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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
f69a1175aca2404165e86b48399f9d2bead7a6b7 | 1,606 | py | Python | tests/explanation_utils.py | ruiwang1994/interpret-community | 21c343b8743ebab9f3c5628a1b0e991c9437af51 | [
"MIT"
] | 1 | 2022-03-07T10:32:54.000Z | 2022-03-07T10:32:54.000Z | tests/explanation_utils.py | ruiwang1994/interpret-community | 21c343b8743ebab9f3c5628a1b0e991c9437af51 | [
"MIT"
] | null | null | null | tests/explanation_utils.py | ruiwang1994/interpret-community | 21c343b8743ebab9f3c5628a1b0e991c9437af51 | [
"MIT"
] | null | null | null | # ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
# Defines common test utilities for validating explanation objects
def validate_global_classification_explanation_shape(explanation, evaluation_examples, num_classes=2):
assert explanation._global_importance_values.shape[0] == evaluation_examples.shape[1]
assert explanation._per_class_values.shape[0] == num_classes
assert explanation._per_class_values.shape[1] == evaluation_examples.shape[1]
validate_local_classification_explanation_shape(explanation, evaluation_examples, num_classes)
def validate_local_classification_explanation_shape(explanation, evaluation_examples, num_classes=2):
assert explanation._local_importance_values.shape[0] == num_classes
assert explanation._local_importance_values.shape[1] == evaluation_examples.shape[0]
assert explanation._local_importance_values.shape[2] == evaluation_examples.shape[1]
def validate_global_regression_explanation_shape(explanation, evaluation_examples, num_classes=2):
assert explanation._global_importance_values.shape[0] == evaluation_examples.shape[1]
validate_local_regression_explanation_shape(explanation, evaluation_examples, num_classes)
def validate_local_regression_explanation_shape(explanation, evaluation_examples, num_classes=2):
assert explanation._local_importance_values.shape[0] == evaluation_examples.shape[0]
assert explanation._local_importance_values.shape[1] == evaluation_examples.shape[1]
| 57.357143 | 102 | 0.775841 | 178 | 1,606 | 6.595506 | 0.191011 | 0.199319 | 0.125213 | 0.189097 | 0.861158 | 0.861158 | 0.833049 | 0.759796 | 0.759796 | 0.759796 | 0 | 0.013559 | 0.081569 | 1,606 | 27 | 103 | 59.481481 | 0.782373 | 0.148194 | 0 | 0.133333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.6 | 1 | 0.266667 | false | 0 | 0.466667 | 0 | 0.733333 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 10 |
f6aa38ad2b1f2cd10c5ccf59cbde5b24709cc03d | 2,358 | py | Python | models/CNN.py | shuoli90/PAC-confidence-set | ab8dcd5205f9aba6b490aabe7bfc74e1410d0f26 | [
"Apache-2.0"
] | 6 | 2020-04-05T18:55:15.000Z | 2021-08-23T02:22:48.000Z | models/CNN.py | shuoli90/PAC-confidence-set | ab8dcd5205f9aba6b490aabe7bfc74e1410d0f26 | [
"Apache-2.0"
] | null | null | null | models/CNN.py | shuoli90/PAC-confidence-set | ab8dcd5205f9aba6b490aabe7bfc74e1410d0f26 | [
"Apache-2.0"
] | 1 | 2021-03-29T15:06:43.000Z | 2021-03-29T15:06:43.000Z | import os, sys
import numpy as np
import torch as tc
import torch.tensor as T
import torch.nn as nn
import torch.nn.functional as F
import torchvision
from .BaseForecasters import *
class AlexNet(Forecaster):
def __init__(self, pretrained=True, n_labels = 1000, load_type='none'):
super().__init__()
model = torchvision.models.alexnet(pretrained=pretrained)
model = model.eval()
self.load_type = load_type
if load_type == 'none':
self.pred = model
elif 'feature' in load_type:
self.pred = model.fc
elif 'logit' in load_type:
self.pred = lambda xs: xs
else:
raise NotImplementedError
def forward(self, xs):
return self.pred(xs)
def eval(self):
self.training = False
if 'logit' not in self.load_type:
self.pred.eval()
return self
class GoogLeNet(Forecaster):
def __init__(self, pretrained=True, n_labels = 1000, load_type='none'):
super().__init__()
model = torchvision.models.googlenet(pretrained=pretrained)
model = model.eval()
self.load_type = load_type
if load_type == 'none':
self.pred = model
elif 'feature' in load_type:
self.pred = model.fc
elif 'logit' in load_type:
self.pred = lambda xs: xs
else:
raise NotImplementedError
def forward(self, xs):
return self.pred(xs)
def eval(self):
self.training = False
if 'logit' not in self.load_type:
self.pred.eval()
return self
class VGG19(Forecaster):
def __init__(self, pretrained=True, n_labels = 1000, load_type='none'):
super().__init__()
model = torchvision.models.vgg19(pretrained=pretrained)
model = model.eval()
self.load_type = load_type
if load_type == 'none':
self.pred = model
elif 'feature' in load_type:
self.pred = model.fc
elif 'logit' in load_type:
self.pred = lambda xs: xs
else:
raise NotImplementedError
def forward(self, xs):
return self.pred(xs)
def eval(self):
self.training = False
if 'logit' not in self.load_type:
self.pred.eval()
return self
| 26.2 | 75 | 0.58609 | 287 | 2,358 | 4.648084 | 0.184669 | 0.125937 | 0.08096 | 0.107946 | 0.855322 | 0.855322 | 0.855322 | 0.855322 | 0.855322 | 0.855322 | 0 | 0.009988 | 0.320611 | 2,358 | 89 | 76 | 26.494382 | 0.822722 | 0 | 0 | 0.802817 | 0 | 0 | 0.03182 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.126761 | false | 0 | 0.112676 | 0.042254 | 0.366197 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
f6b5c49e05658f44ab4d97132ec04728ed5198ab | 83 | py | Python | dots_connect_deta/users/views/__init__.py | Sinha-Ujjawal/dots-connect-deta | cdb9ab69a22b8623a1265d565ee2e9dd91b1f1fe | [
"MIT"
] | null | null | null | dots_connect_deta/users/views/__init__.py | Sinha-Ujjawal/dots-connect-deta | cdb9ab69a22b8623a1265d565ee2e9dd91b1f1fe | [
"MIT"
] | null | null | null | dots_connect_deta/users/views/__init__.py | Sinha-Ujjawal/dots-connect-deta | cdb9ab69a22b8623a1265d565ee2e9dd91b1f1fe | [
"MIT"
] | null | null | null | from .user_me import UserMe
from .user_change_password import UserChangePassword
| 27.666667 | 53 | 0.855422 | 11 | 83 | 6.181818 | 0.727273 | 0.235294 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.120482 | 83 | 2 | 54 | 41.5 | 0.931507 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.5 | 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 | 1 | 1 | 0 | 1 | 0 | 0 | 7 |
100ff761a87167e8e19b712815ae5391d95a332a | 7,414 | py | Python | tests/test_api/test_tags.py | MyOpenPantry/flask-backend | e94702bfa04f36c1a6015ae3e9c37dfb7b923279 | [
"MIT"
] | null | null | null | tests/test_api/test_tags.py | MyOpenPantry/flask-backend | e94702bfa04f36c1a6015ae3e9c37dfb7b923279 | [
"MIT"
] | 4 | 2021-03-28T19:47:04.000Z | 2021-05-04T00:59:46.000Z | tests/test_api/test_tags.py | MyOpenPantry/flask-backend | e94702bfa04f36c1a6015ae3e9c37dfb7b923279 | [
"MIT"
] | null | null | null |
class TestTags:
def test_get_empty(self, app):
client = app.test_client()
response = client.get('tags/')
assert response.status_code == 200
assert len(response.json) == 0
def test_get_nonempty(self, app):
client = app.test_client()
tag = {
'name': 'greek',
}
response = client.post(
'tags/',
headers={"Content-Type": "application/json"},
json=tag,
)
response = client.get('tags/')
assert response.status_code == 200
assert len(response.json) == 1
def test_get_by_id(self, app):
client = app.test_client()
tag = {
'name': 'chicken',
}
response = client.post(
'tags/',
headers={"Content-Type": "application/json"},
json=tag,
)
id = response.json['id']
response = client.get(f'tags/{id}')
assert response.status_code == 200
assert response.json['id'] == 1
assert response.json['name'] == tag['name']
def test_get_invalid(self, app):
client = app.test_client()
tag = {
'name': 'beef',
}
response = client.post(
'tags/',
headers={"Content-Type": "application/json"},
json=tag,
)
id = response.json['id']
response = client.get(f'tags/{id+1}')
assert response.status_code == 404
def test_post(self, app):
client = app.test_client()
tag = {
'name': 'chicken',
}
response = client.post(
'tags/',
headers={"Content-Type": "application/json"},
json=tag,
)
assert response.status_code == 201
assert response.json['name'] == tag['name']
def test_post_invalid(self, app):
client = app.test_client()
tag = {}
response = client.post(
'tags/',
headers={"Content-Type": "application/json"},
json=tag,
)
assert response.status_code == 422
def test_post_existing(self, app):
client = app.test_client()
tag = {
'name': 'chicken',
}
response = client.post(
'tags/',
headers={"Content-Type": "application/json"},
json=tag,
)
id = response.json['id']
response = client.get(f'tags/{id}')
assert response.status_code == 200
response = client.post(
'tags/',
headers={"Content-Type": "application/json"},
json=tag,
)
assert response.status_code == 422
def test_put(self, app):
client = app.test_client()
tag = {
'name': 'vegetaria',
}
response = client.post(
'tags/',
headers={"Content-Type": "application/json"},
json=tag,
)
assert response.status_code == 201
assert response.json['name'] == tag['name']
etag = response.headers['ETag']
id = response.json['id']
tag['name'] = 'vegetarian'
response = client.put(
f'tags/{id}',
headers={'If-Match': etag},
json=tag,
)
assert response.status_code == 200
assert response.json['name'] == tag['name']
def test_put_invalid(self, app):
client = app.test_client()
tag = {'name': 'kosher'}
response = client.post(
'tags/',
headers={"Content-Type": "application/json"},
json=tag,
)
assert response.status_code == 201
tag = {
'name': 'vegetaria',
}
response = client.post(
'tags/',
headers={"Content-Type": "application/json"},
json=tag,
)
assert response.status_code == 201
assert response.json['name'] == tag['name']
etag = response.headers['ETag']
id = response.json['id']
# try to change vegetaria to kosher to check name integrity
response = client.put(
f'tags/{id}',
headers={'If-Match': etag},
json={'name': 'kosher'},
)
assert response.status_code == 422
# no etag
response = client.put(
f'tags/{id}',
headers={'If-Match': ''},
json=tag,
)
assert response.status_code == 428
# no name
del tag['name']
response = client.put(
f'tags/{id}',
headers={'If-Match': etag},
json=tag,
)
assert response.status_code == 422
def test_query(self, app):
client = app.test_client()
tags = (
{'name': 'creole'},
{'name': 'vegetarian'},
{'name': 'side'},
{'name': 'meat'},
{'name': 'chicken thigh'},
{'name': 'chicken breast'}
)
for tag in tags:
response = client.post(
'tags/',
headers={"Content-Type": "application/json"},
json=tag,
)
assert response.status_code == 201
# start of queries
query_resp = (
({'name': ''}, {'code': 422}),
({'name': 'meat'}, {'code': 200, 'len': 1}),
({'name': 'chicken'}, {'code': 200, 'len': 2}),
({'name': 'no such tag'}, {'code': 200, 'len': 0}),
)
for query, resp in query_resp:
response = client.get(
'tags/',
headers={'Content-Type': 'application/json'},
query_string=query
)
assert response.status_code == resp['code']
if resp.get('len', None):
assert len(response.json) == resp['len']
def test_delete(self, app):
client = app.test_client()
tag = {
'name': 'vegetaria',
}
response = client.post(
'tags/',
headers={"Content-Type": "application/json"},
json=tag,
)
assert response.status_code == 201
assert response.json['name'] == tag['name']
etag = response.headers['ETag']
id = response.json['id']
response = client.delete(
f'tags/{id}',
headers={'If-Match': etag},
)
assert response.status_code == 204
def test_delete_invalid(self, app):
client = app.test_client()
tag = {
'name': 'savory',
}
response = client.post(
'tags/',
headers={"Content-Type": "application/json"},
json=tag,
)
assert response.status_code == 201
etag = response.headers['ETag']
id = response.json['id']
# no etag
response = client.delete(
f'tags/{id}',
headers={'If-Match': ''},
)
assert response.status_code == 428
response = client.delete(
f'tags/{id}',
headers={'If-Match': etag},
)
assert response.status_code == 204
response = client.delete(
f'tags/{id}',
headers={'If-Match': etag},
)
assert response.status_code == 404
| 23.536508 | 67 | 0.471136 | 716 | 7,414 | 4.796089 | 0.100559 | 0.122306 | 0.133955 | 0.160745 | 0.831974 | 0.806639 | 0.771695 | 0.771695 | 0.711124 | 0.650262 | 0 | 0.018616 | 0.384138 | 7,414 | 314 | 68 | 23.611465 | 0.733465 | 0.013218 | 0 | 0.657895 | 0 | 0 | 0.137091 | 0 | 0 | 0 | 0 | 0 | 0.144737 | 1 | 0.052632 | false | 0 | 0 | 0 | 0.057018 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
101b057a95044417e4ba2a710e05ecff29200e98 | 107 | py | Python | cryptoBar/__init__.py | ihtiht/CryptowatchForMac | 9a5153a420a03d14c40f1c7eecb36f77890a5a22 | [
"MIT"
] | null | null | null | cryptoBar/__init__.py | ihtiht/CryptowatchForMac | 9a5153a420a03d14c40f1c7eecb36f77890a5a22 | [
"MIT"
] | null | null | null | cryptoBar/__init__.py | ihtiht/CryptowatchForMac | 9a5153a420a03d14c40f1c7eecb36f77890a5a22 | [
"MIT"
] | null | null | null | try:
from cryptoBar.cryptoBar import CryptoBar
except ImportError:
from cryptoBar import CryptoBar
| 21.4 | 45 | 0.794393 | 12 | 107 | 7.083333 | 0.5 | 0.305882 | 0.564706 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.17757 | 107 | 4 | 46 | 26.75 | 0.965909 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.75 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
63f2167bcdb85485f24fd11e648be925fcb79aae | 4,832 | py | Python | tests/test_safety_checker.py | sreeja/soteria_tool | c6332520b06dae7bc76737683614ab4225ef0608 | [
"MIT"
] | 2 | 2019-03-25T10:57:04.000Z | 2022-02-16T13:44:05.000Z | tests/test_safety_checker.py | sreeja/soteria_tool | c6332520b06dae7bc76737683614ab4225ef0608 | [
"MIT"
] | null | null | null | tests/test_safety_checker.py | sreeja/soteria_tool | c6332520b06dae7bc76737683614ab4225ef0608 | [
"MIT"
] | 1 | 2022-02-16T13:44:16.000Z | 2022-02-16T13:44:16.000Z | from soteria.exceptions import SafetyError
from soteria.checks.safety_checker import SafetyChecker
from soteria.components.specification import Specification
from soteria.components.function import Function
from soteria.components.parameter import Parameter
from soteria.components.variable import Variable
from soteria.components.procedure import Procedure
import pytest
class TestSafetyChecker:
def test_unsafe_proc(self):
spec = Specification('sample')
spec.add_variable(Variable('counter', 'int', 1))
procedure = Procedure('dec', 15)
procedure.add_parameter(Parameter('value', 'int'))
procedure.add_modifies('counter')
procedure.set_implementation('counter := counter - value;')
spec.add_procedure(procedure)
merge = Procedure('merge', 15)
merge.add_parameter(Parameter('counter1', 'int'))
merge.add_modifies('counter')
merge.set_implementation('counter := (if counter1 > counter then counter1 else counter);')
spec.set_merge(merge)
invariant = Function('inv', 10)
invariant.add_param(Parameter('counter', 'int'))
invariant.set_return('bool')
spec.set_invariant(invariant)
spec.set_preface('var counter :int;\n//@invariant\nfunction inv(counter:int) returns(bool)\n{\n counter >= 0\n}')
checker = SafetyChecker()
with pytest.raises(SafetyError):
checker.check_safety(spec, procedure)
def test_safe_proc(self):
spec = Specification('sample')
spec.add_variable(Variable('counter', 'int', 1))
procedure = Procedure('inc', 15)
procedure.add_parameter(Parameter('value', 'int'))
procedure.add_modifies('counter')
procedure.add_requires('value > 0')
procedure.add_ensures('counter == old(counter) + value')
procedure.set_implementation('counter := counter + value;')
spec.add_procedure(procedure)
merge = Procedure('merge', 15)
merge.add_parameter(Parameter('counter1', 'int'))
merge.add_modifies('counter')
merge.set_implementation('counter := (if counter1 > counter then counter1 else counter);')
spec.set_merge(merge)
invariant = Function('inv', 10)
invariant.add_param(Parameter('counter', 'int'))
invariant.set_return('bool')
spec.set_invariant(invariant)
spec.set_preface('var counter :int;\n//@invariant\nfunction inv(counter:int) returns(bool)\n{\n counter >= 0\n}')
checker = SafetyChecker()
assert checker.check_safety(spec, procedure) == True
def test_unstable_pair(self):
spec = Specification('sample')
spec.add_variable(Variable('counter', 'int', 1))
procedure = Procedure('dec', 15)
procedure.add_parameter(Parameter('value', 'int'))
procedure.add_modifies('counter')
procedure.add_requires('counter > value')
procedure.set_implementation('counter := counter - value;')
spec.add_procedure(procedure)
merge = Procedure('merge', 15)
merge.add_parameter(Parameter('counter1', 'int'))
merge.add_modifies('counter')
merge.add_requires('counter > 0')
merge.set_implementation('counter := (if counter1 > counter then counter1 else counter);')
spec.set_merge(merge)
invariant = Function('inv', 10)
invariant.add_param(Parameter('counter', 'int'))
invariant.set_return('bool')
spec.set_invariant(invariant)
spec.set_preface('var counter :int;\n//@invariant\nfunction inv(counter:int) returns(bool)\n{\n counter >= 0\n}')
checker = SafetyChecker()
with pytest.raises(SafetyError):
checker.check_stability(spec, procedure)
def test_stable_pair(self):
spec = Specification('sample')
spec.add_variable(Variable('counter', 'int', 1))
procedure = Procedure('inc', 15)
procedure.add_parameter(Parameter('value', 'int'))
procedure.add_modifies('counter')
procedure.add_requires('value > 0')
procedure.set_implementation('counter := counter + value;')
spec.add_procedure(procedure)
merge = Procedure('merge', 15)
merge.add_parameter(Parameter('counter1', 'int'))
merge.add_modifies('counter')
merge.set_implementation('counter := (if counter1 > counter then counter1 else counter);')
spec.set_merge(merge)
invariant = Function('inv', 10)
invariant.add_param(Parameter('counter', 'int'))
invariant.set_return('bool')
spec.set_invariant(invariant)
spec.set_preface('var counter :int;\n//@invariant\nfunction inv(counter:int) returns(bool)\n{\n counter >= 0\n}')
checker = SafetyChecker()
assert checker.check_stability(spec, procedure) == True
| 47.841584 | 122 | 0.664114 | 533 | 4,832 | 5.889306 | 0.123827 | 0.050972 | 0.05352 | 0.034406 | 0.856961 | 0.830838 | 0.830838 | 0.830838 | 0.830838 | 0.830838 | 0 | 0.012249 | 0.205919 | 4,832 | 100 | 123 | 48.32 | 0.805838 | 0 | 0 | 0.791667 | 0 | 0.041667 | 0.22827 | 0.024007 | 0 | 0 | 0 | 0 | 0.020833 | 1 | 0.041667 | false | 0 | 0.083333 | 0 | 0.135417 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
1213ffc203f278748ee33fcac749c8f77de899a3 | 467 | py | Python | unicaps/captcha.py | sergey-scat/unicaps | 8f4a3c3f802c58464e93f953bcf11ecf44ef8f3b | [
"Apache-2.0"
] | 8 | 2020-07-27T19:18:27.000Z | 2022-02-23T04:05:56.000Z | unicaps/captcha.py | sergey-scat/unicaps | 8f4a3c3f802c58464e93f953bcf11ecf44ef8f3b | [
"Apache-2.0"
] | 2 | 2021-01-19T07:06:03.000Z | 2021-09-03T13:27:12.000Z | unicaps/captcha.py | sergey-scat/unicaps | 8f4a3c3f802c58464e93f953bcf11ecf44ef8f3b | [
"Apache-2.0"
] | 5 | 2021-03-22T23:09:05.000Z | 2022-01-21T09:00:14.000Z | # -*- coding: UTF-8 -*-
"""
Supported CAPTCHAs
~~~~~~~~~~~~~~~~~~
"""
# pylint: disable=unused-import,import-error
from ._captcha import (ImageCaptcha, TextCaptcha, RecaptchaV2, RecaptchaV3, HCaptcha, FunCaptcha,
KeyCaptcha, GeeTest, Capy, TikTokCaptcha, CaptchaType)
__all__ = ('ImageCaptcha', 'TextCaptcha', 'RecaptchaV2', 'RecaptchaV3', 'HCaptcha', 'FunCaptcha',
'KeyCaptcha', 'GeeTest', 'Capy', 'TikTokCaptcha', 'CaptchaType')
| 35.923077 | 97 | 0.650964 | 37 | 467 | 8.081081 | 0.648649 | 0.153846 | 0.227425 | 0.301003 | 0.722408 | 0.722408 | 0.722408 | 0.722408 | 0.722408 | 0.722408 | 0 | 0.012788 | 0.162741 | 467 | 12 | 98 | 38.916667 | 0.751918 | 0.220557 | 0 | 0 | 0 | 0 | 0.304225 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.25 | 0 | 0.25 | 0 | 0 | 0 | 0 | null | 0 | 1 | 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 | 7 |
123552a90c76fbaccebd9e39c3c25692214e24d1 | 3,186 | py | Python | genes/migrations/0022_auto_20210225_1521.py | SACGF/variantgrid | 515195e2f03a0da3a3e5f2919d8e0431babfd9c9 | [
"RSA-MD"
] | 5 | 2021-01-14T03:34:42.000Z | 2022-03-07T15:34:18.000Z | genes/migrations/0022_auto_20210225_1521.py | SACGF/variantgrid | 515195e2f03a0da3a3e5f2919d8e0431babfd9c9 | [
"RSA-MD"
] | 551 | 2020-10-19T00:02:38.000Z | 2022-03-30T02:18:22.000Z | genes/migrations/0022_auto_20210225_1521.py | SACGF/variantgrid | 515195e2f03a0da3a3e5f2919d8e0431babfd9c9 | [
"RSA-MD"
] | null | null | null | # Generated by Django 3.1.3 on 2021-02-25 04:51
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('genes', '0021_delete_rvis'),
]
operations = [
migrations.AlterField(
model_name='gene',
name='summary',
field=models.TextField(blank=True, null=True),
),
migrations.AlterField(
model_name='hgnc',
name='ccds_ids',
field=models.TextField(blank=True, null=True),
),
migrations.AlterField(
model_name='hgnc',
name='ensembl_gene_id',
field=models.TextField(blank=True, null=True),
),
migrations.AlterField(
model_name='hgnc',
name='gene_group_ids',
field=models.TextField(blank=True, null=True),
),
migrations.AlterField(
model_name='hgnc',
name='gene_groups',
field=models.TextField(blank=True, null=True),
),
migrations.AlterField(
model_name='hgnc',
name='location',
field=models.TextField(blank=True, null=True),
),
migrations.AlterField(
model_name='hgnc',
name='mgd_ids',
field=models.TextField(blank=True, null=True),
),
migrations.AlterField(
model_name='hgnc',
name='omim_ids',
field=models.TextField(blank=True, null=True),
),
migrations.AlterField(
model_name='hgnc',
name='previous_symbols',
field=models.TextField(blank=True, null=True),
),
migrations.AlterField(
model_name='hgnc',
name='refseq_ids',
field=models.TextField(blank=True, null=True),
),
migrations.AlterField(
model_name='hgnc',
name='rgd_ids',
field=models.TextField(blank=True, null=True),
),
migrations.AlterField(
model_name='hgnc',
name='ucsc_ids',
field=models.TextField(blank=True, null=True),
),
migrations.AlterField(
model_name='hgnc',
name='uniprot_ids',
field=models.TextField(blank=True, null=True),
),
migrations.AlterField(
model_name='uniprot',
name='function',
field=models.TextField(blank=True, null=True),
),
migrations.AlterField(
model_name='uniprot',
name='pathway',
field=models.TextField(blank=True, null=True),
),
migrations.AlterField(
model_name='uniprot',
name='pathway_interaction_db',
field=models.TextField(blank=True, null=True),
),
migrations.AlterField(
model_name='uniprot',
name='reactome',
field=models.TextField(blank=True, null=True),
),
migrations.AlterField(
model_name='uniprot',
name='tissue_specificity',
field=models.TextField(blank=True, null=True),
),
]
| 30.634615 | 58 | 0.534212 | 296 | 3,186 | 5.628378 | 0.189189 | 0.216086 | 0.270108 | 0.313325 | 0.813926 | 0.813926 | 0.813926 | 0.791717 | 0.791717 | 0.791717 | 0 | 0.0091 | 0.344633 | 3,186 | 103 | 59 | 30.932039 | 0.788793 | 0.014124 | 0 | 0.731959 | 1 | 0 | 0.09589 | 0.007009 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.010309 | 0 | 0.041237 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 |
12715f6598bf730541916432e8503ad57b2b1022 | 166 | py | Python | wafw00f/plugins/missioncontrol.py | wizard531/wafw00f | dce0d0616db0f970013432c520b51aeef62d387f | [
"BSD-3-Clause"
] | null | null | null | wafw00f/plugins/missioncontrol.py | wizard531/wafw00f | dce0d0616db0f970013432c520b51aeef62d387f | [
"BSD-3-Clause"
] | null | null | null | wafw00f/plugins/missioncontrol.py | wizard531/wafw00f | dce0d0616db0f970013432c520b51aeef62d387f | [
"BSD-3-Clause"
] | null | null | null | #!/usr/bin/env python
NAME = 'Mission Control Application Shield'
def is_waf(self):
return self.matchheader(('server', 'Mission Control Application Shield'))
| 18.444444 | 77 | 0.728916 | 21 | 166 | 5.714286 | 0.761905 | 0.233333 | 0.416667 | 0.516667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.144578 | 166 | 8 | 78 | 20.75 | 0.84507 | 0.120482 | 0 | 0 | 0 | 0 | 0.510345 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0 | 0.333333 | 0.666667 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 8 |
89df0c2db59e4cd8cc83d21bf560dfbe02285f0c | 2,564 | py | Python | lenv/lib/python3.6/site-packages/Crypto/SelfTest/Hash/test_vectors/BLAKE2s/tv2.txt.py | shrey-c/DataLeakageDjango | a827c5a09e5501921f9fb97b656755671238dd63 | [
"BSD-3-Clause"
] | 6 | 2020-05-03T12:03:21.000Z | 2020-09-07T08:33:58.000Z | lenv/lib/python3.6/site-packages/Crypto/SelfTest/Hash/test_vectors/BLAKE2s/tv2.txt.py | shrey-c/DataLeakageDjango | a827c5a09e5501921f9fb97b656755671238dd63 | [
"BSD-3-Clause"
] | 3 | 2020-04-17T06:50:44.000Z | 2022-01-13T02:16:48.000Z | lenv/lib/python3.6/site-packages/Crypto/SelfTest/Hash/test_vectors/BLAKE2s/tv2.txt.py | shrey-c/DataLeakageDjango | a827c5a09e5501921f9fb97b656755671238dd63 | [
"BSD-3-Clause"
] | null | null | null | X
X XXXXXXX XXX XXXXXXXX XXXXXX XXX XXXX XXX XXXX XXXX X XX XXX XXXXXX
X XXXX X XXXXXXXXXXXXX
X XXXX X XXXXXXXXXXXXXXXX X XXXXXX X XXX X XXXXX
X XXXXX XXX XXX XX X XXXXXX XX XXXXX
X
XXXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
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| 67.473684 | 76 | 0.959048 | 101 | 2,564 | 24.346535 | 0.128713 | 0.671004 | 1.243595 | 1.281009 | 0.941846 | 0.941846 | 0.941846 | 0.941846 | 0.941846 | 0.941846 | 0 | 0 | 0.040952 | 2,564 | 37 | 77 | 69.297297 | 1 | 0 | 0 | 0.837838 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | null | 0 | 0 | null | null | 0 | 0 | 0 | 1 | null | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 13 |
89fd6000e3dca54fab4bf2bca11c6c98797176c0 | 11,153 | py | Python | src/2_updating_strategy/modules/AdaptiveREP/Cases.py | akxen/adaptive-rep-scheme | 6ded4e6c46b994e16d747c1e65544f740a70ce9d | [
"CC-BY-4.0"
] | null | null | null | src/2_updating_strategy/modules/AdaptiveREP/Cases.py | akxen/adaptive-rep-scheme | 6ded4e6c46b994e16d747c1e65544f740a70ce9d | [
"CC-BY-4.0"
] | 7 | 2020-03-24T17:06:24.000Z | 2022-03-11T23:46:50.000Z | src/2_updating_strategy/modules/AdaptiveREP/Cases.py | akxen/adaptive-rep-scheme | 6ded4e6c46b994e16d747c1e65544f740a70ce9d | [
"CC-BY-4.0"
] | null | null | null | """Define cases to investigate"""
# Parameters
# ----------
# Seed for random number generator
SEED = 10
# Number of calibration intervals
MODEL_HORIZON = 52
# Calibration interval index at which a structural (emissions intensity) shock occurs
SHOCK_INDEX = 10
# Permit price applying for all intervals [$/tCO2]
PERMIT_PRICE = 40
# Number of forecast intervals used by MPC controller
FORECAST_INTERVALS_MPC = 6
# Number of forecast intervals when revenue rebalancing
FORECAST_INTERVALS_REVENUE_REBALANCE = 1
# Used to scale forecast values. Realised (perfect forecast) values from benchmark cases
# are scaled by a uniformly distributed random number in the interval, with interval widening by
# FORECAST_UNCERTAINTY_INCREMENT when moving further into the future.
# E.g. for the first calibration interval the scaling factor will be in (0.95, 1.05), for the second (0.9, 1.1)
FORECAST_UNCERTAINTY_INCREMENT = 0.05
# Scheme revenue during first week
INITIAL_ROLLING_SCHEME_REVENUE = 0
# If ramping scheme revenue, the calibration interval index at which revenue ramp begins
START_REVENUE_RAMP_INDEX = 10
# Number of intervals over which revenue is ramped
REVENUE_RAMP_INTERVALS = 10
# Amount the revenue target is incremented each calibration interval
REVENUE_RAMP_INCREMENT = 3e6
# Cases to investigate
# --------------------
# Benchmark cases (results used to generate forecasts for updating cases)
benchmark_cases = [
{'description': 'business as usual - no shocks',
'model_horizon': MODEL_HORIZON,
'shock_option': 'NO_SHOCKS',
# 'forecast_shock': False,
# 'shock_index': SHOCK_INDEX,
'seed': SEED,
'update_mode': 'NO_UPDATE',
'default_baseline': 0,
'initial_permit_price': 0,
'initial_rolling_scheme_revenue': INITIAL_ROLLING_SCHEME_REVENUE,
# 'forecast_intervals': 2,
# 'forecast_uncertainty_increment': 0.05,
# 'revenue_target': 'neutral',
# 'renewables_eligibility': 'ineligible',
# 'revenue_ramp_calibration_interval_start': 1,
# 'revenue_ramp_intervals': 10,
# 'revenue_ramp_increment': 1e6
},
{'description': 'business as usual - emissions intensity shock',
'model_horizon': MODEL_HORIZON,
'shock_option': 'EMISSIONS_INTENSITY_SHOCK',
# 'forecast_shock': False,
'shock_index': SHOCK_INDEX,
'seed': SEED,
'update_mode': 'NO_UPDATE',
'default_baseline': 0,
'initial_permit_price': 0,
'initial_rolling_scheme_revenue': INITIAL_ROLLING_SCHEME_REVENUE,
# 'forecast_intervals': 2,
# 'forecast_uncertainty_increment': 0.05,
# 'revenue_target': 'neutral',
# 'renewables_eligibility': 'ineligible',
# 'revenue_ramp_calibration_interval_start': 1,
# 'revenue_ramp_intervals': 10,
# 'revenue_ramp_increment': 1e6
},
{'description': 'carbon tax - no shocks',
'model_horizon': MODEL_HORIZON,
'shock_option': 'NO_SHOCKS',
# 'shock_index': SHOCK_INDEX,
'seed': SEED,
'update_mode': 'NO_UPDATE',
'default_baseline': 0,
'initial_permit_price': PERMIT_PRICE,
'initial_rolling_scheme_revenue': INITIAL_ROLLING_SCHEME_REVENUE,
# 'forecast_intervals': 2,
# 'forecast_uncertainty_increment': 0.05,
# 'revenue_target': 'neutral',
# 'renewables_eligibility': 'ineligible',
# 'revenue_ramp_calibration_interval_start': 1,
# 'revenue_ramp_intervals': 10,
# 'revenue_ramp_increment': 1e6
},
{'description': 'carbon tax - emissions intensity shock',
'model_horizon': MODEL_HORIZON,
'shock_option': 'EMISSIONS_INTENSITY_SHOCK',
# 'forecast_shock': False,
'shock_index': SHOCK_INDEX,
'seed': SEED,
'update_mode': 'NO_UPDATE',
'default_baseline': 0,
'initial_permit_price': PERMIT_PRICE,
'initial_rolling_scheme_revenue': INITIAL_ROLLING_SCHEME_REVENUE,
# 'forecast_intervals': 2,
# 'forecast_uncertainty_increment': 0.05,
# 'revenue_target': 'neutral',
# 'renewables_eligibility': 'ineligible',
# 'revenue_ramp_calibration_interval_start': 1,
# 'revenue_ramp_intervals': 10,
# 'revenue_ramp_increment': 1e6
},
]
# Updating cases
updating_cases = [
{'description': 'revenue rebalance update - revenue neutral target - no shocks - renewables ineligible',
'model_horizon': MODEL_HORIZON,
'shock_option': 'NO_SHOCKS',
# 'forecast_shock': False,
# 'shock_index': SHOCK_INDEX,
'seed': SEED,
'update_mode': 'REVENUE_REBALANCE_UPDATE',
'default_baseline': 1.02,
'initial_permit_price': PERMIT_PRICE,
'initial_rolling_scheme_revenue': INITIAL_ROLLING_SCHEME_REVENUE,
'forecast_intervals': FORECAST_INTERVALS_REVENUE_REBALANCE,
'forecast_uncertainty_increment': FORECAST_UNCERTAINTY_INCREMENT,
'revenue_target': 'neutral',
'renewables_eligibility': 'ineligible',
# 'revenue_ramp_calibration_interval_start': 1,
# 'revenue_ramp_intervals': 10,
# 'revenue_ramp_increment': 1e6
},
{'description': 'revenue rebalance update - revenue neutral target - emissions intensity shock - renewables ineligible',
'model_horizon': MODEL_HORIZON,
'shock_option': 'EMISSIONS_INTENSITY_SHOCK',
# 'forecast_shock': False,
'shock_index': SHOCK_INDEX,
'seed': SEED,
'update_mode': 'REVENUE_REBALANCE_UPDATE',
'default_baseline': 1.02,
'initial_permit_price': PERMIT_PRICE,
'initial_rolling_scheme_revenue': INITIAL_ROLLING_SCHEME_REVENUE,
'forecast_intervals': FORECAST_INTERVALS_REVENUE_REBALANCE,
'forecast_uncertainty_increment': FORECAST_UNCERTAINTY_INCREMENT,
'revenue_target': 'neutral',
'renewables_eligibility': 'ineligible',
# 'revenue_ramp_calibration_interval_start': 1,
# 'revenue_ramp_intervals': 10,
# 'revenue_ramp_increment': 1e6
},
{'description': 'mpc update - revenue neutral target - no shocks - renewables ineligible',
'model_horizon': MODEL_HORIZON,
'shock_option': 'NO_SHOCKS',
# 'forecast_shock': False,
# 'shock_index': SHOCK_INDEX,
'seed': SEED,
'update_mode': 'MPC_UPDATE',
'default_baseline': 1.02,
'initial_permit_price': PERMIT_PRICE,
'initial_rolling_scheme_revenue': INITIAL_ROLLING_SCHEME_REVENUE,
'forecast_intervals': FORECAST_INTERVALS_MPC,
'forecast_uncertainty_increment': FORECAST_UNCERTAINTY_INCREMENT,
'revenue_target': 'neutral',
'renewables_eligibility': 'ineligible',
# 'revenue_ramp_calibration_interval_start': 1,
# 'revenue_ramp_intervals': 10,
# 'revenue_ramp_increment': 1e6
},
{'description': 'mpc update - revenue neutral target - emissions intensity shock - renewables ineligible',
'model_horizon': MODEL_HORIZON,
'shock_option': 'EMISSIONS_INTENSITY_SHOCK',
# 'forecast_shock': False,
'shock_index': SHOCK_INDEX,
'seed': SEED,
'update_mode': 'MPC_UPDATE',
'default_baseline': 1.02,
'initial_permit_price': PERMIT_PRICE,
'initial_rolling_scheme_revenue': INITIAL_ROLLING_SCHEME_REVENUE,
'forecast_intervals': FORECAST_INTERVALS_MPC,
'forecast_uncertainty_increment': FORECAST_UNCERTAINTY_INCREMENT,
'revenue_target': 'neutral',
'renewables_eligibility': 'ineligible',
# 'revenue_ramp_calibration_interval_start': 1,
# 'revenue_ramp_intervals': 10,
# 'revenue_ramp_increment': 1e6
},
{'description': 'revenue rebalance update - revenue ramp up target - no shocks - renewables ineligible',
'model_horizon': MODEL_HORIZON,
'shock_option': 'NO_SHOCKS',
# 'forecast_shock': False,
# 'shock_index': SHOCK_INDEX,
'seed': SEED,
'update_mode': 'REVENUE_REBALANCE_UPDATE',
'default_baseline': 1.02,
'initial_permit_price': PERMIT_PRICE,
'initial_rolling_scheme_revenue': INITIAL_ROLLING_SCHEME_REVENUE,
'forecast_intervals': FORECAST_INTERVALS_REVENUE_REBALANCE,
'forecast_uncertainty_increment': FORECAST_UNCERTAINTY_INCREMENT,
'revenue_target': 'ramp_up',
'renewables_eligibility': 'ineligible',
'revenue_ramp_calibration_interval_start': START_REVENUE_RAMP_INDEX,
'revenue_ramp_intervals': REVENUE_RAMP_INTERVALS,
'revenue_ramp_increment': REVENUE_RAMP_INCREMENT,
},
{'description': 'mpc update - revenue ramp up target - no shocks - renewables ineligible',
'model_horizon': MODEL_HORIZON,
'shock_option': 'NO_SHOCKS',
# 'forecast_shock': False,
# 'shock_index': SHOCK_INDEX,
'seed': SEED,
'update_mode': 'MPC_UPDATE',
'default_baseline': 1.02,
'initial_permit_price': PERMIT_PRICE,
'initial_rolling_scheme_revenue': INITIAL_ROLLING_SCHEME_REVENUE,
'forecast_intervals': FORECAST_INTERVALS_MPC,
'forecast_uncertainty_increment': FORECAST_UNCERTAINTY_INCREMENT,
'revenue_target': 'ramp_up',
'renewables_eligibility': 'ineligible',
'revenue_ramp_calibration_interval_start': START_REVENUE_RAMP_INDEX,
'revenue_ramp_intervals': REVENUE_RAMP_INTERVALS,
'revenue_ramp_increment': REVENUE_RAMP_INCREMENT,
},
{'description': 'revenue rebalance update - revenue neutral target - emissions intensity shock unanticipated - renewables ineligible',
'model_horizon': MODEL_HORIZON,
'shock_option': 'EMISSIONS_INTENSITY_SHOCK',
'forecast_shock': True,
'shock_index': SHOCK_INDEX,
'seed': SEED,
'update_mode': 'REVENUE_REBALANCE_UPDATE',
'default_baseline': 1.02,
'initial_permit_price': PERMIT_PRICE,
'initial_rolling_scheme_revenue': INITIAL_ROLLING_SCHEME_REVENUE,
'forecast_intervals': FORECAST_INTERVALS_REVENUE_REBALANCE,
'forecast_uncertainty_increment': FORECAST_UNCERTAINTY_INCREMENT,
'revenue_target': 'neutral',
'renewables_eligibility': 'ineligible',
'revenue_ramp_calibration_interval_start': START_REVENUE_RAMP_INDEX,
'revenue_ramp_intervals': REVENUE_RAMP_INTERVALS,
'revenue_ramp_increment': REVENUE_RAMP_INCREMENT,
},
{'description': 'mpc update - revenue neutral target - emissions intensity shock unanticipated - renewables ineligible',
'model_horizon': MODEL_HORIZON,
'shock_option': 'EMISSIONS_INTENSITY_SHOCK',
'forecast_shock': True,
'shock_index': SHOCK_INDEX,
'seed': SEED,
'update_mode': 'MPC_UPDATE',
'default_baseline': 1.02,
'initial_permit_price': PERMIT_PRICE,
'initial_rolling_scheme_revenue': INITIAL_ROLLING_SCHEME_REVENUE,
'forecast_intervals': FORECAST_INTERVALS_MPC,
'forecast_uncertainty_increment': FORECAST_UNCERTAINTY_INCREMENT,
'revenue_target': 'neutral',
'renewables_eligibility': 'ineligible',
'revenue_ramp_calibration_interval_start': START_REVENUE_RAMP_INDEX,
'revenue_ramp_intervals': REVENUE_RAMP_INTERVALS,
'revenue_ramp_increment': REVENUE_RAMP_INCREMENT,
},
]
| 40.40942 | 139 | 0.701964 | 1,171 | 11,153 | 6.274979 | 0.104184 | 0.080838 | 0.068046 | 0.091862 | 0.854382 | 0.841726 | 0.841726 | 0.838051 | 0.838051 | 0.838051 | 0 | 0.013022 | 0.194387 | 11,153 | 275 | 140 | 40.556364 | 0.804786 | 0.256075 | 0 | 0.777143 | 0 | 0 | 0.486527 | 0.170486 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
c389718f0445146db89cc5fd6bda63a550850ff1 | 132 | py | Python | bioplatform/types/__init__.py | jimtheplant/bioplatform | 5097fae3a03c48338b552ad15d02b29e408ddbb9 | [
"Apache-2.0"
] | null | null | null | bioplatform/types/__init__.py | jimtheplant/bioplatform | 5097fae3a03c48338b552ad15d02b29e408ddbb9 | [
"Apache-2.0"
] | null | null | null | bioplatform/types/__init__.py | jimtheplant/bioplatform | 5097fae3a03c48338b552ad15d02b29e408ddbb9 | [
"Apache-2.0"
] | null | null | null | from .action import *
from .initializer import *
from .metadata import *
from .sequence import *
from .sequence_collection import *
| 22 | 34 | 0.772727 | 16 | 132 | 6.3125 | 0.4375 | 0.39604 | 0.356436 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.151515 | 132 | 5 | 35 | 26.4 | 0.901786 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
c3a01531b7687ef38ef0ae2c4bec17f7d4477fbf | 124 | py | Python | file_secrets/__init__.py | nalabelle/py-file_secrets | 35379b62bcc03955b7394d17ff8f5621131be15a | [
"MIT"
] | 1 | 2017-03-22T19:13:09.000Z | 2017-03-22T19:13:09.000Z | file_secrets/__init__.py | nalabelle/py-file_secrets | 35379b62bcc03955b7394d17ff8f5621131be15a | [
"MIT"
] | 1 | 2021-11-13T04:17:21.000Z | 2021-11-13T04:17:21.000Z | file_secrets/__init__.py | nalabelle/py-file_secrets | 35379b62bcc03955b7394d17ff8f5621131be15a | [
"MIT"
] | 3 | 2017-03-22T19:13:34.000Z | 2019-03-14T21:11:52.000Z | from file_secrets.file_secrets import FileSecrets
def secret(key: str) -> str:
return FileSecrets.Instance().get(key)
| 20.666667 | 49 | 0.758065 | 17 | 124 | 5.411765 | 0.705882 | 0.23913 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.137097 | 124 | 5 | 50 | 24.8 | 0.859813 | 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 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 7 |
c3b2e17e013391a4a9d1556bf10e7ee0ef93147b | 6,710 | py | Python | QC/set2set.py | phcavelar/graph-odenet | cba1224c041e53ea221e31bf9103ef950b8bd460 | [
"MIT"
] | 4 | 2019-12-10T18:49:03.000Z | 2022-02-16T03:21:30.000Z | QC/set2set.py | phcavelar/graph-odenet | cba1224c041e53ea221e31bf9103ef950b8bd460 | [
"MIT"
] | 1 | 2020-11-04T04:41:09.000Z | 2021-01-07T18:52:37.000Z | QC/set2set.py | phcavelar/graph-odenet | cba1224c041e53ea221e31bf9103ef950b8bd460 | [
"MIT"
] | 2 | 2020-04-03T12:05:33.000Z | 2020-10-10T11:57:48.000Z | # Adapted from https://github.com/rusty1s/pytorch_geometric
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_scatter import scatter_add
from torch_geometric_utils import softmax
class Set2Set(nn.Module):
r"""The global pooling operator based on iterative content-based attention
from the `"Order Matters: Sequence to sequence for sets"
<https://arxiv.org/abs/1511.06391>`_ paper
.. math::
\mathbf{q}_t &= \mathrm{LSTM}(\mathbf{q}^{*}_{t-1})
\alpha_{i,t} &= \mathrm{softmax}(\mathbf{x}_i \cdot \mathbf{q}_t)
\mathbf{r}_t &= \sum_{i=1}^N \alpha_{i,t} \mathbf{x}_i
\mathbf{q}^{*}_t &= \mathbf{q}_t \, \Vert \, \mathbf{r}_t,
where :math:`\mathbf{q}^{*}_T` defines the output of the layer with twice
the dimensionality as the input.
Args:
in_channels (int): Size of each input sample.
processing_steps (int): Number of iterations :math:`T`.
num_layers (int, optional): Number of recurrent layers, *.e.g*, setting
:obj:`num_layers=2` would mean stacking two LSTMs together to form
a stacked LSTM, with the second LSTM taking in outputs of the first
LSTM and computing the final results. (default: :obj:`1`)
"""
def __init__(self, in_channels, processing_steps, num_layers=1):
super(Set2Set, self).__init__()
self.in_channels = in_channels
self.out_channels = 2 * in_channels
self.processing_steps = processing_steps
self.num_layers = num_layers
self.lstm = nn.LSTM(self.out_channels, self.in_channels,
num_layers)
self.reset_parameters()
def reset_parameters(self):
self.lstm.reset_parameters()
def forward(self, x, batch):
""""""
batch_size = batch.max().item() + 1
h = (x.new_zeros((self.num_layers, batch_size, self.in_channels)),
x.new_zeros((self.num_layers, batch_size, self.in_channels)))
q_star = x.new_zeros(batch_size, self.out_channels)
one_t = torch.ones_like( batch , dtype=batch.dtype, device=batch.device)
for i in range(self.processing_steps):
q, h = self.lstm(q_star.unsqueeze(0), h)
q = q.view(batch_size, self.in_channels)
e = (x * q[batch]).sum(dim=-1, keepdim=True)
# Softmax
a = torch.zeros( [x.size()[0]], dtype=x.dtype, device=x.device )
for i in range(batch_size):
mask = batch.eq(one_t*i)
elements_to_softmax = torch.masked_select( e.squeeze(-1), mask )
softmaxed_elements = F.softmax(elements_to_softmax, dim=0 )
a[mask] += softmaxed_elements
#end for
a = a.unsqueeze(1)
r = scatter_add(a * x, batch, dim=0, dim_size=batch_size)
q_star = torch.cat([q, r], dim=-1)
#end for
return q_star
def __repr__(self):
return '{}({}, {})'.format(self.__class__.__name__, self.in_channels,
self.out_channels)
class Set2Set__UNUSED(nn.Module):
r"""The global pooling operator based on iterative content-based attention
from the `"Order Matters: Sequence to sequence for sets"
<https://arxiv.org/abs/1511.06391>`_ paper
.. math::
\mathbf{q}_t &= \mathrm{LSTM}(\mathbf{q}^{*}_{t-1})
\alpha_{i,t} &= \mathrm{softmax}(\mathbf{x}_i \cdot \mathbf{q}_t)
\mathbf{r}_t &= \sum_{i=1}^N \alpha_{i,t} \mathbf{x}_i
\mathbf{q}^{*}_t &= \mathbf{q}_t \, \Vert \, \mathbf{r}_t,
where :math:`\mathbf{q}^{*}_T` defines the output of the layer with twice
the dimensionality as the input.
Args:
in_channels (int): Size of each input sample.
processing_steps (int): Number of iterations :math:`T`.
num_layers (int, optional): Number of recurrent layers, *.e.g*, setting
:obj:`num_layers=2` would mean stacking two LSTMs together to form
a stacked LSTM, with the second LSTM taking in outputs of the first
LSTM and computing the final results. (default: :obj:`1`)
"""
def __init__(self, in_channels, processing_steps, num_layers=1):
raise NotImplementedError( "This model's implementation does an inplace operation that doesn't allow backpropagation" )
super(Set2Set__UNUSED, self).__init__()
self.in_channels = in_channels
self.out_channels = 2 * in_channels
self.processing_steps = processing_steps
self.num_layers = num_layers
self.lstm = nn.LSTM(self.out_channels, self.in_channels,
num_layers)
self.reset_parameters()
def reset_parameters(self):
self.lstm.reset_parameters()
def forward(self, x, batch):
""""""
batch_size = batch.max().item() + 1
h = (x.new_zeros((self.num_layers, batch_size, self.in_channels)),
x.new_zeros((self.num_layers, batch_size, self.in_channels)))
q_star = x.new_zeros(batch_size, self.out_channels)
if not x.is_cuda: # CPU ver
for i in range(self.processing_steps):
q, h = self.lstm(q_star.unsqueeze(0), h)
q = q.view(batch_size, self.in_channels)
e = (x * q[batch]).sum(dim=-1, keepdim=True)
a = softmax(e, batch, num_nodes=batch_size)
r = scatter_add(a * x, batch, dim=0, dim_size=batch_size)
q_star = torch.cat([q, r], dim=-1)
#end for
else: # CUDA version
a = torch.zeros( [x.size()[0],1], dtype=x.dtype, device=x.device )
one_t = torch.ones_like( batch , dtype=batch.dtype, device=batch.device)
for i in range(self.processing_steps):
q, h = self.lstm(q_star.unsqueeze(0), h)
q = q.view(batch_size, self.in_channels)
e = (x * q[batch]).sum(dim=-1, keepdim=True)
# Softmax
for i in range(batch_size):
mask = batch.eq(one_t*i)
elements_to_softmax = torch.masked_select( e.squeeze(), mask )
softmaxed_elements = F.softmax(elements_to_softmax, dim=0 ).unsqueeze(1)
a[mask] = softmaxed_elements
#end for
r = scatter_add(a * x, batch, dim=0, dim_size=batch_size)
q_star = torch.cat([q, r], dim=-1)
#end for
#end if-else
return q_star
def __repr__(self):
return '{}({}, {})'.format(self.__class__.__name__, self.in_channels,
self.out_channels)
| 39.011628 | 127 | 0.591803 | 916 | 6,710 | 4.116812 | 0.183406 | 0.055688 | 0.055688 | 0.027844 | 0.886767 | 0.886767 | 0.850172 | 0.850172 | 0.850172 | 0.850172 | 0 | 0.011865 | 0.284054 | 6,710 | 171 | 128 | 39.239766 | 0.773106 | 0.304471 | 0 | 0.72093 | 0 | 0 | 0.024027 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.093023 | false | 0 | 0.05814 | 0.023256 | 0.22093 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
c3e5f454cb54db3d34294b4d7de7c72cbdf5a095 | 30,300 | py | Python | huaweicloud-sdk-antiddos/huaweicloudsdkantiddos/v1/antiddos_async_client.py | huaweicloud/huaweicloud-sdk-python-v3 | 7a6270390fcbf192b3882bf763e7016e6026ef78 | [
"Apache-2.0"
] | 64 | 2020-06-12T07:05:07.000Z | 2022-03-30T03:32:50.000Z | huaweicloud-sdk-antiddos/huaweicloudsdkantiddos/v1/antiddos_async_client.py | huaweicloud/huaweicloud-sdk-python-v3 | 7a6270390fcbf192b3882bf763e7016e6026ef78 | [
"Apache-2.0"
] | 11 | 2020-07-06T07:56:54.000Z | 2022-01-11T11:14:40.000Z | huaweicloud-sdk-antiddos/huaweicloudsdkantiddos/v1/antiddos_async_client.py | huaweicloud/huaweicloud-sdk-python-v3 | 7a6270390fcbf192b3882bf763e7016e6026ef78 | [
"Apache-2.0"
] | 24 | 2020-06-08T11:42:13.000Z | 2022-03-04T06:44:08.000Z | # coding: utf-8
from __future__ import absolute_import
import datetime
import re
import importlib
import six
from huaweicloudsdkcore.client import Client, ClientBuilder
from huaweicloudsdkcore.exceptions import exceptions
from huaweicloudsdkcore.utils import http_utils
from huaweicloudsdkcore.sdk_stream_request import SdkStreamRequest
class AntiDDoSAsyncClient(Client):
"""
:param configuration: .Configuration object for this client
:param pool_threads: The number of threads to use for async requests
to the API. More threads means more concurrent API requests.
"""
PRIMITIVE_TYPES = (float, bool, bytes, six.text_type) + six.integer_types
NATIVE_TYPES_MAPPING = {
'int': int,
'long': int if six.PY3 else long,
'float': float,
'str': str,
'bool': bool,
'date': datetime.date,
'datetime': datetime.datetime,
'object': object,
}
def __init__(self):
super(AntiDDoSAsyncClient, self).__init__()
self.model_package = importlib.import_module("huaweicloudsdkantiddos.v1.model")
self.preset_headers = {'User-Agent': 'HuaweiCloud-SDK-Python'}
@classmethod
def new_builder(cls, clazz=None):
if clazz is None:
return ClientBuilder(cls)
if clazz.__name__ != "AntiDDoSClient":
raise TypeError("client type error, support client type is AntiDDoSClient")
return ClientBuilder(clazz)
def create_default_config_async(self, request):
"""配置Anti-DDoS默认防护策略
配置用户的默认防护策略。配置防护策略后，新购买的资源在自动开启防护时，会按照该默认防护策略进行配置。
:param CreateDefaultConfigRequest request
:return: CreateDefaultConfigResponse
"""
return self.create_default_config_with_http_info(request)
def create_default_config_with_http_info(self, request):
"""配置Anti-DDoS默认防护策略
配置用户的默认防护策略。配置防护策略后，新购买的资源在自动开启防护时，会按照该默认防护策略进行配置。
:param CreateDefaultConfigRequest request
:return: CreateDefaultConfigResponse
"""
all_params = ['create_default_config_request_body']
local_var_params = {}
for attr in request.attribute_map:
if hasattr(request, attr):
local_var_params[attr] = getattr(request, attr)
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = {}
body_params = None
if 'body' in local_var_params:
body_params = local_var_params['body']
if isinstance(request, SdkStreamRequest):
body_params = request.get_file_stream()
response_headers = []
header_params['Content-Type'] = http_utils.select_header_content_type(
['application/json'])
auth_settings = []
return self.call_api(
resource_path='/v1/{project_id}/antiddos/default-config',
method='POST',
path_params=path_params,
query_params=query_params,
header_params=header_params,
body=body_params,
post_params=form_params,
response_type='CreateDefaultConfigResponse',
response_headers=response_headers,
auth_settings=auth_settings,
collection_formats=collection_formats,
request_type=request.__class__.__name__)
def delete_default_config_async(self, request):
"""删除Ani-DDoS默认防护策略
删除用户配置的默认防护策略。
:param DeleteDefaultConfigRequest request
:return: DeleteDefaultConfigResponse
"""
return self.delete_default_config_with_http_info(request)
def delete_default_config_with_http_info(self, request):
"""删除Ani-DDoS默认防护策略
删除用户配置的默认防护策略。
:param DeleteDefaultConfigRequest request
:return: DeleteDefaultConfigResponse
"""
all_params = []
local_var_params = {}
for attr in request.attribute_map:
if hasattr(request, attr):
local_var_params[attr] = getattr(request, attr)
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = {}
body_params = None
if isinstance(request, SdkStreamRequest):
body_params = request.get_file_stream()
response_headers = []
header_params['Content-Type'] = http_utils.select_header_content_type(
['application/json'])
auth_settings = []
return self.call_api(
resource_path='/v1/{project_id}/antiddos/default-config',
method='DELETE',
path_params=path_params,
query_params=query_params,
header_params=header_params,
body=body_params,
post_params=form_params,
response_type='DeleteDefaultConfigResponse',
response_headers=response_headers,
auth_settings=auth_settings,
collection_formats=collection_formats,
request_type=request.__class__.__name__)
def show_alert_config_async(self, request):
"""查询告警配置信息
查询用户配置信息，用户可以通过此接口查询是否接收某类告警，同时可以配置是手机短信还是电子邮件接收告警信息。
:param ShowAlertConfigRequest request
:return: ShowAlertConfigResponse
"""
return self.show_alert_config_with_http_info(request)
def show_alert_config_with_http_info(self, request):
"""查询告警配置信息
查询用户配置信息，用户可以通过此接口查询是否接收某类告警，同时可以配置是手机短信还是电子邮件接收告警信息。
:param ShowAlertConfigRequest request
:return: ShowAlertConfigResponse
"""
all_params = []
local_var_params = {}
for attr in request.attribute_map:
if hasattr(request, attr):
local_var_params[attr] = getattr(request, attr)
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = {}
body_params = None
if isinstance(request, SdkStreamRequest):
body_params = request.get_file_stream()
response_headers = []
header_params['Content-Type'] = http_utils.select_header_content_type(
['application/json'])
auth_settings = []
return self.call_api(
resource_path='/v2/{project_id}/warnalert/alertconfig/query',
method='GET',
path_params=path_params,
query_params=query_params,
header_params=header_params,
body=body_params,
post_params=form_params,
response_type='ShowAlertConfigResponse',
response_headers=response_headers,
auth_settings=auth_settings,
collection_formats=collection_formats,
request_type=request.__class__.__name__)
def show_default_config_async(self, request):
"""查询Ani-DDoS默认防护策略
查询用户配置的默认防护策略。
:param ShowDefaultConfigRequest request
:return: ShowDefaultConfigResponse
"""
return self.show_default_config_with_http_info(request)
def show_default_config_with_http_info(self, request):
"""查询Ani-DDoS默认防护策略
查询用户配置的默认防护策略。
:param ShowDefaultConfigRequest request
:return: ShowDefaultConfigResponse
"""
all_params = []
local_var_params = {}
for attr in request.attribute_map:
if hasattr(request, attr):
local_var_params[attr] = getattr(request, attr)
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = {}
body_params = None
if isinstance(request, SdkStreamRequest):
body_params = request.get_file_stream()
response_headers = []
header_params['Content-Type'] = http_utils.select_header_content_type(
['application/json'])
auth_settings = []
return self.call_api(
resource_path='/v1/{project_id}/antiddos/default-config',
method='GET',
path_params=path_params,
query_params=query_params,
header_params=header_params,
body=body_params,
post_params=form_params,
response_type='ShowDefaultConfigResponse',
response_headers=response_headers,
auth_settings=auth_settings,
collection_formats=collection_formats,
request_type=request.__class__.__name__)
def update_alert_config_async(self, request):
"""更新告警配置信息
更新用户配置信息，用户可以通过此接口更新是否接收某类告警，同时可以配置是手机短信还是电子邮件接收告警信息。
:param UpdateAlertConfigRequest request
:return: UpdateAlertConfigResponse
"""
return self.update_alert_config_with_http_info(request)
def update_alert_config_with_http_info(self, request):
"""更新告警配置信息
更新用户配置信息，用户可以通过此接口更新是否接收某类告警，同时可以配置是手机短信还是电子邮件接收告警信息。
:param UpdateAlertConfigRequest request
:return: UpdateAlertConfigResponse
"""
all_params = ['update_alert_config_request_body']
local_var_params = {}
for attr in request.attribute_map:
if hasattr(request, attr):
local_var_params[attr] = getattr(request, attr)
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = {}
body_params = None
if 'body' in local_var_params:
body_params = local_var_params['body']
if isinstance(request, SdkStreamRequest):
body_params = request.get_file_stream()
response_headers = []
header_params['Content-Type'] = http_utils.select_header_content_type(
['application/json'])
auth_settings = []
return self.call_api(
resource_path='/v2/{project_id}/warnalert/alertconfig/update',
method='POST',
path_params=path_params,
query_params=query_params,
header_params=header_params,
body=body_params,
post_params=form_params,
response_type='UpdateAlertConfigResponse',
response_headers=response_headers,
auth_settings=auth_settings,
collection_formats=collection_formats,
request_type=request.__class__.__name__)
def list_d_dos_status_async(self, request):
"""查询EIP防护状态列表
查询用户所有EIP的Anti-DDoS防护状态信息，用户的EIP无论是否绑定到云服务器，都可以进行查询。
:param ListDDosStatusRequest request
:return: ListDDosStatusResponse
"""
return self.list_d_dos_status_with_http_info(request)
def list_d_dos_status_with_http_info(self, request):
"""查询EIP防护状态列表
查询用户所有EIP的Anti-DDoS防护状态信息，用户的EIP无论是否绑定到云服务器，都可以进行查询。
:param ListDDosStatusRequest request
:return: ListDDosStatusResponse
"""
all_params = ['status', 'limit', 'offset', 'ip']
local_var_params = {}
for attr in request.attribute_map:
if hasattr(request, attr):
local_var_params[attr] = getattr(request, attr)
collection_formats = {}
path_params = {}
query_params = []
if 'status' in local_var_params:
query_params.append(('status', local_var_params['status']))
if 'limit' in local_var_params:
query_params.append(('limit', local_var_params['limit']))
if 'offset' in local_var_params:
query_params.append(('offset', local_var_params['offset']))
if 'ip' in local_var_params:
query_params.append(('ip', local_var_params['ip']))
header_params = {}
form_params = {}
body_params = None
if isinstance(request, SdkStreamRequest):
body_params = request.get_file_stream()
response_headers = []
header_params['Content-Type'] = http_utils.select_header_content_type(
['application/json'])
auth_settings = []
return self.call_api(
resource_path='/v1/{project_id}/antiddos',
method='GET',
path_params=path_params,
query_params=query_params,
header_params=header_params,
body=body_params,
post_params=form_params,
response_type='ListDDosStatusResponse',
response_headers=response_headers,
auth_settings=auth_settings,
collection_formats=collection_formats,
request_type=request.__class__.__name__)
def list_daily_log_async(self, request):
"""查询指定EIP异常事件
查询指定EIP在过去24小时之内的异常事件信息，异常事件包括清洗事件和黑洞事件，查询延迟在5分钟之内。
:param ListDailyLogRequest request
:return: ListDailyLogResponse
"""
return self.list_daily_log_with_http_info(request)
def list_daily_log_with_http_info(self, request):
"""查询指定EIP异常事件
查询指定EIP在过去24小时之内的异常事件信息，异常事件包括清洗事件和黑洞事件，查询延迟在5分钟之内。
:param ListDailyLogRequest request
:return: ListDailyLogResponse
"""
all_params = ['floating_ip_id', 'sort_dir', 'limit', 'offset', 'ip']
local_var_params = {}
for attr in request.attribute_map:
if hasattr(request, attr):
local_var_params[attr] = getattr(request, attr)
collection_formats = {}
path_params = {}
if 'floating_ip_id' in local_var_params:
path_params['floating_ip_id'] = local_var_params['floating_ip_id']
query_params = []
if 'sort_dir' in local_var_params:
query_params.append(('sort_dir', local_var_params['sort_dir']))
if 'limit' in local_var_params:
query_params.append(('limit', local_var_params['limit']))
if 'offset' in local_var_params:
query_params.append(('offset', local_var_params['offset']))
if 'ip' in local_var_params:
query_params.append(('ip', local_var_params['ip']))
header_params = {}
form_params = {}
body_params = None
if isinstance(request, SdkStreamRequest):
body_params = request.get_file_stream()
response_headers = []
header_params['Content-Type'] = http_utils.select_header_content_type(
['application/json'])
auth_settings = []
return self.call_api(
resource_path='/v1/{project_id}/antiddos/{floating_ip_id}/logs',
method='GET',
path_params=path_params,
query_params=query_params,
header_params=header_params,
body=body_params,
post_params=form_params,
response_type='ListDailyLogResponse',
response_headers=response_headers,
auth_settings=auth_settings,
collection_formats=collection_formats,
request_type=request.__class__.__name__)
def list_daily_report_async(self, request):
"""查询指定EIP防护流量
查询指定EIP在过去24小时之内的防护流量信息，流量的间隔时间单位为5分钟。
:param ListDailyReportRequest request
:return: ListDailyReportResponse
"""
return self.list_daily_report_with_http_info(request)
def list_daily_report_with_http_info(self, request):
"""查询指定EIP防护流量
查询指定EIP在过去24小时之内的防护流量信息，流量的间隔时间单位为5分钟。
:param ListDailyReportRequest request
:return: ListDailyReportResponse
"""
all_params = ['floating_ip_id', 'ip']
local_var_params = {}
for attr in request.attribute_map:
if hasattr(request, attr):
local_var_params[attr] = getattr(request, attr)
collection_formats = {}
path_params = {}
if 'floating_ip_id' in local_var_params:
path_params['floating_ip_id'] = local_var_params['floating_ip_id']
query_params = []
if 'ip' in local_var_params:
query_params.append(('ip', local_var_params['ip']))
header_params = {}
form_params = {}
body_params = None
if isinstance(request, SdkStreamRequest):
body_params = request.get_file_stream()
response_headers = []
header_params['Content-Type'] = http_utils.select_header_content_type(
['application/json'])
auth_settings = []
return self.call_api(
resource_path='/v1/{project_id}/antiddos/{floating_ip_id}/daily',
method='GET',
path_params=path_params,
query_params=query_params,
header_params=header_params,
body=body_params,
post_params=form_params,
response_type='ListDailyReportResponse',
response_headers=response_headers,
auth_settings=auth_settings,
collection_formats=collection_formats,
request_type=request.__class__.__name__)
def list_new_configs_async(self, request):
"""查询Anti-DDoS配置可选范围
查询系统支持的Anti-DDoS防护策略配置的可选范围，用户根据范围列表选择适合自已业务的防护策略进行Anti-DDoS流量清洗。
:param ListNewConfigsRequest request
:return: ListNewConfigsResponse
"""
return self.list_new_configs_with_http_info(request)
def list_new_configs_with_http_info(self, request):
"""查询Anti-DDoS配置可选范围
查询系统支持的Anti-DDoS防护策略配置的可选范围，用户根据范围列表选择适合自已业务的防护策略进行Anti-DDoS流量清洗。
:param ListNewConfigsRequest request
:return: ListNewConfigsResponse
"""
all_params = []
local_var_params = {}
for attr in request.attribute_map:
if hasattr(request, attr):
local_var_params[attr] = getattr(request, attr)
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = {}
body_params = None
if isinstance(request, SdkStreamRequest):
body_params = request.get_file_stream()
response_headers = []
header_params['Content-Type'] = http_utils.select_header_content_type(
['application/json'])
auth_settings = []
return self.call_api(
resource_path='/v2/{project_id}/antiddos/query-config-list',
method='GET',
path_params=path_params,
query_params=query_params,
header_params=header_params,
body=body_params,
post_params=form_params,
response_type='ListNewConfigsResponse',
response_headers=response_headers,
auth_settings=auth_settings,
collection_formats=collection_formats,
request_type=request.__class__.__name__)
def list_weekly_reports_async(self, request):
"""查询周防护统计情况
查询用户所有Anti-DDoS防护周统计情况，包括一周内DDoS拦截次数和攻击次数、以及按照被攻击次数进行的排名信息等统计数据。系统支持当前时间之前四周的周统计数据查询，超过这个时间的请求是查询不到统计数据的。
:param ListWeeklyReportsRequest request
:return: ListWeeklyReportsResponse
"""
return self.list_weekly_reports_with_http_info(request)
def list_weekly_reports_with_http_info(self, request):
"""查询周防护统计情况
查询用户所有Anti-DDoS防护周统计情况，包括一周内DDoS拦截次数和攻击次数、以及按照被攻击次数进行的排名信息等统计数据。系统支持当前时间之前四周的周统计数据查询，超过这个时间的请求是查询不到统计数据的。
:param ListWeeklyReportsRequest request
:return: ListWeeklyReportsResponse
"""
all_params = ['period_start_date']
local_var_params = {}
for attr in request.attribute_map:
if hasattr(request, attr):
local_var_params[attr] = getattr(request, attr)
collection_formats = {}
path_params = {}
query_params = []
if 'period_start_date' in local_var_params:
query_params.append(('period_start_date', local_var_params['period_start_date']))
header_params = {}
form_params = {}
body_params = None
if isinstance(request, SdkStreamRequest):
body_params = request.get_file_stream()
response_headers = []
header_params['Content-Type'] = http_utils.select_header_content_type(
['application/json'])
auth_settings = []
return self.call_api(
resource_path='/v1/{project_id}/antiddos/weekly',
method='GET',
path_params=path_params,
query_params=query_params,
header_params=header_params,
body=body_params,
post_params=form_params,
response_type='ListWeeklyReportsResponse',
response_headers=response_headers,
auth_settings=auth_settings,
collection_formats=collection_formats,
request_type=request.__class__.__name__)
def show_d_dos_async(self, request):
"""查询Anti-DDoS服务
查询配置的Anti-DDoS防护策略，用户可以查询指定EIP的Anti-DDoS防护策略。
:param ShowDDosRequest request
:return: ShowDDosResponse
"""
return self.show_d_dos_with_http_info(request)
def show_d_dos_with_http_info(self, request):
"""查询Anti-DDoS服务
查询配置的Anti-DDoS防护策略，用户可以查询指定EIP的Anti-DDoS防护策略。
:param ShowDDosRequest request
:return: ShowDDosResponse
"""
all_params = ['floating_ip_id', 'ip']
local_var_params = {}
for attr in request.attribute_map:
if hasattr(request, attr):
local_var_params[attr] = getattr(request, attr)
collection_formats = {}
path_params = {}
if 'floating_ip_id' in local_var_params:
path_params['floating_ip_id'] = local_var_params['floating_ip_id']
query_params = []
if 'ip' in local_var_params:
query_params.append(('ip', local_var_params['ip']))
header_params = {}
form_params = {}
body_params = None
if isinstance(request, SdkStreamRequest):
body_params = request.get_file_stream()
response_headers = []
header_params['Content-Type'] = http_utils.select_header_content_type(
['application/json'])
auth_settings = []
return self.call_api(
resource_path='/v1/{project_id}/antiddos/{floating_ip_id}',
method='GET',
path_params=path_params,
query_params=query_params,
header_params=header_params,
body=body_params,
post_params=form_params,
response_type='ShowDDosResponse',
response_headers=response_headers,
auth_settings=auth_settings,
collection_formats=collection_formats,
request_type=request.__class__.__name__)
def show_d_dos_status_async(self, request):
"""查询指定EIP防护状态
查询指定EIP的Anti-DDoS防护状态。
:param ShowDDosStatusRequest request
:return: ShowDDosStatusResponse
"""
return self.show_d_dos_status_with_http_info(request)
def show_d_dos_status_with_http_info(self, request):
"""查询指定EIP防护状态
查询指定EIP的Anti-DDoS防护状态。
:param ShowDDosStatusRequest request
:return: ShowDDosStatusResponse
"""
all_params = ['floating_ip_id', 'ip']
local_var_params = {}
for attr in request.attribute_map:
if hasattr(request, attr):
local_var_params[attr] = getattr(request, attr)
collection_formats = {}
path_params = {}
if 'floating_ip_id' in local_var_params:
path_params['floating_ip_id'] = local_var_params['floating_ip_id']
query_params = []
if 'ip' in local_var_params:
query_params.append(('ip', local_var_params['ip']))
header_params = {}
form_params = {}
body_params = None
if isinstance(request, SdkStreamRequest):
body_params = request.get_file_stream()
response_headers = []
header_params['Content-Type'] = http_utils.select_header_content_type(
['application/json'])
auth_settings = []
return self.call_api(
resource_path='/v1/{project_id}/antiddos/{floating_ip_id}/status',
method='GET',
path_params=path_params,
query_params=query_params,
header_params=header_params,
body=body_params,
post_params=form_params,
response_type='ShowDDosStatusResponse',
response_headers=response_headers,
auth_settings=auth_settings,
collection_formats=collection_formats,
request_type=request.__class__.__name__)
def show_new_task_status_async(self, request):
"""查询Anti-DDoS任务
用户查询指定的Anti-DDoS防护配置任务，得到任务当前执行的状态。
:param ShowNewTaskStatusRequest request
:return: ShowNewTaskStatusResponse
"""
return self.show_new_task_status_with_http_info(request)
def show_new_task_status_with_http_info(self, request):
"""查询Anti-DDoS任务
用户查询指定的Anti-DDoS防护配置任务，得到任务当前执行的状态。
:param ShowNewTaskStatusRequest request
:return: ShowNewTaskStatusResponse
"""
all_params = ['task_id']
local_var_params = {}
for attr in request.attribute_map:
if hasattr(request, attr):
local_var_params[attr] = getattr(request, attr)
collection_formats = {}
path_params = {}
query_params = []
if 'task_id' in local_var_params:
query_params.append(('task_id', local_var_params['task_id']))
header_params = {}
form_params = {}
body_params = None
if isinstance(request, SdkStreamRequest):
body_params = request.get_file_stream()
response_headers = []
header_params['Content-Type'] = http_utils.select_header_content_type(
['application/json'])
auth_settings = []
return self.call_api(
resource_path='/v2/{project_id}/query-task-status',
method='GET',
path_params=path_params,
query_params=query_params,
header_params=header_params,
body=body_params,
post_params=form_params,
response_type='ShowNewTaskStatusResponse',
response_headers=response_headers,
auth_settings=auth_settings,
collection_formats=collection_formats,
request_type=request.__class__.__name__)
def update_d_dos_async(self, request):
"""更新Anti-DDoS服务
更新指定EIP的Anti-DDoS防护策略配置。调用成功，只是说明服务节点收到了关闭更新配置请求，操作是否成功需要通过任务查询接口查询该任务的执行状态，具体请参考查询Anti-DDoS任务。
:param UpdateDDosRequest request
:return: UpdateDDosResponse
"""
return self.update_d_dos_with_http_info(request)
def update_d_dos_with_http_info(self, request):
"""更新Anti-DDoS服务
更新指定EIP的Anti-DDoS防护策略配置。调用成功，只是说明服务节点收到了关闭更新配置请求，操作是否成功需要通过任务查询接口查询该任务的执行状态，具体请参考查询Anti-DDoS任务。
:param UpdateDDosRequest request
:return: UpdateDDosResponse
"""
all_params = ['floating_ip_id', 'update_d_dos_request_body', 'ip']
local_var_params = {}
for attr in request.attribute_map:
if hasattr(request, attr):
local_var_params[attr] = getattr(request, attr)
collection_formats = {}
path_params = {}
if 'floating_ip_id' in local_var_params:
path_params['floating_ip_id'] = local_var_params['floating_ip_id']
query_params = []
if 'ip' in local_var_params:
query_params.append(('ip', local_var_params['ip']))
header_params = {}
form_params = {}
body_params = None
if 'body' in local_var_params:
body_params = local_var_params['body']
if isinstance(request, SdkStreamRequest):
body_params = request.get_file_stream()
response_headers = []
header_params['Content-Type'] = http_utils.select_header_content_type(
['application/json'])
auth_settings = []
return self.call_api(
resource_path='/v1/{project_id}/antiddos/{floating_ip_id}',
method='PUT',
path_params=path_params,
query_params=query_params,
header_params=header_params,
body=body_params,
post_params=form_params,
response_type='UpdateDDosResponse',
response_headers=response_headers,
auth_settings=auth_settings,
collection_formats=collection_formats,
request_type=request.__class__.__name__)
def call_api(self, resource_path, method, path_params=None, query_params=None, header_params=None, body=None,
post_params=None, response_type=None, response_headers=None, auth_settings=None,
collection_formats=None, request_type=None):
"""Makes the HTTP request and returns deserialized data.
:param resource_path: Path to method endpoint.
:param method: Method to call.
:param path_params: Path parameters in the url.
:param query_params: Query parameters in the url.
:param header_params: Header parameters to be
placed in the request header.
:param body: Request body.
:param post_params dict: Request post form parameters,
for `application/x-www-form-urlencoded`, `multipart/form-data`.
:param auth_settings list: Auth Settings names for the request.
:param response_type: Response data type.
:param response_headers: Header should be added to response data.
:param collection_formats: dict of collection formats for path, query,
header, and post parameters.
:param request_type: Request data type.
:return:
Return the response directly.
"""
return self.do_http_request(
method=method,
resource_path=resource_path,
path_params=path_params,
query_params=query_params,
header_params=header_params,
body=body,
post_params=post_params,
response_type=response_type,
response_headers=response_headers,
collection_formats=collection_formats,
request_type=request_type,
async_request=True)
| 30.761421 | 113 | 0.633465 | 2,992 | 30,300 | 6.030414 | 0.088235 | 0.031924 | 0.055867 | 0.027933 | 0.850357 | 0.836557 | 0.818822 | 0.780912 | 0.773042 | 0.773042 | 0 | 0.001334 | 0.282475 | 30,300 | 984 | 114 | 30.792683 | 0.828573 | 0.160132 | 0 | 0.774368 | 0 | 0 | 0.090169 | 0.04052 | 0 | 0 | 0 | 0 | 0 | 1 | 0.055957 | false | 0 | 0.018051 | 0 | 0.135379 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
61479e0c6e15f0f144f5570163388b3fd7d478a1 | 23,703 | py | Python | tests/fields/test_methods.py | iyanmv/galois | a5e6386a684e3e0b47af608217002795dc25c702 | [
"MIT"
] | 65 | 2021-02-20T04:07:59.000Z | 2022-03-13T10:14:58.000Z | tests/fields/test_methods.py | iyanmv/galois | a5e6386a684e3e0b47af608217002795dc25c702 | [
"MIT"
] | 303 | 2021-02-22T19:36:25.000Z | 2022-03-31T14:48:15.000Z | tests/fields/test_methods.py | iyanmv/galois | a5e6386a684e3e0b47af608217002795dc25c702 | [
"MIT"
] | 9 | 2021-03-11T07:40:51.000Z | 2022-03-06T20:13:17.000Z | """
A pytest module to test methods of Galois field array classes.
"""
import pytest
import galois
def test_display_method():
GF = galois.GF(2**3)
assert str(GF(1)) == "GF(1, order=2^3)"
assert str(GF(0)) == "GF(0, order=2^3)"
assert str(GF(5)) == "GF(5, order=2^3)"
assert str(GF(2)) == "GF(2, order=2^3)"
assert str(GF([1, 0, 5, 2])) == "GF([1, 0, 5, 2], order=2^3)"
GF.display("poly")
assert str(GF(1)) == "GF(1, order=2^3)"
assert str(GF(0)) == "GF(0, order=2^3)"
assert str(GF(5)) == "GF(α^2 + 1, order=2^3)"
assert str(GF(2)) == "GF(α, order=2^3)"
assert str(GF([1, 0, 5, 2])) == "GF([1, 0, α^2 + 1, α], order=2^3)"
GF.display("power")
assert str(GF(1)) == "GF(1, order=2^3)"
assert str(GF(0)) == "GF(0, order=2^3)"
assert str(GF(5)) == "GF(α^6, order=2^3)"
assert str(GF(2)) == "GF(α, order=2^3)"
assert str(GF([1, 0, 5, 2])) == "GF([1, 0, α^6, α], order=2^3)"
GF.display()
assert str(GF(1)) == "GF(1, order=2^3)"
assert str(GF(0)) == "GF(0, order=2^3)"
assert str(GF(5)) == "GF(5, order=2^3)"
assert str(GF(2)) == "GF(2, order=2^3)"
assert str(GF([1, 0, 5, 2])) == "GF([1, 0, 5, 2], order=2^3)"
def test_display_context_manager():
GF = galois.GF(2**3)
a = GF([1, 0, 5, 2])
assert str(a) == "GF([1, 0, 5, 2], order=2^3)"
with GF.display("poly"):
assert str(a) == "GF([1, 0, α^2 + 1, α], order=2^3)"
with GF.display("power"):
assert str(a) == "GF([1, 0, α^6, α], order=2^3)"
assert str(a) == "GF([1, 0, 5, 2], order=2^3)"
def test_display_exceptions():
GF = galois.GF(2**3)
a = GF([1, 0, 5, 2])
with pytest.raises(ValueError):
GF.display("invalid-display-type")
def test_arithmetic_table():
GF = galois.GF(2**3)
with GF.display("int"):
assert GF.arithmetic_table("+") == "╔═══════╦═══╤═══╤═══╤═══╤═══╤═══╤═══╤═══╗\n║ x + y ║ 0 │ 1 │ 2 │ 3 │ 4 │ 5 │ 6 │ 7 ║\n╠═══════╬═══╪═══╪═══╪═══╪═══╪═══╪═══╪═══╣\n║ 0 ║ 0 │ 1 │ 2 │ 3 │ 4 │ 5 │ 6 │ 7 ║\n╟───────╫───┼───┼───┼───┼───┼───┼───┼───╢\n║ 1 ║ 1 │ 0 │ 3 │ 2 │ 5 │ 4 │ 7 │ 6 ║\n╟───────╫───┼───┼───┼───┼───┼───┼───┼───╢\n║ 2 ║ 2 │ 3 │ 0 │ 1 │ 6 │ 7 │ 4 │ 5 ║\n╟───────╫───┼───┼───┼───┼───┼───┼───┼───╢\n║ 3 ║ 3 │ 2 │ 1 │ 0 │ 7 │ 6 │ 5 │ 4 ║\n╟───────╫───┼───┼───┼───┼───┼───┼───┼───╢\n║ 4 ║ 4 │ 5 │ 6 │ 7 │ 0 │ 1 │ 2 │ 3 ║\n╟───────╫───┼───┼───┼───┼───┼───┼───┼───╢\n║ 5 ║ 5 │ 4 │ 7 │ 6 │ 1 │ 0 │ 3 │ 2 ║\n╟───────╫───┼───┼───┼───┼───┼───┼───┼───╢\n║ 6 ║ 6 │ 7 │ 4 │ 5 │ 2 │ 3 │ 0 │ 1 ║\n╟───────╫───┼───┼───┼───┼───┼───┼───┼───╢\n║ 7 ║ 7 │ 6 │ 5 │ 4 │ 3 │ 2 │ 1 │ 0 ║\n╚═══════╩═══╧═══╧═══╧═══╧═══╧═══╧═══╧═══╝"
assert GF.arithmetic_table("-") == "╔═══════╦═══╤═══╤═══╤═══╤═══╤═══╤═══╤═══╗\n║ x - y ║ 0 │ 1 │ 2 │ 3 │ 4 │ 5 │ 6 │ 7 ║\n╠═══════╬═══╪═══╪═══╪═══╪═══╪═══╪═══╪═══╣\n║ 0 ║ 0 │ 1 │ 2 │ 3 │ 4 │ 5 │ 6 │ 7 ║\n╟───────╫───┼───┼───┼───┼───┼───┼───┼───╢\n║ 1 ║ 1 │ 0 │ 3 │ 2 │ 5 │ 4 │ 7 │ 6 ║\n╟───────╫───┼───┼───┼───┼───┼───┼───┼───╢\n║ 2 ║ 2 │ 3 │ 0 │ 1 │ 6 │ 7 │ 4 │ 5 ║\n╟───────╫───┼───┼───┼───┼───┼───┼───┼───╢\n║ 3 ║ 3 │ 2 │ 1 │ 0 │ 7 │ 6 │ 5 │ 4 ║\n╟───────╫───┼───┼───┼───┼───┼───┼───┼───╢\n║ 4 ║ 4 │ 5 │ 6 │ 7 │ 0 │ 1 │ 2 │ 3 ║\n╟───────╫───┼───┼───┼───┼───┼───┼───┼───╢\n║ 5 ║ 5 │ 4 │ 7 │ 6 │ 1 │ 0 │ 3 │ 2 ║\n╟───────╫───┼───┼───┼───┼───┼───┼───┼───╢\n║ 6 ║ 6 │ 7 │ 4 │ 5 │ 2 │ 3 │ 0 │ 1 ║\n╟───────╫───┼───┼───┼───┼───┼───┼───┼───╢\n║ 7 ║ 7 │ 6 │ 5 │ 4 │ 3 │ 2 │ 1 │ 0 ║\n╚═══════╩═══╧═══╧═══╧═══╧═══╧═══╧═══╧═══╝"
assert GF.arithmetic_table("*") == "╔═══════╦═══╤═══╤═══╤═══╤═══╤═══╤═══╤═══╗\n║ x * y ║ 0 │ 1 │ 2 │ 3 │ 4 │ 5 │ 6 │ 7 ║\n╠═══════╬═══╪═══╪═══╪═══╪═══╪═══╪═══╪═══╣\n║ 0 ║ 0 │ 0 │ 0 │ 0 │ 0 │ 0 │ 0 │ 0 ║\n╟───────╫───┼───┼───┼───┼───┼───┼───┼───╢\n║ 1 ║ 0 │ 1 │ 2 │ 3 │ 4 │ 5 │ 6 │ 7 ║\n╟───────╫───┼───┼───┼───┼───┼───┼───┼───╢\n║ 2 ║ 0 │ 2 │ 4 │ 6 │ 3 │ 1 │ 7 │ 5 ║\n╟───────╫───┼───┼───┼───┼───┼───┼───┼───╢\n║ 3 ║ 0 │ 3 │ 6 │ 5 │ 7 │ 4 │ 1 │ 2 ║\n╟───────╫───┼───┼───┼───┼───┼───┼───┼───╢\n║ 4 ║ 0 │ 4 │ 3 │ 7 │ 6 │ 2 │ 5 │ 1 ║\n╟───────╫───┼───┼───┼───┼───┼───┼───┼───╢\n║ 5 ║ 0 │ 5 │ 1 │ 4 │ 2 │ 7 │ 3 │ 6 ║\n╟───────╫───┼───┼───┼───┼───┼───┼───┼───╢\n║ 6 ║ 0 │ 6 │ 7 │ 1 │ 5 │ 3 │ 2 │ 4 ║\n╟───────╫───┼───┼───┼───┼───┼───┼───┼───╢\n║ 7 ║ 0 │ 7 │ 5 │ 2 │ 1 │ 6 │ 4 │ 3 ║\n╚═══════╩═══╧═══╧═══╧═══╧═══╧═══╧═══╧═══╝"
assert GF.arithmetic_table("/") == "╔═══════╦═══╤═══╤═══╤═══╤═══╤═══╤═══╗\n║ x / y ║ 1 │ 2 │ 3 │ 4 │ 5 │ 6 │ 7 ║\n╠═══════╬═══╪═══╪═══╪═══╪═══╪═══╪═══╣\n║ 0 ║ 0 │ 0 │ 0 │ 0 │ 0 │ 0 │ 0 ║\n╟───────╫───┼───┼───┼───┼───┼───┼───╢\n║ 1 ║ 1 │ 5 │ 6 │ 7 │ 2 │ 3 │ 4 ║\n╟───────╫───┼───┼───┼───┼───┼───┼───╢\n║ 2 ║ 2 │ 1 │ 7 │ 5 │ 4 │ 6 │ 3 ║\n╟───────╫───┼───┼───┼───┼───┼───┼───╢\n║ 3 ║ 3 │ 4 │ 1 │ 2 │ 6 │ 5 │ 7 ║\n╟───────╫───┼───┼───┼───┼───┼───┼───╢\n║ 4 ║ 4 │ 2 │ 5 │ 1 │ 3 │ 7 │ 6 ║\n╟───────╫───┼───┼───┼───┼───┼───┼───╢\n║ 5 ║ 5 │ 7 │ 3 │ 6 │ 1 │ 4 │ 2 ║\n╟───────╫───┼───┼───┼───┼───┼───┼───╢\n║ 6 ║ 6 │ 3 │ 2 │ 4 │ 7 │ 1 │ 5 ║\n╟───────╫───┼───┼───┼───┼───┼───┼───╢\n║ 7 ║ 7 │ 6 │ 4 │ 3 │ 5 │ 2 │ 1 ║\n╚═══════╩═══╧═══╧═══╧═══╧═══╧═══╧═══╝"
with GF.display("poly"):
assert GF.arithmetic_table("+") == "╔═════════════╦═════════════╤═════════════╤═════════════╤═════════════╤═════════════╤═════════════╤═════════════╤═════════════╗\n║ x + y ║ 0 │ 1 │ α │ α + 1 │ α^2 │ α^2 + 1 │ α^2 + α │ α^2 + α + 1 ║\n╠═════════════╬═════════════╪═════════════╪═════════════╪═════════════╪═════════════╪═════════════╪═════════════╪═════════════╣\n║ 0 ║ 0 │ 1 │ α │ α + 1 │ α^2 │ α^2 + 1 │ α^2 + α │ α^2 + α + 1 ║\n╟─────────────╫─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────╢\n║ 1 ║ 1 │ 0 │ α + 1 │ α │ α^2 + 1 │ α^2 │ α^2 + α + 1 │ α^2 + α ║\n╟─────────────╫─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────╢\n║ α ║ α │ α + 1 │ 0 │ 1 │ α^2 + α │ α^2 + α + 1 │ α^2 │ α^2 + 1 ║\n╟─────────────╫─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────╢\n║ α + 1 ║ α + 1 │ α │ 1 │ 0 │ α^2 + α + 1 │ α^2 + α │ α^2 + 1 │ α^2 ║\n╟─────────────╫─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────╢\n║ α^2 ║ α^2 │ α^2 + 1 │ α^2 + α │ α^2 + α + 1 │ 0 │ 1 │ α │ α + 1 ║\n╟─────────────╫─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────╢\n║ α^2 + 1 ║ α^2 + 1 │ α^2 │ α^2 + α + 1 │ α^2 + α │ 1 │ 0 │ α + 1 │ α ║\n╟─────────────╫─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────╢\n║ α^2 + α ║ α^2 + α │ α^2 + α + 1 │ α^2 │ α^2 + 1 │ α │ α + 1 │ 0 │ 1 ║\n╟─────────────╫─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────╢\n║ α^2 + α + 1 ║ α^2 + α + 1 │ α^2 + α │ α^2 + 1 │ α^2 │ α + 1 │ α │ 1 │ 0 ║\n╚═════════════╩═════════════╧═════════════╧═════════════╧═════════════╧═════════════╧═════════════╧═════════════╧═════════════╝"
assert GF.arithmetic_table("-") == "╔═════════════╦═════════════╤═════════════╤═════════════╤═════════════╤═════════════╤═════════════╤═════════════╤═════════════╗\n║ x - y ║ 0 │ 1 │ α │ α + 1 │ α^2 │ α^2 + 1 │ α^2 + α │ α^2 + α + 1 ║\n╠═════════════╬═════════════╪═════════════╪═════════════╪═════════════╪═════════════╪═════════════╪═════════════╪═════════════╣\n║ 0 ║ 0 │ 1 │ α │ α + 1 │ α^2 │ α^2 + 1 │ α^2 + α │ α^2 + α + 1 ║\n╟─────────────╫─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────╢\n║ 1 ║ 1 │ 0 │ α + 1 │ α │ α^2 + 1 │ α^2 │ α^2 + α + 1 │ α^2 + α ║\n╟─────────────╫─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────╢\n║ α ║ α │ α + 1 │ 0 │ 1 │ α^2 + α │ α^2 + α + 1 │ α^2 │ α^2 + 1 ║\n╟─────────────╫─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────╢\n║ α + 1 ║ α + 1 │ α │ 1 │ 0 │ α^2 + α + 1 │ α^2 + α │ α^2 + 1 │ α^2 ║\n╟─────────────╫─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────╢\n║ α^2 ║ α^2 │ α^2 + 1 │ α^2 + α │ α^2 + α + 1 │ 0 │ 1 │ α │ α + 1 ║\n╟─────────────╫─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────╢\n║ α^2 + 1 ║ α^2 + 1 │ α^2 │ α^2 + α + 1 │ α^2 + α │ 1 │ 0 │ α + 1 │ α ║\n╟─────────────╫─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────╢\n║ α^2 + α ║ α^2 + α │ α^2 + α + 1 │ α^2 │ α^2 + 1 │ α │ α + 1 │ 0 │ 1 ║\n╟─────────────╫─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────╢\n║ α^2 + α + 1 ║ α^2 + α + 1 │ α^2 + α │ α^2 + 1 │ α^2 │ α + 1 │ α │ 1 │ 0 ║\n╚═════════════╩═════════════╧═════════════╧═════════════╧═════════════╧═════════════╧═════════════╧═════════════╧═════════════╝"
assert GF.arithmetic_table("*") == "╔═════════════╦═════════════╤═════════════╤═════════════╤═════════════╤═════════════╤═════════════╤═════════════╤═════════════╗\n║ x * y ║ 0 │ 1 │ α │ α + 1 │ α^2 │ α^2 + 1 │ α^2 + α │ α^2 + α + 1 ║\n╠═════════════╬═════════════╪═════════════╪═════════════╪═════════════╪═════════════╪═════════════╪═════════════╪═════════════╣\n║ 0 ║ 0 │ 0 │ 0 │ 0 │ 0 │ 0 │ 0 │ 0 ║\n╟─────────────╫─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────╢\n║ 1 ║ 0 │ 1 │ α │ α + 1 │ α^2 │ α^2 + 1 │ α^2 + α │ α^2 + α + 1 ║\n╟─────────────╫─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────╢\n║ α ║ 0 │ α │ α^2 │ α^2 + α │ α + 1 │ 1 │ α^2 + α + 1 │ α^2 + 1 ║\n╟─────────────╫─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────╢\n║ α + 1 ║ 0 │ α + 1 │ α^2 + α │ α^2 + 1 │ α^2 + α + 1 │ α^2 │ 1 │ α ║\n╟─────────────╫─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────╢\n║ α^2 ║ 0 │ α^2 │ α + 1 │ α^2 + α + 1 │ α^2 + α │ α │ α^2 + 1 │ 1 ║\n╟─────────────╫─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────╢\n║ α^2 + 1 ║ 0 │ α^2 + 1 │ 1 │ α^2 │ α │ α^2 + α + 1 │ α + 1 │ α^2 + α ║\n╟─────────────╫─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────╢\n║ α^2 + α ║ 0 │ α^2 + α │ α^2 + α + 1 │ 1 │ α^2 + 1 │ α + 1 │ α │ α^2 ║\n╟─────────────╫─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────╢\n║ α^2 + α + 1 ║ 0 │ α^2 + α + 1 │ α^2 + 1 │ α │ 1 │ α^2 + α │ α^2 │ α + 1 ║\n╚═════════════╩═════════════╧═════════════╧═════════════╧═════════════╧═════════════╧═════════════╧═════════════╧═════════════╝"
assert GF.arithmetic_table("/") == "╔═════════════╦═════════════╤═════════════╤═════════════╤═════════════╤═════════════╤═════════════╤═════════════╗\n║ x / y ║ 1 │ α │ α + 1 │ α^2 │ α^2 + 1 │ α^2 + α │ α^2 + α + 1 ║\n╠═════════════╬═════════════╪═════════════╪═════════════╪═════════════╪═════════════╪═════════════╪═════════════╣\n║ 0 ║ 0 │ 0 │ 0 │ 0 │ 0 │ 0 │ 0 ║\n╟─────────────╫─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────╢\n║ 1 ║ 1 │ α^2 + 1 │ α^2 + α │ α^2 + α + 1 │ α │ α + 1 │ α^2 ║\n╟─────────────╫─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────╢\n║ α ║ α │ 1 │ α^2 + α + 1 │ α^2 + 1 │ α^2 │ α^2 + α │ α + 1 ║\n╟─────────────╫─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────╢\n║ α + 1 ║ α + 1 │ α^2 │ 1 │ α │ α^2 + α │ α^2 + 1 │ α^2 + α + 1 ║\n╟─────────────╫─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────╢\n║ α^2 ║ α^2 │ α │ α^2 + 1 │ 1 │ α + 1 │ α^2 + α + 1 │ α^2 + α ║\n╟─────────────╫─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────╢\n║ α^2 + 1 ║ α^2 + 1 │ α^2 + α + 1 │ α + 1 │ α^2 + α │ 1 │ α^2 │ α ║\n╟─────────────╫─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────╢\n║ α^2 + α ║ α^2 + α │ α + 1 │ α │ α^2 │ α^2 + α + 1 │ 1 │ α^2 + 1 ║\n╟─────────────╫─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────╢\n║ α^2 + α + 1 ║ α^2 + α + 1 │ α^2 + α │ α^2 │ α + 1 │ α^2 + 1 │ α │ 1 ║\n╚═════════════╩═════════════╧═════════════╧═════════════╧═════════════╧═════════════╧═════════════╧═════════════╝"
with GF.display("power"):
assert GF.arithmetic_table("+") == "╔═══════╦═════╤═════╤═════╤═════╤═════╤═════╤═════╤═════╗\n║ x + y ║ 0 │ 1 │ α │ α^2 │ α^3 │ α^4 │ α^5 │ α^6 ║\n╠═══════╬═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╣\n║ 0 ║ 0 │ 1 │ α │ α^2 │ α^3 │ α^4 │ α^5 │ α^6 ║\n╟───────╫─────┼─────┼─────┼─────┼─────┼─────┼─────┼─────╢\n║ 1 ║ 1 │ 0 │ α^3 │ α^6 │ α │ α^5 │ α^4 │ α^2 ║\n╟───────╫─────┼─────┼─────┼─────┼─────┼─────┼─────┼─────╢\n║ α ║ α │ α^3 │ 0 │ α^4 │ 1 │ α^2 │ α^6 │ α^5 ║\n╟───────╫─────┼─────┼─────┼─────┼─────┼─────┼─────┼─────╢\n║ α^2 ║ α^2 │ α^6 │ α^4 │ 0 │ α^5 │ α │ α^3 │ 1 ║\n╟───────╫─────┼─────┼─────┼─────┼─────┼─────┼─────┼─────╢\n║ α^3 ║ α^3 │ α │ 1 │ α^5 │ 0 │ α^6 │ α^2 │ α^4 ║\n╟───────╫─────┼─────┼─────┼─────┼─────┼─────┼─────┼─────╢\n║ α^4 ║ α^4 │ α^5 │ α^2 │ α │ α^6 │ 0 │ 1 │ α^3 ║\n╟───────╫─────┼─────┼─────┼─────┼─────┼─────┼─────┼─────╢\n║ α^5 ║ α^5 │ α^4 │ α^6 │ α^3 │ α^2 │ 1 │ 0 │ α ║\n╟───────╫─────┼─────┼─────┼─────┼─────┼─────┼─────┼─────╢\n║ α^6 ║ α^6 │ α^2 │ α^5 │ 1 │ α^4 │ α^3 │ α │ 0 ║\n╚═══════╩═════╧═════╧═════╧═════╧═════╧═════╧═════╧═════╝"
assert GF.arithmetic_table("-") == "╔═══════╦═════╤═════╤═════╤═════╤═════╤═════╤═════╤═════╗\n║ x - y ║ 0 │ 1 │ α │ α^2 │ α^3 │ α^4 │ α^5 │ α^6 ║\n╠═══════╬═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╣\n║ 0 ║ 0 │ 1 │ α │ α^2 │ α^3 │ α^4 │ α^5 │ α^6 ║\n╟───────╫─────┼─────┼─────┼─────┼─────┼─────┼─────┼─────╢\n║ 1 ║ 1 │ 0 │ α^3 │ α^6 │ α │ α^5 │ α^4 │ α^2 ║\n╟───────╫─────┼─────┼─────┼─────┼─────┼─────┼─────┼─────╢\n║ α ║ α │ α^3 │ 0 │ α^4 │ 1 │ α^2 │ α^6 │ α^5 ║\n╟───────╫─────┼─────┼─────┼─────┼─────┼─────┼─────┼─────╢\n║ α^2 ║ α^2 │ α^6 │ α^4 │ 0 │ α^5 │ α │ α^3 │ 1 ║\n╟───────╫─────┼─────┼─────┼─────┼─────┼─────┼─────┼─────╢\n║ α^3 ║ α^3 │ α │ 1 │ α^5 │ 0 │ α^6 │ α^2 │ α^4 ║\n╟───────╫─────┼─────┼─────┼─────┼─────┼─────┼─────┼─────╢\n║ α^4 ║ α^4 │ α^5 │ α^2 │ α │ α^6 │ 0 │ 1 │ α^3 ║\n╟───────╫─────┼─────┼─────┼─────┼─────┼─────┼─────┼─────╢\n║ α^5 ║ α^5 │ α^4 │ α^6 │ α^3 │ α^2 │ 1 │ 0 │ α ║\n╟───────╫─────┼─────┼─────┼─────┼─────┼─────┼─────┼─────╢\n║ α^6 ║ α^6 │ α^2 │ α^5 │ 1 │ α^4 │ α^3 │ α │ 0 ║\n╚═══════╩═════╧═════╧═════╧═════╧═════╧═════╧═════╧═════╝"
assert GF.arithmetic_table("*") == "╔═══════╦═════╤═════╤═════╤═════╤═════╤═════╤═════╤═════╗\n║ x * y ║ 0 │ 1 │ α │ α^2 │ α^3 │ α^4 │ α^5 │ α^6 ║\n╠═══════╬═════╪═════╪═════╪═════╪═════╪═════╪═════╪═════╣\n║ 0 ║ 0 │ 0 │ 0 │ 0 │ 0 │ 0 │ 0 │ 0 ║\n╟───────╫─────┼─────┼─────┼─────┼─────┼─────┼─────┼─────╢\n║ 1 ║ 0 │ 1 │ α │ α^2 │ α^3 │ α^4 │ α^5 │ α^6 ║\n╟───────╫─────┼─────┼─────┼─────┼─────┼─────┼─────┼─────╢\n║ α ║ 0 │ α │ α^2 │ α^3 │ α^4 │ α^5 │ α^6 │ 1 ║\n╟───────╫─────┼─────┼─────┼─────┼─────┼─────┼─────┼─────╢\n║ α^2 ║ 0 │ α^2 │ α^3 │ α^4 │ α^5 │ α^6 │ 1 │ α ║\n╟───────╫─────┼─────┼─────┼─────┼─────┼─────┼─────┼─────╢\n║ α^3 ║ 0 │ α^3 │ α^4 │ α^5 │ α^6 │ 1 │ α │ α^2 ║\n╟───────╫─────┼─────┼─────┼─────┼─────┼─────┼─────┼─────╢\n║ α^4 ║ 0 │ α^4 │ α^5 │ α^6 │ 1 │ α │ α^2 │ α^3 ║\n╟───────╫─────┼─────┼─────┼─────┼─────┼─────┼─────┼─────╢\n║ α^5 ║ 0 │ α^5 │ α^6 │ 1 │ α │ α^2 │ α^3 │ α^4 ║\n╟───────╫─────┼─────┼─────┼─────┼─────┼─────┼─────┼─────╢\n║ α^6 ║ 0 │ α^6 │ 1 │ α │ α^2 │ α^3 │ α^4 │ α^5 ║\n╚═══════╩═════╧═════╧═════╧═════╧═════╧═════╧═════╧═════╝"
assert GF.arithmetic_table("/") == "╔═══════╦═════╤═════╤═════╤═════╤═════╤═════╤═════╗\n║ x / y ║ 1 │ α │ α^2 │ α^3 │ α^4 │ α^5 │ α^6 ║\n╠═══════╬═════╪═════╪═════╪═════╪═════╪═════╪═════╣\n║ 0 ║ 0 │ 0 │ 0 │ 0 │ 0 │ 0 │ 0 ║\n╟───────╫─────┼─────┼─────┼─────┼─────┼─────┼─────╢\n║ 1 ║ 1 │ α^6 │ α^5 │ α^4 │ α^3 │ α^2 │ α ║\n╟───────╫─────┼─────┼─────┼─────┼─────┼─────┼─────╢\n║ α ║ α │ 1 │ α^6 │ α^5 │ α^4 │ α^3 │ α^2 ║\n╟───────╫─────┼─────┼─────┼─────┼─────┼─────┼─────╢\n║ α^2 ║ α^2 │ α │ 1 │ α^6 │ α^5 │ α^4 │ α^3 ║\n╟───────╫─────┼─────┼─────┼─────┼─────┼─────┼─────╢\n║ α^3 ║ α^3 │ α^2 │ α │ 1 │ α^6 │ α^5 │ α^4 ║\n╟───────╫─────┼─────┼─────┼─────┼─────┼─────┼─────╢\n║ α^4 ║ α^4 │ α^3 │ α^2 │ α │ 1 │ α^6 │ α^5 ║\n╟───────╫─────┼─────┼─────┼─────┼─────┼─────┼─────╢\n║ α^5 ║ α^5 │ α^4 │ α^3 │ α^2 │ α │ 1 │ α^6 ║\n╟───────╫─────┼─────┼─────┼─────┼─────┼─────┼─────╢\n║ α^6 ║ α^6 │ α^5 │ α^4 │ α^3 │ α^2 │ α │ 1 ║\n╚═══════╩═════╧═════╧═════╧═════╧═════╧═════╧═════╝"
def test_repr_table():
GF = galois.GF(2**3)
assert GF.repr_table() == "╔═══════╤═════════════╤═══════════╤═════════╗\n║ Power │ Polynomial │ Vector │ Integer ║\n║═══════╪═════════════╪═══════════╪═════════║\n║ 0 │ 0 │ [0, 0, 0] │ 0 ║\n╟───────┼─────────────┼───────────┼─────────╢\n║ x^0 │ 1 │ [0, 0, 1] │ 1 ║\n╟───────┼─────────────┼───────────┼─────────╢\n║ x^1 │ x │ [0, 1, 0] │ 2 ║\n╟───────┼─────────────┼───────────┼─────────╢\n║ x^2 │ x^2 │ [1, 0, 0] │ 4 ║\n╟───────┼─────────────┼───────────┼─────────╢\n║ x^3 │ x + 1 │ [0, 1, 1] │ 3 ║\n╟───────┼─────────────┼───────────┼─────────╢\n║ x^4 │ x^2 + x │ [1, 1, 0] │ 6 ║\n╟───────┼─────────────┼───────────┼─────────╢\n║ x^5 │ x^2 + x + 1 │ [1, 1, 1] │ 7 ║\n╟───────┼─────────────┼───────────┼─────────╢\n║ x^6 │ x^2 + 1 │ [1, 0, 1] │ 5 ║\n╚═══════╧═════════════╧═══════════╧═════════╝"
assert GF.repr_table(sort="int") == "╔═══════╤═════════════╤═══════════╤═════════╗\n║ Power │ Polynomial │ Vector │ Integer ║\n║═══════╪═════════════╪═══════════╪═════════║\n║ 0 │ 0 │ [0, 0, 0] │ 0 ║\n╟───────┼─────────────┼───────────┼─────────╢\n║ x^0 │ 1 │ [0, 0, 1] │ 1 ║\n╟───────┼─────────────┼───────────┼─────────╢\n║ x^1 │ x │ [0, 1, 0] │ 2 ║\n╟───────┼─────────────┼───────────┼─────────╢\n║ x^3 │ x + 1 │ [0, 1, 1] │ 3 ║\n╟───────┼─────────────┼───────────┼─────────╢\n║ x^2 │ x^2 │ [1, 0, 0] │ 4 ║\n╟───────┼─────────────┼───────────┼─────────╢\n║ x^6 │ x^2 + 1 │ [1, 0, 1] │ 5 ║\n╟───────┼─────────────┼───────────┼─────────╢\n║ x^4 │ x^2 + x │ [1, 1, 0] │ 6 ║\n╟───────┼─────────────┼───────────┼─────────╢\n║ x^5 │ x^2 + x + 1 │ [1, 1, 1] │ 7 ║\n╚═══════╧═════════════╧═══════════╧═════════╝"
alpha = GF.primitive_elements[-1]
assert GF.repr_table(alpha) == "╔═════════════════╤═════════════╤═══════════╤═════════╗\n║ Power │ Polynomial │ Vector │ Integer ║\n║═════════════════╪═════════════╪═══════════╪═════════║\n║ 0 │ 0 │ [0, 0, 0] │ 0 ║\n╟─────────────────┼─────────────┼───────────┼─────────╢\n║ (x^2 + x + 1)^0 │ 1 │ [0, 0, 1] │ 1 ║\n╟─────────────────┼─────────────┼───────────┼─────────╢\n║ (x^2 + x + 1)^1 │ x^2 + x + 1 │ [1, 1, 1] │ 7 ║\n╟─────────────────┼─────────────┼───────────┼─────────╢\n║ (x^2 + x + 1)^2 │ x + 1 │ [0, 1, 1] │ 3 ║\n╟─────────────────┼─────────────┼───────────┼─────────╢\n║ (x^2 + x + 1)^3 │ x │ [0, 1, 0] │ 2 ║\n╟─────────────────┼─────────────┼───────────┼─────────╢\n║ (x^2 + x + 1)^4 │ x^2 + 1 │ [1, 0, 1] │ 5 ║\n╟─────────────────┼─────────────┼───────────┼─────────╢\n║ (x^2 + x + 1)^5 │ x^2 + x │ [1, 1, 0] │ 6 ║\n╟─────────────────┼─────────────┼───────────┼─────────╢\n║ (x^2 + x + 1)^6 │ x^2 │ [1, 0, 0] │ 4 ║\n╚═════════════════╧═════════════╧═══════════╧═════════╝"
assert GF.repr_table(alpha, sort="int") == "╔═════════════════╤═════════════╤═══════════╤═════════╗\n║ Power │ Polynomial │ Vector │ Integer ║\n║═════════════════╪═════════════╪═══════════╪═════════║\n║ 0 │ 0 │ [0, 0, 0] │ 0 ║\n╟─────────────────┼─────────────┼───────────┼─────────╢\n║ (x^2 + x + 1)^0 │ 1 │ [0, 0, 1] │ 1 ║\n╟─────────────────┼─────────────┼───────────┼─────────╢\n║ (x^2 + x + 1)^3 │ x │ [0, 1, 0] │ 2 ║\n╟─────────────────┼─────────────┼───────────┼─────────╢\n║ (x^2 + x + 1)^2 │ x + 1 │ [0, 1, 1] │ 3 ║\n╟─────────────────┼─────────────┼───────────┼─────────╢\n║ (x^2 + x + 1)^6 │ x^2 │ [1, 0, 0] │ 4 ║\n╟─────────────────┼─────────────┼───────────┼─────────╢\n║ (x^2 + x + 1)^4 │ x^2 + 1 │ [1, 0, 1] │ 5 ║\n╟─────────────────┼─────────────┼───────────┼─────────╢\n║ (x^2 + x + 1)^5 │ x^2 + x │ [1, 1, 0] │ 6 ║\n╟─────────────────┼─────────────┼───────────┼─────────╢\n║ (x^2 + x + 1)^1 │ x^2 + x + 1 │ [1, 1, 1] │ 7 ║\n╚═════════════════╧═════════════╧═══════════╧═════════╝"
| 278.858824 | 2,494 | 0.152175 | 3,926 | 23,703 | 3.966378 | 0.021905 | 0.047778 | 0.029861 | 0.017467 | 0.930452 | 0.894105 | 0.849024 | 0.799512 | 0.751991 | 0.735808 | 0 | 0.07499 | 0.268067 | 23,703 | 84 | 2,495 | 282.178571 | 0.131304 | 0.002616 | 0 | 0.446154 | 0 | 0.246154 | 0.913299 | 0.491855 | 0 | 0 | 0 | 0 | 0.615385 | 1 | 0.076923 | false | 0 | 0.030769 | 0 | 0.107692 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | null | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 13 |
6151a47006a5a351207adbe2b34b2318fdcc179a | 1,530 | py | Python | liveclock.py | Fabiomazzoty/oub-remix | bf9ce0cb492fed6a828caf14191cb29027da4b44 | [
"Naumen",
"Condor-1.1",
"MS-PL"
] | null | null | null | liveclock.py | Fabiomazzoty/oub-remix | bf9ce0cb492fed6a828caf14191cb29027da4b44 | [
"Naumen",
"Condor-1.1",
"MS-PL"
] | null | null | null | liveclock.py | Fabiomazzoty/oub-remix | bf9ce0cb492fed6a828caf14191cb29027da4b44 | [
"Naumen",
"Condor-1.1",
"MS-PL"
] | null | null | null | import time
from time import sleep
from pyrogram import Filters, Message
from pyrobot import BOT, LOGGER_GROUP
@BOT.on_message(Filters.command("time", ".") & Filters.me)
def delete(bot: BOT, message: Message):
if not len(message.command) > 1:
message.edit("<b>Wrong input!</b>\nCorrect Syntax:\n.time 20\nShow the current time for 20 seconds")
sleep(3)
message.delete()
return
i=0
if len(message.command) > 1 and int(message.command[1]):
endtimestr = message.command[1]
endtime = int(endtimestr)
while i < endtime:
currenttime = time.localtime()
clock = time.strftime("Current local time: %H:%M:%S", currenttime)
bot.edit_message_text(message.chat.id, message.message_id, clock)
i=i+1
time.sleep(1)
@BOT.on_message(Filters.command("timedate", ".") & Filters.me)
def delete(bot: BOT, message: Message):
if not len(message.command) > 1:
message.edit("<b>Wrong input!</b>\nCorrect Syntax:\n.timedate 20\nShow the current time for 20 seconds")
sleep(3)
message.delete()
return
i=0
if len(message.command) > 1 and int(message.command[1]):
endtimestr = message.command[1]
endtime = int(endtimestr)
while i < endtime:
currenttime = time.localtime()
clock = time.strftime("Current local time/date:\n %H:%M:%S, %d.%m.%Y", currenttime)
bot.edit_message_text(message.chat.id, message.message_id, clock)
i=i+1
time.sleep(1)
| 31.22449 | 112 | 0.635948 | 212 | 1,530 | 4.54717 | 0.268868 | 0.116183 | 0.124481 | 0.074689 | 0.865145 | 0.811203 | 0.811203 | 0.811203 | 0.811203 | 0.811203 | 0 | 0.020391 | 0.230719 | 1,530 | 48 | 113 | 31.875 | 0.798641 | 0 | 0 | 0.736842 | 0 | 0.052632 | 0.169281 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.052632 | false | 0 | 0.105263 | 0 | 0.210526 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
61525bffa2cbfd9dc11e21dd10bec91d84891557 | 8,230 | py | Python | lib/svtplay_dl/tests/test_stream.py | myhrmans/svtplay-dl | b806a72b2349f0cb6ec9af7ad355f1766d961922 | [
"MIT"
] | 543 | 2015-01-01T14:38:24.000Z | 2022-02-08T03:26:30.000Z | lib/svtplay_dl/tests/test_stream.py | myhrmans/svtplay-dl | b806a72b2349f0cb6ec9af7ad355f1766d961922 | [
"MIT"
] | 1,208 | 2015-01-01T19:20:51.000Z | 2022-03-31T21:56:40.000Z | lib/svtplay_dl/tests/test_stream.py | myhrmans/svtplay-dl | b806a72b2349f0cb6ec9af7ad355f1766d961922 | [
"MIT"
] | 146 | 2015-02-15T18:23:40.000Z | 2022-01-31T21:25:05.000Z | import unittest
from svtplay_dl.fetcher.dash import DASH
from svtplay_dl.fetcher.hls import HLS
from svtplay_dl.fetcher.http import HTTP
from svtplay_dl.subtitle import subtitle
from svtplay_dl.utils.parser import setup_defaults
from svtplay_dl.utils.stream import audio_role
from svtplay_dl.utils.stream import format_prio
from svtplay_dl.utils.stream import language_prio
from svtplay_dl.utils.stream import sort_quality
from svtplay_dl.utils.stream import subtitle_filter
class streamTest_sort(unittest.TestCase):
def test_sort(self):
data = [
DASH(setup_defaults(), "http://example.com", 3000, None),
HLS(setup_defaults(), "http://example.com", 2000, None),
HTTP(setup_defaults(), "http://example.com", 3001, None),
]
assert all(
[
a[0] == b.bitrate
for a, b in zip(
sort_quality(data),
[
HTTP(setup_defaults(), "http://example.com", 3001, None),
DASH(setup_defaults(), "http://example.com", 3000, None),
HLS(setup_defaults(), "http://example.com", 2000, None),
],
)
],
)
class streamTestLanguage(unittest.TestCase):
def test_language_prio(self):
config = setup_defaults()
test_streams = [
DASH(setup_defaults(), "http://example.com", 3000, None),
DASH(setup_defaults(), "http://example.com", 3001, None),
DASH(setup_defaults(), "http://example.com", 3002, None),
]
streams = language_prio(config, test_streams)
assert len(streams) == 3
def test_language_prio_select(self):
config = setup_defaults()
config.set("audio_language", "en")
test_streams = [
DASH(setup_defaults(), "http://example.com", 3000, None, language="en"),
DASH(setup_defaults(), "http://example.com", 3001, None),
DASH(setup_defaults(), "http://example.com", 3002, None, language="sv"),
]
streams = language_prio(config, test_streams)
assert len(streams) == 1
class streamTestFormat(unittest.TestCase):
def test_language_prio(self):
test_streams = [
DASH(setup_defaults(), "http://example.com", 3000, None),
DASH(setup_defaults(), "http://example.com", 3001, None, channels="51"),
DASH(setup_defaults(), "http://example.com", 3002, None),
]
streams = format_prio(test_streams, ["h264-51"])
assert len(streams) == 1
def test_language_prio2(self):
test_streams = [
DASH(setup_defaults(), "http://example.com", 3000, None),
DASH(setup_defaults(), "http://example.com", 3001, None, channels="51"),
DASH(setup_defaults(), "http://example.com", 3001, None, codec="h264", channels="51"),
DASH(setup_defaults(), "http://example.com", 3002, None),
]
streams = format_prio(test_streams, ["h264"])
assert len(streams) == 2
def test_language_prio3(self):
test_streams = [
DASH(setup_defaults(), "http://example.com", 3000, None),
DASH(setup_defaults(), "http://example.com", 3001, None, channels="51"),
DASH(setup_defaults(), "http://example.com", 3002, None),
]
streams = format_prio(test_streams, ["h26e4"])
assert len(streams) == 0
class streamTestRole(unittest.TestCase):
def test_language_prio(self):
test_streams = [
DASH(setup_defaults(), "http://example.com", 3000, None),
DASH(setup_defaults(), "http://example.com", 3001, None),
DASH(setup_defaults(), "http://example.com", 3002, None),
]
streams = audio_role(setup_defaults(), test_streams)
assert len(streams) == 3
def test_language_prio2(self):
test_streams = [
DASH(setup_defaults(), "http://example.com", 3000, None),
DASH(setup_defaults(), "http://example.com", 3001, None, role="x-sv"),
DASH(setup_defaults(), "http://example.com", 3002, None),
]
config = setup_defaults()
config.set("audio_role", "x-sv")
streams = audio_role(config, test_streams)
assert len(streams) == 1
def test_language_prio3(self):
test_streams = [
DASH(setup_defaults(), "http://example.com", 3000, None),
DASH(setup_defaults(), "http://example.com", 3001, None, role="x-sv"),
DASH(setup_defaults(), "http://example.com", 3002, None),
]
config = setup_defaults()
config.set("audio_role", "sv")
streams = audio_role(config, test_streams)
assert len(streams) == 0
def test_language_prio4(self):
test_streams = [
DASH(setup_defaults(), "http://example.com", 3000, None),
DASH(setup_defaults(), "http://example.com", 3001, None, role="x-sv"),
DASH(setup_defaults(), "http://example.com", 3002, None),
]
config = setup_defaults()
config.set("audio_language", "sv")
streams = audio_role(config, test_streams)
assert len(streams) == 3
def test_language_prio5(self):
test_streams = [
DASH(setup_defaults(), "http://example.com", 3000, None),
DASH(setup_defaults(), "http://example.com", 3001, None, role="x-sv"),
DASH(setup_defaults(), "http://example.com", 3002, None),
]
config = setup_defaults()
config.set("audio_role", "isii")
config.set("audio_language", "sv")
streams = audio_role(config, test_streams)
assert len(streams) == 0
class streamSubtile(unittest.TestCase):
def test_subtitleFilter(self):
test_subs = [
subtitle(setup_defaults(), "wrst", "http://example.com"),
subtitle(setup_defaults(), "wrst", "http://example.com", "sv"),
subtitle(setup_defaults(), "wrst", "http://example.com", "dk"),
subtitle(setup_defaults(), "wrst", "http://example.com", "sv"),
]
subs = subtitle_filter(test_subs)
assert len(subs) == 3
def test_subtitleFilter2(self):
config = setup_defaults()
config.set("get_all_subtitles", True)
test_subs = [
subtitle(config, "wrst", "http://example.com"),
subtitle(config, "wrst", "http://example.com", subfix="sv"),
subtitle(config, "wrst", "http://example.com", subfix="dk"),
subtitle(config, "wrst", "http://example.com", subfix="no"),
]
subs = subtitle_filter(test_subs)
assert len(subs) == 4
def test_subtitleFilter3(self):
config = setup_defaults()
config.set("subtitle_preferred", "sv")
test_subs = [
subtitle(config, "wrst", "http://example.com"),
subtitle(config, "wrst", "http://example.com", subfix="sv"),
subtitle(config, "wrst", "http://example.com", subfix="dk"),
subtitle(config, "wrst", "http://example.com", subfix="no"),
]
subs = subtitle_filter(test_subs)
assert len(subs) == 1
def test_subtitleFilter4(self):
config = setup_defaults()
config.set("subtitle_preferred", "gr")
test_subs = [
subtitle(config, "wrst", "http://example.com"),
subtitle(config, "wrst", "http://example.com", subfix="sv"),
subtitle(config, "wrst", "http://example.com", subfix="dk"),
subtitle(config, "wrst", "http://example.com", subfix="no"),
]
subs = subtitle_filter(test_subs)
assert len(subs) == 0
def test_subtitleFilter5(self):
config = setup_defaults()
config.set("get_all_subtitles", True)
test_subs = [
subtitle(config, "wrst", "http://example.com"),
subtitle(config, "wrst", "http://example.com", subfix="sv"),
subtitle(config, "wrst", "http://example.com", subfix="sv"),
subtitle(config, "wrst", "http://example.com", subfix="no"),
]
subs = subtitle_filter(test_subs)
assert len(subs) == 3
| 40.343137 | 98 | 0.580073 | 921 | 8,230 | 5.016287 | 0.094463 | 0.135714 | 0.172727 | 0.192208 | 0.850433 | 0.844589 | 0.824242 | 0.771212 | 0.721212 | 0.692424 | 0 | 0.032365 | 0.264156 | 8,230 | 203 | 99 | 40.541872 | 0.730515 | 0 | 0 | 0.618785 | 0 | 0 | 0.163548 | 0 | 0 | 0 | 0 | 0 | 0.088398 | 1 | 0.088398 | false | 0 | 0.060773 | 0 | 0.176796 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
61648ffd89278da17d2cb4927590296de5bb5886 | 137,347 | py | Python | frame_level_models.py | YongyiTang92/youtube-8m | f7f29ad75964e8d5007beeeba815e223cb79a010 | [
"Apache-2.0"
] | 1 | 2019-02-28T01:56:56.000Z | 2019-02-28T01:56:56.000Z | frame_level_models.py | YongyiTang92/youtube-8m | f7f29ad75964e8d5007beeeba815e223cb79a010 | [
"Apache-2.0"
] | null | null | null | frame_level_models.py | YongyiTang92/youtube-8m | f7f29ad75964e8d5007beeeba815e223cb79a010 | [
"Apache-2.0"
] | null | null | null | # Copyright 2017 Antoine Miech All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS-IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Contains a collection of models which operate on variable-length sequences.
