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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2544523311cc08e244cfc6c3f7029491eb958c6b
| 65
|
py
|
Python
|
data/scrape/link_extractors/__init__.py
|
jamesrharwood/journal-guidelines
|
fe6c0a6d3c0443df6fc816b9503fad24459ddb4a
|
[
"MIT"
] | null | null | null |
data/scrape/link_extractors/__init__.py
|
jamesrharwood/journal-guidelines
|
fe6c0a6d3c0443df6fc816b9503fad24459ddb4a
|
[
"MIT"
] | null | null | null |
data/scrape/link_extractors/__init__.py
|
jamesrharwood/journal-guidelines
|
fe6c0a6d3c0443df6fc816b9503fad24459ddb4a
|
[
"MIT"
] | null | null | null |
from .extractors import extract_links, extract_links_by_strategy
| 32.5
| 64
| 0.892308
| 9
| 65
| 6
| 0.777778
| 0.444444
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.076923
| 65
| 1
| 65
| 65
| 0.9
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
c25f383b08dc16b275556e63301d0fafc05360a0
| 124
|
py
|
Python
|
strongr/core/exception/__init__.py
|
bigr-erasmusmc/StrongR
|
48573e170771a251f629f2d13dba7173f010a38c
|
[
"Apache-2.0"
] | null | null | null |
strongr/core/exception/__init__.py
|
bigr-erasmusmc/StrongR
|
48573e170771a251f629f2d13dba7173f010a38c
|
[
"Apache-2.0"
] | null | null | null |
strongr/core/exception/__init__.py
|
bigr-erasmusmc/StrongR
|
48573e170771a251f629f2d13dba7173f010a38c
|
[
"Apache-2.0"
] | null | null | null |
from .isnotcallableexception import IsNotCallableException
from .invalidparameterexception import InvalidParameterException
| 41.333333
| 64
| 0.919355
| 8
| 124
| 14.25
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.064516
| 124
| 2
| 65
| 62
| 0.982759
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
c261396a1367bc9b5512a504dcafa2e8bcfd1807
| 102
|
py
|
Python
|
vnpy/app/algo_trading/__init__.py
|
Billy-Meng/vnpy_origin
|
b0b0868027d70b1ba5dac65aa1a6d5e4246a0900
|
[
"MIT"
] | 1
|
2020-06-18T16:38:29.000Z
|
2020-06-18T16:38:29.000Z
|
vnpy/app/algo_trading/__init__.py
|
Billy-Meng/vnpy_origin
|
b0b0868027d70b1ba5dac65aa1a6d5e4246a0900
|
[
"MIT"
] | 2
|
2020-06-22T12:12:43.000Z
|
2020-06-23T01:26:10.000Z
|
vnpy/app/algo_trading/__init__.py
|
Billy-Meng/vnpy_origin
|
b0b0868027d70b1ba5dac65aa1a6d5e4246a0900
|
[
"MIT"
] | null | null | null |
# -*- coding:utf-8 -*-
import sys
import vnpy_algotrading
sys.modules[__name__] = vnpy_algotrading
| 12.75
| 40
| 0.735294
| 13
| 102
| 5.307692
| 0.692308
| 0.434783
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.011364
| 0.137255
| 102
| 7
| 41
| 14.571429
| 0.772727
| 0.196078
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.666667
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
c2aad64a2aad2f61466e44cae8495b6cafe36146
| 5,168
|
py
|
Python
|
test/test_get_transaction_details_by_transaction_id_response_item_blockchain_specific.py
|
xan187/Crypto_APIs_2.0_SDK_Python
|
a56c75df54ef037b39be1315ed6e54de35bed55b
|
[
"MIT"
] | null | null | null |
test/test_get_transaction_details_by_transaction_id_response_item_blockchain_specific.py
|
xan187/Crypto_APIs_2.0_SDK_Python
|
a56c75df54ef037b39be1315ed6e54de35bed55b
|
[
"MIT"
] | null | null | null |
test/test_get_transaction_details_by_transaction_id_response_item_blockchain_specific.py
|
xan187/Crypto_APIs_2.0_SDK_Python
|
a56c75df54ef037b39be1315ed6e54de35bed55b
|
[
"MIT"
] | 1
|
2021-07-21T03:35:18.000Z
|
2021-07-21T03:35:18.000Z
|
"""
CryptoAPIs
Crypto APIs 2.0 is a complex and innovative infrastructure layer that radically simplifies the development of any Blockchain and Crypto related applications. Organized around REST, Crypto APIs 2.0 can assist both novice Bitcoin/Ethereum enthusiasts and crypto experts with the development of their blockchain applications. Crypto APIs 2.0 provides unified endpoints and data, raw data, automatic tokens and coins forwardings, callback functionalities, and much more. # noqa: E501
The version of the OpenAPI document: 2.0.0
Contact: developers@cryptoapis.io
Generated by: https://openapi-generator.tech
"""
import sys
import unittest
import cryptoapis
from cryptoapis.model.get_transaction_details_by_transaction_id_response_item_blockchain_specific_bitcoin import GetTransactionDetailsByTransactionIDResponseItemBlockchainSpecificBitcoin
from cryptoapis.model.get_transaction_details_by_transaction_id_response_item_blockchain_specific_bitcoin_cash import GetTransactionDetailsByTransactionIDResponseItemBlockchainSpecificBitcoinCash
from cryptoapis.model.get_transaction_details_by_transaction_id_response_item_blockchain_specific_dash import GetTransactionDetailsByTransactionIDResponseItemBlockchainSpecificDash
from cryptoapis.model.get_transaction_details_by_transaction_id_response_item_blockchain_specific_dash_vin import GetTransactionDetailsByTransactionIDResponseItemBlockchainSpecificDashVin
from cryptoapis.model.get_transaction_details_by_transaction_id_response_item_blockchain_specific_dash_vout import GetTransactionDetailsByTransactionIDResponseItemBlockchainSpecificDashVout
from cryptoapis.model.get_transaction_details_by_transaction_id_response_item_blockchain_specific_dogecoin import GetTransactionDetailsByTransactionIDResponseItemBlockchainSpecificDogecoin
from cryptoapis.model.get_transaction_details_by_transaction_id_response_item_blockchain_specific_ethereum import GetTransactionDetailsByTransactionIDResponseItemBlockchainSpecificEthereum
from cryptoapis.model.get_transaction_details_by_transaction_id_response_item_blockchain_specific_ethereum_classic import GetTransactionDetailsByTransactionIDResponseItemBlockchainSpecificEthereumClassic
from cryptoapis.model.get_transaction_details_by_transaction_id_response_item_blockchain_specific_ethereum_classic_gas_price import GetTransactionDetailsByTransactionIDResponseItemBlockchainSpecificEthereumClassicGasPrice
from cryptoapis.model.get_transaction_details_by_transaction_id_response_item_blockchain_specific_litecoin import GetTransactionDetailsByTransactionIDResponseItemBlockchainSpecificLitecoin
globals()['GetTransactionDetailsByTransactionIDResponseItemBlockchainSpecificBitcoin'] = GetTransactionDetailsByTransactionIDResponseItemBlockchainSpecificBitcoin
globals()['GetTransactionDetailsByTransactionIDResponseItemBlockchainSpecificBitcoinCash'] = GetTransactionDetailsByTransactionIDResponseItemBlockchainSpecificBitcoinCash
globals()['GetTransactionDetailsByTransactionIDResponseItemBlockchainSpecificDash'] = GetTransactionDetailsByTransactionIDResponseItemBlockchainSpecificDash
globals()['GetTransactionDetailsByTransactionIDResponseItemBlockchainSpecificDashVin'] = GetTransactionDetailsByTransactionIDResponseItemBlockchainSpecificDashVin
globals()['GetTransactionDetailsByTransactionIDResponseItemBlockchainSpecificDashVout'] = GetTransactionDetailsByTransactionIDResponseItemBlockchainSpecificDashVout
globals()['GetTransactionDetailsByTransactionIDResponseItemBlockchainSpecificDogecoin'] = GetTransactionDetailsByTransactionIDResponseItemBlockchainSpecificDogecoin
globals()['GetTransactionDetailsByTransactionIDResponseItemBlockchainSpecificEthereum'] = GetTransactionDetailsByTransactionIDResponseItemBlockchainSpecificEthereum
globals()['GetTransactionDetailsByTransactionIDResponseItemBlockchainSpecificEthereumClassic'] = GetTransactionDetailsByTransactionIDResponseItemBlockchainSpecificEthereumClassic
globals()['GetTransactionDetailsByTransactionIDResponseItemBlockchainSpecificEthereumClassicGasPrice'] = GetTransactionDetailsByTransactionIDResponseItemBlockchainSpecificEthereumClassicGasPrice
globals()['GetTransactionDetailsByTransactionIDResponseItemBlockchainSpecificLitecoin'] = GetTransactionDetailsByTransactionIDResponseItemBlockchainSpecificLitecoin
from cryptoapis.model.get_transaction_details_by_transaction_id_response_item_blockchain_specific import GetTransactionDetailsByTransactionIDResponseItemBlockchainSpecific
class TestGetTransactionDetailsByTransactionIDResponseItemBlockchainSpecific(unittest.TestCase):
"""GetTransactionDetailsByTransactionIDResponseItemBlockchainSpecific unit test stubs"""
def setUp(self):
pass
def tearDown(self):
pass
def testGetTransactionDetailsByTransactionIDResponseItemBlockchainSpecific(self):
"""Test GetTransactionDetailsByTransactionIDResponseItemBlockchainSpecific"""
# FIXME: construct object with mandatory attributes with example values
# model = GetTransactionDetailsByTransactionIDResponseItemBlockchainSpecific() # noqa: E501
pass
if __name__ == '__main__':
unittest.main()
| 90.666667
| 484
| 0.909443
| 348
| 5,168
| 13.149425
| 0.33046
| 0.033654
| 0.045673
| 0.052885
| 0.218313
| 0.218313
| 0.218313
| 0.218313
| 0.218313
| 0.218313
| 0
| 0.003083
| 0.05863
| 5,168
| 56
| 485
| 92.285714
| 0.937513
| 0.180147
| 0
| 0.090909
| 0
| 0
| 0.18275
| 0.180843
| 0
| 1
| 0
| 0.017857
| 0
| 1
| 0.090909
| false
| 0.090909
| 0.424242
| 0
| 0.545455
| 0
| 0
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 1
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 0
|
0
| 6
|
c2b5b21620b9700cb8ad7260e94e93fb05dfdd01
| 417
|
py
|
Python
|
templatedir/nice/objective.py
|
Kooper95/Shape-optimiser
|
caff58644bf64f5425fef5047688098d71b062b0
|
[
"MIT"
] | null | null | null |
templatedir/nice/objective.py
|
Kooper95/Shape-optimiser
|
caff58644bf64f5425fef5047688098d71b062b0
|
[
"MIT"
] | null | null | null |
templatedir/nice/objective.py
|
Kooper95/Shape-optimiser
|
caff58644bf64f5425fef5047688098d71b062b0
|
[
"MIT"
] | null | null | null |
import sys
#if len(sys.argv) == 1:
# print(200000)
#else:
Powerout = (float(sys.argv[1]) - 298.15) * 3.14159265 * 0.1 * 0.1 * 0.22 / 0.005
#p = 611.21 * 2.718281828 ** ((18.678 - (float(sys.argv[1])-273.15)/234.5)*((float(sys.argv[1])-273.15)/(float(sys.argv[1])-16.01)))
#Powerin = float(sys.argv[2]) * 2256600 * 18.01528 * p/(1000 * 8.31446261815324 * float(sys.argv[1]))
print(Powerout + float(sys.argv[2]))
| 34.75
| 132
| 0.609113
| 76
| 417
| 3.342105
| 0.473684
| 0.220472
| 0.330709
| 0.255906
| 0.141732
| 0.141732
| 0
| 0
| 0
| 0
| 0
| 0.303867
| 0.131894
| 417
| 11
| 133
| 37.909091
| 0.39779
| 0.659472
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.333333
| 0
| 0.333333
| 0.333333
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
665d4ee8e498a7f6eb6e8727b64c0dc527112f25
| 101
|
py
|
Python
|
src/articles/utils.py
|
robzzy/articles-service
|
a37b0f382ec5544c9f67236672e8325de8d8cf6b
|
[
"MIT"
] | null | null | null |
src/articles/utils.py
|
robzzy/articles-service
|
a37b0f382ec5544c9f67236672e8325de8d8cf6b
|
[
"MIT"
] | null | null | null |
src/articles/utils.py
|
robzzy/articles-service
|
a37b0f382ec5544c9f67236672e8325de8d8cf6b
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
from datetime import datetime
def utcnow():
return datetime.utcnow()
| 11.222222
| 29
| 0.643564
| 12
| 101
| 5.416667
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.0125
| 0.207921
| 101
| 8
| 30
| 12.625
| 0.8
| 0.207921
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| true
| 0
| 0.333333
| 0.333333
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 1
| 0
| 0
|
0
| 6
|
666f9404de84023163423cae5f0e889d7d73b5cf
| 114
|
py
|
Python
|
tatc/tatc/__init__.py
|
code-lab-org/tatc
|
51ab32d69923e99637b8939bca6965ba218d6056
|
[
"BSD-3-Clause"
] | null | null | null |
tatc/tatc/__init__.py
|
code-lab-org/tatc
|
51ab32d69923e99637b8939bca6965ba218d6056
|
[
"BSD-3-Clause"
] | null | null | null |
tatc/tatc/__init__.py
|
code-lab-org/tatc
|
51ab32d69923e99637b8939bca6965ba218d6056
|
[
"BSD-3-Clause"
] | null | null | null |
from . import analysis
from . import generation
from . import schemas
from . import constants
from . import utils
| 19
| 24
| 0.780702
| 15
| 114
| 5.933333
| 0.466667
| 0.561798
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.175439
| 114
| 5
| 25
| 22.8
| 0.946809
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
6698840643e395fa623f15782641264872cbd6d1
| 77
|
py
|
Python
|
Code_Challenges/fizz_buzz.py
|
fuse999/Python_Sandbox
|
83d9c33a9c9e6e5cff40bbc6be525c9e604e9e41
|
[
"MIT"
] | null | null | null |
Code_Challenges/fizz_buzz.py
|
fuse999/Python_Sandbox
|
83d9c33a9c9e6e5cff40bbc6be525c9e604e9e41
|
[
"MIT"
] | null | null | null |
Code_Challenges/fizz_buzz.py
|
fuse999/Python_Sandbox
|
83d9c33a9c9e6e5cff40bbc6be525c9e604e9e41
|
[
"MIT"
] | null | null | null |
def fizz_buzz(num):
return "Fizz"*(num%3==0) + "Buzz"*(num%5==0) or str(num)
| 38.5
| 57
| 0.61039
| 16
| 77
| 2.875
| 0.625
| 0.304348
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.057971
| 0.103896
| 77
| 2
| 57
| 38.5
| 0.608696
| 0
| 0
| 0
| 0
| 0
| 0.102564
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| false
| 0
| 0
| 0.5
| 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
| 0
| 1
| 1
| 0
|
0
| 6
|
669f4cf088ed7bab4dba3af533966368a6931102
| 5,790
|
py
|
Python
|
tsfeatures/tsfeatures_r.py
|
vishalbelsare/tsfeatures-1
|
554581d795344a023d14cddcbbf52c491b2d6e14
|
[
"MIT"
] | 57
|
2020-01-28T02:00:19.000Z
|
2021-08-20T19:19:51.000Z
|
tsfeatures/tsfeatures_r.py
|
vishalbelsare/tsfeatures-1
|
554581d795344a023d14cddcbbf52c491b2d6e14
|
[
"MIT"
] | 9
|
2019-11-30T23:56:39.000Z
|
2021-09-01T17:27:13.000Z
|
tsfeatures/tsfeatures_r.py
|
vishalbelsare/tsfeatures-1
|
554581d795344a023d14cddcbbf52c491b2d6e14
|
[
"MIT"
] | 18
|
2020-01-28T02:00:34.000Z
|
2021-07-29T19:57:22.000Z
|
#!/usr/bin/env python
# coding: utf-8
from typing import List
import pandas as pd
import rpy2.robjects as robjects
from rpy2.robjects import pandas2ri
def tsfeatures_r(ts: pd.DataFrame,
freq: int,
features: List[str] = ["length", "acf_features", "arch_stat",
"crossing_points", "entropy", "flat_spots",
"heterogeneity", "holt_parameters",
"hurst", "hw_parameters", "lumpiness",
"nonlinearity", "pacf_features", "stability",
"stl_features", "unitroot_kpss", "unitroot_pp"],
**kwargs) -> pd.DataFrame:
"""tsfeatures wrapper using r.
Parameters
----------
ts: pandas df
Pandas DataFrame with columns ['unique_id', 'ds', 'y'].
Long panel of time series.
freq: int
Frequency of the time series.
features: List[str]
String list of features to calculate.
**kwargs:
Arguments used by the original tsfeatures function.
References
----------
https://pkg.robjhyndman.com/tsfeatures/reference/tsfeatures.html
"""
rstring = """
function(df, freq, features, ...){
suppressMessages(library(data.table))
suppressMessages(library(tsfeatures))
dt <- as.data.table(df)
setkey(dt, unique_id)
series_list <- split(dt, by = "unique_id", keep.by = FALSE)
series_list <- lapply(series_list,
function(serie) serie[, ts(y, frequency = freq)])
if("hw_parameters" %in% features){
features <- setdiff(features, "hw_parameters")
if(length(features)>0){
hw_series_features <- suppressMessages(tsfeatures(series_list, "hw_parameters", ...))
names(hw_series_features) <- paste0("hw_", names(hw_series_features))
series_features <- suppressMessages(tsfeatures(series_list, features, ...))
series_features <- cbind(series_features, hw_series_features)
} else {
series_features <- suppressMessages(tsfeatures(series_list, "hw_parameters", ...))
names(series_features) <- paste0("hw_", names(series_features))
}
} else {
series_features <- suppressMessages(tsfeatures(series_list, features, ...))
}
setDT(series_features)
series_features[, unique_id := names(series_list)]
}
"""
pandas2ri.activate()
rfunc = robjects.r(rstring)
feats = rfunc(ts, freq, features, **kwargs)
pandas2ri.deactivate()
renamer={'ARCH.LM': 'arch_lm', 'length': 'series_length'}
feats = feats.rename(columns=renamer)
return feats
def tsfeatures_r_wide(ts: pd.DataFrame,
features: List[str] = ["length", "acf_features", "arch_stat",
"crossing_points", "entropy", "flat_spots",
"heterogeneity", "holt_parameters",
"hurst", "hw_parameters", "lumpiness",
"nonlinearity", "pacf_features", "stability",
"stl_features", "unitroot_kpss", "unitroot_pp"],
**kwargs) -> pd.DataFrame:
"""tsfeatures wrapper using r.
Parameters
----------
ts: pandas df
Pandas DataFrame with columns ['unique_id', 'seasonality', 'y'].
Wide panel of time series.
features: List[str]
String list of features to calculate.
**kwargs:
Arguments used by the original tsfeatures function.
References
----------
https://pkg.robjhyndman.com/tsfeatures/reference/tsfeatures.html
"""
rstring = """
function(uids, seasonalities, ys, features, ...){
suppressMessages(library(data.table))
suppressMessages(library(tsfeatures))
suppressMessages(library(purrr))
series_list <- pmap(
list(uids, seasonalities, ys),
function(uid, seasonality, y) ts(y, frequency=seasonality)
)
names(series_list) <- uids
if("hw_parameters" %in% features){
features <- setdiff(features, "hw_parameters")
if(length(features)>0){
hw_series_features <- suppressMessages(tsfeatures(series_list, "hw_parameters", ...))
names(hw_series_features) <- paste0("hw_", names(hw_series_features))
series_features <- suppressMessages(tsfeatures(series_list, features, ...))
series_features <- cbind(series_features, hw_series_features)
} else {
series_features <- suppressMessages(tsfeatures(series_list, "hw_parameters", ...))
names(series_features) <- paste0("hw_", names(series_features))
}
} else {
series_features <- suppressMessages(tsfeatures(series_list, features, ...))
}
setDT(series_features)
series_features[, unique_id := names(series_list)]
}
"""
pandas2ri.activate()
rfunc = robjects.r(rstring)
uids = ts['unique_id'].to_list()
seasonalities = ts['seasonality'].to_list()
ys = ts['y'].to_list()
feats = rfunc(uids, seasonalities, ys, features, **kwargs)
pandas2ri.deactivate()
renamer={'ARCH.LM': 'arch_lm', 'length': 'series_length'}
feats = feats.rename(columns=renamer)
return feats
| 37.115385
| 105
| 0.54905
| 526
| 5,790
| 5.857414
| 0.222433
| 0.127231
| 0.041545
| 0.103862
| 0.790652
| 0.790652
| 0.790652
| 0.790652
| 0.743265
| 0.743265
| 0
| 0.003612
| 0.33057
| 5,790
| 155
| 106
| 37.354839
| 0.79128
| 0.143523
| 0
| 0.666667
| 0
| 0
| 0.675564
| 0.17546
| 0
| 0
| 0
| 0
| 0
| 1
| 0.021505
| false
| 0
| 0.043011
| 0
| 0.086022
| 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
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
66a8f9b8cc1a5e38469c82b0fbb8d4d59fa5a00b
| 48
|
py
|
Python
|
ravel/ext/grpc/proto/__init__.py
|
gigaquads/pybiz
|
e9654592246be06a777934e889e03407c5c1673e
|
[
"MIT"
] | 2
|
2021-02-26T15:30:44.000Z
|
2021-05-22T14:06:17.000Z
|
ravel/ext/grpc/proto/__init__.py
|
gigaquads/ravel
|
e9654592246be06a777934e889e03407c5c1673e
|
[
"MIT"
] | null | null | null |
ravel/ext/grpc/proto/__init__.py
|
gigaquads/ravel
|
e9654592246be06a777934e889e03407c5c1673e
|
[
"MIT"
] | null | null | null |
from .message_generator import MessageGenerator
| 24
| 47
| 0.895833
| 5
| 48
| 8.4
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.083333
| 48
| 1
| 48
| 48
| 0.954545
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
66b770c881e21fad34af50dcae6abd39179c47c7
| 24
|
py
|
Python
|
IBRAHIM/OPENCV(gözdengeçir)/opencv13.py
|
vektorelpython24proje/temelbilgiler
|
bced2723d247dbb8b10cf86e25ee209635f82921
|
[
"MIT"
] | null | null | null |
IBRAHIM/OPENCV(gözdengeçir)/opencv13.py
|
vektorelpython24proje/temelbilgiler
|
bced2723d247dbb8b10cf86e25ee209635f82921
|
[
"MIT"
] | null | null | null |
IBRAHIM/OPENCV(gözdengeçir)/opencv13.py
|
vektorelpython24proje/temelbilgiler
|
bced2723d247dbb8b10cf86e25ee209635f82921
|
[
"MIT"
] | 3
|
2020-10-24T14:36:14.000Z
|
2020-10-24T14:41:13.000Z
|
import cv2,numpy as np
| 8
| 22
| 0.75
| 5
| 24
| 3.6
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.052632
| 0.208333
| 24
| 2
| 23
| 12
| 0.894737
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
dd2a5cf8f71832711ba0041d79936803628b02c3
| 88
|
py
|
Python
|
bot/utils/__init__.py
|
famaxth/Russian-Qiwi-Bot
|
d5b0f23516343205ca7bad15b2d2fae7b675f584
|
[
"MIT"
] | null | null | null |
bot/utils/__init__.py
|
famaxth/Russian-Qiwi-Bot
|
d5b0f23516343205ca7bad15b2d2fae7b675f584
|
[
"MIT"
] | null | null | null |
bot/utils/__init__.py
|
famaxth/Russian-Qiwi-Bot
|
d5b0f23516343205ca7bad15b2d2fae7b675f584
|
[
"MIT"
] | null | null | null |
from . import db_api
from . import misc
from .notify_admins import on_startup_notify
| 22
| 45
| 0.795455
| 14
| 88
| 4.714286
| 0.642857
| 0.30303
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.170455
| 88
| 3
| 46
| 29.333333
| 0.90411
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
dd3a98d9913755c248179ace7fd4339b164d8375
| 60
|
py
|
Python
|
reprlearn/data/__init__.py
|
cocoaaa/ReprLearn
|
58dc682aa62dbd59201ccc55b9b26480ff3d6773
|
[
"MIT"
] | null | null | null |
reprlearn/data/__init__.py
|
cocoaaa/ReprLearn
|
58dc682aa62dbd59201ccc55b9b26480ff3d6773
|
[
"MIT"
] | null | null | null |
reprlearn/data/__init__.py
|
cocoaaa/ReprLearn
|
58dc682aa62dbd59201ccc55b9b26480ff3d6773
|
[
"MIT"
] | null | null | null |
def data_fn():
print("src.data.__init__.py")
# data_fn()
| 20
| 33
| 0.65
| 10
| 60
| 3.3
| 0.7
| 0.363636
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.133333
| 60
| 3
| 34
| 20
| 0.634615
| 0.15
| 0
| 0
| 0
| 0
| 0.4
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| true
| 0
| 0
| 0
| 0.5
| 0.5
| 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
| 0
| 0
| 0
| 1
|
0
| 6
|
dd58ae1c08481fd8fa7bad513b2a0bc59315eb1c
| 6,721
|
py
|
Python
|
src/cogs/image.py
|
Alone-ankush/Credo
|
cb7789589e31c46c8e1d699590a2abe6e8fe8d07
|
[
"MIT"
] | null | null | null |
src/cogs/image.py
|
Alone-ankush/Credo
|
cb7789589e31c46c8e1d699590a2abe6e8fe8d07
|
[
"MIT"
] | null | null | null |
src/cogs/image.py
|
Alone-ankush/Credo
|
cb7789589e31c46c8e1d699590a2abe6e8fe8d07
|
[
"MIT"
] | 1
|
2021-11-22T16:11:52.000Z
|
2021-11-22T16:11:52.000Z
|
import discord
from discord.ext import commands
import aiohttp
import requests
class Image(commands.Cog, name='Image'):
def __init__(self, bot):
self.bot = bot
@commands.command()
@commands.cooldown(1, 10, commands.BucketType.user)
async def cat(self, ctx):
"""Gives You Random Image Of Cat"""
async with ctx.channel.typing():
async with aiohttp.ClientSession() as cs:
async with cs.get('http://aws.random.cat/meow') as r:
data = await r.json()
em = discord.Embed(
title='Cat', timestamp=ctx.message.created_at, color=self.bot.color)
em.set_image(url=data['file'])
em.set_footer(icon_url=ctx.author.avatar_url,
text=f"Requested By {ctx.author.name}")
await ctx.send(embed=em)
@commands.command()
@commands.cooldown(1, 10, commands.BucketType.user)
async def dog(self, ctx):
"""Gives You Random Image Of Dog"""
async with ctx.channel.typing():
async with aiohttp.ClientSession() as cs:
async with cs.get('http://random.dog/woof.json') as r:
data = await r.json()
em = discord.Embed(
title='Dog', timestamp=ctx.message.created_at, color=self.bot.color)
em.set_image(url=data['url'])
em.set_footer(icon_url=ctx.author.avatar_url,
text=f"Requested By {ctx.author.name}")
await ctx.send(embed=em)
@commands.command()
@commands.cooldown(1, 10, commands.BucketType.user)
async def fox(self, ctx):
"""Gives You Random Image Of Fox"""
async with ctx.channel.typing():
async with aiohttp.ClientSession() as cs:
async with cs.get('https://some-random-api.ml/img/fox') as r:
data = await r.json()
em = discord.Embed(
title='Fox', timestamp=ctx.message.created_at, color=self.bot.color)
em.set_image(url=data['link'])
em.set_footer(icon_url=ctx.author.avatar_url,
text=f"Requested By {ctx.author.name}")
await ctx.send(embed=em)
@commands.command()
@commands.cooldown(1, 10, commands.BucketType.user)
async def panda(self, ctx):
"""Gives You Random Image Of Panda"""
async with ctx.channel.typing():
async with aiohttp.ClientSession() as cs:
async with cs.get('https://some-random-api.ml/img/panda') as r:
data = await r.json()
em = discord.Embed(
title='Panda', timestamp=ctx.message.created_at, color=self.bot.color)
em.set_image(url=data['link'])
em.set_footer(icon_url=ctx.author.avatar_url,
text=f"Requested By {ctx.author.name}")
await ctx.send(embed=em)
@commands.command()
@commands.cooldown(1, 10, commands.BucketType.user)
async def red_panda(self, ctx):
"""Gives You Random Image Of Red Panda"""
async with ctx.channel.typing():
async with aiohttp.ClientSession() as cs:
async with cs.get('https://some-random-api.ml/img/red_panda') as r:
data = await r.json()
em = discord.Embed(
title='Red Panda', timestamp=ctx.message.created_at, color=self.bot.color)
em.set_image(url=data['link'])
em.set_footer(icon_url=ctx.author.avatar_url,
text=f"Requested By {ctx.author.name}")
await ctx.send(embed=em)
@commands.command()
@commands.cooldown(1, 10, commands.BucketType.user)
async def bird(self, ctx):
"""Gives You Random Image Of Bird"""
async with ctx.channel.typing():
async with aiohttp.ClientSession() as cs:
async with cs.get('https://some-random-api.ml/img/birb') as r:
data = await r.json()
em = discord.Embed(
title='Bird', timestamp=ctx.message.created_at, color=self.bot.color)
em.set_image(url=data['link'])
em.set_footer(icon_url=ctx.author.avatar_url,
text=f"Requested By {ctx.author.name}")
await ctx.send(embed=em)
@commands.command()
@commands.cooldown(1, 10, commands.BucketType.user)
async def kola(self, ctx):
"""Gives You Random Image Of Kola"""
async with ctx.channel.typing():
async with aiohttp.ClientSession() as cs:
async with cs.get('https://some-random-api.ml/img/koala') as r:
data = await r.json()
em = discord.Embed(
title='kola', timestamp=ctx.message.created_at, color=self.bot.color)
em.set_image(url=data['link'])
em.set_footer(icon_url=ctx.author.avatar_url,
text=f"Requested By {ctx.author.name}")
await ctx.send(embed=em)
@commands.command()
@commands.cooldown(1, 10, commands.BucketType.user)
async def pikachu(self, ctx):
"""Gives You Random Image Or GIF Of Pikachu"""
async with ctx.channel.typing():
async with aiohttp.ClientSession() as cs:
async with cs.get('https://some-random-api.ml/img/pikachu') as r:
data = await r.json()
em = discord.Embed(
title='Pikachu', timestamp=ctx.message.created_at, color=self.bot.color)
em.set_image(url=data['link'])
em.set_footer(icon_url=ctx.author.avatar_url,
text=f"Requested By {ctx.author.name}")
await ctx.send(embed=em)
# @commands.command()
# @commands.cooldown(1, 10, commands.BucketType.user)
# async def yt(self,ctx,comment:str):
# """Comments On Youtube"""
# url = f"https://some-random-api.ml/canvas/youtube-comment?avatar={ctx.author.avatar_url_as(format='png')}&username={ctx.author}&comment={comment}"
# em = discord.Embed(color = ctx.author.color)
# em.set_image(url=url)
# em.set_footer(text=f"Requested by {ctx.author}", icon_url=ctx.author.avatar_url)
# await ctx.send(embed=em)
def setup(bot):
bot.add_cog(Image(bot))
| 44.217105
| 156
| 0.545752
| 820
| 6,721
| 4.408537
| 0.112195
| 0.059751
| 0.041494
| 0.049793
| 0.870539
| 0.854772
| 0.833748
| 0.795021
| 0.776763
| 0.776763
| 0
| 0.006
| 0.330457
| 6,721
| 151
| 157
| 44.509934
| 0.797333
| 0.070972
| 0
| 0.690265
| 0
| 0
| 0.09877
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.017699
| false
| 0
| 0.035398
| 0
| 0.061947
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
dd645fa056ebfd23c4caebdfdc2152ec117fdf48
| 214
|
py
|
Python
|
programmers/blind_phone_number.py
|
schio/algorithm_test
|
c240faca428a9adb2970591338d4792b2f4fb7f3
|
[
"MIT"
] | null | null | null |
programmers/blind_phone_number.py
|
schio/algorithm_test
|
c240faca428a9adb2970591338d4792b2f4fb7f3
|
[
"MIT"
] | null | null | null |
programmers/blind_phone_number.py
|
schio/algorithm_test
|
c240faca428a9adb2970591338d4792b2f4fb7f3
|
[
"MIT"
] | null | null | null |
# https://programmers.co.kr/learn/courses/30/lessons/12948
def solution(phone_number):
phone_number = list(phone_number)
phone_number[:-4] = ["*"] * (len(phone_number) - 4)
return "".join(phone_number)
| 35.666667
| 58
| 0.691589
| 29
| 214
| 4.896552
| 0.62069
| 0.464789
| 0.225352
| 0.309859
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.048387
| 0.130841
| 214
| 5
| 59
| 42.8
| 0.715054
| 0.261682
| 0
| 0
| 0
| 0
| 0.00641
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0
| 0
| 0.5
| 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
| 0
| 0
| 0
|
0
| 6
|
dd7a97cab10721a7ac3a4595ec291fc2eb2c99df
| 27
|
py
|
Python
|
models/det/__init__.py
|
BruceHan98/OCHTPS
|
5bee02bcbff36029cd47b4802178216f980a4298
|
[
"MIT"
] | null | null | null |
models/det/__init__.py
|
BruceHan98/OCHTPS
|
5bee02bcbff36029cd47b4802178216f980a4298
|
[
"MIT"
] | null | null | null |
models/det/__init__.py
|
BruceHan98/OCHTPS
|
5bee02bcbff36029cd47b4802178216f980a4298
|
[
"MIT"
] | null | null | null |
from .pannet import PANNet
| 13.5
| 26
| 0.814815
| 4
| 27
| 5.5
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.148148
| 27
| 1
| 27
| 27
| 0.956522
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
dd959355e365d9613862ee93daf2b03fcc0afbb8
| 32
|
py
|
Python
|
main.py
|
Abhishek-P/py-hello-world-run-from-colab
|
efd9539f49dfd324e1e475321e2c7c5ecb70e3ac
|
[
"MIT"
] | null | null | null |
main.py
|
Abhishek-P/py-hello-world-run-from-colab
|
efd9539f49dfd324e1e475321e2c7c5ecb70e3ac
|
[
"MIT"
] | null | null | null |
main.py
|
Abhishek-P/py-hello-world-run-from-colab
|
efd9539f49dfd324e1e475321e2c7c5ecb70e3ac
|
[
"MIT"
] | null | null | null |
print("Hello World! from Colab")
| 32
| 32
| 0.75
| 5
| 32
| 4.8
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.09375
| 32
| 1
| 32
| 32
| 0.827586
| 0
| 0
| 0
| 0
| 0
| 0.69697
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 6
|
b09e86d17222e2c84aa7c7682a198b25873a6309
| 28
|
py
|
Python
|
flask_web/flask_app/deep_learning/machine_learning/ml_utils.py
|
Yakings/system_demo
|
6ec9596db1e60e221054282a06d9129246e88f54
|
[
"Apache-2.0"
] | 7
|
2021-09-02T06:47:35.000Z
|
2022-03-09T05:13:00.000Z
|
data/shapenet.py
|
pkudba/SCL
|
78e85344a579075d3d07ed77eab8e13144321c6a
|
[
"MIT"
] | null | null | null |
data/shapenet.py
|
pkudba/SCL
|
78e85344a579075d3d07ed77eab8e13144321c6a
|
[
"MIT"
] | 1
|
2020-08-18T10:55:10.000Z
|
2020-08-18T10:55:10.000Z
|
import os
import numpy as np
| 14
| 18
| 0.821429
| 6
| 28
| 3.833333
| 0.833333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.178571
| 28
| 2
| 18
| 14
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
b09f6e59e32b5245279365673bc84979d193225e
| 177
|
py
|
Python
|
experiments/series_1/experiment_2/experiment_setup.py
|
TomaszOdrzygozdz/gym-splendor
|
aeb00605e105628188143a4bbd6280e9eb41c4f9
|
[
"MIT"
] | 1
|
2020-03-09T18:56:01.000Z
|
2020-03-09T18:56:01.000Z
|
experiments/series_1/experiment_2/experiment_setup.py
|
TomaszOdrzygozdz/gym-splendor
|
aeb00605e105628188143a4bbd6280e9eb41c4f9
|
[
"MIT"
] | null | null | null |
experiments/series_1/experiment_2/experiment_setup.py
|
TomaszOdrzygozdz/gym-splendor
|
aeb00605e105628188143a4bbd6280e9eb41c4f9
|
[
"MIT"
] | 1
|
2019-10-25T13:09:40.000Z
|
2019-10-25T13:09:40.000Z
|
TRAIN_DIR = '/net/archive/groups/plggluna/plgtodrzygozdz/lvl1/train_epochs_new_eval'
VALID_FILE = '/net/archive/groups/plggluna/plgtodrzygozdz/lvl1/valid_new/valid_eval.pickle'
| 59
| 91
| 0.841808
| 25
| 177
| 5.68
| 0.56
| 0.140845
| 0.225352
| 0.338028
| 0.591549
| 0.591549
| 0
| 0
| 0
| 0
| 0
| 0.011696
| 0.033898
| 177
| 2
| 92
| 88.5
| 0.818713
| 0
| 0
| 0
| 0
| 0
| 0.824859
| 0.824859
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 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
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
9ff0b81e53133aba91462aecf89de89bda59ec1a
| 129
|
py
|
Python
|
file_converters/ifcjson/__init__.py
|
IFCJSON-Team/IFC2JSON_python
|
20452c1c5d4461e6dc462c0c3855f3a213197279
|
[
"MIT"
] | 15
|
2020-05-28T16:12:08.000Z
|
2022-02-17T15:12:19.000Z
|
file_converters/ifcjson/__init__.py
|
claudioperez/IFC2JSON_python
|
20452c1c5d4461e6dc462c0c3855f3a213197279
|
[
"MIT"
] | 2
|
2020-08-03T07:06:21.000Z
|
2020-10-03T12:29:33.000Z
|
file_converters/ifcjson/__init__.py
|
claudioperez/IFC2JSON_python
|
20452c1c5d4461e6dc462c0c3855f3a213197279
|
[
"MIT"
] | 8
|
2020-09-03T06:44:34.000Z
|
2021-05-19T06:11:05.000Z
|
from ifcjson.ifc2json4 import IFC2JSON4
from ifcjson.ifc2json5a import IFC2JSON5a
# from ifcjson.to_ifcopenshell import JSON2IFC
| 32.25
| 46
| 0.868217
| 16
| 129
| 6.9375
| 0.5
| 0.297297
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.077586
| 0.100775
| 129
| 3
| 47
| 43
| 0.87931
| 0.341085
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
9ff12bd165478b168116c20184aa85f7204d868f
| 424
|
py
|
Python
|
src/07_mongoengine/service_central/nosql/mongo_setup.py
|
jabelk/mongodb-for-python-developers
|
36df20d18b6e74fca986d6b01a58f32e983efdbf
|
[
"MIT"
] | null | null | null |
src/07_mongoengine/service_central/nosql/mongo_setup.py
|
jabelk/mongodb-for-python-developers
|
36df20d18b6e74fca986d6b01a58f32e983efdbf
|
[
"MIT"
] | null | null | null |
src/07_mongoengine/service_central/nosql/mongo_setup.py
|
jabelk/mongodb-for-python-developers
|
36df20d18b6e74fca986d6b01a58f32e983efdbf
|
[
"MIT"
] | null | null | null |
import mongoengine
def global_init():
# this is where would pass in creds and port and such
# name= is the database db name
# when we define our classes we will refer to the "core" connection
# default localhost and port
mongoengine.register_connection(alias='core', name='demo_dealership')
# could have multiple like
# mongoengine.register_connection(alias='analytics', name='anotherDBname')
| 32.615385
| 78
| 0.731132
| 57
| 424
| 5.368421
| 0.719298
| 0.045752
| 0.189542
| 0.222222
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.193396
| 424
| 12
| 79
| 35.333333
| 0.894737
| 0.641509
| 0
| 0
| 0
| 0
| 0.131034
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| true
| 0
| 0.333333
| 0
| 0.666667
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
c680f37234049d05b70d11aac85295f6c4622c68
| 350
|
py
|
Python
|
clusterwrapper/clustermetrics.py
|
opennlp/DeepPhrase
|
54bd6ca96c12475e3c3ff3745a4eb7c245b6e870
|
[
"MIT"
] | 2
|
2019-06-19T12:52:31.000Z
|
2020-05-20T15:29:56.000Z
|
clusterwrapper/clustermetrics.py
|
opennlp/DeepPhrase
|
54bd6ca96c12475e3c3ff3745a4eb7c245b6e870
|
[
"MIT"
] | 5
|
2019-12-17T05:44:10.000Z
|
2022-02-10T00:29:31.000Z
|
clusterwrapper/clustermetrics.py
|
opennlp/DeepPhrase
|
54bd6ca96c12475e3c3ff3745a4eb7c245b6e870
|
[
"MIT"
] | 3
|
2019-10-06T13:31:31.000Z
|
2022-03-16T16:13:09.000Z
|
from sklearn.metrics import silhouette_score, calinski_harabaz_score
def get_silhouette_coefficient(cluster_train_data,labels_assigned):
return silhouette_score(cluster_train_data,labels_assigned)
def get_calinski_harabaz_coefficient(cluster_train_data, labels_assigned):
return calinski_harabaz_score(cluster_train_data, labels_assigned)
| 38.888889
| 74
| 0.88
| 45
| 350
| 6.333333
| 0.377778
| 0.168421
| 0.224561
| 0.308772
| 0.575439
| 0.575439
| 0.329825
| 0
| 0
| 0
| 0
| 0
| 0.074286
| 350
| 9
| 75
| 38.888889
| 0.87963
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.4
| false
| 0
| 0.2
| 0.4
| 1
| 0
| 0
| 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
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 6
|
c6d555d859bb876aa1349e06152d5eef3dc029ae
| 117
|
py
|
Python
|
0x02-python-import_modules/5-variable_load.py
|
darkares23/holbertonschool-higher_level_programming
|
931b1b701d8a1d990b7cd931486496c0b5502e21
|
[
"MIT"
] | null | null | null |
0x02-python-import_modules/5-variable_load.py
|
darkares23/holbertonschool-higher_level_programming
|
931b1b701d8a1d990b7cd931486496c0b5502e21
|
[
"MIT"
] | null | null | null |
0x02-python-import_modules/5-variable_load.py
|
darkares23/holbertonschool-higher_level_programming
|
931b1b701d8a1d990b7cd931486496c0b5502e21
|
[
"MIT"
] | null | null | null |
#!/usr/bin/python3
if __name__ == "__main__":
import variable_load_5
print("{:d}".format(variable_load_5.a))
| 23.4
| 43
| 0.683761
| 17
| 117
| 4
| 0.823529
| 0.352941
| 0.382353
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.029703
| 0.136752
| 117
| 4
| 44
| 29.25
| 0.643564
| 0.145299
| 0
| 0
| 0
| 0
| 0.121212
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.333333
| 0
| 0.333333
| 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
| 0
| 0
|
0
| 6
|
c6d683a4278c405775be1daf2baed45d4e050d96
| 126
|
py
|
Python
|
7kyu/jaden_casing_strings.py
|
nhsz/codewars
|
82703959e910254d6feff4162f78c6dbd7a1c3ed
|
[
"MIT"
] | 1
|
2018-12-02T23:04:38.000Z
|
2018-12-02T23:04:38.000Z
|
7kyu/jaden_casing_strings.py
|
nhsz/codewars
|
82703959e910254d6feff4162f78c6dbd7a1c3ed
|
[
"MIT"
] | null | null | null |
7kyu/jaden_casing_strings.py
|
nhsz/codewars
|
82703959e910254d6feff4162f78c6dbd7a1c3ed
|
[
"MIT"
] | null | null | null |
# http://www.codewars.com/kata/5390bac347d09b7da40006f6/
import string
def to_jaden_case(s):
return string.capwords(s)
| 15.75
| 56
| 0.761905
| 17
| 126
| 5.529412
| 0.882353
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.144144
| 0.119048
| 126
| 7
| 57
| 18
| 0.702703
| 0.428571
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0.333333
| 0.333333
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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py
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Python
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__init__.py
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pslustig/galfitwrap
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f8f695083e3b10806aeb6fb0f748234bd840a0d2
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[
"MIT"
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__init__.py
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pslustig/galfitwrap
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f8f695083e3b10806aeb6fb0f748234bd840a0d2
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[
"MIT"
] | null | null | null |
__init__.py
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pslustig/galfitwrap
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f8f695083e3b10806aeb6fb0f748234bd840a0d2
|
[
"MIT"
] | null | null | null |
from .galaxywrap import *
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afd143bd5e84c3a69dfc14385c7dd489f2e9fbb7
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py
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Python
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Bin/init.py
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mfneirae/GrupLAC-Complete
|
f4ccefe2553b90015d28df0e8d7730b4bad37d84
|
[
"MIT"
] | null | null | null |
Bin/init.py
|
mfneirae/GrupLAC-Complete
|
f4ccefe2553b90015d28df0e8d7730b4bad37d84
|
[
"MIT"
] | null | null | null |
Bin/init.py
|
mfneirae/GrupLAC-Complete
|
f4ccefe2553b90015d28df0e8d7730b4bad37d84
|
[
"MIT"
