(self, *args): return _CreditLink_vector.vector_CreditLink___setslice__(self, *args)
def __delslice__(self, *args): return _CreditLink_vector.vector_CreditLink___delslice__(self, *args)
def __delitem__(self, *args): return _CreditLink_vector.vector_CreditLink___delitem__(self, *args)
def __getitem__(self, *args): return _CreditLink_vector.vector_CreditLink___getitem__(self, *args)
def __setitem__(self, *args): return _CreditLink_vector.vector_CreditLink___setitem__(self, *args)
def append(self, *args): return _CreditLink_vector.vector_CreditLink_append(self, *args)
def empty(self): return _CreditLink_vector.vector_CreditLink_empty(self)
def size(self): return _CreditLink_vector.vector_CreditLink_size(self)
def clear(self): return _CreditLink_vector.vector_CreditLink_clear(self)
def swap(self, *args): return _CreditLink_vector.vector_CreditLink_swap(self, *args)
def get_allocator(self): return _CreditLink_vector.vector_CreditLink_get_allocator(self)
def begin(self): return _CreditLink_vector.vector_CreditLink_begin(self)
def end(self): return _CreditLink_vector.vector_CreditLink_end(self)
def rbegin(self): return _CreditLink_vector.vector_CreditLink_rbegin(self)
def rend(self): return _CreditLink_vector.vector_CreditLink_rend(self)
def pop_back(self): return _CreditLink_vector.vector_CreditLink_pop_back(self)
def erase(self, *args): return _CreditLink_vector.vector_CreditLink_erase(self, *args)
def __init__(self, *args):
this = _CreditLink_vector.new_vector_CreditLink(*args)
try: self.this.append(this)
except: self.this = this
def push_back(self, *args): return _CreditLink_vector.vector_CreditLink_push_back(self, *args)
def front(self): return _CreditLink_vector.vector_CreditLink_front(self)
def back(self): return _CreditLink_vector.vector_CreditLink_back(self)
def assign(self, *args): return _CreditLink_vector.vector_CreditLink_assign(self, *args)
def resize(self, *args): return _CreditLink_vector.vector_CreditLink_resize(self, *args)
def insert(self, *args): return _CreditLink_vector.vector_CreditLink_insert(self, *args)
def reserve(self, *args): return _CreditLink_vector.vector_CreditLink_reserve(self, *args)
def capacity(self): return _CreditLink_vector.vector_CreditLink_capacity(self)
__swig_destroy__ = _CreditLink_vector.delete_vector_CreditLink
__del__ = lambda self : None;
vector_CreditLink_swigregister = _CreditLink_vector.vector_CreditLink_swigregister
vector_CreditLink_swigregister(vector_CreditLink)
| 48.958084
| 107
| 0.756727
| 1,032
| 8,176
| 5.516473
| 0.17345
| 0.163007
| 0.185491
| 0.17987
| 0.496575
| 0.440014
| 0.26717
| 0.109433
| 0.082031
| 0.069032
| 0
| 0.004027
| 0.149584
| 8,176
| 166
| 108
| 49.253012
| 0.814756
| 0.028131
| 0
| 0.145833
| 1
| 0
| 0.030242
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.402778
| false
| 0.013889
| 0.152778
| 0.347222
| 0.673611
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 6
|
870633847caa0416890f0d64e5fc0ad34c7e4180
| 20
|
py
|
Python
|
nytram_box2d/shapes/__init__.py
|
cloew/NytramBox2D
|
0c01bd592322fcfa8bbf6fc9374e4958edc69f0b
|
[
"MIT"
] | null | null | null |
nytram_box2d/shapes/__init__.py
|
cloew/NytramBox2D
|
0c01bd592322fcfa8bbf6fc9374e4958edc69f0b
|
[
"MIT"
] | 1
|
2021-10-17T10:18:04.000Z
|
2021-10-17T10:18:04.000Z
|
nytram_box2d/shapes/__init__.py
|
cloew/NytramBox2D
|
0c01bd592322fcfa8bbf6fc9374e4958edc69f0b
|
[
"MIT"
] | null | null | null |
from .box import Box
| 20
| 20
| 0.8
| 4
| 20
| 4
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.15
| 20
| 1
| 20
| 20
| 0.941176
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 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
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
871ebc6e1f97d365b4a7e4805eb6210fd26cef50
| 37
|
py
|
Python
|
vedastr/optimizers/__init__.py
|
csmasters/vedastr
|
7513384ab503f15dc574c7d92b75ff2092354757
|
[
"Apache-2.0"
] | 475
|
2020-03-17T01:46:32.000Z
|
2022-03-29T23:30:15.000Z
|
vedastr/optimizers/__init__.py
|
csmasters/vedastr
|
7513384ab503f15dc574c7d92b75ff2092354757
|
[
"Apache-2.0"
] | 71
|
2020-04-01T04:17:47.000Z
|
2021-11-18T06:55:14.000Z
|
vedastr/optimizers/__init__.py
|
csmasters/vedastr
|
7513384ab503f15dc574c7d92b75ff2092354757
|
[
"Apache-2.0"
] | 108
|
2020-02-21T10:30:37.000Z
|
2022-03-21T12:03:30.000Z
|
from .builder import build_optimizer
| 18.5
| 36
| 0.864865
| 5
| 37
| 6.2
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.108108
| 37
| 1
| 37
| 37
| 0.939394
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 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
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
872c1e9bced709a6d81e485c6b2f93d21b7f7960
| 65
|
py
|
Python
|
jptools/mocks/languageHandler.py
|
riku22/nvdajp
|
66a828ea89d317e4aa0ad2aed4b3b1e08920afb6
|
[
"bzip2-1.0.6"
] | 19
|
2016-05-11T05:15:31.000Z
|
2022-03-17T12:40:10.000Z
|
jptools/mocks/languageHandler.py
|
riku22/nvdajp
|
66a828ea89d317e4aa0ad2aed4b3b1e08920afb6
|
[
"bzip2-1.0.6"
] | 307
|
2015-08-27T11:22:33.000Z
|
2022-03-29T10:43:34.000Z
|
jptools/mocks/languageHandler.py
|
riku22/nvdajp
|
66a828ea89d317e4aa0ad2aed4b3b1e08920afb6
|
[
"bzip2-1.0.6"
] | 14
|
2016-03-28T07:31:49.000Z
|
2022-03-30T04:56:35.000Z
|
def setLanguage(lang):
pass
def getLanguage():
return 'ja-JP'
| 10.833333
| 22
| 0.707692
| 9
| 65
| 5.111111
| 0.888889
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.153846
| 65
| 5
| 23
| 13
| 0.836364
| 0
| 0
| 0
| 0
| 0
| 0.076923
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| false
| 0.25
| 0
| 0.25
| 0.75
| 0
| 1
| 0
| 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
| 1
| 0
| 1
| 0
| 1
| 1
| 0
|
0
| 6
|
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