"""
import math
import models
import video_level_models
import tensorflow as tf
import model_utils as utils
import tensorflow.contrib.slim as slim
from tensorflow import flags
import scipy.io as sio
import numpy as np
FLAGS = flags.FLAGS
flags.DEFINE_bool("gating_remove_diag", False,
"Remove diag for self gating")
flags.DEFINE_bool("lightvlad", False,
"Light or full NetVLAD")
flags.DEFINE_bool("vlagd", False,
"vlagd of vlad")
flags.DEFINE_bool("graphvlad", False,
"graphical vlad")
flags.DEFINE_bool("simplegraphvlad", False,
"graphical vlad")
flags.DEFINE_bool("addvlad", False,
"add vlad")
flags.DEFINE_bool("gruvlad", False,
"gru vlad")
flags.DEFINE_bool("acvlad", False,
"ac vlad")
flags.DEFINE_bool("localvlad", False,
"local vlad")
flags.DEFINE_bool("glulightvlad", False,
"GLU light vlad")
flags.DEFINE_bool("convglulightvlad", False,
"Conv GLU light vlad")
flags.DEFINE_bool("sqrtvlad", False,
"sqrt normalization vlad")
flags.DEFINE_bool("bilinear", False,
"bilinear pooling")
flags.DEFINE_bool("nonlocalvlad", False,
"nonlocal vlad")
flags.DEFINE_bool("nonlocalvlad_shared", False,
"nonlocalvlad_shared")
flags.DEFINE_bool("nonlocalvlad_unique", False,
"nonlocalvlad_unique")
flags.DEFINE_bool("gruthenvlad", False,
"gruthenvlad")
flags.DEFINE_bool("sevlad", False,
"sevlad")
flags.DEFINE_bool("seresvlad", False,
"seresvlad")
flags.DEFINE_bool("beforeNorm", False,
"nonlocal before norm")
flags.DEFINE_bool("afterNorm", False,
"nonlocal after norm")
flags.DEFINE_integer("iterations", 30,
"Number of frames per batch for DBoF.")
flags.DEFINE_bool("dbof_add_batch_norm", True,
"Adds batch normalization to the DBoF model.")
flags.DEFINE_bool(
"sample_random_frames", True,
"If true samples random frames (for frame level models). If false, a random"
"sequence of frames is sampled instead.")
flags.DEFINE_integer("dbof_cluster_size", 16384,
"Number of units in the DBoF cluster layer.")
flags.DEFINE_integer("dbof_hidden_size", 2048,
"Number of units in the DBoF hidden layer.")
flags.DEFINE_bool("dbof_relu", True, 'add ReLU to hidden layer')
flags.DEFINE_integer("dbof_var_features", 0,
"Variance features on top of Dbof cluster layer.")
flags.DEFINE_string("dbof_activation", "relu", 'dbof activation')
flags.DEFINE_bool("softdbof_maxpool", False, 'add max pool to soft dbof')
flags.DEFINE_integer("netvlad_cluster_size", 64,
"Number of units in the NetVLAD cluster layer.")
flags.DEFINE_bool("netvlad_relu", True, 'add ReLU to hidden layer')
flags.DEFINE_integer("netvlad_dimred", -1,
"NetVLAD output dimension reduction")
flags.DEFINE_integer("gatednetvlad_dimred", 1024,
"GatedNetVLAD output dimension reduction")
flags.DEFINE_bool("gating", False,
"Gating for NetVLAD")
flags.DEFINE_integer("hidden_size", 1024,
"size of hidden layer for BasicStatModel.")
flags.DEFINE_integer("netvlad_hidden_size", 1024,
"Number of units in the NetVLAD hidden layer.")
flags.DEFINE_integer("netvlad_hidden_size_video", 1024,
"Number of units in the NetVLAD video hidden layer.")
flags.DEFINE_integer("netvlad_hidden_size_audio", 64,
"Number of units in the NetVLAD audio hidden layer.")
flags.DEFINE_bool("netvlad_add_batch_norm", True,
"Adds batch normalization to the DBoF model.")
flags.DEFINE_integer("fv_cluster_size", 64,
"Number of units in the NetVLAD cluster layer.")
flags.DEFINE_integer("fv_hidden_size", 2048,
"Number of units in the NetVLAD hidden layer.")
flags.DEFINE_bool("fv_relu", True,
"ReLU after the NetFV hidden layer.")
flags.DEFINE_bool("fv_couple_weights", True,
"Coupling cluster weights or not")
flags.DEFINE_float("fv_coupling_factor", 0.01,
"Coupling factor")
flags.DEFINE_string("dbof_pooling_method", "max",
"The pooling method used in the DBoF cluster layer. "
"Choices are 'average' and 'max'.")
flags.DEFINE_string("video_level_classifier_model", "MoeModel",
"Some Frame-Level models can be decomposed into a "
"generalized pooling operation followed by a "
"classifier layer")
flags.DEFINE_integer("lstm_cells", 1024, "Number of LSTM cells.")
flags.DEFINE_integer("lstm_layers", 2, "Number of LSTM layers.")
flags.DEFINE_integer("lstm_cells_video", 1024, "Number of LSTM cells (video).")
flags.DEFINE_integer("lstm_cells_audio", 128, "Number of LSTM cells (audio).")
flags.DEFINE_integer("gru_cells", 1024, "Number of GRU cells.")
flags.DEFINE_integer("gru_cells_video", 1024, "Number of GRU cells (video).")
flags.DEFINE_integer("gru_cells_audio", 128, "Number of GRU cells (audio).")
flags.DEFINE_integer("gru_layers", 2, "Number of GRU layers.")
flags.DEFINE_bool("lstm_random_sequence", False,
"Random sequence input for lstm.")
flags.DEFINE_bool("gru_random_sequence", False,
"Random sequence input for gru.")
flags.DEFINE_bool("gru_backward", False, "BW reading for GRU")
flags.DEFINE_bool("lstm_backward", False, "BW reading for LSTM")
flags.DEFINE_bool("fc_dimred", True, "Adding FC dimred after pooling")
flags.DEFINE_integer("ConvLayers", 1, "Number of conv layers for ConvSquare_Moe.")
flags.DEFINE_integer("Conv_temporal_field", 2, "Receptive field for convolution.")
flags.DEFINE_bool("avg_netvlad", False, "use avgpooling for reducing vladnet feature size")
flags.DEFINE_bool("max_netvlad", False, "use maxpooling for reducing vladnet feature size")
flags.DEFINE_integer("num_nonlocal_block", 1, "Number of non-local module.")
class LightVLAD():
def __init__(self, feature_size,max_frames,cluster_size, add_batch_norm, is_training):
self.feature_size = feature_size
self.max_frames = max_frames
self.is_training = is_training
self.add_batch_norm = add_batch_norm
self.cluster_size = cluster_size
def forward(self,reshaped_input):
cluster_weights = tf.get_variable("cluster_weights",
[self.feature_size, self.cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
activation = tf.matmul(reshaped_input, cluster_weights)
if self.add_batch_norm:
activation = slim.batch_norm(
activation,
center=True,
scale=True,
is_training=self.is_training,
scope="cluster_bn")
else:
cluster_biases = tf.get_variable("cluster_biases",
[cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
tf.summary.histogram("cluster_biases", cluster_biases)
activation += cluster_biases
activation = tf.nn.softmax(activation)
activation = tf.reshape(activation, [-1, self.max_frames, self.cluster_size])
activation = tf.transpose(activation,perm=[0,2,1])
reshaped_input = tf.reshape(reshaped_input,[-1,self.max_frames,self.feature_size])
vlad = tf.matmul(activation,reshaped_input)
vlad = tf.transpose(vlad,perm=[0,2,1])
vlad = tf.nn.l2_normalize(vlad,1)
vlad = tf.reshape(vlad,[-1,self.cluster_size*self.feature_size])
vlad = tf.nn.l2_normalize(vlad,1)
return vlad
class Bilinear_pooling():
def __init__(self, feature_size,max_frames,cluster_size, add_batch_norm, is_training):
self.feature_size = feature_size
self.max_frames = max_frames
self.is_training = is_training
self.add_batch_norm = add_batch_norm
self.cluster_size = cluster_size
def forward(self,reshaped_input):
cluster_weights_1 = tf.get_variable("cluster_weights_1",
[self.feature_size, self.cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
cluster_weights_2 = tf.get_variable("cluster_weights_2",
[self.feature_size, self.cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
activation_1 = tf.matmul(reshaped_input, cluster_weights_1)
activation_2 = tf.matmul(reshaped_input, cluster_weights_2)
if self.add_batch_norm:
activation_1 = slim.batch_norm(
activation_1,
center=True,
scale=True,
is_training=self.is_training,
scope="cluster_bn_1")
activation_2 = slim.batch_norm(
activation_2,
center=True,
scale=True,
is_training=self.is_training,
scope="cluster_bn_2")
else:
cluster_biases_1 = tf.get_variable("cluster_biases_1",
[cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
cluster_biases_2 = tf.get_variable("cluster_biases_2",
[cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
# tf.summary.histogram("cluster_biases", cluster_biases)
activation_1 += cluster_biases_1
activation_2 += cluster_biases_2
# activation = tf.nn.softmax(activation)
activation_1 = tf.reshape(activation_1, [-1, self.max_frames, self.cluster_size])
activation_2 = tf.reshape(activation_2, [-1, self.max_frames, self.cluster_size])
activation_1 = tf.transpose(activation_1,perm=[0,2,1])
# reshaped_input = tf.reshape(reshaped_input,[-1,self.max_frames,self.feature_size])
vlad = tf.matmul(activation_1,activation_2)
vlad = tf.transpose(vlad,perm=[0,2,1])
vlad = tf.nn.l2_normalize(vlad,1)
vlad = tf.reshape(vlad,[-1,self.cluster_size*self.cluster_size])
vlad = tf.nn.l2_normalize(vlad,1)
return vlad
class GLULightVLAD():
def __init__(self, feature_size,max_frames,cluster_size, add_batch_norm, is_training):
self.feature_size = feature_size
self.max_frames = max_frames
self.is_training = is_training
self.add_batch_norm = add_batch_norm
self.cluster_size = cluster_size
def forward(self,reshaped_input):
cluster_weights = tf.get_variable("cluster_weights",
[self.feature_size, self.cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
activation = tf.matmul(reshaped_input, cluster_weights)
if self.add_batch_norm:
activation = slim.batch_norm(
activation,
center=True,
scale=True,
is_training=self.is_training,
scope="cluster_bn")
else:
cluster_biases = tf.get_variable("cluster_biases",
[cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
tf.summary.histogram("cluster_biases", cluster_biases)
activation += cluster_biases
activation = tf.nn.softmax(activation)
activation = tf.reshape(activation, [-1, self.max_frames, self.cluster_size])
activation = tf.transpose(activation,perm=[0,2,1])
# Gated Linear Unit
linear_reshaped_input = slim.fully_connected(reshaped_input, self.cluster_size, activation_fn=None, scope='GLU_linear')
gated_reshaped_input = slim.fully_connected(reshaped_input, self.cluster_size, activation_fn=tf.sigmoid, scope='GLU_gated')
glu_reshape_input = linear_reshaped_input*gated_reshaped_input
gated_reshaped_input = tf.reshape(gated_reshaped_input,[-1,self.max_frames,self.cluster_size])
vlad = tf.matmul(activation, gated_reshaped_input)
vlad = tf.transpose(vlad, perm=[0,2,1])
vlad = tf.nn.l2_normalize(vlad,1)
vlad = tf.reshape(vlad,[-1,self.cluster_size*self.cluster_size])
vlad = tf.nn.l2_normalize(vlad,1)
return vlad
class ConvGLULightVLAD():
def __init__(self, feature_size,max_frames,cluster_size, add_batch_norm, is_training):
self.feature_size = feature_size
self.max_frames = max_frames
self.is_training = is_training
self.add_batch_norm = add_batch_norm
self.cluster_size = cluster_size
def forward(self,reshaped_input):
# Conv
reshaped_input = tf.reshape(reshaped_input,[-1,self.max_frames, 1, self.feature_size])
conv1_output = tf.contrib.layers.conv2d(reshaped_input, self.feature_size, [4,1], stride=[1,1], scope='conv1')
conv2_output = tf.contrib.layers.conv2d(reshaped_input, self.feature_size, [4,1], stride=[1,1], scope='conv2')
reshaped_input = tf.reshape(conv2_output,[-1, self.feature_size])
cluster_weights = tf.get_variable("cluster_weights",
[self.feature_size, self.cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
activation = tf.matmul(reshaped_input, cluster_weights)
if self.add_batch_norm:
activation = slim.batch_norm(
activation,
center=True,
scale=True,
is_training=self.is_training,
scope="cluster_bn")
else:
cluster_biases = tf.get_variable("cluster_biases",
[cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
tf.summary.histogram("cluster_biases", cluster_biases)
activation += cluster_biases
activation = tf.nn.softmax(activation)
activation = tf.reshape(activation, [-1, self.max_frames, self.cluster_size])
activation = tf.transpose(activation,perm=[0,2,1])
# Gated Linear Unit
linear_reshaped_input = slim.fully_connected(reshaped_input, self.cluster_size, activation_fn=None, scope='GLU_linear')
gated_reshaped_input = slim.fully_connected(reshaped_input, self.cluster_size, activation_fn=tf.sigmoid, scope='GLU_gated')
glu_reshape_input = linear_reshaped_input*gated_reshaped_input
gated_reshaped_input = tf.reshape(gated_reshaped_input,[-1,self.max_frames,self.cluster_size])
vlad = tf.matmul(activation, gated_reshaped_input)
vlad = tf.transpose(vlad, perm=[0,2,1])
vlad = tf.nn.l2_normalize(vlad,1)
vlad = tf.reshape(vlad,[-1,self.cluster_size*self.cluster_size])
vlad = tf.nn.l2_normalize(vlad,1)
return vlad
class LocalLightVLAD():
def __init__(self, feature_size,max_frames,cluster_size, add_batch_norm, is_training):
self.feature_size = feature_size
self.max_frames = max_frames
self.is_training = is_training
self.add_batch_norm = add_batch_norm
self.cluster_size = cluster_size
def forward(self,reshaped_input):
cluster_weights = tf.get_variable("cluster_weights",
[self.feature_size, self.cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
activation = tf.matmul(reshaped_input, cluster_weights)
if self.add_batch_norm:
activation = slim.batch_norm(
activation,
center=True,
scale=True,
is_training=self.is_training,
scope="cluster_bn")
else:
cluster_biases = tf.get_variable("cluster_biases",
[cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
tf.summary.histogram("cluster_biases", cluster_biases)
activation += cluster_biases
activation = tf.nn.softmax(activation)
activation = tf.reshape(activation, [-1, self.max_frames, self.cluster_size])
activation = tf.transpose(activation,perm=[0,2,1])
reshaped_input = tf.reshape(reshaped_input,[-1,self.max_frames,self.feature_size])
vlad = tf.matmul(activation,reshaped_input)
vlad = tf.nn.l2_normalize(vlad,2)
# local_weights = tf.get_variable("local_weights",
# [self.feature_size, self.cluster_size],
# initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
vlad = tf.reshape(vlad,[-1,self.feature_size])
vlad = slim.fully_connected(vlad, self.cluster_size, scope='local_connection')
vlad = tf.reshape(vlad,[-1,self.cluster_size, self.cluster_size])
vlad = tf.transpose(vlad,perm=[0,2,1])
vlad = tf.nn.l2_normalize(vlad,1)
vlad = tf.reshape(vlad,[-1,self.cluster_size*self.cluster_size])
vlad = tf.nn.l2_normalize(vlad,1)
return vlad
class AC_VLAD():
def __init__(self, feature_size,max_frames,cluster_size, add_batch_norm, is_training):
self.feature_size = feature_size
self.max_frames = max_frames
self.is_training = is_training
self.add_batch_norm = add_batch_norm
self.cluster_size = cluster_size
def forward(self,reshaped_input):
cluster_weights = tf.get_variable("cluster_weights",
[self.feature_size, self.cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
activation = tf.matmul(reshaped_input, cluster_weights)
if self.add_batch_norm:
activation = slim.batch_norm(
activation,
center=True,
scale=True,
is_training=self.is_training,
scope="cluster_bn")
else:
cluster_biases = tf.get_variable("cluster_biases",
[cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
tf.summary.histogram("cluster_biases", cluster_biases)
activation += cluster_biases
activation = tf.nn.softmax(activation)
activation = tf.reshape(activation, [-1, self.max_frames, self.cluster_size])
activation = tf.transpose(activation,perm=[0,2,1])
reshaped_input = tf.reshape(reshaped_input,[-1,self.max_frames,self.feature_size])
vlad = tf.matmul(activation,reshaped_input)
c_vlad = tf.matmul(vlad, tf.transpose(vlad,perm=[0,2,1]))
c_vlad = tf.transpose(c_vlad,perm=[0,2,1])
c_vlad = tf.nn.l2_normalize(c_vlad,1)
vlad = tf.transpose(vlad,perm=[0,2,1])
vlad = tf.nn.l2_normalize(vlad,1)
vlad = tf.reshape(vlad,[-1,self.cluster_size*self.feature_size])
c_vlad = tf.reshape(c_vlad, [-1, self.cluster_size*self.cluster_size])
vlad = tf.concat([vlad, c_vlad], -1)
vlad = tf.nn.l2_normalize(vlad,1)
return vlad
class NetVLAD():
def __init__(self, feature_size,max_frames,cluster_size, add_batch_norm, is_training):
self.feature_size = feature_size
self.max_frames = max_frames
self.is_training = is_training
self.add_batch_norm = add_batch_norm
self.cluster_size = cluster_size
def forward(self,reshaped_input):
cluster_weights = tf.get_variable("cluster_weights",
[self.feature_size, self.cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
tf.summary.histogram("cluster_weights", cluster_weights)
activation = tf.matmul(reshaped_input, cluster_weights)
if self.add_batch_norm:
activation = slim.batch_norm(
activation,
center=True,
scale=True,
is_training=self.is_training,
scope="cluster_bn")
else:
cluster_biases = tf.get_variable("cluster_biases",
[cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
tf.summary.histogram("cluster_biases", cluster_biases)
activation += cluster_biases
activation = tf.nn.softmax(activation)
tf.summary.histogram("cluster_output", activation)
activation = tf.reshape(activation, [-1, self.max_frames, self.cluster_size])
a_sum = tf.reduce_sum(activation,-2,keep_dims=True)
cluster_weights2 = tf.get_variable("cluster_weights2",
[1,self.feature_size, self.cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
a = tf.multiply(a_sum,cluster_weights2)
activation = tf.transpose(activation,perm=[0,2,1])
reshaped_input = tf.reshape(reshaped_input,[-1,self.max_frames,self.feature_size])
vlad = tf.matmul(activation,reshaped_input)
vlad = tf.transpose(vlad,perm=[0,2,1])
vlad = tf.subtract(vlad,a)
vlad = tf.nn.l2_normalize(vlad,1)
vlad = tf.reshape(vlad,[-1,self.cluster_size*self.feature_size])
vlad = tf.nn.l2_normalize(vlad,1)
return vlad
class SE_NetVLAD():
def __init__(self, feature_size,max_frames,cluster_size, add_batch_norm, is_training):
self.feature_size = feature_size
self.max_frames = max_frames
self.is_training = is_training
self.add_batch_norm = add_batch_norm
self.cluster_size = cluster_size
def forward(self,reshaped_input):
cluster_weights = tf.get_variable("cluster_weights",
[self.feature_size, self.cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
tf.summary.histogram("cluster_weights", cluster_weights)
activation = tf.matmul(reshaped_input, cluster_weights)
if self.add_batch_norm:
activation = slim.batch_norm(
activation,
center=True,
scale=True,
is_training=self.is_training,
scope="cluster_bn")
else:
cluster_biases = tf.get_variable("cluster_biases",
[cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
tf.summary.histogram("cluster_biases", cluster_biases)
activation += cluster_biases
activation = tf.nn.softmax(activation)
tf.summary.histogram("cluster_output", activation)
activation = tf.reshape(activation, [-1, self.max_frames, self.cluster_size])
a_sum = tf.reduce_sum(activation,-2,keep_dims=True)
cluster_weights2 = tf.get_variable("cluster_weights2",
[1,self.feature_size, self.cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
a = tf.multiply(a_sum,cluster_weights2)
activation = tf.transpose(activation,perm=[0,2,1])
reshaped_input = tf.reshape(reshaped_input,[-1,self.max_frames,self.feature_size])
vlad = tf.matmul(activation,reshaped_input)
vlad = tf.transpose(vlad,perm=[0,2,1])
vlad = tf.subtract(vlad,a)
vlad = tf.nn.l2_normalize(vlad,1) # batch_size, feature_size, cluster_size
## SE layer
vlad = tf.transpose(vlad,perm=[0,2,1]) # batch_size, cluster_size, feature_size
### side path
vlad_path = tf.reduce_mean(vlad, 1)
SE_weights2 = tf.get_variable("SE_weights2",
[self.feature_size//16, self.feature_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size//16)))
vlad_path = slim.fully_connected(vlad_path, self.feature_size//16, activation_fn=tf.nn.relu, scope='SE_weights1', weights_regularizer=slim.l2_regularizer(8e-4))
vlad_path = tf.sigmoid(tf.matmul(vlad_path,SE_weights2))
vlad_path = tf.expand_dims(vlad_path, 1) # batch_size, 1, feature_size
vlad = vlad*vlad_path
vlad = tf.transpose(vlad,perm=[0,2,1])
vlad = tf.nn.l2_normalize(vlad,1)
##
vlad = tf.reshape(vlad,[-1,self.cluster_size*self.feature_size])
vlad = tf.nn.l2_normalize(vlad,1)
return vlad
class SE_res_NetVLAD():
def __init__(self, feature_size,max_frames,cluster_size, add_batch_norm, is_training):
self.feature_size = feature_size
self.max_frames = max_frames
self.is_training = is_training
self.add_batch_norm = add_batch_norm
self.cluster_size = cluster_size
def forward(self,reshaped_input):
cluster_weights = tf.get_variable("cluster_weights",
[self.feature_size, self.cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
tf.summary.histogram("cluster_weights", cluster_weights)
activation = tf.matmul(reshaped_input, cluster_weights)
if self.add_batch_norm:
activation = slim.batch_norm(
activation,
center=True,
scale=True,
is_training=self.is_training,
scope="cluster_bn")
else:
cluster_biases = tf.get_variable("cluster_biases",
[cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
tf.summary.histogram("cluster_biases", cluster_biases)
activation += cluster_biases
activation = tf.nn.softmax(activation)
tf.summary.histogram("cluster_output", activation)
activation = tf.reshape(activation, [-1, self.max_frames, self.cluster_size])
a_sum = tf.reduce_sum(activation,-2,keep_dims=True)
cluster_weights2 = tf.get_variable("cluster_weights2",
[1,self.feature_size, self.cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
a = tf.multiply(a_sum,cluster_weights2)
activation = tf.transpose(activation,perm=[0,2,1])
reshaped_input = tf.reshape(reshaped_input,[-1,self.max_frames,self.feature_size])
vlad = tf.matmul(activation,reshaped_input)
vlad = tf.transpose(vlad,perm=[0,2,1])
vlad = tf.subtract(vlad,a)
vlad = tf.nn.l2_normalize(vlad,1) # batch_size, feature_size, cluster_size
## SE layer
vlad = tf.transpose(vlad,perm=[0,2,1]) # batch_size, cluster_size, feature_size
### side path
vlad_path = tf.reduce_mean(vlad, 1)
SE_weights2 = tf.get_variable("SE_weights2",
[self.feature_size//16, self.feature_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size//16)))
vlad_path = slim.fully_connected(vlad_path, self.feature_size//16, activation_fn=tf.nn.relu, scope='SE_weights1', weights_regularizer=slim.l2_regularizer(8e-4))
vlad_path = tf.sigmoid(tf.matmul(vlad_path,SE_weights2))
vlad_path = tf.expand_dims(vlad_path, 1) # batch_size, 1, feature_size
vlad = vlad*vlad_path + vlad
vlad = tf.transpose(vlad,perm=[0,2,1])
vlad = tf.nn.l2_normalize(vlad,1)
##
vlad = tf.reshape(vlad,[-1,self.cluster_size*self.feature_size])
vlad = tf.nn.l2_normalize(vlad,1)
return vlad
class NetVLAD_Flexable():
def __init__(self, feature_size,cluster_size, add_batch_norm, is_training):
self.feature_size = feature_size
# self.max_frames = max_frames
self.is_training = is_training
self.add_batch_norm = add_batch_norm
self.cluster_size = cluster_size
def forward(self,reshaped_input, max_frames):
cluster_weights = tf.get_variable("cluster_weights",
[self.feature_size, self.cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
tf.summary.histogram("cluster_weights", cluster_weights)
activation = tf.matmul(reshaped_input, cluster_weights)
if self.add_batch_norm:
activation = slim.batch_norm(
activation,
center=True,
scale=True,
is_training=self.is_training,
scope="cluster_bn")
else:
cluster_biases = tf.get_variable("cluster_biases",
[cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
tf.summary.histogram("cluster_biases", cluster_biases)
activation += cluster_biases
activation = tf.nn.softmax(activation)
tf.summary.histogram("cluster_output", activation)
activation = tf.reshape(activation, [-1, max_frames, self.cluster_size])
a_sum = tf.reduce_sum(activation,-2,keep_dims=True)
cluster_weights2 = tf.get_variable("cluster_weights2",
[1,self.feature_size, self.cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
a = tf.multiply(a_sum,cluster_weights2)
activation = tf.transpose(activation,perm=[0,2,1])
reshaped_input = tf.reshape(reshaped_input,[-1,max_frames,self.feature_size])
vlad = tf.matmul(activation,reshaped_input)
vlad = tf.transpose(vlad,perm=[0,2,1])
vlad = tf.subtract(vlad,a)
vlad = tf.nn.l2_normalize(vlad,1)
vlad = tf.reduce_mean(vlad,2)
vlad = tf.nn.l2_normalize(vlad,1)
return vlad
class NetVLAD_NonLocal():
def __init__(self, feature_size,max_frames,cluster_size, add_batch_norm, is_training):
self.feature_size = feature_size
self.max_frames = max_frames
self.is_training = is_training
self.add_batch_norm = add_batch_norm
self.cluster_size = cluster_size
def forward(self,reshaped_input):
cluster_weights = tf.get_variable("cluster_weights",
[self.feature_size, self.cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
tf.summary.histogram("cluster_weights", cluster_weights)
activation = tf.matmul(reshaped_input, cluster_weights)
if self.add_batch_norm:
activation = slim.batch_norm(
activation,
center=True,
scale=True,
is_training=self.is_training,
scope="cluster_bn")
else:
cluster_biases = tf.get_variable("cluster_biases",
[cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
tf.summary.histogram("cluster_biases", cluster_biases)
activation += cluster_biases
activation = tf.nn.softmax(activation)
tf.summary.histogram("cluster_output", activation)
activation = tf.reshape(activation, [-1, self.max_frames, self.cluster_size])
a_sum = tf.reduce_sum(activation,-2,keep_dims=True)
cluster_weights2 = tf.get_variable("cluster_weights2",
[1,self.feature_size, self.cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
a = tf.multiply(a_sum,cluster_weights2)
activation = tf.transpose(activation,perm=[0,2,1])
reshaped_input = tf.reshape(reshaped_input,[-1,self.max_frames,self.feature_size])
vlad = tf.matmul(activation,reshaped_input)
vlad = tf.transpose(vlad,perm=[0,2,1])
vlad = tf.subtract(vlad,a)
if FLAGS.afterNorm:
vlad = tf.nn.l2_normalize(vlad,1) # [b,f,c]
vlad = tf.transpose(vlad,perm=[0,2,1])
vlad = tf.reshape(vlad, [-1, self.feature_size])
nonlocal_theta = tf.get_variable("nonlocal_theta",
[self.feature_size, self.cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
nonlocal_phi = tf.get_variable("nonlocal_phi",
[self.feature_size, self.cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
nonlocal_g = tf.get_variable("nonlocal_g",
[self.feature_size, self.cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
nonlocal_out = tf.get_variable("nonlocal_out",
[self.cluster_size, self.feature_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.cluster_size)))
vlad_theta = tf.matmul(vlad, nonlocal_theta)
vlad_phi = tf.matmul(vlad, nonlocal_phi)
vlad_g = tf.matmul(vlad, nonlocal_g)
vlad_theta = tf.reshape(vlad_theta, [-1, self.cluster_size, self.cluster_size])
vlad_phi = tf.reshape(vlad_phi, [-1, self.cluster_size, self.cluster_size])
vlad_g = tf.reshape(vlad_phi, [-1, self.cluster_size, self.cluster_size])
vlad_softmax = tf.nn.softmax(self.feature_size**-.5 * tf.matmul(vlad_theta, tf.transpose(vlad_phi,perm=[0,2,1])))
vlad_g = tf.matmul(vlad_softmax, vlad_g)
vlad_g = tf.reshape(vlad_g, [-1, self.cluster_size])
vlad_g = tf.matmul(vlad_g, nonlocal_out)
vlad_g = tf.reshape(vlad_g, [-1, self.cluster_size, self.feature_size])
vlad = tf.reshape(vlad, [-1, self.cluster_size, self.feature_size])
vlad = vlad + vlad_g
vlad = tf.transpose(vlad,perm=[0,2,1])
if FLAGS.beforeNorm:
vlad = tf.nn.l2_normalize(vlad,1) # [b,f,c]
if FLAGS.avg_netvlad:
vlad = tf.reduce_mean(vlad,2)
elif FLAGS.max_netvlad:
vlad = tf.reduce_max(vlad,2)
else:
vlad = tf.reshape(vlad,[-1,self.cluster_size*self.feature_size])
vlad = tf.nn.l2_normalize(vlad,1)
return vlad
class NetVLAD_NonLocal_modularize_shared():
def __init__(self, feature_size,max_frames,cluster_size, add_batch_norm, is_training):
self.feature_size = feature_size
self.max_frames = max_frames
self.is_training = is_training
self.add_batch_norm = add_batch_norm
self.cluster_size = cluster_size
def forward(self,reshaped_input):
cluster_weights = tf.get_variable("cluster_weights",
[self.feature_size, self.cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
tf.summary.histogram("cluster_weights", cluster_weights)
activation = tf.matmul(reshaped_input, cluster_weights)
if self.add_batch_norm:
activation = slim.batch_norm(
activation,
center=True,
scale=True,
is_training=self.is_training,
scope="cluster_bn")
else:
cluster_biases = tf.get_variable("cluster_biases",
[cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
tf.summary.histogram("cluster_biases", cluster_biases)
activation += cluster_biases
activation = tf.nn.softmax(activation)
tf.summary.histogram("cluster_output", activation)
activation = tf.reshape(activation, [-1, self.max_frames, self.cluster_size])
a_sum = tf.reduce_sum(activation,-2,keep_dims=True)
cluster_weights2 = tf.get_variable("cluster_weights2",
[1,self.feature_size, self.cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
a = tf.multiply(a_sum,cluster_weights2)
activation = tf.transpose(activation,perm=[0,2,1])
reshaped_input = tf.reshape(reshaped_input,[-1,self.max_frames,self.feature_size])
vlad = tf.matmul(activation,reshaped_input)
vlad = tf.transpose(vlad,perm=[0,2,1])
vlad = tf.subtract(vlad,a)
if FLAGS.afterNorm:
vlad = tf.nn.l2_normalize(vlad,1) # [b,f,c]
vlad = tf.transpose(vlad,perm=[0,2,1])
vlad = tf.reshape(vlad, [-1, self.feature_size])
for nonlocal_i in range(FLAGS.num_nonlocal_block):
with (tf.variable_scope(("nonlocal_layer"), reuse=True if nonlocal_i > 0 else None)):
vlad = nonLocal_block(vlad, feature_size=self.feature_size, hidden_size=self.cluster_size, cluster_size=self.cluster_size)
vlad = tf.reshape(vlad, [-1, self.cluster_size, self.feature_size])
vlad = tf.transpose(vlad,perm=[0,2,1])
if FLAGS.beforeNorm:
vlad = tf.nn.l2_normalize(vlad,1) # [b,f,c]
if FLAGS.avg_netvlad:
vlad = tf.reduce_mean(vlad,2)
elif FLAGS.max_netvlad:
vlad = tf.reduce_max(vlad,2)
else:
vlad = tf.reshape(vlad,[-1,self.cluster_size*self.feature_size])
vlad = tf.nn.l2_normalize(vlad,1)
return vlad
class NetVLAD_NonLocal_modularize_unique():
def __init__(self, feature_size,max_frames,cluster_size, add_batch_norm, is_training):
self.feature_size = feature_size
self.max_frames = max_frames
self.is_training = is_training
self.add_batch_norm = add_batch_norm
self.cluster_size = cluster_size
def forward(self,reshaped_input):
cluster_weights = tf.get_variable("cluster_weights",
[self.feature_size, self.cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
tf.summary.histogram("cluster_weights", cluster_weights)
activation = tf.matmul(reshaped_input, cluster_weights)
if self.add_batch_norm:
activation = slim.batch_norm(
activation,
center=True,
scale=True,
is_training=self.is_training,
scope="cluster_bn")
else:
cluster_biases = tf.get_variable("cluster_biases",
[cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
tf.summary.histogram("cluster_biases", cluster_biases)
activation += cluster_biases
activation = tf.nn.softmax(activation)
tf.summary.histogram("cluster_output", activation)
activation = tf.reshape(activation, [-1, self.max_frames, self.cluster_size])
a_sum = tf.reduce_sum(activation,-2,keep_dims=True)
cluster_weights2 = tf.get_variable("cluster_weights2",
[1,self.feature_size, self.cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
a = tf.multiply(a_sum,cluster_weights2)
activation = tf.transpose(activation,perm=[0,2,1])
reshaped_input = tf.reshape(reshaped_input,[-1,self.max_frames,self.feature_size])
vlad = tf.matmul(activation,reshaped_input)
vlad = tf.transpose(vlad,perm=[0,2,1])
vlad = tf.subtract(vlad,a)
if FLAGS.afterNorm:
vlad = tf.nn.l2_normalize(vlad,1) # [b,f,c]
vlad = tf.transpose(vlad,perm=[0,2,1])
vlad = tf.reshape(vlad, [-1, self.feature_size])
for nonlocal_i in range(FLAGS.num_nonlocal_block):
with tf.variable_scope(("nonlocal_layer_%d") % nonlocal_i):
vlad = nonLocal_block(vlad, feature_size=self.feature_size, hidden_size=self.cluster_size, cluster_size=self.cluster_size)
vlad = tf.reshape(vlad, [-1, self.cluster_size, self.feature_size])
vlad = tf.transpose(vlad,perm=[0,2,1])
if FLAGS.beforeNorm:
vlad = tf.nn.l2_normalize(vlad,1) # [b,f,c]
if FLAGS.avg_netvlad:
vlad = tf.reduce_mean(vlad,2)
elif FLAGS.max_netvlad:
vlad = tf.reduce_max(vlad,2)
else:
vlad = tf.reshape(vlad,[-1,self.cluster_size*self.feature_size])
vlad = tf.nn.l2_normalize(vlad,1)
return vlad
class sqrtNetVLAD():
def __init__(self, feature_size,max_frames,cluster_size, add_batch_norm, is_training):
self.feature_size = feature_size
self.max_frames = max_frames
self.is_training = is_training
self.add_batch_norm = add_batch_norm
self.cluster_size = cluster_size
def forward(self,reshaped_input):
cluster_weights = tf.get_variable("cluster_weights",
[self.feature_size, self.cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
tf.summary.histogram("cluster_weights", cluster_weights)
activation = tf.matmul(reshaped_input, cluster_weights)
if self.add_batch_norm:
activation = slim.batch_norm(
activation,
center=True,
scale=True,
is_training=self.is_training,
scope="cluster_bn")
else:
cluster_biases = tf.get_variable("cluster_biases",
[cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
tf.summary.histogram("cluster_biases", cluster_biases)
activation += cluster_biases
activation = tf.nn.softmax(activation)
tf.summary.histogram("cluster_output", activation)
activation = tf.reshape(activation, [-1, self.max_frames, self.cluster_size])
a_sum = tf.reduce_sum(activation,-2,keep_dims=True)
cluster_weights2 = tf.get_variable("cluster_weights2",
[1,self.feature_size, self.cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
a = tf.multiply(a_sum,cluster_weights2)
activation = tf.transpose(activation,perm=[0,2,1])
reshaped_input = tf.reshape(reshaped_input,[-1,self.max_frames,self.feature_size])
vlad = tf.matmul(activation,reshaped_input)
vlad = tf.transpose(vlad,perm=[0,2,1])
vlad = tf.subtract(vlad,a)
vlad = tf.sign(vlad)*tf.sqrt(tf.abs(vlad))
vlad = tf.nn.l2_normalize(vlad,1)
vlad = tf.reshape(vlad,[-1,self.cluster_size*self.feature_size])
vlad = tf.nn.l2_normalize(vlad,1)
return vlad
class AddNetVLAD():
def __init__(self, feature_size,max_frames,cluster_size, add_batch_norm, is_training):
self.feature_size = feature_size
self.max_frames = max_frames
self.is_training = is_training
self.add_batch_norm = add_batch_norm
self.cluster_size = cluster_size
def forward(self,reshaped_input):
cluster_weights = tf.get_variable("cluster_weights",
[self.feature_size, self.cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
tf.summary.histogram("cluster_weights", cluster_weights)
activation = tf.matmul(reshaped_input, cluster_weights)
if self.add_batch_norm:
activation = slim.batch_norm(
activation,
center=True,
scale=True,
is_training=self.is_training,
scope="cluster_bn")
else:
cluster_biases = tf.get_variable("cluster_biases",
[cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
tf.summary.histogram("cluster_biases", cluster_biases)
activation += cluster_biases
activation = tf.nn.softmax(activation)
tf.summary.histogram("cluster_output", activation)
activation = tf.reshape(activation, [-1, self.max_frames, self.cluster_size])
a_sum = tf.reduce_sum(activation,-2,keep_dims=True)
cluster_weights2 = tf.get_variable("cluster_weights2",
[1,self.feature_size, self.cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
a = tf.multiply(a_sum,cluster_weights2)
activation = tf.transpose(activation,perm=[0,2,1])
reshaped_input = tf.reshape(reshaped_input,[-1,self.max_frames,self.feature_size])
vlad = tf.matmul(activation,reshaped_input)
vlad = tf.transpose(vlad,perm=[0,2,1])
vlad = tf.subtract(vlad,a)
vlad = tf.nn.l2_normalize(vlad,1)
# vlad = tf.reshape(vlad,[-1,self.cluster_size*self.feature_size])
vlad = tf.reduce_sum(vlad, 2)
vlad = tf.nn.l2_normalize(vlad,1)
return vlad
class GRUNetVLAD():
def __init__(self, feature_size,max_frames,cluster_size, add_batch_norm, is_training):
self.feature_size = feature_size
self.max_frames = max_frames
self.is_training = is_training
self.add_batch_norm = add_batch_norm
self.cluster_size = cluster_size
def forward(self,reshaped_input):
gru_size = FLAGS.gru_cells
number_of_layers = FLAGS.gru_layers
random_frames = FLAGS.gru_random_sequence
iterations = FLAGS.iterations
cluster_weights = tf.get_variable("cluster_weights",
[self.feature_size, self.cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
tf.summary.histogram("cluster_weights", cluster_weights)
activation = tf.matmul(reshaped_input, cluster_weights)
if self.add_batch_norm:
activation = slim.batch_norm(
activation,
center=True,
scale=True,
is_training=self.is_training,
scope="cluster_bn")
else:
cluster_biases = tf.get_variable("cluster_biases",
[cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
tf.summary.histogram("cluster_biases", cluster_biases)
activation += cluster_biases
activation = tf.nn.softmax(activation)
tf.summary.histogram("cluster_output", activation)
activation = tf.reshape(activation, [-1, self.max_frames, self.cluster_size])
a_sum = tf.reduce_sum(activation,-2,keep_dims=True)
cluster_weights2 = tf.get_variable("cluster_weights2",
[1,self.feature_size, self.cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
a = tf.multiply(a_sum,cluster_weights2)
activation = tf.transpose(activation,perm=[0,2,1])
reshaped_input = tf.reshape(reshaped_input,[-1,self.max_frames,self.feature_size])
vlad = tf.matmul(activation,reshaped_input)
vlad = tf.transpose(vlad,perm=[0,2,1])
vlad = tf.subtract(vlad,a)
if FLAGS.afterNorm:
vlad = tf.nn.l2_normalize(vlad,1) # [b,f,c]
# vlad = tf.reshape(vlad,[-1,self.cluster_size*self.feature_size])
vlad = tf.transpose(vlad,perm=[0,2,1]) # [b, c, f]
stacked_GRU = tf.contrib.rnn.MultiRNNCell(
[
tf.contrib.rnn.GRUCell(gru_size)
for _ in range(number_of_layers)
], state_is_tuple=False)
with tf.variable_scope("RNN"):
outputs, state = tf.nn.dynamic_rnn(stacked_GRU, vlad,
dtype=tf.float32)
# vlad = tf.reduce_sum(vlad, 2)
state = tf.nn.l2_normalize(state,1)
return state
class GRUthenNetVLAD():
def __init__(self, feature_size,max_frames,cluster_size, add_batch_norm, is_training):
self.feature_size = feature_size
self.max_frames = max_frames
self.is_training = is_training
self.add_batch_norm = add_batch_norm
self.cluster_size = cluster_size
def forward(self,reshaped_input):
gru_size = FLAGS.gru_cells
number_of_layers = FLAGS.gru_layers
random_frames = FLAGS.gru_random_sequence
iterations = FLAGS.iterations
reshaped_input = tf.reshape(reshaped_input, [-1, self.max_frames, self.feature_size])
stacked_GRU = tf.contrib.rnn.MultiRNNCell(
[
tf.contrib.rnn.GRUCell(gru_size)
for _ in range(number_of_layers)
], state_is_tuple=False)
with tf.variable_scope("RNN"):
outputs, state = tf.nn.dynamic_rnn(stacked_GRU, reshaped_input,
dtype=tf.float32)
reshaped_input = tf.reshape(outputs, [-1, gru_size])
cluster_weights = tf.get_variable("cluster_weights",
[gru_size, self.cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(gru_size)))
tf.summary.histogram("cluster_weights", cluster_weights)
activation = tf.matmul(reshaped_input, cluster_weights)
if self.add_batch_norm:
activation = slim.batch_norm(
activation,
center=True,
scale=True,
is_training=self.is_training,
scope="cluster_bn")
else:
cluster_biases = tf.get_variable("cluster_biases",
[cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(gru_size)))
tf.summary.histogram("cluster_biases", cluster_biases)
activation += cluster_biases
activation = tf.nn.softmax(activation)
tf.summary.histogram("cluster_output", activation)
activation = tf.reshape(activation, [-1, self.max_frames, self.cluster_size])
a_sum = tf.reduce_sum(activation,-2,keep_dims=True)
cluster_weights2 = tf.get_variable("cluster_weights2",
[1,gru_size, self.cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(gru_size)))
a = tf.multiply(a_sum,cluster_weights2)
activation = tf.transpose(activation,perm=[0,2,1])
reshaped_input = tf.reshape(reshaped_input,[-1,self.max_frames,gru_size])
vlad = tf.matmul(activation,reshaped_input)
vlad = tf.transpose(vlad,perm=[0,2,1])
vlad = tf.subtract(vlad,a)
vlad = tf.nn.l2_normalize(vlad,1)
# vlad = tf.reshape(vlad,[-1,self.cluster_size*self.feature_size])
vlad = tf.transpose(vlad,perm=[0,2,1]) # [b, c, f]
# vlad = tf.reduce_sum(vlad, 2)
state = tf.nn.l2_normalize(state,1)
return state
class GraphicalNetVLAD():
def __init__(self, feature_size,max_frames,cluster_size, add_batch_norm, is_training):
self.feature_size = feature_size
self.max_frames = max_frames
self.is_training = is_training
self.add_batch_norm = add_batch_norm
self.cluster_size = cluster_size
def forward(self,reshaped_input):
cluster_weights = tf.get_variable("cluster_weights",
[self.feature_size, self.cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
tf.summary.histogram("cluster_weights", cluster_weights)
activation = tf.matmul(reshaped_input, cluster_weights)
if self.add_batch_norm:
activation = slim.batch_norm(
activation,
center=True,
scale=True,
is_training=self.is_training,
scope="cluster_bn")
else:
cluster_biases = tf.get_variable("cluster_biases",
[cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
tf.summary.histogram("cluster_biases", cluster_biases)
activation += cluster_biases
activation = tf.nn.softmax(activation)
tf.summary.histogram("cluster_output", activation)
activation = tf.reshape(activation, [-1, self.max_frames, self.cluster_size])
a_sum = tf.reduce_sum(activation,-2,keep_dims=True)
cluster_weights2 = tf.get_variable("cluster_weights2",
[1,self.feature_size, self.cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
a = tf.multiply(a_sum,cluster_weights2)
activation = tf.transpose(activation,perm=[0,2,1])
reshaped_input = tf.reshape(reshaped_input,[-1,self.max_frames,self.feature_size])
vlad = tf.matmul(activation,reshaped_input)
vlad = tf.transpose(vlad,perm=[0,2,1])
vlad = tf.subtract(vlad,a)
vlad = tf.nn.l2_normalize(vlad,1)
graph_weights = tf.get_variable("graph_weights",
[self.feature_size, self.feature_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
vlad_proj = tf.transpose(vlad,perm=[0,2,1])
vlad_proj = tf.reshape(vlad_proj, [-1, self.feature_size])
vlad_proj = tf.matmul(vlad_proj, graph_weights) #[batch, cluster, feature]
vlad_proj = tf.reshape(vlad_proj, [-1, self.cluster_size, self.feature_size])
G_vlad = tf.matmul(vlad_proj, tf.transpose(vlad_proj,perm=[0,2,1]))
G_vlad = tf.nn.softmax(G_vlad, dim=2)
graph_weights2 = tf.get_variable("graph_weights2",
[self.feature_size, self.feature_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
vlad = tf.matmul(G_vlad, tf.transpose(vlad,perm=[0,2,1]))
vlad = tf.reshape(vlad, [-1, self.feature_size])
vlad = tf.matmul(vlad,graph_weights2)
vlad = tf.reshape(vlad, [-1, self.cluster_size, self.feature_size])
vlad = tf.transpose(vlad,perm=[0,2,1])
vlad = tf.nn.l2_normalize(vlad,1)
vlad = tf.reshape(vlad,[-1,self.cluster_size*self.feature_size])
vlad = tf.nn.l2_normalize(vlad,1)
return vlad
class GraphicalNetVLAD_simple():
def __init__(self, feature_size,max_frames,cluster_size, add_batch_norm, is_training):
self.feature_size = feature_size
self.max_frames = max_frames
self.is_training = is_training
self.add_batch_norm = add_batch_norm
self.cluster_size = cluster_size
def forward(self,reshaped_input):
cluster_weights = tf.get_variable("cluster_weights",
[self.feature_size, self.cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
tf.summary.histogram("cluster_weights", cluster_weights)
activation = tf.matmul(reshaped_input, cluster_weights)
if self.add_batch_norm:
activation = slim.batch_norm(
activation,
center=True,
scale=True,
is_training=self.is_training,
scope="cluster_bn")
else:
cluster_biases = tf.get_variable("cluster_biases",
[cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
tf.summary.histogram("cluster_biases", cluster_biases)
activation += cluster_biases
activation = tf.nn.softmax(activation)
tf.summary.histogram("cluster_output", activation)
activation = tf.reshape(activation, [-1, self.max_frames, self.cluster_size])
a_sum = tf.reduce_sum(activation,-2,keep_dims=True)
cluster_weights2 = tf.get_variable("cluster_weights2",
[1,self.feature_size, self.cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
a = tf.multiply(a_sum,cluster_weights2)
activation = tf.transpose(activation,perm=[0,2,1])
reshaped_input = tf.reshape(reshaped_input,[-1,self.max_frames,self.feature_size])
vlad = tf.matmul(activation,reshaped_input)
vlad = tf.transpose(vlad,perm=[0,2,1])
vlad = tf.subtract(vlad,a)
vlad = tf.nn.l2_normalize(vlad,1)
graph_weights = tf.get_variable("graph_weights",
[self.feature_size, self.cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
vlad_proj = tf.transpose(vlad,perm=[0,2,1])
vlad_proj = tf.reshape(vlad_proj, [-1, self.feature_size])
vlad_proj = tf.matmul(vlad_proj, graph_weights) #[batch, cluster, feature]
vlad_proj = tf.reshape(vlad_proj, [-1, self.cluster_size, self.cluster_size])
G_vlad = tf.matmul(vlad_proj, tf.transpose(vlad_proj,perm=[0,2,1]))
G_vlad = tf.nn.softmax(G_vlad, dim=2)
# graph_weights2 = tf.get_variable("graph_weights2",
# [self.feature_size, self.feature_size],
# initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
vlad = tf.matmul(G_vlad, tf.transpose(vlad,perm=[0,2,1]))
# vlad = tf.reshape(vlad, [-1, self.feature_size])
# vlad = tf.matmul(vlad,graph_weights2)
# vlad = tf.reshape(vlad, [-1, self.cluster_size, self.feature_size])
# vlad = tf.transpose(vlad,perm=[0,2,1])
vlad = tf.nn.l2_normalize(vlad,1)
vlad = tf.reshape(vlad,[-1,self.cluster_size*self.feature_size])
vlad = tf.nn.l2_normalize(vlad,1)
return vlad
class NetVLAGD():
def __init__(self, feature_size,max_frames,cluster_size, add_batch_norm, is_training):
self.feature_size = feature_size
self.max_frames = max_frames
self.is_training = is_training
self.add_batch_norm = add_batch_norm
self.cluster_size = cluster_size
def forward(self,reshaped_input):
cluster_weights = tf.get_variable("cluster_weights",
[self.feature_size, self.cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
activation = tf.matmul(reshaped_input, cluster_weights)
if self.add_batch_norm:
activation = slim.batch_norm(
activation,
center=True,
scale=True,
is_training=self.is_training,
scope="cluster_bn")
else:
cluster_biases = tf.get_variable("cluster_biases",
[cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
activation = tf.nn.softmax(activation)
activation = tf.reshape(activation, [-1, self.max_frames, self.cluster_size])
gate_weights = tf.get_variable("gate_weights",
[1, self.cluster_size,self.feature_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
gate_weights = tf.sigmoid(gate_weights)
activation = tf.transpose(activation,perm=[0,2,1])
reshaped_input = tf.reshape(reshaped_input,[-1,self.max_frames,self.feature_size])
vlagd = tf.matmul(activation,reshaped_input)
vlagd = tf.multiply(vlagd,gate_weights)
vlagd = tf.transpose(vlagd,perm=[0,2,1])
vlagd = tf.nn.l2_normalize(vlagd,1)
vlagd = tf.reshape(vlagd,[-1,self.cluster_size*self.feature_size])
vlagd = tf.nn.l2_normalize(vlagd,1)
return vlagd
class GatedDBoF():
def __init__(self, feature_size,max_frames,cluster_size, max_pool, add_batch_norm, is_training):
self.feature_size = feature_size
self.max_frames = max_frames
self.is_training = is_training
self.add_batch_norm = add_batch_norm
self.cluster_size = cluster_size
self.max_pool = max_pool
def forward(self, reshaped_input):
feature_size = self.feature_size
cluster_size = self.cluster_size
add_batch_norm = self.add_batch_norm
max_frames = self.max_frames
is_training = self.is_training
max_pool = self.max_pool
cluster_weights = tf.get_variable("cluster_weights",
[feature_size, cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(feature_size)))
tf.summary.histogram("cluster_weights", cluster_weights)
activation = tf.matmul(reshaped_input, cluster_weights)
if add_batch_norm:
activation = slim.batch_norm(
activation,
center=True,
scale=True,
is_training=is_training,
scope="cluster_bn")
else:
cluster_biases = tf.get_variable("cluster_biases",
[cluster_size],
initializer = tf.random_normal(stddev=1 / math.sqrt(feature_size)))
tf.summary.histogram("cluster_biases", cluster_biases)
activation += cluster_biases
activation = tf.nn.softmax(activation)
activation = tf.reshape(activation, [-1, max_frames, cluster_size])
activation_sum = tf.reduce_sum(activation,1)
activation_max = tf.reduce_max(activation,1)
activation_max = tf.nn.l2_normalize(activation_max,1)
dim_red = tf.get_variable("dim_red",
[cluster_size, feature_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(feature_size)))
cluster_weights_2 = tf.get_variable("cluster_weights_2",
[feature_size, cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(feature_size)))
tf.summary.histogram("cluster_weights_2", cluster_weights_2)
activation = tf.matmul(activation_max, dim_red)
activation = tf.matmul(activation, cluster_weights_2)
if add_batch_norm:
activation = slim.batch_norm(
activation,
center=True,
scale=True,
is_training=is_training,
scope="cluster_bn_2")
else:
cluster_biases = tf.get_variable("cluster_biases_2",
[cluster_size],
initializer = tf.random_normal(stddev=1 / math.sqrt(feature_size)))
tf.summary.histogram("cluster_biases_2", cluster_biases)
activation += cluster_biases
activation = tf.sigmoid(activation)
activation = tf.multiply(activation,activation_sum)
activation = tf.nn.l2_normalize(activation,1)
return activation
class SoftDBoF():
def __init__(self, feature_size,max_frames,cluster_size, max_pool, add_batch_norm, is_training):
self.feature_size = feature_size
self.max_frames = max_frames
self.is_training = is_training
self.add_batch_norm = add_batch_norm
self.cluster_size = cluster_size
self.max_pool = max_pool
def forward(self, reshaped_input):
feature_size = self.feature_size
cluster_size = self.cluster_size
add_batch_norm = self.add_batch_norm
max_frames = self.max_frames
is_training = self.is_training
max_pool = self.max_pool
cluster_weights = tf.get_variable("cluster_weights",
[feature_size, cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(feature_size)))
tf.summary.histogram("cluster_weights", cluster_weights)
activation = tf.matmul(reshaped_input, cluster_weights)
if add_batch_norm:
activation = slim.batch_norm(
activation,
center=True,
scale=True,
is_training=is_training,
scope="cluster_bn")
else:
cluster_biases = tf.get_variable("cluster_biases",
[cluster_size],
initializer = tf.random_normal(stddev=1 / math.sqrt(feature_size)))
tf.summary.histogram("cluster_biases", cluster_biases)
activation += cluster_biases
activation = tf.nn.softmax(activation)
activation = tf.reshape(activation, [-1, max_frames, cluster_size])
activation_sum = tf.reduce_sum(activation,1)
activation_sum = tf.nn.l2_normalize(activation_sum,1)
if max_pool:
activation_max = tf.reduce_max(activation,1)
activation_max = tf.nn.l2_normalize(activation_max,1)
activation = tf.concat([activation_sum,activation_max],1)
else:
activation = activation_sum
return activation
class DBoF():
def __init__(self, feature_size,max_frames,cluster_size,activation, add_batch_norm, is_training):
self.feature_size = feature_size
self.max_frames = max_frames
self.is_training = is_training
self.add_batch_norm = add_batch_norm
self.cluster_size = cluster_size
self.activation = activation
def forward(self, reshaped_input):
feature_size = self.feature_size
cluster_size = self.cluster_size
add_batch_norm = self.add_batch_norm
max_frames = self.max_frames
is_training = self.is_training
cluster_weights = tf.get_variable("cluster_weights",
[feature_size, cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(feature_size)))
tf.summary.histogram("cluster_weights", cluster_weights)
activation = tf.matmul(reshaped_input, cluster_weights)
if add_batch_norm:
activation = slim.batch_norm(
activation,
center=True,
scale=True,
is_training=is_training,
scope="cluster_bn")
else:
cluster_biases = tf.get_variable("cluster_biases",
[cluster_size],
initializer = tf.random_normal(stddev=1 / math.sqrt(feature_size)))
tf.summary.histogram("cluster_biases", cluster_biases)
activation += cluster_biases
if activation == 'glu':
space_ind = range(cluster_size/2)
gate_ind = range(cluster_size/2,cluster_size)
gates = tf.sigmoid(activation[:,gate_ind])
activation = tf.multiply(activation[:,space_ind],gates)
elif activation == 'relu':
activation = tf.nn.relu6(activation)
tf.summary.histogram("cluster_output", activation)
activation = tf.reshape(activation, [-1, max_frames, cluster_size])
avg_activation = utils.FramePooling(activation, 'average')
avg_activation = tf.nn.l2_normalize(avg_activation,1)
max_activation = utils.FramePooling(activation, 'max')
max_activation = tf.nn.l2_normalize(max_activation,1)
return tf.concat([avg_activation,max_activation],1)
class NetFV():
def __init__(self, feature_size,max_frames,cluster_size, add_batch_norm, is_training):
self.feature_size = feature_size
self.max_frames = max_frames
self.is_training = is_training
self.add_batch_norm = add_batch_norm
self.cluster_size = cluster_size
def forward(self,reshaped_input):
cluster_weights = tf.get_variable("cluster_weights",
[self.feature_size, self.cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
covar_weights = tf.get_variable("covar_weights",
[self.feature_size, self.cluster_size],
initializer = tf.random_normal_initializer(mean=1.0, stddev=1 /math.sqrt(self.feature_size)))
covar_weights = tf.square(covar_weights)
eps = tf.constant([1e-6])
covar_weights = tf.add(covar_weights,eps)
tf.summary.histogram("cluster_weights", cluster_weights)
activation = tf.matmul(reshaped_input, cluster_weights)
if self.add_batch_norm:
activation = slim.batch_norm(
activation,
center=True,
scale=True,
is_training=self.is_training,
scope="cluster_bn")
else:
cluster_biases = tf.get_variable("cluster_biases",
[self.cluster_size],
initializer = tf.random_normal(stddev=1 / math.sqrt(self.feature_size)))
tf.summary.histogram("cluster_biases", cluster_biases)
activation += cluster_biases
activation = tf.nn.softmax(activation)
tf.summary.histogram("cluster_output", activation)
activation = tf.reshape(activation, [-1, self.max_frames, self.cluster_size])
a_sum = tf.reduce_sum(activation,-2,keep_dims=True)
if not FLAGS.fv_couple_weights:
cluster_weights2 = tf.get_variable("cluster_weights2",
[1,self.feature_size, self.cluster_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
else:
cluster_weights2 = tf.scalar_mul(FLAGS.fv_coupling_factor,cluster_weights)
a = tf.multiply(a_sum,cluster_weights2)
activation = tf.transpose(activation,perm=[0,2,1])
reshaped_input = tf.reshape(reshaped_input,[-1,self.max_frames,self.feature_size])
fv1 = tf.matmul(activation,reshaped_input)
fv1 = tf.transpose(fv1,perm=[0,2,1])
# computing second order FV
a2 = tf.multiply(a_sum,tf.square(cluster_weights2))
b2 = tf.multiply(fv1,cluster_weights2)
fv2 = tf.matmul(activation,tf.square(reshaped_input))
fv2 = tf.transpose(fv2,perm=[0,2,1])
fv2 = tf.add_n([a2,fv2,tf.scalar_mul(-2,b2)])
fv2 = tf.divide(fv2,tf.square(covar_weights))
fv2 = tf.subtract(fv2,a_sum)
fv2 = tf.reshape(fv2,[-1,self.cluster_size*self.feature_size])
fv2 = tf.nn.l2_normalize(fv2,1)
fv2 = tf.reshape(fv2,[-1,self.cluster_size*self.feature_size])
fv2 = tf.nn.l2_normalize(fv2,1)
fv1 = tf.subtract(fv1,a)
fv1 = tf.divide(fv1,covar_weights)
fv1 = tf.nn.l2_normalize(fv1,1)
fv1 = tf.reshape(fv1,[-1,self.cluster_size*self.feature_size])
fv1 = tf.nn.l2_normalize(fv1,1)
return tf.concat([fv1,fv2],1)
class NetVLADModelLF(models.BaseModel):
"""Creates a NetVLAD based model.
Args:
model_input: A 'batch_size' x 'max_frames' x 'num_features' matrix of
input features.
vocab_size: The number of classes in the dataset.
num_frames: A vector of length 'batch' which indicates the number of
frames for each video (before padding).
Returns:
A dictionary with a tensor containing the probability predictions of the
model in the 'predictions' key. The dimensions of the tensor are
'batch_size' x 'num_classes'.
"""
def create_model(self,
model_input,
vocab_size,
num_frames,
iterations=None,
add_batch_norm=None,
sample_random_frames=None,
cluster_size=None,
hidden_size=None,
is_training=True,
**unused_params):
iterations = iterations or FLAGS.iterations
add_batch_norm = add_batch_norm or FLAGS.netvlad_add_batch_norm
random_frames = sample_random_frames or FLAGS.sample_random_frames
cluster_size = cluster_size or FLAGS.netvlad_cluster_size
hidden1_size = hidden_size or FLAGS.netvlad_hidden_size
relu = FLAGS.netvlad_relu
dimred = FLAGS.netvlad_dimred
gating = FLAGS.gating
remove_diag = FLAGS.gating_remove_diag
lightvlad = FLAGS.lightvlad
vlagd = FLAGS.vlagd
graphvlad = FLAGS.graphvlad
addvlad = FLAGS.addvlad
simplegraphvlad = FLAGS.simplegraphvlad
gruvlad = FLAGS.gruvlad
acvlad = FLAGS.acvlad
localvlad = FLAGS.localvlad
glulightvlad = FLAGS.glulightvlad
convglulightvlad = FLAGS.convglulightvlad
sqrtvlad = FLAGS.sqrtvlad
nonlocalvlad = FLAGS.nonlocalvlad
bilinear = FLAGS.bilinear
nonlocalvlad_shared = FLAGS.nonlocalvlad_shared
nonlocalvlad_unique = FLAGS.nonlocalvlad_unique
gruthenvlad = FLAGS.gruthenvlad
sevlad = FLAGS.sevlad
seresvlad = FLAGS.seresvlad
num_frames = tf.cast(tf.expand_dims(num_frames, 1), tf.float32)
if random_frames:
model_input = utils.SampleRandomFrames(model_input, num_frames,
iterations)
else:
model_input = utils.SampleRandomSequence(model_input, num_frames,
iterations)
max_frames = model_input.get_shape().as_list()[1]
feature_size = model_input.get_shape().as_list()[2]
reshaped_input = tf.reshape(model_input, [-1, feature_size])
if lightvlad:
video_NetVLAD = LightVLAD(1024,max_frames,cluster_size, add_batch_norm, is_training)
audio_NetVLAD = LightVLAD(128,max_frames,cluster_size/2, add_batch_norm, is_training)
elif vlagd:
video_NetVLAD = NetVLAGD(1024,max_frames,cluster_size, add_batch_norm, is_training)
audio_NetVLAD = NetVLAGD(128,max_frames,cluster_size/2, add_batch_norm, is_training)
elif graphvlad:
video_NetVLAD = GraphicalNetVLAD(1024,max_frames,cluster_size, add_batch_norm, is_training)
audio_NetVLAD = GraphicalNetVLAD(128,max_frames,cluster_size/2, add_batch_norm, is_training)
elif simplegraphvlad:
video_NetVLAD = GraphicalNetVLAD_simple(1024,max_frames,cluster_size, add_batch_norm, is_training)
audio_NetVLAD = GraphicalNetVLAD_simple(128,max_frames,cluster_size/2, add_batch_norm, is_training)
elif addvlad:
video_NetVLAD = AddNetVLAD(1024,max_frames,cluster_size, add_batch_norm, is_training)
audio_NetVLAD = AddNetVLAD(128,max_frames,cluster_size/2, add_batch_norm, is_training)
elif gruvlad:
video_NetVLAD = GRUNetVLAD(1024,max_frames,cluster_size, add_batch_norm, is_training)
audio_NetVLAD = GRUNetVLAD(128,max_frames,cluster_size/2, add_batch_norm, is_training)
elif acvlad:
video_NetVLAD = AC_VLAD(1024,max_frames,cluster_size, add_batch_norm, is_training)
audio_NetVLAD = AC_VLAD(128,max_frames,cluster_size/2, add_batch_norm, is_training)
elif localvlad:
video_NetVLAD = LocalLightVLAD(1024,max_frames,cluster_size, add_batch_norm, is_training)
audio_NetVLAD = LocalLightVLAD(128,max_frames,cluster_size/2, add_batch_norm, is_training)
elif glulightvlad:
video_NetVLAD = GLULightVLAD(1024,max_frames,cluster_size, add_batch_norm, is_training)
audio_NetVLAD = GLULightVLAD(128,max_frames,cluster_size/2, add_batch_norm, is_training)
elif convglulightvlad:
video_NetVLAD = ConvGLULightVLAD(1024,max_frames,cluster_size, add_batch_norm, is_training)
audio_NetVLAD = ConvGLULightVLAD(128,max_frames,cluster_size/2, add_batch_norm, is_training)
elif sqrtvlad:
video_NetVLAD = sqrtNetVLAD(1024,max_frames,cluster_size, add_batch_norm, is_training)
audio_NetVLAD = sqrtNetVLAD(128,max_frames,cluster_size/2, add_batch_norm, is_training)
elif bilinear:
video_NetVLAD = Bilinear_pooling(1024,max_frames,cluster_size, add_batch_norm, is_training)
audio_NetVLAD = Bilinear_pooling(128,max_frames,cluster_size/2, add_batch_norm, is_training)
elif nonlocalvlad:
video_NetVLAD = NetVLAD_NonLocal(1024,max_frames,cluster_size, add_batch_norm, is_training)
audio_NetVLAD = NetVLAD_NonLocal(128,max_frames,cluster_size/2, add_batch_norm, is_training)
elif nonlocalvlad_shared:
video_NetVLAD = NetVLAD_NonLocal_modularize_shared(1024,max_frames,cluster_size, add_batch_norm, is_training)
audio_NetVLAD = NetVLAD_NonLocal_modularize_shared(128,max_frames,cluster_size/2, add_batch_norm, is_training)
elif nonlocalvlad_unique:
video_NetVLAD = NetVLAD_NonLocal_modularize_unique(1024,max_frames,cluster_size, add_batch_norm, is_training)
audio_NetVLAD = NetVLAD_NonLocal_modularize_unique(128,max_frames,cluster_size/2, add_batch_norm, is_training)
elif gruthenvlad:
video_NetVLAD = GRUthenNetVLAD(1024,max_frames,cluster_size, add_batch_norm, is_training)
audio_NetVLAD = GRUthenNetVLAD(128,max_frames,cluster_size/2, add_batch_norm, is_training)
elif sevlad:
video_NetVLAD = SE_NetVLAD(1024,max_frames,cluster_size, add_batch_norm, is_training)
audio_NetVLAD = SE_NetVLAD(128,max_frames,cluster_size/2, add_batch_norm, is_training)
elif seresvlad:
video_NetVLAD = SE_res_NetVLAD(1024,max_frames,cluster_size, add_batch_norm, is_training)
audio_NetVLAD = SE_res_NetVLAD(128,max_frames,cluster_size/2, add_batch_norm, is_training)
else:
video_NetVLAD = NetVLAD(1024,max_frames,cluster_size, add_batch_norm, is_training)
audio_NetVLAD = NetVLAD(128,max_frames,cluster_size/2, add_batch_norm, is_training)
if add_batch_norm:# and not lightvlad:
reshaped_input = slim.batch_norm(
reshaped_input,
center=True,
scale=True,
is_training=is_training,
scope="input_bn")
with tf.variable_scope("video_VLAD"):
vlad_video = video_NetVLAD.forward(reshaped_input[:,0:1024])
with tf.variable_scope("audio_VLAD"):
vlad_audio = audio_NetVLAD.forward(reshaped_input[:,1024:])
vlad = tf.concat([vlad_video, vlad_audio],1)
vlad_dim = vlad.get_shape().as_list()[1]
hidden1_weights = tf.get_variable("hidden1_weights",
[vlad_dim, hidden1_size],
initializer=tf.random_normal_initializer(stddev=1 / math.sqrt(cluster_size)))
activation = tf.matmul(vlad, hidden1_weights)
if add_batch_norm and relu:
activation = slim.batch_norm(
activation,
center=True,
scale=True,
is_training=is_training,
scope="hidden1_bn")
else:
hidden1_biases = tf.get_variable("hidden1_biases",
[hidden1_size],
initializer = tf.random_normal_initializer(stddev=0.01))
tf.summary.histogram("hidden1_biases", hidden1_biases)
activation += hidden1_biases
if relu:
activation = tf.nn.relu6(activation)
if gating:
gating_weights = tf.get_variable("gating_weights_2",
[hidden1_size, hidden1_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(hidden1_size)))
gates = tf.matmul(activation, gating_weights)
if remove_diag:
#removes diagonals coefficients
diagonals = tf.matrix_diag_part(gating_weights)
gates = gates - tf.multiply(diagonals,activation)
if add_batch_norm:
gates = slim.batch_norm(
gates,
center=True,
scale=True,
is_training=is_training,
scope="gating_bn")
else:
gating_biases = tf.get_variable("gating_biases",
[cluster_size],
initializer = tf.random_normal(stddev=1 / math.sqrt(feature_size)))
gates += gating_biases
gates = tf.sigmoid(gates)
activation = tf.multiply(activation,gates)
aggregated_model = getattr(video_level_models,
FLAGS.video_level_classifier_model)
return aggregated_model().create_model(
model_input=activation,
vocab_size=vocab_size,
is_training=is_training,
**unused_params)
class DbofModelLF(models.BaseModel):
"""Creates a Deep Bag of Frames model.
The model projects the features for each frame into a higher dimensional
'clustering' space, pools across frames in that space, and then
uses a configurable video-level model to classify the now aggregated features.
The model will randomly sample either frames or sequences of frames during
training to speed up convergence.
Args:
model_input: A 'batch_size' x 'max_frames' x 'num_features' matrix of
input features.
vocab_size: The number of classes in the dataset.
num_frames: A vector of length 'batch' which indicates the number of
frames for each video (before padding).
Returns:
A dictionary with a tensor containing the probability predictions of the
model in the 'predictions' key. The dimensions of the tensor are
'batch_size' x 'num_classes'.
"""
def create_model(self,
model_input,
vocab_size,
num_frames,
iterations=None,
add_batch_norm=None,
sample_random_frames=None,
cluster_size=None,
hidden_size=None,
is_training=True,
**unused_params):
iterations = iterations or FLAGS.iterations
add_batch_norm = add_batch_norm or FLAGS.dbof_add_batch_norm
random_frames = sample_random_frames or FLAGS.sample_random_frames
cluster_size = cluster_size or FLAGS.dbof_cluster_size
hidden1_size = hidden_size or FLAGS.dbof_hidden_size
relu = FLAGS.dbof_relu
cluster_activation = FLAGS.dbof_activation
num_frames = tf.cast(tf.expand_dims(num_frames, 1), tf.float32)
if random_frames:
model_input = utils.SampleRandomFrames(model_input, num_frames,
iterations)
else:
model_input = utils.SampleRandomSequence(model_input, num_frames,
iterations)
max_frames = model_input.get_shape().as_list()[1]
feature_size = model_input.get_shape().as_list()[2]
reshaped_input = tf.reshape(model_input, [-1, feature_size])
tf.summary.histogram("input_hist", reshaped_input)
if cluster_activation == 'glu':
cluster_size = 2*cluster_size
video_Dbof = DBoF(1024,max_frames,cluster_size, cluster_activation, add_batch_norm, is_training)
audio_Dbof = DBoF(128,max_frames,cluster_size/8, cluster_activation, add_batch_norm, is_training)
if add_batch_norm:
reshaped_input = slim.batch_norm(
reshaped_input,
center=True,
scale=True,
is_training=is_training,
scope="input_bn")
with tf.variable_scope("video_DBOF"):
dbof_video = video_Dbof.forward(reshaped_input[:,0:1024])
with tf.variable_scope("audio_DBOF"):
dbof_audio = audio_Dbof.forward(reshaped_input[:,1024:])
dbof = tf.concat([dbof_video, dbof_audio],1)
dbof_dim = dbof.get_shape().as_list()[1]
hidden1_weights = tf.get_variable("hidden1_weights",
[dbof_dim, hidden1_size],
initializer=tf.random_normal_initializer(stddev=1 / math.sqrt(cluster_size)))
tf.summary.histogram("hidden1_weights", hidden1_weights)
activation = tf.matmul(dbof, hidden1_weights)
if add_batch_norm and relu:
activation = slim.batch_norm(
activation,
center=True,
scale=True,
is_training=is_training,
scope="hidden1_bn")
else:
hidden1_biases = tf.get_variable("hidden1_biases",
[hidden1_size],
initializer = tf.random_normal_initializer(stddev=0.01))
tf.summary.histogram("hidden1_biases", hidden1_biases)
activation += hidden1_biases
if relu:
activation = tf.nn.relu6(activation)
tf.summary.histogram("hidden1_output", activation)
aggregated_model = getattr(video_level_models,
FLAGS.video_level_classifier_model)
return aggregated_model().create_model(
model_input=activation,
vocab_size=vocab_size,
**unused_params)
class GatedDbofModelLF(models.BaseModel):
"""Creates a Gated Deep Bag of Frames model.
The model projects the features for each frame into a higher dimensional
'clustering' space, pools across frames in that space, and then
uses a configurable video-level model to classify the now aggregated features.
The model will randomly sample either frames or sequences of frames during
training to speed up convergence.
Args:
model_input: A 'batch_size' x 'max_frames' x 'num_features' matrix of
input features.
vocab_size: The number of classes in the dataset.
num_frames: A vector of length 'batch' which indicates the number of
frames for each video (before padding).
Returns:
A dictionary with a tensor containing the probability predictions of the
model in the 'predictions' key. The dimensions of the tensor are
'batch_size' x 'num_classes'.
"""
def create_model(self,
model_input,
vocab_size,
num_frames,
iterations=None,
add_batch_norm=None,
sample_random_frames=None,
cluster_size=None,
hidden_size=None,
is_training=True,
**unused_params):
iterations = iterations or FLAGS.iterations
add_batch_norm = add_batch_norm or FLAGS.dbof_add_batch_norm
random_frames = sample_random_frames or FLAGS.sample_random_frames
cluster_size = cluster_size or FLAGS.dbof_cluster_size
hidden1_size = hidden_size or FLAGS.dbof_hidden_size
fc_dimred = FLAGS.fc_dimred
relu = FLAGS.dbof_relu
max_pool = FLAGS.softdbof_maxpool
num_frames = tf.cast(tf.expand_dims(num_frames, 1), tf.float32)
if random_frames:
model_input = utils.SampleRandomFrames(model_input, num_frames,
iterations)
else:
model_input = utils.SampleRandomSequence(model_input, num_frames,
iterations)
max_frames = model_input.get_shape().as_list()[1]
feature_size = model_input.get_shape().as_list()[2]
reshaped_input = tf.reshape(model_input, [-1, feature_size])
tf.summary.histogram("input_hist", reshaped_input)
video_Dbof = GatedDBoF(1024,max_frames,cluster_size, max_pool, add_batch_norm, is_training)
audio_Dbof = SoftDBoF(128,max_frames,cluster_size/8, max_pool, add_batch_norm, is_training)
if add_batch_norm:
reshaped_input = slim.batch_norm(
reshaped_input,
center=True,
scale=True,
is_training=is_training,
scope="input_bn")
with tf.variable_scope("video_DBOF"):
dbof_video = video_Dbof.forward(reshaped_input[:,0:1024])
with tf.variable_scope("audio_DBOF"):
dbof_audio = audio_Dbof.forward(reshaped_input[:,1024:])
dbof = tf.concat([dbof_video, dbof_audio],1)
dbof_dim = dbof.get_shape().as_list()[1]
if fc_dimred:
hidden1_weights = tf.get_variable("hidden1_weights",
[dbof_dim, hidden1_size],
initializer=tf.random_normal_initializer(stddev=1 / math.sqrt(cluster_size)))
tf.summary.histogram("hidden1_weights", hidden1_weights)
activation = tf.matmul(dbof, hidden1_weights)
if add_batch_norm and relu:
activation = slim.batch_norm(
activation,
center=True,
scale=True,
is_training=is_training,
scope="hidden1_bn")
else:
hidden1_biases = tf.get_variable("hidden1_biases",
[hidden1_size],
initializer = tf.random_normal_initializer(stddev=0.01))
tf.summary.histogram("hidden1_biases", hidden1_biases)
activation += hidden1_biases
if relu:
activation = tf.nn.relu6(activation)
tf.summary.histogram("hidden1_output", activation)
else:
activation = dbof
aggregated_model = getattr(video_level_models,
FLAGS.video_level_classifier_model)
return aggregated_model().create_model(
model_input=activation,
vocab_size=vocab_size,
is_training=is_training,
**unused_params)
class SoftDbofModelLF(models.BaseModel):
"""Creates a Soft Deep Bag of Frames model.
The model projects the features for each frame into a higher dimensional
'clustering' space, pools across frames in that space, and then
uses a configurable video-level model to classify the now aggregated features.
The model will randomly sample either frames or sequences of frames during
training to speed up convergence.
Args:
model_input: A 'batch_size' x 'max_frames' x 'num_features' matrix of
input features.
vocab_size: The number of classes in the dataset.
num_frames: A vector of length 'batch' which indicates the number of
frames for each video (before padding).
Returns:
A dictionary with a tensor containing the probability predictions of the
model in the 'predictions' key. The dimensions of the tensor are
'batch_size' x 'num_classes'.
"""
def create_model(self,
model_input,
vocab_size,
num_frames,
iterations=None,
add_batch_norm=None,
sample_random_frames=None,
cluster_size=None,
hidden_size=None,
is_training=True,
**unused_params):
iterations = iterations or FLAGS.iterations
add_batch_norm = add_batch_norm or FLAGS.dbof_add_batch_norm
random_frames = sample_random_frames or FLAGS.sample_random_frames
cluster_size = cluster_size or FLAGS.dbof_cluster_size
hidden1_size = hidden_size or FLAGS.dbof_hidden_size
fc_dimred = FLAGS.fc_dimred
relu = FLAGS.dbof_relu
max_pool = FLAGS.softdbof_maxpool
num_frames = tf.cast(tf.expand_dims(num_frames, 1), tf.float32)
if random_frames:
model_input = utils.SampleRandomFrames(model_input, num_frames,
iterations)
else:
model_input = utils.SampleRandomSequence(model_input, num_frames,
iterations)
max_frames = model_input.get_shape().as_list()[1]
feature_size = model_input.get_shape().as_list()[2]
reshaped_input = tf.reshape(model_input, [-1, feature_size])
tf.summary.histogram("input_hist", reshaped_input)
video_Dbof = SoftDBoF(1024,max_frames,cluster_size, max_pool, add_batch_norm, is_training)
audio_Dbof = SoftDBoF(128,max_frames,cluster_size/8, max_pool, add_batch_norm, is_training)
if add_batch_norm:
reshaped_input = slim.batch_norm(
reshaped_input,
center=True,
scale=True,
is_training=is_training,
scope="input_bn")
with tf.variable_scope("video_DBOF"):
dbof_video = video_Dbof.forward(reshaped_input[:,0:1024])
with tf.variable_scope("audio_DBOF"):
dbof_audio = audio_Dbof.forward(reshaped_input[:,1024:])
dbof = tf.concat([dbof_video, dbof_audio],1)
dbof_dim = dbof.get_shape().as_list()[1]
if fc_dimred:
hidden1_weights = tf.get_variable("hidden1_weights",
[dbof_dim, hidden1_size],
initializer=tf.random_normal_initializer(stddev=1 / math.sqrt(cluster_size)))
tf.summary.histogram("hidden1_weights", hidden1_weights)
activation = tf.matmul(dbof, hidden1_weights)
if add_batch_norm and relu:
activation = slim.batch_norm(
activation,
center=True,
scale=True,
is_training=is_training,
scope="hidden1_bn")
else:
hidden1_biases = tf.get_variable("hidden1_biases",
[hidden1_size],
initializer = tf.random_normal_initializer(stddev=0.01))
tf.summary.histogram("hidden1_biases", hidden1_biases)
activation += hidden1_biases
if relu:
activation = tf.nn.relu6(activation)
tf.summary.histogram("hidden1_output", activation)
else:
activation = dbof
aggregated_model = getattr(video_level_models,
FLAGS.video_level_classifier_model)
return aggregated_model().create_model(
model_input=activation,
vocab_size=vocab_size,
is_training=is_training,
**unused_params)
class LstmModel(models.BaseModel):
def create_model(self, model_input, vocab_size, num_frames, is_training=True, **unused_params):
"""Creates a model which uses a stack of LSTMs to represent the video.
Args:
model_input: A 'batch_size' x 'max_frames' x 'num_features' matrix of
input features.
vocab_size: The number of classes in the dataset.
num_frames: A vector of length 'batch' which indicates the number of
frames for each video (before padding).
Returns:
A dictionary with a tensor containing the probability predictions of the
model in the 'predictions' key. The dimensions of the tensor are
'batch_size' x 'num_classes'.
"""
lstm_size = FLAGS.lstm_cells
number_of_layers = FLAGS.lstm_layers
random_frames = FLAGS.lstm_random_sequence
iterations = FLAGS.iterations
backward = FLAGS.lstm_backward
if random_frames:
num_frames_2 = tf.cast(tf.expand_dims(num_frames, 1), tf.float32)
model_input = utils.SampleRandomFrames(model_input, num_frames_2,
iterations)
if backward:
model_input = tf.reverse_sequence(model_input, num_frames, seq_axis=1)
stacked_lstm = tf.contrib.rnn.MultiRNNCell(
[
tf.contrib.rnn.BasicLSTMCell(
lstm_size, forget_bias=1.0, state_is_tuple=False)
for _ in range(number_of_layers)
], state_is_tuple=False)
loss = 0.0
with tf.variable_scope("RNN"):
outputs, state = tf.nn.dynamic_rnn(stacked_lstm, model_input,
sequence_length=num_frames,
dtype=tf.float32)
aggregated_model = getattr(video_level_models,
FLAGS.video_level_classifier_model)
return aggregated_model().create_model(
model_input=state,
vocab_size=vocab_size,
is_training=is_training,
**unused_params)
class GruModel(models.BaseModel):
def create_model(self, model_input, vocab_size, num_frames, is_training=True, **unused_params):
"""Creates a model which uses a stack of GRUs to represent the video.
Args:
model_input: A 'batch_size' x 'max_frames' x 'num_features' matrix of
input features.
vocab_size: The number of classes in the dataset.
num_frames: A vector of length 'batch' which indicates the number of
frames for each video (before padding).
Returns:
A dictionary with a tensor containing the probability predictions of the
model in the 'predictions' key. The dimensions of the tensor are
'batch_size' x 'num_classes'.
"""
gru_size = FLAGS.gru_cells
number_of_layers = FLAGS.gru_layers
backward = FLAGS.gru_backward
random_frames = FLAGS.gru_random_sequence
iterations = FLAGS.iterations
if random_frames:
num_frames_2 = tf.cast(tf.expand_dims(num_frames, 1), tf.float32)
model_input = utils.SampleRandomFrames(model_input, num_frames_2,
iterations)
if backward:
model_input = tf.reverse_sequence(model_input, num_frames, seq_axis=1)
stacked_GRU = tf.contrib.rnn.MultiRNNCell(
[
tf.contrib.rnn.GRUCell(gru_size)
for _ in range(number_of_layers)
], state_is_tuple=False)
loss = 0.0
with tf.variable_scope("RNN"):
outputs, state = tf.nn.dynamic_rnn(stacked_GRU, model_input,
sequence_length=num_frames,
dtype=tf.float32)
aggregated_model = getattr(video_level_models,
FLAGS.video_level_classifier_model)
return aggregated_model().create_model(
model_input=state,
vocab_size=vocab_size,
is_training=is_training,
**unused_params)
class NetFVModelLF(models.BaseModel):
"""Creates a NetFV based model.
It emulates a Gaussian Mixture Fisher Vector pooling operations
Args:
model_input: A 'batch_size' x 'max_frames' x 'num_features' matrix of
input features.
vocab_size: The number of classes in the dataset.
num_frames: A vector of length 'batch' which indicates the number of
frames for each video (before padding).
Returns:
A dictionary with a tensor containing the probability predictions of the
model in the 'predictions' key. The dimensions of the tensor are
'batch_size' x 'num_classes'.
"""
def create_model(self,
model_input,
vocab_size,
num_frames,
iterations=None,
add_batch_norm=None,
sample_random_frames=None,
cluster_size=None,
hidden_size=None,
is_training=True,
**unused_params):
iterations = iterations or FLAGS.iterations
add_batch_norm = add_batch_norm or FLAGS.netvlad_add_batch_norm
random_frames = sample_random_frames or FLAGS.sample_random_frames
cluster_size = cluster_size or FLAGS.fv_cluster_size
hidden1_size = hidden_size or FLAGS.fv_hidden_size
relu = FLAGS.fv_relu
gating = FLAGS.gating
num_frames = tf.cast(tf.expand_dims(num_frames, 1), tf.float32)
if random_frames:
model_input = utils.SampleRandomFrames(model_input, num_frames,
iterations)
else:
model_input = utils.SampleRandomSequence(model_input, num_frames,
iterations)
max_frames = model_input.get_shape().as_list()[1]
feature_size = model_input.get_shape().as_list()[2]
reshaped_input = tf.reshape(model_input, [-1, feature_size])
tf.summary.histogram("input_hist", reshaped_input)
video_NetFV = NetFV(1024,max_frames,cluster_size, add_batch_norm, is_training)
audio_NetFV = NetFV(128,max_frames,cluster_size/2, add_batch_norm, is_training)
if add_batch_norm:
reshaped_input = slim.batch_norm(
reshaped_input,
center=True,
scale=True,
is_training=is_training,
scope="input_bn")
with tf.variable_scope("video_FV"):
fv_video = video_NetFV.forward(reshaped_input[:,0:1024])
with tf.variable_scope("audio_FV"):
fv_audio = audio_NetFV.forward(reshaped_input[:,1024:])
fv = tf.concat([fv_video, fv_audio],1)
fv_dim = fv.get_shape().as_list()[1]
hidden1_weights = tf.get_variable("hidden1_weights",
[fv_dim, hidden1_size],
initializer=tf.random_normal_initializer(stddev=1 / math.sqrt(cluster_size)))
activation = tf.matmul(fv, hidden1_weights)
if add_batch_norm and relu:
activation = slim.batch_norm(
activation,
center=True,
scale=True,
is_training=is_training,
scope="hidden1_bn")
else:
hidden1_biases = tf.get_variable("hidden1_biases",
[hidden1_size],
initializer = tf.random_normal_initializer(stddev=0.01))
tf.summary.histogram("hidden1_biases", hidden1_biases)
activation += hidden1_biases
if relu:
activation = tf.nn.relu6(activation)
if gating:
gating_weights = tf.get_variable("gating_weights_2",
[hidden1_size, hidden1_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(hidden1_size)))
gates = tf.matmul(activation, gating_weights)
if add_batch_norm:
gates = slim.batch_norm(
gates,
center=True,
scale=True,
is_training=is_training,
scope="gating_bn")
else:
gating_biases = tf.get_variable("gating_biases",
[cluster_size],
initializer = tf.random_normal(stddev=1 / math.sqrt(feature_size)))
gates += gating_biases
gates = tf.sigmoid(gates)
activation = tf.multiply(activation,gates)
aggregated_model = getattr(video_level_models,
FLAGS.video_level_classifier_model)
return aggregated_model().create_model(
model_input=activation,
vocab_size=vocab_size,
is_training=is_training,
**unused_params)
class ConvSquare(models.BaseModel):
def create_model(self, model_input, vocab_size, num_frames, is_training=True, **unused_params):
num_layers = FLAGS.ConvLayers
temporal_field = FLAGS.Conv_temporal_field
gating = FLAGS.gating
add_batch_norm = FLAGS.netvlad_add_batch_norm
if random_frames:
num_frames_2 = tf.cast(tf.expand_dims(num_frames, 1), tf.float32)
model_input = utils.SampleRandomFrames(model_input, num_frames_2,
iterations)
max_frames = model_input.get_shape().as_list()[1]
feature_size = model_input.get_shape().as_list()[2]
reshaped_input = tf.reshape(model_input, [-1, feature_size])
if add_batch_norm:
reshaped_input = slim.batch_norm(
reshaped_input,
center=True,
scale=True,
is_training=is_training,
scope="input_bn")
model_input = tf.reshape(reshaped_input, [-1, max_frames, feature_size])
input_ = model_input
for i in range(num_layers):
with tf.name_scope('conv1_%d' % i) as scope:
kernel = tf.get_variable("kernel",
[temporal_field, feature_size, feature_size // 2],
initializer = tf.random_normal_initializer(stddev=0.01))
conv = tf.nn.conv1d(input_, kernel, 1, padding='SAME')
conv = tf.sqrt(tf.square(conv))
# biases = tf.Variable(tf.constant(0.0, shape=[feature_size // 2], dtype=tf.float32),
# trainable=True, name='biases')
biases = tf.get_variable("biases",
[feature_size // 2],
initializer = tf.constant_initializer(0.0))
bias = tf.nn.bias_add(conv, biases)
input_ = tf.nn.relu(bias, name=scope)
feature_size = feature_size // 2
input_ = tf.reduce_mean(input_, 1)
if gating:
gating_weights = tf.get_variable("gating_weights_2",
[feature_size, feature_size],
initializer=tf.random_normal_initializer(stddev=1 / math.sqrt(feature_size)))
gates = tf.matmul(input_, gating_weights)
if add_batch_norm:
gates = slim.batch_norm(
gates,
center=True,
scale=True,
is_training=is_training,
scope="gating_bn")
gates = tf.sigmoid(gates)
input_ = tf.multiply(input_, gates)
aggregated_model = getattr(video_level_models,
FLAGS.video_level_classifier_model)
return aggregated_model().create_model(
model_input=input_,
vocab_size=vocab_size, is_training=is_training,
**unused_params)
class twoStreamLstmModel(models.BaseModel):
def create_model(self, model_input, vocab_size, num_frames, is_training=True, **unused_params):
lstm_size = FLAGS.lstm_cells
number_of_layers = FLAGS.lstm_layers
random_frames = FLAGS.lstm_random_sequence
iterations = FLAGS.iterations
backward = FLAGS.lstm_backward
if random_frames:
num_frames_2 = tf.cast(tf.expand_dims(num_frames, 1), tf.float32)
model_input = utils.SampleRandomFrames(model_input, num_frames_2,
iterations)
video_input = model_input[:,:,0:1024]
audio_input = model_input[:,:,1024:]
video_dim = 1024
audio_dim = 128
# input embedding
video_embedding_weights = tf.get_variable("video_embedding_weights",
[video_dim, lstm_size//2],
initializer=tf.random_normal_initializer(stddev=1 / math.sqrt(video_dim)))
audio_embedding_weights = tf.get_variable("audio_embedding_weights",
[audio_dim, lstm_size//2],
initializer=tf.random_normal_initializer(stddev=1 / math.sqrt(audio_dim)))
max_num_frames = video_input.get_shape().as_list()[1]
video_input = tf.reshape(video_input, [-1, video_dim])
# video_input = tf.matmul(video_input, video_embedding_weights)
video_input = slim.fully_connected(video_input, lstm_size//2, activation_fn=tf.tanh, scope='video_embedding', weights_regularizer=slim.l2_regularizer(8e-4))
video_input = tf.reshape(video_input, [-1, max_num_frames, lstm_size//2])
audio_input = tf.reshape(audio_input, [-1, audio_dim])
# audio_input = tf.matmul(audio_input, audio_embedding_weights)
audio_input = slim.fully_connected(audio_input, lstm_size//2, activation_fn=tf.tanh, scope='audio_embedding', weights_regularizer=slim.l2_regularizer(8e-4))
audio_input = tf.reshape(audio_input, [-1, max_num_frames, lstm_size//2])
# if backward:
# model_input = tf.reverse_sequence(model_input, num_frames, seq_axis=1)
video_forward_lstm = tf.contrib.rnn.BasicLSTMCell(
lstm_size, forget_bias=1.0, state_is_tuple=False)
video_backward_lstm = tf.contrib.rnn.BasicLSTMCell(
lstm_size, forget_bias=1.0, state_is_tuple=False)
audio_forward_lstm = tf.contrib.rnn.BasicLSTMCell(
lstm_size, forget_bias=1.0, state_is_tuple=False)
audio_backward_lstm = tf.contrib.rnn.BasicLSTMCell(
lstm_size, forget_bias=1.0, state_is_tuple=False)
loss = 0.0
with tf.variable_scope("Video_RNN"):
# outputs, state = tf.nn.dynamic_rnn(stacked_lstm, model_input,
# sequence_length=num_frames,
# dtype=tf.float32)
v_outputs, v_state = tf.nn.bidirectional_dynamic_rnn(video_forward_lstm, video_backward_lstm,
video_input, sequence_length=num_frames,
dtype=tf.float32)
v_outputs = tf.concat(v_outputs, 2)
with tf.variable_scope("Audio_RNN"):
a_outputs, a_state = tf.nn.bidirectional_dynamic_rnn(audio_forward_lstm, audio_backward_lstm,
audio_input, sequence_length=num_frames,
dtype=tf.float32)
a_outputs = tf.concat(a_outputs, 2)
video_att_weights = tf.get_variable("video_att_weights",
[lstm_size*2, 1],
initializer=tf.random_normal_initializer(stddev=1 / math.sqrt(lstm_size*2)))
audio_att_weights = tf.get_variable("audio_att_weights",
[lstm_size*2, 1],
initializer=tf.random_normal_initializer(stddev=1 / math.sqrt(lstm_size*2)))
v_outputs_reshape = tf.reshape(v_outputs, [-1, lstm_size*2])
video_attention = tf.matmul(v_outputs_reshape, video_att_weights)
video_attention = tf.reshape(video_attention, [-1, max_num_frames, 1])
video_attention = tf.nn.softmax(video_attention, dim=1)
v_outputs = tf.multiply(v_outputs, video_attention)
v_outputs = tf.reduce_sum(v_outputs, 1)
a_outputs_reshape = tf.reshape(a_outputs, [-1, lstm_size*2])
audio_attention = tf.matmul(a_outputs_reshape, audio_att_weights)
audio_attention = tf.reshape(audio_attention, [-1, max_num_frames, 1])
audio_attention = tf.nn.softmax(audio_attention, dim=1)
a_outputs = tf.multiply(a_outputs, audio_attention)
a_outputs = tf.reduce_sum(a_outputs, 1)
lstm_out = tf.concat([v_outputs, a_outputs], 1)
lstm_out_up = slim.fully_connected(lstm_out, lstm_size*8, scope='up_proj')
lstm_out_hidden = slim.fully_connected(lstm_out_up, lstm_size*4, activation_fn=tf.tanh, scope='hidden')
aggregated_model = getattr(video_level_models,
FLAGS.video_level_classifier_model)
return aggregated_model().create_model(
model_input=lstm_out_hidden,
vocab_size=vocab_size,
is_training=is_training,
**unused_params)
class TRN(models.BaseModel):
def create_model(self, model_input, vocab_size, num_frames, is_training=True, **unused_params):
iterations = FLAGS.iterations
gating = FLAGS.gating
add_batch_norm = FLAGS.netvlad_add_batch_norm
random_frames = FLAGS.lstm_random_sequence
if random_frames:
num_frames_2 = tf.cast(tf.expand_dims(num_frames, 1), tf.float32)
model_input = utils.SampleRandomFrames(model_input, num_frames_2,
iterations)
max_frames = model_input.get_shape().as_list()[1]
feature_size = model_input.get_shape().as_list()[2]
reshaped_input = tf.reshape(model_input, [-1, feature_size])
if add_batch_norm:
reshaped_input = slim.batch_norm(
reshaped_input,
center=True,
scale=True,
is_training=is_training,
scope="input_bn")
model_input = tf.reshape(reshaped_input, [-1, max_frames, feature_size])
input_ = model_input
with tf.name_scope('TRN_2') as scope:
kernel = tf.get_variable("kernel_2",
[2, feature_size, feature_size // 2],
initializer = tf.random_normal_initializer(stddev=0.01))
conv = tf.nn.conv1d(input_, kernel, 1, padding='SAME')
conv = tf.sqrt(tf.square(conv))
# biases = tf.Variable(tf.constant(0.0, shape=[feature_size // 2], dtype=tf.float32),
# trainable=True, name='biases')
biases = tf.get_variable("biases_2",
[feature_size // 2],
initializer = tf.constant_initializer(0.0))
bias = tf.nn.bias_add(conv, biases)
TRN_2_output = tf.nn.relu(bias, name=scope)
TRN_2_output = tf.reduce_mean(TRN_2_output, 1)
with tf.name_scope('TRN_4') as scope:
kernel = tf.get_variable("kernel_4",
[4, feature_size, feature_size // 2],
initializer = tf.random_normal_initializer(stddev=0.01))
conv = tf.nn.conv1d(input_, kernel, 1, padding='SAME')
conv = tf.sqrt(tf.square(conv))
# biases = tf.Variable(tf.constant(0.0, shape=[feature_size // 2], dtype=tf.float32),
# trainable=True, name='biases')
biases = tf.get_variable("biases_4",
[feature_size // 2],
initializer = tf.constant_initializer(0.0))
bias = tf.nn.bias_add(conv, biases)
TRN_4_output = tf.nn.relu(bias, name=scope)
TRN_4_output = tf.reduce_mean(TRN_4_output, 1)
with tf.name_scope('TRN_8') as scope:
kernel = tf.get_variable("kernel_8",
[8, feature_size, feature_size // 2],
initializer = tf.random_normal_initializer(stddev=0.01))
conv = tf.nn.conv1d(input_, kernel, 1, padding='SAME')
conv = tf.sqrt(tf.square(conv))
# biases = tf.Variable(tf.constant(0.0, shape=[feature_size // 2], dtype=tf.float32),
# trainable=True, name='biases')
biases = tf.get_variable("biases_8",
[feature_size // 2],
initializer = tf.constant_initializer(0.0))
bias = tf.nn.bias_add(conv, biases)
TRN_8_output = tf.nn.relu(bias, name=scope)
TRN_8_output = tf.reduce_mean(TRN_8_output, 1)
avg_TRN = TRN_2_output + TRN_4_output + TRN_8_output
avg_TRN = avg_TRN / 3.0
if gating:
gating_weights = tf.get_variable("gating_weights_2",
[feature_size, feature_size],
initializer=tf.random_normal_initializer(stddev=1 / math.sqrt(feature_size)))
gates = tf.matmul(avg_TRN, gating_weights)
if add_batch_norm:
gates = slim.batch_norm(
gates,
center=True,
scale=True,
is_training=is_training,
scope="gating_bn")
gates = tf.sigmoid(gates)
avg_TRN = tf.multiply(avg_TRN, gates)
aggregated_model = getattr(video_level_models,
FLAGS.video_level_classifier_model)
return aggregated_model().create_model(
model_input=avg_TRN,
vocab_size=vocab_size, is_training=is_training,
**unused_params)
class CDCModel(models.BaseModel):
def create_model(self, model_input, vocab_size, num_frames, is_training=True, **unused_params):
"""Creates a model which uses a stack of GRUs to represent the video.
Args:
model_input: A 'batch_size' x 'max_frames' x 'num_features' matrix of
input features.
vocab_size: The number of classes in the dataset.
num_frames: A vector of length 'batch' which indicates the number of
frames for each video (before padding).
Returns:
A dictionary with a tensor containing the probability predictions of the
model in the 'predictions' key. The dimensions of the tensor are
'batch_size' x 'num_classes'.
"""
gru_size = FLAGS.gru_cells
number_of_layers = FLAGS.gru_layers
backward = FLAGS.gru_backward
random_frames = FLAGS.gru_random_sequence
iterations = FLAGS.iterations
add_batch_norm = FLAGS.netvlad_add_batch_norm
if random_frames:
num_frames_2 = tf.cast(tf.expand_dims(num_frames, 1), tf.float32)
model_input = utils.SampleRandomFrames(model_input, num_frames_2,
iterations)
max_frames = model_input.get_shape().as_list()[1]
feature_size = model_input.get_shape().as_list()[2]
if add_batch_norm:
reshaped_input = tf.reshape(model_input, [-1, feature_size])
reshaped_input = slim.batch_norm(
reshaped_input,
center=True,
scale=True,
is_training=is_training,
scope="input_bn")
model_input = tf.reshape(reshaped_input, [-1, max_frames, 1, feature_size])
conv1_output = tf.contrib.layers.conv2d(model_input, feature_size*4, [8,1], stride=[1,1], scope='conv1')
conv1_output = tf.contrib.layers.max_pool2d(conv1_output, [8,1], stride=[8,1], scope='pool1')
conv2_output = tf.contrib.layers.conv2d(conv1_output, feature_size*4, [8,1], stride=[1,1], scope='conv2')
conv2_output = tf.contrib.layers.max_pool2d(conv2_output, [8,1], stride=[8,1], scope='pool2')
deconv1_output = tf.contrib.layers.conv2d_transpose(conv2_output, feature_size*4, [8,1], stride=[8,1], scope='conv1_trans')
deconv2_output = tf.contrib.layers.conv2d_transpose(deconv1_output, feature_size, [8,1], stride=[8,1], scope='conv2_trans')
model_input = tf.reshape(reshaped_input, [-1, max_frames, feature_size])
deconv2_output = tf.reshape(deconv2_output, [-1, feature_size])
deconv2_output = slim.batch_norm(
deconv2_output,
center=True,
scale=True,
is_training=is_training,
scope="deconv_bn")
deconv2_output = tf.reshape(deconv2_output, [-1, max_frames, feature_size])
model_input = tf.concat([model_input, deconv2_output], 2)
stacked_GRU = tf.contrib.rnn.MultiRNNCell(
[
tf.contrib.rnn.GRUCell(gru_size)
for _ in range(number_of_layers)
], state_is_tuple=False)
loss = 0.0
with tf.variable_scope("RNN"):
outputs, state = tf.nn.dynamic_rnn(stacked_GRU, model_input,
sequence_length=num_frames,
dtype=tf.float32)
aggregated_model = getattr(video_level_models,
FLAGS.video_level_classifier_model)
return aggregated_model().create_model(
model_input=state,
vocab_size=vocab_size,
is_training=is_training,
**unused_params)
class multiScale_NetVLADModelLF(models.BaseModel):
"""Creates a NetVLAD based model.
Args:
model_input: A 'batch_size' x 'max_frames' x 'num_features' matrix of
input features.
vocab_size: The number of classes in the dataset.
num_frames: A vector of length 'batch' which indicates the number of
frames for each video (before padding).
Returns:
A dictionary with a tensor containing the probability predictions of the
model in the 'predictions' key. The dimensions of the tensor are
'batch_size' x 'num_classes'.
"""
def create_model(self,
model_input,
vocab_size,
num_frames,
iterations=None,
add_batch_norm=None,
sample_random_frames=None,
cluster_size=None,
hidden_size=None,
is_training=True,
**unused_params):
iterations = iterations or FLAGS.iterations
add_batch_norm = add_batch_norm or FLAGS.netvlad_add_batch_norm
random_frames = sample_random_frames or FLAGS.sample_random_frames
cluster_size = cluster_size or FLAGS.netvlad_cluster_size
hidden1_size = hidden_size or FLAGS.netvlad_hidden_size
relu = FLAGS.netvlad_relu
dimred = FLAGS.netvlad_dimred
gating = FLAGS.gating
remove_diag = FLAGS.gating_remove_diag
nonlocalvlad = FLAGS.nonlocalvlad
nonlocalvlad_shared = FLAGS.nonlocalvlad_shared
nonlocalvlad_unique = FLAGS.nonlocalvlad_unique
# if nonlocalvlad:
# video_NetVLAD = NetVLAD_NonLocal(1024,max_frames,cluster_size, add_batch_norm, is_training)
# audio_NetVLAD = NetVLAD_NonLocal(128,max_frames,cluster_size/2, add_batch_norm, is_training)
# elif nonlocalvlad_shared:
# video_NetVLAD = NetVLAD_NonLocal_modularize_shared(1024,max_frames,cluster_size, add_batch_norm, is_training)
# audio_NetVLAD = NetVLAD_NonLocal_modularize_shared(128,max_frames,cluster_size/2, add_batch_norm, is_training)
# elif nonlocalvlad_unique:
# video_NetVLAD = NetVLAD_NonLocal_modularize_unique(1024,max_frames,cluster_size, add_batch_norm, is_training)
# audio_NetVLAD = NetVLAD_NonLocal_modularize_unique(128,max_frames,cluster_size/2, add_batch_norm, is_training)
# else:
# video_NetVLAD = NetVLAD_Flexable(1024,max_frames,cluster_size, add_batch_norm, is_training)
# audio_NetVLAD = NetVLAD_Flexable(128,max_frames,cluster_size/2, add_batch_norm, is_training)
video_NetVLAD = NetVLAD_Flexable(1024,cluster_size, add_batch_norm, is_training)
audio_NetVLAD = NetVLAD_Flexable(128,cluster_size/2, add_batch_norm, is_training)
num_frames = tf.cast(tf.expand_dims(num_frames, 1), tf.float32)
feature_size = model_input.get_shape().as_list()[2]
iterations_list = [300, 150, 150, 70, 70, 70, 70]
input_list = []
for iter_i, iter_size in enumerate(iterations_list):
model_input_tmp = utils.SampleRandomSequence(model_input, num_frames,
iter_size)
max_frames = model_input_tmp.get_shape().as_list()[1]
reshaped_input = tf.reshape(model_input_tmp, [-1, feature_size])
with (tf.variable_scope(("input_bn"), reuse=True if iter_i > 0 else None)):
reshaped_input = slim.batch_norm(
reshaped_input,
center=True,
scale=True,
is_training=is_training)
with (tf.variable_scope(("video_VLAD"), reuse=True if iter_i > 0 else None)):
vlad_video = video_NetVLAD.forward(reshaped_input[:,0:1024],max_frames)
with (tf.variable_scope(("audio_VLAD"), reuse=True if iter_i > 0 else None)):
vlad_audio = audio_NetVLAD.forward(reshaped_input[:,1024:],max_frames)
vlad_tmp = tf.concat([vlad_video, vlad_audio],1)
input_list.append(vlad_tmp)
vlad = tf.concat(input_list, 1)
vlad_dim = vlad.get_shape().as_list()[1]
hidden1_weights = tf.get_variable("hidden1_weights",
[vlad_dim, hidden1_size],
initializer=tf.random_normal_initializer(stddev=1 / math.sqrt(cluster_size)))
activation = tf.matmul(vlad, hidden1_weights)
if add_batch_norm and relu:
activation = slim.batch_norm(
activation,
center=True,
scale=True,
is_training=is_training,
scope="hidden1_bn")
else:
hidden1_biases = tf.get_variable("hidden1_biases",
[hidden1_size],
initializer = tf.random_normal_initializer(stddev=0.01))
tf.summary.histogram("hidden1_biases", hidden1_biases)
activation += hidden1_biases
if relu:
activation = tf.nn.relu6(activation)
if gating:
gating_weights = tf.get_variable("gating_weights_2",
[hidden1_size, hidden1_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(hidden1_size)))
gates = tf.matmul(activation, gating_weights)
if remove_diag:
#removes diagonals coefficients
diagonals = tf.matrix_diag_part(gating_weights)
gates = gates - tf.multiply(diagonals,activation)
if add_batch_norm:
gates = slim.batch_norm(
gates,
center=True,
scale=True,
is_training=is_training,
scope="gating_bn")
else:
gating_biases = tf.get_variable("gating_biases",
[cluster_size],
initializer = tf.random_normal(stddev=1 / math.sqrt(feature_size)))
gates += gating_biases
gates = tf.sigmoid(gates)
activation = tf.multiply(activation,gates)
aggregated_model = getattr(video_level_models,
FLAGS.video_level_classifier_model)
return aggregated_model().create_model(
model_input=activation,
vocab_size=vocab_size,
is_training=is_training,
**unused_params)
class TLEmax_NetVLADModelLF(models.BaseModel):
"""Creates a NetVLAD based model.
Args:
model_input: A 'batch_size' x 'max_frames' x 'num_features' matrix of
input features.
vocab_size: The number of classes in the dataset.
num_frames: A vector of length 'batch' which indicates the number of
frames for each video (before padding).
Returns:
A dictionary with a tensor containing the probability predictions of the
model in the 'predictions' key. The dimensions of the tensor are
'batch_size' x 'num_classes'.
"""
def create_model(self,
model_input,
vocab_size,
num_frames,
iterations=None,
add_batch_norm=None,
sample_random_frames=None,
cluster_size=None,
hidden_size=None,
is_training=True,
**unused_params):
iterations = iterations or FLAGS.iterations
add_batch_norm = add_batch_norm or FLAGS.netvlad_add_batch_norm
random_frames = sample_random_frames or FLAGS.sample_random_frames
cluster_size = cluster_size or FLAGS.netvlad_cluster_size
hidden1_size = hidden_size or FLAGS.netvlad_hidden_size
relu = FLAGS.netvlad_relu
dimred = FLAGS.netvlad_dimred
gating = FLAGS.gating
remove_diag = FLAGS.gating_remove_diag
nonlocalvlad = FLAGS.nonlocalvlad
nonlocalvlad_shared = FLAGS.nonlocalvlad_shared
nonlocalvlad_unique = FLAGS.nonlocalvlad_unique
# if nonlocalvlad:
# video_NetVLAD = NetVLAD_NonLocal(1024,max_frames,cluster_size, add_batch_norm, is_training)
# audio_NetVLAD = NetVLAD_NonLocal(128,max_frames,cluster_size/2, add_batch_norm, is_training)
# elif nonlocalvlad_shared:
# video_NetVLAD = NetVLAD_NonLocal_modularize_shared(1024,max_frames,cluster_size, add_batch_norm, is_training)
# audio_NetVLAD = NetVLAD_NonLocal_modularize_shared(128,max_frames,cluster_size/2, add_batch_norm, is_training)
# elif nonlocalvlad_unique:
# video_NetVLAD = NetVLAD_NonLocal_modularize_unique(1024,max_frames,cluster_size, add_batch_norm, is_training)
# audio_NetVLAD = NetVLAD_NonLocal_modularize_unique(128,max_frames,cluster_size/2, add_batch_norm, is_training)
# else:
# video_NetVLAD = NetVLAD_Flexable(1024,max_frames,cluster_size, add_batch_norm, is_training)
# audio_NetVLAD = NetVLAD_Flexable(128,max_frames,cluster_size/2, add_batch_norm, is_training)
num_frames = tf.cast(tf.expand_dims(num_frames, 1), tf.float32)
feature_size = model_input.get_shape().as_list()[2]
model_input_tmp = utils.SampleRandomSequence(model_input, num_frames,
iterations)
max_frames = model_input_tmp.get_shape().as_list()[1]
reshaped_input = tf.reshape(model_input_tmp, [-1, max_frames//10, 10, feature_size])
reshaped_input = tf.reduce_max(reshaped_input, 2)
max_frames = reshaped_input.get_shape().as_list()[1]
reshaped_input = tf.reshape(reshaped_input, [-1, feature_size])
with tf.variable_scope("input_bn"):
reshaped_input = slim.batch_norm(
reshaped_input,
center=True,
scale=True,
is_training=is_training)
video_NetVLAD = NetVLAD(1024,max_frames, cluster_size, add_batch_norm, is_training)
audio_NetVLAD = NetVLAD(128,max_frames, cluster_size/2, add_batch_norm, is_training)
with tf.variable_scope("video_VLAD"):
vlad_video = video_NetVLAD.forward(reshaped_input[:,0:1024])
with tf.variable_scope("audio_VLAD"):
vlad_audio = audio_NetVLAD.forward(reshaped_input[:,1024:])
vlad = tf.concat([vlad_video, vlad_audio],1)
vlad_dim = vlad.get_shape().as_list()[1]
hidden1_weights = tf.get_variable("hidden1_weights",
[vlad_dim, hidden1_size],
initializer=tf.random_normal_initializer(stddev=1 / math.sqrt(cluster_size)))
activation = tf.matmul(vlad, hidden1_weights)
if add_batch_norm and relu:
activation = slim.batch_norm(
activation,
center=True,
scale=True,
is_training=is_training,
scope="hidden1_bn")
else:
hidden1_biases = tf.get_variable("hidden1_biases",
[hidden1_size],
initializer = tf.random_normal_initializer(stddev=0.01))
tf.summary.histogram("hidden1_biases", hidden1_biases)
activation += hidden1_biases
if relu:
activation = tf.nn.relu6(activation)
if gating:
gating_weights = tf.get_variable("gating_weights_2",
[hidden1_size, hidden1_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(hidden1_size)))
gates = tf.matmul(activation, gating_weights)
if remove_diag:
#removes diagonals coefficients
diagonals = tf.matrix_diag_part(gating_weights)
gates = gates - tf.multiply(diagonals,activation)
if add_batch_norm:
gates = slim.batch_norm(
gates,
center=True,
scale=True,
is_training=is_training,
scope="gating_bn")
else:
gating_biases = tf.get_variable("gating_biases",
[cluster_size],
initializer = tf.random_normal(stddev=1 / math.sqrt(feature_size)))
gates += gating_biases
gates = tf.sigmoid(gates)
activation = tf.multiply(activation,gates)
aggregated_model = getattr(video_level_models,
FLAGS.video_level_classifier_model)
return aggregated_model().create_model(
model_input=activation,
vocab_size=vocab_size,
is_training=is_training,
**unused_params)
class TLEmul_NetVLADModelLF(models.BaseModel):
"""Creates a NetVLAD based model.
Args:
model_input: A 'batch_size' x 'max_frames' x 'num_features' matrix of
input features.
vocab_size: The number of classes in the dataset.
num_frames: A vector of length 'batch' which indicates the number of
frames for each video (before padding).
Returns:
A dictionary with a tensor containing the probability predictions of the
model in the 'predictions' key. The dimensions of the tensor are
'batch_size' x 'num_classes'.
"""
def create_model(self,
model_input,
vocab_size,
num_frames,
iterations=None,
add_batch_norm=None,
sample_random_frames=None,
cluster_size=None,
hidden_size=None,
is_training=True,
**unused_params):
iterations = iterations or FLAGS.iterations
add_batch_norm = add_batch_norm or FLAGS.netvlad_add_batch_norm
random_frames = sample_random_frames or FLAGS.sample_random_frames
cluster_size = cluster_size or FLAGS.netvlad_cluster_size
hidden1_size = hidden_size or FLAGS.netvlad_hidden_size
relu = FLAGS.netvlad_relu
dimred = FLAGS.netvlad_dimred
gating = FLAGS.gating
remove_diag = FLAGS.gating_remove_diag
nonlocalvlad = FLAGS.nonlocalvlad
nonlocalvlad_shared = FLAGS.nonlocalvlad_shared
nonlocalvlad_unique = FLAGS.nonlocalvlad_unique
# if nonlocalvlad:
# video_NetVLAD = NetVLAD_NonLocal(1024,max_frames,cluster_size, add_batch_norm, is_training)
# audio_NetVLAD = NetVLAD_NonLocal(128,max_frames,cluster_size/2, add_batch_norm, is_training)
# elif nonlocalvlad_shared:
# video_NetVLAD = NetVLAD_NonLocal_modularize_shared(1024,max_frames,cluster_size, add_batch_norm, is_training)
# audio_NetVLAD = NetVLAD_NonLocal_modularize_shared(128,max_frames,cluster_size/2, add_batch_norm, is_training)
# elif nonlocalvlad_unique:
# video_NetVLAD = NetVLAD_NonLocal_modularize_unique(1024,max_frames,cluster_size, add_batch_norm, is_training)
# audio_NetVLAD = NetVLAD_NonLocal_modularize_unique(128,max_frames,cluster_size/2, add_batch_norm, is_training)
# else:
# video_NetVLAD = NetVLAD_Flexable(1024,max_frames,cluster_size, add_batch_norm, is_training)
# audio_NetVLAD = NetVLAD_Flexable(128,max_frames,cluster_size/2, add_batch_norm, is_training)
num_frames = tf.cast(tf.expand_dims(num_frames, 1), tf.float32)
feature_size = model_input.get_shape().as_list()[2]
model_input_tmp = utils.SampleRandomSequence(model_input, num_frames,
iterations)
max_frames = model_input_tmp.get_shape().as_list()[1]
reshaped_input = tf.reshape(model_input_tmp, [-1, max_frames//10, 10, feature_size])
reshaped_input = tf.reduce_prod(reshaped_input, 2)
max_frames = reshaped_input.get_shape().as_list()[1]
reshaped_input = tf.reshape(reshaped_input, [-1, feature_size])
with tf.variable_scope("input_bn"):
reshaped_input = slim.batch_norm(
reshaped_input,
center=True,
scale=True,
is_training=is_training)
video_NetVLAD = NetVLAD(1024,max_frames, cluster_size, add_batch_norm, is_training)
audio_NetVLAD = NetVLAD(128,max_frames, cluster_size/2, add_batch_norm, is_training)
with tf.variable_scope("video_VLAD"):
vlad_video = video_NetVLAD.forward(reshaped_input[:,0:1024])
with tf.variable_scope("audio_VLAD"):
vlad_audio = audio_NetVLAD.forward(reshaped_input[:,1024:])
vlad = tf.concat([vlad_video, vlad_audio],1)
vlad_dim = vlad.get_shape().as_list()[1]
hidden1_weights = tf.get_variable("hidden1_weights",
[vlad_dim, hidden1_size],
initializer=tf.random_normal_initializer(stddev=1 / math.sqrt(cluster_size)))
activation = tf.matmul(vlad, hidden1_weights)
if add_batch_norm and relu:
activation = slim.batch_norm(
activation,
center=True,
scale=True,
is_training=is_training,
scope="hidden1_bn")
else:
hidden1_biases = tf.get_variable("hidden1_biases",
[hidden1_size],
initializer = tf.random_normal_initializer(stddev=0.01))
tf.summary.histogram("hidden1_biases", hidden1_biases)
activation += hidden1_biases
if relu:
activation = tf.nn.relu6(activation)
if gating:
gating_weights = tf.get_variable("gating_weights_2",
[hidden1_size, hidden1_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(hidden1_size)))
gates = tf.matmul(activation, gating_weights)
if remove_diag:
#removes diagonals coefficients
diagonals = tf.matrix_diag_part(gating_weights)
gates = gates - tf.multiply(diagonals,activation)
if add_batch_norm:
gates = slim.batch_norm(
gates,
center=True,
scale=True,
is_training=is_training,
scope="gating_bn")
else:
gating_biases = tf.get_variable("gating_biases",
[cluster_size],
initializer = tf.random_normal(stddev=1 / math.sqrt(feature_size)))
gates += gating_biases
gates = tf.sigmoid(gates)
activation = tf.multiply(activation,gates)
aggregated_model = getattr(video_level_models,
FLAGS.video_level_classifier_model)
return aggregated_model().create_model(
model_input=activation,
vocab_size=vocab_size,
is_training=is_training,
**unused_params)
def nonLocal_block(vlad, feature_size, hidden_size, cluster_size):
nonlocal_theta = tf.get_variable("nonlocal_theta",
[feature_size, hidden_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(feature_size)))
nonlocal_phi = tf.get_variable("nonlocal_phi",
[feature_size, hidden_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(feature_size)))
nonlocal_g = tf.get_variable("nonlocal_g",
[feature_size, hidden_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(feature_size)))
nonlocal_out = tf.get_variable("nonlocal_out",
[hidden_size, feature_size],
initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(hidden_size)))
vlad_theta = tf.matmul(vlad, nonlocal_theta)
vlad_phi = tf.matmul(vlad, nonlocal_phi)
vlad_g = tf.matmul(vlad, nonlocal_g)
vlad_theta = tf.reshape(vlad_theta, [-1, cluster_size, hidden_size])
vlad_phi = tf.reshape(vlad_phi, [-1, cluster_size, hidden_size])
vlad_g = tf.reshape(vlad_phi, [-1, cluster_size, hidden_size])
vlad_softmax = tf.nn.softmax(feature_size**-.5 * tf.matmul(vlad_theta, tf.transpose(vlad_phi,perm=[0,2,1])))
vlad_g = tf.matmul(vlad_softmax, vlad_g)
vlad_g = tf.reshape(vlad_g, [-1, hidden_size])
vlad_g = tf.matmul(vlad_g, nonlocal_out)
vlad = vlad + vlad_g
return vlad | 40.419953 | 168 | 0.651605 | 17,030 | 137,347 | 4.960893 | 0.027657 | 0.048826 | 0.034373 | 0.03551 | 0.902763 | 0.884594 | 0.873006 | 0.861086 | 0.850492 | 0.842799 | 0 | 0.01711 | 0.252754 | 137,347 | 3,398 | 169 | 40.419953 | 0.806064 | 0.091127 | 0 | 0.801633 | 0 | 0 | 0.053163 | 0.001175 | 0 | 0 | 0 | 0 | 0 | 1 | 0.025714 | false | 0 | 0.003673 | 0 | 0.060816 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
f62bcff76940df000fac928e916b6fd195e7f8fc | 25,819 | py | Python | mundiapi/controllers/charges_controller.py | hugocpolos/MundiAPI-PYTHON | 164545cc58bf18c946d5456e9ba4d55a378a339a | [
"MIT"
] | 7 | 2017-08-30T15:54:22.000Z | 2020-10-09T01:01:00.000Z | mundiapi/controllers/charges_controller.py | hugocpolos/MundiAPI-PYTHON | 164545cc58bf18c946d5456e9ba4d55a378a339a | [
"MIT"
] | 4 | 2018-05-05T15:15:09.000Z | 2021-12-22T00:52:41.000Z | mundiapi/controllers/charges_controller.py | hugocpolos/MundiAPI-PYTHON | 164545cc58bf18c946d5456e9ba4d55a378a339a | [
"MIT"
] | 4 | 2017-12-07T16:40:19.000Z | 2020-11-02T11:56:13.000Z | # -*- coding: utf-8 -*-
"""
mundiapi
This file was automatically generated by APIMATIC v2.0 ( https://apimatic.io ).
"""
from mundiapi.api_helper import APIHelper
from mundiapi.configuration import Configuration
from mundiapi.controllers.base_controller import BaseController
from mundiapi.http.auth.basic_auth import BasicAuth
from mundiapi.models.get_charge_response import GetChargeResponse
from mundiapi.models.list_charges_response import ListChargesResponse
from mundiapi.models.list_charge_transactions_response import ListChargeTransactionsResponse
from mundiapi.models.get_charges_summary_response import GetChargesSummaryResponse
class ChargesController(BaseController):
"""A Controller to access Endpoints in the mundiapi API."""
def update_charge_card(self,
charge_id,
request,
idempotency_key=None):
"""Does a PATCH request to /charges/{charge_id}/card.
Updates the card from a charge
Args:
charge_id (string): Charge id
request (UpdateChargeCardRequest): Request for updating a charge's
card
idempotency_key (string, optional): TODO: type description here.
Example:
Returns:
GetChargeResponse: Response from the API.
Raises:
APIException: When an error occurs while fetching the data from
the remote API. This exception includes the HTTP Response
code, an error message, and the HTTP body that was received in
the request.
"""
# Prepare query URL
_url_path = '/charges/{charge_id}/card'
_url_path = APIHelper.append_url_with_template_parameters(_url_path, {
'charge_id': charge_id
})
_query_builder = Configuration.base_uri
_query_builder += _url_path
_query_url = APIHelper.clean_url(_query_builder)
# Prepare headers
_headers = {
'accept': 'application/json',
'content-type': 'application/json; charset=utf-8',
'idempotency-key': idempotency_key
}
# Prepare and execute request
_request = self.http_client.patch(_query_url, headers=_headers, parameters=APIHelper.json_serialize(request))
BasicAuth.apply(_request)
_context = self.execute_request(_request)
self.validate_response(_context)
# Return appropriate type
return APIHelper.json_deserialize(_context.response.raw_body, GetChargeResponse.from_dictionary)
def update_charge_payment_method(self,
charge_id,
request,
idempotency_key=None):
"""Does a PATCH request to /charges/{charge_id}/payment-method.
Updates a charge's payment method
Args:
charge_id (string): Charge id
request (UpdateChargePaymentMethodRequest): Request for updating
the payment method from a charge
idempotency_key (string, optional): TODO: type description here.
Example:
Returns:
GetChargeResponse: Response from the API.
Raises:
APIException: When an error occurs while fetching the data from
the remote API. This exception includes the HTTP Response
code, an error message, and the HTTP body that was received in
the request.