] | 1
|
2021-06-10T09:21:18.000Z
|
2021-06-10T09:21:18.000Z
|
#
#
# #############################################################################
# Copyright (c) 2018 Universidad Nacional de Colombia All Rights Reserved.
#
# This work was made as a development to improve data collection
# for self-assessment and accreditation processes in the Vicedeanship
# of academic affairs in the Engineering Faculty of the Universidad
# Nacional de Colombia and is licensed under a Creative Commons
# Attribution-NonCommercial - ShareAlike 4.0 International License
# and MIT Licence.
#
# by Manuel Embus.
#
# For more information write me to jai@mfneirae.com
# Or visit my webpage at https://mfneirae.com/
# #############################################################################
#
#
def inicio():
global GP_DATOS_BASE
global GP_DATOS_BASE_CSV
global GP_DATOS_INSTITUCIONES
global GP_DATOS_INSTITUCIONES_CSV
global GP_DATOS_LINEAS
global GP_DATOS_LINEAS_CSV
global GP_DATOS_SECTORES
global GP_DATOS_SECTORES_CSV
global GP_DATOS_INTEGRANTES
global GP_DATOS_INTEGRANTES_CSV
global REL_GRUPO_PRODUCTO
global REL_GRUPO_PRODUCTO_CSV
global GP_PROD_BIB
global GP_PROD_BIB_CSV
global GP_PROD_TEC
global GP_PROD_TEC_CSV
global GP_APROPIACION
global GP_APROPIACION_CSV
global GP_OBRAS
global GP_OBRAS_CSV
global GP_ACTIVIDADES
global GP_ACTIVIDADES_CSV
global v_colciencias_tipo_producto
global inv_colciencias_tipo_producto
GP_DATOS_BASE = []
GP_DATOS_INSTITUCIONES = []
GP_DATOS_LINEAS = []
GP_DATOS_SECTORES = []
GP_DATOS_INTEGRANTES = []
REL_GRUPO_PRODUCTO = []
GP_PROD_BIB = []
GP_PROD_TEC = []
GP_APROPIACION = []
GP_OBRAS = []
GP_ACTIVIDADES = []
GP_PROD_BIB_CSV=["CODGP_PROD_BIB; \
CODGP_PROD:\
Revista; \
Autor Original; \
Nombre Libro; \
ISBN/ISSN; \
Medio de Divulgación; \
URL; \
Fasciculos; \
Idioma Original; \
Idioma Traduccion; \
Edición; \
Serie; \
Página Inicial; \
Página Final ; \
\n"]
GP_PROD_TEC_CSV=["CODGP_PROD_TEC; \
CODGP_PROD; \
Tema; \
Nombre Comerial; \
Nombre Proyecto; \
Tipo de Ciclo; \
NIT; \
Fecha de Registro; \
Tiene Productos; \
Disponibilidad; \
Objeto; \
Fecha Publicación; \
Número de Contrato; \
Acto Administrativo; \
\n"]
GP_APROPIACION_CSV=["CODGP_PROD_APROPIACION; \
CODGP_PROD; \
Tipos de Participación; \
Fecha Inicio; \
Fecha Fin; \
Proyecto de Inv; \
Medio de publicación; \
Emisora; \
Número de Participantes; \
\n"]
GP_OBRAS_CSV=["CODGP_PROD_OBRAS; \
CODGP_PROD; \
Fecha Creación; \
Disiplina de origen; \
Institución Licencia; \
Fecha Licencia; \
Distinciones; \
Selección Distinción; \
Productos Asociados; \
Número Derechos Autor/NIT; \
\n"]
GP_ACTIVIDADES_CSV=["CODGP_PROD_FORM; \
CODGP_PROD; \
Nombre de Ferias; \
Fecha Inicio Curso; \
Tipo Orientación; \
Nombre Estudiante; \
Programa Académico; \
Valoración; \
Fecha fin Curso; \
Finalidad; \
Duración; \
\n"]
REL_GRUPO_PRODUCTO_CSV =["CODGP_PROD; \
CODGP; \
GP_TIPO_PROD; \
Nombre Producto; \
Lugar; \
Año; \
Idioma; \
Páginas; \
Volumen; \
Editorial; \
Ambito; \
DOI; \
Descripción; \
Instituciones; \
Tipo Vincula Institu; \
Autores\n"]
GP_DATOS_BASE_CSV = ["CODGP;\
Año Formación;\
Mes Formación;\
Lugar;\
Nombre Lider;\
Información Certificada;\
Página Web;\
Correo;\
Clasificación;\
Área del Conocimiento;\
Programa Nacional;\
Programa Nacional 2;\
Plan de trabajo;\
Estado del Arte;\
Objetivos;\
Retos;\
Visión\n"]
GP_DATOS_INSTITUCIONES_CSV = ["CODGP_INSTI;\
CODGP;\
Nombre Institución\n"]
GP_DATOS_LINEAS_CSV = ["CODGP_LINEA;\
CODGP;\
Línea de Investigación\n"]
GP_DATOS_SECTORES_CSV = ["CODGP_SECTOR;\
CODGP;\
Sector\n"]
GP_DATOS_INTEGRANTES_CSV = ["CODGP_INTEGRANTE;\
CODGP;\
COD_RG;\
CVLAC;\
NOMBRE COMPLETO;\
Tipo Vinculación;\
Horas de Dedicación;\
Duración Vinculación;\
Inicio Vinculación;\
Fin Vinculación;\
Fin Vinculación\n"]
v_colciencias_tipo_producto = [ "COD_TIPO_PRODUCTO; \
TIPO_PRODUCTO_COL; \
SUB_TIPO_PRODUCTO_COL; \
TIPO_UAPA\n\
0; \
Evento sin producto asociado; \
Evento sin producto asociado; \
Evento sin producto asociado\n\
1; \
Redes de conocimiento; \
Redes de conocimiento; \
Redes de conocimiento\n\
2; \
Producción bibliográfica - Trabajos en eventos (Capítulos de memoria) - Completo; \
Capítulos de memoria; \
Capítulos de memoria\n\
3; \
Producción técnica - Presentación de trabajo - Comunicación; \
Presentación de trabajo; \
Trabajo de Comunicación\n\
4; \
Demás trabajos - Demás trabajos - Póster; \
Demás trabajos; \
Poster\n\
5; \
Producción técnica - Presentación de trabajo - Conferencia; \
Presentación de trabajo; \
Conferencia\n\
6; \
Producción técnica - Presentación de trabajo - Ponencia; \
Presentación de trabajo; \
Ponencia\n\
7; \
Estrategias pedagógicas para el fomento a la CTI; \
Estrategias pedagógicas; \
Estrategias pedagógicas\n\
8; \
Producción bibliográfica - Artículo - Publicado en revista especializada; \
Publicado en revista especializada; \
Artículo\n\
9; \
Producción bibliográfica - Artículo - Corto (Resumen); \
Corto (Resumen); \
Artículo\n\
10; \
Estrategias pedagógicas para el fomento a la CTI; \
Estrategias pedagógicas; \
Estrategias pedagógicas\n\
11; \
Producción bibliográfica - Artículo - Caso clínico; \
Caso Clínico; \
Artículo\n\
12; \
Producción bibliográfica - Trabajos en eventos (Capítulos de memoria) - Resumen; \
Capítulo de Memoria; \
Resumen\n\
13; \
Producción técnica - Presentación de trabajo - Congreso; \
Congreso; \
Congreso\n\
14; \
Producción técnica - Presentación de trabajo - Simposio; \
Simposio; \
Simposio\n\
15; \
Producción técnica - Presentación de trabajo - Seminario; \
Seminario; \
Seminario\n\
16; \
Producción técnica - Presentación de trabajo - Otro; \
Otro; \
Otro\n\
17; \
Producción bibliográfica - Libro - Libro resultado de investigación; \
Libro resultado de investigación; \
Libro\n\
18; \
Producción bibliográfica - Libro - Otro libro publicado; \
Otro libro publicado; \
Libro - Otro\n\
19; \
Producción bibliográfica - Libro - Libro pedagógico y/o de divulgación; \
Libro pedagógico y/o de divulgación; \
Libro - pedagógico\n\
20; \
Otro capítulo de libro publicado; \
Otro capítulo de libro; \
Capítulo de libro - Otro\n\
21; \
Capítulo de libro; \
Capítulo de libro; \
Capítulo de libro\n\
22; \
Producción bibliográfica - Otro artículo publicado - Periódico de noticias; \
Periódico de noticias; \
Otro\n\
23; \
Producción bibliográfica - Otro artículo publicado - Revista de divulgación; \
Revista de divulgación; \
Otro\n\
24; \
Producción bibliográfica - Otro artículo publicado - Cartas al editor; \
Cartas al editor; \
Otro\n\
25; \
Producción bibliográfica - Otro artículo publicado - Reseñas de libros; \
Reseñas de libros; \
Otro\n\
26; \
Producción bibliográfica - Otro artículo publicado - Columna de opinión; \
Columnas de opinión; \
Otro\n\
27; \
Producción bibliográfica - Documento de trabajo (Working Paper); \
Documento de trabajo (Working Paper); \
Otro\n\
28; \
Producción bibliográfica - Traducciones - Artículo; \
Traducciones - Artículo; \
Traducciones\n\
29; \
Producción bibliográfica - Traducciones - Libro; \
Traducciones - Libro; \
Traducciones\n\
30; \
Producción bibliográfica - Traducciones - Otra; \
Traducciones - Otra; \
Traducciones\n\
31; \
Producción bibliográfica - Otra producción bibliográfica - Introducción; \
Introducción; \
Otro\n\
32; \
Producción bibliográfica - Otra producción bibliográfica - Prólogo; \
Prólogo; \
Otro\n\
33; \
Producción bibliográfica - Otra producción bibliográfica - Epílogo; \
Epílogo; \
Otro\n\
34; \
Producción bibliográfica - Otra producción bibliográfica - Otra; \
Otra; \
Otro\n\
35; \
Producción técnica - Softwares - Computacional; \
Software; \
Software\n\
36; \
Producción técnica - Productos tecnológicos - Gen Clonado; \
Productos tecnológicos - Gen Clonado; \
Productos tecnológicos\n\
37; \
Producción técnica - Productos tecnológicos - Coleccion biologica de referencia con informacion sistematizada; \
Productos tecnológicos - Coleccion biologica de referencia con informacion sistematizada; \
Productos tecnológicos\n\
38; \
Producción técnica - Productos tecnológicos - Otro; \
Productos tecnológicos - Otro; \
Productos tecnológicos\n\
39; \
Producción técnica - Productos tecnológicos - Base de datos de referencia para investigación; \
Productos tecnológicos - Base de datos de referencia para investigación; \
Productos tecnológicos\n\
40; \
Producción técnica - Diseño Industrial; \
Diseño Industrial; \
Otro\n\
41; \
Producción técnica - Esquema de circuito integrado; \
Esquema de circuito integrado; \
Otro\n\
42; \
Producción técnica - Innovaciones generadas de producción empresarial - Organizacional; \
Innovaciones generadas de producción empresarial - Organizacional; \
Innovaciones\n\
43; \
Producción técnica - Innovaciones generadas de producción empresarial - Empresarial; \
Innovaciones generadas de producción empresarial - Empresarial; \
Innovaciones\n\
44; \
Producción técnica - Variedad animal; \
Variedad animal; \
Otro\n\
45; \
Producción técnica - Innovación de proceso o procedimiento; \
Innovación de proceso o procedimiento; \
Innovación\n\
46; \
Producción técnica - Cartas, mapas o similares - Aerofotograma; \
Aerofotograma; \
Otro\n\
47; \
Producción técnica - Cartas, mapas o similares - Carta; \
Carta; \
Otro\n\
48; \
Producción técnica - Cartas, mapas o similares - Fotograma; \
Fotograma; \
Otro\n\
49; \
Producción técnica - Cartas, mapas o similares - Mapa; \
Mapa; \
Otro\n\
50; \
Producción técnica - Cartas, mapas o similares - Otra; \
Otra; \
Otro\n\
51; \
Producción técnica - Variedad vegetal; \
Variedad vegetal; \
Otro\n\
52; \
Producción técnica - Consultoría Científico Tecnológica e Informe Técnico - Servicios de proyectos de IDI; \
Servicios de proyectos de IDI; \
Otro\n\
53; \
Producción técnica - Consultoría Científico Tecnológica e Informe Técnico - Comercialización de tecnología; \
Comercialización de tecnología; \
Otro\n\
54; \
Producción técnica - Consultoría Científico Tecnológica e Informe Técnico - Análisis de competitividad; \
Análisis de competitividad; \
Otro\n\
55; \
Producción técnica - Consultoría Científico Tecnológica e Informe Técnico - Informe técnico; \
Informe técnico; \
Otro\n\
56; \
Producción técnica - Consultoría Científico Tecnológica e Informe Técnico - Otro; \
Otro; \
Otro\n\
57; \
Producción técnica - Consultoría Científico Tecnológica e Informe Técnico - Acciones de transferencia tecnológica; \
Acciones de transferencia tecnológica; \
Otro\n\
58; \
Producción técnica - Consultoría Científico Tecnológica e Informe Técnico - Desarrollo de productos; \
Desarrollo de productos; \
Otro\n\
59; \
Producción técnica - Consultoría Científico Tecnológica e Informe Técnico - Implementación de sistemas de análisis; \
Implementación de sistemas de análisis; \
Otro\n\
60; \
Producción técnica - Consultoría Científico Tecnológica e Informe Técnico - Consultoría en artes,arquitectura y diseño; \
Consultoría en artes,arquitectura y diseño; \
Otro\n\
61; \
Producción técnica - Regulación, norma, reglamento o legislación - Ambiental o de Salud; \
Regulación, norma, reglamento o legislación - Ambiental o de Salud; \
Otro\n\
62; \
Producción técnica - Regulación, norma, reglamento o legislación - Educativa; \
Regulación, norma, reglamento o legislación - Educativa; \
Otro\n\
63; \
Producción técnica - Regulación, norma, reglamento o legislación - Social; \
Regulación, norma, reglamento o legislación - Social; \
Otro\n\
64; \
Producción técnica - Regulación, norma, reglamento o legislación - Técnica; \
Regulación, norma, reglamento o legislación - Técnica; \
Otro\n\
65; \
Producción técnica - Regulación, norma, reglamento o legislación - Guía de práctica clínica; \
Regulación, norma, reglamento o legislación - Guía de práctica clínica; \
Otro\n\
66; \
Producción técnica - Regulación, norma, reglamento o legislación - Proyecto de ley; \
Regulación, norma, reglamento o legislación - Proyecto de ley; \
Otro\n\
67; \
Producción técnica - Reglamento Técnico; \
Reglamento Técnico; \
Otro\n\
68; \
Producción técnica - Empresa de base tecnológica - Spin-off; \
Empresa de base tecnológica - Spin-off; \
Otro\n\
69; \
Producción técnica - Empresa de base tecnológica - Start-up; \
Empresa de base tecnológica - Start-up; \
Otro\n\
70; \
Demás trabajos - Demás trabajos; \
Demás trabajos; \
Otro\n\
71; \
Producción técnica - Signos; \
Signos; \
Otro\n\
72; \
Producción técnica - Softwares - Multimedia; \
Multimedia; \
Software\n\
73; \
Producción técnica - Softwares - Otra; \
Softwares - Otra; \
Software\n\
74; \
Producción técnica - Regulación, norma, reglamento o legislación - Técnica - Básica; \
Técnica - Básica; \
Otro\n\
75; \
Producción técnica - Regulación, norma, reglamento o legislación - Técnica - Ensayo; \
Técnica - Ensayo; \
Otro\n\
76; \
Producción técnica - Consultoría Científico Tecnológica e Informe Técnico - Servicios de Proyectos de I+D+I; \
Servicios de Proyectos de I+D+I; \
Otro\n\
77; \
Producción técnica - Regulación, norma, reglamento o legislación - Técnica - Proceso; \
Técnica - Proceso; \
Otro\n\
78; \
Datos complementarios - Participación en comités de evaluación - Profesor titular; \
Participación en comités de evaluación - Profesor titular; \
Comités\n\
79; \
Datos complementarios - Participación en comités de evaluación - Concurso docente; \
Participación en comités de evaluación - Concurso docente; \
Comités\n\
80; \
Datos complementarios - Participación en comités de evaluación - Jefe de cátedra; \
Participación en comités de evaluación - Jefe de cátedra; \
Comités\n\
81; \
Datos complementarios - Participación en comités de evaluación - Evaluación de cursos; \
Participación en comités de evaluación - Evaluación de cursos; \
Comités\n\
82; \
Datos complementarios - Participación en comités de evaluación - Acreditación de programas; \
Participación en comités de evaluación - Acreditación de programas; \
Comités\n\
83; \
Datos complementarios - Participación en comités de evaluación - Asignación de becas; \
Participación en comités de evaluación - Asignación de becas; \
Comités\n\
84; \
Datos complementarios - Participación en comités de evaluación - Otra; \
Participación en comités de evaluación - Otra; \
Comités\n\
85; \
Datos complementarios - Jurado/Comisiones evaluadoras de trabajo de grado - Pregrado; \
Jurado Pregrado; \
Comités\n\
86; \
Datos complementarios - Jurado/Comisiones evaluadoras de trabajo de grado - Especialización; \
Jurado Especialización; \
Comités\n\
87; \
Datos complementarios - Jurado/Comisiones evaluadoras de trabajo de grado - Especialidad Médica; \
Jurado Especialidad Médica; \
Comités\n\
88; \
Datos complementarios - Jurado/Comisiones evaluadoras de trabajo de grado - Maestría; \
Jurado Maestría; \
Comités\n\
89; \
Datos complementarios - Jurado/Comisiones evaluadoras de trabajo de grado - Doctorado; \
Jurado Doctorado; \
Comités\n\
90; \
Datos complementarios - Jurado/Comisiones evaluadoras de trabajo de grado - Otra; \
Jurado Otra; \
Comités\n\
91; \
Datos complementarios - Jurado/Comisiones evaluadoras de trabajo de grado - Curso de perfeccionamiento/especialización; \
Jurado Especializaciones; \
Comités\n\
96; \
Producción técnica - Signos Distintivos - Nombres comerciales; \
Signos Distintivos - Nombres comerciales; \
Nombres comerciales\n\
92; \
Datos complementarios - Jurado/Comisiones evaluadoras de trabajo de grado - Curso de perfeccionamiento/especialización; \
Jurado Especializaciones; \
Comités\n\
93; \
Producción técnica - Plantas piloto - Planta piloto; \
Plantas piloto - Planta piloto; \
Planta piloto\n\
94; \
Producción técnica - Prototipo - Industrial; \
Prototipo - Industrial; \
Industrial\n\
95; \
Producción técnica - Signos Distintivos - Marcas; \
Signos Distintivos - Marcas; \
Marcas\n\
96; \
Producción técnica - Signos Distintivos - Nombres comerciales; \
Signos Distintivos - Nombres comerciales; \
Nombres comerciales\n\
97; \
Apropiación social y circularción del conocimiento - Ediciones - Anales; \
Ediciones - Anales; \
Analess\n\
98; \
Apropiación social y circularción del conocimiento - Ediciones - Libro; \
Ediciones - Libro; \
Libro\n\
92; \
Producción técnica - Prototipo - Servicios; \
Prototipo - Servicios; \
Servicios\n"]
#***************************************************************************
#Insert
#***************************************************************************
inv_colciencias_tipo_producto = [ "REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`cod_tipo_producto`,\
`tipo_producto_col`,\
`sub_tipo_producto_col`,\
`tipo_uapa`) VALUES (\
0,\
'Evento sin producto asociado',\
'Evento sin producto asociado',\
'Evento sin producto asociado');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
1,\
'Redes de conocimiento',\
'Redes de conocimiento',\
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'Estrategias pedagógicas');\n\
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'Congreso');\n\
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'Simposio');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
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'Seminario');\n\
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'Otro',\
'Otro');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
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'Libro resultado de investigación',\
'Libro');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
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'Producción bibliográfica - Libro - Otro libro publicado',\
'Otro libro publicado',\
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'Capítulo de libro',\
'Capítulo de libro',\
'Capítulo de libro');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
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'Producción bibliográfica - Otro artículo publicado - Periódico de noticias',\
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'Otro');\n\
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'Otro');\n\
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'Otro');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
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'Otro');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
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'Otro');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
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'Producción bibliográfica - Documento de trabajo (Working Paper)',\
'Documento de trabajo (Working Paper)',\
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REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
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'Producción bibliográfica - Traducciones - Artículo',\
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'Traducciones');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
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'Producción bibliográfica - Traducciones - Libro',\
'Traducciones - Libro',\
'Traducciones');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
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'Producción bibliográfica - Traducciones - Otra',\
'Traducciones - Otra',\
'Traducciones');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
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'Producción bibliográfica - Otra producción bibliográfica - Introducción',\
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'Otro');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
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'Producción bibliográfica - Otra producción bibliográfica - Prólogo',\
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'Otro');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
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'Producción bibliográfica - Otra producción bibliográfica - Epílogo',\
'Epílogo',\
'Otro');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
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34,\
'Producción bibliográfica - Otra producción bibliográfica - Otra',\
'Otra',\
'Otro');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
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'Producción técnica - Softwares - Computacional',\
'Software',\
'Software');\n\
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REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
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'Producción técnica - Productos tecnológicos - Otro',\
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REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
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'Producción técnica - Diseño Industrial',\
'Diseño Industrial',\
'Otro');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
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'Producción técnica - Esquema de circuito integrado',\
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'Otro');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
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'Producción técnica - Innovaciones generadas de producción empresarial - Organizacional',\
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'Innovaciones');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
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'Innovaciones');\n\
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'Producción técnica - Variedad animal',\
'Variedad animal',\
'Otro');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
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'Producción técnica - Innovación de proceso o procedimiento',\
'Innovación de proceso o procedimiento',\
'Innovación');\n\
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'Producción técnica - Cartas, mapas o similares - Aerofotograma',\
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'Otro');\n\
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'Otro');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
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'Producción técnica - Variedad vegetal',\
'Variedad vegetal',\
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REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
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REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
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'Producción técnica - Consultoría Científico Tecnológica e Informe Técnico - Comercialización de tecnología',\
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REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
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'Producción técnica - Consultoría Científico Tecnológica e Informe Técnico - Análisis de competitividad',\
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REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
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'Producción técnica - Consultoría Científico Tecnológica e Informe Técnico - Informe técnico',\
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REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
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'Producción técnica - Consultoría Científico Tecnológica e Informe Técnico - Otro',\
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'Otro');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
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REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
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REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
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REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
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REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
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61,\
'Producción técnica - Regulación, norma, reglamento o legislación - Ambiental o de Salud',\
'Regulación, norma, reglamento o legislación - Ambiental o de Salud',\
'Otro');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
62,\
'Producción técnica - Regulación, norma, reglamento o legislación - Educativa',\
'Regulación, norma, reglamento o legislación - Educativa',\
'Otro');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
63,\
'Producción técnica - Regulación, norma, reglamento o legislación - Social',\
'Regulación, norma, reglamento o legislación - Social',\
'Otro');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
64,\
'Producción técnica - Regulación, norma, reglamento o legislación - Técnica',\
'Regulación, norma, reglamento o legislación - Técnica',\
'Otro');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
65,\
'Producción técnica - Regulación, norma, reglamento o legislación - Guía de práctica clínica',\
'Regulación, norma, reglamento o legislación - Guía de práctica clínica',\
'Otro');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
66,\
'Producción técnica - Regulación, norma, reglamento o legislación - Proyecto de ley',\
'Regulación, norma, reglamento o legislación - Proyecto de ley',\
'Otro');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
67,\
'Producción técnica - Reglamento Técnico',\
'Reglamento Técnico',\
'Otro');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
68,\
'Producción técnica - Empresa de base tecnológica - Spin-off',\
'Empresa de base tecnológica - Spin-off',\
'Otro');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
69,\
'Producción técnica - Empresa de base tecnológica - Start-up',\
'Empresa de base tecnológica - Start-up',\
'Otro');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
70,\
'Demás trabajos - Demás trabajos',\
'Demás trabajos',\
'Otro');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
71,\
'Producción técnica - Signos',\
'Signos',\
'Otro');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
72,\
'Producción técnica - Softwares - Multimedia',\
'Multimedia',\
'Software');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
73,\
'Producción técnica - Softwares - Otra',\
'Softwares - Otra',\
'Software');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
74,\
'Producción técnica - Regulación, norma, reglamento o legislación - Técnica - Básica',\
'Técnica - Básica',\
'Otro');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
75,\
'Producción técnica - Regulación, norma, reglamento o legislación - Técnica - Ensayo',\
'Técnica - Ensayo',\
'Otro');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
76,\
'Producción técnica - Consultoría Científico Tecnológica e Informe Técnico - Servicios de Proyectos de I+D+I',\
'Servicios de Proyectos de I+D+I',\
'Otro');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
77,\
'Producción técnica - Regulación, norma, reglamento o legislación - Técnica - Proceso',\
'Técnica - Proceso',\
'Otro');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
78,\
'Datos complementarios - Participación en comités de evaluación - Profesor titular',\
'Participación en comités de evaluación - Profesor titular',\
'Comités');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
79,\
'Datos complementarios - Participación en comités de evaluación - Concurso docente',\
'Participación en comités de evaluación - Concurso docente',\
'Comités');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
80,\
'Datos complementarios - Participación en comités de evaluación - Jefe de cátedra',\
'articipación en comités de evaluación - Jefe de cátedra',\
'Comités');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
81,\
'Datos complementarios - Participación en comités de evaluación - Evaluación de cursos',\
'Participación en comités de evaluación - Evaluación de cursos',\
'Comités');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
82,\
'Datos complementarios - Participación en comités de evaluación - Acreditación de programas',\
'Participación en comités de evaluación - Acreditación de programas',\
'Comités');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
83,\
'Datos complementarios - Participación en comités de evaluación - Asignación de becas',\
'Participación en comités de evaluación - Asignación de becas',\
'Comités');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
84,\
'Datos complementarios - Participación en comités de evaluación - Otra',\
'Participación en comités de evaluación - Otra',\
'Comités');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
85,\
'Datos complementarios - Jurado/Comisiones evaluadoras de trabajo de grado - Pregrado',\
'Jurado Pregrado',\
'Comités');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
86,\
'Datos complementarios - Jurado/Comisiones evaluadoras de trabajo de grado - Especialización',\
'Jurado Especialización',\
'Comités');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
87,\
'Datos complementarios - Jurado/Comisiones evaluadoras de trabajo de grado - Especialidad Médica',\
'Jurado Especialidad Médica',\
'Comités');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
88,\
'Datos complementarios - Jurado/Comisiones evaluadoras de trabajo de grado - Maestría',\
'Jurado Maestría',\
'Comités');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
89,\
'Datos complementarios - Jurado/Comisiones evaluadoras de trabajo de grado - Doctorado',\
'Jurado Doctorado',\
'Comités');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
90, \
'Datos complementarios - Jurado/Comisiones evaluadoras de trabajo de grado - Otra',\
'Jurado Otra',\
'Comités');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
92, \
'Producción técnica - Prototipo - Servicios',\
'Prototipo - Servicios',\
'Servicios');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
93, \
'Producción técnica - Plantas piloto - Planta piloto',\
'Plantas piloto - Planta piloto',\
'Planta piloto');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
94, \
'Producción técnica - Prototipo - Industrial',\
'Prototipo - Industrial',\
'Industrial');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
95, \
'Producción técnica - Signos Distintivos - Marcas',\
'Signos Distintivos - Marcas',\
'Marcas');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
96, \
'Producción técnica - Signos Distintivos - Nombres comerciales',\
'Signos Distintivos - Nombres comerciales',\
'Nombres comerciales');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
97, \
'Apropiación - Eventos Cientificos - Otro',\
'Eventos Cientificos - Otro',\
'Otro');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
98, \
'Apropiación - Eventos Cientificos - Taller',\
'Eventos Cientificos - Taller',\
'Taller');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
99, \
'Apropiación - Eventos Cientificos - Congreso',\
'Eventos Cientificos - Congreso',\
'Congreso');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
100, \
'Apropiación - Eventos Cientificos - Encuentro',\
'Eventos Cientificos - Encuentro',\
'Encuentro');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
101, \
'Apropiación - Eventos Cientificos - Seminario',\
'Eventos Cientificos - Seminario',\
'Seminario');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
102, \
'Apropiación - Eventos Cientificos - Simposio',\
'Eventos Cientificos - Simposio',\
'Simposio');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
103, \
'Apropiación - Eventos Cientificos - Informes de investigación',\
'Eventos Cientificos - Informes de investigación',\
'Informes de investigación');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
104, \
'Apropiación - Impresos - Manual',\
'Impresos - Manual',\
'Manual');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
105, \
'Apropiación - Impresos - Boletín',\
'Impresos - Boletín',\
'Boletín');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
106, \
'Apropiación - Contenido Multimedia - Comentario',\
'Contenido Multimedia - Comentario',\
'Comentario');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
107, \
'Apropiación - Contenido Multimedia - Entrevista',\
'Contenido Multimedia - Entrevista',\
'Entrevista');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
108, \
'Apropiación - Contenido Virtual - Página Web',\
'Contenido Virtual - Página Web',\
'Página Web');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
109, \
'Apropiación - Estrategias de Comunicación - Estrategias de Comunicación',\
'Estrategias de Comunicación - Estrategias de Comunicación',\
'Estrategias de Comunicación');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
110, \
'Apropiación - Estrategias Pedagógicas - Estrategias Pedagógicas para el fomento a la CTI',\
'Estrategias Pedagógicas - Estrategias Pedagógicas para el fomento a la CTI',\
'Estrategias Pedagógicas para el fomento a la CTI');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
111, \
'Apropiación - Participación Ciudadana - Participación Ciudadana en Proyectos de CTI',\
'Participación Ciudadana - Participación Ciudadana en Proyectos de CTI',\
'Participación Ciudadana en Proyectos de CTI');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
112, \
'Apropiación - Participación Ciudadana - Espacios de Participación Ciudadana',\
'Participación Ciudadana - Espacios de Participación Ciudadana',\
'Espacios de Participación Ciudadana');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
113, \
'Producción en arte, arquitectura y diseño - Obras o productos - Obras o productos',\
'Obras o productos - Obras o productos',\
'Obras o productos');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
114, \
'Actividades de Formación - Actividades de Formación - Asesorías al Programa Ondas',\
'Actividades de Formación - Asesorías al Programa Ondas',\
'Asesorías al Programa Ondas');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
115, \
'Actividades de Formación - Curso de Corta Duración Dictados - Perfeccionamiento',\
'Curso de Corta Duración Dictados - Perfeccionamiento',\
'Perfeccionamiento');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
116, \
'Actividades de Formación - Curso de Corta Duración Dictados - Extensión Extracurricular',\
'Curso de Corta Duración Dictados - Extensión Extracurricular',\
'Extensión Extracurricular');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
117, \
'Actividades de Formación - Trabajos dirigidos/turorías - Monografía de conclusión de curso',\
'Trabajos dirigidos/turorías - Monografía de conclusión de curso',\
'Monografía de conclusión de curso');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
118, \
'Actividades de Formación - Curso de Corta Duración Dictados - Otro',\
'Curso de Corta Duración Dictados - Otro',\
'Otro');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
119, \
'Proyectos - Investigación, desarrollo e innovación - Proyectos',\
'Investigación, desarrollo e innovación - Proyectos',\
'Proyectos');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
120, \
'Apropiación social y circularción del conocimiento - Revista',\
'Investigación, desarrollo e innovación - Revista',\
'Revista');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
121, \
'Apropiación social y circularción del conocimiento - Cartilla',\
'Contenidos Impresos - Cartilla',\
'Cartilla');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
122, \
'Actividades de Formación - Cursos de Corta Duración - Especialización',\
'Cursos de Corta Duración - Especialización',\
'Especialización');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
123, \
'Apropiación - Contenidos Multimedia - Otro',\
'Contenidos Multimedia - Otro',\
'Otro');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
124, \
'Apropiación - Contenidos Virtuales - Blog',\
'Contenidos Virtuales - Blog',\
'Blog');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
125, \
'Apropiación - Contenidos Virtuales - Aplicativo',\
'Contenidos Virtuales - Aplicativo',\
'Aplicativo');\n\
REPLACE INTO `uapa_db`.`v_colciencias_tipo_producto` ( \
`COD_TIPO_PRODUCTO`,\
`TIPO_PRODUCTO_COL`,\
`SUB_TIPO_PRODUCTO_COL`,\
`TIPO_UAPA`) VALUES (\
91, \
'Datos complementarios - Jurado/Comisiones evaluadoras de trabajo de grado - Curso de perfeccionamiento/especialización',\
'Jurado Especial',\
'Comités');\n"]
| 29.831986
| 122
| 0.744953
| 6,414
| 51,669
| 5.743374
| 0.082476
| 0.166459
| 0.103426
| 0.083392
| 0.890466
| 0.885499
| 0.873772
| 0.864461
| 0.850915
| 0.814539
| 0
| 0.010224
| 0.116008
| 51,669
| 1,731
| 123
| 29.84922
| 0.7963
| 0.014361
| 0
| 0.503835
| 0
| 0.00118
| 0.262223
| 0.00065
| 0
| 0
| 0
| 0
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| 1
| 0.00059
| false
| 0
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| 0
| 0.00059
| 0
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| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
a550fbf0806ccc2e40e0a75340a7b677dae1c755
| 78
|
py
|
Python
|
tests/printing/test_registry_rendering.py
|
anna-naden/qalgebra
|
e7641ef77a2433caf2f587df27235800b894b631
|
[
"MIT"
] | 2
|
2020-08-17T12:18:19.000Z
|
2020-08-25T11:17:27.000Z
|
tests/printing/test_registry_rendering.py
|
anna-naden/qalgebra
|
e7641ef77a2433caf2f587df27235800b894b631
|
[
"MIT"
] | 1
|
2022-01-13T10:29:18.000Z
|
2022-01-13T10:29:18.000Z
|
tests/printing/test_registry_rendering.py
|
anna-naden/qalgebra
|
e7641ef77a2433caf2f587df27235800b894b631
|
[
"MIT"
] | null | null | null |
import os
import pytest
from qalgebra.utils.testing import datadir
# TODO
| 8.666667
| 42
| 0.782051
| 11
| 78
| 5.545455
| 0.818182
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.179487
| 78
| 8
| 43
| 9.75
| 0.953125
| 0.051282
| 0
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| 0
| 0
| 0
| 0
| 0
| 0.125
| 0
| 1
| 0
| true
| 0
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| 1
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| 0
| null | 0
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| 0
| 0
| 0
| 0
| 1
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| 0
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| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
a552f91d3303c3c4ba023cfcd62f634b5b719020
| 44
|
py
|
Python
|
plugin/lighthouse/reader/__init__.py
|
x9090/lighthouse
|
b7378ee5948ba81900ef80538870f2e2b47610f4
|
[
"MIT"
] | 1,741
|
2017-02-21T14:09:27.000Z
|
2022-03-30T19:49:25.000Z
|
plugin/lighthouse/reader/__init__.py
|
x9090/lighthouse
|
b7378ee5948ba81900ef80538870f2e2b47610f4
|
[
"MIT"
] | 114
|
2017-03-12T21:46:16.000Z
|
2022-03-16T22:10:49.000Z
|
plugin/lighthouse/reader/__init__.py
|
x9090/lighthouse
|
b7378ee5948ba81900ef80538870f2e2b47610f4
|
[
"MIT"
] | 264
|
2017-02-21T14:46:16.000Z
|
2022-03-14T12:21:15.000Z
|
from .coverage_reader import CoverageReader
| 22
| 43
| 0.886364
| 5
| 44
| 7.6
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.090909
| 44
| 1
| 44
| 44
| 0.95
| 0
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| 0
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| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
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| 1
| 0
| null | 0
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| 0
| 0
| 0
| 0
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| 1
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
a5b0d52e0078ea20025f0753051928c6476236c3
| 43
|
py
|
Python
|
h1st/core/__init__.py
|
Shiti/h1st
|
0805452bda2453924663203b11f448e31525d596
|
[
"Apache-2.0"
] | null | null | null |
h1st/core/__init__.py
|
Shiti/h1st
|
0805452bda2453924663203b11f448e31525d596
|
[
"Apache-2.0"
] | null | null | null |
h1st/core/__init__.py
|
Shiti/h1st
|
0805452bda2453924663203b11f448e31525d596
|
[
"Apache-2.0"
] | null | null | null |
from .dataclass import NodeInfo, GraphInfo
| 21.5
| 42
| 0.837209
| 5
| 43
| 7.2
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.116279
| 43
| 1
| 43
| 43
| 0.947368
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
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| 1
| 1
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| null | 0
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| 0
| 0
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| 1
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| 0
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| 0
| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
a5b3fe8a62484a08f85e5b9ff9a70af0ed0c8faf
| 225
|
py
|
Python
|
utils/start_server.py
|
FGAUnB-REQ-GM/2021.2-PousadaAnimal
|
b7371aebccad0da23073de0db642a6ce824f919e
|
[
"MIT"
] | null | null | null |
utils/start_server.py
|
FGAUnB-REQ-GM/2021.2-PousadaAnimal
|
b7371aebccad0da23073de0db642a6ce824f919e
|
[
"MIT"
] | 95
|
2022-02-04T19:40:09.000Z
|
2022-03-31T20:24:11.000Z
|
utils/start_server.py
|
FGAUnB-REQ-GM/2021.2-PousadaAnimal
|
b7371aebccad0da23073de0db642a6ce824f919e
|
[
"MIT"
] | 4
|
2022-01-26T23:51:48.000Z
|
2022-01-27T18:28:16.000Z
|
from os import system
# Database
system('python3 manage.py makemigrations users pets hosting services message payment host')
system('python3 manage.py migrate')
# Server
system('python3 manage.py runserver localhost:8000')
| 25
| 91
| 0.8
| 30
| 225
| 6
| 0.7
| 0.216667
| 0.316667
| 0.35
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.035354
| 0.12
| 225
| 9
| 92
| 25
| 0.873737
| 0.066667
| 0
| 0
| 0
| 0
| 0.711538
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.25
| 0
| 0.25
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
3c3b1f75cf099592f20b9f3fb8aa02e9dab8062a
| 148
|
py
|
Python
|
Chapter 2/wall_time.py
|
indrag49/Computational-Stat-Mech
|
0877f54a0245fce815f03478f4fb219fd6314951
|
[
"MIT"
] | 19
|
2018-06-29T12:22:47.000Z
|
2022-03-10T03:18:18.000Z
|
Chapter 2/wall_time.py
|
indrag49/Computational-Stat-Mech
|
0877f54a0245fce815f03478f4fb219fd6314951
|
[
"MIT"
] | null | null | null |
Chapter 2/wall_time.py
|
indrag49/Computational-Stat-Mech
|
0877f54a0245fce815f03478f4fb219fd6314951
|
[
"MIT"
] | 7
|
2018-11-30T01:56:36.000Z
|
2021-12-23T15:29:56.000Z
|
from sympy import oo
def wall_time(pos, vel, radius): return (1.0-radius-pos)/vel if vel>0.0 else (pos-radius)/abs(vel) if vel<0.0 else float(oo)
| 49.333333
| 125
| 0.709459
| 32
| 148
| 3.25
| 0.53125
| 0.115385
| 0.153846
| 0.173077
| 0.269231
| 0.269231
| 0
| 0
| 0
| 0
| 0
| 0.046875
| 0.135135
| 148
| 2
| 126
| 74
| 0.765625
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| false
| 0
| 0.5
| 0.5
| 1
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| 0
| 0
| null | 0
| 0
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| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 1
| 1
| 0
|
0
| 6
|
3c5deb62501fc581506d694af7891b5f422871dc
| 22,655
|
py
|
Python
|
app/utils.py
|
mirsazzathossain/SPMS-Project
|
eb2b9144b6ddb8d18c146a4c4d6f79b9f7a7eeb5
|
[
"MIT"
] | null | null | null |
app/utils.py
|
mirsazzathossain/SPMS-Project
|
eb2b9144b6ddb8d18c146a4c4d6f79b9f7a7eeb5
|
[
"MIT"
] | null | null | null |
app/utils.py
|
mirsazzathossain/SPMS-Project
|
eb2b9144b6ddb8d18c146a4c4d6f79b9f7a7eeb5
|
[
"MIT"
] | null | null | null |
from django.db import connection
import numpy as np
def getstudentcoursewisePLO(studentID, courseID):
with connection.cursor() as cursor:
cursor.execute('''
SELECT p.ploNum as plonum,100*(sum(e.obtainedMarks)/sum(a.totalMarks)) as plopercent
FROM app_registration_t r,
app_assessment_t a,
app_evaluation_t e,
app_co_t co,
app_plo_t p
WHERE r.registrationID = e.registration_id
and e.assessment_id = a.assessmentID
and a.co_id=co.coID
and co.plo_id = p.ploID
and r.student_id = '{}'
and co.course_id = '{}'
GROUP BY p.ploID
'''.format(studentID, courseID))
row = cursor.fetchall()
return row
def getcoursewiseavgPLO(courseID):
with connection.cursor() as cursor:
cursor.execute('''
SELECT p.ploNum as plonum, avg(100*e.obtainedMarks/a.totalMarks)
FROM app_registration_t r,
app_assessment_t a,
app_evaluation_t e,
app_co_t co,
app_plo_t p
WHERE r.registrationID = e.registration_id
and e.assessment_id = a.assessmentID
and a.co_id=co.coID
and co.plo_id = p.ploID
and co.course_id = '{}'
GROUP BY p.ploID
'''.format(courseID))
row = cursor.fetchall()
return row
def getcompletedcourses(studentID):
with connection.cursor() as cursor:
cursor.execute(
'''
SELECT distinct s.course_id
FROM app_registration_t r,
app_evaluation_t e,
app_section_t s
WHERE r.registrationID = e.registration_id
and r.section_id = s.sectionID
and r.student_id = '{}'
'''.format(studentID))
row = cursor.fetchall()
return row
def getcorrespondingstudentid(userID):
with connection.cursor() as cursor:
cursor.execute(
'''
SELECT studentID
FROM app_student_t s
WHERE s.user_ptr_id = '{}'
'''.format(userID))
row = cursor.fetchall()
return row
def getstudentprogramwisePLO(studentID):
with connection.cursor() as cursor:
cursor.execute('''
SELECT p.ploNum as plonum,100*(sum(e.obtainedMarks)/sum(a.totalMarks)) as plopercent
FROM app_registration_t r,
app_assessment_t a,
app_evaluation_t e,
app_co_t co,
app_plo_t p,
app_student_t s,
app_program_t pr
WHERE r.registrationID = e.registration_id
and e.assessment_id = a.assessmentID
and a.co_id=co.coID
and co.plo_id = p.ploID
and r.student_id = '{}'
and s.studentID = r.student_id
and s.program_id = pr.programID
GROUP BY p.ploID
'''.format(studentID))
row = cursor.fetchall()
return row
def getprogramwiseavgPLO(programID):
with connection.cursor() as cursor:
cursor.execute('''
SELECT p.ploNum as plonum, avg(100*e.obtainedMarks/a.totalMarks)
FROM app_registration_t r,
app_assessment_t a,
app_evaluation_t e,
app_co_t co,
app_plo_t p
WHERE r.registrationID = e.registration_id
and e.assessment_id = a.assessmentID
and a.co_id=co.coID
and co.plo_id = p.ploID
and p.program_id = '{}'
GROUP BY p.ploID
'''.format(programID))
row = cursor.fetchall()
return row
def getstudentprogramid(studentID):
with connection.cursor() as cursor:
cursor.execute('''
SELECT s.program_id
FROM app_student_t s
WHERE s.studentID = '{}'
'''.format(studentID))
row = cursor.fetchall()
return row
def getstudentallcoursePLO(studentID, category):
with connection.cursor() as cursor:
cursor.execute('''
SELECT p.ploNum as ploNum,co.course_id,sum(e.obtainedMarks),sum(a.totalMarks), derived.Total
FROM app_registration_t r,
app_assessment_t a,
app_evaluation_t e,
app_co_t co,
app_plo_t p,
(
SELECT p.ploNum as ploNum,sum(a.totalMarks) as Total, r.student_id as StudentID
FROM app_registration_t r,
app_assessment_t a,
app_evaluation_t e,
app_co_t co,
app_plo_t p
WHERE r.registrationID = e.registration_id
and e.assessment_id = a.assessmentID
and a.co_id=co.coID
and co.plo_id = p.ploID
and r.student_id = '{}'
GROUP BY r.student_id,p.ploID) derived
WHERE r.student_id = derived.StudentID
and e.registration_id = r.registrationID
and e.assessment_id = a.assessmentID
and a.co_id=co.coID
and co.plo_id = p.ploID
and p.ploNum = derived.ploNum
GROUP BY p.ploID,co.course_id
'''.format(studentID))
row = cursor.fetchall()
table = []
courses = []
for entry in row:
if entry[1] not in courses:
courses.append(entry[1])
courses.sort()
plo = ["PLO1", "PLO2", "PLO3", "PLO4", "PLO5", "PLO6", "PLO7", "PLO8", "PLO9", "PLO10", "PLO11", "PLO12"]
for i in courses:
temptable = []
if category == 'report':
temptable = [i]
for j in plo:
found = False
for k in row:
if j == k[0] and i == k[1]:
if category == 'report':
temptable.append(np.round(100 * k[2] / k[3], 2))
elif category == 'chart':
temptable.append(np.round(100 * k[2] / k[4], 2))
found = True
if not found:
if category == 'report':
temptable.append('N/A')
elif category == 'chart':
temptable.append(0)
table.append(temptable)
return plo, courses, table
def getfacultycoursewisePLO(courseID, semesters):
sem = '';
for semester in semesters:
sem += '"'
sem += semester
sem += '",'
sem = sem[:-1]
with connection.cursor() as cursor:
cursor.execute('''
SELECT f.first_name, f.last_name, f.plonum, COUNT(*) as achieved_cnt
FROM
(
SELECT u.first_name, u.last_name, p.ploNum as plonum, 100*e.obtainedMarks/a.totalMarks as percentage
FROM app_registration_t r,
app_assessment_t a,
app_evaluation_t e,
app_co_t co,
app_plo_t p,
app_section_t s,
accounts_user u,
app_employee_t emp
WHERE r.registrationID = e.registration_id
and e.assessment_id = a.assessmentID
and a.co_id=co.coID
and co.plo_id = p.ploID
and a.section_id = s.sectionID
and s.faculty_id IN
(
SELECT DISTINCT s.faculty_id
FROM app_section_t s
WHERE s.course_id = '{}'
)
and s.semester IN ({})
and s.course_id ='{}'
and s.faculty_id = emp.employeeID
and emp.user_ptr_id = u.id
)f
WHERE f.percentage >= 40
GROUP BY f.first_name, f.plonum;
'''.format(courseID, sem, courseID))
row1 = cursor.fetchall()
cursor.execute('''
SELECT COUNT(*)
FROM
(
SELECT u.first_name, u.last_name, p.ploNum as plonum, 100*e.obtainedMarks/a.totalMarks as percentage
FROM app_registration_t r,
app_assessment_t a,
app_evaluation_t e,
app_co_t co,
app_plo_t p,
app_section_t s,
accounts_user u,
app_employee_t emp
WHERE r.registrationID = e.registration_id
and e.assessment_id = a.assessmentID
and a.co_id=co.coID
and co.plo_id = p.ploID
and a.section_id = s.sectionID
and s.faculty_id IN
(
SELECT DISTINCT s.faculty_id
FROM app_section_t s
WHERE s.course_id = '{}'
)
and s.semester IN ({})
and s.course_id ='{}'
and s.faculty_id = emp.employeeID
and emp.user_ptr_id = u.id
)f
GROUP BY f.first_name, f.plonum;
'''.format(courseID, sem, courseID))
row2 = cursor.fetchall()
faculty = []
plonum = []
plos1 = []
plos2 = []
for record in row1:
faculty.append(record[0]+' '+record[1])
plonum.append(record[2])
plos1.append(record[3])
for record in row2:
plos2.append(record[0])
plos = 100*(np.array(plos1)/np.array(plos2))
plos = plos.tolist()
faculty = list(set(faculty))
plonum = list(set(plonum))
plonum.sort()
plonum.sort(key=len, reverse=False)
plos = np.array(plos)
plos = np.split(plos, len(plos)/len(plonum))
new_plo=[]
for plo in plos:
new_plo.append(plo.tolist())
return faculty, plonum, new_plo
def getsemestercoursewisePLO(courseID, semesters):
sem = '';
for semester in semesters:
sem += '"'
sem += semester
sem += '",'
sem = sem[:-1]
with connection.cursor() as cursor:
cursor.execute('''
SELECT f.semester, f.plonum, COUNT(*) as achieved_cnt
FROM
(
SELECT s.semester, p.ploNum as plonum, s.course_id, 100*e.obtainedMarks/a.totalMarks as percentage
FROM app_registration_t r,
app_assessment_t a,
app_evaluation_t e,
app_co_t co,
app_plo_t p,
app_section_t s
WHERE r.registrationID = e.registration_id
and e.assessment_id = a.assessmentID
and a.co_id=co.coID
and co.plo_id = p.ploID
and a.section_id = s.sectionID
and s.semester IN ({})
and co.course_id ='{}'
and s.course_id = co.course_id
)f
WHERE f.percentage >= 40
GROUP BY f.semester, f.plonum;
'''.format(sem, courseID))
row1 = cursor.fetchall()
cursor.execute('''
SELECT COUNT(*) as all_cnt
FROM
(
SELECT s.semester, p.ploNum as plonum, s.course_id, 100*e.obtainedMarks/a.totalMarks as percentage
FROM app_registration_t r,
app_assessment_t a,
app_evaluation_t e,
app_co_t co,
app_plo_t p,
app_section_t s
WHERE r.registrationID = e.registration_id
and e.assessment_id = a.assessmentID
and a.co_id=co.coID
and co.plo_id = p.ploID
and a.section_id = s.sectionID
and s.semester IN ({})
and co.course_id ='{}'
and s.course_id = co.course_id
)f
GROUP BY f.semester, f.plonum;
'''.format(sem, courseID))
row2 = cursor.fetchall()
semester = []
plonum = []
acheived = []
all_cnt = []
for record in row1:
semester.append(record[0])
plonum.append(record[1])
acheived.append(record[2])
for record in row2:
all_cnt.append(record[0])
acheived_per = 100*(np.array(acheived)/np.array(all_cnt))
semester = list(set(semester))
plonum = list(set(plonum))
failed_per = 100 - acheived_per
acheived_per = np.split(acheived_per, len(acheived_per)/len(semester))
failed_per = np.split(failed_per, len(failed_per)/len(semester))
acheived=[]
for plo in acheived_per:
acheived.append(plo.tolist())
failed=[]
for plo in failed_per:
failed.append(plo.tolist())
return semester, plonum, acheived, failed
def getplowisecoursecomparism(plos, semesters):
sem = '';
for semester in semesters:
sem += '"'
sem += semester
sem += '",'
sem = sem[:-1]
ploo = '';
for plo in plos:
ploo += '"'
ploo += plo
ploo += '",'
ploo = ploo[:-1]
with connection.cursor() as cursor:
cursor.execute('''
SELECT f.course_id, f.ploNum, COUNT(*)
FROM
(
SELECT s.course_id, p.ploNum, 100*e.obtainedMarks/a.totalMarks as percentage
FROM app_registration_t r,
app_assessment_t a,
app_evaluation_t e,
app_co_t co,
app_plo_t p,
app_section_t s
WHERE r.registrationID = e.registration_id
and e.assessment_id = a.assessmentID
and a.co_id=co.coID
and co.plo_id = p.ploID
and p.ploNum in ({})
and a.section_id = s.sectionID
and s.semester IN ({})
)f
WHERE f.percentage >= 40
GROUP BY f.ploNum, f.course_id;
'''.format(ploo, sem))
row1 = cursor.fetchall()
with connection.cursor() as cursor:
cursor.execute('''
SELECT COUNT(*)
FROM
(
SELECT s.course_id, p.ploNum, 100*e.obtainedMarks/a.totalMarks as percentage
FROM app_registration_t r,
app_assessment_t a,
app_evaluation_t e,
app_co_t co,
app_plo_t p,
app_section_t s
WHERE r.registrationID = e.registration_id
and e.assessment_id = a.assessmentID
and a.co_id=co.coID
and co.plo_id = p.ploID
and p.ploNum in ({})
and a.section_id = s.sectionID
and s.semester IN ({})
)f
GROUP BY f.ploNum, f.course_id;
'''.format(ploo, sem))
row2 = cursor.fetchall()
courses = []
plonum = []
acheived = []
all_cnt = []
for record in row1:
courses.append(record[0])
plonum.append(record[1])
acheived.append(record[2])
for record in row2:
all_cnt.append(record[0])
acheived_per = 100*(np.array(acheived)/np.array(all_cnt))
courses = list(set(courses))
plonum = list(set(plonum))
acheived_per = np.split(acheived_per, len(acheived_per)/len(plonum))
acheived=[]
for plo in acheived_per:
acheived.append(plo.tolist())
return courses, plonum, acheived
def getprogramsemesterwiseplocount(program, semesters):
sem = '';
for semester in semesters:
sem += '"'
sem += semester
sem += '",'
sem = sem[:-1]
with connection.cursor() as cursor:
cursor.execute('''
SELECT f.plonum, COUNT(*)
FROM
(
SELECT p.ploNum as plonum, r.student_id, 100*e.obtainedMarks/a.totalMarks as percentage
FROM app_registration_t r,
app_assessment_t a,
app_evaluation_t e,
app_co_t co,
app_plo_t p,
app_section_t s,
app_program_t prog
WHERE r.registrationID = e.registration_id
and e.assessment_id = a.assessmentID
and a.co_id=co.coID
and co.plo_id = p.ploID
and p.program_id = prog.programID
and prog.programName = '{}'
and a.section_id = s.sectionID
and s.semester IN ({})
)f
WHERE f.percentage>=40
GROUP BY f.plonum;
'''.format(program, sem))
row1 = cursor.fetchall()
with connection.cursor() as cursor:
cursor.execute('''
SELECT COUNT(*)
FROM
(
SELECT p.ploNum as plonum, r.student_id, 100*e.obtainedMarks/a.totalMarks as percentage
FROM app_registration_t r,
app_assessment_t a,
app_evaluation_t e,
app_co_t co,
app_plo_t p,
app_section_t s,
app_program_t prog
WHERE r.registrationID = e.registration_id
and e.assessment_id = a.assessmentID
and a.co_id=co.coID
and co.plo_id = p.ploID
and p.program_id = prog.programID
and prog.programName = '{}'
and a.section_id = s.sectionID
and s.semester IN ({})
)f
GROUP BY f.plonum;
'''.format(program, sem))
row2 = cursor.fetchall()
plonum = []
acheived = []
attempted = []
for record in row1:
plonum.append(record[0])
acheived.append(record[1])
for record in row2:
attempted.append(record[0])
plonum = list(set(plonum))
acheived = np.array(acheived)
attempted = np.array(attempted)
new_acheived=[]
for plo in acheived:
new_acheived.append(plo.tolist())
new_attempted=[]
for plo in attempted:
new_attempted.append(plo.tolist())
plonum.sort()
plonum.sort(key=len, reverse=False)
return plonum, new_acheived, new_attempted
def getprogramwiseploandcourses(program, semesters):
sem = '';
for semester in semesters:
sem += '"'
sem += semester
sem += '",'
sem = sem[:-1]
with connection.cursor() as cursor:
cursor.execute('''
SELECT f.ploNum, f.course_id, COUNT(*)
FROM
(
SELECT p.ploNum as plonum, s.course_id, r.student_id, 100*e.obtainedMarks/a.totalMarks as percentage
FROM app_registration_t r,
app_assessment_t a,
app_evaluation_t e,
app_co_t co,
app_plo_t p,
app_section_t s,
app_program_t prog
WHERE r.registrationID = e.registration_id
and e.assessment_id = a.assessmentID
and a.co_id=co.coID
and co.plo_id = p.ploID
and p.program_id = prog.programID
and prog.programName = '{}'
and a.section_id = s.sectionID
and s.semester IN ({})
)f
WHERE f.percentage>=40
GROUP BY f.ploNum, f.course_id
'''.format(program, sem))
row = cursor.fetchall()
plonum = []
courses = []
counts = []
for record in row:
plonum.append(record[0])
courses.append(record[1])
plonum = list(set(plonum))
plonum.sort()
plonum.sort(key=len, reverse=False)
courses = list(set(courses))
courses.sort()
table = np.zeros((len(courses), len(plonum)))
for record in row:
table[courses.index(record[1])][plonum.index(record[0])] += record[2]
table = table.tolist()
return plonum, courses, table
| 35.343214
| 122
| 0.455352
| 2,317
| 22,655
| 4.294778
| 0.067328
| 0.020098
| 0.032459
| 0.032158
| 0.773792
| 0.743543
| 0.72445
| 0.705758
| 0.666566
| 0.62848
| 0
| 0.011656
| 0.462238
| 22,655
| 641
| 123
| 35.343214
| 0.805138
| 0
| 0
| 0.761194
| 0
| 0.020522
| 0.636851
| 0.025595
| 0
| 0
| 0
| 0
| 0
| 1
| 0.024254
| false
| 0
| 0.003731
| 0
| 0.052239
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
3c66bd87520d7163328c7042283d619a6348309f
| 45
|
py
|
Python
|
pyaugmecon/__init__.py
|
vishalbelsare/pyaugmecon
|
b9b6310b66007d1be7035f50a7e2691e7669f74e
|
[
"MIT"
] | 5
|
2021-05-29T20:18:06.000Z
|
2022-01-20T08:56:26.000Z
|
pyaugmecon/__init__.py
|
vishalbelsare/pyaugmecon
|
b9b6310b66007d1be7035f50a7e2691e7669f74e
|
[
"MIT"
] | null | null | null |
pyaugmecon/__init__.py
|
vishalbelsare/pyaugmecon
|
b9b6310b66007d1be7035f50a7e2691e7669f74e
|
[
"MIT"
] | 3
|
2021-08-20T19:27:28.000Z
|
2022-01-21T13:42:49.000Z
|
from pyaugmecon.pyaugmecon import PyAugmecon
| 22.5
| 44
| 0.888889
| 5
| 45
| 8
| 0.6
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.088889
| 45
| 1
| 45
| 45
| 0.97561
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
593e1e17a425e56881cf41a7f836ee82374b8d59
| 171
|
py
|
Python
|
OpenGLCffi/GLX/EXT/SGIX/dmbuffer.py
|
cydenix/OpenGLCffi
|
c78f51ae5e6b655eb2ea98f072771cf69e2197f3
|
[
"MIT"
] | null | null | null |
OpenGLCffi/GLX/EXT/SGIX/dmbuffer.py
|
cydenix/OpenGLCffi
|
c78f51ae5e6b655eb2ea98f072771cf69e2197f3
|
[
"MIT"
] | null | null | null |
OpenGLCffi/GLX/EXT/SGIX/dmbuffer.py
|
cydenix/OpenGLCffi
|
c78f51ae5e6b655eb2ea98f072771cf69e2197f3
|
[
"MIT"
] | null | null | null |
from OpenGLCffi.GLX import params
@params(api='glx', prms=['dpy', 'pbuffer', 'params', 'dmbuffer'])
def glXAssociateDMPbufferSGIX(dpy, pbuffer, params, dmbuffer):
pass
| 24.428571
| 65
| 0.730994
| 20
| 171
| 6.25
| 0.65
| 0.16
| 0.256
| 0.384
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.105263
| 171
| 6
| 66
| 28.5
| 0.816993
| 0
| 0
| 0
| 0
| 0
| 0.159763
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0.25
| 0.25
| 0
| 0.5
| 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
| 1
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 6
|
3ca729505e43392eecb38753ae74fd29ff951cd7
| 1,202
|
py
|
Python
|
code/functions/segment/__init__.py
|
a9w/Fat2_polarizes_WAVE
|
be39ba21245a9b532a70954a38139976a2355a7d
|
[
"MIT"
] | null | null | null |
code/functions/segment/__init__.py
|
a9w/Fat2_polarizes_WAVE
|
be39ba21245a9b532a70954a38139976a2355a7d
|
[
"MIT"
] | null | null | null |
code/functions/segment/__init__.py
|
a9w/Fat2_polarizes_WAVE
|
be39ba21245a9b532a70954a38139976a2355a7d
|
[
"MIT"
] | null | null | null |
"""Functions for segmenting images."""