"""
# Prepare query URL
_url_path = '/charges/{charge_id}/payment-method'
_url_path = APIHelper.append_url_with_template_parameters(_url_path, {
'charge_id': charge_id
})
_query_builder = Configuration.base_uri
_query_builder += _url_path
_query_url = APIHelper.clean_url(_query_builder)
# Prepare headers
_headers = {
'accept': 'application/json',
'content-type': 'application/json; charset=utf-8',
'idempotency-key': idempotency_key
}
# Prepare and execute request
_request = self.http_client.patch(_query_url, headers=_headers, parameters=APIHelper.json_serialize(request))
BasicAuth.apply(_request)
_context = self.execute_request(_request)
self.validate_response(_context)
# Return appropriate type
return APIHelper.json_deserialize(_context.response.raw_body, GetChargeResponse.from_dictionary)
def create_charge(self,
request,
idempotency_key=None):
"""Does a POST request to /Charges.
Creates a new charge
Args:
request (CreateChargeRequest): Request for creating a charge
idempotency_key (string, optional): TODO: type description here.
Example:
Returns:
GetChargeResponse: Response from the API.
Raises:
APIException: When an error occurs while fetching the data from
the remote API. This exception includes the HTTP Response
code, an error message, and the HTTP body that was received in
the request.
"""
# Prepare query URL
_url_path = '/Charges'
_query_builder = Configuration.base_uri
_query_builder += _url_path
_query_url = APIHelper.clean_url(_query_builder)
# Prepare headers
_headers = {
'accept': 'application/json',
'content-type': 'application/json; charset=utf-8',
'idempotency-key': idempotency_key
}
# Prepare and execute request
_request = self.http_client.post(_query_url, headers=_headers, parameters=APIHelper.json_serialize(request))
BasicAuth.apply(_request)
_context = self.execute_request(_request)
self.validate_response(_context)
# Return appropriate type
return APIHelper.json_deserialize(_context.response.raw_body, GetChargeResponse.from_dictionary)
def get_charge(self,
charge_id):
"""Does a GET request to /charges/{charge_id}.
Get a charge from its id
Args:
charge_id (string): Charge id
Returns:
GetChargeResponse: Response from the API.
Raises:
APIException: When an error occurs while fetching the data from
the remote API. This exception includes the HTTP Response
code, an error message, and the HTTP body that was received in
the request.
"""
# Prepare query URL
_url_path = '/charges/{charge_id}'
_url_path = APIHelper.append_url_with_template_parameters(_url_path, {
'charge_id': charge_id
})
_query_builder = Configuration.base_uri
_query_builder += _url_path
_query_url = APIHelper.clean_url(_query_builder)
# Prepare headers
_headers = {
'accept': 'application/json'
}
# Prepare and execute request
_request = self.http_client.get(_query_url, headers=_headers)
BasicAuth.apply(_request)
_context = self.execute_request(_request)
self.validate_response(_context)
# Return appropriate type
return APIHelper.json_deserialize(_context.response.raw_body, GetChargeResponse.from_dictionary)
def retry_charge(self,
charge_id,
idempotency_key=None):
"""Does a POST request to /charges/{charge_id}/retry.
Retries a charge
Args:
charge_id (string): Charge id
idempotency_key (string, optional): TODO: type description here.
Example:
Returns:
GetChargeResponse: Response from the API.
Raises:
APIException: When an error occurs while fetching the data from
the remote API. This exception includes the HTTP Response
code, an error message, and the HTTP body that was received in
the request.
"""
# Prepare query URL
_url_path = '/charges/{charge_id}/retry'
_url_path = APIHelper.append_url_with_template_parameters(_url_path, {
'charge_id': charge_id
})
_query_builder = Configuration.base_uri
_query_builder += _url_path
_query_url = APIHelper.clean_url(_query_builder)
# Prepare headers
_headers = {
'accept': 'application/json',
'idempotency-key': idempotency_key
}
# Prepare and execute request
_request = self.http_client.post(_query_url, headers=_headers)
BasicAuth.apply(_request)
_context = self.execute_request(_request)
self.validate_response(_context)
# Return appropriate type
return APIHelper.json_deserialize(_context.response.raw_body, GetChargeResponse.from_dictionary)
def get_charges(self,
page=None,
size=None,
code=None,
status=None,
payment_method=None,
customer_id=None,
order_id=None,
created_since=None,
created_until=None):
"""Does a GET request to /charges.
Lists all charges
Args:
page (int, optional): Page number
size (int, optional): Page size
code (string, optional): Filter for charge's code
status (string, optional): Filter for charge's status
payment_method (string, optional): Filter for charge's payment
method
customer_id (string, optional): Filter for charge's customer id
order_id (string, optional): Filter for charge's order id
created_since (datetime, optional): Filter for the beginning of
the range for charge's creation
created_until (datetime, optional): Filter for the end of the
range for charge's creation
Returns:
ListChargesResponse: Response from the API.
Raises:
APIException: When an error occurs while fetching the data from
the remote API. This exception includes the HTTP Response
code, an error message, and the HTTP body that was received in
the request.
"""
# Prepare query URL
_url_path = '/charges'
_query_builder = Configuration.base_uri
_query_builder += _url_path
_query_parameters = {
'page': page,
'size': size,
'code': code,
'status': status,
'payment_method': payment_method,
'customer_id': customer_id,
'order_id': order_id,
'created_since': APIHelper.when_defined(APIHelper.RFC3339DateTime, created_since),
'created_until': APIHelper.when_defined(APIHelper.RFC3339DateTime, created_until)
}
_query_builder = APIHelper.append_url_with_query_parameters(_query_builder,
_query_parameters, Configuration.array_serialization)
_query_url = APIHelper.clean_url(_query_builder)
# Prepare headers
_headers = {
'accept': 'application/json'
}
# Prepare and execute request
_request = self.http_client.get(_query_url, headers=_headers)
BasicAuth.apply(_request)
_context = self.execute_request(_request)
self.validate_response(_context)
# Return appropriate type
return APIHelper.json_deserialize(_context.response.raw_body, ListChargesResponse.from_dictionary)
def update_charge_metadata(self,
charge_id,
request,
idempotency_key=None):
"""Does a PATCH request to /Charges/{charge_id}/metadata.
Updates the metadata from a charge
Args:
charge_id (string): The charge id
request (UpdateMetadataRequest): Request for updating the charge
metadata
idempotency_key (string, optional): TODO: type description here.
Example:
Returns:
GetChargeResponse: Response from the API.
Raises:
APIException: When an error occurs while fetching the data from
the remote API. This exception includes the HTTP Response
code, an error message, and the HTTP body that was received in
the request.
"""
# Prepare query URL
_url_path = '/Charges/{charge_id}/metadata'
_url_path = APIHelper.append_url_with_template_parameters(_url_path, {
'charge_id': charge_id
})
_query_builder = Configuration.base_uri
_query_builder += _url_path
_query_url = APIHelper.clean_url(_query_builder)
# Prepare headers
_headers = {
'accept': 'application/json',
'content-type': 'application/json; charset=utf-8',
'idempotency-key': idempotency_key
}
# Prepare and execute request
_request = self.http_client.patch(_query_url, headers=_headers, parameters=APIHelper.json_serialize(request))
BasicAuth.apply(_request)
_context = self.execute_request(_request)
self.validate_response(_context)
# Return appropriate type
return APIHelper.json_deserialize(_context.response.raw_body, GetChargeResponse.from_dictionary)
def cancel_charge(self,
charge_id,
request=None,
idempotency_key=None):
"""Does a DELETE request to /charges/{charge_id}.
Cancel a charge
Args:
charge_id (string): Charge id
request (CreateCancelChargeRequest, optional): Request for
cancelling a charge
idempotency_key (string, optional): TODO: type description here.
Example:
Returns:
GetChargeResponse: Response from the API.
Raises:
APIException: When an error occurs while fetching the data from
the remote API. This exception includes the HTTP Response
code, an error message, and the HTTP body that was received in
the request.
"""
# Prepare query URL
_url_path = '/charges/{charge_id}'
_url_path = APIHelper.append_url_with_template_parameters(_url_path, {
'charge_id': charge_id
})
_query_builder = Configuration.base_uri
_query_builder += _url_path
_query_url = APIHelper.clean_url(_query_builder)
# Prepare headers
_headers = {
'accept': 'application/json',
'content-type': 'application/json; charset=utf-8',
'idempotency-key': idempotency_key
}
# Prepare and execute request
_request = self.http_client.delete(_query_url, headers=_headers, parameters=APIHelper.json_serialize(request))
BasicAuth.apply(_request)
_context = self.execute_request(_request)
self.validate_response(_context)
# Return appropriate type
return APIHelper.json_deserialize(_context.response.raw_body, GetChargeResponse.from_dictionary)
def capture_charge(self,
charge_id,
request=None,
idempotency_key=None):
"""Does a POST request to /charges/{charge_id}/capture.
Captures a charge
Args:
charge_id (string): Charge id
request (CreateCaptureChargeRequest, optional): Request for
capturing a charge
idempotency_key (string, optional): TODO: type description here.
Example:
Returns:
GetChargeResponse: Response from the API.
Raises:
APIException: When an error occurs while fetching the data from
the remote API. This exception includes the HTTP Response
code, an error message, and the HTTP body that was received in
the request.
"""
# Prepare query URL
_url_path = '/charges/{charge_id}/capture'
_url_path = APIHelper.append_url_with_template_parameters(_url_path, {
'charge_id': charge_id
})
_query_builder = Configuration.base_uri
_query_builder += _url_path
_query_url = APIHelper.clean_url(_query_builder)
# Prepare headers
_headers = {
'accept': 'application/json',
'content-type': 'application/json; charset=utf-8',
'idempotency-key': idempotency_key
}
# Prepare and execute request
_request = self.http_client.post(_query_url, headers=_headers, parameters=APIHelper.json_serialize(request))
BasicAuth.apply(_request)
_context = self.execute_request(_request)
self.validate_response(_context)
# Return appropriate type
return APIHelper.json_deserialize(_context.response.raw_body, GetChargeResponse.from_dictionary)
def update_charge_due_date(self,
charge_id,
request,
idempotency_key=None):
"""Does a PATCH request to /Charges/{charge_id}/due-date.
Updates the due date from a charge
Args:
charge_id (string): Charge Id
request (UpdateChargeDueDateRequest): Request for updating the due
date
idempotency_key (string, optional): TODO: type description here.
Example:
Returns:
GetChargeResponse: Response from the API.
Raises:
APIException: When an error occurs while fetching the data from
the remote API. This exception includes the HTTP Response
code, an error message, and the HTTP body that was received in
the request.
"""
# Prepare query URL
_url_path = '/Charges/{charge_id}/due-date'
_url_path = APIHelper.append_url_with_template_parameters(_url_path, {
'charge_id': charge_id
})
_query_builder = Configuration.base_uri
_query_builder += _url_path
_query_url = APIHelper.clean_url(_query_builder)
# Prepare headers
_headers = {
'accept': 'application/json',
'content-type': 'application/json; charset=utf-8',
'idempotency-key': idempotency_key
}
# Prepare and execute request
_request = self.http_client.patch(_query_url, headers=_headers, parameters=APIHelper.json_serialize(request))
BasicAuth.apply(_request)
_context = self.execute_request(_request)
self.validate_response(_context)
# Return appropriate type
return APIHelper.json_deserialize(_context.response.raw_body, GetChargeResponse.from_dictionary)
def confirm_payment(self,
charge_id,
request=None,
idempotency_key=None):
"""Does a POST request to /charges/{charge_id}/confirm-payment.
TODO: type endpoint description here.
Args:
charge_id (string): TODO: type description here. Example:
request (CreateConfirmPaymentRequest, optional): Request for
confirm payment
idempotency_key (string, optional): TODO: type description here.
Example:
Returns:
GetChargeResponse: Response from the API.
Raises:
APIException: When an error occurs while fetching the data from
the remote API. This exception includes the HTTP Response
code, an error message, and the HTTP body that was received in
the request.
"""
# Prepare query URL
_url_path = '/charges/{charge_id}/confirm-payment'
_url_path = APIHelper.append_url_with_template_parameters(_url_path, {
'charge_id': charge_id
})
_query_builder = Configuration.base_uri
_query_builder += _url_path
_query_url = APIHelper.clean_url(_query_builder)
# Prepare headers
_headers = {
'accept': 'application/json',
'content-type': 'application/json; charset=utf-8',
'idempotency-key': idempotency_key
}
# Prepare and execute request
_request = self.http_client.post(_query_url, headers=_headers, parameters=APIHelper.json_serialize(request))
BasicAuth.apply(_request)
_context = self.execute_request(_request)
self.validate_response(_context)
# Return appropriate type
return APIHelper.json_deserialize(_context.response.raw_body, GetChargeResponse.from_dictionary)
def get_charge_transactions(self,
charge_id,
page=None,
size=None):
"""Does a GET request to /charges/{charge_id}/transactions.
TODO: type endpoint description here.
Args:
charge_id (string): Charge Id
page (int, optional): Page number
size (int, optional): Page size
Returns:
ListChargeTransactionsResponse: Response from the API.
Raises:
APIException: When an error occurs while fetching the data from
the remote API. This exception includes the HTTP Response
code, an error message, and the HTTP body that was received in
the request.
"""
# Prepare query URL
_url_path = '/charges/{charge_id}/transactions'
_url_path = APIHelper.append_url_with_template_parameters(_url_path, {
'charge_id': charge_id
})
_query_builder = Configuration.base_uri
_query_builder += _url_path
_query_parameters = {
'page': page,
'size': size
}
_query_builder = APIHelper.append_url_with_query_parameters(_query_builder,
_query_parameters, Configuration.array_serialization)
_query_url = APIHelper.clean_url(_query_builder)
# Prepare headers
_headers = {
'accept': 'application/json'
}
# Prepare and execute request
_request = self.http_client.get(_query_url, headers=_headers)
BasicAuth.apply(_request)
_context = self.execute_request(_request)
self.validate_response(_context)
# Return appropriate type
return APIHelper.json_deserialize(_context.response.raw_body, ListChargeTransactionsResponse.from_dictionary)
def get_charges_summary(self,
status,
created_since=None,
created_until=None):
"""Does a GET request to /charges/summary.
TODO: type endpoint description here.
Args:
status (string): TODO: type description here. Example:
created_since (datetime, optional): TODO: type description here.
Example:
created_until (datetime, optional): TODO: type description here.
Example:
Returns:
GetChargesSummaryResponse: Response from the API.
Raises:
APIException: When an error occurs while fetching the data from
the remote API. This exception includes the HTTP Response
code, an error message, and the HTTP body that was received in
the request.
"""
# Prepare query URL
_url_path = '/charges/summary'
_query_builder = Configuration.base_uri
_query_builder += _url_path
_query_parameters = {
'status': status,
'created_since': APIHelper.when_defined(APIHelper.RFC3339DateTime, created_since),
'created_until': APIHelper.when_defined(APIHelper.RFC3339DateTime, created_until)
}
_query_builder = APIHelper.append_url_with_query_parameters(_query_builder,
_query_parameters, Configuration.array_serialization)
_query_url = APIHelper.clean_url(_query_builder)
# Prepare headers
_headers = {
'accept': 'application/json'
}
# Prepare and execute request
_request = self.http_client.get(_query_url, headers=_headers)
BasicAuth.apply(_request)
_context = self.execute_request(_request)
self.validate_response(_context)
# Return appropriate type
return APIHelper.json_deserialize(_context.response.raw_body, GetChargesSummaryResponse.from_dictionary)
| 37.310694 | 119 | 0.596963 | 2,555 | 25,819 | 5.770254 | 0.066928 | 0.037442 | 0.037035 | 0.044089 | 0.864749 | 0.850438 | 0.833752 | 0.81442 | 0.807841 | 0.792851 | 0 | 0.001579 | 0.337736 | 25,819 | 691 | 120 | 37.364689 | 0.860635 | 0.347922 | 0 | 0.763699 | 1 | 0 | 0.088958 | 0.016684 | 0 | 0 | 0 | 0.023155 | 0 | 1 | 0.044521 | false | 0 | 0.027397 | 0 | 0.119863 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
f63914e5dcf42ac04eecaa001e0866eb56240191 | 13,179 | py | Python | response.py | salimsuprayogi/test_automation_whatsapp | 8b980fdb0e8e30a9b253742a704f1ecbc3cc4a34 | [
"MIT"
] | 2 | 2020-10-28T13:33:38.000Z | 2022-03-27T17:53:01.000Z | response.py | salimsuprayogi/test_automation_whatsapp | 8b980fdb0e8e30a9b253742a704f1ecbc3cc4a34 | [
"MIT"
] | null | null | null | response.py | salimsuprayogi/test_automation_whatsapp | 8b980fdb0e8e30a9b253742a704f1ecbc3cc4a34 | [
"MIT"
] | null | null | null | #!python3
# -*- coding: utf-8 -*-
# automationpy/response
# salim suprayogi
# created 21 Oktober 2020
import time
def waiting(driver, class_name="z_tTQ", content="_2hqOq", msgs="hello", message_content="_3Whw5"):
# function for waiting reply chat
print("````1 stoper````")
send_msgs = msgs
stoper = True
while stoper:
try:
len_last_chat = driver.find_element_by_class_name(class_name)
elem_last_chat = len_last_chat.find_elements_by_class_name(
content)[-1]
elem_next_text = elem_last_chat.find_elements_by_class_name(
message_content)
# log
# len_msg_content = len(elem_next_text)
# print("len_msg_content:", len_msg_content)
reply = []
for i, val in enumerate(elem_next_text):
time.sleep(0.5)
text_list = val.text
reply.append(text_list)
get_reply = "\n".join(reply)
# log
# print("elem_text:", get_reply)
# print("profile:", send_msgs)
try:
if get_reply.lower().strip() == send_msgs.lower().strip():
# print("Please wait....!!!")
pass
elif get_reply.lower().strip() == "":
# print("Please wait....!!!")
pass
else:
if get_reply.lower().strip() == "menu":
stoper = False
else:
stoper = False
except:
pass
except:
pass
print("````2 stoper````")
def get_reply_chat(driver, class_name="z_tTQ", content="_2hqOq", messages="hai", message_content="_3Whw5"):
# function get reply chat
message = messages
print("$ 1 Retrieve Text Reply Chat")
# if there is 1 chat reply
reply = []
try:
len_last_second = driver.find_element_by_class_name(class_name)
elem_last_second = len_last_second.find_elements_by_class_name(
content)[-2]
elem_second_text = elem_last_second.find_elements_by_class_name(
message_content)
# log
# len_msg_content = len(elem_second_text)
# print("len_msg_content:", len_msg_content)
reply_text = []
for i, val in enumerate(elem_second_text):
time.sleep(0.5)
text_list = val.text
reply_text.append(text_list)
get_reply_text = "\n".join(reply_text)
# jika elemen kedua terakhir sama dengan text yang dikirim
# if last second element equals the sent text
if get_reply_text.lower().strip() == message.lower().strip():
# if there is 1 chat reply
try:
time.sleep(0.5)
first_text = driver.find_element_by_class_name(class_name)
elem_last_first = first_text.find_elements_by_class_name(
content)[-1]
elem_first_text = elem_last_first.find_elements_by_class_name(
message_content)
# log
# len_msg_content = len(elem_first_text)
# print("len_msg_content:", len_msg_content)
# reply = []
for i, val in enumerate(elem_first_text):
time.sleep(0.5)
text_list = val.text
reply.append(text_list)
# log
# print("text_data {} :".format(i), text_list)
# log
# print(reply)
# get_reply = "\n".join(reply)
# print("Balasan Chat hanya satu:\n---\n",
# get_reply.strip(), "\n---")
print("+1 One chat reply")
except:
pass
else:
pass
except:
pass
# if there is 2 chat reply
try:
len_last_third = driver.find_element_by_class_name(class_name)
elem_last_third = len_last_third.find_elements_by_class_name(
content)[-3]
elem_last_third = elem_last_third.find_elements_by_class_name(
message_content)
# log
# len_msg_content = len(elem_last_third)
# print("len_msg_content:", len_msg_content)
reply_text = []
for i, val in enumerate(elem_last_third):
time.sleep(0.5)
text_list = val.text
reply_text.append(text_list)
get_reply_text = "\n".join(reply_text)
# jika elemen ketiga terakhir sama dengan text yang dikirim
if get_reply_text.lower().strip() == message.lower().strip():
try:
# reply = []
loop = -2 # -2,-1 < 0
while loop < 0:
try:
time.sleep(0.5)
first_text = driver.find_element_by_class_name(
class_name)
elem_last_first = first_text.find_elements_by_class_name(content)[
loop]
elem_first_text = elem_last_first.find_elements_by_class_name(
message_content)
# log
# len_msg_content = len(elem_first_text)
# print("len_msg_content:", len_msg_content)
for i, val in enumerate(elem_first_text):
time.sleep(0.5)
text_list = val.text
reply.append(text_list)
# log
# print("text_data {} :".format(i), text_list)
loop += 1
# jika 0 < 0 = Flase [stop]
except:
pass
# log
# print(reply)
# get_reply = "\n".join(reply)
# print("Balasan Chat Ada 2:\n---\n",
# get_reply.strip(), "\n---")
print("+2 Two chat reply")
except:
pass
else:
pass
except:
pass
# if there is 3 chat reply
try:
len_last_third = driver.find_element_by_class_name(class_name)
elem_third_text = len_last_third.find_elements_by_class_name(
content)[-4]
elem_last_third = elem_third_text.find_elements_by_class_name(
message_content)
# len_msg_content = len(elem_last_third)
# print("len_msg_content:", len_msg_content)
reply_text = []
for i, val in enumerate(elem_last_third):
time.sleep(0.5)
text_list = val.text
reply_text.append(text_list)
get_reply_text = "\n".join(reply_text)
# jika elemen keempat terakhir sama dengan text yang dikirim
if get_reply_text.lower().strip() == message.lower().strip():
try:
# reply = []
loop = -3 # -2,-1 < 0
while loop < 0:
try:
time.sleep(0.5)
first_text = driver.find_element_by_class_name(
class_name)
elem_last_first = first_text.find_elements_by_class_name(content)[
loop]
elem_first_text = elem_last_first.find_elements_by_class_name(
message_content)
# log
# len_msg_content = len(elem_first_text)
# print("len_msg_content:", len_msg_content)
for i, val in enumerate(elem_first_text):
time.sleep(0.5)
text_list = val.text
reply.append(text_list)
# log
# print("text_data {} :".format(i), text_list)
loop += 1
# jika 0 < 0 = Flase [stop]
except:
pass
# log
# print(reply)
# get_reply = "\n".join(reply)
# print("Balasan Chat Ada 3:\n---\n",
# get_reply.strip(), "\n---")
print("+3 Three chat reply")
except:
pass
else:
pass
except:
pass
# if there is 4 chat reply
try:
len_last_forth = driver.find_element_by_class_name(class_name)
elem_forth_text = len_last_forth.find_elements_by_class_name(
content)[-5]
elem_last_forth = elem_forth_text.find_elements_by_class_name(
message_content)
len_msg_content = len(elem_last_forth)
# print("len_msg_content:", len_msg_content)
reply_text = []
for i, val in enumerate(elem_last_forth):
time.sleep(0.5)
text_list = val.text
reply_text.append(text_list)
get_reply_text = "\n".join(reply_text)
# jika elemen keempat terakhir sama dengan text yang dikirim
if get_reply_text.lower().strip() == message.lower().strip():
try:
# reply = []
loop = -4 # -2,-1 < 0
while loop < 0:
try:
time.sleep(0.5)
first_text = driver.find_element_by_class_name(
class_name)
elem_last_first = first_text.find_elements_by_class_name(content)[
loop]
elem_first_text = elem_last_first.find_elements_by_class_name(
message_content)
# log
# len_msg_content = len(elem_first_text)
# print("len_msg_content:", len_msg_content)
for i, val in enumerate(elem_first_text):
time.sleep(0.5)
text_list = val.text
reply.append(text_list)
# log
# print("text_data {} :".format(i), text_list)
loop += 1
# jika 0 < 0 = Flase [stop]
except:
pass
# log
# print(reply)
# get_reply = "\n".join(reply)
# print("Balasan Chat Ada 4:\n---\n",
# get_reply.strip(), "\n---")
print("+4 Four chat reply")
except:
pass
else:
pass
except:
pass
# if there is 5 chat reply
try:
len_last_five = driver.find_element_by_class_name(class_name)
elem_five_text = len_last_five.find_elements_by_class_name(content)[-5]
elem_last_five = elem_five_text.find_elements_by_class_name(
message_content)
len_msg_content = len(elem_last_five)
# print("len_msg_content:", len_msg_content)
reply_text = []
for i, val in enumerate(elem_last_five):
time.sleep(0.5)
text_list = val.text
reply_text.append(text_list)
get_reply_text = "\n".join(reply_text)
# jika elemen keempat terakhir sama dengan text yang dikirim
if get_reply_text.lower().strip() == message.lower().strip():
try:
# reply = []
loop = -5 # -2,-1 < 0
while loop < 0:
try:
time.sleep(0.5)
first_text = driver.find_element_by_class_name(
class_name)
elem_last_first = first_text.find_elements_by_class_name(content)[
loop]
elem_first_text = elem_last_first.find_elements_by_class_name(
message_content)
# log
# len_msg_content = len(elem_first_text)
# print("len_msg_content:", len_msg_content)
for i, val in enumerate(elem_first_text):
time.sleep(0.5)
text_list = val.text
reply.append(text_list)
# log
# print("text_data {} :".format(i), text_list)
loop += 1
# jika 0 < 0 = Flase [stop]
except:
pass
# log
# print(reply)
# get_reply = "\n".join(reply)
# print("Balasan Chat Ada 5:\n---\n",
# get_reply.strip(), "\n---")
print("+5 Five chat reply")
except:
pass
else:
pass
except:
pass
# log
# print(reply)
print("$ 2 Retrieve Text Reply Chat")
return reply
| 40.179878 | 107 | 0.479399 | 1,402 | 13,179 | 4.17689 | 0.080599 | 0.070697 | 0.061988 | 0.07138 | 0.861851 | 0.843238 | 0.825649 | 0.779542 | 0.767077 | 0.717725 | 0 | 0.0139 | 0.432279 | 13,179 | 327 | 108 | 40.302752 | 0.768778 | 0.201533 | 0 | 0.762332 | 0 | 0 | 0.022551 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.008969 | false | 0.103139 | 0.004484 | 0 | 0.017937 | 0.040359 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 8 |
9c919ac9c4641f1da907002a7d6ab68920775072 | 760 | py | Python | saleor/seo/models.py | ruszhov/saleor-shop | df0c27c61055a0f20eb27fe2aa5082970397f553 | [
"BSD-3-Clause"
] | null | null | null | saleor/seo/models.py | ruszhov/saleor-shop | df0c27c61055a0f20eb27fe2aa5082970397f553 | [
"BSD-3-Clause"
] | 5 | 2021-03-09T16:22:37.000Z | 2022-02-10T19:10:03.000Z | saleor/seo/models.py | ruszhov/saleor-shop | df0c27c61055a0f20eb27fe2aa5082970397f553 | [
"BSD-3-Clause"
] | null | null | null | from django.core.validators import MaxLengthValidator
from django.db import models
class SeoModel(models.Model):
seo_title = models.CharField(
max_length=100, blank=True, null=True,
validators=[MaxLengthValidator(100)])
seo_description = models.CharField(
max_length=600, blank=True, null=True,
validators=[MaxLengthValidator(600)])
class Meta:
abstract = True
class SeoModelTranslation(models.Model):
seo_title = models.CharField(
max_length=100, blank=True, null=True,
validators=[MaxLengthValidator(100)])
seo_description = models.CharField(
max_length=600, blank=True, null=True,
validators=[MaxLengthValidator(600)])
class Meta:
abstract = True
| 28.148148 | 53 | 0.690789 | 83 | 760 | 6.228916 | 0.301205 | 0.116054 | 0.139265 | 0.185687 | 0.789168 | 0.789168 | 0.789168 | 0.789168 | 0.789168 | 0.789168 | 0 | 0.04 | 0.210526 | 760 | 26 | 54 | 29.230769 | 0.821667 | 0 | 0 | 0.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.1 | 0 | 0.5 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
140e65ca442bb7bc0d3a942baac4dd3fcf3a2010 | 157 | py | Python | partI_basics/Chapter2_variables_and_simple_data_types/Apostrophe.py | hao-beixi/PythonCrashCourse | 194736bac3c22976d7e3fbdc8ea1f13fd30e9879 | [
"MIT"
] | null | null | null | partI_basics/Chapter2_variables_and_simple_data_types/Apostrophe.py | hao-beixi/PythonCrashCourse | 194736bac3c22976d7e3fbdc8ea1f13fd30e9879 | [
"MIT"
] | null | null | null | partI_basics/Chapter2_variables_and_simple_data_types/Apostrophe.py | hao-beixi/PythonCrashCourse | 194736bac3c22976d7e3fbdc8ea1f13fd30e9879 | [
"MIT"
] | null | null | null | message = "one of Python's strengths is its diverse community."
print(message)
message ='one of Python's strengths is its diverse community.'
print(message) | 31.4 | 63 | 0.77707 | 24 | 157 | 5.083333 | 0.458333 | 0.163934 | 0.196721 | 0.295082 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0.133758 | 157 | 5 | 64 | 31.4 | 0.897059 | 0 | 0 | 0.5 | 0 | 0 | 0.405063 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0 | null | null | 0.5 | 1 | 0 | 0 | null | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 12 |
14202f579260ccd5e6e4034029b8bd86c8254343 | 23,388 | py | Python | tests/test_convolution.py | JulianPL/Non-Rectangular_Convolution | 383e217b6559c486557545a1a7b29294ed39c01b | [
"MIT"
] | null | null | null | tests/test_convolution.py | JulianPL/Non-Rectangular_Convolution | 383e217b6559c486557545a1a7b29294ed39c01b | [
"MIT"
] | null | null | null | tests/test_convolution.py | JulianPL/Non-Rectangular_Convolution | 383e217b6559c486557545a1a7b29294ed39c01b | [
"MIT"
] | null | null | null | #!/usr/bin/python3
from fractions import Fraction
import unittest
import nrconv
import sympy
class TestEdgeCase(unittest.TestCase):
def test_non_rectangular_convolution_edge_diagonal1(self):
list1 = [1, 1, 1, 1, 1, 1, 1, 1]
list2 = [1, 1, 1, 1, 1, 1, 1, 1]
geometry = [(0, 0), (7, 7)]
prime = nrconv.create_ntt_prime(list1, list2)
result, _ = nrconv.convolution.non_rectangular_convolution_edge(
list1, list2, geometry, prime)
want = [1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1]
self.assertEqual(result, want)
def test_non_rectangular_convolution_edge_diagonal2(self):
list1 = [1, 1, 1, 1, 1, 1, 1, 1]
list2 = [1, 1, 1, 1, 1, 1, 1, 1]
geometry = [(Fraction(0, 1), Fraction(7, 1)),
(Fraction(7, 1), Fraction(0, 1))]
prime = nrconv.create_ntt_prime(list1, list2)
result, _ = nrconv.convolution.non_rectangular_convolution_edge(
list1, list2, geometry, prime)
want = [0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0]
self.assertEqual(result, want)
def test_non_rectangular_convolution_edge_halfdiagonal1(self):
list1 = [1, 2, 3, 4, 5, 6, 7, 8]
list2 = [1, 1, 1, 1, 1, 1, 1, 1]
geometry = [(Fraction(0, 1), Fraction(0, 1)),
(Fraction(7, 1), Fraction(7, 2))]
prime = nrconv.create_ntt_prime(list1, list2)
result, _ = nrconv.convolution.non_rectangular_convolution_edge(
list1, list2, geometry, prime)
want = [1, 0, 0, 3, 0, 0, 5, 0, 0, 7, 0]
self.assertEqual(result, want)
def test_non_rectangular_convolution_edge_halfdiagonal2(self):
list1 = [1, 2, 3, 4, 5, 6, 7, 8]
list2 = [1, 1, 1, 1, 1, 1, 1, 1]
geometry = [(Fraction(7, 2), Fraction(7, 1)),
(Fraction(0, 1), Fraction(0, 1))]
prime = nrconv.create_ntt_prime(list1, list2)
result, _ = nrconv.convolution.non_rectangular_convolution_edge(
list1, list2, geometry, prime)
want = [1, 0, 0, 2, 0, 0, 3, 0, 0, 4, 0]
self.assertEqual(result, want)
def test_non_rectangular_convolution_edge_vertical(self):
list1 = [1, 2, 3, 4, 5, 6, 7, 8]
list2 = [1, 1, 1, 1, 1, 1, 1, 1]
geometry = [(Fraction(2, 1), Fraction(0, 1)),
(Fraction(2, 1), Fraction(7, 1))]
prime = nrconv.create_ntt_prime(list1, list2)
result, _ = nrconv.convolution.non_rectangular_convolution_edge(
list1, list2, geometry, prime)
want = [3, 3, 3, 3, 3, 3, 3, 3]
self.assertEqual(result, want)
def test_non_rectangular_convolution_edge_offset(self):
list1 = [1, 2, 3, 4, 5, 6, 7, 8]
list2 = [1, 1, 1, 1, 1, 1, 1, 1]
geometry = [(Fraction(1, 1), Fraction(7, 2)),
(Fraction(7, 1), Fraction(1, 2))]
prime = nrconv.create_ntt_prime(list1, list2)
_, result = nrconv.convolution.non_rectangular_convolution_edge(
list1, list2, geometry, prime)
want = 2
self.assertEqual(result, want)
class TestRectangleCase(unittest.TestCase):
def test_non_rectangular_convolution_rectangle_full(self):
list1 = [1, 1, 1, 1, 1, 1, 1, 1]
list2 = [1, 1, 1, 1, 1, 1, 1, 1]
geometry = [(0, 0), (7, 7)]
prime = nrconv.create_ntt_prime(list1, list2)
result, _ = nrconv.convolution.non_rectangular_convolution_rectangle(
list1, list2, geometry, prime)
want = [1, 2, 3, 4, 5, 6, 7, 8, 7, 6, 5, 4, 3, 2, 1]
self.assertEqual(result, want)
def test_non_rectangular_convolution_rectangle_int_part(self):
list1 = [1, 1, 1, 1, 1, 1, 1, 1]
list2 = [1, 1, 1, 1, 1, 1, 1, 1]
geometry = [(1, 2), (4, 6)]
prime = nrconv.create_ntt_prime(list1, list2)
result, _ = nrconv.convolution.non_rectangular_convolution_rectangle(
list1, list2, geometry, prime)
want = [1, 2, 3, 4, 4, 3, 2, 1]
self.assertEqual(result, want)
def test_non_rectangular_convolution_rectangle_frac_part(self):
list1 = [1, 1, 1, 1, 1, 1, 1, 1]
list2 = [1, 1, 1, 1, 1, 1, 1, 1]
geometry = [(Fraction(3, 4), Fraction(13, 2)),
(Fraction(13, 3), Fraction(5, 3))]
prime = nrconv.create_ntt_prime(list1, list2)
result, _ = nrconv.convolution.non_rectangular_convolution_rectangle(
list1, list2, geometry, prime)
want = [1, 2, 3, 4, 4, 3, 2, 1]
self.assertEqual(result, want)
def test_non_rectangular_convolution_rectangle_int_dot(self):
list1 = [1, 1, 3, 1, 1, 1, 1, 1]
list2 = [1, 1, 1, 1, 1, 7, 1, 1]
geometry = [(2, 5), (2, 5)]
prime = nrconv.create_ntt_prime(list1, list2)
result, _ = nrconv.convolution.non_rectangular_convolution_rectangle(
list1, list2, geometry, prime)
want = [21]
self.assertEqual(result, want)
def test_non_rectangular_convolution_rectangle_intfrac_dot(self):
list1 = [1, 1, 3, 1, 1, 1, 1, 1]
list2 = [1, 1, 1, 1, 1, 7, 1, 1]
geometry = [(Fraction(6, 3), Fraction(5, 1)),
(Fraction(6, 3), Fraction(5, 1))]
prime = nrconv.create_ntt_prime(list1, list2)
result, _ = nrconv.convolution.non_rectangular_convolution_rectangle(
list1, list2, geometry, prime)
want = [21]
self.assertEqual(result, want)
def test_non_rectangular_convolution_rectangle_frac_dot(self):
list1 = [1, 1, 3, 1, 1, 1, 1, 1]
list2 = [1, 1, 1, 1, 1, 7, 1, 1]
geometry = [(Fraction(3, 2), Fraction(17, 4)),
(Fraction(5, 2), Fraction(16, 3))]
prime = nrconv.create_ntt_prime(list1, list2)
result, _ = nrconv.convolution.non_rectangular_convolution_rectangle(
list1, list2, geometry, prime)
want = [21]
self.assertEqual(result, want)
def test_non_rectangular_convolution_rectangle_frac_empty1(self):
list1 = [1, 1, 3, 1, 1, 1, 1, 1]
list2 = [1, 1, 1, 1, 1, 7, 1, 1]
geometry = [(Fraction(4, 3), Fraction(17, 8)),
(Fraction(5, 3), Fraction(16, 3))]
prime = nrconv.create_ntt_prime(list1, list2)
result, _ = nrconv.convolution.non_rectangular_convolution_rectangle(
list1, list2, geometry, prime)
want = []
self.assertEqual(result, want)
self.assertEqual(result, want)
def test_non_rectangular_convolution_rectangle_frac_empty2(self):
list1 = [1, 1, 3, 1, 1, 1, 1, 1]
list2 = [1, 1, 1, 1, 1, 7, 1, 1]
geometry = [(Fraction(17, 8), Fraction(4, 3)),
(Fraction(16, 3), Fraction(5, 3))]
prime = nrconv.create_ntt_prime(list1, list2)
result, _ = nrconv.convolution.non_rectangular_convolution_rectangle(
list1, list2, geometry, prime)
want = []
self.assertEqual(result, want)
def test_non_rectangular_convolution_rectangle_frac_part_offset(self):
list1 = [1, 1, 1, 1, 1, 1, 1, 1]
list2 = [1, 1, 1, 1, 1, 1, 1, 1]
geometry = [(Fraction(3, 4), Fraction(13, 2)),
(Fraction(13, 3), Fraction(5, 3))]
prime = nrconv.create_ntt_prime(list1, list2)
_, result = nrconv.convolution.non_rectangular_convolution_rectangle(
list1, list2, geometry, prime)
want = 3
self.assertEqual(result, want)
class TestAxisAlignedTriangleCase(unittest.TestCase):
def test_non_rectangular_convolution_triangle_axis_aligned_degenerated1(
self):
list1 = [1, 2, 3, 4, 5, 6, 7, 8]
list2 = [1, 1, 1, 1, 1, 1, 1, 1]
geometry = [(4, 4), (4, 7), (4, 4)]
prime = nrconv.create_ntt_prime(list1, list2)
result, _ = nrconv.convolution.non_rectangular_convolution_triangle_axis_aligned(
list1, list2, geometry, prime)
want = [5, 5, 5, 5]
self.assertEqual(result, want)
def test_non_rectangular_convolution_triangle_axis_aligned_degenerated2(
self):
list1 = [1, 2, 3, 4, 5, 6, 7, 8]
list2 = [1, 1, 1, 1, 1, 1, 1, 1]
geometry = [(Fraction(4, 3), Fraction(8, 2)),
(Fraction(19, 4), Fraction(4, 1)),
(Fraction(8, 6), Fraction(12, 3))]
prime = nrconv.create_ntt_prime(list1, list2)
result, _ = nrconv.convolution.non_rectangular_convolution_triangle_axis_aligned(
list1, list2, geometry, prime)
want = [3, 4, 5]
self.assertEqual(result, want)
def test_non_rectangular_convolution_triangle_axis_aligned_degenerated3(
self):
list1 = [1, 2, 3, 4, 5, 6, 7, 8]
list2 = [1, 1, 1, 1, 1, 1, 1, 1]
geometry = [(Fraction(4, 3), Fraction(5, 2)),
(Fraction(19, 4), Fraction(10, 4)),
(Fraction(8, 6), Fraction(15, 6))]
prime = nrconv.create_ntt_prime(list1, list2)
result, _ = nrconv.convolution.non_rectangular_convolution_triangle_axis_aligned(
list1, list2, geometry, prime)
want = []
self.assertEqual(result, want)
def test_non_rectangular_convolution_triangle_axis_aligned_degenerated_offset(
self):
list1 = [1, 2, 3, 4, 5, 6, 7, 8]
list2 = [1, 1, 1, 1, 1, 1, 1, 1]
geometry = [(Fraction(4, 3), Fraction(8, 2)),
(Fraction(19, 4), Fraction(4, 1)),
(Fraction(8, 6), Fraction(12, 3))]
prime = nrconv.create_ntt_prime(list1, list2)
_, result = nrconv.convolution.non_rectangular_convolution_triangle_axis_aligned(
list1, list2, geometry, prime)
want = 6
self.assertEqual(result, want)
def test_non_rectangular_convolution_triangle_axis_aligned_small1(self):
list1 = [0, 1, 2, 3, 4, 5, 6, 7]
list2 = [0, 1, 2, 3, 4, 5, 6, 7]
geometry = [(Fraction(39, 10), Fraction(32, 10)),
(Fraction(41, 10), Fraction(28, 10)),
(Fraction(41, 10), Fraction(32, 10))]
prime = nrconv.create_ntt_prime(list1, list2)
result, _ = nrconv.convolution.non_rectangular_convolution_triangle_axis_aligned(
list1, list2, geometry, prime)
want = [12]
self.assertEqual(result, want)
def test_non_rectangular_convolution_triangle_axis_aligned_small2(self):
list1 = [0, 1, 2, 3, 4, 5, 6, 7]
list2 = [0, 1, 2, 3, 4, 5, 6, 7]
geometry = [(Fraction(39, 10), Fraction(32, 10)),
(Fraction(42, 10), Fraction(28, 10)),
(Fraction(42, 10), Fraction(32, 10))]
prime = nrconv.create_ntt_prime(list1, list2)
result, _ = nrconv.convolution.non_rectangular_convolution_triangle_axis_aligned(
list1, list2, geometry, prime)
want = []
self.assertEqual(result, want)
def test_non_rectangular_convolution_triangle_axis_aligned_small3(self):
list1 = [0, 1, 2, 3, 4, 5, 6, 7]
list2 = [0, 1, 2, 3, 4, 5, 6, 7]
geometry = [(Fraction(39, 10), Fraction(32, 10)),
(Fraction(39, 10), Fraction(28, 10)),
(Fraction(41, 10), Fraction(28, 10))]
prime = nrconv.create_ntt_prime(list1, list2)
result, _ = nrconv.convolution.non_rectangular_convolution_triangle_axis_aligned(
list1, list2, geometry, prime)
want = [12]
self.assertEqual(result, want)
def test_non_rectangular_convolution_triangle_axis_aligned_small4(self):
list1 = [0, 1, 2, 3, 4, 5, 6, 7]
list2 = [0, 1, 2, 3, 4, 5, 6, 7]
geometry = [(Fraction(39, 10), Fraction(32, 10)),
(Fraction(39, 10), Fraction(28, 10)),
(Fraction(42, 10), Fraction(28, 10))]
prime = nrconv.create_ntt_prime(list1, list2)
result, _ = nrconv.convolution.non_rectangular_convolution_triangle_axis_aligned(
list1, list2, geometry, prime)
want = [12]
self.assertEqual(result, want)
def test_non_rectangular_convolution_triangle_axis_aligned_small_offset(
self):
list1 = [0, 1, 2, 3, 4, 5, 6, 7]
list2 = [0, 1, 2, 3, 4, 5, 6, 7]
geometry = [(Fraction(39, 10), Fraction(32, 10)),
(Fraction(39, 10), Fraction(28, 10)),
(Fraction(41, 10), Fraction(28, 10))]
prime = nrconv.create_ntt_prime(list1, list2)
_, result = nrconv.convolution.non_rectangular_convolution_triangle_axis_aligned(
list1, list2, geometry, prime)
want = 7
self.assertEqual(result, want)
def test_non_rectangular_convolution_triangle_axis_aligned_large(self):
list1 = [0, 1, 2, 3, 4, 5, 6, 7]
list2 = [0, 1, 2, 3, 4, 5, 6, 7]
geometry = [(Fraction(0, 1), Fraction(0, 1)),
(Fraction(6, 1), Fraction(6, 1)),
(Fraction(0, 1), Fraction(6, 1))]
prime = nrconv.create_ntt_prime(list1, list2)
result, _ = nrconv.convolution.non_rectangular_convolution_triangle_axis_aligned(
list1, list2, geometry, prime)
want = [0, 0, 1, 2, 7, 10, 22, 28, 43, 38, 49, 30, 36]
self.assertEqual(result, want)
def test_non_rectangular_convolution_triangle_axis_aligned_big_offset(
self):
list1 = [0, 1, 2, 3, 4, 5, 6, 7]
list2 = [0, 1, 2, 3, 4, 5, 6, 7]
geometry = [(Fraction(0, 1), Fraction(0, 1)),
(Fraction(6, 1), Fraction(6, 1)),
(Fraction(0, 1), Fraction(6, 1))]
prime = nrconv.create_ntt_prime(list1, list2)
_, result = nrconv.convolution.non_rectangular_convolution_triangle_axis_aligned(
list1, list2, geometry, prime)
want = 0
self.assertEqual(result, want)
class TestAxisArbitraryTriangleCase(unittest.TestCase):
def test_non_rectangular_convolution_triangle_case_1_1_1(self):
list1 = [1, 1, 1, 1, 1, 1, 1, 1]
list2 = [1, 1, 1, 1, 1, 1, 1, 1]
geometry = [(Fraction(0, 1), Fraction(0, 1)),
(Fraction(4, 1), Fraction(2, 1)),
(Fraction(6, 1), Fraction(6, 1))]
prime = nrconv.create_ntt_prime(list1, list2)
result, _ = nrconv.convolution.non_rectangular_convolution_triangle(
list1, list2, geometry, prime)
want = [1, 0, 1, 1, 1, 1, 2, 1, 1, 1, 1, 0, 1]
self.assertEqual(result, want)
def test_non_rectangular_convolution_triangle_case_1_1_2(self):
list1 = [1, 1, 1, 1, 1, 1, 1, 1]
list2 = [1, 1, 1, 1, 1, 1, 1, 1]
geometry = [(Fraction(0, 1), Fraction(0, 1)),
(Fraction(2, 1), Fraction(4, 1)),
(Fraction(6, 1), Fraction(6, 1))]
prime = nrconv.create_ntt_prime(list1, list2)
result, _ = nrconv.convolution.non_rectangular_convolution_triangle(
list1, list2, geometry, prime)
want = [1, 0, 1, 1, 1, 1, 2, 1, 1, 1, 1, 0, 1]
self.assertEqual(result, want)
def test_non_rectangular_convolution_triangle_case_1_2_1(self):
list1 = [1, 1, 1, 1, 1, 1, 1, 1]
list2 = [1, 1, 1, 1, 1, 1, 1, 1]
geometry = [(Fraction(0, 1), Fraction(6, 1)),
(Fraction(4, 1), Fraction(4, 1)),
(Fraction(6, 1), Fraction(0, 1))]
prime = nrconv.create_ntt_prime(list1, list2)
result, _ = nrconv.convolution.non_rectangular_convolution_triangle(
list1, list2, geometry, prime)
want = [0, 0, 0, 0, 0, 0, 7, 4, 1, 0, 0, 0, 0]
self.assertEqual(result, want)
def test_non_rectangular_convolution_triangle_case_1_2_2(self):
list1 = [1, 1, 1, 1, 1, 1, 1, 1]
list2 = [1, 1, 1, 1, 1, 1, 1, 1]
geometry = [(Fraction(0, 1), Fraction(6, 1)),
(Fraction(2, 1), Fraction(2, 1)),
(Fraction(6, 1), Fraction(0, 1))]
prime = nrconv.create_ntt_prime(list1, list2)
result, _ = nrconv.convolution.non_rectangular_convolution_triangle(
list1, list2, geometry, prime)
want = [0, 0, 0, 0, 1, 4, 7, 0, 0, 0, 0, 0, 0]
self.assertEqual(result, want)
def test_non_rectangular_convolution_triangle_case_2_1_2_1(self):
list1 = [1, 1, 1, 1, 1, 1, 1, 1]
list2 = [1, 1, 1, 1, 1, 1, 1, 1]
geometry = [(Fraction(0, 1), Fraction(0, 1)),
(Fraction(3, 1), Fraction(6, 1)),
(Fraction(6, 1), Fraction(0, 1))]
prime = nrconv.create_ntt_prime(list1, list2)
result, _ = nrconv.convolution.non_rectangular_convolution_triangle(
list1, list2, geometry, prime)
want = [1, 1, 2, 3, 3, 4, 5, 3, 2, 1, 0, 0, 0]
self.assertEqual(result, want)
def test_non_rectangular_convolution_triangle_case_2_1_2_2(self):
list1 = [1, 1, 1, 1, 1, 1, 1, 1]
list2 = [1, 1, 1, 1, 1, 1, 1, 1]
geometry = [(Fraction(0, 1), Fraction(6, 1)),
(Fraction(3, 1), Fraction(0, 1)),
(Fraction(6, 1), Fraction(6, 1))]
prime = nrconv.create_ntt_prime(list1, list2)
result, _ = nrconv.convolution.non_rectangular_convolution_triangle(
list1, list2, geometry, prime)
want = [0, 0, 0, 1, 2, 3, 5, 4, 3, 3, 2, 1, 1]
self.assertEqual(result, want)
def test_non_rectangular_convolution_triangle_case_2_1_1_1(self):
list1 = [1, 1, 1, 1, 1, 1, 1, 1]
list2 = [1, 1, 1, 1, 1, 1, 1, 1]
geometry = [(Fraction(0, 1), Fraction(0, 1)),
(Fraction(6, 1), Fraction(3, 1)),
(Fraction(0, 1), Fraction(6, 1))]
prime = nrconv.create_ntt_prime(list1, list2)
result, _ = nrconv.convolution.non_rectangular_convolution_triangle(
list1, list2, geometry, prime)
want = [1, 1, 2, 3, 3, 4, 5, 3, 2, 1, 0, 0, 0]
self.assertEqual(result, want)
def test_non_rectangular_convolution_triangle_case_2_1_1_2(self):
list1 = [1, 1, 1, 1, 1, 1, 1, 1]
list2 = [1, 1, 1, 1, 1, 1, 1, 1]
geometry = [(Fraction(6, 1), Fraction(0, 1)),
(Fraction(0, 1), Fraction(3, 1)),
(Fraction(6, 1), Fraction(6, 1))]
prime = nrconv.create_ntt_prime(list1, list2)
result, _ = nrconv.convolution.non_rectangular_convolution_triangle(
list1, list2, geometry, prime)
want = [0, 0, 0, 1, 2, 3, 5, 4, 3, 3, 2, 1, 1]
self.assertEqual(result, want)
def test_non_rectangular_convolution_triangle_case_2_2_1(self):
list1 = [1, 1, 1, 1, 1, 1, 1, 1]
list2 = [1, 1, 1, 1, 1, 1, 1, 1]
geometry = [(Fraction(0, 1), Fraction(0, 1)),
(Fraction(6, 1), Fraction(3, 1)),
(Fraction(3, 1), Fraction(6, 1))]
prime = nrconv.create_ntt_prime(list1, list2)
result, _ = nrconv.convolution.non_rectangular_convolution_triangle(
list1, list2, geometry, prime)
want = [1, 0, 1, 2, 1, 2, 3, 2, 3, 4, 0, 0, 0]
self.assertEqual(result, want)
def test_non_rectangular_convolution_triangle_case_2_2_2(self):
list1 = [1, 1, 1, 1, 1, 1, 1, 1]
list2 = [1, 1, 1, 1, 1, 1, 1, 1]
geometry = [(Fraction(0, 1), Fraction(0, 1)),
(Fraction(3, 1), Fraction(6, 1)),
(Fraction(6, 1), Fraction(3, 1))]
prime = nrconv.create_ntt_prime(list1, list2)
result, _ = nrconv.convolution.non_rectangular_convolution_triangle(
list1, list2, geometry, prime)
want = [1, 0, 1, 2, 1, 2, 3, 2, 3, 4, 0, 0, 0]
self.assertEqual(result, want)
def test_non_rectangular_convolution_triangle_case_2_2_3(self):
list1 = [1, 1, 1, 1, 1, 1, 1, 1]
list2 = [1, 1, 1, 1, 1, 1, 1, 1]
geometry = [(Fraction(6, 1), Fraction(6, 1)),
(Fraction(0, 1), Fraction(3, 1)),
(Fraction(3, 1), Fraction(0, 1))]
prime = nrconv.create_ntt_prime(list1, list2)
result, _ = nrconv.convolution.non_rectangular_convolution_triangle(
list1, list2, geometry, prime)
want = [0, 0, 0, 4, 3, 2, 3, 2, 1, 2, 1, 0, 1]
self.assertEqual(result, want)
def test_non_rectangular_convolution_triangle_case_2_2_4(self):
list1 = [1, 1, 1, 1, 1, 1, 1, 1]
list2 = [1, 1, 1, 1, 1, 1, 1, 1]
geometry = [(Fraction(6, 1), Fraction(6, 1)),
(Fraction(3, 1), Fraction(0, 1)),
(Fraction(0, 1), Fraction(3, 1))]
prime = nrconv.create_ntt_prime(list1, list2)
result, _ = nrconv.convolution.non_rectangular_convolution_triangle(
list1, list2, geometry, prime)
want = [0, 0, 0, 4, 3, 2, 3, 2, 1, 2, 1, 0, 1]
self.assertEqual(result, want)
class TestAxisArbitraryConvexCase(unittest.TestCase):
def test_non_rectangular_convolution_convex_polygon_triangle(self):
list1 = [1, 1, 1, 1, 1, 1, 1, 1]
list2 = [1, 1, 1, 1, 1, 1, 1, 1]
geometry = [(Fraction(6, 1), Fraction(6, 1)),
(Fraction(3, 1), Fraction(0, 1)),
(Fraction(0, 1), Fraction(3, 1))]
prime = nrconv.create_ntt_prime(list1, list2)
result, _ = nrconv.convolution.non_rectangular_convolution_convex_polygon(
list1, list2, geometry, prime)
want = [0, 0, 0, 4, 3, 2, 3, 2, 1, 2, 1, 0, 1]
self.assertEqual(result, want)
def test_non_rectangular_convolution_convex_polygon_quadrilateral(self):
list1 = [1, 1, 1, 1, 1, 1, 1, 1]
list2 = [1, 1, 1, 1, 1, 1, 1, 1]
geometry = [(Fraction(0, 1), Fraction(0, 1)),
(Fraction(4, 1), Fraction(2, 1)),
(Fraction(6, 1), Fraction(4, 1)),
(Fraction(2, 1), Fraction(4, 1))]
prime = nrconv.create_ntt_prime(list1, list2)
result, _ = nrconv.convolution.non_rectangular_convolution_convex_polygon(
list1, list2, geometry, prime)
want = [1, 0, 1, 2, 1, 2, 3, 2, 2, 1, 1]
self.assertEqual(result, want)
def test_non_rectangular_convolution_convex_polygon_12_edges(self):
list1 = [1, 1, 1, 1, 1, 1, 1, 1]
list2 = [1, 1, 1, 1, 1, 1, 1, 1]
geometry = [(Fraction(3, 1), Fraction(0, 1)),
(Fraction(4, 1), Fraction(0, 1)),
(Fraction(6, 1), Fraction(1, 1)),
(Fraction(7, 1), Fraction(3, 1)),
(Fraction(7, 1), Fraction(4, 1)),
(Fraction(6, 1), Fraction(6, 1)),
(Fraction(4, 1), Fraction(7, 1)),
(Fraction(3, 1), Fraction(7, 1)),
(Fraction(1, 1), Fraction(6, 1)),
(Fraction(0, 1), Fraction(4, 1)),
(Fraction(0, 1), Fraction(3, 1)),
(Fraction(1, 1), Fraction(1, 1))]
prime = nrconv.create_ntt_prime(list1, list2)
result, _ = nrconv.convolution.non_rectangular_convolution_convex_polygon(
list1, list2, geometry, prime)
want = [0, 0, 1, 4, 5, 4, 5, 6, 5, 4, 5, 4, 1, 0, 0]
self.assertEqual(result, want)
if __name__ == '__main__':
unittest.main()
| 45.679688 | 89 | 0.561313 | 3,176 | 23,388 | 3.956549 | 0.030856 | 0.06812 | 0.081171 | 0.087219 | 0.955435 | 0.944294 | 0.938485 | 0.920102 | 0.897501 | 0.894 | 0 | 0.110496 | 0.297289 | 23,388 | 511 | 90 | 45.76908 | 0.654092 | 0.000727 | 0 | 0.740175 | 0 | 0 | 0.000342 | 0 | 0 | 0 | 0 | 0 | 0.091703 | 1 | 0.08952 | false | 0 | 0.008734 | 0 | 0.10917 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
14568fd81474b0b07197f218602ffca2017da064 | 1,053 | py | Python | tests/test_engine/test_update/test_update_upsert.py | bobuk/montydb | 9ee299e7f1d3a7236abb683e0dfe4f7817859b2c | [
"BSD-3-Clause"
] | 478 | 2019-07-31T00:48:11.000Z | 2022-03-18T09:12:29.000Z | tests/test_engine/test_update/test_update_upsert.py | bobuk/montydb | 9ee299e7f1d3a7236abb683e0dfe4f7817859b2c | [
"BSD-3-Clause"
] | 47 | 2019-07-28T10:12:22.000Z | 2022-01-04T16:25:12.000Z | tests/test_engine/test_update/test_update_upsert.py | bobuk/montydb | 9ee299e7f1d3a7236abb683e0dfe4f7817859b2c | [
"BSD-3-Clause"
] | 26 | 2019-08-09T14:28:29.000Z | 2022-02-22T02:49:51.000Z |
def test_upsert_1(monty_update, mongo_update):
docs = [
{"a": 0}
]
spec = {"$inc": {"a.$[]": 9}}
find = {"a": [1]}
monty_c = monty_update(docs, spec, find, upsert=True)
mongo_c = mongo_update(docs, spec, find, upsert=True)
assert mongo_c[1] == monty_c[1]
monty_c.rewind()
assert monty_c[1] == {"a": [10]}
def test_upsert_2(monty_update, mongo_update):
docs = [
{"a": 0}
]
spec = {"$inc": {"a": 9}}
find = {"a": 6}
monty_c = monty_update(docs, spec, find, upsert=True)
mongo_c = mongo_update(docs, spec, find, upsert=True)
assert mongo_c[1] == monty_c[1]
monty_c.rewind()
assert monty_c[1] == {"a": 15}
def test_upsert_3(monty_update, mongo_update):
docs = [
{"a": 0}
]
spec = {"$inc": {"b": 9}}
find = {"a.b": {"$gt": 6}}
monty_c = monty_update(docs, spec, find, upsert=True)
mongo_c = mongo_update(docs, spec, find, upsert=True)
assert mongo_c[1] == monty_c[1]
monty_c.rewind()
assert monty_c[1] == {"b": 9}
| 22.891304 | 57 | 0.557455 | 158 | 1,053 | 3.487342 | 0.158228 | 0.130672 | 0.088929 | 0.196007 | 0.892922 | 0.892922 | 0.892922 | 0.892922 | 0.892922 | 0.829401 | 0 | 0.032995 | 0.251662 | 1,053 | 45 | 58 | 23.4 | 0.666244 | 0 | 0 | 0.545455 | 0 | 0 | 0.031399 | 0 | 0 | 0 | 0 | 0 | 0.181818 | 1 | 0.090909 | false | 0 | 0 | 0 | 0.090909 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
1497c6d94488f80f1ca85842910f517c1e23c801 | 26,323 | py | Python | unreleased/azure-mgmt-machinelearning/azure/mgmt/machinelearning/operations/web_services_operations.py | v-Ajnava/azure-sdk-for-python | a1f6f80eb5869c5b710e8bfb66146546697e2a6f | [
"MIT"
] | 4 | 2016-06-17T23:25:29.000Z | 2022-03-30T22:37:45.000Z | unreleased/azure-mgmt-machinelearning/azure/mgmt/machinelearning/operations/web_services_operations.py | v-Ajnava/azure-sdk-for-python | a1f6f80eb5869c5b710e8bfb66146546697e2a6f | [
"MIT"
] | 54 | 2016-03-25T17:25:01.000Z | 2018-10-22T17:27:54.000Z | unreleased/azure-mgmt-machinelearning/azure/mgmt/machinelearning/operations/web_services_operations.py | v-Ajnava/azure-sdk-for-python | a1f6f80eb5869c5b710e8bfb66146546697e2a6f | [
"MIT"
] | 3 | 2016-05-03T20:49:46.000Z | 2017-10-05T21:05:27.000Z | # coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
#
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may cause incorrect behavior and will be lost if the code is
# regenerated.
# --------------------------------------------------------------------------
from msrest.pipeline import ClientRawResponse
from msrestazure.azure_exceptions import CloudError
from msrestazure.azure_operation import AzureOperationPoller
import uuid
from .. import models
class WebServicesOperations(object):
"""WebServicesOperations operations.
:param client: Client for service requests.
:param config: Configuration of service client.
:param serializer: An object model serializer.
:param deserializer: An objec model deserializer.
"""
def __init__(self, client, config, serializer, deserializer):
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self.config = config
def create_or_update(
self, resource_group_name, web_service_name, create_or_update_payload, custom_headers=None, raw=False, **operation_config):
"""Create or update a web service. This call will overwrite an existing
web service. Note that there is no warning or confirmation. This is a
nonrecoverable operation. If your intent is to create a new web
service, call the Get operation first to verify that it does not exist.
:param resource_group_name: Name of the resource group in which the
web service is located.
:type resource_group_name: str
:param web_service_name: The name of the web service.
:type web_service_name: str
:param create_or_update_payload: The payload that is used to create or
update the web service.
:type create_or_update_payload: :class:`WebService
<azure.mgmt.machinelearning.models.WebService>`
:param dict custom_headers: headers that will be added to the request
:param bool raw: returns the direct response alongside the
deserialized response
:rtype:
:class:`AzureOperationPoller<msrestazure.azure_operation.AzureOperationPoller>`
instance that returns :class:`WebService
<azure.mgmt.machinelearning.models.WebService>`
:rtype: :class:`ClientRawResponse<msrest.pipeline.ClientRawResponse>`
if raw=true
:raises: :class:`CloudError<msrestazure.azure_exceptions.CloudError>`
"""
# Construct URL
url = '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearning/webServices/{webServiceName}'
path_format_arguments = {
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'webServiceName': self._serialize.url("web_service_name", web_service_name, 'str'),
'subscriptionId': self._serialize.url("self.config.subscription_id", self.config.subscription_id, 'str')
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {}
query_parameters['api-version'] = self._serialize.query("self.config.api_version", self.config.api_version, 'str')
# Construct headers
header_parameters = {}
header_parameters['Content-Type'] = 'application/json; charset=utf-8'
if self.config.generate_client_request_id:
header_parameters['x-ms-client-request-id'] = str(uuid.uuid1())
if custom_headers:
header_parameters.update(custom_headers)
if self.config.accept_language is not None:
header_parameters['accept-language'] = self._serialize.header("self.config.accept_language", self.config.accept_language, 'str')
# Construct body
body_content = self._serialize.body(create_or_update_payload, 'WebService')
# Construct and send request
def long_running_send():
request = self._client.put(url, query_parameters)
return self._client.send(
request, header_parameters, body_content, **operation_config)
def get_long_running_status(status_link, headers=None):
request = self._client.get(status_link)
if headers:
request.headers.update(headers)
return self._client.send(
request, header_parameters, **operation_config)
def get_long_running_output(response):
if response.status_code not in [200, 201]:
exp = CloudError(response)
exp.request_id = response.headers.get('x-ms-request-id')
raise exp
deserialized = None
if response.status_code == 200:
deserialized = self._deserialize('WebService', response)
if response.status_code == 201:
deserialized = self._deserialize('WebService', response)
if raw:
client_raw_response = ClientRawResponse(deserialized, response)
return client_raw_response
return deserialized
if raw:
response = long_running_send()
return get_long_running_output(response)
long_running_operation_timeout = operation_config.get(
'long_running_operation_timeout',
self.config.long_running_operation_timeout)
return AzureOperationPoller(
long_running_send, get_long_running_output,
get_long_running_status, long_running_operation_timeout)
def get(
self, resource_group_name, web_service_name, custom_headers=None, raw=False, **operation_config):
"""Gets the Web Service Definiton as specified by a subscription, resource
group, and name. Note that the storage credentials and web service keys
are not returned by this call. To get the web service access keys, call
List Keys.
:param resource_group_name: Name of the resource group in which the
web service is located.
:type resource_group_name: str
:param web_service_name: The name of the web service.
:type web_service_name: str
:param dict custom_headers: headers that will be added to the request
:param bool raw: returns the direct response alongside the
deserialized response
:param operation_config: :ref:`Operation configuration
overrides<msrest:optionsforoperations>`.
:rtype: :class:`WebService
<azure.mgmt.machinelearning.models.WebService>`
:rtype: :class:`ClientRawResponse<msrest.pipeline.ClientRawResponse>`
if raw=true
:raises: :class:`CloudError<msrestazure.azure_exceptions.CloudError>`
"""
# Construct URL
url = '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearning/webServices/{webServiceName}'
path_format_arguments = {
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'webServiceName': self._serialize.url("web_service_name", web_service_name, 'str'),
'subscriptionId': self._serialize.url("self.config.subscription_id", self.config.subscription_id, 'str')
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {}
query_parameters['api-version'] = self._serialize.query("self.config.api_version", self.config.api_version, 'str')
# Construct headers
header_parameters = {}
header_parameters['Content-Type'] = 'application/json; charset=utf-8'
if self.config.generate_client_request_id:
header_parameters['x-ms-client-request-id'] = str(uuid.uuid1())
if custom_headers:
header_parameters.update(custom_headers)
if self.config.accept_language is not None:
header_parameters['accept-language'] = self._serialize.header("self.config.accept_language", self.config.accept_language, 'str')
# Construct and send request
request = self._client.get(url, query_parameters)
response = self._client.send(request, header_parameters, **operation_config)
if response.status_code not in [200]:
exp = CloudError(response)
exp.request_id = response.headers.get('x-ms-request-id')
raise exp
deserialized = None
if response.status_code == 200:
deserialized = self._deserialize('WebService', response)
if raw:
client_raw_response = ClientRawResponse(deserialized, response)
return client_raw_response
return deserialized
def patch(
self, resource_group_name, web_service_name, patch_payload, custom_headers=None, raw=False, **operation_config):
"""Modifies an existing web service resource. The PATCH API call is an
asynchronous operation. To determine whether it has completed
successfully, you must perform a Get operation.
:param resource_group_name: Name of the resource group in which the
web service is located.
:type resource_group_name: str
:param web_service_name: The name of the web service.
:type web_service_name: str
:param patch_payload: The payload to use to patch the web service.
:type patch_payload: :class:`WebService
<azure.mgmt.machinelearning.models.WebService>`
:param dict custom_headers: headers that will be added to the request
:param bool raw: returns the direct response alongside the
deserialized response
:rtype:
:class:`AzureOperationPoller<msrestazure.azure_operation.AzureOperationPoller>`
instance that returns :class:`WebService
<azure.mgmt.machinelearning.models.WebService>`
:rtype: :class:`ClientRawResponse<msrest.pipeline.ClientRawResponse>`
if raw=true
:raises: :class:`CloudError<msrestazure.azure_exceptions.CloudError>`
"""
# Construct URL
url = '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearning/webServices/{webServiceName}'
path_format_arguments = {
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'webServiceName': self._serialize.url("web_service_name", web_service_name, 'str'),
'subscriptionId': self._serialize.url("self.config.subscription_id", self.config.subscription_id, 'str')
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {}
query_parameters['api-version'] = self._serialize.query("self.config.api_version", self.config.api_version, 'str')
# Construct headers
header_parameters = {}
header_parameters['Content-Type'] = 'application/json; charset=utf-8'
if self.config.generate_client_request_id:
header_parameters['x-ms-client-request-id'] = str(uuid.uuid1())
if custom_headers:
header_parameters.update(custom_headers)
if self.config.accept_language is not None:
header_parameters['accept-language'] = self._serialize.header("self.config.accept_language", self.config.accept_language, 'str')
# Construct body
body_content = self._serialize.body(patch_payload, 'WebService')
# Construct and send request
def long_running_send():
request = self._client.patch(url, query_parameters)
return self._client.send(
request, header_parameters, body_content, **operation_config)
def get_long_running_status(status_link, headers=None):
request = self._client.get(status_link)
if headers:
request.headers.update(headers)
return self._client.send(
request, header_parameters, **operation_config)
def get_long_running_output(response):
if response.status_code not in [200]:
exp = CloudError(response)
exp.request_id = response.headers.get('x-ms-request-id')
raise exp
deserialized = None
if response.status_code == 200:
deserialized = self._deserialize('WebService', response)
if raw:
client_raw_response = ClientRawResponse(deserialized, response)
return client_raw_response
return deserialized
if raw:
response = long_running_send()
return get_long_running_output(response)
long_running_operation_timeout = operation_config.get(
'long_running_operation_timeout',
self.config.long_running_operation_timeout)
return AzureOperationPoller(
long_running_send, get_long_running_output,
get_long_running_status, long_running_operation_timeout)
def remove(
self, resource_group_name, web_service_name, custom_headers=None, raw=False, **operation_config):
"""Deletes the specified web service.
:param resource_group_name: Name of the resource group in which the
web service is located.
:type resource_group_name: str
:param web_service_name: The name of the web service.
:type web_service_name: str
:param dict custom_headers: headers that will be added to the request
:param bool raw: returns the direct response alongside the
deserialized response
:rtype:
:class:`AzureOperationPoller<msrestazure.azure_operation.AzureOperationPoller>`
instance that returns None
:rtype: :class:`ClientRawResponse<msrest.pipeline.ClientRawResponse>`
if raw=true
:raises: :class:`CloudError<msrestazure.azure_exceptions.CloudError>`
"""
# Construct URL
url = '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearning/webServices/{webServiceName}'
path_format_arguments = {
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'webServiceName': self._serialize.url("web_service_name", web_service_name, 'str'),
'subscriptionId': self._serialize.url("self.config.subscription_id", self.config.subscription_id, 'str')
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {}
query_parameters['api-version'] = self._serialize.query("self.config.api_version", self.config.api_version, 'str')
# Construct headers
header_parameters = {}
header_parameters['Content-Type'] = 'application/json; charset=utf-8'
if self.config.generate_client_request_id:
header_parameters['x-ms-client-request-id'] = str(uuid.uuid1())
if custom_headers:
header_parameters.update(custom_headers)
if self.config.accept_language is not None:
header_parameters['accept-language'] = self._serialize.header("self.config.accept_language", self.config.accept_language, 'str')
# Construct and send request
def long_running_send():
request = self._client.delete(url, query_parameters)
return self._client.send(request, header_parameters, **operation_config)
def get_long_running_status(status_link, headers=None):
request = self._client.get(status_link)
if headers:
request.headers.update(headers)
return self._client.send(
request, header_parameters, **operation_config)
def get_long_running_output(response):
if response.status_code not in [202, 204]:
exp = CloudError(response)
exp.request_id = response.headers.get('x-ms-request-id')
raise exp
if raw:
client_raw_response = ClientRawResponse(None, response)
return client_raw_response
if raw:
response = long_running_send()
return get_long_running_output(response)
long_running_operation_timeout = operation_config.get(
'long_running_operation_timeout',
self.config.long_running_operation_timeout)
return AzureOperationPoller(
long_running_send, get_long_running_output,
get_long_running_status, long_running_operation_timeout)
def list_keys(
self, resource_group_name, web_service_name, custom_headers=None, raw=False, **operation_config):
"""Gets the access keys for the specified web service.
:param resource_group_name: Name of the resource group in which the
web service is located.
:type resource_group_name: str
:param web_service_name: The name of the web service.
:type web_service_name: str
:param dict custom_headers: headers that will be added to the request
:param bool raw: returns the direct response alongside the
deserialized response
:param operation_config: :ref:`Operation configuration
overrides<msrest:optionsforoperations>`.
:rtype: :class:`WebServiceKeys
<azure.mgmt.machinelearning.models.WebServiceKeys>`
:rtype: :class:`ClientRawResponse<msrest.pipeline.ClientRawResponse>`
if raw=true
:raises: :class:`CloudError<msrestazure.azure_exceptions.CloudError>`
"""
# Construct URL
url = '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearning/webServices/{webServiceName}/listKeys'
path_format_arguments = {
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'webServiceName': self._serialize.url("web_service_name", web_service_name, 'str'),
'subscriptionId': self._serialize.url("self.config.subscription_id", self.config.subscription_id, 'str')
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {}
query_parameters['api-version'] = self._serialize.query("self.config.api_version", self.config.api_version, 'str')
# Construct headers
header_parameters = {}
header_parameters['Content-Type'] = 'application/json; charset=utf-8'
if self.config.generate_client_request_id:
header_parameters['x-ms-client-request-id'] = str(uuid.uuid1())
if custom_headers:
header_parameters.update(custom_headers)
if self.config.accept_language is not None:
header_parameters['accept-language'] = self._serialize.header("self.config.accept_language", self.config.accept_language, 'str')
# Construct and send request
request = self._client.get(url, query_parameters)
response = self._client.send(request, header_parameters, **operation_config)
if response.status_code not in [200]:
exp = CloudError(response)
exp.request_id = response.headers.get('x-ms-request-id')
raise exp
deserialized = None
if response.status_code == 200:
deserialized = self._deserialize('WebServiceKeys', response)
if raw:
client_raw_response = ClientRawResponse(deserialized, response)
return client_raw_response
return deserialized
def list_by_resource_group(
self, resource_group_name, skiptoken=None, custom_headers=None, raw=False, **operation_config):
"""Gets the web services in the specified resource group.
:param resource_group_name: Name of the resource group in which the
web service is located.
:type resource_group_name: str
:param skiptoken: Continuation token for pagination.
:type skiptoken: str
:param dict custom_headers: headers that will be added to the request
:param bool raw: returns the direct response alongside the
deserialized response
:param operation_config: :ref:`Operation configuration
overrides<msrest:optionsforoperations>`.
:rtype: :class:`WebServicePaged
<azure.mgmt.machinelearning.models.WebServicePaged>`
:raises: :class:`CloudError<msrestazure.azure_exceptions.CloudError>`
"""
def internal_paging(next_link=None, raw=False):
if not next_link:
# Construct URL
url = '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearning/webServices'
path_format_arguments = {
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'subscriptionId': self._serialize.url("self.config.subscription_id", self.config.subscription_id, 'str')
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {}
if skiptoken is not None:
query_parameters['$skiptoken'] = self._serialize.query("skiptoken", skiptoken, 'str')
query_parameters['api-version'] = self._serialize.query("self.config.api_version", self.config.api_version, 'str')
else:
url = next_link
query_parameters = {}
# Construct headers
header_parameters = {}
header_parameters['Content-Type'] = 'application/json; charset=utf-8'
if self.config.generate_client_request_id:
header_parameters['x-ms-client-request-id'] = str(uuid.uuid1())
if custom_headers:
header_parameters.update(custom_headers)
if self.config.accept_language is not None:
header_parameters['accept-language'] = self._serialize.header("self.config.accept_language", self.config.accept_language, 'str')
# Construct and send request
request = self._client.get(url, query_parameters)
response = self._client.send(
request, header_parameters, **operation_config)
if response.status_code not in [200]:
exp = CloudError(response)
exp.request_id = response.headers.get('x-ms-request-id')
raise exp
return response
# Deserialize response
deserialized = models.WebServicePaged(internal_paging, self._deserialize.dependencies)
if raw:
header_dict = {}
client_raw_response = models.WebServicePaged(internal_paging, self._deserialize.dependencies, header_dict)
return client_raw_response
return deserialized
def list(
self, skiptoken=None, custom_headers=None, raw=False, **operation_config):
"""Gets the web services in the specified subscription.
:param skiptoken: Continuation token for pagination.
:type skiptoken: str
:param dict custom_headers: headers that will be added to the request
:param bool raw: returns the direct response alongside the
deserialized response
:param operation_config: :ref:`Operation configuration
overrides<msrest:optionsforoperations>`.
:rtype: :class:`WebServicePaged
<azure.mgmt.machinelearning.models.WebServicePaged>`
:raises: :class:`CloudError<msrestazure.azure_exceptions.CloudError>`
"""
def internal_paging(next_link=None, raw=False):
if not next_link:
# Construct URL
url = '/subscriptions/{subscriptionId}/providers/Microsoft.MachineLearning/webServices'
path_format_arguments = {
'subscriptionId': self._serialize.url("self.config.subscription_id", self.config.subscription_id, 'str')
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {}
if skiptoken is not None:
query_parameters['$skiptoken'] = self._serialize.query("skiptoken", skiptoken, 'str')
query_parameters['api-version'] = self._serialize.query("self.config.api_version", self.config.api_version, 'str')
else:
url = next_link
query_parameters = {}
# Construct headers
header_parameters = {}
header_parameters['Content-Type'] = 'application/json; charset=utf-8'
if self.config.generate_client_request_id:
header_parameters['x-ms-client-request-id'] = str(uuid.uuid1())
if custom_headers:
header_parameters.update(custom_headers)
if self.config.accept_language is not None:
header_parameters['accept-language'] = self._serialize.header("self.config.accept_language", self.config.accept_language, 'str')
# Construct and send request
request = self._client.get(url, query_parameters)
response = self._client.send(
request, header_parameters, **operation_config)
if response.status_code not in [200]:
exp = CloudError(response)
exp.request_id = response.headers.get('x-ms-request-id')
raise exp
return response
# Deserialize response
deserialized = models.WebServicePaged(internal_paging, self._deserialize.dependencies)
if raw:
header_dict = {}
client_raw_response = models.WebServicePaged(internal_paging, self._deserialize.dependencies, header_dict)
return client_raw_response
return deserialized
| 45.77913 | 156 | 0.660297 | 2,809 | 26,323 | 5.96618 | 0.077608 | 0.035802 | 0.030431 | 0.030073 | 0.897607 | 0.894683 | 0.892356 | 0.88442 | 0.878155 | 0.878155 | 0 | 0.002882 | 0.24868 | 26,323 | 574 | 157 | 45.858885 | 0.844516 | 0.266345 | 0 | 0.857143 | 0 | 0 | 0.159233 | 0.089863 | 0 | 0 | 0 | 0 | 0 | 1 | 0.063123 | false | 0 | 0.016611 | 0 | 0.172757 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
14af12efc5ce3cbb4632c1cb05564550697f9be2 | 16,776 | py | Python | algorithms.py | adamsolomou/second-order-random-search | 212e8ed3b081c8a7928ebcf5de99d012218a15c6 | [
"Apache-2.0"
] | null | null | null | algorithms.py | adamsolomou/second-order-random-search | 212e8ed3b081c8a7928ebcf5de99d012218a15c6 | [
"Apache-2.0"
] | null | null | null | algorithms.py | adamsolomou/second-order-random-search | 212e8ed3b081c8a7928ebcf5de99d012218a15c6 | [
"Apache-2.0"
] | null | null | null | import math
import time
import itertools
import autograd.numpy as np
from utils import uniform_angles_pss
def STP(f, x, a_init, step_upd='half', distribution='Uniform', T=10000):
# Initialization
y = x
a = a_init
# Function values
f_values = []
f_values.append((0 ,f.eval(y)))
# Gradient norm
g_norm = []
g_norm.append((0, np.linalg.norm(f.gradient(y))))
# Execution time per iteration
timer = []
for t in range(1, T):
# Start timer
s_time = time.time()
if distribution == 'Uniform':
s = np.random.multivariate_normal(np.zeros(f.d), np.identity(f.d))
s = s/np.linalg.norm(s)
elif distribution == 'Normal':
s = np.random.multivariate_normal(np.zeros(f.d), np.identity(f.d))
else:
raise ValueError('The option %s is not a supported sampling distribution.' %(distribution))
# List possible next iterates
V = [y+a*s, y-a*s, y]
f_v = []
for v in V:
f_v.append(f.eval(v))
# Select optimal point
i_star = np.argmin(np.array(f_v))
# Update step
y = V[i_star]
# Step size update
if step_upd == 'half':
if t%10 == 0:
a = a/2
elif step_upd == 'inv':
a = a_init/(t+1)
elif step_upd == 'inv_sqrt':
a = a_init/np.sqrt(t+1)
else:
raise ValueError('The option %s is not a supported step size update rule.' %(step_upd))
# Stop timer
e_time = time.time()
timer.append(e_time - s_time)
f_values.append((t, f.eval(y)))
g_norm.append((t, np.linalg.norm(f.gradient(y))))
# Summary
summary = {}
summary['x_T'] = y
summary['fval'] = np.array(f_values)
summary['gnorm'] = np.array(g_norm)
summary['time'] = np.mean(timer)
return summary
def BDS(f, x, a_init, a_max, theta, gamma, rho, T=10000):
# Initialization
y = x # iterate @ t
a = a_init # step size
# Function values
f_values = []
f_values.append((0, f.eval(y)))
# Gradient norm
g_norm = []
g_norm.append((0, np.linalg.norm(f.gradient(y))))
# Execution time per iteration
timer = []
for t in range(1,T):
# Start timer
s_time = time.time()
# Reset variables
successful = False
d_opt = np.zeros(f.d)
f_y = f.eval(y) # function value at current iterate
# Generate a polling set
D, D_symmetric, D_size = uniform_angles_pss(f.d)
# Search the polling set
for i in np.random.permutation(D_size):
d = D[:,i]
if f.eval(y + a*d) < f_y - rho(a):
# Iteration succesful
d_opt = d
successful = True
# Stop searching PSS
break
# Update step
if successful:
y = y + a*d_opt
a = np.minimum(gamma*a, a_max)
else:
a = theta*a
# Stop timer
e_time = time.time()
timer.append(e_time - s_time)
f_values.append((t, f.eval(y)))
g_norm.append((t, np.linalg.norm(f.gradient(y))))
# Summary
summary = {}
summary['x_T'] = y
summary['fval'] = np.array(f_values)
summary['gnorm'] = np.array(g_norm)
summary['time'] = np.mean(timer)
return summary
def AHDS(f, x, a_init, a_max, theta, gamma, rho, T=10000):
# Initialization
y = x # iterate @ t
a = a_init # step size
# Function values
f_values = []
f_values.append((0, f.eval(y)))
# Gradient norm
g_norm = []
g_norm.append((0, np.linalg.norm(f.gradient(y))))
# Execution time per iteration
timer = []
# Flag to indicate if the algorithms get's stucked
stacked = False
for t in range(1,T):
# Start timer
s_time = time.time()
# Reset variables
successful = False
H = np.zeros((f.d, f.d)) # Hessian
f_y = f.eval(y) # function value at current iterate
d_opt = np.zeros(f.d) # descent direction
B_opt = np.zeros((f.d, f.d)) # independent set of vectors
D_table = np.zeros(f.d) # store function values for Hessian computation
B_table = np.zeros((f.d, f.d)) # store function values for Hessian computation
""" ========= Step 1 ========= """
# Generate a PSS D
D, D_symmetric, D_size = uniform_angles_pss(f.d)
# Search the PSS
for i in np.random.permutation(D_size):
d = D[:,i]
if f.eval(y + a*d) < f_y - rho(a):
# Iteration succesful
d_opt = d
successful = True
# Stop searching PSS
break
""" ========= Step 2 ========= """
# Search opposite directions in PSS
if not D_symmetric and not successful:
for i in np.random.permutation(D_size):
d = -D[:,i]
if f.eval(y + a*d) < f_y - rho(a):
# Iteration succesful
d_opt = d
successful = True
# Stop searching PSS
break
""" ========= Step 3 ========= """
if not successful:
# Choose B as a subset of D with f.d linearly independent vectors
subsets = itertools.combinations(range(D_size), f.d)
for subset in np.random.permutation(list(subsets)):
B = D[:,subset]
if np.linalg.matrix_rank(B) == f.d:
B_opt = B
# Stop search
break
break_outer = False
for i in range(f.d-1):
for j in range(i+1,f.d):
d = B_opt[:,i] + B_opt[:,j]
B_table[i,j] = f.eval(y + a*d)
if B_table[i,j] < f_y - rho(a):
# Iteration successful
d_opt = d
successful = True
# Stop searching
break_outer = True
break
if break_outer:
# Stop searching
break
""" ========= Step 4 ========= """
# if not successful and not stacked:
if not successful:
# Hessian approximation: Diagonal elements
for i in range(f.d):
di = B_opt[:,i]
D_table[i] = f.eval(y+a*di)
H[i,i] = D_table[i] - 2*f_y + f.eval(y-a*di)
# Hessian approximation: Off-diagonal elements
for i in range(f.d-1):
for j in range(i+1,f.d):
H[i,j] = B_table[i,j] - D_table[i] - D_table[j] + f_y
H[j,i] = H[i,j]
# Complete computation
H = H/(a**2)
# When iterates get very close to a minimizer the Hessian approximation
# may result to NaN values. The try statement avoids such errors.
try:
# Eigendecomposition
L, V = np.linalg.eig(H)
# Eigenvector corresponding to minimum eigenvalue
idx = np.argmin(L)
d = V[:,idx]
# Check d
if f.eval(y + a*d) < f.eval(y) - rho(a):
# Iteration successful
d_opt = d
successful = True
# Check -d
if f.eval(y - a*d) < f.eval(y) - rho(a) and f.eval(y - a*d) < f.eval(y + a*d):
# Iteration successful
d_opt = -d
successful = True
except:
pass
""" ========= Step 5 ========= """
# Update step
if successful:
y = y + a*d_opt
a = np.minimum(gamma*a, a_max)
stacked = False
else:
a = theta*a
stacked = True
# Stop timer
e_time = time.time()
timer.append(e_time - s_time)
f_values.append((t, f.eval(y)))
g_norm.append((t, np.linalg.norm(f.gradient(y))))
# Summary
summary = {}
summary['x_T'] = y
summary['fval'] = np.array(f_values)
summary['gnorm'] = np.array(g_norm)
summary['time'] = np.mean(timer)
return summary
def RS(f, x, a_init, sigma_1, sigma_2, distribution='Normal', step_upd='half', theta=0.6, T_half=10, T=10000):
# Initialization
y = x # iterate @ t
a = a_init # step size
# Function values
f_values = []
f_values.append((0, f.eval(y)))
# Gradient norm
g_norm = []
g_norm.append((0, np.linalg.norm(f.gradient(y))))
# Execution time per iteration
timer = []
for t in range(1,T):
# Start timer
s_time = time.time()
""" ========= Random Step 1 ========= """
if distribution == 'Uniform':
d1 = np.random.multivariate_normal(np.zeros(f.d), np.identity(f.d))
d1 = sigma_1*(d1/np.linalg.norm(d1))
elif distribution == 'Normal':
d1 = np.random.multivariate_normal(np.zeros(f.d), np.power(sigma_1,2.0)*np.identity(f.d))
else:
raise ValueError('The option %s is not a supported sampling distribution.' %(distribution))
V = [y, y+a*d1, y-a*d1]
f_v = []
for v in V:
f_v.append(f.eval(v))
# Select optimal point
i_star = np.argmin(np.array(f_v))
# Update iterate
y = V[i_star]
""" ========= Random Step 2 ========= """
if i_star == 0:
if distribution == 'Uniform':
d2 = np.random.multivariate_normal(np.zeros(f.d), np.identity(f.d))
d2 = sigma_2*(d2/np.linalg.norm(d2))
elif distribution == 'Normal':
d2 = np.random.multivariate_normal(np.zeros(f.d), np.power(sigma_2,2.0)*np.identity(f.d))
else:
raise ValueError('The option %s is not a supported sampling distribution.' %(distribution))
V = [y, y+a*d2, y-a*d2]
f_v = []
for v in V:
f_v.append(f.eval(v))
# Select optimal point
i_star = np.argmin(np.array(f_v))
# Update iterate
y = V[i_star]
# Update step-size
if step_upd == 'half':
if t%T_half == 0:
a = theta*a
elif step_upd == 'inv':
a = a_init/(t+1)
elif step_upd == 'inv_sqrt':
a = a_init/np.sqrt(t+1)
else:
raise ValueError('The option %s is not a supported step size update rule.' %(step_upd))
# Stop timer
e_time = time.time()
timer.append(e_time - s_time)
f_values.append((t, f.eval(y)))
g_norm.append((t, np.linalg.norm(f.gradient(y))))
# Summary
summary = {}
summary['x_T'] = y
summary['fval'] = np.array(f_values)
summary['gnorm'] = np.array(g_norm)
summary['time'] = np.mean(timer)
return summary
def DFPI_SPSA(f, y, c_init, beta, T_power):
# Power iteration - Compute eigenvector for max eigenvalue
r = 0.001
T_power_approx = 5
d2 = np.random.rand(f.d)
c = c_init
for i in range(T_power_approx):
Delta = np.random.binomial(n=1, p=0.5, size=f.d)
Delta[Delta == 0] = -1
# Approximate gradient vectors
d_rplus = f.eval(y + r*d2 + c*Delta) - f.eval(y + r*d2 - c*Delta)
G_rplus = np.divide(d_rplus, 2*c*Delta)
d_rminus = f.eval(y - r*d2 + c*Delta) - f.eval(y - r*d2 - c*Delta)
G_rminus = np.divide(d_rminus, 2*c*Delta)
# Approximate Hessian-vector product
Hd = (G_rplus - G_rminus)/(2*r)
# Power iteration - update
d2 = Hd/np.linalg.norm(Hd)
# Approximate gradient vectors
d_rplus = f.eval(y + r*d2 + c*Delta) - f.eval(y + r*d2 - c*Delta)
G_rplus = np.divide(d_rplus, 2*c*Delta)
d_rminus = f.eval(y - r*d2 + c*Delta) - f.eval(y - r*d2 - c*Delta)
G_rminus = np.divide(d_rminus, 2*c*Delta)
# Approximate Hessian-vector product
Hd = (G_rplus - G_rminus)/(2*r)
# Largest eigenvalue
lmax = np.linalg.norm(Hd)/np.linalg.norm(d2)
# Power iteration - Compute eigenvector for min eigenvalue
b_power = 1/lmax
d2 = np.random.rand(f.d)
for i in range(T_power):
Delta = np.random.binomial(n=1, p=0.5, size=f.d)
Delta[Delta == 0] = -1
# Approximate gradient vectors
d_rplus = f.eval(y + r*d2 + c*Delta) - f.eval(y + r*d2 - c*Delta)
G_rplus = np.divide(d_rplus, 2*c*Delta)
d_rminus = f.eval(y - r*d2 + c*Delta) - f.eval(y - r*d2 - c*Delta)
G_rminus = np.divide(d_rminus, 2*c*Delta)
# Approximate Hessian-vector product
Hd = (G_rplus - G_rminus)/(2*r)
# Power iteration - update
d2_ = d2 - b_power*Hd
d2 = d2_/np.linalg.norm(d2_)
# Negative curvature
return d2
def DFPI_FD(f, y, c, T_power):
r = 0.01
# Power iteration - Compute eigenvector for max eigenvalue
T_power_approx = 15
d2 = np.random.rand(f.d)
# Basis vectors
I = np.identity(f.d)
for i in range(T_power_approx):
# Initialize
g_p = np.empty(f.d)
g_m = np.empty(f.d)
# Approximate gradient vectors
for j in range(f.d):
g_p[j] = (f.eval(y + r*d2 + c*I[:,j]) - f.eval(y + r*d2 - c*I[:,j]))/(2*c)
g_m[j] = (f.eval(y - r*d2 + c*I[:,j]) - f.eval(y - r*d2 - c*I[:,j]))/(2*c)
# Approximate Hessian-vector product
Hd = (g_p - g_m)/(2*r)
# Power iteration - update
d2 = Hd/np.linalg.norm(Hd)
# Approximate gradient vectors
g_p = np.empty(f.d)
g_m = np.empty(f.d)
for j in range(f.d):
g_p[j] = (f.eval(y + r*d2 + c*I[:,j]) - f.eval(y + r*d2 - c*I[:,j]))/(2*c)
g_m[j] = (f.eval(y - r*d2 + c*I[:,j]) - f.eval(y - r*d2 - c*I[:,j]))/(2*c)
# Approximate Hessian-vector product
Hd = (g_p - g_m)/(2*r)
# Largest eigenvalue
lmax = np.linalg.norm(Hd)/np.linalg.norm(d2)
# Power iteration - Compute eigenvector for min eigenvalue
b_power = 1/lmax
d2 = np.random.rand(f.d)
for i in range(T_power):
# Initialize
g_p = np.empty(f.d)
g_m = np.empty(f.d)
# Approximate gradient vectors
for j in range(f.d):
g_p[j] = (f.eval(y + r*d2 + c*I[:,j]) - f.eval(y + r*d2 - c*I[:,j]))/(2*c)
g_m[j] = (f.eval(y - r*d2 + c*I[:,j]) - f.eval(y - r*d2 - c*I[:,j]))/(2*c)
# Approximate Hessian-vector product
Hd = (g_p - g_m)/(2*r)
# Power iteration - update
d2_ = d2 - b_power*Hd
d2 = d2_/np.linalg.norm(d2_)
return d2
def RSPI_SPSA(f, x, a_init, c_init, beta, sigma_1, sigma_2, distribution='Normal', step_upd='half', theta=0.6, T_half=10, T_power=100, T=10000):
# Initialization
y = x # iterate @ t
a = a_init # step size
c = c_init # SPSA step
# Function values
f_values = []
f_values.append((0, f.eval(y)))
# Gradient norm
g_norm = []
g_norm.append((0, np.linalg.norm(f.gradient(y))))
# Execution time per iteration
timer = []
for t in range(1,T):
# Start timer
s_time = time.time()
""" ========= Random Step ========= """
if distribution == 'Uniform':
d1 = np.random.multivariate_normal(np.zeros(f.d), np.identity(f.d))
d1 = sigma_1*(d1/np.linalg.norm(d1))
elif distribution == 'Normal':
d1 = np.random.multivariate_normal(np.zeros(f.d), np.power(sigma_1,2.0)*np.identity(f.d))
else:
raise ValueError('The option %s is not a supported sampling distribution.' %(distribution))
V = [y, y+a*d1, y-a*d1]
f_v = []
for v in V:
f_v.append(f.eval(v))
# Select optimal point
i_star = np.argmin(np.array(f_v))
# Update iterate
y = V[i_star]
""" ========= Negative Curvature ========= """
if i_star == 0:
d2 = DFPI_SPSA(f, y, c, beta, T_power)
while d2 is None:
d2 = DFPI_SPSA(f, y, c, beta, T_power)
""" ========= Update Step ========= """
V = [y, y+sigma_2*d2, y-sigma_2*d2]
f_v = []
for v in V:
f_v.append(f.eval(v))
# Select optimal point
i_star = np.argmin(np.array(f_v))
# Update iterate
y = V[i_star]
# Decrease SPSA parameter
c = c_init/pow(t,beta)
# Update step-size
if step_upd == 'half':
if t%T_half == 0:
a = theta*a
elif step_upd == 'inv':
a = a_init/(t+1)
elif step_upd == 'inv_sqrt':
a = a_init/np.sqrt(t+1)
else:
raise ValueError('The option %s is not a supported step size update rule.' %(step_upd))
# Stop timer
e_time = time.time()
timer.append(e_time - s_time)
f_values.append((t, f.eval(y)))
g_norm.append((t, np.linalg.norm(f.gradient(y))))
# Summary
summary = {}
summary['x_T'] = y
summary['fval'] = np.array(f_values)
summary['gnorm'] = np.array(g_norm)
summary['time'] = np.mean(timer)
return summary
def RSPI_FD(f, x, a_init, c_init, beta, sigma_1, sigma_2, distribution='Normal', step_upd='half', theta=0.6, T_half=10, T_power=100, T=10000):
# Initialization
y = x # iterate @ t
a = a_init # step size
c = c_init # SPSA step
# Function values
f_values = []
f_values.append((0, f.eval(y)))
# Gradient norm
g_norm = []
g_norm.append((0, np.linalg.norm(f.gradient(y))))
# Execution time per iteration
timer = []
for t in range(1,T):
# Start timer
s_time = time.time()
""" ========= Random Step ========= """
if distribution == 'Uniform':
d1 = np.random.multivariate_normal(np.zeros(f.d), np.identity(f.d))
d1 = sigma_1*(d1/np.linalg.norm(d1))
elif distribution == 'Normal':
d1 = np.random.multivariate_normal(np.zeros(f.d), np.power(sigma_1,2.0)*np.identity(f.d))
else:
raise ValueError('The option %s is not a supported sampling distribution.' %(distribution))
V = [y, y+a*d1, y-a*d1]
f_v = []
for v in V:
f_v.append(f.eval(v))
# Select optimal point
i_star = np.argmin(np.array(f_v))
# Update iterate
y = V[i_star]
""" ========= Negative Curvature ========= """
if i_star == 0:
d2 = DFPI_FD(f, y, c, T_power)
while d2 is None:
d2 = DFPI_FD(f, y, c, T_power)
""" ========= Update Step ========= """
V = [y, y+sigma_2*d2, y-sigma_2*d2]
f_v = []
for v in V:
f_v.append(f.eval(v))
# Select optimal point
i_star = np.argmin(np.array(f_v))
# Update iterate
y = V[i_star]
# Decrease SPSA parameter
c = c_init/pow(t,beta)
# Update step-size
if step_upd == 'half':
if t%T_half == 0:
a = theta*a
elif step_upd == 'inv':
a = a_init/(t+1)
elif step_upd == 'inv_sqrt':
a = a_init/np.sqrt(t+1)
else:
raise ValueError('The option %s is not a supported step size update rule.' %(step_upd))
# Stop timer
e_time = time.time()
timer.append(e_time - s_time)
f_values.append((t, f.eval(y)))
g_norm.append((t, np.linalg.norm(f.gradient(y))))
# Summary
summary = {}
summary['x_T'] = y
summary['fval'] = np.array(f_values)
summary['gnorm'] = np.array(g_norm)
summary['time'] = np.mean(timer)
return summary | 24.490511 | 144 | 0.6067 | 2,876 | 16,776 | 3.422462 | 0.077191 | 0.028955 | 0.030479 | 0.017068 | 0.857564 | 0.845068 | 0.834095 | 0.814691 | 0.793356 | 0.789901 | 0 | 0.018771 | 0.221984 | 16,776 | 685 | 145 | 24.490511 | 0.735366 | 0.186695 | 0 | 0.850374 | 0 | 0 | 0.058222 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.01995 | false | 0.002494 | 0.012469 | 0 | 0.052369 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
1ae9a10d036b77eb9cd4d0996190f2e37fc0d7a6 | 39,490 | py | Python | NER/model.py | moasgh/BumbleBee | 2b0aae7970ab316c7b8b12dd4032b41ee1772aad | [
"MIT"
] | 7 | 2020-03-06T05:53:43.000Z | 2022-01-30T17:31:18.000Z | NER/model.py | moasgh/BumbleBee | 2b0aae7970ab316c7b8b12dd4032b41ee1772aad | [
"MIT"
] | null | null | null | NER/model.py | moasgh/BumbleBee | 2b0aae7970ab316c7b8b12dd4032b41ee1772aad | [
"MIT"
] | null | null | null | from NER.utils import *
from NER.embedding import embed
import torch
import matplotlib.pyplot as plt
# single Task
class rnn_single_crf(nn.Module):
def __init__(self, cti_size, wti_size, num_tags , params):
super().__init__()
self.rnn = rnn(cti_size, wti_size, num_tags , params)
self.crf = crf(num_tags , params)
self = self.cuda(ACTIVE_DEVICE) if CUDA else self
self.HRE = params['HRE']
self.BATCH_SIZE = params['BATCH_SIZE']
self.params = params
def forward(self, xc, xw, y0): # for training
self.zero_grad()
self.rnn.batch_size = y0.size(0)
self.crf.batch_size = y0.size(0)
mask = y0[:, 1:].gt(PAD_IDX).float()
#print("xw", xw.shape)
#print('mask' , mask.shape)
h = self.rnn(xc, xw, mask)
#print("h :" , h.shape)
Z = self.crf.forward(h, mask)
#print("Y0 :" , y0 , Z)
score = self.crf.score(h, y0, mask)
#print("score :", score)
return torch.mean(Z - score) # NLL loss
def decode(self, xc, xw, doc_lens): # for inference
self.rnn.batch_size = len(doc_lens) if self.HRE else xw.size(0)
self.crf.batch_size = len(doc_lens) if self.HRE else xw.size(0)
if self.HRE:
mask = Tensor([[1] * x + [PAD_IDX] * (doc_lens[0] - x) for x in doc_lens])
else:
mask = xw.gt(PAD_IDX).float()
h = self.rnn(xc, xw, mask)
return self.crf.decode(h, mask)
def loaddata(self, sentences, cti, wti, itt , y0 = None):
data = dataloader()
block = []
for si, sent in enumerate(sentences):
sent = normalize(sent)
words = tokenize(sent) #sent.split(' ')
x = []
for w in words:
w = normalize(w)
wxc = [cti[c] if c in cti else UNK_IDX for c in w]
x.append((wxc , wti[w] if w in wti else UNK_IDX))
xc , xw = zip(*x)
if y0:
assert len(y0[si]) == len(xw) , "Tokens length is not the same as Target length (y0)!"
block.append((sent, xc,xw , y0[si]))
else:
block.append((sent, xc,xw))
for s in block:
if y0:
data.append_item( x0 = [s[0]] , xc= [list(s[1])] , xw =[list(s[2])] , y0 = s[3])
data.append_row()
else:
data.append_item( x0 = [s[0]] , xc= [list(s[1])] , xw =[list(s[2])] , y0 = [])
data.append_row()
data.strip()
data.sort()
return data
def predict(self , sentences , cti , wti , itt ):
"""
sentences : List of sentence space seperated (tokenization will be done simply by spliting the space between words)
cti : Character to Index that model trained
wti : Word to Index that model trained
itt : Index To Tag (Inside Other Begin)
"""
data = self.loaddata(sentences,cti,wti,itt )
for batch in data.split(self.BATCH_SIZE, self.HRE):
xc , xw = data.tensor(batch.xc , batch.xw , batch.lens)
y1 = self.decode(xc, xw, batch.lens)
data.y1.extend([[itt[i] for i in x] for x in y1])
data.unsort()
return data
def evaluate(self, sentences, cti , wti , itt , y0 , parameters = [] , model_name = None , save = False , filename = None ):
"""
sentences : List of sentence space seperated (tokenization will be done simply by spliting the space between words)
cti : Character to Index that model trained
wti : Word to Index that model trained
itt : Index To Tag (Inside Other Begin)
y0 : Target values to evaluate
parameters : 'macro_precision','macro_recall','hmacro_f1', 'amacro_f1','micro_f1','auc'
"""
data = self.loaddata(sentences, cti, wti , itt , y0)
for batch in data.split(self.BATCH_SIZE, self.HRE):
xc , xw = data.tensor(batch.xc , batch.xw , batch.lens)
y1 = self.decode(xc, xw, batch.lens)
data.y1.extend([[itt[i] for i in x] for x in y1])
data.unsort()
result = metrics(data.y0 , data.y1 , model_name=model_name,save=save,filename=filename)
if parameters:
print("============ evaluation results ============")
for m in parameters:
if m in result:
print("\t" + m +" = %f"% result[m])
return data
# Two Sequential
# this is tested based on jupyter Run-rnn_two_crf_seq
class rnn_two_crf_seq(nn.Module):
def __init__(self, cti_size, wti_size , num_tags_iob , num_tag_ner , params):
"""
num_output_rnn : Maximum number of out put for the two iob and ner
"""
super().__init__()
self.rnn_iob = rnn(cti_size, wti_size, num_tags_iob , params)
self.crfiob = crf(num_tags_iob , params)
self.HRE = params['HRE']
self.BATCH_SIZE = params['BATCH_SIZE']
self.NUM_DIRS = params['NUM_DIRS']
self.NUM_LAYERS = params['NUM_LAYERS']
self.DROPOUT = params['DROPOUT']
self.RNN_TYPE = params['RNN_TYPE']
self.HIDDEN_SIZE = params['HIDDEN_SIZE']
self.rnn_ner = getattr(nn, self.RNN_TYPE)(
input_size = num_tags_iob,
hidden_size = self.HIDDEN_SIZE // self.NUM_DIRS,
num_layers = self.NUM_LAYERS,
bias = True,
batch_first = True,
dropout = self.DROPOUT,
bidirectional = (self.NUM_DIRS == 2)
)
self.out = nn.Linear(self.HIDDEN_SIZE, num_tag_ner) # RNN output to tag
self.crfner = crf(num_tag_ner , params)
self.params = params
self = self.cuda(ACTIVE_DEVICE) if CUDA else self
def forward(self, xc, xw, yiob , yner): # for training
self.zero_grad()
self.rnn_iob.batch_size = xw.size(0)
self.rnn_ner.batch_size = xw.size(0)
self.crfiob.batch_size = xw.size(0)
self.crfner.batch_size = xw.size(0)
# Mask on sentence
mask = xw[:,1:].gt(PAD_IDX).float()
# Layer one get the embed and then go to crf for IOB
h_iob = self.rnn_iob(xc, xw, mask)
#mask_iob = yiob[:, 1:].gt(PAD_IDX).float()
#print('MASK_IOB')
#print(mask_iob)
# this need to be backward when we train it ()
Ziob = self.crfiob.forward(h_iob, mask)
# Result of IOB will go to the RNN and Predict the NER
h_iob *= mask.unsqueeze(2)
h_ner , _ = self.rnn_ner(h_iob)
ner_out = self.out(h_ner)
t = 2
# to see how CRF converge the model to the output
#for _nerpred, _nery in zip(ner_out , yner):
# plt.plot(_nerpred[_nery[t+1]].cpu().data.numpy())
# plt.vlines(x=_nery[t+1].cpu().data.numpy(),ymin= 0 , ymax=1)
# plt.show()
#print(_nerpred[_nery[t+1]].data , _nery[t+1].data)
#ner_out *= mask.unsqueeze(2)
#mask_ner = yner[:, 1:].gt(PAD_IDX).float()
#print('MASK_NER')
#print(mask_ner)
Zner = self.crfner.forward(ner_out, mask)
scoreiob = self.crfiob.score(h_iob, yiob, mask)
scorener = self.crfner.score(ner_out, yner, mask)
return torch.mean(Ziob - scoreiob) , torch.mean(Zner - scorener) # NLL loss
def decode(self, xc, xw, doc_lens): # for inference
self.rnn_iob.batch_size = len(doc_lens) if self.HRE else xw.size(0)
self.rnn_ner.batch_size = len(doc_lens) if self.HRE else xw.size(0)
self.crfiob.batch_size = len(doc_lens) if self.HRE else xw.size(0)
self.crfner.batch_size = len(doc_lens) if self.HRE else xw.size(0)
if self.HRE:
mask = Tensor([[1] * x + [PAD_IDX] * (doc_lens[0] - x) for x in doc_lens])
else:
mask = xw.gt(PAD_IDX).float()
iob_pred = self.rnn_iob(xc, xw, mask)
#iob_pred = self.iob(h_bio)
h_ner , _ = self.rnn_ner(iob_pred)
ner_pred = self.out(h_ner)
return self.crfiob.decode(iob_pred, mask) , self.crfner.decode(ner_pred, mask)
def loaddata(self, sentences , cti , wti , itt_iob , itt_ner , yiob=None , yner =None):
data = dataloader()
block = []
for si ,sent in enumerate(sentences) :
sent = normalize(sent)
words = tokenize(sent) #sent.split(' ')
x = []
tokens = []
for w in words:
w = normalize(w)
wxc = [cti[c] if c in cti else UNK_IDX for c in w]
x.append((wxc , wti[w] if w in wti else UNK_IDX))
tokens.append(w)
xc , xw = zip(*x)
if yiob and yner:
assert len(yiob[si]) == len(xw) , "Tokens length is not the same as Target length (y0)!"
assert len(yner[si]) == len(xw) , "Tokens length is not the same as Target length (y0)!"
block.append((sent,tokens, xc,xw , yiob[si] , yner[si]))
else:
block.append((sent,tokens, xc,xw))
for s in block:
if yiob and yner:
data.append_item( x0 = [s[0]] , x1 = [s[1]] , xc= [list(s[2])] , xw =[list(s[3])] , yiob=s[4] , yner =s[5])
data.append_row()
else:
data.append_item( x0 = [s[0]] , x1 = [s[1]] , xc= [list(s[2])] , xw =[list(s[3])] , yiob=[] , yner =[] )
data.append_row()
data.strip()
data.sort()
return data
def predict(self , sentences , cti , wti , itt_iob , itt_ner):
"""
sentences : List of sentence space seperated (tokenization will be done simply by spliting the space between words)
cti : Character to Index that model trained
wti : Word to Index that model trained
itt_iob : Index To Tag IOB (Inside Other Begin)
itt_ner : Index To Tag Named Entity REcognition
"""
#itt_iob = {v:k for k,v in tti_iob.items()}
#itt_ner = {v:k for k,v in tti_ner.items()}
data = self.loaddata(sentences , cti , wti , itt_iob , itt_ner)
for batch in data.split(self.BATCH_SIZE, self.HRE):
xc , xw = data.tensor(batch.xc , batch.xw , batch.lens)
yiob , yner = self.decode(xc, xw, batch.lens)
#print(yiob , yner)
#print([[itt_iob[i] if i in itt_iob else O for i in x] for x in yiob])
data.y1iob.extend([[itt_iob[i] for i in x] for x in yiob])
data.y1ner.extend([[itt_ner[i] for i in x] for x in yner])
data.unsort()
#print(data.y1iob , data.x1)
return data
def evaluate(self, sentences , cti , wti , itt_iob , itt_ner , y0iob , y0ner , parameters = [] , model_name = None , save = False , filename = None ):
"""
sentences : List of sentence space seperated (tokenization will be done simply by spliting the space between words)
cti : Character to Index that model trained
wti : Word to Index that model trained
itt : Index To Tag (Inside Other Begin)
y0iob : Target values to evaluate (Inside Other Begin)
y0ner : Target values to evaluate (Named Entity Recognition)
parameters : 'macro_precision','macro_recall','hmacro_f1', 'amacro_f1','micro_f1','auc'
"""
data = self.loaddata(sentences , cti , wti , itt_iob , itt_ner , y0iob , y0ner)
for batch in data.split(self.BATCH_SIZE, self.HRE):
xc , xw = data.tensor(batch.xc , batch.xw , batch.lens)
y1iob , y1ner = self.decode(xc, xw, batch.lens)
data.y1iob.extend([[itt_iob[i] for i in x] for x in y1iob])
data.y1ner.extend([[itt_ner[i] for i in x] for x in y1ner])
data.unsort()
result_ner = metrics(data.yner , data.y1ner , model_name=model_name + "_ner",save=save,filename=filename)
result_iob = metrics(data.yiob , data.y1iob , model_name=model_name + "_iob",save=save,filename=filename)
if parameters:
print("============ evaluation results ============")
for m in parameters:
if m in result_ner:
print("\t" + m +" (ner) = %f"% result_ner[m])
if m in result_iob:
print("\t" + m +" (iob) = %f"% result_iob[m])
return data
class rnn_two_crf_seq2(nn.Module):
def __init__(self, cti_size, wti_size , num_tags_iob , num_tags_ner , params):
"""
num_output_rnn : Maximum number of out put for the two iob and ner
"""
super().__init__()
self.rnn_ner = rnn(cti_size, wti_size, num_tags_ner , params)
self.crfner = crf(num_tags_ner , params)
self.HRE = params['HRE']
self.BATCH_SIZE = params['BATCH_SIZE']
self.NUM_DIRS = params['NUM_DIRS']
self.NUM_LAYERS = params['NUM_LAYERS']
self.DROPOUT = params['DROPOUT']
self.RNN_TYPE = params['RNN_TYPE']
self.HIDDEN_SIZE = params['HIDDEN_SIZE']
self.rnn_iob = getattr(nn, self.RNN_TYPE)(
input_size = num_tags_ner,
hidden_size = self.HIDDEN_SIZE // self.NUM_DIRS,
num_layers = self.NUM_LAYERS,
bias = True,
batch_first = True,
dropout = self.DROPOUT,
bidirectional = (self.NUM_DIRS == 2)
)
self.out = nn.Linear(self.HIDDEN_SIZE, num_tags_iob) # RNN output to tag
self.crfiob = crf(num_tags_iob , params)
self.params = params
self = self.cuda(ACTIVE_DEVICE) if CUDA else self
def forward(self, xc, xw, yiob , yner): # for training
self.zero_grad()
self.rnn_iob.batch_size = xw.size(0)
self.rnn_ner.batch_size = xw.size(0)
self.crfiob.batch_size = xw.size(0)
self.crfner.batch_size = xw.size(0)
# Mask on sentence
mask = xw[:,1:].gt(PAD_IDX).float()
# Layer one get the embed and then go to crf for IOB
h_ner = self.rnn_ner(xc, xw, mask)
#mask_iob = yiob[:, 1:].gt(PAD_IDX).float()
#print('MASK_IOB')
#print(mask_iob)
# this need to be backward when we train it ()
Zner = self.crfner.forward(h_ner, mask)
# Result of IOB will go to the RNN and Predict the NER
h_ner *= mask.unsqueeze(2)
h_iob , _ = self.rnn_iob(h_ner)
iob_out = self.out(h_iob)
#t = 2
# to see how CRF converge the model to the output
# for _nerpred, _nery in zip(ner_out , yner):
# plt.plot(_nerpred[_nery[t+1]].cpu().data.numpy())
# plt.vlines(x=_nery[t+1].cpu().data.numpy(),ymin= 0 , ymax=1)
# plt.show()
#print(_nerpred[_nery[t+1]].data , _nery[t+1].data)
#ner_out *= mask.unsqueeze(2)
#mask_ner = yner[:, 1:].gt(PAD_IDX).float()
#print('MASK_NER')
#print(mask_ner)
Ziob = self.crfiob.forward(iob_out, mask)
scorener = self.crfner.score(h_ner, yner, mask)
scoreiob = self.crfiob.score(iob_out, yiob, mask)
return torch.mean(Ziob - scoreiob) , torch.mean(Zner - scorener) # NLL loss
def decode(self, xc, xw, doc_lens): # for inference
self.rnn_iob.batch_size = len(doc_lens) if self.HRE else xw.size(0)
self.rnn_ner.batch_size = len(doc_lens) if self.HRE else xw.size(0)
self.crfiob.batch_size = len(doc_lens) if self.HRE else xw.size(0)
self.crfner.batch_size = len(doc_lens) if self.HRE else xw.size(0)
if self.HRE:
mask = Tensor([[1] * x + [PAD_IDX] * (doc_lens[0] - x) for x in doc_lens])
else:
mask = xw.gt(PAD_IDX).float()
ner_pred = self.rnn_ner(xc, xw, mask)
#iob_pred = self.iob(h_bio)
h_iob , _ = self.rnn_iob(ner_pred)
iob_pred = self.out(h_iob)
return self.crfiob.decode(iob_pred, mask) , self.crfner.decode(ner_pred, mask)
def loaddata(self, sentences , cti , wti , itt_iob , itt_ner , yiob=None , yner =None):
data = dataloader()
block = []
for si ,sent in enumerate(sentences) :
sent = normalize(sent)
words = tokenize(sent) #sent.split(' ')
x = []
tokens = []
for w in words:
w = normalize(w)
wxc = [cti[c] if c in cti else UNK_IDX for c in w]
x.append((wxc , wti[w] if w in wti else UNK_IDX))
tokens.append(w)
xc , xw = zip(*x)
if yiob and yner:
assert len(yiob[si]) == len(xw) , "Tokens length is not the same as Target length (y0)!"
assert len(yner[si]) == len(xw) , "Tokens length is not the same as Target length (y0)!"
block.append((sent, tokens, xc,xw , yiob[si] , yner[si] ))
else:
block.append((sent, tokens, xc,xw))
for s in block:
if yiob and yner:
data.append_item( x0 = [s[0]] , x1 = [s[1]] , xc= [list(s[2])] , xw =[list(s[3])] , yiob=s[4] , yner =s[5])
data.append_row()
else:
data.append_item( x0 = [s[0]] , x1 = [s[1]] , xc= [list(s[2])] , xw =[list(s[3])] , yiob=[] , yner =[] )
data.append_row()
data.strip()
data.sort()
return data
def predict(self , sentences , cti , wti , itt_iob , itt_ner):
"""
sentences : List of sentence space seperated (tokenization will be done simply by spliting the space between words)
cti : Character to Index that model trained
wti : Word to Index that model trained
itt_iob : Index To Tag IOB (Inside Other Begin)
itt_ner : Index To Tag Named Entity REcognition
"""
#itt_iob = {v:k for k,v in tti_iob.items()}
#itt_ner = {v:k for k,v in tti_ner.items()}
data = self.loaddata(sentences , cti , wti , itt_iob , itt_ner)
for batch in data.split(self.BATCH_SIZE, self.HRE):
xc , xw = data.tensor(batch.xc , batch.xw , batch.lens)
yiob , yner = self.decode(xc, xw, batch.lens)
#print(yiob , yner)
#print([[itt_iob[i] if i in itt_iob else O for i in x] for x in yiob])
data.y1iob.extend([[itt_iob[i] for i in x] for x in yiob])
data.y1ner.extend([[itt_ner[i] for i in x] for x in yner])
data.unsort()
return data
def evaluate(self, sentences , cti , wti , itt_iob , itt_ner , y0iob , y0ner , parameters = [] , model_name = None , save = False , filename = None ):
"""
sentences : List of sentence space seperated (tokenization will be done simply by spliting the space between words)
cti : Character to Index that model trained
wti : Word to Index that model trained
itt : Index To Tag (Inside Other Begin)
y0iob : Target values to evaluate (Inside Other Begin)
y0ner : Target values to evaluate (Named Entity Recognition)
parameters : 'macro_precision','macro_recall','hmacro_f1', 'amacro_f1','micro_f1','auc'
"""
data = self.loaddata(sentences , cti , wti , itt_iob , itt_ner , y0iob , y0ner)
for batch in data.split(self.BATCH_SIZE, self.HRE):
xc , xw = data.tensor(batch.xc , batch.xw , batch.lens)
y1iob , y1ner = self.decode(xc, xw, batch.lens)
data.y1iob.extend([[itt_iob[i] for i in x] for x in y1iob])
data.y1ner.extend([[itt_ner[i] for i in x] for x in y1ner])
data.unsort()
result_ner = metrics(data.yner , data.y1ner , model_name=model_name + "_ner",save=save,filename=filename)
result_iob = metrics(data.yiob , data.y1iob , model_name=model_name + "_iob",save=save,filename=filename)
if parameters:
print("============ evaluation results ============")
for m in parameters:
if m in result_ner:
print("\t" + m +" (ner) = %f"% result_ner[m])
if m in result_iob:
print("\t" + m +" (iob) = %f"% result_iob[m])
return data
# Two Task Prallel
# This is tested based on Jupyter Run-rnn_two_crf_par
class rnn_two_crf_par(nn.Module):
def __init__(self, cti_size, wti_size , num_tags_iob , num_tag_ner , params):
"""
num_output_rnn : Maximum number of out put for the two iob and ner
"""
super().__init__()
self.rnn_iob = rnn(cti_size, wti_size, num_tags_iob , params)
self.crfiob = crf(num_tags_iob , params)
self.rnn_ner = rnn(cti_size, wti_size, num_tag_ner , params)
self.crfner = crf(num_tag_ner , params)
self = self.cuda(ACTIVE_DEVICE) if CUDA else self
self.HRE = params['HRE']
self.BATCH_SIZE = params['BATCH_SIZE']
self.params = params
def forward(self, xc, xw, yiob , yner): # for training
self.zero_grad()
self.rnn_iob.batch_size = xw.size(0)
self.rnn_ner.batch_size = xw.size(0)
self.crfiob.batch_size = xw.size(0)
self.crfner.batch_size = xw.size(0)
mask = xw[:,1:].gt(PAD_IDX).float()
h_iob = self.rnn_iob(xc, xw, mask)
mask_iob = yiob[:, 1:].gt(PAD_IDX).float()
Ziob = self.crfiob.forward(h_iob, mask_iob)
h_ner = self.rnn_ner(xc, xw, mask)
mask_ner = yner[:, 1:].gt(PAD_IDX).float()
Zner = self.crfner.forward(h_ner, mask_ner)
scoreiob = self.crfiob.score(h_iob, yiob, mask_iob)
scorener = self.crfner.score(h_ner, yner, mask_ner)
#print("score :", score)
#loss = torch.mean((Ziob + Zner) - (scoreiob + scorener))
loss = torch.mean(torch.pow((Ziob+Zner) - (scoreiob+scorener) , 2))
return loss #torch.abs( torch.mean(Ziob - scoreiob) + torch.mean(Zner - scorener)) # NLL loss
def decode(self, xc, xw, doc_lens): # for inference
self.rnn_iob.batch_size = len(doc_lens) if self.HRE else xw.size(0)
self.rnn_ner.batch_size = len(doc_lens) if self.HRE else xw.size(0)
self.crfiob.batch_size = len(doc_lens) if self.HRE else xw.size(0)
self.crfner.batch_size = len(doc_lens) if self.HRE else xw.size(0)
if self.HRE:
mask = Tensor([[1] * x + [PAD_IDX] * (doc_lens[0] - x) for x in doc_lens])
else:
mask = xw.gt(PAD_IDX).float()
iob_pred = self.rnn_iob(xc, xw, mask)
#iob_pred = self.iob(h_bio)
ner_pred = self.rnn_ner(xc, xw, mask)
#ner_pred = self.ner(h_ner)
return self.crfiob.decode(iob_pred, mask) , self.crfner.decode(ner_pred, mask)
def loaddata(self, sentences , cti , wti , itt_iob , itt_ner , yiob=None , yner =None):
data = dataloader()
block = []
for si ,sent in enumerate(sentences) :
sent = normalize(sent)
words = tokenize(sent) #sent.split(' ')
#print(len(words) , words)
x = []
tokens = []
for w in words:
w = normalize(w)
wxc = [cti[c] if c in cti else UNK_IDX for c in w]
x.append((wxc , wti[w] if w in wti else UNK_IDX))
tokens.append(w)
xc , xw = zip(*x)
if yiob and yner:
assert len(yiob[si]) == len(xw) , "Tokens length is not the same as Target length (y0)!"
assert len(yner[si]) == len(xw) , "Tokens length is not the same as Target length (y0)!"