from .interface import (
interface_endpoints_mask,
interface_endpoints_coords,
interface_shape_edge_method,
trim_interface,
refine_junction,
edge_between_neighbors,
)
from .timelapse import (
segment_epithelium_timelapse,
largest_object_mask_timelapse,
segment_hemijunctions_timelapse,
)
from .tissue import (
epithelium_watershed,
largest_object_mask,
select_border_adjacent,
select_in_field,
select_mask_adjacent,
segment_hemijunctions,
cell_edges_mask,
cell_interiors_mask,
cell_vertices_mask,
neighbor_array_nr,
)
__all__ = [
"interface_endpoints_mask",
"interface_endpoints_coords",
"interface_shape_edge_method",
"trim_interface",
"refine_junction",
"edge_between_neighbors",
"segment_epithelium_timelapse",
"largest_object_mask_timelapse",
"segment_hemijunctions_timelapse",
"epithelium_watershed",
"largest_object_mask",
"select_border_adjacent",
"select_in_field",
"select_mask_adjacent",
"segment_hemijunctions",
"cell_edges_mask",
"cell_interiors_mask",
"cell_vertices_mask",
"neighbor_array_nr"
]
| 24.04
| 38
| 0.742928
| 124
| 1,202
| 6.58871
| 0.314516
| 0.088127
| 0.083231
| 0.075887
| 0.895961
| 0.895961
| 0.895961
| 0.895961
| 0.895961
| 0.895961
| 0
| 0
| 0.178869
| 1,202
| 49
| 39
| 24.530612
| 0.827761
| 0.026622
| 0
| 0
| 0
| 0
| 0.345361
| 0.197595
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.065217
| 0
| 0.065217
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
3cc05d6b6890f5cd2771c0bf4e20222a059024a6
| 31
|
py
|
Python
|
snippets/python/automation/beep.py
|
c6401/Snippets
|
a88d97005658eeda99f1a2766e3d069a64e142cb
|
[
"MIT"
] | null | null | null |
snippets/python/automation/beep.py
|
c6401/Snippets
|
a88d97005658eeda99f1a2766e3d069a64e142cb
|
[
"MIT"
] | null | null | null |
snippets/python/automation/beep.py
|
c6401/Snippets
|
a88d97005658eeda99f1a2766e3d069a64e142cb
|
[
"MIT"
] | null | null | null |
def beep():
print('\007')
| 7.75
| 17
| 0.483871
| 4
| 31
| 3.75
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.130435
| 0.258065
| 31
| 3
| 18
| 10.333333
| 0.521739
| 0
| 0
| 0
| 0
| 0
| 0.133333
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| true
| 0
| 0
| 0
| 0.5
| 0.5
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 6
|
3ce5fd9779d7d65bfe16fb75039683fefaa02664
| 36
|
py
|
Python
|
src/cryptos/__init__.py
|
villoro/airflow_tasks
|
81bd892744a9bbbf6e01903649b6c3786a955a5a
|
[
"MIT"
] | null | null | null |
src/cryptos/__init__.py
|
villoro/airflow_tasks
|
81bd892744a9bbbf6e01903649b6c3786a955a5a
|
[
"MIT"
] | 4
|
2020-10-09T15:59:09.000Z
|
2020-11-18T08:34:44.000Z
|
src/cryptos/__init__.py
|
villoro/airflow_tasks
|
81bd892744a9bbbf6e01903649b6c3786a955a5a
|
[
"MIT"
] | null | null | null |
from .process import update_cryptos
| 18
| 35
| 0.861111
| 5
| 36
| 6
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.111111
| 36
| 1
| 36
| 36
| 0.9375
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
a70d88641d80e3344d467bea499761a70a1897c3
| 21,155
|
py
|
Python
|
tests/test_mc_cnn.py
|
CNES/Pandora_MCCNN
|
54d4423f88d31831065ff28ccb5affb724239988
|
[
"Apache-2.0"
] | 3
|
2021-07-20T09:41:56.000Z
|
2021-12-13T08:29:43.000Z
|
tests/test_mc_cnn.py
|
qfardet/Pandora_MCCNN
|
0bd26d78f2f4dc1d8571f2cdf47e327dc1628c9e
|
[
"Apache-2.0"
] | null | null | null |
tests/test_mc_cnn.py
|
qfardet/Pandora_MCCNN
|
0bd26d78f2f4dc1d8571f2cdf47e327dc1628c9e
|
[
"Apache-2.0"
] | 2
|
2021-07-09T15:08:05.000Z
|
2022-01-20T16:27:03.000Z
|
#!/usr/bin/env python
# coding: utf8
#
# Copyright (c) 2021 Centre National d'Etudes Spatiales (CNES).
#
# This file is part of PANDORA_MCCNN
#
# https://github.com/CNES/Pandora_MCCNN
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
"""
This module contains functions to test the cost volume create by mc_cnn
"""
import unittest
import numpy as np
import torch
import torch.nn as nn
from mc_cnn.run import computes_cost_volume_mc_cnn_fast
from mc_cnn.model.mc_cnn_accurate import AccMcCnnInfer
from mc_cnn.dataset_generator.middlebury_generator import MiddleburyGenerator
from mc_cnn.dataset_generator.datas_fusion_contest_generator import DataFusionContestGenerator
# pylint: disable=no-self-use
class TestMCCNN(unittest.TestCase):
"""
TestMCCNN class allows to test the cost volume create by mc_cnn
"""
def setUp(self):
"""
Method called to prepare the test fixture
"""
self.ref_img_0 = np.tile(np.arange(13, dtype=np.float32), (13, 1))
self.sec_img_0 = np.tile(np.arange(13, dtype=np.float32), (13, 1)) + 1
self.ref_img_1 = np.tile(np.arange(13, dtype=np.float32), (13, 1))
self.sec_img_2 = np.tile(np.arange(13, dtype=np.float32), (13, 1)) - 1
def test_computes_cost_volume_mc_cnn_fast(self):
""" "
Test the computes_cost_volume_mc_cnn_fast function
"""
# create reference and secondary features
ref_feature = torch.randn((64, 4, 4), dtype=torch.float64)
sec_features = torch.randn((64, 4, 4), dtype=torch.float64)
cos = nn.CosineSimilarity(dim=0, eps=1e-6)
# Create the ground truth cost volume (row, col, disp)
cv_gt = np.full((4, 4, 5), np.nan)
# disparity -2
cv_gt[:, 2:, 0] = cos(ref_feature[:, :, 2:], sec_features[:, :, 0:2]).cpu().detach().numpy()
# disparity -1
cv_gt[:, 1:, 1] = cos(ref_feature[:, :, 1:], sec_features[:, :, 0:3]).cpu().detach().numpy()
# disparity 0
cv_gt[:, :, 2] = cos(ref_feature[:, :, :], sec_features[:, :, :]).cpu().detach().numpy()
# disparity 1
cv_gt[:, :3, 3] = cos(ref_feature[:, :, :3], sec_features[:, :, 1:4]).cpu().detach().numpy()
# disparity 2
cv_gt[:, :2, 4] = cos(ref_feature[:, :, :2], sec_features[:, :, 2:4]).cpu().detach().numpy()
# The minus sign converts the similarity score to a matching cost
cv_gt *= -1
cv = computes_cost_volume_mc_cnn_fast(ref_feature, sec_features, -2, 2)
# Check if the calculated cost volume is equal to the ground truth (same shape and all elements equals)
np.testing.assert_allclose(cv, cv_gt, rtol=1e-05)
def test_computes_cost_volume_mc_cnn_fast_negative_disp(self):
""" "
Test the computes_cost_volume_mc_cnn_fast function with negative disparities
"""
# create reference and secondary features
ref_feature = torch.randn((64, 4, 4), dtype=torch.float64)
sec_features = torch.randn((64, 4, 4), dtype=torch.float64)
cos = nn.CosineSimilarity(dim=0, eps=1e-6)
# Create the ground truth cost volume (row, col, disp)
cv_gt = np.full((4, 4, 4), np.nan)
# disparity -4
# all nan
# disparity -3
cv_gt[:, 3:, 1] = cos(ref_feature[:, :, 3:], sec_features[:, :, 0:1]).cpu().detach().numpy()
# disparity -2
cv_gt[:, 2:, 2] = cos(ref_feature[:, :, 2:], sec_features[:, :, 0:2]).cpu().detach().numpy()
# disparity -1
cv_gt[:, 1:, 3] = cos(ref_feature[:, :, 1:], sec_features[:, :, 0:3]).cpu().detach().numpy()
# The minus sign converts the similarity score to a matching cost
cv_gt *= -1
cv = computes_cost_volume_mc_cnn_fast(ref_feature, sec_features, -4, -1)
# Check if the calculated cost volume is equal to the ground truth (same shape and all elements equals)
np.testing.assert_allclose(cv, cv_gt, rtol=1e-05)
def test_computes_cost_volume_mc_cnn_fast_positive_disp(self):
""" "
Test the computes_cost_volume_mc_cnn_fast function with positive disparities
"""
# create reference and secondary features
ref_feature = torch.randn((64, 4, 4), dtype=torch.float64)
sec_features = torch.randn((64, 4, 4), dtype=torch.float64)
cos = nn.CosineSimilarity(dim=0, eps=1e-6)
# Create the ground truth cost volume (row, col, disp)
cv_gt = np.full((4, 4, 4), np.nan)
# disparity 1
cv_gt[:, :3, 0] = cos(ref_feature[:, :, :3], sec_features[:, :, 1:4]).cpu().detach().numpy()
# disparity 2
cv_gt[:, :2, 1] = cos(ref_feature[:, :, :2], sec_features[:, :, 2:4]).cpu().detach().numpy()
# disparity 3
cv_gt[:, :1, 2] = cos(ref_feature[:, :, :1], sec_features[:, :, 3:]).cpu().detach().numpy()
# disparity 4
# all nan
# The minus sign converts the similarity score to a matching cost
cv_gt *= -1
cv = computes_cost_volume_mc_cnn_fast(ref_feature, sec_features, 1, 4)
# Check if the calculated cost volume is equal to the ground truth (same shape and all elements equals)
np.testing.assert_allclose(cv, cv_gt, rtol=1e-05)
def sad_cost(self, ref_features, sec_features):
"""
Useful to test the computes_cost_volume_mc_cnn_accurate function
"""
return torch.sum(abs(ref_features[0, :, :, :] - sec_features[0, :, :, :]), dim=0)
def test_computes_cost_volume_mc_cnn_accurate(self):
""" "
Test the computes_cost_volume_mc_cnn_accurate function
"""
# create reference and secondary features
ref_feature = torch.randn((1, 112, 4, 4), dtype=torch.float64)
sec_features = torch.randn((1, 112, 4, 4), dtype=torch.float64)
# Create the ground truth cost volume (row, col, disp)
cv_gt = np.full((4, 4, 5), np.nan)
# disparity -2
cv_gt[:, 2:, 0] = self.sad_cost(ref_feature[:, :, :, 2:], sec_features[:, :, :, 0:2]).cpu().detach().numpy()
# disparity -1
cv_gt[:, 1:, 1] = self.sad_cost(ref_feature[:, :, :, 1:], sec_features[:, :, :, 0:3]).cpu().detach().numpy()
# disparity 0
cv_gt[:, :, 2] = self.sad_cost(ref_feature[:, :, :, :], sec_features[:, :, :, :]).cpu().detach().numpy()
# disparity 1
cv_gt[:, :3, 3] = self.sad_cost(ref_feature[:, :, :, :3], sec_features[:, :, :, 1:4]).cpu().detach().numpy()
# disparity 2
cv_gt[:, :2, 4] = self.sad_cost(ref_feature[:, :, :, :2], sec_features[:, :, :, 2:4]).cpu().detach().numpy()
# The minus sign converts the similarity score to a matching cost
cv_gt *= -1
acc = AccMcCnnInfer()
# Because input shape of nn.Conv2d is (Batch_size, Channel, H, W), we add 1 dimensions
cv = acc.computes_cost_volume_mc_cnn_accurate(ref_feature, sec_features, -2, 2, self.sad_cost)
# Check if the calculated cost volume is equal to the ground truth (same shape and all elements equals)
np.testing.assert_allclose(cv, cv_gt, rtol=1e-05)
def test_computes_cost_volume_mc_cnn_accuratenegative_disp(self):
""" "
Test the computes_cost_volume_mc_cnn_accurate function with negative disparities
"""
# create reference and secondary features
ref_feature = torch.randn((1, 112, 4, 4), dtype=torch.float64)
sec_features = torch.randn((1, 112, 4, 4), dtype=torch.float64)
# Create the ground truth cost volume (row, col, disp)
cv_gt = np.full((4, 4, 4), np.nan)
# disparity -4
# all nan
# disparity -3
cv_gt[:, 3:, 1] = self.sad_cost(ref_feature[:, :, :, 3:], sec_features[:, :, :, 0:1]).cpu().detach().numpy()
# disparity -2
cv_gt[:, 2:, 2] = self.sad_cost(ref_feature[:, :, :, 2:], sec_features[:, :, :, 0:2]).cpu().detach().numpy()
# disparity -1
cv_gt[:, 1:, 3] = self.sad_cost(ref_feature[:, :, :, 1:], sec_features[:, :, :, 0:3]).cpu().detach().numpy()
# The minus sign converts the similarity score to a matching cost
cv_gt *= -1
acc = AccMcCnnInfer()
# Because input shape of nn.Conv2d is (Batch_size, Channel, H, W), we add 1 dimensions
cv = acc.computes_cost_volume_mc_cnn_accurate(ref_feature, sec_features, -4, -1, self.sad_cost)
# Check if the calculated cost volume is equal to the ground truth (same shape and all elements equals)
np.testing.assert_allclose(cv, cv_gt, rtol=1e-05)
def test_computes_cost_volume_mc_cnn_accurate_positive_disp(self):
""" "
Test the computes_cost_volume_mc_cnn_accurate function with positive disparities
"""
# create reference and secondary features
ref_feature = torch.randn((1, 112, 4, 4), dtype=torch.float64)
sec_features = torch.randn((1, 112, 4, 4), dtype=torch.float64)
# Create the ground truth cost volume (row, col, disp)
cv_gt = np.full((4, 4, 4), np.nan)
# disparity 1
cv_gt[:, :3, 0] = self.sad_cost(ref_feature[:, :, :, :3], sec_features[:, :, :, 1:4]).cpu().detach().numpy()
# disparity 2
cv_gt[:, :2, 1] = self.sad_cost(ref_feature[:, :, :, :2], sec_features[:, :, :, 2:4]).cpu().detach().numpy()
# disparity 3
cv_gt[:, :1, 2] = self.sad_cost(ref_feature[:, :, :, :1], sec_features[:, :, :, 3:]).cpu().detach().numpy()
# disparity 4
# all nan
# The minus sign converts the similarity score to a matching cost
cv_gt *= -1
acc = AccMcCnnInfer()
# Because input shape of nn.Conv2d is (Batch_size, Channel, H, W), we add 1 dimensions
cv = acc.computes_cost_volume_mc_cnn_accurate(ref_feature, sec_features, 1, 4, self.sad_cost)
# Check if the calculated cost volume is equal to the ground truth (same shape and all elements equals)
np.testing.assert_allclose(cv, cv_gt, rtol=1e-05)
# pylint: disable=invalid-name
# -> because changing the name here loses the reference to the actual name of the checked function
def test_MiddleburyGenerator(self):
"""
test the function MiddleburyGenerator
"""
# Script use to create images_middlebury and samples_middlebury :
# pylint: disable=pointless-string-statement
"""
# shape 1, 2, 13, 13 : 1 exposures, 2 = left and right images
image_pairs_0 = np.zeros((1, 2, 13, 13))
# left
image_pairs_0[0, 0, :, :] = np.tile(np.arange(13), (13, 1))
# right
image_pairs_0[0, 1, :, :] = np.tile(np.arange(13), (13, 1)) + 1
image_pairs_1 = np.zeros((1, 2, 13, 13))
image_pairs_1[0, 0, :, :] = np.tile(np.arange(13), (13, 1))
image_pairs_1[0, 1, :, :] = np.tile(np.arange(13), (13, 1)) - 1
img_file = h5py.File('images_middlebury.hdf5', 'w')
img_0 = [image_pairs_0]
grp = img_file.create_group(str(0))
# 1 illumination
for light in range(len(img_0)):
dset = grp.create_dataset(str(light), data=img_0[light])
img_1 = [image_pairs_1]
grp = img_file.create_group(str(1))
for light in range(len(img_1)):
dset = grp.create_dataset(str(light), data=img_1[light])
sampl_file = h5py.File('sample_middlebury.hdf5', 'w')
# disparity of image_pairs_0
x0 = np.array([[0., 5., 6., 1.]
[0., 7., 7., 1.]])
# disparity of image_pairs_1
x1 = np.array([[ 1., 7., 5., -1.]
[ 0., 0., 0., 0.]])
sampl_file.create_dataset(str(0), data=x0)
sampl_file.create_dataset(str(1), data=x1)
"""
# Positive disparity
cfg = {
"data_augmentation": False,
"dataset_neg_low": 1,
"dataset_neg_high": 1,
"dataset_pos": 0,
"augmentation_param": {
"vertical_disp": 0,
"scale": 0.8,
"hscale": 0.8,
"hshear": 0.1,
"trans": 0,
"rotate": 28,
"brightness": 1.3,
"contrast": 1.1,
"d_hscale": 0.9,
"d_hshear": 0.3,
"d_vtrans": 1,
"d_rotate": 3,
"d_brightness": 0.7,
"d_contrast": 1.1,
},
}
training_loader = MiddleburyGenerator("tests/sample_middlebury.hdf5", "tests/images_middlebury.hdf5", cfg)
# Patch of shape 3, 11, 11
# With the firt dimension = left patch, right positive patch, right negative patch
patch = training_loader.__getitem__(0)
x_ref_patch = 6
y_ref_patch = 5
patch_size = 5
gt_ref_patch = self.ref_img_0[
y_ref_patch - patch_size : y_ref_patch + patch_size + 1,
x_ref_patch - patch_size : x_ref_patch + patch_size + 1,
]
# disp = 1, with middlebury image convention img_ref(x,y) = img_sec(x-d,y)
disp = 1
x_sec_pos_patch = x_ref_patch - disp
y_sec_pos_patch = 5
gt_sec_pos_patch = self.sec_img_0[
y_sec_pos_patch - patch_size : y_sec_pos_patch + patch_size + 1,
x_sec_pos_patch - patch_size : x_sec_pos_patch + patch_size + 1,
]
# dataset_neg_low & dataset_neg_high = 1, with middlebury image convention img_ref(x,y) = img_sec(x-d,y)
dataset_neg = 1
x_sec_neg_patch = x_ref_patch - disp + dataset_neg
y_sec_neg_patch = 5
gt_sec_neg_patch = self.sec_img_0[
y_sec_neg_patch - patch_size : y_sec_neg_patch + patch_size + 1,
x_sec_neg_patch - patch_size : x_sec_neg_patch + patch_size + 1,
]
gt_path = np.stack((gt_ref_patch, gt_sec_pos_patch, gt_sec_neg_patch), axis=0)
# Check if the calculated patch is equal to the ground truth (same shape and all elements equals)
np.testing.assert_array_equal(patch, gt_path)
# negative disparity
patch = training_loader.__getitem__(2)
x_ref_patch = 5
y_ref_patch = 7
patch_size = 5
gt_ref_patch = self.ref_img_0[
y_ref_patch - patch_size : y_ref_patch + patch_size + 1,
x_ref_patch - patch_size : x_ref_patch + patch_size + 1,
]
# disp = -1, with middlebury image convention img_ref(x,y) = img_sec(x-d,y)
disp = -1
x_sec_pos_patch = x_ref_patch - disp
y_sec_pos_patch = 5
gt_sec_pos_patch = self.sec_img_0[
y_sec_pos_patch - patch_size : y_sec_pos_patch + patch_size + 1,
x_sec_pos_patch - patch_size : x_sec_pos_patch + patch_size + 1,
]
# dataset_neg_low & dataset_neg_high = 1, with middlebury image convention img_ref(x,y) = img_sec(x-d,y)
dataset_neg = 1
x_sec_neg_patch = x_ref_patch - disp + dataset_neg
y_sec_neg_patch = 5
gt_sec_neg_patch = self.sec_img_0[
y_sec_neg_patch - patch_size : y_sec_neg_patch + patch_size + 1,
x_sec_neg_patch - patch_size : x_sec_neg_patch + patch_size + 1,
]
gt_path = np.stack((gt_ref_patch, gt_sec_pos_patch, gt_sec_neg_patch), axis=0)
# Check if the calculated patch is equal to the ground truth (same shape and all elements equals)
np.testing.assert_array_equal(patch, gt_path)
# pylint: disable=invalid-name
# -> because changing the name here loses the reference to the actual name of the checked function
def test_DataFusionContestGenerator(self):
"""
test the function DataFusionContestGenerator
"""
# pylint: disable=pointless-string-statement
"""
# Script use to create images_middlebury and samples_middlebury :
# shape 2, 13, 13 : 2 = left and right images, row, col
image_pairs_0 = np.zeros((2, 13, 13))
# left
image_pairs_0[0, :, :] = np.tile(np.arange(13), (13, 1))
# right
image_pairs_0[1, :, :] = np.tile(np.arange(13), (13, 1)) + 1
image_pairs_1 = np.zeros((2, 13, 13))
image_pairs_1[0, :, :] = np.tile(np.arange(13), (13, 1))
image_pairs_1[1, :, :] = np.tile(np.arange(13), (13, 1)) - 1
img_file = h5py.File('images_dfc.hdf5', 'w')
img_file.create_dataset(str(0), data=image_pairs_0)
img_file.create_dataset(str(1), data=image_pairs_1)
sampl_file = h5py.File('sample_dfc.hdf5', 'w')
# disparity of image_pairs_0
x0 = np.array([[0., 5., 6., 1.],
[0., 7., 7., 1.]])
# disparity of image_pairs_1
x1 = np.array([[ 1., 7., 5., -1.],
[ 0., 0., 0., 0.]])
sampl_file.create_dataset(str(0), data=x0)
sampl_file.create_dataset(str(1), data=x1)
"""
# Positive disparity
cfg = {
"data_augmentation": False,
"dataset_neg_low": 1,
"dataset_neg_high": 1,
"dataset_pos": 0,
"vertical_disp": 0,
"augmentation_param": {
"scale": 0.8,
"hscale": 0.8,
"hshear": 0.1,
"trans": 0,
"rotate": 28,
"brightness": 1.3,
"contrast": 1.1,
"d_hscale": 0.9,
"d_hshear": 0.3,
"d_vtrans": 1,
"d_rotate": 3,
"d_brightness": 0.7,
"d_contrast": 1.1,
},
}
training_loader = DataFusionContestGenerator("tests/sample_dfc.hdf5", "tests/images_dfc.hdf5", cfg)
# Patch of shape 3, 11, 11
# With the firt dimension = left patch, right positive patch, right negative patch
patch = training_loader.__getitem__(0)
x_ref_patch = 6
y_ref_patch = 5
patch_size = 5
gt_ref_patch = self.ref_img_0[
y_ref_patch - patch_size : y_ref_patch + patch_size + 1,
x_ref_patch - patch_size : x_ref_patch + patch_size + 1,
]
# disp = 1, with middlebury image convention img_ref(x,y) = img_sec(x-d,y)
disp = 1
x_sec_pos_patch = x_ref_patch - disp
y_sec_pos_patch = 5
gt_sec_pos_patch = self.sec_img_0[
y_sec_pos_patch - patch_size : y_sec_pos_patch + patch_size + 1,
x_sec_pos_patch - patch_size : x_sec_pos_patch + patch_size + 1,
]
# dataset_neg_low & dataset_neg_high = 1, with middlebury image convention img_ref(x,y) = img_sec(x-d,y)
dataset_neg = 1
x_sec_neg_patch = x_ref_patch - disp + dataset_neg
y_sec_neg_patch = 5
gt_sec_neg_patch = self.sec_img_0[
y_sec_neg_patch - patch_size : y_sec_neg_patch + patch_size + 1,
x_sec_neg_patch - patch_size : x_sec_neg_patch + patch_size + 1,
]
gt_path = np.stack((gt_ref_patch, gt_sec_pos_patch, gt_sec_neg_patch), axis=0)
# Check if the calculated patch is equal to the ground truth (same shape and all elements equals)
np.testing.assert_array_equal(patch, gt_path)
# negative disparity
patch = training_loader.__getitem__(2)
x_ref_patch = 5
y_ref_patch = 7
patch_size = 5
gt_ref_patch = self.ref_img_1[
y_ref_patch - patch_size : y_ref_patch + patch_size + 1,
x_ref_patch - patch_size : x_ref_patch + patch_size + 1,
]
# disp = -1, with middlebury image convention img_ref(x,y) = img_sec(x-d,y)
disp = -1
x_sec_pos_patch = x_ref_patch - disp
y_sec_pos_patch = 7
gt_sec_pos_patch = self.sec_img_2[
y_sec_pos_patch - patch_size : y_sec_pos_patch + patch_size + 1,
x_sec_pos_patch - patch_size : x_sec_pos_patch + patch_size + 1,
]
# dataset_neg_low & dataset_neg_high = 1, with middlebury image convention img_ref(x,y) = img_sec(x-d,y)
dataset_neg = 1
x_sec_neg_patch = x_ref_patch - disp + dataset_neg
y_sec_neg_patch = 7
gt_sec_neg_patch = self.sec_img_2[
y_sec_neg_patch - patch_size : y_sec_neg_patch + patch_size + 1,
x_sec_neg_patch - patch_size : x_sec_neg_patch + patch_size + 1,
]
gt_path = np.stack((gt_ref_patch, gt_sec_pos_patch, gt_sec_neg_patch), axis=0)
# Check if the calculated patch is equal to the ground truth (same shape and all elements equals)
np.testing.assert_array_equal(patch, gt_path)
if __name__ == "__main__":
unittest.main()
| 40.918762
| 116
| 0.597636
| 3,049
| 21,155
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| 0.090521
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| 21,155
| 516
| 117
| 40.998062
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| 0.007612
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| 1
| 0.043103
| false
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| 0
| null | 0
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| 1
| 1
| 1
| 1
| 1
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|
0
| 6
|
5966d63840b7ab8028ae290541bcf4513c66c13a
| 211
|
py
|
Python
|
tests/conftest.py
|
Agilicus/copper-sdk
|
dfdecd4aa76bdd47661fdd4bfada7781f8eae835
|
[
"MIT"
] | 4
|
2021-01-03T07:40:01.000Z
|
2021-09-03T09:21:02.000Z
|
tests/conftest.py
|
Agilicus/copper-sdk
|
dfdecd4aa76bdd47661fdd4bfada7781f8eae835
|
[
"MIT"
] | 5
|
2020-09-03T17:28:13.000Z
|
2021-10-04T22:47:23.000Z
|
tests/conftest.py
|
Agilicus/copper-sdk
|
dfdecd4aa76bdd47661fdd4bfada7781f8eae835
|
[
"MIT"
] | 4
|
2021-01-07T05:30:49.000Z
|
2021-09-13T08:08:54.000Z
|
import pytest
from copper_sdk import COPPER_API_TOKEN, COPPER_API_EMAIL
from copper_sdk.copper import Copper
@pytest.fixture(scope='session')
def copper():
return Copper(COPPER_API_TOKEN, COPPER_API_EMAIL)
| 26.375
| 57
| 0.824645
| 32
| 211
| 5.125
| 0.40625
| 0.219512
| 0.158537
| 0.243902
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| 0.341463
| 0
| 0
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| 211
| 7
| 58
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| 0.166667
| true
| 0
| 0.5
| 0.166667
| 0.833333
| 0
| 0
| 0
| 0
| null | 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 1
| 0
| 0
|
0
| 6
|
59a2b7f390bd55399c067382bffd0b88305d91cf
| 3,770
|
py
|
Python
|
pirates/leveleditor/worldData/CaveBTemplate.py
|
itsyaboyrocket/pirates
|
6ca1e7d571c670b0d976f65e608235707b5737e3
|
[
"BSD-3-Clause"
] | 3
|
2021-02-25T06:38:13.000Z
|
2022-03-22T07:00:15.000Z
|
pirates/leveleditor/worldData/CaveBTemplate.py
|
itsyaboyrocket/pirates
|
6ca1e7d571c670b0d976f65e608235707b5737e3
|
[
"BSD-3-Clause"
] | null | null | null |
pirates/leveleditor/worldData/CaveBTemplate.py
|
itsyaboyrocket/pirates
|
6ca1e7d571c670b0d976f65e608235707b5737e3
|
[
"BSD-3-Clause"
] | 1
|
2021-02-25T06:38:17.000Z
|
2021-02-25T06:38:17.000Z
|
# uncompyle6 version 3.2.0
# Python bytecode 2.4 (62061)
# Decompiled from: Python 2.7.14 (v2.7.14:84471935ed, Sep 16 2017, 20:19:30) [MSC v.1500 32 bit (Intel)]
# Embedded file name: pirates.leveleditor.worldData.CaveBTemplate
from pandac.PandaModules import Point3, VBase3
objectStruct = {'Objects': {'1172185213.66sdnaik': {'Type': 'Island Game Area', 'Name': 'CaveBTemplate', 'File': '', 'Instanced': True, 'Objects': {'1172185301.05sdnaik': {'Type': 'Locator Node', 'Name': 'portal_interior_1', 'Hpr': VBase3(-92.814, 0.0, 0.0), 'Pos': Point3(408.102, 203.835, 1.938), 'Scale': VBase3(1.0, 1.0, 1.0)}, '1172185301.08sdnaik': {'Type': 'Locator Node', 'Name': 'portal_interior_2', 'Hpr': VBase3(-0.234, -0.244, 0.739), 'Pos': Point3(-535.085, 236.444, 77.638), 'Scale': VBase3(1.0, 1.0, 1.0)}, '1172893180.14kmuller': {'Type': 'Tunnel Cap', 'Hpr': VBase3(-89.933, 0.0, 0.0), 'Pos': Point3(-530.764, 233.107, 82.679), 'Scale': VBase3(1.0, 1.0, 1.0), 'Visual': {'Model': 'models/tunnels/tunnelcap_cave_interior'}}, '1172893192.18kmuller': {'Type': 'Tunnel Cap', 'Hpr': Point3(0.0, 0.0, 0.0), 'Pos': Point3(-476.043, 262.701, 122.229), 'Scale': VBase3(1.0, 1.0, 1.0), 'Visual': {'Model': 'models/tunnels/tunnelcap_cave_interior'}}, '1172893216.81kmuller': {'Type': 'Tunnel Cap', 'Hpr': Point3(0.0, 0.0, 0.0), 'Pos': Point3(-436.771, 259.368, 146.301), 'Scale': VBase3(1.0, 1.0, 1.0), 'Visual': {'Model': 'models/tunnels/tunnelcap_cave_interior'}}, '1172893544.75kmuller': {'Type': 'Tunnel Cap', 'Hpr': VBase3(-29.142, 0.38, 0.0), 'Pos': Point3(408.785, 196.489, 3.052), 'Scale': VBase3(1.0, 1.0, 1.0), 'Visual': {'Color': (0.6000000238418579, 0.6000000238418579, 0.6000000238418579, 1.0), 'Model': 'models/tunnels/tunnelcap_cave_interior'}}, '1176755520.41dzlu': {'Type': 'Light - Dynamic', 'Attenuation': '0.005', 'ConeAngle': '120.0000', 'DropOff': '6.8182', 'Flickering': False, 'Hpr': VBase3(-110.238, -3.38, 94.315), 'Intensity': '1.5758', 'LightType': 'SPOT', 'Pos': Point3(-538.19, 242.893, 99.248), 'Visual': {'Color': (1, 1, 1, 1), 'Model': 'models/props/light_tool_bulb'}}, '1176755691.11dzlu': {'Type': 'Light - Dynamic', 'Attenuation': '0.005', 'ConeAngle': '120.0000', 'DropOff': '2.7273', 'Flickering': False, 'Hpr': VBase3(42.452, 40.037, -92.62), 'Intensity': '1.4545', 'LightType': 'SPOT', 'Pos': Point3(-301.72, -166.094, 66.363), 'Visual': {'Color': (1, 1, 1, 1), 'Model': 'models/props/light_tool_bulb'}}, '1176756704.88dzlu': {'Type': 'Light - Dynamic', 'Attenuation': '0.005', 'ConeAngle': '60.0000', 'DropOff': '0.0000', 'Flickering': False, 'Hpr': Point3(0.0, 0.0, 0.0), 'Intensity': '0.1515', 'LightType': 'AMBIENT', 'Pos': Point3(66.477, -201.119, 35.177), 'Visual': {'Color': (1, 1, 1, 1), 'Model': 'models/props/light_tool_bulb'}}}, 'Visual': {'Model': 'models/caves/cave_b_zero'}}}, 'Node Links': [], 'Layers': {}, 'ObjectIds': {'1172185213.66sdnaik': '["Objects"]["1172185213.66sdnaik"]', '1172185301.05sdnaik': '["Objects"]["1172185213.66sdnaik"]["Objects"]["1172185301.05sdnaik"]', '1172185301.08sdnaik': '["Objects"]["1172185213.66sdnaik"]["Objects"]["1172185301.08sdnaik"]', '1172893180.14kmuller': '["Objects"]["1172185213.66sdnaik"]["Objects"]["1172893180.14kmuller"]', '1172893192.18kmuller': '["Objects"]["1172185213.66sdnaik"]["Objects"]["1172893192.18kmuller"]', '1172893216.81kmuller': '["Objects"]["1172185213.66sdnaik"]["Objects"]["1172893216.81kmuller"]', '1172893544.75kmuller': '["Objects"]["1172185213.66sdnaik"]["Objects"]["1172893544.75kmuller"]', '1176755520.41dzlu': '["Objects"]["1172185213.66sdnaik"]["Objects"]["1176755520.41dzlu"]', '1176755691.11dzlu': '["Objects"]["1172185213.66sdnaik"]["Objects"]["1176755691.11dzlu"]', '1176756704.88dzlu': '["Objects"]["1172185213.66sdnaik"]["Objects"]["1176756704.88dzlu"]'}}
| 628.333333
| 3,495
| 0.659416
| 513
| 3,770
| 4.807018
| 0.352827
| 0.017843
| 0.019465
| 0.019465
| 0.360097
| 0.301298
| 0.248175
| 0.231955
| 0.212084
| 0.202758
| 0
| 0.270348
| 0.077719
| 3,770
| 6
| 3,495
| 628.333333
| 0.438884
| 0.05809
| 0
| 0
| 0
| 0
| 0.564421
| 0.254863
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.5
| 0
| 0.5
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 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
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
59b9ab7aa44e85318936fa3ee35b19c5aa8d531f
| 824
|
py
|
Python
|
qork/easy.py
|
flipcoder/qork
|
86f10f0db2edc82786516fd30bbd9f046b1a27aa
|
[
"MIT"
] | 3
|
2020-03-19T06:31:32.000Z
|
2021-08-24T19:19:50.000Z
|
qork/easy.py
|
flipcoder/qork
|
86f10f0db2edc82786516fd30bbd9f046b1a27aa
|
[
"MIT"
] | null | null | null |
qork/easy.py
|
flipcoder/qork
|
86f10f0db2edc82786516fd30bbd9f046b1a27aa
|
[
"MIT"
] | null | null | null |
#!/usr/bin/python
from collections import defaultdict
from qork.signal import Signal
from qork.reactive import *
APP = None
def qork_app(a=None):
global APP
if a is None:
return APP
APP = a
return APP
def cache(*args, **kwargs):
return APP.cache(*args, **kwargs)
def add(*args, **kwargs):
return APP.add(*args, **kwargs)
def find(*args, **kwargs):
return APP.world.find(*args, **kwargs)
def find_one(*args, **kwargs):
return APP.world.find(*args, one=True, **kwargs)
def remove(*args, **kwargs):
return APP.remove(*args, **kwargs)
def create(*args, **kwargs):
return APP.create(*args, **kwargs)
def clear():
return APP.scene.clear()
def play(*args, **kwargs):
return APP.play(*args, **kwargs)
# def music(fn):
# return APP.add(fn, loop=True)
| 15.846154
| 52
| 0.634709
| 118
| 824
| 4.415254
| 0.288136
| 0.24952
| 0.214971
| 0.255278
| 0.122841
| 0.122841
| 0.122841
| 0
| 0
| 0
| 0
| 0
| 0.20267
| 824
| 51
| 53
| 16.156863
| 0.792998
| 0.078884
| 0
| 0.076923
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.346154
| false
| 0
| 0.115385
| 0.307692
| 0.846154
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 6
|
ab7f0aaf5c29c103ff087961487efc01b174671f
| 82
|
py
|
Python
|
paths_cli/__init__.py
|
dwhswenson/openpathsampling-cli
|
9d20fb069b08ea516174607fe4464fb5f9a74b12
|
[
"MIT"
] | 1
|
2020-02-11T13:31:53.000Z
|
2020-02-11T13:31:53.000Z
|
paths_cli/__init__.py
|
dwhswenson/openpathsampling-cli
|
9d20fb069b08ea516174607fe4464fb5f9a74b12
|
[
"MIT"
] | null | null | null |
paths_cli/__init__.py
|
dwhswenson/openpathsampling-cli
|
9d20fb069b08ea516174607fe4464fb5f9a74b12
|
[
"MIT"
] | null | null | null |
from .cli import OpenPathSamplingCLI
from . import commands
from . import version
| 20.5
| 36
| 0.817073
| 10
| 82
| 6.7
| 0.6
| 0.298507
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.146341
| 82
| 3
| 37
| 27.333333
| 0.957143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
ab902eebf9ec437998e96a8a77820cb0063c7fc1
| 35
|
py
|
Python
|
piwebasync/websockets/__init__.py
|
newvicx/piwebasync
|
fc0d159aa4b99667777f428a090fe7a102481fea
|
[
"MIT"
] | null | null | null |
piwebasync/websockets/__init__.py
|
newvicx/piwebasync
|
fc0d159aa4b99667777f428a090fe7a102481fea
|
[
"MIT"
] | 2
|
2022-03-02T17:42:21.000Z
|
2022-03-29T19:24:01.000Z
|
piwebasync/websockets/__init__.py
|
newvicx/piwebasync
|
fc0d159aa4b99667777f428a090fe7a102481fea
|
[
"MIT"
] | null | null | null |
from .client import WebsocketClient
| 35
| 35
| 0.885714
| 4
| 35
| 7.75
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.085714
| 35
| 1
| 35
| 35
| 0.96875
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
abb9f37a6fa130250d38acc4dde44fb5a49531ff
| 131
|
py
|
Python
|
orb_simulator/orbsim_language/orbsim_ast/bitwise_shift_right_node.py
|
dmguezjaviersnet/IA-Sim-Comp-Project
|
8165b9546efc45f98091a3774e2dae4f45942048
|
[
"MIT"
] | 1
|
2022-01-19T22:49:09.000Z
|
2022-01-19T22:49:09.000Z
|
orb_simulator/orbsim_language/orbsim_ast/bitwise_shift_right_node.py
|
dmguezjaviersnet/IA-Sim-Comp-Project
|
8165b9546efc45f98091a3774e2dae4f45942048
|
[
"MIT"
] | 15
|
2021-11-10T14:25:02.000Z
|
2022-02-12T19:17:11.000Z
|
orb_simulator/orbsim_language/orbsim_ast/bitwise_shift_right_node.py
|
dmguezjaviersnet/IA-Sim-Comp-Project
|
8165b9546efc45f98091a3774e2dae4f45942048
|
[
"MIT"
] | null | null | null |
from orbsim_language.orbsim_ast.binary_expr_node import BinaryExprNode
# >>
class BitwiseShiftRightNode(BinaryExprNode):
pass
| 21.833333
| 70
| 0.832061
| 14
| 131
| 7.5
| 0.857143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.10687
| 131
| 5
| 71
| 26.2
| 0.897436
| 0.015267
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.333333
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 6
|
abce9487f37e68f9114d032ce10077abc1f38211
| 29
|
py
|
Python
|
library/source1/mdl/v44/__init__.py
|
anderlli0053/SourceIO
|
3c0c4839939ce698439987ac52154f89ee2f5341
|
[
"MIT"
] | null | null | null |
library/source1/mdl/v44/__init__.py
|
anderlli0053/SourceIO
|
3c0c4839939ce698439987ac52154f89ee2f5341
|
[
"MIT"
] | null | null | null |
library/source1/mdl/v44/__init__.py
|
anderlli0053/SourceIO
|
3c0c4839939ce698439987ac52154f89ee2f5341
|
[
"MIT"
] | null | null | null |
from .mdl_file import MdlV44
| 14.5
| 28
| 0.827586
| 5
| 29
| 4.6
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.08
| 0.137931
| 29
| 1
| 29
| 29
| 0.84
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
abd455a86a3ffc917698b585fae0d39293200f58
| 40
|
py
|
Python
|
problems/DivisionTwo_Practice/solution.py
|
Pactionly/SpringComp2019
|
f684b84375b0d1fe98f3dffac6d2fac26ba6e2f1
|
[
"MIT"
] | 1
|
2020-04-21T00:42:47.000Z
|
2020-04-21T00:42:47.000Z
|
problems/DivisionTwo_Practice/solution.py
|
Pactionly/SpringComp2019
|
f684b84375b0d1fe98f3dffac6d2fac26ba6e2f1
|
[
"MIT"
] | null | null | null |
problems/DivisionTwo_Practice/solution.py
|
Pactionly/SpringComp2019
|
f684b84375b0d1fe98f3dffac6d2fac26ba6e2f1
|
[
"MIT"
] | 1
|
2020-04-23T02:09:45.000Z
|
2020-04-23T02:09:45.000Z
|
print("Hello World from Division Two!")
| 20
| 39
| 0.75
| 6
| 40
| 5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.125
| 40
| 1
| 40
| 40
| 0.857143
| 0
| 0
| 0
| 0
| 0
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 6
|
abe2175d966ffb568f2530920d141df8286c646b
| 10,354
|
py
|
Python
|
sjb/hack/determine_install_upgrade_version_test.py
|
brenton/aos-cd-jobs
|
34e427bb7091c52791bc93a34f062e57dc005082
|
[
"Apache-2.0"
] | 45
|
2017-05-09T15:49:06.000Z
|
2021-11-07T19:48:35.000Z
|
sjb/hack/determine_install_upgrade_version_test.py
|
brenton/aos-cd-jobs
|
34e427bb7091c52791bc93a34f062e57dc005082
|
[
"Apache-2.0"
] | 1,313
|
2017-01-19T13:40:43.000Z
|
2022-03-30T14:25:44.000Z
|
sjb/hack/determine_install_upgrade_version_test.py
|
brenton/aos-cd-jobs
|
34e427bb7091c52791bc93a34f062e57dc005082
|
[
"Apache-2.0"
] | 165
|
2017-01-17T22:19:04.000Z
|
2022-03-02T12:15:13.000Z
|
import unittest
from determine_install_upgrade_version import *
class TestPackage(object):
def __init__(self, name, version, release, epoch, vra, pkgtup):
self.name = name
self.version = version
self.release = release
self.epoch = epoch
self.vra = vra
self.pkgtup = pkgtup
def __eq__(self, other):
return self.__dict__ == other.__dict__
@classmethod
def create_test_packages(self, test_pkgs):
test_pkgs_objs = []
for pkg in test_pkgs:
pkg_name, pkg_version, pkg_release, pkg_epoch, pkg_arch = rpmutils.splitFilename(pkg)
pkg_vra = pkg_version + "-" + pkg_release + "." + pkg_arch
pkg_tup = (pkg_name , pkg_arch, pkg_epoch, pkg_version, pkg_release)
test_pkgs_objs.append(TestPackage(pkg_name, pkg_version, pkg_release, pkg_epoch, pkg_vra, pkg_tup))
return test_pkgs_objs
class RemoveDuplicatePackages(unittest.TestCase):
"Test for `determine_install_upgrade_version.py`"
def test_removing_single_duplicate_package(self):
""" when is multiple duplicate packages, return only one """
test_pkgs = ["origin-1.4.1-1.el7.x86_64", "origin-1.5.0-0.4.el7.x86_64", "origin-1.5.0-0.4.el7.x86_64"]
test_pkgs_objs = TestPackage.create_test_packages(test_pkgs)
result_pkgs_objs = test_pkgs_objs[:2]
self.assertEqual(remove_duplicate_pkgs(test_pkgs_objs), result_pkgs_objs)
def test_removing_no_duplicate_package(self):
""" when there is no duplicate package, return the single one """
test_pkgs = ["origin-1.4.1-1.el7.x86_64", "origin-1.5.0-0.4.el7.x86_64"]
test_pkgs_objs = TestPackage.create_test_packages(test_pkgs)
result_pkgs_objs = test_pkgs_objs[:2]
self.assertEqual(remove_duplicate_pkgs(test_pkgs_objs), result_pkgs_objs)
class GetMatchingVersionTestCase(unittest.TestCase):
"Test for `determine_install_upgrade_version.py`"
def test_get_matching_versions(self):
""" when only one matching version exist and its pre-release, it is returned """
test_pkgs = ["origin-1.4.1-1.el7.x86_64", "origin-1.5.0-0.4.el7.x86_64"]
test_pkgs_objs = TestPackage.create_test_packages(test_pkgs)
self.assertEqual(get_matching_versions('origin', test_pkgs_objs, '1.5'), ['1.5.0-0.4.el7'])
def test_with_single_pre_release(self):
""" when only one pre-release version exist, it is returned """
test_pkgs = ["origin-1.5.0-0.4.el7.x86_64"]
test_pkgs_objs = TestPackage.create_test_packages(test_pkgs)
self.assertEqual(get_matching_versions('origin', test_pkgs_objs, '1.5'), ['1.5.0-0.4.el7'])
def test_with_multiple_pre_release(self):
""" when only one pre-release version exist, it is returned """
test_pkgs = ["origin-1.5.0-0.4.el7.x86_64", "origin-1.5.2-0.1.el7.x86_64"]
test_pkgs_objs = TestPackage.create_test_packages(test_pkgs)
self.assertEqual(get_matching_versions('origin', test_pkgs_objs, '1.5'), ['1.5.0-0.4.el7', '1.5.2-0.1.el7'])
def test_with_single_release(self):
""" when both release and pre-release versions exist, only release versions are returned """
test_pkgs = ["origin-1.5.0-0.4.el7.x86_64", "origin-1.5.0-1.1.el7.x86_64"]
test_pkgs_objs = TestPackage.create_test_packages(test_pkgs)
self.assertEqual(get_matching_versions('origin', test_pkgs_objs, '1.5'), ["1.5.0-1.1.el7"])
def test_with_muptiple_release(self):
""" when both release and pre-release versions exist, only release version is returned """
test_pkgs = ["origin-1.5.0-0.4.el7.x86_64", "origin-1.5.0-1.1.el7.x86_64", "origin-1.5.2-1.1.el7.x86_64"]
test_pkgs_objs = TestPackage.create_test_packages(test_pkgs)
self.assertEqual(get_matching_versions('origin', test_pkgs_objs, '1.5'), ["1.5.0-1.1.el7", "1.5.2-1.1.el7"])
def test_with_no_matches(self):
test_pkgs = ["origin-1.2.0-0.4.el7.x86_64", "origin-1.3.0-1.1.el7.x86_64", "origin-1.4.2-1.1.el7.x86_64"]
test_pkgs_objs = TestPackage.create_test_packages(test_pkgs)
self.assertRaises(SystemExit, get_matching_versions, 'origin', test_pkgs_objs, '1.5')
class DetermineSearchVersionTestCase(unittest.TestCase):
"Test for `determine_install_upgrade_version.py`"
def test_origin_with_standard_versioning_schema(self):
""" when the origin version is higher then the first version of the new origin versioning schema - origin-3.6 """
self.assertEqual(determine_search_versions("origin", "3.7.0"), ("3.6", "3.7"))
def test_origin_with_short_standard_versioning_schema(self):
""" when the origin version is in short format and higher then the first version of the new origin versioning schema - origin-3.6 """
self.assertEqual(determine_search_versions("origin", "3.7"), ("3.6", "3.7"))
def test_origin_with_standard_to_legacy_versioning_schema(self):
""" when the origin version is the first from the new origin versioning schema - origin-3.6 """
self.assertEqual(determine_search_versions("origin", "3.6.0"), ("1.5", "3.6"))
def test_origin_with_short_standard_to_legacy_versioning_schema(self):
""" when the origin version is in short format and first from the new origin versioning schema - origin-3.6 """
self.assertEqual(determine_search_versions("origin", "3.6"), ("1.5", "3.6"))
def test_origin_with_legacy_schema(self):
""" when the origin version is in the old versioning schema """
self.assertEqual(determine_search_versions("origin", "1.5.0"), ("1.4", "1.5"))
def test_origin_with_short_legacy_schema(self):
""" when the origin version is in short and old versioning schema """
self.assertEqual(determine_search_versions("origin", "1.5"), ("1.4", "1.5"))
def test_openshift_ansible_with_standard_versioning_schema(self):
""" when openshift-ansible, which doesnt have different versioning schema, is in 3.7 version """
self.assertEqual(determine_search_versions("openshift-ansible", "3.7.0"), ("3.6", "3.7"))
def test_openshift_ansible_with_standard_to_legacy_versioning_schema(self):
""" when openshift-ansible, which doesnt have different versioning schema is in 3.6 version """
self.assertEqual(determine_search_versions("openshift-ansible", "3.6.0"), ("3.5", "3.6"))
def test_openshift_ansible_with_short_standard_to_legacy_versioning_schema(self):
""" when openshift-ansible, which doesnt have different versioning schema, is in short format and in 3.6 version """
self.assertEqual(determine_search_versions("openshift-ansible", "3.6"), ("3.5", "3.6"))
def test_openshift_ansible_with_legacy_versioning_schema(self):
""" when openshift-ansible, which doesnt have different versioning schema is in 3.4 version """
self.assertEqual(determine_search_versions("openshift-ansible", "3.5.0"), ("3.4", "3.5"))
class SchemaChangeCheckTestCase(unittest.TestCase):
"Test for `determine_install_upgrade_version.py`"
def test_origin_package_with_new_schema(self):
""" when origin package is in 3.6 version """
self.assertEqual(schema_change_check("origin", "3", "6"), "3.6")
def test_origin_package_with_old_schema(self):
""" when origin package is in 1.5 version """
self.assertEqual(schema_change_check("origin", "3", "5"), "1.5")
def test_non_origin_package_with_new_schema(self):
""" when origin package is in 3.6 version """
self.assertEqual(schema_change_check("openshift-ansible", "3", "6"), "3.6")
def test_non_origin_package_with_old_schema(self):
""" when origin package is in 3.5 version """
self.assertEqual(schema_change_check("openshift-ansible", "3", "5"), "3.5")
class GetLastVersionTestCase(unittest.TestCase):
"Test for `determine_install_upgrade_version.py`"
def test_with_multiple_matching_release_versions(self):
""" when multiple matching version are present in released versions """
matching_versions = ["1.2.0-1.el7", "1.2.2-1.el7", "1.2.5-1.el7"]
install_version = "1.2.5-1.el7"
self.assertEqual(get_last_version(matching_versions), install_version)
def test_with_single_matching_release_version(self):
""" when only a single matching version is present in released versions """
matching_versions = ["1.5.0-1.4.el7"]
install_version = "1.5.0-1.4.el7"
self.assertEqual(get_last_version(matching_versions), install_version)
def test_with_multiple_matching_pre_release_versions(self):
""" when multiple matching pre-release version are present in pre-released versions """
matching_versions = ["1.2.0-0.el7", "1.2.2-0.el7", "1.2.5-0.el7"]
install_version = "1.2.5-0.el7"
self.assertEqual(get_last_version(matching_versions), install_version)
def test_with_single_matching_pre_release_version(self):
""" when only single matching pre-release version is present in pre-released versions """
matching_versions = ["1.5.0-0.4.el7"]
install_version = "1.5.0-0.4.el7"
self.assertEqual(get_last_version(matching_versions), install_version)
class SortPackagesTestCase(unittest.TestCase):
"Test for `determine_install_upgrade_version.py`"
def test_sort_packages_with_exceptional_origin_pkg(self):
""" when sorting origin packages with exceptional origin-3.6.0-0.0.alpha.0.1 package """
test_pkgs = ["origin-3.6.0-0.0.alpha.0.1.el7", "origin-3.6.0-0.alpha.0.2.el7"]
properly_sorted_pkgs = ["origin-3.6.0-0.alpha.0.2.el7"]
test_pkgs_obj = TestPackage.create_test_packages(test_pkgs)
properly_sorted_pkgs_obj = TestPackage.create_test_packages(properly_sorted_pkgs)
sorted_test_pkgs_obj = sort_pkgs(test_pkgs_obj)
self.assertEqual(sorted_test_pkgs_obj, properly_sorted_pkgs_obj)
def test_sort_packages_with_same_minor_version(self):
""" when sorting origin packages within the same minor version """
test_pkgs = ["origin-1.5.1-1.el7", "origin-1.5.0-1.el7"]
properly_sorted_pkgs = ["origin-1.5.0-1.el7", "origin-1.5.1-1.el7"]
test_pkgs_obj = TestPackage.create_test_packages(test_pkgs)
properly_sorted_pkgs_obj = TestPackage.create_test_packages(properly_sorted_pkgs)
sorted_test_pkgs_obj = sort_pkgs(test_pkgs_obj)
self.assertEqual(sorted_test_pkgs_obj, properly_sorted_pkgs_obj)
def test_sort_packages_with_different_minor_version(self):
""" when sorting origin packages with different minor version """
test_pkgs = ["origin-1.5.1-1.el7", "origin-1.4.0-1.el7"]
properly_sorted_pkgs = ["origin-1.4.0-1.el7", "origin-1.5.1-1.el7"]
test_pkgs_obj = TestPackage.create_test_packages(test_pkgs)
properly_sorted_pkgs_obj = TestPackage.create_test_packages(properly_sorted_pkgs)
sorted_test_pkgs_obj = sort_pkgs(test_pkgs_obj)
self.assertEqual(sorted_test_pkgs_obj, properly_sorted_pkgs_obj)
if __name__ == '__main__':
unittest.main()
| 51.257426
| 135
| 0.758934
| 1,662
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| 0.075812
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| 51.257426
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| 1
| 0.238806
| false
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| null | 0
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0
| 6
|
abef62196bfa039670d1b1408e40569773e9a263
| 38,079
|
py
|
Python
|
instances/passenger_demand/pas-20210421-2109-int16e/45.py
|
LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure
|
bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11
|
[
"BSD-3-Clause"
] | null | null | null |
instances/passenger_demand/pas-20210421-2109-int16e/45.py
|
LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure
|
bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11
|
[
"BSD-3-Clause"
] | null | null | null |
instances/passenger_demand/pas-20210421-2109-int16e/45.py
|
LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure
|
bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11
|
[
"BSD-3-Clause"
] | null | null | null |
"""
PASSENGERS
"""
numPassengers = 3619
passenger_arriving = (
(4, 8, 8, 4, 6, 0, 7, 6, 4, 7, 5, 0), # 0
(8, 5, 5, 1, 0, 0, 5, 9, 6, 2, 4, 0), # 1
(4, 14, 10, 6, 1, 0, 8, 9, 5, 4, 0, 0), # 2
(8, 9, 4, 4, 3, 0, 7, 13, 8, 2, 2, 0), # 3
(1, 17, 7, 2, 0, 0, 13, 10, 1, 5, 2, 0), # 4
(4, 8, 7, 6, 3, 0, 7, 5, 6, 6, 1, 0), # 5
(1, 7, 7, 4, 2, 0, 5, 7, 7, 6, 1, 0), # 6
(6, 14, 7, 5, 1, 0, 7, 8, 11, 4, 2, 0), # 7
(5, 10, 9, 4, 3, 0, 2, 10, 6, 6, 1, 0), # 8
(2, 12, 8, 2, 2, 0, 14, 10, 5, 9, 2, 0), # 9
(5, 13, 12, 7, 4, 0, 9, 15, 10, 3, 2, 0), # 10
(5, 13, 8, 6, 2, 0, 9, 10, 9, 2, 3, 0), # 11
(4, 6, 5, 3, 3, 0, 7, 8, 7, 8, 0, 0), # 12
(6, 13, 8, 9, 4, 0, 10, 9, 8, 9, 3, 0), # 13
(6, 8, 9, 6, 1, 0, 7, 6, 2, 7, 4, 0), # 14
(3, 9, 8, 8, 3, 0, 8, 7, 5, 7, 3, 0), # 15
(4, 10, 10, 3, 5, 0, 10, 8, 10, 6, 2, 0), # 16
(1, 8, 9, 5, 0, 0, 12, 10, 3, 14, 3, 0), # 17
(5, 8, 6, 3, 2, 0, 7, 9, 9, 8, 0, 0), # 18
(1, 6, 7, 5, 6, 0, 3, 10, 5, 7, 2, 0), # 19
(5, 12, 10, 6, 4, 0, 8, 15, 4, 4, 3, 0), # 20
(6, 8, 9, 4, 2, 0, 7, 10, 4, 5, 0, 0), # 21
(7, 5, 13, 9, 4, 0, 6, 9, 10, 1, 4, 0), # 22
(4, 7, 6, 6, 3, 0, 12, 14, 5, 4, 1, 0), # 23
(4, 14, 13, 3, 2, 0, 13, 8, 7, 4, 7, 0), # 24
(3, 7, 6, 6, 2, 0, 6, 16, 5, 8, 2, 0), # 25
(3, 11, 7, 6, 3, 0, 8, 12, 6, 7, 1, 0), # 26
(7, 9, 9, 0, 1, 0, 4, 5, 6, 6, 1, 0), # 27
(7, 14, 8, 5, 5, 0, 9, 10, 6, 2, 1, 0), # 28
(8, 12, 5, 2, 3, 0, 5, 9, 7, 3, 2, 0), # 29
(4, 9, 7, 3, 5, 0, 15, 13, 7, 3, 2, 0), # 30
(9, 10, 13, 4, 5, 0, 6, 7, 7, 5, 2, 0), # 31
(6, 11, 8, 7, 2, 0, 11, 7, 4, 7, 4, 0), # 32
(7, 14, 10, 1, 4, 0, 4, 5, 6, 8, 1, 0), # 33
(4, 18, 11, 6, 6, 0, 7, 13, 4, 6, 2, 0), # 34
(6, 9, 9, 5, 3, 0, 5, 16, 12, 3, 3, 0), # 35
(7, 8, 12, 3, 3, 0, 9, 12, 8, 2, 4, 0), # 36
(7, 9, 5, 4, 3, 0, 6, 13, 5, 5, 1, 0), # 37
(8, 10, 4, 5, 3, 0, 7, 12, 7, 2, 2, 0), # 38
(5, 6, 8, 8, 3, 0, 3, 6, 10, 4, 2, 0), # 39
(5, 6, 6, 6, 1, 0, 6, 8, 10, 5, 6, 0), # 40
(8, 7, 8, 2, 2, 0, 9, 6, 5, 5, 4, 0), # 41
(10, 10, 6, 1, 1, 0, 10, 7, 8, 4, 2, 0), # 42
(5, 12, 7, 7, 3, 0, 6, 6, 8, 3, 1, 0), # 43
(2, 9, 5, 3, 4, 0, 2, 7, 8, 4, 4, 0), # 44
(6, 12, 3, 5, 1, 0, 9, 8, 5, 4, 4, 0), # 45
(7, 13, 4, 5, 2, 0, 9, 10, 7, 0, 4, 0), # 46
(10, 10, 6, 3, 3, 0, 7, 14, 5, 4, 3, 0), # 47
(3, 8, 10, 2, 2, 0, 7, 13, 12, 6, 4, 0), # 48
(3, 11, 13, 0, 1, 0, 6, 7, 3, 5, 1, 0), # 49
(5, 13, 8, 5, 1, 0, 6, 10, 5, 6, 2, 0), # 50
(3, 11, 11, 5, 2, 0, 7, 8, 5, 7, 3, 0), # 51
(3, 12, 8, 3, 2, 0, 5, 10, 6, 5, 1, 0), # 52
(5, 14, 10, 5, 4, 0, 7, 7, 7, 6, 1, 0), # 53
(7, 9, 6, 4, 4, 0, 4, 9, 8, 2, 1, 0), # 54
(4, 13, 12, 3, 2, 0, 6, 9, 8, 6, 7, 0), # 55
(2, 10, 10, 5, 2, 0, 6, 8, 5, 5, 2, 0), # 56
(8, 15, 8, 2, 2, 0, 10, 15, 6, 6, 3, 0), # 57
(6, 9, 4, 4, 2, 0, 6, 11, 7, 4, 3, 0), # 58
(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), # 59
)
station_arriving_intensity = (
(4.239442493415277, 10.874337121212122, 12.79077763496144, 10.138043478260869, 11.428846153846154, 7.610869565217392), # 0
(4.27923521607648, 10.995266557940518, 12.859864860039991, 10.194503019323673, 11.51450641025641, 7.608275422705315), # 1
(4.318573563554774, 11.114402244668911, 12.927312196515281, 10.249719806763286, 11.598358974358975, 7.60560193236715), # 2
(4.357424143985952, 11.231615625000002, 12.993070372750644, 10.303646739130434, 11.680326923076926, 7.60284945652174), # 3
(4.395753565505805, 11.346778142536477, 13.057090117109396, 10.356236714975847, 11.760333333333335, 7.600018357487922), # 4
(4.433528436250122, 11.459761240881035, 13.11932215795487, 10.407442632850241, 11.838301282051281, 7.597108997584541), # 5
(4.470715364354698, 11.570436363636365, 13.179717223650389, 10.457217391304349, 11.914153846153846, 7.594121739130435), # 6
(4.507280957955322, 11.678674954405162, 13.238226042559269, 10.50551388888889, 11.987814102564105, 7.591056944444445), # 7
(4.543191825187787, 11.784348456790122, 13.294799343044847, 10.552285024154589, 12.059205128205129, 7.587914975845411), # 8
(4.578414574187884, 11.88732831439394, 13.34938785347044, 10.597483695652175, 12.12825, 7.584696195652175), # 9
(4.612915813091406, 11.987485970819305, 13.401942302199371, 10.64106280193237, 12.194871794871796, 7.581400966183574), # 10
(4.646662150034143, 12.084692869668913, 13.452413417594972, 10.682975241545895, 12.25899358974359, 7.578029649758455), # 11
(4.679620193151888, 12.178820454545454, 13.500751928020566, 10.723173913043478, 12.320538461538462, 7.574582608695652), # 12
(4.71175655058043, 12.26974016905163, 13.546908561839473, 10.761611714975846, 12.37942948717949, 7.5710602053140095), # 13
(4.743037830455566, 12.357323456790127, 13.590834047415022, 10.798241545893719, 12.435589743589743, 7.567462801932367), # 14
(4.773430640913081, 12.441441761363635, 13.632479113110538, 10.833016304347826, 12.488942307692309, 7.563790760869566), # 15
(4.802901590088772, 12.521966526374861, 13.671794487289347, 10.86588888888889, 12.539410256410257, 7.560044444444445), # 16
(4.831417286118428, 12.598769195426486, 13.708730898314768, 10.896812198067634, 12.586916666666667, 7.556224214975846), # 17
(4.8589443371378405, 12.671721212121213, 13.74323907455013, 10.925739130434785, 12.631384615384619, 7.552330434782609), # 18
(4.8854493512828014, 12.740694020061728, 13.775269744358756, 10.952622584541063, 12.67273717948718, 7.5483634661835755), # 19
(4.910898936689104, 12.805559062850728, 13.804773636103969, 10.9774154589372, 12.710897435897436, 7.544323671497584), # 20
(4.935259701492538, 12.866187784090906, 13.831701478149103, 11.000070652173914, 12.74578846153846, 7.540211413043479), # 21
(4.958498253828894, 12.922451627384962, 13.856003998857469, 11.020541062801932, 12.777333333333331, 7.5360270531400975), # 22
(4.980581201833967, 12.97422203633558, 13.877631926592404, 11.038779589371982, 12.805455128205129, 7.531770954106282), # 23
(5.001475153643547, 13.021370454545455, 13.896535989717222, 11.054739130434783, 12.830076923076923, 7.52744347826087), # 24
(5.0211467173934246, 13.063768325617284, 13.91266691659526, 11.068372584541065, 12.851121794871794, 7.523044987922706), # 25
(5.039562501219393, 13.101287093153758, 13.925975435589832, 11.079632850241545, 12.86851282051282, 7.518575845410628), # 26
(5.056689113257243, 13.133798200757575, 13.936412275064265, 11.088472826086958, 12.88217307692308, 7.514036413043479), # 27
(5.072493161642767, 13.161173092031426, 13.943928163381893, 11.09484541062802, 12.89202564102564, 7.509427053140097), # 28
(5.086941254511755, 13.183283210578004, 13.948473828906026, 11.09870350241546, 12.89799358974359, 7.504748128019324), # 29
(5.1000000000000005, 13.200000000000001, 13.950000000000001, 11.100000000000001, 12.9, 7.5), # 30
(5.112219245524297, 13.213886079545453, 13.948855917874395, 11.099765849673204, 12.89926985815603, 7.4934020156588375), # 31
(5.124174680306906, 13.227588636363638, 13.945456038647343, 11.099067973856208, 12.897095035460993, 7.483239613526571), # 32
(5.135871675191815, 13.241105965909092, 13.93984891304348, 11.097913235294119, 12.893498936170213, 7.469612293853072), # 33
(5.147315601023018, 13.254436363636366, 13.93208309178744, 11.096308496732028, 12.888504964539008, 7.452619556888223), # 34
(5.158511828644501, 13.267578124999998, 13.922207125603865, 11.094260620915033, 12.882136524822696, 7.432360902881893), # 35
(5.169465728900256, 13.280529545454549, 13.91026956521739, 11.091776470588236, 12.874417021276598, 7.408935832083959), # 36
(5.180182672634271, 13.293288920454547, 13.896318961352657, 11.088862908496733, 12.865369858156027, 7.382443844744294), # 37
(5.190668030690537, 13.305854545454546, 13.8804038647343, 11.08552679738562, 12.855018439716313, 7.352984441112776), # 38
(5.200927173913044, 13.318224715909091, 13.862572826086955, 11.081775, 12.843386170212765, 7.32065712143928), # 39
(5.21096547314578, 13.330397727272729, 13.842874396135267, 11.077614379084968, 12.830496453900707, 7.285561385973679), # 40
(5.220788299232737, 13.342371874999998, 13.821357125603866, 11.073051797385622, 12.816372695035462, 7.247796734965852), # 41
(5.230401023017903, 13.354145454545458, 13.798069565217393, 11.068094117647059, 12.801038297872342, 7.207462668665667), # 42
(5.239809015345269, 13.365716761363636, 13.773060265700483, 11.06274820261438, 12.784516666666667, 7.164658687323005), # 43
(5.249017647058824, 13.377084090909092, 13.746377777777779, 11.05702091503268, 12.76683120567376, 7.119484291187739), # 44
(5.258032289002557, 13.388245738636364, 13.718070652173916, 11.050919117647059, 12.748005319148938, 7.072038980509745), # 45
(5.266858312020461, 13.399200000000002, 13.688187439613529, 11.044449673202614, 12.72806241134752, 7.022422255538898), # 46
(5.275501086956522, 13.409945170454547, 13.656776690821255, 11.037619444444445, 12.707025886524825, 6.970733616525071), # 47
(5.283965984654732, 13.420479545454548, 13.623886956521739, 11.030435294117646, 12.68491914893617, 6.9170725637181425), # 48
(5.292258375959079, 13.430801420454543, 13.589566787439615, 11.022904084967323, 12.66176560283688, 6.861538597367982), # 49
(5.300383631713555, 13.440909090909088, 13.553864734299518, 11.015032679738564, 12.63758865248227, 6.804231217724471), # 50
(5.308347122762149, 13.450800852272728, 13.516829347826087, 11.006827941176471, 12.612411702127659, 6.7452499250374816), # 51
(5.316154219948849, 13.460475, 13.47850917874396, 10.998296732026144, 12.58625815602837, 6.684694219556889), # 52
(5.3238102941176475, 13.469929829545457, 13.438952777777779, 10.98944591503268, 12.559151418439718, 6.622663601532567), # 53
(5.331320716112533, 13.479163636363635, 13.398208695652173, 10.980282352941177, 12.531114893617023, 6.559257571214393), # 54
(5.338690856777493, 13.488174715909091, 13.356325483091787, 10.970812908496733, 12.502171985815604, 6.494575628852241), # 55
(5.3459260869565215, 13.496961363636363, 13.313351690821257, 10.961044444444445, 12.472346099290782, 6.428717274695986), # 56
(5.353031777493607, 13.505521875000003, 13.269335869565218, 10.950983823529413, 12.441660638297872, 6.361782008995502), # 57
(5.360013299232737, 13.513854545454544, 13.224326570048309, 10.940637908496733, 12.410139007092198, 6.293869332000667), # 58
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 59
)
passenger_arriving_acc = (
(4, 8, 8, 4, 6, 0, 7, 6, 4, 7, 5, 0), # 0
(12, 13, 13, 5, 6, 0, 12, 15, 10, 9, 9, 0), # 1
(16, 27, 23, 11, 7, 0, 20, 24, 15, 13, 9, 0), # 2
(24, 36, 27, 15, 10, 0, 27, 37, 23, 15, 11, 0), # 3
(25, 53, 34, 17, 10, 0, 40, 47, 24, 20, 13, 0), # 4
(29, 61, 41, 23, 13, 0, 47, 52, 30, 26, 14, 0), # 5
(30, 68, 48, 27, 15, 0, 52, 59, 37, 32, 15, 0), # 6
(36, 82, 55, 32, 16, 0, 59, 67, 48, 36, 17, 0), # 7
(41, 92, 64, 36, 19, 0, 61, 77, 54, 42, 18, 0), # 8
(43, 104, 72, 38, 21, 0, 75, 87, 59, 51, 20, 0), # 9
(48, 117, 84, 45, 25, 0, 84, 102, 69, 54, 22, 0), # 10
(53, 130, 92, 51, 27, 0, 93, 112, 78, 56, 25, 0), # 11
(57, 136, 97, 54, 30, 0, 100, 120, 85, 64, 25, 0), # 12
(63, 149, 105, 63, 34, 0, 110, 129, 93, 73, 28, 0), # 13
(69, 157, 114, 69, 35, 0, 117, 135, 95, 80, 32, 0), # 14
(72, 166, 122, 77, 38, 0, 125, 142, 100, 87, 35, 0), # 15
(76, 176, 132, 80, 43, 0, 135, 150, 110, 93, 37, 0), # 16
(77, 184, 141, 85, 43, 0, 147, 160, 113, 107, 40, 0), # 17
(82, 192, 147, 88, 45, 0, 154, 169, 122, 115, 40, 0), # 18
(83, 198, 154, 93, 51, 0, 157, 179, 127, 122, 42, 0), # 19
(88, 210, 164, 99, 55, 0, 165, 194, 131, 126, 45, 0), # 20
(94, 218, 173, 103, 57, 0, 172, 204, 135, 131, 45, 0), # 21
(101, 223, 186, 112, 61, 0, 178, 213, 145, 132, 49, 0), # 22
(105, 230, 192, 118, 64, 0, 190, 227, 150, 136, 50, 0), # 23
(109, 244, 205, 121, 66, 0, 203, 235, 157, 140, 57, 0), # 24
(112, 251, 211, 127, 68, 0, 209, 251, 162, 148, 59, 0), # 25
(115, 262, 218, 133, 71, 0, 217, 263, 168, 155, 60, 0), # 26
(122, 271, 227, 133, 72, 0, 221, 268, 174, 161, 61, 0), # 27
(129, 285, 235, 138, 77, 0, 230, 278, 180, 163, 62, 0), # 28
(137, 297, 240, 140, 80, 0, 235, 287, 187, 166, 64, 0), # 29
(141, 306, 247, 143, 85, 0, 250, 300, 194, 169, 66, 0), # 30
(150, 316, 260, 147, 90, 0, 256, 307, 201, 174, 68, 0), # 31
(156, 327, 268, 154, 92, 0, 267, 314, 205, 181, 72, 0), # 32
(163, 341, 278, 155, 96, 0, 271, 319, 211, 189, 73, 0), # 33
(167, 359, 289, 161, 102, 0, 278, 332, 215, 195, 75, 0), # 34
(173, 368, 298, 166, 105, 0, 283, 348, 227, 198, 78, 0), # 35
(180, 376, 310, 169, 108, 0, 292, 360, 235, 200, 82, 0), # 36
(187, 385, 315, 173, 111, 0, 298, 373, 240, 205, 83, 0), # 37
(195, 395, 319, 178, 114, 0, 305, 385, 247, 207, 85, 0), # 38
(200, 401, 327, 186, 117, 0, 308, 391, 257, 211, 87, 0), # 39
(205, 407, 333, 192, 118, 0, 314, 399, 267, 216, 93, 0), # 40
(213, 414, 341, 194, 120, 0, 323, 405, 272, 221, 97, 0), # 41
(223, 424, 347, 195, 121, 0, 333, 412, 280, 225, 99, 0), # 42
(228, 436, 354, 202, 124, 0, 339, 418, 288, 228, 100, 0), # 43
(230, 445, 359, 205, 128, 0, 341, 425, 296, 232, 104, 0), # 44
(236, 457, 362, 210, 129, 0, 350, 433, 301, 236, 108, 0), # 45
(243, 470, 366, 215, 131, 0, 359, 443, 308, 236, 112, 0), # 46
(253, 480, 372, 218, 134, 0, 366, 457, 313, 240, 115, 0), # 47
(256, 488, 382, 220, 136, 0, 373, 470, 325, 246, 119, 0), # 48
(259, 499, 395, 220, 137, 0, 379, 477, 328, 251, 120, 0), # 49
(264, 512, 403, 225, 138, 0, 385, 487, 333, 257, 122, 0), # 50
(267, 523, 414, 230, 140, 0, 392, 495, 338, 264, 125, 0), # 51
(270, 535, 422, 233, 142, 0, 397, 505, 344, 269, 126, 0), # 52
(275, 549, 432, 238, 146, 0, 404, 512, 351, 275, 127, 0), # 53
(282, 558, 438, 242, 150, 0, 408, 521, 359, 277, 128, 0), # 54
(286, 571, 450, 245, 152, 0, 414, 530, 367, 283, 135, 0), # 55
(288, 581, 460, 250, 154, 0, 420, 538, 372, 288, 137, 0), # 56
(296, 596, 468, 252, 156, 0, 430, 553, 378, 294, 140, 0), # 57
(302, 605, 472, 256, 158, 0, 436, 564, 385, 298, 143, 0), # 58
(302, 605, 472, 256, 158, 0, 436, 564, 385, 298, 143, 0), # 59
)
passenger_arriving_rate = (
(4.239442493415277, 8.699469696969697, 7.674466580976864, 4.055217391304347, 2.2857692307692306, 0.0, 7.610869565217392, 9.143076923076922, 6.082826086956521, 5.1163110539845755, 2.174867424242424, 0.0), # 0
(4.27923521607648, 8.796213246352414, 7.715918916023995, 4.077801207729468, 2.3029012820512818, 0.0, 7.608275422705315, 9.211605128205127, 6.116701811594203, 5.1439459440159965, 2.1990533115881035, 0.0), # 1
(4.318573563554774, 8.891521795735128, 7.7563873179091685, 4.099887922705314, 2.3196717948717946, 0.0, 7.60560193236715, 9.278687179487179, 6.1498318840579715, 5.170924878606112, 2.222880448933782, 0.0), # 2
(4.357424143985952, 8.9852925, 7.795842223650386, 4.121458695652173, 2.336065384615385, 0.0, 7.60284945652174, 9.34426153846154, 6.18218804347826, 5.197228149100257, 2.246323125, 0.0), # 3
(4.395753565505805, 9.07742251402918, 7.834254070265637, 4.142494685990338, 2.352066666666667, 0.0, 7.600018357487922, 9.408266666666668, 6.213742028985508, 5.222836046843758, 2.269355628507295, 0.0), # 4
(4.433528436250122, 9.167808992704828, 7.8715932947729215, 4.1629770531400965, 2.367660256410256, 0.0, 7.597108997584541, 9.470641025641024, 6.244465579710145, 5.247728863181948, 2.291952248176207, 0.0), # 5
(4.470715364354698, 9.25634909090909, 7.907830334190233, 4.182886956521739, 2.382830769230769, 0.0, 7.594121739130435, 9.531323076923076, 6.274330434782609, 5.271886889460156, 2.3140872727272725, 0.0), # 6
(4.507280957955322, 9.34293996352413, 7.942935625535561, 4.2022055555555555, 2.397562820512821, 0.0, 7.591056944444445, 9.590251282051284, 6.303308333333334, 5.295290417023708, 2.3357349908810323, 0.0), # 7
(4.543191825187787, 9.427478765432097, 7.976879605826908, 4.220914009661835, 2.4118410256410256, 0.0, 7.587914975845411, 9.647364102564103, 6.3313710144927535, 5.317919737217938, 2.3568696913580243, 0.0), # 8
(4.578414574187884, 9.509862651515151, 8.009632712082263, 4.23899347826087, 2.4256499999999996, 0.0, 7.584696195652175, 9.702599999999999, 6.358490217391305, 5.339755141388175, 2.377465662878788, 0.0), # 9
(4.612915813091406, 9.589988776655444, 8.041165381319622, 4.256425120772947, 2.438974358974359, 0.0, 7.581400966183574, 9.755897435897436, 6.384637681159421, 5.360776920879748, 2.397497194163861, 0.0), # 10
(4.646662150034143, 9.66775429573513, 8.071448050556983, 4.273190096618357, 2.4517987179487175, 0.0, 7.578029649758455, 9.80719487179487, 6.409785144927537, 5.380965367037988, 2.4169385739337823, 0.0), # 11
(4.679620193151888, 9.743056363636363, 8.100451156812339, 4.289269565217391, 2.4641076923076923, 0.0, 7.574582608695652, 9.85643076923077, 6.433904347826087, 5.400300771208226, 2.4357640909090907, 0.0), # 12
(4.71175655058043, 9.815792135241303, 8.128145137103683, 4.304644685990338, 2.475885897435898, 0.0, 7.5710602053140095, 9.903543589743592, 6.456967028985507, 5.418763424735789, 2.4539480338103257, 0.0), # 13
(4.743037830455566, 9.8858587654321, 8.154500428449014, 4.3192966183574875, 2.4871179487179482, 0.0, 7.567462801932367, 9.948471794871793, 6.478944927536231, 5.4363336189660085, 2.471464691358025, 0.0), # 14
(4.773430640913081, 9.953153409090907, 8.179487467866322, 4.33320652173913, 2.4977884615384616, 0.0, 7.563790760869566, 9.991153846153846, 6.499809782608695, 5.452991645244214, 2.488288352272727, 0.0), # 15
(4.802901590088772, 10.017573221099887, 8.203076692373608, 4.346355555555555, 2.507882051282051, 0.0, 7.560044444444445, 10.031528205128204, 6.519533333333333, 5.468717794915738, 2.504393305274972, 0.0), # 16
(4.831417286118428, 10.079015356341188, 8.22523853898886, 4.358724879227053, 2.517383333333333, 0.0, 7.556224214975846, 10.069533333333332, 6.538087318840581, 5.483492359325907, 2.519753839085297, 0.0), # 17
(4.8589443371378405, 10.13737696969697, 8.245943444730077, 4.370295652173914, 2.5262769230769235, 0.0, 7.552330434782609, 10.105107692307694, 6.55544347826087, 5.4972956298200515, 2.5343442424242424, 0.0), # 18
(4.8854493512828014, 10.192555216049382, 8.265161846615253, 4.381049033816424, 2.534547435897436, 0.0, 7.5483634661835755, 10.138189743589743, 6.571573550724637, 5.510107897743501, 2.5481388040123454, 0.0), # 19
(4.910898936689104, 10.244447250280581, 8.282864181662381, 4.3909661835748794, 2.542179487179487, 0.0, 7.544323671497584, 10.168717948717948, 6.58644927536232, 5.5219094544415865, 2.5611118125701453, 0.0), # 20
(4.935259701492538, 10.292950227272724, 8.299020886889462, 4.400028260869565, 2.5491576923076917, 0.0, 7.540211413043479, 10.196630769230767, 6.600042391304348, 5.53268059125964, 2.573237556818181, 0.0), # 21
(4.958498253828894, 10.337961301907969, 8.313602399314481, 4.408216425120773, 2.555466666666666, 0.0, 7.5360270531400975, 10.221866666666664, 6.6123246376811595, 5.542401599542987, 2.584490325476992, 0.0), # 22
(4.980581201833967, 10.379377629068463, 8.326579155955441, 4.415511835748792, 2.5610910256410255, 0.0, 7.531770954106282, 10.244364102564102, 6.623267753623189, 5.551052770636961, 2.5948444072671157, 0.0), # 23
(5.001475153643547, 10.417096363636363, 8.337921593830332, 4.421895652173912, 2.5660153846153846, 0.0, 7.52744347826087, 10.264061538461538, 6.632843478260869, 5.558614395886888, 2.6042740909090907, 0.0), # 24
(5.0211467173934246, 10.451014660493826, 8.347600149957156, 4.427349033816426, 2.5702243589743587, 0.0, 7.523044987922706, 10.280897435897435, 6.641023550724639, 5.565066766638103, 2.6127536651234564, 0.0), # 25
(5.039562501219393, 10.481029674523006, 8.355585261353898, 4.431853140096617, 2.5737025641025637, 0.0, 7.518575845410628, 10.294810256410255, 6.647779710144927, 5.570390174235932, 2.6202574186307515, 0.0), # 26
(5.056689113257243, 10.507038560606059, 8.361847365038559, 4.435389130434783, 2.5764346153846156, 0.0, 7.514036413043479, 10.305738461538462, 6.653083695652175, 5.574564910025706, 2.6267596401515148, 0.0), # 27
(5.072493161642767, 10.52893847362514, 8.366356898029135, 4.437938164251207, 2.578405128205128, 0.0, 7.509427053140097, 10.313620512820512, 6.656907246376812, 5.5775712653527565, 2.632234618406285, 0.0), # 28
(5.086941254511755, 10.546626568462402, 8.369084297343615, 4.439481400966184, 2.579598717948718, 0.0, 7.504748128019324, 10.318394871794872, 6.659222101449276, 5.57938953156241, 2.6366566421156006, 0.0), # 29
(5.1000000000000005, 10.56, 8.370000000000001, 4.44, 2.58, 0.0, 7.5, 10.32, 6.660000000000001, 5.58, 2.64, 0.0), # 30
(5.112219245524297, 10.571108863636361, 8.369313550724637, 4.439906339869282, 2.5798539716312057, 0.0, 7.4934020156588375, 10.319415886524823, 6.659859509803923, 5.579542367149758, 2.6427772159090903, 0.0), # 31
(5.124174680306906, 10.582070909090909, 8.367273623188405, 4.439627189542483, 2.5794190070921985, 0.0, 7.483239613526571, 10.317676028368794, 6.659440784313724, 5.578182415458937, 2.6455177272727273, 0.0), # 32
(5.135871675191815, 10.592884772727274, 8.363909347826088, 4.439165294117647, 2.5786997872340423, 0.0, 7.469612293853072, 10.314799148936169, 6.658747941176471, 5.575939565217392, 2.6482211931818185, 0.0), # 33
(5.147315601023018, 10.603549090909091, 8.359249855072465, 4.438523398692811, 2.5777009929078014, 0.0, 7.452619556888223, 10.310803971631206, 6.657785098039217, 5.572833236714976, 2.6508872727272728, 0.0), # 34
(5.158511828644501, 10.614062499999998, 8.353324275362318, 4.437704248366013, 2.576427304964539, 0.0, 7.432360902881893, 10.305709219858157, 6.65655637254902, 5.568882850241546, 2.6535156249999994, 0.0), # 35
(5.169465728900256, 10.624423636363638, 8.346161739130434, 4.436710588235294, 2.5748834042553193, 0.0, 7.408935832083959, 10.299533617021277, 6.655065882352941, 5.564107826086956, 2.6561059090909094, 0.0), # 36
(5.180182672634271, 10.634631136363637, 8.337791376811595, 4.435545163398693, 2.573073971631205, 0.0, 7.382443844744294, 10.29229588652482, 6.65331774509804, 5.558527584541062, 2.6586577840909094, 0.0), # 37
(5.190668030690537, 10.644683636363636, 8.32824231884058, 4.4342107189542475, 2.5710036879432625, 0.0, 7.352984441112776, 10.28401475177305, 6.651316078431372, 5.5521615458937195, 2.661170909090909, 0.0), # 38
(5.200927173913044, 10.654579772727272, 8.317543695652173, 4.43271, 2.568677234042553, 0.0, 7.32065712143928, 10.274708936170212, 6.649065, 5.545029130434782, 2.663644943181818, 0.0), # 39
(5.21096547314578, 10.664318181818182, 8.305724637681159, 4.431045751633987, 2.566099290780141, 0.0, 7.285561385973679, 10.264397163120565, 6.646568627450981, 5.537149758454106, 2.6660795454545454, 0.0), # 40
(5.220788299232737, 10.673897499999997, 8.29281427536232, 4.429220718954248, 2.563274539007092, 0.0, 7.247796734965852, 10.253098156028368, 6.643831078431373, 5.5285428502415455, 2.6684743749999993, 0.0), # 41
(5.230401023017903, 10.683316363636365, 8.278841739130435, 4.427237647058823, 2.560207659574468, 0.0, 7.207462668665667, 10.240830638297872, 6.640856470588235, 5.519227826086957, 2.6708290909090913, 0.0), # 42
(5.239809015345269, 10.692573409090908, 8.26383615942029, 4.4250992810457515, 2.556903333333333, 0.0, 7.164658687323005, 10.227613333333332, 6.637648921568627, 5.509224106280192, 2.673143352272727, 0.0), # 43
(5.249017647058824, 10.701667272727272, 8.247826666666667, 4.422808366013072, 2.5533662411347517, 0.0, 7.119484291187739, 10.213464964539007, 6.634212549019608, 5.498551111111111, 2.675416818181818, 0.0), # 44
(5.258032289002557, 10.71059659090909, 8.23084239130435, 4.420367647058823, 2.5496010638297872, 0.0, 7.072038980509745, 10.198404255319149, 6.630551470588235, 5.487228260869566, 2.6776491477272724, 0.0), # 45
(5.266858312020461, 10.71936, 8.212912463768117, 4.417779869281045, 2.5456124822695037, 0.0, 7.022422255538898, 10.182449929078015, 6.626669803921568, 5.475274975845411, 2.67984, 0.0), # 46
(5.275501086956522, 10.727956136363636, 8.194066014492753, 4.415047777777778, 2.5414051773049646, 0.0, 6.970733616525071, 10.165620709219858, 6.6225716666666665, 5.462710676328501, 2.681989034090909, 0.0), # 47
(5.283965984654732, 10.736383636363637, 8.174332173913044, 4.412174117647059, 2.536983829787234, 0.0, 6.9170725637181425, 10.147935319148935, 6.618261176470588, 5.449554782608695, 2.6840959090909093, 0.0), # 48
(5.292258375959079, 10.744641136363633, 8.15374007246377, 4.409161633986929, 2.5323531205673757, 0.0, 6.861538597367982, 10.129412482269503, 6.613742450980394, 5.435826714975845, 2.6861602840909082, 0.0), # 49
(5.300383631713555, 10.752727272727268, 8.13231884057971, 4.406013071895425, 2.527517730496454, 0.0, 6.804231217724471, 10.110070921985816, 6.6090196078431385, 5.421545893719807, 2.688181818181817, 0.0), # 50
(5.308347122762149, 10.760640681818181, 8.110097608695652, 4.4027311764705885, 2.5224823404255314, 0.0, 6.7452499250374816, 10.089929361702126, 6.604096764705883, 5.406731739130435, 2.6901601704545453, 0.0), # 51
(5.316154219948849, 10.768379999999999, 8.087105507246376, 4.399318692810457, 2.517251631205674, 0.0, 6.684694219556889, 10.069006524822695, 6.5989780392156865, 5.391403671497584, 2.6920949999999997, 0.0), # 52
(5.3238102941176475, 10.775943863636364, 8.063371666666667, 4.395778366013072, 2.5118302836879436, 0.0, 6.622663601532567, 10.047321134751774, 6.593667549019608, 5.375581111111111, 2.693985965909091, 0.0), # 53
(5.331320716112533, 10.783330909090907, 8.038925217391304, 4.392112941176471, 2.5062229787234043, 0.0, 6.559257571214393, 10.024891914893617, 6.5881694117647065, 5.359283478260869, 2.6958327272727267, 0.0), # 54
(5.338690856777493, 10.790539772727271, 8.013795289855072, 4.388325163398693, 2.5004343971631204, 0.0, 6.494575628852241, 10.001737588652482, 6.58248774509804, 5.342530193236715, 2.697634943181818, 0.0), # 55
(5.3459260869565215, 10.79756909090909, 7.988011014492754, 4.384417777777777, 2.494469219858156, 0.0, 6.428717274695986, 9.977876879432625, 6.576626666666667, 5.325340676328502, 2.6993922727272723, 0.0), # 56
(5.353031777493607, 10.804417500000001, 7.96160152173913, 4.380393529411765, 2.4883321276595742, 0.0, 6.361782008995502, 9.953328510638297, 6.570590294117648, 5.307734347826087, 2.7011043750000003, 0.0), # 57
(5.360013299232737, 10.811083636363634, 7.934595942028984, 4.376255163398692, 2.4820278014184396, 0.0, 6.293869332000667, 9.928111205673758, 6.564382745098039, 5.289730628019323, 2.7027709090909084, 0.0), # 58
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 59
)
passenger_allighting_rate = (
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 0
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 1
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 2
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 3
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 4
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 5
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 6
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 7
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 8
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 9
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 10
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 11
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 12
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 13
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 14
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 15
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 16
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 17
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 18
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 19
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 20
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 21
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 22
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 23
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 24
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 25
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 26
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 27
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 28
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 29
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 30
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 31
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 32
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 33
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 34
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 35
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 36
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 37
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 38
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 39
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 40
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 41
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 42
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 43
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 44
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 45
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 46
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 47
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 48
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 49
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 50
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 51
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 52
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 53
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 54
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 55
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 56
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 57
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 58
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 59
)
"""
parameters for reproducibiliy. More information: https://numpy.org/doc/stable/reference/random/parallel.html
"""
#initial entropy
entropy = 258194110137029475889902652135037600173
#index for seed sequence child
child_seed_index = (
1, # 0
44, # 1
)
| 113.668657
| 214
| 0.730455
| 5,147
| 38,079
| 5.401982
| 0.22926
| 0.310747
| 0.246008
| 0.46612
| 0.326284
| 0.325565
| 0.325565
| 0.325565
| 0.325565
| 0.325565
| 0
| 0.820051
| 0.118543
| 38,079
| 334
| 215
| 114.008982
| 0.008312
| 0.031802
| 0
| 0.202532
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0.015823
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
f9fc952d1ccf29274ec0973eb4a467f04bcaa21d
| 146
|
py
|
Python
|
flog/user/__init__.py
|
mutalisk999/Flog
|
5d836e26967b39faebdf2d5a2c558316bf93221b
|
[
"MIT"
] | 1
|
2020-08-24T03:39:52.000Z
|
2020-08-24T03:39:52.000Z
|
flog/user/__init__.py
|
mutalisk999/Flog
|
5d836e26967b39faebdf2d5a2c558316bf93221b
|
[
"MIT"
] | null | null | null |
flog/user/__init__.py
|
mutalisk999/Flog
|
5d836e26967b39faebdf2d5a2c558316bf93221b
|
[
"MIT"
] | null | null | null |
"""
MIT License
Copyright (c) 2020 Andy Zhou
"""
from flask import Blueprint
user_bp = Blueprint("user", __name__)
from . import views
| 14.6
| 38
| 0.678082
| 19
| 146
| 4.947368
| 0.789474
| 0.276596
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.035088
| 0.219178
| 146
| 9
| 39
| 16.222222
| 0.789474
| 0.273973
| 0
| 0
| 0
| 0
| 0.044944
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 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
| 0
| 0
| 1
| 0
| 1
| 1
|
0
| 6
|
e6583e674ba2e81a101b4e7e7b28bee80e278c5c
| 35
|
py
|
Python
|
swf/responses/__init__.py
|
nstott/simpleflow
|
483602deb745a09b59ad6e24052dd5096c54fad2
|
[
"MIT"
] | 69
|
2015-02-24T00:49:40.000Z
|
2022-02-05T02:35:04.000Z
|
swf/responses/__init__.py
|
nstott/simpleflow
|
483602deb745a09b59ad6e24052dd5096c54fad2
|
[
"MIT"
] | 295
|
2015-02-06T11:02:00.000Z
|
2022-03-21T11:01:34.000Z
|
swf/responses/__init__.py
|
nstott/simpleflow
|
483602deb745a09b59ad6e24052dd5096c54fad2
|
[
"MIT"
] | 27
|
2015-08-31T22:14:42.000Z
|
2022-02-08T07:25:01.000Z
|
from .base import Response # NOQA
| 17.5
| 34
| 0.742857
| 5
| 35
| 5.2
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.2
| 35
| 1
| 35
| 35
| 0.928571
| 0.114286
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
0510b47d60ee89fe0f4905a512bdc61e7ec08cb3
| 2,805
|
py
|
Python
|
ML/code/linear_model.py
|
DistributedML/Biscotti
|
dfba71b3924e1bafd2ab2545881fb741193f224e
|
[
"BSD-2-Clause"
] | 61
|
2019-01-13T22:07:00.000Z
|
2022-02-16T16:53:13.000Z
|
ML/code/linear_model.py
|
cm20210602/Biscotti
|
dfba71b3924e1bafd2ab2545881fb741193f224e
|
[
"BSD-2-Clause"
] | null | null | null |
ML/code/linear_model.py
|
cm20210602/Biscotti
|
dfba71b3924e1bafd2ab2545881fb741193f224e
|
[
"BSD-2-Clause"
] | 14
|
2019-05-26T15:11:39.000Z
|
2022-03-02T16:10:24.000Z
|
from __future__ import division
import numpy as np
import utils
import pdb
lammy = 0.1
verbose = 1
maxEvals = 10000
X = 0
y = 0
iteration = 1
alpha = 1e-2
d = 0
hist_grad = 0
def init(dataset):
global X
X = utils.load_dataset(dataset)['X']
global y
y = utils.load_dataset(dataset)['y']
global d
d = X.shape[1]
global hist_grad
hist_grad = np.zeros(d)
return d
def funObj(ww, X, y):
xwy = (X.dot(ww) - y)
f = 0.5 * xwy.T.dot(xwy)
g = X.T.dot(xwy)
return f, g
def funObjL2(ww, X, y):
xwy = (X.dot(ww) - y)
f = 0.5 * xwy.T.dot(xwy) + 0.5 * self.lammy * ww.T.dot(ww)