block.append((sent,tokens, xc,xw , yiob[si] , yner[si]))
else:
block.append((sent,tokens, xc,xw))
for s in block:
if yiob and yner:
data.append_item( x0 = [s[0]] , x1 = [s[1]] , xc= [list(s[2])] , xw =[list(s[3])] , yiob=s[4] , yner =s[5])
data.append_row()
else:
data.append_item( x0 = [s[0]] , x1 = [s[1]] , xc= [list(s[2])] , xw =[list(s[3])] , yiob=[] , yner =[] )
data.append_row()
data.strip()
data.sort()
return data
def predict(self , sentences , cti , wti , itt_iob , itt_ner):
"""
sentences : List of sentence space seperated (tokenization will be done simply by spliting the space between words)
cti : Character to Index that model trained
wti : Word to Index that model trained
itt_iob : Index To Tag IOB (Inside Other Begin)
itt_ner : Index To Tag Named Entity REcognition
"""
#itt_iob = {v:k for k,v in tti_iob.items()}
#itt_ner = {v:k for k,v in tti_ner.items()}
data = self.loaddata(sentences , cti , wti , itt_iob , itt_ner)
for batch in data.split(self.BATCH_SIZE, self.HRE):
xc , xw = data.tensor(batch.xc , batch.xw , batch.lens)
yiob , yner = self.decode(xc, xw, batch.lens)
#print(yiob , yner)
#print([[itt_iob[i] if i in itt_iob else O for i in x] for x in yiob])
data.y1iob.extend([[itt_iob[i] for i in x] for x in yiob])
data.y1ner.extend([[itt_ner[i] for i in x] for x in yner])
data.unsort()
return data
def evaluate(self, sentences , cti , wti , itt_iob , itt_ner , y0iob , y0ner , parameters = [] , model_name = None , save = False , filename = None ):
"""
sentences : List of sentence space seperated (tokenization will be done simply by spliting the space between words)
cti : Character to Index that model trained
wti : Word to Index that model trained
itt : Index To Tag (Inside Other Begin)
y0iob : Target values to evaluate (Inside Other Begin)
y0ner : Target values to evaluate (Named Entity Recognition)
parameters : 'macro_precision','macro_recall','hmacro_f1', 'amacro_f1','micro_f1','auc'
"""
data = self.loaddata(sentences , cti , wti , itt_iob , itt_ner , y0iob , y0ner)
for batch in data.split(self.BATCH_SIZE, self.HRE):
xc , xw = data.tensor(batch.xc , batch.xw , batch.lens)
y1iob , y1ner = self.decode(xc, xw, batch.lens)
data.y1iob.extend([[itt_iob[i] for i in x] for x in y1iob])
data.y1ner.extend([[itt_ner[i] for i in x] for x in y1ner])
data.unsort()
result_ner = metrics(data.yner , data.y1ner , model_name=model_name + "_ner",save=save,filename=filename)
result_iob = metrics(data.yiob , data.y1iob , model_name=model_name + "_iob",save=save,filename=filename)
if parameters:
print("============ evaluation results ============")
for m in parameters:
if m in result_ner:
print("\t" + m +" (ner) = %f"% result_ner[m])
if m in result_iob:
print("\t" + m +" (iob) = %f"% result_iob[m])
return data
# Two task Model NER , Word Segmenting
# this is test based on jupyter RUN_rnn_two_crf
class rnn_two_crf(nn.Module):
def __init__(self, cti_size, wti_size, num_output_rnn , num_tags_iob , num_tag_ner , params):
"""
num_output_rnn : Maximum number of out put for the two iob and ner
"""
super().__init__()
#self.Tensor = lambda *x: torch.FloatTensor(*x).cuda(ACTIVE_DEVICE) if CUDA else torch.FloatTensor
#self.LongTensor = lambda *x: torch.LongTensor(*x).cuda(ACTIVE_DEVICE) if CUDA else torch.LongTensor
#self.randn = lambda *x: torch.randn(*x).cuda(ACTIVE_DEVICE) if CUDA else torch.randn
#self.zeros = lambda *x: torch.zeros(*x).cuda(ACTIVE_DEVICE) if CUDA else torch.zeros
self.rnn = rnn(cti_size, wti_size, num_output_rnn , params)
self.iob = nn.Linear(num_output_rnn , num_tags_iob)
self.crfiob = crf(num_tags_iob , params)
self.ner = nn.Linear(num_output_rnn , num_tag_ner)
self.crfner = crf(num_tag_ner , params)
self = self.cuda(ACTIVE_DEVICE) if CUDA else self
self.HRE = params['HRE']
self.BATCH_SIZE = params['BATCH_SIZE']
self.params = params
def forward(self, xc, xw, yiob , yner): # for training
self.zero_grad()
self.rnn.batch_size = xw.size(0)
self.crfiob.batch_size = xw.size(0)
self.crfner.batch_size = xw.size(0)
mask = xw[:,1:].gt(PAD_IDX).float()
#print("xw", xw.shape)
h = self.rnn(xc, xw, mask)
mask_iob = yiob[:, 1:].gt(PAD_IDX).float()
mask_ner = yner[:, 1:].gt(PAD_IDX).float()
#print("h :" , h.shape)
iob_fc = self.iob(h)
Ziob = self.crfiob.forward(iob_fc, mask_iob)
ner_fc = self.ner(h)
Zner = self.crfner.forward(ner_fc, mask_ner)
#print("Y0 :" , y0 , Z)
#print(Ziob)
#print(Zner)
scoreiob = self.crfiob.score(iob_fc, yiob, mask_iob)
scorener = self.crfner.score(ner_fc, yner, mask_ner)
#print(scoreiob)
#print(scorener)
#print("score :", score)
#yi = torch.mean(scoreiob + scorener)
#yi_ = torch.mean(Ziob + Zner)
loss = torch.mean(torch.pow((Ziob+Zner) - (scoreiob+scorener) , 2))
#loss = torch.mean((Ziob + Zner) - (scoreiob + scorener))
#loss = - ((yi* torch.log(yi_)) + ((1-yi) * torch.log(1-yi_)))
return loss # NLL loss
def decode(self, xc, xw, doc_lens): # for inference
self.rnn.batch_size = len(doc_lens) if self.HRE else xw.size(0)
self.crfiob.batch_size = len(doc_lens) if self.HRE else xw.size(0)
self.crfner.batch_size = len(doc_lens) if self.HRE else xw.size(0)
if self.HRE:
mask = Tensor([[1] * x + [PAD_IDX] * (doc_lens[0] - x) for x in doc_lens])
else:
mask = xw.gt(PAD_IDX).float()
h = self.rnn(xc, xw, mask)
iob_pred = self.iob(h)
ner_pred = self.ner(h)
return self.crfiob.decode(iob_pred, mask) , self.crfner.decode(ner_pred, mask)
def loaddata(self, sentences , cti , wti , itt_iob , itt_ner , yiob=None , yner =None):
data = dataloader()
block = []
for si ,sent in enumerate(sentences) :
sent = normalize(sent)
words = tokenize(sent) #sent.split(' ')
x = []
tokens = []
for w in words:
w = normalize(w)
wxc = [cti[c] if c in cti else UNK_IDX for c in w]
x.append((wxc , wti[w] if w in wti else UNK_IDX))
tokens.append(w)
xc , xw = zip(*x)
if yiob and yner:
assert len(yiob[si]) == len(xw) , "Tokens length is not the same as Target length (y0)!"
assert len(yner[si]) == len(xw) , "Tokens length is not the same as Target length (y0)!"
block.append((sent,tokens, xc,xw , yiob[si] , yner[si]))
else:
block.append((sent,tokens, xc,xw))
for s in block:
if yiob and yner:
data.append_item( x0 = [s[0]] , x1 = [s[1]] , xc= [list(s[2])] , xw =[list(s[3])] , yiob=s[4] , yner =s[5])
data.append_row()
else:
data.append_item( x0 = [s[0]] , x1 = [s[1]] , xc= [list(s[2])] , xw =[list(s[3])] , yiob=[] , yner =[] )
data.append_row()
data.strip()
data.sort()
return data
def predict(self , sentences , cti , wti , itt_iob , itt_ner):
"""
sentences : List of sentence space seperated (tokenization will be done simply by spliting the space between words)
cti : Character to Index that model trained
wti : Word to Index that model trained
itt_iob : Index To Tag IOB (Inside Other Begin)
itt_ner : Index To Tag Named Entity REcognition
"""
#itt_iob = {v:k for k,v in tti_iob.items()}
#itt_ner = {v:k for k,v in tti_ner.items()}
data = self.loaddata(sentences , cti , wti , itt_iob , itt_ner)
for batch in data.split(self.BATCH_SIZE, self.HRE):
xc , xw = data.tensor(batch.xc , batch.xw , batch.lens)
yiob , yner = self.decode(xc, xw, batch.lens)
#print(yiob , yner)
#print([[itt_iob[i] if i in itt_iob else O for i in x] for x in yiob])
data.y1iob.extend([[itt_iob[i] for i in x] for x in yiob])
data.y1ner.extend([[itt_ner[i] for i in x] for x in yner])
data.unsort()
return data
def evaluate(self, sentences , cti , wti , itt_iob , itt_ner , y0iob , y0ner , parameters = [] , model_name = None , save = False , filename = None ):
"""
sentences : List of sentence space seperated (tokenization will be done simply by spliting the space between words)
cti : Character to Index that model trained
wti : Word to Index that model trained
itt : Index To Tag (Inside Other Begin)
y0iob : Target values to evaluate (Inside Other Begin)
y0ner : Target values to evaluate (Named Entity Recognition)
parameters : 'macro_precision','macro_recall','hmacro_f1', 'amacro_f1','micro_f1','auc'
"""
data = self.loaddata(sentences , cti , wti , itt_iob , itt_ner , y0iob , y0ner)
for batch in data.split(self.BATCH_SIZE, self.HRE):
xc , xw = data.tensor(batch.xc , batch.xw , batch.lens)
y1iob , y1ner = self.decode(xc, xw, batch.lens)
data.y1iob.extend([[itt_iob[i] for i in x] for x in y1iob])
data.y1ner.extend([[itt_ner[i] for i in x] for x in y1ner])
data.unsort()
result_ner = metrics(data.yner , data.y1ner , model_name=model_name + "_ner",save=save,filename=filename)
result_iob = metrics(data.yiob , data.y1iob , model_name=model_name + "_iob",save=save,filename=filename)
if parameters:
print("============ evaluation results ============")
for m in parameters:
if m in result_ner:
print("\t" + m +" (ner) = %f"% result_ner[m])
if m in result_iob:
print("\t" + m +" (iob) = %f"% result_iob[m])
return data
class rnn(nn.Module):
def __init__(self, cti_size, wti_size, num_tags , params):
super().__init__()
self.batch_size = 0
self.EMBED_SIZE = params['EMBED_SIZE']
self.HIDDEN_SIZE = params['HIDDEN_SIZE']
self.NUM_DIRS = params['NUM_DIRS']
self.NUM_LAYERS = params['NUM_LAYERS']
self.DROPOUT = params['DROPOUT']
self.RNN_TYPE = params['RNN_TYPE']
self.HRE = params['HRE']
self.params = params
# architecture
self.embed = embed(cti_size, wti_size, self.params, self.HRE)
self.rnn = getattr(nn, self.RNN_TYPE)(
input_size = self.EMBED_SIZE,
hidden_size = self.HIDDEN_SIZE // self.NUM_DIRS,
num_layers = self.NUM_LAYERS,
bias = True,
batch_first = True,
dropout = self.DROPOUT,
bidirectional = (self.NUM_DIRS == 2)
)
self.out = nn.Linear(self.HIDDEN_SIZE, num_tags) # RNN output to tag
def init_state(self, b): # initialize RNN states
n = self.NUM_LAYERS * self.NUM_DIRS
h = self.HIDDEN_SIZE // self.NUM_DIRS
hs = zeros(n, b, h) # hidden state
if self.RNN_TYPE == "LSTM":
cs = zeros(n, b, h) # LSTM cell state
return (hs, cs)
return hs
def forward(self, xc, xw, mask):
hs = self.init_state(self.batch_size)
x = self.embed(xc, xw)
if self.HRE: # [B * doc_len, 1, H] -> [B, doc_len, H]
x = x.view(self.batch_size, -1, self.EMBED_SIZE)
x = nn.utils.rnn.pack_padded_sequence(x, mask.sum(1).int(), batch_first = True)
h, _ = self.rnn(x, hs)
h, _ = nn.utils.rnn.pad_packed_sequence(h, batch_first = True)
h = self.out(h)
h *= mask.unsqueeze(2)
return h
class crf(nn.Module):
def __init__(self, num_tags,params):
super().__init__()
self.batch_size = 0
self.num_tags = num_tags
# matrix of transition scores from j to i
#rnd = torch.rand(num_tags, num_tags).cuda(ACTIVE_DEVICE) if CUDA else torch.rand(num_tags, num_tags)
self.trans = nn.Parameter(randn(num_tags, num_tags))
self.trans.data[SOS_IDX, :] = -10000 # no transition to SOS
self.trans.data[:, EOS_IDX] = -10000 # no transition from EOS except to PAD
self.trans.data[:, PAD_IDX] = -10000 # no transition from PAD except to PAD
self.trans.data[PAD_IDX, :] = -10000 # no transition to PAD except from EOS
self.trans.data[PAD_IDX, EOS_IDX] = 0
self.trans.data[PAD_IDX, PAD_IDX] = 0
def forward(self, h, mask): # forward algorithm
# initialize forward variables in log space
score = Tensor(self.batch_size, self.num_tags).fill_(-10000) # [B, C]
score[:, SOS_IDX] = 0.
trans = self.trans.unsqueeze(0) # [1, C, C]
for t in range(h.size(1)): # recursion through the sequence
mask_t = mask[:, t].unsqueeze(1)
emit_t = h[:, t].unsqueeze(2) # [B, C, 1]
score_t = score.unsqueeze(1) + emit_t + trans # [B, 1, C] -> [B, C, C]
score_t = log_sum_exp(score_t) # [B, C, C] -> [B, C]
score = score_t * mask_t + score * (1 - mask_t)
score = log_sum_exp(score + self.trans[EOS_IDX])
return score # partition function
def score(self, h, y0, mask): # calculate the score of a given sequence
score = Tensor(self.batch_size).fill_(0.)
h = h.unsqueeze(3)
trans = self.trans.unsqueeze(2)
for t in range(h.size(1)): # recursion through the sequence
mask_t = mask[:, t]
emit_t = torch.cat([h[t, y0[t + 1]] for h, y0 in zip(h, y0)])
trans_t = torch.cat([trans[y0[t + 1], y0[t]] for y0 in y0])
score += (emit_t + trans_t) * mask_t
last_tag = y0.gather(1, mask.sum(1).long().unsqueeze(1)).squeeze(1)
score += self.trans[EOS_IDX, last_tag]
return score
def decode(self, h, mask): # Viterbi decoding
# initialize backpointers and viterbi variables in log space
bptr = LongTensor()
score = Tensor(self.batch_size, self.num_tags).fill_(-10000)
score[:, SOS_IDX] = 0.
for t in range(h.size(1)): # recursion through the sequence
mask_t = mask[:, t].unsqueeze(1)
score_t = score.unsqueeze(1) + self.trans # [B, 1, C] -> [B, C, C]
score_t, bptr_t = score_t.max(2) # best previous scores and tags
score_t += h[:, t] # plus emission scores
bptr = torch.cat((bptr, bptr_t.unsqueeze(1)), 1)
score = score_t * mask_t + score * (1 - mask_t)
score += self.trans[EOS_IDX]
best_score, best_tag = torch.max(score, 1)
# back-tracking
bptr = bptr.tolist()
best_path = [[i] for i in best_tag.tolist()]
for b in range(self.batch_size):
i = best_tag[b] # best tag
j = int(mask[b].sum().item())
for bptr_t in reversed(bptr[b][:j]):
i = bptr_t[i]
best_path[b].append(i)
best_path[b].pop()
best_path[b].reverse()
return best_path
| 45.286697 | 155 | 0.564776 | 5,675 | 39,490 | 3.790132 | 0.054626 | 0.025943 | 0.010414 | 0.008787 | 0.878702 | 0.851132 | 0.838207 | 0.821052 | 0.793668 | 0.781301 | 0 | 0.012508 | 0.307597 | 39,490 | 871 | 156 | 45.338691 | 0.774129 | 0.208129 | 0 | 0.748333 | 0 | 0 | 0.034348 | 0 | 0 | 0 | 0 | 0 | 0.015 | 1 | 0.061667 | false | 0 | 0.006667 | 0 | 0.131667 | 0.023333 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
1af7c2e502d42550a977b81ffb32f89abb76c2e0 | 2,517 | py | Python | tests/migrator/utilities/test_lambda_utilities.py | bblommers/dynamodb-migrator | 3b9e97a631125fc6ba45ecc1339b917af3caa9cf | [
"MIT"
] | 2 | 2020-04-15T18:11:26.000Z | 2021-03-09T03:38:32.000Z | tests/migrator/utilities/test_lambda_utilities.py | bblommers/dynamodb-migrator | 3b9e97a631125fc6ba45ecc1339b917af3caa9cf | [
"MIT"
] | 14 | 2019-09-29T08:41:46.000Z | 2020-03-09T12:31:01.000Z | tests/migrator/utilities/test_lambda_utilities.py | bblommers/dynamodb-migrator | 3b9e97a631125fc6ba45ecc1339b917af3caa9cf | [
"MIT"
] | null | null | null | from migrator.utilities.LambdaUtilities import update_boto_client_endpoints
def test_none_returns_none():
assert update_boto_client_endpoints(None, None) is None
assert update_boto_client_endpoints("", None) == ""
def test_code_without_clients_returned_as_is():
existing_code = """
def handler():
return 'some data'
"""
assert update_boto_client_endpoints(existing_code, None) == existing_code
def test_code_with_one_client_returned_with_specified_endpoint():
existing_code = """
import boto3
def handler():
client = boto3.client('dynamodb2')
return 'some data'
"""
location_url = "http://localhost:5000"
expected_code = f"""
import boto3
def handler():
client = boto3.client('dynamodb2', endpoint_url='{location_url}')
return 'some data'
"""
assert update_boto_client_endpoints(existing_code, location_url) == expected_code
def test_code_with_multiple_clients_returned_with_specified_endpoint():
existing_code = """
import boto3
def handler():
client = boto3.client('dynamodb2')
client = boto3.client("sqs")
return 'some data'
"""
location_url = "http://localhost:5000"
expected_code = f"""
import boto3
def handler():
client = boto3.client('dynamodb2', endpoint_url='{location_url}')
client = boto3.client("sqs", endpoint_url='{location_url}')
return 'some data'
"""
assert update_boto_client_endpoints(existing_code, location_url) == expected_code
def test_code_with_multiple_clients_with_existing_endpoint_returned_with_specified_endpoint():
existing_code = """
import boto3
def handler():
client = boto3.client('dynamodb2', endpoint_url="http://dynamodb.aws.amazon.com")
client = boto3.client("sqs")
return 'some data'
"""
location_url = "http://localhost:5000"
expected_code = f"""
import boto3
def handler():
client = boto3.client('dynamodb2', endpoint_url='{location_url}')
client = boto3.client("sqs", endpoint_url='{location_url}')
return 'some data'
"""
assert update_boto_client_endpoints(existing_code, location_url) == expected_code
| 35.957143 | 101 | 0.607469 | 259 | 2,517 | 5.559846 | 0.169884 | 0.084028 | 0.118056 | 0.121528 | 0.850694 | 0.8375 | 0.8375 | 0.783333 | 0.783333 | 0.783333 | 0 | 0.019069 | 0.291617 | 2,517 | 69 | 102 | 36.478261 | 0.788559 | 0 | 0 | 0.830508 | 0 | 0 | 0.560191 | 0.11919 | 0 | 0 | 0 | 0 | 0.101695 | 1 | 0.084746 | false | 0 | 0.118644 | 0 | 0.322034 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
211ea17d9fadb748cba7871483f502fc842510d1 | 850,523 | py | Python | multi_script_editor/hqt.py | paulwinex/pw_multiScriptEditor | e447e99f87cb07e238baf693b7e124e50efdbc51 | [
"MIT"
] | 142 | 2015-03-21T12:56:21.000Z | 2022-02-08T04:42:46.000Z | multi_script_editor/hqt.py | paulwinex/pw_multiScriptEditor | e447e99f87cb07e238baf693b7e124e50efdbc51 | [
"MIT"
] | 8 | 2018-04-04T16:35:26.000Z | 2022-02-10T09:56:30.000Z | multi_script_editor/hqt.py | paulwinex/pw_multiScriptEditor | e447e99f87cb07e238baf693b7e124e50efdbc51 | [
"MIT"
] | 51 | 2016-05-07T14:27:42.000Z | 2022-02-10T05:55:11.000Z | """
hqt - QT helper for Houdini v1.3
Use function "show"
========================================================================================
manual:
help(hqt.show)
========================================================================================
By default for Windows and Houdini 13 script append path <C:/Python27/Lib/site-packages>
to environment PATH. If PySide installed in different folder you mast append this path manually.
"""
qt = 0
import hou
import sys, os, inspect
# import hqt to main
main = __import__('__main__')
ns = main.__dict__
if not __name__ in ns:
exec 'import ' + __name__ in ns
ver = hou.applicationVersion()
# check Houdini version
if ver[0] <= 13:
QT = False
# we are in Houdini 13 or lower
################# if PySide not installed inside houdini libs
import platform
if platform.system() == 'Windows':
sp = 'C:/Python27/Lib/site-packages'
if not sp in sys.path and os.path.exists(sp):
# if os.path.exists(sp):
sys.path.insert(0, sp)
################# IMPORTS
try:
from PySide.QtCore import *
from PySide.QtGui import *
qt = 1
except:
try:
from PyQt4.QtCore import *
from PyQt4.QtGui import *
qt = 2
except:
try:
from PySide2.QtCore import *
from PySide2.QtGui import *
from PySide2.QtWidgets import *
qt = 3
except:
raise Exception('Error load PyQt or PySide')
import inspect
else:
# we use Houdini 14 or higher
QT = True
import tempfile
try:
from PySide.QtCore import *
from PySide.QtGui import *
except:
from PySide2.QtCore import *
from PySide2.QtGui import *
from PySide2.QtWidgets import *
qt = 1
############################################################
############ GENERAL METHODS #############################
############################################################
def show(cls, clear=False, ontop=False, name=None, floating=False, position=(), size=(), pane=None, replacePyPanel=False, hideTitleMenu=True, dialog=False, useThisPanel=None):
"""
Main hqt function
Parameters:
cls : class of widget. NOT instance!
clear=False : delete exists window. For h13 only
ontop=False : window always on top (only floating window). For h13 only
name=None : window title in h13 or tab title in h14
floating=False : floating window or insert in tab Pane. For h14 only
position=() : tuple of int. Window Position. For floating window only
size=() : tuple of int. Window Size. For floating window only
pane=None : int number of pane to insert new tab. For h14 only
replacePyPanel=False : replace exists PythonPanel or create new. For h14 only
hideTitleMenu=True : True = hide PythonPanel menu, False = collapse only. For h14 only
useThisPanel : hou.PythonPanel, set special pythonPanel to insert widget. For h14 only
----------------------------------------------------------------------------------------------------------
Other functions:
hqt.houdiniColors() # list of colors in current Houdini theme
hqt.applyStyle(widget) # apply QT stile and Houdini icon for widget
hqt.setIcon(widget) # set Houdini icon for widget
hqt.getHouWindow() # return main Qt widget of Houdini
hqt.showWidget() # Just show widget
hqt.get_h14_style() # return qt stylesheet for current Houdini theme
"""
if QT: # h14
return showUi14( cls, name=name, floating=floating, position=position, size=size, pane=pane, replacePyPanel=replacePyPanel, hideTitleMenu=hideTitleMenu, dialog=dialog,useThisPanel=useThisPanel)
else: # h13
return showUi13( cls, app=None, clear=clear, ontop=ontop, position=position, size=size, name=name)
############################################################
############ METHODS FOR HOU 13 ###########################
############################################################
def showUi13(widget, app=None, clear=False, ontop=False, position=(), size=(), name=None):
"""
open pyqt ui in houdini 13
"""
#check application
if not app:
app = application()
if not isinstance(app, QApplication):
app = application()
#check widget
if inspect.isclass(widget): #object not created
window = widget()
else:
window = widget
#remove if exists
#need to object name
if clear:
clearUi(window.objectName())
if position:
window.move(position)
if size:
window.resize(size)
if name:
window.setWindowTitle(name)
# exec
if isinstance(window, QMenu): #for menu
action = window.exec_(QCursor().pos())
return action
elif isinstance(window, QDialog): #for dialog
window.setWindowFlags(Qt.WindowStaysOnTopHint)
setIcon(window)
result = window.exec_()
main.hqt.exec_(app, window)
return result, window
else: #for simple window
if ontop:
window.setWindowFlags(Qt.WindowStaysOnTopHint)
setIcon(window)
window.show()
window.setFocus()
main.hqt.exec_(app, window)
return window
def anyQtWindowsAreOpen():
return any(w.isVisible() for w in QApplication.topLevelWidgets())
def exec_(app, *args):
IntegratedEventLoop(app, args).exec_()
def execSynchronously(application, *args):
exec_(application, *args)
hou.ui.waitUntil(lambda: not anyQtWindowsAreOpen())
class IntegratedEventLoop(object):
def __init__(self, application, dialogs):
self.application = application
self.dialogs = dialogs
self.event_loop = QEventLoop()
def exec_(self):
hou.ui.addEventLoopCallback(self.processEvents)
def processEvents(self):
if not anyQtWindowsAreOpen():
hou.ui.removeEventLoopCallback(self.processEvents)
self.event_loop.processEvents()
self.application.sendPostedEvents(None, 0)
################################### Search application
def getApp():
qApp = QApplication.instance()
if qApp is None:
qApp = QApplication(['houdini'])
#houdini style
applyStyle(qApp)
return qApp
################################## Get main application in 13
def application():
return main.hqt.getApp()
###################################### CLEAR
def clearUi(name):
if name:
for w in application().topLevelWidgets():
if w.objectName() == name:
try:
w.close()
except:
pass
##################################### STYLE
def applyStyle(widget, theme=False, h13=False):
widget.setStyleSheet('')
widget.setStyleSheet(qss13() if h13 else get_h14_style(theme))
setIcon(widget)
def setIcon(widget):
if hou.applicationVersion()[0] < 15:
if widget.windowIcon().isNull():
ico = QIcon(':/houdini.png')
widget.setWindowIcon(ico)
else:
ico = hou.ui.createQtIcon('DESKTOP_application')
widget.setWindowIcon(ico)
############################################################
############ METHODS FOR HOU 14 ###########################
############################################################
def getHouWindow(): # temporary method
# check Houdini version
version = hou.applicationVersion()[0]
if 13 <= version:
app = QApplication.instance()
for w in app.topLevelWidgets():
if w.windowIconText():
return w
elif 13 < version < 17:
return hou.ui.mainQtWindow()
elif version > 16:
return hou.qt.mainWindow()
houWindow = getHouWindow()
def showUi14(cls, name=None, floating=False, position=(),
size=(), pane=None, replacePyPanel=False,
hideTitleMenu=True, dialog=False, useThisPanel=None, args=None, kwargs=None):
"""
open qt ui in houdini 14
"""
if not inspect.isclass(cls):
raise Exception('Object should be class, not instance')
if dialog:
h = getHouWindow()
dial = cls(h, *(args or []), **(kwargs or {}))
dial.setStyleSheet('')
dial.setStyleSheet(get_h14_style())
res = dial.exec_()
return (res, dial)
panFile = createPanelFile(cls, name)
panFile = os.path.normpath(panFile).replace('\\', '/')
hou.pypanel.installFile(panFile)
pypan = hou.pypanel.interfacesInFile(panFile)[0]
menu = installedInterfaces()
menu.append(pypan.name())
menu = [x for x in menu if not x == '__separator__']
new = []
for m in menu:
if not m in new:
new.append(m)
hou.pypanel.setMenuInterfaces(tuple(new))
if pane is None:
pane = max(0,len(hou.ui.curDesktop().panes())-1)
if useThisPanel:
python_panel = useThisPanel
else:
python_panel = None
if floating:
python_panel = hou.ui.curDesktop().createFloatingPaneTab(hou.paneTabType.PythonPanel, position, size)
else:
if replacePyPanel:
for p in hou.ui.curDesktop().panes():
for t in p.tabs():
if t.type() == hou.paneTabType.PythonPanel:
python_panel = t.setType(hou.paneTabType.PythonPanel)
if not python_panel:
python_panel = hou.ui.curDesktop().panes()[pane].createTab(hou.paneTabType.PythonPanel)
else:
python_panel = hou.ui.curDesktop().panes()[pane].createTab(hou.paneTabType.PythonPanel)
python_panel.setIsCurrentTab()
if hideTitleMenu:
python_panel.showToolbar(0)
else:
python_panel.showToolbar(1)
python_panel.expandToolbar(0)
if hou.applicationVersion()[0] < 15:
python_panel.setInterface(pypan)
else:
python_panel.setActiveInterface(pypan)
QTimer.singleShot(2000, lambda x=panFile:delPanFile(x))
def showWidget(widget, tool=False):
"""
Just show widget
"""
if inspect.isclass(widget): #object not created
widget = widget()
widget.setParent(getHouWindow())
if tool:
widget.setWindowFlags(Qt.Tool)
else:
widget.setWindowFlags(Qt.Window)
applyStyle(widget)
widget.show()
return widget
def delPanFile(path):
try:
os.remove(path)
except:
pass
def installedInterfaces():
res = []
menu = hou.pypanel.menuInterfaces()
for i in menu:
try:
hou.pypanel.setMenuInterfaces((i,))
res.append(i)
except:
pass
return res
def createPanelFile(cls, name=None):
"""
quick save python panel file
"""
main.__dict__[cls.__name__] = cls
if not name:
name = cls.__name__
xml = '''<?xml version="1.0" encoding="UTF-8"?>
<pythonPanelDocument>
<interface name="{0}" label="{1}" icon="MISC_python">
<script><![CDATA[main = __import__('__main__')
def createInterface():
w = main.__dict__['{0}']()
w.setStyleSheet('')
w.setStyleSheet( main.__dict__['hqt'].get_h14_style() )
return w
]]></script>
</interface>
</pythonPanelDocument>'''.format(cls.__name__, name)
tmp = tempfile.NamedTemporaryFile(delete = False, suffix='.pypanel')
tmp.write(xml)
tmp.close()
return tmp.name
class houdiniMenu(QMenu):
def __init__(self):
super(houdiniMenu, self).__init__(getHouWindow())
self.par = getHouWindow()
def addItem(self, name, callback, icon=None):
if not isinstance(name, str):
return False
if not hasattr(callback, '__call__'):
return False
act = QAction(name, self.par)
act.triggered.connect(callback)
if icon:
if isinstance(icon, str):
if os.path.exists(icon):
try:
icon = QIcon(icon)
act.setIcon(icon)
except:
print 'Error create icon:', icon
else:
try:
icon = hou.ui.createQtIcon(icon)
act.setIcon(icon)
except:
print 'Icon not found:', icon
elif isinstance(icon, QIcon):
act.setIcon(icon)
self.addAction(act)
def show(self, *args, **kwargs):
return self.exec_(QCursor.pos())
############################################################
############ RESOURCES ###################################
############################################################
import os, re, glob
# import hqt
# s = hqt.get_h14_style('Houdini Dark')
# w.setStyleSheet(s)
def get_h14_style(theme=None):
colors = getThemeColors(theme)
if colors:
style = []
mid = sum(colors['BackColor'])/3
gamma = qss14ImagesDark if mid < 0.5 else qss14ImagesLight
for l in qss14().split('\n'):
variables = re.findall('(@.*@)', l)
if variables:
for v in variables:
val = v[1:-1]
br = 1
if ':Brightness' in val:
name, br = val.split(':')
br = float(br.split('=')[-1])
else:
name = val
if name in colors:
c = ','.join([str( min(int(x*br*255),255) ) for x in colors[name]])
else:
#use default
print 'Color not found', name
c = ','.join([str( min(int(x*br*255),255) ) for x in colors['BackColor']])
l = l.replace(v, c)
images = re.findall('(\$.*\$)', l)
if images:
for i in images:
img = i[1:-1]
if img in gamma:
l = l.replace(i, gamma[img])
style.append(l)
return '\n'.join(style)
# return hou.ui.qtStyleSheet()+'\n'.join(style)
else:
return ''
def getCurrentColorTheme():
pref = hou.homeHoudiniDirectory()
uipref = os.path.join(pref, 'ui.pref')
if os.path.exists(uipref):
with open(uipref) as f:
for l in f.readlines():
if l.startswith('colors.scheme'):
theme = re.findall('\"(.*)\"', l)[0]
return theme
def getThemeColors(theme=None):
if not theme:
theme = getCurrentColorTheme()
conf = os.path.join(hou.getenv('HFS'), 'houdini', 'config')
if os.path.exists(conf):
for uif in glob.glob1(conf, "*.hcs"):
path = os.path.join(conf, uif)
with open(path) as f:
name = f.readline().split(':')[-1].strip()
if name == theme:
reader = colorReader(path)
colors = reader.parse()
return colors
class houdiniColorsClass(QMainWindow):
def __init__(self, theme=None):
super(houdiniColorsClass, self).__init__(getHouWindow(), Qt.WindowStaysOnTopHint)
# self.setWindowFlags()
self.centralwidget = QWidget(self)
self.verticalLayout = QVBoxLayout(self.centralwidget)
self.scrollArea = QScrollArea(self.centralwidget)
self.scrollArea.setWidgetResizable(True)
self.scrollAreaWidgetContents = QWidget()
self.verticalLayout_2 = QVBoxLayout(self.scrollAreaWidgetContents)
self.widget = QWidget(self.scrollAreaWidgetContents)
self.verticalLayout_3 = QGridLayout(self.widget)
self.verticalLayout_2.addWidget(self.widget)
self.scrollArea.setWidget(self.scrollAreaWidgetContents)
self.verticalLayout.addWidget(self.scrollArea)
self.setCentralWidget(self.centralwidget)
self.setStyleSheet(get_h14_style())
colors = getThemeColors(theme)
for i, name in enumerate(sorted(colors, key=lambda x:x)):
color = colors[name]
if not color:continue
if len(color) != 3:
continue
l = QLineEdit()
l.setMinimumWidth(200)
l.setText(name)
l.setReadOnly(1)
self.verticalLayout_3.addWidget(l, i, 0)
c = QLabel()
c.setText(str(color))
col = QColor()
if (sum(color)/3) < 0.5:
text = '#fff'
else:
text = '#000'
col.setRgbF(*color)
style = 'background-color: %s; color: %s;' % (col.name(), text)
# print style
c.setStyleSheet(style)
self.verticalLayout_3.addWidget(c, i, 1)
def houdiniColors():
show(houdiniColorsClass, name='Colors', hideTitleMenu=True)
# showUi14(houdiniColorsClass, name='Colors', floating=False)
class colorReader(object):
'''
Read houdini color theme
'''
def __init__(self, path):
self.lines = []
self.colors = {}
if os.path.exists(path):
self.lines = open(path).readlines()
self.preDef = dict(white=[1,1,1],
black=[0,0,0],
red=[1,0,0],
green=[0,1,0],
blue=[0,0,1])
def parse(self):
for l in self.lines:
if l.startswith('//'):
continue
l = l.split('//')[0].strip()
if not l:
continue
name, value = self.getColor(l)
if name:
if not name in self.colors:
if isinstance(value,str):
if value in self.colors.keys():
self.colors[name] = self.colors[value]
else:
self.colors[name] = value
return self.colors
def getColor(self, line):
name = val = None
if line.startswith('#define'):
spl = line.split()[1:]
name = spl[0]
val = ' '.join(spl[1:])
else:
parts = line.split(':')
if len(parts) == 2:
name, val = parts
val = self.strToColor(val)
return name, val
def strToColor(self, line):
if not line:
return None
line = line.strip()
if line.lower() in self.preDef:
return self.preDef[line.lower()]
gr = re.findall("grey\(([0-9\.]+)\)", line ,flags=re.IGNORECASE)
if gr:
g = round(float(gr[0]),4)
return [g, g, g]
gr = re.findall("grey([0-9\.]+)", line.strip() ,flags=re.IGNORECASE)
if gr:
g = round(float(gr[0])/255,4)
return [g,g,g]
hsv = re.findall("HSV\s+([\d\.]*\s+[\d\.]*\s+[\d\.]*)", line, flags=re.IGNORECASE)
if hsv:
h,s,v = [float(x) for x in hsv[0].split()]
qc = QColor()
qc.setHsv(h,min(s,1)*255,min(v,1)*255)
return [round(qc.red()*1.0/255, 4), round(qc.green()*1.0/255, 4), round(qc.blue()*1.0/255, 4)]
rgb = re.findall("([\d\.]*\s+[\d\.]*\s+[\d\.]*)", line, flags=re.IGNORECASE)
if rgb:
return [round(float(x),4) for x in rgb[0].split()]
hx = re.findall("#[a-zA-Z0-9]+\s*[a-zA-Z0-9]+\s*[a-zA-Z0-9]+", line ,flags=re.IGNORECASE)
if hx:
qc = QColor(''.join(hx[0].split()))
return [round(qc.red()*1.0/255, 4), round(qc.green()*1.0/255, 4), round(qc.blue()*1.0/255, 4)]
shd = re.findall("^SHADOW([0-9_]+)", line ,flags=re.IGNORECASE) # For some "Houdini Pro" lines
if shd:
if not line in self.colors:
g = float( '0.'+shd[0].replace('_','') )
return [g, g, g]
return line
#custum icons resource
if qt == 2:#PyQt
qt_resource_data = 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qt_resource_struct = "\x00\x00\x00\x00\x00\x02\x00\x00\x00\x20\x00\x00\x00\x01\x00\x00\x01\xd6\x00\x00\x00\x00\x00\x01\x00\x00\x67\xc8\x00\x00\x03\xca\x00\x01\x00\x00\x00\x01\x00\x00\xc9\xdd\x00\x00\x04\x2e\x00\x01\x00\x00\x00\x01\x00\x00\xe4\xad\x00\x00\x01\x26\x00\x01\x00\x00\x00\x01\x00\x00\x32\x83\x00\x00\x00\xca\x00\x01\x00\x00\x00\x01\x00\x00\x24\x6c\x00\x00\x00\x3c\x00\x01\x00\x00\x00\x01\x00\x00\x05\xd2\x00\x00\x01\xba\x00\x01\x00\x00\x00\x01\x00\x00\x57\xae\x00\x00\x04\x18\x00\x00\x00\x00\x00\x01\x00\x00\xe4\x33\x00\x00\x01\x74\x00\x00\x00\x00\x00\x01\x00\x00\x46\xd1\x00\x00\x00\x00\x00\x00\x00\x00\x00\x01\x00\x00\x00\x00\x00\x00\x02\x90\x00\x01\x00\x00\x00\x01\x00\x00\x93\xc5\x00\x00\x02\x66\x00\x01\x00\x00\x00\x01\x00\x00\x8a\x91\x00\x00\x02\x3a\x00\x01\x00\x00\x00\x01\x00\x00\x7a\x3f\x00\x00\x03\x48\x00\x01\x00\x00\x00\x01\x00\x00\xaf\xc8\x00\x00\x04\x6c\x00\x00\x00\x00\x00\x01\x00\x01\x04\xf4\x00\x00\x00\xf4\x00\x01\x00\x00\x00\x01\x00\x00\x29\x4d\x00\x00\x03\x98\x00\x01\x00\x00\x00\x01\x00\x00\xc5\x24\x00\x00\x00\x98\x00\x01\x00\x00\x00\x01\x00\x00\x1b\x73\x00\x00\x02\xba\x00\x01\x00\x00\x00\x01\x00\x00\x9c\x83\x00\x00\x03\xea\x00\x01\x00\x00\x00\x01\x00\x00\xd9\xf5\x00\x00\x03\x6a\x00\x01\x00\x00\x00\x01\x00\x00\xbf\x9f\x00\x00\x03\x1a\x00\x01\x00\x00\x00\x01\x00\x00\xa6\xc5\x00\x00\x02\xec\x00\x01\x00\x00\x00\x01\x00\x00\xa1\x6b\x00\x00\x04\x4e\x00\x01\x00\x00\x00\x01\x00\x00\xf5\x33\x00\x00\x00\x84\x00\x00\x00\x00\x00\x01\x00\x00\x1a\xf4\x00\x00\x00\x5e\x00\x01\x00\x00\x00\x01\x00\x00\x15\x96\x00\x00\x02\x14\x00\x01\x00\x00\x00\x01\x00\x00\x70\xfd\x00\x00\x00\x16\x00\x01\x00\x00\x00\x01\x00\x00\x00\xdc\x00\x00\x01\xee\x00\x01\x00\x00\x00\x01\x00\x00\x68\x2b\x00\x00\x01\x92\x00\x01\x00\x00\x00\x01\x00\x00\x47\xaa\x00\x00\x04\x86\x00\x00\x00\x00\x00\x01\x00\x01\x05\xdd\x00\x00\x01\x50\x00\x01\x00\x00\x00\x01\x00\x00\x36\xf6"
def qInitResources():
qRegisterResourceData(0x01, qt_resource_struct, qt_resource_name, qt_resource_data)
def qCleanupResources():
qUnregisterResourceData(0x01, qt_resource_struct, qt_resource_name, qt_resource_data)
qInitResources()
elif qt == 1: #PySide
qt_resource_data = "\x00\x00\x00\xd8\x89PNG\x0d\x0a\x1a\x0a\x00\x00\x00\x0dIHDR\x00\x00\x00\x14\x00\x00\x00\x14\x08\x06\x00\x00\x00\x8d\x89\x1d\x0d\x00\x00\x00\x9fIDAT8\x8dc` \x12\xf4\xf4\xf4\xfc'F\x1d\x13\xb1\x06\x12\x0bF\x0d\xa4\x1c0\xe2\x92\xa8i\xa8\x22\x18\xab-\x0dm\x18\xfaY\xf0i\x88\x0a\x8b\xc2)\xb7l\xd52\xac\xe2\x83?\x0c\xf1z\x99\x81\x81\x81!82\x16Cl\xed\xf2\xc5\x0c\x0c\x0c\x0c\x0c\x1d\x1d\x1d\xff\xff\xff\xff\xcf\xc0\xc8\x08\x09\xca\xff\xff\xff\x136\x10\xa6\x19\x1b\xa8\xa8\xa8\xc0\x88\x14\x9c^\xfe\xf0\xfe\x03^\x8b\xde\xbd}\x87U\x1c\xa7\x81\xef\xde\xbd\xc7o \x0ey\x9c^~\xf7\xf6\x1dCaI1\x84\xf3\x1f\x92$\xe1\x09\xf3?Q\x05\x0f~0Z|\xd1\xcf@\x00\xd9\xc4/\xb0s\x9d\xad8\x00\x00\x00\x00IEND\xaeB`\x82\x00\x00:\xe2\x89PNG\x0d\x0a\x1a\x0a\x00\x00\x00\x0dIHDR\x00\x00\x00\x0e\x00\x00\x00\x0e\x08\x00\x00\x00\x00:#r\x0d\x00\x00\x00\x09pHYs\x00\x00\x0b\x13\x00\x00\x0b\x13\x01\x00\x9a\x9c\x18\x00\x009\xcfiTXtXML:com.adobe.xmp\x00\x00\x00\x00\x00<?xpacket begin=\x22\xef\xbb\xbf\x22 id=\x22W5M0MpCehiHzreSzNTczkc9d\x22?>\x0a<x:xmpmeta xmlns:x=\x22adobe:ns:meta/\x22 x:xmptk=\x22Adobe XMP Core 5.5-c014 79.151481, 2013/03/13-12:09:15 \x22>\x0a <rdf:RDF xmlns:rdf=\x22http://www.w3.org/1999/02/22-rdf-syntax-ns#\x22>\x0a <rdf:Description rdf:about=\x22\x22\x0a xmlns:xmp=\x22http://ns.adobe.com/xap/1.0/\x22\x0a xmlns:xmpMM=\x22http://ns.adobe.com/xap/1.0/mm/\x22\x0a xmlns:stEvt=\x22http://ns.adobe.com/xap/1.0/sType/ResourceEvent#\x22\x0a xmlns:dc=\x22http://purl.org/dc/elements/1.1/\x22\x0a xmlns:photoshop=\x22http://ns.adobe.com/photoshop/1.0/\x22\x0a xmlns:tiff=\x22http://ns.adobe.com/tiff/1.0/\x22\x0a xmlns:exif=\x22http://ns.adobe.com/exif/1.0/\x22>\x0a <xmp:CreatorTool>Adobe Photoshop CC (Windows)</xmp:CreatorTool>\x0a <xmp:CreateDate>2014-01-22T14:00:13+04:00</xmp:CreateDate>\x0a <xmp:MetadataDate>2014-01-22T14:00:13+04:00</xmp:MetadataDate>\x0a <xmp:ModifyDate>2014-01-22T14:00:13+04:00</xmp:ModifyDate>\x0a 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qt_resource_struct = "\x00\x00\x00\x00\x00\x02\x00\x00\x00 \x00\x00\x00\x01\x00\x00\x01\xd6\x00\x00\x00\x00\x00\x01\x00\x02\x8b\x0b\x00\x00\x03\xca\x00\x00\x00\x00\x00\x01\x00\x05JV\x00\x00\x04.\x00\x00\x00\x00\x00\x01\x00\x05\xdd\xcd\x00\x00\x01&\x00\x00\x00\x00\x00\x01\x00\x01}p\x00\x00\x00\xca\x00\x00\x00\x00\x00\x01\x00\x00\xffU\x00\x00\x00<\x00\x00\x00\x00\x00\x01\x00\x00;\xc2\x00\x00\x01\xba\x00\x00\x00\x00\x00\x01\x00\x02C\xdc\x00\x00\x04\x18\x00\x00\x00\x00\x00\x01\x00\x05\xddS\x00\x00\x01t\x00\x00\x00\x00\x00\x01\x00\x01\xfdf\x00\x00\x00\x00\x00\x00\x00\x00\x00\x01\x00\x00\x00\x00\x00\x00\x02\x90\x00\x00\x00\x00\x00\x01\x00\x03\x92Z\x00\x00\x02f\x00\x00\x00\x00\x00\x01\x00\x03Q9\x00\x00\x02:\x00\x00\x00\x00\x00\x01\x00\x03\x0bL\x00\x00\x03H\x00\x00\x00\x00\x00\x01\x00\x04\x8b&\x00\x00\x04l\x00\x00\x00\x00\x00\x01\x00\x06iO\x00\x00\x00\xf4\x00\x00\x00\x00\x00\x01\x00\x01<P\x00\x00\x03\x98\x00\x00\x00\x00\x00\x01\x00\x05\x0e\x14\x00\x00\x00\x98\x00\x00\x00\x00\x00\x01\x00\x00\xbe\xfa\x00\x00\x02\xba\x00\x00\x00\x00\x00\x01\x00\x03\xd0\xff\x00\x00\x03\xea\x00\x00\x00\x00\x00\x01\x00\x05\x91\x86\x00\x00\x03j\x00\x00\x00\x00\x00\x01\x00\x04\xd0\x99\x00\x00\x03\x1a\x00\x00\x00\x00\x00\x01\x00\x04J\xc3\x00\x00\x02\xec\x00\x00\x00\x00\x00\x01\x00\x04\x0e\x06\x00\x00\x04N\x00\x00\x00\x00\x00\x01\x00\x06#\xef\x00\x00\x00\x84\x00\x00\x00\x00\x00\x01\x00\x00\xbe{\x00\x00\x00^\x00\x00\x00\x00\x00\x01\x00\x00\x81\x1f\x00\x00\x02\x14\x00\x00\x00\x00\x00\x01\x00\x02\xca\x1f\x00\x00\x00\x16\x00\x00\x00\x00\x00\x01\x00\x00\x00\xdc\x00\x00\x01\xee\x00\x00\x00\x00\x00\x01\x00\x02\x8bn\x00\x00\x01\x92\x00\x00\x00\x00\x00\x01\x00\x01\xfe?\x00\x00\x04\x86\x00\x00\x00\x00\x00\x01\x00\x06j8\x00\x00\x01P\x00\x00\x00\x00\x00\x01\x00\x01\xb7\xf0"
def qInitResources():
qRegisterResourceData(0x01, qt_resource_struct, qt_resource_name, qt_resource_data)
def qCleanupResources():
qUnregisterResourceData(0x01, qt_resource_struct, qt_resource_name, qt_resource_data)
qInitResources()
else:
raise Exception( "Can find PySide or PyQt library")
qss14ImagesDark = dict(
cb_unchecked = ':/cb_unchecked_d.png',
cb_unchecked_dis = ':/cb_unchecked_dis_d.png',
cb_checked = ':/cb_checked_d.png',
cb_checked_dis = ':/cb_checked_dis_d.png',
rb_unchecked = ':/rb_unchecked_d.png',
rb_unchecked_dis = ':/rb_unchecked_dis_d.png',
rb_checked = ':/rb_checked_d.png',
rb_checked_dis = ':/rb_checked_dis_d.png'
)
qss14ImagesLight = dict(
cb_unchecked = ':/cb_unchecked_l.png',
cb_unchecked_dis = ':/cb_unchecked_dis_l.png',
cb_checked = ':/cb_checked_l.png',
cb_checked_dis = ':/cb_checked_dis_l.png',
rb_unchecked = ':/rb_unchecked_l.png',
rb_unchecked_dis = ':/rb_unchecked_dis_l.png',
rb_checked = ':/rb_checked_l.png',
rb_checked_dis = ':/rb_checked_dis_l.png'
)
# houdini QSS Style
def qss13():
'''
custom Qt qss for houdini 13. Black theme only
'''
s = '''/******* QWidget ********/
QWidget
{
color: #b1b1b1;
background-color:#3a3a3a;
}
QWidget:disabled
{
color: #b1b1b1;
background-color: #252525;
}
QAbstractScrollArea,QTableView
{
border: 1px solid #222;
}
/************** QMainWindow *************/
QMainWindow::separator
{
background-color: QLinearGradient(x1:0, y1:0, x2:0, y2:1, stop:0 #161616, stop: 0.5 #151515, stop: 0.6 #212121, stop:1 #343434);
color: white;
padding-left: 4px;
border: 1px solid #4c4c4c;
spacing: 2px;
}
QMainWindow::separator:hover
{
background-color: QLinearGradient(x1:0, y1:0, x2:0, y2:1, stop:0 #d7801a, stop:0.5 #b56c17 stop:1 #ffa02f);
color: white;
padding-left: 4px;
border: 1px solid #6c6c6c;
spacing: 3px;
}
/************** QToolTip **************/
QToolTip
{
border: 1px solid black;
background-color: #000;
padding: 1px;
padding-left: 4px;
padding-right: 4px;
border-radius: 3px;
color: white;
opacity: 100;
}
/***************** QMenuBar *************/
QMenuBar::item
{
background: transparent;
}
QMenuBar::item:selected
{
background-color: #555555;
color: #fff;
}
QMenuBar::item:pressed
{
background: #444;
border: 1px solid #000;
background-color: QLinearGradient(
x1:0, y1:0,
x2:0, y2:1,
stop:1 #212121,
stop:0.4 #343434
);
margin-bottom:-1px;
padding-bottom:1px;
}
/**************** QMenu **********/
QMenu
{
border: 1px solid #000;
}
QMenu::item
{
background-color: #3a3a3a;
padding: 2px 20px 2px 20px;
margin-left: 14px;
}
QMenu::item:selected
{
color: #fff;
background-color: #555555;
}
QMenu::separator
{
height: 2px;
background-color: QLinearGradient(x1:0, y1:0, x2:0, y2:1, stop:0 #161616, stop: 0.5 #151515, stop: 0.6 #212121, stop:1 #343434);
color: white;
padding-left: 4px;
margin-left: 20px;
margin-right: 5px;
}
/************* QAbstractItemView ***********/
QAbstractItemView
{
background-color: #353535;
alternate-background-color: #323232;
outline: 0;
height: 20px;
}
/************ QTreeView **********/
QTreeView::item:alternate,
QListView::item:alternate {
background-color: #323232;
}
QTreeView::branch:has-siblings:!adjoins-item
{
border-image: url(:/vline.png) 0;
}
QTreeView::branch:has-siblings:adjoins-item
{
border-image: url(:/more.png) 0;
}
QTreeView::branch:!has-children:!has-siblings:adjoins-item
{
border-image: url(:/end.png) 0;
}
QTreeView::branch:closed:has-children:has-siblings
{
border-image: url(:/closed.png) 0;
}
QTreeView::branch:closed:has-children:!has-siblings
{
border-image: url(:/closed_end.png) 0;
}
QTreeView::branch:open:has-children:!has-siblings
{
border-image: url(:/open_end.png) 0;
}
QTreeView::branch:open:has-children:has-siblings
{
border-image: url(:/open.png) 0;
}
/********************* QListView ************/
QListView::item,
QTreeView::item
{
color: rgb(220,220,220);
border-color: rgba(0,0,0,0);
border-width: 1px;
border-style: solid;
}
QListView::item:selected,
QTreeView::item:selected
{
background: #605132;
border-color: #b98620;
}
/*************** QTableView ********/
QHeaderView::section
{
background-color: QLinearGradient( x1: 0, y1: 0, x2: 0, y2: 1, stop: 0 #393939, stop: 1 #272727);
color: #b1b1b1;
border: 1px solid #191919;
border-top-width: 0px;
border-left-width: 0px;
padding-left: 10px;
padding-right: 10px;
padding-top: 3px;
padding-bottom: 3px;
}
QTableView {
alternate-background-color: #2e2e2e
}
QTableView::item:selected {
background: #605132;
border: 1px solid #b98620;
color: rgb(220,220,220);
}
QTableView QTableCornerButton::section {
background-color: QLinearGradient( x1: 0, y1: 0, x2: 0, y2: 1, stop: 0 #393939, stop: 1 #272727);
border: 1px solid #191919;
border-top-width: 0px;
border-left-width: 0px;
}
/*************** QlineEdit ************/
QLineEdit,QDateEdit,QDateTimeEdit,QSpinBox
{
background-color: #000;
padding: 1px;
border-style: solid;
border: 2px solid #2b2b2b;
border-radius: 0;
color:rgb(255,255,255);
min-height: 18px;
selection-background-color: rgb(185,134,32);
selection-color: rgb(0,0,0);
}
/*************** QPushButton ***********/
QPushButton
{
color: #b1b1b1;
background-color: QLinearGradient( x1: 0, y1: 0, x2: 0, y2: 1, stop: 0 #535353, stop: 0.1 #515151, stop: 0.5 #474747, stop: 0.9 #3d3d3d, stop: 1 #3a3a3a);
border: 2px solid #232323;
border-top-width: 2px;
border-left-width: 2px;
border-top-color: QLinearGradient( x1: 0, y1: 0, x2: 0, y2: 1, stop: 0 #101010, stop: 1 #818181);
border-left-color: QLinearGradient( x1: 0, y1: 0, x2: 1, y2: 0, stop: 0 #101010, stop: 1 #818181);
border-radius: 0;
padding: 3px;
font-size: 12px;
padding-left: 10px;
padding-right: 10px;
}
QPushButton:disabled
{
background-color: #424242;
border: 2px solid #313131;
border-top-width: 2px;
border-left-width: 2px;
border-top-color: QLinearGradient( x1: 0, y1: 0, x2: 0, y2: 1, stop: 0 #151515, stop: 1 #777777);
border-left-color: QLinearGradient( x1: 0, y1: 0, x2: 1, y2: 0, stop: 0 #151515, stop: 1 #777777);
color: #777;
}
QPushButton:checked
{
border-color: #000;
background-color: #2d2d2d;
color: #cacaca;
border-width: 1px;
}
QPushButton:hover
{
background-color: QLinearGradient( x1: 0, y1: 0, x2: 0, y2: 1, stop: 0 #606060, stop: 0.1 #585858, stop: 0.5 #545454, stop: 0.9 #3d3d3d, stop: 1 #3a3a3a);
}
QPushButton:pressed
{
background-color: #af8021;
color: #fff;
}
/*********** QScrollBar ***************/
QScrollBar:horizontal {
border: 1px solid #222222;
background: #222;
height: 15px;
margin: 0px 14px 0 14px;
}
QScrollBar:vertical
{
border: 1px solid #222222;
background: #222;
width: 15px;
margin: 14px 0 14px 0;
border: 1px solid #222222;
}
QScrollBar::handle:vertical
{
background: QLinearGradient( x1: 0, y1: 0, x2: 1, y2: 0, stop: 0 #535353, stop: 0.1 #515151, stop: 0.5 #474747, stop: 0.9 #3d3d3d, stop: 1 #3a3a3a);
min-height: 20px;
border-radius: 0px;
border: 1px solid #222222;
border-left-width: 0px;
border-right-width: 0px;
}
QScrollBar::handle:horizontal
{
background: QLinearGradient( x1: 0, y1: 0, x2: 0, y2: 1, stop: 0 #535353, stop: 0.1 #515151, stop: 0.5 #474747, stop: 0.9 #3d3d3d, stop: 1 #3a3a3a);
min-height: 20px;
border-radius: 0px;
border: 1px solid #222222;
border-top-width: 0px;
border-bottom-width: 0px;
}
QScrollBar::add-line:horizontal, QScrollBar::sub-line:horizontal
{
border: 1px solid #1b1b19;
border-radius: 0px;
background:QLinearGradient( x1: 0, y1: 0, x2: 0, y2: 1, stop: 0 #535353, stop: 0.1 #515151, stop: 0.5 #474747, stop: 0.9 #3d3d3d, stop: 1 #3a3a3a);
width: 14px;
subcontrol-origin: margin;
}
QScrollBar::add-line:vertical, QScrollBar::sub-line:vertical
{
border: 1px solid #1b1b19;
border-radius: 1px;
background: QLinearGradient( x1: 0, y1: 0, x2: 1, y2: 0, stop: 0 #535353, stop: 0.1 #515151, stop: 0.5 #474747, stop: 0.9 #3d3d3d, stop: 1 #3a3a3a);
height: 14px;
subcontrol-origin: margin;
}
QScrollBar::add-line:horizontal:pressed, QScrollBar::sub-line:horizontal:pressed ,
QScrollBar::add-line:vertical:pressed, QScrollBar::sub-line:vertical:pressed
{
background: #5b5a5a;
}
QScrollBar::sub-line:vertical
{
subcontrol-position: top;
}
QScrollBar::add-line:vertical
{
subcontrol-position: bottom;
}
QScrollBar::sub-line:horizontal
{
subcontrol-position: left;
}
QScrollBar::add-line:horizontal
{
subcontrol-position: right;
}
QScrollBar::add-page:horizontal, QScrollBar::sub-page:horizontal
{
background: none;
}
QScrollBar::up-arrow:vertical
{
border-image: url(:/arrow_up.png) 1;
}
QScrollBar::down-arrow:vertical
{
border-image: url(:/arrow_down.png) 1;
}
QScrollBar::right-arrow:horizontal
{
border-image: url(:/arrow_right.png) 1;
}
QScrollBar::left-arrow:horizontal
{
border-image: url(:/arrow_left.png) 1;
}
QScrollBar::add-page:vertical, QScrollBar::sub-page:vertical
{
background: none;
}
/********* QSlider **************/
QSlider::groove:horizontal {
border: 1px solid #000;
background: #000;
height: 3px;
border-radius: 0px;
}
QSlider::sub-page:horizontal {
background: #404040;
border: 1px solid #000;
height: 10px;
border-radius: 0px;
}
QSlider::add-page:horizontal {
background: #626262;
border: 1px solid #000;
height: 10px;
border-radius: 0px;
}
QSlider::handle:horizontal {
background: qlineargradient(x1:0, y1:0, x2:1, y2:1, stop:0 #696969, stop:1 #505050);
border: 1px solid #000;
width: 5px;
margin-top: -8px;
margin-bottom: -8px;
border-radius: 0px;
}
QSlider::hover
{
background: #3f3f3f;
}
QSlider::groove:vertical {
border: 1px solid #ffaa00;
background: #ffaa00;
width: 3px;
border-radius: 0px;
}
QSlider::add-page:vertical {
background: qlineargradient(x1: 0, y1: 0, x2: 0, y2: 1, stop: 0 #ffaa00, stop: 1 #ffaa00);
background:#404040;
border: 1px solid #000;
width: 8px;
border-radius: 0px;
}
QSlider::sub-page:vertical {
background: #626262;
border: 1px solid #000;
width: 8px;
border-radius: 0px;
}
QSlider::handle:vertical {
background: qlineargradient(x1:0, y1:0, x2:1, y2:1, stop:0 #696969, stop:1 #505050);
border: 1px solid #000;
height: 5px;
margin-left: -8px;
margin-right: -8px;
border-radius: 0px;
}
/* disabled */
QSlider::sub-page:disabled, QSlider::add-page:disabled
{
border-color: #3a3a3a;
background: #414141;
border-radius: 0px;
}
QSlider::handle:disabled {
background: #3a3a3a;
border: 1px solid #242424;
}
QSlider::disabled {
background: #3a3a3a;
}
/********* QProgressBar ***********/
QProgressBar
{
border: 1px solid #6d6c6c;
border-radius: 0px;
text-align: center;
background:#262626;
color: gray;
border-bottom: 1px #545353;
}
QProgressBar::chunk
{
background-color: QLinearGradient( x1: 0, y1: 0, x2: 0, y2: 1,
stop: 0 #f0d66e,
stop: 0.09 #f0d66e,
stop: 0.1 #ecdfa8,
stop: 0.7 #d9a933,
stop: 0.91 #b88822);
}
/************ QComboBox ************/
QComboBox
{
selection-background-color: #ffaa00;
background-color: QLinearGradient( x1: 0, y1: 0, x2: 0, y2: 1, stop: 0 #515151, stop: 0.5 #484848, stop: 1 #3d3d3d);
border-style: solid;
border: 1px solid #000;
border-radius: 0;
padding-left: 9px;
min-height: 20px;
font: 10pt;
}
QComboBox:hover
{
background-color: QLinearGradient( x1: 0, y1: 0, x2: 0, y2: 1, stop: 0 #555555, stop: 0.5 #4d4d4d, stop: 1 #414141);
/* font: 14pt;*/
}
QComboBox:on
{
background-color: #b98620;
color:#fff;
selection-background-color: #494949;
}
QComboBox::drop-down
{
subcontrol-origin: padding;
subcontrol-position: top right;
width: 25px;
background-color:QLinearGradient( x1: 0, y1: 0, x2: 0, y2: 1, stop: 0 #3d3d3d, stop: 1 #282828);
border-width: 0px;
}
QComboBox::down-arrow
{
image: url(:/arrow_up_down.png);
}
QComboBox QAbstractItemView
{
background-color: #3a3a3a;
border-radius: 0px;
border: 1px solid #101010;
border-top-color: #818181;
border-left-color: #818181;
selection-background-color: #606060;
padding: 2px;
}
QComboBox QAbstractItemView::item
{
margin-top: 3px;
}
QListView#comboListView {
background: rgb(80, 80, 80);
color: rgb(220, 220, 220);
min-height: 90px;
margin: 0 0 0 0;
}
QListView#comboListView::item {
background-color: rgb(80, 80, 80);
}
QListView#comboListView::item:hover {
background-color: rgb(95, 95, 95);
}
/************ QCheckBox *********/
QCheckBox::indicator:unchecked {
background:black;
image: url(:/cb_unchecked_d.png);
}
QCheckBox::indicator:checked {
image: url(:/cb_checked_d.png);
}
QCheckBox::indicator:unchecked:disabled {
background:black;
image: url(:/cb_unchecked_dis_d.png);
}
QCheckBox::indicator:checked:disabled {
image: url(:/cb_checked_dis_d.png);
}
/****** QRadioButton ***********/
QRadioButton::indicator:unchecked
{
image: url(:/rb_unchecked_d.png);
}
QRadioButton::indicator:checked
{
image: url(:/rb_checked_d.png);
}
QRadioButton::indicator:unchecked:disabled
{
image: url(:/rb_unchecked_dis_d.png);
}
QRadioButton::indicator:checked:disabled
{
image: url(:/rb_checked_dis_d.png);
}
/****** QTabWidget *************/
QTabWidget::pane {
border: 1px solid #111111;
margin-top:-1px; /* hide line under selected tab*/
}
QTabWidget::tab-bar {
left: 0px; /* move to the right by 5px */
}
QTabBar::tab {
border: 1px solid #111;
border-radius: 0px;
min-width: 15ex;
padding-left: 3px;
padding-right: 5px;
padding-top: 3px;
padding-bottom: 2px;
background-color:QLinearGradient( x1: 0, y1: 0, x2: 0, y2: 1, stop: 0 #313131, stop: 1 #252525);
}
QTabBar::tab:selected {
border-bottom: 0px;
background-color:QLinearGradient( x1: 0, y1: 0, x2: 0, y2: 1, stop: 0 #4b4b4b, stop: 1 #3a3a3a)
}
QTabBar::tab:only-one {
margin: 0;
}
/************** QGroupBox *************/
QGroupBox {
border-left-color:QLinearGradient( x1: 0, y1: 0, x2: 1, y2: 0, stop: 0 #4b4b4b, stop: 1 #3a3a3a);
border-right-color:QLinearGradient( x1: 0, y1: 0, x2: 1, y2: 0, stop: 0 #111, stop: 1 #3a3a3a);
border-top-color:QLinearGradient( x1: 0, y1: 0, x2: 0, y2:1, stop: 0 #4b4b4b, stop: 1 #3a3a3a);
border-bottom-color:QLinearGradient( x1: 0, y1: 0, x2: 0, y2: 1, stop: 0 #111, stop: 1 #3a3a3a);
border-width: 2px;
border-style: solid;
border-radius: 0px;
padding-top: 10px;
}
QGroupBox::title {
background-color: transparent;
subcontrol-position: top left;
padding:4 10px;
}
/************************ QSpinBox *******************/
/*,QDoubleSpinBox*/
QSpinBox::up-button, QDoubleSpinBox::up-button, QTimeEdit::up-button {
/*background:QLinearGradient( x1: 0, y1: 0, x2: 0, y2: 1, stop: 0 #535353, stop: 1 #3a3a3a);*/
subcontrol-origin: border;
subcontrol-position: top right;
width: 16px;
/*border: 1px solid #333;*/
}
QSpinBox::down-button, QDoubleSpinBox::down-button, QTimeEdit::down-button{
/* background:QLinearGradient( x1: 0, y1: 0, x2: 0, y2: 1, stop: 0 #535353, stop: 1 #3a3a3a);*/
subcontrol-origin: border;
subcontrol-position: bottom right;
width: 16px;
/* border: 1px solid #333;*/
}
QSpinBox::down-button,QDoubleSpinBox::down-button, QTimeEdit::down-button,
QSpinBox::up-button, QDoubleSpinBox::up-button,QTimeEdit::up-button
{
color: #b1b1b1;
background-color: QLinearGradient( x1: 0, y1: 0, x2: 0, y2: 1, stop: 0 #535353, stop: 0.1 #515151, stop: 0.5 #474747, stop: 0.9 #3d3d3d, stop: 1 #3a3a3a);
border: 2px solid #232323;
border-top-width: 2px;
border-left-width: 2px;
border-top-color: QLinearGradient( x1: 0, y1: 0, x2: 0, y2: 1, stop: 0 #101010, stop: 1 #818181);
border-left-color: QLinearGradient( x1: 0, y1: 0, x2: 1, y2: 0, stop: 0 #101010, stop: 1 #818181);
border-radius: 0;
}
QSpinBox::up-button:pressed, QDoubleSpinBox::up-button:pressed, QSpinBox::down-button:pressed,
QTimeEdit::up-button:pressed ,QDoubleSpinBox::up-button:pressed , QTimeEdit::down-button:pressed
{
background-color: #828282;
}
QSpinBox::up-button, QDoubleSpinBox::up-button {
image: url(:/spin_up.png);
}
QSpinBox::down-button, QDoubleSpinBox::down-button {
image: url(:/spin_down.png);
}
QPlainTextEdit, QTextEdit {
background: #000;
color: white;
}
QTextBrowser {
background-color:#3a3a3a;
}
QTabBar::close-button {
image: url(:/tab_close.png);
subcontrol-position: right;
}
QTabBar::close-button:hover {
image: url(:/tab_close_hover.png);
}
QToolTip {
color: #ffffff;
background-color: #2a82da;
border: 1px solid white;
}
'''
return s
# different icons for light and black themes
def qss14():
'''
this is fixed copy of default qss houdini\config\Styles\base.qss
:return: new QSS style
'''
s = '''
QWidget
{
font-family: "DejaVu Sans";
font-size: 11px;
color: rgb(@TextColor@);
background-color: rgb(@BackColor@);
}
QDialog, QFrame, QGroupBox
{
background: rgb(@BackColor@);
color: rgb(@TextColor@);
}
QAbstractItemView
{
alternate-background-color: rgb(@ListEntry2@);
outline: 0;
height: 20px;
}
#central_widget
{
background: rgb(@BackColor@);
color: rgb(@TextColor@);
}
QStatusBar
{
background: rgb(@BackColor@);
color: rgb(@TextColor@);
}
QTextEdit, QPlainTextEdit
{
background: rgb(@TextboxBG@);
color: rgb(@TextColor@);
selection-background-color: rgb(@SelectedTextBG@);
selection-color: rgb(@SelectedTextFG@);
}
QTextEdit#code_edit
{
background: rgb(@ButtonGradHi@);
font-size: 15px;
border: none;
}
QCheckBox
{
background: rgb(@BackColor@);
}
QCheckBox::indicator:unchecked {
image: url($cb_unchecked$);
}
QCheckBox::indicator:checked {
image: url($cb_checked$);
}
QCheckBox::indicator:unchecked:disabled {
image: url($cb_unchecked_dis$);
}
QCheckBox::indicator:checked:disabled {
image: url($cb_checked_dis$);
}
QRadioButton::indicator:unchecked
{
image: url($rb_unchecked$);
}
QRadioButton::indicator:checked
{
image: url($rb_checked$);
}
QRadioButton::indicator:unchecked:disabled
{
image: url($rb_unchecked_dis$);
}
QRadioButton::indicator:checked:disabled
{
image: url($rb_checked_dis$);
}
QCheckBox:disabled,QRadioButton:disabled {
color: rgb(@DisabledTextColor@);
}
QSplitter::handle
{
background-color: rgb(@BackColor:Brightness=1.2@);
margin:2px;
}
QSplitter::handle:horizontal
{
width: 1px;
}
QSplitter::handle:vertical
{
height: 1px;
}
QSplitter::handle:pressed
{
background-color: rgb(@SplitBarHighlight@);
}
QSplitter::handle:hover
{
background-color: rgb(@SplitBarHighlight@);
}
QLineEdit,QDateEdit,QDateTimeEdit,QSpinBox
{
border: 1px solid rgb(@TextboxBorderPrimary@);
border-radius: 2px;
padding: 2px 4px;
background: rgb(@TextboxBG@);
selection-color: rgb(@SelectedTextFG@);
selection-background-color: rgb(@SelectedTextBG@);
}
QLineEdit:disabled,QDateEdit:disabled,QDateTimeEdit:disabled,QSpinBox:disabled
{
border: 1px solid rgba(@TextboxBorderPrimary@, 40);
border-radius: 2px;
padding: 2px 4px;
background: rgba(@TextboxBG@, 40);
color: rgb(@DisabledTextColor@);
}
QLineEdit[invalid="true"]
{
background: rgb(@TextboxInvalidBG@);
}
QLabel:enabled
{
color: rgb(@TextColor@);
}
#big_text
{
background: qlineargradient(x1: 0, y1: 0, x2: 0, y2: 1,
stop: 0.0 rgb(@ButtonGradHi@),
stop: 1.0 rgb(@BackColor@));
font-size: 16px;
font-weight: bold;
padding: 10px;
border: none;
border-bottom: 2px solid rgb(@ToolbarBevelLight@);
height: 20px;
margin-left: -1px;
}
QPushButton:pressed#big_text
{
background: qlineargradient(x1: 0, y1: 0, x2: 0, y2: 1,
stop: 0.0 rgb(@ButtonPressedGradHi@),
stop: 1.0 rgb(@ButtonPressedGradLow@));
color: rgb(@ButtonPressedText@);
}
QPushButton:hover#big_text
{
background: qlineargradient(x1: 0, y1: 0, x2: 0, y2: 1,
stop: 0.0 rgb(@ButtonGradHi:Brightness=1.05@),
stop: 1.0 rgb(@ButtonGradLow:Brightness=1.05@));
}
QPushButton
{
border: 1px solid rgb(@BorderLight@);
border-radius: 1px;
padding-top: 4px;
padding-bottom: 4px;
padding-right: 15px;
padding-left: 15px;
background: qlineargradient(x1: 0, y1: 0, x2: 0, y2: 1,
stop: 0.0 rgb(@ButtonGradHi@),
stop: 1.0 rgb(@ButtonGradLow@));
}
QRadioButton
{
margin: 5px;
}
QToolButton
{
border: 1px solid rgb(@BorderLight@);
border-radius: 0px;
padding-top: 2px;
padding-bottom: 2px;
padding-right: 3px;
padding-left: 3px;
margin: 1px;
background: qlineargradient(x1: 0, y1: 0, x2: 0, y2: 1,
stop: 0.0 rgb(@ButtonGradHi@),
stop: 1.0 rgb(@ButtonGradLow@));
}
QPushButton:hover, QToolButton:hover
{
background: qlineargradient(x1: 0, y1: 0, x2: 0, y2: 1,
stop: 0.0 rgb(@ButtonGradHi:Brightness=1.05@),
stop: 1.0 rgb(@ButtonGradLow:Brightness=1.05@));
}
QPushButton:pressed, QToolButton:pressed
{
background: qlineargradient(x1: 0, y1: 0, x2: 0, y2: 1,
stop: 0.0 rgb(@ButtonPressedGradHi@),
stop: 1.0 rgb(@ButtonPressedGradLow@));
color: rgb(@ButtonPressedText@);
}
QPushButton:checked, QToolButton:checked
{
background: qlineargradient(x1: 0, y1: 0, x2: 0, y2: 1,
stop: 0.0 rgb(@ButtonPressedGradHi:Brightness=0.75@),
stop: 1.0 rgb(@ButtonPressedGradLow:Brightness=0.75@));
color: rgb(@ButtonPressedText@);
}
QPushButton:disabled, QToolButton:disabled
{
background: qlineargradient(x1: 0, y1: 0, x2: 0, y2: 1,
stop: 0.0 rgba(@ButtonGradHi@, 40),
stop: 1.0 rgba(@ButtonGradLow@, 40));
color: rgb(@DisabledTextColor@);
}
QPushButton:flat:hover:!pressed, QToolButton:flat:hover:!pressed
{
background: rgb(@ButtonGradHi@);
}
QPushButton:flat::pressed, QToolButton:flat::pressed
{
background: qlineargradient(x1: 0, y1: 0, x2: 0, y2: 1,
stop: 0.0 rgb(@ButtonPressedGradHi@),
stop: 1.0 rgb(@ButtonPressedGradLow@));
}
QPushButton:flat:!pressed, QToolButton:flat:!pressed
{
border: none;
background: rgb(@BackColor@);
}
QToolButton[transparent="true"]
{
background: none;
border: none;
}
QToolButton[transparent="true"]:hover
{
background:black;
border: outset 1px;
}
QToolButton[transparent="true"]:pressed
{
background: qlineargradient(x1: 0, y1: 0, x2: 0, y2: 1,
stop: 0.0 rgb(@ButtonPressedGradHi@),
stop: 1.0 rgb(@ButtonPressedGradLow@));
color: rgb(@ButtonPressedText@);
}
QToolButton[transparent="true"]:disabled
{
background: none;
border: none;
}
QAbstractScrollArea,QTableView
{
border: none;
}
QTableView
{
alternate-background-color: rgb(@ListEntry2@);
background: rgb(@ListEntry1@);
selection-background-color: rgba(@ListEntrySelected@, 77);
selection-color: rgb(@SelectedTextFG@);
color: rgb(@ListText@);
border: none;
}
QTableView::item
{
border-right: 1px solid rgb(@ListBorder@);
border-left: 0;
border-top: 0;
border-bottom: 0;
outline: none;
}
QTableView::item:selected
{
border-top: 1px solid rgb(@ListEntrySelected@);
border-bottom: 1px solid rgb(@ListEntrySelected@);
color: rgb(@TextColor@);
background: rgba(@ListEntrySelected@, 77);
outline: none;
}
QTreeView, QListView, QTableView
{
alternate-background-color: rgb(@ListEntry2@);
background: rgb(@ListEntry1@);
selection-background-color: rgba(@ListEntrySelected@, 77);
/*selection-color: rgb(@SelectedTextFG@);*/
color: rgb(@ListText@);
border-top: 0;
border-bottom: 1px solid rgb(@ListBorder@);
border-left: 1px solid rgb(@ListBorder@);
border-right: 1px solid rgb(@ListBorder@);
}
QListView, QTableView{
border-top: 1px solid rgb(@ListBorder@);
}
QTreeView::item, QListView::item
{
border-right: 1px solid rgb(@ListBorder@);
border-left: 0;
border-top: 0;
border-bottom: 0;
height: 20px;
}
QTreeView::item:selected, QListView::item:selected
{
border-top: 1px solid rgb(@ListEntrySelected@);
border-bottom: 1px solid rgb(@ListEntrySelected@);
color: rgb(@TextColor@);
background: rgba(@ListEntrySelected@, 77);
outline: none;
}
QHeaderView::section, QTableCornerButton::section
{
border: 1px solid rgb(@ListTitleShadow@);
border-top: 1;
border-left: 0;
padding: 4px;
background: qlineargradient(x1: 0, y1: 0, x2: 0, y2: 1,
stop: 0.0 rgb(@ListTitleGradHi@),
stop: 1.0 rgb(@ListTitleGradLow@) );
}
QTabWidget
{
background: rgb(@BackColor@);
border: none;
}
QTabBar
{
background: rgb(@BackColor@);
border: none;
}
QTabWidget
{
background: rgb(@BackColor@);
}
QTabWidget::pane
{
border: 1px solid rgb(@PaneTabShadow@);
background: rgb(@BackColor@);
}
QTabWidget::tab-bar
{
alignment: left;
left: 1px;
border: none;
background: rgb(@BackColor@);
}
QTabBar::tab
{
padding-left: 6px;
padding-right: 6px;
padding-top: 2px;
padding-bottom: 2px;
height: 18px;
margin-top: 1px;
margin-left: -1px;
border: 1px solid rgb(@PaneTabInactiveHi:Brightness=0.75@);
border-radius: 0px;
background: rgb(@PaneTabInactiveHi@);
}
QTabBar[webbrowser="true"]::tab
{
width: 100px;
border-top-left-radius: 3px;
border-top-right-radius: 3px;
border-bottom-left-radius: 0px;
border-bottom-right-radius: 0px;
}
QTabBar[webbrowser="true"]::tab:last
{
border-color: rgb(@PaneTabInactiveLow@);
border-radius: 0px;
background: none;
}
QTabBar::tab:selected
{
border: 2px solid rgb(@PaneTabInactiveLow@);
border-bottom: 0;
background: qlineargradient(x1: 0, y1: 0, x2: 0, y2: 1,
stop: 0.0 rgb(@PaneTabActiveHi@),
stop: 1.0 rgb(@BackColor@));
}
QTabBar[webbrowser="true"]::close-button
{
subcontrol-position: right;
image: url(:/BUTTONS/delete.svg);
width: 8px;
height: 8px;
margin: 4px;
}
QTabBar::close-button
{
subcontrol-position: right;
image: url(:/BUTTONS/delete.svg);
width: 8px;
height: 8px;
margin: 4px;
}
QTabBar::close-button:hover
{
background: rgb(@ButtonMenuArrow@);
}
QMenu
{
background-color: rgb(@MenuBG@);
border-top: 1px solid rgb(@MenuHighlight@);
border-left: 1px solid rgb(@MenuHighlight@);
border-bottom: 1px solid rgb(@MenuShadow@);
border-right: 1px solid rgb(@MenuShadow@);
padding: 0px;
}
QMenu::item
{
padding: 2px 15px 2px 20px;
margin-left: 1px;
margin-right: 1px;
}
QMenuBar::item
{
background: transparent;
padding: 4px;
}
QMenu::item:selected:!disabled
{
background-color: rgb(@MenuSelectedBG@);
color: rgb(@MenuTextSelected@);
}
QMenu::item:disabled
{
color: rgb(@MenuTextDisabled@);
}
QMenu::tearoff
{
background-color: rgb(@MenuBG@);
border: none;
}
QMenu::tearoff:selected
{
background-color: rgb(@MenuSelectedBG@);
}
QMenuBar
{
background: qlineargradient(x1: 0, y1: 0, x2: 0, y2: 1,
stop: 0.0 rgb(@MenuBG@),
stop: 1.0 rgb(@MenuBG:Brightness=0.85@));
border: 1px solid rgb(@BorderLight@);
}
QMenuBar::item:pressed
{
background: rgb(@MenuSelectedBG@);
color: rgb(@MenuTextSelected@);
}
QProgressBar
{
border-top: 1px solid rgb(@ProgressMeterTopBorder@);
border-bottom: 1px rgb(@ProgressMeterBottomBorder@);
border-radius: 0px;
text-align: center;
background: rgb(@ProgressMeterWellGradLo@);
color: rgb(@ProgressMeterText@);
}
QProgressBar::chunk
{
background-color: qlineargradient(x1:0, y1:0, x2:0, y2:1,
stop:0 rgb(@ProgressMeterGradHi@),
stop:0.07 rgb(@ProgressMeterGradLo@),
stop:0.8 rgb(@ProgressMeterGradLo:Brightness=0.7@),
stop:0.95 rgb(@ProgressMeterGradLo:Brightness=0.65@),
stop:1.0 rgb(@ProgressMeterGradLo:Brightness=0.4@));
}
QScrollBar:horizontal
{
border: 1px solid rgb(@ScrollbarUpperBorder@);
background: rgb(@ScrollbarWell@);
height: 15px;
margin: 0 17px 0 17px;
}
QScrollBar::handle:horizontal
{
background: qlineargradient(x1: 0, y1: 0, x2: 0, y2: 1,
stop: 0.0 rgb(@ButtonGradHi@),
stop: 1.0 rgb(@ButtonGradLow@));
min-width: 30px;
}
QScrollBar::add-line:horizontal
{
border: 1px solid rgb(@ScrollbarUpperBorder@);
background: qlineargradient(x1: 0, y1: 0, x2: 0, y2: 1,
stop: 0.0 rgb(@ButtonGradHi@),
stop: 1.0 rgb(@ButtonGradLow@));
width: 15px;
subcontrol-position: right;
subcontrol-origin: margin;
}
QScrollBar::sub-line:horizontal
{
border: 1px solid rgb(@ScrollbarUpperBorder@);
background: qlineargradient(x1: 0, y1: 0, x2: 0, y2: 1,
stop: 0.0 rgb(@ButtonGradHi@),
stop: 1.0 rgb(@ButtonGradLow@));
width: 15px;
subcontrol-position: left;
subcontrol-origin: margin;
}
QScrollBar::left-arrow:horizontal
{
width: 0;
height: 0;
border-top: 3px solid rgb(@ButtonGradHi@);
border-bottom: 3px solid rgb(@ButtonGradHi@);
border-right: 5px solid rgb(@ScrollArrow@);
}
QScrollBar::right-arrow:horizontal
{
width: 0;
height: 0;
border-top: 3px solid rgb(@ButtonGradHi@);
border-bottom: 3px solid rgb(@ButtonGradHi@);
border-left: 5px solid rgb(@ScrollArrow@);
}
QScrollBar::add-page:horizontal, QScrollBar::sub-page:horizontal
{
background: none;
}
QScrollBar:vertical
{
border: 1px solid rgb(@ScrollbarUpperBorder@);
background: rgb(@ScrollbarWell@);
width: 15px;
margin: 17px 0 17px 0;
}
QScrollBar:horizontal
{
border: 1px solid rgb(@ScrollbarUpperBorder@);
}
QScrollBar::handle:vertical
{
background: qlineargradient(x1: 0, y1: 0, x2: 1, y2: 0,
stop: 0.0 rgb(@ButtonGradHi@),
stop: 1.0 rgb(@ButtonGradLow@));
min-height: 30px;
}
QScrollBar::add-line:vertical
{
border: 1px solid rgb(@ScrollbarUpperBorder@);
background: qlineargradient(x1: 0, y1: 0, x2: 1, y2: 0,
stop: 0.0 rgb(@ButtonGradHi@),
stop: 1.0 rgb(@ButtonGradLow@));
height: 15px;
subcontrol-position: bottom;
subcontrol-origin: margin;
}
QScrollBar::sub-line:vertical
{
border: 1px solid rgb(@ScrollbarUpperBorder@);
background: qlineargradient(x1: 0, y1: 0, x2: 1, y2: 0,
stop: 0.0 rgb(@ButtonGradHi@),
stop: 1.0 rgb(@ButtonGradLow@));
height: 15px;
subcontrol-position: top;
subcontrol-origin: margin;
}
QScrollBar::up-arrow:vertical
{
width: 0;
height: 0;
border-left: 3px solid rgba(@ButtonGradHi@, 0);
border-right: 3px solid rgba(@ButtonGradHi@, 0);
border-bottom: 5px solid rgb(@ScrollArrow@);
}
QScrollBar::down-arrow:vertical
{
width: 0;
height: 0;
border-left: 3px solid rgba(@ButtonGradHi@, 0);
border-right: 3px solid rgba(@ButtonGradHi@, 0);
border-top: 5px solid rgb(@ScrollArrow@);
}
QScrollBar::add-page:vertical, QScrollBar::sub-page:vertical
{
background: none;
}
QSlider
{
height: 20px;
}
QSlider::groove::horizontal
{
border-top: 1px solid rgb(@SliderTopBorder@);
border-bottom: 1px solid rgb(@SliderBottomBorder@);
border-radius: 1px;
background: qlineargradient(x1:0, y1:0, x2:0, y2:1,
stop:0.4 rgb(@SliderRemainingBevel@),
stop:0.5 rgb(@SliderRemainingGroove@));
height: 3px;
margin: 2px 0;
}
QSlider::handle:horizontal
{
background: qlineargradient(x1:0, y1:0, x2:0, y2:1,
stop:0 rgb(@ButtonGradHi@),
stop:1 rgb(@ButtonGradLow@));
border-top: 1px solid rgb(@SliderThumbTopBorder@);
border-left: 1px solid rgb(@SliderThumbTopBorder@);
border-right: 1px solid rgb(@SliderThumbBottomBorder@);
border-bottom: 1px solid rgb(@SliderThumbBottomBorder@);
width: 4px;
margin: -8px 0;
border-radius: 1px;
}
QSlider::sub-page:horizontal
{
border-top: 1px solid rgb(@SliderTopBorder@);
border-bottom: 1px solid rgb(@SliderBottomBorder@);
border-radius: 1px;
background: qlineargradient(x1:0, y1:0, x2:0, y2:1,
stop:0.4 rgb(@SliderAdvancedBevel@),
stop:0.5 rgb(@SliderAdvancedGroove@));
height: 3px;
margin: 2px 0;
}
QTreeView::branch:has-siblings:!adjoins-item
{
border-image: url(:/vline.png) 0;
}
QTreeView::branch:has-siblings:adjoins-item
{
border-image: url(:/more.png) 0;
}
QTreeView::branch:!has-children:!has-siblings:adjoins-item
{
border-image: url(:/end.png) 0;
}
QTreeView::branch:closed:has-children:has-siblings
{
border-image: url(:/closed.png) 0;
}
QTreeView::branch:closed:has-children:!has-siblings
{
border-image: url(:/closed_end.png) 0;
}
QTreeView::branch:open:has-children:!has-siblings
{
border-image: url(:/open_end.png) 0;
}
QTreeView::branch:open:has-children:has-siblings
{
border-image: url(:/open.png) 0;
}
QGroupBox
{
border: 1px solid rgb(@ToolbarBevelLight@);
border-radius: 4px;
padding: 7 -1 px;
margin-top: 1ex;
margin-bottom: 1.5ex;
}
QGroupBox::title {
subcontrol-position: top left;
subcontrol-origin: margin;
padding: -6 6 -5 px;
margin: 0 0 0 5 px;
left: 15px;
}
QSpinBox, QDoubleSpinBox, QTimeEdit {
border-radius: 2px;
}
QSpinBox::up-button, QDoubleSpinBox::up-button, QTimeEdit::up-button {
subcontrol-origin: border;
subcontrol-position: top right;
width: 16px;
border: 1px solid rgb(@TextboxBorderPrimary@);
border-top-right-radius: 2px;
}
QSpinBox::down-button, QDoubleSpinBox::down-button, QTimeEdit::down-button{
subcontrol-origin: border;
subcontrol-position: bottom right;
width: 16px;
border: 1px solid rgb(@TextboxBorderPrimary@);
border-bottom-right-radius: 2px;
}
QSpinBox::down-button,QDoubleSpinBox::down-button, QTimeEdit::down-button,
QSpinBox::up-button, QDoubleSpinBox::up-button,QTimeEdit::up-button
{
/*border: 1px solid rgb(@ButtonShadow@);
border-radius: 0px;*/
padding-top: 1px;
padding-bottom: 1px;
padding-right: 1px;
padding-left: 1px;
background: qlineargradient(x1: 0, y1: 0, x2: 0, y2: 1,
stop: 0.0 rgb(@ButtonGradHi@),
stop: 1.0 rgb(@ButtonGradLow@));
}
QSpinBox::up-button:pressed, QDoubleSpinBox::up-button:pressed, QSpinBox::down-button:pressed,
QTimeEdit::up-button:pressed ,QDoubleSpinBox::up-button:pressed , QTimeEdit::down-button:pressed
{
background: qlineargradient(x1: 0, y1: 0, x2: 0, y2: 1,
stop: 0.0 rgb(@ButtonPressedGradHi@),
stop: 1.0 rgb(@ButtonPressedGradLow@));
color: rgb(@ButtonPressedText@);
}
QSpinBox::up-button, QDoubleSpinBox::up-button {
image: url(:/spin_up.png);
}
QSpinBox::down-button, QDoubleSpinBox::down-button {
image: url(:/spin_down.png);
}
QComboBox
{
border: 1px solid rgba(@ButtonShadow@, 102);
border-radius: 1px;
padding-top: 4px;
padding-bottom: 4px;
padding-right: 15px;
padding-left: 15px;
background: qlineargradient(x1: 0, y1: 0, x2: 0, y2: 1,
stop: 0.0 rgb(@ButtonGradHi@),
stop: 1.0 rgb(@ButtonGradLow@));
}
QComboBox:hover
{
background: qlineargradient(x1: 0, y1: 0, x2: 0, y2: 1,
stop: 0.0 rgb(@ButtonMenuArrow@),
stop: 1.0 rgb(@ButtonGradLow@));
}
QComboBox::drop-down, QDateEdit::drop-down,QDateTimeEdit::drop-down
{
background: qlineargradient(x1: 0, y1: 0, x2: 0, y2: 1,
stop: 0.0 rgb(@ButtonMenuArrowHi@),
stop: 1.0 rgb(@ButtonMenuArrowLow@));
width: 20px;
}
QComboBox::down-arrow, QDateEdit::down-arrow,QDateTimeEdit::down-arrow
{
width: 0;
height: 0;
border-left: 3px solid rgba(@ButtonMenuArrowHi@, 0);
border-right: 3px solid rgba(@ButtonMenuArrowHi@, 0);
border-top: 5px solid rgb(@ButtonMenuArrow@);
}
QComboBox QAbstractItemView
{
background-color: rgb(@MenuBG@);
border-top: 1px solid rgb(@BackColor@);
border-left: 1px solid rgb(@ButtonGradHi@);
border-bottom: 1px solid rgb(@ButtonMenuArrowLow@);
border-right: 1px solid rgb(@ButtonMenuArrowLow@);
padding: 3px;
outline: none;
selection-background-color: rgb(@MenuSelectedBG@);
}
QComboBox QAbstractItemView:item
{
padding: 4px 15px 4px 15px;
selection-background-color: rgb(@MenuSelectedBG@);
border-radius: 0px;
color: rgb(@MenuTextSelected@);
}
QComboBox:on
{
background-color: rgb(@ButtonPressedGradHi@);
color: rgb(@ButtonPressedText@);
}
QToolBar {
border: 1px solid rgb(@ProgressMeterBottomBorder@);
background: qlineargradient(x1:0, y1:0, x2:0, y2:1,
stop:0.0 rgb(@BackColor:Brightness=1.2@),
stop:1.0 rgb(@BackColor@));
}
QToolBar::handle:horizontal {
background: qlineargradient(x1:0, y1:0, x2:1, y2:0,
stop:0.0 rgb(@PaneTabShadow@),
stop:0.2 rgb(@BackColor:Brightness=1.8@),
stop:0.4 transparent
);
width: 10px;
}
QToolBar::handle:vertical {
background: qlineargradient(x1:0, y1:0, x2:0, y2:1,
stop:0.0 rgb(@PaneTabShadow@),
stop:0.2 rgb(@BackColor:Brightness=1.8@),
stop:0.4 transparent
);
height: 10px;
}
QToolBar::separator:horizontal {
width: 2;
margin: 2px;
background: rgb(@BackColor:Brightness=0.8@);
}
QToolBar::separator:vertical {
height: 2;
margin: 2px;
background: rgb(@BackColor:Brightness=0.8@);
}
QToolBar QToolButton {
background: transparent;
border: none;
border-radius: 0px;
}
QToolBar QToolButton:hover {
background: qlineargradient(x1:0, y1:0, x2:0, y2:1,
stop:0.0 rgb(@BackColor:Brightness=1.3@),
stop:1.0 rgb(@BackColor@));
}
QToolBar QToolButton:pressed {
background: qlineargradient(x1:0, y1:0, x2:0, y2:1,
stop:0.0 rgb(@BackColor:Brightness=0.9@),
stop:1.0 rgb(@BackColor:Brightness=0.6@));
}
'''
return s
# QToolBar::handle:horizontal {
# background: qlineargradient(x1:0, y1:0, x2:1, y2:0,
# stop:0.0 rgb(@PaneTabShadow@),
# stop:0.2 rgb(@BackColor:Brightness=1.6@),
# stop:0.6 rgb(@BackColor:Brightness=1.5@),
# stop:0.8 rgb(@BackColor@),
# stop:0.9 rgb(@PaneTabShadow@)
# );
# width: 10px;
# }
# QToolBar::handle:vertical {
# background: qlineargradient(x1:0, y1:0, x2:0, y2:1,
# stop:0.0 rgb(@PaneTabShadow@),
# stop:0.2 rgb(@BackColor:Brightness=1.6@),
# stop:0.6 rgb(@BackColor:Brightness=1.5@),
# stop:0.8 rgb(@BackColor@),
# stop:0.9 rgb(@PaneTabShadow@)
# );
# height: 10px;
# } | 365.03133 | 510,606 | 0.465498 | 117,382 | 850,523 | 3.36889 | 0.021886 | 0.045533 | 0.067708 | 0.089519 | 0.446237 | 0.43643 | 0.430402 | 0.423521 | 0.412558 | 0.406102 | 0 | 0.259365 | 0.385964 | 850,523 | 2,330 | 510,607 | 365.03133 | 0.497831 | 0.001699 | 0 | 0.368004 | 0 | 0.023759 | 0.980155 | 0.540129 | 0 | 1 | 0.000019 | 0 | 0 | 0 | null | null | 0.001584 | 0.011088 | null | null | 0.001584 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | null | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
dcdb762926a0ee3b7961d72a5d6eb5963d5a77ba | 14,988 | py | Python | tests/core/test_data_test.py | thanhlelgg/golem | 5245c880d00d40c57581a7397a606359fb37cd59 | [
"MIT"
] | null | null | null | tests/core/test_data_test.py | thanhlelgg/golem | 5245c880d00d40c57581a7397a606359fb37cd59 | [
"MIT"
] | null | null | null | tests/core/test_data_test.py | thanhlelgg/golem | 5245c880d00d40c57581a7397a606359fb37cd59 | [
"MIT"
] | null | null | null | import os
from golem.core import test_data
class Test_save_external_test_data_file:
def test_save_external_data(self, project_function_clean, test_utils):
testdir = project_function_clean['testdir']
project = project_function_clean['name']
test_name = test_utils.random_string(10, 'test')
input_test_data = [{'key1': 'value1', 'key2': 'value2'},
{'key1': 'value3', 'key2': 'value4'}]
test_data.save_external_test_data_file(testdir, project, test_name,
input_test_data)
data_path = os.path.join(testdir, 'projects', project, 'tests', test_name+'.csv')
with open(data_path) as f:
result = f.read()
expected = ('key1,key2\nvalue1,value2\nvalue3,value4\n')
expected_var = ('key2,key1\nvalue2,value1\nvalue4,value3\n')
assert result == expected or result == expected_var
def test_save_external_data_empty_data(self, project_function_clean,
test_utils):
testdir = project_function_clean['testdir']
project = project_function_clean['name']
test_name = test_utils.random_string(10, 'test')
input_test_data = []
test_data.save_external_test_data_file(testdir, project, test_name,
input_test_data)
data_path = os.path.join(testdir, 'projects', project, 'tests', test_name+'.csv')
assert not os.path.isfile(data_path)
def test_save_external_data_empty_data_file_exists(self, project_function_clean,
test_utils):
testdir = project_function_clean['testdir']
project = project_function_clean['name']
test_name = test_utils.random_string(10, 'test')
input_test_data = []
data_path = os.path.join(testdir, 'projects', project, 'tests',
test_name+'.csv')
open(data_path, 'w+').close()
test_data.save_external_test_data_file(testdir, project, test_name,
input_test_data)
with open(data_path) as f:
assert f.read() == ''
def test_save_external_data_special_cases(self, project_function_clean,
test_utils):
testdir = project_function_clean['testdir']
project = project_function_clean['name']
test_name = test_utils.random_string(10, 'test')
input_test_data = [{'key1': 'string with spaces'},
{'key1': 'string "with" quotes'},
{'key1': 'string \'with\' quotes'},
{'key1': '"quoted_string"'},
{'key1': '\'quoted_string\''}]
test_data.save_external_test_data_file(testdir, project, test_name,
input_test_data)
data_path = os.path.join(testdir, 'projects', project, 'tests', test_name+'.csv')
with open(data_path) as f:
result = f.read()
expected = ('key1\n'
'string with spaces\n'
'"string ""with"" quotes"\n'
'string \'with\' quotes\n'
'"""quoted_string"""\n'
'\'quoted_string\'\n')
assert result == expected
class Test_get_external_test_data:
def test_get_external_test_data(self, project_function_clean, test_utils):
testdir = project_function_clean['testdir']
project = project_function_clean['name']
test_name = test_utils.random_string(10, 'test')
data_path = os.path.join(testdir, 'projects',
project, 'tests', test_name + '.csv')
input = ('key1,key2\nvalue1,value2\nvalue3,value4\n')
with open(data_path, 'w+') as f:
f.write(input)
result = test_data.get_external_test_data(testdir, project, test_name)
expected = [{'key1': 'value1', 'key2': 'value2'},
{'key1': 'value3', 'key2': 'value4'}]
assert result == expected
def test_get_external_test_data_file_not_exists(self, project_function_clean,
test_utils):
testdir = project_function_clean['testdir']
project = project_function_clean['name']
test_name = test_utils.random_string(10, 'test')
result = test_data.get_external_test_data(testdir, project, test_name)
assert result == []
def test_get_external_test_data_special_cases(self, project_function_clean,
test_utils):
testdir = project_function_clean['testdir']
project = project_function_clean['name']
test_name = test_utils.random_string(10, 'test')
data_path = os.path.join(testdir, 'projects',
project, 'tests', test_name + '.csv')
input = ('key1\n'
'string with spaces\n'
'"string ""with"" quotes"\n'
'string \'with\' quotes\n'
'"""quoted_string"""\n'
'\'quoted_string\'\n')
with open(data_path, 'w+') as f:
f.write(input)
result = test_data.get_external_test_data(testdir, project, test_name)
expected = [{'key1': 'string with spaces'},
{'key1': 'string "with" quotes'},
{'key1': 'string \'with\' quotes'},
{'key1': '"quoted_string"'},
{'key1': '\'quoted_string\''}]
assert result == expected
class Test_get_internal_test_data:
def test_get_internal_test_data_list(self, project_function_clean):
testdir = project_function_clean['testdir']
project = project_function_clean['name']
test_name = 'test_get_internal_test_data'
test_content = ("data = [\n"
" {\n"
" 'key1': 'value1',\n"
" 'key2': 'value2',\n"
" },\n"
" {\n"
" 'key1': 'value3',\n"
" 'key2': 'value4',\n"
" },\n"
"]\n")
test_path = os.path.join(testdir, 'projects', project, 'tests', test_name+'.py')
with open(test_path, 'w+') as f:
f.write(test_content)
expected = [
{'key1': 'value1', 'key2': 'value2'},
{'key1': 'value3', 'key2': 'value4'}
]
internal_data = test_data.get_internal_test_data(testdir, project, test_name)
assert internal_data == expected
def test_get_internal_test_data_dict(self, project_function_clean):
testdir = project_function_clean['testdir']
project = project_function_clean['name']
test_name = 'test_get_internal_test_data'
test_content = ("data = {\n"
" 'key1': 'value1',\n"
" 'key2': 'value2',\n"
"}\n")
test_path = os.path.join(testdir, 'projects', project, 'tests', test_name+'.py')
with open(test_path, 'w+') as f:
f.write(test_content)
expected = [{'key1': 'value1', 'key2': 'value2'}]
internal_data = test_data.get_internal_test_data(testdir, project, test_name)
assert internal_data == expected
def test_get_internal_test_data_special_cases(self, project_function_clean):
testdir = project_function_clean['testdir']
project = project_function_clean['name']
test_name = 'test_get_internal_test_data'
test_content = ("data = [\n"
" {\n"
" 'key1': 12,\n"
" 'key2': 1.2,\n"
" 'key3': False,\n"
" 'key4': None,\n"
" 'key5': [1,2,3],\n"
" 'key6': ['a','b'],\n"
" 'key7': {'key1': 'a', \"key2\": \"b\"},\n"
" 'key8': (1, '2'),\n"
" 'key9': 'test',\n"
" 'key10': \"test\",\n"
" 'key11': 'te\"s\"t',\n"
" 'key12': \"te's't\",\n"
" }\n"
"]\n")
test_path = os.path.join(testdir, 'projects', project, 'tests', test_name+'.py')
with open(test_path, 'w+') as f:
f.write(test_content)
expected = [
{'key1': 12,
'key2': 1.2,
'key3': False,
'key4': None,
'key5': [1,2,3],
'key6': ['a', 'b'],
'key7': {'key1': 'a', 'key2': 'b'},
'key8': (1, '2'),
'key9': 'test',
'key10': 'test',
'key11': 'te"s"t',
'key12': "te's't"}
]
internal_data = test_data.get_internal_test_data(testdir, project, test_name)
assert internal_data == expected
def test_get_internal_test_data_repr(self, project_function_clean):
testdir = project_function_clean['testdir']
project = project_function_clean['name']
test_name = 'test_get_internal_test_data'
test_content = ("data = [\n"
" {\n"
" 'key1': 12,\n"
" 'key2': 1.2,\n"
" 'key3': False,\n"
" 'key4': None,\n"
" 'key5': [1,2,3],\n"
" 'key6': ['a','b'],\n"
" 'key7': {'key1': 'a', \"key2\": \"b\"},\n"
" 'key8': (1, '2'),\n"
" 'key9': 'test',\n"
" 'key10': \"test\",\n"
" 'key11': 'te\"s\"t',\n"
" 'key12': \"te's't\",\n"
" }\n"
"]\n")
test_path = os.path.join(testdir, 'projects', project, 'tests', test_name+'.py')
with open(test_path, 'w+') as f:
f.write(test_content)
expected = [
{'key1': 12,
'key2': 1.2,
'key3': False,
'key4': None,
'key5': [1,2,3],
'key6': ['a', 'b'],
'key7': {'key1': 'a', 'key2': 'b'},
'key8': (1, '2'),
'key9': "'test'",
'key10': "'test'",
'key11': '\'te"s"t\'',
'key12': '"te\'s\'t"'}
]
internal_data = test_data.get_internal_test_data(testdir, project,
test_name, repr_strings=True)
assert internal_data == expected
def test_get_internal_test_data_no_data_var(self, project_function_clean):
testdir = project_function_clean['testdir']
project = project_function_clean['name']
test_name = 'test_get_internal_test_data_no_data_var'
test_content = ("there_is = 'no data here'\n")
test_path = os.path.join(testdir, 'projects', project, 'tests', test_name+'.py')
with open(test_path, 'w+') as f:
f.write(test_content)
internal_data = test_data.get_internal_test_data(testdir, project, test_name)
assert internal_data == []
def test_get_internal_test_data_not_dict_not_list(self, project_function_clean,
capsys):
testdir = project_function_clean['testdir']
project = project_function_clean['name']
test_name = 'test_get_internal_test_data'
test_content = ("data = 'just a string'\n")
test_path = os.path.join(testdir, 'projects', project, 'tests', test_name+'.py')
with open(test_path, 'w+') as f:
f.write(test_content)
internal_data = test_data.get_internal_test_data(testdir, project, test_name)
assert internal_data == []
captured = capsys.readouterr()
msg = 'Warning: infile test data must be a dictionary or a list of dictionaries'
assert msg in captured.out
class Test_get_test_data:
def test_get_test_data_from_infile(self, project_class, test_utils):
testdir = project_class['testdir']
project = project_class['name']
test_name = test_utils.random_string(5, 'test')
test_content = ("data = {\n"
" 'key1': 'value1',\n"
" 'key2': 'value2',\n"
"}\n")
test_path = os.path.join(testdir, 'projects', project, 'tests',
test_name + '.py')
with open(test_path, 'w+') as f:
f.write(test_content)
expected = [
{'key1': 'value1', 'key2': 'value2'},
]
returned_data = test_data.get_test_data(testdir, project, test_name)
assert returned_data == expected
def test_get_test_data_from_csv(self, project_class, test_utils):
"""when there is csv and infile data, csv has priority"""
testdir = project_class['testdir']
project = project_class['name']
test_name = test_utils.random_string(5, 'test')
test_content = ("data = {\n"
" 'key1': 'value1',\n"
" 'key2': 'value2',\n"
"}\n")
test_path = os.path.join(testdir, 'projects', project, 'tests',
test_name + '.py')
with open(test_path, 'w+') as f:
f.write(test_content)
data_path = os.path.join(testdir, 'projects', project, 'tests',
test_name + '.csv')
with open(data_path, 'w+') as f:
f.write('key1,key2\nvalue3,value4\n')
returned_data = test_data.get_test_data(testdir, project, test_name)
expected = [
{'key1': 'value3', 'key2': 'value4'},
]
assert returned_data == expected
def test_get_test_data_no_data(self, project_class, test_utils):
"""when there is csv and infile data, csv has priority"""
testdir = project_class['testdir']
project = project_class['name']
test_name = test_utils.random_string(5, 'test')
test_content = ("there_is = 'no data'\n")
test_path = os.path.join(testdir, 'projects', project, 'tests',
test_name + '.py')
with open(test_path, 'w+') as f:
f.write(test_content)
returned_data = test_data.get_test_data(testdir, project, test_name)
assert returned_data == [{}]
| 45.97546 | 89 | 0.504337 | 1,584 | 14,988 | 4.481692 | 0.072601 | 0.074377 | 0.109875 | 0.050852 | 0.93647 | 0.911537 | 0.881673 | 0.855895 | 0.841668 | 0.82251 | 0 | 0.022101 | 0.363024 | 14,988 | 325 | 90 | 46.116923 | 0.721483 | 0.006872 | 0 | 0.777027 | 0 | 0 | 0.17971 | 0.024539 | 0 | 0 | 0 | 0 | 0.057432 | 1 | 0.054054 | false | 0 | 0.006757 | 0 | 0.074324 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
dce925adb49fd5a447b02343ca94483b8d5adc5c | 17,695 | py | Python | test/test_move_first.py | Nexuscompute/inplace | 37bc1d3b4450be91c6625910adff9f221e94e0e9 | [
"MIT"
] | 23 | 2016-12-24T03:21:16.000Z | 2021-10-03T10:38:10.000Z | test/test_move_first.py | Nexuscompute/inplace | 37bc1d3b4450be91c6625910adff9f221e94e0e9 | [
"MIT"
] | 5 | 2018-04-27T06:07:33.000Z | 2021-02-19T22:12:29.000Z | test/test_move_first.py | Nexuscompute/inplace | 37bc1d3b4450be91c6625910adff9f221e94e0e9 | [
"MIT"
] | 4 | 2017-01-20T19:39:19.000Z | 2021-12-22T22:24:47.000Z | import os
import platform
import pytest
from in_place import InPlace
from test_in_place_util import TEXT, pylistdir
def test_move_first_nobackup(tmpdir):
assert pylistdir(tmpdir) == []
p = tmpdir.join("file.txt")
p.write(TEXT)
with InPlace(str(p), move_first=True) as fp:
assert not fp.closed
for line in fp:
assert isinstance(line, str)
fp.write(line.swapcase())
assert not fp.closed
assert fp.closed
assert pylistdir(tmpdir) == ["file.txt"]
assert p.read() == TEXT.swapcase()
def test_move_first_backup_ext(tmpdir):
assert pylistdir(tmpdir) == []
p = tmpdir.join("file.txt")
p.write(TEXT)
with InPlace(str(p), backup_ext="~", move_first=True) as fp:
for line in fp:
fp.write(line.swapcase())
assert pylistdir(tmpdir) == ["file.txt", "file.txt~"]
assert p.new(ext="txt~").read() == TEXT
assert p.read() == TEXT.swapcase()
def test_move_first_backup(tmpdir):
assert pylistdir(tmpdir) == []
p = tmpdir.join("file.txt")
p.write(TEXT)
bkp = tmpdir.join("backup.txt")
with InPlace(str(p), backup=str(bkp), move_first=True) as fp:
assert not fp.closed
for line in fp:
fp.write(line.swapcase())
assert not fp.closed
assert fp.closed
assert pylistdir(tmpdir) == ["backup.txt", "file.txt"]
assert bkp.read() == TEXT
assert p.read() == TEXT.swapcase()
def test_move_first_backup_ext_and_backup(tmpdir):
assert pylistdir(tmpdir) == []
p = tmpdir.join("file.txt")
p.write(TEXT)
bkp = tmpdir.join("backup.txt")
with pytest.raises(ValueError):
with InPlace(str(p), backup=str(bkp), backup_ext="~", move_first=True):
assert False
assert pylistdir(tmpdir) == ["file.txt"]
assert p.read() == TEXT
def test_move_first_empty_backup_ext(tmpdir):
assert pylistdir(tmpdir) == []
p = tmpdir.join("file.txt")
p.write(TEXT)
with pytest.raises(ValueError):
with InPlace(str(p), backup_ext="", move_first=True):
assert False
assert pylistdir(tmpdir) == ["file.txt"]
assert p.read() == TEXT
def test_move_first_error_backup_ext(tmpdir):
assert pylistdir(tmpdir) == []
p = tmpdir.join("file.txt")
p.write(TEXT)
with pytest.raises(RuntimeError):
with InPlace(str(p), backup_ext="~", move_first=True) as fp:
for i, line in enumerate(fp):
fp.write(line.swapcase())
if i > 5:
raise RuntimeError("I changed my mind.")
assert pylistdir(tmpdir) == ["file.txt"]
assert p.read() == TEXT
def test_move_first_pass_nobackup(tmpdir):
assert pylistdir(tmpdir) == []
p = tmpdir.join("file.txt")
p.write(TEXT)
with InPlace(str(p), move_first=True):
pass
assert pylistdir(tmpdir) == ["file.txt"]
assert p.read() == ""
@pytest.mark.skipif(
platform.system() == "Windows",
reason="Cannot delete open file on Windows",
)
def test_move_first_delete_nobackup(tmpdir):
assert pylistdir(tmpdir) == []
p = tmpdir.join("file.txt")
p.write(TEXT)
with InPlace(str(p), move_first=True) as fp:
for i, line in enumerate(fp):
fp.write(line.swapcase())
if i == 5:
p.remove()
assert pylistdir(tmpdir) == []
@pytest.mark.skipif(
platform.system() == "Windows",
reason="Cannot delete open file on Windows",
)
def test_move_first_delete_backup(tmpdir):
assert pylistdir(tmpdir) == []
p = tmpdir.join("file.txt")
p.write(TEXT)
bkp = tmpdir.join("backup.txt")
with InPlace(str(p), backup=str(bkp), move_first=True) as fp:
for i, line in enumerate(fp):
fp.write(line.swapcase())
if i == 5:
p.remove()
assert pylistdir(tmpdir) == ["backup.txt"]
assert bkp.read() == TEXT
def test_move_first_early_close_nobackup(tmpdir):
assert pylistdir(tmpdir) == []
p = tmpdir.join("file.txt")
p.write(TEXT)
with InPlace(str(p), move_first=True) as fp:
for line in fp:
fp.write(line.swapcase())
fp.close()
assert pylistdir(tmpdir) == ["file.txt"]
assert p.read() == TEXT.swapcase()
def test_move_first_early_close_and_write_nobackup(tmpdir):
assert pylistdir(tmpdir) == []
p = tmpdir.join("file.txt")
p.write(TEXT)
with pytest.raises(ValueError):
with InPlace(str(p), move_first=True) as fp:
for line in fp:
fp.write(line.swapcase())
fp.close()
fp.write("And another thing...\n")
assert pylistdir(tmpdir) == ["file.txt"]
assert p.read() == TEXT.swapcase()
def test_move_first_early_close_backup(tmpdir):
assert pylistdir(tmpdir) == []
p = tmpdir.join("file.txt")
p.write(TEXT)
bkp = tmpdir.join("backup.txt")
with InPlace(str(p), backup=str(bkp), move_first=True) as fp:
for line in fp:
fp.write(line.swapcase())
fp.close()
assert pylistdir(tmpdir) == ["backup.txt", "file.txt"]
assert bkp.read() == TEXT
assert p.read() == TEXT.swapcase()
def test_move_first_early_close_and_write_backup(tmpdir):
assert pylistdir(tmpdir) == []
p = tmpdir.join("file.txt")
p.write(TEXT)
bkp = tmpdir.join("backup.txt")
with pytest.raises(ValueError):
with InPlace(str(p), backup=str(bkp), move_first=True) as fp:
for line in fp:
fp.write(line.swapcase())
fp.close()
fp.write("And another thing...\n")
assert pylistdir(tmpdir) == ["backup.txt", "file.txt"]
assert bkp.read() == TEXT
assert p.read() == TEXT.swapcase()
def test_move_first_rollback_nobackup(tmpdir):
assert pylistdir(tmpdir) == []
p = tmpdir.join("file.txt")
p.write(TEXT)
with InPlace(str(p), move_first=True) as fp:
for line in fp:
fp.write(line.swapcase())
fp.rollback()
assert pylistdir(tmpdir) == ["file.txt"]
assert p.read() == TEXT
def test_move_first_rollback_and_write_nobackup(tmpdir):
assert pylistdir(tmpdir) == []
p = tmpdir.join("file.txt")
p.write(TEXT)
with pytest.raises(ValueError):
with InPlace(str(p), move_first=True) as fp:
for line in fp:
fp.write(line.swapcase())
fp.rollback()
fp.write("And another thing...\n")
assert pylistdir(tmpdir) == ["file.txt"]
assert p.read() == TEXT
def test_move_first_rollback_backup(tmpdir):
assert pylistdir(tmpdir) == []
p = tmpdir.join("file.txt")
p.write(TEXT)
bkp = tmpdir.join("backup.txt")
with InPlace(str(p), backup=str(bkp), move_first=True) as fp:
for line in fp:
fp.write(line.swapcase())
fp.rollback()
assert pylistdir(tmpdir) == ["file.txt"]
assert p.read() == TEXT
def test_move_first_rollback_and_write_backup(tmpdir):
assert pylistdir(tmpdir) == []
p = tmpdir.join("file.txt")
p.write(TEXT)
bkp = tmpdir.join("backup.txt")
with pytest.raises(ValueError):
with InPlace(str(p), backup=str(bkp), move_first=True) as fp:
for line in fp:
fp.write(line.swapcase())
fp.rollback()
fp.write("And another thing...\n")
assert pylistdir(tmpdir) == ["file.txt"]
assert p.read() == TEXT
def test_move_first_overwrite_backup(tmpdir):
assert pylistdir(tmpdir) == []
p = tmpdir.join("file.txt")
p.write(TEXT)
bkp = tmpdir.join("backup.txt")
bkp.write("This is not the file you are looking for.\n")
with InPlace(str(p), backup=str(bkp), move_first=True) as fp:
for line in fp:
fp.write(line.swapcase())
assert pylistdir(tmpdir) == ["backup.txt", "file.txt"]
assert bkp.read() == TEXT
assert p.read() == TEXT.swapcase()
def test_move_first_rollback_overwrite_backup(tmpdir):
assert pylistdir(tmpdir) == []
p = tmpdir.join("file.txt")
p.write(TEXT)
bkp = tmpdir.join("backup.txt")
bkp.write("This is not the file you are looking for.\n")
with InPlace(str(p), backup=str(bkp), move_first=True) as fp:
for line in fp:
fp.write(line.swapcase())
fp.rollback()
assert pylistdir(tmpdir) == ["backup.txt", "file.txt"]
assert bkp.read() == "This is not the file you are looking for.\n"
assert p.read() == TEXT
def test_move_first_prechdir_backup(tmpdir, monkeypatch):
assert pylistdir(tmpdir) == []
monkeypatch.chdir(tmpdir)
p = tmpdir.join("file.txt")
p.write(TEXT)
with InPlace(str(p), backup="backup.txt", move_first=True) as fp:
for line in fp:
fp.write(line.swapcase())
assert pylistdir(tmpdir) == ["backup.txt", "file.txt"]
assert tmpdir.join("backup.txt").read() == TEXT
assert p.read() == TEXT.swapcase()
def test_move_first_midchdir_backup(tmpdir, monkeypatch):
"""
Assert that changing directory between creating an InPlace object and
opening it works
"""
filedir = tmpdir.mkdir("filedir")
wrongdir = tmpdir.mkdir("wrongdir")
p = filedir.join("file.txt")
p.write(TEXT)
monkeypatch.chdir(filedir)
fp = InPlace("file.txt", backup="backup.txt", delay_open=True, move_first=True)
monkeypatch.chdir(wrongdir)
assert fp.closed
with fp:
assert not fp.closed
for line in fp:
fp.write(line.swapcase())
assert not fp.closed
assert fp.closed
assert os.getcwd() == str(wrongdir)
assert pylistdir(wrongdir) == []
assert pylistdir(filedir) == ["backup.txt", "file.txt"]
assert filedir.join("backup.txt").read() == TEXT
assert p.read() == TEXT.swapcase()
def test_move_first_postchdir_backup(tmpdir, monkeypatch):
"""Assert that changing directory after opening an InPlace object works"""
filedir = tmpdir.mkdir("filedir")
wrongdir = tmpdir.mkdir("wrongdir")
p = filedir.join("file.txt")
p.write(TEXT)
monkeypatch.chdir(filedir)
with InPlace("file.txt", backup="backup.txt", move_first=True) as fp:
monkeypatch.chdir(wrongdir)
for line in fp:
fp.write(line.swapcase())
assert os.getcwd() == str(wrongdir)
assert pylistdir(wrongdir) == []
assert pylistdir(filedir) == ["backup.txt", "file.txt"]
assert filedir.join("backup.txt").read() == TEXT
assert p.read() == TEXT.swapcase()
def test_move_first_different_dir_backup(tmpdir, monkeypatch):
monkeypatch.chdir(tmpdir)
filedir = tmpdir.mkdir("filedir")
bkpdir = tmpdir.mkdir("bkpdir")
p = filedir.join("file.txt")
p.write(TEXT)
with InPlace(
os.path.join("filedir", "file.txt"),
backup=os.path.join("bkpdir", "backup.txt"),
move_first=True,
) as fp:
for line in fp:
fp.write(line.swapcase())
assert pylistdir(filedir) == ["file.txt"]
assert pylistdir(bkpdir) == ["backup.txt"]
assert bkpdir.join("backup.txt").read() == TEXT
assert p.read() == TEXT.swapcase()
def test_move_first_different_dir_file_backup(tmpdir, monkeypatch):
"""
Assert that if the input filepath contains a directory component and the
backup path does not, the backup file will be created in the current
directory
"""
monkeypatch.chdir(tmpdir)
filedir = tmpdir.mkdir("filedir")
p = filedir.join("file.txt")
p.write(TEXT)
with InPlace(
os.path.join("filedir", "file.txt"),
backup="backup.txt",
move_first=True,
) as fp:
for line in fp:
fp.write(line.swapcase())
assert pylistdir(tmpdir) == ["backup.txt", "filedir"]
assert pylistdir(filedir) == ["file.txt"]
assert tmpdir.join("backup.txt").read() == TEXT
assert p.read() == TEXT.swapcase()
def test_move_first_backup_dirpath(tmpdir):
"""
Assert that using a path to a directory as the backup path raises an error
when closing
"""
assert pylistdir(tmpdir) == []
p = tmpdir.join("file.txt")
p.write(TEXT)
not_a_file = tmpdir.join("not-a-file")
not_a_file.mkdir()
assert pylistdir(not_a_file) == []
fp = InPlace(str(p), backup=str(not_a_file), move_first=True)
fp.write("This will be discarded.\n")
with pytest.raises(EnvironmentError):
fp.close()
assert pylistdir(tmpdir) == ["file.txt", "not-a-file"]
assert p.read() == TEXT
assert pylistdir(not_a_file) == []
def test_move_first_backup_nosuchdir(tmpdir):
"""
Assert that using a path to a file in a nonexistent directory as the backup
path raises an error when opening
"""
assert pylistdir(tmpdir) == []
p = tmpdir.join("file.txt")
p.write(TEXT)
fp = InPlace(
str(p),
backup=str(tmpdir.join("nonexistent", "backup.txt")),
move_first=True,
delay_open=True,
)
with pytest.raises(EnvironmentError):
fp.open()
assert pylistdir(tmpdir) == ["file.txt"]
assert p.read() == TEXT
def test_move_first_double_open_nobackup(tmpdir):
assert pylistdir(tmpdir) == []
p = tmpdir.join("file.txt")
p.write(TEXT)
with InPlace(str(p), move_first=True) as fp:
with pytest.raises(ValueError):
fp.open()
assert not fp.closed
for line in fp:
fp.write(line.swapcase())
assert not fp.closed
assert fp.closed
assert pylistdir(tmpdir) == ["file.txt"]
assert p.read() == TEXT.swapcase()
def test_move_first_nonexistent(tmpdir):
assert pylistdir(tmpdir) == []
p = tmpdir.join("file.txt")
fp = InPlace(str(p), move_first=True, delay_open=True)
with pytest.raises(EnvironmentError):
fp.open()
assert pylistdir(tmpdir) == []
def test_move_first_with_nonexistent(tmpdir):
assert pylistdir(tmpdir) == []
p = tmpdir.join("file.txt")
with pytest.raises(EnvironmentError):
with InPlace(str(p), move_first=True):
assert False
assert pylistdir(tmpdir) == []
def test_move_first_nonexistent_backup_ext(tmpdir):
assert pylistdir(tmpdir) == []
p = tmpdir.join("file.txt")
fp = InPlace(str(p), backup_ext="~", move_first=True, delay_open=True)
with pytest.raises(EnvironmentError):
fp.open()
assert pylistdir(tmpdir) == []
def test_move_first_with_nonexistent_backup_ext(tmpdir):
assert pylistdir(tmpdir) == []
p = tmpdir.join("file.txt")
with pytest.raises(EnvironmentError):
with InPlace(str(p), backup_ext="~", move_first=True):
assert False
assert pylistdir(tmpdir) == []
def test_move_first_reentrant_backup_ext(tmpdir):
assert pylistdir(tmpdir) == []
p = tmpdir.join("file.txt")
p.write(TEXT)
with InPlace(str(p), backup_ext="~", move_first=True) as fp:
with fp:
for line in fp:
fp.write(line.swapcase())
assert pylistdir(tmpdir) == ["file.txt", "file.txt~"]
assert p.new(ext="txt~").read() == TEXT
assert p.read() == TEXT.swapcase()
def test_move_first_use_and_reenter_backup_ext(tmpdir):
assert pylistdir(tmpdir) == []
p = tmpdir.join("file.txt")
p.write(TEXT)
with InPlace(str(p), backup_ext="~", move_first=True) as fp:
fp.write(fp.readline().swapcase())
with fp:
for line in fp:
fp.write(line.swapcase())
assert pylistdir(tmpdir) == ["file.txt", "file.txt~"]
assert p.new(ext="txt~").read() == TEXT
assert p.read() == TEXT.swapcase()
def test_move_first_var_changes(tmpdir):
assert pylistdir(tmpdir) == []
p = tmpdir.join("file.txt")
p.write(TEXT)
with InPlace(str(p), backup_ext="~", move_first=True) as fp:
assert not fp.closed
assert fp.input is not None
assert fp.output is not None
assert fp._tmppath is not None
assert fp._state == fp.OPEN
assert fp.closed
assert fp.input is None
assert fp.output is None
assert fp._tmppath is None
assert fp._state == fp.CLOSED
def test_move_first_useless_after_close(tmpdir):
assert pylistdir(tmpdir) == []
p = tmpdir.join("file.txt")
p.write(TEXT)
with InPlace(str(p), backup_ext="~", move_first=True) as fp:
assert not fp.closed
assert fp.closed
with pytest.raises(ValueError):
fp.flush()
with pytest.raises(ValueError):
iter(fp)
with pytest.raises(ValueError):
fp.read()
with pytest.raises(ValueError):
fp.readline()
with pytest.raises(ValueError):
fp.readlines()
with pytest.raises(ValueError):
fp.write("")
with pytest.raises(ValueError):
fp.writelines([""])
def test_move_first_rollback_too_late(tmpdir):
assert pylistdir(tmpdir) == []
p = tmpdir.join("file.txt")
p.write(TEXT)
with InPlace(str(p), backup_ext="~", move_first=True) as fp:
for line in fp:
fp.write(line.swapcase())
with pytest.raises(ValueError):
fp.rollback()
assert pylistdir(tmpdir) == ["file.txt", "file.txt~"]
assert p.new(ext="txt~").read() == TEXT
assert p.read() == TEXT.swapcase()
def test_move_first_rollback_too_early(tmpdir):
assert pylistdir(tmpdir) == []
p = tmpdir.join("file.txt")
p.write(TEXT)
fp = InPlace(str(p), backup_ext="~", delay_open=True, move_first=True)
with pytest.raises(ValueError):
fp.rollback()
assert fp.closed
assert pylistdir(tmpdir) == ["file.txt"]
assert p.read() == TEXT
with fp:
for line in fp:
fp.write(line.swapcase())
assert pylistdir(tmpdir) == ["file.txt", "file.txt~"]
assert p.new(ext="txt~").read() == TEXT
assert p.read() == TEXT.swapcase()
| 31.654741 | 83 | 0.622775 | 2,364 | 17,695 | 4.556684 | 0.059222 | 0.049387 | 0.128667 | 0.054957 | 0.904567 | 0.864742 | 0.835964 | 0.807464 | 0.794746 | 0.784348 | 0 | 0.000221 | 0.231704 | 17,695 | 558 | 84 | 31.71147 | 0.792129 | 0.028539 | 0 | 0.800429 | 0 | 0 | 0.082086 | 0 | 0 | 0 | 0 | 0 | 0.330472 | 1 | 0.079399 | false | 0.004292 | 0.01073 | 0 | 0.090129 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
dcec5cc753fe1b7b046b7f6f4853cf3888afdfae | 32 | py | Python | tests/fixtures/foo.py | divykj/importit | 15350c4b622c67b92aed9015630428c51f8c7f31 | [
"MIT"
] | 9 | 2020-06-08T16:51:30.000Z | 2021-03-28T06:19:44.000Z | tests/fixtures/foo.py | divykj/importit | 15350c4b622c67b92aed9015630428c51f8c7f31 | [
"MIT"
] | 2 | 2020-07-06T14:44:09.000Z | 2020-10-03T04:33:10.000Z | tests/fixtures/foo.py | divykj/importit | 15350c4b622c67b92aed9015630428c51f8c7f31 | [
"MIT"
] | 2 | 2020-07-06T13:41:12.000Z | 2020-09-01T16:53:59.000Z | def say_bar():
return "bar"
| 10.666667 | 16 | 0.59375 | 5 | 32 | 3.6 | 0.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.25 | 32 | 2 | 17 | 16 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0.09375 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | true | 0 | 0 | 0.5 | 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 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 7 |
0d0254107bcc440702c9334dce022879579e8239 | 76 | py | Python | typed_models/__init__.py | lockhaty/typed-models | 75005b84cf78bc58d9a760eef34d42095ca4f726 | [
"MIT"
] | 1 | 2020-09-06T13:55:58.000Z | 2020-09-06T13:55:58.000Z | typed_models/__init__.py | lockhaty/typed-models | 75005b84cf78bc58d9a760eef34d42095ca4f726 | [
"MIT"
] | 3 | 2020-09-06T13:54:33.000Z | 2020-10-13T10:57:15.000Z | typed_models/__init__.py | lockhaty/typed-models | 75005b84cf78bc58d9a760eef34d42095ca4f726 | [
"MIT"
] | 1 | 2020-10-05T11:29:17.000Z | 2020-10-05T11:29:17.000Z | from .base import Model
from .base import Field
from .base import FieldValue | 25.333333 | 28 | 0.815789 | 12 | 76 | 5.166667 | 0.5 | 0.387097 | 0.677419 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.144737 | 76 | 3 | 28 | 25.333333 | 0.953846 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
0d156945ebe9277f9a3102bed0a8e047fc7c2e9c | 157 | py | Python | prune/__init__.py | hey-yahei/FPruning.MXNet | b8ef8582b7953dc1268dbfebd9fc3ab024374a69 | [
"MIT"
] | 6 | 2019-07-11T11:10:15.000Z | 2021-02-08T05:20:43.000Z | prune/__init__.py | hey-yahei/FPruning.MXNet | b8ef8582b7953dc1268dbfebd9fc3ab024374a69 | [
"MIT"
] | 1 | 2019-08-21T06:00:45.000Z | 2019-08-21T06:00:45.000Z | prune/__init__.py | hey-yahei/FPruning.MXNet | b8ef8582b7953dc1268dbfebd9fc3ab024374a69 | [
"MIT"
] | null | null | null | #-*- coding: utf-8 -*-
from .pruner import *
from .weight_rank_pruner import *
from .activation_rank_pruner import *
from .gradient_rank_pruner import *
| 15.7 | 37 | 0.738854 | 21 | 157 | 5.238095 | 0.47619 | 0.436364 | 0.436364 | 0.363636 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.007519 | 0.152866 | 157 | 9 | 38 | 17.444444 | 0.819549 | 0.133758 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 8 |
b4970a757d738d7e3c5462fe72dedd6dd56bb349 | 3,727 | py | Python | data.py | augustinharter/sba | 43c08c02fe98a007b9f9dcad53db0c0410bc4f0f | [
"MIT"
] | null | null | null | data.py | augustinharter/sba | 43c08c02fe98a007b9f9dcad53db0c0410bc4f0f | [
"MIT"
] | null | null | null | data.py | augustinharter/sba | 43c08c02fe98a007b9f9dcad53db0c0410bc4f0f | [
"MIT"
] | null | null | null | from typing import Union
from torchvision import transforms
from torchvision.datasets import MNIST, CIFAR10
from torch.utils.data import DataLoader, random_split
import pytorch_lightning as pl
class MNISTDataModule(pl.LightningDataModule):
def __init__(self, dir, batch_size=32, num_workers=2):
super().__init__()
self.dir = dir
self.batch_size = batch_size
self.num_workers = num_workers
# When doing distributed training, Datamodules have two optional arguments for
# granular control over download/prepare/splitting data:
# OPTIONAL, called only on 1 GPU/machine
def prepare_data(self):
MNIST(self.dir, train=True, download=True)
MNIST(self.dir, train=False, download=True)
# OPTIONAL, called for every GPU/machine (assigning state is OK)
def setup(self, stage: Union[str, None] = None):
# transforms
#transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
transform = transforms.Compose([transforms.ToTensor()])
# split dataset
if stage in (None, "fit"):
mnist_train = MNIST(self.dir, train=True, transform=transform)
self.data_train, self.data_val = random_split(mnist_train, [55000, 5000])
if stage == (None, "test"):
self.data_test = MNIST(self.dir, train=False, transform=transform)
# return the dataloader for each split
def train_dataloader(self):
data_train = DataLoader(self.data_train, batch_size=self.batch_size, num_workers=self.num_workers)
return data_train
def val_dataloader(self):
data_val = DataLoader(self.data_val, batch_size=self.batch_size, num_workers=self.num_workers)
return data_val
def test_dataloader(self):
data_test = DataLoader(self.data_test, batch_size=self.batch_size, num_workers=self.num_workers)
return data_test
class CIFAR10DataModule(pl.LightningDataModule):
def __init__(self, dir, batch_size=32, num_workers=2):
super().__init__()
self.dir = dir
self.batch_size = batch_size
self.num_workers = num_workers
# When doing distributed training, Datamodules have two optional arguments for
# granular control over download/prepare/splitting data:
# OPTIONAL, called only on 1 GPU/machine
def prepare_data(self):
CIFAR10(self.dir, train=True, download=True)
CIFAR10(self.dir, train=False, download=True)
# OPTIONAL, called for every GPU/machine (assigning state is OK)
def setup(self, stage: Union[str, None] = None):
# transforms
#transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
transform = transforms.Compose([transforms.ToTensor()])
# split dataset
if stage in (None, "fit"):
data_train = CIFAR10(self.dir, train=True, transform=transform)
len = data_train.__len__()
self.data_train, self.data_val = random_split(data_train, [int(len*0.8), int(len*0.2)])
if stage == (None, "test"):
self.data_test = CIFAR10(self.dir, train=False, transform=transform)
# return the dataloader for each split
def train_dataloader(self):
data_train = DataLoader(self.data_train, batch_size=self.batch_size, num_workers=self.num_workers)
return data_train
def val_dataloader(self):
data_val = DataLoader(self.data_val, batch_size=self.batch_size, num_workers=self.num_workers)
return data_val
def test_dataloader(self):
data_test = DataLoader(self.data_test, batch_size=self.batch_size, num_workers=self.num_workers)
return data_test | 42.83908 | 108 | 0.691441 | 483 | 3,727 | 5.134576 | 0.184265 | 0.065323 | 0.087097 | 0.043548 | 0.885484 | 0.866129 | 0.816129 | 0.794355 | 0.766129 | 0.766129 | 0 | 0.017966 | 0.208479 | 3,727 | 87 | 109 | 42.83908 | 0.822712 | 0.211698 | 0 | 0.678571 | 0 | 0 | 0.004791 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.214286 | false | 0 | 0.089286 | 0 | 0.446429 | 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 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
b4cc3f42efcf2bf7474d192f52cb6eae911983fc | 12,351 | py | Python | pydy/viz/camera.py | nouiz/pydy | 20c8ca9fc521208ae2144b5b453c14ed4a22a0ec | [
"BSD-3-Clause"
] | 1 | 2021-08-10T08:48:45.000Z | 2021-08-10T08:48:45.000Z | pydy/viz/camera.py | nouiz/pydy | 20c8ca9fc521208ae2144b5b453c14ed4a22a0ec | [
"BSD-3-Clause"
] | null | null | null | pydy/viz/camera.py | nouiz/pydy | 20c8ca9fc521208ae2144b5b453c14ed4a22a0ec | [
"BSD-3-Clause"
] | 1 | 2016-10-02T13:43:48.000Z | 2016-10-02T13:43:48.000Z | from sympy.matrices.expressions import Identity
from .visualization_frame import VisualizationFrame
__all__ = ['PerspectiveCamera', 'OrthoGraphicCamera']
class PerspectiveCamera(VisualizationFrame):
"""
Creates a Perspective Camera for visualization.
The camera is inherited from VisualizationFrame,
It can be attached to dynamics objects, hence we can
get a moving camera. All the transformation matrix generation
methods are applicable to a Perspective Camera.
Like VisualizationFrame,
It can also be initialized using:
1)Rigidbody
2)ReferenceFrame, Point
3)ReferenceFrame, Particle
Either one of these must be supplied during initialization
Unlike VisualizationFrame, It doesnt require a Shape argument.
Parameters
==========
name : str
a name for the PerspectiveCamera(optional). Default is 'unnamed'
fov : int or float
Field Of View, It determines the angle between the top and bottom
of the viewable area(in degrees). Default is 45 (degrees)
near : int or float
The distance of near plane of the PerspectiveCamera.
All objects closer to this distance are not displayed.
far : int or float
The distance of far plane of the PerspectiveCamera
All objects farther than this distance are not displayed.
"""
def __init__(self, *args, **kwargs):
"""
Initialises a PerspectiveCamera object.
To initialize a visualization frame, we need to supply
a name(optional), a reference frame, a point,
field of view(fov) (optional), near plane distance(optional)
and far plane distance(optional).
Examples
========
>>> from pydy.viz import VisualizationFrame, Shape
>>> from sympy.physics.mechanics import \
ReferenceFrame, Point, RigidBody, \
Particle, inertia
>>> from sympy import symbols
>>> I = ReferenceFrame('I')
>>> O = Point('O')
>>> shape = Shape()
>>> #initializing with reference frame, point
>>> camera1 = PerspectiveCamera('frame1', I, O)
>>> Ixx, Iyy, Izz, mass = symbols('Ixx Iyy Izz mass')
>>> i = inertia(I, Ixx, Iyy, Izz)
>>> rbody = RigidBody('rbody', O, I, mass, (inertia, O))
>>> # Initializing with a rigidbody ..
>>> camera2 = PerspectiveCamera('frame2', rbody)
>>> Pa = Particle('Pa', O, mass)
>>> #initializing with Particle, reference_frame ...
>>> camera3 = PerspectiveCamera('frame3', I, Pa)
"""
try:
self._fov = kwargs['fov']
except KeyError:
self._fov = 45
try:
self._near = kwargs['near']
except KeyError:
self._near = 1
try:
self._far = kwargs['far']
except KeyError:
self._far = 1000
#Now we use same approach as in VisualizationFrame
#for setting reference_frame and origin
i = 0
#If first arg is not str, name the visualization frame 'unnamed'
if isinstance(args[i], str):
self._name = args[i]
i += 1
else:
self._name = 'unnamed'
try:
self._reference_frame = args[i].get_frame()
self._origin = args[i].get_masscenter()
except AttributeError:
#It is not a rigidbody, hence this arg should be a
#reference frame
try:
dcm = args[i]._dcm_dict
self._reference_frame = args[i]
i += 1
except AttributeError:
raise TypeError(''' A ReferenceFrame is to be supplied
before a Particle/Point. ''')
#Now next arg can either be a Particle or point
try:
self._origin = args[i].get_point()
except AttributeError:
self._origin = args[i]
#basic thing required, transform matrix
self._transform = Identity(4).as_mutable()
def __str__(self):
return 'PerspectiveCamera: ' + self._name
def __repr__(self):
return 'PerspectiveCamera'
@property
def fov(self):
"""
attribute for Field Of view of a PerspectiveCamera
Default is 45 degrees
"""
return self._fov
@fov.setter
def fov(self, new_fov):
if not isinstance(new_fov, (int, str)):
raise TypeError(''' fov should be supplied in
int or float ''')
else:
self._fov = new_fov
@property
def near(self):
"""
attribute for Near Plane distance of a PerspectiveCamera
Default is 1
"""
return self._near
@near.setter
def near(self, new_near):
if not isinstance(new_near, (int, str)):
raise TypeError(''' near should be supplied in
int or float ''')
else:
self._near = new_near
@property
def far(self):
"""
attribute for Far Plane distance of a PerspectiveCamera
Default is 1000
"""
return self._far
@far.setter
def far(self, new_far):
if not isinstance(new_far, (int, str)):
raise TypeError(''' far should be supplied in
int or float ''')
else:
self._far = new_far
def generate_visualization_dict(self):
"""
Returns a dictionary of all the info required
for the visualization of this Camera
Before calling this method, all the transformation matrix
generation methods should be called, or it will give an error.
Returns
=======
a dictionary containing following keys:
name : name of the PerspectiveCamera
fov : Field Of View of the PerspectiveCamera
simulation_matrix : a N*4*4 matrix, converted to list, for
passing to Javascript for animation purposes, where N is the
number of timesteps for animations.
"""
self._data = {}
self._data['name'] = self.name
self._data['type'] = self.__repr__()
self._data['fov'] = self.fov
self._data['near'] = self.near
self._data['far'] = self.far
try:
self._data['simulation_matrix'] = self._visualization_matrix.tolist()
except:
#Not sure which error to call here.
raise RuntimeError('''Please call the numerical
transformation methods,
before generating simulation dict ''')
return self._data
class OrthoGraphicCamera(VisualizationFrame):
"""
Creates a OrthoGraphic Camera for visualization.
The camera is inherited from VisualizationFrame,
It can be attached to dynamics objects, hence we can
get a moving camera. All the transformation matrix generation
methods are applicable to a Perspective Camera.
Like VisualizationFrame,
It can also be initialized using:
1)Rigidbody
2)ReferenceFrame, Point
3)ReferenceFrame, Particle
Either one of these must be supplied during initialization
Unlike VisualizationFrame, It doesnt require a Shape argument.
Parameters
==========
name : str
a name for the PerspectiveCamera(optional). Default is 'unnamed'
near : int or float
The distance of near plane of the PerspectiveCamera.
All objects closer to this distance are not displayed.
far : int or float
The distance of far plane of the PerspectiveCamera
All objects farther than this distance are not displayed.
"""
def __init__(self, *args, **kwargs):
"""
Initialises an OrthoGraphicCamera object.
To initialize a visualization frame, we need to supply
a name(optional), a reference frame, a point,
near plane distance(optional)
and far plane distance(optional).
Examples
========
>>> from pydy.viz import OrthoGraphicCamera
>>> from sympy.physics.mechanics import \
ReferenceFrame, Point, RigidBody, \
Particle, inertia
>>> from sympy import symbols
>>> I = ReferenceFrame('I')
>>> O = Point('O')
>>> shape = Shape()
>>> #initializing with reference frame, point
>>> camera1 = OrthoGraphicCamera('frame1', I, O)
>>> Ixx, Iyy, Izz, mass = symbols('Ixx Iyy Izz mass')
>>> i = inertia(I, Ixx, Iyy, Izz)
>>> rbody = RigidBody('rbody', O, I, mass, (inertia, O))
>>> # Initializing with a rigidbody ..
>>> camera2 = OrthoGraphicCamera('frame2', rbody)
>>> Pa = Particle('Pa', O, mass)
>>> #initializing with Particle, reference_frame ...
>>> camera3 = OrthoGraphicCamera('frame3', I, Pa)
"""
try:
self._near = kwargs['near']
except KeyError:
self._near = 1
try:
self._far = kwargs['far']
except KeyError:
self._far = 1000
#Now we use same approach as in VisualizationFrame
#for setting reference_frame and origin
i = 0
#If first arg is not str, name the visualization frame 'unnamed'
if isinstance(args[i], str):
self._name = args[i]
i += 1
else:
self._name = 'unnamed'
try:
self._reference_frame = args[i].get_frame()
self._origin = args[i].get_masscenter()
except AttributeError:
#It is not a rigidbody, hence this arg should be a
#reference frame
self._reference_frame = args[i]
i += 1
#Now next arg can either be a Particle or point
try:
self._origin = args[i].get_point()
except AttributeError:
self._origin = args[i]
#basic thing required, transform matrix
self._transform = Identity(4).as_mutable()
def __str__(self):
return 'OrthoGraphicCamera: ' + self._name
def __repr__(self):
return 'OrthoGraphicCamera'
@property
def near(self):
"""
attribute for Near Plane distance of an OrthoGraphicCamera
Default is 1
"""
return self._near
@near.setter
def near(self, new_near):
if not isinstance(new_near, (int, str)):
raise TypeError(''' near should be supplied in
int or float ''')
else:
self._near = new_near
@property
def far(self):
"""
attribute for Far Plane distance of an OrthoGraphicCamera
Default is 1000
"""
return self._far
@far.setter
def far(self, new_far):
if not isinstance(new_far, (int, str)):
raise TypeError(''' far should be supplied in
int or float ''')
else:
self._far = new_far
def generate_visualization_dict(self):
"""
Returns a dictionary of all the info required
for the visualization of this Camera
Before calling this method, all the transformation matrix
generation methods should be called, or it will give an error.
Returns
=======
a dictionary containing following keys:
name : name of the OrthoGraphicCamera
simulation_matrix : a N*4*4 matrix, converted to list, for
passing to Javascript for animation purposes, where N is the
number of timesteps for animations.
"""
self._data = {}
self._data['name'] = self.name
self._data['type'] = self.__repr__()
self._data['near'] = self.near
self._data['far'] = self.far
try:
self._data['simulation_matrix'] = self.simulation_matrix.tolist()
except:
#Not sure which error to call here.
raise RuntimeError('''Please call the numerical
transformation methods,
before generating simulation dict ''')
return self._data
| 31.750643 | 81 | 0.576553 | 1,364 | 12,351 | 5.122434 | 0.150293 | 0.010734 | 0.014312 | 0.012881 | 0.855016 | 0.847288 | 0.840132 | 0.822098 | 0.822098 | 0.816946 | 0 | 0.006864 | 0.339487 | 12,351 | 388 | 82 | 31.832474 | 0.849595 | 0.47486 | 0 | 0.84106 | 0 | 0 | 0.180325 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.119205 | false | 0 | 0.013245 | 0.02649 | 0.218543 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
b4d3921a45a0db2938a529ce887538c0d4060f95 | 8,337 | py | Python | pyclustering/cluster/tests/unit/ut_clique.py | JosephChataignon/pyclustering | bf4f51a472622292627ec8c294eb205585e50f52 | [
"BSD-3-Clause"
] | 1,013 | 2015-01-26T19:50:14.000Z | 2022-03-31T07:38:48.000Z | pyclustering/cluster/tests/unit/ut_clique.py | peterlau0626/pyclustering | bf4f51a472622292627ec8c294eb205585e50f52 | [
"BSD-3-Clause"
] | 542 | 2015-01-20T16:44:32.000Z | 2022-01-29T14:57:20.000Z | pyclustering/cluster/tests/unit/ut_clique.py | peterlau0626/pyclustering | bf4f51a472622292627ec8c294eb205585e50f52 | [
"BSD-3-Clause"
] | 262 | 2015-03-19T07:28:12.000Z | 2022-03-30T07:28:24.000Z | """!
@brief Unit-tests for CLIQUE algorithm.
@authors Andrei Novikov (pyclustering@yandex.ru)
@date 2014-2020
@copyright BSD-3-Clause
"""
import unittest
# Generate images without having a window appear.
import matplotlib
matplotlib.use('Agg')
from pyclustering.cluster.clique import clique_block
from pyclustering.cluster.tests.clique_templates import clique_test_template
from pyclustering.tests.assertion import assertion
from pyclustering.samples.definitions import SIMPLE_SAMPLES, FCPS_SAMPLES
class clique_unit_test(unittest.TestCase):
def test_clustering_sample_simple_1(self):
clique_test_template.clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, 8, 0, [5, 5], 0, False)
clique_test_template.clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, 7, 0, [5, 5], 0, False)
clique_test_template.clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, 6, 0, [5, 5], 0, False)
clique_test_template.clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, 5, 0, [5, 5], 0, False)
def test_clustering_sample_simple_1_one_cluster(self):
clique_test_template.clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, 1, 0, [10], 0, False)
def test_clustering_diagonal_blocks_arent_neoghbors(self):
clique_test_template.clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, 2, 0, [5, 5], 0, False)
def test_clustering_sample_simple_1_noise_only(self):
clique_test_template.clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, 6, 1000, [], 10, False)
clique_test_template.clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, 6, 10, [], 10, False)
clique_test_template.clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, 2, 5, [], 10, False)
def test_clustering_sample_simple_2(self):
clique_test_template.clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE2, 7, 0, [5, 8, 10], 0, False)
clique_test_template.clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE2, 6, 0, [5, 8, 10], 0, False)
clique_test_template.clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE2, 1, 0, [23], 0, False)
clique_test_template.clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE2, 6, 500, [], 23, False)
def test_clustering_sample_simple_3(self):
clique_test_template.clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, 9, 0, [10, 10, 10, 30], 0, False)
clique_test_template.clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, 8, 0, [10, 10, 10, 30], 0, False)
clique_test_template.clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, 1, 0, [60], 0, False)
clique_test_template.clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, 6, 500, [], 60, False)
def test_clustering_sample_simple_3_one_point_noise(self):
clique_test_template.clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, 2, 9, [59], 1, False)
def test_clustering_sample_simple_4_one_cluster(self):
clique_test_template.clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE4, 1, 0, [75], 0, False)
def test_clustering_sample_simple_5(self):
clique_test_template.clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE5, 8, 0, [15, 15, 15, 15], 0, False)
clique_test_template.clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE5, 7, 0, [15, 15, 15, 15], 0, False)
clique_test_template.clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE5, 6, 0, [15, 15, 15, 15], 0, False)
clique_test_template.clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE5, 5, 0, [15, 15, 15, 15], 0, False)
clique_test_template.clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE5, 1, 0, [60], 0, False)
def test_clustering_one_dimensional_data1(self):
clique_test_template.clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE7, 4, 0, [10, 10], 0, False)
clique_test_template.clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE7, 2, 0, [20], 0, False)
def test_clustering_one_dimensional_data2(self):
clique_test_template.clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE8, 15, 0, [15, 20, 30, 80], 0, False)
clique_test_template.clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE8, 2, 0, [145], 0, False)
def test_clustering_one_dimensional_data_3_similar(self):
clique_test_template.clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE9, 7, 0, [10, 20], 0, False)
clique_test_template.clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE9, 2, 0, [30], 0, False)
def test_clustering_sample_simple_10(self):
clique_test_template.clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE10, 8, 0, [11, 11, 11], 0, False)
clique_test_template.clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE10, 7, 0, [11, 11, 11], 0, False)
clique_test_template.clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE10, 2, 0, [33], 0, False)
clique_test_template.clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE10, 1, 0, [33], 0, False)
def test_clustering_three_dimensional_data1(self):
clique_test_template.clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE11, 6, 0, [10, 10], 0, False)
clique_test_template.clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE11, 5, 0, [10, 10], 0, False)
clique_test_template.clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE11, 1, 0, [20], 0, False)
def test_clustering_similar_points(self):
clique_test_template.clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE12, 8, 0, [5, 5, 5], 0, False)
clique_test_template.clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE12, 7, 0, [5, 5, 5], 0, False)
clique_test_template.clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE12, 5, 0, [5, 5, 5], 0, False)
clique_test_template.clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE12, 2, 0, [15], 0, False)
def test_clustering_zero_column(self):
clique_test_template.clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE13, 3, 0, [5, 5], 0, False)
clique_test_template.clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE13, 2, 0, [5, 5], 0, False)
clique_test_template.clustering(SIMPLE_SAMPLES.SAMPLE_SIMPLE13, 1, 0, [10], 0, False)
def test_clustering_fcps_lsun(self):
clique_test_template.clustering(FCPS_SAMPLES.SAMPLE_LSUN, 15, 0, [100, 101, 202], 0, False)
def test_clustering_fcps_hepta(self):
clique_test_template.clustering(FCPS_SAMPLES.SAMPLE_HEPTA, 9, 0, [30, 30, 30, 30, 30, 30, 32], 0, False)
def test_visualize_no_failure_one_dimensional(self):
clique_test_template.visualize(SIMPLE_SAMPLES.SAMPLE_SIMPLE7, 4, 0, False)
clique_test_template.visualize(SIMPLE_SAMPLES.SAMPLE_SIMPLE8, 7, 0, False)
def test_visualize_no_failure_two_dimensional(self):
clique_test_template.visualize(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, 8, 0, False)
clique_test_template.visualize(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, 1, 0, False)
def test_visualize_no_failure_three_dimensional(self):
clique_test_template.visualize(SIMPLE_SAMPLES.SAMPLE_SIMPLE11, 3, 0, False)
def test_argument_invalid_levels(self):
clique_test_template.exception(ValueError, SIMPLE_SAMPLES.SAMPLE_SIMPLE1, 0, 0.0, False)
clique_test_template.exception(ValueError, SIMPLE_SAMPLES.SAMPLE_SIMPLE1, -1, 0.0, False)
clique_test_template.exception(ValueError, SIMPLE_SAMPLES.SAMPLE_SIMPLE1, -10, 0.0, False)
def test_argument_invalid_density(self):
clique_test_template.exception(ValueError, SIMPLE_SAMPLES.SAMPLE_SIMPLE1, 1, -1.0, False)
clique_test_template.exception(ValueError, SIMPLE_SAMPLES.SAMPLE_SIMPLE1, 1, -2.0, False)
def test_argument_empty_data(self):
clique_test_template.exception(ValueError, [], 1, 0.0, False)
def test_logical_block_neighbors(self):
block = clique_block()
block.logical_location = [1, 1]
neighbors = block.get_location_neighbors(3)
assertion.eq(4, len(neighbors))
assertion.true([0, 1] in neighbors)
assertion.true([2, 1] in neighbors)
assertion.true([1, 0] in neighbors)
assertion.true([1, 2] in neighbors)
def test_logical_block_neighbors_on_edge(self):
block = clique_block()
block.logical_location = [1, 1]
neighbors = block.get_location_neighbors(2)
assertion.eq(2, len(neighbors))
assertion.true([0, 1] in neighbors)
assertion.true([1, 0] in neighbors)
block.logical_location = [0, 0]
neighbors = block.get_location_neighbors(2)
assertion.eq(2, len(neighbors))
assertion.true([0, 1] in neighbors)
assertion.true([1, 0] in neighbors)
| 52.765823 | 113 | 0.721123 | 1,139 | 8,337 | 4.964881 | 0.112379 | 0.102564 | 0.184615 | 0.227763 | 0.860831 | 0.831653 | 0.78939 | 0.725376 | 0.61733 | 0.488064 | 0 | 0.068129 | 0.174283 | 8,337 | 157 | 114 | 53.101911 | 0.753341 | 0.02171 | 0 | 0.127273 | 1 | 0 | 0.000375 | 0 | 0 | 0 | 0 | 0 | 0.109091 | 1 | 0.236364 | false | 0 | 0.054545 | 0 | 0.3 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 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 | 0 | 0 | 0 | 0 | 8 |
3707f777fcfd1bfc1bc87b8118cfeb081a7b3ae7 | 7,505 | py | Python | python/test_alpenglow/experiments/test_ALSOnlineFactorExperiment.py | rpalovics/Alpenglow | 63472ce667d517d6c7f47c9d0559861392fca3f9 | [
"Apache-2.0"
] | 28 | 2017-07-23T22:47:44.000Z | 2022-03-12T15:11:13.000Z | python/test_alpenglow/experiments/test_ALSOnlineFactorExperiment.py | proto-n/Alpenglow | 7a15d5c57b511787379f095e7310e67423159fa0 | [
"Apache-2.0"
] | 4 | 2017-05-10T10:23:17.000Z | 2019-05-23T14:07:09.000Z | python/test_alpenglow/experiments/test_ALSOnlineFactorExperiment.py | proto-n/Alpenglow | 7a15d5c57b511787379f095e7310e67423159fa0 | [
"Apache-2.0"
] | 9 | 2017-05-04T09:20:58.000Z | 2021-12-14T08:19:01.000Z | import alpenglow as prs
import alpenglow.Getter as rs
import alpenglow.experiments
import alpenglow.evaluation
import pandas as pd
import math
import numpy as np
import pytest
import sys
from alpenglow.evaluation import DcgScore
import alpenglow.cpp
compiler = alpenglow.cpp.__compiler
stdlib = alpenglow.cpp.__stdlib
class TestALSFactorExperiment:
def test_ALSFactorExperiment(self):
experiment = alpenglow.experiments.ALSOnlineFactorExperiment(
period_length=500,
)
rankings = experiment.run("python/test_alpenglow/test_data_4", experimentType="online_id", verbose=True, exclude_known=True)
assert rankings.top_k == 100
desired_ranks = [101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 7.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 5.0, 101.0, 101.0, 3.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 1.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 1.0, 101.0, 101.0, 101.0, 16.0, 15.0, 20.0, 101.0, 101.0, 23.0, 101.0, 1.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 16.0, 101.0, 19.0, 44.0, 46.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 3.0, 101.0, 7.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 1.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 17.0, 101.0, 101.0, 101.0, 101.0, 101.0, 3.0, 101.0, 7.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 2.0, 101.0, 101.0, 101.0, 101.0, 81.0, 101.0, 101.0, 25.0, 101.0, 101.0, 101.0, 101.0, 101.0, 22.0, 60.0, 101.0, 101.0, 101.0, 1.0, 101.0, 1.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 3.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 13.0, 4.0, 101.0, 101.0, 101.0, 1.0, 101.0, 80.0, 4.0, 31.0, 5.0, 101.0, 32.0, 14.0, 9.0, 34.0, 20.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 7.0, 101.0, 101.0, 101.0, 16.0, 28.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 3.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 2.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 47.0, 101.0, 101.0, 101.0, 66.0, 101.0, 101.0, 101.0, 101.0, 26.0, 101.0, 97.0, 25.0, 70.0, 101.0, 24.0, 101.0, 101.0, 1.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 9.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 2.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 12.0, 101.0, 15.0, 30.0, 101.0, 101.0, 54.0, 62.0, 101.0, 101.0, 101.0, 20.0, 52.0, 10.0, 101.0, 53.0, 4.0, 101.0, 5.0, 101.0, 101.0, 101.0, 45.0, 2.0, 101.0, 3.0, 14.0, 101.0, 101.0, 101.0, 13.0, 101.0, 101.0, 101.0, 5.0, 6.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 19.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 38.0, 10.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 8.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 2.0, 10.0, 10.0, 13.0, 101.0, 101.0, 13.0, 101.0, 101.0, 63.0, 101.0, 101.0, 18.0, 101.0, 4.0, 101.0, 51.0, 9.0, 101.0, 101.0, 101.0, 51.0, 101.0, 101.0, 20.0, 101.0, 101.0, 101.0, 23.0, 101.0, 101.0, 65.0, 93.0, 101.0, 15.0, 101.0, 101.0, 101.0, 17.0, 13.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 6.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 3.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 31.0, 66.0, 28.0, 101.0, 101.0, 62.0, 45.0, 101.0, 36.0, 101.0, 101.0, 15.0, 22.0, 101.0, 19.0, 1.0, 8.0, 1.0, 2.0, 101.0, 13.0, 31.0, 9.0, 101.0, 101.0, 2.0, 101.0, 101.0, 63.0, 29.0, 17.0, 33.0, 12.0, 3.0, 4.0, 101.0, 64.0, 101.0, 41.0, 53.0, 23.0, 101.0, 1.0, 101.0, 101.0, 40.0, 101.0, 101.0, 17.0, 13.0, 5.0, 101.0, 7.0, 6.0, 9.0, 12.0, 25.0, 101.0, 38.0, 22.0, 101.0, 101.0, 101.0, 101.0, 101.0, 18.0, 29.0, 39.0, 27.0, 101.0, 101.0, 101.0, 53.0, 39.0, 11.0, 101.0, 66.0, 20.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 27.0, 101.0, 101.0, 23.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 17.0, 101.0, 101.0, 101.0, 40.0, 4.0, 45.0, 4.0, 3.0, 19.0, 101.0, 4.0, 22.0, 8.0, 101.0, 101.0, 13.0, 101.0, 101.0, 47.0, 101.0, 22.0, 15.0, 101.0, 25.0, 101.0, 101.0, 101.0, 41.0, 101.0, 101.0, 101.0, 41.0, 13.0, 9.0, 48.0, 12.0, 9.0, 101.0, 6.0, 101.0, 6.0, 101.0, 101.0, 101.0, 12.0, 3.0, 19.0, 101.0, 34.0, 7.0, 99.0, 22.0, 101.0, 11.0, 7.0, 10.0, 29.0, 101.0, 101.0, 101.0, 54.0, 101.0, 101.0, 10.0, 101.0, 8.0, 101.0, 8.0, 101.0, 101.0, 101.0, 20.0, 101.0, 101.0, 101.0, 1.0, 8.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 3.0, 101.0, 101.0, 6.0, 101.0, 101.0, 101.0, 101.0, 101.0, 16.0, 2.0, 101.0, 101.0, 101.0, 101.0, 101.0, 101.0, 4.0, 101.0, 15.0, 49.0, 20.0, 5.0, 101.0, 7.0, 101.0, 101.0, 28.0, 23.0, 4.0, 30.0, 64.0, 101.0, 80.0, 101.0]
assert DcgScore(rankings).mean() == pytest.approx(0.0663298214520355, abs=1e-3) | 300.2 | 6,744 | 0.57535 | 2,093 | 7,505 | 2.056856 | 0.054945 | 0.737747 | 0.921022 | 1.261789 | 0.825319 | 0.780023 | 0.780023 | 0.741928 | 0.741928 | 0.700581 | 0 | 0.587608 | 0.150566 | 7,505 | 25 | 6,745 | 300.2 | 0.087686 | 0 | 0 | 0 | 0 | 0 | 0.005596 | 0.004396 | 0 | 0 | 0 | 0 | 0.090909 | 1 | 0.045455 | false | 0 | 0.5 | 0 | 0.590909 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 15 |
2eae18ee527e318c613ad1d625f61d21812d404a | 1,402 | py | Python | chart/migrations/0002_auto_20190811_1347.py | KATO-Hiro/star-chart | c3e12e413012c2677ab7c2516a01d21c41cd2998 | [
"MIT"
] | 1 | 2019-12-20T13:48:36.000Z | 2019-12-20T13:48:36.000Z | chart/migrations/0002_auto_20190811_1347.py | KATO-Hiro/star-chart | c3e12e413012c2677ab7c2516a01d21c41cd2998 | [
"MIT"
] | 15 | 2019-12-15T18:00:44.000Z | 2021-09-22T23:36:57.000Z | chart/migrations/0002_auto_20190811_1347.py | KATO-Hiro/star-chart | c3e12e413012c2677ab7c2516a01d21c41cd2998 | [
"MIT"
] | 1 | 2019-12-20T13:48:39.000Z | 2019-12-20T13:48:39.000Z | # Generated by Django 2.2.2 on 2019-08-11 13:47
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('chart', '0001_initial'),
]
operations = [
migrations.AddField(
model_name='combination',
name='avatar_url1',
field=models.CharField(blank=True, default='', max_length=255, null=True),
),
migrations.AddField(
model_name='combination',
name='avatar_url2',
field=models.CharField(blank=True, default='', max_length=255, null=True),
),
migrations.AddField(
model_name='combination',
name='avatar_url3',
field=models.CharField(blank=True, default='', max_length=255, null=True),
),
migrations.AddField(
model_name='combination',
name='name_owner1',
field=models.CharField(blank=True, default='', max_length=100, null=True),
),
migrations.AddField(
model_name='combination',
name='name_owner2',
field=models.CharField(blank=True, default='', max_length=100, null=True),
),
migrations.AddField(
model_name='combination',
name='name_owner3',
field=models.CharField(blank=True, default='', max_length=100, null=True),
),
]
| 31.863636 | 86 | 0.574893 | 141 | 1,402 | 5.58156 | 0.304965 | 0.13723 | 0.175349 | 0.205845 | 0.78526 | 0.78526 | 0.78526 | 0.724269 | 0.724269 | 0.719187 | 0 | 0.043522 | 0.295292 | 1,402 | 43 | 87 | 32.604651 | 0.753036 | 0.032097 | 0 | 0.648649 | 1 | 0 | 0.109963 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.027027 | 0 | 0.108108 | 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 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
2ec6fb19aa0f5f19cd9f16046880190909ff18a2 | 1,079 | py | Python | scripts/encoding model 15 ROIs/qsub_jobs_en.py | nmningmei/METASEMA_encoding_model | ce4b9b0935e5a1b04de77174236905f7d8d267f8 | [
"MIT"
] | null | null | null | scripts/encoding model 15 ROIs/qsub_jobs_en.py | nmningmei/METASEMA_encoding_model | ce4b9b0935e5a1b04de77174236905f7d8d267f8 | [
"MIT"
] | null | null | null | scripts/encoding model 15 ROIs/qsub_jobs_en.py | nmningmei/METASEMA_encoding_model | ce4b9b0935e5a1b04de77174236905f7d8d267f8 | [
"MIT"
] | 1 | 2019-06-13T07:47:55.000Z | 2019-06-13T07:47:55.000Z |
import os
import time
os.system("qsub enc_1")
time.sleep(30)
os.system("qsub enc_2")
time.sleep(30)
os.system("qsub enc_3")
time.sleep(30)
os.system("qsub enc_4")
time.sleep(30)
os.system("qsub enc_5")
time.sleep(30)
os.system("qsub enc_6")
time.sleep(30)
os.system("qsub enc_7")
time.sleep(30)
os.system("qsub enc_8")
time.sleep(30)
os.system("qsub enc_9")
time.sleep(30)
os.system("qsub enc_10")
time.sleep(30)
os.system("qsub enc_11")
time.sleep(30)
os.system("qsub enc_12")
time.sleep(30)
os.system("qsub enc_13")
time.sleep(30)
os.system("qsub enc_14")
time.sleep(30)
os.system("qsub enc_15")
time.sleep(30)
os.system("qsub enc_16")
time.sleep(30)
os.system("qsub enc_17")
time.sleep(30)
os.system("qsub enc_18")
time.sleep(30)
os.system("qsub enc_19")
time.sleep(30)
os.system("qsub enc_20")
time.sleep(30)
os.system("qsub enc_21")
time.sleep(30)
os.system("qsub enc_22")
time.sleep(30)
os.system("qsub enc_23")
time.sleep(30)
os.system("qsub enc_24")
time.sleep(30)
os.system("qsub enc_25")
time.sleep(30)
os.system("qsub enc_26")
time.sleep(30)
os.system("qsub enc_27")
| 18.929825 | 24 | 0.723818 | 217 | 1,079 | 3.474654 | 0.16129 | 0.286472 | 0.429708 | 0.537135 | 0.896552 | 0.896552 | 0.896552 | 0 | 0 | 0 | 0 | 0.097586 | 0.078777 | 1,079 | 56 | 25 | 19.267857 | 0.660966 | 0 | 0 | 0.472727 | 0 | 0 | 0.267161 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.036364 | 0 | 0.036364 | 0 | 0 | 0 | 0 | null | 1 | 1 | 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 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
2ed0469664dfca998ae98cc1caa8bf4b612cb426 | 196 | py | Python | plotly/graph_objs/heatmapgl/__init__.py | gnestor/plotly.py | a8ae062795ddbf9867b8578fe6d9e244948c15ff | [
"MIT"
] | 12 | 2020-04-18T18:10:22.000Z | 2021-12-06T10:11:15.000Z | plotly/graph_objs/heatmapgl/__init__.py | Vesauza/plotly.py | e53e626d59495d440341751f60aeff73ff365c28 | [
"MIT"
] | 27 | 2020-04-28T21:23:12.000Z | 2021-06-25T15:36:38.000Z | plotly/graph_objs/heatmapgl/__init__.py | Vesauza/plotly.py | e53e626d59495d440341751f60aeff73ff365c28 | [
"MIT"
] | 6 | 2020-04-18T23:07:08.000Z | 2021-11-18T07:53:06.000Z | from ._stream import Stream
from ._hoverlabel import Hoverlabel
from plotly.graph_objs.heatmapgl import hoverlabel
from ._colorbar import ColorBar
from plotly.graph_objs.heatmapgl import colorbar
| 32.666667 | 50 | 0.862245 | 26 | 196 | 6.307692 | 0.346154 | 0.195122 | 0.243902 | 0.231707 | 0.414634 | 0.414634 | 0 | 0 | 0 | 0 | 0 | 0 | 0.102041 | 196 | 5 | 51 | 39.2 | 0.931818 | 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 | 1 | 0 | 0 | 7 |
25cc7ceeffc65d1bf1bcbffa3afb03f1425f21ef | 1,255 | py | Python | Buscador/src/model/CoefficientStategy.py | Llambi/Web_Semantica | 16f98a7d78ba08366a67caf2bd44f3f45af6ee21 | [
"MIT"
] | null | null | null | Buscador/src/model/CoefficientStategy.py | Llambi/Web_Semantica | 16f98a7d78ba08366a67caf2bd44f3f45af6ee21 | [
"MIT"
] | null | null | null | Buscador/src/model/CoefficientStategy.py | Llambi/Web_Semantica | 16f98a7d78ba08366a67caf2bd44f3f45af6ee21 | [
"MIT"
] | null | null | null | from abc import ABC
from src.model.BagOfWords import BagOfWords
class CoefficientStrategy(ABC):
def exec(self):
pass
class DiceStrategy(CoefficientStrategy):
def __init__(self, bag1: BagOfWords, bag2: BagOfWords):
self.__bag1 = bag1
self.__bag2 = bag2
def exec(self):
return 2.0 * len(self.__bag1.intersection(self.__bag2)) / (len(self.__bag1) + len(self.__bag2))
class JaccardStrategy(CoefficientStrategy):
def __init__(self, bag1: BagOfWords, bag2: BagOfWords):
self.__bag1 = bag1
self.__bag2 = bag2
def exec(self):
return len(self.__bag1.intersection(self.__bag2)) / len(self.__bag1.union(self.__bag2))
class CosineStrategy(CoefficientStrategy):
def __init__(self, bag1: BagOfWords, bag2: BagOfWords):
self.__bag1 = bag1
self.__bag2 = bag2
def exec(self):
return len(self.__bag1.intersection(self.__bag2)) / (len(self.__bag1) * len(self.__bag2))
class OverlappingStrategy(CoefficientStrategy):
def __init__(self, bag1: BagOfWords, bag2: BagOfWords):
self.__bag1 = bag1
self.__bag2 = bag2
def exec(self):
return len(self.__bag1.intersection(self.__bag2)) / min(len(self.__bag1), len(self.__bag2))
| 27.282609 | 103 | 0.681275 | 148 | 1,255 | 5.344595 | 0.175676 | 0.16182 | 0.111252 | 0.151707 | 0.767383 | 0.767383 | 0.73957 | 0.73957 | 0.73957 | 0.73957 | 0 | 0.042042 | 0.203984 | 1,255 | 45 | 104 | 27.888889 | 0.74975 | 0 | 0 | 0.586207 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.310345 | false | 0.034483 | 0.068966 | 0.137931 | 0.689655 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 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 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 8 |
25e55f4b3557b5903a1ae99c6b70f80cca076c8a | 185 | py | Python | tests/test_matching_types.py | welchbj/almanac | 91db5921a27f7d089b4ad8463ffb6e1453c5126a | [
"MIT"
] | 4 | 2020-08-04T10:59:10.000Z | 2021-08-23T13:42:03.000Z | tests/test_matching_types.py | welchbj/almanac | 91db5921a27f7d089b4ad8463ffb6e1453c5126a | [
"MIT"
] | null | null | null | tests/test_matching_types.py | welchbj/almanac | 91db5921a27f7d089b4ad8463ffb6e1453c5126a | [
"MIT"
] | 2 | 2021-07-20T04:49:22.000Z | 2021-08-23T13:42:23.000Z | """Tests for comparisons between types."""
from typing import Union
from almanac import is_matching_type
def test_union():
assert is_matching_type(int, Union[int, str]) is True
| 18.5 | 57 | 0.756757 | 28 | 185 | 4.821429 | 0.678571 | 0.148148 | 0.207407 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.156757 | 185 | 9 | 58 | 20.555556 | 0.865385 | 0.194595 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.25 | 1 | 0.25 | true | 0 | 0.5 | 0 | 0.75 | 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 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
d38d5fbb78c41c067e3ada4afb653a5e3a5e7da0 | 137 | py | Python | tests/rcf/__init__.py | sourya/compas_rcf | 61a829f5ac6b95ff853e42b5338ad5a52455dd55 | [
"MIT"
] | null | null | null | tests/rcf/__init__.py | sourya/compas_rcf | 61a829f5ac6b95ff853e42b5338ad5a52455dd55 | [
"MIT"
] | 21 | 2020-04-01T10:00:59.000Z | 2020-04-23T14:08:05.000Z | tests/rcf/__init__.py | sourya/compas_rcf | 61a829f5ac6b95ff853e42b5338ad5a52455dd55 | [
"MIT"
] | null | null | null | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
# container for unit tests
| 22.833333 | 38 | 0.861314 | 18 | 137 | 5.777778 | 0.611111 | 0.288462 | 0.461538 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.131387 | 137 | 5 | 39 | 27.4 | 0.87395 | 0.175182 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0.333333 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
6ce1149bc05f30f334338bac057bd794a15e595e | 1,236 | py | Python | data/source/KBP/generateBClusterInput.py | INK-USC/shifted-label-distribution | 3cf2b7ced3b2e18234db405f6014f049c4830d71 | [
"Apache-2.0"
] | 37 | 2019-10-29T13:12:41.000Z | 2022-01-20T02:42:28.000Z | data/source/KBP/generateBClusterInput.py | cherry979988/feedforward-RE | 546a608a8cb5b35c475e577995df70a89affa15e | [
"MIT"
] | 5 | 2020-07-23T10:32:59.000Z | 2021-09-01T11:37:15.000Z | data/source/KBP/generateBClusterInput.py | cherry979988/feedforward-RE | 546a608a8cb5b35c475e577995df70a89affa15e | [
"MIT"
] | 2 | 2020-05-27T06:00:56.000Z | 2021-02-08T10:45:41.000Z | __author__ = 'ZeqiuWu'
import json
import sys
from collections import defaultdict
import unicodedata
file = open('./train_split.json', 'r')
f = open('./bc_input.txt', 'w')
writtenSents = set()
for line in file.readlines():
sent = json.loads(line)
sentText = unicodedata.normalize('NFKD', sent['sentText']).encode('ascii','ignore').decode('ascii').rstrip('\n').rstrip('\r')
if sentText in writtenSents:
continue
f.write(sentText)
f.write('\n')
writtenSents.add(sentText)
file.close()
file = open('./test.json', 'r')
for line in file.readlines():
sent = json.loads(line)
sentText = unicodedata.normalize('NFKD', sent['sentText']).encode('ascii','ignore').decode('ascii').rstrip('\n').rstrip('\r')
if sentText in writtenSents:
continue
f.write(sentText)
f.write('\n')
writtenSents.add(sentText)
file.close()
file = open('./dev.json', 'r')
for line in file.readlines():
sent = json.loads(line)
sentText = unicodedata.normalize('NFKD', sent['sentText']).encode('ascii','ignore').decode('ascii').rstrip('\n').rstrip('\r')
if sentText in writtenSents:
continue
f.write(sentText)
f.write('\n')
writtenSents.add(sentText)
file.close()
f.close()
| 28.744186 | 129 | 0.65534 | 158 | 1,236 | 5.088608 | 0.272152 | 0.044776 | 0.033582 | 0.048507 | 0.81592 | 0.81592 | 0.81592 | 0.81592 | 0.81592 | 0.81592 | 0 | 0 | 0.156149 | 1,236 | 42 | 130 | 29.428571 | 0.770853 | 0 | 0 | 0.710526 | 0 | 0 | 0.134304 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.105263 | 0 | 0.105263 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
6ceb71ad8dd50844a606314cdd15de1edf78797b | 79 | py | Python | django_describer/adapters/__init__.py | karlosss/django_describer | 2c1e12ad1cf99d1b72dba16cd5d0401787c3ac58 | [
"MIT"
] | 4 | 2019-09-29T08:13:52.000Z | 2019-10-11T19:41:54.000Z | django_describer/adapters/__init__.py | karlosss/django_describer | 2c1e12ad1cf99d1b72dba16cd5d0401787c3ac58 | [
"MIT"
] | null | null | null | django_describer/adapters/__init__.py | karlosss/django_describer | 2c1e12ad1cf99d1b72dba16cd5d0401787c3ac58 | [
"MIT"
] | null | null | null | import django_describer.adapters.base
import django_describer.adapters.graphql
| 26.333333 | 40 | 0.898734 | 10 | 79 | 6.9 | 0.6 | 0.347826 | 0.608696 | 0.84058 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.050633 | 79 | 2 | 41 | 39.5 | 0.92 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 8 |
6cff5e8b52b0108b8ea08e79eed19c5bd0e2bd70 | 3,187 | py | Python | tests/stubs/workspace_management_api/sample_model.py | PSE-TECO-2020-TEAM1/e2e-ml_model-management | 7f01a008648e25a29c639a5e16124b2e399eb821 | [
"MIT"
] | 1 | 2021-05-04T08:46:19.000Z | 2021-05-04T08:46:19.000Z | tests/stubs/workspace_management_api/sample_model.py | PSE-TECO-2020-TEAM1/e2e-ml_model-management | 7f01a008648e25a29c639a5e16124b2e399eb821 | [
"MIT"
] | null | null | null | tests/stubs/workspace_management_api/sample_model.py | PSE-TECO-2020-TEAM1/e2e-ml_model-management | 7f01a008648e25a29c639a5e16124b2e399eb821 | [
"MIT"
] | 1 | 2022-01-28T21:21:32.000Z | 2022-01-28T21:21:32.000Z | from app.workspace_management_api.sample_model import DataPoint, SampleFromWorkspace, Timeframe, DataPointsPerSensor
# TODO replace values with better ones
def get_sample_from_workspace_stub_1():
return SampleFromWorkspace(
label="Shake",
start=1617981582100,
end=1617981582200,
timeFrames=[Timeframe(1617981582100, 1617981582200)],
sensorDataPoints=[DataPointsPerSensor(
sensorName="Accelerometer",
dataPoints=[
DataPoint(data=[1.0, 1.0, 1.0], timestamp=1617981582100),
DataPoint(data=[1.0, 1.0, 1.0], timestamp=1617981582117),
DataPoint(data=[1.0, 1.0, 1.0], timestamp=1617981582133),
DataPoint(data=[1.0, 1.0, 1.0], timestamp=1617981582150),
DataPoint(data=[1.0, 1.0, 1.0], timestamp=1617981582167),
DataPoint(data=[1.0, 1.0, 1.0], timestamp=1617981582183),
DataPoint(data=[1.0, 1.0, 1.0], timestamp=1617981582200),
]
), DataPointsPerSensor(
sensorName="Gyroscope",
dataPoints=[
DataPoint(data=[1.0, 1.0, 1.0], timestamp=1617981582100),
DataPoint(data=[1.0, 1.0, 1.0], timestamp=1617981582120),
DataPoint(data=[1.0, 1.0, 1.0], timestamp=1617981582140),
DataPoint(data=[1.0, 1.0, 1.0], timestamp=1617981582160),
DataPoint(data=[1.0, 1.0, 1.0], timestamp=1617981582180),
DataPoint(data=[1.0, 1.0, 1.0], timestamp=1617981582200)
]
)]
)
def get_sample_from_workspace_stub_2():
return SampleFromWorkspace(
label="Rotate",
start=1617981582010,
end=1617981582260,
timeFrames=[Timeframe(1617981582050, 1617981582080), Timeframe(1617981582100, 1617981582200),
Timeframe(1617981582210, 1617981582230)],
sensorDataPoints=[DataPointsPerSensor(
sensorName="Accelerometer",
dataPoints=[
DataPoint(data=[1.0, 1.0, 1.0], timestamp=1617981582100),
DataPoint(data=[1.0, 1.0, 1.0], timestamp=1617981582117),
DataPoint(data=[1.0, 1.0, 1.0], timestamp=1617981582133),
DataPoint(data=[1.0, 1.0, 1.0], timestamp=1617981582150),
DataPoint(data=[1.0, 1.0, 1.0], timestamp=1617981582167),
DataPoint(data=[1.0, 1.0, 1.0], timestamp=1617981582183),
DataPoint(data=[1.0, 1.0, 1.0], timestamp=1617981582200),
DataPoint(data=[1.0, 1.0, 1.0], timestamp=1617981582217)
]
), DataPointsPerSensor(
sensorName="Gyroscope",
dataPoints=[
DataPoint(data=[1.0, 1.0, 1.0], timestamp=1617981582100),
DataPoint(data=[1.0, 1.0, 1.0], timestamp=1617981582120),
DataPoint(data=[1.0, 1.0, 1.0], timestamp=1617981582140),
DataPoint(data=[1.0, 1.0, 1.0], timestamp=1617981582160),
DataPoint(data=[1.0, 1.0, 1.0], timestamp=1617981582180),
DataPoint(data=[1.0, 1.0, 1.0], timestamp=1617981582200)
]
)]
)
| 46.188406 | 116 | 0.578601 | 350 | 3,187 | 5.231429 | 0.16 | 0.088476 | 0.088476 | 0.117968 | 0.755871 | 0.755871 | 0.724194 | 0.724194 | 0.724194 | 0.708902 | 0 | 0.293013 | 0.281456 | 3,187 | 68 | 117 | 46.867647 | 0.50655 | 0.011296 | 0 | 0.688525 | 0 | 0 | 0.017466 | 0 | 0 | 0 | 0 | 0.014706 | 0 | 1 | 0.032787 | true | 0 | 0.016393 | 0.032787 | 0.081967 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
9f1a234b57a3f2fa3514a38a2e310c6af32f8811 | 83 | py | Python | src/channel/postpro/tke/__init__.py | andyandreolli/channel_pytools | 3268b92aebd0d8859358a0610865559308a2cb01 | [
"MIT"
] | null | null | null | src/channel/postpro/tke/__init__.py | andyandreolli/channel_pytools | 3268b92aebd0d8859358a0610865559308a2cb01 | [
"MIT"
] | null | null | null | src/channel/postpro/tke/__init__.py | andyandreolli/channel_pytools | 3268b92aebd0d8859358a0610865559308a2cb01 | [
"MIT"
] | null | null | null | from channel.postpro.tke.read_uiuj import *
from channel.postpro.tke.ebox import *
| 27.666667 | 43 | 0.807229 | 13 | 83 | 5.076923 | 0.615385 | 0.333333 | 0.545455 | 0.636364 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.096386 | 83 | 2 | 44 | 41.5 | 0.88 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 7 |
9f3227be18bfec055668d3a9e2f4294e894ff8d4 | 4,885 | py | Python | api/v1/tests/test_rides.py | Quantum-35/Ride_My_Way_Challenge2 | de08415a5222a3b0c6b8f7b4b7a0cdb7addb6438 | [
"MIT"
] | null | null | null | api/v1/tests/test_rides.py | Quantum-35/Ride_My_Way_Challenge2 | de08415a5222a3b0c6b8f7b4b7a0cdb7addb6438 | [
"MIT"
] | 6 | 2018-06-23T06:38:36.000Z | 2018-06-28T07:52:27.000Z | api/v1/tests/test_rides.py | Quantum-35/Ride_My_Way_Challenge | de08415a5222a3b0c6b8f7b4b7a0cdb7addb6438 | [
"MIT"
] | 1 | 2018-06-26T10:21:31.000Z | 2018-06-26T10:21:31.000Z | from flask import json
from tests import BaseTests
RIDES_URL = '/api/v1/rides/'
class TestRides(BaseTests):
def test_user_can_get_all_available_rides(self):
response = self.register_user()
self.assertTrue(response.status_code == 201)
response = self.login_user()
self.assertTrue(response.status_code == 200)
access_token = json.loads(response.data)['token']
headers = dict(Authorization='Bearer {}'.format(access_token))
response = self.client.get(RIDES_URL,
content_type='application/json',
headers=headers)
self.assertTrue(response.status_code == 200)
def test_user_can_post_and_fetch_single_ride(self):
response = self.register_user()
self.assertTrue(response.status_code == 201)
response = self.login_user()
self.assertTrue(response.status_code == 200)
access_token = json.loads(response.data)['token']
headers = dict(Authorization='Bearer {}'.format(access_token))
response = self.client.post('/api/v1/rides/',
data=json.dumps(self.test_ride_data),
content_type='application/json',
headers=headers)
self.assertTrue(response.status_code == 201)
response = self.client.get('/api/v1/rides/1',
content_type='application/json',
headers=headers)
self.assertTrue(response.status_code == 200)
expected = {'status': 'ok'}
self.assertEquals(expected['status'], json.loads(response.data)['status'])
def test_user_can_get_that_does_not_exist(self):
response = self.register_user()
self.assertTrue(response.status_code == 201)
response = self.login_user()
self.assertTrue(response.status_code == 200)
access_token = json.loads(response.data)['token']
headers = dict(Authorization='Bearer {}'.format(access_token))
response = self.client.post(
'/api/v1/rides/12/requests',
data=json.dumps({
"pickup": "Nairobi",
"destination": "Nakuru",
"pickuptime": "122312"}),
headers=headers,
content_type='application/json')
self.assertTrue(response.status_code == 404)
expected = {'message': 'Riide with that id Does not exist'}
self.assertEquals(expected['message'], json.loads(response.data)['message'])
response = self.client.get('/api/v1/rides/1',
content_type='application/json',
headers=headers)
self.assertTrue(response.status_code == 404)
expected = {'message': 'Ride with that id Does not exist'}
self.assertEquals(expected['message'], json.loads(response.data)['message'])
response = self.client.post('/api/v1/rides/',
data=json.dumps(self.test_ride_data),
content_type='application/json',
headers=headers)
self.assertTrue(response.status_code == 201)
response = self.client.get('/api/v1/rides/11',
content_type='application/json',
headers=headers)
self.assertTrue(response.status_code == 404)
expected = {'message': 'Ride with that id Does not exist'}
self.assertEquals(expected['message'], json.loads(response.data)['message'])
response = self.client.post(
'/api/v1/rides/1/requests',
data=json.dumps({
"pickup": "Nairobi",
"destination": "Nakuru",
"pickuptime": "122312"}),
headers=headers,
content_type='application/json')
print(response.data)
self.assertTrue(response.status_code == 201)
response = self.client.post(
'/api/v1/rides/12/requests',
data=json.dumps({
"pickup": "Nairobi",
"destination": "Nakuru",
"pickuptime": "122312"}),
headers=headers,
content_type='application/json')
self.assertTrue(response.status_code == 404)
expected = {'message': 'Ride with that id Does not exist'}
self.assertEquals(expected['message'], json.loads(response.data)['message'])
| 46.52381 | 84 | 0.526919 | 454 | 4,885 | 5.53304 | 0.154185 | 0.071656 | 0.131369 | 0.167197 | 0.895701 | 0.882166 | 0.882166 | 0.882166 | 0.882166 | 0.868232 | 0 | 0.025854 | 0.358649 | 4,885 | 104 | 85 | 46.971154 | 0.775934 | 0 | 0 | 0.842697 | 0 | 0 | 0.147185 | 0.015148 | 0 | 0 | 0 | 0 | 0.224719 | 1 | 0.033708 | false | 0 | 0.022472 | 0 | 0.067416 | 0.011236 | 0 | 0 | 0 | null | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
4cd2709e82be21b33907a6afcbf6d71fc0040cda | 167 | py | Python | sysopt/symbolic/__init__.py | csp-at-unimelb/sysopt | f33653623e19d9fff3bae279210de8fac6c67b42 | [
"Apache-2.0"
] | null | null | null | sysopt/symbolic/__init__.py | csp-at-unimelb/sysopt | f33653623e19d9fff3bae279210de8fac6c67b42 | [
"Apache-2.0"
] | null | null | null | sysopt/symbolic/__init__.py | csp-at-unimelb/sysopt | f33653623e19d9fff3bae279210de8fac6c67b42 | [
"Apache-2.0"
] | 1 | 2022-03-09T03:59:49.000Z | 2022-03-09T03:59:49.000Z | """Sysopt symbolic and function manipulations."""
from sysopt.symbolic.symbols import *
from sysopt.symbolic.scalar_ops import *
from sysopt.backends import lambdify
| 27.833333 | 49 | 0.808383 | 21 | 167 | 6.380952 | 0.571429 | 0.313433 | 0.268657 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.107784 | 167 | 5 | 50 | 33.4 | 0.899329 | 0.257485 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
e2414828134aa2e8868d2ac8483824c352f5fbf3 | 161 | py | Python | examples/objects/vx300s/__init__.py | eager-dev/eagerx_pybullet | a67c14399564c4c261d1d4f6512380697a043e27 | [
"Apache-2.0"
] | 1 | 2022-03-24T12:14:21.000Z | 2022-03-24T12:14:21.000Z | examples/objects/vx300s/__init__.py | eager-dev/eagerx_pybullet | a67c14399564c4c261d1d4f6512380697a043e27 | [
"Apache-2.0"
] | 1 | 2022-03-29T14:33:23.000Z | 2022-03-29T14:33:23.000Z | examples/objects/vx300s/__init__.py | eager-dev/eagerx_pybullet | a67c14399564c4c261d1d4f6512380697a043e27 | [
"Apache-2.0"
] | null | null | null | import examples.objects.vx300s.objects # noqa # pylint: disable=unused-import
import examples.objects.vx300s.converters # noqa # pylint: disable=unused-import
| 53.666667 | 81 | 0.801242 | 20 | 161 | 6.45 | 0.45 | 0.217054 | 0.325581 | 0.418605 | 0.449612 | 0 | 0 | 0 | 0 | 0 | 0 | 0.041379 | 0.099379 | 161 | 2 | 82 | 80.5 | 0.848276 | 0.440994 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 8 |
e248d7f07f9c209fcbbb081610a72a0774d8b220 | 12,239 | py | Python | t/checkpoint_split3_test.py | rohankumardubey/orioledb | a736714ed5d71c7f85bee23150c4432b3fbdff1f | [
"PostgreSQL"
] | 947 | 2021-11-29T10:58:09.000Z | 2022-03-31T18:14:07.000Z | t/checkpoint_split3_test.py | rohankumardubey/orioledb | a736714ed5d71c7f85bee23150c4432b3fbdff1f | [
"PostgreSQL"
] | 22 | 2021-12-12T22:02:32.000Z | 2022-03-30T19:31:46.000Z | t/checkpoint_split3_test.py | rohankumardubey/orioledb | a736714ed5d71c7f85bee23150c4432b3fbdff1f | [
"PostgreSQL"
] | 30 | 2021-12-15T01:11:09.000Z | 2022-03-27T11:22:16.000Z | #!/usr/bin/env python3
# coding: utf-8
import time
from .base_test import ThreadQueryExecutor
from .base_test import wait_checkpointer_stopevent
from .checkpoint_split_base_test import CheckpointSplitBaseTest
class CheckpointSplit3Test(CheckpointSplitBaseTest):
def test_checkpoint_split_concurrent_begin(self):
node = self.node
node.append_conf('postgresql.conf',
"orioledb.enable_stopevents = true\n")
node.start()
node.safe_psql('postgres',
"CREATE EXTENSION IF NOT EXISTS orioledb;\n"
"CREATE TABLE IF NOT EXISTS o_checkpoint (\n"
" id text NOT NULL,\n"
" PRIMARY KEY (id)\n"
") USING orioledb\n")
node.safe_psql('postgres',
"INSERT INTO o_checkpoint\n"
" (SELECT to_char(id, 'fm0000') || repeat('x', 2500) FROM generate_series(20, 400, 40) id);\n"
)
con1 = node.connect()
con2 = node.connect()
con3 = node.connect()
con3.execute("SELECT pg_stopevent_set('checkpoint_step',\n"
"'$.action == \"walkDownwards\" && "
"$.treeName == \"o_checkpoint_pkey\" && "
"$.lokey.id > \"0020\" &&"
"$.level == 1');")
t1 = ThreadQueryExecutor(con1, "CHECKPOINT;")
t1.start()
wait_checkpointer_stopevent(node)
con2.begin()
t2 = ThreadQueryExecutor(con2, "INSERT INTO o_checkpoint\n"
"(SELECT to_char(id, 'fm0000') || repeat('x', 2500) FROM generate_series(111, 120, 1) id);")
t2.start()
con3.execute("SELECT pg_stopevent_reset('checkpoint_step')")
t1.join()
t2.join()
con2.commit()
self.assertEqual(node.execute("SELECT COUNT(*) FROM o_checkpoint;")[0][0], 20)
node.execute("SELECT orioledb_tbl_check('o_checkpoint'::regclass)") # no errors, can be true or false
node.safe_psql('postgres', "CHECKPOINT;")
# no incomplete split
self.assertTrue(node.execute("SELECT orioledb_tbl_check('o_checkpoint'::regclass)")[0][0])
con1.close()
con2.close()
con3.close()
node.stop(['-m', 'immediate'])
node.start()
self.assertEqual(node.execute("SELECT COUNT(*) FROM o_checkpoint;")[0][0], 20)
self.assertTrue(node.execute("SELECT orioledb_tbl_check('o_checkpoint'::regclass)")[0][0])
node.stop()
def test_checkpoint_split_concurrent_midle(self):
node = self.node
node.append_conf('postgresql.conf',
"orioledb.enable_stopevents = true\n")
node.start()
node.safe_psql('postgres',
"CREATE EXTENSION IF NOT EXISTS orioledb;\n"
"CREATE TABLE IF NOT EXISTS o_checkpoint (\n"
" id text NOT NULL,\n"
" PRIMARY KEY (id)\n"
") USING orioledb;\n")
node.safe_psql('postgres',
"INSERT INTO o_checkpoint\n"
" (SELECT to_char(id, 'fm0000') || repeat('x', 2500) FROM generate_series(10, 300, 40) id);\n"
)
con1 = node.connect()
con2 = node.connect()
con3 = node.connect()
con3.execute("SELECT pg_stopevent_set('checkpoint_step',\n"
"'$.action == \"walkDownwards\" && "
"$.treeName == \"o_checkpoint_pkey\" && "
"$.lokey.id > \"0020\" &&"
"$.level == 1');")
t1 = ThreadQueryExecutor(con1, "CHECKPOINT;")
t1.start()
wait_checkpointer_stopevent(node)
con2.begin()
t2 = ThreadQueryExecutor(con2, "INSERT INTO o_checkpoint\n"
"(SELECT to_char(id, 'fm0000') || repeat('x', 2500) FROM generate_series(110, 120, 1) id);")
t2.start()
con3.execute("SELECT pg_stopevent_reset('checkpoint_step')")
t1.join()
t2.join()
con2.commit()
self.assertEqual(node.execute("SELECT COUNT(*) FROM o_checkpoint;")[0][0], 19)
node.execute("SELECT orioledb_tbl_check('o_checkpoint'::regclass)") # no errors, can be true or false
node.safe_psql('postgres', "CHECKPOINT;")
# no incomplete split
self.assertTrue(node.execute("SELECT orioledb_tbl_check('o_checkpoint'::regclass)")[0][0])
con1.close()
con2.close()
con3.close()
node.stop(['-m', 'immediate'])
node.start()
self.assertEqual(node.execute("SELECT COUNT(*) FROM o_checkpoint;")[0][0], 19)
self.assertTrue(node.execute("SELECT orioledb_tbl_check('o_checkpoint'::regclass)")[0][0])
node.stop()
def test_checkpoint_split_concurrent_end(self):
node = self.node
node.append_conf('postgresql.conf',
"orioledb.enable_stopevents = true\n")
node.start()
node.safe_psql('postgres',
"CREATE EXTENSION IF NOT EXISTS orioledb;\n"
"CREATE TABLE IF NOT EXISTS o_checkpoint (\n"
" id text NOT NULL,\n"
" PRIMARY KEY (id)\n"
") USING orioledb;\n")
node.safe_psql('postgres',
"INSERT INTO o_checkpoint\n"
" (SELECT to_char(id, 'fm0000') || repeat('x', 2500) FROM generate_series(1, 30, 4) id);\n"
)
con1 = node.connect()
con2 = node.connect()
con3 = node.connect()
con3.execute("SELECT pg_stopevent_set('checkpoint_step',\n"
"'$.action == \"walkDownwards\" && "
"$.treeName == \"o_checkpoint_pkey\" && "
"$.level == 1');")
t1 = ThreadQueryExecutor(con1, "CHECKPOINT;")
t1.start()
wait_checkpointer_stopevent(node)
con2.begin()
con2.execute("INSERT INTO o_checkpoint\n"
"(SELECT to_char(id, 'fm0000') || repeat('x', 2500) FROM generate_series(11, 12, 1) id);")
con2.commit()
con2.begin()
t2 = ThreadQueryExecutor(con2, "INSERT INTO o_checkpoint\n"
"(SELECT to_char(id, 'fm0000') || repeat('x', 2500) FROM generate_series(410, 440, 1) id);")
t2.start()
con3.execute("SELECT pg_stopevent_reset('checkpoint_step')")
t1.join()
t2.join()
con2.commit()
self.assertEqual(node.execute("SELECT COUNT(*) FROM o_checkpoint;")[0][0], 41)
node.execute("SELECT orioledb_tbl_check('o_checkpoint'::regclass)")
node.safe_psql('postgres', "CHECKPOINT;")
# no incomplete split
self.assertTrue(node.execute("SELECT orioledb_tbl_check('o_checkpoint'::regclass)")[0][0])
con1.close()
con2.close()
con3.close()
node.stop(['-m', 'immediate'])
node.start()
self.assertEqual(node.execute("SELECT COUNT(*) FROM o_checkpoint;")[0][0], 41)
self.assertTrue(node.execute("SELECT orioledb_tbl_check('o_checkpoint'::regclass)")[0][0])
node.stop()
def test_checkpoint_split_no_deadlock(self):
node = self.node
node.append_conf('postgresql.conf',
"orioledb.enable_stopevents = true\n")
node.start()
node.safe_psql('postgres',
"CREATE EXTENSION IF NOT EXISTS orioledb;\n"
"CREATE TABLE IF NOT EXISTS o_checkpoint (\n"
" id text NOT NULL,\n"
" PRIMARY KEY (id)\n"
") USING orioledb;\n")
node.safe_psql('postgres',
"INSERT INTO o_checkpoint\n"
" (SELECT to_char(id, 'fm0000') || repeat('x', 2500) FROM generate_series(1, 40, 4) id);\n"
)
con1 = node.connect()
con2 = node.connect()
con3 = node.connect()
con3.execute("SELECT pg_stopevent_set('checkpoint_step',\n"
"'$.action == \"walkDownwards\" && "
"$.treeName == \"o_checkpoint_pkey\" && "
"$.lokey.id > \"0030\"');")
t1 = ThreadQueryExecutor(con1, "CHECKPOINT;")
t1.start()
wait_checkpointer_stopevent(node)
con2.execute("INSERT INTO o_checkpoint (SELECT to_char(id, 'fm0000') || repeat('x', 2500) FROM generate_series(90, 95, 1) id);")
con2.commit()
con2.execute("INSERT INTO o_checkpoint (SELECT '0330' || repeat('x', 2500));")
con3.execute("SELECT pg_stopevent_reset('checkpoint_step')")
t1.join()
con2.commit()
con1.close()
con2.close()
con3.close()
node.stop()
def test_checkpoint_split_root(self):
node = self.node
node.append_conf('postgresql.conf',
"log_min_messages = notice\n"
"orioledb.enable_stopevents = true\n")
node.start()
node.safe_psql('postgres',
"CREATE EXTENSION IF NOT EXISTS orioledb;\n"
"CREATE TABLE IF NOT EXISTS o_checkpoint (\n"
" id text NOT NULL,\n"
" PRIMARY KEY (id)\n"
") USING orioledb;\n")
node.safe_psql('postgres',
"INSERT INTO o_checkpoint\n"
" (SELECT to_char(id, 'fm0000') || repeat('x', 2500) FROM generate_series(10, 300, 40) id);\n")
con1 = node.connect()
con2 = node.connect()
con2.execute("SELECT pg_stopevent_set('checkpoint_step',\n"
"'$.action == \"walkDownwards\" && "
"$.treeName == \"o_checkpoint_pkey\" && "
"$.lokey.id > \"0200\"');")
t1 = ThreadQueryExecutor(con1, "CHECKPOINT;")
t1.start()
wait_checkpointer_stopevent(node)
con2.begin()
con2.execute("INSERT INTO o_checkpoint\n"
"(SELECT to_char(id, 'fm0000') || repeat('x', 2500) FROM generate_series(911, 930, 1) id);")
con2.commit()
con2.execute("SELECT pg_stopevent_reset('checkpoint_step')")
t1.join()
self.assertEqual(node.execute("SELECT COUNT(*) FROM o_checkpoint;")[0][0], 28)
# autonomous checkpoint write happens
self.assertFalse(node.execute("SELECT orioledb_tbl_check('o_checkpoint'::regclass)")[0][0])
con1.close()
con2.close()
node.stop(['-m', 'immediate'])
node.start()
self.assertEqual(node.execute("SELECT COUNT(*) FROM o_checkpoint;")[0][0], 28)
self.assertTrue(node.execute("SELECT orioledb_tbl_check('o_checkpoint'::regclass)")[0][0])
node.stop()
def test_checkpoint_split_root_v2(self):
node = self.node
node.append_conf('postgresql.conf',
"log_min_messages = notice\n"
"orioledb.enable_stopevents = true\n")
node.start()
node.safe_psql('postgres',
"CREATE EXTENSION IF NOT EXISTS orioledb;\n"
"CREATE TABLE IF NOT EXISTS o_checkpoint (\n"
" id text NOT NULL,\n"
" PRIMARY KEY (id)\n"
") USING orioledb;\n")
node.safe_psql('postgres',
"INSERT INTO o_checkpoint\n"
" (SELECT to_char(id, 'fm0000') || repeat('x', 2500) FROM generate_series(10, 300, 40) id);\n")
con1 = node.connect()
con2 = node.connect()
con2.execute("SELECT pg_stopevent_set('checkpoint_step',\n"
"'$.action == \"walkDownwards\" && "
"$.treeName == \"o_checkpoint_pkey\" && "
"$.lokey.id > \"0100\"');")
t1 = ThreadQueryExecutor(con1, "CHECKPOINT;")
t1.start()
wait_checkpointer_stopevent(node)
con2.begin()
con2.execute("INSERT INTO o_checkpoint\n"
"(SELECT to_char(id, 'fm0000') || repeat('x', 2500) FROM generate_series(211, 231, 1) id);")
con2.commit()
con2.execute("SELECT pg_stopevent_reset('checkpoint_step')")
t1.join()
self.assertEqual(node.execute("SELECT COUNT(*) FROM o_checkpoint;")[0][0], 29)
# autonomous checkpoint page write happens
self.assertFalse(node.execute("SELECT orioledb_tbl_check('o_checkpoint'::regclass)")[0][0])
con1.execute("CHECKPOINT;")
self.assertTrue(node.execute("SELECT orioledb_tbl_check('o_checkpoint'::regclass)")[0][0])
con1.close()
con2.close()
node.stop(['-m', 'immediate'])
node.start()
self.assertEqual(node.execute("SELECT COUNT(*) FROM o_checkpoint;")[0][0], 29)
self.assertTrue(node.execute("SELECT orioledb_tbl_check('o_checkpoint'::regclass)")[0][0])
node.stop()
def test_checkpoint_split_ok(self):
node = self.node
node.append_conf('postgresql.conf',
"orioledb.enable_stopevents = true\n")
node.start()
node.safe_psql('postgres',
"CREATE EXTENSION IF NOT EXISTS orioledb;\n"
"CREATE TABLE IF NOT EXISTS o_checkpoint (\n"
" id text NOT NULL,\n"
" PRIMARY KEY (id)\n"
") USING orioledb;\n")
node.safe_psql('postgres',
"INSERT INTO o_checkpoint\n"
" (SELECT to_char(id, 'fm0000') || repeat('x', 2500) FROM generate_series(1, 30, 4) id);\n")
con1 = node.connect()
con2 = node.connect()
con2.execute("SELECT pg_stopevent_set('checkpoint_step',\n"
"'$.action == \"walkUpwards\" && "
"$.treeName == \"o_checkpoint_pkey\" && "
"$.nextKey.type == \"value\" && "
"$.nextKey.value.id > \"0010\"');")
t1 = ThreadQueryExecutor(con1, "CHECKPOINT;")
t1.start()
wait_checkpointer_stopevent(node)
con2.begin()
con2.execute("INSERT INTO o_checkpoint\n"
"(SELECT to_char(id, 'fm0000') || repeat('x', 2500) FROM generate_series(11, 12, 1) id);")
con2.commit()
con2.execute("SELECT pg_stopevent_reset('checkpoint_step')")
t1.join()
con1.close()
con2.close()
node.stop(['-m', 'immediate'])
node.start()
self.assertEqual(node.execute("SELECT count(*) FROM o_checkpoint;")[0][0], 10)
node.stop()
| 34.379213 | 130 | 0.64989 | 1,583 | 12,239 | 4.870499 | 0.094125 | 0.07847 | 0.055123 | 0.044099 | 0.940597 | 0.936446 | 0.93476 | 0.922309 | 0.917769 | 0.911025 | 0 | 0.042596 | 0.179018 | 12,239 | 355 | 131 | 34.476056 | 0.724721 | 0.019283 | 0 | 0.875421 | 0 | 0.050505 | 0.471903 | 0.109722 | 0 | 0 | 0 | 0 | 0.074074 | 1 | 0.023569 | false | 0 | 0.013468 | 0 | 0.040404 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
e28d4340285eabbb44e9524c8681a747e87067cb | 6,109 | py | Python | tests/unit/test_world_manager.py | buxx/rolling | ef1268fe6ddabe768a125c3ce8b37e0b9cbad4a5 | [
"MIT"
] | 14 | 2019-11-16T18:51:51.000Z | 2022-01-15T17:50:34.000Z | tests/unit/test_world_manager.py | buxx/rolling | ef1268fe6ddabe768a125c3ce8b37e0b9cbad4a5 | [
"MIT"
] | 148 | 2018-12-10T09:07:45.000Z | 2022-03-08T10:51:04.000Z | tests/unit/test_world_manager.py | buxx/rolling | ef1268fe6ddabe768a125c3ce8b37e0b9cbad4a5 | [
"MIT"
] | 1 | 2020-08-05T14:25:48.000Z | 2020-08-05T14:25:48.000Z | # # coding: utf-8
# import pytest
# import typing
# from rolling.game.world import WorldManager
# from rolling.kernel import Kernel
# # NOTE: test are based on tile_clutter_capacity config
# class TestWorldManager:
# def _find_available_place_where_drop(
# self,
# world_manager: WorldManager,
# resource_id: typing.Optional[str] = None,
# resource_quantity: typing.Optional[float] = None,
# stuff_id: typing.Optional[str] = None,
# ) -> typing.List[typing.Tuple[typing.Tuple[int, int], typing.Optional[float]]]:
# return world_manager.find_available_place_where_drop(
# resource_id=resource_id,
# resource_quantity=resource_quantity,
# stuff_id=stuff_id,
# world_row_i=1,
# world_col_i=1,
# start_from_zone_row_i=69,
# start_from_zone_col_i=40,
# allow_fallback_on_start_coordinates=False,
# )
# @pytest.mark.parametrize(
# "resource_id,quantity,expected",
# [
# ("WOOD", 0.005, [((69, 40), 0.005)]),
# ("WOOD", 0.05, [((69, 40), 0.02), ((70, 39), 0.02), ((71, 39), 0.01)]),
# ],
# )
# def test_find_available_place_where_drop_when_place_resource_on_full_free_space(
# self,
# worldmapc_kernel: Kernel,
# resource_id: str,
# quantity: float,
# expected: typing.List[
# typing.Tuple[typing.Tuple[int, int], typing.Optional[float]]
# ],
# ) -> None:
# # Given
# kernel = worldmapc_kernel
# # When
# places = self._find_available_place_where_drop(
# kernel.game.world_manager,
# resource_id=resource_id,
# resource_quantity=quantity,
# )
# # Then
# assert places == expected
# @pytest.mark.parametrize(
# "resource_id,quantity,expected",
# [
# ("WOOD", 0.005, [((69, 40), 0.002), ((70, 39), 0.003)]),
# (
# "WOOD",
# 0.05,
# [
# ((69, 40), 0.002),
# ((70, 39), 0.02),
# ((71, 39), 0.02),
# ((68, 39), 0.008),
# ],
# ),
# ],
# )
# def test_find_available_place_where_drop_when_place_resource_on_occupied_space(
# self,
# worldmapc_kernel: Kernel,
# resource_id: str,
# quantity: float,
# expected: typing.List[
# typing.Tuple[typing.Tuple[int, int], typing.Optional[float]]
# ],
# ) -> None:
# # Given
# kernel = worldmapc_kernel
# kernel.resource_lib.add_resource_to(
# resource_id="STONE",
# quantity=9,
# ground=True,
# world_row_i=1,
# world_col_i=1,
# zone_row_i=69,
# zone_col_i=40,
# )
# # When
# places = self._find_available_place_where_drop(
# kernel.game.world_manager,
# resource_id=resource_id,
# resource_quantity=quantity,
# )
# # Then
# assert places == expected
# @pytest.mark.parametrize(
# "resource_id,quantity,expected",
# [
# ("WOOD", 0.005, [((69, 40), 0.005)]),
# ("WOOD", 0.05, [((69, 40), 0.02), ((70, 39), 0.02), ((71, 39), 0.01)]),
# ],
# )
# def test_find_available_place_where_drop_when_place_resource_on_walled_space(
# self,
# worldmapc_kernel: Kernel,
# resource_id: str,
# quantity: float,
# expected: typing.List[
# typing.Tuple[typing.Tuple[int, int], typing.Optional[float]]
# ],
# ) -> None:
# # Given
# kernel = worldmapc_kernel
# kernel.build_lib.place_build(
# world_row_i=1,
# world_col_i=1,
# zone_row_i=69,
# zone_col_i=41,
# build_id="STONE_WALL",
# under_construction=False,
# )
# # When
# places = self._find_available_place_where_drop(
# kernel.game.world_manager,
# resource_id=resource_id,
# resource_quantity=quantity,
# )
# # Then
# assert places == expected
# def test_find_available_place_where_drop_when_place_stuff_on_full_free_space(
# self, worldmapc_kernel: Kernel
# ) -> None:
# # Given
# kernel = worldmapc_kernel
# # When
# places = self._find_available_place_where_drop(
# kernel.game.world_manager, stuff_id="STONE_HAXE"
# )
# # Then
# assert places == [((69, 40), 1.0)]
# def test_find_available_place_where_drop_when_place_stuff_on_occupied_space(
# self, worldmapc_kernel: Kernel
# ) -> None:
# # Given
# kernel = worldmapc_kernel
# kernel.resource_lib.add_resource_to(
# resource_id="STONE",
# quantity=9,
# ground=True,
# world_row_i=1,
# world_col_i=1,
# zone_row_i=69,
# zone_col_i=40,
# )
# # When
# places = self._find_available_place_where_drop(
# kernel.game.world_manager, stuff_id="STONE_HAXE"
# )
# # Then
# assert places == [((69, 40), 1.0)]
# def test_find_available_place_where_drop_when_place_stuff_on_walled_space(
# self, worldmapc_kernel: Kernel
# ) -> None:
# # Given
# kernel = worldmapc_kernel
# kernel.build_lib.place_build(
# world_row_i=1,
# world_col_i=1,
# zone_row_i=69,
# zone_col_i=41,
# build_id="STONE_WALL",
# under_construction=False,
# )
# # When
# places = self._find_available_place_where_drop(
# kernel.game.world_manager, stuff_id="STONE_HAXE"
# )
# # Then
# assert places == [((69, 40), 1.0)]
| 30.545 | 86 | 0.522508 | 646 | 6,109 | 4.592879 | 0.147059 | 0.057297 | 0.084934 | 0.108527 | 0.870913 | 0.837209 | 0.822717 | 0.803842 | 0.791709 | 0.791709 | 0 | 0.044534 | 0.353086 | 6,109 | 199 | 87 | 30.698492 | 0.706225 | 0.934687 | 0 | null | 0 | null | 0 | 0 | null | 0 | 0 | 0 | null | 1 | null | true | 0 | 0 | null | null | null | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
e2914af4f9049cf44eaabf43368c546fd5a5bb6e | 125 | py | Python | pyfbook/facebook/__init__.py | Leouldan/pyfbook | 642c514d560f32a48585fc5e3f2c852c25bc76b2 | [
"BSD-2-Clause"
] | null | null | null | pyfbook/facebook/__init__.py | Leouldan/pyfbook | 642c514d560f32a48585fc5e3f2c852c25bc76b2 | [
"BSD-2-Clause"
] | null | null | null | pyfbook/facebook/__init__.py | Leouldan/pyfbook | 642c514d560f32a48585fc5e3f2c852c25bc76b2 | [
"BSD-2-Clause"
] | null | null | null | from . import marketing
from . import path
from . import graph
from . import page
from . import post
from . import page_post
| 17.857143 | 23 | 0.76 | 19 | 125 | 4.947368 | 0.368421 | 0.638298 | 0.297872 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.192 | 125 | 6 | 24 | 20.833333 | 0.930693 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
e2f77700b1d630dc9a84c0667f5c0e3abb0892b9 | 9,001 | py | Python | tests/image_tools_test.py | andrewferlitsch/Gap | b8e79d549d9cfb3537216ea941749f7c2688bdc2 | [
"Apache-2.0"
] | 39 | 2018-07-20T02:06:29.000Z | 2022-01-10T07:01:51.000Z | tests/image_tools_test.py | andrewferlitsch/Gap | b8e79d549d9cfb3537216ea941749f7c2688bdc2 | [
"Apache-2.0"
] | 94 | 2018-07-18T07:00:41.000Z | 2022-03-11T23:34:38.000Z | tests/image_tools_test.py | andrewferlitsch/Gap | b8e79d549d9cfb3537216ea941749f7c2688bdc2 | [
"Apache-2.0"
] | 34 | 2018-07-18T02:55:36.000Z | 2021-07-21T07:53:58.000Z | from gapml.utils.img_tools import ImgUtils
import unittest
import pytest
import os
import shutil
class MyTest(unittest.TestCase):
def setup_class(self):
pass
def teardown_class(self):
pass
def test_001(self):
""" ImgUtils Constructor - directory = not a string """
with pytest.raises(TypeError):
gap = ImgUtils(root_path=1)
def test_002(self):
""" transform dataset from tree 1 to tree 2 """
gap = ImgUtils(root_path='files/imtest4')
gap.transform(shufle=True, img_split=0.2)
self.assertTrue(os.path.exists('files/imtest4_t2'))
self.assertTrue(os.path.exists('files/imtest4_t2/errors'))
self.assertTrue(os.path.exists('files/imtest4_t2/test'))
self.assertTrue(os.path.exists('files/imtest4_t2/test/test'))
self.assertTrue(os.path.exists('files/imtest4_t2/train_tr'))
self.assertTrue(os.path.exists('files/imtest4_t2/train_tr/daisy'))
self.assertTrue(os.path.exists('files/imtest4_t2/train_tr/dandelion'))
self.assertTrue(os.path.exists('files/imtest4_t2/train_tr/roses'))
self.assertTrue(os.path.exists('files/imtest4_t2/train_val'))
self.assertTrue(os.path.exists('files/imtest4_t2/train_val/daisy'))
self.assertTrue(os.path.exists('files/imtest4_t2/train_val/dandelion'))
self.assertTrue(os.path.exists('files/imtest4_t2/train_val/roses'))
def test_003(self):
""" Setter - transform """
gap = ImgUtils(root_path='files/imtest4_t2/train_tr')
gap.transf = '2to1'
self.assertEqual(gap.transf, '2to1')
def test_004(self):
""" transform dataset from tree 2 to tree 1 """
gap = ImgUtils(root_path='files/imtest4_t2/train_tr')
gap.transf = '2to1'
gap.transform()
self.assertTrue(os.path.exists('files/imtest4'))
self.assertTrue(os.path.exists('files/imtest4/daisy'))
self.assertTrue(os.path.exists('files/imtest4/dandelion'))
self.assertTrue(os.path.exists('files/imtest4/roses'))
def test_005(self):
""" getting a sample from tree 1 to tree 1 """
gap = ImgUtils(root_path='files/imtest4')
gap.img_container(action='copy', spl=5)
self.assertTrue(os.path.exists('files/imtest4_spl'))
self.assertTrue(os.path.exists('files/imtest4_spl/daisy'))
self.assertTrue(os.path.exists('files/imtest4_spl/dandelion'))
self.assertTrue(os.path.exists('files/imtest4_spl/roses'))
def test_006(self):
""" getting a sample from tree 1 to tree 2 """
gap = ImgUtils(root_path='files/imtest4', tree=2)
gap.img_container(action='copy', spl=6, shufle=True, img_split=0.5)
self.assertTrue(os.path.exists('files/imtest4_t2_spl'))
self.assertTrue(os.path.exists('files/imtest4_t2_spl/errors'))
self.assertTrue(os.path.exists('files/imtest4_t2_spl/test'))
self.assertTrue(os.path.exists('files/imtest4_t2_spl/test/test'))
self.assertTrue(os.path.exists('files/imtest4_t2_spl/train_tr'))
self.assertTrue(os.path.exists('files/imtest4_t2_spl/train_tr/daisy'))
self.assertTrue(os.path.exists('files/imtest4_t2_spl/train_tr/dandelion'))
self.assertTrue(os.path.exists('files/imtest4_t2_spl/train_tr/roses'))
self.assertTrue(os.path.exists('files/imtest4_t2_spl/train_val'))
self.assertTrue(os.path.exists('files/imtest4_t2_spl/train_val/daisy'))
self.assertTrue(os.path.exists('files/imtest4_t2_spl/train_val/dandelion'))
self.assertTrue(os.path.exists('files/imtest4_t2_spl/train_val/roses'))
def test_007(self):
""" rename images with an id - tree 1"""
gap = ImgUtils(root_path='files/imtest4_spl')
gap.img_rename()
self.assertTrue(os.path.isfile('files/imtest4_spl/daisy/0.jpg'))
self.assertTrue(os.path.isfile('files/imtest4_spl/daisy/1.jpg'))
self.assertTrue(os.path.isfile('files/imtest4_spl/daisy/2.jpg'))
self.assertTrue(os.path.isfile('files/imtest4_spl/daisy/3.jpg'))
self.assertTrue(os.path.isfile('files/imtest4_spl/daisy/4.jpg'))
def test_008(self):
""" rename images with label name_id - tree 1 """
gap = ImgUtils(root_path='files/imtest4_spl')
gap.img_rename(text=True)
self.assertTrue(os.path.isfile('files/imtest4_spl/daisy/daisy_0.jpg'))
self.assertTrue(os.path.isfile('files/imtest4_spl/daisy/daisy_1.jpg'))
self.assertTrue(os.path.isfile('files/imtest4_spl/daisy/daisy_2.jpg'))
self.assertTrue(os.path.isfile('files/imtest4_spl/roses/roses_0.jpg'))
self.assertTrue(os.path.isfile('files/imtest4_spl/roses/roses_1.jpg'))
self.assertTrue(os.path.isfile('files/imtest4_spl/roses/roses_2.jpg'))
def test_009(self):
""" rename images with a specific word_id - tree 1 """
gap = ImgUtils(root_path='files/imtest4_spl')
gap.img_rename(text='test')
self.assertTrue(os.path.isfile('files/imtest4_spl/daisy/test_0.jpg'))
self.assertTrue(os.path.isfile('files/imtest4_spl/daisy/test_1.jpg'))
self.assertTrue(os.path.isfile('files/imtest4_spl/daisy/test_2.jpg'))
self.assertTrue(os.path.isfile('files/imtest4_spl/roses/test_0.jpg'))
self.assertTrue(os.path.isfile('files/imtest4_spl/roses/test_1.jpg'))
self.assertTrue(os.path.isfile('files/imtest4_spl/roses/test_2.jpg'))
def test_010(self):
""" replace part of the image name with a specific woRD_id - tree 1 """
gap = ImgUtils(root_path='files/imtest4_spl')
gap.img_replace(old='st', new='sted', img_id=False)
self.assertTrue(os.path.isfile('files/imtest4_spl/daisy/tested_0.jpg'))
self.assertTrue(os.path.isfile('files/imtest4_spl/daisy/tested_1.jpg'))
self.assertTrue(os.path.isfile('files/imtest4_spl/daisy/tested_2.jpg'))
self.assertTrue(os.path.isfile('files/imtest4_spl/roses/tested_0.jpg'))
self.assertTrue(os.path.isfile('files/imtest4_spl/roses/tested_1.jpg'))
self.assertTrue(os.path.isfile('files/imtest4_spl/roses/tested_2.jpg'))
shutil.rmtree('files/imtest4_spl')
def test_011(self):
""" rename images with an id - tree 2 """
gap = ImgUtils(root_path='files/imtest4_t2_spl/train_tr', tree=2)
gap.img_rename()
self.assertTrue(os.path.isfile('files/imtest4_t2_spl/train_tr/daisy/0.jpg'))
self.assertTrue(os.path.isfile('files/imtest4_t2_spl/train_tr/daisy/1.jpg'))
self.assertTrue(os.path.isfile('files/imtest4_t2_spl/train_tr/daisy/2.jpg'))
self.assertTrue(os.path.isfile('files/imtest4_t2_spl/train_val/daisy/0.jpg'))
self.assertTrue(os.path.isfile('files/imtest4_t2_spl/train_val/daisy/1.jpg'))
def test_012(self):
""" rename images with label name_id - tree 2 """
gap = ImgUtils(root_path='files/imtest4_t2_spl/train_tr', tree=2)
gap.img_rename(text=True)
self.assertTrue(os.path.isfile('files/imtest4_t2_spl/train_tr/daisy/daisy_0.jpg'))
self.assertTrue(os.path.isfile('files/imtest4_t2_spl/train_tr/daisy/daisy_1.jpg'))
self.assertTrue(os.path.isfile('files/imtest4_t2_spl/train_tr/daisy/daisy_2.jpg'))
self.assertTrue(os.path.isfile('files/imtest4_t2_spl/train_val/roses/roses_0.jpg'))
self.assertTrue(os.path.isfile('files/imtest4_t2_spl/train_val/roses/roses_1.jpg'))
def test_013(self):
""" rename images with a specific word_id - tree 2 """
gap = ImgUtils(root_path='files/imtest4_t2_spl/train_tr', tree=2)
gap.img_rename(text='test')
self.assertTrue(os.path.isfile('files/imtest4_t2_spl/train_val/daisy/test_0.jpg'))
self.assertTrue(os.path.isfile('files/imtest4_t2_spl/train_val/daisy/test_1.jpg'))
self.assertTrue(os.path.isfile('files/imtest4_t2_spl/train_tr/roses/test_0.jpg'))
self.assertTrue(os.path.isfile('files/imtest4_t2_spl/train_tr/roses/test_1.jpg'))
self.assertTrue(os.path.isfile('files/imtest4_t2_spl/train_tr/roses/test_2.jpg'))
def test_014(self):
""" replace part of the image name with a specific woRD_id - tree 2 """
gap = ImgUtils(root_path='files/imtest4_t2_spl/train_tr', tree=2)
gap.img_replace(old='st', new='sted', img_id=False)
self.assertTrue(os.path.isfile('files/imtest4_t2_spl/train_tr/daisy/tested_0.jpg'))
self.assertTrue(os.path.isfile('files/imtest4_t2_spl/train_tr/daisy/tested_1.jpg'))
self.assertTrue(os.path.isfile('files/imtest4_t2_spl/train_tr/daisy/tested_2.jpg'))
self.assertTrue(os.path.isfile('files/imtest4_t2_spl/train_val/roses/tested_0.jpg'))
self.assertTrue(os.path.isfile('files/imtest4_t2_spl/train_val/roses/tested_1.jpg'))
shutil.rmtree('files/imtest4_t2_spl')
| 55.220859 | 93 | 0.677591 | 1,310 | 9,001 | 4.467939 | 0.079389 | 0.184521 | 0.205023 | 0.256279 | 0.920383 | 0.893217 | 0.880403 | 0.858022 | 0.788314 | 0.735862 | 0 | 0.035078 | 0.176536 | 9,001 | 162 | 94 | 55.561728 | 0.754587 | 0.068548 | 0 | 0.183206 | 0 | 0 | 0.359267 | 0.323869 | 0 | 0 | 0 | 0 | 0.580153 | 1 | 0.122137 | false | 0.015267 | 0.038168 | 0 | 0.167939 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
e2fc1bf6bb647236abbb35f4f0cbf05e498dca8f | 199 | py | Python | check_bounds.py | ks8/conformation | f470849d5b7b90dc5a65bab8a536de1d57c1021a | [
"MIT"
] | null | null | null | check_bounds.py | ks8/conformation | f470849d5b7b90dc5a65bab8a536de1d57c1021a | [
"MIT"
] | null | null | null | check_bounds.py | ks8/conformation | f470849d5b7b90dc5a65bab8a536de1d57c1021a | [
"MIT"
] | null | null | null | """ Generate distance matrices from molecular conformations. """
from conformation.check_bounds import conf_to_distmat, Args
if __name__ == '__main__':
conf_to_distmat(Args().parse_args())
| 33.166667 | 65 | 0.753769 | 24 | 199 | 5.666667 | 0.75 | 0.088235 | 0.191176 | 0.25 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.140704 | 199 | 5 | 66 | 39.8 | 0.795322 | 0.281407 | 0 | 0 | 1 | 0 | 0.061538 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.333333 | 0 | 0.333333 | 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 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 7 |
390c1fb27b62fa080b8512270f90bc58031b8cd0 | 32,674 | py | Python | napalm_yang/models/openconfig/network_instances/network_instance/protocols/protocol/isis/levels/level/link_state_database/lsp/__init__.py | ckishimo/napalm-yang | 8f2bd907bd3afcde3c2f8e985192de74748baf6c | [
"Apache-2.0"
] | 64 | 2016-10-20T15:47:18.000Z | 2021-11-11T11:57:32.000Z | napalm_yang/models/openconfig/network_instances/network_instance/protocols/protocol/isis/levels/level/link_state_database/lsp/__init__.py | ckishimo/napalm-yang | 8f2bd907bd3afcde3c2f8e985192de74748baf6c | [
"Apache-2.0"
] | 126 | 2016-10-05T10:36:14.000Z | 2019-05-15T08:43:23.000Z | napalm_yang/models/openconfig/network_instances/network_instance/protocols/protocol/isis/levels/level/link_state_database/lsp/__init__.py | ckishimo/napalm-yang | 8f2bd907bd3afcde3c2f8e985192de74748baf6c | [
"Apache-2.0"
] | 63 | 2016-11-07T15:23:08.000Z | 2021-09-22T14:41:16.000Z | # -*- coding: utf-8 -*-
from operator import attrgetter
from pyangbind.lib.yangtypes import RestrictedPrecisionDecimalType
from pyangbind.lib.yangtypes import RestrictedClassType
from pyangbind.lib.yangtypes import TypedListType
from pyangbind.lib.yangtypes import YANGBool
from pyangbind.lib.yangtypes import YANGListType
from pyangbind.lib.yangtypes import YANGDynClass
from pyangbind.lib.yangtypes import ReferenceType
from pyangbind.lib.base import PybindBase
from collections import OrderedDict
from decimal import Decimal
from bitarray import bitarray
import six
# PY3 support of some PY2 keywords (needs improved)
if six.PY3:
import builtins as __builtin__
long = int
elif six.PY2:
import __builtin__
from . import state
from . import tlvs
from . import undefined_tlvs
class lsp(PybindBase):
"""
This class was auto-generated by the PythonClass plugin for PYANG
from YANG module openconfig-network-instance - based on the path /network-instances/network-instance/protocols/protocol/isis/levels/level/link-state-database/lsp. Each member element of
the container is represented as a class variable - with a specific
YANG type.
YANG Description: This list describes LSPs in LSDB.
"""
__slots__ = (
"_path_helper",
"_extmethods",
"__lsp_id",
"__state",
"__tlvs",
"__undefined_tlvs",
)
_yang_name = "lsp"
_pybind_generated_by = "container"
def __init__(self, *args, **kwargs):
self._path_helper = False
self._extmethods = False
self.__lsp_id = YANGDynClass(
base=six.text_type,
is_leaf=True,
yang_name="lsp-id",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
is_keyval=True,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="leafref",
is_config=False,
)
self.__state = YANGDynClass(
base=state.state,
is_container="container",
yang_name="state",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
extensions=None,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="container",
is_config=False,
)
self.__tlvs = YANGDynClass(
base=tlvs.tlvs,
is_container="container",
yang_name="tlvs",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
extensions=None,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="container",
is_config=False,
)
self.__undefined_tlvs = YANGDynClass(
base=undefined_tlvs.undefined_tlvs,
is_container="container",
yang_name="undefined-tlvs",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
extensions=None,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="container",
is_config=False,
)
load = kwargs.pop("load", None)
if args:
if len(args) > 1:
raise TypeError("cannot create a YANG container with >1 argument")
all_attr = True
for e in self._pyangbind_elements:
if not hasattr(args[0], e):
all_attr = False
break
if not all_attr:
raise ValueError("Supplied object did not have the correct attributes")
for e in self._pyangbind_elements:
nobj = getattr(args[0], e)
if nobj._changed() is False:
continue
setmethod = getattr(self, "_set_%s" % e)
if load is None:
setmethod(getattr(args[0], e))
else:
setmethod(getattr(args[0], e), load=load)
def _path(self):
if hasattr(self, "_parent"):
return self._parent._path() + [self._yang_name]
else:
return [
"network-instances",
"network-instance",
"protocols",
"protocol",
"isis",
"levels",
"level",
"link-state-database",
"lsp",
]
def _get_lsp_id(self):
"""
Getter method for lsp_id, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/levels/level/link_state_database/lsp/lsp_id (leafref)
YANG Description: A reference to the Link State PDU ID.
"""
return self.__lsp_id
def _set_lsp_id(self, v, load=False):
"""
Setter method for lsp_id, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/levels/level/link_state_database/lsp/lsp_id (leafref)
If this variable is read-only (config: false) in the
source YANG file, then _set_lsp_id is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_lsp_id() directly.
YANG Description: A reference to the Link State PDU ID.
"""
parent = getattr(self, "_parent", None)
if parent is not None and load is False:
raise AttributeError(
"Cannot set keys directly when" + " within an instantiated list"
)
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(
v,
base=six.text_type,
is_leaf=True,
yang_name="lsp-id",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
is_keyval=True,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="leafref",
is_config=False,
)
except (TypeError, ValueError):
raise ValueError(
{
"error-string": """lsp_id must be of a type compatible with leafref""",
"defined-type": "leafref",
"generated-type": """YANGDynClass(base=six.text_type, is_leaf=True, yang_name="lsp-id", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, is_keyval=True, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='leafref', is_config=False)""",
}
)
self.__lsp_id = t
if hasattr(self, "_set"):
self._set()
def _unset_lsp_id(self):
self.__lsp_id = YANGDynClass(
base=six.text_type,
is_leaf=True,
yang_name="lsp-id",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
is_keyval=True,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="leafref",
is_config=False,
)
def _get_state(self):
"""
Getter method for state, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/levels/level/link_state_database/lsp/state (container)
YANG Description: State parameters of Link State PDU.
"""
return self.__state
def _set_state(self, v, load=False):
"""
Setter method for state, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/levels/level/link_state_database/lsp/state (container)
If this variable is read-only (config: false) in the
source YANG file, then _set_state is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_state() directly.
YANG Description: State parameters of Link State PDU.
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(
v,
base=state.state,
is_container="container",
yang_name="state",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
extensions=None,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="container",
is_config=False,
)
except (TypeError, ValueError):
raise ValueError(
{
"error-string": """state must be of a type compatible with container""",
"defined-type": "container",
"generated-type": """YANGDynClass(base=state.state, is_container='container', yang_name="state", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='container', is_config=False)""",
}
)
self.__state = t
if hasattr(self, "_set"):
self._set()
def _unset_state(self):
self.__state = YANGDynClass(
base=state.state,
is_container="container",
yang_name="state",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
extensions=None,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="container",
is_config=False,
)
def _get_tlvs(self):
"""
Getter method for tlvs, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/levels/level/link_state_database/lsp/tlvs (container)
YANG Description: This container defines Link State PDU State TLVs.
"""
return self.__tlvs
def _set_tlvs(self, v, load=False):
"""
Setter method for tlvs, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/levels/level/link_state_database/lsp/tlvs (container)
If this variable is read-only (config: false) in the
source YANG file, then _set_tlvs is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_tlvs() directly.
YANG Description: This container defines Link State PDU State TLVs.
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(
v,
base=tlvs.tlvs,
is_container="container",
yang_name="tlvs",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
extensions=None,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="container",
is_config=False,
)
except (TypeError, ValueError):
raise ValueError(
{
"error-string": """tlvs must be of a type compatible with container""",
"defined-type": "container",
"generated-type": """YANGDynClass(base=tlvs.tlvs, is_container='container', yang_name="tlvs", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='container', is_config=False)""",
}
)
self.__tlvs = t
if hasattr(self, "_set"):
self._set()
def _unset_tlvs(self):
self.__tlvs = YANGDynClass(
base=tlvs.tlvs,
is_container="container",
yang_name="tlvs",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
extensions=None,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="container",
is_config=False,
)
def _get_undefined_tlvs(self):
"""
Getter method for undefined_tlvs, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/levels/level/link_state_database/lsp/undefined_tlvs (container)
YANG Description: Surrounding container for a list of unknown TLVs.
"""
return self.__undefined_tlvs
def _set_undefined_tlvs(self, v, load=False):
"""
Setter method for undefined_tlvs, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/levels/level/link_state_database/lsp/undefined_tlvs (container)
If this variable is read-only (config: false) in the
source YANG file, then _set_undefined_tlvs is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_undefined_tlvs() directly.
YANG Description: Surrounding container for a list of unknown TLVs.
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(
v,
base=undefined_tlvs.undefined_tlvs,
is_container="container",
yang_name="undefined-tlvs",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
extensions=None,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="container",
is_config=False,
)
except (TypeError, ValueError):
raise ValueError(
{
"error-string": """undefined_tlvs must be of a type compatible with container""",
"defined-type": "container",
"generated-type": """YANGDynClass(base=undefined_tlvs.undefined_tlvs, is_container='container', yang_name="undefined-tlvs", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='container', is_config=False)""",
}
)
self.__undefined_tlvs = t
if hasattr(self, "_set"):
self._set()
def _unset_undefined_tlvs(self):
self.__undefined_tlvs = YANGDynClass(
base=undefined_tlvs.undefined_tlvs,
is_container="container",
yang_name="undefined-tlvs",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
extensions=None,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="container",
is_config=False,
)
lsp_id = __builtin__.property(_get_lsp_id)
state = __builtin__.property(_get_state)
tlvs = __builtin__.property(_get_tlvs)
undefined_tlvs = __builtin__.property(_get_undefined_tlvs)
_pyangbind_elements = OrderedDict(
[
("lsp_id", lsp_id),
("state", state),
("tlvs", tlvs),
("undefined_tlvs", undefined_tlvs),
]
)
from . import state
from . import tlvs
from . import undefined_tlvs
class lsp(PybindBase):
"""
This class was auto-generated by the PythonClass plugin for PYANG
from YANG module openconfig-network-instance-l2 - based on the path /network-instances/network-instance/protocols/protocol/isis/levels/level/link-state-database/lsp. Each member element of
the container is represented as a class variable - with a specific
YANG type.
YANG Description: This list describes LSPs in LSDB.
"""
__slots__ = (
"_path_helper",
"_extmethods",
"__lsp_id",
"__state",
"__tlvs",
"__undefined_tlvs",
)
_yang_name = "lsp"
_pybind_generated_by = "container"
def __init__(self, *args, **kwargs):
self._path_helper = False
self._extmethods = False
self.__lsp_id = YANGDynClass(
base=six.text_type,
is_leaf=True,
yang_name="lsp-id",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
is_keyval=True,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="leafref",
is_config=False,
)
self.__state = YANGDynClass(
base=state.state,
is_container="container",
yang_name="state",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
extensions=None,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="container",
is_config=False,
)
self.__tlvs = YANGDynClass(
base=tlvs.tlvs,
is_container="container",
yang_name="tlvs",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
extensions=None,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="container",
is_config=False,
)
self.__undefined_tlvs = YANGDynClass(
base=undefined_tlvs.undefined_tlvs,
is_container="container",
yang_name="undefined-tlvs",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
extensions=None,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="container",
is_config=False,
)
load = kwargs.pop("load", None)
if args:
if len(args) > 1:
raise TypeError("cannot create a YANG container with >1 argument")
all_attr = True
for e in self._pyangbind_elements:
if not hasattr(args[0], e):
all_attr = False
break
if not all_attr:
raise ValueError("Supplied object did not have the correct attributes")
for e in self._pyangbind_elements:
nobj = getattr(args[0], e)
if nobj._changed() is False:
continue
setmethod = getattr(self, "_set_%s" % e)
if load is None:
setmethod(getattr(args[0], e))
else:
setmethod(getattr(args[0], e), load=load)
def _path(self):
if hasattr(self, "_parent"):
return self._parent._path() + [self._yang_name]
else:
return [
"network-instances",
"network-instance",
"protocols",
"protocol",
"isis",
"levels",
"level",
"link-state-database",
"lsp",
]
def _get_lsp_id(self):
"""
Getter method for lsp_id, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/levels/level/link_state_database/lsp/lsp_id (leafref)
YANG Description: A reference to the Link State PDU ID.
"""
return self.__lsp_id
def _set_lsp_id(self, v, load=False):
"""
Setter method for lsp_id, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/levels/level/link_state_database/lsp/lsp_id (leafref)
If this variable is read-only (config: false) in the
source YANG file, then _set_lsp_id is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_lsp_id() directly.
YANG Description: A reference to the Link State PDU ID.
"""
parent = getattr(self, "_parent", None)
if parent is not None and load is False:
raise AttributeError(
"Cannot set keys directly when" + " within an instantiated list"
)
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(
v,
base=six.text_type,
is_leaf=True,
yang_name="lsp-id",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
is_keyval=True,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="leafref",
is_config=False,
)
except (TypeError, ValueError):
raise ValueError(
{
"error-string": """lsp_id must be of a type compatible with leafref""",
"defined-type": "leafref",
"generated-type": """YANGDynClass(base=six.text_type, is_leaf=True, yang_name="lsp-id", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, is_keyval=True, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='leafref', is_config=False)""",
}
)
self.__lsp_id = t
if hasattr(self, "_set"):
self._set()
def _unset_lsp_id(self):
self.__lsp_id = YANGDynClass(
base=six.text_type,
is_leaf=True,
yang_name="lsp-id",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
is_keyval=True,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="leafref",
is_config=False,
)
def _get_state(self):
"""
Getter method for state, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/levels/level/link_state_database/lsp/state (container)
YANG Description: State parameters of Link State PDU.
"""
return self.__state
def _set_state(self, v, load=False):
"""
Setter method for state, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/levels/level/link_state_database/lsp/state (container)
If this variable is read-only (config: false) in the
source YANG file, then _set_state is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_state() directly.
YANG Description: State parameters of Link State PDU.
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(
v,
base=state.state,
is_container="container",
yang_name="state",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
extensions=None,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="container",
is_config=False,
)
except (TypeError, ValueError):
raise ValueError(
{
"error-string": """state must be of a type compatible with container""",
"defined-type": "container",
"generated-type": """YANGDynClass(base=state.state, is_container='container', yang_name="state", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='container', is_config=False)""",
}
)
self.__state = t
if hasattr(self, "_set"):
self._set()
def _unset_state(self):
self.__state = YANGDynClass(
base=state.state,
is_container="container",
yang_name="state",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
extensions=None,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="container",
is_config=False,
)
def _get_tlvs(self):
"""
Getter method for tlvs, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/levels/level/link_state_database/lsp/tlvs (container)
YANG Description: This container defines Link State PDU State TLVs.
"""
return self.__tlvs
def _set_tlvs(self, v, load=False):
"""
Setter method for tlvs, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/levels/level/link_state_database/lsp/tlvs (container)
If this variable is read-only (config: false) in the
source YANG file, then _set_tlvs is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_tlvs() directly.
YANG Description: This container defines Link State PDU State TLVs.
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(
v,
base=tlvs.tlvs,
is_container="container",
yang_name="tlvs",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
extensions=None,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="container",
is_config=False,
)
except (TypeError, ValueError):
raise ValueError(
{
"error-string": """tlvs must be of a type compatible with container""",
"defined-type": "container",
"generated-type": """YANGDynClass(base=tlvs.tlvs, is_container='container', yang_name="tlvs", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='container', is_config=False)""",
}
)
self.__tlvs = t
if hasattr(self, "_set"):
self._set()
def _unset_tlvs(self):
self.__tlvs = YANGDynClass(
base=tlvs.tlvs,
is_container="container",
yang_name="tlvs",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
extensions=None,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="container",
is_config=False,
)
def _get_undefined_tlvs(self):
"""
Getter method for undefined_tlvs, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/levels/level/link_state_database/lsp/undefined_tlvs (container)
YANG Description: Surrounding container for a list of unknown TLVs.
"""
return self.__undefined_tlvs
def _set_undefined_tlvs(self, v, load=False):
"""
Setter method for undefined_tlvs, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/levels/level/link_state_database/lsp/undefined_tlvs (container)
If this variable is read-only (config: false) in the
source YANG file, then _set_undefined_tlvs is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_undefined_tlvs() directly.
YANG Description: Surrounding container for a list of unknown TLVs.
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(
v,
base=undefined_tlvs.undefined_tlvs,
is_container="container",
yang_name="undefined-tlvs",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
extensions=None,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="container",
is_config=False,
)
except (TypeError, ValueError):
raise ValueError(
{
"error-string": """undefined_tlvs must be of a type compatible with container""",
"defined-type": "container",
"generated-type": """YANGDynClass(base=undefined_tlvs.undefined_tlvs, is_container='container', yang_name="undefined-tlvs", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='container', is_config=False)""",
}
)
self.__undefined_tlvs = t
if hasattr(self, "_set"):
self._set()
def _unset_undefined_tlvs(self):
self.__undefined_tlvs = YANGDynClass(
base=undefined_tlvs.undefined_tlvs,
is_container="container",
yang_name="undefined-tlvs",
parent=self,
path_helper=self._path_helper,
extmethods=self._extmethods,
register_paths=True,
extensions=None,
namespace="http://openconfig.net/yang/network-instance",
defining_module="openconfig-network-instance",
yang_type="container",
is_config=False,
)
lsp_id = __builtin__.property(_get_lsp_id)
state = __builtin__.property(_get_state)
tlvs = __builtin__.property(_get_tlvs)
undefined_tlvs = __builtin__.property(_get_undefined_tlvs)
_pyangbind_elements = OrderedDict(
[
("lsp_id", lsp_id),
("state", state),
("tlvs", tlvs),
("undefined_tlvs", undefined_tlvs),
]
)
| 39.083732 | 402 | 0.596866 | 3,468 | 32,674 | 5.387543 | 0.056805 | 0.069043 | 0.049454 | 0.056412 | 0.979929 | 0.968315 | 0.968315 | 0.968315 | 0.968315 | 0.968315 | 0 | 0.000796 | 0.307951 | 32,674 | 835 | 403 | 39.130539 | 0.825491 | 0.194803 | 0 | 0.878882 | 0 | 0.012422 | 0.250726 | 0.080969 | 0 | 0 | 0 | 0 | 0 | 1 | 0.043478 | false | 0 | 0.032609 | 0 | 0.122671 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
392e7dae15052c0e88974c2e8c1a7adb3878e323 | 7,291 | py | Python | tests/test_provider_philips_software_hsdp.py | mjuenema/python-terrascript | 6d8bb0273a14bfeb8ff8e950fe36f97f7c6e7b1d | [
"BSD-2-Clause"
] | 507 | 2017-07-26T02:58:38.000Z | 2022-01-21T12:35:13.000Z | tests/test_provider_philips_software_hsdp.py | mjuenema/python-terrascript | 6d8bb0273a14bfeb8ff8e950fe36f97f7c6e7b1d | [
"BSD-2-Clause"
] | 135 | 2017-07-20T12:01:59.000Z | 2021-10-04T22:25:40.000Z | tests/test_provider_philips_software_hsdp.py | mjuenema/python-terrascript | 6d8bb0273a14bfeb8ff8e950fe36f97f7c6e7b1d | [
"BSD-2-Clause"
] | 81 | 2018-02-20T17:55:28.000Z | 2022-01-31T07:08:40.000Z | # tests/test_provider_philips-software_hsdp.py
# Automatically generated by tools/makecode.py (24-Sep-2021 15:18:56 UTC)
def test_provider_import():
import terrascript.provider.philips_software.hsdp
def test_resource_import():
from terrascript.resource.philips_software.hsdp import (
hsdp_ai_inference_compute_environment,
)
from terrascript.resource.philips_software.hsdp import (
hsdp_ai_inference_compute_target,
)
from terrascript.resource.philips_software.hsdp import hsdp_ai_inference_job
from terrascript.resource.philips_software.hsdp import hsdp_ai_inference_model
from terrascript.resource.philips_software.hsdp import hsdp_ai_workspace
from terrascript.resource.philips_software.hsdp import (
hsdp_ai_workspace_compute_target,
)
from terrascript.resource.philips_software.hsdp import hsdp_cdl_data_type_definition
from terrascript.resource.philips_software.hsdp import hsdp_cdl_export_route
from terrascript.resource.philips_software.hsdp import hsdp_cdl_label_definition
from terrascript.resource.philips_software.hsdp import hsdp_cdl_research_study
from terrascript.resource.philips_software.hsdp import hsdp_cdr_org
from terrascript.resource.philips_software.hsdp import hsdp_cdr_subscription
from terrascript.resource.philips_software.hsdp import hsdp_container_host
from terrascript.resource.philips_software.hsdp import hsdp_container_host_exec
from terrascript.resource.philips_software.hsdp import hsdp_dicom_gateway_config
from terrascript.resource.philips_software.hsdp import hsdp_dicom_object_store
from terrascript.resource.philips_software.hsdp import hsdp_dicom_remote_node
from terrascript.resource.philips_software.hsdp import hsdp_dicom_repository
from terrascript.resource.philips_software.hsdp import hsdp_dicom_store_config
from terrascript.resource.philips_software.hsdp import hsdp_edge_app
from terrascript.resource.philips_software.hsdp import hsdp_edge_config
from terrascript.resource.philips_software.hsdp import hsdp_edge_custom_cert
from terrascript.resource.philips_software.hsdp import hsdp_edge_sync
from terrascript.resource.philips_software.hsdp import hsdp_function
from terrascript.resource.philips_software.hsdp import hsdp_iam_application
from terrascript.resource.philips_software.hsdp import hsdp_iam_client
from terrascript.resource.philips_software.hsdp import hsdp_iam_email_template
from terrascript.resource.philips_software.hsdp import hsdp_iam_group
from terrascript.resource.philips_software.hsdp import hsdp_iam_mfa_policy
from terrascript.resource.philips_software.hsdp import hsdp_iam_org
from terrascript.resource.philips_software.hsdp import hsdp_iam_password_policy
from terrascript.resource.philips_software.hsdp import hsdp_iam_proposition
from terrascript.resource.philips_software.hsdp import hsdp_iam_role
from terrascript.resource.philips_software.hsdp import hsdp_iam_service
from terrascript.resource.philips_software.hsdp import hsdp_iam_user
from terrascript.resource.philips_software.hsdp import hsdp_metrics_autoscaler
from terrascript.resource.philips_software.hsdp import hsdp_notification_producer
from terrascript.resource.philips_software.hsdp import hsdp_notification_subscriber
from terrascript.resource.philips_software.hsdp import (
hsdp_notification_subscription,
)
from terrascript.resource.philips_software.hsdp import hsdp_notification_topic
from terrascript.resource.philips_software.hsdp import hsdp_pki_cert
from terrascript.resource.philips_software.hsdp import hsdp_pki_tenant
from terrascript.resource.philips_software.hsdp import hsdp_s3creds_policy
def test_datasource_import():
from terrascript.data.philips_software.hsdp import (
hsdp_ai_inference_compute_environments,
)
from terrascript.data.philips_software.hsdp import hsdp_ai_inference_compute_targets
from terrascript.data.philips_software.hsdp import hsdp_ai_inference_jobs
from terrascript.data.philips_software.hsdp import hsdp_ai_inference_models
from terrascript.data.philips_software.hsdp import (
hsdp_ai_inference_service_instance,
)
from terrascript.data.philips_software.hsdp import hsdp_ai_workspace
from terrascript.data.philips_software.hsdp import hsdp_ai_workspace_compute_targets
from terrascript.data.philips_software.hsdp import (
hsdp_ai_workspace_service_instance,
)
from terrascript.data.philips_software.hsdp import hsdp_cdl_data_type_definition
from terrascript.data.philips_software.hsdp import hsdp_cdl_data_type_definitions
from terrascript.data.philips_software.hsdp import hsdp_cdl_export_route
from terrascript.data.philips_software.hsdp import hsdp_cdl_instance
from terrascript.data.philips_software.hsdp import hsdp_cdl_label_definition
from terrascript.data.philips_software.hsdp import hsdp_cdl_research_studies
from terrascript.data.philips_software.hsdp import hsdp_cdl_research_study
from terrascript.data.philips_software.hsdp import hsdp_cdr_fhir_store
from terrascript.data.philips_software.hsdp import hsdp_config
from terrascript.data.philips_software.hsdp import hsdp_container_host_instances
from terrascript.data.philips_software.hsdp import hsdp_container_host_subnet_types
from terrascript.data.philips_software.hsdp import hsdp_edge_device
from terrascript.data.philips_software.hsdp import hsdp_iam_application
from terrascript.data.philips_software.hsdp import hsdp_iam_introspect
from terrascript.data.philips_software.hsdp import hsdp_iam_org
from terrascript.data.philips_software.hsdp import hsdp_iam_permissions
from terrascript.data.philips_software.hsdp import hsdp_iam_proposition
from terrascript.data.philips_software.hsdp import hsdp_iam_service
from terrascript.data.philips_software.hsdp import hsdp_iam_user
from terrascript.data.philips_software.hsdp import hsdp_notification_producer
from terrascript.data.philips_software.hsdp import hsdp_notification_producers
from terrascript.data.philips_software.hsdp import hsdp_notification_subscriber
from terrascript.data.philips_software.hsdp import hsdp_notification_subscription
from terrascript.data.philips_software.hsdp import hsdp_notification_topic
from terrascript.data.philips_software.hsdp import hsdp_notification_topics
from terrascript.data.philips_software.hsdp import hsdp_pki_policy
from terrascript.data.philips_software.hsdp import hsdp_pki_root
from terrascript.data.philips_software.hsdp import hsdp_s3creds_access
from terrascript.data.philips_software.hsdp import hsdp_s3creds_policy
# TODO: Shortcut imports without namespace for official and supported providers.
# TODO: This has to be moved into a required_providers block.
# def test_version_source():
#
# import terrascript.provider.philips_software.hsdp
#
# t = terrascript.provider.philips_software.hsdp.hsdp()
# s = str(t)
#
# assert 'https://github.com/philips-software/terraform-provider-hsdp' in s
# assert '0.20.5' in s
| 36.638191 | 88 | 0.828144 | 940 | 7,291 | 6.105319 | 0.143617 | 0.222164 | 0.278097 | 0.348493 | 0.885346 | 0.878725 | 0.862171 | 0.85886 | 0.811814 | 0.370796 | 0 | 0.002983 | 0.12632 | 7,291 | 198 | 89 | 36.823232 | 0.897959 | 0.070909 | 0 | 0.071429 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0.005051 | 0 | 1 | 0.030612 | true | 0.010204 | 0.857143 | 0 | 0.887755 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 12 |
1ac60e75841c64653af7461b4eea8229b7fb7560 | 13,178 | py | Python | src/fqe/unittest_data/build_hamiltonian.py | rmlarose/OpenFermion-FQE | 54489126725fe3bb83218b6fde9d44f6cf130359 | [
"Apache-2.0"
] | null | null | null | src/fqe/unittest_data/build_hamiltonian.py | rmlarose/OpenFermion-FQE | 54489126725fe3bb83218b6fde9d44f6cf130359 | [
"Apache-2.0"
] | null | null | null | src/fqe/unittest_data/build_hamiltonian.py | rmlarose/OpenFermion-FQE | 54489126725fe3bb83218b6fde9d44f6cf130359 | [
"Apache-2.0"
] | null | null | null | # Copyright 2020 Google LLC
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Build Hamiltonian is a convenience routine for initializing unittest data.