g = X.T.dot(xwy) + self.lammy * ww
return f, g
# Reports the direct change to w, based on the given one.
# Batch size could be 1 for SGD, or 0 for full gradient.
def privateFun(theta, ww, batch_size=0):
global iteration
print 'python iteration ' + str(iteration) + ' starting'
ww = np.array(ww)
# Define constants and params
nn, dd = X.shape
threshold = int(d * theta)
if batch_size > 0 and batch_size < nn:
idx = np.random.choice(nn, batch_size, replace=False)
else:
# Just take the full range
idx = range(nn)
f, g = funObj(ww, X[idx, :], y[idx])
# AdaGrad
global hist_grad
hist_grad += g**2
ada_grad = g / (1e-6 + np.sqrt(hist_grad))
# Determine the actual step magnitude
delta = -alpha * ada_grad
# Weird way to get NON top k values
if theta < 1:
param_filter = np.argpartition(
abs(delta), -threshold)[:d - threshold]
delta[param_filter] = 0
w_new = ww + delta
f_new, g_new = funObj(w_new, X[idx, :], y[idx])
print 'python iteration ' + str(iteration) + ' ending'
iteration = iteration + 1
return delta
def privateFunL2(theta, ww, batch_size=0):
global iteration
print 'python iteration ' + str(iteration) + ' starting'
ww = np.array(ww)
# Define constants and params
nn, dd = X.shape
threshold = int(d * theta)
if batch_size > 0 and batch_size < nn:
idx = np.random.choice(nn, batch_size, replace=False)
else:
# Just take the full range
idx = range(nn)
f, g = funObjL2(ww, X[idx, :], y[idx])
# AdaGrad
global hist_grad
hist_grad += g**2
ada_grad = g / (1e-6 + np.sqrt(hist_grad))
# Determine the actual step magnitude
delta = -alpha * ada_grad
# Weird way to get NON top k values
if theta < 1:
param_filter = np.argpartition(
abs(delta), -threshold)[:d - threshold]
delta[param_filter] = 0
w_new = ww + delta
f_new, g_new = funObjL2(w_new, X[idx, :], y[idx])
print 'python iteration ' + str(iteration) + ' ending'
iteration = iteration + 1
return delta
| 21.576923
| 62
| 0.596435
| 434
| 2,805
| 3.764977
| 0.251152
| 0.044064
| 0.017136
| 0.056304
| 0.734394
| 0.709914
| 0.709914
| 0.709914
| 0.709914
| 0.709914
| 0
| 0.021923
| 0.284492
| 2,805
| 130
| 63
| 21.576923
| 0.792227
| 0.13262
| 0
| 0.580247
| 0
| 0
| 0.042131
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0.049383
| null | null | 0.049383
| 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
057920a1e2854b49360f4c734ccb7bc39d232c72
| 98
|
py
|
Python
|
lockbot/controllers/ping.py
|
preyneyv/iot-door-opener
|
dc84803e8a853dd4db2fbc8310f16381da9dfffa
|
[
"MIT"
] | null | null | null |
lockbot/controllers/ping.py
|
preyneyv/iot-door-opener
|
dc84803e8a853dd4db2fbc8310f16381da9dfffa
|
[
"MIT"
] | null | null | null |
lockbot/controllers/ping.py
|
preyneyv/iot-door-opener
|
dc84803e8a853dd4db2fbc8310f16381da9dfffa
|
[
"MIT"
] | null | null | null |
from starlette.responses import PlainTextResponse
def ping(_):
return PlainTextResponse('')
| 16.333333
| 49
| 0.77551
| 9
| 98
| 8.333333
| 0.888889
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.142857
| 98
| 5
| 50
| 19.6
| 0.892857
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0.333333
| 0.333333
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 1
| 1
| 0
|
0
| 6
|
553e3c05ff43b750f67e560612f2b93db0cef109
| 94
|
py
|
Python
|
tests/test_dummy.py
|
rmflight/GOcats
|
fc7b367583a5a579a76c58a83a37fe13c69ebccc
|
[
"Unlicense"
] | 10
|
2017-03-31T19:12:22.000Z
|
2021-09-28T01:29:38.000Z
|
tests/test_dummy.py
|
rmflight/GOcats
|
fc7b367583a5a579a76c58a83a37fe13c69ebccc
|
[
"Unlicense"
] | 8
|
2018-04-23T15:40:56.000Z
|
2021-03-31T14:22:06.000Z
|
tests/test_dummy.py
|
rmflight/GOcats
|
fc7b367583a5a579a76c58a83a37fe13c69ebccc
|
[
"Unlicense"
] | 3
|
2017-04-23T14:15:41.000Z
|
2021-06-20T18:38:01.000Z
|
import pytest
def test_run_script():
# run 1
# run 2
# assert
assert 1 == 1
| 10.444444
| 22
| 0.553191
| 14
| 94
| 3.571429
| 0.642857
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.066667
| 0.361702
| 94
| 8
| 23
| 11.75
| 0.766667
| 0.191489
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.333333
| 1
| 0.333333
| true
| 0
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
55410510df321bd146836470591e53e089a30174
| 181
|
py
|
Python
|
fjlt/__init__.py
|
gabobert/fast-jlt
|
b4f1156fd355ae4dca53b2d661f4bdd5a74fb8fa
|
[
"MIT"
] | 8
|
2015-09-29T11:41:37.000Z
|
2022-01-31T17:59:58.000Z
|
fjlt/__init__.py
|
gabobert/fast-jlt
|
b4f1156fd355ae4dca53b2d661f4bdd5a74fb8fa
|
[
"MIT"
] | 1
|
2017-07-13T11:00:35.000Z
|
2017-07-14T00:42:32.000Z
|
fjlt/__init__.py
|
gabobert/fast-jlt
|
b4f1156fd355ae4dca53b2d661f4bdd5a74fb8fa
|
[
"MIT"
] | 8
|
2015-08-14T18:33:38.000Z
|
2020-02-10T07:56:21.000Z
|
import os
from .version import __version__
def get_include():
''' Path of cython headers for compiling cython modules '''
return os.path.dirname(os.path.abspath(__file__))
| 25.857143
| 63
| 0.740331
| 25
| 181
| 5
| 0.72
| 0.096
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.160221
| 181
| 6
| 64
| 30.166667
| 0.822368
| 0.281768
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| true
| 0
| 0.5
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
55450421321a9d78d6344e925ca710a027620a80
| 23,700
|
py
|
Python
|
tests/vcf_chunker_test.py
|
oxfordfun/minos
|
e7165f1a398b1003e82a8aa00480ef5cd65fa834
|
[
"MIT"
] | 14
|
2018-01-25T15:20:42.000Z
|
2022-03-25T07:57:19.000Z
|
tests/vcf_chunker_test.py
|
oxfordfun/minos
|
e7165f1a398b1003e82a8aa00480ef5cd65fa834
|
[
"MIT"
] | 41
|
2018-01-25T15:47:13.000Z
|
2021-11-04T10:30:21.000Z
|
tests/vcf_chunker_test.py
|
oxfordfun/minos
|
e7165f1a398b1003e82a8aa00480ef5cd65fa834
|
[
"MIT"
] | 11
|
2018-01-25T15:11:32.000Z
|
2021-11-04T08:59:55.000Z
|
import filecmp
import shutil
import os
import unittest
import cluster_vcf_records
from minos import vcf_chunker
this_dir = os.path.dirname(os.path.abspath(__file__))
data_dir = os.path.join(this_dir, "data", "vcf_chunker")
class TestVcfChunker(unittest.TestCase):
def test_total_variants_and_alleles_in_vcf_dict(self):
"""test _total_variants_and_alleles_in_vcf_dict"""
class FakeVcf:
def __init__(self, alt):
self.ALT = alt
test_dict = {
"chrom1": [FakeVcf("123"), FakeVcf("1"), FakeVcf("123456789")],
"chrom2": [FakeVcf("12"), FakeVcf("1234")],
}
expect_variants = 5
expect_alleles = 24
(
got_variants,
got_alleles,
) = vcf_chunker.VcfChunker._total_variants_and_alleles_in_vcf_dict(test_dict)
self.assertEqual(expect_variants, got_variants)
self.assertEqual(expect_alleles, got_alleles)
def test_chunk_end_indexes_from_vcf_record_list(self):
"""test _chunk_end_indexes_from_vcf_record_list"""
record_list = [
cluster_vcf_records.vcf_record.VcfRecord("ref\t1\t.\tA\tG\t.\t.\t.\t."),
cluster_vcf_records.vcf_record.VcfRecord(
"ref\t2\t.\tC\tT,A,G,TA\t.\t.\t.\t."
),
cluster_vcf_records.vcf_record.VcfRecord("ref\t3\t.\tT\tA,C\t.\t.\t.\t."),
cluster_vcf_records.vcf_record.VcfRecord(
"ref\t5\t.\tAGAGTCACGTA\tG\t.\t.\t.\t."
),
cluster_vcf_records.vcf_record.VcfRecord("ref\t18\t.\tA\tG\t.\t.\t.\t."),
cluster_vcf_records.vcf_record.VcfRecord("ref\t21\t.\tG\tT\t.\t.\t.\t."),
]
self.assertEqual(
(0, 0, 1),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 0, 1, total_alleles=1
),
)
self.assertEqual(
(0, 0, 1),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 0, 1, total_alleles=2
),
)
self.assertEqual(
(0, 0, 1),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 0, 1, total_alleles=3
),
)
self.assertEqual(
(0, 0, 1),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 0, 1, total_alleles=4
),
)
self.assertEqual(
(0, 0, 1),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 0, 1, total_alleles=5
),
)
self.assertEqual(
(0, 0, 1),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 0, 1, total_alleles=6
),
)
self.assertEqual(
(0, 1, 2),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 0, 1, total_alleles=7
),
)
self.assertEqual(
(0, 1, 2),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 0, 1, total_alleles=8
),
)
self.assertEqual(
(0, 1, 2),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 0, 1, total_alleles=9
),
)
self.assertEqual(
(0, 2, 2),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 0, 1, total_alleles=10
),
)
self.assertEqual(
(0, 2, 2),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 0, 1, total_alleles=11
),
)
self.assertEqual(
(0, 3, 3),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 0, 1, total_alleles=12
),
)
self.assertEqual(
(0, 0, 1),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 0, 1, total_sites=1
),
)
self.assertEqual(
(0, 1, 2),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 0, 1, total_sites=2
),
)
self.assertEqual(
(0, 2, 2),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 0, 1, total_sites=3
),
)
self.assertEqual(
(0, 3, 3),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 0, 1, total_sites=4
),
)
self.assertEqual(
(0, 4, 4),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 0, 1, total_sites=5
),
)
self.assertEqual(
(0, 5, 5),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 0, 1, total_sites=6
),
)
self.assertEqual(
(0, 5, 5),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 0, 1, total_sites=7
),
)
self.assertEqual(
(0, 5, 5),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 0, 1, total_sites=8
),
)
self.assertEqual(
(0, 0, 2),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 0, 2, total_sites=1
),
)
self.assertEqual(
(0, 1, 2),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 0, 2, total_sites=2
),
)
self.assertEqual(
(0, 2, 3),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 0, 2, total_sites=3
),
)
self.assertEqual(
(0, 3, 3),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 0, 2, total_sites=4
),
)
self.assertEqual(
(0, 4, 4),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 0, 2, total_sites=5
),
)
self.assertEqual(
(0, 5, 5),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 0, 2, total_sites=6
),
)
self.assertEqual(
(0, 5, 5),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 0, 2, total_sites=7
),
)
self.assertEqual(
(0, 5, 5),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 0, 2, total_sites=8
),
)
self.assertEqual(
(0, 0, 2),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 0, 3, total_sites=1
),
)
self.assertEqual(
(0, 1, 3),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 0, 3, total_sites=2
),
)
self.assertEqual(
(0, 2, 3),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 0, 3, total_sites=3
),
)
self.assertEqual(
(0, 3, 4),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 0, 3, total_sites=4
),
)
self.assertEqual(
(0, 4, 5),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 0, 3, total_sites=5
),
)
self.assertEqual(
(0, 5, 5),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 0, 3, total_sites=6
),
)
self.assertEqual(
(0, 5, 5),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 0, 3, total_sites=7
),
)
self.assertEqual(
(0, 5, 5),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 0, 3, total_sites=8
),
)
self.assertEqual(
(0, 0, 3),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 0, 4, total_sites=1
),
)
self.assertEqual(
(0, 1, 3),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 0, 4, total_sites=2
),
)
self.assertEqual(
(0, 2, 3),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 0, 4, total_sites=3
),
)
self.assertEqual(
(0, 3, 4),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 0, 4, total_sites=4
),
)
self.assertEqual(
(0, 4, 5),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 0, 4, total_sites=5
),
)
self.assertEqual(
(0, 5, 5),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 0, 4, total_sites=6
),
)
self.assertEqual(
(0, 5, 5),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 0, 4, total_sites=7
),
)
self.assertEqual(
(0, 1, 2),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 1, 1, total_sites=1
),
)
self.assertEqual(
(0, 1, 2),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 1, 2, total_sites=1
),
)
self.assertEqual(
(0, 1, 3),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 1, 3, total_sites=1
),
)
self.assertEqual(
(0, 1, 3),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 1, 15, total_sites=1
),
)
self.assertEqual(
(0, 1, 4),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 1, 16, total_sites=1
),
)
self.assertEqual(
(0, 5, 5),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 1, 1, total_sites=6
),
)
self.assertEqual(
(4, 4, 4),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 4, 1, total_sites=1
),
)
self.assertEqual(
(4, 4, 4),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 4, 2, total_sites=1
),
)
self.assertEqual(
(3, 4, 5),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 4, 3, total_sites=1
),
)
self.assertEqual(
(4, 5, 5),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 4, 1, total_sites=2
),
)
self.assertEqual(
(5, 5, 5),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 5, 1, total_sites=1
),
)
self.assertEqual(
(5, 5, 5),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 5, 1, total_sites=2
),
)
self.assertEqual(
(5, 5, 5),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 5, 2, total_sites=2
),
)
self.assertEqual(
(4, 5, 5),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 5, 3, total_sites=2
),
)
self.assertEqual(
(4, 5, 5),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 5, 4, total_sites=2
),
)
self.assertEqual(
(4, 5, 5),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 5, 5, total_sites=2
),
)
self.assertEqual(
(3, 5, 5),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 5, 6, total_sites=2
),
)
self.assertEqual(
(3, 5, 5),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 5, 7, total_sites=2
),
)
self.assertEqual(
(3, 5, 5),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 5, 17, total_sites=2
),
)
self.assertEqual(
(2, 5, 5),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 5, 18, total_sites=2
),
)
self.assertEqual(
(1, 5, 5),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 5, 19, total_sites=2
),
)
self.assertEqual(
(0, 5, 5),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 5, 20, total_sites=2
),
)
self.assertEqual(
(0, 5, 5),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 5, 21, total_sites=2
),
)
# These records caused minos error because variant at 800
# was included in the last split file, but the use_end_index was at
# position of the variant at 610. So the one at 800 was not getting used.
record_list = [
cluster_vcf_records.vcf_record.VcfRecord("ref\t75\t.\tA\tG\t.\t.\t.\t."),
cluster_vcf_records.vcf_record.VcfRecord("ref\t150\t.\tG\tA,T\t.\t.\t.\t."),
cluster_vcf_records.vcf_record.VcfRecord("ref\t450\t.\tT\tC\t.\t.\t.\t."),
cluster_vcf_records.vcf_record.VcfRecord("ref\t610\t.\tA\tG\t.\t.\t.\t."),
cluster_vcf_records.vcf_record.VcfRecord("ref\t800\t.\tC\tCA\t.\t.\t.\t."),
]
self.assertEqual(
(0, 1, 1),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 0, 100, total_sites=2
),
)
self.assertEqual(
(2, 3, 3),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 2, 100, total_sites=2
),
)
self.assertEqual(
(4, 4, 4),
vcf_chunker.VcfChunker._chunk_end_indexes_from_vcf_record_list(
record_list, 4, 100, total_sites=2
),
)
def test_make_split_files(self):
"""test make_split_files"""
infile = os.path.join(data_dir, "make_split_files.in.vcf")
tmp_out = "tmp.vcf_chunker.make_split_files"
ref_fa = os.path.join(data_dir, "make_split_files.in.ref.fa")
if os.path.exists(tmp_out):
shutil.rmtree(tmp_out)
vcf1 = cluster_vcf_records.vcf_record.VcfRecord(
"ref1\t1\t.\tG\tT\t.\tPASS\t.\t.\t."
)
vcf2 = cluster_vcf_records.vcf_record.VcfRecord(
"ref1\t2\t.\tC\tT\t.\tPASS\t.\t.\t."
)
vcf3 = cluster_vcf_records.vcf_record.VcfRecord(
"ref1\t3\t.\tT\tA\t.\tPASS\t.\t.\t."
)
vcf4 = cluster_vcf_records.vcf_record.VcfRecord(
"ref1\t5\t.\tAGAGTCACGTA\tG\t.\tPASS\t.\t.\t."
)
vcf5 = cluster_vcf_records.vcf_record.VcfRecord(
"ref1\t18\t.\tA\tG\t.\tPASS\t.\t.\t."
)
vcf6 = cluster_vcf_records.vcf_record.VcfRecord(
"ref1\t21\t.\tG\tT\t.\tPASS\t.\t.\t."
)
vcf7 = cluster_vcf_records.vcf_record.VcfRecord(
"ref2\t42\t.\tC\tG\t.\tPASS\t.\t.\t."
)
header_lines = [
"##header1",
"##header2",
"#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\tFORMAT\tsample_name",
]
chunker = vcf_chunker.VcfChunker(
tmp_out,
vcf_infile=infile,
ref_fasta=ref_fa,
variants_per_split=2,
flank_length=1,
gramtools_kmer_size=5,
)
chunker.make_split_files()
self.assertTrue(os.path.exists(chunker.metadata_pickle))
got_header, got_records = cluster_vcf_records.vcf_file_read.vcf_file_to_list(
os.path.join(tmp_out, "split.0.in.vcf")
)
self.assertEqual(header_lines, got_header)
self.assertEqual([vcf1, vcf2, vcf3], got_records)
got_header, got_records = cluster_vcf_records.vcf_file_read.vcf_file_to_list(
os.path.join(tmp_out, "split.1.in.vcf")
)
self.assertEqual(header_lines, got_header)
self.assertEqual([vcf2, vcf3, vcf4], got_records)
got_header, got_records = cluster_vcf_records.vcf_file_read.vcf_file_to_list(
os.path.join(tmp_out, "split.2.in.vcf")
)
self.assertEqual(header_lines, got_header)
self.assertEqual([vcf5, vcf6], got_records)
got_header, got_records = cluster_vcf_records.vcf_file_read.vcf_file_to_list(
os.path.join(tmp_out, "split.3.in.vcf")
)
self.assertEqual(header_lines, got_header)
self.assertEqual([vcf7], got_records)
self.assertFalse(os.path.exists(os.path.join(tmp_out, "split.4.in.vcf")))
shutil.rmtree(tmp_out)
chunker = vcf_chunker.VcfChunker(
tmp_out,
vcf_infile=infile,
ref_fasta=ref_fa,
variants_per_split=4,
flank_length=3,
gramtools_kmer_size=5,
)
chunker.make_split_files()
self.assertTrue(os.path.exists(chunker.metadata_pickle))
got_header, got_records = cluster_vcf_records.vcf_file_read.vcf_file_to_list(
os.path.join(tmp_out, "split.0.in.vcf")
)
self.assertEqual(header_lines, got_header)
self.assertEqual([vcf1, vcf2, vcf3, vcf4, vcf5], got_records)
got_header, got_records = cluster_vcf_records.vcf_file_read.vcf_file_to_list(
os.path.join(tmp_out, "split.1.in.vcf")
)
self.assertEqual(header_lines, got_header)
self.assertEqual([vcf4, vcf5, vcf6], got_records)
got_header, got_records = cluster_vcf_records.vcf_file_read.vcf_file_to_list(
os.path.join(tmp_out, "split.2.in.vcf")
)
self.assertEqual(header_lines, got_header)
self.assertEqual([vcf7], got_records)
self.assertFalse(os.path.exists(os.path.join(tmp_out, "split.3.in.vcf")))
chunker2 = vcf_chunker.VcfChunker(tmp_out, gramtools_kmer_size=5)
self.assertEqual(chunker.vcf_infile, chunker2.vcf_infile)
self.assertEqual(chunker.ref_fasta, chunker2.ref_fasta)
self.assertEqual(chunker.variants_per_split, chunker2.variants_per_split)
self.assertEqual(chunker.total_splits, chunker2.total_splits)
self.assertEqual(chunker.flank_length, chunker2.flank_length)
self.assertEqual(chunker.gramtools_kmer_size, chunker2.gramtools_kmer_size)
self.assertEqual(chunker.total_split_files, chunker2.total_split_files)
self.assertEqual(chunker.vcf_split_files, chunker2.vcf_split_files)
shutil.rmtree(tmp_out)
def test_make_split_files_2(self):
"""test make_split_files with different input from previous test"""
# These records cause a minos bug. Last record was not being used
# when merging because the index was wrong.
# They are test data from multi_sample_pipeline tests
infile = os.path.join(data_dir, "make_split_files2.in.vcf")
tmp_out = "tmp.vcf_chunker.make_split_files2"
ref_fa = os.path.join(data_dir, "make_split_files2.in.ref.fa")
if os.path.exists(tmp_out):
shutil.rmtree(tmp_out)
chunker = vcf_chunker.VcfChunker(
tmp_out,
vcf_infile=infile,
ref_fasta=ref_fa,
variants_per_split=2,
flank_length=200,
gramtools_kmer_size=5,
)
chunker.make_split_files()
self.assertTrue(os.path.exists(chunker.metadata_pickle))
chunker2 = vcf_chunker.VcfChunker(tmp_out, gramtools_kmer_size=5)
self.assertEqual(1, len(chunker2.vcf_split_files))
self.assertEqual(3, len(chunker2.vcf_split_files["ref.0"]))
self.assertEqual(4, chunker2.vcf_split_files["ref.0"][-1].use_end_index)
shutil.rmtree(tmp_out)
# Test with two threads
chunker = vcf_chunker.VcfChunker(
tmp_out,
vcf_infile=infile,
ref_fasta=ref_fa,
variants_per_split=2,
flank_length=200,
threads=2,
gramtools_kmer_size=5,
)
chunker.make_split_files()
self.assertTrue(os.path.exists(chunker.metadata_pickle))
chunker2 = vcf_chunker.VcfChunker(tmp_out, gramtools_kmer_size=5)
self.assertEqual(1, len(chunker2.vcf_split_files))
self.assertEqual(3, len(chunker2.vcf_split_files["ref.0"]))
self.assertEqual(4, chunker2.vcf_split_files["ref.0"][-1].use_end_index)
shutil.rmtree(tmp_out)
def test_merge_files(self):
"""test merge_files"""
vcf_to_split = os.path.join(data_dir, "merge_files.in.vcf")
ref_fasta = os.path.join(data_dir, "merge_files.in.ref.fa")
tmp_outdir = "tmp.vcf_chunker.merge_files"
chunker = vcf_chunker.VcfChunker(
tmp_outdir,
vcf_infile=vcf_to_split,
ref_fasta=ref_fasta,
variants_per_split=4,
flank_length=3,
gramtools_kmer_size=5,
)
chunker.make_split_files()
to_merge = {}
for ref, split_list in chunker.vcf_split_files.items():
to_merge[ref] = [x.filename for x in split_list]
tmp_vcf_out = "tmp.vcf_chunker.merge_files.out.vcf"
chunker.merge_files(to_merge, tmp_vcf_out)
self.assertTrue(filecmp.cmp(vcf_to_split, tmp_vcf_out, shallow=False))
os.unlink(tmp_vcf_out)
shutil.rmtree(tmp_outdir)
| 35.585586
| 88
| 0.567511
| 2,873
| 23,700
| 4.298991
| 0.068918
| 0.11497
| 0.126306
| 0.109222
| 0.848838
| 0.829002
| 0.809732
| 0.782447
| 0.765363
| 0.738078
| 0
| 0.03759
| 0.333249
| 23,700
| 665
| 89
| 35.639098
| 0.74402
| 0.02384
| 0
| 0.573744
| 0
| 0.001621
| 0.048608
| 0.03874
| 0
| 0
| 0
| 0
| 0.171799
| 1
| 0.009724
| false
| 0.011345
| 0.009724
| 0
| 0.02269
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
557abe7d3140721c2a79214e8a58388bd9de455d
| 33
|
py
|
Python
|
grasshopper/__init__.py
|
aholyoke/grasshopper
|
b9e11ac3aafdb6e2a61cc8a74ca67e36b690da69
|
[
"BSD-3-Clause"
] | null | null | null |
grasshopper/__init__.py
|
aholyoke/grasshopper
|
b9e11ac3aafdb6e2a61cc8a74ca67e36b690da69
|
[
"BSD-3-Clause"
] | null | null | null |
grasshopper/__init__.py
|
aholyoke/grasshopper
|
b9e11ac3aafdb6e2a61cc8a74ca67e36b690da69
|
[
"BSD-3-Clause"
] | null | null | null |
from .framework import Framework
| 16.5
| 32
| 0.848485
| 4
| 33
| 7
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.121212
| 33
| 1
| 33
| 33
| 0.965517
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
55984b7c0e3a37ed751f5916d817fb1f0c1d3de6
| 313
|
py
|
Python
|
python scripts/detect_winning_team.py
|
Geoffry-Skionfinschii/Datapack_SurvivalGames
|
476a7da18e6b5a1eca96fcb7969b63b0c0ddc87f
|
[
"Apache-2.0"
] | 10
|
2020-05-30T09:08:47.000Z
|
2022-01-28T07:07:56.000Z
|
python scripts/detect_winning_team.py
|
Geoffry-Skionfinschii/Datapack_SurvivalGames
|
476a7da18e6b5a1eca96fcb7969b63b0c0ddc87f
|
[
"Apache-2.0"
] | 18
|
2020-05-31T15:16:00.000Z
|
2022-03-13T13:34:17.000Z
|
python scripts/detect_winning_team.py
|
Geoffry-Skionfinschii/Datapack_SurvivalGames
|
476a7da18e6b5a1eca96fcb7969b63b0c0ddc87f
|
[
"Apache-2.0"
] | 5
|
2020-04-17T15:07:12.000Z
|
2020-12-02T01:03:45.000Z
|
def main():
print("# Generated by python script")
for i in range(1, 22):
print("execute as @r[team=TEAM_{0}, tag=InGame] run tag @a[team=TEAM_{0}] add Winner".format(i))
print("execute as @r[team=TEAM_{0}, tag=InGame] run scoreboard players add @a[team=TEAM_{0}] Wins 1".format(i))
main()
| 44.714286
| 119
| 0.638978
| 54
| 313
| 3.62963
| 0.518519
| 0.163265
| 0.183673
| 0.153061
| 0.367347
| 0.367347
| 0.367347
| 0.367347
| 0.367347
| 0.367347
| 0
| 0.03125
| 0.182109
| 313
| 7
| 120
| 44.714286
| 0.734375
| 0
| 0
| 0
| 1
| 0.333333
| 0.627389
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.166667
| false
| 0
| 0
| 0
| 0.166667
| 0.5
| 0
| 0
| 0
| null | 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 6
|
559dba20f272da8e85bd02cd1799c9bf91921491
| 10,862
|
py
|
Python
|
09_road_limit/model.py
|
yeodongbin/2020AIChallengeCode
|
776c686b65a67bc0d71eed1118eed6cf45ea17c6
|
[
"MIT"
] | null | null | null |
09_road_limit/model.py
|
yeodongbin/2020AIChallengeCode
|
776c686b65a67bc0d71eed1118eed6cf45ea17c6
|
[
"MIT"
] | null | null | null |
09_road_limit/model.py
|
yeodongbin/2020AIChallengeCode
|
776c686b65a67bc0d71eed1118eed6cf45ea17c6
|
[
"MIT"
] | null | null | null |
import torchvision
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
from torchvision.models.detection.mask_rcnn import MaskRCNNPredictor
from efficientnet_pytorch import EfficientNet
from torchvision.models.detection import FasterRCNN
from torchvision.models.detection import MaskRCNN
from torchvision.models.detection.rpn import AnchorGenerator
from custom_model.faster_rcnn import fasterrcnn_resnet50_fpn
from custom_model.mask_rcnn import maskrcnn_resnet50_fpn
def get_model_instance_segmentation_custom0(num_classes):
model = fasterrcnn_resnet50_fpn(pretrained=False, pretrained_backbone=False)
print("fasterrcnn_resnet50_fpn custom call - 41,755,286 (resnet50) / 28,730,006 (resnet18) / 28,730,006 resnet / 22,463,126 / 오잉..light resnet : 22,468,758/ 19,333,398 / custom resent (64 쭉..) 17,664,662")
return model
def get_model_instance_segmentation0(num_classes):
model = fasterrcnn_resnet50_fpn(pretrained=False, pretrained_backbone=False)
print("fasterrcnn_resnet50_fpn custom call - 41,755,286 / ")
return model
def get_model_instance_segmentation(num_classes):
# COCO 에서 미리 학습된 인스턴스 분할 모델을 읽어옵니다
#model = torchvision.models.detection.maskrcnn_resnet50_fpn(pretrained=False, pretrained_backbone=False)
model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=False, pretrained_backbone=False)
#backbone = torchvision.models.mobilenet_v2(pretrained=False).features
#backbone.out_channels = 1280
#anchor_generator = AnchorGenerator(sizes=((32, 64, 128, 256, 512),),
# aspect_ratios=((0.5, 1.0, 2.0),))
#roi_pooler = torchvision.ops.MultiScaleRoIAlign(featmap_names=[0],
# output_size=1,
# sampling_ratio=2)
#model = FasterRCNN(backbone,
# num_classes=num_classes,
# rpn_anchor_generator=anchor_generator,
# box_roi_pool=roi_pooler)
print("fasterrcnn_resnet50_fpn call - 41,401,661 / 41,532,886")
# 분류를 위한 입력 특징 차원을 얻습니다
#in_features = model.roi_heads.box_predictor.cls_score.in_features
# 미리 학습된 헤더를 새로운 것으로 바꿉니다
#model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)
#in_features_mask = model.roi_heads.mask_predictor.conv5_mask.in_channels
#hidden_layer = 1
# and replace the mask predictor with a new one
#model.roi_heads.mask_predictor = MaskRCNNPredictor(in_features_mask,
# hidden_layer,
# num_classes)
return model
def get_model_instance_segmentation_custom1(num_classes):
# COCO 에서 미리 학습된 인스턴스 분할 모델을 읽어옵니다
model = maskrcnn_resnet50_fpn(pretrained=False, pretrained_backbone=False)
#model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=False, pretrained_backbone=False)
#backbone = torchvision.models.mobilenet_v2(pretrained=False).features
#backbone.out_channels = 1280
#anchor_generator = AnchorGenerator(sizes=((32, 64, 128, 256, 512),),
# aspect_ratios=((0.5, 1.0, 2.0),))
#roi_pooler = torchvision.ops.MultiScaleRoIAlign(featmap_names=[0],
# output_size=1,
# sampling_ratio=2)
#model = FasterRCNN(backbone,
# num_classes=num_classes,
# rpn_anchor_generator=anchor_generator,
# box_roi_pool=roi_pooler)
print("maskrcnn_resnet50_fpn custom call1 - resnet : 24,743,507 mobilenet : 87,366,291 squeezenet : 33,161,683 densnet : 43,702,739, resnet basicblock 3*3 -> 1*1 : 20,549,203 / basic : 20,543,571 / basicblock con1 : 20,195,411 / 채널 : 강제로 128 지정시 13,033,555 / 128 all 변경 : 9,465,555 ")
# 분류를 위한 입력 특징 차원을 얻습니다
in_features = model.roi_heads.box_predictor.cls_score.in_features
# 미리 학습된 헤더를 새로운 것으로 바꿉니다
model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)
in_features_mask = model.roi_heads.mask_predictor.conv5_mask.in_channels
hidden_layer = 128
# and replace the mask predictor with a new one
model.roi_heads.mask_predictor = MaskRCNNPredictor(in_features_mask,
hidden_layer,
num_classes)
return model
def get_model_instance_segmentation2(num_classes):
# COCO 에서 미리 학습된 인스턴스 분할 모델을 읽어옵니다
#model = torchvision.models.detection.maskrcnn_resnet50_fpn(pretrained=False, pretrained_backbone=False)
#model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=False, pretrained_backbone=False)
backbone = torchvision.models.mobilenet_v2(pretrained=False).features
#backbone.out_channels = 1
backbone.out_channels = 1280
anchor_generator = AnchorGenerator(sizes=((32, 64, 128, 256, 512),),
aspect_ratios=((0.5, 1.0, 2.0),))
roi_pooler = torchvision.ops.MultiScaleRoIAlign(featmap_names=[0],
output_size=1,
sampling_ratio=2)
model = FasterRCNN(backbone,
num_classes=num_classes,
rpn_anchor_generator=anchor_generator,
box_roi_pool=roi_pooler)
print("mobilenet_v2 call2 - out_channels :1280, 19,540,921")
# 분류를 위한 입력 특징 차원을 얻습니다
#in_features = backbone
# 미리 학습된 헤더를 새로운 것으로 바꿉니다
#model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)
#in_features_mask = model.roi_heads.mask_predictor.conv5_mask.in_channels
#hidden_layer = 1
# and replace the mask predictor with a new one
#model.roi_heads.mask_predictor = MaskRCNNPredictor(in_features_mask,
# hidden_layer,
# num_classes)
return model
def get_model_instance_segmentation4(num_classes):
# COCO 에서 미리 학습된 인스턴스 분할 모델을 읽어옵니다
#model = torchvision.models.detection.maskrcnn_resnet50_fpn(pretrained=False, pretrained_backbone=False)
#model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=False, pretrained_backbone=False)
backbone = torchvision.models.squeezenet1_1(pretrained=False).features
#backbone.out_channels = 1
backbone.out_channels = 512
anchor_generator = AnchorGenerator(sizes=((32, 64, 128, 256, 512),),
aspect_ratios=((0.5, 1.0, 2.0),))
roi_pooler = torchvision.ops.MultiScaleRoIAlign(featmap_names=[0],
output_size=7,
sampling_ratio=2)
mask_roi_pooler = torchvision.ops.MultiScaleRoIAlign(featmap_names=['0'],
output_size=14,
sampling_ratio=2)
model = MaskRCNN(backbone,
num_classes=num_classes,
box_roi_pool =roi_pooler,
mask_roi_pool = mask_roi_pooler
)
#print("squeezenet1_0 call2 - out_channels :1280, 18,052,473 / 72M")
#print("squeezenet1_0 call2 - out_channels :516, 4,862,777 / 19.5M")
#print("squeezenet1_1 call2 - out_channels :516, 4,849,849 4,862,777 / 19.5M")
print("squeezenet1_1 call2 - out_channels :256, 2,757,369 / 11M (15,000,000 / 15,000,000)")
print("squeezenet1_1 call2 - out_channels :512, 4,808,441 / 19.2M (15,000,000)")
print("squeezenet1_1 call2 - out_channels :512, 33,192,463 33,161,683 / 172M (15,000,000)")
#
# 분류를 위한 입력 특징 차원을 얻습니다
#in_features = backbone
# 미리 학습된 헤더를 새로운 것으로 바꿉니다
#model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)
#in_features_mask = model.roi_heads.mask_predictor.conv5_mask.in_channels
#hidden_layer = 1
# and replace the mask predictor with a new one
#model.roi_heads.mask_predictor = MaskRCNNPredictor(in_features_mask,
# hidden_layer,
# num_classes)
return model
def get_model_instance_segmentation5(num_classes):
# COCO 에서 미리 학습된 인스턴스 분할 모델을 읽어옵니다
#model = torchvision.models.detection.maskrcnn_resnet50_fpn(pretrained=False, pretrained_backbone=False)
#model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=False, pretrained_backbone=False)
backbone = torchvision.models.densenet161(pretrained=False).features
#backbone.out_channels = 1
backbone.out_channels = 256
anchor_generator = AnchorGenerator(sizes=((32, 64, 128, 256, 512),),
aspect_ratios=((0.5, 1.0, 2.0),))
roi_pooler = torchvision.ops.MultiScaleRoIAlign(featmap_names=[0],
output_size=1,
sampling_ratio=2)
model = FasterRCNN(backbone,
num_classes=num_classes,
rpn_anchor_generator=anchor_generator,
box_roi_pool=roi_pooler)
print("densenet161 call2 - out_channels :256, 28,506,873 / 150M")
# 분류를 위한 입력 특징 차원을 얻습니다
#in_features = backbone
# 미리 학습된 헤더를 새로운 것으로 바꿉니다
#model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)
#in_features_mask = model.roi_heads.mask_predictor.conv5_mask.in_channels
#hidden_layer = 1
# and replace the mask predictor with a new one
#model.roi_heads.mask_predictor = MaskRCNNPredictor(in_features_mask,
# hidden_layer,
# num_classes)
return model
def get_model_instance_segmentation6(num_classes):
backbone = torchvision.models.squeezenet1_1(pretrained=False).features
backbone.out_channels = 512
anchor_generator = AnchorGenerator(sizes=((32, 64, 128, 256, 512),),
aspect_ratios=((0.5, 1.0, 2.0),))
roi_pooler = torchvision.ops.MultiScaleRoIAlign(featmap_names=[0],
output_size=1,
sampling_ratio=2)
model = FasterRCNN(backbone,
num_classes=num_classes,
rpn_anchor_generator=anchor_generator,
box_roi_pool=roi_pooler)
print("get_model_instance_segmentation6 call6 - out_channels :512, 4,808,441 / (15,000,000) ")
return model
| 45.638655
| 290
| 0.62981
| 1,254
| 10,862
| 5.208134
| 0.157895
| 0.045935
| 0.033839
| 0.047772
| 0.836166
| 0.812127
| 0.79314
| 0.784872
| 0.784872
| 0.784872
| 0
| 0.076306
| 0.288161
| 10,862
| 237
| 291
| 45.831224
| 0.768365
| 0.409593
| 0
| 0.5
| 0
| 0.053191
| 0.159317
| 0.019282
| 0
| 0
| 0
| 0
| 0
| 1
| 0.085106
| false
| 0
| 0.095745
| 0
| 0.265957
| 0.106383
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
559e2f16b9c8d8149f2ea49ac5c688b029b0d86d
| 27
|
py
|
Python
|
steamprofile/__init__.py
|
aaronlyy/steamprofile
|
43002e62f4924a2a2040a240ed1362c28ad7a8f5
|
[
"MIT"
] | null | null | null |
steamprofile/__init__.py
|
aaronlyy/steamprofile
|
43002e62f4924a2a2040a240ed1362c28ad7a8f5
|
[
"MIT"
] | null | null | null |
steamprofile/__init__.py
|
aaronlyy/steamprofile
|
43002e62f4924a2a2040a240ed1362c28ad7a8f5
|
[
"MIT"
] | null | null | null |
from .steamprofile import *
| 27
| 27
| 0.814815
| 3
| 27
| 7.333333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.111111
| 27
| 1
| 27
| 27
| 0.916667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
e94a4e5cc42d1e471dd1294588d5acc65b8a33e1
| 12,946
|
py
|
Python
|
tests/test_views.py
|
yezyilomo/drf-pretty-put
|
1bc77f5f8fea58b2c30e4e3d7c0837b55b679d59
|
[
"MIT"
] | 28
|
2019-08-27T14:27:41.000Z
|
2020-02-04T18:54:18.000Z
|
tests/test_views.py
|
yezyilomo/drf-pretty-put
|
1bc77f5f8fea58b2c30e4e3d7c0837b55b679d59
|
[
"MIT"
] | 3
|
2019-09-04T10:06:15.000Z
|
2019-09-06T10:48:42.000Z
|
tests/test_views.py
|
yezyilomo/drf-pretty-update
|
1bc77f5f8fea58b2c30e4e3d7c0837b55b679d59
|
[
"MIT"
] | null | null | null |
from django.urls import reverse
from rest_framework.test import APITestCase
from tests.testapp.models import Book, Course, Student, Phone
class ViewTests(APITestCase):
def setUp(self):
self.book1 = Book.objects.create(title="Advanced Data Structures", author="S.Mobit")
self.book2 = Book.objects.create(title="Basic Data Structures", author="S.Mobit")
self.course1 = Course.objects.create(
name="Data Structures", code="CS210"
)
self.course2 = Course.objects.create(
name="Programming", code="CS150"
)
self.course1.books.set([self.book1, self.book2])
self.course2.books.set([self.book1])
self.student = Student.objects.create(
name="Yezy", age=24, course=self.course1
)
self.phone1 = Phone.objects.create(number="076711110", type="Office", student=self.student)
self.phone2 = Phone.objects.create(number="073008880", type="Home", student=self.student)
def tearDown(self):
Book.objects.all().delete()
Course.objects.all().delete()
Student.objects.all().delete()
# **************** POST Tests ********************* #
def test_post_on_pk_nested_foreignkey_related_field(self):
url = reverse("rstudent-list")
data = {
"name": "yezy",
"age": 33,
"course": 2
}
response = self.client.post(url, data, format="json")
self.assertEqual(
response.data,
{
'name': 'yezy',
'age': 33,
'course': {
'name': 'Programming',
'code': 'CS150',
'books': [
{"title": "Advanced Data Structures", "author": "S.Mobit"}
]
},
'phone_numbers': []
}
)
def test_post_on_writable_nested_foreignkey_related_field(self):
url = reverse("wstudent-list")
data = {
"name": "yezy",
"age": 33,
"course": {"name": "Programming", "code": "CS50"},
}
response = self.client.post(url, data, format="json")
self.assertEqual(
response.data,
{
'name': 'yezy',
'age': 33,
'course': {
'name': 'Programming',
'code': 'CS50',
'books': []
},
'phone_numbers': []
}
)
def test_post_with_add_operation(self):
url = reverse("rcourse-list")
data = {
"name": "Data Structures",
"code": "CS310",
"books": {"add":[1,2]}
}
response = self.client.post(url, data, format="json")
self.assertEqual(
response.data,
{
"name": "Data Structures",
"code": "CS310",
"books": [
{'title': 'Advanced Data Structures', 'author': 'S.Mobit'},
{'title': 'Basic Data Structures', 'author': 'S.Mobit'}
]
}
)
def test_post_with_create_operation(self):
data = {
"name": "Data Structures",
"code": "CS310",
"books": {"create": [
{"title": "Linear Math", "author": "Me"},
{"title": "Algebra Three", "author": "Me"}
]}
}
url = reverse("wcourse-list")
response = self.client.post(url, data, format="json")
self.assertEqual(
response.data,
{
"name": "Data Structures",
"code": "CS310",
"books": [
{"title": "Linear Math", "author": "Me"},
{"title": "Algebra Three", "author": "Me"}
]
}
)
def test_post_on_deep_nested_fields(self):
url = reverse("wstudent-list")
data = {
"name": "yezy",
"age": 33,
"course": {
"name": "Programming",
"code": "CS50",
"books": {"create": [
{"title": "Python Tricks", "author": "Dan Bader"}
]}
}
}
response = self.client.post(url, data, format="json")
self.assertEqual(
response.data,
{
'name': 'yezy',
'age': 33,
'course': {
'name': 'Programming',
'code': 'CS50',
'books': [
{"title": "Python Tricks", "author": "Dan Bader"}
]
},
'phone_numbers': []
}
)
def test_post_on_many_2_one_relation(self):
url = reverse("wstudent-list")
data = {
"name": "yezy",
"age": 33,
"course": {"name": "Programming", "code": "CS50"},
"phone_numbers": {
'create': [
{'number': '076750000', 'type': 'office'}
]
}
}
response = self.client.post(url, data, format="json")
self.assertEqual(
response.data,
{
'name': 'yezy',
'age': 33,
'course': {
'name': 'Programming',
'code': 'CS50',
'books': []
},
'phone_numbers': [
{'number': '076750000', 'type': 'office', 'student': 2}
]
}
)
# **************** PUT Tests ********************* #
def test_put_on_pk_nested_foreignkey_related_field(self):
url = reverse("rstudent-detail", args=[self.student.id])
data = {
"name": "yezy",
"age": 33,
"course": 2
}
response = self.client.put(url, data, format="json")
self.assertEqual(
response.data,
{
'name': 'yezy', 'age': 33,
'course': {
'name': 'Programming', 'code': 'CS150',
'books': [
{"title": "Advanced Data Structures", "author": "S.Mobit"}
]
},
'phone_numbers': [
{'number': '076711110', 'type': 'Office', 'student': 1},
{'number': '073008880', 'type': 'Home', 'student': 1}
]
}
)
def test_put_on_writable_nested_foreignkey_related_field(self):
url = reverse("wstudent-detail", args=[self.student.id])
data = {
"name": "yezy",
"age": 33,
"course": {"name": "Programming", "code": "CS50"}
}
response = self.client.put(url, data, format="json")
self.assertEqual(
response.data,
{
'name': 'yezy', 'age': 33,
'course': {
'name': 'Programming', 'code': 'CS50',
'books': [
{'title': 'Advanced Data Structures', 'author': 'S.Mobit'},
{'title': 'Basic Data Structures', 'author': 'S.Mobit'}
]
},
'phone_numbers': [
{'number': '076711110', 'type': 'Office', 'student': 1},
{'number': '073008880', 'type': 'Home', 'student': 1}
]
}
)
def test_put_with_add_operation(self):
url = reverse("rcourse-detail", args=[self.course2.id])
data = {
"name": "Data Structures",
"code": "CS410",
"books": {
"add": [2]
}
}
response = self.client.put(url, data, format="json")
self.assertEqual(
response.data,
{
"name": "Data Structures",
"code": "CS410",
"books": [
{'title': 'Advanced Data Structures', 'author': 'S.Mobit'},
{'title': 'Basic Data Structures', 'author': 'S.Mobit'}
]
}
)
def test_put_with_remove_operation(self):
url = reverse("rcourse-detail", args=[self.course2.id])
data = {
"name": "Data Structures",
"code": "CS410",
"books": {
"remove": [1]
}
}
response = self.client.put(url, data, format="json")
self.assertEqual(
response.data,
{
"name": "Data Structures",
"code": "CS410",
"books": []
}
)
def test_put_with_create_operation(self):
url = reverse("wcourse-detail", args=[self.course2.id])
data = {
"name": "Data Structures",
"code": "CS310",
"books": {
"create": [
{"title": "Primitive Data Types", "author": "S.Mobit"}
]
}
}
response = self.client.put(url, data, format="json")
self.assertEqual(
response.data,
{
"name": "Data Structures",
"code": "CS310",
"books": [
{'title': 'Advanced Data Structures', 'author': 'S.Mobit'},
{"title": "Primitive Data Types", "author": "S.Mobit"}
]
}
)
def test_put_with_update_operation(self):
url = reverse("wcourse-detail", args=[self.course2.id])
data = {
"name": "Data Structures",
"code": "CS310",
"books": {
"update": {
1: {"title": "React Programming", "author": "M.Json"}
}
}
}
response = self.client.put(url, data, format="json")
self.assertEqual(
response.data,
{
"name": "Data Structures",
"code": "CS310",
"books": [
{"title": "React Programming", "author": "M.Json"}
]
}
)
def test_put_on_deep_nested_fields(self):
url = reverse("wstudent-detail", args=[self.student.id])
data = {
"name": "yezy",
"age": 33,
"course": {
"name": "Programming",
"code": "CS50",
"books": {
"remove": [1]
}
}
}
response = self.client.put(url, data, format="json")
self.assertEqual(
response.data,
{
'name': 'yezy', 'age': 33,
'course': {
'name': 'Programming', 'code': 'CS50',
'books': [
{'title': 'Basic Data Structures', 'author': 'S.Mobit'}
]
},
'phone_numbers': [
{'number': '076711110', 'type': 'Office', 'student': 1},
{'number': '073008880', 'type': 'Home', 'student': 1}
]
}
)
def test_put_on_many_2_one_relation(self):
url = reverse("wstudent-detail", args=[self.student.id])
data = {
"name": "yezy",
"age": 33,
"course": {"name": "Programming", "code": "CS50"},
"phone_numbers": {
'update': {
1: {'number': '073008811', 'type': 'office'}
},
'create': [
{'number': '076750000', 'type': 'office'}
]
}
}
response = self.client.put(url, data, format="json")
self.assertEqual(
response.data,
{
'name': 'yezy', 'age': 33,
'course': {
'name': 'Programming', 'code': 'CS50',
'books': [
{'title': 'Advanced Data Structures', 'author': 'S.Mobit'},
{'title': 'Basic Data Structures', 'author': 'S.Mobit'}
]
},
'phone_numbers': [
{'number': '073008811', 'type': 'office', 'student': 1},
{'number': '073008880', 'type': 'Home', 'student': 1},
{'number': '076750000', 'type': 'office', 'student': 1}
]
}
)
| 32.691919
| 99
| 0.400046
| 996
| 12,946
| 5.114458
| 0.111446
| 0.043973
| 0.03671
| 0.047114
| 0.851786
| 0.816647
| 0.78563
| 0.738516
| 0.719278
| 0.702199
| 0
| 0.038811
| 0.446702
| 12,946
| 396
| 100
| 32.691919
| 0.672344
| 0.007647
| 0
| 0.581267
| 0
| 0
| 0.217489
| 0
| 0
| 0
| 0
| 0
| 0.038567
| 1
| 0.044077
| false
| 0
| 0.008264
| 0
| 0.055096
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
e98574d4316ffc2546acdf0d678a3e6348fee7c4
| 1,319
|
py
|
Python
|
prob08/prob8.py
|
speyejack/EulersProblems
|
b13714c9bab15d8ac31ea66b4ddc5de944e4f8d9
|
[
"MIT"
] | null | null | null |
prob08/prob8.py
|
speyejack/EulersProblems
|
b13714c9bab15d8ac31ea66b4ddc5de944e4f8d9
|
[
"MIT"
] | null | null | null |
prob08/prob8.py
|
speyejack/EulersProblems
|
b13714c9bab15d8ac31ea66b4ddc5de944e4f8d9
|
[
"MIT"
] | null | null | null |
from operator import itemgetter
from functools import reduce
num = """73167176531330624919225119674426574742355349194934
96983520312774506326239578318016984801869478851843
85861560789112949495459501737958331952853208805511
12540698747158523863050715693290963295227443043557
66896648950445244523161731856403098711121722383113
62229893423380308135336276614282806444486645238749
30358907296290491560440772390713810515859307960866
70172427121883998797908792274921901699720888093776
65727333001053367881220235421809751254540594752243
52584907711670556013604839586446706324415722155397
53697817977846174064955149290862569321978468622482
83972241375657056057490261407972968652414535100474
82166370484403199890008895243450658541227588666881
16427171479924442928230863465674813919123162824586
17866458359124566529476545682848912883142607690042
24219022671055626321111109370544217506941658960408
07198403850962455444362981230987879927244284909188
84580156166097919133875499200524063689912560717606
05886116467109405077541002256983155200055935729725
71636269561882670428252483600823257530420752963450""".replace("\n", "").strip()
search_range = 13
product = max([reduce(lambda x,y:int(x)*int(y),sub) for sub in [num[sub:sub+search_range] for sub in range(len(num)-search_range)]])
print("Greatest product: {}".format(product))
| 45.482759
| 132
| 0.91812
| 67
| 1,319
| 18.029851
| 0.686567
| 0.027318
| 0.013245
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.790845
| 0.039424
| 1,319
| 28
| 133
| 47.107143
| 0.162589
| 0
| 0
| 0
| 0
| 0
| 0.789833
| 0.758725
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.08
| 0
| 0.08
| 0.04
| 0
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
e9a259e76f8beb10a60cd94e22813838141dff75
| 37
|
py
|
Python
|
setwallpaper/__init__.py
|
tinaxd/setwallpaper
|
199787c7603d4ac7bdf0c2bdbaa09720ed53f93f
|
[
"MIT"
] | null | null | null |
setwallpaper/__init__.py
|
tinaxd/setwallpaper
|
199787c7603d4ac7bdf0c2bdbaa09720ed53f93f
|
[
"MIT"
] | 1
|
2021-07-29T10:45:29.000Z
|
2021-07-29T10:45:29.000Z
|
setwallpaper/__init__.py
|
tinaxd/setwallpaper
|
199787c7603d4ac7bdf0c2bdbaa09720ed53f93f
|
[
"MIT"
] | null | null | null |
from .wallpaper import set_wallpaper
| 18.5
| 36
| 0.864865
| 5
| 37
| 6.2
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.108108
| 37
| 1
| 37
| 37
| 0.939394
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
75d7b71b9034ef64fd2b359b3feab75f7cb6ee30
| 943
|
py
|
Python
|
Easy/Repeated String/repeatedString.py
|
Zealll/HackerRank
|
0f03ba284a699f5f37138e866b348c616d4d101a
|
[
"MIT"
] | null | null | null |
Easy/Repeated String/repeatedString.py
|
Zealll/HackerRank
|
0f03ba284a699f5f37138e866b348c616d4d101a
|
[
"MIT"
] | null | null | null |
Easy/Repeated String/repeatedString.py
|
Zealll/HackerRank
|
0f03ba284a699f5f37138e866b348c616d4d101a
|
[
"MIT"
] | null | null | null |
def repeatedString(s, n):
dictionary = {'a': 0}
length = n // len(s)
if 'a' not in s:
return 0
for i in s:
if i == 'a':
dictionary['a'] += 1
remaining = n - len(s) * length
total = int(dictionary['a'] * length)
if remaining > 0:
for i in range(remaining):
if s[i] == 'a':
total += 1
return total
# def repeatedString(s, n):
# dictionary = {}
# length = n // len(s)
# if 'a' not in s:
# return 0
# for i in s:
# if i == 'a':
# if 'a' not in dictionary:
# dictionary['a'] = 1
# else:
# dictionary['a'] += 1
# remaining = n - len(s) * length
# total = int(dictionary['a'] * length)
# if remaining > 0:
# for i in range(remaining):
# if s[i] == 'a':
# total += 1
# return total
| 15.983051
| 43
| 0.415695
| 115
| 943
| 3.408696
| 0.182609
| 0.168367
| 0.05102
| 0.071429
| 0.908163
| 0.760204
| 0.760204
| 0.760204
| 0.760204
| 0.760204
| 0
| 0.018868
| 0.437964
| 943
| 59
| 44
| 15.983051
| 0.720755
| 0.510074
| 0
| 0
| 0
| 0
| 0.013514
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.066667
| false
| 0
| 0
| 0
| 0.2
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
f93ed093ff43559433edfd036a4c4cb26d6e5443
| 24,160
|
py
|
Python
|
experiments/utils/nets/cnn_factory.py
|
ezetl/deep-learning-techniques-thesis
|
8092dfc8bbd99d9a0d148139a381363b46fe09b4
|
[
"CC0-1.0"
] | null | null | null |
experiments/utils/nets/cnn_factory.py
|
ezetl/deep-learning-techniques-thesis
|
8092dfc8bbd99d9a0d148139a381363b46fe09b4
|
[
"CC0-1.0"
] | null | null | null |
experiments/utils/nets/cnn_factory.py
|
ezetl/deep-learning-techniques-thesis
|
8092dfc8bbd99d9a0d148139a381363b46fe09b4
|
[
"CC0-1.0"
] | null | null | null |
#!/usr/bin/env python2.7
import caffe
from caffe import (layers as L, params as P)
from layers_wrappers import *
caffe.set_device(0)
caffe.set_mode_gpu()
class MNISTNetFactory:
@staticmethod
def standar(lmdb_path=None, batch_size=125, scale=1.0, is_train=True, learn_all=True):
"""
Creates a protoxt similar to the first layers of AlexNet architecture for the MNIST experiment
:param lmdb_path: str. Path to train LMDB
:param batch_size: int. Batch size
:param scale: float. How to scale the images
:param is_train: bool. Flag indicating if this is for testing or training
:returns: Caffe NetSpec, tuple with names of loss blobs, tuple with name of accuracy blobs
"""
n = caffe.NetSpec()
phase = caffe.TRAIN if is_train else caffe.TEST
n.data, n.label = L.Data(include=dict(phase=phase), batch_size=batch_size, backend=P.Data.LMDB, source=lmdb_path, transform_param=dict(scale=scale), ntop=2)
n.conv1 = L.Convolution(n.data, kernel_size=11, stride=4, num_output=96, param=[weight_param('conv1_w', learn_all=learn_all), bias_param('conv1_b', learn_all=learn_all)], weight_filler=weight_filler, bias_filler=bias_filler_0)
n.relu1 = L.ReLU(n.conv1, in_place=True)
n.pool1 = L.Pooling(n.relu1, pool=P.Pooling.MAX, kernel_size=3, stride=2)
n.norm1 = L.LRN(n.pool1, local_size=5, alpha=1e-4, beta=0.75)
n.conv2 = L.Convolution(n.norm1, kernel_size=5, num_output=256, pad=2, group=2, param=[weight_param('conv2_w', learn_all=learn_all), bias_param('conv2_b', learn_all=learn_all)], weight_filler=weight_filler, bias_filler=bias_filler_0)
n.relu2 = L.ReLU(n.conv2, in_place=True)
n.pool2 = L.Pooling(n.relu2, pool=P.Pooling.MAX, kernel_size=3, stride=2)
n.norm2 = L.LRN(n.pool2, local_size=5, alpha=1e-4, beta=0.75)
n.fc500 = L.InnerProduct(n.norm2, num_output=500, param=[weight_param('fc500_w', learn_all=True), bias_param('fc500_b', learn_all=True)], weight_filler=weight_filler_fc, bias_filler=bias_filler_1)
n.relu3 = L.ReLU(n.fc500, in_place=True)
if is_train:
n.dropout = fc10input = L.Dropout(n.relu3, in_place=True)
else:
fc10input = n.relu3
# Learn all true because we always want to train the top classifier no matter if we are training from scratch or finetuning
n.fc10 = L.InnerProduct(fc10input, num_output=10, param=[weight_param('fc10_w', learn_all=True), bias_param('fc10_b', learn_all=True)], weight_filler=weight_filler_fc, bias_filler=bias_filler_1)
if is_train:
n.loss = L.SoftmaxWithLoss(n.fc10, n.label)
n.acc = L.Accuracy(n.fc10, n.label, include=dict(phase=caffe.TEST))
# Returning the name of the loss/acc layers is useful because then we can
# know which outputs of the net we can track to test the 'health'
# of the training process
return n, ('loss',), ('acc',)
@staticmethod
def siamese_egomotion(lmdb_path=None, labels_lmdb_path=None,
batch_size=125, scale=1.0, is_train=True, learn_all=False, sfa=False):
"""
Creates a protoxt for the AlexNet architecture for the MNIST experiment
Uses Egomotion as stated in the paper
:param lmdb_path: str. Path to train LMDB
:param labels_lmdb_path: str. Path to train LMDB labels
:param batch_size: int. Batch size
:param scale: float. How to scale the images
:param is_train: bool. Flag indicating if this is for testing or training
:param learn_all: bool. Flag indicating if we should learn all the layers from scratch
:returns: Caffe NetSpec, tuple with names of loss blobs, tuple with name of accuracy blobs
"""
n = caffe.NetSpec()
n.data, n.label = input_layers(lmdb_path=lmdb_path, labels_lmdb_path=labels_lmdb_path, batch_size=batch_size, scale=scale, is_train=is_train)
# Slice data/labels for MNIST
n.data0, n.data1 = L.Slice(n.data, slice_param=dict(axis=1, slice_point=1), ntop=2)
n.labelx, n.labely, n.labelz = L.Slice(n.label, slice_param=dict(axis=1, slice_point=[1,2]), ntop=3)
# BCNN
n.norm2, n.norm2_p = bcnn(n.data0, n.data1, n, learn_all, True)
# TCNN
n.concat = L.Concat(n.norm2, n.norm2_p, concat_param=dict(axis=1))
n.fc1000 = L.InnerProduct(n.concat, num_output=1000, param=[weight_param('fc1000_w', learn_all=True), bias_param('fc1000_b', learn_all=True)], weight_filler=weight_filler_fc, bias_filler=bias_filler_1)
n.relu3 = L.ReLU(n.fc1000, in_place=True)
if is_train:
n.dropout = fcxinput = fcyinput = fczinput = L.Dropout(n.relu3, in_place=True)
else:
fcxinput = fcyinput = fczinput = n.relu3
# Classifiers
n.fcx = L.InnerProduct(fcxinput, num_output=7, param=[weight_param('fcx_w', learn_all=True), bias_param('fcx_b', learn_all=True)], weight_filler=weight_filler_fc, bias_filler=bias_filler_1)
n.fcy = L.InnerProduct(fcyinput, num_output=7, param=[weight_param('fcy_w', learn_all=True), bias_param('fcy_b', learn_all=True)], weight_filler=weight_filler_fc, bias_filler=bias_filler_1)
n.fcz = L.InnerProduct(fczinput, num_output=20, param=[weight_param('fcz_w', learn_all=True), bias_param('fcz_b', learn_all=True)], weight_filler=weight_filler_fc, bias_filler=bias_filler_1)
n.loss_x = L.SoftmaxWithLoss(n.fcx, n.labelx)
n.loss_y = L.SoftmaxWithLoss(n.fcy, n.labely)
n.loss_z = L.SoftmaxWithLoss(n.fcz, n.labelz)
n.acc_x = L.Accuracy(n.fcx, n.labelx, include=dict(phase=caffe.TEST))
n.acc_y = L.Accuracy(n.fcy, n.labely, include=dict(phase=caffe.TEST))
n.acc_z = L.Accuracy(n.fcz, n.labelz, include=dict(phase=caffe.TEST))
return n, ('loss_x', 'loss_y', 'loss_z'), ('acc_x', 'acc_y', 'acc_z')
@staticmethod
def siamese_contrastive(lmdb_path=None, labels_lmdb_path=None,
batch_size=125, scale=1.0, contrastive_margin=10, is_train=True, learn_all=False, sfa=False):
"""
Creates a protoxt for the AlexNet architecture for the MNIST experiment
Uses Contrastive loss
:param lmdb_path: str. Path to train LMDB
:param labels_lmdb_path: str. Path to train LMDB labels
:param batch_size: int. Batch size
:param scale: float. How to scale the images
:param contrastive_margin: int. Margin for the contrastive loss layer
:param is_train: bool. Flag indicating if this is for testing or training
:param learn_all: bool. Flag indicating if we should learn all the layers from scratch
:returns: Caffe NetSpec, tuple with names of loss blobs, tuple with name of accuracy blobs
"""
n = caffe.NetSpec()
n.data, n.label = input_layers(lmdb_path=lmdb_path, labels_lmdb_path=labels_lmdb_path, batch_size=batch_size, scale=scale, is_train=is_train)
# Slice data/labels for MNIST
n.data0, n.data1 = L.Slice(n.data, slice_param=dict(axis=1, slice_point=1), ntop=2)
# BCNN
n.norm2, n.norm2_p = bcnn(n.data0, n.data1, n, learn_all, True)
# TCNNs
n.fc1 = L.InnerProduct(n.norm2, num_output=500, param=[weight_param('fc1_p_w', learn_all=True), bias_param('fc1_p_b', learn_all=True)], weight_filler=weight_filler_fc, bias_filler=bias_filler_1)
n.relu3 = L.ReLU(n.fc1, in_place=True)
n.dropout1 = L.Dropout(n.relu3, in_place=True)
n.fc2 = L.InnerProduct(n.relu3, num_output=100, param=[weight_param('fc2_p_w', learn_all=True), bias_param('fc2_p_b', learn_all=True)], weight_filler=weight_filler_fc, bias_filler=bias_filler_1)
n.fc1_p = L.InnerProduct(n.norm2_p, num_output=500, param=[weight_param('fc1_p_w', learn_all=True), bias_param('fc1_p_b', learn_all=True)], weight_filler=weight_filler_fc, bias_filler=bias_filler_1)
n.relu3_p = L.ReLU(n.fc1_p, in_place=True)
n.dropout1_p = L.Dropout(n.relu3_p, in_place=True)
n.fc2_p = L.InnerProduct(n.relu3_p, num_output=100, param=[weight_param('fc2_p_w', learn_all=True), bias_param('fc2_p_b', learn_all=True)], weight_filler=weight_filler_fc, bias_filler=bias_filler_1)
n.contrastive = L.ContrastiveLoss(n.fc2, n.fc2_p, n.label, contrastive_loss_param=dict(margin=contrastive_margin))
return n, ('contrastive',), None
class KITTINetFactory:
@staticmethod
def siamese_egomotion(lmdb_path=None, labels_lmdb_path=None, mean_file=None,
batch_size=125, scale=1.0, is_train=True, learn_all=True):
"""
Creates a protoxt for the AlexNet architecture
:param lmdb_path: str. Path to train LMDB
:param labels_lmdb_path: str. Path to train LMDB labels
:param test_lmdb: str. Path to train LMDB
:param test_labels_lmdb: str. Path to test LMDB labels
:param batch_size: int. Batch size
:param scale: float. How to scale the images
:param is_train: bool. Flag indicating if this is for testing or training
:param learn_all: bool. Flag indicating if we should learn all the layers from scratch
:returns: Caffe NetSpec, tuple with names of loss blobs, tuple with name of accuracy blobs
"""
n = caffe.NetSpec()
n.data, n.label = input_layers(lmdb_path=lmdb_path, labels_lmdb_path=labels_lmdb_path, mean_file=mean_file, batch_size=batch_size, scale=scale, is_train=is_train)
# Slice data/labels
n.data0, n.data1 = L.Slice(n.data, slice_param=dict(axis=1, slice_point=3), ntop=2)
n.labelx, n.labely, n.labelz = L.Slice(n.label, slice_param=dict(axis=1, slice_point=[1,2]), ntop=3)
# BCNN
relu5, relu5_p = bcnn(n.data0, n.data1, n, learn_all, False)
# TCNN
n.concat = L.Concat(relu5, relu5_p, concat_param=dict(axis=1))
n.conv6 = L.Convolution(n.concat, kernel_size=3, stride=2, num_output=256, param=[weight_param('conv6_w', learn_all=learn_all), bias_param('conv6_b', learn_all=learn_all)], weight_filler=weight_filler_fc, bias_filler=bias_filler_0)
n.relu6 = L.ReLU(n.conv6, in_place=True)
n.conv7 = L.Convolution(n.relu6, kernel_size=3, stride=2, num_output=128, param=[weight_param('conv7_w', learn_all=learn_all), bias_param('conv7_b', learn_all=learn_all)], weight_filler=weight_filler, bias_filler=bias_filler_0)
n.relu7 = L.ReLU(n.conv7, in_place=True)
n.fc7_ego = L.InnerProduct(n.relu7, num_output=500, param=[weight_param('fc7_ego_w', learn_all=True), bias_param('fc7_ego_b', learn_all=True)], weight_filler=weight_filler_fc, bias_filler=bias_filler_1)
n.relu8 = L.ReLU(n.fc7_ego, in_place=True)
if is_train:
n.drop = fcxinput = fcyinput = fczinput = L.Dropout(n.relu8, dropout_param=dict(dropout_ratio=0.5), in_place=True)
else:
fcxinput = fcyinput = fczinput = n.relu8
# Classifiers
n.fcx = L.InnerProduct(fcxinput, num_output=20, param=[weight_param('fcx_w', learn_all=True), bias_param('fcx_b', learn_all=True)], weight_filler=weight_filler_fc, bias_filler=bias_filler_1)
n.fcy = L.InnerProduct(fcyinput, num_output=20, param=[weight_param('fcy_w', learn_all=True), bias_param('fcy_b', learn_all=True)], weight_filler=weight_filler_fc, bias_filler=bias_filler_1)
n.fcz = L.InnerProduct(fczinput, num_output=20, param=[weight_param('fcz_w', learn_all=True), bias_param('fcz_b', learn_all=True)], weight_filler=weight_filler_fc, bias_filler=bias_filler_1)
if is_train:
n.loss_x = L.SoftmaxWithLoss(n.fcx, n.labelx)
n.loss_y = L.SoftmaxWithLoss(n.fcy, n.labely)
n.loss_z = L.SoftmaxWithLoss(n.fcz, n.labelz)
n.acc_x = L.Accuracy(n.fcx, n.labelx, include=dict(phase=caffe.TEST))
n.acc_y = L.Accuracy(n.fcy, n.labely, include=dict(phase=caffe.TEST))
n.acc_z = L.Accuracy(n.fcz, n.labelz, include=dict(phase=caffe.TEST))
return n, ('loss_x', 'loss_y', 'loss_z'), ('acc_x', 'acc_y', 'acc_z')
@staticmethod
def siamese_contrastive(lmdb_path=None, labels_lmdb_path=None, mean_file=None,
batch_size=125, scale=1.0, contrastive_margin=10, is_train=True, learn_all=True):
"""
Creates a protoxt for siamese AlexNet architecture with a contrastive loss layer on top
:param lmdb_path: str. Path to train LMDB
:param labels_lmdb_path: str. Path to train LMDB labels
:param test_lmdb: str. Path to train LMDB
:param test_labels_lmdb: str. Path to test LMDB labels
:param batch_size: int. Batch size
:param scale: float. How to scale the images
:param contrastive_margin: int. Margin for the contrastive loss layer
:param is_train: bool. Flag indicating if this is for testing or training
:param learn_all: bool. Flag indicating if we should learn all the layers from scratch
:returns: Caffe NetSpec, tuple with names of loss blobs, tuple with name of accuracy blobs
"""
n = caffe.NetSpec()
n.data, n.label = input_layers(lmdb_path=lmdb_path, labels_lmdb_path=labels_lmdb_path, mean_file=mean_file, batch_size=batch_size, scale=scale, is_train=is_train)
# Slice data/labels
n.data0, n.data1 = L.Slice(n.data, slice_param=dict(axis=1, slice_point=3), ntop=2)
# BCNN
relu5, relu5_p = bcnn(n.data0, n.data1, n, learn_all, False)
# TCNNs
n.fc1 = L.InnerProduct(relu5, num_output=500, param=[weight_param('fc1_p_w', learn_all=True), bias_param('fc1_p_b', learn_all=True)], weight_filler=weight_filler_fc, bias_filler=bias_filler_1)
n.relu6 = L.ReLU(n.fc1, in_place=True)
n.dropout1 = L.Dropout(n.relu6, in_place=True)
n.fc2 = L.InnerProduct(n.relu6, num_output=100, param=[weight_param('fc2_p_w', learn_all=True), bias_param('fc2_p_b', learn_all=True)], weight_filler=weight_filler_fc, bias_filler=bias_filler_1)
n.fc1_p = L.InnerProduct(relu5_p, num_output=500, param=[weight_param('fc1_p_w', learn_all=True), bias_param('fc1_p_b', learn_all=True)], weight_filler=weight_filler_fc, bias_filler=bias_filler_1)
n.relu6_p = L.ReLU(n.fc1_p, in_place=True)
n.dropout1_p = L.Dropout(n.relu6_p, in_place=True)
n.fc2_p = L.InnerProduct(n.relu6_p, num_output=100, param=[weight_param('fc2_p_w', learn_all=True), bias_param('fc2_p_b', learn_all=True)], weight_filler=weight_filler_fc, bias_filler=bias_filler_1)
n.contrastive = L.ContrastiveLoss(n.fc2, n.fc2_p, n.label, contrastive_loss_param=dict(margin=contrastive_margin))
return n, ('contrastive',), None
@staticmethod
def standar(lmdb_path=None, labels_lmdb_path=None, batch_size=126, mean_file=None,
scale=1.0, is_train=True, num_classes=397, learn_all=True, layers='5', is_imagenet=False):
"""
Creates a protoxt for the AlexNet architecture
:param lmdb_path: str. Path to train LMDB
:param labels_lmdb_path: str. Path to train LMDB labels
:param test_lmdb: str. Path to train LMDB
:param test_labels_lmdb: str. Path to test LMDB labels
:param batch_size: int. Batch size
:param scale: float. How to scale the images
:param is_train: bool. Flag indicating if this is for testing or training
:param num_classes: int. number of classes for the top classifier
:param classifier_name: str. name of the top classifier
:param learn_all: bool. Flag indicating if we should learn all the layers from scratch
:param layers: str. from which layer we will extract features to train a classifier
:returns: Caffe NetSpec, tuple with names of loss blobs, tuple with name of accuracy blobs
"""
n = caffe.NetSpec()
n.data, n.label = input_layers(lmdb_path=lmdb_path, labels_lmdb_path=labels_lmdb_path, mean_file=mean_file, batch_size=batch_size, scale=scale, is_train=is_train)
n.conv1 = L.Convolution(n.data, kernel_size=11, stride=4, num_output=96, param=[weight_param('conv1_w', learn_all=learn_all), bias_param('conv1_b', learn_all=learn_all)], weight_filler=weight_filler, bias_filler=bias_filler_0)
n.relu1 = L.ReLU(n.conv1, in_place=True)
n.pool1 = L.Pooling(n.relu1, pool=P.Pooling.MAX, kernel_size=3, stride=2)
n.norm1 = L.LRN(n.pool1, local_size=5, alpha=1e-4, beta=0.75)
if layers == '1':
n.fc_intermediate = L.InnerProduct(n.norm1, num_output=num_classes, param=[weight_param('fc_intermediate_w', learn_all=True), bias_param('fc_intermediate_b', learn_all=True)], weight_filler=weight_filler_fc, bias_filler=bias_filler_1)
if is_train:
n.loss = L.SoftmaxWithLoss(n.fc_intermediate, n.label)
n.acc = L.Accuracy(n.fc_intermediate, n.label, include=dict(phase=caffe.TEST))
return n, ('loss',), ('acc',)
n.conv2 = L.Convolution(n.norm1, kernel_size=5, num_output=256, pad=2, group=2, param=[weight_param('conv2_w', learn_all=learn_all), bias_param('conv2_b', learn_all=learn_all)], weight_filler=weight_filler, bias_filler=bias_filler_0)
n.relu2 = L.ReLU(n.conv2, in_place=True)
n.pool2 = L.Pooling(n.relu2, pool=P.Pooling.MAX, kernel_size=3, stride=2)
n.norm2 = L.LRN(n.pool2, local_size=5, alpha=1e-4, beta=0.75)
if layers == '2':
n.fc_intermediate = L.InnerProduct(n.norm2, num_output=num_classes, param=[weight_param('fc_intermediate_w', learn_all=True), bias_param('fc_intermediate_b', learn_all=True)], weight_filler=weight_filler_fc, bias_filler=bias_filler_1)
if is_train:
n.loss = L.SoftmaxWithLoss(n.fc_intermediate, n.label)
n.acc = L.Accuracy(n.fc_intermediate, n.label, include=dict(phase=caffe.TEST))
return n, ('loss',), ('acc',)
n.conv3 = L.Convolution(n.norm2, kernel_size=3, num_output=384, pad=1, param=[weight_param('conv3_w', learn_all=learn_all), bias_param('conv3_b', learn_all=learn_all)], weight_filler=weight_filler, bias_filler=bias_filler_0)
n.relu3 = L.ReLU(n.conv3, in_place=True)
if layers == '3':
n.fc_prev = L.InnerProduct(n.relu3, num_output=1000, param=[weight_param('fc_prev_w', learn_all=True), bias_param('fc_prev_b', learn_all=True)], weight_filler=weight_filler_fc, bias_filler=bias_filler_1)
n.relu_prev = L.ReLU(n.fc_prev, in_place=True)
n.fc_intermediate = L.InnerProduct(n.relu_prev, num_output=num_classes, param=[weight_param('fc_intermediate_w', learn_all=True), bias_param('fc_intermediate_b', learn_all=True)], weight_filler=weight_filler_fc, bias_filler=bias_filler_1)
if is_train:
n.loss = L.SoftmaxWithLoss(n.fc_intermediate, n.label)
n.acc = L.Accuracy(n.fc_intermediate, n.label, include=dict(phase=caffe.TEST))
return n, ('loss',), ('acc',)
n.conv4 = L.Convolution(n.relu3, kernel_size=3, num_output=384, pad=1, group=2, param=[weight_param('conv4_w', learn_all=learn_all), bias_param('conv4_b', learn_all=learn_all)], weight_filler=weight_filler, bias_filler=bias_filler_0)
n.relu4 = L.ReLU(n.conv4, in_place=True)
if layers == '4':
n.fc_prev = L.InnerProduct(n.relu4, num_output=1000, param=[weight_param('fc_prev_w', learn_all=True), bias_param('fc_prev_b', learn_all=True)], weight_filler=weight_filler_fc, bias_filler=bias_filler_1)
n.relu_prev = L.ReLU(n.fc_prev, in_place=True)
n.fc_intermediate = L.InnerProduct(n.relu_prev, num_output=num_classes, param=[weight_param('fc_intermediate_w', learn_all=True), bias_param('fc_intermediate_b', learn_all=True)], weight_filler=weight_filler_fc, bias_filler=bias_filler_1)
if is_train:
n.loss = L.SoftmaxWithLoss(n.fc_intermediate, n.label)
n.acc = L.Accuracy(n.fc_intermediate, n.label, include=dict(phase=caffe.TEST))
return n, ('loss',), ('acc',)
n.conv5 = L.Convolution(n.relu4, kernel_size=3, num_output=256, pad=1, group=2, param=[weight_param('conv5_w', learn_all=learn_all), bias_param('conv5_b', learn_all=learn_all)], weight_filler=weight_filler, bias_filler=bias_filler_0)
n.relu5 = L.ReLU(n.conv5, in_place=True)
if not is_imagenet:
if layers == '5':
n.fc_prev = L.InnerProduct(n.relu5, num_output=1000, param=[weight_param('fc_prev_w', learn_all=True), bias_param('fc_prev_b', learn_all=True)], weight_filler=weight_filler_fc, bias_filler=bias_filler_1)
n.relu_prev = L.ReLU(n.fc_prev, in_place=True)
n.fc_intermediate = L.InnerProduct(n.relu_prev, num_output=num_classes, param=[weight_param('fc_intermediate_w', learn_all=True), bias_param('fc_intermediate_b', learn_all=True)], weight_filler=weight_filler_fc, bias_filler=bias_filler_1)
if is_train:
n.loss = L.SoftmaxWithLoss(n.fc_intermediate, n.label)
n.acc = L.Accuracy(n.fc_intermediate, n.label, include=dict(phase=caffe.TEST))
return n, ('loss',), ('acc',)
n.fc6 = L.InnerProduct(n.relu5, num_output=4096, param=[weight_param('fc6_w', learn_all=True), bias_param('fc6_b', learn_all=learn_all)], weight_filler=weight_filler_fc, bias_filler=bias_filler_1)
n.relu6 = L.ReLU(n.fc6, in_place=True)
if is_train:
n.drop6 = fc7input = L.Dropout(n.relu6, in_place=True)
else:
fc7input = n.relu6
n.fc7 = L.InnerProduct(fc7input, num_output=4096, param=[weight_param('fc7_w', learn_all=True), bias_param('fc7_b', learn_all=True)], weight_filler=weight_filler_fc, bias_filler=bias_filler_1)
n.relu7 = L.ReLU(n.fc7, in_place=True)
if is_train:
n.drop7 = fc8input = L.Dropout(n.relu7, in_place=True)
else:
fc8input = n.relu7
n.fc8 = L.InnerProduct(fc8input, num_output=num_classes, param=[weight_param('fc8_w', learn_all=True), bias_param('fc8_b', learn_all=True)], weight_filler=weight_filler_fc, bias_filler=bias_filler_1)
if is_train:
n.loss = L.SoftmaxWithLoss(n.fc8, n.label)
n.acc = L.Accuracy(n.fc8, n.label, include=dict(phase=caffe.TEST))
else:
if layers == '5':
n.fc_imgnet = L.InnerProduct(n.relu5, num_output=num_classes, param=[weight_param('fc_w', learn_all=True), bias_param('fc_b', learn_all=True)], weight_filler=weight_filler_fc, bias_filler=bias_filler_1)
if is_train:
n.loss = L.SoftmaxWithLoss(n.fc_imgnet, n.label)
n.acc = L.Accuracy(n.fc_imgnet, n.label, include=dict(phase=caffe.TEST))
return n, ('loss',), ('acc',)
n.fc6_imgnet = L.InnerProduct(n.relu5, num_output=4096, param=[weight_param('fc6_w', learn_all=True), bias_param('fc6_b', learn_all=learn_all)], weight_filler=weight_filler_fc, bias_filler=bias_filler_1)
n.relu6 = L.ReLU(n.fc6_imgnet, in_place=True)
if is_train:
n.drop6 = fc7input = L.Dropout(n.relu6, in_place=True)
else:
fc7input = n.relu6
n.fc7_imgnet = L.InnerProduct(fc7input, num_output=4096, param=[weight_param('fc7_w', learn_all=True), bias_param('fc7_b', learn_all=True)], weight_filler=weight_filler_fc, bias_filler=bias_filler_1)
n.relu7 = L.ReLU(n.fc7_imgnet, in_place=True)
if is_train:
n.drop7 = fc8input = L.Dropout(n.relu7, in_place=True)
else:
fc8input = n.relu7
n.fc8_imgnet = L.InnerProduct(fc8input, num_output=num_classes, param=[weight_param('fc8_w', learn_all=True), bias_param('fc8_b', learn_all=True)], weight_filler=weight_filler_fc, bias_filler=bias_filler_1)
if is_train:
n.loss = L.SoftmaxWithLoss(n.fc8_imgnet, n.label)
n.acc = L.Accuracy(n.fc8_imgnet, n.label, include=dict(phase=caffe.TEST))
return n, ('loss',), ('acc',)
| 61.319797
| 254
| 0.683733
| 3,825
| 24,160
| 4.072157
| 0.062745
| 0.064201
| 0.0547
| 0.064715
| 0.900616
| 0.888354
| 0.860683
| 0.830573
| 0.797509
| 0.793015
| 0
| 0.027645
| 0.195985
| 24,160
| 393
| 255
| 61.475827
| 0.774208
| 0.166556
| 0
| 0.564815
| 0
| 0
| 0.040963
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| 0
| 0
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| 0
| 0
| 1
| 0.027778
| false
| 0
| 0.013889
| 0
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| null | 0
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| 1
| 1
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| null | 0
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| 0
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| 0
| 0
| 0
|
0
| 6
|
f95d80f6b2f10443f11498a8fbea081fb1bc38e2
| 38
|
py
|
Python
|
src/affe/tests/__init__.py
|
eliavw/affe
|
0e57d7f40cb67f9a300292e03e3f83b4b591d1e3
|
[
"MIT"
] | 1
|
2020-12-02T06:16:00.000Z
|
2020-12-02T06:16:00.000Z
|
src/affe/tests/__init__.py
|
eliavw/affe
|
0e57d7f40cb67f9a300292e03e3f83b4b591d1e3
|
[
"MIT"
] | null | null | null |
src/affe/tests/__init__.py
|
eliavw/affe
|
0e57d7f40cb67f9a300292e03e3f83b4b591d1e3
|
[
"MIT"
] | null | null | null |
from .resources import get_dummy_flow
| 19
| 37
| 0.868421
| 6
| 38
| 5.166667
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0.105263
| 38
| 1
| 38
| 38
| 0.911765
| 0
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| 0
| 1
| 0
| true
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| 1
| 1
| 0
| null | 0
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| 0
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| 0
| 0
| 0
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| null | 0
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| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
f9bdd7bc3ffc492d558ea976403f626c443d40c0
| 1,840
|
py
|
Python
|
tests/test_logic/test_tree/test_functions.py
|
cdhiraj40/wemake-python-styleguide
|
7cef9be081d594c30045b7a98cae77a9be46e1aa
|
[
"MIT"
] | 1,931
|
2018-03-17T13:52:45.000Z
|
2022-03-27T09:39:17.000Z
|
tests/test_logic/test_tree/test_functions.py
|
amansr02/wemake-python-styleguide
|
681035ed21fbe28ebfb32b8807b98e8de76b64aa
|
[
"MIT"
] | 2,231
|
2018-03-09T21:19:05.000Z
|
2022-03-31T08:35:37.000Z
|
tests/test_logic/test_tree/test_functions.py
|
amansr02/wemake-python-styleguide
|
681035ed21fbe28ebfb32b8807b98e8de76b64aa
|
[
"MIT"
] | 492
|
2018-05-18T21:20:28.000Z
|
2022-03-20T14:11:50.000Z
|
import pytest
from wemake_python_styleguide.logic.tree import functions
@pytest.mark.parametrize(('function_call', 'function_name'), [
# Simple builtin functions
('print("Hello world!")', 'print'),
('int("10")', 'int'),
('bool(1)', 'bool'),
('open("/tmp/file.txt", "r")', 'open'),
('str(10)', 'str'),
# Functions in modules
('datetime.timedelta(days=1)', 'datetime.timedelta'),
('cmath.sqrt(100)', 'cmath.sqrt'),
# Functions in (made up) objects
('dt.strftime("%H:%M")', 'dt.strftime'),
('obj.funct()', 'obj.funct'),
])
def test_given_function_called_no_split(
parse_ast_tree, function_call: str, function_name: str,
) -> None:
"""Test given_function_called without splitting the modules."""
tree = parse_ast_tree(function_call)
node = tree.body[0].value
called_function = functions.given_function_called(node, [function_name])
assert called_function == function_name
@pytest.mark.parametrize(('function_call', 'function_name'), [
# Simple builtin functions
('print("Hello world!")', 'print'),
('int("10")', 'int'),
('bool(1)', 'bool'),
('open("/tmp/file.txt", "r")', 'open'),
('str(10)', 'str'),
# Functions in modules
('datetime.timedelta(days=1)', 'timedelta'),
('cmath.sqrt(100)', 'sqrt'),
# Functions in (made up) objects
('dt.strftime("%H:%M")', 'strftime'),
('obj.funct()', 'funct'),
])
def test_given_function_called_with_split(
parse_ast_tree, function_call: str, function_name: str,
) -> None:
"""Test given_function_called splitting the modules."""
tree = parse_ast_tree(function_call)
node = tree.body[0].value
called_function = functions.given_function_called(
node,
[function_name],
split_modules=True,
)
assert called_function == function_name
| 30.666667
| 76
| 0.636957
| 224
| 1,840
| 5.022321
| 0.290179
| 0.085333
| 0.101333
| 0.081778
| 0.837333
| 0.780444
| 0.725333
| 0.725333
| 0.725333
| 0.725333
| 0
| 0.013201
| 0.17663
| 1,840
| 59
| 77
| 31.186441
| 0.729373
| 0.142391
| 0
| 0.571429
| 0
| 0
| 0.286812
| 0.060179
| 0
| 0
| 0
| 0
| 0.047619
| 1
| 0.047619
| false
| 0
| 0.047619
| 0
| 0.095238
| 0.047619
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
f9c085c8c1b92b87c14d3482f5a9791aa3b801ae
| 106
|
py
|
Python
|
weld-python/weld/grizzly/core/indexes/base.py
|
tustvold/weld
|
dcbba9a45ae2a190b31badec530ea54a58437606
|
[
"BSD-3-Clause"
] | 2,912
|
2017-03-16T19:32:54.000Z
|
2022-03-30T09:03:11.000Z
|
weld-python/weld/grizzly/core/indexes/base.py
|
QiangHeisenberg/weld
|
0926f84f6f4361e40842fcd6e00b7afdcc10a87f
|
[
"BSD-3-Clause"
] | 285
|
2017-03-16T18:01:00.000Z
|
2021-08-12T10:58:23.000Z
|
weld-python/weld/grizzly/core/indexes/base.py
|
QiangHeisenberg/weld
|
0926f84f6f4361e40842fcd6e00b7afdcc10a87f
|
[
"BSD-3-Clause"
] | 272
|
2017-03-17T06:28:58.000Z
|
2022-02-24T04:22:02.000Z
|
from abc import ABC
class Index(ABC):
"""
Base class for an index in Grizzly.