"""
# pylint: disable=line-too-long
# pylint: disable=missing-docstring
# pylint: disable=invalid-name
# pylint: disable=too-many-nested-blocks
from typing import Tuple
import numpy as np
from openfermion import FermionOperator
from openfermion.utils import hermitian_conjugated
def number_nonconserving_fop(rank: int, norb: int) -> FermionOperator:
# TODO: Complete docstring.
"""Returns a FermionOperator Hamiltonian which...
Args:
rank:
norb:
"""
hamil = FermionOperator()
if rank >= 0:
hamil += FermionOperator("", 6.0)
if rank >= 2:
for i in range(0, 2 * norb, 2):
for j in range(0, 2 * norb, 2):
opstring = str(i) + " " + str(j + 1)
hamil += FermionOperator(
opstring,
(i + 1 + j * 2) * 0.1 - (i + 1 + 2 * (j + 1)) * 0.1j,
)
opstring = str(i) + "^ " + str(j + 1) + "^ "
hamil += FermionOperator(opstring,
(i + 1 + j) * 0.1 + (i + 1 + j) * 0.1j)
opstring = str(i + 1) + " " + str(j)
hamil += FermionOperator(opstring,
(i + 1 + j) * 0.1 - (i + 1 + j) * 0.1j)
opstring = str(i + 1) + "^ " + str(j) + "^ "
hamil += FermionOperator(
opstring,
(i + 1 + j * 2) * 0.1 + (i + 1 + 2 * (j + 1)) * 0.1j,
)
return (hamil + hermitian_conjugated(hamil)) / 2.0
def build_restricted(norb: int, full: bool = False) -> Tuple[np.ndarray, ...]:
# TODO: Complete docstring.
"""Build data structures for evolution tests to avoid large amount of data
being saved in remote repository.
Args:
norb:
full:
"""
if full:
orb_use = 2 * norb
else:
orb_use = norb
h1e = np.zeros((orb_use,) * 2, dtype=np.complex128)
h2e = np.zeros((orb_use,) * 4, dtype=np.complex128)
h3e = np.zeros((orb_use,) * 6, dtype=np.complex128)
h4e = np.zeros((orb_use,) * 8, dtype=np.complex128)
for i in range(norb):
h1e[i, i] += i * 2.0
for j in range(norb):
h1e[i, j] += (i + j) * 0.02
for k in range(norb):
for l in range(norb):
h2e[i, j, k, l] += (i + k) * (j + l) * 0.02
for m in range(norb):
for n in range(norb):
h3e[i, j, k, l, m, n] += ((i + l) * (j + m) *
(k + n) * 0.002)
for o in range(norb):
for p in range(norb):
h4e[i, j, k, l, m, n, o, p] += ((i + m) *
(j + n) *
(k + o) *
(l + p) *
0.001)
# TODO: Simplify.
if full:
h1e[norb:, norb:] = h1e[:norb, :norb]
h2e[:norb, norb:, :norb, norb:] = h2e[:norb, :norb, :norb, :norb]
h2e[norb:, :norb, norb:, :norb] = h2e[:norb, :norb, :norb, :norb]
h2e[norb:, norb:, norb:, norb:] = h2e[:norb, :norb, :norb, :norb]
h3e[:norb, norb:, :norb, :norb, norb:, :
norb] = h3e[:norb, :norb, :norb, :norb, :norb, :norb]
h3e[norb:, :norb, :norb, norb:, :norb, :
norb] = h3e[:norb, :norb, :norb, :norb, :norb, :norb]
h3e[:norb, :norb, norb:, :norb, :norb,
norb:] = h3e[:norb, :norb, :norb, :norb, :norb, :norb]
h3e[norb:, norb:, :norb, norb:, norb:, :
norb] = h3e[:norb, :norb, :norb, :norb, :norb, :norb]
h3e[:norb, norb:, norb:, :norb, norb:,
norb:] = h3e[:norb, :norb, :norb, :norb, :norb, :norb]
h3e[norb:, :norb, norb:, norb:, :norb,
norb:] = h3e[:norb, :norb, :norb, :norb, :norb, :norb]
h3e[norb:, norb:, norb:, norb:, norb:,
norb:] = h3e[:norb, :norb, :norb, :norb, :norb, :norb]
h4e[:norb, norb:, :norb, :norb, :norb, norb:, :norb, :
norb] = h4e[:norb, :norb, :norb, :norb, :norb, :norb, :norb, :norb]
h4e[norb:, :norb, :norb, :norb, norb:, :norb, :norb, :
norb] = h4e[:norb, :norb, :norb, :norb, :norb, :norb, :norb, :norb]
h4e[:norb, :norb, norb:, :norb, :norb, :norb, norb:, :
norb] = h4e[:norb, :norb, :norb, :norb, :norb, :norb, :norb, :norb]
h4e[:norb, :norb, :norb, norb:, :norb, :norb, :norb,
norb:] = h4e[:norb, :norb, :norb, :norb, :norb, :norb, :norb, :norb]
h4e[norb:, norb:, :norb, :norb, norb:, norb:, :norb, :
norb] = h4e[:norb, :norb, :norb, :norb, :norb, :norb, :norb, :norb]
h4e[:norb, norb:, norb:, :norb, :norb, norb:, norb:, :
norb] = h4e[:norb, :norb, :norb, :norb, :norb, :norb, :norb, :norb]
h4e[norb:, :norb, norb:, :norb, norb:, :norb, norb:, :
norb] = h4e[:norb, :norb, :norb, :norb, :norb, :norb, :norb, :norb]
h4e[:norb, norb:, :norb, norb:, :norb, norb:, :norb,
norb:] = h4e[:norb, :norb, :norb, :norb, :norb, :norb, :norb, :norb]
h4e[norb:, :norb, :norb, norb:, norb:, :norb, :norb,
norb:] = h4e[:norb, :norb, :norb, :norb, :norb, :norb, :norb, :norb]
h4e[:norb, :norb, norb:, norb:, :norb, :norb, norb:,
norb:] = h4e[:norb, :norb, :norb, :norb, :norb, :norb, :norb, :norb]
h4e[norb:, norb:, norb:, :norb, norb:, norb:, norb:, :
norb] = h4e[:norb, :norb, :norb, :norb, :norb, :norb, :norb, :norb]
h4e[norb:, norb:, :norb, norb:, norb:, norb:, :norb,
norb:] = h4e[:norb, :norb, :norb, :norb, :norb, :norb, :norb, :norb]
h4e[:norb, norb:, norb:, norb:, :norb, norb:, norb:,
norb:] = h4e[:norb, :norb, :norb, :norb, :norb, :norb, :norb, :norb]
h4e[norb:, :norb, norb:, norb:, norb:, :norb, norb:,
norb:] = h4e[:norb, :norb, :norb, :norb, :norb, :norb, :norb, :norb]
h4e[norb:, norb:, norb:, norb:, norb:, norb:, norb:,
norb:] = h4e[:norb, :norb, :norb, :norb, :norb, :norb, :norb, :norb]
return h1e, h2e, h3e, h4e
def build_gso(norb: int) -> Tuple[np.ndarray, ...]:
"""TODO: Add docstring."""
# TODO:
# I think this can be implemented by
# > return build_restricted(2 * norb, full=False)
# ?
h1e = np.zeros((norb * 2,) * 2, dtype=np.complex128)
h2e = np.zeros((norb * 2,) * 4, dtype=np.complex128)
h3e = np.zeros((norb * 2,) * 6, dtype=np.complex128)
h4e = np.zeros((norb * 2,) * 8, dtype=np.complex128)
for i in range(norb * 2):
h1e[i, i] += i * 2.0
for j in range(norb * 2):
h1e[i, j] += (i + j) * 0.02
for k in range(norb * 2):
for l in range(norb * 2):
h2e[i, j, k, l] += (i + k) * (j + l) * 0.02
for m in range(norb * 2):
for n in range(norb * 2):
h3e[i, j, k, l, m, n] += ((i + l) * (j + m) *
(k + n) * 0.002)
for o in range(norb * 2):
for p in range(norb * 2):
h4e[i, j, k, l, m, n, o, p] += ((i + m) *
(j + n) *
(k + o) *
(l + p) *
0.001)
return h1e, h2e, h3e, h4e
def build_sso(norb: int):
"""Build data structures for evolution tests to avoid large amount of data
being saved in remote repository.
"""
h1e = np.zeros((norb * 2,) * 2, dtype=np.complex128)
h2e = np.zeros((norb * 2,) * 4, dtype=np.complex128)
h3e = np.zeros((norb * 2,) * 6, dtype=np.complex128)
h4e = np.zeros((norb * 2,) * 8, dtype=np.complex128)
for i in range(norb):
h1e[i, i] += i * 2.0
for j in range(norb):
h1e[i, j] += (i + j) * 0.02
for k in range(norb):
for l in range(norb):
h2e[i, j, k, l] += (i + k) * (j + l) * 0.02
for m in range(norb):
for n in range(norb):
h3e[i, j, k, l, m, n] += ((i + l) * (j + m) *
(k + n) * 0.002)
for o in range(norb):
for p in range(norb):
h4e[i, j, k, l, m, n, o, p] += ((i + m) *
(j + n) *
(k + o) *
(l + p) *
0.001)
h1e[norb:, norb:] = 2.0 * h1e[:norb, :norb]
h2e[:norb, norb:, :norb, norb:] = 2.0 * h2e[:norb, :norb, :norb, :norb]
h2e[norb:, :norb, norb:, :norb] = 2.0 * h2e[:norb, :norb, :norb, :norb]
h2e[norb:, norb:, norb:, norb:] = 4.0 * h2e[:norb, :norb, :norb, :norb]
h3e[:norb, norb:, :norb, :norb, norb:, :norb] = (
2.0 * h3e[:norb, :norb, :norb, :norb, :norb, :norb])
h3e[norb:, :norb, :norb, norb:, :norb, :norb] = (
2.0 * h3e[:norb, :norb, :norb, :norb, :norb, :norb])
h3e[:norb, :norb, norb:, :norb, :norb, norb:] = (
2.0 * h3e[:norb, :norb, :norb, :norb, :norb, :norb])
h3e[norb:, norb:, :norb, norb:, norb:, :norb] = (
4.0 * h3e[:norb, :norb, :norb, :norb, :norb, :norb])
h3e[:norb, norb:, norb:, :norb, norb:, norb:] = (
4.0 * h3e[:norb, :norb, :norb, :norb, :norb, :norb])
h3e[norb:, :norb, norb:, norb:, :norb, norb:] = (
4.0 * h3e[:norb, :norb, :norb, :norb, :norb, :norb])
h3e[norb:, norb:, norb:, norb:, norb:, norb:] = (
6.0 * h3e[:norb, :norb, :norb, :norb, :norb, :norb])
h4e[:norb, norb:, :norb, :norb, :norb, norb:, :norb, :norb] = (
2.0 * h4e[:norb, :norb, :norb, :norb, :norb, :norb, :norb, :norb])
h4e[norb:, :norb, :norb, :norb, norb:, :norb, :norb, :norb] = (
2.0 * h4e[:norb, :norb, :norb, :norb, :norb, :norb, :norb, :norb])
h4e[:norb, :norb, norb:, :norb, :norb, :norb, norb:, :norb] = (
2.0 * h4e[:norb, :norb, :norb, :norb, :norb, :norb, :norb, :norb])
h4e[:norb, :norb, :norb, norb:, :norb, :norb, :norb, norb:] = (
2.0 * h4e[:norb, :norb, :norb, :norb, :norb, :norb, :norb, :norb])
h4e[norb:, norb:, :norb, :norb, norb:, norb:, :norb, :norb] = (
4.0 * h4e[:norb, :norb, :norb, :norb, :norb, :norb, :norb, :norb])
h4e[:norb, norb:, norb:, :norb, :norb, norb:, norb:, :norb] = (
4.0 * h4e[:norb, :norb, :norb, :norb, :norb, :norb, :norb, :norb])
h4e[norb:, :norb, norb:, :norb, norb:, :norb, norb:, :norb] = (
4.0 * h4e[:norb, :norb, :norb, :norb, :norb, :norb, :norb, :norb])
h4e[:norb, norb:, :norb, norb:, :norb, norb:, :norb, norb:] = (
4.0 * h4e[:norb, :norb, :norb, :norb, :norb, :norb, :norb, :norb])
h4e[norb:, :norb, :norb, norb:, norb:, :norb, :norb, norb:] = (
4.0 * h4e[:norb, :norb, :norb, :norb, :norb, :norb, :norb, :norb])
h4e[:norb, :norb, norb:, norb:, :norb, :norb, norb:, norb:] = (
4.0 * h4e[:norb, :norb, :norb, :norb, :norb, :norb, :norb, :norb])
h4e[norb:, norb:, norb:, :norb, norb:, norb:, norb:, :norb] = (
6.0 * h4e[:norb, :norb, :norb, :norb, :norb, :norb, :norb, :norb])
h4e[norb:, norb:, :norb, norb:, norb:, norb:, :norb, norb:] = (
6.0 * h4e[:norb, :norb, :norb, :norb, :norb, :norb, :norb, :norb])
h4e[:norb, norb:, norb:, norb:, :norb, norb:, norb:, norb:] = (
6.0 * h4e[:norb, :norb, :norb, :norb, :norb, :norb, :norb, :norb])
h4e[norb:, :norb, norb:, norb:, norb:, :norb, norb:, norb:] = (
6.0 * h4e[:norb, :norb, :norb, :norb, :norb, :norb, :norb, :norb])
h4e[norb:, norb:, norb:, norb:, norb:, norb:, norb:, norb:] = (
8.0 * h4e[:norb, :norb, :norb, :norb, :norb, :norb, :norb, :norb])
return h1e, h2e, h3e, h4e
| 47.402878 | 80 | 0.447261 | 1,655 | 13,178 | 3.55287 | 0.096073 | 0.816327 | 1.012245 | 1.077551 | 0.784354 | 0.77619 | 0.770748 | 0.75068 | 0.74932 | 0.74932 | 0 | 0.043307 | 0.36394 | 13,178 | 277 | 81 | 47.574007 | 0.658196 | 0.096449 | 0 | 0.532338 | 0 | 0 | 0.000847 | 0 | 0 | 0 | 0 | 0.01083 | 0 | 1 | 0.019901 | false | 0 | 0.019901 | 0 | 0.059701 | 0 | 0 | 0 | 0 | null | 1 | 1 | 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 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
1acb2c958f5944826622b4fec2d0e2127d4335be | 13,911 | py | Python | vespa/analysis/prefs.py | vespa-mrs/vespa | 6d3e84a206ec427ac1304e70c7fadf817432956b | [
"BSD-3-Clause"
] | null | null | null | vespa/analysis/prefs.py | vespa-mrs/vespa | 6d3e84a206ec427ac1304e70c7fadf817432956b | [
"BSD-3-Clause"
] | 4 | 2021-04-17T13:58:31.000Z | 2022-01-20T14:19:57.000Z | vespa/analysis/prefs.py | vespa-mrs/vespa | 6d3e84a206ec427ac1304e70c7fadf817432956b | [
"BSD-3-Clause"
] | 3 | 2021-06-05T16:34:57.000Z | 2022-01-19T16:13:22.000Z | # Python modules
import abc
# 3rd party modules
# Our modules
import vespa.analysis.util_menu as util_menu
import vespa.analysis.util_analysis_config as util_analysis_config
import vespa.common.prefs as prefs
import vespa.common.util.xml_ as util_xml
"""See common/prefs.py for info on the classes below."""
class AnalysisPrefs(prefs.Prefs, metaclass=abc.ABCMeta):
def __init__(self, id_class):
prefs.Prefs.__init__(self, util_menu.bar, id_class)
@property
def _ConfigClass(self):
"""Returns the appropriate ConfigObj class for this app, specifically
util_analysis_config.Config.
"""
return util_analysis_config.Config
class PrefsPrepFidsum(AnalysisPrefs):
def __init__(self):
AnalysisPrefs.__init__(self, util_menu.ViewIdsPrepFidsum)
@property
def _ini_section_name(self):
return "prep_fidsum_prefs"
def deflate(self):
# Call my base class deflate
d = AnalysisPrefs.deflate(self)
# Add my custom stuff
for name in ("sash_position",
"line_color_imaginary",
"line_color_magnitude",
"line_color_real",
"zero_line_color",
"zero_line_style",
"zero_line_plot_color",
"zero_line_plot_style",
"line_width",
):
d[name] = getattr(self, name)
return d
def inflate(self, source):
# Call my base class inflate
AnalysisPrefs.inflate(self, source)
# Add my custom stuff
for name in ("sash_position", ):
setattr(self, name, int(source[name]))
for name in ("line_color_imaginary",
"line_color_magnitude",
"line_color_real",
"zero_line_color",
"zero_line_style",
"zero_line_plot_color",
"zero_line_plot_style",
):
setattr(self, name, source[name])
for name in ("line_width", ):
setattr(self, name, float(source[name]))
class PrefsSpectral(AnalysisPrefs):
def __init__(self):
AnalysisPrefs.__init__(self, util_menu.ViewIdsSpectral)
@property
def _ini_section_name(self):
return "spectral_prefs"
def deflate(self):
# Call my base class deflate
d = AnalysisPrefs.deflate(self)
# Add my custom stuff
for name in ("sash_position_main",
"sash_position_svd",
"line_color_imaginary",
"line_color_magnitude",
"line_color_real",
"line_color_svd",
"zero_line_color",
"zero_line_style",
"line_width",
):
d[name] = getattr(self, name)
return d
def inflate(self, source):
# Call my base class inflate
AnalysisPrefs.inflate(self, source)
# Add my custom stuff
for name in ("sash_position_main",
"sash_position_svd", ):
setattr(self, name, int(source[name]))
for name in ("line_color_imaginary",
"line_color_magnitude",
"line_color_real",
"line_color_svd",
"zero_line_color",
"zero_line_style",
):
setattr(self, name, source[name])
for name in ("line_width", ):
setattr(self, name, float(source[name]))
class PrefsVoigt(AnalysisPrefs):
def __init__(self):
AnalysisPrefs.__init__(self, util_menu.ViewIdsVoigt)
# FIXME PS - magic number
self.plotx = [PrefsVoigtPlotX(i) for i in range(4)]
@property
def _ini_section_name(self):
return "voigt_prefs"
@property
def menu_state(self):
state = { }
for menu_item_id in self._id_name_map:
name = self._id_name_map[menu_item_id]
state[menu_item_id] = getattr(self, name)
for plotx in self.plotx:
state.update(plotx.menu_state)
return state
@property
def n_plots(self):
"""Returns the # of plots that should be visible. Read-only."""
if self.n_plots_1:
return 1
elif self.n_plots_2:
return 2
elif self.n_plots_3:
return 3
elif self.n_plots_4:
return 4
def handle_event(self, menu_item_id):
if menu_item_id in self._id_name_map:
# This is an ordinary boolean. My base class can handle it.
return AnalysisPrefs.handle_event(self, menu_item_id)
else:
for i, plotx in enumerate(self.plotx):
if plotx.handle_event(menu_item_id):
return True
return False
def deflate(self):
# Call my base class deflate
d = AnalysisPrefs.deflate(self)
# Get rid of the plotx items
del d["plotx"]
# Add my custom stuff
for name in ("sash_position",
"line_color_raw",
"line_color_fit",
"line_color_base",
"line_color_init",
"line_color_weight",
"line_color_imaginary",
"line_color_magnitude",
"line_color_real",
"zero_line_color",
"zero_line_style",
"line_width",
"csv_qa_metab_labels",
):
d[name] = getattr(self, name)
return d
def inflate(self, source):
# Due to a bug, for a while we wrote "n_plots" to the INI file, so it
# might be present in source. If it is, we need to get rid of it,
# otherwise we'll try to overwrite the read-only "n_plots" property
# during inflate.
if "n_plots" in source:
del source["n_plots"]
# Call my base class inflate
AnalysisPrefs.inflate(self, source)
# Add my custom stuff
self.sash_position = int(source.get("sash_position", 0))
for name in ("line_color_raw",
"line_color_fit",
"line_color_base",
"line_color_init",
"line_color_weight",
"line_color_imaginary",
"line_color_magnitude",
"line_color_real",
"zero_line_color",
"zero_line_style",
):
setattr(self, name, source[name])
for name in ("line_width",
):
setattr(self, name, float(source[name]))
self.csv_qa_metab_labels = util_xml.BOOLEANS[source.get("csv_qa_metab_labels", False)]
def save(self):
AnalysisPrefs.save(self)
for plotx in self.plotx:
plotx.save()
class PrefsVoigtPlotX(AnalysisPrefs):
def __init__(self, index):
self._index = index
AnalysisPrefs.__init__(self, util_menu.PlotXIds)
# At this point (after my base class init), the keys in _id_name_map
# are 4-tuples. I need to reduce them to single ints to make this
# _id_name_map look mimic the behavior of _id_name_map in all of the
# other Prefs classes.
keys = list(self._id_name_map.keys())
self._id_name_map = \
dict( (key[self.index], value) for key, value
in self._id_name_map.items())
@property
def index(self):
return self._index
@property
def _ini_section_name(self):
# The ini section name is 'voigt_plot_x' where x = 'a', 'b', 'c', or 'd'.
return "voigt_plot_%s" % chr(ord('a') + self.index)
def handle_event(self, menu_item_id):
# This version of handle_event differs from the base class version
# because I can't guarantee that the id passed to me belongs to my
# menu at all.
if menu_item_id in self._id_name_map:
return AnalysisPrefs.handle_event(self, menu_item_id)
else:
# Not my menu
return False
class PrefsGiso(AnalysisPrefs):
def __init__(self):
AnalysisPrefs.__init__(self, util_menu.ViewIdsGiso)
# FIXME PS - magic number
self.plotx = [PrefsGisoPlotX(i) for i in range(4)]
@property
def _ini_section_name(self):
return "giso_prefs"
@property
def menu_state(self):
state = { }
for menu_item_id in self._id_name_map:
name = self._id_name_map[menu_item_id]
state[menu_item_id] = getattr(self, name)
for plotx in self.plotx:
state.update(plotx.menu_state)
return state
@property
def n_plots(self):
"""Returns the # of plots that should be visible. Read-only."""
if self.n_plots_1:
return 1
elif self.n_plots_2:
return 2
elif self.n_plots_3:
return 3
elif self.n_plots_4:
return 4
def handle_event(self, menu_item_id):
if menu_item_id in self._id_name_map:
# This is an ordinary boolean. My base class can handle it.
return AnalysisPrefs.handle_event(self, menu_item_id)
else:
for i, plotx in enumerate(self.plotx):
if plotx.handle_event(menu_item_id):
return True
return False
def deflate(self):
# Call my base class deflate
d = AnalysisPrefs.deflate(self)
# Get rid of the plotx items
del d["plotx"]
# Add my custom stuff
for name in ("sash_position",
"line_color_raw",
"line_color_fit",
"line_color_base",
"line_color_init",
"line_color_weight",
"line_color_imaginary",
"line_color_magnitude",
"line_color_real",
"zero_line_color",
"zero_line_style",
"line_width",
):
d[name] = getattr(self, name)
return d
def inflate(self, source):
# Due to a bug, for a while we wrote "n_plots" to the INI file, so it
# might be present in source. If it is, we need to get rid of it,
# otherwise we'll try to overwrite the read-only "n_plots" property
# during inflate.
if "n_plots" in source:
del source["n_plots"]
# Call my base class inflate
AnalysisPrefs.inflate(self, source)
# Add my custom stuff
self.sash_position = int(source.get("sash_position", 0))
for name in ("line_color_raw",
"line_color_fit",
"line_color_base",
"line_color_init",
"line_color_weight",
"line_color_imaginary",
"line_color_magnitude",
"line_color_real",
"zero_line_color",
"zero_line_style", ):
setattr(self, name, source[name])
for name in ("line_width", ):
setattr(self, name, float(source[name]))
def save(self):
AnalysisPrefs.save(self)
for plotx in self.plotx:
plotx.save()
class PrefsGisoPlotX(AnalysisPrefs):
def __init__(self, index):
self._index = index
AnalysisPrefs.__init__(self, util_menu.PlotXIds)
# At this point (after my base class init), the keys in _id_name_map
# are 4-tuples. I need to reduce them to single ints to make this
# _id_name_map look mimic the behavior of _id_name_map in all of the
# other Prefs classes.
keys = list(self._id_name_map.keys())
self._id_name_map = \
dict( (key[self.index], value) for key, value
in self._id_name_map.items())
@property
def index(self):
return self._index
@property
def _ini_section_name(self):
# The ini section name is 'voigt_plot_x' where x = 'a', 'b', 'c', or 'd'.
return "giso_plot_%s" % chr(ord('a') + self.index)
def handle_event(self, menu_item_id):
# This version of handle_event differs from the base class version
# because I can't guarantee that the id passed to me belongs to my
# menu at all.
if menu_item_id in self._id_name_map:
return AnalysisPrefs.handle_event(self, menu_item_id)
else:
# Not my menu
return False
class PrefsWatref(AnalysisPrefs):
def __init__(self):
AnalysisPrefs.__init__(self, util_menu.ViewIdsWatref)
@property
def _ini_section_name(self):
return "watref_prefs"
def deflate(self):
# Call my base class deflate
d = AnalysisPrefs.deflate(self)
# Add my custom stuff
for name in ("sash_position_main",
"csv_qa_metab_labels",
):
d[name] = getattr(self, name)
return d
def inflate(self, source):
# Call my base class inflate
AnalysisPrefs.inflate(self, source)
# Add my custom stuff
for name in ("sash_position_main",):
setattr(self, name, int(source[name]))
self.csv_qa_metab_labels = util_xml.BOOLEANS[source.get("csv_qa_metab_labels", False)]
| 29.22479 | 94 | 0.547409 | 1,632 | 13,911 | 4.389093 | 0.122549 | 0.067849 | 0.027921 | 0.025408 | 0.9006 | 0.9006 | 0.890549 | 0.87589 | 0.87589 | 0.838894 | 0 | 0.00263 | 0.371289 | 13,911 | 475 | 95 | 29.286316 | 0.816373 | 0.163396 | 0 | 0.871972 | 0 | 0 | 0.135462 | 0 | 0 | 0 | 0 | 0.002105 | 0 | 1 | 0.131488 | false | 0 | 0.017301 | 0.031142 | 0.297578 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
46e3eff7b89ec0bb6c8f1fe0047f31cfbed5ab72 | 4,138 | py | Python | tests/converter/example_tx_results.py | geometry-labs/icon-sdk-python | e530df02eb16b394c3022d2d7d0383bd972e129a | [
"Apache-2.0"
] | 51 | 2018-08-29T04:15:36.000Z | 2022-03-14T10:02:08.000Z | tests/converter/example_tx_results.py | geometry-labs/icon-sdk-python | e530df02eb16b394c3022d2d7d0383bd972e129a | [
"Apache-2.0"
] | 24 | 2018-09-03T03:16:19.000Z | 2022-01-17T08:28:04.000Z | tests/converter/example_tx_results.py | geometry-labs/icon-sdk-python | e530df02eb16b394c3022d2d7d0383bd972e129a | [
"Apache-2.0"
] | 44 | 2018-09-06T22:36:16.000Z | 2022-03-15T06:46:05.000Z | # -*- coding: utf-8 -*-
# Copyright 2018 ICON Foundation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
TX_RESULT_0 = {
"status": "0x0",
"failure": {
"code": "0x7d00",
"message": "Out of balance"
},
"to": "cx4d6f646441a3f9c9b91019c9b98e3c342cceb114",
"txHash": "0xb903239f8543d04b5dc1ba6579132b143087c68db1b2168786408fcbce568238",
"txIndex": "0x1",
"blockHeight": "0x1234",
"blockHash": "0xc71303ef8543d04b5dc1ba6579132b143087c68db1b2168786408fcbce568238",
"cumulativeStepUsed": "0x1234",
"stepUsed": "0x1234",
"stepPrice": "0x5678"
}
TX_RESULT_1 = {
"txHash": "0x36d46e6f8ce3fb037f72c227214a391ba680fb771bb8062b7391a9ef084fdebc",
"blockHeight": "0x7",
"blockHash": "0x6979f9ad26fcf54a59998337fe6383c1feb32ef111d0cc9b3a78eec595e1bf4e",
"txIndex": "0x0",
"to": "cx0000000000000000000000000000000000000000",
"scoreAddress": "cx6d34efb31a521f680a77d736678de30ef9aff9ce",
"stepUsed": "0x298f290",
"stepPrice": "0x0",
"cumulativeStepUsed": "0x298f290",
"eventLogs": [],
"logsBloom": "0x00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000",
"status": "0x1"
}
TX_RESULT_2 = {
"txHash": "0xd34941501ef27bd2eba6c35b382e5ca2da5f5e44bec3bf460367a8ee04fd3fae",
"blockHeight": "0x7",
"blockHash": "0x6979f9ad26fcf54a59998337fe6383c1feb32ef111d0cc9b3a78eec595e1bf4e",
"txIndex": "0x1",
"to": "cx0000000000000000000000000000000000000001",
"stepUsed": "0xf9e70",
"stepPrice": "0x0",
"cumulativeStepUsed": "0x2a89100",
"eventLogs": [],
"logsBloom": "0x00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000",
"status": "0x0",
"failure": {
"code": "0x7d64",
"message": "Invalid sender: no permission"
}
}
TX_RESULT_3 = {
"txHash": "0xa24ffb1152aa9c58dab9b9b2b7102d5b238e0076222991ba289581a63b6ac0a5",
"blockHeight": "0x4",
"blockHash": "0x255822446aca1cf42e0a68302560f6f7e6e33ff9beea25c3ee23255bb5504165",
"txIndex": "0x0",
"to": "hxf1d9719d29488684039712e881830b5b37a64f11",
"stepUsed": "0xf4240",
"stepPrice": "0x0",
"cumulativeStepUsed": "0xf4240",
"eventLogs": [],
"logsBloom": "0x00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000",
"status": "0x1"
}
| 53.051282 | 534 | 0.808603 | 203 | 4,138 | 16.44335 | 0.566502 | 0.017975 | 0.026962 | 0.483523 | 0.381666 | 0 | 0 | 0 | 0 | 0 | 0 | 0.577579 | 0.10754 | 4,138 | 77 | 535 | 53.74026 | 0.326293 | 0.137748 | 0 | 0.389831 | 0 | 0 | 0.803546 | 0.641711 | 0 | 1 | 0.618069 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | null | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
46ed916bbe7e7c185798d488ce1e9d3fc810cbbf | 224 | py | Python | tests/unit/test_formatters.py | akhand2222/olypy | d2b62a590c416a7d919e6e7bfcabf35873bc13c4 | [
"Apache-2.0"
] | null | null | null | tests/unit/test_formatters.py | akhand2222/olypy | d2b62a590c416a7d919e6e7bfcabf35873bc13c4 | [
"Apache-2.0"
] | null | null | null | tests/unit/test_formatters.py | akhand2222/olypy | d2b62a590c416a7d919e6e7bfcabf35873bc13c4 | [
"Apache-2.0"
] | null | null | null | import pytest
import olypy.formatters
def test_print_one_thing():
with pytest.raises(KeyError):
olypy.formatters.print_one_thing({'asdf': 'asdf'})
olypy.formatters.print_one_thing({'CO': {'asdf': 1}})
| 22.4 | 61 | 0.691964 | 29 | 224 | 5.103448 | 0.517241 | 0.304054 | 0.263514 | 0.310811 | 0.378378 | 0 | 0 | 0 | 0 | 0 | 0 | 0.005319 | 0.160714 | 224 | 9 | 62 | 24.888889 | 0.781915 | 0 | 0 | 0 | 0 | 0 | 0.0625 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.166667 | true | 0 | 0.333333 | 0 | 0.5 | 0.5 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 8 |
46f6c1ff6185eac69469c872a58a5eae055575cf | 5,210 | py | Python | datamaps/migrations/0001_initial.py | rochapps/django-datamaps | 74d50077d31c79095122968bc3c2fde9628da69a | [
"BSD-2-Clause"
] | 3 | 2016-07-13T17:19:12.000Z | 2017-09-07T01:49:48.000Z | datamaps/migrations/0001_initial.py | rochapps/django-datamaps | 74d50077d31c79095122968bc3c2fde9628da69a | [
"BSD-2-Clause"
] | null | null | null | datamaps/migrations/0001_initial.py | rochapps/django-datamaps | 74d50077d31c79095122968bc3c2fde9628da69a | [
"BSD-2-Clause"
] | null | null | null | # -*- coding: 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 'World'
db.create_table(u'datamaps_world', (
(u'id', self.gf('django.db.models.fields.AutoField')(primary_key=True)),
('topo', self.gf('django.db.models.fields.TextField')(blank=True)),
('color', self.gf('django.db.models.fields.CharField')(default='#EDDC4E', max_length=8)),
))
db.send_create_signal(u'datamaps', ['World'])
# Adding model 'Scope'
db.create_table(u'datamaps_scope', (
(u'id', self.gf('django.db.models.fields.AutoField')(primary_key=True)),
('code', self.gf('django.db.models.fields.CharField')(max_length=2)),
('name', self.gf('django.db.models.fields.CharField')(max_length=20)),
('slug', self.gf('django.db.models.fields.SlugField')(max_length=50)),
('topo', self.gf('django.db.models.fields.TextField')(blank=True)),
('lat', self.gf('django.db.models.fields.FloatField')(default=0)),
('lon', self.gf('django.db.models.fields.FloatField')(default=0)),
('scale', self.gf('django.db.models.fields.DecimalField')(default=0, max_digits=12, decimal_places=8)),
('color', self.gf('django.db.models.fields.CharField')(default='#EDDC4E', max_length=8)),
))
db.send_create_signal(u'datamaps', ['Scope'])
# Adding model 'Country'
db.create_table(u'datamaps_country', (
(u'id', self.gf('django.db.models.fields.AutoField')(primary_key=True)),
('code', self.gf('django.db.models.fields.CharField')(max_length=3)),
('name', self.gf('django.db.models.fields.CharField')(max_length=40)),
('slug', self.gf('django.db.models.fields.SlugField')(max_length=50)),
('scope', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['datamaps.Scope'], null=True, blank=True)),
('topo', self.gf('django.db.models.fields.TextField')(blank=True)),
('lat', self.gf('django.db.models.fields.FloatField')(default=0)),
('lon', self.gf('django.db.models.fields.FloatField')(default=0)),
('radius', self.gf('django.db.models.fields.PositiveIntegerField')(default=10)),
('color', self.gf('django.db.models.fields.CharField')(default='#EDDC4E', max_length=8)),
))
db.send_create_signal(u'datamaps', ['Country'])
def backwards(self, orm):
# Deleting model 'World'
db.delete_table(u'datamaps_world')
# Deleting model 'Scope'
db.delete_table(u'datamaps_scope')
# Deleting model 'Country'
db.delete_table(u'datamaps_country')
models = {
u'datamaps.country': {
'Meta': {'ordering': "('name',)", 'object_name': 'Country'},
'code': ('django.db.models.fields.CharField', [], {'max_length': '3'}),
'color': ('django.db.models.fields.CharField', [], {'default': "'#EDDC4E'", 'max_length': '8'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'lat': ('django.db.models.fields.FloatField', [], {'default': '0'}),
'lon': ('django.db.models.fields.FloatField', [], {'default': '0'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '40'}),
'radius': ('django.db.models.fields.PositiveIntegerField', [], {'default': '10'}),
'scope': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['datamaps.Scope']", 'null': 'True', 'blank': 'True'}),
'slug': ('django.db.models.fields.SlugField', [], {'max_length': '50'}),
'topo': ('django.db.models.fields.TextField', [], {'blank': 'True'})
},
u'datamaps.scope': {
'Meta': {'object_name': 'Scope'},
'code': ('django.db.models.fields.CharField', [], {'max_length': '2'}),
'color': ('django.db.models.fields.CharField', [], {'default': "'#EDDC4E'", 'max_length': '8'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'lat': ('django.db.models.fields.FloatField', [], {'default': '0'}),
'lon': ('django.db.models.fields.FloatField', [], {'default': '0'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '20'}),
'scale': ('django.db.models.fields.DecimalField', [], {'default': '0', 'max_digits': '12', 'decimal_places': '8'}),
'slug': ('django.db.models.fields.SlugField', [], {'max_length': '50'}),
'topo': ('django.db.models.fields.TextField', [], {'blank': 'True'})
},
u'datamaps.world': {
'Meta': {'object_name': 'World'},
'color': ('django.db.models.fields.CharField', [], {'default': "'#EDDC4E'", 'max_length': '8'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'topo': ('django.db.models.fields.TextField', [], {'blank': 'True'})
}
}
complete_apps = ['datamaps'] | 55.425532 | 139 | 0.572937 | 598 | 5,210 | 4.908027 | 0.132107 | 0.122658 | 0.209881 | 0.29983 | 0.837138 | 0.792164 | 0.786031 | 0.70494 | 0.659285 | 0.657922 | 0 | 0.013006 | 0.203071 | 5,210 | 94 | 140 | 55.425532 | 0.693882 | 0.030134 | 0 | 0.44 | 0 | 0 | 0.457788 | 0.302616 | 0 | 0 | 0 | 0 | 0 | 1 | 0.026667 | false | 0 | 0.053333 | 0 | 0.12 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
200309873cfdf2f628a4e011163ad9dfb79d72b9 | 23,416 | py | Python | Ryven/packages/auto_generated/cgi/nodes.py | tfroehlich82/Ryven | cb57c91d13949712844a4410a9302c4a90d28dcd | [
"MIT"
] | 2,872 | 2020-07-01T09:06:34.000Z | 2022-03-31T05:52:32.000Z | Ryven/packages/auto_generated/cgi/nodes.py | dhf327/Ryven | a11e361528d982a9dd3c489dd536f8b05ffd56e1 | [
"MIT"
] | 59 | 2020-06-28T12:50:50.000Z | 2022-03-27T19:07:54.000Z | Ryven/packages/auto_generated/cgi/nodes.py | dhf327/Ryven | a11e361528d982a9dd3c489dd536f8b05ffd56e1 | [
"MIT"
] | 339 | 2020-07-05T04:36:20.000Z | 2022-03-24T07:25:18.000Z |
from NENV import *
import cgi
class NodeBase(Node):
pass
class _Parseparam_Node(NodeBase):
"""
"""
title = '_parseparam'
type_ = 'cgi'
init_inputs = [
NodeInputBP(label='s'),
]
init_outputs = [
NodeOutputBP(type_='data'),
]
color = '#32DA22'
def update_event(self, inp=-1):
self.set_output_val(0, cgi._parseparam(self.input(0)))
class Closelog_Node(NodeBase):
"""
Close the log file."""
title = 'closelog'
type_ = 'cgi'
init_inputs = [
]
init_outputs = [
NodeOutputBP(type_='data'),
]
color = '#32DA22'
def update_event(self, inp=-1):
self.set_output_val(0, cgi.closelog())
class Dolog_Node(NodeBase):
"""
Write a log message to the log file. See initlog() for docs."""
title = 'dolog'
type_ = 'cgi'
init_inputs = [
NodeInputBP(label='fmt'),
]
init_outputs = [
NodeOutputBP(type_='data'),
]
color = '#32DA22'
def update_event(self, inp=-1):
self.set_output_val(0, cgi.dolog(self.input(0)))
class Initlog_Node(NodeBase):
"""
Write a log message, if there is a log file.
Even though this function is called initlog(), you should always
use log(); log is a variable that is set either to initlog
(initially), to dolog (once the log file has been opened), or to
nolog (when logging is disabled).
The first argument is a format string; the remaining arguments (if
any) are arguments to the % operator, so e.g.
log("%s: %s", "a", "b")
will write "a: b" to the log file, followed by a newline.
If the global logfp is not None, it should be a file object to
which log data is written.
If the global logfp is None, the global logfile may be a string
giving a filename to open, in append mode. This file should be
world writable!!! If the file can't be opened, logging is
silently disabled (since there is no safe place where we could
send an error message).
"""
title = 'initlog'
type_ = 'cgi'
init_inputs = [
]
init_outputs = [
NodeOutputBP(type_='data'),
]
color = '#32DA22'
def update_event(self, inp=-1):
self.set_output_val(0, cgi.initlog())
class Log_Node(NodeBase):
"""
Write a log message, if there is a log file.
Even though this function is called initlog(), you should always
use log(); log is a variable that is set either to initlog
(initially), to dolog (once the log file has been opened), or to
nolog (when logging is disabled).
The first argument is a format string; the remaining arguments (if
any) are arguments to the % operator, so e.g.
log("%s: %s", "a", "b")
will write "a: b" to the log file, followed by a newline.
If the global logfp is not None, it should be a file object to
which log data is written.
If the global logfp is None, the global logfile may be a string
giving a filename to open, in append mode. This file should be
world writable!!! If the file can't be opened, logging is
silently disabled (since there is no safe place where we could
send an error message).
"""
title = 'log'
type_ = 'cgi'
init_inputs = [
]
init_outputs = [
NodeOutputBP(type_='data'),
]
color = '#32DA22'
def update_event(self, inp=-1):
self.set_output_val(0, cgi.log())
class Nolog_Node(NodeBase):
"""
Dummy function, assigned to log when logging is disabled."""
title = 'nolog'
type_ = 'cgi'
init_inputs = [
]
init_outputs = [
NodeOutputBP(type_='data'),
]
color = '#32DA22'
def update_event(self, inp=-1):
self.set_output_val(0, cgi.nolog())
class Parse_Node(NodeBase):
"""
Parse a query in the environment or from a file (default stdin)
Arguments, all optional:
fp : file pointer; default: sys.stdin.buffer
environ : environment dictionary; default: os.environ
keep_blank_values: flag indicating whether blank values in
percent-encoded forms should be treated as blank strings.
A true value indicates that blanks should be retained as
blank strings. The default false value indicates that
blank values are to be ignored and treated as if they were
not included.
strict_parsing: flag indicating what to do with parsing errors.
If false (the default), errors are silently ignored.
If true, errors raise a ValueError exception.
separator: str. The symbol to use for separating the query arguments.
Defaults to &.
"""
title = 'parse'
type_ = 'cgi'
init_inputs = [
NodeInputBP(label='fp', dtype=dtypes.Data(default=None, size='s')),
NodeInputBP(label='environ', dtype=dtypes.Data(default=environ({'ALLUSERSPROFILE': 'C:\\ProgramData', 'APPDATA': 'C:\\Users\\nutri\\AppData\\Roaming', 'COMMONPROGRAMFILES': 'C:\\Program Files\\Common Files', 'COMMONPROGRAMFILES(X86)': 'C:\\Program Files (x86)\\Common Files', 'COMMONPROGRAMW6432': 'C:\\Program Files\\Common Files', 'COMPUTERNAME': 'DESKTOP-0N3SJ6C', 'COMSPEC': 'C:\\WINDOWS\\system32\\cmd.exe', 'DRIVERDATA': 'C:\\Windows\\System32\\Drivers\\DriverData', 'HOMEDRIVE': 'C:', 'HOMEPATH': '\\Users\\nutri', 'IDEA_INITIAL_DIRECTORY': 'C:\\Users\\nutri\\AppData\\Local\\programs\\JetBrains\\PyCharm 2021.1.1\\bin', 'LOCALAPPDATA': 'C:\\Users\\nutri\\AppData\\Local', 'LOGONSERVER': '\\\\DESKTOP-0N3SJ6C', 'NUMBER_OF_PROCESSORS': '8', 'ONEDRIVE': 'C:\\Users\\nutri\\OneDrive - ETH Zurich', 'ONEDRIVECOMMERCIAL': 'C:\\Users\\nutri\\OneDrive - ETH Zurich', 'ONEDRIVECONSUMER': 'C:\\Users\\nutri\\OneDrive', 'ONLINESERVICES': 'Online Services', 'OS': 'Windows_NT', 'PATH': 'C:\\Users\\nutri\\projects\\ryven projects\\venv\\Scripts;C:\\Program Files\\Common Files\\Oracle\\Java\\javapath;C:\\WINDOWS\\system32;C:\\WINDOWS;C:\\WINDOWS\\System32\\Wbem;C:\\WINDOWS\\System32\\WindowsPowerShell\\v1.0\\;C:\\WINDOWS\\System32\\OpenSSH\\;C:\\Users\\nutri\\AppData\\Local\\Programs\\Git\\cmd;C:\\Program Files\\Wolfram Research\\WolframScript\\;C:\\Users\\nutri\\AppData\\Local\\programs\\nodejs\\;C:\\Users\\nutri\\AppData\\Local\\Programs\\Python\\Python39\\Scripts\\;C:\\Users\\nutri\\AppData\\Local\\Programs\\Python\\Python39\\;C:\\Users\\nutri\\AppData\\Local\\Programs\\Python\\Launcher\\;C:\\Users\\nutri\\AppData\\Local\\Microsoft\\WindowsApps;C:\\Users\\nutri\\AppData\\Local\\GitHubDesktop\\bin;C:\\Users\\nutri\\AppData\\Local\\Programs\\MiKTeX\\miktex\\bin\\x64\\;C:\\Users\\nutri\\AppData\\Roaming\\npm', 'PATHEXT': '.COM;.EXE;.BAT;.CMD;.VBS;.VBE;.JS;.JSE;.WSF;.WSH;.MSC', 'PLATFORMCODE': 'KV', 'PROCESSOR_ARCHITECTURE': 'AMD64', 'PROCESSOR_IDENTIFIER': 'Intel64 Family 6 Model 126 Stepping 5, GenuineIntel', 'PROCESSOR_LEVEL': '6', 'PROCESSOR_REVISION': '7e05', 'PROGRAMDATA': 'C:\\ProgramData', 'PROGRAMFILES': 'C:\\Program Files', 'PROGRAMFILES(X86)': 'C:\\Program Files (x86)', 'PROGRAMW6432': 'C:\\Program Files', 'PROMPT': '(venv) $P$G', 'PSMODULEPATH': 'C:\\Program Files\\WindowsPowerShell\\Modules;C:\\WINDOWS\\system32\\WindowsPowerShell\\v1.0\\Modules', 'PUBLIC': 'C:\\Users\\Public', 'PYCHARM_DISPLAY_PORT': '63342', 'PYCHARM_HOSTED': '1', 'PYTHONIOENCODING': 'UTF-8', 'PYTHONPATH': 'C:\\Users\\nutri\\projects\\ryven projects\\Ryven\\Ryven;C:\\Users\\nutri\\projects\\ryven projects\\Ryven\\Ryven\\src;C:\\Users\\nutri\\AppData\\Local\\programs\\JetBrains\\PyCharm 2021.1.1\\plugins\\python\\helpers\\pycharm_matplotlib_backend;C:\\Users\\nutri\\AppData\\Local\\programs\\JetBrains\\PyCharm 2021.1.1\\plugins\\python\\helpers\\pycharm_display', 'PYTHONUNBUFFERED': '1', 'REGIONCODE': 'EMEA', 'SESSIONNAME': 'Console', 'SYSTEMDRIVE': 'C:', 'SYSTEMROOT': 'C:\\WINDOWS', 'TEMP': 'C:\\Users\\nutri\\AppData\\Local\\Temp', 'TMP': 'C:\\Users\\nutri\\AppData\\Local\\Temp', 'USERDOMAIN': 'DESKTOP-0N3SJ6C', 'USERDOMAIN_ROAMINGPROFILE': 'DESKTOP-0N3SJ6C', 'USERNAME': 'nutri', 'USERPROFILE': 'C:\\Users\\nutri', 'VIRTUAL_ENV': 'C:\\Users\\nutri\\projects\\ryven projects\\venv', 'WINDIR': 'C:\\WINDOWS', 'ZES_ENABLE_SYSMAN': '1', '_OLD_VIRTUAL_PATH': 'C:\\Program Files\\Common Files\\Oracle\\Java\\javapath;C:\\WINDOWS\\system32;C:\\WINDOWS;C:\\WINDOWS\\System32\\Wbem;C:\\WINDOWS\\System32\\WindowsPowerShell\\v1.0\\;C:\\WINDOWS\\System32\\OpenSSH\\;C:\\Users\\nutri\\AppData\\Local\\Programs\\Git\\cmd;C:\\Program Files\\Wolfram Research\\WolframScript\\;C:\\Users\\nutri\\AppData\\Local\\programs\\nodejs\\;C:\\Users\\nutri\\AppData\\Local\\Programs\\Python\\Python39\\Scripts\\;C:\\Users\\nutri\\AppData\\Local\\Programs\\Python\\Python39\\;C:\\Users\\nutri\\AppData\\Local\\Programs\\Python\\Launcher\\;C:\\Users\\nutri\\AppData\\Local\\Microsoft\\WindowsApps;C:\\Users\\nutri\\AppData\\Local\\GitHubDesktop\\bin;C:\\Users\\nutri\\AppData\\Local\\Programs\\MiKTeX\\miktex\\bin\\x64\\;C:\\Users\\nutri\\AppData\\Roaming\\npm', '_OLD_VIRTUAL_PROMPT': '$P$G'}), size='s')),
NodeInputBP(label='keep_blank_values', dtype=dtypes.Data(default=0, size='s')),
NodeInputBP(label='strict_parsing', dtype=dtypes.Data(default=0, size='s')),
NodeInputBP(label='separator', dtype=dtypes.Data(default='&', size='s')),
]
init_outputs = [
NodeOutputBP(type_='data'),
]
color = '#32DA22'
def update_event(self, inp=-1):
self.set_output_val(0, cgi.parse(self.input(0), self.input(1), self.input(2), self.input(3), self.input(4)))
class Parse_Header_Node(NodeBase):
"""
Parse a Content-type like header.
Return the main content-type and a dictionary of options.
"""
title = 'parse_header'
type_ = 'cgi'
init_inputs = [
NodeInputBP(label='line'),
]
init_outputs = [
NodeOutputBP(type_='data'),
]
color = '#32DA22'
def update_event(self, inp=-1):
self.set_output_val(0, cgi.parse_header(self.input(0)))
class Parse_Multipart_Node(NodeBase):
"""
Parse multipart input.
Arguments:
fp : input file
pdict: dictionary containing other parameters of content-type header
encoding, errors: request encoding and error handler, passed to
FieldStorage
Returns a dictionary just like parse_qs(): keys are the field names, each
value is a list of values for that field. For non-file fields, the value
is a list of strings.
"""
title = 'parse_multipart'
type_ = 'cgi'
init_inputs = [
NodeInputBP(label='fp'),
NodeInputBP(label='pdict'),
NodeInputBP(label='encoding', dtype=dtypes.Data(default='utf-8', size='s')),
NodeInputBP(label='errors', dtype=dtypes.Data(default='replace', size='s')),
NodeInputBP(label='separator', dtype=dtypes.Data(default='&', size='s')),
]
init_outputs = [
NodeOutputBP(type_='data'),
]
color = '#32DA22'
def update_event(self, inp=-1):
self.set_output_val(0, cgi.parse_multipart(self.input(0), self.input(1), self.input(2), self.input(3), self.input(4)))
class Print_Arguments_Node(NodeBase):
"""
"""
title = 'print_arguments'
type_ = 'cgi'
init_inputs = [
]
init_outputs = [
NodeOutputBP(type_='data'),
]
color = '#32DA22'
def update_event(self, inp=-1):
self.set_output_val(0, cgi.print_arguments())
class Print_Directory_Node(NodeBase):
"""
Dump the current directory as HTML."""
title = 'print_directory'
type_ = 'cgi'
init_inputs = [
]
init_outputs = [
NodeOutputBP(type_='data'),
]
color = '#32DA22'
def update_event(self, inp=-1):
self.set_output_val(0, cgi.print_directory())
class Print_Environ_Node(NodeBase):
"""
Dump the shell environment as HTML."""
title = 'print_environ'
type_ = 'cgi'
init_inputs = [
NodeInputBP(label='environ', dtype=dtypes.Data(default=environ({'ALLUSERSPROFILE': 'C:\\ProgramData', 'APPDATA': 'C:\\Users\\nutri\\AppData\\Roaming', 'COMMONPROGRAMFILES': 'C:\\Program Files\\Common Files', 'COMMONPROGRAMFILES(X86)': 'C:\\Program Files (x86)\\Common Files', 'COMMONPROGRAMW6432': 'C:\\Program Files\\Common Files', 'COMPUTERNAME': 'DESKTOP-0N3SJ6C', 'COMSPEC': 'C:\\WINDOWS\\system32\\cmd.exe', 'DRIVERDATA': 'C:\\Windows\\System32\\Drivers\\DriverData', 'HOMEDRIVE': 'C:', 'HOMEPATH': '\\Users\\nutri', 'IDEA_INITIAL_DIRECTORY': 'C:\\Users\\nutri\\AppData\\Local\\programs\\JetBrains\\PyCharm 2021.1.1\\bin', 'LOCALAPPDATA': 'C:\\Users\\nutri\\AppData\\Local', 'LOGONSERVER': '\\\\DESKTOP-0N3SJ6C', 'NUMBER_OF_PROCESSORS': '8', 'ONEDRIVE': 'C:\\Users\\nutri\\OneDrive - ETH Zurich', 'ONEDRIVECOMMERCIAL': 'C:\\Users\\nutri\\OneDrive - ETH Zurich', 'ONEDRIVECONSUMER': 'C:\\Users\\nutri\\OneDrive', 'ONLINESERVICES': 'Online Services', 'OS': 'Windows_NT', 'PATH': 'C:\\Users\\nutri\\projects\\ryven projects\\venv\\Scripts;C:\\Program Files\\Common Files\\Oracle\\Java\\javapath;C:\\WINDOWS\\system32;C:\\WINDOWS;C:\\WINDOWS\\System32\\Wbem;C:\\WINDOWS\\System32\\WindowsPowerShell\\v1.0\\;C:\\WINDOWS\\System32\\OpenSSH\\;C:\\Users\\nutri\\AppData\\Local\\Programs\\Git\\cmd;C:\\Program Files\\Wolfram Research\\WolframScript\\;C:\\Users\\nutri\\AppData\\Local\\programs\\nodejs\\;C:\\Users\\nutri\\AppData\\Local\\Programs\\Python\\Python39\\Scripts\\;C:\\Users\\nutri\\AppData\\Local\\Programs\\Python\\Python39\\;C:\\Users\\nutri\\AppData\\Local\\Programs\\Python\\Launcher\\;C:\\Users\\nutri\\AppData\\Local\\Microsoft\\WindowsApps;C:\\Users\\nutri\\AppData\\Local\\GitHubDesktop\\bin;C:\\Users\\nutri\\AppData\\Local\\Programs\\MiKTeX\\miktex\\bin\\x64\\;C:\\Users\\nutri\\AppData\\Roaming\\npm', 'PATHEXT': '.COM;.EXE;.BAT;.CMD;.VBS;.VBE;.JS;.JSE;.WSF;.WSH;.MSC', 'PLATFORMCODE': 'KV', 'PROCESSOR_ARCHITECTURE': 'AMD64', 'PROCESSOR_IDENTIFIER': 'Intel64 Family 6 Model 126 Stepping 5, GenuineIntel', 'PROCESSOR_LEVEL': '6', 'PROCESSOR_REVISION': '7e05', 'PROGRAMDATA': 'C:\\ProgramData', 'PROGRAMFILES': 'C:\\Program Files', 'PROGRAMFILES(X86)': 'C:\\Program Files (x86)', 'PROGRAMW6432': 'C:\\Program Files', 'PROMPT': '(venv) $P$G', 'PSMODULEPATH': 'C:\\Program Files\\WindowsPowerShell\\Modules;C:\\WINDOWS\\system32\\WindowsPowerShell\\v1.0\\Modules', 'PUBLIC': 'C:\\Users\\Public', 'PYCHARM_DISPLAY_PORT': '63342', 'PYCHARM_HOSTED': '1', 'PYTHONIOENCODING': 'UTF-8', 'PYTHONPATH': 'C:\\Users\\nutri\\projects\\ryven projects\\Ryven\\Ryven;C:\\Users\\nutri\\projects\\ryven projects\\Ryven\\Ryven\\src;C:\\Users\\nutri\\AppData\\Local\\programs\\JetBrains\\PyCharm 2021.1.1\\plugins\\python\\helpers\\pycharm_matplotlib_backend;C:\\Users\\nutri\\AppData\\Local\\programs\\JetBrains\\PyCharm 2021.1.1\\plugins\\python\\helpers\\pycharm_display', 'PYTHONUNBUFFERED': '1', 'REGIONCODE': 'EMEA', 'SESSIONNAME': 'Console', 'SYSTEMDRIVE': 'C:', 'SYSTEMROOT': 'C:\\WINDOWS', 'TEMP': 'C:\\Users\\nutri\\AppData\\Local\\Temp', 'TMP': 'C:\\Users\\nutri\\AppData\\Local\\Temp', 'USERDOMAIN': 'DESKTOP-0N3SJ6C', 'USERDOMAIN_ROAMINGPROFILE': 'DESKTOP-0N3SJ6C', 'USERNAME': 'nutri', 'USERPROFILE': 'C:\\Users\\nutri', 'VIRTUAL_ENV': 'C:\\Users\\nutri\\projects\\ryven projects\\venv', 'WINDIR': 'C:\\WINDOWS', 'ZES_ENABLE_SYSMAN': '1', '_OLD_VIRTUAL_PATH': 'C:\\Program Files\\Common Files\\Oracle\\Java\\javapath;C:\\WINDOWS\\system32;C:\\WINDOWS;C:\\WINDOWS\\System32\\Wbem;C:\\WINDOWS\\System32\\WindowsPowerShell\\v1.0\\;C:\\WINDOWS\\System32\\OpenSSH\\;C:\\Users\\nutri\\AppData\\Local\\Programs\\Git\\cmd;C:\\Program Files\\Wolfram Research\\WolframScript\\;C:\\Users\\nutri\\AppData\\Local\\programs\\nodejs\\;C:\\Users\\nutri\\AppData\\Local\\Programs\\Python\\Python39\\Scripts\\;C:\\Users\\nutri\\AppData\\Local\\Programs\\Python\\Python39\\;C:\\Users\\nutri\\AppData\\Local\\Programs\\Python\\Launcher\\;C:\\Users\\nutri\\AppData\\Local\\Microsoft\\WindowsApps;C:\\Users\\nutri\\AppData\\Local\\GitHubDesktop\\bin;C:\\Users\\nutri\\AppData\\Local\\Programs\\MiKTeX\\miktex\\bin\\x64\\;C:\\Users\\nutri\\AppData\\Roaming\\npm', '_OLD_VIRTUAL_PROMPT': '$P$G'}), size='s')),
]
init_outputs = [
NodeOutputBP(type_='data'),
]
color = '#32DA22'
def update_event(self, inp=-1):
self.set_output_val(0, cgi.print_environ(self.input(0)))
class Print_Environ_Usage_Node(NodeBase):
"""
Dump a list of environment variables used by CGI as HTML."""
title = 'print_environ_usage'
type_ = 'cgi'
init_inputs = [
]
init_outputs = [
NodeOutputBP(type_='data'),
]
color = '#32DA22'
def update_event(self, inp=-1):
self.set_output_val(0, cgi.print_environ_usage())
class Print_Exception_Node(NodeBase):
"""
"""
title = 'print_exception'
type_ = 'cgi'
init_inputs = [
NodeInputBP(label='type', dtype=dtypes.Data(default=None, size='s')),
NodeInputBP(label='value', dtype=dtypes.Data(default=None, size='s')),
NodeInputBP(label='tb', dtype=dtypes.Data(default=None, size='s')),
NodeInputBP(label='limit', dtype=dtypes.Data(default=None, size='s')),
]
init_outputs = [
NodeOutputBP(type_='data'),
]
color = '#32DA22'
def update_event(self, inp=-1):
self.set_output_val(0, cgi.print_exception(self.input(0), self.input(1), self.input(2), self.input(3)))
class Print_Form_Node(NodeBase):
"""
Dump the contents of a form as HTML."""
title = 'print_form'
type_ = 'cgi'
init_inputs = [
NodeInputBP(label='form'),
]
init_outputs = [
NodeOutputBP(type_='data'),
]
color = '#32DA22'
def update_event(self, inp=-1):
self.set_output_val(0, cgi.print_form(self.input(0)))
class Test_Node(NodeBase):
"""
Robust test CGI script, usable as main program.
Write minimal HTTP headers and dump all information provided to
the script in HTML form.
"""
title = 'test'
type_ = 'cgi'
init_inputs = [
NodeInputBP(label='environ', dtype=dtypes.Data(default=environ({'ALLUSERSPROFILE': 'C:\\ProgramData', 'APPDATA': 'C:\\Users\\nutri\\AppData\\Roaming', 'COMMONPROGRAMFILES': 'C:\\Program Files\\Common Files', 'COMMONPROGRAMFILES(X86)': 'C:\\Program Files (x86)\\Common Files', 'COMMONPROGRAMW6432': 'C:\\Program Files\\Common Files', 'COMPUTERNAME': 'DESKTOP-0N3SJ6C', 'COMSPEC': 'C:\\WINDOWS\\system32\\cmd.exe', 'DRIVERDATA': 'C:\\Windows\\System32\\Drivers\\DriverData', 'HOMEDRIVE': 'C:', 'HOMEPATH': '\\Users\\nutri', 'IDEA_INITIAL_DIRECTORY': 'C:\\Users\\nutri\\AppData\\Local\\programs\\JetBrains\\PyCharm 2021.1.1\\bin', 'LOCALAPPDATA': 'C:\\Users\\nutri\\AppData\\Local', 'LOGONSERVER': '\\\\DESKTOP-0N3SJ6C', 'NUMBER_OF_PROCESSORS': '8', 'ONEDRIVE': 'C:\\Users\\nutri\\OneDrive - ETH Zurich', 'ONEDRIVECOMMERCIAL': 'C:\\Users\\nutri\\OneDrive - ETH Zurich', 'ONEDRIVECONSUMER': 'C:\\Users\\nutri\\OneDrive', 'ONLINESERVICES': 'Online Services', 'OS': 'Windows_NT', 'PATH': 'C:\\Users\\nutri\\projects\\ryven projects\\venv\\Scripts;C:\\Program Files\\Common Files\\Oracle\\Java\\javapath;C:\\WINDOWS\\system32;C:\\WINDOWS;C:\\WINDOWS\\System32\\Wbem;C:\\WINDOWS\\System32\\WindowsPowerShell\\v1.0\\;C:\\WINDOWS\\System32\\OpenSSH\\;C:\\Users\\nutri\\AppData\\Local\\Programs\\Git\\cmd;C:\\Program Files\\Wolfram Research\\WolframScript\\;C:\\Users\\nutri\\AppData\\Local\\programs\\nodejs\\;C:\\Users\\nutri\\AppData\\Local\\Programs\\Python\\Python39\\Scripts\\;C:\\Users\\nutri\\AppData\\Local\\Programs\\Python\\Python39\\;C:\\Users\\nutri\\AppData\\Local\\Programs\\Python\\Launcher\\;C:\\Users\\nutri\\AppData\\Local\\Microsoft\\WindowsApps;C:\\Users\\nutri\\AppData\\Local\\GitHubDesktop\\bin;C:\\Users\\nutri\\AppData\\Local\\Programs\\MiKTeX\\miktex\\bin\\x64\\;C:\\Users\\nutri\\AppData\\Roaming\\npm', 'PATHEXT': '.COM;.EXE;.BAT;.CMD;.VBS;.VBE;.JS;.JSE;.WSF;.WSH;.MSC', 'PLATFORMCODE': 'KV', 'PROCESSOR_ARCHITECTURE': 'AMD64', 'PROCESSOR_IDENTIFIER': 'Intel64 Family 6 Model 126 Stepping 5, GenuineIntel', 'PROCESSOR_LEVEL': '6', 'PROCESSOR_REVISION': '7e05', 'PROGRAMDATA': 'C:\\ProgramData', 'PROGRAMFILES': 'C:\\Program Files', 'PROGRAMFILES(X86)': 'C:\\Program Files (x86)', 'PROGRAMW6432': 'C:\\Program Files', 'PROMPT': '(venv) $P$G', 'PSMODULEPATH': 'C:\\Program Files\\WindowsPowerShell\\Modules;C:\\WINDOWS\\system32\\WindowsPowerShell\\v1.0\\Modules', 'PUBLIC': 'C:\\Users\\Public', 'PYCHARM_DISPLAY_PORT': '63342', 'PYCHARM_HOSTED': '1', 'PYTHONIOENCODING': 'UTF-8', 'PYTHONPATH': 'C:\\Users\\nutri\\projects\\ryven projects\\Ryven\\Ryven;C:\\Users\\nutri\\projects\\ryven projects\\Ryven\\Ryven\\src;C:\\Users\\nutri\\AppData\\Local\\programs\\JetBrains\\PyCharm 2021.1.1\\plugins\\python\\helpers\\pycharm_matplotlib_backend;C:\\Users\\nutri\\AppData\\Local\\programs\\JetBrains\\PyCharm 2021.1.1\\plugins\\python\\helpers\\pycharm_display', 'PYTHONUNBUFFERED': '1', 'REGIONCODE': 'EMEA', 'SESSIONNAME': 'Console', 'SYSTEMDRIVE': 'C:', 'SYSTEMROOT': 'C:\\WINDOWS', 'TEMP': 'C:\\Users\\nutri\\AppData\\Local\\Temp', 'TMP': 'C:\\Users\\nutri\\AppData\\Local\\Temp', 'USERDOMAIN': 'DESKTOP-0N3SJ6C', 'USERDOMAIN_ROAMINGPROFILE': 'DESKTOP-0N3SJ6C', 'USERNAME': 'nutri', 'USERPROFILE': 'C:\\Users\\nutri', 'VIRTUAL_ENV': 'C:\\Users\\nutri\\projects\\ryven projects\\venv', 'WINDIR': 'C:\\WINDOWS', 'ZES_ENABLE_SYSMAN': '1', '_OLD_VIRTUAL_PATH': 'C:\\Program Files\\Common Files\\Oracle\\Java\\javapath;C:\\WINDOWS\\system32;C:\\WINDOWS;C:\\WINDOWS\\System32\\Wbem;C:\\WINDOWS\\System32\\WindowsPowerShell\\v1.0\\;C:\\WINDOWS\\System32\\OpenSSH\\;C:\\Users\\nutri\\AppData\\Local\\Programs\\Git\\cmd;C:\\Program Files\\Wolfram Research\\WolframScript\\;C:\\Users\\nutri\\AppData\\Local\\programs\\nodejs\\;C:\\Users\\nutri\\AppData\\Local\\Programs\\Python\\Python39\\Scripts\\;C:\\Users\\nutri\\AppData\\Local\\Programs\\Python\\Python39\\;C:\\Users\\nutri\\AppData\\Local\\Programs\\Python\\Launcher\\;C:\\Users\\nutri\\AppData\\Local\\Microsoft\\WindowsApps;C:\\Users\\nutri\\AppData\\Local\\GitHubDesktop\\bin;C:\\Users\\nutri\\AppData\\Local\\Programs\\MiKTeX\\miktex\\bin\\x64\\;C:\\Users\\nutri\\AppData\\Roaming\\npm', '_OLD_VIRTUAL_PROMPT': '$P$G'}), size='s')),
]
init_outputs = [
NodeOutputBP(type_='data'),
]
color = '#32DA22'
def update_event(self, inp=-1):
self.set_output_val(0, cgi.test(self.input(0)))
class Valid_Boundary_Node(NodeBase):
"""
"""
title = 'valid_boundary'
type_ = 'cgi'
init_inputs = [
NodeInputBP(label='s'),
]
init_outputs = [
NodeOutputBP(type_='data'),
]
color = '#32DA22'
def update_event(self, inp=-1):
self.set_output_val(0, cgi.valid_boundary(self.input(0)))
export_nodes(
_Parseparam_Node,
Closelog_Node,
Dolog_Node,
Initlog_Node,
Log_Node,
Nolog_Node,
Parse_Node,
Parse_Header_Node,
Parse_Multipart_Node,
Print_Arguments_Node,
Print_Directory_Node,
Print_Environ_Node,
Print_Environ_Usage_Node,
Print_Exception_Node,
Print_Form_Node,
Test_Node,
Valid_Boundary_Node,
)
| 54.329466 | 4,205 | 0.659293 | 2,936 | 23,416 | 5.162807 | 0.127384 | 0.040375 | 0.071843 | 0.089062 | 0.833355 | 0.828473 | 0.817918 | 0.811585 | 0.811585 | 0.795817 | 0 | 0.022723 | 0.148616 | 23,416 | 430 | 4,206 | 54.455814 | 0.73761 | 0.147036 | 0 | 0.471861 | 0 | 0.077922 | 0.595919 | 0.385721 | 0 | 0 | 0 | 0 | 0 | 1 | 0.073593 | false | 0.004329 | 0.008658 | 0 | 0.528139 | 0.051948 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 8 |
20338a83b796616bc6ac3bc0e8adac74510da35d | 4,501 | py | Python | settings.py | kunjmehta/alien-shooter | efdcca8d3e9b4712803bc00f8236c86462deadd2 | [
"MIT"
] | null | null | null | settings.py | kunjmehta/alien-shooter | efdcca8d3e9b4712803bc00f8236c86462deadd2 | [
"MIT"
] | null | null | null | settings.py | kunjmehta/alien-shooter | efdcca8d3e9b4712803bc00f8236c86462deadd2 | [
"MIT"
] | 2 | 2019-03-25T19:03:31.000Z | 2020-11-18T04:35:03.000Z | class Settings:
def __init__(self):
"""Initialize the game's screen settings."""
# Screen settings.
self.screen_width = 1366
self.screen_height = 768
self.bg_color = (230, 230, 230)
class ArcadeSettings(Settings):
"""A class to store all the settings of the arcade game"""
def __init__(self):
"""Initialize the timed game's static settings."""
super().__init__()
# Ship settings.
self.ship_limit = 3
# Bullet settings.
self.bullet_width = 3
self.bullet_height = 15
self.bullet_color = 60, 60, 60
self.bullets_allowed = 3
# Alien settings.
self.fleet_drop_speed = 15
# How quickly the game speeds up.
self.speedup_scale = 1.1
# How quickly the alien point values increase.
self.score_scale = 1.5
self.initialize_dynamic_settings()
def initialize_dynamic_settings(self):
"""Initialize settings that change throughout the timed game."""
self.ship_speed_factor = 1.5
self.bullet_speed_factor = 2
self.alien_speed_factor = 0.75
# Scoring.
self.alien_points = 50
# fleet_direction of 1 represents right, -1 represents left.
self.fleet_direction = 1
def increase_speed(self):
"""Increase speed settings and alien point values."""
# Factor * scale
self.ship_speed_factor *= self.speedup_scale
self.bullet_speed_factor *= self.speedup_scale
self.alien_speed_factor *= self.speedup_scale
self.alien_points = int(self.alien_points * self.score_scale)
class TimedSettings(Settings):
"""A class to store all the settings of the timed game"""
def __init__(self):
"""Initialize the timed game's static settings."""
super().__init__()
# Ship settings.
self.ship_limit = 3
# Bullet settings.
self.bullet_width = 3
self.bullet_height = 15
self.bullet_color = 60, 60, 60
self.bullets_allowed = 3
# Alien settings.
self.fleet_drop_speed = 10
# How quickly the game speeds up.
self.speedup_scale = 1.1
# How quickly the alien point values increase.
self.score_scale = 1.5
self.initialize_dynamic_settings()
def initialize_dynamic_settings(self):
"""Initialize settings that change throughout the timed game."""
self.ship_speed_factor = 2
self.bullet_speed_factor = 7
self.alien_speed_factor = 1.5
# Scoring.
self.alien_points = 50
# fleet_direction of 1 represents right, -1 represents left.
self.fleet_direction = 1
def increase_speed(self):
"""Increase speed settings and alien point values."""
# Factor * scale
self.ship_speed_factor *= self.speedup_scale
self.bullet_speed_factor *= self.speedup_scale
self.alien_speed_factor *= self.speedup_scale
self.alien_points = int(self.alien_points * self.score_scale)
class SurvivalSettings(Settings):
"""A class to store all the settings of the survival game"""
def __init__(self):
"""Initialize the timed game's static settings."""
super().__init__()
# Ship settings.
self.ship_limit = 1
# Bullet settings.
self.bullet_width = 3
self.bullet_height = 15
self.bullet_color = 60, 60, 60
self.bullets_allowed = 3
# Alien settings.
self.fleet_drop_speed = 15
# How quickly the game speeds up.
self.speedup_scale = 1.1
# How quickly the alien point values increase.
self.score_scale = 1.5
self.initialize_dynamic_settings()
def initialize_dynamic_settings(self):
"""Initialize settings that change throughout the timed game."""
self.ship_speed_factor = 2
self.bullet_speed_factor = 3
self.alien_speed_factor = 1
# Scoring.
self.alien_points = 50
# fleet_direction of 1 represents right, -1 represents left.
self.fleet_direction = 1
def increase_speed(self):
"""Increase speed settings and alien point values."""
# Factor * scale
self.ship_speed_factor *= self.speedup_scale
self.bullet_speed_factor *= self.speedup_scale
self.alien_speed_factor *= self.speedup_scale
self.alien_points = int(self.alien_points * self.score_scale)
| 29.611842 | 72 | 0.632526 | 558 | 4,501 | 4.858423 | 0.130824 | 0.073036 | 0.070823 | 0.073036 | 0.917374 | 0.893028 | 0.893028 | 0.893028 | 0.893028 | 0.893028 | 0 | 0.029512 | 0.284826 | 4,501 | 151 | 73 | 29.807947 | 0.812675 | 0.287936 | 0 | 0.783784 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.135135 | false | 0 | 0 | 0 | 0.189189 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
203a98857b3afdb55fd9cee8548fb1c0ca4f8243 | 18,350 | py | Python | quay/api/user_api.py | angeiv/python-quay | 16072f87956d8f581ac9ebccc67f6563e977cf52 | [
"MIT"
] | null | null | null | quay/api/user_api.py | angeiv/python-quay | 16072f87956d8f581ac9ebccc67f6563e977cf52 | [
"MIT"
] | null | null | null | quay/api/user_api.py | angeiv/python-quay | 16072f87956d8f581ac9ebccc67f6563e977cf52 | [
"MIT"
] | null | null | null | # coding: utf-8
"""
Quay Frontend
This API allows you to perform many of the operations required to work with Quay repositories, users, and organizations. You can find out more at <a href=\"https://quay.io\">Quay</a>. # noqa: E501
OpenAPI spec version: v1
Contact: support@quay.io
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
import re # noqa: F401
# python 2 and python 3 compatibility library
import six
from quay.api_client import ApiClient
class UserApi(object):
"""NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
Ref: https://github.com/swagger-api/swagger-codegen
"""
def __init__(self, api_client=None):
if api_client is None:
api_client = ApiClient()
self.api_client = api_client
def create_star(self, body, **kwargs): # noqa: E501
"""create_star # noqa: E501
Star a repository. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.create_star(body, async_req=True)
>>> result = thread.get()
:param async_req bool
:param NewStarredRepository body: Request body contents. (required)
:return: None
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.create_star_with_http_info(body, **kwargs) # noqa: E501
else:
(data) = self.create_star_with_http_info(body, **kwargs) # noqa: E501
return data
def create_star_with_http_info(self, body, **kwargs): # noqa: E501
"""create_star # noqa: E501
Star a repository. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.create_star_with_http_info(body, async_req=True)
>>> result = thread.get()
:param async_req bool
:param NewStarredRepository body: Request body contents. (required)
:return: None
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['body'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method create_star" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'body' is set
if ('body' not in params or
params['body'] is None):
raise ValueError("Missing the required parameter `body` when calling `create_star`") # noqa: E501
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'body' in params:
body_params = params['body']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['*/*']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['*/*']) # noqa: E501
# Authentication setting
auth_settings = ['oauth2_implicit'] # noqa: E501
return self.api_client.call_api(
'/api/v1/user/starred', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type=None, # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def delete_star(self, repository, **kwargs): # noqa: E501
"""delete_star # noqa: E501
Removes a star from a repository. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_star(repository, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str repository: The full path of the repository. e.g. namespace/name (required)
:return: None
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.delete_star_with_http_info(repository, **kwargs) # noqa: E501
else:
(data) = self.delete_star_with_http_info(repository, **kwargs) # noqa: E501
return data
def delete_star_with_http_info(self, repository, **kwargs): # noqa: E501
"""delete_star # noqa: E501
Removes a star from a repository. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_star_with_http_info(repository, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str repository: The full path of the repository. e.g. namespace/name (required)
:return: None
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['repository'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method delete_star" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'repository' is set
if ('repository' not in params or
params['repository'] is None):
raise ValueError("Missing the required parameter `repository` when calling `delete_star`") # noqa: E501
collection_formats = {}
path_params = {}
if 'repository' in params:
path_params['repository'] = params['repository'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['*/*']) # noqa: E501
# Authentication setting
auth_settings = ['oauth2_implicit'] # noqa: E501
return self.api_client.call_api(
'/api/v1/user/starred/{repository}', 'DELETE',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type=None, # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def get_logged_in_user(self, **kwargs): # noqa: E501
"""get_logged_in_user # noqa: E501
Get user information for the authenticated user. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_logged_in_user(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: UserView
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_logged_in_user_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.get_logged_in_user_with_http_info(**kwargs) # noqa: E501
return data
def get_logged_in_user_with_http_info(self, **kwargs): # noqa: E501
"""get_logged_in_user # noqa: E501
Get user information for the authenticated user. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_logged_in_user_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: UserView
If the method is called asynchronously,
returns the request thread.
"""
all_params = [] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_logged_in_user" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['*/*']) # noqa: E501
# Authentication setting
auth_settings = ['oauth2_implicit'] # noqa: E501
return self.api_client.call_api(
'/api/v1/user/', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='UserView', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def get_user_information(self, username, **kwargs): # noqa: E501
"""get_user_information # noqa: E501
Get user information for the specified user. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_user_information(username, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str username: (required)
:return: None
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_user_information_with_http_info(username, **kwargs) # noqa: E501
else:
(data) = self.get_user_information_with_http_info(username, **kwargs) # noqa: E501
return data
def get_user_information_with_http_info(self, username, **kwargs): # noqa: E501
"""get_user_information # noqa: E501
Get user information for the specified user. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_user_information_with_http_info(username, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str username: (required)
:return: None
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['username'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_user_information" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'username' is set
if ('username' not in params or
params['username'] is None):
raise ValueError("Missing the required parameter `username` when calling `get_user_information`") # noqa: E501
collection_formats = {}
path_params = {}
if 'username' in params:
path_params['username'] = params['username'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['*/*']) # noqa: E501
# Authentication setting
auth_settings = [] # noqa: E501
return self.api_client.call_api(
'/api/v1/users/{username}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type=None, # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def list_starred_repos(self, **kwargs): # noqa: E501
"""list_starred_repos # noqa: E501
List all starred repositories. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.list_starred_repos(async_req=True)
>>> result = thread.get()
:param async_req bool
:param str next_page: The page token for the next page
:return: None
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.list_starred_repos_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.list_starred_repos_with_http_info(**kwargs) # noqa: E501
return data
def list_starred_repos_with_http_info(self, **kwargs): # noqa: E501
"""list_starred_repos # noqa: E501
List all starred repositories. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.list_starred_repos_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:param str next_page: The page token for the next page
:return: None
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['next_page'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method list_starred_repos" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
if 'next_page' in params:
query_params.append(('next_page', params['next_page'])) # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['*/*']) # noqa: E501
# Authentication setting
auth_settings = ['oauth2_implicit'] # noqa: E501
return self.api_client.call_api(
'/api/v1/user/starred', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type=None, # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
| 36.626747 | 201 | 0.60049 | 2,102 | 18,350 | 4.985728 | 0.08706 | 0.052672 | 0.026718 | 0.034351 | 0.894847 | 0.877099 | 0.866698 | 0.843702 | 0.829962 | 0.825954 | 0 | 0.017578 | 0.308665 | 18,350 | 500 | 202 | 36.7 | 0.808529 | 0.333134 | 0 | 0.737643 | 0 | 0 | 0.161146 | 0.036515 | 0 | 0 | 0 | 0 | 0 | 1 | 0.041825 | false | 0 | 0.015209 | 0 | 0.117871 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
2056f25ee2be259d1c1f100be2532d74022e9155 | 3,205 | py | Python | 25/00/textwrap.shorten.py | pylangstudy/201708 | 126b1af96a1d1f57522d5a1d435b58597bea2e57 | [
"CC0-1.0"
] | null | null | null | 25/00/textwrap.shorten.py | pylangstudy/201708 | 126b1af96a1d1f57522d5a1d435b58597bea2e57 | [
"CC0-1.0"
] | 39 | 2017-07-31T22:54:01.000Z | 2017-08-31T00:19:03.000Z | 25/00/textwrap.shorten.py | pylangstudy/201708 | 126b1af96a1d1f57522d5a1d435b58597bea2e57 | [
"CC0-1.0"
] | null | null | null | import textwrap
text = '1234567890'
print(textwrap.shorten(text, width=10))
print(textwrap.shorten(text, width=9))
text = '1234567890 1234567890'
print(textwrap.shorten(text, width=999, expand_tabs=True))
text = '1234567890 1234567890'
print(textwrap.shorten(text, width=999, expand_tabs=True, tabsize=4))
print(textwrap.shorten(text, width=999, expand_tabs=True, replace_whitespace=True))
print(textwrap.shorten(text, width=999, expand_tabs=True, replace_whitespace=False))
print(textwrap.shorten(text, width=999, expand_tabs=False, replace_whitespace=True))#1Tab→1Space
print(textwrap.shorten(text, width=999, expand_tabs=False, replace_whitespace=False))#1Tab→\t
#Space&Tab
text = ' 1234567890 1234567890 '
print(textwrap.shorten(text, width=999, ))
print(textwrap.shorten(text, width=999, drop_whitespace=True))
print(textwrap.shorten(text, width=999, drop_whitespace=False))
#Space
text = ' 1234567890 1234567890 '
print(textwrap.shorten(text, width=999))
print(textwrap.shorten(text, width=999, drop_whitespace=True))
print(textwrap.shorten(text, width=999, drop_whitespace=False))
#Tab
text = ' 1234567890 1234567890 '
text = ' 1234567890 1234567890 '
print(textwrap.shorten(text, width=999))
print(textwrap.shorten(text, width=999, drop_whitespace=True))
print(textwrap.shorten(text, width=999, drop_whitespace=False))
text = '12345678901234567890123456789012345678901234567890123456789012345678901234567890'
print(textwrap.shorten(text, width=40, initial_indent=' '), '\n')
print(textwrap.shorten(text, width=40, subsequent_indent=' '), '\n')
#fix_sentence_endingsは`([a-z]+\. )`の文字列を半角スペース2つ間を空けて区切る。じつに理解しがたい謎API。
text = 'abc. def! ghi? jkl. mn. opqrstu. vwxyz.'
print(textwrap.shorten(text, width=999, fix_sentence_endings=True))
text = '1234567890. 1234567890. 1234567890. 1234567890. 1234567890. 1234567890. 1234567890. 1234567890.'
print(textwrap.shorten(text, width=999, fix_sentence_endings=True))
text = 'ABC. DEF! GHI? JKL. MN. OPQRSTU. VWXYZ.'
print(textwrap.shorten(text, width=999, fix_sentence_endings=True))
text = '12345678901234567890123456789012345678901234567890123456789012345678901234567890'
print(textwrap.shorten(text, width=999, break_long_words=True))
print(textwrap.shorten(text, width=999, break_long_words=False))
#何も変わらない… https://github.com/pallets/jinja/issues/550
text = 'abc-def'
print(textwrap.shorten(text, width=999, break_on_hyphens=True, break_long_words=True))
print(textwrap.shorten(text, width=999, break_on_hyphens=False, break_long_words=True))
text = 'ABC-DEF'
print(textwrap.shorten(text, width=999, break_on_hyphens=True, break_long_words=True))
print(textwrap.shorten(text, width=999, break_on_hyphens=False, break_long_words=True))
text = '1234567890-1234567890'
print(textwrap.shorten(text, width=999, break_on_hyphens=True, break_long_words=True))
print(textwrap.shorten(text, width=999, break_on_hyphens=False, break_long_words=True))
#TypeError: type object got multiple values for keyword argument 'max_lines'
#text = '12345678901234567890123456789012345678901234567890123456789012345678901234567890'
#print(textwrap.shorten(text, width=10, max_lines=3))
#print(textwrap.shorten(text, width=10, max_lines=3, placeholder='続く…'))
| 50.078125 | 104 | 0.795944 | 424 | 3,205 | 5.900943 | 0.174528 | 0.166267 | 0.255795 | 0.306954 | 0.8753 | 0.863709 | 0.834133 | 0.69944 | 0.689848 | 0.641487 | 0 | 0.190876 | 0.069891 | 3,205 | 63 | 105 | 50.873016 | 0.645756 | 0.138534 | 0 | 0.543478 | 0 | 0 | 0.191273 | 0.065818 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.021739 | 0 | 0.021739 | 0.652174 | 0 | 0 | 0 | null | 0 | 1 | 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 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 7 |
644ab0a8da2d5db24e3adcb449afc8cc24a8a0c9 | 1,042 | py | Python | experiments/depth/TBLogger.py | qabach/coen-342-project | 70713ccbda4d53b18d29405b454c06a9dd6946cb | [
"BSD-3-Clause"
] | 59 | 2020-07-28T03:18:31.000Z | 2022-02-08T22:43:30.000Z | experiments/depth/TBLogger.py | qabach/coen-342-project | 70713ccbda4d53b18d29405b454c06a9dd6946cb | [
"BSD-3-Clause"
] | 9 | 2020-07-29T09:14:43.000Z | 2021-09-12T01:51:31.000Z | experiments/depth/TBLogger.py | qabach/coen-342-project | 70713ccbda4d53b18d29405b454c06a9dd6946cb | [
"BSD-3-Clause"
] | 6 | 2020-07-29T21:29:37.000Z | 2021-09-11T10:56:22.000Z | '''
TensorBoard logger.
https://pytorch.org/docs/stable/tensorboard.html
'''
import config
from torch.utils.tensorboard import SummaryWriter
class TBLogger(object):
def __init__(self, folder, flush_secs=60):
self.writer = SummaryWriter(log_dir = folder, flush_secs=flush_secs)
def add_value(self, name, value, step):
self.writer.add_scalar(tag = name, scalar_value = value, global_step=step)
def add_image(self, name, value, step, dataformats):
self.writer.add_image(tag = name, img_tensor = value, global_step=step, dataformats=dataformats)
class TBLoggerX(object):
def __init__(self, folder, flush_secs=60):
self.writer = SummaryWriter(log_dir = folder, flush_secs=flush_secs)
def add_value(self, name, value, step):
self.writer.add_scalar(tag = name, scalar_value = value, global_step=step)
def add_image(self, name, value, step, dataformats):
self.writer.add_image(tag = name, img_tensor = value, global_step=step, dataformats=dataformats)
| 38.592593 | 104 | 0.712092 | 139 | 1,042 | 5.107914 | 0.280576 | 0.076056 | 0.084507 | 0.095775 | 0.802817 | 0.802817 | 0.802817 | 0.802817 | 0.802817 | 0.802817 | 0 | 0.004667 | 0.177543 | 1,042 | 26 | 105 | 40.076923 | 0.823804 | 0.069098 | 0 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.375 | false | 0 | 0.125 | 0 | 0.625 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 9 |
6468fd8cb1e25330708b768738ea3305d0030c0b | 178 | py | Python | quax/__init__.py | ferchault/Quax | 53950d03b6b50a3e092f18aed7a607a6318fc6d7 | [
"BSD-3-Clause"
] | null | null | null | quax/__init__.py | ferchault/Quax | 53950d03b6b50a3e092f18aed7a607a6318fc6d7 | [
"BSD-3-Clause"
] | null | null | null | quax/__init__.py | ferchault/Quax | 53950d03b6b50a3e092f18aed7a607a6318fc6d7 | [
"BSD-3-Clause"
] | null | null | null | from . import integrals
from . import constants
if constants.libint_imported:
from . import external_integrals
from . import methods
from . import core
from . import utils
| 17.8 | 36 | 0.775281 | 23 | 178 | 5.913043 | 0.478261 | 0.441176 | 0.279412 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.179775 | 178 | 9 | 37 | 19.777778 | 0.931507 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
64b096bfc0bc9aafa43a4904e02694c4d370a8c5 | 120 | py | Python | python/testData/inspections/unusedImport/usedLastImport/test1.py | tgodzik/intellij-community | f5ef4191fc30b69db945633951fb160c1cfb7b6f | [
"Apache-2.0"
] | 2 | 2019-04-28T07:48:50.000Z | 2020-12-11T14:18:08.000Z | python/testData/inspections/unusedImport/usedLastImport/test1.py | Cyril-lamirand/intellij-community | 60ab6c61b82fc761dd68363eca7d9d69663cfa39 | [
"Apache-2.0"
] | 2 | 2022-02-19T09:45:05.000Z | 2022-02-27T20:32:55.000Z | python/testData/inspections/unusedImport/usedLastImport/test1.py | Cyril-lamirand/intellij-community | 60ab6c61b82fc761dd68363eca7d9d69663cfa39 | [
"Apache-2.0"
] | 2 | 2020-03-15T08:57:37.000Z | 2020-04-07T04:48:14.000Z | <warning descr="Unused import statement 'from a import foo'">from a import foo</warning>
from b import foo
print(foo)
| 20 | 88 | 0.75 | 20 | 120 | 4.5 | 0.5 | 0.3 | 0.244444 | 0.311111 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.15 | 120 | 5 | 89 | 24 | 0.882353 | 0 | 0 | 0 | 0 | 0 | 0.358333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0.666667 | null | null | 0.333333 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 7 |
3761de0441e195feafcbd962c6dae48a8cf92ae4 | 192 | py | Python | DSToolkit/sorting/__init__.py | AndreaFerrante/DSToolkit | 6f527cb4c19127cecd74bb682330236aa4e41839 | [
"MIT"
] | null | null | null | DSToolkit/sorting/__init__.py | AndreaFerrante/DSToolkit | 6f527cb4c19127cecd74bb682330236aa4e41839 | [
"MIT"
] | null | null | null | DSToolkit/sorting/__init__.py | AndreaFerrante/DSToolkit | 6f527cb4c19127cecd74bb682330236aa4e41839 | [
"MIT"
] | null | null | null | from .bubble_sort import *
from .counting_sort import *
from .heap_sort import *
from .insertion_sort import *
from .merge_sort import *
from .quick_sort import *
from .selection_sort import * | 27.428571 | 29 | 0.786458 | 28 | 192 | 5.142857 | 0.357143 | 0.486111 | 0.583333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.140625 | 192 | 7 | 30 | 27.428571 | 0.872727 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
378653c779be88c9ceba787151f4b0a9baf151ae | 1,205 | py | Python | instruments/instrumentinfo.py | paulscottrobson/animonica | 06345fd2ce6c715e0040f93312af477cff413c63 | [
"MIT"
] | null | null | null | instruments/instrumentinfo.py | paulscottrobson/animonica | 06345fd2ce6c715e0040f93312af477cff413c63 | [
"MIT"
] | null | null | null | instruments/instrumentinfo.py | paulscottrobson/animonica | 06345fd2ce6c715e0040f93312af477cff413c63 | [
"MIT"
] | null | null | null | class InstrumentInfo(object):
def getRawInfo(self):
return [[1,"CDIAT","Diatonic Harmonica C",10,0,0,0,0,0,0,0,0,0,0,0,0,13,15,5,17,20,6,20,24,7,25,27,5,29,30,0,32,34,5,37,36,0,44,39,1,41,42,1,49,46,2],[1,"ADIAT","Diatonic Harmonica A",10,0,0,0,0,0,0,0,0,0,0,0,0,22,24,5,26,29,6,29,33,7,34,36,5,38,39,0,41,43,5,46,45,0,53,48,1,50,51,1,58,55,2],[1,"GDIAT","Diatonic Harmonica G",10,0,0,0,0,0,0,0,0,0,0,0,0,20,22,5,24,27,6,27,31,7,32,34,5,36,37,0,39,41,5,44,43,0,51,46,1,48,49,1,56,53,2],[1,"DDIAT","Diatonic Harmonica D",10,0,0,0,0,0,0,0,0,0,0,0,0,15,17,5,19,22,6,22,26,7,27,29,5,31,32,0,34,36,5,39,38,0,46,41,1,43,44,1,51,48,2],[1,"EDIAT","Diatonic Harmonica E",10,0,0,0,0,0,0,0,0,0,0,0,0,17,19,5,21,24,6,24,28,7,29,31,5,33,34,0,36,38,5,41,40,0,48,43,1,45,46,1,53,50,2],[1,"FDIAT","Diatonic Harmonica F",10,0,0,0,0,0,0,0,0,0,0,0,0,18,20,5,22,25,6,25,29,7,30,32,5,34,35,0,37,39,5,42,41,0,49,44,1,46,47,1,54,51,2],[1,"BBDIAT","Diatonic Harmonica Bb",10,0,0,0,0,0,0,0,0,0,0,0,0,23,25,5,27,30,6,30,34,7,35,37,5,39,40,0,42,44,5,47,46,0,54,49,1,51,52,1,59,56,2],[1,"EBDIAT","Diatonic Harmonica Eb",10,0,0,0,0,0,0,0,0,0,0,0,0,16,18,5,20,23,6,23,27,7,28,30,5,32,33,0,35,37,5,40,39,0,47,42,1,44,45,1,52,49,2]]
| 241 | 1,150 | 0.627386 | 391 | 1,205 | 1.933504 | 0.196931 | 0.232804 | 0.31746 | 0.380952 | 0.148148 | 0.148148 | 0.148148 | 0.148148 | 0.148148 | 0.148148 | 0 | 0.441052 | 0.021577 | 1,205 | 4 | 1,151 | 301.25 | 0.20017 | 0 | 0 | 0 | 0 | 0 | 0.169435 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0 | 0.333333 | 1 | 0 | 0 | 0 | 1 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 12 |
37bf506d430a347a9740fc5e0ac3a164ae50d4bd | 1,094 | py | Python | exabel_data_sdk/stubs/exabel/api/data/v1/all_pb2_grpc.py | aksestok/python-sdk | 520a3d9822ffa9a023262b379ea3b3d19cb10853 | [
"MIT"
] | 1 | 2021-12-22T11:23:57.000Z | 2021-12-22T11:23:57.000Z | exabel_data_sdk/stubs/exabel/api/data/v1/all_pb2_grpc.py | aksestok/python-sdk | 520a3d9822ffa9a023262b379ea3b3d19cb10853 | [
"MIT"
] | 18 | 2021-01-13T16:24:38.000Z | 2022-03-15T13:32:29.000Z | exabel_data_sdk/stubs/exabel/api/data/v1/all_pb2_grpc.py | aksestok/python-sdk | 520a3d9822ffa9a023262b379ea3b3d19cb10853 | [
"MIT"
] | 10 | 2021-01-11T13:24:51.000Z | 2021-12-17T20:53:06.000Z | # Generated by generate_protobuf_stubs.sh.
# Contains all messages in *_pb2_grpc.py in a single module.
from exabel_data_sdk.stubs.exabel.api.data.v1.data_set_messages_pb2_grpc import *
from exabel_data_sdk.stubs.exabel.api.data.v1.data_set_service_pb2_grpc import *
from exabel_data_sdk.stubs.exabel.api.data.v1.entity_messages_pb2_grpc import *
from exabel_data_sdk.stubs.exabel.api.data.v1.entity_service_pb2_grpc import *
from exabel_data_sdk.stubs.exabel.api.data.v1.internal_entity_service_pb2_grpc import *
from exabel_data_sdk.stubs.exabel.api.data.v1.relationship_messages_pb2_grpc import *
from exabel_data_sdk.stubs.exabel.api.data.v1.relationship_service_pb2_grpc import *
from exabel_data_sdk.stubs.exabel.api.data.v1.search_messages_pb2_grpc import *
from exabel_data_sdk.stubs.exabel.api.data.v1.signal_messages_pb2_grpc import *
from exabel_data_sdk.stubs.exabel.api.data.v1.signal_service_pb2_grpc import *
from exabel_data_sdk.stubs.exabel.api.data.v1.time_series_messages_pb2_grpc import *
from exabel_data_sdk.stubs.exabel.api.data.v1.time_series_service_pb2_grpc import *
| 68.375 | 87 | 0.859232 | 190 | 1,094 | 4.584211 | 0.168421 | 0.104478 | 0.192882 | 0.234214 | 0.870264 | 0.870264 | 0.870264 | 0.870264 | 0.870264 | 0.870264 | 0 | 0.024248 | 0.057587 | 1,094 | 15 | 88 | 72.933333 | 0.820563 | 0.090494 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 12 |
806a24b89426ac61fc5f3ce6170a30fa276b2efc | 143 | py | Python | tests/conftest.py | MasonMcGill/artisan | f24932289bfe4f606b30516d429dc982df27ffdd | [
"MIT"
] | null | null | null | tests/conftest.py | MasonMcGill/artisan | f24932289bfe4f606b30516d429dc982df27ffdd | [
"MIT"
] | 9 | 2019-06-10T11:27:56.000Z | 2022-01-20T15:53:28.000Z | tests/conftest.py | MasonMcGill/artisan | f24932289bfe4f606b30516d429dc982df27ffdd | [
"MIT"
] | 1 | 2019-06-07T15:44:33.000Z | 2019-06-07T15:44:33.000Z | import hypothesis
hypothesis.settings.register_profile('dev', max_examples=10)
hypothesis.settings.register_profile('dist', max_examples=100)
| 28.6 | 62 | 0.839161 | 18 | 143 | 6.444444 | 0.611111 | 0.310345 | 0.448276 | 0.568966 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.036765 | 0.048951 | 143 | 4 | 63 | 35.75 | 0.816176 | 0 | 0 | 0 | 0 | 0 | 0.048951 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 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 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 7 |
808cf994003fb8a49c4d39188f8a0bde57b55e96 | 7,488 | py | Python | core/migrations/0019_auto_20210528_0818.py | tanyutao544/digitalace-backend | 3607b1325856eafa4e1c96d6189f7aed1b163a19 | [
"MIT"
] | 1 | 2021-05-28T05:22:54.000Z | 2021-05-28T05:22:54.000Z | core/migrations/0019_auto_20210528_0818.py | tanyutao544/digitalace-backend | 3607b1325856eafa4e1c96d6189f7aed1b163a19 | [
"MIT"
] | 3 | 2021-05-31T15:44:14.000Z | 2021-06-29T07:48:13.000Z | core/migrations/0019_auto_20210528_0818.py | tanyutao544/digitalace-backend | 3607b1325856eafa4e1c96d6189f7aed1b163a19 | [
"MIT"
] | 1 | 2021-05-30T07:42:54.000Z | 2021-05-30T07:42:54.000Z | # Generated by Django 3.2.3 on 2021-05-28 08:18
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('core', '0018_auto_20210528_0808'),
]
operations = [
migrations.AlterField(
model_name='invoice',
name='discount_amount',
field=models.DecimalField(decimal_places=2, max_digits=10),
),
migrations.AlterField(
model_name='invoice',
name='discount_rate',
field=models.DecimalField(decimal_places=2, max_digits=10),
),
migrations.AlterField(
model_name='invoice',
name='grand_total',
field=models.DecimalField(decimal_places=2, max_digits=10),
),
migrations.AlterField(
model_name='invoice',
name='gst_amount',
field=models.DecimalField(decimal_places=2, max_digits=10),
),
migrations.AlterField(
model_name='invoice',
name='gst_rate',
field=models.DecimalField(decimal_places=2, max_digits=10),
),
migrations.AlterField(
model_name='invoice',
name='net',
field=models.DecimalField(decimal_places=2, max_digits=10),
),
migrations.AlterField(
model_name='invoice',
name='total_amount',
field=models.DecimalField(decimal_places=2, max_digits=10),
),
migrations.AlterField(
model_name='invoiceitem',
name='cost',
field=models.DecimalField(decimal_places=2, max_digits=10),
),
migrations.AlterField(
model_name='invoiceitem',
name='unit_price',
field=models.DecimalField(decimal_places=2, max_digits=10),
),
migrations.AlterField(
model_name='product',
name='cost',
field=models.DecimalField(decimal_places=2, max_digits=10),
),
migrations.AlterField(
model_name='product',
name='unit_price',
field=models.DecimalField(decimal_places=2, max_digits=10),
),
migrations.AlterField(
model_name='purchaseorder',
name='discount_amount',
field=models.DecimalField(decimal_places=2, max_digits=10),
),
migrations.AlterField(
model_name='purchaseorder',
name='discount_rate',
field=models.DecimalField(decimal_places=2, max_digits=10),
),
migrations.AlterField(
model_name='purchaseorder',
name='grand_total',
field=models.DecimalField(decimal_places=2, max_digits=10),
),
migrations.AlterField(
model_name='purchaseorder',
name='gst_amount',
field=models.DecimalField(decimal_places=2, max_digits=10),
),
migrations.AlterField(
model_name='purchaseorder',
name='gst_rate',
field=models.DecimalField(decimal_places=2, max_digits=10),
),
migrations.AlterField(
model_name='purchaseorder',
name='net',
field=models.DecimalField(decimal_places=2, max_digits=10),
),
migrations.AlterField(
model_name='purchaseorder',
name='total_amount',
field=models.DecimalField(decimal_places=2, max_digits=10),
),
migrations.AlterField(
model_name='purchaseorderitem',
name='cost',
field=models.DecimalField(decimal_places=2, max_digits=10),
),
migrations.AlterField(
model_name='purchaseorderitem',
name='unit_price',
field=models.DecimalField(decimal_places=2, max_digits=10),
),
migrations.AlterField(
model_name='receive',
name='discount_amount',
field=models.DecimalField(decimal_places=2, max_digits=10),
),
migrations.AlterField(
model_name='receive',
name='discount_rate',
field=models.DecimalField(decimal_places=2, max_digits=10),
),
migrations.AlterField(
model_name='receive',
name='grand_total',
field=models.DecimalField(decimal_places=2, max_digits=10),
),
migrations.AlterField(
model_name='receive',
name='gst_amount',
field=models.DecimalField(decimal_places=2, max_digits=10),
),
migrations.AlterField(
model_name='receive',
name='gst_rate',
field=models.DecimalField(decimal_places=2, max_digits=10),
),
migrations.AlterField(
model_name='receive',
name='net',
field=models.DecimalField(decimal_places=2, max_digits=10),
),
migrations.AlterField(
model_name='receive',
name='total_amount',
field=models.DecimalField(decimal_places=2, max_digits=10),
),
migrations.AlterField(
model_name='receiveitem',
name='cost',
field=models.DecimalField(decimal_places=2, max_digits=10),
),
migrations.AlterField(
model_name='receiveitem',
name='unit_price',
field=models.DecimalField(decimal_places=2, max_digits=10),
),
migrations.AlterField(
model_name='salesorder',
name='discount_amount',
field=models.DecimalField(decimal_places=2, max_digits=10),
),
migrations.AlterField(
model_name='salesorder',
name='discount_rate',
field=models.DecimalField(decimal_places=2, max_digits=10),
),
migrations.AlterField(
model_name='salesorder',
name='grand_total',
field=models.DecimalField(decimal_places=2, max_digits=10),
),
migrations.AlterField(
model_name='salesorder',
name='gst_amount',
field=models.DecimalField(decimal_places=2, max_digits=10),
),
migrations.AlterField(
model_name='salesorder',
name='gst_rate',
field=models.DecimalField(decimal_places=2, max_digits=10),
),
migrations.AlterField(
model_name='salesorder',
name='net',
field=models.DecimalField(decimal_places=2, max_digits=10),
),
migrations.AlterField(
model_name='salesorder',
name='total_amount',
field=models.DecimalField(decimal_places=2, max_digits=10),
),
migrations.AlterField(
model_name='salesorderitem',
name='cost',
field=models.DecimalField(decimal_places=2, max_digits=10),
),
migrations.AlterField(
model_name='salesorderitem',
name='unit_price',
field=models.DecimalField(decimal_places=2, max_digits=10),
),
migrations.AlterField(
model_name='userconfig',
name='discount_rate',
field=models.DecimalField(decimal_places=2, max_digits=10),
),
migrations.AlterField(
model_name='userconfig',
name='gst_rate',
field=models.DecimalField(decimal_places=2, max_digits=10),
),
]
| 34.990654 | 71 | 0.57265 | 700 | 7,488 | 5.905714 | 0.078571 | 0.193517 | 0.241896 | 0.2806 | 0.964441 | 0.964441 | 0.964441 | 0.955733 | 0.955733 | 0.955733 | 0 | 0.029527 | 0.317041 | 7,488 | 213 | 72 | 35.15493 | 0.778842 | 0.00601 | 0 | 0.966184 | 1 | 0 | 0.108184 | 0.003091 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.004831 | 0 | 0.019324 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
80973f20a5c9572d5733a74e51d169aff3b6666f | 3,361 | py | Python | pyMSpec/pyisocalc/tools.py | SpatialMetabolomics/pyMS | 52c4dce2c4c0eba3c6d447565f3296252f882f9e | [
"Apache-2.0"
] | 5 | 2017-12-18T06:03:51.000Z | 2019-04-05T21:12:54.000Z | pyMSpec/pyisocalc/tools.py | alexandrovteam/pyMS | 52c4dce2c4c0eba3c6d447565f3296252f882f9e | [
"Apache-2.0"
] | null | null | null | pyMSpec/pyisocalc/tools.py | alexandrovteam/pyMS | 52c4dce2c4c0eba3c6d447565f3296252f882f9e | [
"Apache-2.0"
] | 3 | 2018-10-23T13:13:19.000Z | 2021-02-22T09:19:26.000Z | from __future__ import print_function
def make_sf_adduct_database(sum_formulae, adducts, output_filename, sigma=0.001, resolution=10000, charge=1):
from pyMSpec.pyisocalc import pyisocalc
# Extract variables from config dict
# Check if already genrated and load if possible, otherwise calculate fresh
with open(output_filename, 'a') as f_out:
for sum_formula in sum_formulae:
# print sum_formula
for adduct in adducts:
try:
sf = pyisocalc.complex_to_simple(sum_formula + adduct)
if sf is None: # not possible to form adduct
continue
isotope_ms = pyisocalc.isodist(sf, plot=False, sigma=sigma, charges=charge,
resolution=resolution)
except KeyError as e:
if str(e).startswith("KeyError:"):
print(str(e))
continue
except ValueError as e:
if str(e).startswith("Element not recognised"):
print(str(e))
continue
except:
print(sf == "", sum_formula, adduct)
raise
f_out.write("{},[M{}],{},{}\n".format(sum_formula, adduct, isotope_ms.get_spectrum(
source='centroids')[0], isotope_ms.get_spectrum(source='centroids')[1]))
def make_sf_adduct_optimusfilter(sum_formulae, adducts, output_filename, sigma=0.001, resolution=10000, charge=1):
from pyMSpec.pyisocalc import pyisocalc
# Extract variables from config dict
# Check if already genrated and load if possible, otherwise calculate fresh
with open(output_filename, 'a') as f_out:
for sum_formula in sum_formulae:
# print sum_formula
for adduct in adducts:
try:
sf = pyisocalc.complex_to_simple(sum_formula + adduct)
if sf is None: # not possible to form adduct
continue
isotope_ms = pyisocalc.isodist(sf, plot=False, sigma=sigma, charges=charge,
resolution=resolution)
except KeyError as e:
if str(e).startswith("KeyError:"):
print(str(e))
continue
except ValueError as e:
if str(e).startswith("Element not recognised"):
print(str(e))
continue
except:
print(sf == "", sum_formula, adduct)
raise
f_out.write("{} [M{}],-1,{}\n".format(sum_formula, adduct,
isotope_ms.get_spectrum(source='centroids')[0][0]))
def normalise_sf(sf_string):
from pyMSpec.pyisocalc.pyisocalc import InvalidFormulaError, ParseError
from pyMSpec.pyisocalc.pyisocalc import parseSumFormula
import logging
try:
sf = parseSumFormula(sf_string)
except (ParseError, InvalidFormulaError) as e:
logging.debug(e)
return ""
except:
logging.warning("failed to parse: {}".format(sf_string))
return ""
return sf.__unicode__()
| 44.813333 | 114 | 0.542993 | 345 | 3,361 | 5.136232 | 0.266667 | 0.056433 | 0.054176 | 0.018059 | 0.822235 | 0.782731 | 0.76298 | 0.76298 | 0.76298 | 0.76298 | 0 | 0.011916 | 0.375781 | 3,361 | 74 | 115 | 45.418919 | 0.832698 | 0.091937 | 0 | 0.741935 | 0 | 0 | 0.046664 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.048387 | false | 0 | 0.096774 | 0 | 0.193548 | 0.112903 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
03a2be682bc0ab6644f6f70d05c74e3dcd8a1898 | 146,585 | py | Python | venv/lib/python3.8/site-packages/openapi_client/api/admin_api.py | akshitgoyal/csc398nlp | 6adf80cb7fa3737f88faf73a6e818da495b95ab4 | [
"MIT"
] | 1 | 2020-09-28T10:09:25.000Z | 2020-09-28T10:09:25.000Z | venv/lib/python3.8/site-packages/openapi_client/api/admin_api.py | akshitgoyal/NLP-Research-Project | 6adf80cb7fa3737f88faf73a6e818da495b95ab4 | [
"MIT"
] | null | null | null | venv/lib/python3.8/site-packages/openapi_client/api/admin_api.py | akshitgoyal/NLP-Research-Project | 6adf80cb7fa3737f88faf73a6e818da495b95ab4 | [
"MIT"
] | 1 | 2020-07-01T18:46:20.000Z | 2020-07-01T18:46:20.000Z | # coding: utf-8
"""
NamSor API v2
NamSor API v2 : enpoints to process personal names (gender, cultural origin or ethnicity) in all alphabets or languages. Use GET methods for small tests, but prefer POST methods for higher throughput (batch processing of up to 100 names at a time). Need something you can't find here? We have many more features coming soon. Let us know, we'll do our best to add it! # noqa: E501
OpenAPI spec version: 2.0.10
Contact: contact@namsor.com
Generated by: https://openapi-generator.tech
"""
from __future__ import absolute_import
import re # noqa: F401
# python 2 and python 3 compatibility library
import six
from openapi_client.api_client import ApiClient
class AdminApi(object):
"""NOTE: This class is auto generated by OpenAPI Generator
Ref: https://openapi-generator.tech
Do not edit the class manually.