"""
pass
| 10.6
| 39
| 0.575472
| 15
| 106
| 4.066667
| 0.733333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.330189
| 106
| 9
| 40
| 11.777778
| 0.859155
| 0.330189
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.333333
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 1
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| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 6
|
ddbbdc7b1e6e50953351ff74a0aa07962f6666af
| 58
|
py
|
Python
|
jd/api/__init__.py
|
fengjinqi/linjuanbang
|
8cdc4e81df73ccd737ac547da7f2c7dca545862a
|
[
"MIT"
] | 5
|
2019-10-30T01:16:30.000Z
|
2020-06-14T03:32:19.000Z
|
jd/api/__init__.py
|
fengjinqi/linjuanbang
|
8cdc4e81df73ccd737ac547da7f2c7dca545862a
|
[
"MIT"
] | 2
|
2020-10-12T07:12:48.000Z
|
2021-06-02T03:15:47.000Z
|
jd/api/__init__.py
|
fengjinqi/linjuanbang
|
8cdc4e81df73ccd737ac547da7f2c7dca545862a
|
[
"MIT"
] | 3
|
2019-12-06T17:33:49.000Z
|
2021-03-01T13:24:22.000Z
|
from jd.api.rest import *
from jd.api.base import FileItem
| 29
| 32
| 0.793103
| 11
| 58
| 4.181818
| 0.636364
| 0.26087
| 0.391304
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0.12069
| 58
| 2
| 32
| 29
| 0.901961
| 0
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| 0
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| 0
| 0
| 1
| 0
| true
| 0
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| 1
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| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
fb06cfeab0c901cc01b748c3466219b1ee6e39dc
| 120
|
py
|
Python
|
musictools/custom_exceptions.py
|
zfazli/zfmusicc
|
7620b70699f26837b30dc4039cb997ec3c2b5cc3
|
[
"MIT"
] | 49
|
2017-03-14T14:35:31.000Z
|
2017-04-07T09:15:29.000Z
|
musictools/custom_exceptions.py
|
zulfazliansyah/music
|
7620b70699f26837b30dc4039cb997ec3c2b5cc3
|
[
"MIT"
] | 5
|
2017-09-13T02:58:01.000Z
|
2021-07-12T10:23:48.000Z
|
musictools/custom_exceptions.py
|
zulfazliansyah/music
|
7620b70699f26837b30dc4039cb997ec3c2b5cc3
|
[
"MIT"
] | 7
|
2017-06-21T13:21:20.000Z
|
2020-09-11T21:31:36.000Z
|
class SongNotFound(Exception):
def __init__(self, message, dErrorArg):
Exception.__init__(self, message, dErrorArg)
| 24
| 46
| 0.783333
| 13
| 120
| 6.615385
| 0.615385
| 0.186047
| 0.348837
| 0.55814
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.108333
| 120
| 4
| 47
| 30
| 0.803738
| 0
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| 0
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| 0
| 0
| 0
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| 1
| 0.333333
| false
| 0
| 0
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| 0.666667
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| 0
| null | 0
| 1
| 1
| 0
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| 0
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| 0
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| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 6
|
fb12e527ea3fb00741b7c06ae9f7e2c83dd9fa0b
| 121
|
py
|
Python
|
blog/views/post_view.py
|
ShahadatShuvo/blood_lagbe
|
c5edb52bf20c084425e2a89e3bddc7e5705edf30
|
[
"Apache-2.0"
] | 3
|
2021-04-24T16:30:09.000Z
|
2021-06-19T08:02:22.000Z
|
blog/views/post_view.py
|
ShahadatShuvo/blood_lagbe
|
c5edb52bf20c084425e2a89e3bddc7e5705edf30
|
[
"Apache-2.0"
] | 16
|
2021-04-24T07:44:34.000Z
|
2021-04-28T17:12:25.000Z
|
blog/views/post_view.py
|
ShahadatShuvo/blood_lagbe
|
c5edb52bf20c084425e2a89e3bddc7e5705edf30
|
[
"Apache-2.0"
] | 4
|
2021-04-24T23:42:51.000Z
|
2021-06-20T16:53:00.000Z
|
from django.shortcuts import render
def postView(request, id):
return render(request, 'blog/blog.html', context={})
| 24.2
| 56
| 0.735537
| 16
| 121
| 5.5625
| 0.8125
| 0
| 0
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| 0
| 0
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| 0
| 0
| 0.132231
| 121
| 5
| 56
| 24.2
| 0.847619
| 0
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| 0
| 0.114754
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0.333333
| 0.333333
| 1
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| 1
| 0
| 0
| null | 0
| 0
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| 0
| 0
| 0
| 0
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| 0
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| 1
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| null | 0
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| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 1
| 1
| 0
|
0
| 6
|
fb1dcdfba92bbedd533afca86a61f62410952118
| 159
|
py
|
Python
|
backend/schema.py
|
ReynaldoCC/arango-backend
|
fe7e28b7ee266ce9b50054758018cfad976bc7c3
|
[
"BSD-3-Clause"
] | null | null | null |
backend/schema.py
|
ReynaldoCC/arango-backend
|
fe7e28b7ee266ce9b50054758018cfad976bc7c3
|
[
"BSD-3-Clause"
] | null | null | null |
backend/schema.py
|
ReynaldoCC/arango-backend
|
fe7e28b7ee266ce9b50054758018cfad976bc7c3
|
[
"BSD-3-Clause"
] | null | null | null |
from abc import ABC
from django.db.backends.base.schema import BaseDatabaseSchemaEditor
class DatabaseSchemaEditor(BaseDatabaseSchemaEditor, ABC):
pass
| 19.875
| 67
| 0.830189
| 17
| 159
| 7.764706
| 0.705882
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.119497
| 159
| 7
| 68
| 22.714286
| 0.942857
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.25
| 0.5
| 0
| 0.75
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 6
|
34bb50770b6d16ee92d7df01119fd24059f26213
| 21
|
py
|
Python
|
Unidad 2/packages/extra/ugly/omega.py
|
angelxehg/utzac-ppy
|
fb88bcc661518bb35c08a102a67c20d0659f71db
|
[
"MIT"
] | null | null | null |
Unidad 2/packages/extra/ugly/omega.py
|
angelxehg/utzac-ppy
|
fb88bcc661518bb35c08a102a67c20d0659f71db
|
[
"MIT"
] | null | null | null |
Unidad 2/packages/extra/ugly/omega.py
|
angelxehg/utzac-ppy
|
fb88bcc661518bb35c08a102a67c20d0659f71db
|
[
"MIT"
] | null | null | null |
def funO():
pass
| 7
| 11
| 0.52381
| 3
| 21
| 3.666667
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.333333
| 21
| 2
| 12
| 10.5
| 0.785714
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| true
| 0.5
| 0
| 0
| 0.5
| 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
| 1
| 0
| 0
| 0
| 0
|
0
| 6
|
55069127b3a4bb0fc89225d0f89824fe31ac92cf
| 384
|
py
|
Python
|
utils/__init__.py
|
JacobChen258/AI-Constraints-Satisfaction
|
9b01cfce447e40678eb2e426413b4e2e437257f0
|
[
"MIT"
] | null | null | null |
utils/__init__.py
|
JacobChen258/AI-Constraints-Satisfaction
|
9b01cfce447e40678eb2e426413b4e2e437257f0
|
[
"MIT"
] | null | null | null |
utils/__init__.py
|
JacobChen258/AI-Constraints-Satisfaction
|
9b01cfce447e40678eb2e426413b4e2e437257f0
|
[
"MIT"
] | null | null | null |
from .directions import Direction
from .directions import direction_to_vector
from .directions import vector_to_direction
from .constants import ASSETS
from .constants import LINE_LIMIT
from .constants import TILESIZE
from .constants import TETROMINO_GRID_SIZE
from .constants import BORDER
from .utils import load_grid
from .matrix_util import MatrixUtil
from .gframe import GFrame
| 32
| 43
| 0.854167
| 53
| 384
| 6.018868
| 0.396226
| 0.203762
| 0.297806
| 0.181818
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.117188
| 384
| 11
| 44
| 34.909091
| 0.941003
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
9b64b2a7431b2238a96db3dce1afd55264830e4e
| 36
|
py
|
Python
|
src/melissa/__init__.py
|
aleksandrgordienko/melissa-quiz
|
49b165acc9aae0ad84cf751cbeb4f6a27dd5ab0f
|
[
"MIT"
] | null | null | null |
src/melissa/__init__.py
|
aleksandrgordienko/melissa-quiz
|
49b165acc9aae0ad84cf751cbeb4f6a27dd5ab0f
|
[
"MIT"
] | null | null | null |
src/melissa/__init__.py
|
aleksandrgordienko/melissa-quiz
|
49b165acc9aae0ad84cf751cbeb4f6a27dd5ab0f
|
[
"MIT"
] | null | null | null |
from melissa.melissa import Melissa
| 18
| 35
| 0.861111
| 5
| 36
| 6.2
| 0.6
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.111111
| 36
| 1
| 36
| 36
| 0.96875
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
9b7c8b16f664fc8e6e0bdb34685ccbe413103c9b
| 82
|
py
|
Python
|
plugins/supervisor/__init__.py
|
ajenti/ajen
|
177c1a67278a7763ed06eb2f773d7b409a85ec77
|
[
"MIT"
] | 3,777
|
2015-02-21T00:10:12.000Z
|
2022-03-30T15:33:22.000Z
|
plugins/supervisor/__init__.py
|
ajenti/ajen
|
177c1a67278a7763ed06eb2f773d7b409a85ec77
|
[
"MIT"
] | 749
|
2015-03-12T14:17:03.000Z
|
2022-03-25T13:22:28.000Z
|
plugins/supervisor/__init__.py
|
ajenti/ajen
|
177c1a67278a7763ed06eb2f773d7b409a85ec77
|
[
"MIT"
] | 687
|
2015-03-21T10:42:33.000Z
|
2022-03-21T23:18:12.000Z
|
# pyflakes: disable-all
from .api import *
from .aug import *
from .main import *
| 16.4
| 23
| 0.707317
| 12
| 82
| 4.833333
| 0.666667
| 0.344828
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.182927
| 82
| 4
| 24
| 20.5
| 0.865672
| 0.256098
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
9b8c163ee58a3767776a54eaed4e118faa56927f
| 7,793
|
py
|
Python
|
foundation/organisation/migrations/0001_initial.py
|
Mindelirium/foundation
|
2d07e430915d696ca7376afea633692119c4d30e
|
[
"MIT"
] | null | null | null |
foundation/organisation/migrations/0001_initial.py
|
Mindelirium/foundation
|
2d07e430915d696ca7376afea633692119c4d30e
|
[
"MIT"
] | null | null | null |
foundation/organisation/migrations/0001_initial.py
|
Mindelirium/foundation
|
2d07e430915d696ca7376afea633692119c4d30e
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
from south.utils import datetime_utils as datetime
from south.db import db
from south.v2 import SchemaMigration
from django.db import models
class Migration(SchemaMigration):
def forwards(self, orm):
# Adding model 'Person'
db.create_table(u'organisation_person', (
(u'id', self.gf('django.db.models.fields.AutoField')(primary_key=True)),
('created_at', self.gf('django.db.models.fields.DateTimeField')(auto_now_add=True, blank=True)),
('updated_at', self.gf('django.db.models.fields.DateTimeField')(auto_now=True, blank=True)),
('name', self.gf('django.db.models.fields.CharField')(max_length=100)),
('email', self.gf('django.db.models.fields.EmailField')(max_length=75)),
('photo', self.gf('django.db.models.fields.files.ImageField')(max_length=100, blank=True)),
('twitter', self.gf('django.db.models.fields.CharField')(max_length=18, blank=True)),
('url', self.gf('django.db.models.fields.URLField')(max_length=200, blank=True)),
))
db.send_create_signal(u'organisation', ['Person'])
# Adding model 'Unit'
db.create_table(u'organisation_unit', (
(u'id', self.gf('django.db.models.fields.AutoField')(primary_key=True)),
('created_at', self.gf('django.db.models.fields.DateTimeField')(auto_now_add=True, blank=True)),
('updated_at', self.gf('django.db.models.fields.DateTimeField')(auto_now=True, blank=True)),
('name', self.gf('django.db.models.fields.CharField')(max_length=100)),
))
db.send_create_signal(u'organisation', ['Unit'])
# Adding model 'UnitMembership'
db.create_table(u'organisation_unitmembership', (
(u'id', self.gf('django.db.models.fields.AutoField')(primary_key=True)),
('created_at', self.gf('django.db.models.fields.DateTimeField')(auto_now_add=True, blank=True)),
('updated_at', self.gf('django.db.models.fields.DateTimeField')(auto_now=True, blank=True)),
('title', self.gf('django.db.models.fields.CharField')(max_length=100)),
('person', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['organisation.Person'])),
('unit', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['organisation.Unit'])),
))
db.send_create_signal(u'organisation', ['UnitMembership'])
# Adding model 'Board'
db.create_table(u'organisation_board', (
(u'id', self.gf('django.db.models.fields.AutoField')(primary_key=True)),
('created_at', self.gf('django.db.models.fields.DateTimeField')(auto_now_add=True, blank=True)),
('updated_at', self.gf('django.db.models.fields.DateTimeField')(auto_now=True, blank=True)),
('name', self.gf('django.db.models.fields.CharField')(max_length=100)),
('description', self.gf('django.db.models.fields.TextField')()),
))
db.send_create_signal(u'organisation', ['Board'])
# Adding model 'BoardMembership'
db.create_table(u'organisation_boardmembership', (
(u'id', self.gf('django.db.models.fields.AutoField')(primary_key=True)),
('created_at', self.gf('django.db.models.fields.DateTimeField')(auto_now_add=True, blank=True)),
('updated_at', self.gf('django.db.models.fields.DateTimeField')(auto_now=True, blank=True)),
('title', self.gf('django.db.models.fields.CharField')(max_length=100)),
('person', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['organisation.Person'])),
('board', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['organisation.Board'])),
))
db.send_create_signal(u'organisation', ['BoardMembership'])
def backwards(self, orm):
# Deleting model 'Person'
db.delete_table(u'organisation_person')
# Deleting model 'Unit'
db.delete_table(u'organisation_unit')
# Deleting model 'UnitMembership'
db.delete_table(u'organisation_unitmembership')
# Deleting model 'Board'
db.delete_table(u'organisation_board')
# Deleting model 'BoardMembership'
db.delete_table(u'organisation_boardmembership')
models = {
u'organisation.board': {
'Meta': {'object_name': 'Board'},
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'description': ('django.db.models.fields.TextField', [], {}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'})
},
u'organisation.boardmembership': {
'Meta': {'object_name': 'BoardMembership'},
'board': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['organisation.Board']"}),
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'person': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['organisation.Person']"}),
'title': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'})
},
u'organisation.person': {
'Meta': {'object_name': 'Person'},
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'email': ('django.db.models.fields.EmailField', [], {'max_length': '75'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'photo': ('django.db.models.fields.files.ImageField', [], {'max_length': '100', 'blank': 'True'}),
'twitter': ('django.db.models.fields.CharField', [], {'max_length': '18', 'blank': 'True'}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}),
'url': ('django.db.models.fields.URLField', [], {'max_length': '200', 'blank': 'True'})
},
u'organisation.unit': {
'Meta': {'object_name': 'Unit'},
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'})
},
u'organisation.unitmembership': {
'Meta': {'object_name': 'UnitMembership'},
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'person': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['organisation.Person']"}),
'title': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'unit': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['organisation.Unit']"}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'})
}
}
complete_apps = ['organisation']
| 59.946154
| 115
| 0.603747
| 889
| 7,793
| 5.163105
| 0.086614
| 0.102832
| 0.176906
| 0.252723
| 0.820915
| 0.755991
| 0.712636
| 0.712636
| 0.694771
| 0.693464
| 0
| 0.00825
| 0.191197
| 7,793
| 130
| 116
| 59.946154
| 0.719975
| 0.035801
| 0
| 0.466019
| 0
| 0
| 0.485006
| 0.312675
| 0
| 0
| 0
| 0
| 0
| 1
| 0.019417
| false
| 0
| 0.038835
| 0
| 0.087379
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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0
| 6
|
fd5aaecf58a2361f54fe46788333b514263a9988
| 39
|
py
|
Python
|
torchnet/containers/models/__init__.py
|
a5chin/torchnet
|
6895735a3def7be03b04ae330d06eaf7e6258f10
|
[
"MIT"
] | null | null | null |
torchnet/containers/models/__init__.py
|
a5chin/torchnet
|
6895735a3def7be03b04ae330d06eaf7e6258f10
|
[
"MIT"
] | null | null | null |
torchnet/containers/models/__init__.py
|
a5chin/torchnet
|
6895735a3def7be03b04ae330d06eaf7e6258f10
|
[
"MIT"
] | null | null | null |
from .classification import Classifier
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0
| 6
|
b5cac31572b2496bccbfdfd8efd0fdf9ff3a7b3a
| 8,700
|
py
|
Python
|
backend/lib/google/cloud/grpc/datastore/v1/datastore_pb2_grpc.py
|
isaiah-solo/Droptalk
|
578a647adceecfae9d30ca6b98fdaae7077d683f
|
[
"MIT"
] | null | null | null |
backend/lib/google/cloud/grpc/datastore/v1/datastore_pb2_grpc.py
|
isaiah-solo/Droptalk
|
578a647adceecfae9d30ca6b98fdaae7077d683f
|
[
"MIT"
] | null | null | null |
backend/lib/google/cloud/grpc/datastore/v1/datastore_pb2_grpc.py
|
isaiah-solo/Droptalk
|
578a647adceecfae9d30ca6b98fdaae7077d683f
|
[
"MIT"
] | null | null | null |
import grpc
from grpc.framework.common import cardinality
from grpc.framework.interfaces.face import utilities as face_utilities
import google.cloud.grpc.datastore.v1.datastore_pb2 as google_dot_cloud_dot_grpc_dot_datastore_dot_v1_dot_datastore__pb2
import google.cloud.grpc.datastore.v1.datastore_pb2 as google_dot_cloud_dot_grpc_dot_datastore_dot_v1_dot_datastore__pb2
import google.cloud.grpc.datastore.v1.datastore_pb2 as google_dot_cloud_dot_grpc_dot_datastore_dot_v1_dot_datastore__pb2
import google.cloud.grpc.datastore.v1.datastore_pb2 as google_dot_cloud_dot_grpc_dot_datastore_dot_v1_dot_datastore__pb2
import google.cloud.grpc.datastore.v1.datastore_pb2 as google_dot_cloud_dot_grpc_dot_datastore_dot_v1_dot_datastore__pb2
import google.cloud.grpc.datastore.v1.datastore_pb2 as google_dot_cloud_dot_grpc_dot_datastore_dot_v1_dot_datastore__pb2
import google.cloud.grpc.datastore.v1.datastore_pb2 as google_dot_cloud_dot_grpc_dot_datastore_dot_v1_dot_datastore__pb2
import google.cloud.grpc.datastore.v1.datastore_pb2 as google_dot_cloud_dot_grpc_dot_datastore_dot_v1_dot_datastore__pb2
import google.cloud.grpc.datastore.v1.datastore_pb2 as google_dot_cloud_dot_grpc_dot_datastore_dot_v1_dot_datastore__pb2
import google.cloud.grpc.datastore.v1.datastore_pb2 as google_dot_cloud_dot_grpc_dot_datastore_dot_v1_dot_datastore__pb2
import google.cloud.grpc.datastore.v1.datastore_pb2 as google_dot_cloud_dot_grpc_dot_datastore_dot_v1_dot_datastore__pb2
import google.cloud.grpc.datastore.v1.datastore_pb2 as google_dot_cloud_dot_grpc_dot_datastore_dot_v1_dot_datastore__pb2
class DatastoreStub(object):
"""Each RPC normalizes the partition IDs of the keys in its input entities,
and always returns entities with keys with normalized partition IDs.
This applies to all keys and entities, including those in values, except keys
with both an empty path and an empty or unset partition ID. Normalization of
input keys sets the project ID (if not already set) to the project ID from
the request.
"""
def __init__(self, channel):
"""Constructor.
Args:
channel: A grpc.Channel.
"""
self.Lookup = channel.unary_unary(
'/google.datastore.v1.Datastore/Lookup',
request_serializer=google_dot_cloud_dot_grpc_dot_datastore_dot_v1_dot_datastore__pb2.LookupRequest.SerializeToString,
response_deserializer=google_dot_cloud_dot_grpc_dot_datastore_dot_v1_dot_datastore__pb2.LookupResponse.FromString,
)
self.RunQuery = channel.unary_unary(
'/google.datastore.v1.Datastore/RunQuery',
request_serializer=google_dot_cloud_dot_grpc_dot_datastore_dot_v1_dot_datastore__pb2.RunQueryRequest.SerializeToString,
response_deserializer=google_dot_cloud_dot_grpc_dot_datastore_dot_v1_dot_datastore__pb2.RunQueryResponse.FromString,
)
self.BeginTransaction = channel.unary_unary(
'/google.datastore.v1.Datastore/BeginTransaction',
request_serializer=google_dot_cloud_dot_grpc_dot_datastore_dot_v1_dot_datastore__pb2.BeginTransactionRequest.SerializeToString,
response_deserializer=google_dot_cloud_dot_grpc_dot_datastore_dot_v1_dot_datastore__pb2.BeginTransactionResponse.FromString,
)
self.Commit = channel.unary_unary(
'/google.datastore.v1.Datastore/Commit',
request_serializer=google_dot_cloud_dot_grpc_dot_datastore_dot_v1_dot_datastore__pb2.CommitRequest.SerializeToString,
response_deserializer=google_dot_cloud_dot_grpc_dot_datastore_dot_v1_dot_datastore__pb2.CommitResponse.FromString,
)
self.Rollback = channel.unary_unary(
'/google.datastore.v1.Datastore/Rollback',
request_serializer=google_dot_cloud_dot_grpc_dot_datastore_dot_v1_dot_datastore__pb2.RollbackRequest.SerializeToString,
response_deserializer=google_dot_cloud_dot_grpc_dot_datastore_dot_v1_dot_datastore__pb2.RollbackResponse.FromString,
)
self.AllocateIds = channel.unary_unary(
'/google.datastore.v1.Datastore/AllocateIds',
request_serializer=google_dot_cloud_dot_grpc_dot_datastore_dot_v1_dot_datastore__pb2.AllocateIdsRequest.SerializeToString,
response_deserializer=google_dot_cloud_dot_grpc_dot_datastore_dot_v1_dot_datastore__pb2.AllocateIdsResponse.FromString,
)
class DatastoreServicer(object):
"""Each RPC normalizes the partition IDs of the keys in its input entities,
and always returns entities with keys with normalized partition IDs.
This applies to all keys and entities, including those in values, except keys
with both an empty path and an empty or unset partition ID. Normalization of
input keys sets the project ID (if not already set) to the project ID from
the request.
"""
def Lookup(self, request, context):
"""Looks up entities by key.
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def RunQuery(self, request, context):
"""Queries for entities.
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def BeginTransaction(self, request, context):
"""Begins a new transaction.
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def Commit(self, request, context):
"""Commits a transaction, optionally creating, deleting or modifying some
entities.
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def Rollback(self, request, context):
"""Rolls back a transaction.
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def AllocateIds(self, request, context):
"""Allocates IDs for the given keys, which is useful for referencing an entity
before it is inserted.
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def add_DatastoreServicer_to_server(servicer, server):
rpc_method_handlers = {
'Lookup': grpc.unary_unary_rpc_method_handler(
servicer.Lookup,
request_deserializer=google_dot_cloud_dot_grpc_dot_datastore_dot_v1_dot_datastore__pb2.LookupRequest.FromString,
response_serializer=google_dot_cloud_dot_grpc_dot_datastore_dot_v1_dot_datastore__pb2.LookupResponse.SerializeToString,
),
'RunQuery': grpc.unary_unary_rpc_method_handler(
servicer.RunQuery,
request_deserializer=google_dot_cloud_dot_grpc_dot_datastore_dot_v1_dot_datastore__pb2.RunQueryRequest.FromString,
response_serializer=google_dot_cloud_dot_grpc_dot_datastore_dot_v1_dot_datastore__pb2.RunQueryResponse.SerializeToString,
),
'BeginTransaction': grpc.unary_unary_rpc_method_handler(
servicer.BeginTransaction,
request_deserializer=google_dot_cloud_dot_grpc_dot_datastore_dot_v1_dot_datastore__pb2.BeginTransactionRequest.FromString,
response_serializer=google_dot_cloud_dot_grpc_dot_datastore_dot_v1_dot_datastore__pb2.BeginTransactionResponse.SerializeToString,
),
'Commit': grpc.unary_unary_rpc_method_handler(
servicer.Commit,
request_deserializer=google_dot_cloud_dot_grpc_dot_datastore_dot_v1_dot_datastore__pb2.CommitRequest.FromString,
response_serializer=google_dot_cloud_dot_grpc_dot_datastore_dot_v1_dot_datastore__pb2.CommitResponse.SerializeToString,
),
'Rollback': grpc.unary_unary_rpc_method_handler(
servicer.Rollback,
request_deserializer=google_dot_cloud_dot_grpc_dot_datastore_dot_v1_dot_datastore__pb2.RollbackRequest.FromString,
response_serializer=google_dot_cloud_dot_grpc_dot_datastore_dot_v1_dot_datastore__pb2.RollbackResponse.SerializeToString,
),
'AllocateIds': grpc.unary_unary_rpc_method_handler(
servicer.AllocateIds,
request_deserializer=google_dot_cloud_dot_grpc_dot_datastore_dot_v1_dot_datastore__pb2.AllocateIdsRequest.FromString,
response_serializer=google_dot_cloud_dot_grpc_dot_datastore_dot_v1_dot_datastore__pb2.AllocateIdsResponse.SerializeToString,
),
}
generic_handler = grpc.method_handlers_generic_handler(
'google.datastore.v1.Datastore', rpc_method_handlers)
server.add_generic_rpc_handlers((generic_handler,))
| 55.063291
| 139
| 0.811034
| 1,124
| 8,700
| 5.80605
| 0.118327
| 0.132394
| 0.07723
| 0.093779
| 0.824088
| 0.824088
| 0.824088
| 0.749617
| 0.749617
| 0.749617
| 0
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| 0.127356
| 8,700
| 157
| 140
| 55.414013
| 0.846022
| 0.128276
| 0
| 0.333333
| 0
| 0
| 0.080358
| 0.036101
| 0
| 0
| 0
| 0
| 0
| 1
| 0.074074
| false
| 0
| 0.138889
| 0
| 0.231481
| 0
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| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
bd3b5192e37e67b890d6f238c4fa601930e90daa
| 27,353
|
py
|
Python
|
post_optimization_studies/mad_analyses/ma100MeV_L2TeV_deta2_1/Output/Histos/MadAnalysis5job_0/selection_3.py
|
sheride/axion_pheno
|
7d3fc08f5ae5b17a3500eba19a2e43f87f076ce5
|
[
"MIT"
] | null | null | null |
post_optimization_studies/mad_analyses/ma100MeV_L2TeV_deta2_1/Output/Histos/MadAnalysis5job_0/selection_3.py
|
sheride/axion_pheno
|
7d3fc08f5ae5b17a3500eba19a2e43f87f076ce5
|
[
"MIT"
] | null | null | null |
post_optimization_studies/mad_analyses/ma100MeV_L2TeV_deta2_1/Output/Histos/MadAnalysis5job_0/selection_3.py
|
sheride/axion_pheno
|
7d3fc08f5ae5b17a3500eba19a2e43f87f076ce5
|
[
"MIT"
] | null | null | null |
def selection_3():
# Library import
import numpy
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
# Library version
matplotlib_version = matplotlib.__version__
numpy_version = numpy.__version__
# Histo binning
xBinning = numpy.linspace(0.0,1000.0,101,endpoint=True)
# Creating data sequence: middle of each bin
xData = numpy.array([5.0,15.0,25.0,35.0,45.0,55.0,65.0,75.0,85.0,95.0,105.0,115.0,125.0,135.0,145.0,155.0,165.0,175.0,185.0,195.0,205.0,215.0,225.0,235.0,245.0,255.0,265.0,275.0,285.0,295.0,305.0,315.0,325.0,335.0,345.0,355.0,365.0,375.0,385.0,395.0,405.0,415.0,425.0,435.0,445.0,455.0,465.0,475.0,485.0,495.0,505.0,515.0,525.0,535.0,545.0,555.0,565.0,575.0,585.0,595.0,605.0,615.0,625.0,635.0,645.0,655.0,665.0,675.0,685.0,695.0,705.0,715.0,725.0,735.0,745.0,755.0,765.0,775.0,785.0,795.0,805.0,815.0,825.0,835.0,845.0,855.0,865.0,875.0,885.0,895.0,905.0,915.0,925.0,935.0,945.0,955.0,965.0,975.0,985.0,995.0])
# Creating weights for histo: y4_PT_0
y4_PT_0_weights = numpy.array([0.0,0.0,3.90559020362,3.4098395493,3.184383199,3.05922062016,2.90005937365,2.69067250471,2.59447024398,2.33587893166,2.18523204026,2.12003125671,1.8892599792,1.66270571154,1.62632415953,1.44260945213,1.39024714742,1.25777804247,1.16363732442,1.0803099133,0.944656967911,0.902964485484,0.808927284215,0.7608055704,0.720230589587,0.684924972608,0.648649335451,0.502277007629,0.526816080088,0.464822718018,0.461619693006,0.412484793714,0.39755051973,0.337661545893,0.325900960506,0.28000465763,0.255365785403,0.250059771033,0.22869290836,0.223345647149,0.201964196204,0.200894176418,0.179527673455,0.150678106305,0.15068326216,0.137849059879,0.12715281884,0.107934748702,0.112197401854,0.116476082122,0.0822722593839,0.0780036110513,0.0790783470457,0.0812097935254,0.0737402784453,0.0630358040256,0.0448726458182,0.061973338167,0.0545157734797,0.0576995339793,0.039533074426,0.0363344179017,0.0406034379355,0.0459482611057,0.0416654641475,0.0384728147938,0.0203040730752,0.0320609174698,0.0256479210294,0.0224402757314,0.0245828332786,0.0256441920272,0.0160344015652,0.0213785812527,0.0213699282091,0.0128207331093,0.0149610924238,0.0117553096284,0.0128248937644,0.0160306765598,0.0138933747873,0.00962064173846,0.00641112994094,0.0160276110243,0.00641375982671,0.00855215671885,0.00427087054611,0.00748071407694,0.00641112994094,0.00427569466788,0.00106771763653,0.00534417968749,0.00320688031303,0.00213916307619,0.00427383216519,0.00855292410193,0.00534154980172,0.00106958133825,0.00106771763653,0.00213543527306])
# Creating weights for histo: y4_PT_1
y4_PT_1_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating weights for histo: y4_PT_2
y4_PT_2_weights = numpy.array([0.0,0.0,1.05462838872,0.0,1.0521138287,0.0,1.0529581672,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating weights for histo: y4_PT_3
y4_PT_3_weights = numpy.array([0.0,0.0,1.15196926193,4.14546686726,2.07128556314,2.30314234879,2.99532358213,1.38295763269,1.15081073498,1.84150989919,1.84187686212,1.61263992282,2.53659494287,0.461124849562,1.15077768911,0.691380062969,0.460707165054,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating weights for histo: y4_PT_4
y4_PT_4_weights = numpy.array([0.0,0.0,2.35403599972,1.41266419099,2.07671152374,1.99354845762,1.85609859839,1.74406233686,1.49540870465,1.74443741835,1.8551733974,1.57821630893,1.8827986294,1.79972135115,1.32958075756,1.57824439195,1.46788080148,1.41290885953,1.35653545928,1.27375786152,0.969080905097,0.636699853394,1.02415209938,0.747512002845,0.221556745765,0.36000362928,0.360011284789,0.166236651256,0.0828748881291,0.0277222376876,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating weights for histo: y4_PT_5
y4_PT_5_weights = numpy.array([0.0,0.0,0.816624299929,0.847103616213,0.695752625962,0.55432358588,0.665464465348,0.554366550392,0.594913641312,0.796518364615,0.675453714448,0.665563987664,0.766337615281,0.685355578101,0.654902962926,0.625159140836,0.675573262597,0.614919872257,0.645341538418,0.544521487282,0.625120909702,0.504131447432,0.544501764871,0.524144657076,0.705610190047,0.554607042429,0.443654163045,0.433346685268,0.494214716117,0.292538445978,0.413215748006,0.342808624486,0.221770189954,0.161297092786,0.130998008991,0.191592110731,0.0705828653668,0.0503668486607,0.0403519907686,0.0100846024034,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating weights for histo: y4_PT_6
y4_PT_6_weights = numpy.array([0.0,0.0,0.461223304328,0.401713933697,0.345168970089,0.339518636703,0.282930004188,0.257400083665,0.297117705146,0.274452849692,0.265985660171,0.274443577263,0.248954016813,0.200886592451,0.248980141208,0.217799810348,0.226396352175,0.203669167531,0.220590503613,0.240494291405,0.263167726742,0.22908173989,0.246120962784,0.229171463143,0.220680034492,0.294228593322,0.271553580518,0.280044047298,0.260194932209,0.288645244576,0.282900878759,0.229198318559,0.257496540009,0.277205414422,0.288555521324,0.243280329218,0.240484018631,0.183940978764,0.260225519682,0.229204589952,0.198067273928,0.20088170615,0.135804415171,0.124498169551,0.115999045939,0.0961765169427,0.0481013976183,0.0735709126931,0.0651046850421,0.0480742344037,0.0594057964477,0.0452637650872,0.0396098535436,0.0367849407841,0.0198036724931,0.0197992940599,0.0141489683684,0.0028271100558,0.00566047149844,0.00565071043959,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating weights for histo: y4_PT_7
y4_PT_7_weights = numpy.array([0.0,0.0,0.0578886571164,0.0654949359643,0.047269924506,0.0381081870264,0.0274217245724,0.0380153952501,0.0290052464243,0.0380934759909,0.0259196392317,0.0228540839485,0.0274196567562,0.00456656063309,0.0106596009304,0.0137082272975,0.0151919858784,0.0151841163607,0.016728456049,0.0121775233802,0.0228492866148,0.0152297028464,0.0121575541835,0.0197920262287,0.0137063130905,0.00912570430477,0.0136821137326,0.0136781789737,0.0076256276998,0.0228986896985,0.0182696999211,0.0228629223858,0.0152104189831,0.0212864074769,0.0304524223821,0.0274217363885,0.0228417006833,0.0197749756069,0.0213510060559,0.0303954097346,0.0320008504385,0.02895551249,0.0304396373697,0.0303826365384,0.0472342871703,0.0365619803954,0.050328827329,0.0425762888852,0.0442239248562,0.039660845244,0.0335054053426,0.0380489293213,0.0395747531924,0.0411030111785,0.0548946364586,0.057941616844,0.0320023747145,0.0380960637153,0.054860287077,0.0441785156118,0.0274625491728,0.031996324875,0.0198249104148,0.0274270418142,0.0106799529685,0.0152205571907,0.0167747869486,0.0197493465017,0.010646577233,0.010684560063,0.00610276966803,0.00457786981545,0.0,0.00153528737971,0.00610901329144,0.00152607673542,0.00302457943684,0.003052912064,0.00151690862907,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating weights for histo: y4_PT_8
y4_PT_8_weights = numpy.array([0.0,0.0,0.00830935147317,0.00686212867026,0.0057777918495,0.00487499237596,0.00343193664794,0.0039737719234,0.00252637273229,0.00234767030544,0.00162461810849,0.00252753697142,0.00216603821697,0.00162427804128,0.00126508311697,0.00162446251715,0.00126490672877,0.00108334627393,0.00162441360601,0.00216564115096,0.00198639299905,0.001985104749,0.000901942565811,0.000722357812845,0.00108287449327,0.00126317211644,0.0018061512193,0.000721643787256,0.00252674938656,0.00162505330208,0.00162595103328,0.00108327849157,0.00126367932879,0.00108453400582,0.00144331453341,0.00216528644893,0.00198653357043,0.00234658540248,0.00198651855048,0.000722846924224,0.00253033029806,0.00234643943932,0.00325011469182,0.00162371036398,0.00343175448284,0.00360926417115,0.0016237034317,0.00324671517517,0.00433281048613,0.00379432543106,0.00451221808065,0.00325053216957,0.00216682657208,0.00361096912869,0.00632018170794,0.00415376105979,0.00415287141626,0.00270884940443,0.00740285092848,0.00559668969588,0.00613692284273,0.00758210832344,0.00866876360916,0.00938883569284,0.0079450212645,0.00866896002397,0.00938716039008,0.0131790021367,0.00848917885619,0.00957163240558,0.0121009891024,0.0106549216807,0.0106480856752,0.00974956881468,0.00957010730238,0.00902793311509,0.0113765304214,0.0108339942147,0.0106544595282,0.00975270374903,0.00993226539437,0.0093889589335,0.00848701444206,0.00559722117123,0.00740305119456,0.0061388715857,0.00794359629434,0.00325065078871,0.00541598422314,0.00343001717462,0.00523492058222,0.0066811227984,0.00379199310152,0.00397064854286,0.00288920710742,0.00307069746913,0.00306901523409,0.00234680723568,0.00289091437573,0.00271114129562])
# Creating weights for histo: y4_PT_9
y4_PT_9_weights = numpy.array([0.0,0.0,0.012170493784,0.0242554668822,0.0121753353338,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating weights for histo: y4_PT_10
y4_PT_10_weights = numpy.array([0.0,0.0,0.120516882096,0.190667418918,0.170688939005,0.230969775691,0.220865995678,0.150608273824,0.0702865634698,0.0100459438961,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating weights for histo: y4_PT_11
y4_PT_11_weights = numpy.array([0.0,0.0,0.198084037561,0.31351194449,0.445496209225,0.467619888339,0.583054986195,0.577571193396,0.693118827227,0.555574269604,0.544584339317,0.494883658319,0.50052345767,0.373980165186,0.324499762188,0.247559646607,0.176019389051,0.115393049215,0.0439894192711,0.0274758738871,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating weights for histo: y4_PT_12
y4_PT_12_weights = numpy.array([0.0,0.0,0.107569510644,0.159834387706,0.237835485183,0.296068725784,0.341467706071,0.373026348684,0.375033292643,0.39866176095,0.369072103919,0.34638109128,0.41743953349,0.372032175906,0.369074949795,0.368120939945,0.350345637316,0.399686516877,0.307892219056,0.316796364428,0.279250118549,0.20230063245,0.190451887037,0.180576456145,0.117451715524,0.0858512665881,0.0631590514663,0.0434230603867,0.0148022518436,0.00986973513175,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating weights for histo: y4_PT_13
y4_PT_13_weights = numpy.array([0.0,0.0,0.0287386225353,0.0471453137393,0.064036059712,0.0892481061597,0.0995732117025,0.105116064126,0.112683447048,0.128803190991,0.120988463256,0.141919667971,0.140142882391,0.131331293443,0.140156966081,0.149220140954,0.143433424629,0.143181558631,0.137888531671,0.148983358908,0.141911065717,0.13007164337,0.126282850572,0.120244948429,0.123264339616,0.120001604664,0.104859476891,0.104607890967,0.102586681338,0.10411896285,0.0909966843491,0.0791581022219,0.0579749114769,0.0524290982777,0.0395763103928,0.0342805427144,0.0289909606569,0.0138615482158,0.00882280788765,0.00377996328405,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating weights for histo: y4_PT_14
y4_PT_14_weights = numpy.array([0.0,0.0,0.0151722744734,0.0257760572781,0.0277619033204,0.0352127451226,0.0395232531673,0.0400689339222,0.0503777511844,0.0518301437998,0.0460993661199,0.0529711944513,0.0472398568938,0.0466623430952,0.0466740105456,0.0497999774426,0.0483796878119,0.0552642833532,0.0577990894338,0.0569929755901,0.0509945763924,0.0558091842786,0.0501156484605,0.0460905380491,0.056957813274,0.0544144290679,0.0461057547239,0.0532696492313,0.0535481183788,0.0489525326307,0.0538078616183,0.0532481939199,0.0538143302047,0.0501142287707,0.0609924216098,0.0526891660816,0.0506668380116,0.0483805876152,0.0509633732111,0.0418048545796,0.04465198241,0.0426608875147,0.0338083720137,0.0289025640571,0.0291987393353,0.0220538906659,0.0231870930324,0.0186147222113,0.0157373709855,0.0123215574274,0.0134671070957,0.00801535744122,0.010879552542,0.00544157087883,0.00629035739746,0.0048590431759,0.00315123337563,0.00228740414421,0.000570437544937,0.000573476980743,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating weights for histo: y4_PT_15
y4_PT_15_weights = numpy.array([0.0,0.0,0.00203023686697,0.00330505940149,0.00457813469553,0.00444700574288,0.00433982433676,0.00500579160733,0.00468589865485,0.00490187501373,0.00490069733424,0.00472927162631,0.00429701296335,0.00466560568666,0.00451344195349,0.00410183752552,0.00518109820923,0.00488013194909,0.00453648423748,0.00446931878763,0.00434253174226,0.00468651054526,0.00455639163089,0.00449244908325,0.00427686249019,0.00412531609576,0.00433829041971,0.00485652806705,0.0049028431417,0.00457613557413,0.00472786344016,0.00451228103812,0.00516050348687,0.00550521572015,0.0055270174592,0.00546343534016,0.00582993254838,0.00587162910776,0.00602748932957,0.00664594708189,0.00675932702167,0.006825587209,0.00691048909863,0.00662695333306,0.00715041300402,0.00695376567639,0.00762135906668,0.00652188420615,0.00807411605831,0.00770777810924,0.00788062876744,0.00917801664672,0.00773262253621,0.00818296968548,0.0082691205021,0.00794606332182,0.0075794403827,0.00755999819364,0.00826910792901,0.00766849139222,0.00684624898785,0.00654375300169,0.00563605549171,0.00472775866441,0.00440546006059,0.00449218085732,0.00380075433564,0.00339095330796,0.00246257935053,0.00235287871616,0.00185822064288,0.00149050300667,0.00146861535149,0.00125084229797,0.000775942417429,0.000799464574381,0.000775939064605,0.000194590956758,0.00021602262785,6.48123121329e-05,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
# Creating weights for histo: y4_PT_16
y4_PT_16_weights = numpy.array([0.0,0.0,0.000508893869518,0.000879128628745,0.000992943272196,0.000737860541265,0.000507704851964,0.000738067423785,0.000709235159,0.000822779949229,0.000766849490227,0.000650196412665,0.000454346241198,0.000650855377302,0.000651784640709,0.000593312779211,0.000623069703412,0.000511161557381,0.000623539607427,0.000793008173447,0.000510494127343,0.000597687460833,0.000678863527872,0.000538564209004,0.000568217469172,0.000453062025012,0.000539805949669,0.000878340752387,0.000396081410735,0.0005928335187,0.000624078571292,0.000738070542617,0.000593818029988,0.00048155730743,0.000510886951653,0.000624431741882,0.000764373137654,0.000565531857827,0.000709441447457,0.000710160560996,0.00102190697347,0.000595592348338,0.00042465332731,0.000738077968407,0.000678803230454,0.000992078316133,0.000650647900719,0.000851640283501,0.00118927686262,0.000936860287459,0.000707942180381,0.0011320459992,0.00079299955953,0.000766869985408,0.00130461156535,0.00107612192638,0.00116265109718,0.00101848323856,0.00110836708442,0.00110386571881,0.00150483047781,0.00127272581557,0.00107824273211,0.00153224204038,0.00180962798417,0.00164447543603,0.0018163646612,0.0017347775024,0.00133396452656,0.00153737029121,0.00164394969007,0.00161533217915,0.00175967469236,0.00141904745043,0.00161779308608,0.00190046916293,0.00190158303148,0.00192184207277,0.00193016044314,0.00175371326785,0.00176071875848,0.00153214847542,0.00124928541717,0.00135856230864,0.00133031044363,0.00107794035393,0.00110429626614,0.000964978636758,0.000850867555755,0.000566123693319,0.000705250925435,0.000820178546347,0.000564525960263,0.000595245563928,0.000677627430807,0.000654230399025,0.000511285122533,0.000339645106893,0.000508386093972,0.000312252108798])
# Creating a new Canvas
fig = plt.figure(figsize=(12,6),dpi=80)
frame = gridspec.GridSpec(1,1,right=0.7)
pad = fig.add_subplot(frame[0])
# Creating a new Stack
pad.hist(x=xData, bins=xBinning, weights=y4_PT_0_weights+y4_PT_1_weights+y4_PT_2_weights+y4_PT_3_weights+y4_PT_4_weights+y4_PT_5_weights+y4_PT_6_weights+y4_PT_7_weights+y4_PT_8_weights+y4_PT_9_weights+y4_PT_10_weights+y4_PT_11_weights+y4_PT_12_weights+y4_PT_13_weights+y4_PT_14_weights+y4_PT_15_weights+y4_PT_16_weights,\
label="$bg\_vbf\_1600\_inf$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#e5e5e5", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y4_PT_0_weights+y4_PT_1_weights+y4_PT_2_weights+y4_PT_3_weights+y4_PT_4_weights+y4_PT_5_weights+y4_PT_6_weights+y4_PT_7_weights+y4_PT_8_weights+y4_PT_9_weights+y4_PT_10_weights+y4_PT_11_weights+y4_PT_12_weights+y4_PT_13_weights+y4_PT_14_weights+y4_PT_15_weights,\
label="$bg\_vbf\_1200\_1600$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#f2f2f2", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y4_PT_0_weights+y4_PT_1_weights+y4_PT_2_weights+y4_PT_3_weights+y4_PT_4_weights+y4_PT_5_weights+y4_PT_6_weights+y4_PT_7_weights+y4_PT_8_weights+y4_PT_9_weights+y4_PT_10_weights+y4_PT_11_weights+y4_PT_12_weights+y4_PT_13_weights+y4_PT_14_weights,\
label="$bg\_vbf\_800\_1200$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#ccc6aa", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y4_PT_0_weights+y4_PT_1_weights+y4_PT_2_weights+y4_PT_3_weights+y4_PT_4_weights+y4_PT_5_weights+y4_PT_6_weights+y4_PT_7_weights+y4_PT_8_weights+y4_PT_9_weights+y4_PT_10_weights+y4_PT_11_weights+y4_PT_12_weights+y4_PT_13_weights,\
label="$bg\_vbf\_600\_800$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#ccc6aa", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y4_PT_0_weights+y4_PT_1_weights+y4_PT_2_weights+y4_PT_3_weights+y4_PT_4_weights+y4_PT_5_weights+y4_PT_6_weights+y4_PT_7_weights+y4_PT_8_weights+y4_PT_9_weights+y4_PT_10_weights+y4_PT_11_weights+y4_PT_12_weights,\
label="$bg\_vbf\_400\_600$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#c1bfa8", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y4_PT_0_weights+y4_PT_1_weights+y4_PT_2_weights+y4_PT_3_weights+y4_PT_4_weights+y4_PT_5_weights+y4_PT_6_weights+y4_PT_7_weights+y4_PT_8_weights+y4_PT_9_weights+y4_PT_10_weights+y4_PT_11_weights,\
label="$bg\_vbf\_200\_400$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#bab5a3", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y4_PT_0_weights+y4_PT_1_weights+y4_PT_2_weights+y4_PT_3_weights+y4_PT_4_weights+y4_PT_5_weights+y4_PT_6_weights+y4_PT_7_weights+y4_PT_8_weights+y4_PT_9_weights+y4_PT_10_weights,\
label="$bg\_vbf\_100\_200$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#b2a596", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y4_PT_0_weights+y4_PT_1_weights+y4_PT_2_weights+y4_PT_3_weights+y4_PT_4_weights+y4_PT_5_weights+y4_PT_6_weights+y4_PT_7_weights+y4_PT_8_weights+y4_PT_9_weights,\
label="$bg\_vbf\_0\_100$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#b7a39b", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y4_PT_0_weights+y4_PT_1_weights+y4_PT_2_weights+y4_PT_3_weights+y4_PT_4_weights+y4_PT_5_weights+y4_PT_6_weights+y4_PT_7_weights+y4_PT_8_weights,\
label="$bg\_dip\_1600\_inf$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#ad998c", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y4_PT_0_weights+y4_PT_1_weights+y4_PT_2_weights+y4_PT_3_weights+y4_PT_4_weights+y4_PT_5_weights+y4_PT_6_weights+y4_PT_7_weights,\
label="$bg\_dip\_1200\_1600$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#9b8e82", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y4_PT_0_weights+y4_PT_1_weights+y4_PT_2_weights+y4_PT_3_weights+y4_PT_4_weights+y4_PT_5_weights+y4_PT_6_weights,\
label="$bg\_dip\_800\_1200$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#876656", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y4_PT_0_weights+y4_PT_1_weights+y4_PT_2_weights+y4_PT_3_weights+y4_PT_4_weights+y4_PT_5_weights,\
label="$bg\_dip\_600\_800$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#afcec6", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y4_PT_0_weights+y4_PT_1_weights+y4_PT_2_weights+y4_PT_3_weights+y4_PT_4_weights,\
label="$bg\_dip\_400\_600$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#84c1a3", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y4_PT_0_weights+y4_PT_1_weights+y4_PT_2_weights+y4_PT_3_weights,\
label="$bg\_dip\_200\_400$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#89a8a0", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y4_PT_0_weights+y4_PT_1_weights+y4_PT_2_weights,\
label="$bg\_dip\_100\_200$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#829e8c", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y4_PT_0_weights+y4_PT_1_weights,\
label="$bg\_dip\_0\_100$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#adbcc6", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
pad.hist(x=xData, bins=xBinning, weights=y4_PT_0_weights,\
label="$signal$", histtype="step", rwidth=1.0,\
color=None, edgecolor="#7a8e99", linewidth=1, linestyle="solid",\
bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
# Axis
plt.rc('text',usetex=False)
plt.xlabel(r"p_{T} [ j_{2} ] ( GeV ) ",\
fontsize=16,color="black")
plt.ylabel(r"$\mathrm{Events}$ $(\mathcal{L}_{\mathrm{int}} = 40.0\ \mathrm{fb}^{-1})$ ",\
fontsize=16,color="black")
# Boundary of y-axis
ymax=(y4_PT_0_weights+y4_PT_1_weights+y4_PT_2_weights+y4_PT_3_weights+y4_PT_4_weights+y4_PT_5_weights+y4_PT_6_weights+y4_PT_7_weights+y4_PT_8_weights+y4_PT_9_weights+y4_PT_10_weights+y4_PT_11_weights+y4_PT_12_weights+y4_PT_13_weights+y4_PT_14_weights+y4_PT_15_weights+y4_PT_16_weights).max()*1.1
ymin=0 # linear scale
#ymin=min([x for x in (y4_PT_0_weights+y4_PT_1_weights+y4_PT_2_weights+y4_PT_3_weights+y4_PT_4_weights+y4_PT_5_weights+y4_PT_6_weights+y4_PT_7_weights+y4_PT_8_weights+y4_PT_9_weights+y4_PT_10_weights+y4_PT_11_weights+y4_PT_12_weights+y4_PT_13_weights+y4_PT_14_weights+y4_PT_15_weights+y4_PT_16_weights) if x])/100. # log scale
plt.gca().set_ylim(ymin,ymax)
# Log/Linear scale for X-axis
plt.gca().set_xscale("linear")
#plt.gca().set_xscale("log",nonposx="clip")
# Log/Linear scale for Y-axis
plt.gca().set_yscale("linear")
#plt.gca().set_yscale("log",nonposy="clip")
# Legend
plt.legend(bbox_to_anchor=(1.05,1), loc=2, borderaxespad=0.)