"""
def __init__(self, api_client=None):
if api_client is None:
api_client = ApiClient()
self.api_client = api_client
def add_credits(self, api_key, usage_credits, user_message, **kwargs): # noqa: E501
"""Add usage credits to an API Key. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.add_credits(api_key, usage_credits, user_message, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str api_key: (required)
:param int usage_credits: (required)
:param str user_message: (required)
:return: SystemMetricsOut
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.add_credits_with_http_info(api_key, usage_credits, user_message, **kwargs) # noqa: E501
else:
(data) = self.add_credits_with_http_info(api_key, usage_credits, user_message, **kwargs) # noqa: E501
return data
def add_credits_with_http_info(self, api_key, usage_credits, user_message, **kwargs): # noqa: E501
"""Add usage credits to an API Key. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.add_credits_with_http_info(api_key, usage_credits, user_message, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str api_key: (required)
:param int usage_credits: (required)
:param str user_message: (required)
:return: SystemMetricsOut
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = ['api_key', 'usage_credits', 'user_message'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method add_credits" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'api_key' is set
if ('api_key' not in local_var_params or
local_var_params['api_key'] is None):
raise ValueError("Missing the required parameter `api_key` when calling `add_credits`") # noqa: E501
# verify the required parameter 'usage_credits' is set
if ('usage_credits' not in local_var_params or
local_var_params['usage_credits'] is None):
raise ValueError("Missing the required parameter `usage_credits` when calling `add_credits`") # noqa: E501
# verify the required parameter 'user_message' is set
if ('user_message' not in local_var_params or
local_var_params['user_message'] is None):
raise ValueError("Missing the required parameter `user_message` when calling `add_credits`") # noqa: E501
collection_formats = {}
path_params = {}
if 'api_key' in local_var_params:
path_params['apiKey'] = local_var_params['api_key'] # noqa: E501
if 'usage_credits' in local_var_params:
path_params['usageCredits'] = local_var_params['usage_credits'] # noqa: E501
if 'user_message' in local_var_params:
path_params['userMessage'] = local_var_params['user_message'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['api_key'] # noqa: E501
return self.api_client.call_api(
'/api2/json/addCredits/{apiKey}/{usageCredits}/{userMessage}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='SystemMetricsOut', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def anonymize(self, source, anonymized, **kwargs): # noqa: E501
"""Activate/deactivate anonymization for a source. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.anonymize(source, anonymized, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str source: (required)
:param bool anonymized: (required)
:return: None
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.anonymize_with_http_info(source, anonymized, **kwargs) # noqa: E501
else:
(data) = self.anonymize_with_http_info(source, anonymized, **kwargs) # noqa: E501
return data
def anonymize_with_http_info(self, source, anonymized, **kwargs): # noqa: E501
"""Activate/deactivate anonymization for a source. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.anonymize_with_http_info(source, anonymized, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str source: (required)
:param bool anonymized: (required)
:return: None
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = ['source', 'anonymized'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method anonymize" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'source' is set
if ('source' not in local_var_params or
local_var_params['source'] is None):
raise ValueError("Missing the required parameter `source` when calling `anonymize`") # noqa: E501
# verify the required parameter 'anonymized' is set
if ('anonymized' not in local_var_params or
local_var_params['anonymized'] is None):
raise ValueError("Missing the required parameter `anonymized` when calling `anonymize`") # noqa: E501
collection_formats = {}
path_params = {}
if 'source' in local_var_params:
path_params['source'] = local_var_params['source'] # noqa: E501
if 'anonymized' in local_var_params:
path_params['anonymized'] = local_var_params['anonymized'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# Authentication setting
auth_settings = ['api_key'] # noqa: E501
return self.api_client.call_api(
'/api2/json/anonymize/{source}/{anonymized}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type=None, # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def api_status(self, **kwargs): # noqa: E501
"""Prints the current status of the classifiers. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.api_status(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: APIPlansOut
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.api_status_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.api_status_with_http_info(**kwargs) # noqa: E501
return data
def api_status_with_http_info(self, **kwargs): # noqa: E501
"""Prints the current status of the classifiers. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.api_status_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: APIPlansOut
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method api_status" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['api_key'] # noqa: E501
return self.api_client.call_api(
'/api2/json/apiStatus', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='APIPlansOut', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def api_usage(self, **kwargs): # noqa: E501
"""Print current API usage. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.api_usage(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: APIPeriodUsageOut
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.api_usage_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.api_usage_with_http_info(**kwargs) # noqa: E501
return data
def api_usage_with_http_info(self, **kwargs): # noqa: E501
"""Print current API usage. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.api_usage_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: APIPeriodUsageOut
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method api_usage" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['api_key'] # noqa: E501
return self.api_client.call_api(
'/api2/json/apiUsage', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='APIPeriodUsageOut', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def api_usage_history(self, **kwargs): # noqa: E501
"""Print historical API usage. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.api_usage_history(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: APIPeriodUsageOut
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.api_usage_history_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.api_usage_history_with_http_info(**kwargs) # noqa: E501
return data
def api_usage_history_with_http_info(self, **kwargs): # noqa: E501
"""Print historical API usage. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.api_usage_history_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: APIPeriodUsageOut
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method api_usage_history" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['api_key'] # noqa: E501
return self.api_client.call_api(
'/api2/json/apiUsageHistory', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='APIPeriodUsageOut', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def api_usage_history_aggregate(self, **kwargs): # noqa: E501
"""Print historical API usage (in an aggregated view, by service, by day/hour/min). # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.api_usage_history_aggregate(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: APIPeriodUsageOut
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.api_usage_history_aggregate_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.api_usage_history_aggregate_with_http_info(**kwargs) # noqa: E501
return data
def api_usage_history_aggregate_with_http_info(self, **kwargs): # noqa: E501
"""Print historical API usage (in an aggregated view, by service, by day/hour/min). # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.api_usage_history_aggregate_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: APIPeriodUsageOut
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method api_usage_history_aggregate" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['api_key'] # noqa: E501
return self.api_client.call_api(
'/api2/json/apiUsageHistoryAggregate', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='APIPeriodUsageOut', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def available_plans(self, **kwargs): # noqa: E501
"""List all available plans in the default currency (usd). # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.available_plans(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: APIPlansOut
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.available_plans_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.available_plans_with_http_info(**kwargs) # noqa: E501
return data
def available_plans_with_http_info(self, **kwargs): # noqa: E501
"""List all available plans in the default currency (usd). # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.available_plans_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: APIPlansOut
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method available_plans" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['api_key'] # noqa: E501
return self.api_client.call_api(
'/api2/json/availablePlans', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='APIPlansOut', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def available_plans1(self, token, **kwargs): # noqa: E501
"""List all available plans in the user's preferred currency. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.available_plans1(token, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str token: (required)
:return: APIPlansOut
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.available_plans1_with_http_info(token, **kwargs) # noqa: E501
else:
(data) = self.available_plans1_with_http_info(token, **kwargs) # noqa: E501
return data
def available_plans1_with_http_info(self, token, **kwargs): # noqa: E501
"""List all available plans in the user's preferred currency. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.available_plans1_with_http_info(token, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str token: (required)
:return: APIPlansOut
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = ['token'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method available_plans1" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'token' is set
if ('token' not in local_var_params or
local_var_params['token'] is None):
raise ValueError("Missing the required parameter `token` when calling `available_plans1`") # noqa: E501
collection_formats = {}
path_params = {}
if 'token' in local_var_params:
path_params['token'] = local_var_params['token'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['api_key'] # noqa: E501
return self.api_client.call_api(
'/api2/json/availablePlans/{token}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='APIPlansOut', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def available_services(self, **kwargs): # noqa: E501
"""List of API services and usage cost in Units (default is 1=ONE Unit). # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.available_services(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: APIPlansOut
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.available_services_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.available_services_with_http_info(**kwargs) # noqa: E501
return data
def available_services_with_http_info(self, **kwargs): # noqa: E501
"""List of API services and usage cost in Units (default is 1=ONE Unit). # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.available_services_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: APIPlansOut
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method available_services" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['api_key'] # noqa: E501
return self.api_client.call_api(
'/api2/json/apiServices', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='APIPlansOut', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def billing_currencies(self, **kwargs): # noqa: E501
"""List possible currency options for billing (USD, EUR, GBP, ...) # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.billing_currencies(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: CurrenciesOut
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.billing_currencies_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.billing_currencies_with_http_info(**kwargs) # noqa: E501
return data
def billing_currencies_with_http_info(self, **kwargs): # noqa: E501
"""List possible currency options for billing (USD, EUR, GBP, ...) # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.billing_currencies_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: CurrenciesOut
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method billing_currencies" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['api_key'] # noqa: E501
return self.api_client.call_api(
'/api2/json/billingCurrencies', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='CurrenciesOut', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def billing_history(self, token, **kwargs): # noqa: E501
"""Read the history billing information (invoices paid via Stripe or manually). # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.billing_history(token, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str token: (required)
:return: BillingHistoryOut
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.billing_history_with_http_info(token, **kwargs) # noqa: E501
else:
(data) = self.billing_history_with_http_info(token, **kwargs) # noqa: E501
return data
def billing_history_with_http_info(self, token, **kwargs): # noqa: E501
"""Read the history billing information (invoices paid via Stripe or manually). # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.billing_history_with_http_info(token, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str token: (required)
:return: BillingHistoryOut
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = ['token'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method billing_history" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'token' is set
if ('token' not in local_var_params or
local_var_params['token'] is None):
raise ValueError("Missing the required parameter `token` when calling `billing_history`") # noqa: E501
collection_formats = {}
path_params = {}
if 'token' in local_var_params:
path_params['token'] = local_var_params['token'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['api_key'] # noqa: E501
return self.api_client.call_api(
'/api2/json/billingHistory/{token}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='BillingHistoryOut', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def billing_info(self, token, **kwargs): # noqa: E501
"""Read the billing information (company name, address, phone, vat ID) # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.billing_info(token, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str token: (required)
:return: BillingInfoInOut
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.billing_info_with_http_info(token, **kwargs) # noqa: E501
else:
(data) = self.billing_info_with_http_info(token, **kwargs) # noqa: E501
return data
def billing_info_with_http_info(self, token, **kwargs): # noqa: E501
"""Read the billing information (company name, address, phone, vat ID) # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.billing_info_with_http_info(token, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str token: (required)
:return: BillingInfoInOut
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = ['token'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method billing_info" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'token' is set
if ('token' not in local_var_params or
local_var_params['token'] is None):
raise ValueError("Missing the required parameter `token` when calling `billing_info`") # noqa: E501
collection_formats = {}
path_params = {}
if 'token' in local_var_params:
path_params['token'] = local_var_params['token'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['api_key'] # noqa: E501
return self.api_client.call_api(
'/api2/json/billingInfo/{token}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='BillingInfoInOut', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def charge(self, **kwargs): # noqa: E501
"""Create a Stripe Customer, based on a payment card token (from secure StripeJS) and email. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.charge(async_req=True)
>>> result = thread.get()
:param async_req bool
:param InlineObject inline_object:
:return: APIKeyOut
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.charge_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.charge_with_http_info(**kwargs) # noqa: E501
return data
def charge_with_http_info(self, **kwargs): # noqa: E501
"""Create a Stripe Customer, based on a payment card token (from secure StripeJS) and email. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.charge_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:param InlineObject inline_object:
:return: APIKeyOut
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = ['inline_object'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method charge" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'inline_object' in local_var_params:
body_params = local_var_params['inline_object']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['api_key'] # noqa: E501
return self.api_client.call_api(
'/api2/json/charge', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='APIKeyOut', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def corporate_key(self, api_key, corporate, **kwargs): # noqa: E501
"""Setting an API Key to a corporate status. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.corporate_key(api_key, corporate, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str api_key: (required)
:param bool corporate: (required)
:return: None
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.corporate_key_with_http_info(api_key, corporate, **kwargs) # noqa: E501
else:
(data) = self.corporate_key_with_http_info(api_key, corporate, **kwargs) # noqa: E501
return data
def corporate_key_with_http_info(self, api_key, corporate, **kwargs): # noqa: E501
"""Setting an API Key to a corporate status. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.corporate_key_with_http_info(api_key, corporate, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str api_key: (required)
:param bool corporate: (required)
:return: None
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = ['api_key', 'corporate'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method corporate_key" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'api_key' is set
if ('api_key' not in local_var_params or
local_var_params['api_key'] is None):
raise ValueError("Missing the required parameter `api_key` when calling `corporate_key`") # noqa: E501
# verify the required parameter 'corporate' is set
if ('corporate' not in local_var_params or
local_var_params['corporate'] is None):
raise ValueError("Missing the required parameter `corporate` when calling `corporate_key`") # noqa: E501
collection_formats = {}
path_params = {}
if 'api_key' in local_var_params:
path_params['apiKey'] = local_var_params['api_key'] # noqa: E501
if 'corporate' in local_var_params:
path_params['corporate'] = local_var_params['corporate'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# Authentication setting
auth_settings = ['api_key'] # noqa: E501
return self.api_client.call_api(
'/api2/json/corporateKey/{apiKey}/{corporate}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type=None, # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def debug_level(self, logger, level, **kwargs): # noqa: E501
"""Update debug level for a classifier # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.debug_level(logger, level, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str logger: (required)
:param str level: (required)
:return: None
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.debug_level_with_http_info(logger, level, **kwargs) # noqa: E501
else:
(data) = self.debug_level_with_http_info(logger, level, **kwargs) # noqa: E501
return data
def debug_level_with_http_info(self, logger, level, **kwargs): # noqa: E501
"""Update debug level for a classifier # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.debug_level_with_http_info(logger, level, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str logger: (required)
:param str level: (required)
:return: None
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = ['logger', 'level'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method debug_level" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'logger' is set
if ('logger' not in local_var_params or
local_var_params['logger'] is None):
raise ValueError("Missing the required parameter `logger` when calling `debug_level`") # noqa: E501
# verify the required parameter 'level' is set
if ('level' not in local_var_params or
local_var_params['level'] is None):
raise ValueError("Missing the required parameter `level` when calling `debug_level`") # noqa: E501
collection_formats = {}
path_params = {}
if 'logger' in local_var_params:
path_params['logger'] = local_var_params['logger'] # noqa: E501
if 'level' in local_var_params:
path_params['level'] = local_var_params['level'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# Authentication setting
auth_settings = ['api_key'] # noqa: E501
return self.api_client.call_api(
'/api2/json/debugLevel/{logger}/{level}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type=None, # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def flush(self, **kwargs): # noqa: E501
"""Flush counters. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.flush(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: None
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.flush_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.flush_with_http_info(**kwargs) # noqa: E501
return data
def flush_with_http_info(self, **kwargs): # noqa: E501
"""Flush counters. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.flush_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: None
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method flush" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# Authentication setting
auth_settings = ['api_key'] # noqa: E501
return self.api_client.call_api(
'/api2/json/flush', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type=None, # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def invalidate_cache(self, **kwargs): # noqa: E501
"""Invalidate system caches. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.invalidate_cache(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: None
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.invalidate_cache_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.invalidate_cache_with_http_info(**kwargs) # noqa: E501
return data
def invalidate_cache_with_http_info(self, **kwargs): # noqa: E501
"""Invalidate system caches. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.invalidate_cache_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: None
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method invalidate_cache" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# Authentication setting
auth_settings = ['api_key'] # noqa: E501
return self.api_client.call_api(
'/api2/json/invalidateCache', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type=None, # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def learnable(self, source, learnable, **kwargs): # noqa: E501
"""Activate/deactivate learning from a source. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.learnable(source, learnable, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str source: (required)
:param bool learnable: (required)
:return: None
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.learnable_with_http_info(source, learnable, **kwargs) # noqa: E501
else:
(data) = self.learnable_with_http_info(source, learnable, **kwargs) # noqa: E501
return data
def learnable_with_http_info(self, source, learnable, **kwargs): # noqa: E501
"""Activate/deactivate learning from a source. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.learnable_with_http_info(source, learnable, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str source: (required)
:param bool learnable: (required)
:return: None
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = ['source', 'learnable'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method learnable" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'source' is set
if ('source' not in local_var_params or
local_var_params['source'] is None):
raise ValueError("Missing the required parameter `source` when calling `learnable`") # noqa: E501
# verify the required parameter 'learnable' is set
if ('learnable' not in local_var_params or
local_var_params['learnable'] is None):
raise ValueError("Missing the required parameter `learnable` when calling `learnable`") # noqa: E501
collection_formats = {}
path_params = {}
if 'source' in local_var_params:
path_params['source'] = local_var_params['source'] # noqa: E501
if 'learnable' in local_var_params:
path_params['learnable'] = local_var_params['learnable'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# Authentication setting
auth_settings = ['api_key'] # noqa: E501
return self.api_client.call_api(
'/api2/json/learnable/{source}/{learnable}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type=None, # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def namsor_counter(self, **kwargs): # noqa: E501
"""Get the overall API counter # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.namsor_counter(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: SoftwareVersionOut
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.namsor_counter_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.namsor_counter_with_http_info(**kwargs) # noqa: E501
return data
def namsor_counter_with_http_info(self, **kwargs): # noqa: E501
"""Get the overall API counter # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.namsor_counter_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: SoftwareVersionOut
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method namsor_counter" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['api_key'] # noqa: E501
return self.api_client.call_api(
'/api2/json/namsorCounter', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='SoftwareVersionOut', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def payment_info(self, token, **kwargs): # noqa: E501
"""Get the Stripe payment information associated with the current google auth session token. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.payment_info(token, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str token: (required)
:return: APIKeyOut
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.payment_info_with_http_info(token, **kwargs) # noqa: E501
else:
(data) = self.payment_info_with_http_info(token, **kwargs) # noqa: E501
return data
def payment_info_with_http_info(self, token, **kwargs): # noqa: E501
"""Get the Stripe payment information associated with the current google auth session token. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.payment_info_with_http_info(token, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str token: (required)
:return: APIKeyOut
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = ['token'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method payment_info" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'token' is set
if ('token' not in local_var_params or
local_var_params['token'] is None):
raise ValueError("Missing the required parameter `token` when calling `payment_info`") # noqa: E501
collection_formats = {}
path_params = {}
if 'token' in local_var_params:
path_params['token'] = local_var_params['token'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['api_key'] # noqa: E501
return self.api_client.call_api(
'/api2/json/paymentInfo/{token}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='APIKeyOut', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def procure_key(self, token, **kwargs): # noqa: E501
"""Procure an API Key (sent via Email), based on an auth token. Keep your API Key secret. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.procure_key(token, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str token: (required)
:return: APIKeyOut
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.procure_key_with_http_info(token, **kwargs) # noqa: E501
else:
(data) = self.procure_key_with_http_info(token, **kwargs) # noqa: E501
return data
def procure_key_with_http_info(self, token, **kwargs): # noqa: E501
"""Procure an API Key (sent via Email), based on an auth token. Keep your API Key secret. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.procure_key_with_http_info(token, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str token: (required)
:return: APIKeyOut
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = ['token'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method procure_key" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'token' is set
if ('token' not in local_var_params or
local_var_params['token'] is None):
raise ValueError("Missing the required parameter `token` when calling `procure_key`") # noqa: E501
collection_formats = {}
path_params = {}
if 'token' in local_var_params:
path_params['token'] = local_var_params['token'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['api_key'] # noqa: E501
return self.api_client.call_api(
'/api2/json/procureKey/{token}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='APIKeyOut', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def redeploy_ui(self, **kwargs): # noqa: E501
"""Redeploy UI from current dev branch. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.redeploy_ui(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: None
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.redeploy_ui_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.redeploy_ui_with_http_info(**kwargs) # noqa: E501
return data
def redeploy_ui_with_http_info(self, **kwargs): # noqa: E501
"""Redeploy UI from current dev branch. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.redeploy_ui_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: None
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method redeploy_ui" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# Authentication setting
auth_settings = ['api_key'] # noqa: E501
return self.api_client.call_api(
'/api2/json/redeployUI', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type=None, # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def redeploy_ui1(self, live, **kwargs): # noqa: E501
"""Redeploy UI from current dev branch. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.redeploy_ui1(live, async_req=True)
>>> result = thread.get()
:param async_req bool
:param bool live: (required)
:return: None
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.redeploy_ui1_with_http_info(live, **kwargs) # noqa: E501
else:
(data) = self.redeploy_ui1_with_http_info(live, **kwargs) # noqa: E501
return data
def redeploy_ui1_with_http_info(self, live, **kwargs): # noqa: E501
"""Redeploy UI from current dev branch. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.redeploy_ui1_with_http_info(live, async_req=True)
>>> result = thread.get()
:param async_req bool
:param bool live: (required)
:return: None
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = ['live'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method redeploy_ui1" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'live' is set
if ('live' not in local_var_params or
local_var_params['live'] is None):
raise ValueError("Missing the required parameter `live` when calling `redeploy_ui1`") # noqa: E501
collection_formats = {}
path_params = {}
if 'live' in local_var_params:
path_params['live'] = local_var_params['live'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# Authentication setting
auth_settings = ['api_key'] # noqa: E501
return self.api_client.call_api(
'/api2/json/redeployUI/{live}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type=None, # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def remove_user_account(self, token, **kwargs): # noqa: E501
"""Remove the user account. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.remove_user_account(token, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str token: (required)
:return: APIPlanSubscriptionOut
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.remove_user_account_with_http_info(token, **kwargs) # noqa: E501
else:
(data) = self.remove_user_account_with_http_info(token, **kwargs) # noqa: E501
return data
def remove_user_account_with_http_info(self, token, **kwargs): # noqa: E501
"""Remove the user account. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.remove_user_account_with_http_info(token, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str token: (required)
:return: APIPlanSubscriptionOut
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = ['token'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method remove_user_account" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'token' is set
if ('token' not in local_var_params or
local_var_params['token'] is None):
raise ValueError("Missing the required parameter `token` when calling `remove_user_account`") # noqa: E501
collection_formats = {}
path_params = {}
if 'token' in local_var_params:
path_params['token'] = local_var_params['token'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['api_key'] # noqa: E501
return self.api_client.call_api(
'/api2/json/removeUserAccount/{token}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='APIPlanSubscriptionOut', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def remove_user_account_on_behalf(self, api_key, **kwargs): # noqa: E501
"""Remove (on behalf) a user account. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.remove_user_account_on_behalf(api_key, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str api_key: (required)
:return: APIPlanSubscriptionOut
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.remove_user_account_on_behalf_with_http_info(api_key, **kwargs) # noqa: E501
else:
(data) = self.remove_user_account_on_behalf_with_http_info(api_key, **kwargs) # noqa: E501
return data
def remove_user_account_on_behalf_with_http_info(self, api_key, **kwargs): # noqa: E501
"""Remove (on behalf) a user account. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.remove_user_account_on_behalf_with_http_info(api_key, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str api_key: (required)
:return: APIPlanSubscriptionOut
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = ['api_key'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method remove_user_account_on_behalf" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'api_key' is set
if ('api_key' not in local_var_params or
local_var_params['api_key'] is None):
raise ValueError("Missing the required parameter `api_key` when calling `remove_user_account_on_behalf`") # noqa: E501
collection_formats = {}
path_params = {}
if 'api_key' in local_var_params:
path_params['apiKey'] = local_var_params['api_key'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['api_key'] # noqa: E501
return self.api_client.call_api(
'/api2/json/removeUserAccountOnBehalf/{apiKey}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='APIPlanSubscriptionOut', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def shutdown(self, **kwargs): # noqa: E501
"""Stop learning and shutdown system. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.shutdown(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: None
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.shutdown_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.shutdown_with_http_info(**kwargs) # noqa: E501
return data
def shutdown_with_http_info(self, **kwargs): # noqa: E501
"""Stop learning and shutdown system. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.shutdown_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: None
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method shutdown" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# Authentication setting
auth_settings = ['api_key'] # noqa: E501
return self.api_client.call_api(
'/api2/json/shutdown', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type=None, # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def software_version(self, **kwargs): # noqa: E501
"""Get the current software version # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.software_version(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: SoftwareVersionOut
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.software_version_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.software_version_with_http_info(**kwargs) # noqa: E501
return data
def software_version_with_http_info(self, **kwargs): # noqa: E501
"""Get the current software version # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.software_version_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: SoftwareVersionOut
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method software_version" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['api_key'] # noqa: E501
return self.api_client.call_api(
'/api2/json/softwareVersion', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='SoftwareVersionOut', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def source_stats(self, source, **kwargs): # noqa: E501
"""Print basic source statistics. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.source_stats(source, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str source: (required)
:return: SystemMetricsOut
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.source_stats_with_http_info(source, **kwargs) # noqa: E501
else:
(data) = self.source_stats_with_http_info(source, **kwargs) # noqa: E501
return data
def source_stats_with_http_info(self, source, **kwargs): # noqa: E501
"""Print basic source statistics. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.source_stats_with_http_info(source, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str source: (required)
:return: SystemMetricsOut
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = ['source'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method source_stats" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'source' is set
if ('source' not in local_var_params or
local_var_params['source'] is None):
raise ValueError("Missing the required parameter `source` when calling `source_stats`") # noqa: E501
collection_formats = {}
path_params = {}
if 'source' in local_var_params:
path_params['source'] = local_var_params['source'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['api_key'] # noqa: E501
return self.api_client.call_api(
'/api2/json/sourceStats/{source}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='SystemMetricsOut', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def stats(self, **kwargs): # noqa: E501
"""Print basic system statistics. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.stats(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: SystemMetricsOut
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.stats_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.stats_with_http_info(**kwargs) # noqa: E501
return data
def stats_with_http_info(self, **kwargs): # noqa: E501
"""Print basic system statistics. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.stats_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: SystemMetricsOut
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method stats" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['api_key'] # noqa: E501
return self.api_client.call_api(
'/api2/json/stats', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='SystemMetricsOut', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def stripe_connect(self, **kwargs): # noqa: E501
"""Connects a Stripe Account. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.stripe_connect(async_req=True)
>>> result = thread.get()
:param async_req bool
:param str scope:
:param str code:
:param str error:
:param str error_description:
:return: None
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.stripe_connect_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.stripe_connect_with_http_info(**kwargs) # noqa: E501
return data
def stripe_connect_with_http_info(self, **kwargs): # noqa: E501
"""Connects a Stripe Account. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.stripe_connect_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:param str scope:
:param str code:
:param str error:
:param str error_description:
:return: None
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = ['scope', 'code', 'error', 'error_description'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method stripe_connect" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
if 'scope' in local_var_params:
query_params.append(('scope', local_var_params['scope'])) # noqa: E501
if 'code' in local_var_params:
query_params.append(('code', local_var_params['code'])) # noqa: E501
if 'error' in local_var_params:
query_params.append(('error', local_var_params['error'])) # noqa: E501
if 'error_description' in local_var_params:
query_params.append(('error_description', local_var_params['error_description'])) # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# Authentication setting
auth_settings = ['api_key'] # noqa: E501
return self.api_client.call_api(
'/api2/json/stripeConnect', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type=None, # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def subscribe_plan(self, plan_name, token, **kwargs): # noqa: E501
"""Subscribe to a give API plan, using the user's preferred or default currency. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.subscribe_plan(plan_name, token, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str plan_name: (required)
:param str token: (required)
:return: APIPlanSubscriptionOut
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.subscribe_plan_with_http_info(plan_name, token, **kwargs) # noqa: E501
else:
(data) = self.subscribe_plan_with_http_info(plan_name, token, **kwargs) # noqa: E501
return data
def subscribe_plan_with_http_info(self, plan_name, token, **kwargs): # noqa: E501
"""Subscribe to a give API plan, using the user's preferred or default currency. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.subscribe_plan_with_http_info(plan_name, token, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str plan_name: (required)
:param str token: (required)
:return: APIPlanSubscriptionOut
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = ['plan_name', 'token'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method subscribe_plan" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'plan_name' is set
if ('plan_name' not in local_var_params or
local_var_params['plan_name'] is None):
raise ValueError("Missing the required parameter `plan_name` when calling `subscribe_plan`") # noqa: E501
# verify the required parameter 'token' is set
if ('token' not in local_var_params or
local_var_params['token'] is None):
raise ValueError("Missing the required parameter `token` when calling `subscribe_plan`") # noqa: E501
collection_formats = {}
path_params = {}
if 'plan_name' in local_var_params:
path_params['planName'] = local_var_params['plan_name'] # noqa: E501
if 'token' in local_var_params:
path_params['token'] = local_var_params['token'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['api_key'] # noqa: E501
return self.api_client.call_api(
'/api2/json/subscribePlan/{planName}/{token}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='APIPlanSubscriptionOut', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def subscribe_plan_on_behalf(self, plan_name, api_key, **kwargs): # noqa: E501
"""Subscribe to a give API plan, using the user's preferred or default currency (admin only). # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.subscribe_plan_on_behalf(plan_name, api_key, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str plan_name: (required)
:param str api_key: (required)
:return: APIPlanSubscriptionOut
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.subscribe_plan_on_behalf_with_http_info(plan_name, api_key, **kwargs) # noqa: E501
else:
(data) = self.subscribe_plan_on_behalf_with_http_info(plan_name, api_key, **kwargs) # noqa: E501
return data
def subscribe_plan_on_behalf_with_http_info(self, plan_name, api_key, **kwargs): # noqa: E501
"""Subscribe to a give API plan, using the user's preferred or default currency (admin only). # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.subscribe_plan_on_behalf_with_http_info(plan_name, api_key, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str plan_name: (required)
:param str api_key: (required)
:return: APIPlanSubscriptionOut
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = ['plan_name', 'api_key'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method subscribe_plan_on_behalf" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'plan_name' is set
if ('plan_name' not in local_var_params or
local_var_params['plan_name'] is None):
raise ValueError("Missing the required parameter `plan_name` when calling `subscribe_plan_on_behalf`") # noqa: E501
# verify the required parameter 'api_key' is set
if ('api_key' not in local_var_params or
local_var_params['api_key'] is None):
raise ValueError("Missing the required parameter `api_key` when calling `subscribe_plan_on_behalf`") # noqa: E501
collection_formats = {}
path_params = {}
if 'plan_name' in local_var_params:
path_params['planName'] = local_var_params['plan_name'] # noqa: E501
if 'api_key' in local_var_params:
path_params['apiKey'] = local_var_params['api_key'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['api_key'] # noqa: E501
return self.api_client.call_api(
'/api2/json/subscribePlanOnBehalf/{planName}/{apiKey}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='APIPlanSubscriptionOut', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def taxonomy_classes(self, classifier_name, **kwargs): # noqa: E501
"""Print the taxonomy classes valid for the given classifier. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.taxonomy_classes(classifier_name, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str classifier_name: (required)
:return: APIPlansOut
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.taxonomy_classes_with_http_info(classifier_name, **kwargs) # noqa: E501
else:
(data) = self.taxonomy_classes_with_http_info(classifier_name, **kwargs) # noqa: E501
return data
def taxonomy_classes_with_http_info(self, classifier_name, **kwargs): # noqa: E501
"""Print the taxonomy classes valid for the given classifier. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.taxonomy_classes_with_http_info(classifier_name, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str classifier_name: (required)
:return: APIPlansOut
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = ['classifier_name'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method taxonomy_classes" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'classifier_name' is set
if ('classifier_name' not in local_var_params or
local_var_params['classifier_name'] is None):
raise ValueError("Missing the required parameter `classifier_name` when calling `taxonomy_classes`") # noqa: E501
collection_formats = {}
path_params = {}
if 'classifier_name' in local_var_params:
path_params['classifierName'] = local_var_params['classifier_name'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['api_key'] # noqa: E501
return self.api_client.call_api(
'/api2/json/taxonomyClasses/{classifierName}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='APIPlansOut', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def update_billing_info(self, token, **kwargs): # noqa: E501
"""Sets or update the billing information (company name, address, phone, vat ID) # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.update_billing_info(token, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str token: (required)
:param BillingInfoInOut billing_info_in_out:
:return: BillingInfoInOut
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.update_billing_info_with_http_info(token, **kwargs) # noqa: E501
else:
(data) = self.update_billing_info_with_http_info(token, **kwargs) # noqa: E501
return data
def update_billing_info_with_http_info(self, token, **kwargs): # noqa: E501
"""Sets or update the billing information (company name, address, phone, vat ID) # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.update_billing_info_with_http_info(token, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str token: (required)
:param BillingInfoInOut billing_info_in_out:
:return: BillingInfoInOut
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = ['token', 'billing_info_in_out'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method update_billing_info" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'token' is set
if ('token' not in local_var_params or
local_var_params['token'] is None):
raise ValueError("Missing the required parameter `token` when calling `update_billing_info`") # noqa: E501
collection_formats = {}
path_params = {}
if 'token' in local_var_params:
path_params['token'] = local_var_params['token'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'billing_info_in_out' in local_var_params:
body_params = local_var_params['billing_info_in_out']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json;charset=UTF-8']) # noqa: E501
# Authentication setting
auth_settings = ['api_key'] # noqa: E501
return self.api_client.call_api(
'/api2/json/updateBillingInfo/{token}', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='BillingInfoInOut', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def update_limit(self, usage_limit, hard_or_soft, token, **kwargs): # noqa: E501
"""Modifies the hard/soft limit on the API plan's overages (default is 0$ soft limit). # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.update_limit(usage_limit, hard_or_soft, token, async_req=True)
>>> result = thread.get()
:param async_req bool
:param int usage_limit: (required)
:param bool hard_or_soft: (required)
:param str token: (required)
:return: APIPeriodUsageOut
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.update_limit_with_http_info(usage_limit, hard_or_soft, token, **kwargs) # noqa: E501
else:
(data) = self.update_limit_with_http_info(usage_limit, hard_or_soft, token, **kwargs) # noqa: E501
return data
def update_limit_with_http_info(self, usage_limit, hard_or_soft, token, **kwargs): # noqa: E501
"""Modifies the hard/soft limit on the API plan's overages (default is 0$ soft limit). # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.update_limit_with_http_info(usage_limit, hard_or_soft, token, async_req=True)
>>> result = thread.get()
:param async_req bool
:param int usage_limit: (required)
:param bool hard_or_soft: (required)
:param str token: (required)
:return: APIPeriodUsageOut
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = ['usage_limit', 'hard_or_soft', 'token'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method update_limit" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'usage_limit' is set
if ('usage_limit' not in local_var_params or
local_var_params['usage_limit'] is None):
raise ValueError("Missing the required parameter `usage_limit` when calling `update_limit`") # noqa: E501
# verify the required parameter 'hard_or_soft' is set
if ('hard_or_soft' not in local_var_params or
local_var_params['hard_or_soft'] is None):
raise ValueError("Missing the required parameter `hard_or_soft` when calling `update_limit`") # noqa: E501
# verify the required parameter 'token' is set
if ('token' not in local_var_params or
local_var_params['token'] is None):
raise ValueError("Missing the required parameter `token` when calling `update_limit`") # noqa: E501
collection_formats = {}
path_params = {}
if 'usage_limit' in local_var_params:
path_params['usageLimit'] = local_var_params['usage_limit'] # noqa: E501
if 'hard_or_soft' in local_var_params:
path_params['hardOrSoft'] = local_var_params['hard_or_soft'] # noqa: E501
if 'token' in local_var_params:
path_params['token'] = local_var_params['token'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['api_key'] # noqa: E501
return self.api_client.call_api(
'/api2/json/updateLimit/{usageLimit}/{hardOrSoft}/{token}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='APIPeriodUsageOut', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def update_payment_default(self, defaut_source_id, token, **kwargs): # noqa: E501
"""Update the default Stripe card associated with the current google auth session token. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.update_payment_default(defaut_source_id, token, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str defaut_source_id: (required)
:param str token: (required)
:return: APIKeyOut
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.update_payment_default_with_http_info(defaut_source_id, token, **kwargs) # noqa: E501
else:
(data) = self.update_payment_default_with_http_info(defaut_source_id, token, **kwargs) # noqa: E501
return data
def update_payment_default_with_http_info(self, defaut_source_id, token, **kwargs): # noqa: E501
"""Update the default Stripe card associated with the current google auth session token. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.update_payment_default_with_http_info(defaut_source_id, token, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str defaut_source_id: (required)
:param str token: (required)
:return: APIKeyOut
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = ['defaut_source_id', 'token'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method update_payment_default" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'defaut_source_id' is set
if ('defaut_source_id' not in local_var_params or
local_var_params['defaut_source_id'] is None):
raise ValueError("Missing the required parameter `defaut_source_id` when calling `update_payment_default`") # noqa: E501
# verify the required parameter 'token' is set
if ('token' not in local_var_params or
local_var_params['token'] is None):
raise ValueError("Missing the required parameter `token` when calling `update_payment_default`") # noqa: E501
collection_formats = {}
path_params = {}
if 'defaut_source_id' in local_var_params:
path_params['defautSourceId'] = local_var_params['defaut_source_id'] # noqa: E501
if 'token' in local_var_params:
path_params['token'] = local_var_params['token'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['api_key'] # noqa: E501
return self.api_client.call_api(
'/api2/json/updatePaymentDefault/{defautSourceId}/{token}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='APIKeyOut', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def user_info(self, token, **kwargs): # noqa: E501
"""Get the user profile associated with the current google auth session token. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.user_info(token, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str token: (required)
:return: APIKeyOut
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.user_info_with_http_info(token, **kwargs) # noqa: E501
else:
(data) = self.user_info_with_http_info(token, **kwargs) # noqa: E501
return data
def user_info_with_http_info(self, token, **kwargs): # noqa: E501
"""Get the user profile associated with the current google auth session token. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.user_info_with_http_info(token, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str token: (required)
:return: APIKeyOut
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = ['token'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method user_info" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'token' is set
if ('token' not in local_var_params or
local_var_params['token'] is None):
raise ValueError("Missing the required parameter `token` when calling `user_info`") # noqa: E501
collection_formats = {}
path_params = {}
if 'token' in local_var_params:
path_params['token'] = local_var_params['token'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['api_key'] # noqa: E501
return self.api_client.call_api(
'/api2/json/userInfo/{token}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='APIKeyOut', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def verify_email(self, email_token, **kwargs): # noqa: E501
"""Verifies an email, based on token sent to that email # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.verify_email(email_token, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str email_token: (required)
:return: APIKeyOut
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.verify_email_with_http_info(email_token, **kwargs) # noqa: E501
else:
(data) = self.verify_email_with_http_info(email_token, **kwargs) # noqa: E501
return data
def verify_email_with_http_info(self, email_token, **kwargs): # noqa: E501
"""Verifies an email, based on token sent to that email # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.verify_email_with_http_info(email_token, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str email_token: (required)
:return: APIKeyOut
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = ['email_token'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method verify_email" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'email_token' is set
if ('email_token' not in local_var_params or
local_var_params['email_token'] is None):
raise ValueError("Missing the required parameter `email_token` when calling `verify_email`") # noqa: E501
collection_formats = {}
path_params = {}
if 'email_token' in local_var_params:
path_params['emailToken'] = local_var_params['email_token'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['api_key'] # noqa: E501
return self.api_client.call_api(
'/api2/json/verifyEmail/{emailToken}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='APIKeyOut', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def verify_remove_email(self, email_token, **kwargs): # noqa: E501
"""Verifies an email, based on token sent to that email # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.verify_remove_email(email_token, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str email_token: (required)
:return: APIKeyOut
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.verify_remove_email_with_http_info(email_token, **kwargs) # noqa: E501
else:
(data) = self.verify_remove_email_with_http_info(email_token, **kwargs) # noqa: E501
return data
def verify_remove_email_with_http_info(self, email_token, **kwargs): # noqa: E501
"""Verifies an email, based on token sent to that email # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.verify_remove_email_with_http_info(email_token, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str email_token: (required)
:return: APIKeyOut
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = ['email_token'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method verify_remove_email" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'email_token' is set
if ('email_token' not in local_var_params or
local_var_params['email_token'] is None):
raise ValueError("Missing the required parameter `email_token` when calling `verify_remove_email`") # noqa: E501
collection_formats = {}
path_params = {}
if 'email_token' in local_var_params:
path_params['emailToken'] = local_var_params['email_token'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['api_key'] # noqa: E501
return self.api_client.call_api(
'/api2/json/verifyRemoveEmail/{emailToken}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='APIKeyOut', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def vet(self, source, vetted, **kwargs): # noqa: E501
"""Vetting of a source. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.vet(source, vetted, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str source: (required)
:param bool vetted: (required)
:return: None
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.vet_with_http_info(source, vetted, **kwargs) # noqa: E501
else:
(data) = self.vet_with_http_info(source, vetted, **kwargs) # noqa: E501
return data
def vet_with_http_info(self, source, vetted, **kwargs): # noqa: E501
"""Vetting of a source. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.vet_with_http_info(source, vetted, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str source: (required)
:param bool vetted: (required)
:return: None
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = ['source', 'vetted'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method vet" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'source' is set
if ('source' not in local_var_params or
local_var_params['source'] is None):
raise ValueError("Missing the required parameter `source` when calling `vet`") # noqa: E501
# verify the required parameter 'vetted' is set
if ('vetted' not in local_var_params or
local_var_params['vetted'] is None):
raise ValueError("Missing the required parameter `vetted` when calling `vet`") # noqa: E501
collection_formats = {}
path_params = {}
if 'source' in local_var_params:
path_params['source'] = local_var_params['source'] # noqa: E501
if 'vetted' in local_var_params:
path_params['vetted'] = local_var_params['vetted'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# Authentication setting
auth_settings = ['api_key'] # noqa: E501
return self.api_client.call_api(
'/api2/json/vetting/{source}/{vetted}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type=None, # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
| 39.12063 | 385 | 0.611836 | 16,982 | 146,585 | 4.987163 | 0.021847 | 0.05252 | 0.078685 | 0.034006 | 0.961791 | 0.953845 | 0.941258 | 0.935082 | 0.924373 | 0.904985 | 0 | 0.015588 | 0.299771 | 146,585 | 3,746 | 386 | 39.131073 | 0.809524 | 0.300495 | 0 | 0.801067 | 0 | 0 | 0.173656 | 0.044769 | 0 | 0 | 0 | 0 | 0 | 1 | 0.039301 | false | 0 | 0.001941 | 0 | 0.099951 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
03ca24ca871f7abf621163623a03a59264a3fd93 | 124 | py | Python | python-student/exercise-01/param_echo.py | yahav876/Yahav-DevOps | 308a10758150824061f14cc2589738355dec91e8 | [
"CNRI-Python"
] | null | null | null | python-student/exercise-01/param_echo.py | yahav876/Yahav-DevOps | 308a10758150824061f14cc2589738355dec91e8 | [
"CNRI-Python"
] | null | null | null | python-student/exercise-01/param_echo.py | yahav876/Yahav-DevOps | 308a10758150824061f14cc2589738355dec91e8 | [
"CNRI-Python"
] | null | null | null | #!/bin/python3
import sys
print(f"First argument to echo {sys.argv[2:]}")
print(f"Second argument to echo {sys.argv[1]}")
| 17.714286 | 47 | 0.693548 | 22 | 124 | 3.909091 | 0.636364 | 0.139535 | 0.325581 | 0.395349 | 0.488372 | 0 | 0 | 0 | 0 | 0 | 0 | 0.027523 | 0.120968 | 124 | 6 | 48 | 20.666667 | 0.761468 | 0.104839 | 0 | 0 | 0 | 0 | 0.672727 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.333333 | 0 | 0.333333 | 0.666667 | 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 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 8 |
03df966d7780a1057e346910223ec198083a2776 | 243 | py | Python | 2021/01_Introduction/fish.py | lfrommelt/monty | e8cabf0e4ac01ab3d97eecee5e699139076d6544 | [
"MIT"
] | null | null | null | 2021/01_Introduction/fish.py | lfrommelt/monty | e8cabf0e4ac01ab3d97eecee5e699139076d6544 | [
"MIT"
] | null | null | null | 2021/01_Introduction/fish.py | lfrommelt/monty | e8cabf0e4ac01ab3d97eecee5e699139076d6544 | [
"MIT"
] | 1 | 2020-03-20T14:26:28.000Z | 2020-03-20T14:26:28.000Z | print("blubb")
print("blubb")
print("blubb")
print("blubb")
print("blubb")
print("blubb")
print("blubb")
print("blubb")
print("blubb")
print("blubb")
print("blubb")
print("blubb")
print("blubb")
print("blubb")
print("blubb")
print("blubb")
| 12.15 | 14 | 0.658436 | 32 | 243 | 5 | 0.0625 | 1 | 1.40625 | 1.875 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0.078189 | 243 | 19 | 15 | 12.789474 | 0.714286 | 0 | 0 | 1 | 0 | 0 | 0.329218 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 12 |
ff038787fb83d80fd4142a166ea85a0a5dd001e0 | 6,936 | py | Python | test/hummingbot/connector/exchange/bitfinex/test_bitfinex_in_flight_order.py | BGTCapital/hummingbot | 2c50f50d67cedccf0ef4d8e3f4c8cdce3dc87242 | [
"Apache-2.0"
] | 3,027 | 2019-04-04T18:52:17.000Z | 2022-03-30T09:38:34.000Z | test/hummingbot/connector/exchange/bitfinex/test_bitfinex_in_flight_order.py | BGTCapital/hummingbot | 2c50f50d67cedccf0ef4d8e3f4c8cdce3dc87242 | [
"Apache-2.0"
] | 4,080 | 2019-04-04T19:51:11.000Z | 2022-03-31T23:45:21.000Z | test/hummingbot/connector/exchange/bitfinex/test_bitfinex_in_flight_order.py | BGTCapital/hummingbot | 2c50f50d67cedccf0ef4d8e3f4c8cdce3dc87242 | [
"Apache-2.0"
] | 1,342 | 2019-04-04T20:50:53.000Z | 2022-03-31T15:22:36.000Z | from decimal import Decimal
from unittest import TestCase
from hummingbot.connector.exchange.bitfinex import OrderStatus
from hummingbot.connector.exchange.bitfinex.bitfinex_in_flight_order import BitfinexInFlightOrder
from hummingbot.core.event.events import OrderType, TradeType
class BitfinexInFlightOrderTests(TestCase):
def setUp(self):
super().setUp()
self.base_token = "BTC"
self.quote_token = "USDT"
self.trading_pair = f"{self.base_token}-{self.quote_token}"
def test_update_with_partial_trade_event(self):
order = BitfinexInFlightOrder(
client_order_id="OID1",
exchange_order_id="EOID1",
trading_pair=self.trading_pair,
order_type=OrderType.LIMIT,
trade_type=TradeType.BUY,
price=Decimal(10000),
amount=Decimal(1)
)
trade_event_info = {
"order_id": "EOID1",
"trade_id": 1,
"amount": 0.1,
"price": 10050.0,
"fee": 10.0,
"fee_currency": "USDC",
}
update_result = order.update_with_trade_update(trade_event_info)
self.assertTrue(update_result)
self.assertTrue(order.is_open)
self.assertEqual(OrderStatus.PARTIALLY, order.last_state)
self.assertEqual(Decimal(str(trade_event_info["amount"])), order.executed_amount_base)
expected_executed_quote_amount = Decimal(str(trade_event_info["amount"])) * Decimal(str(trade_event_info["price"]))
self.assertEqual(expected_executed_quote_amount, order.executed_amount_quote)
self.assertEqual(Decimal(trade_event_info["fee"]), order.fee_paid)
self.assertEqual(trade_event_info["fee_currency"], order.fee_asset)
def test_update_with_full_fill_trade_event(self):
order = BitfinexInFlightOrder(
client_order_id="OID1",
exchange_order_id="EOID1",
trading_pair=self.trading_pair,
order_type=OrderType.LIMIT,
trade_type=TradeType.BUY,
price=Decimal(10000),
amount=Decimal(1)
)
trade_event_info = {
"order_id": "EOID1",
"trade_id": 1,
"amount": 0.1,
"price": 10050.0,
"fee": 10.0,
"fee_currency": "USDC",
}
update_result = order.update_with_trade_update(trade_event_info)
self.assertTrue(update_result)
self.assertTrue(order.is_open)
self.assertEqual(OrderStatus.PARTIALLY, order.last_state)
self.assertEqual(Decimal(str(trade_event_info["amount"])), order.executed_amount_base)
expected_executed_quote_amount = Decimal(str(trade_event_info["amount"])) * Decimal(str(trade_event_info["price"]))
self.assertEqual(expected_executed_quote_amount, order.executed_amount_quote)
self.assertEqual(Decimal(trade_event_info["fee"]), order.fee_paid)
self.assertEqual(trade_event_info["fee_currency"], order.fee_asset)
complete_event_info = {
"order_id": "EOID1",
"trade_id": 2,
"amount": 0.9,
"price": 10060.0,
"fee": 50.0,
"fee_currency": "USDC",
}
update_result = order.update_with_trade_update(complete_event_info)
self.assertTrue(update_result)
self.assertFalse(order.is_open)
self.assertTrue(order.is_done)
self.assertEqual(OrderStatus.EXECUTED, order.last_state)
self.assertEqual(order.amount, order.executed_amount_base)
expected_executed_quote_amount += Decimal(str(complete_event_info["amount"])) * Decimal(
str(complete_event_info["price"]))
self.assertEqual(expected_executed_quote_amount, order.executed_amount_quote)
self.assertEqual(Decimal(trade_event_info["fee"]) + Decimal(complete_event_info["fee"]), order.fee_paid)
self.assertEqual(complete_event_info["fee_currency"], order.fee_asset)
def test_update_with_repeated_trade_id_is_ignored(self):
order = BitfinexInFlightOrder(
client_order_id="OID1",
exchange_order_id="EOID1",
trading_pair=self.trading_pair,
order_type=OrderType.LIMIT,
trade_type=TradeType.BUY,
price=Decimal(10000),
amount=Decimal(1)
)
trade_event_info = {
"order_id": "EOID1",
"trade_id": 1,
"amount": 0.1,
"price": 10050.0,
"fee": 10.0,
"fee_currency": "USDC",
}
update_result = order.update_with_trade_update(trade_event_info)
self.assertTrue(update_result)
self.assertTrue(order.is_open)
self.assertEqual(OrderStatus.PARTIALLY, order.last_state)
self.assertEqual(Decimal(str(trade_event_info["amount"])), order.executed_amount_base)
expected_executed_quote_amount = Decimal(str(trade_event_info["amount"])) * Decimal(str(trade_event_info["price"]))
self.assertEqual(expected_executed_quote_amount, order.executed_amount_quote)
self.assertEqual(Decimal(trade_event_info["fee"]), order.fee_paid)
self.assertEqual(trade_event_info["fee_currency"], order.fee_asset)
complete_event_info = {
"order_id": "EOID1",
"trade_id": 1,
"amount": 1,
"price": 10060.0,
"fee": 50.0,
"fee_currency": "USDC",
}
update_result = order.update_with_trade_update(complete_event_info)
self.assertFalse(update_result)
self.assertTrue(order.is_open)
self.assertEqual(OrderStatus.PARTIALLY, order.last_state)
self.assertEqual(Decimal(str(trade_event_info["amount"])), order.executed_amount_base)
expected_executed_quote_amount = Decimal(str(trade_event_info["amount"])) * Decimal(
str(trade_event_info["price"]))
self.assertEqual(expected_executed_quote_amount, order.executed_amount_quote)
self.assertEqual(Decimal(trade_event_info["fee"]), order.fee_paid)
self.assertEqual(trade_event_info["fee_currency"], order.fee_asset)
def test_fee_currency_is_translated_when_processing_trade_event(self):
order = BitfinexInFlightOrder(
client_order_id="OID1",
exchange_order_id="EOID1",
trading_pair=self.trading_pair,
order_type=OrderType.LIMIT,
trade_type=TradeType.BUY,
price=Decimal(10000),
amount=Decimal(1)
)
trade_event_info = {
"order_id": "EOID1",
"trade_id": 1,
"amount": 0.1,
"price": 10050.0,
"fee": 10.0,
"fee_currency": "UST",
}
update_result = order.update_with_trade_update(trade_event_info)
self.assertTrue(update_result)
self.assertEqual("USDT", order.fee_asset)
| 39.186441 | 123 | 0.644752 | 785 | 6,936 | 5.359236 | 0.107006 | 0.079154 | 0.096506 | 0.057048 | 0.866889 | 0.841455 | 0.831709 | 0.825529 | 0.816259 | 0.814595 | 0 | 0.020881 | 0.247405 | 6,936 | 176 | 124 | 39.409091 | 0.785057 | 0 | 0 | 0.726667 | 0 | 0 | 0.078576 | 0.00519 | 0 | 0 | 0 | 0 | 0.253333 | 1 | 0.033333 | false | 0 | 0.033333 | 0 | 0.073333 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
ff084599427e4e9670ab8bf445de699b7096dd35 | 89,646 | py | Python | archived/common/get_worlds.py | jotalanusse/minety | bed3cb0e62257c0e9ef556df8c23b4b6748a7571 | [
"MIT"
] | 1 | 2021-05-12T02:01:59.000Z | 2021-05-12T02:01:59.000Z | archived/common/get_worlds.py | jotalanusse/minety | bed3cb0e62257c0e9ef556df8c23b4b6748a7571 | [
"MIT"
] | null | null | null | archived/common/get_worlds.py | jotalanusse/minety | bed3cb0e62257c0e9ef556df8c23b4b6748a7571 | [
"MIT"
] | null | null | null | def get_worlds():
return [
# ##############################################################################################################################
# ######################################################### [SEASON 2] #########################################################
# ##############################################################################################################################
# {
# 'name': 'BCC Season 2 - overworld', # Name of the world
# 'region_files_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/servers/season-2/original/Lelo_world_2.0/region/', # Directory of the world region files to be scanned
# 'scans': [
# {
# 'name': '0.05 seconds scan', # Name of the scan
# 'scan_all_chunks': True, # When enabled, the script will check all the chunks even if the region is already marked as important (the only good thing about this is you get to see an awesome world map)
# 'inhabited_ticks_threshold': 20 * 0.05, # How many ticks a chunk has to be inhabited for so that it is considered as an important chunk
# 'region_output': {
# 'enabled': True, # When disabled, no files will be copied to the output
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-2/overworld/seconds-005/', # Output directory for the processed region files
# },
# 'map': {
# 'enabled': True, # Is a map going to be graphed or not
# 'scale_factor': 1.0, # How large the map image output will be (if value is 1.0 one chunk = one pixel)
# 'file_name': 'heatmap-005.png', # Filename of the map file
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-2/overworld/heatmaps/', # Output directory for the generated heatmaps
# 'colors': {
# 'default': [0, 0, 0], # Default color for the map
# 'regions': {
# 'inhabited_colormap': 'jet', # Colormap used when a region is inhabited
# 'non_inhabited_colormap': 'PRGn' # Colormap used when a region is not inhabited
# }
# },
# 'realtime_graph': {
# 'enabled': False, # When enabled, the script will show a real time window showing the map being graphed once the region scanning process is finished
# 'max_size': 1000, # If you are using a small screen you can modify this value to be able to fit the whole map graph in your screen
# 'update_interval': 1 # How many chunk renders to skip before displaying the updated graph again
# }
# },
# },
# {
# 'name': '5 seconds scan', # Name of the scan
# 'scan_all_chunks': True, # When enabled, the script will check all the chunks even if the region is already marked as important (the only good thing about this is you get to see an awesome world map)
# 'inhabited_ticks_threshold': 20 * 5, # How many ticks a chunk has to be inhabited for so that it is considered as an important chunk
# 'region_output': {
# 'enabled': True, # When disabled, no files will be copied to the output
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-2/overworld/seconds-5/', # Output directory for the processed region files
# },
# 'map': {
# 'enabled': True, # Is a map going to be graphed or not
# 'scale_factor': 1.0, # How large the map image output will be (if value is 1.0 one chunk = one pixel)
# 'file_name': 'heatmap-5.png', # Filename of the map file
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-2/overworld/heatmaps/', # Output directory for the generated heatmaps
# 'colors': {
# 'default': [0, 0, 0], # Default color for the map
# 'regions': {
# 'inhabited_colormap': 'jet', # Colormap used when a region is inhabited
# 'non_inhabited_colormap': 'PRGn' # Colormap used when a region is not inhabited
# }
# },
# 'realtime_graph': {
# 'enabled': False, # When enabled, the script will show a real time window showing the map being graphed once the region scanning process is finished
# 'max_size': 1000, # If you are using a small screen you can modify this value to be able to fit the whole map graph in your screen
# 'update_interval': 1 # How many chunk renders to skip before displaying the updated graph again
# }
# },
# },
# {
# 'name': '30 seconds scan', # Name of the scan
# 'scan_all_chunks': True, # When enabled, the script will check all the chunks even if the region is already marked as important (the only good thing about this is you get to see an awesome world map)
# 'inhabited_ticks_threshold': 20 * 30, # How many ticks a chunk has to be inhabited for so that it is considered as an important chunk
# 'region_output': {
# 'enabled': True, # When disabled, no files will be copied to the output
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-2/overworld/seconds-30/', # Output directory for the processed region files
# },
# 'map': {
# 'enabled': True, # Is a map going to be graphed or not
# 'scale_factor': 1.0, # How large the map image output will be (if value is 1.0 one chunk = one pixel)
# 'file_name': 'heatmap-30.png', # Filename of the map file
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-2/overworld/heatmaps/', # Output directory for the generated heatmaps
# 'colors': {
# 'default': [0, 0, 0], # Default color for the map
# 'regions': {
# 'inhabited_colormap': 'jet', # Colormap used when a region is inhabited
# 'non_inhabited_colormap': 'PRGn' # Colormap used when a region is not inhabited
# }
# },
# 'realtime_graph': {
# 'enabled': False, # When enabled, the script will show a real time window showing the map being graphed once the region scanning process is finished
# 'max_size': 1000, # If you are using a small screen you can modify this value to be able to fit the whole map graph in your screen
# 'update_interval': 1 # How many chunk renders to skip before displaying the updated graph again
# }
# },
# }
# ]
# },
# {
# 'name': 'BCC Season 2 - nether', # Name of the world
# 'region_files_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/servers/season-2/original/Lelo_world_2.0/DIM-1/region/', # Directory of the world region files to be scanned
# 'scans': [
# {
# 'name': '0.05 seconds scan', # Name of the scan
# 'scan_all_chunks': True, # When enabled, the script will check all the chunks even if the region is already marked as important (the only good thing about this is you get to see an awesome world map)
# 'inhabited_ticks_threshold': 20 * 0.05, # How many ticks a chunk has to be inhabited for so that it is considered as an important chunk
# 'region_output': {
# 'enabled': True, # When disabled, no files will be copied to the output
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-2/the-nether/seconds-005/', # Output directory for the processed region files
# },
# 'map': {
# 'enabled': True, # Is a map going to be graphed or not
# 'scale_factor': 1.0, # How large the map image output will be (if value is 1.0 one chunk = one pixel)
# 'file_name': 'heatmap-005.png', # Filename of the map file
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-2/the-nether/heatmaps/', # Output directory for the generated heatmaps
# 'colors': {
# 'default': [0, 0, 0], # Default color for the map
# 'regions': {
# 'inhabited_colormap': 'jet', # Colormap used when a region is inhabited
# 'non_inhabited_colormap': 'PRGn' # Colormap used when a region is not inhabited
# }
# },
# 'realtime_graph': {
# 'enabled': False, # When enabled, the script will show a real time window showing the map being graphed once the region scanning process is finished
# 'max_size': 1000, # If you are using a small screen you can modify this value to be able to fit the whole map graph in your screen
# 'update_interval': 1 # How many chunk renders to skip before displaying the updated graph again
# }
# },
# },
# {
# 'name': '5 seconds scan', # Name of the scan
# 'scan_all_chunks': True, # When enabled, the script will check all the chunks even if the region is already marked as important (the only good thing about this is you get to see an awesome world map)
# 'inhabited_ticks_threshold': 20 * 5, # How many ticks a chunk has to be inhabited for so that it is considered as an important chunk
# 'region_output': {
# 'enabled': True, # When disabled, no files will be copied to the output
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-2/the-nether/seconds-5/', # Output directory for the processed region files
# },
# 'map': {
# 'enabled': True, # Is a map going to be graphed or not
# 'scale_factor': 1.0, # How large the map image output will be (if value is 1.0 one chunk = one pixel)
# 'file_name': 'heatmap-5.png', # Filename of the map file
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-2/the-nether/heatmaps/', # Output directory for the generated heatmaps
# 'colors': {
# 'default': [0, 0, 0], # Default color for the map
# 'regions': {
# 'inhabited_colormap': 'jet', # Colormap used when a region is inhabited
# 'non_inhabited_colormap': 'PRGn' # Colormap used when a region is not inhabited
# }
# },
# 'realtime_graph': {
# 'enabled': False, # When enabled, the script will show a real time window showing the map being graphed once the region scanning process is finished
# 'max_size': 1000, # If you are using a small screen you can modify this value to be able to fit the whole map graph in your screen
# 'update_interval': 1 # How many chunk renders to skip before displaying the updated graph again
# }
# },
# },
# {
# 'name': '30 seconds scan', # Name of the scan
# 'scan_all_chunks': True, # When enabled, the script will check all the chunks even if the region is already marked as important (the only good thing about this is you get to see an awesome world map)
# 'inhabited_ticks_threshold': 20 * 30, # How many ticks a chunk has to be inhabited for so that it is considered as an important chunk
# 'region_output': {
# 'enabled': True, # When disabled, no files will be copied to the output
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-2/the-nether/seconds-30/', # Output directory for the processed region files
# },
# 'map': {
# 'enabled': True, # Is a map going to be graphed or not
# 'scale_factor': 1.0, # How large the map image output will be (if value is 1.0 one chunk = one pixel)
# 'file_name': 'heatmap-30.png', # Filename of the map file
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-2/the-nether/heatmaps/', # Output directory for the generated heatmaps
# 'colors': {
# 'default': [0, 0, 0], # Default color for the map
# 'regions': {
# 'inhabited_colormap': 'jet', # Colormap used when a region is inhabited
# 'non_inhabited_colormap': 'PRGn' # Colormap used when a region is not inhabited
# }
# },
# 'realtime_graph': {
# 'enabled': False, # When enabled, the script will show a real time window showing the map being graphed once the region scanning process is finished
# 'max_size': 1000, # If you are using a small screen you can modify this value to be able to fit the whole map graph in your screen
# 'update_interval': 1 # How many chunk renders to skip before displaying the updated graph again
# }
# },
# }
# ]
# },
# ##############################################################################################################################
# ######################################################### [SEASON 3] #########################################################
# ##############################################################################################################################
# {
# 'name': 'BCC Season 3 - overworld', # Name of the world
# 'region_files_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/servers/season-3/original/BCC Server/region/', # Directory of the world region files to be scanned
# 'scans': [
# {
# 'name': '0.05 seconds scan', # Name of the scan
# 'scan_all_chunks': True, # When enabled, the script will check all the chunks even if the region is already marked as important (the only good thing about this is you get to see an awesome world map)
# 'inhabited_ticks_threshold': 20 * 0.05, # How many ticks a chunk has to be inhabited for so that it is considered as an important chunk
# 'region_output': {
# 'enabled': True, # When disabled, no files will be copied to the output
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-3/overworld/seconds-005/', # Output directory for the processed region files
# },
# 'map': {
# 'enabled': True, # Is a map going to be graphed or not
# 'scale_factor': 1.0, # How large the map image output will be (if value is 1.0 one chunk = one pixel)
# 'file_name': 'heatmap-005.png', # Filename of the map file
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-3/overworld/heatmaps/', # Output directory for the generated heatmaps
# 'colors': {
# 'default': [0, 0, 0], # Default color for the map
# 'regions': {
# 'inhabited_colormap': 'jet', # Colormap used when a region is inhabited
# 'non_inhabited_colormap': 'PRGn' # Colormap used when a region is not inhabited
# }
# },
# 'realtime_graph': {
# 'enabled': False, # When enabled, the script will show a real time window showing the map being graphed once the region scanning process is finished
# 'max_size': 1000, # If you are using a small screen you can modify this value to be able to fit the whole map graph in your screen
# 'update_interval': 1 # How many chunk renders to skip before displaying the updated graph again
# }
# },
# },
# {
# 'name': '5 seconds scan', # Name of the scan
# 'scan_all_chunks': True, # When enabled, the script will check all the chunks even if the region is already marked as important (the only good thing about this is you get to see an awesome world map)
# 'inhabited_ticks_threshold': 20 * 5, # How many ticks a chunk has to be inhabited for so that it is considered as an important chunk
# 'region_output': {
# 'enabled': True, # When disabled, no files will be copied to the output
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-3/overworld/seconds-5/', # Output directory for the processed region files
# },
# 'map': {
# 'enabled': True, # Is a map going to be graphed or not
# 'scale_factor': 1.0, # How large the map image output will be (if value is 1.0 one chunk = one pixel)
# 'file_name': 'heatmap-5.png', # Filename of the map file
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-3/overworld/heatmaps/', # Output directory for the generated heatmaps
# 'colors': {
# 'default': [0, 0, 0], # Default color for the map
# 'regions': {
# 'inhabited_colormap': 'jet', # Colormap used when a region is inhabited
# 'non_inhabited_colormap': 'PRGn' # Colormap used when a region is not inhabited
# }
# },
# 'realtime_graph': {
# 'enabled': False, # When enabled, the script will show a real time window showing the map being graphed once the region scanning process is finished
# 'max_size': 1000, # If you are using a small screen you can modify this value to be able to fit the whole map graph in your screen
# 'update_interval': 1 # How many chunk renders to skip before displaying the updated graph again
# }
# },
# },
# {
# 'name': '30 seconds scan', # Name of the scan
# 'scan_all_chunks': True, # When enabled, the script will check all the chunks even if the region is already marked as important (the only good thing about this is you get to see an awesome world map)
# 'inhabited_ticks_threshold': 20 * 30, # How many ticks a chunk has to be inhabited for so that it is considered as an important chunk
# 'region_output': {
# 'enabled': True, # When disabled, no files will be copied to the output
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-3/overworld/seconds-30/', # Output directory for the processed region files
# },
# 'map': {
# 'enabled': True, # Is a map going to be graphed or not
# 'scale_factor': 1.0, # How large the map image output will be (if value is 1.0 one chunk = one pixel)
# 'file_name': 'heatmap-30.png', # Filename of the map file
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-3/overworld/heatmaps/', # Output directory for the generated heatmaps
# 'colors': {
# 'default': [0, 0, 0], # Default color for the map
# 'regions': {
# 'inhabited_colormap': 'jet', # Colormap used when a region is inhabited
# 'non_inhabited_colormap': 'PRGn' # Colormap used when a region is not inhabited
# }
# },
# 'realtime_graph': {
# 'enabled': False, # When enabled, the script will show a real time window showing the map being graphed once the region scanning process is finished
# 'max_size': 1000, # If you are using a small screen you can modify this value to be able to fit the whole map graph in your screen
# 'update_interval': 1 # How many chunk renders to skip before displaying the updated graph again
# }
# },
# }
# ]
# },
# {
# 'name': 'BCC Season 3 - nether', # Name of the world
# 'region_files_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/servers/season-3/original/BCC Server/DIM-1/region/', # Directory of the world region files to be scanned
# 'scans': [
# {
# 'name': '0.05 seconds scan', # Name of the scan
# 'scan_all_chunks': True, # When enabled, the script will check all the chunks even if the region is already marked as important (the only good thing about this is you get to see an awesome world map)
# 'inhabited_ticks_threshold': 20 * 0.05, # How many ticks a chunk has to be inhabited for so that it is considered as an important chunk
# 'region_output': {
# 'enabled': True, # When disabled, no files will be copied to the output
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-3/the-nether/seconds-005/', # Output directory for the processed region files
# },
# 'map': {
# 'enabled': True, # Is a map going to be graphed or not
# 'scale_factor': 1.0, # How large the map image output will be (if value is 1.0 one chunk = one pixel)
# 'file_name': 'heatmap-005.png', # Filename of the map file
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-3/the-nether/heatmaps/', # Output directory for the generated heatmaps
# 'colors': {
# 'default': [0, 0, 0], # Default color for the map
# 'regions': {
# 'inhabited_colormap': 'jet', # Colormap used when a region is inhabited
# 'non_inhabited_colormap': 'PRGn' # Colormap used when a region is not inhabited
# }
# },
# 'realtime_graph': {
# 'enabled': False, # When enabled, the script will show a real time window showing the map being graphed once the region scanning process is finished
# 'max_size': 1000, # If you are using a small screen you can modify this value to be able to fit the whole map graph in your screen
# 'update_interval': 1 # How many chunk renders to skip before displaying the updated graph again
# }
# },
# },
# {
# 'name': '5 seconds scan', # Name of the scan
# 'scan_all_chunks': True, # When enabled, the script will check all the chunks even if the region is already marked as important (the only good thing about this is you get to see an awesome world map)
# 'inhabited_ticks_threshold': 20 * 5, # How many ticks a chunk has to be inhabited for so that it is considered as an important chunk
# 'region_output': {
# 'enabled': True, # When disabled, no files will be copied to the output
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-3/the-nether/seconds-5/', # Output directory for the processed region files
# },
# 'map': {
# 'enabled': True, # Is a map going to be graphed or not
# 'scale_factor': 1.0, # How large the map image output will be (if value is 1.0 one chunk = one pixel)
# 'file_name': 'heatmap-5.png', # Filename of the map file
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-3/the-nether/heatmaps/', # Output directory for the generated heatmaps
# 'colors': {
# 'default': [0, 0, 0], # Default color for the map
# 'regions': {
# 'inhabited_colormap': 'jet', # Colormap used when a region is inhabited
# 'non_inhabited_colormap': 'PRGn' # Colormap used when a region is not inhabited
# }
# },
# 'realtime_graph': {
# 'enabled': False, # When enabled, the script will show a real time window showing the map being graphed once the region scanning process is finished
# 'max_size': 1000, # If you are using a small screen you can modify this value to be able to fit the whole map graph in your screen
# 'update_interval': 1 # How many chunk renders to skip before displaying the updated graph again
# }
# },
# },
# {
# 'name': '30 seconds scan', # Name of the scan
# 'scan_all_chunks': True, # When enabled, the script will check all the chunks even if the region is already marked as important (the only good thing about this is you get to see an awesome world map)
# 'inhabited_ticks_threshold': 20 * 30, # How many ticks a chunk has to be inhabited for so that it is considered as an important chunk
# 'region_output': {
# 'enabled': True, # When disabled, no files will be copied to the output
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-3/the-nether/seconds-30/', # Output directory for the processed region files
# },
# 'map': {
# 'enabled': True, # Is a map going to be graphed or not
# 'scale_factor': 1.0, # How large the map image output will be (if value is 1.0 one chunk = one pixel)
# 'file_name': 'heatmap-30.png', # Filename of the map file
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-3/the-nether/heatmaps/', # Output directory for the generated heatmaps
# 'colors': {
# 'default': [0, 0, 0], # Default color for the map
# 'regions': {
# 'inhabited_colormap': 'jet', # Colormap used when a region is inhabited
# 'non_inhabited_colormap': 'PRGn' # Colormap used when a region is not inhabited
# }
# },
# 'realtime_graph': {
# 'enabled': False, # When enabled, the script will show a real time window showing the map being graphed once the region scanning process is finished
# 'max_size': 1000, # If you are using a small screen you can modify this value to be able to fit the whole map graph in your screen
# 'update_interval': 1 # How many chunk renders to skip before displaying the updated graph again
# }
# },
# }
# ]
# },
# {
# 'name': 'BCC Season 3 - end', # Name of the world
# 'region_files_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/servers/season-3/original/BCC Server/DIM1/region/', # Directory of the world region files to be scanned
# 'scans': [
# {
# 'name': '0.05 seconds scan', # Name of the scan
# 'scan_all_chunks': True, # When enabled, the script will check all the chunks even if the region is already marked as important (the only good thing about this is you get to see an awesome world map)
# 'inhabited_ticks_threshold': 20 * 0.05, # How many ticks a chunk has to be inhabited for so that it is considered as an important chunk
# 'region_output': {
# 'enabled': True, # When disabled, no files will be copied to the output
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-3/the-end/seconds-005/', # Output directory for the processed region files
# },
# 'map': {
# 'enabled': True, # Is a map going to be graphed or not
# 'scale_factor': 1.0, # How large the map image output will be (if value is 1.0 one chunk = one pixel)
# 'file_name': 'heatmap-005.png', # Filename of the map file
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-3/the-end/heatmaps/', # Output directory for the generated heatmaps
# 'colors': {
# 'default': [0, 0, 0], # Default color for the map
# 'regions': {
# 'inhabited_colormap': 'jet', # Colormap used when a region is inhabited
# 'non_inhabited_colormap': 'PRGn' # Colormap used when a region is not inhabited
# }
# },
# 'realtime_graph': {
# 'enabled': False, # When enabled, the script will show a real time window showing the map being graphed once the region scanning process is finished
# 'max_size': 1000, # If you are using a small screen you can modify this value to be able to fit the whole map graph in your screen
# 'update_interval': 1 # How many chunk renders to skip before displaying the updated graph again
# }
# },
# },
# {
# 'name': '5 seconds scan', # Name of the scan
# 'scan_all_chunks': True, # When enabled, the script will check all the chunks even if the region is already marked as important (the only good thing about this is you get to see an awesome world map)
# 'inhabited_ticks_threshold': 20 * 5, # How many ticks a chunk has to be inhabited for so that it is considered as an important chunk
# 'region_output': {
# 'enabled': True, # When disabled, no files will be copied to the output
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-3/the-end/seconds-5/', # Output directory for the processed region files
# },
# 'map': {
# 'enabled': True, # Is a map going to be graphed or not
# 'scale_factor': 1.0, # How large the map image output will be (if value is 1.0 one chunk = one pixel)
# 'file_name': 'heatmap-5.png', # Filename of the map file
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-3/the-end/heatmaps/', # Output directory for the generated heatmaps
# 'colors': {
# 'default': [0, 0, 0], # Default color for the map
# 'regions': {
# 'inhabited_colormap': 'jet', # Colormap used when a region is inhabited
# 'non_inhabited_colormap': 'PRGn' # Colormap used when a region is not inhabited
# }
# },
# 'realtime_graph': {
# 'enabled': False, # When enabled, the script will show a real time window showing the map being graphed once the region scanning process is finished
# 'max_size': 1000, # If you are using a small screen you can modify this value to be able to fit the whole map graph in your screen
# 'update_interval': 1 # How many chunk renders to skip before displaying the updated graph again
# }
# },
# },
# {
# 'name': '30 seconds scan', # Name of the scan
# 'scan_all_chunks': True, # When enabled, the script will check all the chunks even if the region is already marked as important (the only good thing about this is you get to see an awesome world map)
# 'inhabited_ticks_threshold': 20 * 30, # How many ticks a chunk has to be inhabited for so that it is considered as an important chunk
# 'region_output': {
# 'enabled': True, # When disabled, no files will be copied to the output
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-3/the-end/seconds-30/', # Output directory for the processed region files
# },
# 'map': {
# 'enabled': True, # Is a map going to be graphed or not
# 'scale_factor': 1.0, # How large the map image output will be (if value is 1.0 one chunk = one pixel)
# 'file_name': 'heatmap-30.png', # Filename of the map file
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-3/the-end/heatmaps/', # Output directory for the generated heatmaps
# 'colors': {
# 'default': [0, 0, 0], # Default color for the map
# 'regions': {
# 'inhabited_colormap': 'jet', # Colormap used when a region is inhabited
# 'non_inhabited_colormap': 'PRGn' # Colormap used when a region is not inhabited
# }
# },
# 'realtime_graph': {
# 'enabled': False, # When enabled, the script will show a real time window showing the map being graphed once the region scanning process is finished
# 'max_size': 1000, # If you are using a small screen you can modify this value to be able to fit the whole map graph in your screen
# 'update_interval': 1 # How many chunk renders to skip before displaying the updated graph again
# }
# },
# }
# ]
# },
# ##############################################################################################################################
# ######################################################### [SEASON 4] #########################################################
# ##############################################################################################################################
# {
# 'name': 'BCC Season 4 - overworld', # Name of the world
# 'region_files_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/servers/season-4/original/BCC Server/region/', # Directory of the world region files to be scanned
# 'scans': [
# {
# 'name': '0.05 seconds scan', # Name of the scan
# 'scan_all_chunks': True, # When enabled, the script will check all the chunks even if the region is already marked as important (the only good thing about this is you get to see an awesome world map)
# 'inhabited_ticks_threshold': 20 * 0.05, # How many ticks a chunk has to be inhabited for so that it is considered as an important chunk
# 'region_output': {
# 'enabled': True, # When disabled, no files will be copied to the output
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-4/overworld/seconds-005/', # Output directory for the processed region files
# },
# 'map': {
# 'enabled': True, # Is a map going to be graphed or not
# 'scale_factor': 1.0, # How large the map image output will be (if value is 1.0 one chunk = one pixel)
# 'file_name': 'heatmap-005.png', # Filename of the map file
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-4/overworld/heatmaps/', # Output directory for the generated heatmaps
# 'colors': {
# 'default': [0, 0, 0], # Default color for the map
# 'regions': {
# 'inhabited_colormap': 'jet', # Colormap used when a region is inhabited
# 'non_inhabited_colormap': 'PRGn' # Colormap used when a region is not inhabited
# }
# },
# 'realtime_graph': {
# 'enabled': False, # When enabled, the script will show a real time window showing the map being graphed once the region scanning process is finished
# 'max_size': 1000, # If you are using a small screen you can modify this value to be able to fit the whole map graph in your screen
# 'update_interval': 1 # How many chunk renders to skip before displaying the updated graph again
# }
# },
# },
# {
# 'name': '5 seconds scan', # Name of the scan
# 'scan_all_chunks': True, # When enabled, the script will check all the chunks even if the region is already marked as important (the only good thing about this is you get to see an awesome world map)
# 'inhabited_ticks_threshold': 20 * 5, # How many ticks a chunk has to be inhabited for so that it is considered as an important chunk
# 'region_output': {
# 'enabled': True, # When disabled, no files will be copied to the output
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-4/overworld/seconds-5/', # Output directory for the processed region files
# },
# 'map': {
# 'enabled': True, # Is a map going to be graphed or not
# 'scale_factor': 1.0, # How large the map image output will be (if value is 1.0 one chunk = one pixel)
# 'file_name': 'heatmap-5.png', # Filename of the map file
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-4/overworld/heatmaps/', # Output directory for the generated heatmaps
# 'colors': {
# 'default': [0, 0, 0], # Default color for the map
# 'regions': {
# 'inhabited_colormap': 'jet', # Colormap used when a region is inhabited
# 'non_inhabited_colormap': 'PRGn' # Colormap used when a region is not inhabited
# }
# },
# 'realtime_graph': {
# 'enabled': False, # When enabled, the script will show a real time window showing the map being graphed once the region scanning process is finished
# 'max_size': 1000, # If you are using a small screen you can modify this value to be able to fit the whole map graph in your screen
# 'update_interval': 1 # How many chunk renders to skip before displaying the updated graph again
# }
# },
# },
# {
# 'name': '20 seconds scan', # Name of the scan
# 'scan_all_chunks': True, # When enabled, the script will check all the chunks even if the region is already marked as important (the only good thing about this is you get to see an awesome world map)
# 'inhabited_ticks_threshold': 20 * 20, # How many ticks a chunk has to be inhabited for so that it is considered as an important chunk
# 'region_output': {
# 'enabled': True, # When disabled, no files will be copied to the output
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-4/overworld/seconds-20/', # Output directory for the processed region files
# },
# 'map': {
# 'enabled': True, # Is a map going to be graphed or not
# 'scale_factor': 1.0, # How large the map image output will be (if value is 1.0 one chunk = one pixel)
# 'file_name': 'heatmap-20.png', # Filename of the map file
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-4/overworld/heatmaps/', # Output directory for the generated heatmaps
# 'colors': {
# 'default': [0, 0, 0], # Default color for the map
# 'regions': {
# 'inhabited_colormap': 'jet', # Colormap used when a region is inhabited
# 'non_inhabited_colormap': 'PRGn' # Colormap used when a region is not inhabited
# }
# },
# 'realtime_graph': {
# 'enabled': False, # When enabled, the script will show a real time window showing the map being graphed once the region scanning process is finished
# 'max_size': 1000, # If you are using a small screen you can modify this value to be able to fit the whole map graph in your screen
# 'update_interval': 1 # How many chunk renders to skip before displaying the updated graph again
# }
# },
# },
# {
# 'name': '25 seconds scan', # Name of the scan
# 'scan_all_chunks': True, # When enabled, the script will check all the chunks even if the region is already marked as important (the only good thing about this is you get to see an awesome world map)
# 'inhabited_ticks_threshold': 20 * 25, # How many ticks a chunk has to be inhabited for so that it is considered as an important chunk
# 'region_output': {
# 'enabled': True, # When disabled, no files will be copied to the output
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-4/overworld/seconds-25/', # Output directory for the processed region files
# },
# 'map': {
# 'enabled': True, # Is a map going to be graphed or not
# 'scale_factor': 1.0, # How large the map image output will be (if value is 1.0 one chunk = one pixel)
# 'file_name': 'heatmap-25.png', # Filename of the map file
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-4/overworld/heatmaps/', # Output directory for the generated heatmaps
# 'colors': {
# 'default': [0, 0, 0], # Default color for the map
# 'regions': {
# 'inhabited_colormap': 'jet', # Colormap used when a region is inhabited
# 'non_inhabited_colormap': 'PRGn' # Colormap used when a region is not inhabited
# }
# },
# 'realtime_graph': {
# 'enabled': False, # When enabled, the script will show a real time window showing the map being graphed once the region scanning process is finished
# 'max_size': 1000, # If you are using a small screen you can modify this value to be able to fit the whole map graph in your screen
# 'update_interval': 1 # How many chunk renders to skip before displaying the updated graph again
# }
# },
# },
# {
# 'name': '30 seconds scan', # Name of the scan
# 'scan_all_chunks': True, # When enabled, the script will check all the chunks even if the region is already marked as important (the only good thing about this is you get to see an awesome world map)
# 'inhabited_ticks_threshold': 20 * 30, # How many ticks a chunk has to be inhabited for so that it is considered as an important chunk
# 'region_output': {
# 'enabled': True, # When disabled, no files will be copied to the output
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-4/overworld/seconds-30/', # Output directory for the processed region files
# },
# 'map': {
# 'enabled': True, # Is a map going to be graphed or not
# 'scale_factor': 1.0, # How large the map image output will be (if value is 1.0 one chunk = one pixel)
# 'file_name': 'heatmap-30.png', # Filename of the map file
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-4/overworld/heatmaps/', # Output directory for the generated heatmaps
# 'colors': {
# 'default': [0, 0, 0], # Default color for the map
# 'regions': {
# 'inhabited_colormap': 'jet', # Colormap used when a region is inhabited
# 'non_inhabited_colormap': 'PRGn' # Colormap used when a region is not inhabited
# }
# },
# 'realtime_graph': {
# 'enabled': False, # When enabled, the script will show a real time window showing the map being graphed once the region scanning process is finished
# 'max_size': 1000, # If you are using a small screen you can modify this value to be able to fit the whole map graph in your screen
# 'update_interval': 1 # How many chunk renders to skip before displaying the updated graph again
# }
# },
# }
# ]
# },
# {
# 'name': 'BCC Season 4 - nether', # Name of the world
# 'region_files_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/servers/season-4/original/BCC Server_nether/DIM-1/region', # Directory of the world region files to be scanned
# 'scans': [
# {
# 'name': '0.05 seconds scan', # Name of the scan
# 'scan_all_chunks': True, # When enabled, the script will check all the chunks even if the region is already marked as important (the only good thing about this is you get to see an awesome world map)
# 'inhabited_ticks_threshold': 20 * 0.05, # How many ticks a chunk has to be inhabited for so that it is considered as an important chunk
# 'region_output': {
# 'enabled': True, # When disabled, no files will be copied to the output
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-4/the-nether/seconds-005/', # Output directory for the processed region files
# },
# 'map': {
# 'enabled': True, # Is a map going to be graphed or not
# 'scale_factor': 1.0, # How large the map image output will be (if value is 1.0 one chunk = one pixel)
# 'file_name': 'heatmap-005.png', # Filename of the map file
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-4/the-nether/heatmaps/', # Output directory for the generated heatmaps
# 'colors': {
# 'default': [0, 0, 0], # Default color for the map
# 'regions': {
# 'inhabited_colormap': 'jet', # Colormap used when a region is inhabited
# 'non_inhabited_colormap': 'PRGn' # Colormap used when a region is not inhabited
# }
# },
# 'realtime_graph': {
# 'enabled': False, # When enabled, the script will show a real time window showing the map being graphed once the region scanning process is finished
# 'max_size': 1000, # If you are using a small screen you can modify this value to be able to fit the whole map graph in your screen
# 'update_interval': 1 # How many chunk renders to skip before displaying the updated graph again
# }
# },
# },
# {
# 'name': '5 seconds scan', # Name of the scan
# 'scan_all_chunks': True, # When enabled, the script will check all the chunks even if the region is already marked as important (the only good thing about this is you get to see an awesome world map)
# 'inhabited_ticks_threshold': 20 * 5, # How many ticks a chunk has to be inhabited for so that it is considered as an important chunk
# 'region_output': {
# 'enabled': True, # When disabled, no files will be copied to the output
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-4/the-nether/seconds-5/', # Output directory for the processed region files
# },
# 'map': {
# 'enabled': True, # Is a map going to be graphed or not
# 'scale_factor': 1.0, # How large the map image output will be (if value is 1.0 one chunk = one pixel)
# 'file_name': 'heatmap-5.png', # Filename of the map file
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-4/the-nether/heatmaps/', # Output directory for the generated heatmaps
# 'colors': {
# 'default': [0, 0, 0], # Default color for the map
# 'regions': {
# 'inhabited_colormap': 'jet', # Colormap used when a region is inhabited
# 'non_inhabited_colormap': 'PRGn' # Colormap used when a region is not inhabited
# }
# },
# 'realtime_graph': {
# 'enabled': False, # When enabled, the script will show a real time window showing the map being graphed once the region scanning process is finished
# 'max_size': 1000, # If you are using a small screen you can modify this value to be able to fit the whole map graph in your screen
# 'update_interval': 1 # How many chunk renders to skip before displaying the updated graph again
# }
# },
# },
# {
# 'name': '30 seconds scan', # Name of the scan
# 'scan_all_chunks': True, # When enabled, the script will check all the chunks even if the region is already marked as important (the only good thing about this is you get to see an awesome world map)
# 'inhabited_ticks_threshold': 20 * 30, # How many ticks a chunk has to be inhabited for so that it is considered as an important chunk
# 'region_output': {
# 'enabled': True, # When disabled, no files will be copied to the output
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-4/the-nether/seconds-30/', # Output directory for the processed region files
# },
# 'map': {
# 'enabled': True, # Is a map going to be graphed or not
# 'scale_factor': 1.0, # How large the map image output will be (if value is 1.0 one chunk = one pixel)
# 'file_name': 'heatmap-30.png', # Filename of the map file
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-4/the-nether/heatmaps/', # Output directory for the generated heatmaps
# 'colors': {
# 'default': [0, 0, 0], # Default color for the map
# 'regions': {
# 'inhabited_colormap': 'jet', # Colormap used when a region is inhabited
# 'non_inhabited_colormap': 'PRGn' # Colormap used when a region is not inhabited
# }
# },
# 'realtime_graph': {
# 'enabled': False, # When enabled, the script will show a real time window showing the map being graphed once the region scanning process is finished
# 'max_size': 1000, # If you are using a small screen you can modify this value to be able to fit the whole map graph in your screen
# 'update_interval': 1 # How many chunk renders to skip before displaying the updated graph again
# }
# },
# }
# ]
# },
# {
# 'name': 'BCC Season 4 - end', # Name of the world
# 'region_files_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/servers/season-4/original/BCC Server_the_end/DIM1/region', # Directory of the world region files to be scanned
# 'scans': [
# {
# 'name': '0.05 seconds scan', # Name of the scan
# 'scan_all_chunks': True, # When enabled, the script will check all the chunks even if the region is already marked as important (the only good thing about this is you get to see an awesome world map)
# 'inhabited_ticks_threshold': 20 * 0.05, # How many ticks a chunk has to be inhabited for so that it is considered as an important chunk
# 'region_output': {
# 'enabled': True, # When disabled, no files will be copied to the output
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-4/the-end/seconds-005/', # Output directory for the processed region files
# },
# 'map': {
# 'enabled': True, # Is a map going to be graphed or not
# 'scale_factor': 1.0, # How large the map image output will be (if value is 1.0 one chunk = one pixel)
# 'file_name': 'heatmap-005.png', # Filename of the map file
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-4/the-end/heatmaps/', # Output directory for the generated heatmaps
# 'colors': {
# 'default': [0, 0, 0], # Default color for the map
# 'regions': {
# 'inhabited_colormap': 'jet', # Colormap used when a region is inhabited
# 'non_inhabited_colormap': 'PRGn' # Colormap used when a region is not inhabited
# }
# },
# 'realtime_graph': {
# 'enabled': False, # When enabled, the script will show a real time window showing the map being graphed once the region scanning process is finished
# 'max_size': 1000, # If you are using a small screen you can modify this value to be able to fit the whole map graph in your screen
# 'update_interval': 1 # How many chunk renders to skip before displaying the updated graph again
# }
# },
# },
# {
# 'name': '5 seconds scan', # Name of the scan
# 'scan_all_chunks': True, # When enabled, the script will check all the chunks even if the region is already marked as important (the only good thing about this is you get to see an awesome world map)
# 'inhabited_ticks_threshold': 20 * 5, # How many ticks a chunk has to be inhabited for so that it is considered as an important chunk
# 'region_output': {
# 'enabled': True, # When disabled, no files will be copied to the output
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-4/the-end/seconds-5/', # Output directory for the processed region files
# },
# 'map': {
# 'enabled': True, # Is a map going to be graphed or not
# 'scale_factor': 1.0, # How large the map image output will be (if value is 1.0 one chunk = one pixel)
# 'file_name': 'heatmap-5.png', # Filename of the map file
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-4/the-end/heatmaps/', # Output directory for the generated heatmaps
# 'colors': {
# 'default': [0, 0, 0], # Default color for the map
# 'regions': {
# 'inhabited_colormap': 'jet', # Colormap used when a region is inhabited
# 'non_inhabited_colormap': 'PRGn' # Colormap used when a region is not inhabited
# }
# },
# 'realtime_graph': {
# 'enabled': False, # When enabled, the script will show a real time window showing the map being graphed once the region scanning process is finished
# 'max_size': 1000, # If you are using a small screen you can modify this value to be able to fit the whole map graph in your screen
# 'update_interval': 1 # How many chunk renders to skip before displaying the updated graph again
# }
# },
# },
# {
# 'name': '30 seconds scan', # Name of the scan
# 'scan_all_chunks': True, # When enabled, the script will check all the chunks even if the region is already marked as important (the only good thing about this is you get to see an awesome world map)
# 'inhabited_ticks_threshold': 20 * 30, # How many ticks a chunk has to be inhabited for so that it is considered as an important chunk
# 'region_output': {
# 'enabled': True, # When disabled, no files will be copied to the output
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-4/the-end/seconds-30/', # Output directory for the processed region files
# },
# 'map': {
# 'enabled': True, # Is a map going to be graphed or not
# 'scale_factor': 1.0, # How large the map image output will be (if value is 1.0 one chunk = one pixel)
# 'file_name': 'heatmap-30.png', # Filename of the map file
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-4/the-end/heatmaps/', # Output directory for the generated heatmaps
# 'colors': {
# 'default': [0, 0, 0], # Default color for the map
# 'regions': {
# 'inhabited_colormap': 'jet', # Colormap used when a region is inhabited
# 'non_inhabited_colormap': 'PRGn' # Colormap used when a region is not inhabited
# }
# },
# 'realtime_graph': {
# 'enabled': False, # When enabled, the script will show a real time window showing the map being graphed once the region scanning process is finished
# 'max_size': 1000, # If you are using a small screen you can modify this value to be able to fit the whole map graph in your screen
# 'update_interval': 1 # How many chunk renders to skip before displaying the updated graph again
# }
# },
# }
# ]
# },
# ##############################################################################################################################
# ######################################################### [SEASON 5] #########################################################
# ##############################################################################################################################
# {
# 'name': 'BCC Season 5 - overworld', # Name of the world
# 'region_files_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/servers/season-5/original/world/region/', # Directory of the world region files to be scanned
# 'scans': [
# {
# 'name': '0.05 seconds scan', # Name of the scan
# 'scan_all_chunks': True, # When enabled, the script will check all the chunks even if the region is already marked as important (the only good thing about this is you get to see an awesome world map)
# 'inhabited_ticks_threshold': 20 * 0.05, # How many ticks a chunk has to be inhabited for so that it is considered as an important chunk
# 'region_output': {
# 'enabled': True, # When disabled, no files will be copied to the output
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-5/overworld/seconds-005/', # Output directory for the processed region files
# },
# 'map': {
# 'enabled': True, # Is a map going to be graphed or not
# 'scale_factor': 1.0, # How large the map image output will be (if value is 1.0 one chunk = one pixel)
# 'file_name': 'heatmap-005.png', # Filename of the map file
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-5/overworld/heatmaps/', # Output directory for the generated heatmaps
# 'colors': {
# 'default': [0, 0, 0], # Default color for the map
# 'regions': {
# 'inhabited_colormap': 'jet', # Colormap used when a region is inhabited
# 'non_inhabited_colormap': 'PRGn' # Colormap used when a region is not inhabited
# }
# },
# 'realtime_graph': {
# 'enabled': False, # When enabled, the script will show a real time window showing the map being graphed once the region scanning process is finished
# 'max_size': 1000, # If you are using a small screen you can modify this value to be able to fit the whole map graph in your screen
# 'update_interval': 1 # How many chunk renders to skip before displaying the updated graph again
# }
# },
# },
# {
# 'name': '5 seconds scan', # Name of the scan
# 'scan_all_chunks': True, # When enabled, the script will check all the chunks even if the region is already marked as important (the only good thing about this is you get to see an awesome world map)
# 'inhabited_ticks_threshold': 20 * 5, # How many ticks a chunk has to be inhabited for so that it is considered as an important chunk
# 'region_output': {
# 'enabled': True, # When disabled, no files will be copied to the output
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-5/overworld/seconds-5/', # Output directory for the processed region files
# },
# 'map': {
# 'enabled': True, # Is a map going to be graphed or not
# 'scale_factor': 1.0, # How large the map image output will be (if value is 1.0 one chunk = one pixel)
# 'file_name': 'heatmap-5.png', # Filename of the map file
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-5/overworld/heatmaps/', # Output directory for the generated heatmaps
# 'colors': {
# 'default': [0, 0, 0], # Default color for the map
# 'regions': {
# 'inhabited_colormap': 'jet', # Colormap used when a region is inhabited
# 'non_inhabited_colormap': 'PRGn' # Colormap used when a region is not inhabited
# }
# },
# 'realtime_graph': {
# 'enabled': False, # When enabled, the script will show a real time window showing the map being graphed once the region scanning process is finished
# 'max_size': 1000, # If you are using a small screen you can modify this value to be able to fit the whole map graph in your screen
# 'update_interval': 1 # How many chunk renders to skip before displaying the updated graph again
# }
# },
# },
# {
# 'name': '30 seconds scan', # Name of the scan
# 'scan_all_chunks': True, # When enabled, the script will check all the chunks even if the region is already marked as important (the only good thing about this is you get to see an awesome world map)
# 'inhabited_ticks_threshold': 20 * 30, # How many ticks a chunk has to be inhabited for so that it is considered as an important chunk
# 'region_output': {
# 'enabled': True, # When disabled, no files will be copied to the output
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-5/overworld/seconds-30/', # Output directory for the processed region files
# },
# 'map': {
# 'enabled': True, # Is a map going to be graphed or not
# 'scale_factor': 1.0, # How large the map image output will be (if value is 1.0 one chunk = one pixel)
# 'file_name': 'heatmap-30.png', # Filename of the map file
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-5/overworld/heatmaps/', # Output directory for the generated heatmaps
# 'colors': {
# 'default': [0, 0, 0], # Default color for the map
# 'regions': {
# 'inhabited_colormap': 'jet', # Colormap used when a region is inhabited
# 'non_inhabited_colormap': 'PRGn' # Colormap used when a region is not inhabited
# }
# },
# 'realtime_graph': {
# 'enabled': False, # When enabled, the script will show a real time window showing the map being graphed once the region scanning process is finished
# 'max_size': 1000, # If you are using a small screen you can modify this value to be able to fit the whole map graph in your screen
# 'update_interval': 1 # How many chunk renders to skip before displaying the updated graph again
# }
# },
# }
# ]
# },
# {
# 'name': 'BCC Season 5 - nether', # Name of the world
# 'region_files_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/servers/season-5/original/world/DIM-1/region', # Directory of the world region files to be scanned
# 'scans': [
# {
# 'name': '0.05 seconds scan', # Name of the scan
# 'scan_all_chunks': True, # When enabled, the script will check all the chunks even if the region is already marked as important (the only good thing about this is you get to see an awesome world map)
# 'inhabited_ticks_threshold': 20 * 0.05, # How many ticks a chunk has to be inhabited for so that it is considered as an important chunk
# 'region_output': {
# 'enabled': True, # When disabled, no files will be copied to the output
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-5/the-nether/seconds-005/', # Output directory for the processed region files
# },
# 'map': {
# 'enabled': True, # Is a map going to be graphed or not
# 'scale_factor': 1.0, # How large the map image output will be (if value is 1.0 one chunk = one pixel)
# 'file_name': 'heatmap-005.png', # Filename of the map file
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-5/the-nether/heatmaps/', # Output directory for the generated heatmaps
# 'colors': {
# 'default': [0, 0, 0], # Default color for the map
# 'regions': {
# 'inhabited_colormap': 'jet', # Colormap used when a region is inhabited
# 'non_inhabited_colormap': 'PRGn' # Colormap used when a region is not inhabited
# }
# },
# 'realtime_graph': {
# 'enabled': False, # When enabled, the script will show a real time window showing the map being graphed once the region scanning process is finished
# 'max_size': 1000, # If you are using a small screen you can modify this value to be able to fit the whole map graph in your screen
# 'update_interval': 1 # How many chunk renders to skip before displaying the updated graph again
# }
# },
# },
# {
# 'name': '5 seconds scan', # Name of the scan
# 'scan_all_chunks': True, # When enabled, the script will check all the chunks even if the region is already marked as important (the only good thing about this is you get to see an awesome world map)
# 'inhabited_ticks_threshold': 20 * 5, # How many ticks a chunk has to be inhabited for so that it is considered as an important chunk
# 'region_output': {
# 'enabled': True, # When disabled, no files will be copied to the output
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-5/the-nether/seconds-5/', # Output directory for the processed region files
# },
# 'map': {
# 'enabled': True, # Is a map going to be graphed or not
# 'scale_factor': 1.0, # How large the map image output will be (if value is 1.0 one chunk = one pixel)
# 'file_name': 'heatmap-5.png', # Filename of the map file
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-5/the-nether/heatmaps/', # Output directory for the generated heatmaps
# 'colors': {
# 'default': [0, 0, 0], # Default color for the map
# 'regions': {
# 'inhabited_colormap': 'jet', # Colormap used when a region is inhabited
# 'non_inhabited_colormap': 'PRGn' # Colormap used when a region is not inhabited
# }
# },
# 'realtime_graph': {
# 'enabled': False, # When enabled, the script will show a real time window showing the map being graphed once the region scanning process is finished
# 'max_size': 1000, # If you are using a small screen you can modify this value to be able to fit the whole map graph in your screen
# 'update_interval': 1 # How many chunk renders to skip before displaying the updated graph again
# }
# },
# },
# {
# 'name': '30 seconds scan', # Name of the scan
# 'scan_all_chunks': True, # When enabled, the script will check all the chunks even if the region is already marked as important (the only good thing about this is you get to see an awesome world map)
# 'inhabited_ticks_threshold': 20 * 30, # How many ticks a chunk has to be inhabited for so that it is considered as an important chunk
# 'region_output': {
# 'enabled': True, # When disabled, no files will be copied to the output
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-5/the-nether/seconds-30/', # Output directory for the processed region files
# },
# 'map': {
# 'enabled': True, # Is a map going to be graphed or not
# 'scale_factor': 1.0, # How large the map image output will be (if value is 1.0 one chunk = one pixel)
# 'file_name': 'heatmap-30.png', # Filename of the map file
# 'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-5/the-nether/heatmaps/', # Output directory for the generated heatmaps
# 'colors': {
# 'default': [0, 0, 0], # Default color for the map
# 'regions': {
# 'inhabited_colormap': 'jet', # Colormap used when a region is inhabited
# 'non_inhabited_colormap': 'PRGn' # Colormap used when a region is not inhabited
# }
# },
# 'realtime_graph': {
# 'enabled': False, # When enabled, the script will show a real time window showing the map being graphed once the region scanning process is finished
# 'max_size': 1000, # If you are using a small screen you can modify this value to be able to fit the whole map graph in your screen
# 'update_interval': 1 # How many chunk renders to skip before displaying the updated graph again
# }
# },
# }
# ]
# },
##############################################################################################################################
################################################### [SEASON 6 - EPISODE 1] ###################################################
##############################################################################################################################
{
'name': 'BCC Season 6 [Episode 1] - overworld', # Name of the world
'region_files_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/season 6/bcc/region/', # Directory of the world region files to be scanned
'scans': [
{
'name': '0.05 seconds scan', # Name of the scan
'scan_all_chunks': True, # When enabled, the script will check all the chunks even if the region is already marked as important (the only good thing about this is you get to see an awesome world map)
'inhabited_ticks_threshold': 20 * 0.05, # How many ticks a chunk has to be inhabited for so that it is considered as an important chunk
'region_output': {
'enabled': True, # When disabled, no files will be copied to the output
'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-6/overworld/seconds-005/', # Output directory for the processed region files
},
'map': {
'enabled': True, # Is a map going to be graphed or not
'scale_factor': 1.0, # How large the map image output will be (if value is 1.0 one chunk = one pixel)
'file_name': 'heatmap-005.png', # Filename of the map file
'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-6/overworld/heatmaps/', # Output directory for the generated heatmaps
'colors': {
'default': [0, 0, 0], # Default color for the map
'regions': {
'inhabited_colormap': 'jet', # Colormap used when a region is inhabited
'non_inhabited_colormap': 'PRGn' # Colormap used when a region is not inhabited
}
},
'realtime_graph': {
'enabled': False, # When enabled, the script will show a real time window showing the map being graphed once the region scanning process is finished
'max_size': 1000, # If you are using a small screen you can modify this value to be able to fit the whole map graph in your screen
'update_interval': 1 # How many chunk renders to skip before displaying the updated graph again
}
},
},
{
'name': '5 seconds scan', # Name of the scan
'scan_all_chunks': True, # When enabled, the script will check all the chunks even if the region is already marked as important (the only good thing about this is you get to see an awesome world map)
'inhabited_ticks_threshold': 20 * 5, # How many ticks a chunk has to be inhabited for so that it is considered as an important chunk
'region_output': {
'enabled': True, # When disabled, no files will be copied to the output
'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-6/overworld/seconds-5/', # Output directory for the processed region files
},
'map': {
'enabled': True, # Is a map going to be graphed or not
'scale_factor': 1.0, # How large the map image output will be (if value is 1.0 one chunk = one pixel)
'file_name': 'heatmap-5.png', # Filename of the map file
'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-6/overworld/heatmaps/', # Output directory for the generated heatmaps
'colors': {
'default': [0, 0, 0], # Default color for the map
'regions': {
'inhabited_colormap': 'jet', # Colormap used when a region is inhabited
'non_inhabited_colormap': 'PRGn' # Colormap used when a region is not inhabited
}
},
'realtime_graph': {
'enabled': False, # When enabled, the script will show a real time window showing the map being graphed once the region scanning process is finished
'max_size': 1000, # If you are using a small screen you can modify this value to be able to fit the whole map graph in your screen
'update_interval': 1 # How many chunk renders to skip before displaying the updated graph again
}
},
},
{
'name': '30 seconds scan', # Name of the scan
'scan_all_chunks': True, # When enabled, the script will check all the chunks even if the region is already marked as important (the only good thing about this is you get to see an awesome world map)
'inhabited_ticks_threshold': 20 * 30, # How many ticks a chunk has to be inhabited for so that it is considered as an important chunk
'region_output': {
'enabled': True, # When disabled, no files will be copied to the output
'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-6/overworld/seconds-30/', # Output directory for the processed region files
},
'map': {
'enabled': True, # Is a map going to be graphed or not
'scale_factor': 1.0, # How large the map image output will be (if value is 1.0 one chunk = one pixel)
'file_name': 'heatmap-30.png', # Filename of the map file
'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-6/overworld/heatmaps/', # Output directory for the generated heatmaps
'colors': {
'default': [0, 0, 0], # Default color for the map
'regions': {
'inhabited_colormap': 'jet', # Colormap used when a region is inhabited
'non_inhabited_colormap': 'PRGn' # Colormap used when a region is not inhabited
}
},
'realtime_graph': {
'enabled': False, # When enabled, the script will show a real time window showing the map being graphed once the region scanning process is finished
'max_size': 1000, # If you are using a small screen you can modify this value to be able to fit the whole map graph in your screen
'update_interval': 1 # How many chunk renders to skip before displaying the updated graph again
}
},
}
]
},
{
'name': 'BCC Season 6 [Episode 1] - nether', # Name of the world
'region_files_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/season 6/bcc_nether/DIM-1/region', # Directory of the world region files to be scanned
'scans': [
{
'name': '0.05 seconds scan', # Name of the scan
'scan_all_chunks': True, # When enabled, the script will check all the chunks even if the region is already marked as important (the only good thing about this is you get to see an awesome world map)
'inhabited_ticks_threshold': 20 * 0.05, # How many ticks a chunk has to be inhabited for so that it is considered as an important chunk
'region_output': {
'enabled': True, # When disabled, no files will be copied to the output
'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-6/the-nether/seconds-005/', # Output directory for the processed region files
},
'map': {
'enabled': True, # Is a map going to be graphed or not
'scale_factor': 1.0, # How large the map image output will be (if value is 1.0 one chunk = one pixel)
'file_name': 'heatmap-005.png', # Filename of the map file
'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-6/the-nether/heatmaps/', # Output directory for the generated heatmaps
'colors': {
'default': [0, 0, 0], # Default color for the map
'regions': {
'inhabited_colormap': 'jet', # Colormap used when a region is inhabited
'non_inhabited_colormap': 'PRGn' # Colormap used when a region is not inhabited
}
},
'realtime_graph': {
'enabled': False, # When enabled, the script will show a real time window showing the map being graphed once the region scanning process is finished
'max_size': 1000, # If you are using a small screen you can modify this value to be able to fit the whole map graph in your screen
'update_interval': 1 # How many chunk renders to skip before displaying the updated graph again
}
},
},
{
'name': '5 seconds scan', # Name of the scan
'scan_all_chunks': True, # When enabled, the script will check all the chunks even if the region is already marked as important (the only good thing about this is you get to see an awesome world map)
'inhabited_ticks_threshold': 20 * 5, # How many ticks a chunk has to be inhabited for so that it is considered as an important chunk
'region_output': {
'enabled': True, # When disabled, no files will be copied to the output
'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-6/the-nether/seconds-5/', # Output directory for the processed region files
},
'map': {
'enabled': True, # Is a map going to be graphed or not
'scale_factor': 1.0, # How large the map image output will be (if value is 1.0 one chunk = one pixel)
'file_name': 'heatmap-5.png', # Filename of the map file
'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-6/the-nether/heatmaps/', # Output directory for the generated heatmaps
'colors': {
'default': [0, 0, 0], # Default color for the map
'regions': {
'inhabited_colormap': 'jet', # Colormap used when a region is inhabited
'non_inhabited_colormap': 'PRGn' # Colormap used when a region is not inhabited
}
},
'realtime_graph': {
'enabled': False, # When enabled, the script will show a real time window showing the map being graphed once the region scanning process is finished
'max_size': 1000, # If you are using a small screen you can modify this value to be able to fit the whole map graph in your screen
'update_interval': 1 # How many chunk renders to skip before displaying the updated graph again
}
},
},
{
'name': '30 seconds scan', # Name of the scan
'scan_all_chunks': True, # When enabled, the script will check all the chunks even if the region is already marked as important (the only good thing about this is you get to see an awesome world map)
'inhabited_ticks_threshold': 20 * 30, # How many ticks a chunk has to be inhabited for so that it is considered as an important chunk
'region_output': {
'enabled': True, # When disabled, no files will be copied to the output
'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-6/the-nether/seconds-30/', # Output directory for the processed region files
},
'map': {
'enabled': True, # Is a map going to be graphed or not
'scale_factor': 1.0, # How large the map image output will be (if value is 1.0 one chunk = one pixel)
'file_name': 'heatmap-30.png', # Filename of the map file
'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-6/the-nether/heatmaps/', # Output directory for the generated heatmaps
'colors': {
'default': [0, 0, 0], # Default color for the map
'regions': {
'inhabited_colormap': 'jet', # Colormap used when a region is inhabited
'non_inhabited_colormap': 'PRGn' # Colormap used when a region is not inhabited
}
},
'realtime_graph': {
'enabled': False, # When enabled, the script will show a real time window showing the map being graphed once the region scanning process is finished
'max_size': 1000, # If you are using a small screen you can modify this value to be able to fit the whole map graph in your screen
'update_interval': 1 # How many chunk renders to skip before displaying the updated graph again
}
},
}
]
},
{
'name': 'BCC Season 6 [Episode 1] - end', # Name of the world
'region_files_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/season 6/bcc_the_end/DIM1/region', # Directory of the world region files to be scanned
'scans': [
{
'name': '0.05 seconds scan', # Name of the scan
'scan_all_chunks': True, # When enabled, the script will check all the chunks even if the region is already marked as important (the only good thing about this is you get to see an awesome world map)
'inhabited_ticks_threshold': 20 * 0.05, # How many ticks a chunk has to be inhabited for so that it is considered as an important chunk
'region_output': {
'enabled': True, # When disabled, no files will be copied to the output
'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-6/the-end/seconds-005/', # Output directory for the processed region files
},
'map': {
'enabled': True, # Is a map going to be graphed or not
'scale_factor': 1.0, # How large the map image output will be (if value is 1.0 one chunk = one pixel)
'file_name': 'heatmap-005.png', # Filename of the map file
'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-6/the-end/heatmaps/', # Output directory for the generated heatmaps
'colors': {
'default': [0, 0, 0], # Default color for the map
'regions': {
'inhabited_colormap': 'jet', # Colormap used when a region is inhabited
'non_inhabited_colormap': 'PRGn' # Colormap used when a region is not inhabited
}
},
'realtime_graph': {
'enabled': False, # When enabled, the script will show a real time window showing the map being graphed once the region scanning process is finished
'max_size': 1000, # If you are using a small screen you can modify this value to be able to fit the whole map graph in your screen
'update_interval': 1 # How many chunk renders to skip before displaying the updated graph again
}
},
},
{
'name': '5 seconds scan', # Name of the scan
'scan_all_chunks': True, # When enabled, the script will check all the chunks even if the region is already marked as important (the only good thing about this is you get to see an awesome world map)
'inhabited_ticks_threshold': 20 * 5, # How many ticks a chunk has to be inhabited for so that it is considered as an important chunk
'region_output': {
'enabled': True, # When disabled, no files will be copied to the output
'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-6/the-end/seconds-5/', # Output directory for the processed region files
},
'map': {
'enabled': True, # Is a map going to be graphed or not
'scale_factor': 1.0, # How large the map image output will be (if value is 1.0 one chunk = one pixel)
'file_name': 'heatmap-5.png', # Filename of the map file
'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-6/the-end/heatmaps/', # Output directory for the generated heatmaps
'colors': {
'default': [0, 0, 0], # Default color for the map
'regions': {
'inhabited_colormap': 'jet', # Colormap used when a region is inhabited
'non_inhabited_colormap': 'PRGn' # Colormap used when a region is not inhabited
}
},
'realtime_graph': {
'enabled': False, # When enabled, the script will show a real time window showing the map being graphed once the region scanning process is finished
'max_size': 1000, # If you are using a small screen you can modify this value to be able to fit the whole map graph in your screen
'update_interval': 1 # How many chunk renders to skip before displaying the updated graph again
}
},
},
{
'name': '30 seconds scan', # Name of the scan
'scan_all_chunks': True, # When enabled, the script will check all the chunks even if the region is already marked as important (the only good thing about this is you get to see an awesome world map)
'inhabited_ticks_threshold': 20 * 30, # How many ticks a chunk has to be inhabited for so that it is considered as an important chunk
'region_output': {
'enabled': True, # When disabled, no files will be copied to the output
'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-6/the-end/seconds-30/', # Output directory for the processed region files
},
'map': {
'enabled': True, # Is a map going to be graphed or not
'scale_factor': 1.0, # How large the map image output will be (if value is 1.0 one chunk = one pixel)
'file_name': 'heatmap-30.png', # Filename of the map file
'output_directory': '/media/jotalanusse/windows-drive/Servers/Minecraft/backup/clean/season-6/the-end/heatmaps/', # Output directory for the generated heatmaps
'colors': {
'default': [0, 0, 0], # Default color for the map
'regions': {
'inhabited_colormap': 'jet', # Colormap used when a region is inhabited
'non_inhabited_colormap': 'PRGn' # Colormap used when a region is not inhabited
}
},
'realtime_graph': {
'enabled': False, # When enabled, the script will show a real time window showing the map being graphed once the region scanning process is finished
'max_size': 1000, # If you are using a small screen you can modify this value to be able to fit the whole map graph in your screen
'update_interval': 1 # How many chunk renders to skip before displaying the updated graph again
}
},
}
]
},
] | 74.518703 | 211 | 0.599313 | 11,602 | 89,646 | 4.574556 | 0.013101 | 0.04635 | 0.044749 | 0.057279 | 0.997909 | 0.997909 | 0.997494 | 0.997494 | 0.997494 | 0.997494 | 0 | 0.01621 | 0.29052 | 89,646 | 1,203 | 212 | 74.518703 | 0.81826 | 0.803416 | 0 | 0.659091 | 0 | 0.075758 | 0.316533 | 0.167159 | 0 | 0 | 0 | 0 | 0 | 1 | 0.003788 | true | 0 | 0 | 0.003788 | 0.007576 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
ff0b2c61650512f363dea73a04495f6dcb58958e | 92 | py | Python | libfuturize/test_scripts/py2/print_range.py | kojoidrissa/python-future | 16635ad986ab10a337267f637a8b040a70bc5258 | [
"MIT"
] | 1 | 2018-01-18T00:46:19.000Z | 2018-01-18T00:46:19.000Z | libfuturize/test_scripts/py2/print_range.py | kojoidrissa/python-future | 16635ad986ab10a337267f637a8b040a70bc5258 | [
"MIT"
] | null | null | null | libfuturize/test_scripts/py2/print_range.py | kojoidrissa/python-future | 16635ad986ab10a337267f637a8b040a70bc5258 | [
"MIT"
] | null | null | null | from __future__ import print_function
from future.builtins import *
print(list(range(10)))
| 18.4 | 37 | 0.804348 | 13 | 92 | 5.307692 | 0.692308 | 0.289855 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.02439 | 0.108696 | 92 | 4 | 38 | 23 | 0.817073 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.666667 | 0 | 0.666667 | 0.666667 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 7 |
20635b8612d0540326a2dc52aafe2e8ebc2325f9 | 1,040 | py | Python | units/volume/u_s_teaspoons.py | putridparrot/PyUnits | 4f1095c6fc0bee6ba936921c391913dbefd9307c | [
"MIT"
] | null | null | null | units/volume/u_s_teaspoons.py | putridparrot/PyUnits | 4f1095c6fc0bee6ba936921c391913dbefd9307c | [
"MIT"
] | null | null | null | units/volume/u_s_teaspoons.py | putridparrot/PyUnits | 4f1095c6fc0bee6ba936921c391913dbefd9307c | [
"MIT"
] | null | null | null | # <auto-generated>
# This code was generated by the UnitCodeGenerator tool
#
# Changes to this file will be lost if the code is regenerated
# </auto-generated>
def to_millilitres(value):
return value * 4.928921593749999616
def to_litres(value):
return value * 0.004928921593749999616
def to_kilolitres(value):
return value * 0.000004928921593749999
def to_teaspoons(value):
return value * 0.83267384046639071232
def to_tablespoons(value):
return value * 0.27755794682213023744
def to_quarts(value):
return value * 0.004336842919095784243
def to_pints(value):
return value * 0.008673685838191568486
def to_gallons(value):
return value * 0.00108421072977394606
def to_fluid_ounces(value):
return value * 0.1734737167638313984
def to_u_s_tablespoons(value):
return value / 3.0
def to_u_s_quarts(value):
return value / 192.0
def to_u_s_pints(value):
return value / 96.0
def to_u_s_gallons(value):
return value / 768.0
def to_u_s_fluid_ounces(value):
return value / 6.0
def to_u_s_cups(value):
return value / 48.0
| 28.108108 | 62 | 0.774038 | 158 | 1,040 | 4.911392 | 0.322785 | 0.096649 | 0.309278 | 0.175258 | 0.121134 | 0 | 0 | 0 | 0 | 0 | 0 | 0.233184 | 0.142308 | 1,040 | 36 | 63 | 28.888889 | 0.636771 | 0.143269 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | false | 0 | 0 | 0.5 | 1 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 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 | 7 |
20c6dfd805d746ef8f192ef46080f019c67435be | 1,157 | py | Python | testsuite/E20.py | andriyor/pycodestyle | 79e191d7c68b7c0d77f1f94a53558004c477cf8b | [
"MIT"
] | 3,594 | 2016-02-23T07:13:52.000Z | 2022-03-31T21:15:06.000Z | testsuite/E20.py | andriyor/pycodestyle | 79e191d7c68b7c0d77f1f94a53558004c477cf8b | [
"MIT"
] | 581 | 2016-02-23T15:19:11.000Z | 2022-03-31T23:47:20.000Z | testsuite/E20.py | andriyor/pycodestyle | 79e191d7c68b7c0d77f1f94a53558004c477cf8b | [
"MIT"
] | 476 | 2016-02-25T01:27:27.000Z | 2022-03-26T23:58:31.000Z | #: E201:1:6
spam( ham[1], {eggs: 2})
#: E201:1:10
spam(ham[ 1], {eggs: 2})
#: E201:1:15
spam(ham[1], { eggs: 2})
#: E201:1:6
spam( ham[1], {eggs: 2})
#: E201:1:10
spam(ham[ 1], {eggs: 2})
#: E201:1:15
spam(ham[1], { eggs: 2})
#: Okay
spam(ham[1], {eggs: 2})
#:
#: E202:1:23
spam(ham[1], {eggs: 2} )
#: E202:1:22
spam(ham[1], {eggs: 2 })
#: E202:1:11
spam(ham[1 ], {eggs: 2})
#: E202:1:23
spam(ham[1], {eggs: 2} )
#: E202:1:22
spam(ham[1], {eggs: 2 })
#: E202:1:11
spam(ham[1 ], {eggs: 2})
#: Okay
spam(ham[1], {eggs: 2})
result = func(
arg1='some value',
arg2='another value',
)
result = func(
arg1='some value',
arg2='another value'
)
result = [
item for item in items
if item > 5
]
#:
#: E203:1:10
if x == 4 :
print x, y
x, y = y, x
#: E203:1:10
if x == 4 :
print x, y
x, y = y, x
#: E203:2:15 E702:2:16
if x == 4:
print x, y ; x, y = y, x
#: E203:2:15 E702:2:16
if x == 4:
print x, y ; x, y = y, x
#: E203:3:13
if x == 4:
print x, y
x, y = y , x
#: E203:3:13
if x == 4:
print x, y
x, y = y , x
#: Okay
if x == 4:
print x, y
x, y = y, x
a[b1, :] == a[b1, ...]
b = a[:, b1]
#:
| 14.64557 | 28 | 0.480553 | 235 | 1,157 | 2.365957 | 0.165957 | 0.176259 | 0.201439 | 0.302158 | 0.929856 | 0.929856 | 0.929856 | 0.929856 | 0.929856 | 0.778777 | 0 | 0.189635 | 0.266206 | 1,157 | 78 | 29 | 14.833333 | 0.465253 | 0.20484 | 0 | 0.787234 | 0 | 0 | 0.051339 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0 | null | null | 0.148936 | 0 | 0 | 0 | null | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 |
4553741fcfd35f7e9989aa9dec4bb70c3d0f47e3 | 2,804 | py | Python | tests/test_messages.py | adsr303/unjabber | 159f5fc8468e51c885a97c215196241c63b42a1e | [
"MIT"
] | null | null | null | tests/test_messages.py | adsr303/unjabber | 159f5fc8468e51c885a97c215196241c63b42a1e | [
"MIT"
] | null | null | null | tests/test_messages.py | adsr303/unjabber | 159f5fc8468e51c885a97c215196241c63b42a1e | [
"MIT"
] | null | null | null | import unittest
from datetime import datetime
from unjabberlib.messages import Message
class TestMessage(unittest.TestCase):
def test_properties(self):
d = datetime(2017, 9, 5, 14, 32, 16, 14230)
m = Message(d, 'johnny.b.goode@rocknroll.com', 'some payload')
self.assertEqual(m.day, '2017-09-05')
self.assertEqual(m.hour, '14:32')
self.assertEqual(m.shortname, 'johnny.b.goode')
def test_after_same_user_other_day(self):
m1 = Message(datetime(2017, 9, 4, 12, 1), 'chuck.berry@guitar.info',
'Sing la la la')
m2 = Message(datetime(2017, 9, 6, 9, 12), 'chuck.berry@guitar.info',
'Play more')
self.assertEqual(m2.after(m1), ('2017-09-06', '09:12', 'chuck.berry'))
def test_after_same_user_same_day(self):
m1 = Message(datetime(2017, 9, 4, 12, 1), 'chuck.berry@guitar.info',
'Sing la la la')
m2 = Message(datetime(2017, 9, 4, 12, 3), 'chuck.berry@guitar.info',
'Play more')
self.assertEqual(m2.after(m1), (None, '12:03', None))
def test_after_same_user_same_hour(self):
m1 = Message(datetime(2017, 9, 4, 12, 1), 'chuck.berry@guitar.info',
'Sing la la la')
m2 = Message(datetime(2017, 9, 4, 12, 1), 'chuck.berry@guitar.info',
'Play more')
self.assertEqual(m2.after(m1), (None, None, None))
def test_after_different_user_other_day(self):
m1 = Message(datetime(2017, 9, 4, 12, 1), 'chuck.berry@guitar.info',
'Sing la la la')
m2 = Message(datetime(2017, 9, 6, 9, 12),
'johnny.b.goode@rocknroll.com', 'Play more')
self.assertEqual(m2.after(m1),
('2017-09-06', '09:12', 'johnny.b.goode'))
def test_after_different_user_same_day(self):
m1 = Message(datetime(2017, 9, 4, 12, 1), 'chuck.berry@guitar.info',
'Sing la la la')
m2 = Message(datetime(2017, 9, 4, 12, 3),
'johnny.b.goode@rocknroll.com', 'Play more')
self.assertEqual(m2.after(m1), (None, '12:03', 'johnny.b.goode'))
def test_after_different_user_same_hour(self):
m1 = Message(datetime(2017, 9, 4, 12, 1), 'chuck.berry@guitar.info',
'Sing la la la')
m2 = Message(datetime(2017, 9, 4, 12, 1),
'johnny.b.goode@rocknroll.com', 'Play more')
self.assertEqual(m2.after(m1), (None, '12:01', 'johnny.b.goode'))
def test_after_none(self):
m2 = Message(datetime(2017, 9, 4, 12, 1),
'johnny.b.goode@rocknroll.com', 'Play more')
self.assertEqual(m2.after(None),
('2017-09-04', '12:01', 'johnny.b.goode'))
| 44.507937 | 78 | 0.563124 | 386 | 2,804 | 4.005181 | 0.158031 | 0.108668 | 0.117723 | 0.168176 | 0.810479 | 0.767141 | 0.715395 | 0.715395 | 0.715395 | 0.672704 | 0 | 0.108911 | 0.279601 | 2,804 | 62 | 79 | 45.225806 | 0.656436 | 0 | 0 | 0.403846 | 0 | 0 | 0.233952 | 0.123752 | 0 | 0 | 0 | 0 | 0.192308 | 1 | 0.153846 | false | 0 | 0.057692 | 0 | 0.230769 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
4567e27cdfe03cc9cf769ea0aab06014fc88d01a | 15,474 | py | Python | Assignment01.py | MuhammadNaseerAslam/AddressBook-Assignment-Python | 56b237f26dfd8f3156585d874608094de3e5af0f | [
"BSD-3-Clause"
] | null | null | null | Assignment01.py | MuhammadNaseerAslam/AddressBook-Assignment-Python | 56b237f26dfd8f3156585d874608094de3e5af0f | [
"BSD-3-Clause"
] | null | null | null | Assignment01.py | MuhammadNaseerAslam/AddressBook-Assignment-Python | 56b237f26dfd8f3156585d874608094de3e5af0f | [
"BSD-3-Clause"
] | null | null | null | import os
def AdressBook():
print("We are going to save a contact!")
f_W=open("Address Book.txt", mode='a')
FName=input("Please Enter your First Name: ")
LName=input("Please Enter your Last Name: ")
MobileNo=(input("Please Enter your Mobile Number : "))
City =input("Please Enter your City: ")
Email=input("Please Enter your Email: ")
Profession=input("Please Enter your Profession: ")
f_W.write(FName+","+LName+","+MobileNo+","+City+","+Email+","+Profession+'\n')
print("OH yes Sucessfully Contact Saved!")
f_W.close()
def SearchByFirstName():
print("We are going to search record by First Name")
f_r=open("Address Book.txt", mode='r')
FName_S=input("Please input First name which you want to search: ")
s =' '
count=0
while (s):
s=f_r.readline()
L=s.split(",")
if len(s)>0:
if (L[0]==FName_S):
count=count+1
print("Here is your Record which You want to search")
print("FName is: ", L[0])
print("LName is: ", L[1])
print("Mobile No is: ", L[2])
print("City is: ", L[3])
print("Email is: ", L[4])
print("Profession is: ", L[5])
if count==0:
print("This First Name is not in our record Sorry!")
f_r.close()
def SearchByLastName():
print("We are going to search record by Last Name")
f_r=open("Address Book.txt", mode='r')
LName_S=input("Please input Last name which you want to search: ")
s =' '
count = 0
while (s):
s=f_r.readline()
L=s.split(",")
if len(s)>0:
if (L[1]==LName_S):
count = count+1
print("Here is your Record which You want to search")
print("FName is: ", L[0])
print("LName is: ", L[1])
print("Mobile No is: ", L[2])
print("City is: ", L[3])
print("Email is: ", L[4])
print("Profession is: ", L[5])
if count == 0:
print("This Last Name is not in our record Sorry!")
f_r.close()
def SearchByMobile():
print("We are going to search record by Mobile Number")
f_r=open("Address Book.txt", mode='r')
Mobile_S=input("Please input Last name which you want to search: ")
s =' '
count = 0
while (s):
s=f_r.readline()
L=s.split(",")
if len(s)>0:
if (L[2]==Mobile_S):
count = count + 1
print("Here is your Record which You want to search")
print("FName is: ", L[0])
print("LName is: ", L[1])
print("Mobile No is: ", L[2])
print("City is: ", L[3])
print("Email is: ", L[4])
print("Profession is: ", L[5])
if count == 0:
print("This Mobile Number is not in our record Sorry!")
f_r.close()
def SearchByEmail():
print("We are going to search record by Email")
f_r=open("Address Book.txt", mode='r')
Email_S=input("Please input Email which you want to search: ")
s =' '
count =0
while (s):
s=f_r.readline()
L=s.split(",")
if len(s)>0:
if (L[4]==Email_S):
count = count + 1
print("Here is your Record which You want to search")
print("FName is: ", L[0])
print("LName is: ", L[1])
print("Mobile No is: ", L[2])
print("City is: ", L[3])
print("Email is: ", L[4])
print("Profession is: ", L[5])
if count == 0:
print("This Email is not in our record Sorry!")
f_r.close()
def SearchByCity():
print("We are going to search record by City")
f_r=open("Address Book.txt", mode='r')
City_S=input("Please input City which you want to search: ")
s =' '
count =0
while (s):
s=f_r.readline()
L=s.split(",")
if len(s)>0:
if (L[3]==City_S):
count = count + 1
print("Here is your Record which You want to search")
print("FName is: ", L[0])
print("LName is: ", L[1])
print("Mobile No is: ", L[2])
print("City is: ", L[3])
print("Email is: ", L[4])
print("Profession is: ", L[5])
if count == 0:
print(City_S+" city is not in our record Sorry!")
f_r.close()
def SpecialSearchByFirstName():
print("We are going to search record by First Name key")
f_r=open("Address Book.txt", mode='r')
FName_S=input("Please input First name key which you want to search: ")
s =' '
count=0
while (s):
s=f_r.readline()
L=s.split(",")
if len(s)>0:
if (L[0].find(FName_S)!=-1):
count=count+1
print("Here is your Record which You want to search")
print("FName is: ", L[0])
print("LName is: ", L[1])
print("Mobile No is: ", L[2])
print("City is: ", L[3])
print("Email is: ", L[4])
print("Profession is: ", L[5])
if count==0:
print("This First Name key is not in our record Sorry!")
f_r.close()
def SpecialSearchByLastName():
print("We are going to search record by Last Name key")
f_r=open("Address Book.txt", mode='r')
FName_S=input("Please input Last name key which you want to search: ")
s =' '
count=0
while (s):
s=f_r.readline()
L=s.split(",")
if len(s)>0:
if (L[1].find(FName_S)!=-1):
count=count+1
print("Here is your Record which You want to search")
print("FName is: ", L[0])
print("LName is: ", L[1])
print("Mobile No is: ", L[2])
print("City is: ", L[3])
print("Email is: ", L[4])
print("Profession is: ", L[5])
if count==0:
print("This Last Name key is not in our record Sorry!")
f_r.close()
def SpecialSearchByMobileNumber():
print("We are going to search record by Mobile Number key")
f_r=open("Address Book.txt", mode='r')
FName_S=input("Please input Mobile No key which you want to search: ")
s =' '
count=0
while (s):
s=f_r.readline()
L=s.split(",")
if len(s)>0:
if (L[2].find(FName_S)!=-1):
count=count+1
print("Here is your Record which You want to search")
print("FName is: ", L[0])
print("LName is: ", L[1])
print("Mobile No is: ", L[2])
print("City is: ", L[3])
print("Email is: ", L[4])
print("Profession is: ", L[5])
if count==0:
print("This Moile No key is not in our record Sorry!")
f_r.close()
def SpecialSearchByCity():
print("We are going to search record by City key")
f_r=open("Address Book.txt", mode='r')
FName_S=input("Please input City key which you want to search: ")
s =' '
count=0
while (s):
s=f_r.readline()
L=s.split(",")
if len(s)>0:
if (L[3].find(FName_S)!=-1):
count=count+1
print("Here is your Record which You want to search")
print("FName is: ", L[0])
print("LName is: ", L[1])
print("Mobile No is: ", L[2])
print("City is: ", L[3])
print("Email is: ", L[4])
print("Profession is: ", L[5])
if count==0:
print("This City key is not in our record Sorry!")
f_r.close()
def SpecialSearchByEmail():
print("We are going to search record by Email key")
f_r=open("Address Book.txt", mode='r')
FName_S=input("Please input Email key which you want to search: ")
s =' '
count=0
while (s):
s=f_r.readline()
L=s.split(",")
if len(s)>0:
if (L[4].find(FName_S)!=-1):
count=count+1
print("Here is your Record which You want to search")
print("FName is: ", L[0])
print("LName is: ", L[1])
print("Mobile No is: ", L[2])
print("City is: ", L[3])
print("Email is: ", L[4])
print("Profession is: ", L[5])
if count==0:
print("This Email key is not in our record Sorry!")
f_r.close()
def DeleteContactByName():
print("We are going to delete record by First Name")
f_r = open("Address Book.txt", mode='r')
f_w = open("UpdateAddress Book.txt", mode='w')
FName_SU = input("Please input First Name which you want to Delete: ")
Email_SU = input("Please input also Email which you want to Delete: ")
s = ' '
count = 0
while (s):
s = f_r.readline()
L = s.split(",")
if len(s) > 0:
if (L[0] != FName_SU and L[4]!=Email_SU):
f_w.write(s)
elif (L[0] == FName_SU and L[4]==Email_SU):
count = count + 1
if count == 0:
print("This data is not in our record which you want to delete Sorry!")
else:
print(FName_SU+" Data is sucessfully deleted!")
f_r.close()
f_w.close()
os.remove("Address Book.txt")
os.rename(r'E:\Semester 8\WEB\Labs\UpdateAddress Book.txt',"Address Book.txt")
def DeleteContactByMobile():
print("We are going to delete record by Mobile Number")
f_r = open("Address Book.txt", mode='r')
f_w = open("UpdateAddress Book.txt", mode='w')
Mobile_SU = input("Please input Mobile Number which you want to Delete: ")
Email_SU = input("Please input also Email which you want to Delete: ")
s = ' '
count =0
while (s):
s = f_r.readline()
L = s.split(",")
if len(s) > 0:
if (L[2] != Mobile_SU and L[4]!=Email_SU):
f_w.write(s)
elif (L[2] == Mobile_SU and L[4]==Email_SU):
count = count + 1
if count == 0:
print("This data is not in our record which you want to delete Sorry!")
else:
print(Mobile_SU +" Data is sucessfully deleted!")
f_r.close()
f_w.close()
os.remove("Address Book.txt")
os.rename(r'E:\Semester 8\WEB\Labs\UpdateAddress Book.txt',"Address Book.txt")
def DeleteContactByCity():
print("We are going to delete record by City")
f_r = open("Address Book.txt", mode='r')
f_w = open("UpdateAddress Book.txt", mode='w')
City_SU = input("Please input City which you want to Delete: ")
Email_SU = input("Please input also Email which you want to Delete: ")
s = ' '
count = 0
while (s):
s = f_r.readline()
L = s.split(",")
if len(s) > 0:
if (L[3] != City_SU and L[4]!=Email_SU):
f_w.write(s)
elif (L[3] == City_SU and L[4]==Email_SU):
count = count + 1
if count == 0:
print(City_SU+" data is not in our record which you want to delete Sorry!")
else:
print(City_SU+" Data is sucessfully deleted")
f_r.close()
f_w.close()
os.remove("Address Book.txt")
os.rename(r'E:\Semester 8\WEB\Labs\UpdateAddress Book.txt',"Address Book.txt")
def AllDeleteContactByName():
print("We are going to delete record by First Name all similiar data")
f_r = open("Address Book.txt", mode='r')
f_w = open("UpdateAddress Book.txt", mode='w')
FName_SU = input("Please input First Name which you want to Delete: ")
s = ' '
count =0
while (s):
s = f_r.readline()
L = s.split(",")
if len(s) > 0:
if (L[0] != FName_SU):
f_w.write(s)
elif (L[0] == FName_SU):
count=count+1
if count == 0:
print("This data is not in our record which you want to delete Sorry!")
else:
print(FName_SU+" all Data is sucessfully deleted")
f_r.close()
f_w.close()
os.remove("Address Book.txt")
os.rename(r'E:\Semester 8\WEB\Labs\UpdateAddress Book.txt',"Address Book.txt")
def AllDeleteContactByMobile():
print("We are going to delete record by Mobile Number all similiar data")
f_r = open("Address Book.txt", mode='r')
f_w = open("UpdateAddress Book.txt", mode='w')
Mobile_SU = input("Please input Mobile Number which you want to Delete: ")
s = ' '
count = 0
while (s):
s = f_r.readline()
L = s.split(",")
if len(s) > 0:
if (L[2] != Mobile_SU):
f_w.write(s)
elif (L[2] == Mobile_SU):
count=count+1
if count == 0:
print("This data is not in our record which you want to delete Sorry!")
else:
print(Mobile_SU+" all Data is sucessfully Deleted!")
f_r.close()
f_w.close()
os.remove("Address Book.txt")
os.rename(r'E:\Semester 8\WEB\Labs\UpdateAddress Book.txt',"Address Book.txt")
def AllDeleteContactByCity():
print("We are going to delete record by City all similiar data")
f_r = open("Address Book.txt", mode='r')
f_w = open("UpdateAddress Book.txt", mode='w')
City_SU = input("Please input City which you want to Delete: ")
s = ' '
count =0
while (s):
s = f_r.readline()
L = s.split(",")
if len(s) > 0:
if (L[3] != City_SU):
f_w.write(s)
if count == 0:
print("This data is not in our record which you want to delete Sorry!")
else:
print(City_SU +" all Data is sucessfully Deleted!")
f_r.close()
f_w.close()
os.remove("Address Book.txt")
os.rename(r'E:\Semester 8\WEB\Labs\UpdateAddress Book.txt',"Address Book.txt")
def UpdateContact():
print("We are going to dupdate record by First Name")
f_r = open("Address Book.txt", mode='r')
f_w = open("UpdateAddress Book.txt", mode='w')
FName_SU = input("Please input name which you want to update: ")
s = ' '
count =0
while (s):
s = f_r.readline()
L = s.split(",")
if len(s) > 0:
if (L[0] == FName_SU):
count = count + 1
FName = input("Please Enter your First Name: ")
LName = input("Please Enter your Last Name: ")
Mb= input("Please Enter your Mobile Number : ")
City = input("Please Enter your City: ")
Email= input("Please Enter your Email: ")
Profession = input("Please Enter your Profession: ")
f_w.write(FName+"," +LName+"," +Mb+ "," + City + "," + Email + "," + Profession + '\n')
else:
f_w.write(s)
if count == 0:
print("This data is not in our record which you want to Update Sorry!")
else:
print(FName_SU+" Data is sucessfully Updated!")
f_r.close()
f_w.close()
os.remove("Address Book.txt")
os.rename(r'E:\Semester 8\WEB\Labs\UpdateAddress Book.txt',"Address Book.txt")
AdressBook()
AdressBook()
AllDeleteContactByName()
| 37.376812 | 104 | 0.517578 | 2,197 | 15,474 | 3.584888 | 0.050979 | 0.022854 | 0.056374 | 0.065769 | 0.925089 | 0.908837 | 0.908329 | 0.906932 | 0.896394 | 0.868715 | 0 | 0.01636 | 0.340313 | 15,474 | 413 | 105 | 37.467312 | 0.755192 | 0 | 0 | 0.760494 | 0 | 0 | 0.363359 | 0.01116 | 0 | 0 | 0 | 0 | 0 | 1 | 0.044444 | false | 0 | 0.002469 | 0 | 0.046914 | 0.279012 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
459b47a2b7af34b337cbf933d46a3e647110639a | 7,397 | py | Python | stud_auth/tests.py | PyDev777/studentsdb | 9fd7f7a66b6fe346ad0bc742f154dead5e72f561 | [
"MIT"
] | 11 | 2016-12-05T19:07:05.000Z | 2022-02-15T07:21:28.000Z | stud_auth/tests.py | PyDev777/studentsdb | 9fd7f7a66b6fe346ad0bc742f154dead5e72f561 | [
"MIT"
] | null | null | null | stud_auth/tests.py | PyDev777/studentsdb | 9fd7f7a66b6fe346ad0bc742f154dead5e72f561 | [
"MIT"
] | 4 | 2016-12-06T07:22:30.000Z | 2018-09-25T09:56:50.000Z | from django.test import TestCase, Client, override_settings
from django.core.urlresolvers import reverse
from stud_auth.models import StProfile, User
@override_settings(LANGUAGE_CODE='en')
class TestCustRegFormUniqEmail(TestCase):
def setUp(self):
# remember test browser
self.client = Client()
# remember url to edit form
self.url = reverse('users:registration_register')
def test_form(self):
# try to access form as anonymous user
response = self.client.get(self.url, follow=True)
# we have to get 200 code and login form
self.assertEqual(response.status_code, 200)
# check form content
self.assertIn('Registration Form', response.content)
self.assertIn('form', response.content)
self.assertIn('action="%s"' % self.url, response.content)
self.assertIn('username', response.content)
self.assertIn('email', response.content)
self.assertIn('password1', response.content)
self.assertIn('password2', response.content)
self.assertIn('captcha', response.content)
self.assertIn('name="submit_button"', response.content)
self.assertIn('name="cancel_button"', response.content)
# check form styles
self.assertIn('form-horizontal', response.content)
self.assertIn('form-group', response.content)
self.assertIn('control-label', response.content)
self.assertIn('controls', response.content)
self.assertIn('btn-primary', response.content)
@override_settings(LANGUAGE_CODE='en')
class TestCustPswResetForm(TestCase):
def setUp(self):
# remember test browser
self.client = Client()
# remember url to edit form
self.url = reverse('password_reset')
def test_form(self):
# try to access form as anonymous user
response = self.client.get(self.url, follow=True)
# we have to get 200 code and login form
self.assertEqual(response.status_code, 200)
# check form content
self.assertIn('Reset password', response.content)
self.assertIn('form', response.content)
self.assertIn('action="%s"' % self.url, response.content)
self.assertIn('email', response.content)
self.assertIn('captcha', response.content)
self.assertIn('name="submit_button"', response.content)
self.assertIn('name="cancel_button"', response.content)
# check form styles
self.assertIn('form-horizontal', response.content)
self.assertIn('form-group', response.content)
self.assertIn('control-label', response.content)
self.assertIn('controls', response.content)
self.assertIn('btn-primary', response.content)
@override_settings(LANGUAGE_CODE='en')
class TestCustPswChangeForm(TestCase):
fixtures = ['students_test_data.json']
def setUp(self):
# remember test browser
self.client = Client()
# remember url to edit form
self.url = reverse('password_change')
def test_access(self):
# try to access form as anonymous user
response = self.client.get(self.url, follow=True)
# we have to get 200 code and login form
self.assertEqual(response.status_code, 200)
# check that we're on login form
self.assertIn('form', response.content)
self.assertIn('username', response.content)
self.assertIn('password', response.content)
# check redirect url
self.assertEqual(response.redirect_chain[0], ('http://testserver/users/login/?next=/users/password_change/', 302))
def test_form(self):
self.client.login(username='admin', password='admin')
# try to access form as auth user
response = self.client.get(self.url, follow=True)
# we have to get 200 code and login form
self.assertEqual(response.status_code, 200)
# check form content
self.assertIn('Change password', response.content)
self.assertIn('form', response.content)
self.assertIn('action="%s"' % self.url, response.content)
self.assertIn('new_password1', response.content)
self.assertIn('new_password2', response.content)
self.assertIn('old_password', response.content)
self.assertIn('name="submit_button"', response.content)
self.assertIn('name="cancel_button"', response.content)
# check form styles
self.assertIn('form-horizontal', response.content)
self.assertIn('form-group', response.content)
self.assertIn('control-label', response.content)
self.assertIn('controls', response.content)
self.assertIn('btn-primary', response.content)
@override_settings(LANGUAGE_CODE='en')
class TestUserProfileForm(TestCase):
fixtures = ['students_test_data.json']
def setUp(self):
# remember test browser
self.client = Client()
# remember url to edit form
self.url = reverse('profile')
def test_access(self):
# try to access form as anonymous user
response = self.client.get(self.url, follow=True)
# we have to get 200 code and login form
self.assertEqual(response.status_code, 200)
# check that we're on login form
self.assertIn('form', response.content)
self.assertIn('username', response.content)
self.assertIn('password', response.content)
# check redirect url
self.assertEqual(response.redirect_chain[0], ('http://testserver/users/login/?next=/users/profile/', 302))
def test_form(self):
# login as admin to access student edit form
self.client.login(username='admin', password='admin')
# get form and check few fields there
response = self.client.get(self.url)
self.assertEqual(response.status_code, 200)
# check page title, few field titles and button on edit form
self.assertIn('form', response.content)
self.assertIn('username', response.content)
self.assertIn('first_name', response.content)
self.assertIn('last_name', response.content)
self.assertIn('photo', response.content)
self.assertIn('mobile_phone', response.content)
self.assertIn('address', response.content)
self.assertIn('name="save_button"', response.content)
self.assertIn('name="cancel_button"', response.content)
self.assertIn('You want to change the password?', response.content)
def test_styles(self):
# login as admin to access student edit form
self.client.login(username='admin', password='admin')
# get form and check few fields there
response = self.client.get(self.url)
# check response status
self.assertEqual(response.status_code, 200)
# check classes
self.assertIn('form-horizontal', response.content)
self.assertIn('form-group', response.content)
self.assertIn('control-label', response.content)
self.assertIn('controls', response.content)
self.assertIn('btn-primary', response.content)
class StProfileModelTests(TestCase):
"""Test event model"""
def test_unicode(self):
user = User(username='user1', email='test@test.com')
st_user = StProfile(user=user, mobile_phone='355-355')
self.assertEqual(unicode(st_user), u'user1')
| 36.985 | 122 | 0.663783 | 869 | 7,397 | 5.590334 | 0.140391 | 0.150679 | 0.211198 | 0.28345 | 0.858584 | 0.799094 | 0.79189 | 0.766571 | 0.766571 | 0.75175 | 0 | 0.009705 | 0.219954 | 7,397 | 199 | 123 | 37.170854 | 0.832236 | 0.141679 | 0 | 0.709402 | 0 | 0 | 0.1552 | 0.011573 | 0 | 0 | 0 | 0 | 0.606838 | 1 | 0.102564 | false | 0.136752 | 0.025641 | 0 | 0.188034 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 11 |
b320944030118f4baefbfa15dc1d61eaf02dc341 | 70,416 | py | Python | exact_sync/v1/api/screening_modes_api.py | maubreville/EXACT-Sync | 47a47e5af360292677601a877e0765d5e01bd2df | [
"MIT"
] | 4 | 2020-10-22T08:46:00.000Z | 2021-09-22T21:40:03.000Z | exact_sync/v1/api/screening_modes_api.py | maubreville/EXACT-Sync | 47a47e5af360292677601a877e0765d5e01bd2df | [
"MIT"
] | null | null | null | exact_sync/v1/api/screening_modes_api.py | maubreville/EXACT-Sync | 47a47e5af360292677601a877e0765d5e01bd2df | [
"MIT"
] | 1 | 2020-07-26T15:16:17.000Z | 2020-07-26T15:16:17.000Z | # coding: utf-8
"""
EXACT - API
API to interact with the EXACT Server # noqa: E501
OpenAPI spec version: 1.0.0
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
from exact_sync.v1.api.pagination_base_api import PaginationBaseAPI
import re # noqa: F401
# python 2 and python 3 compatibility library
import six
from exact_sync.v1.api_client import ApiClient
class ScreeningModesApi(PaginationBaseAPI):
"""NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
Ref: https://github.com/swagger-api/swagger-codegen
"""
def __init__(self, api_client=None):
if api_client is None:
api_client = ApiClient()
self.api_client = api_client
def create_screening_mode(self, **kwargs): # noqa: E501
"""create_screening_mode # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.create_screening_mode(async_req=True)
>>> result = thread.get()
:param async_req bool
:param Body54 body:
:return: ScreeningMode
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.create_screening_mode_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.create_screening_mode_with_http_info(**kwargs) # noqa: E501
return data
def create_screening_mode_with_http_info(self, **kwargs): # noqa: E501
"""create_screening_mode # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.create_screening_mode_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:param Body54 body:
:return: ScreeningMode
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['body'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method create_screening_mode" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
if 'image' in params:
form_params.append(('image', params['image'])) # noqa: E501
if 'user' in params:
form_params.append(('user', params['user'])) # noqa: E501
if 'screening_tiles' in params:
form_params.append(('screening_tiles', params['screening_tiles'])) # noqa: E501
if 'x_steps' in params:
form_params.append(('x_steps', params['x_steps'])) # noqa: E501
if 'y_steps' in params:
form_params.append(('y_steps', params['y_steps'])) # noqa: E501
if 'x_resolution' in params:
form_params.append(('x_resolution', params['x_resolution'])) # noqa: E501
if 'y_resolution' in params:
form_params.append(('y_resolution', params['y_resolution'])) # noqa: E501
if 'current_index' in params:
form_params.append(('current_index', params['current_index'])) # noqa: E501
if 'image' in params:
form_params.append(('image', params['image'])) # noqa: E501
if 'user' in params:
form_params.append(('user', params['user'])) # noqa: E501
if 'screening_tiles' in params:
form_params.append(('screening_tiles', params['screening_tiles'])) # noqa: E501
if 'x_steps' in params:
form_params.append(('x_steps', params['x_steps'])) # noqa: E501
if 'y_steps' in params:
form_params.append(('y_steps', params['y_steps'])) # noqa: E501
if 'x_resolution' in params:
form_params.append(('x_resolution', params['x_resolution'])) # noqa: E501
if 'y_resolution' in params:
form_params.append(('y_resolution', params['y_resolution'])) # noqa: E501
if 'current_index' in params:
form_params.append(('current_index', params['current_index'])) # noqa: E501
body_params = None
if 'body' in params:
body_params = params['body']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json', 'application/x-www-form-urlencoded', 'multipart/form-data']) # noqa: E501
# Authentication setting
auth_settings = ['basicAuth'] # noqa: E501
return self.api_client.call_api(
'/api/v1/images/screening_modes/', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ScreeningMode', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def create_screening_mode(self, **kwargs): # noqa: E501
"""create_screening_mode # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.create_screening_mode(async_req=True)
>>> result = thread.get()
:param async_req bool
:param int image:
:param int user:
:param object screening_tiles:
:param int x_steps:
:param int y_steps:
:param int x_resolution:
:param int y_resolution:
:param int current_index:
:return: ScreeningMode
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.create_screening_mode_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.create_screening_mode_with_http_info(**kwargs) # noqa: E501
return data
def create_screening_mode_with_http_info(self, **kwargs): # noqa: E501
"""create_screening_mode # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.create_screening_mode_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:param int image:
:param int user:
:param object screening_tiles:
:param int x_steps:
:param int y_steps:
:param int x_resolution:
:param int y_resolution:
:param int current_index:
:return: ScreeningMode
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['image', 'user', 'screening_tiles', 'x_steps', 'y_steps', 'x_resolution', 'y_resolution', 'current_index'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method create_screening_mode" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
if 'image' in params:
form_params.append(('image', params['image'])) # noqa: E501
if 'user' in params:
form_params.append(('user', params['user'])) # noqa: E501
if 'screening_tiles' in params:
form_params.append(('screening_tiles', params['screening_tiles'])) # noqa: E501
if 'x_steps' in params:
form_params.append(('x_steps', params['x_steps'])) # noqa: E501
if 'y_steps' in params:
form_params.append(('y_steps', params['y_steps'])) # noqa: E501
if 'x_resolution' in params:
form_params.append(('x_resolution', params['x_resolution'])) # noqa: E501
if 'y_resolution' in params:
form_params.append(('y_resolution', params['y_resolution'])) # noqa: E501
if 'current_index' in params:
form_params.append(('current_index', params['current_index'])) # noqa: E501
if 'image' in params:
form_params.append(('image', params['image'])) # noqa: E501
if 'user' in params:
form_params.append(('user', params['user'])) # noqa: E501
if 'screening_tiles' in params:
form_params.append(('screening_tiles', params['screening_tiles'])) # noqa: E501
if 'x_steps' in params:
form_params.append(('x_steps', params['x_steps'])) # noqa: E501
if 'y_steps' in params:
form_params.append(('y_steps', params['y_steps'])) # noqa: E501
if 'x_resolution' in params:
form_params.append(('x_resolution', params['x_resolution'])) # noqa: E501
if 'y_resolution' in params:
form_params.append(('y_resolution', params['y_resolution'])) # noqa: E501
if 'current_index' in params:
form_params.append(('current_index', params['current_index'])) # noqa: E501
body_params = None
if 'body' in params:
body_params = params['body']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json', 'application/x-www-form-urlencoded', 'multipart/form-data']) # noqa: E501
# Authentication setting
auth_settings = ['basicAuth'] # noqa: E501
return self.api_client.call_api(
'/api/v1/images/screening_modes/', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ScreeningMode', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def create_screening_mode(self, **kwargs): # noqa: E501
"""create_screening_mode # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.create_screening_mode(async_req=True)
>>> result = thread.get()
:param async_req bool
:param int image:
:param int user:
:param object screening_tiles:
:param int x_steps:
:param int y_steps:
:param int x_resolution:
:param int y_resolution:
:param int current_index:
:return: ScreeningMode
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.create_screening_mode_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.create_screening_mode_with_http_info(**kwargs) # noqa: E501
return data
def create_screening_mode_with_http_info(self, **kwargs): # noqa: E501
"""create_screening_mode # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.create_screening_mode_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:param int image:
:param int user:
:param object screening_tiles:
:param int x_steps:
:param int y_steps:
:param int x_resolution:
:param int y_resolution:
:param int current_index:
:return: ScreeningMode
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['image', 'user', 'screening_tiles', 'x_steps', 'y_steps', 'x_resolution', 'y_resolution', 'current_index'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method create_screening_mode" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
if 'image' in params:
form_params.append(('image', params['image'])) # noqa: E501
if 'user' in params:
form_params.append(('user', params['user'])) # noqa: E501
if 'screening_tiles' in params:
form_params.append(('screening_tiles', params['screening_tiles'])) # noqa: E501
if 'x_steps' in params:
form_params.append(('x_steps', params['x_steps'])) # noqa: E501
if 'y_steps' in params:
form_params.append(('y_steps', params['y_steps'])) # noqa: E501
if 'x_resolution' in params:
form_params.append(('x_resolution', params['x_resolution'])) # noqa: E501
if 'y_resolution' in params:
form_params.append(('y_resolution', params['y_resolution'])) # noqa: E501
if 'current_index' in params:
form_params.append(('current_index', params['current_index'])) # noqa: E501
if 'image' in params:
form_params.append(('image', params['image'])) # noqa: E501
if 'user' in params:
form_params.append(('user', params['user'])) # noqa: E501
if 'screening_tiles' in params:
form_params.append(('screening_tiles', params['screening_tiles'])) # noqa: E501
if 'x_steps' in params:
form_params.append(('x_steps', params['x_steps'])) # noqa: E501
if 'y_steps' in params:
form_params.append(('y_steps', params['y_steps'])) # noqa: E501
if 'x_resolution' in params:
form_params.append(('x_resolution', params['x_resolution'])) # noqa: E501
if 'y_resolution' in params:
form_params.append(('y_resolution', params['y_resolution'])) # noqa: E501
if 'current_index' in params:
form_params.append(('current_index', params['current_index'])) # noqa: E501
body_params = None
if 'body' in params:
body_params = params['body']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json', 'application/x-www-form-urlencoded', 'multipart/form-data']) # noqa: E501
# Authentication setting
auth_settings = ['basicAuth'] # noqa: E501
return self.api_client.call_api(
'/api/v1/images/screening_modes/', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ScreeningMode', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def destroy_screening_mode(self, id, **kwargs): # noqa: E501
"""destroy_screening_mode # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.destroy_screening_mode(id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str id: (required)
:param str id2: id
:param str image: image
:param str user: user
:return: None
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.destroy_screening_mode_with_http_info(id, **kwargs) # noqa: E501
else:
(data) = self.destroy_screening_mode_with_http_info(id, **kwargs) # noqa: E501
return data
def destroy_screening_mode_with_http_info(self, id, **kwargs): # noqa: E501
"""destroy_screening_mode # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.destroy_screening_mode_with_http_info(id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str id: (required)
:param str id2: id
:param str image: image
:param str user: user
:return: None
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['id', 'id2', 'image', 'user'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method destroy_screening_mode" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'id' is set
if ('id' not in params or
params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `destroy_screening_mode`") # noqa: E501
collection_formats = {}
path_params = {}
if 'id' in params:
path_params['id'] = params['id'] # noqa: E501
query_params = []
if 'id2' in params:
query_params.append(('id', params['id2'])) # noqa: E501
if 'image' in params:
query_params.append(('image', params['image'])) # noqa: E501
if 'user' in params:
query_params.append(('user', params['user'])) # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# Authentication setting
auth_settings = ['basicAuth'] # noqa: E501
return self.api_client.call_api(
'/api/v1/images/screening_modes/{id}/', 'DELETE',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type=None, # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def list_screening_modes(self, pagination:bool=True, **kwargs): # noqa: E501
"""list_screening_modes # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.list_screening_modes(async_req=True)
>>> result = thread.get()
:param async_req bool
:param int limit: Number of results to return per page.
:param int offset: The initial index from which to return the results.
:param str id: id
:param str image: image
:param str user: user
:return: ScreeningModes
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if pagination:
if kwargs.get('async_req'):
return self.list_screening_modes_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.list_screening_modes_with_http_info(**kwargs) # noqa: E501
return data
else:
return self._get_all(self.list_screening_modes_with_http_info, **kwargs)
def list_screening_modes_with_http_info(self, **kwargs): # noqa: E501
"""list_screening_modes # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.list_screening_modes_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:param int limit: Number of results to return per page.
:param int offset: The initial index from which to return the results.
:param str id: id
:param str image: image
:param str user: user
:return: ScreeningModes
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['limit', 'offset', 'id', 'image', 'user'] # noqa: E501
all_params.append('omit')
all_params.append('fields')
all_params.append('expand')
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method list_screening_modes" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
if 'limit' in params:
query_params.append(('limit', params['limit'])) # noqa: E501
if 'offset' in params:
query_params.append(('offset', params['offset'])) # noqa: E501
if 'id' in params:
query_params.append(('id', params['id'])) # noqa: E501
if 'image' in params:
query_params.append(('image', params['image'])) # noqa: E501
if 'user' in params:
query_params.append(('user', params['user'])) # noqa: E501
if 'omit' in params:
query_params.append(('omit', params['omit'])) # noqa: E501
if 'fields' in params:
query_params.append(('fields', params['fields'])) # noqa: E501
if 'expand' in params:
query_params.append(('expand', params['expand'])) # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['basicAuth'] # noqa: E501
return self.api_client.call_api(
'/api/v1/images/screening_modes/', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ScreeningModes', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def partial_update_screening_mode(self, id, **kwargs): # noqa: E501
"""partial_update_screening_mode # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.partial_update_screening_mode(id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str id: (required)
:param Body60 body:
:param str id2: id
:param str image2: image
:param str user2: user
:return: ScreeningMode
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.partial_update_screening_mode_with_http_info(id, **kwargs) # noqa: E501
else:
(data) = self.partial_update_screening_mode_with_http_info(id, **kwargs) # noqa: E501
return data
def partial_update_screening_mode_with_http_info(self, id, **kwargs): # noqa: E501
"""partial_update_screening_mode # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.partial_update_screening_mode_with_http_info(id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str id: (required)
:param Body60 body:
:param str id2: id
:param str image2: image
:param str user2: user
:return: ScreeningMode
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['id', 'body', 'id2', 'image2', 'user2'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method partial_update_screening_mode" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'id' is set
if ('id' not in params or
params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `partial_update_screening_mode`") # noqa: E501
collection_formats = {}
path_params = {}
if 'id' in params:
path_params['id'] = params['id'] # noqa: E501
query_params = []
if 'id2' in params:
query_params.append(('id', params['id2'])) # noqa: E501
if 'image2' in params:
query_params.append(('image', params['image2'])) # noqa: E501
if 'user2' in params:
query_params.append(('user', params['user2'])) # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
if 'image' in params:
form_params.append(('image', params['image'])) # noqa: E501
if 'user' in params:
form_params.append(('user', params['user'])) # noqa: E501
if 'screening_tiles' in params:
form_params.append(('screening_tiles', params['screening_tiles'])) # noqa: E501
if 'x_steps' in params:
form_params.append(('x_steps', params['x_steps'])) # noqa: E501
if 'y_steps' in params:
form_params.append(('y_steps', params['y_steps'])) # noqa: E501
if 'x_resolution' in params:
form_params.append(('x_resolution', params['x_resolution'])) # noqa: E501
if 'y_resolution' in params:
form_params.append(('y_resolution', params['y_resolution'])) # noqa: E501
if 'current_index' in params:
form_params.append(('current_index', params['current_index'])) # noqa: E501
if 'image' in params:
form_params.append(('image', params['image'])) # noqa: E501
if 'user' in params:
form_params.append(('user', params['user'])) # noqa: E501
if 'screening_tiles' in params:
form_params.append(('screening_tiles', params['screening_tiles'])) # noqa: E501
if 'x_steps' in params:
form_params.append(('x_steps', params['x_steps'])) # noqa: E501
if 'y_steps' in params:
form_params.append(('y_steps', params['y_steps'])) # noqa: E501
if 'x_resolution' in params:
form_params.append(('x_resolution', params['x_resolution'])) # noqa: E501
if 'y_resolution' in params:
form_params.append(('y_resolution', params['y_resolution'])) # noqa: E501
if 'current_index' in params:
form_params.append(('current_index', params['current_index'])) # noqa: E501
body_params = None
if 'body' in params:
body_params = params['body']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json', 'application/x-www-form-urlencoded', 'multipart/form-data']) # noqa: E501
# Authentication setting
auth_settings = ['basicAuth'] # noqa: E501
return self.api_client.call_api(
'/api/v1/images/screening_modes/{id}/', 'PATCH',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ScreeningMode', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def partial_update_screening_mode(self, id, **kwargs): # noqa: E501
"""partial_update_screening_mode # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.partial_update_screening_mode(id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str id: (required)
:param int image:
:param int user:
:param object screening_tiles:
:param int x_steps:
:param int y_steps:
:param int x_resolution:
:param int y_resolution:
:param int current_index:
:param str id2: id
:param str image2: image
:param str user2: user
:return: ScreeningMode
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.partial_update_screening_mode_with_http_info(id, **kwargs) # noqa: E501
else:
(data) = self.partial_update_screening_mode_with_http_info(id, **kwargs) # noqa: E501
return data
def partial_update_screening_mode_with_http_info(self, id, **kwargs): # noqa: E501
"""partial_update_screening_mode # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.partial_update_screening_mode_with_http_info(id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str id: (required)
:param int image:
:param int user:
:param object screening_tiles:
:param int x_steps:
:param int y_steps:
:param int x_resolution:
:param int y_resolution:
:param int current_index:
:param str id2: id
:param str image2: image
:param str user2: user
:return: ScreeningMode
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['id', 'image', 'user', 'screening_tiles', 'x_steps', 'y_steps', 'x_resolution', 'y_resolution', 'current_index', 'id2', 'image2', 'user2'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method partial_update_screening_mode" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'id' is set
if ('id' not in params or
params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `partial_update_screening_mode`") # noqa: E501
collection_formats = {}
path_params = {}
if 'id' in params:
path_params['id'] = params['id'] # noqa: E501
query_params = []
if 'id2' in params:
query_params.append(('id', params['id2'])) # noqa: E501
if 'image2' in params:
query_params.append(('image', params['image2'])) # noqa: E501
if 'user2' in params:
query_params.append(('user', params['user2'])) # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
if 'image' in params:
form_params.append(('image', params['image'])) # noqa: E501
if 'user' in params:
form_params.append(('user', params['user'])) # noqa: E501
if 'screening_tiles' in params:
form_params.append(('screening_tiles', params['screening_tiles'])) # noqa: E501
if 'x_steps' in params:
form_params.append(('x_steps', params['x_steps'])) # noqa: E501
if 'y_steps' in params:
form_params.append(('y_steps', params['y_steps'])) # noqa: E501
if 'x_resolution' in params:
form_params.append(('x_resolution', params['x_resolution'])) # noqa: E501
if 'y_resolution' in params:
form_params.append(('y_resolution', params['y_resolution'])) # noqa: E501
if 'current_index' in params:
form_params.append(('current_index', params['current_index'])) # noqa: E501
if 'image' in params:
form_params.append(('image', params['image'])) # noqa: E501
if 'user' in params:
form_params.append(('user', params['user'])) # noqa: E501
if 'screening_tiles' in params:
form_params.append(('screening_tiles', params['screening_tiles'])) # noqa: E501
if 'x_steps' in params:
form_params.append(('x_steps', params['x_steps'])) # noqa: E501
if 'y_steps' in params:
form_params.append(('y_steps', params['y_steps'])) # noqa: E501
if 'x_resolution' in params:
form_params.append(('x_resolution', params['x_resolution'])) # noqa: E501
if 'y_resolution' in params:
form_params.append(('y_resolution', params['y_resolution'])) # noqa: E501
if 'current_index' in params:
form_params.append(('current_index', params['current_index'])) # noqa: E501
body_params = None
if 'body' in params:
body_params = params['body']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json', 'application/x-www-form-urlencoded', 'multipart/form-data']) # noqa: E501
# Authentication setting
auth_settings = ['basicAuth'] # noqa: E501
return self.api_client.call_api(
'/api/v1/images/screening_modes/{id}/', 'PATCH',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ScreeningMode', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def partial_update_screening_mode(self, id, **kwargs): # noqa: E501
"""partial_update_screening_mode # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.partial_update_screening_mode(id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str id: (required)
:param int image:
:param int user:
:param object screening_tiles:
:param int x_steps:
:param int y_steps:
:param int x_resolution:
:param int y_resolution:
:param int current_index:
:param str id2: id
:param str image2: image
:param str user2: user
:return: ScreeningMode
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.partial_update_screening_mode_with_http_info(id, **kwargs) # noqa: E501
else:
(data) = self.partial_update_screening_mode_with_http_info(id, **kwargs) # noqa: E501
return data
def partial_update_screening_mode_with_http_info(self, id, **kwargs): # noqa: E501
"""partial_update_screening_mode # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.partial_update_screening_mode_with_http_info(id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str id: (required)
:param int image:
:param int user:
:param object screening_tiles:
:param int x_steps:
:param int y_steps:
:param int x_resolution:
:param int y_resolution:
:param int current_index:
:param str id2: id
:param str image2: image
:param str user2: user
:return: ScreeningMode
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['id', 'image', 'user', 'screening_tiles', 'x_steps', 'y_steps', 'x_resolution', 'y_resolution', 'current_index', 'id2', 'image2', 'user2'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method partial_update_screening_mode" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'id' is set
if ('id' not in params or
params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `partial_update_screening_mode`") # noqa: E501
collection_formats = {}
path_params = {}
if 'id' in params:
path_params['id'] = params['id'] # noqa: E501
query_params = []
if 'id2' in params:
query_params.append(('id', params['id2'])) # noqa: E501
if 'image2' in params:
query_params.append(('image', params['image2'])) # noqa: E501
if 'user2' in params:
query_params.append(('user', params['user2'])) # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
if 'image' in params:
form_params.append(('image', params['image'])) # noqa: E501
if 'user' in params:
form_params.append(('user', params['user'])) # noqa: E501
if 'screening_tiles' in params:
form_params.append(('screening_tiles', params['screening_tiles'])) # noqa: E501
if 'x_steps' in params:
form_params.append(('x_steps', params['x_steps'])) # noqa: E501
if 'y_steps' in params:
form_params.append(('y_steps', params['y_steps'])) # noqa: E501
if 'x_resolution' in params:
form_params.append(('x_resolution', params['x_resolution'])) # noqa: E501
if 'y_resolution' in params:
form_params.append(('y_resolution', params['y_resolution'])) # noqa: E501
if 'current_index' in params:
form_params.append(('current_index', params['current_index'])) # noqa: E501
if 'image' in params:
form_params.append(('image', params['image'])) # noqa: E501
if 'user' in params:
form_params.append(('user', params['user'])) # noqa: E501
if 'screening_tiles' in params:
form_params.append(('screening_tiles', params['screening_tiles'])) # noqa: E501
if 'x_steps' in params:
form_params.append(('x_steps', params['x_steps'])) # noqa: E501
if 'y_steps' in params:
form_params.append(('y_steps', params['y_steps'])) # noqa: E501
if 'x_resolution' in params:
form_params.append(('x_resolution', params['x_resolution'])) # noqa: E501
if 'y_resolution' in params:
form_params.append(('y_resolution', params['y_resolution'])) # noqa: E501
if 'current_index' in params:
form_params.append(('current_index', params['current_index'])) # noqa: E501
body_params = None
if 'body' in params:
body_params = params['body']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json', 'application/x-www-form-urlencoded', 'multipart/form-data']) # noqa: E501
# Authentication setting
auth_settings = ['basicAuth'] # noqa: E501
return self.api_client.call_api(
'/api/v1/images/screening_modes/{id}/', 'PATCH',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ScreeningMode', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def retrieve_screening_mode(self, id, **kwargs): # noqa: E501
"""retrieve_screening_mode # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.retrieve_screening_mode(id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str id: (required)
:param str id2: id
:param str image: image
:param str user: user
:return: ScreeningMode
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.retrieve_screening_mode_with_http_info(id, **kwargs) # noqa: E501
else:
(data) = self.retrieve_screening_mode_with_http_info(id, **kwargs) # noqa: E501
return data
def retrieve_screening_mode_with_http_info(self, id, **kwargs): # noqa: E501
"""retrieve_screening_mode # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.retrieve_screening_mode_with_http_info(id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str id: (required)
:param str id2: id
:param str image: image
:param str user: user
:return: ScreeningMode
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['id', 'id2', 'image', 'user'] # noqa: E501
all_params.append('omit')
all_params.append('fields')
all_params.append('expand')
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method retrieve_screening_mode" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'id' is set
if ('id' not in params or
params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `retrieve_screening_mode`") # noqa: E501
collection_formats = {}
path_params = {}
if 'id' in params:
path_params['id'] = params['id'] # noqa: E501
query_params = []
if 'id2' in params:
query_params.append(('id', params['id2'])) # noqa: E501
if 'image' in params:
query_params.append(('image', params['image'])) # noqa: E501
if 'user' in params:
query_params.append(('user', params['user'])) # noqa: E501
if 'omit' in params:
query_params.append(('omit', params['omit'])) # noqa: E501
if 'fields' in params:
query_params.append(('fields', params['fields'])) # noqa: E50
if 'expand' in params:
query_params.append(('expand', params['expand'])) # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['basicAuth'] # noqa: E501
return self.api_client.call_api(
'/api/v1/images/screening_modes/{id}/', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ScreeningMode', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def update_screening_mode(self, id, **kwargs): # noqa: E501
"""update_screening_mode # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.update_screening_mode(id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str id: (required)
:param Body57 body:
:param str id2: id
:param str image2: image
:param str user2: user
:return: ScreeningMode
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.update_screening_mode_with_http_info(id, **kwargs) # noqa: E501
else:
(data) = self.update_screening_mode_with_http_info(id, **kwargs) # noqa: E501
return data
def update_screening_mode_with_http_info(self, id, **kwargs): # noqa: E501
"""update_screening_mode # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.update_screening_mode_with_http_info(id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str id: (required)
:param Body57 body:
:param str id2: id
:param str image2: image
:param str user2: user
:return: ScreeningMode
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['id', 'body', 'id2', 'image2', 'user2'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method update_screening_mode" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'id' is set
if ('id' not in params or
params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `update_screening_mode`") # noqa: E501
collection_formats = {}
path_params = {}
if 'id' in params:
path_params['id'] = params['id'] # noqa: E501
query_params = []
if 'id2' in params:
query_params.append(('id', params['id2'])) # noqa: E501
if 'image2' in params:
query_params.append(('image', params['image2'])) # noqa: E501
if 'user2' in params:
query_params.append(('user', params['user2'])) # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
if 'image' in params:
form_params.append(('image', params['image'])) # noqa: E501
if 'user' in params:
form_params.append(('user', params['user'])) # noqa: E501
if 'screening_tiles' in params:
form_params.append(('screening_tiles', params['screening_tiles'])) # noqa: E501
if 'x_steps' in params:
form_params.append(('x_steps', params['x_steps'])) # noqa: E501
if 'y_steps' in params:
form_params.append(('y_steps', params['y_steps'])) # noqa: E501
if 'x_resolution' in params:
form_params.append(('x_resolution', params['x_resolution'])) # noqa: E501
if 'y_resolution' in params:
form_params.append(('y_resolution', params['y_resolution'])) # noqa: E501
if 'current_index' in params:
form_params.append(('current_index', params['current_index'])) # noqa: E501
if 'image' in params:
form_params.append(('image', params['image'])) # noqa: E501
if 'user' in params:
form_params.append(('user', params['user'])) # noqa: E501
if 'screening_tiles' in params:
form_params.append(('screening_tiles', params['screening_tiles'])) # noqa: E501
if 'x_steps' in params:
form_params.append(('x_steps', params['x_steps'])) # noqa: E501
if 'y_steps' in params:
form_params.append(('y_steps', params['y_steps'])) # noqa: E501
if 'x_resolution' in params:
form_params.append(('x_resolution', params['x_resolution'])) # noqa: E501
if 'y_resolution' in params:
form_params.append(('y_resolution', params['y_resolution'])) # noqa: E501
if 'current_index' in params:
form_params.append(('current_index', params['current_index'])) # noqa: E501
body_params = None
if 'body' in params:
body_params = params['body']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json', 'application/x-www-form-urlencoded', 'multipart/form-data']) # noqa: E501
# Authentication setting
auth_settings = ['basicAuth'] # noqa: E501
return self.api_client.call_api(
'/api/v1/images/screening_modes/{id}/', 'PUT',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ScreeningMode', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def update_screening_mode(self, id, **kwargs): # noqa: E501
"""update_screening_mode # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.update_screening_mode(id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str id: (required)
:param int image:
:param int user:
:param object screening_tiles:
:param int x_steps:
:param int y_steps:
:param int x_resolution:
:param int y_resolution:
:param int current_index:
:param str id2: id
:param str image2: image
:param str user2: user
:return: ScreeningMode
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.update_screening_mode_with_http_info(id, **kwargs) # noqa: E501
else:
(data) = self.update_screening_mode_with_http_info(id, **kwargs) # noqa: E501
return data
def update_screening_mode_with_http_info(self, id, **kwargs): # noqa: E501
"""update_screening_mode # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.update_screening_mode_with_http_info(id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str id: (required)
:param int image:
:param int user:
:param object screening_tiles:
:param int x_steps:
:param int y_steps:
:param int x_resolution:
:param int y_resolution:
:param int current_index:
:param str id2: id
:param str image2: image
:param str user2: user
:return: ScreeningMode
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['id', 'image', 'user', 'screening_tiles', 'x_steps', 'y_steps', 'x_resolution', 'y_resolution', 'current_index', 'id2', 'image2', 'user2'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method update_screening_mode" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'id' is set
if ('id' not in params or
params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `update_screening_mode`") # noqa: E501
collection_formats = {}
path_params = {}
if 'id' in params:
path_params['id'] = params['id'] # noqa: E501
query_params = []
if 'id2' in params:
query_params.append(('id', params['id2'])) # noqa: E501
if 'image2' in params:
query_params.append(('image', params['image2'])) # noqa: E501
if 'user2' in params:
query_params.append(('user', params['user2'])) # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
if 'image' in params:
form_params.append(('image', params['image'])) # noqa: E501
if 'user' in params:
form_params.append(('user', params['user'])) # noqa: E501
if 'screening_tiles' in params:
form_params.append(('screening_tiles', params['screening_tiles'])) # noqa: E501
if 'x_steps' in params:
form_params.append(('x_steps', params['x_steps'])) # noqa: E501
if 'y_steps' in params:
form_params.append(('y_steps', params['y_steps'])) # noqa: E501
if 'x_resolution' in params:
form_params.append(('x_resolution', params['x_resolution'])) # noqa: E501
if 'y_resolution' in params:
form_params.append(('y_resolution', params['y_resolution'])) # noqa: E501
if 'current_index' in params:
form_params.append(('current_index', params['current_index'])) # noqa: E501
if 'image' in params:
form_params.append(('image', params['image'])) # noqa: E501
if 'user' in params:
form_params.append(('user', params['user'])) # noqa: E501
if 'screening_tiles' in params:
form_params.append(('screening_tiles', params['screening_tiles'])) # noqa: E501
if 'x_steps' in params:
form_params.append(('x_steps', params['x_steps'])) # noqa: E501
if 'y_steps' in params:
form_params.append(('y_steps', params['y_steps'])) # noqa: E501
if 'x_resolution' in params:
form_params.append(('x_resolution', params['x_resolution'])) # noqa: E501
if 'y_resolution' in params:
form_params.append(('y_resolution', params['y_resolution'])) # noqa: E501
if 'current_index' in params:
form_params.append(('current_index', params['current_index'])) # noqa: E501
body_params = None
if 'body' in params:
body_params = params['body']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json', 'application/x-www-form-urlencoded', 'multipart/form-data']) # noqa: E501
# Authentication setting
auth_settings = ['basicAuth'] # noqa: E501
return self.api_client.call_api(
'/api/v1/images/screening_modes/{id}/', 'PUT',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ScreeningMode', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def update_screening_mode(self, id, **kwargs): # noqa: E501
"""update_screening_mode # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.update_screening_mode(id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str id: (required)
:param int image:
:param int user:
:param object screening_tiles:
:param int x_steps:
:param int y_steps:
:param int x_resolution:
:param int y_resolution:
:param int current_index:
:param str id2: id
:param str image2: image
:param str user2: user
:return: ScreeningMode
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.update_screening_mode_with_http_info(id, **kwargs) # noqa: E501
else:
(data) = self.update_screening_mode_with_http_info(id, **kwargs) # noqa: E501
return data
def update_screening_mode_with_http_info(self, id, **kwargs): # noqa: E501
"""update_screening_mode # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.update_screening_mode_with_http_info(id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str id: (required)
:param int image:
:param int user:
:param object screening_tiles:
:param int x_steps:
:param int y_steps:
:param int x_resolution:
:param int y_resolution:
:param int current_index:
:param str id2: id
:param str image2: image
:param str user2: user
:return: ScreeningMode
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['id', 'image', 'user', 'screening_tiles', 'x_steps', 'y_steps', 'x_resolution', 'y_resolution', 'current_index', 'id2', 'image2', 'user2'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method update_screening_mode" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'id' is set
if ('id' not in params or
params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `update_screening_mode`") # noqa: E501
collection_formats = {}
path_params = {}
if 'id' in params:
path_params['id'] = params['id'] # noqa: E501
query_params = []
if 'id2' in params:
query_params.append(('id', params['id2'])) # noqa: E501
if 'image2' in params:
query_params.append(('image', params['image2'])) # noqa: E501
if 'user2' in params:
query_params.append(('user', params['user2'])) # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
if 'image' in params:
form_params.append(('image', params['image'])) # noqa: E501
if 'user' in params:
form_params.append(('user', params['user'])) # noqa: E501
if 'screening_tiles' in params:
form_params.append(('screening_tiles', params['screening_tiles'])) # noqa: E501
if 'x_steps' in params:
form_params.append(('x_steps', params['x_steps'])) # noqa: E501
if 'y_steps' in params:
form_params.append(('y_steps', params['y_steps'])) # noqa: E501
if 'x_resolution' in params:
form_params.append(('x_resolution', params['x_resolution'])) # noqa: E501
if 'y_resolution' in params:
form_params.append(('y_resolution', params['y_resolution'])) # noqa: E501
if 'current_index' in params:
form_params.append(('current_index', params['current_index'])) # noqa: E501
if 'image' in params:
form_params.append(('image', params['image'])) # noqa: E501
if 'user' in params:
form_params.append(('user', params['user'])) # noqa: E501
if 'screening_tiles' in params:
form_params.append(('screening_tiles', params['screening_tiles'])) # noqa: E501
if 'x_steps' in params:
form_params.append(('x_steps', params['x_steps'])) # noqa: E501
if 'y_steps' in params:
form_params.append(('y_steps', params['y_steps'])) # noqa: E501
if 'x_resolution' in params:
form_params.append(('x_resolution', params['x_resolution'])) # noqa: E501
if 'y_resolution' in params:
form_params.append(('y_resolution', params['y_resolution'])) # noqa: E501
if 'current_index' in params:
form_params.append(('current_index', params['current_index'])) # noqa: E501
body_params = None
if 'body' in params:
body_params = params['body']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json', 'application/x-www-form-urlencoded', 'multipart/form-data']) # noqa: E501
# Authentication setting
auth_settings = ['basicAuth'] # noqa: E501
return self.api_client.call_api(
'/api/v1/images/screening_modes/{id}/', 'PUT',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ScreeningMode', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
| 41.543363 | 174 | 0.595319 | 8,201 | 70,416 | 4.869772 | 0.023778 | 0.066505 | 0.067306 | 0.064902 | 0.982698 | 0.980119 | 0.979292 | 0.974885 | 0.973633 | 0.973433 | 0 | 0.022808 | 0.294535 | 70,416 | 1,694 | 175 | 41.567887 | 0.781142 | 0.2597 | 0 | 0.947783 | 1 | 0 | 0.216519 | 0.040658 | 0 | 0 | 0 | 0 | 0 | 1 | 0.024631 | false | 0 | 0.004926 | 0 | 0.066995 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
2fb9f2912f3b9730b1783a8ad0c5e16bde4e4532 | 6,532 | py | Python | tests/step7.py | hannahjzhang/2048 | d877722be026e2a2b4a90a79be3c66134f276739 | [
"MIT"
] | null | null | null | tests/step7.py | hannahjzhang/2048 | d877722be026e2a2b4a90a79be3c66134f276739 | [
"MIT"
] | null | null | null | tests/step7.py | hannahjzhang/2048 | d877722be026e2a2b4a90a79be3c66134f276739 | [
"MIT"
] | null | null | null | test = {
'name': 'Swap',
'points': 3,
'suites': [
{
'cases': [
{
'code': r"""
>>> assert not starter.swap(board), "An empty board should not perform swap"
>>> calls
['print']
""",
'hidden': False,
'locked': False
}
],
'scored': True,
'setup': r"""
>>> import starter_2048 as starter
>>> import utils
>>> N = 4
>>> board = utils.make_board(N)
>>> calls = []
>>> def test_print(message):
... calls.append('print') # do not actually print
>>> starter.print = test_print
""",
'teardown': '',
'type': 'doctest'
},
{
'cases': [
{
'code': r"""
>>> starter.place_piece('2', 0, 0, board)
True
>>> assert not starter.swap_possible(board), "A board with 1 piece cannot perform swap"
>>> calls
['print']
>>> starter.place_piece('2', 1, 1, board)
True
>>> assert not starter.swap_possible(board), "A board with 2 identical pieces cannot perform swap"
""",
'hidden': False,
'locked': False
}
],
'scored': True,
'setup': r"""
>>> import starter_2048 as starter
>>> import utils
>>> N = 4
>>> board = utils.make_board(N)
>>> calls = []
>>> def test_print(message):
... calls.append('print') # do not actually print
>>> starter.print = test_print
""",
'teardown': '',
'type': 'doctest'
},
{
'cases': [
{
'code': r"""
>>> board = utils.make_board(4);
>>> starter.place_piece('2', 0, 0, board)
True
>>> starter.place_piece('4', 1, 0, board)
True
>>> assert starter.swap(board), "A board with 2 unique pieces should perform swap"
>>> assert starter.get_piece(0,0,board)=='4', "The pieces are not correctly swapped. check your swap functions again."
>>> assert starter.get_piece(1,0,board)=='2', "The pieces are not correctly swapped. check your swap functions again."
>>> assert starter.get_piece(0,1,board)=='*', "There shoudn't be pieces added to empty places. check your swap functions again."
>>> assert starter.get_piece(1,1,board)=='*', "There shoudn't be pieces added to empty places. check your swap functions again."
""",
'hidden': False,
'locked': False
}
],
'scored': True,
'setup': r"""
>>> import starter_2048 as starter
>>> import utils
>>> N = 4
>>> board = utils.make_board(N)
""",
'teardown': '',
'type': 'doctest'
},
{
'cases': [
{
'code': r"""
>>> board = utils.make_board(4);
>>> starter.place_piece('2', 0, 0, board)
True
>>> starter.place_piece('4', 1, 1, board)
True
>>> assert starter.swap(board), "A board with 2 unique pieces should perform swap"
>>> assert starter.get_piece(0,0,board)=='4', "The pieces are not correctly swapped. \nYou shoudn't swap randomly, not staticly. \ncheck your swap functions again."
>>> assert starter.get_piece(1,1,board)=='2', "The pieces are not correctly swapped. \nYou shoudn't swap randomly, not staticly. \ncheck your swap functions again."
>>> assert starter.get_piece(0,1,board)=='*', "There shoudn't be pieces added to empty places. check your swap functions again."
>>> assert starter.get_piece(1,0,board)=='*', "There shoudn't be pieces added to empty places. check your swap functions again."
""",
'hidden': False,
'locked': False
}
],
'scored': True,
'setup': r"""
>>> import starter_2048 as starter
>>> import utils
>>> board = utils.make_board(10)
""",
'teardown': '',
'type': 'doctest'
},
{
'cases': [
{
'code': r"""
>>> board = utils.make_board(4);
>>> entered = False
>>> starter.place_piece('2', 0, 0, board)
True
>>> starter.place_piece('4', 0, 1, board)
True
>>> starter.place_piece('8', 1, 0, board)
True
>>> assert starter.swap(board), "A board with 3 unique pieces should perform swap"
>>> if starter.get_piece(0,0,board) =='2':
... entered = True
... assert starter.get_piece(0,1,board)=='8', "The pieces are not correctly swapped. \nMultiple swaps probably occurred. \ncheck your swap function again.";
... assert starter.get_piece(1,0,board)=='4', "The pieces are not correctly swapped. \nMultiple swaps probably occurred. \ncheck your swap function again.";
... assert starter.get_piece(1,1,board)=='*', "There shoudn't be pieces added to empty places. check your swap function again.";
>>> if starter.get_piece(0,0,board) =='4':
... entered = True
... assert starter.get_piece(0,1,board)=='2', "The pieces are not correctly swapped. \nMultiple swaps probably occurred. \ncheck your swap function again.";
... assert starter.get_piece(1,0,board)=='8', "The pieces are not correctly swapped. \nMultiple swaps probably occurred. \ncheck your swap function again.";
... assert starter.get_piece(1,1,board)=='*', "There shoudn't be pieces added to empty places. check your swap function again.";
>>> if starter.get_piece(0,0,board) =='8':
... entered = True
... assert starter.get_piece(0,1,board)=='4', "The pieces are not correctly swapped. \nMultiple swaps probably occurred. \ncheck your swap function again.";
... assert starter.get_piece(1,0,board)=='2', "The pieces are not correctly swapped. \nMultiple swaps probably occurred. \ncheck your swap function again.";
... assert starter.get_piece(1,1,board)=='*', "There shoudn't be pieces added to empty places. check your swap function again.";
>>> if not entered:
... assert False,"Improper swaps occurred. Check your swap function again";
""",
'hidden': False,
'locked': False
}
],
'scored': True,
'setup': r"""
>>> import starter_2048 as starter
>>> import utils
""",
'teardown': '',
'type': 'doctest'
}
]
}
| 39.829268 | 174 | 0.539039 | 760 | 6,532 | 4.571053 | 0.107895 | 0.074842 | 0.086356 | 0.102763 | 0.917962 | 0.894934 | 0.894934 | 0.887737 | 0.88198 | 0.852332 | 0 | 0.025205 | 0.307563 | 6,532 | 163 | 175 | 40.07362 | 0.74287 | 0 | 0 | 0.668712 | 0 | 0.104294 | 0.85594 | 0.149724 | 0 | 0 | 0 | 0 | 0.147239 | 1 | 0 | false | 0 | 0.06135 | 0 | 0.06135 | 0.04908 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
640ef05d1c3112fdb1d09953d9389b2532765a36 | 177 | py | Python | ENIAC/api/eniac_bps/__init__.py | webclinic017/fast_tools | 144d764e4f169d3ab3753dcc6a79db9f9449de59 | [
"Apache-2.0"
] | 1 | 2021-12-11T16:33:54.000Z | 2021-12-11T16:33:54.000Z | ENIAC/api/eniac_bps/__init__.py | webclinic017/fast_tools | 144d764e4f169d3ab3753dcc6a79db9f9449de59 | [
"Apache-2.0"
] | null | null | null | ENIAC/api/eniac_bps/__init__.py | webclinic017/fast_tools | 144d764e4f169d3ab3753dcc6a79db9f9449de59 | [
"Apache-2.0"
] | 3 | 2021-11-22T09:46:43.000Z | 2022-01-28T22:33:07.000Z | # from . import factors, backtrader, btresultApi, loopback, piplineStatus, trading ,ai,validation, loop_coin, financial
# from . import factors, financial
from . import car_info | 59 | 119 | 0.79096 | 21 | 177 | 6.571429 | 0.714286 | 0.217391 | 0.246377 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.124294 | 177 | 3 | 120 | 59 | 0.890323 | 0.847458 | 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 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 8 |
ff38fa252d914e3b2c6c2b3e8283c448f50c80ef | 35,170 | py | Python | hierarchical_sparse.py | AlbaIII/MMV-CLAMP_Tensorflow | fa19fd224a05fbc4a7c4965c9238103c5f98668a | [
"MIT"
] | null | null | null | hierarchical_sparse.py | AlbaIII/MMV-CLAMP_Tensorflow | fa19fd224a05fbc4a7c4965c9238103c5f98668a | [
"MIT"
] | null | null | null | hierarchical_sparse.py | AlbaIII/MMV-CLAMP_Tensorflow | fa19fd224a05fbc4a7c4965c9238103c5f98668a | [
"MIT"
] | null | null | null | #!/usr/bin/python
from __future__ import division
from __future__ import print_function
import numpy as np
import numpy.linalg as la
import math
import tensorflow as tf
from tools import raputil as ru
# import raputil as ru
class Generator(object):
#def __init__(self, A, **kwargs):
def __init__(self, A, E, N, **kwargs):
self.A = A
[M1, N1] = A.shape
self.E = E
#vars(self).update(kwargs)
#self.x_ = tf.placeholder(tf.float32, (None, N*2), name='x')
#self.y_ = tf.placeholder(tf.float32, (None, M*2), name='y')
#self.x_ = tf.placeholder(tf.float32, (None, N), name='x')
#self.y_ = tf.placeholder(tf.float32, (None, M), name='y')
#self.x_ = tf.placeholder(tf.float32, (None, N*E), name='x')
#self.y_ = tf.placeholder(tf.float32, (None, M*E), name='y')
#self.x_ = tf.placeholder(tf.float32, (N, None), name='x')
#self.y_ = tf.placeholder(tf.float32, (M, None), name='y')
# LAMP-C network
self.x_ = tf.placeholder(tf.float32, (None,E*N1), name='x')
self.y_ = tf.placeholder(tf.float32, (None,E*M1), name='y')
# # LAMP-D network
# self.x_ = tf.placeholder(tf.float32, (None,N1), name='x')
# self.y_ = tf.placeholder(tf.float32, (None,M1), name='y')
# # LAMP-H network
# U = 2
# E1 = int(E/U)
# self.x_ = tf.placeholder(tf.float32, (None,E1*N1), name='x')
# self.y_ = tf.placeholder(tf.float32, (None,E1*M1), name='y')
class TFGenerator(Generator):
def __init__(self, **kwargs):
Generator.__init__(self, **kwargs)
def __call__(self, sess):
'generates y,x pair for training'
return sess.run((self.ygen_, self.xgen_))
class NumpyGenerator(Generator):
def __init__(self, **kwargs):
Generator.__init__(self, **kwargs)
def __call__(self, sess):
'generates y,x pair for training'
return self.p.genYX(self.nbatches, self.nsubprocs)
def bernoulli_gaussian_hierarchical_sparse_trial(M=10, N=200, L=1000, Tg=3, pnz=0.05, kappa=0, SNR=0):
A = np.random.normal(size=(M, N), scale=1.0 / math.sqrt(M)).astype(np.float32)
if kappa >= 1:
# create a random operator with a specific condition number
U, _, V = la.svd(A, full_matrices=False)
s = np.logspace(0, np.log10(1 / kappa), M)
A = np.dot(U * (s * np.sqrt(N) / la.norm(s)), V).astype(np.float32)
A_col_norm = np.linalg.norm(A, ord=2, axis=0, keepdims=True)
A = A / A_col_norm
Ao = A
A_v = np.zeros([M + Tg, N * (Tg + 1)]).astype(np.float32)
for i1 in range(N):
for i2 in range(Tg + 1):
A_v[i2:(i2 + M), i1 * (Tg + 1) + i2] = A[:, i1]
A = A_v
A_ = tf.constant(A, name='A')
prob_hisps = TFGenerator(A=A, A_=A_, pnz=pnz, kappa=kappa, SNR=SNR)
prob_hisps.name = 'Bernoulli-Gaussian-Hierarchical-Sparse, random A'
test_batch = 5000
bernoulli = np.random.uniform(0, 1, size=(N, L)).astype(np.float32)
for col in range(L):
for row in range(N):
if bernoulli[row][col] < pnz:
bernoulli[row][col] = 1
else:
bernoulli[row][col] = 0
sum_ber = np.sum(bernoulli[:, col])
if sum_ber == 0:
bernoulli[0][col] = 1
x_channel = np.random.normal(size=(N, L), scale=1).astype(np.float32)
x_sync = np.multiply(x_channel, bernoulli)
x_sync_test = x_sync[:, 0:test_batch]
prob_hisps.bernoulli = bernoulli
ind_test = bernoulli[:, 0:test_batch]
prob_hisps.ind_test = ind_test
prob_hisps.x_sync_test = x_sync_test
noise_var = 1 / np.sqrt(M) * math.pow(10., -SNR / 10.)
# bernoulli_ = tf.to_float(tf.random_uniform((N, L)) < pnz)
# xgen_ = bernoulli_ * tf.random_normal((N, L))
# noise_var = pnz * N / M * math.pow(10., -SNR / 10.)
# ygen_ = tf.matmul(A_, xgen_) + tf.random_normal((M, L), stddev=math.sqrt(noise_var))
user_delay = np.random.random_integers(0, Tg, size=(N, L))
ud_test = user_delay[:, 0:test_batch]
prob_hisps.ud_test = ud_test
prob_hisps.ud = user_delay
x_vir_channel = np.zeros([N * (Tg + 1), L]).astype(np.float32)
for iL in range(L):
for iu in range(N):
if bernoulli[iu][iL] == 1:
x_vir_channel[iu*(Tg+1) + user_delay[iu,iL],iL] = x_channel[iu,iL]
# print(x_vir_channel[iu][iL])
x_test = x_vir_channel[:, 0:test_batch]
noise = np.random.normal(size=(M+Tg, L), scale=noise_var).astype(np.float32)
SNR_ex = np.array([3, 6, 9, 12])
sigma_w = np.zeros([4]).astype(np.float32)
noise_ex = np.zeros([M+Tg, 4*test_batch]).astype(np.float32)
for iw in range(4):
sigma_w[iw] = 1 / np.sqrt(M) * math.pow(10., -SNR_ex[iw] / 10.)
noise_ex[:,iw*test_batch:(iw+1)*test_batch] = sigma_w[iw]/noise_var*noise[:,0:test_batch]
y_signal_noiseless = np.dot(A_v, x_vir_channel)
y_signal = y_signal_noiseless + noise
y_test = y_signal[:, 0:test_batch]
y_test_ex = np.zeros([M+Tg, 4*test_batch])
x_test_ex = np.zeros([N*(Tg+1), 4*test_batch]).astype(np.float32)
for iw in range(4):
x_test_ex[:,iw*test_batch:(iw+1)*test_batch] = x_test
y_test_ex[:,iw*test_batch:(iw+1)*test_batch] = y_signal_noiseless[:,0:test_batch] + noise_ex[:,iw*test_batch:(iw+1)*test_batch]
prob_hisps.y_test_ex = y_test_ex
prob_hisps.x_test_ex = x_test_ex
y_sync = np.dot(Ao, x_sync) + noise[0:M, :]
y_sync_test = y_sync[:, 0:test_batch]
prob_hisps.y_sync_test = y_sync_test
prob_hisps.Ao = Ao
prob_hisps.x_sync = x_sync
prob_hisps.y_sync = y_sync
xgen_ = tf.constant(x_vir_channel, name='x_vir_channel')
ygen_ = tf.constant(y_signal, name='y_signal')
prob_hisps.x_test = x_test
prob_hisps.y_test = y_test
prob_hisps.xval = x_vir_channel
prob_hisps.yval = y_signal
prob_hisps.xinit = x_vir_channel
prob_hisps.yinit = y_signal
prob_hisps.xgen_ = xgen_
prob_hisps.ygen_ = ygen_
prob_hisps.noise_var = noise_var
return prob_hisps
def bernoulli_gaussian_hierarchical_sparse_MMV_trial(M=10,N=200,E=2,L=200000,Tg=3,pnz=0.05,kappa=0,SNR=0):
A = np.random.normal(size=(M, N), scale=1.0 / math.sqrt(M)).astype(np.float32)
if kappa >= 1:
# create a random operator with a specific condition number
U, _, V = la.svd(A, full_matrices=False)
s = np.logspace(0, np.log10(1 / kappa), M)
A = np.dot(U * (s * np.sqrt(N) / la.norm(s)), V).astype(np.float32)
A_col_norm = np.linalg.norm(A, ord=2, axis=0, keepdims=True)
A = A / A_col_norm
Ao = A
#A_col_norm = np.linalg.norm(A, ord=2, axis=0, keepdims=True)
A_v = np.zeros([M+Tg, N*(Tg+1)]).astype(np.float32)
for i1 in range(N):
for i2 in range(Tg+1):
A_v[i2:(i2 + M), i1 * (Tg + 1) + i2] = A[:, i1]
A = A_v
A_ = tf.constant(A, name='A')
prob_hisps_MMV = TFGenerator(A=A, A_=A_, pnz=pnz, kappa=kappa, SNR=SNR, Ao=Ao, M=M, N=N, Tg=Tg, E=E)
prob_hisps_MMV.name = 'Bernoulli-Gaussian-Hierarchical-Sparse-MMV, random A'
test_batch = 20000
bernoulli = np.random.uniform(0, 1, size=(L, N)).astype(np.float32)
ind_channel = np.zeros(shape=(L, E, N)).astype(np.float32)
for height in range(L):
for row in range(N):
if bernoulli[height,row] < pnz:
bernoulli[height,row] = 1.0
ind_channel[height,:,row] = np.ones(shape=(E)).astype(np.float32)
else:
bernoulli[height,row] = 0.0
ind_channel[height,:,row] = np.zeros(shape=(E)).astype(np.float32)
sum_ber = np.sum(bernoulli[height, :])
if sum_ber == 0:
bernoulli[height,0] = 1.0
ind_channel[height,:,0] = np.ones(shape=(E)).astype(np.float32)
prob_hisps_MMV.ind = np.transpose(bernoulli, (1,0))
x_channel = np.random.normal(size=(L, E, N), scale=1.0).astype(np.float32)
x_sync = np.multiply(x_channel, ind_channel)
x_sync_test = x_sync[0:test_batch,:,:]
x_sync_test_vec = x_sync_test.reshape([test_batch,N*E])
prob_hisps_MMV.x_sync_test_mat = np.transpose(x_sync_test, (2,1,0))
prob_hisps_MMV.x_sync_test = x_sync_test_vec
ind_test = bernoulli[0:test_batch,:]
prob_hisps_MMV.ind_test = np.transpose(ind_test, (1,0))
user_delay = np.random.random_integers(0, Tg, size=(L, N))
prob_hisps_MMV.ud = np.transpose(user_delay, (1,0))
ud_test = user_delay[0:test_batch,:]
prob_hisps_MMV.ud_test = np.transpose(ud_test, (1,0))
x_vir_channel = np.zeros([L,E,N*(Tg+1)]).astype(np.float32)
for iL in range(L):
for iu in range(N):
if bernoulli[iL,iu] == 1.0:
x_vir_channel[iL,:,iu*(Tg+1)+user_delay[iL,iu]] = x_channel[iL,:,iu]
x_vir_channel_vec = x_vir_channel.reshape([L,N*(Tg+1)*E])
prob_hisps_MMV.x_mat = np.transpose(x_vir_channel, (2,1,0))
x_test = x_vir_channel[0:test_batch,:,:]
prob_hisps_MMV.x_test_mat = np.transpose(x_test, (2,1,0))
x_test_vec = x_test.reshape([test_batch,N*(Tg+1)*E])
prob_hisps_MMV.x_test = x_test_vec
noise_var = 1 / np.sqrt(M) * math.pow(10., -SNR / 10.)
noise = np.random.normal(size=(L,E,M+Tg), scale=noise_var).astype(np.float32)
prob_hisps_MMV.noise_mat = np.transpose(noise, (2,1,0))
SNR_ex = np.array([3, 6, 9, 12])
sigma_w = np.zeros([4]).astype(np.float32)
noise_ex = np.zeros([4*test_batch,E,M+Tg]).astype(np.float32)
for iw in range(4):
sigma_w[iw] = 1 / np.sqrt(M) * math.pow(10., -SNR_ex[iw] / 10.)
noise_ex[iw*test_batch:(iw+1)*test_batch,:,:] = sigma_w[iw] / noise_var * noise[0:test_batch,:,:]
y_signal_noiseless = np.dot(np.reshape(x_vir_channel, (E*L,N*(Tg+1))), np.transpose(A_v))
y_signal_noiseless = y_signal_noiseless.reshape([L,E,M+Tg])
y_signal = y_signal_noiseless + noise
prob_hisps_MMV.y_mat = np.transpose(y_signal, (2,1,0))
y_signal_vec = y_signal.reshape([L,(M+Tg)*E])
y_test = y_signal[0:test_batch,:,:]
y_test_vec = y_test.reshape([test_batch,(M+Tg)*E])
prob_hisps_MMV.y_test_mat = np.transpose(y_test, (2,1,0))
prob_hisps_MMV.y_test = y_test_vec
y_test_ex = np.zeros([4*test_batch,E,M+Tg])
x_test_ex = np.zeros([4*test_batch,E,N*(Tg+1)]).astype(np.float32)
for iw in range(4):
x_test_ex[iw*test_batch:(iw+1)*test_batch,:,:] = x_test
y_test_ex[iw*test_batch:(iw+1)*test_batch,:,:] = y_signal_noiseless[0:test_batch,:,:] + noise_ex[iw*test_batch:(iw+1)*test_batch,:,:]
prob_hisps_MMV.y_test_ex_mat = np.transpose(y_test_ex, (2,1,0))
prob_hisps_MMV.x_test_ex_mat = np.transpose(x_test_ex, (2,1,0))
y_test_ex_vec = y_test_ex.reshape([4*test_batch,(M+Tg)*E])
x_test_ex_vec = x_test_ex.reshape([4*test_batch,N*(Tg+1)*E])
prob_hisps_MMV.y_test_ex = y_test_ex_vec
prob_hisps_MMV.x_test_ex = x_test_ex_vec
y_sync_noiseless = np.dot(np.reshape(x_sync, (L*E,N)), np.transpose(Ao))
y_sync_noiseless = y_sync_noiseless.reshape([L,E,M])
y_sync = y_sync_noiseless + noise[:,:,0:M]
y_sync_test = y_sync[0:test_batch,:,:]
y_sync_test_vec = y_sync_test.reshape([test_batch,M*E])
prob_hisps_MMV.y_sync_test = y_sync_test_vec
prob_hisps_MMV.y_sync_test_mat = np.transpose(y_sync_test, (2,1,0))
prob_hisps_MMV.Ao = Ao
prob_hisps_MMV.x_sync_mat = np.transpose(x_sync, (2,1,0))
prob_hisps_MMV.y_sync_mat = np.transpose(y_sync, (2,1,0))
prob_hisps_MMV.x_sync_D = x_sync.reshape([L*E,N])
prob_hisps_MMV.y_sync_D = y_sync.reshape([L*E,M])
prob_hisps_MMV.x_sync_test_D = x_sync_test.reshape([test_batch*E,N])
prob_hisps_MMV.y_sync_test_D = y_sync_test.reshape([test_batch*E,M])
prob_hisps_MMV.y_signal_D = y_signal.reshape([L*E,M+Tg])
prob_hisps_MMV.x_D = x_vir_channel.reshape([L*E,N*(Tg+1)])
prob_hisps_MMV.y_test_D = y_test.reshape([test_batch*E,M+Tg])
prob_hisps_MMV.x_test_D = x_test.reshape([test_batch*E,N*(Tg+1)])
prob_hisps_MMV.y_test_ex_D = y_test_ex.reshape([4*test_batch*E,M+Tg])
prob_hisps_MMV.x_test_ex_D = x_test_ex.reshape([4*test_batch*E,N*(Tg+1)])
#xgen_ = tf.constant(x_vir_channel_vec, name='x_vir_channel')
#ygen_ = tf.constant(y_signal_vec, name='y_signal')
prob_hisps_MMV.xval = x_vir_channel_vec
prob_hisps_MMV.yval = y_signal_vec
prob_hisps_MMV.xinit = x_vir_channel_vec
prob_hisps_MMV.yinit = y_signal_vec
#prob_hisps_MMV.xgen_ = xgen_
#prob_hisps_MMV.ygen_ = ygen_
prob_hisps_MMV.noise_var = noise_var
return prob_hisps_MMV
def bernoulli_gaussian_hierarchical_sparse_randomlocation_trial(M=10, N=200, L=1000, Tg=3, pnz=0.05, kappa=0, SNR=0):
A = np.random.normal(size=(M, N), scale=1.0 / math.sqrt(M)).astype(np.float32)
if kappa >= 1:
# create a random operator with a specific condition number
U, _, V = la.svd(A, full_matrices=False)
s = np.logspace(0, np.log10(1 / kappa), M)
A = np.dot(U * (s * np.sqrt(N) / la.norm(s)), V).astype(np.float32)
A_col_norm = np.linalg.norm(A, ord=2, axis=0, keepdims=True)
A = A / A_col_norm
Ao = A
A_v = np.zeros([M + Tg, N * (Tg + 1)]).astype(np.float32)
for i1 in range(N):
for i2 in range(Tg + 1):
A_v[i2:(i2 + M), i1 * (Tg + 1) + i2] = A[:, i1]
A = A_v
A_ = tf.constant(A, name='A')
prob_hisps = TFGenerator(A=A, A_=A_, pnz=pnz, kappa=kappa, SNR=SNR, iid=False)
prob_hisps.name = 'Bernoulli-Gaussian-Hierarchical-Sparse, random A'
alpha = 15.3
beta = 37.6
lsf_sd = 0.1**(alpha+beta*np.log10(300))
lsf = np.random.uniform(low=100, high=1000, size=N)
lsf_t = np.zeros([N*(Tg+1)]).astype(np.float32)
for il in range(N):
lsf_t[il] = 0.1**(alpha+beta*np.log10(lsf[il]))
for iT in range(Tg+1):
lsf_t[il*(Tg+1)+iT] = lsf[il]
lsf = lsf/lsf_sd
lsf_t = lsf_t/lsf_sd
test_batch = 5000
bernoulli = np.random.uniform(0, 1, size=(N, L)).astype(np.float32)
for col in range(L):
for row in range(N):
if bernoulli[row][col] < pnz:
bernoulli[row][col] = 1
else:
bernoulli[row][col] = 0
sum_ber = np.sum(bernoulli[:, col])
if sum_ber == 0:
bernoulli[0][col] = 1
x_channel = np.random.normal(size=(N, L), scale=1).astype(np.float32)
x_channel = x_channel*lsf.reshape(lsf,[N,1])
x_sync = np.multiply(x_channel, bernoulli)
x_sync_test = x_sync[:, 0:test_batch]
prob_hisps.bernoulli = bernoulli
ind_test = bernoulli[:, 0:test_batch]
prob_hisps.ind_test = ind_test
prob_hisps.x_sync_test = x_sync_test
noise_var = 1 / np.sqrt(M) * math.pow(10., -SNR / 10.)
# bernoulli_ = tf.to_float(tf.random_uniform((N, L)) < pnz)
# xgen_ = bernoulli_ * tf.random_normal((N, L))
# noise_var = pnz * N / M * math.pow(10., -SNR / 10.)
# ygen_ = tf.matmul(A_, xgen_) + tf.random_normal((M, L), stddev=math.sqrt(noise_var))
user_delay = np.random.random_integers(0, Tg, size=(N, L))
ud_test = user_delay[:, 0:test_batch]
prob_hisps.ud_test = ud_test
prob_hisps.ud = user_delay
x_vir_channel = np.zeros([N * (Tg + 1), L]).astype(np.float32)
for iL in range(L):
for iu in range(N):
if bernoulli[iu][iL] == 1:
x_vir_channel[iu*(Tg+1) + user_delay[iu,iL],iL] = x_channel[iu,iL]
# print(x_vir_channel[iu][iL])
x_test = x_vir_channel[:, 0:test_batch]
noise = np.random.normal(size=(M+Tg, L), scale=noise_var).astype(np.float32)
SNR_ex = np.array([3, 6, 9, 12])
sigma_w = np.zeros([4]).astype(np.float32)
noise_ex = np.zeros([M+Tg, 4*test_batch]).astype(np.float32)
for iw in range(4):
sigma_w[iw] = 1 / np.sqrt(M) * math.pow(10., -SNR_ex[iw] / 10.)
noise_ex[:,iw*test_batch:(iw+1)*test_batch] = sigma_w[iw]/noise_var*noise[:,0:test_batch]
y_signal_noiseless = np.dot(A_v, x_vir_channel)
y_signal = y_signal_noiseless + noise
y_test = y_signal[:, 0:test_batch]
y_test_ex = np.zeros([M+Tg, 4*test_batch])
x_test_ex = np.zeros([N*(Tg+1), 4*test_batch]).astype(np.float32)
for iw in range(4):
x_test_ex[:,iw*test_batch:(iw+1)*test_batch] = x_test
y_test_ex[:,iw*test_batch:(iw+1)*test_batch] = y_signal_noiseless[:,0:test_batch] + noise_ex[:,iw*test_batch:(iw+1)*test_batch]
prob_hisps.y_test_ex = y_test_ex
prob_hisps.x_test_ex = x_test_ex
y_sync = np.dot(Ao, x_sync) + noise[0:M, :]
y_sync_test = y_sync[:, 0:test_batch]
prob_hisps.y_sync_test = y_sync_test
prob_hisps.Ao = Ao
prob_hisps.x_sync = x_sync
prob_hisps.y_sync = y_sync
xgen_ = tf.constant(x_vir_channel, name='x_vir_channel')
ygen_ = tf.constant(y_signal, name='y_signal')
prob_hisps.x_test = x_test
prob_hisps.y_test = y_test
prob_hisps.xval = x_vir_channel
prob_hisps.yval = y_signal
prob_hisps.xinit = x_vir_channel
prob_hisps.yinit = y_signal
prob_hisps.xgen_ = xgen_
prob_hisps.ygen_ = ygen_
prob_hisps.noise_var = noise_var
return prob_hisps
def bernoulli_gaussian_hierarchical_sparse_MMV_randomlocation_trial(M=10,N=200,E=2,L=200000,Tg=3,pnz=0.05,kappa=0,SNR=0):
A = np.random.normal(size=(M, N), scale=1.0 / math.sqrt(M)).astype(np.float32)
if kappa >= 1:
# create a random operator with a specific condition number
U, _, V = la.svd(A, full_matrices=False)
s = np.logspace(0, np.log10(1 / kappa), M)
A = np.dot(U * (s * np.sqrt(N) / la.norm(s)), V).astype(np.float32)
A_col_norm = np.linalg.norm(A, ord=2, axis=0, keepdims=True)
A = A / A_col_norm
Ao = A
#A_col_norm = np.linalg.norm(A, ord=2, axis=0, keepdims=True)
A_v = np.zeros([M+Tg, N*(Tg+1)]).astype(np.float32)
for i1 in range(N):
for i2 in range(Tg+1):
A_v[i2:(i2 + M), i1 * (Tg + 1) + i2] = A[:, i1]
A = A_v
A_ = tf.constant(A, name='A')
iid = False
prob_hisps_MMV = TFGenerator(A=A, A_=A_, pnz=pnz, kappa=kappa, SNR=SNR, Ao=Ao, M=M, N=N, Tg=Tg, E=E, iid=iid)
prob_hisps_MMV.name = 'Bernoulli-Gaussian-Hierarchical-Sparse-MMV, random A'
prob_hisps_MMV.iid = iid
alpha = 15.3
beta = 37.6
lsf_sd = 0.1**((alpha+beta*np.log10(500))/10)
distance = np.random.uniform(low=100, high=1000, size=N).astype(np.float32)
lsf = np.zeros([N]).astype(np.float32)
lsf_t = np.zeros([N*(Tg+1)]).astype(np.float32)
for il in range(N):
lsf[il] = 0.1**((alpha+beta*np.log10(distance[il]))/10)
for iT in range(Tg+1):
lsf_t[il*(Tg+1)+iT] = lsf[il]
lsf = 1.0/lsf_sd*lsf
lsf_t = 1.0/lsf_sd*lsf_t
prob_hisps_MMV.lsf = lsf
prob_hisps_MMV.lsf_t = lsf_t
test_batch = 5000
bernoulli = np.random.uniform(0, 1, size=(L, N)).astype(np.float32)
ind_channel = np.zeros(shape=(L, E, N)).astype(np.float32)
for height in range(L):
for row in range(N):
if bernoulli[height,row] < pnz:
bernoulli[height,row] = 1.0
ind_channel[height,:,row] = np.ones(shape=(E)).astype(np.float32)
else:
bernoulli[height,row] = 0.0
ind_channel[height,:,row] = np.zeros(shape=(E)).astype(np.float32)
sum_ber = np.sum(bernoulli[height, :])
if sum_ber == 0:
bernoulli[height,0] = 1.0
ind_channel[height,:,0] = np.ones(shape=(E)).astype(np.float32)
prob_hisps_MMV.ind = np.transpose(bernoulli, (1,0))
x_channel = np.random.normal(size=(L, E, N), scale=1.0).astype(np.float32)
x_channel = x_channel*lsf.reshape([1,1,N])
x_sync = np.multiply(x_channel, ind_channel)
x_sync_test = x_sync[0:test_batch,:,:]
x_sync_test_vec = x_sync_test.reshape([test_batch,N*E])
prob_hisps_MMV.x_sync_test_mat = np.transpose(x_sync_test, (2,1,0))
prob_hisps_MMV.x_sync_test = x_sync_test_vec
ind_test = bernoulli[0:test_batch,:]
prob_hisps_MMV.ind_test = np.transpose(ind_test, (1,0))
user_delay = np.random.random_integers(0, Tg, size=(L, N))
prob_hisps_MMV.ud = np.transpose(user_delay, (1,0))
ud_test = user_delay[0:test_batch,:]
prob_hisps_MMV.ud_test = np.transpose(ud_test, (1,0))
x_vir_channel = np.zeros([L,E,N*(Tg+1)]).astype(np.float32)
for iL in range(L):
for iu in range(N):
if bernoulli[iL,iu] == 1.0:
x_vir_channel[iL,:,iu*(Tg+1)+user_delay[iL,iu]] = x_channel[iL,:,iu]
x_vir_channel_vec = x_vir_channel.reshape([L,N*(Tg+1)*E])
prob_hisps_MMV.x_mat = np.transpose(x_vir_channel, (2,1,0))
x_test = x_vir_channel[0:test_batch,:,:]
prob_hisps_MMV.x_test_mat = np.transpose(x_test, (2,1,0))
x_test_vec = x_test.reshape([test_batch,N*(Tg+1)*E])
prob_hisps_MMV.x_test = x_test_vec
noise_var = 1 / np.sqrt(M) * math.pow(10., -SNR / 10.)
noise = np.random.normal(size=(L,E,M+Tg), scale=noise_var).astype(np.float32)
prob_hisps_MMV.noise_mat = np.transpose(noise, (2,1,0))
SNR_ex = np.array([3, 6, 9, 12])
sigma_w = np.zeros([4]).astype(np.float32)
noise_ex = np.zeros([4*test_batch,E,M+Tg]).astype(np.float32)
for iw in range(4):
sigma_w[iw] = 1 / np.sqrt(M) * math.pow(10., -SNR_ex[iw] / 10.)
noise_ex[iw*test_batch:(iw+1)*test_batch,:,:] = sigma_w[iw] / noise_var * noise[0:test_batch,:,:]
y_signal_noiseless = np.dot(np.reshape(x_vir_channel, (E*L,N*(Tg+1))), np.transpose(A_v))
y_signal_noiseless = y_signal_noiseless.reshape([L,E,M+Tg])
y_signal = y_signal_noiseless + noise
prob_hisps_MMV.y_mat = np.transpose(y_signal, (2,1,0))
y_signal_vec = y_signal.reshape([L,(M+Tg)*E])
y_test = y_signal[0:test_batch,:,:]
y_test_vec = y_test.reshape([test_batch,(M+Tg)*E])
prob_hisps_MMV.y_test_mat = np.transpose(y_test, (2,1,0))
prob_hisps_MMV.y_test = y_test_vec
y_test_ex = np.zeros([4*test_batch,E,M+Tg])
x_test_ex = np.zeros([4*test_batch,E,N*(Tg+1)]).astype(np.float32)
for iw in range(4):
x_test_ex[iw*test_batch:(iw+1)*test_batch,:,:] = x_test
y_test_ex[iw*test_batch:(iw+1)*test_batch,:,:] = y_signal_noiseless[0:test_batch,:,:] + noise_ex[iw*test_batch:(iw+1)*test_batch,:,:]
prob_hisps_MMV.y_test_ex_mat = np.transpose(y_test_ex, (2,1,0))
prob_hisps_MMV.x_test_ex_mat = np.transpose(x_test_ex, (2,1,0))
y_test_ex_vec = y_test_ex.reshape([4*test_batch,(M+Tg)*E])
x_test_ex_vec = x_test_ex.reshape([4*test_batch,N*(Tg+1)*E])
prob_hisps_MMV.y_test_ex = y_test_ex_vec
prob_hisps_MMV.x_test_ex = x_test_ex_vec
y_sync_noiseless = np.dot(np.reshape(x_sync, (L*E,N)), np.transpose(Ao))
y_sync_noiseless = y_sync_noiseless.reshape([L,E,M])
y_sync = y_sync_noiseless + noise[:,:,0:M]
y_sync_test = y_sync[0:test_batch,:,:]
y_sync_test_vec = y_sync_test.reshape([test_batch,M*E])
prob_hisps_MMV.y_sync_test = y_sync_test_vec
prob_hisps_MMV.y_sync_test_mat = np.transpose(y_sync_test, (2,1,0))
prob_hisps_MMV.Ao = Ao
prob_hisps_MMV.x_sync_mat = np.transpose(x_sync, (2,1,0))
prob_hisps_MMV.y_sync_mat = np.transpose(y_sync, (2,1,0))
prob_hisps_MMV.x_sync_D = x_sync.reshape([L*E,N])
prob_hisps_MMV.y_sync_D = y_sync.reshape([L*E,M])
prob_hisps_MMV.x_sync_test_D = x_sync_test.reshape([test_batch*E,N])
prob_hisps_MMV.y_sync_test_D = y_sync_test.reshape([test_batch*E,M])
prob_hisps_MMV.y_signal_D = y_signal.reshape([L*E,M+Tg])
prob_hisps_MMV.x_D = x_vir_channel.reshape([L*E,N*(Tg+1)])
prob_hisps_MMV.y_test_D = y_test.reshape([test_batch*E,M+Tg])
prob_hisps_MMV.x_test_D = x_test.reshape([test_batch*E,N*(Tg+1)])
prob_hisps_MMV.y_test_ex_D = y_test_ex.reshape([4*test_batch*E,M+Tg])
prob_hisps_MMV.x_test_ex_D = x_test_ex.reshape([4*test_batch*E,N*(Tg+1)])
#xgen_ = tf.constant(x_vir_channel_vec, name='x_vir_channel')
#ygen_ = tf.constant(y_signal_vec, name='y_signal')
prob_hisps_MMV.xval = x_vir_channel_vec
prob_hisps_MMV.yval = y_signal_vec
prob_hisps_MMV.xinit = x_vir_channel_vec
prob_hisps_MMV.yinit = y_signal_vec
#prob_hisps_MMV.xgen_ = xgen_
#prob_hisps_MMV.ygen_ = ygen_
prob_hisps_MMV.noise_var = noise_var
return prob_hisps_MMV
def cbg_hisps_MMV_trial(A0r,A0i,M=40,N=200,E=1,L=105000,Tg=3,pnz=0.05,SNR=0,iid=True):
# complex-valued system
Ar = np.zeros([M+Tg,N*(Tg+1)],dtype=np.float32)
Ai = np.zeros([M+Tg,N*(Tg+1)],dtype=np.float32)
for i1 in range(N):
for i2 in range(Tg+1):
Ar[i2:(i2+M),i1*(Tg+1)+i2] = A0r[:,i1]
Ai[i2:(i2+M),i1*(Tg+1)+i2] = A0i[:,i1]
A = np.zeros([2*(M+Tg),2*N*(Tg+1)]).astype(np.float32)
A[0:(M+Tg),0:(N*(Tg+1))] = Ar
A[0:(M+Tg),(N*(Tg+1)):(2*N*(Tg+1))] = -Ai
A[(M+Tg):(2*(M+Tg)),0:(N*(Tg+1))] = Ai
A[(M+Tg):(2*(M+Tg)),(N*(Tg+1)):(2*N*(Tg+1))] = Ar
AT = np.transpose(A)
A0 = np.zeros([2*M,2*N]).astype(np.float32)
A0[0:M,0:N] = A0r
A0[0:M,N:(2*N)] = -A0i
A0[M:(2*M),0:N] = A0i
A0[M:(2*M),N:(2*N)] = A0r
A_ = tf.constant(A, name='A')
prob_hisps_MMV = TFGenerator(A=A, A_=A_, pnz=pnz, SNR=SNR, A0=A0, M=M, N=N, Tg=Tg, E=E, iid=iid)
prob_hisps_MMV.name = 'Complex-Bernoulli-Gaussian-Hierarchical-Sparse-MMV, random A'
prob_hisps_MMV.A = A
prob_hisps_MMV.iid = iid
prob_hisps_MMV.M = M
prob_hisps_MMV.N = N
prob_hisps_MMV.E = E
prob_hisps_MMV.Tg = Tg
prob_hisps_MMV.pnz = pnz
prob_hisps_MMV.Mt = M+Tg
prob_hisps_MMV.Nt = N*(Tg+1)
# large-scale fading
if iid == False:
alpha = 15.3
beta = 37.6
lsf_sd = 0.1 ** ((alpha + beta * np.log10(150)) / 10)
distance = np.random.uniform(low=50, high=250, size=N).astype(np.float32)
lsf = np.zeros([N]).astype(np.float32)
lsf_t = np.zeros([N*(Tg+1)]).astype(np.float32)
for il in range(N):
lsf[il] = 0.1**( (alpha + beta*np.log10(distance[il]))/10 )
for iT in range(Tg+1):
lsf_t[il*(Tg+1)+iT] = lsf[il]
lsf = np.sqrt(1.0/lsf_sd*lsf)
lsf_t = np.sqrt(1.0/lsf_sd*lsf_t)
else:
lsf = np.ones([N]).astype(np.float32)
lsf_t = np.ones([N*(Tg+1)]).astype(np.float32)
prob_hisps_MMV.lsf = lsf
prob_hisps_MMV.lsf_t = lsf_t
# channel
naep = np.floor(pnz*N).astype(np.int32)
test_batch = 5000
bernoulli = (np.random.uniform(0,1,size=(L,N))<pnz).astype(np.float32)
nau = np.sum(bernoulli,axis=1)
for iL in range(test_batch):
if nau[iL] == 0:
bernoulli[iL,0:naep] = np.ones([naep]).astype(np.float32)
oneE = np.ones([E],dtype=np.float32)
ind_channel = bernoulli.reshape([L,1,N]) * np.reshape(oneE,(1,E,1))
prob_hisps_MMV.ind = np.transpose(bernoulli, (1,0))
x_channel_r = np.sqrt(0.5)*np.random.normal(size=(L,E,N),scale=1.0).astype(np.float32)
x_channel_i = np.sqrt(0.5)*np.random.normal(size=(L,E,N),scale=1.0).astype(np.float32)
x_channel_r = x_channel_r*lsf.reshape([1,1,N])
x_channel_i = x_channel_i*lsf.reshape([1,1,N])
x_sync_r = np.multiply(x_channel_r, ind_channel)
x_sync_i = np.multiply(x_channel_i, ind_channel)
x_sync = np.zeros([L,E,2*N]).astype(np.float32)
x_sync[:,:,0:N] = x_sync_r
x_sync[:,:,N:(2*N)] = x_sync_i
x_sync_test = x_sync[0:test_batch,:,:]
#x_sync_test_vec = x_sync_test.reshape([test_batch,2*N*E])
prob_hisps_MMV.x_sync_test_mat = np.transpose(x_sync_test, (2,1,0))
prob_hisps_MMV.x_sync_test = x_sync_test
prob_hisps_MMV.x_sync_train = x_sync[test_batch:L,:,:]
ind_test = bernoulli[0:test_batch,:]
prob_hisps_MMV.ind_test = np.transpose(ind_test,(1,0))
user_delay = np.random.random_integers(0,Tg,size=(L,N))
prob_hisps_MMV.ud = np.transpose(user_delay,(1,0))
ud_test = user_delay[0:test_batch,:]
prob_hisps_MMV.ud_test = np.transpose(ud_test,(1,0))
x_vir_channel = np.zeros([L,E,2*N*(Tg+1)]).astype(np.float32)
x_vir_channel_r = np.zeros([L,E,N*(Tg+1)],dtype=np.float32)
x_vir_channel_i = np.zeros([L,E,N*(Tg+1)],dtype=np.float32)
for iL in range(L):
for iu in range(N):
if bernoulli[iL,iu] == 1.0:
index1 = iu*(Tg+1)+user_delay[iL,iu]
index2 = N*(Tg+1)+iu*(Tg+1)+user_delay[iL,iu]
x_vir_channel[iL,:,index1] = x_channel_r[iL,:,iu]
x_vir_channel[iL,:,index2] = x_channel_i[iL,:,iu]
x_vir_channel_r[iL,:,index1] = x_channel_r[iL,:,iu]
x_vir_channel_i[iL,:,index1] = x_channel_i[iL,:,iu]
#x_vir_channel_vec = x_vir_channel.reshape([L,N*(Tg+1)*E])
#prob_hisps_MMV.x_mat = np.transpose(x_vir_channel, (2,1,0))
x_test = x_vir_channel[0:test_batch,:,:]
prob_hisps_MMV.x_test_mat = np.transpose(x_test, (2,1,0))
#x_test_vec = x_test.reshape([test_batch,N*(Tg+1)*E])
prob_hisps_MMV.x_test = x_test.reshape([test_batch,E*2*N*(Tg+1)])
x_train = x_vir_channel[test_batch:L,:,:]
prob_hisps_MMV.x_train = x_train.reshape([-1,E*2*N*(Tg+1)])
# noise and ex signal with various SNR
noise_var = 1/np.sqrt(M)*np.sqrt(math.pow(10., -SNR/10.))
noise = noise_var*np.sqrt(0.5)*np.random.normal(size=(L,E,2*(M+Tg)), scale=1.0).astype(np.float32)
prob_hisps_MMV.noise_mat = np.transpose(noise, (2,1,0))
SNR_ex = np.array([2,4,6,8])
sigma_w = np.zeros([4]).astype(np.float32)
noise_ex = np.zeros([4*test_batch,E,2*(M+Tg)]).astype(np.float32)
for iw in range(4):
sigma_w[iw] = 1/np.sqrt(M)*math.pow(10., -SNR_ex[iw]/10.)
noise_ex[(iw*test_batch):((iw+1)*test_batch),:,:] = sigma_w[iw]/noise_var*noise[0:test_batch,:,:]
# signal y in sync system (seems useless)
y_sync_noiseless = np.dot(np.reshape(x_sync,(L*E,2*N)), np.transpose(A0))
y_sync_noiseless = y_sync_noiseless.reshape([L,E,2*M])
noise_sync = np.zeros([L,E,2*M])
noise_sync[:,:,0:M] = noise[:,:,0:M]
noise_sync[:,:,M:(2*M)] = noise[:,:,(M+Tg):(2*M+Tg)]
y_sync = y_sync_noiseless + noise_sync
y_sync_test = y_sync[0:test_batch,:,:]
y_sync_train = y_sync[test_batch:L,:,:]
#y_sync_test_vec = y_sync_test.reshape([test_batch,M*E])
prob_hisps_MMV.y_sync_test = y_sync_test.reshape([test_batch,E*2*M])
prob_hisps_MMV.y_sync_train = y_sync_train.reshape([-1,E*2*M])
prob_hisps_MMV.y_sync_test_mat = np.transpose(y_sync_test, (2,1,0))
prob_hisps_MMV.A0 = A0
#prob_hisps_MMV.x_sync_mat = np.transpose(x_sync, (2,1,0))
#prob_hisps_MMV.y_sync_mat = np.transpose(y_sync, (2,1,0))
# signal y in async system
y_signal_noiseless = np.dot(np.reshape(x_vir_channel, (E*L,2*N*(Tg+1))), AT)
y_signal_noiseless = y_signal_noiseless.reshape([L,E,2*(M+Tg)])
y_signal = y_signal_noiseless + noise
#prob_hisps_MMV.y_mat = np.transpose(y_signal, (2,1,0))
#y_signal_vec = y_signal.reshape([L,(M+Tg)*E])
y_test = y_signal[0:test_batch,:,:]
#y_test_vec = y_test.reshape([test_batch, (M+Tg)*E])
prob_hisps_MMV.y_test_mat = np.transpose(y_test, (2,1,0))
prob_hisps_MMV.y_test = y_test.reshape(test_batch,E*2*(M+Tg))
y_train = y_signal[test_batch:L,:,:]
prob_hisps_MMV.y_train = y_train.reshape([-1,E*2*(M+Tg)])
y_test_ex = np.zeros([4*test_batch,E,2*(M+Tg)])
x_test_ex = np.zeros([4*test_batch,E,2*N*(Tg+1)]).astype(np.float32)
for iw in range(4):
x_test_ex[(iw*test_batch):((iw+1)*test_batch),:,:] = x_test
y_test_ex[(iw*test_batch):((iw+1)*test_batch),:,:] = y_signal_noiseless[0:test_batch,:,:] + noise_ex[(iw*test_batch):((iw+1)*test_batch),:,:]
prob_hisps_MMV.y_test_ex_mat = np.transpose(y_test_ex, (2,1,0))
prob_hisps_MMV.x_test_ex_mat = np.transpose(x_test_ex, (2,1,0))
#y_test_ex_vec = y_test_ex.reshape([5*test_batch, (M+Tg)*E])
#x_test_ex_vec = x_test_ex.reshape([5*test_batch, N*(Tg+1)*E])
prob_hisps_MMV.y_test_ex = y_test_ex.reshape([-1,E*2*(M+Tg)])
prob_hisps_MMV.x_test_ex = x_test_ex.reshape([-1,E*2*N*(Tg+1)])
Ac = Ar+1j*Ai
xc = np.reshape(x_vir_channel_r+1j*x_vir_channel_i, (-1,N*(Tg+1)))
AcH = np.transpose(Ac)
y1 = np.transpose(np.matmul(xc,AcH))
y1r = y1.real
y1i = y1.imag
y2r = y_signal_noiseless[:,:,0:(M+Tg)]
y2i = y_signal_noiseless[:,:,(M+Tg):2*(M+Tg)]
# aa1 = y1r-y2r
# aa2 = y1i-y2i
# LAMP-D is considered
prob_hisps_MMV.y_train_D = np.reshape(y_signal[test_batch:L,:,:], (-1,2*(M+Tg)))
prob_hisps_MMV.x_train_D = np.reshape(x_vir_channel[test_batch:L,:,:], (-1,2*N*(Tg+1)))
prob_hisps_MMV.y_test_D = y_test.reshape([test_batch*E,2*(M+Tg)])
prob_hisps_MMV.x_test_D = x_test.reshape([test_batch*E,2*N*(Tg+1)])
prob_hisps_MMV.y_test_ex_D = y_test_ex.reshape([4*test_batch*E,2*(M+Tg)])
prob_hisps_MMV.x_test_ex_D = x_test_ex.reshape([4*test_batch*E,2*N*(Tg+1)])
# LAMP-H is considered
prob_hisps_MMV.y_train_H = np.reshape(y_signal[test_batch:L,:,:], (-1,2*2*(M+Tg)))
prob_hisps_MMV.x_train_H = np.reshape(x_vir_channel[test_batch:L,:,:], (-1,2*2*N*(Tg+1)))
prob_hisps_MMV.y_test_H = y_test.reshape([-1,2*2*(M+Tg)])
prob_hisps_MMV.x_test_H = x_test.reshape([-1,2*2*N*(Tg+1)])
prob_hisps_MMV.y_test_ex_H = y_test_ex.reshape([-1,2*2*(M+Tg)])
prob_hisps_MMV.x_test_ex_H = x_test_ex.reshape([-1,2*2*N*(Tg+1)])
prob_hisps_MMV.xval = x_vir_channel.reshape([L*E,-1])
prob_hisps_MMV.yval = y_signal.reshape([L*E,-1])
prob_hisps_MMV.xinit = x_vir_channel.reshape([L*E,-1])
prob_hisps_MMV.yinit = y_signal.reshape([L*E,-1])
#prob_hisps_MMV.xgen_ = xgen_
#prob_hisps_MMV.ygen_ = ygen_
prob_hisps_MMV.noise_var = noise_var
return prob_hisps_MMV
def random_access_problem(which=1):
# import raputil as ru
if which == 1:
opts = ru.Problem.scenario1()
else:
opts = ru.Problem.scenario2()
p = ru.Problem(**opts)
x1 = p.genX(1)
y1 = p.fwd(x1)
A = p.S
M, N = A.shape
nbatches = int(math.ceil(1000 / x1.shape[1]))
prob = NumpyGenerator(p=p, nbatches=nbatches, A=A, opts=opts, iid=(which == 1))
if which == 2:
prob.maskX_ = tf.expand_dims(tf.constant((np.arange(N) % (N // 2) < opts['Nu']).astype(np.float32)), 1)
_, prob.noise_var = p.add_noise(y1)
unused = p.genYX(nbatches) # for legacy reasons -- want to compare against a previous run
(prob.yval, prob.xval) = p.genYX(nbatches)
(prob.yinit, prob.xinit) = p.genYX(nbatches)
import multiprocessing as mp
prob.nsubprocs = mp.cpu_count()
return prob
| 45.913838 | 150 | 0.623429 | 6,280 | 35,170 | 3.221815 | 0.042357 | 0.085405 | 0.086591 | 0.026986 | 0.898878 | 0.884347 | 0.864034 | 0.848268 | 0.833539 | 0.810508 | 0 | 0.038821 | 0.208246 | 35,170 | 765 | 151 | 45.973856 | 0.687783 | 0.091811 | 0 | 0.720065 | 0 | 0 | 0.011968 | 0.006898 | 0 | 0 | 0 | 0 | 0 | 1 | 0.017799 | false | 0 | 0.012945 | 0 | 0.048544 | 0.001618 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
447b57b77b8cfd2d8971d22cc88b23b8e24b00ef | 24,186 | py | Python | contracts/document_compiled.py | marinimau/public_administration_blockchain_certified_document_sharing | 39936631db9618109cb034c6233f435f47629a97 | [
"MIT"
] | null | null | null | contracts/document_compiled.py | marinimau/public_administration_blockchain_certified_document_sharing | 39936631db9618109cb034c6233f435f47629a97 | [
"MIT"
] | null | null | null | contracts/document_compiled.py | marinimau/public_administration_blockchain_certified_document_sharing | 39936631db9618109cb034c6233f435f47629a97 | [
"MIT"
] | null | null | null | """
This software is distributed under MIT/X11 license
Copyright (c) 2021 Mauro Marini - University of Cagliari
Permission is hereby granted, free of charge, to any person
obtaining a copy of this software and associated documentation
files (the "Software"), to deal in the Software without
restriction, including without limitation the rights to use,
copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the
Software is furnished to do so, subject to the following
conditions:
The above copyright notice and this permission notice shall be
included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
OTHER DEALINGS IN THE SOFTWARE.
"""
import json
abi = json.loads(
'''
[
{
"inputs": [
{
"internalType": "string",
"name": "_documentURI",
"type": "string"
}
],
"stateMutability": "nonpayable",
"type": "constructor"
},
{
"anonymous": false,
"inputs": [
{
"indexed": true,
"internalType": "address",
"name": "account",
"type": "address"
}
],
"name": "AddedToWhitelist",
"type": "event"
},
{
"anonymous": false,
"inputs": [
{
"indexed": true,
"internalType": "address",
"name": "previousOwner",
"type": "address"
},
{
"indexed": true,
"internalType": "address",
"name": "newOwner",
"type": "address"
}
],
"name": "OwnershipTransferred",
"type": "event"
},
{
"anonymous": false,
"inputs": [
{
"indexed": true,
"internalType": "address",
"name": "account",
"type": "address"
}
],
"name": "RemovedFromWhitelist",
"type": "event"
},
{
"inputs": [
{
"internalType": "address[]",
"name": "addrs",
"type": "address[]"
}
],
"name": "addAddressesToWhitelist",
"outputs": [],
"stateMutability": "nonpayable",
"type": "function"
},
{
"inputs": [
{
"internalType": "address",
"name": "_address",
"type": "address"
}
],
"name": "addToWhiteList",
"outputs": [],
"stateMutability": "nonpayable",
"type": "function"
},
{
"inputs": [
{
"internalType": "uint256",
"name": "_documentVersionID",
"type": "uint256"
},
{
"internalType": "string",
"name": "_documentVersionURI",
"type": "string"
},
{
"internalType": "bytes32",
"name": "_fingerPrint",
"type": "bytes32"
}
],
"name": "createDocumentVersion",
"outputs": [],
"stateMutability": "nonpayable",
"type": "function"
},
{
"inputs": [
{
"internalType": "address",
"name": "_address",
"type": "address"
}
],
"name": "isWhitelisted",
"outputs": [
{
"internalType": "bool",
"name": "",
"type": "bool"
}
],
"stateMutability": "view",
"type": "function"
},
{
"inputs": [],
"name": "owner",
"outputs": [
{
"internalType": "address",
"name": "",
"type": "address"
}
],
"stateMutability": "view",
"type": "function"
},
{
"inputs": [
{
"internalType": "address[]",
"name": "addrs",
"type": "address[]"
}
],
"name": "removeAddressesFromWhitelist",
"outputs": [],
"stateMutability": "nonpayable",
"type": "function"
},
{
"inputs": [
{
"internalType": "address",
"name": "_address",
"type": "address"
}
],
"name": "removeFromWhiteList",
"outputs": [],
"stateMutability": "nonpayable",
"type": "function"
},
{
"inputs": [],
"name": "renounceOwnership",
"outputs": [],
"stateMutability": "nonpayable",
"type": "function"
},
{
"inputs": [],
"name": "retrieveDocumentAuthor",
"outputs": [
{
"internalType": "address",
"name": "",
"type": "address"
}
],
"stateMutability": "view",
"type": "function"
},
{
"inputs": [],
"name": "retrieveDocumentURI",
"outputs": [
{
"internalType": "string",
"name": "",
"type": "string"
}
],
"stateMutability": "view",
"type": "function"
},
{
"inputs": [
{
"internalType": "uint256",
"name": "_documentVersionID",
"type": "uint256"
}
],
"name": "retrieveDocumentVersion",
"outputs": [
{
"components": [
{
"internalType": "uint256",
"name": "documentVersionID",
"type": "uint256"
},
{
"internalType": "string",
"name": "documentVersionURI",
"type": "string"
},
{
"internalType": "bytes32",
"name": "fingerPrint",
"type": "bytes32"
},
{
"internalType": "address",
"name": "versionAuthor",
"type": "address"
}
],
"internalType": "struct Document.DocumentVersion",
"name": "version",
"type": "tuple"
}
],
"stateMutability": "view",
"type": "function"
},
{
"inputs": [
{
"internalType": "address",
"name": "newOwner",
"type": "address"
}
],
"name": "transferOwnership",
"outputs": [],
"stateMutability": "nonpayable",
"type": "function"
},
{
"inputs": [
{
"internalType": "uint256",
"name": "",
"type": "uint256"
}
],
"name": "versions",
"outputs": [
{
"internalType": "uint256",
"name": "documentVersionID",
"type": "uint256"
},
{
"internalType": "string",
"name": "documentVersionURI",
"type": "string"
},
{
"internalType": "bytes32",
"name": "fingerPrint",
"type": "bytes32"
},
{
"internalType": "address",
"name": "versionAuthor",
"type": "address"
}
],
"stateMutability": "view",
"type": "function"
}
]
'''
)
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| 75.58125 | 13,451 | 0.694741 | 496 | 24,186 | 33.860887 | 0.318548 | 0.015064 | 0.019172 | 0.01429 | 0.108604 | 0.107949 | 0.102292 | 0.091754 | 0.082703 | 0.071271 | 0 | 0.587449 | 0.257463 | 24,186 | 319 | 13,452 | 75.818182 | 0.347737 | 0.046721 | 0 | 0 | 0 | 0 | 0.995924 | 0.995924 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.25 | 0 | 0.25 | 0 | 0 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
923fec693de5327447989849dc8b7ce83225a72d | 117 | py | Python | hermes/utils.py | eblume/hermes | 6b0c6b2ac14d55170e9eae5b10ef30d3d450809d | [
"Apache-2.0"
] | 4 | 2019-03-19T04:15:06.000Z | 2020-11-02T18:13:50.000Z | hermes/utils.py | eblume/hermes | 6b0c6b2ac14d55170e9eae5b10ef30d3d450809d | [
"Apache-2.0"
] | null | null | null | hermes/utils.py | eblume/hermes | 6b0c6b2ac14d55170e9eae5b10ef30d3d450809d | [
"Apache-2.0"
] | null | null | null | import datetime as dt
import pytz
def get_now() -> dt.datetime:
return dt.datetime.now(pytz.UTC).astimezone()
| 14.625 | 49 | 0.717949 | 18 | 117 | 4.611111 | 0.611111 | 0.240964 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.162393 | 117 | 7 | 50 | 16.714286 | 0.846939 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | true | 0 | 0.5 | 0.25 | 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 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 8 |
2b866ccdf1353035cba953f18b68b34ef3292251 | 195 | py | Python | src/apification/utils/__init__.py | Quantify-world/Apification | 5fc0bd056d0bf430645a2e2c5d7d9435328b9a4a | [
"MIT"
] | 5 | 2016-10-29T14:23:22.000Z | 2017-07-15T08:39:58.000Z | src/apification/utils/__init__.py | Quantify-world/Apification | 5fc0bd056d0bf430645a2e2c5d7d9435328b9a4a | [
"MIT"
] | 32 | 2016-10-23T19:18:26.000Z | 2017-02-27T18:33:36.000Z | src/apification/utils/__init__.py | Quantify-world/apification | 5fc0bd056d0bf430645a2e2c5d7d9435328b9a4a | [
"MIT"
] | null | null | null | from apification.utils.noninstantiable import Noninstantiable, NoninstantiableMeta
from apification.utils.writeonce import writeonce
from apification.utils.instancevisible import instancevisible
| 48.75 | 82 | 0.897436 | 19 | 195 | 9.210526 | 0.421053 | 0.257143 | 0.342857 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.066667 | 195 | 3 | 83 | 65 | 0.961538 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
2ba2d792dabe9199f50b216e508b536c41349c6b | 140 | py | Python | trials/motleytrials/help-motleyformatter.py | ericmotleybytes/motleylog | a389483fd647523e9b582f2f5d0d47143235a645 | [
"MIT"
] | null | null | null | trials/motleytrials/help-motleyformatter.py | ericmotleybytes/motleylog | a389483fd647523e9b582f2f5d0d47143235a645 | [
"MIT"
] | null | null | null | trials/motleytrials/help-motleyformatter.py | ericmotleybytes/motleylog | a389483fd647523e9b582f2f5d0d47143235a645 | [
"MIT"
] | null | null | null | import motleylog.motleyformatter
print('#'*80)
print('# >>> help(motleylog.motleyformatter)')
print('#'*80)
help(motleylog.motleyformatter)
| 23.333333 | 46 | 0.75 | 14 | 140 | 7.5 | 0.428571 | 0.685714 | 0.552381 | 0.590476 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.030303 | 0.057143 | 140 | 5 | 47 | 28 | 0.765152 | 0 | 0 | 0.4 | 0 | 0 | 0.278571 | 0.221429 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.2 | 0 | 0.2 | 0.6 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 7 |
2bfd657bb2d7ef2a30d5f4f5bf48a861ba6e5ba5 | 184 | py | Python | stagesepx/classifier/__init__.py | IcyW/stagesepx | 2b49e815af48f207f8d7b1118099fd84e8e59f81 | [
"MIT"
] | 369 | 2019-07-22T22:38:45.000Z | 2022-03-18T14:06:32.000Z | stagesepx/classifier/__init__.py | AIBiuBiu/stagesepx | 20ccfa8145e5896144702eb1d0db19c4fef737a5 | [
"MIT"
] | 172 | 2019-07-18T10:35:58.000Z | 2022-03-30T03:44:46.000Z | stagesepx/classifier/__init__.py | AIBiuBiu/stagesepx | 20ccfa8145e5896144702eb1d0db19c4fef737a5 | [
"MIT"
] | 104 | 2019-07-23T01:38:55.000Z | 2022-03-15T03:00:10.000Z | from stagesepx.classifier.base import SingleClassifierResult, ClassifierResult
from stagesepx.classifier.ssim import SSIMClassifier
from stagesepx.classifier.svm import SVMClassifier
| 36.8 | 78 | 0.88587 | 19 | 184 | 8.578947 | 0.578947 | 0.239264 | 0.423313 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.076087 | 184 | 4 | 79 | 46 | 0.958824 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
a6179f901527c2d58a3b4f0ec56131ae85a2dbd8 | 809 | py | Python | datasets/__init__.py | ChiragCD/NR-GAN | fc455c6219b09bc8bf605715504b78b2bb801e48 | [
"MIT"
] | 54 | 2020-04-17T03:05:50.000Z | 2022-03-07T20:30:35.000Z | datasets/__init__.py | ChiragCD/NR-GAN | fc455c6219b09bc8bf605715504b78b2bb801e48 | [
"MIT"
] | 8 | 2020-08-24T03:42:42.000Z | 2022-03-12T00:21:33.000Z | datasets/__init__.py | ChiragCD/NR-GAN | fc455c6219b09bc8bf605715504b78b2bb801e48 | [
"MIT"
] | 14 | 2020-06-01T10:21:08.000Z | 2021-12-30T07:24:22.000Z | from .cifar import (CIFAR10AdditiveGaussianNoise, CIFAR10LocalGaussianNoise,
CIFAR10UniformNoise, CIFAR10MixtureNoise,
CIFAR10BrownGaussianNoise,
CIFAR10AdditiveBrownGaussianNoise,
CIFAR10MultiplicativeGaussianNoise,
CIFAR10AdditiveMultiplicativeGaussianNoise,
CIFAR10PoissonNoise, CIFAR10PoissonGaussianNoise)
__all__ = ('CIFAR10AdditiveGaussianNoise', 'CIFAR10LocalGaussianNoise',
'CIFAR10UniformNoise', 'CIFAR10MixtureNoise',
'CIFAR10BrownGaussianNoise', 'CIFAR10AdditiveBrownGaussianNoise',
'CIFAR10MultiplicativeGaussianNoise',
'CIFAR10AdditiveMultiplicativeGaussianNoise', 'CIFAR10PoissonNoise',
'CIFAR10PoissonGaussianNoise')
| 53.933333 | 79 | 0.697157 | 24 | 809 | 23.333333 | 0.583333 | 0.189286 | 0.257143 | 0.325 | 0.967857 | 0.967857 | 0.967857 | 0.967857 | 0.967857 | 0.967857 | 0 | 0.065789 | 0.248455 | 809 | 14 | 80 | 57.785714 | 0.855263 | 0 | 0 | 0 | 0 | 0 | 0.334981 | 0.264524 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.076923 | 0 | 0.076923 | 0 | 1 | 0 | 1 | null | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
a6b177914a2861603bd42c2ee06b6e19e6a6a679 | 9,883 | gyp | Python | binding.gyp | Frequency-I/talib-binding-node | f773a86e429ebc6f84efe6fcd436dcbe526f3022 | [
"MIT"
] | 89 | 2017-10-08T01:28:46.000Z | 2022-03-29T07:01:20.000Z | binding.gyp | Frequency-I/talib-binding-node | f773a86e429ebc6f84efe6fcd436dcbe526f3022 | [
"MIT"
] | 4 | 2019-12-07T15:52:12.000Z | 2021-09-01T02:34:24.000Z | binding.gyp | Frequency-I/talib-binding-node | f773a86e429ebc6f84efe6fcd436dcbe526f3022 | [
"MIT"
] | 22 | 2017-10-08T01:29:07.000Z | 2022-03-30T07:23:26.000Z | {
"targets": [
{
"target_name": "talib_binding",
"sources": [
"ta-lib/c/src/ta_abstract/frames/ta_frame.c",
"ta-lib/c/src/ta_abstract/ta_abstract.c",
"ta-lib/c/src/ta_abstract/ta_def_ui.c",
"ta-lib/c/src/ta_abstract/ta_func_api.c",
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"ta-lib/c/src/ta_func/ta_WILLR.c",
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"src/talib-binding.generated.cc"
],
"include_dirs": [
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"ta-lib/c/src/ta_abstract/",
"ta-lib/c/src/ta_abstract/frames/",
"ta-lib/c/src/ta_abstract/tables/",
"ta-lib/c/src/ta_abstract/templates/",
"ta-lib/c/src/ta_common/",
"ta-lib/c/src/ta_func/",
"<!(node -e \"require('nan')\")"
]
}
]
} | 45.334862 | 56 | 0.61196 | 1,885 | 9,883 | 2.985146 | 0.118302 | 0.182157 | 0.218589 | 0.326284 | 0.733428 | 0.733428 | 0.727741 | 0.716901 | 0.207571 | 0 | 0 | 0.002261 | 0.194374 | 9,883 | 218 | 57 | 45.334862 | 0.704471 | 0 | 0 | 0 | 0 | 0 | 0.739073 | 0.730676 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
5b26212f57b41b23f97a0f29468527a834ed2101 | 8,554 | py | Python | tests/seahub/views/file/test_can_edit_file.py | weimens/seahub | 5ecf78ed7a2ddc72a23961804ee41be21c24893f | [
"Apache-2.0"
] | 420 | 2015-01-03T11:34:46.000Z | 2022-03-10T07:15:41.000Z | tests/seahub/views/file/test_can_edit_file.py | weimens/seahub | 5ecf78ed7a2ddc72a23961804ee41be21c24893f | [
"Apache-2.0"
] | 735 | 2015-01-04T21:22:51.000Z | 2022-03-31T09:26:07.000Z | tests/seahub/views/file/test_can_edit_file.py | weimens/seahub | 5ecf78ed7a2ddc72a23961804ee41be21c24893f | [
"Apache-2.0"
] | 379 | 2015-01-05T17:08:03.000Z | 2022-03-06T00:11:50.000Z | from mock import patch
from seaserv import seafile_api
from seahub.views.file import can_edit_file
from seahub.test_utils import BaseTestCase
from seahub.settings import FILE_PREVIEW_MAX_SIZE
from seahub.utils import OFFICE_PREVIEW_MAX_SIZE
OFFICE_WEB_APP_FILE_EXTENSION = ('doc', 'docx', 'ppt', 'pptx', 'xls', 'xlsx')
OFFICE_WEB_APP_EDIT_FILE_EXTENSION = ('docx', 'pptx', 'xlsx')
ONLYOFFICE_FILE_EXTENSION = ('doc', 'docx', 'ppt', 'pptx', 'xls', 'xlsx')
ONLYOFFICE_EDIT_FILE_EXTENSION = ('doc', 'docx', 'ppt', 'pptx', 'xls', 'xlsx')
class CanEditFileTest(BaseTestCase):
def setUp(self):
self.file_size = 1
self.exceeded_file_size = FILE_PREVIEW_MAX_SIZE + 1
self.office_file_size = 1
self.exceeded_office_file_size = OFFICE_PREVIEW_MAX_SIZE + 1
self.encrypted_repo_id = seafile_api.create_repo('encrypted-repo',
'', self.user.username, 'password')
self.encrypted_repo = seafile_api.get_repo(self.encrypted_repo_id)
def tearDown(self):
self.remove_repo(self.repo.id)
self.remove_repo(self.encrypted_repo.id)
def can_edit_in_normal_repo_normal_size(self, file_name):
if file_name.endswith('.doc') or file_name.endswith('.docx'):
file_size = self.office_file_size
else:
file_size = self.file_size
can_edit, error_msg = can_edit_file(file_name, file_size,
self.repo)
return can_edit
def can_edit_in_encrypted_repo_normal_size(self, file_name):
if file_name.endswith('.doc') or file_name.endswith('.docx'):
file_size = self.office_file_size
else:
file_size = self.file_size
can_edit, error_msg = can_edit_file(file_name, file_size,
self.encrypted_repo)
return can_edit
def can_edit_in_normal_repo_exceeded_size(self, file_name):
if file_name.endswith('.doc') or file_name.endswith('.docx'):
file_size = self.exceeded_office_file_size
else:
file_size = self.exceeded_file_size
can_edit, error_msg = can_edit_file(file_name, file_size,
self.repo)
return can_edit
def can_edit_in_encrypted_repo_exceeded_size(self, file_name):
if file_name.endswith('.doc') or file_name.endswith('.docx'):
file_size = self.exceeded_office_file_size
else:
file_size = self.exceeded_file_size
can_edit, error_msg = can_edit_file(file_name, file_size,
self.encrypted_repo)
return can_edit
def test_iso(self):
file_name = '123.iso'
assert not self.can_edit_in_normal_repo_normal_size(file_name)
assert not self.can_edit_in_normal_repo_exceeded_size(file_name)
assert not self.can_edit_in_encrypted_repo_normal_size(file_name)
assert not self.can_edit_in_encrypted_repo_exceeded_size(file_name)
def test_pdf(self):
file_name = '123.pdf'
assert not self.can_edit_in_normal_repo_normal_size(file_name)
assert not self.can_edit_in_normal_repo_exceeded_size(file_name)
assert not self.can_edit_in_encrypted_repo_normal_size(file_name)
assert not self.can_edit_in_encrypted_repo_exceeded_size(file_name)
def test_jpg(self):
file_name = '123.jpg'
assert not self.can_edit_in_normal_repo_normal_size(file_name)
assert not self.can_edit_in_normal_repo_exceeded_size(file_name)
assert not self.can_edit_in_encrypted_repo_normal_size(file_name)
assert not self.can_edit_in_encrypted_repo_exceeded_size(file_name)
def test_txt(self):
file_name = '123.txt'
assert self.can_edit_in_normal_repo_normal_size(file_name)
assert not self.can_edit_in_normal_repo_exceeded_size(file_name)
assert self.can_edit_in_encrypted_repo_normal_size(file_name)
assert not self.can_edit_in_encrypted_repo_exceeded_size(file_name)
def test_md(self):
file_name = '123.md'
assert self.can_edit_in_normal_repo_normal_size(file_name)
assert not self.can_edit_in_normal_repo_exceeded_size(file_name)
assert self.can_edit_in_encrypted_repo_normal_size(file_name)
assert not self.can_edit_in_encrypted_repo_exceeded_size(file_name)
def test_doc(self):
file_name = '123.doc'
assert not self.can_edit_in_normal_repo_normal_size(file_name)
assert not self.can_edit_in_normal_repo_exceeded_size(file_name)
assert not self.can_edit_in_encrypted_repo_normal_size(file_name)
assert not self.can_edit_in_encrypted_repo_exceeded_size(file_name)
@patch('seahub.views.file.HAS_OFFICE_CONVERTER', True)
def test_doc_has_office_converter(self):
file_name = '123.doc'
assert not self.can_edit_in_normal_repo_normal_size(file_name)
assert not self.can_edit_in_normal_repo_exceeded_size(file_name)
assert not self.can_edit_in_encrypted_repo_normal_size(file_name)
assert not self.can_edit_in_encrypted_repo_exceeded_size(file_name)
@patch('seahub.views.file.ENABLE_ONLYOFFICE', True)
@patch('seahub.views.file.ONLYOFFICE_FILE_EXTENSION',
ONLYOFFICE_FILE_EXTENSION)
@patch('seahub.views.file.ONLYOFFICE_EDIT_FILE_EXTENSION',
ONLYOFFICE_EDIT_FILE_EXTENSION)
def test_doc_enable_onlyoffice(self):
file_name = '123.doc'
assert self.can_edit_in_normal_repo_normal_size(file_name)
assert self.can_edit_in_normal_repo_exceeded_size(file_name)
assert not self.can_edit_in_encrypted_repo_normal_size(file_name)
assert not self.can_edit_in_encrypted_repo_exceeded_size(file_name)
@patch('seahub.views.file.ENABLE_OFFICE_WEB_APP', True)
@patch('seahub.views.file.OFFICE_WEB_APP_FILE_EXTENSION',
OFFICE_WEB_APP_FILE_EXTENSION)
@patch('seahub.views.file.ENABLE_OFFICE_WEB_APP_EDIT', True)
@patch('seahub.views.file.OFFICE_WEB_APP_EDIT_FILE_EXTENSION',
OFFICE_WEB_APP_EDIT_FILE_EXTENSION)
def test_doc_enable_office_web_app(self):
file_name = '123.doc'
assert not self.can_edit_in_normal_repo_normal_size(file_name)
assert not self.can_edit_in_normal_repo_exceeded_size(file_name)
assert not self.can_edit_in_encrypted_repo_normal_size(file_name)
assert not self.can_edit_in_encrypted_repo_exceeded_size(file_name)
def test_docx(self):
file_name = '123.docx'
assert not self.can_edit_in_normal_repo_normal_size(file_name)
assert not self.can_edit_in_normal_repo_exceeded_size(file_name)
assert not self.can_edit_in_encrypted_repo_normal_size(file_name)
assert not self.can_edit_in_encrypted_repo_exceeded_size(file_name)
@patch('seahub.views.file.HAS_OFFICE_CONVERTER', True)
def test_docx_has_office_converter(self):
file_name = '123.docx'
assert not self.can_edit_in_normal_repo_normal_size(file_name)
assert not self.can_edit_in_normal_repo_exceeded_size(file_name)
assert not self.can_edit_in_encrypted_repo_normal_size(file_name)
assert not self.can_edit_in_encrypted_repo_exceeded_size(file_name)
@patch('seahub.views.file.ENABLE_ONLYOFFICE', True)
@patch('seahub.views.file.ONLYOFFICE_FILE_EXTENSION',
ONLYOFFICE_FILE_EXTENSION)
@patch('seahub.views.file.ONLYOFFICE_EDIT_FILE_EXTENSION',
ONLYOFFICE_EDIT_FILE_EXTENSION)
def test_docx_enable_onlyoffice(self):
file_name = '123.docx'
assert self.can_edit_in_normal_repo_normal_size(file_name)
assert self.can_edit_in_normal_repo_exceeded_size(file_name)
assert not self.can_edit_in_encrypted_repo_normal_size(file_name)
assert not self.can_edit_in_encrypted_repo_exceeded_size(file_name)
@patch('seahub.views.file.ENABLE_OFFICE_WEB_APP', True)
@patch('seahub.views.file.OFFICE_WEB_APP_FILE_EXTENSION',
OFFICE_WEB_APP_FILE_EXTENSION)
@patch('seahub.views.file.ENABLE_OFFICE_WEB_APP_EDIT', True)
@patch('seahub.views.file.OFFICE_WEB_APP_EDIT_FILE_EXTENSION',
OFFICE_WEB_APP_EDIT_FILE_EXTENSION)
def test_docx_enable_office_web_app(self):
file_name = '123.docx'
assert self.can_edit_in_normal_repo_normal_size(file_name)
assert self.can_edit_in_normal_repo_exceeded_size(file_name)
assert not self.can_edit_in_encrypted_repo_normal_size(file_name)
assert not self.can_edit_in_encrypted_repo_exceeded_size(file_name)
| 39.419355 | 78 | 0.738602 | 1,263 | 8,554 | 4.508314 | 0.054632 | 0.113804 | 0.088514 | 0.118721 | 0.898314 | 0.879698 | 0.858799 | 0.844222 | 0.818405 | 0.818405 | 0 | 0.006184 | 0.187164 | 8,554 | 216 | 79 | 39.601852 | 0.812743 | 0 | 0 | 0.717949 | 0 | 0 | 0.107435 | 0.080898 | 0 | 0 | 0 | 0 | 0.333333 | 1 | 0.121795 | false | 0.00641 | 0.038462 | 0 | 0.192308 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
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