# Saving the image
plt.savefig('../../HTML/MadAnalysis5job_0/selection_3.png')
plt.savefig('../../PDF/MadAnalysis5job_0/selection_3.png')
plt.savefig('../../DVI/MadAnalysis5job_0/selection_3.eps')
# Running!
if __name__ == '__main__':
selection_3()
| 140.994845
| 1,762
| 0.754177
| 5,390
| 27,353
| 3.691651
| 0.187384
| 0.190471
| 0.280581
| 0.367876
| 0.392803
| 0.392803
| 0.390089
| 0.382652
| 0.364408
| 0.364408
| 0
| 0.523893
| 0.060505
| 27,353
| 193
| 1,763
| 141.725389
| 0.250409
| 0.046905
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| 0.185841
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| 0.00885
| 0.039909
| 0.007682
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| 0
|
0
| 6
|
1fd084450ab7989741cd1d8f31d5b1b6c83f3cdb
| 43
|
py
|
Python
|
altair/vegalite/v2/schema/__init__.py
|
hydrosquall/altair
|
ded897b0967a88a467828b1e2c133bd92862de23
|
[
"BSD-3-Clause"
] | null | null | null |
altair/vegalite/v2/schema/__init__.py
|
hydrosquall/altair
|
ded897b0967a88a467828b1e2c133bd92862de23
|
[
"BSD-3-Clause"
] | null | null | null |
altair/vegalite/v2/schema/__init__.py
|
hydrosquall/altair
|
ded897b0967a88a467828b1e2c133bd92862de23
|
[
"BSD-3-Clause"
] | null | null | null |
from .core import *
from .channels import *
| 21.5
| 23
| 0.744186
| 6
| 43
| 5.333333
| 0.666667
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| 43
| 2
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| 21.5
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| 1
| 0
|
0
| 6
|
1fefd29342dc01347b09406a2334475730114bff
| 133
|
py
|
Python
|
scripts/npc/autogen_10308.py
|
hsienjan/SideQuest-Server
|
3e88debaf45615b759d999255908f99a15283695
|
[
"MIT"
] | null | null | null |
scripts/npc/autogen_10308.py
|
hsienjan/SideQuest-Server
|
3e88debaf45615b759d999255908f99a15283695
|
[
"MIT"
] | null | null | null |
scripts/npc/autogen_10308.py
|
hsienjan/SideQuest-Server
|
3e88debaf45615b759d999255908f99a15283695
|
[
"MIT"
] | null | null | null |
# ObjectID: 1000002
# ParentID: 10308
# Character field ID when accessed: 4000032
# Object Position X: 2522
# Object Position Y: -22
| 22.166667
| 43
| 0.744361
| 18
| 133
| 5.5
| 0.888889
| 0.282828
| 0
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| 0
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| 0
| 0
| 0
| 0
| 0.227273
| 0.172932
| 133
| 5
| 44
| 26.6
| 0.672727
| 0.917293
| 0
| null | 0
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| 0
|
0
| 6
|
1ff0515c6eb3e9318b37eb944d57d34d627b9e8c
| 3,841
|
py
|
Python
|
counter_attack/cli/commands/approximation_dataset.py
|
samuelemarro/anti-attacks
|
f63829ee26e24d40aecdd2d6cc6bd7026d11a016
|
[
"MIT"
] | null | null | null |
counter_attack/cli/commands/approximation_dataset.py
|
samuelemarro/anti-attacks
|
f63829ee26e24d40aecdd2d6cc6bd7026d11a016
|
[
"MIT"
] | null | null | null |
counter_attack/cli/commands/approximation_dataset.py
|
samuelemarro/anti-attacks
|
f63829ee26e24d40aecdd2d6cc6bd7026d11a016
|
[
"MIT"
] | null | null | null |
import pathlib
import click
import torch
from counter_attack import defenses, rejectors, training, utils
from counter_attack.cli import definitions, options, parsing
@click.group(name='approximation-dataset')
def approximation_dataset():
pass
@approximation_dataset.command(name='preprocessor')
@options.global_options
@options.dataset_options('train', 'train')
@options.standard_model_options
@options.pretrained_model_options
@options.preprocessor_options
@options.adversarial_dataset_options
@options.approximation_dataset_options('preprocessor')
def approximation_dataset_preprocessor(options):
"""
Generates the dataset to train a substitute model for models
with preprocessors.
Saves the labels predicted by the defended model, using the genuine
dataset + an adversarial dataset.
"""
adversarial_loader = options['adversarial_loader']
approximation_dataset_path = options['approximation_dataset_path']
foolbox_model = options['foolbox_model']
genuine_loader = options['loader']
preprocessor = options['preprocessor']
defended_model = defenses.PreprocessorDefenseModel(
foolbox_model, preprocessor)
genuine_approximation_dataset = training.generate_approximation_dataset(defended_model, genuine_loader, 'Genuine Approximation Dataset')
adversarial_approximation_dataset = training.generate_approximation_dataset(defended_model, adversarial_loader, 'Adversarial Approximation Dataset')
approximation_dataset = genuine_approximation_dataset + adversarial_approximation_dataset
utils.save_zip(approximation_dataset, approximation_dataset_path)
@approximation_dataset.command(name='model')
@options.global_options
@options.dataset_options('train', 'train')
@options.standard_model_options
@options.custom_model_options
@options.adversarial_dataset_options
@options.approximation_dataset_options('model')
def approximation_dataset_model(options):
adversarial_loader = options['adversarial_loader']
approximation_dataset_path = options['approximation_dataset_path']
custom_foolbox_model = options['custom_foolbox_model']
genuine_loader = options['loader']
genuine_approximation_dataset = training.generate_approximation_dataset(custom_foolbox_model, genuine_loader, 'Genuine Approximation Dataset')
adversarial_approximation_dataset = training.generate_approximation_dataset(custom_foolbox_model, adversarial_loader, 'Adversarial Approximation Dataset')
approximation_dataset = genuine_approximation_dataset + adversarial_approximation_dataset
utils.save_zip(approximation_dataset, approximation_dataset_path)
@approximation_dataset.command(name='rejector')
@options.global_options
@options.dataset_options('train', 'train')
@options.standard_model_options
@options.pretrained_model_options
@options.distance_options
@options.counter_attack_options(False)
@options.detector_options
@options.rejector_options
@options.adversarial_dataset_options
@options.approximation_dataset_options('rejector')
def approximation_dataset_rejector(options):
adversarial_loader = options['adversarial_loader']
approximation_dataset_path = options['approximation_dataset_path']
foolbox_model = options['foolbox_model']
genuine_loader = options['loader']
rejector = options['rejector']
defended_model = rejectors.RejectorModel(foolbox_model, rejector)
genuine_approximation_dataset = training.generate_approximation_dataset(defended_model, genuine_loader, 'Genuine Approximation Dataset')
adversarial_approximation_dataset = training.generate_approximation_dataset(defended_model, adversarial_loader, 'Adversarial Approximation Dataset')
approximation_dataset = genuine_approximation_dataset + adversarial_approximation_dataset
utils.save_zip(approximation_dataset, approximation_dataset_path)
| 41.75
| 158
| 0.829732
| 399
| 3,841
| 7.621554
| 0.14787
| 0.328839
| 0.071029
| 0.071029
| 0.722789
| 0.720816
| 0.70832
| 0.706018
| 0.706018
| 0.607037
| 0
| 0
| 0.096329
| 3,841
| 91
| 159
| 42.208791
| 0.876116
| 0.047644
| 0
| 0.530303
| 0
| 0
| 0.138606
| 0.02728
| 0
| 0
| 0
| 0
| 0
| 1
| 0.060606
| false
| 0.015152
| 0.075758
| 0
| 0.136364
| 0
| 0
| 0
| 0
| null | 1
| 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
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
951352401456a0ad5a4f14435c8689c5d005440b
| 968
|
py
|
Python
|
DQM/CSCMonitorModule/python/csc_dqm_masked_hw_cfi.py
|
ckamtsikis/cmssw
|
ea19fe642bb7537cbf58451dcf73aa5fd1b66250
|
[
"Apache-2.0"
] | 852
|
2015-01-11T21:03:51.000Z
|
2022-03-25T21:14:00.000Z
|
DQM/CSCMonitorModule/python/csc_dqm_masked_hw_cfi.py
|
ckamtsikis/cmssw
|
ea19fe642bb7537cbf58451dcf73aa5fd1b66250
|
[
"Apache-2.0"
] | 30,371
|
2015-01-02T00:14:40.000Z
|
2022-03-31T23:26:05.000Z
|
DQM/CSCMonitorModule/python/csc_dqm_masked_hw_cfi.py
|
ckamtsikis/cmssw
|
ea19fe642bb7537cbf58451dcf73aa5fd1b66250
|
[
"Apache-2.0"
] | 3,240
|
2015-01-02T05:53:18.000Z
|
2022-03-31T17:24:21.000Z
|
import FWCore.ParameterSet.Config as cms
#--------------------------
# Masked HW Elements
#--------------------------
CSCMaskedHW = cms.untracked.vstring(
# == Post LS1 - All ME4/2 chambers should be enabled
# == mask most or ME+4/2 chambers, except 9,10,11,12,13
#'1,4,2,1,*,*,*',
#'1,4,2,2,*,*,*',
#'1,4,2,3,*,*,*',
#'1,4,2,4,*,*,*',
#'1,4,2,5,*,*,*',
#'1,4,2,6,*,*,*',
#'1,4,2,7,*,*,*',
#'1,4,2,8,*,*,*',
#'1,4,2,14,*,*,*',
#'1,4,2,15,*,*,*',
#'1,4,2,16,*,*,*',
#'1,4,2,17,*,*,*',
#'1,4,2,18,*,*,*',
#'1,4,2,19,*,*,*',
#'1,4,2,20,*,*,*',
#'1,4,2,21,*,*,*',
#'1,4,2,22,*,*,*',
#'1,4,2,23,*,*,*',
#'1,4,2,24,*,*,*',
#'1,4,2,25,*,*,*',
#'1,4,2,26,*,*,*',
#'1,4,2,27,*,*,*',
#'1,4,2,28,*,*,*',
#'1,4,2,29,*,*,*',
#'1,4,2,30,*,*,*',
#'1,4,2,31,*,*,*',
#'1,4,2,32,*,*,*',
#'1,4,2,33,*,*,*',
#'1,4,2,34,*,*,*',
#'1,4,2,35,*,*,*',
#'1,4,2,36,*,*,*',
# == mask all ME-4/2 chambers
#'2,4,2,*,*,*,*',
)
| 22
| 57
| 0.334711
| 168
| 968
| 1.928571
| 0.363095
| 0.209877
| 0.287037
| 0.074074
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.201701
| 0.149793
| 968
| 43
| 58
| 22.511628
| 0.191981
| 0.764463
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.333333
| 0
| 0.333333
| 0
| 0
| 0
| 1
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
952ed6d84ab9007d29ee9db38bef13d4465845bd
| 152
|
py
|
Python
|
loop2.py
|
musaibnazir/MixedPy
|
b2911f06e2f99aba9fb5b6fa802471710196ba4b
|
[
"MIT"
] | null | null | null |
loop2.py
|
musaibnazir/MixedPy
|
b2911f06e2f99aba9fb5b6fa802471710196ba4b
|
[
"MIT"
] | null | null | null |
loop2.py
|
musaibnazir/MixedPy
|
b2911f06e2f99aba9fb5b6fa802471710196ba4b
|
[
"MIT"
] | null | null | null |
num = 5
for i in range(0,num):
for j in range(0,num-i-1):
print(end=" ")
for j in range(1,i+1):
print(j," ",end="")
print()
| 19
| 30
| 0.473684
| 29
| 152
| 2.482759
| 0.37931
| 0.291667
| 0.222222
| 0.305556
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.057692
| 0.315789
| 152
| 7
| 31
| 21.714286
| 0.634615
| 0
| 0
| 0
| 0
| 0
| 0.013158
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.428571
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 6
|
20fa196194e51243cbdb7f40115b79715cfb9074
| 139
|
py
|
Python
|
src/nitpick/style/__init__.py
|
jaysonsantos/nitpick
|
34d24993fed4de40c029d676a434761c19029860
|
[
"MIT"
] | null | null | null |
src/nitpick/style/__init__.py
|
jaysonsantos/nitpick
|
34d24993fed4de40c029d676a434761c19029860
|
[
"MIT"
] | 1
|
2021-03-30T09:40:53.000Z
|
2021-03-30T10:08:40.000Z
|
src/nitpick/style/__init__.py
|
jaysonsantos/nitpick
|
34d24993fed4de40c029d676a434761c19029860
|
[
"MIT"
] | null | null | null |
"""Styles parsing and merging."""
from .cache import parse_cache_option
from .core import Style
__all__ = ("Style", "parse_cache_option")
| 23.166667
| 41
| 0.755396
| 19
| 139
| 5.105263
| 0.631579
| 0.206186
| 0.329897
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.122302
| 139
| 5
| 42
| 27.8
| 0.795082
| 0.194245
| 0
| 0
| 0
| 0
| 0.216981
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.666667
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
1f5a6c2662f454cc1ac83a1c3606a8bd4b790ead
| 83
|
py
|
Python
|
app/ctr/__init__.py
|
ihong9059/flasky
|
c1cd3ef83f92fae165ec8794c0a2b7de23757f3d
|
[
"MIT"
] | null | null | null |
app/ctr/__init__.py
|
ihong9059/flasky
|
c1cd3ef83f92fae165ec8794c0a2b7de23757f3d
|
[
"MIT"
] | null | null | null |
app/ctr/__init__.py
|
ihong9059/flasky
|
c1cd3ef83f92fae165ec8794c0a2b7de23757f3d
|
[
"MIT"
] | null | null | null |
from flask import Blueprint
ctr = Blueprint('ctr', __name__)
from . import views
| 13.833333
| 32
| 0.746988
| 11
| 83
| 5.272727
| 0.636364
| 0.413793
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.168675
| 83
| 5
| 33
| 16.6
| 0.84058
| 0
| 0
| 0
| 0
| 0
| 0.036145
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 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
| 0
| 0
| 1
| 0
| 1
| 1
|
0
| 6
|
2f5eec7c331b21462246f58d6ec8f2ef3293a0f4
| 42
|
py
|
Python
|
src/ODM2Sensor/settings/production.py
|
UCHIC/ODM2Sensor
|
488630f3a6535d201c652d9fbcfa1e8269253e0c
|
[
"BSD-3-Clause"
] | 7
|
2015-04-11T19:27:25.000Z
|
2020-10-16T09:14:09.000Z
|
src/ODM2Sensor/settings/production.py
|
UCHIC/ODM2Sensor
|
488630f3a6535d201c652d9fbcfa1e8269253e0c
|
[
"BSD-3-Clause"
] | 193
|
2015-04-13T22:30:40.000Z
|
2018-06-19T19:49:05.000Z
|
src/ODM2Sensor/settings/production.py
|
UCHIC/ODM2Sensor
|
488630f3a6535d201c652d9fbcfa1e8269253e0c
|
[
"BSD-3-Clause"
] | 5
|
2016-03-22T18:57:23.000Z
|
2018-03-23T00:25:29.000Z
|
# TODO: write configuration for production
| 42
| 42
| 0.833333
| 5
| 42
| 7
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.119048
| 42
| 1
| 42
| 42
| 0.945946
| 0.952381
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 1
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 1
| 0
| null | 0
| 0
| 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
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
2f7dcf4c22b4d1c0c7d38301f88b9ef4e705040d
| 4,294
|
py
|
Python
|
python-lib/modellightgbm/dku_lightgbm.py
|
shippeo/dss-plugin-model-lightgbm
|
9c2bf2e010775501d7ff2ffdf25d1b51c01a0187
|
[
"MIT"
] | 3
|
2021-06-15T16:02:38.000Z
|
2021-12-08T06:38:47.000Z
|
python-lib/modellightgbm/dku_lightgbm.py
|
shippeo/dss-plugin-model-lightgbm
|
9c2bf2e010775501d7ff2ffdf25d1b51c01a0187
|
[
"MIT"
] | null | null | null |
python-lib/modellightgbm/dku_lightgbm.py
|
shippeo/dss-plugin-model-lightgbm
|
9c2bf2e010775501d7ff2ffdf25d1b51c01a0187
|
[
"MIT"
] | 1
|
2021-06-15T16:06:02.000Z
|
2021-06-15T16:06:02.000Z
|
from lightgbm import LGBMClassifier, LGBMRegressor
class DkuLGBMClassifier(LGBMClassifier):
def __init__(self, boosting_type='gbdt', num_leaves=31, max_depth=-1, learning_rate=0.1, n_estimators=100,
subsample_for_bin=200000, objective=None, class_weight=None, min_split_gain=0.0, min_child_weight=0.001,
min_child_samples=20, subsample=1.0, subsample_freq=0, colsample_bytree=1.0, reg_alpha=0.0,
reg_lambda=0.0, random_state=None, n_jobs=-1, silent=True, importance_type='split',
early_stopping_rounds=None, early_stopping=None):
self.early_stopping_rounds = early_stopping_rounds
super(DkuLGBMClassifier, self).__init__(boosting_type=boosting_type, num_leaves=num_leaves, max_depth=max_depth, learning_rate=learning_rate, n_estimators=n_estimators,
subsample_for_bin=subsample_for_bin, objective=objective, class_weight=class_weight, min_split_gain=min_split_gain, min_child_weight=min_child_weight,
min_child_samples=20, subsample=1.0, subsample_freq=0, colsample_bytree=1.0, reg_alpha=0.0,
reg_lambda=reg_lambda, random_state=random_state, n_jobs=n_jobs, silent=silent, importance_type=importance_type)
def fit(self, X, y, sample_weight=None, init_score=None, eval_set=None, eval_names=None, eval_sample_weight=None,
eval_class_weight=None, eval_init_score=None, eval_metric=None, early_stopping_rounds=None, verbose=True,
feature_name='auto', categorical_feature='auto', callbacks=None):
return super(DkuLGBMClassifier, self).fit(X, y, init_score=init_score, eval_set=eval_set or [(X, y)], eval_names=eval_names, eval_sample_weight=eval_sample_weight,
eval_class_weight=eval_class_weight, eval_init_score=eval_init_score, eval_metric=eval_metric, verbose=verbose,
feature_name=feature_name, categorical_feature=categorical_feature, callbacks=callbacks, early_stopping_rounds=self.early_stopping_rounds)
class DkuLGBMRegressor(LGBMRegressor):
def __init__(self, boosting_type='gbdt', num_leaves=31, max_depth=-1, learning_rate=0.1, n_estimators=100,
subsample_for_bin=200000, objective=None, class_weight=None, min_split_gain=0.0, min_child_weight=0.001,
min_child_samples=20, subsample=1.0, subsample_freq=0, colsample_bytree=1.0, reg_alpha=0.0,
reg_lambda=0.0, random_state=None, n_jobs=-1, silent=True, importance_type='split',
early_stopping_rounds=None, early_stopping=None):
self.early_stopping_rounds = early_stopping_rounds
super(DkuLGBMRegressor, self).__init__(boosting_type=boosting_type, num_leaves=num_leaves, max_depth=max_depth, learning_rate=learning_rate, n_estimators=n_estimators,
subsample_for_bin=subsample_for_bin, objective=objective, class_weight=class_weight, min_split_gain=min_split_gain, min_child_weight=min_child_weight,
min_child_samples=20, subsample=1.0, subsample_freq=0, colsample_bytree=1.0, reg_alpha=0.0,
reg_lambda=reg_lambda, random_state=random_state, n_jobs=n_jobs, silent=silent, importance_type=importance_type)
def fit(self, X, y, sample_weight=None, init_score=None, eval_set=None, eval_names=None, eval_sample_weight=None,
eval_init_score=None, eval_metric=None, early_stopping_rounds=None, verbose=True,
feature_name='auto', categorical_feature='auto', callbacks=None):
return super(DkuLGBMRegressor, self).fit(X, y, init_score=init_score, eval_set=eval_set or [(X, y)], eval_names=eval_names, eval_sample_weight=eval_sample_weight,
eval_init_score=eval_init_score, eval_metric=eval_metric, verbose=verbose,
feature_name=feature_name, categorical_feature=categorical_feature, callbacks=callbacks, early_stopping_rounds=self.early_stopping_rounds)
| 97.590909
| 196
| 0.688402
| 557
| 4,294
| 4.901257
| 0.131059
| 0.066667
| 0.083516
| 0.024908
| 0.931868
| 0.924542
| 0.924542
| 0.924542
| 0.924542
| 0.924542
| 0
| 0.024677
| 0.226129
| 4,294
| 44
| 197
| 97.590909
| 0.79687
| 0
| 0
| 0.685714
| 0
| 0
| 0.007916
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.114286
| false
| 0
| 0.142857
| 0.057143
| 0.371429
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
2f7eea72e07f1252862fdc72b203bccf4d49ed00
| 6,181
|
py
|
Python
|
examples/pruning_two_instances.py
|
laudv/veritas
|
ba1761cc333b08b4381afa720b24ace065a9f106
|
[
"Apache-2.0"
] | 6
|
2020-10-29T10:20:48.000Z
|
2022-03-31T13:39:47.000Z
|
examples/pruning_two_instances.py
|
laudv/veritas
|
ba1761cc333b08b4381afa720b24ace065a9f106
|
[
"Apache-2.0"
] | 1
|
2021-11-25T13:15:11.000Z
|
2021-12-08T09:23:24.000Z
|
examples/pruning_two_instances.py
|
laudv/veritas
|
ba1761cc333b08b4381afa720b24ace065a9f106
|
[
"Apache-2.0"
] | null | null | null |
import xgboost as xgb
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
import veritas
import veritas.xgb
# Generate a random dataset
np.random.seed(14)
N = 2000
x = np.random.randint(0, 100, size=(N, 1)).astype(float)
y = np.random.randint(0, 100, size=(N, 1)).astype(float)
dist = np.sqrt(x**2 + y**2)
s = x + y
target = ((dist < 50) & (s > 20)) | ((x+2*y) > 200)
# Plot the dataset
#plt.plot(x[target], y[target], '.', color="blue")
#plt.plot(x[~target], y[~target], '.', color="red")
#plt.show()
X = np.concatenate((x, y), axis=1)
# Train a model using XGBoost
xtrain = xgb.DMatrix(X, label=target, missing=None)
params = {
"learning_rate": 0.5,
"max_depth": 4,
"objective": "binary:hinge",
"eval_metric": "error",
"tree_method": "hist",
"seed": 1,
"nthread": 1,
}
bst = xgb.train(params, xtrain, 10, [(xtrain, "train")])
features = ["x", "y"]
feat2id = {f : i for i, f in enumerate(features)}
at = veritas.xgb.addtree_from_xgb_model(bst)
at.base_score = 0.5
# Check whether our "AddTree"'s predictions and XGBoost's match
pred_raw_at = np.array(at.predict(X))
pred_raw = bst.predict(xtrain, output_margin=True)
print("max error", max(pred_raw_at - pred_raw), "(should be no more than float32 rounding error)")
# Look in a 100×100 grid at the values produced by XGBoost
Xv = np.zeros((100*100, 2))
for i, xv in enumerate(range(100)):
for j, yv in enumerate(range(100)):
Xv[i*100+j, 0:2] = [xv, yv]
vs = bst.predict(xgb.DMatrix(Xv), output_margin=True)
fig, (ax0, ax1) = plt.subplots(1, 2, figsize=(16, 6))
pred = (pred_raw.reshape((N,1)) > 0.0)
ax0.plot(x[pred&target], y[pred&target], '.', color="darkblue", alpha=0.5, label="true pos")
ax0.plot(x[~pred&~target], y[~pred&~target], '.', color="darkred", alpha=0.5, label="true neg")
ax0.plot(x[pred&~target], y[pred&~target], 'x', color="blue", label="false pos")
ax0.plot(x[~pred&target], y[~pred&target], 'x', color="red", label="false neg")
im = ax1.imshow(vs.reshape(100,100).T, origin="lower", cmap="Spectral")
fig.colorbar(im, ax=ax1)
plt.show()
# EXAMPLE 1
# Use VERITAS to find the two output configurations
# - one in box x: [25, 75], y: [50, 80]
# - one in box x: [0, 50], y: [0, 50]
# such that the difference in output is maximized
opt = veritas.Optimizer(minimize=at, maximize=at, matches=set(), match_is_reuse=True)
box0 = [
veritas.RealDomain(25, 75),
veritas.RealDomain(50, 80),
]
box1 = [
veritas.RealDomain(0, 50),
veritas.RealDomain(0, 50),
]
print("num reachable leafs before prune", opt.g0.num_vertices(), opt.g1.num_vertices())
opt.prune_box(box0, 0) # prune instance0 (minimized)
opt.prune_box(box1, 1) # prune instance1 (maximized)
print("num reachable leafs after prune", opt.g0.num_vertices(), opt.g1.num_vertices())
opt.steps(2000)
print((opt.num_solutions(), opt.num_rejected(), opt.num_candidate_cliques(), opt.num_steps()))
points = []
for sol in opt.solutions():
# convert Solution object to list of intervals indexes by feature id
intervals = opt.solution_to_intervals(sol, 4)
xv0 = sum(intervals[0][0])/2 # instance0: middle of first feature interval
yv0 = sum(intervals[0][1])/2 # instance0: middle of second feature interval
xv1 = sum(intervals[1][0])/2 # instance1: middle of first feature interval
yv1 = sum(intervals[1][1])/2 # instance1: middle of second feature interval
points.append([xv0, yv0, xv1, yv1, sol.output0, sol.output1])
points = np.array(points)
print(points)
#print(bst.predict(xgb.DMatrix(points), output_margin=True))
fig, ax = plt.subplots()
m, M = abs(min(points[:,2])), max(points[:,2])
im = ax.imshow(vs.reshape(100,100).T, origin="lower", cmap="Spectral")
ax.add_patch(Rectangle((0, 0), 50, 50, fill=False, color="blue"))
ax.add_patch(Rectangle((25, 50), 50, 30, fill=False, color="red"))
for p in points[:3]: # 3 best only
l, = ax.plot([p[0], p[2]], [p[1], p[3]])
ax.scatter([p[0]], [p[1]], marker="v", color=l.get_color()) # min
ax.scatter([p[2]], [p[3]], marker="^", color=l.get_color()) # max
fig.colorbar(im, ax=ax)
plt.show()
# EXAMPLE 2
# Use VERITAS to find the two output configurations
# - one in box x: [25, 75], y: [50, 80]
# - one in box x: [0, 50], y: [0, 50]
# such that the difference in output is maximized
# This time, share attribute x between the two instances
opt = veritas.Optimizer(minimize=at, maximize=at, matches=set([0]), match_is_reuse=True)
box0 = [
veritas.RealDomain(25, 75),
veritas.RealDomain(50, 80),
]
box1 = [
veritas.RealDomain(0, 50),
veritas.RealDomain(0, 50),
]
print("num reachable leafs before prune", opt.g0.num_vertices(), opt.g1.num_vertices())
opt.prune_box(box0, 0) # prune instance0 (minimized)
opt.prune_box(box1, 1) # prune instance1 (maximized)
print("num reachable leafs after prune", opt.g0.num_vertices(), opt.g1.num_vertices())
opt.steps(2000)
print((opt.num_solutions(), opt.num_rejected(), opt.num_candidate_cliques(), opt.num_steps()))
points = []
for sol in opt.solutions():
# convert Solution object to list of intervals indexes by feature id
intervals = opt.solution_to_intervals(sol, 4)
xv0 = sum(intervals[0][0])/2 # instance0: middle of first feature interval
yv0 = sum(intervals[0][1])/2 # instance0: middle of second feature interval
xv1 = sum(intervals[1][0])/2 # instance1: middle of first feature interval
yv1 = sum(intervals[1][1])/2 # instance1: middle of second feature interval
points.append([xv0, yv0, xv1, yv1, sol.output0, sol.output1])
points = np.array(points)
print(points)
#print(bst.predict(xgb.DMatrix(points), output_margin=True))
fig, ax = plt.subplots()
m, M = abs(min(points[:,2])), max(points[:,2])
im = ax.imshow(vs.reshape(100,100).T, origin="lower", cmap="Spectral")
ax.add_patch(Rectangle((0, 0), 50, 50, fill=False, color="blue"))
ax.add_patch(Rectangle((25, 50), 50, 30, fill=False, color="red"))
for p in points[:3]: # 3 best only
l, = ax.plot([p[0], p[2]], [p[1], p[3]])
ax.scatter([p[0]], [p[1]], marker="v", color=l.get_color()) # min
ax.scatter([p[2]], [p[3]], marker="^", color=l.get_color()) # max
fig.colorbar(im, ax=ax)
plt.show()
| 36.791667
| 98
| 0.668986
| 1,019
| 6,181
| 4.002944
| 0.223749
| 0.007355
| 0.027458
| 0.011768
| 0.73572
| 0.727874
| 0.727874
| 0.715126
| 0.715126
| 0.672959
| 0
| 0.056439
| 0.145769
| 6,181
| 167
| 99
| 37.011976
| 0.715909
| 0.239605
| 0
| 0.567797
| 1
| 0
| 0.083942
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.050847
| 0
| 0.050847
| 0.076271
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
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| 0
| 0
| 0
|
0
| 6
|
85cd1711c069b83c769da11d7c138d705bd98d3c
| 22,554
|
py
|
Python
|
Parabola/prop1_probs.py
|
pdcxs/ManimProjects
|
550a281e430a1a1568ae1978ccfe817bedcc9ef8
|
[
"WTFPL"
] | 29
|
2019-12-09T13:57:37.000Z
|
2022-02-15T12:18:25.000Z
|
Parabola/prop1_probs.py
|
pdcxs/ManimProjects
|
550a281e430a1a1568ae1978ccfe817bedcc9ef8
|
[
"WTFPL"
] | 1
|
2019-12-22T09:15:18.000Z
|
2019-12-23T02:16:43.000Z
|
Parabola/prop1_probs.py
|
pdcxs/ManimProjects
|
550a281e430a1a1568ae1978ccfe817bedcc9ef8
|
[
"WTFPL"
] | 4
|
2020-04-16T12:50:09.000Z
|
2021-07-09T12:39:04.000Z
|
from manimlib.imports import *
from ManimProjects.utils.Parabola import Parabola
from ManimProjects.utils.geometry import CText
class Prob1(Parabola):
CONFIG = {
'x_min' : -5
}
def construct(self):
self.adjust_x_range()
graph = self.get_graph(color=LIGHT_BROWN)
directrix = self.get_directrix()
focus = Dot().move_to(self.get_focus())
focus.set_fill(DARK_BROWN)
focus.plot_depth = 1
focusLabel = TexMobject('F').scale(0.7)
focusLabel.next_to(focus, RIGHT)
self.play(*[ShowCreation(e) for\
e in [graph, directrix, focus, focusLabel]])
y_val = ValueTracker(8)
p1 = Dot()
p1.set_color(DARK_BLUE)
p1.add_updater(lambda m:\
m.move_to(self.coords_to_point(
self.func(y_val.get_value()),
y_val.get_value()
)))
p1.plot_depth = 1
p1Label = TexMobject('P_1').scale(0.7)
p1Label.add_updater(lambda m:\
m.next_to(p1, RIGHT, buff=SMALL_BUFF))
p2 = Dot()
p2.set_color(DARK_BLUE)
p2.add_updater(lambda m:\
m.move_to(self.get_opposite(p1)))
p2.plot_depth = 1
p2Label = TexMobject('P_2').scale(0.7)
p2Label.add_updater(lambda m:\
m.next_to(p2, RIGHT, buff=SMALL_BUFF))
focus_chord = Line()
focus_chord.add_updater(lambda m:\
m.put_start_and_end_on(
p1.get_center(),
self.get_opposite(p1)
))
self.play(ShowCreation(p1), ShowCreation(p1Label))
self.play(ShowCreation(focus_chord))
self.play(ShowCreation(p2), ShowCreation(p2Label))
fc_def = CText('焦点弦')
fc_def.move_to(focus_chord.get_center())
fc_def.shift(0.2 * RIGHT + 0.1 * DOWN)
self.play(Write(fc_def))
self.wait(2)
self.play(FadeOut(fc_def))
q_y = ValueTracker(2)
q = Dot()
q.set_fill(DARK_BLUE)
q.plot_depth = 1
q.add_updater(lambda m:\
m.move_to(self.coords_to_point(
self.func(q_y.get_value()),
q_y.get_value()
)))
qLabel = TexMobject('Q').scale(0.7)
qLabel.add_updater(lambda m:\
m.next_to(q, LEFT, buff=SMALL_BUFF))
k1 = Dot()
k1.set_fill(BLUE_E)
k1.plot_depth = 1
k1.add_updater(lambda m:\
m.move_to(self.chord_to_directrix(p1, q)))
k1Label = TexMobject('K_1').scale(0.7)
k1Label.add_updater(lambda m:\
m.next_to(k1, LEFT, buff=SMALL_BUFF))
k2 = Dot()
k2.set_fill(BLUE_E)
k2.plot_depth = 1
k2.add_updater(lambda m:\
m.move_to(self.chord_to_directrix(p2, q)))
k2Label = TexMobject('K_2').scale(0.7)
k2Label.add_updater(lambda m:\
m.next_to(k2, LEFT, buff=SMALL_BUFF))
l1 = Line()
l1.add_updater(lambda m:\
m.put_start_and_end_on(
self.right(p1, q),
self.chord_to_directrix(p1, q)
))
l2 = Line()
l2.add_updater(lambda m:\
m.put_start_and_end_on(
self.right(p2, q),
self.chord_to_directrix(p2, q)
))
self.play(ShowCreation(q), ShowCreation(qLabel))
self.play(ShowCreation(l1), ShowCreation(l2))
self.play(*[ShowCreation(e) for e in [k1, k2, k1Label, k2Label]])
k1f = Line()
k1f.add_updater(lambda m:\
m.put_start_and_end_on(
k1.get_center(), focus.get_center()
))
k2f = Line()
k2f.add_updater(lambda m:\
m.put_start_and_end_on(
k2.get_center(), focus.get_center()
))
self.play(ShowCreation(k1f), ShowCreation(k2f))
self.wait(1)
self.play(ApplyMethod(y_val.set_value,
5))
summary = TexMobject('K_1F \\perp K_2F').scale(2)
summary.to_edge(RIGHT)
self.wait(1)
self.play(Write(summary))
self.wait(5)
qf = Line()
qf.add_updater(lambda m:\
m.put_start_and_end_on(q.get_center(),
focus.get_center()))
self.play(ShowCreation(qf))
self.wait(1)
self.play(ApplyMethod(q_y.set_value,
-1))
self.wait(1)
self.play(ApplyMethod(y_val.set_value,
0.5))
self.wait(1)
self.play(ApplyMethod(y_val.set_value,
3),
ApplyMethod(q_y.set_value, 0.5))
self.wait(10)
class Prob2(Parabola):
CONFIG = {
'focus': 2,
'x_min': -4
}
def construct(self):
self.adjust_x_range()
graph = self.get_graph(color=LIGHT_BROWN)
directrix = self.get_directrix()
focus = Dot().move_to(self.get_focus())
focus.set_fill(DARK_BROWN)
focus.plot_depth = 1
focusLabel = TexMobject('F').scale(0.7)
focusLabel.next_to(focus, RIGHT)
self.play(*[ShowCreation(e) for\
e in [graph, directrix, focus, focusLabel]])
q1_y = ValueTracker(9)
q1 = Dot()
q1.set_fill(DARK_BLUE)
q1.plot_depth = 1
q1.add_updater(lambda m:\
m.move_to(self.coords_to_point(
self.func(q1_y.get_value()),
q1_y.get_value()
)))
q1_label = TexMobject('Q_1').scale(0.5)
q1_label.add_updater(lambda m:\
m.next_to(q1, RIGHT, buff=SMALL_BUFF))
self.play(ShowCreation(q1), ShowCreation(q1_label))
q2 = Dot()
q2.set_fill(DARK_BLUE)
q2.plot_depth = 1
q2.add_updater(lambda m:\
m.move_to(self.get_opposite(q1)))
q2_label = TexMobject('Q_2').scale(0.5)
q2_label.add_updater(lambda m:\
m.next_to(q2, RIGHT, buff=SMALL_BUFF))
q1q2 = Line()
q1q2.add_updater(lambda m:\
m.put_start_and_end_on(
q1.get_center(),
self.get_opposite(q1)
))
self.play(*[ShowCreation(e) for e in\
[q2, q2_label, q1q2]])
p1_y = ValueTracker(2)
p1 = Dot()
p1.set_fill(DARK_BLUE)
p1.plot_depth = 1
p1.add_updater(lambda m:\
m.move_to(self.coords_to_point(
self.func(p1_y.get_value()),
p1_y.get_value()
)))
p1_label = TexMobject('P_1').scale(0.5)
p1_label.add_updater(lambda m:\
m.next_to(p1, RIGHT, buff=SMALL_BUFF))
self.play(ShowCreation(p1), ShowCreation(p1_label))
p2 = Dot()
p2.set_fill(DARK_BLUE)
p2.plot_depth = 1
p2.add_updater(lambda m:\
m.move_to(self.get_opposite(p1)))
p2_label = TexMobject('P_2').scale(0.5)
p2_label.add_updater(lambda m:\
m.next_to(p2, RIGHT, buff=SMALL_BUFF))
p1p2 = Line()
p1p2.add_updater(lambda m:\
m.put_start_and_end_on(
p1.get_center(),
self.get_opposite(p1)
))
self.play(*[ShowCreation(e) for e in\
[p2, p2_label, p1p2]])
k1 = Dot()
k1.set_fill(DARK_BROWN)
k1.plot_depth = 1
k1.add_updater(lambda m:\
m.move_to(self.chord_to_directrix(p1, q1)))
k1_label = TexMobject('K_1').scale(0.5)
k1_label.add_updater(lambda m:\
m.next_to(k1, LEFT, buff=SMALL_BUFF))
p1q1 = Line()
p1q1.add_updater(lambda m:\
m.put_start_and_end_on(
self.right(p1, q1),
self.chord_to_directrix(p1, q1)
))
p2q2 = Line()
p2q2.add_updater(lambda m:\
m.put_start_and_end_on(
self.right(p2, q2),
self.chord_to_directrix(p2, q2)
))
self.play(*[ShowCreation(e) for e in \
[k1, k1_label, p1q1, p2q2]])
k2 = Dot()
k2.set_fill(DARK_BROWN)
k2.plot_depth = 1
k2.add_updater(lambda m:\
m.move_to(self.chord_to_directrix(p2, q1)))
k2_label = TexMobject('K_2').scale(0.5)
k2_label.add_updater(lambda m:\
m.next_to(k2, LEFT, buff=SMALL_BUFF))
p2q1 = Line()
p2q1.add_updater(lambda m:\
m.put_start_and_end_on(
self.right(p2, q1),
self.chord_to_directrix(p2, q1)
))
p1q2 = Line()
p1q2.add_updater(lambda m:\
m.put_start_and_end_on(
self.right(p1, q2),
self.chord_to_directrix(p1, q2)
))
self.play(*[ShowCreation(e) for e in \
[k2, k2_label, p2q1, p1q2]])
explain = CText('这些交点在准线上').scale(0.3)
explain.to_edge(RIGHT)
self.wait(2)
self.play(Write(explain))
self.wait(5)
self.play(ApplyMethod(q1_y.set_value, 0.5),
ApplyMethod(p1_y.set_value, -3))
self.wait(3)
self.play(ApplyMethod(q1_y.set_value, 3),
ApplyMethod(p1_y.set_value, -9))
self.wait(10)
class Prob3(Parabola):
CONFIG = {
'focus': 2,
'x_min': -4
}
def construct(self):
self.adjust_x_range()
graph = self.get_graph(color=LIGHT_BROWN)
directrix = self.get_directrix()
focus = Dot().move_to(self.get_focus())
focus.set_fill(DARK_BROWN)
focus.plot_depth = 1
focusLabel = TexMobject('F').scale(0.7)
focusLabel.next_to(focus, RIGHT)
self.play(*[ShowCreation(e) for\
e in [graph, directrix, focus, focusLabel]])
q1_y = ValueTracker(9)
q1 = Dot()
q1.set_fill(DARK_BLUE)
q1.plot_depth = 1
q1.add_updater(lambda m:\
m.move_to(self.coords_to_point(
self.func(q1_y.get_value()),
q1_y.get_value()
)))
q1_label = TexMobject('Q_1').scale(0.5)
q1_label.add_updater(lambda m:\
m.next_to(q1, RIGHT, buff=SMALL_BUFF))
self.play(ShowCreation(q1), ShowCreation(q1_label))
q2 = Dot()
q2.set_fill(DARK_BLUE)
q2.plot_depth = 1
q2.add_updater(lambda m:\
m.move_to(self.get_opposite(q1)))
q2_label = TexMobject('Q_2').scale(0.5)
q2_label.add_updater(lambda m:\
m.next_to(q2, RIGHT, buff=SMALL_BUFF))
q1q2 = Line()
q1q2.add_updater(lambda m:\
m.put_start_and_end_on(
q1.get_center(),
self.get_opposite(q1)
))
self.play(*[ShowCreation(e) for e in\
[q2, q2_label, q1q2]])
p1_y = ValueTracker(2)
p1 = Dot()
p1.set_fill(DARK_BLUE)
p1.plot_depth = 1
p1.add_updater(lambda m:\
m.move_to(self.coords_to_point(
self.func(p1_y.get_value()),
p1_y.get_value()
)))
p1_label = TexMobject('P_1').scale(0.5)
p1_label.add_updater(lambda m:\
m.next_to(p1, RIGHT, buff=SMALL_BUFF))
self.play(ShowCreation(p1), ShowCreation(p1_label))
p2 = Dot()
p2.set_fill(DARK_BLUE)
p2.plot_depth = 1
p2.add_updater(lambda m:\
m.move_to(self.get_opposite(p1)))
p2_label = TexMobject('P_2').scale(0.5)
p2_label.add_updater(lambda m:\
m.next_to(p2, RIGHT, buff=SMALL_BUFF))
p1p2 = Line()
p1p2.add_updater(lambda m:\
m.put_start_and_end_on(
p1.get_center(),
self.get_opposite(p1)
))
self.play(*[ShowCreation(e) for e in\
[p2, p2_label, p1p2]])
k1 = Dot()
k1.set_fill(DARK_BROWN)
k1.plot_depth = 1
k1.add_updater(lambda m:\
m.move_to(self.chord_to_directrix(p1, q1)))
k1_label = TexMobject('K_1').scale(0.5)
k1_label.add_updater(lambda m:\
m.next_to(k1, LEFT, buff=SMALL_BUFF))
p1q1 = Line()
p1q1.add_updater(lambda m:\
m.put_start_and_end_on(
self.right(p1, q1),
self.chord_to_directrix(p1, q1)
))
p2q2 = Line()
p2q2.add_updater(lambda m:\
m.put_start_and_end_on(
self.right(p2, q2),
self.chord_to_directrix(p2, q2)
))
self.play(*[ShowCreation(e) for e in \
[k1, k1_label, p1q1, p2q2]])
k2 = Dot()
k2.set_fill(DARK_BROWN)
k2.plot_depth = 1
k2.add_updater(lambda m:\
m.move_to(self.chord_to_directrix(p2, q1)))
k2_label = TexMobject('K_2').scale(0.5)
k2_label.add_updater(lambda m:\
m.next_to(k2, LEFT, buff=SMALL_BUFF))
p2q1 = Line()
p2q1.add_updater(lambda m:\
m.put_start_and_end_on(
self.right(p2, q1),
self.chord_to_directrix(p2, q1)
))
p1q2 = Line()
p1q2.add_updater(lambda m:\
m.put_start_and_end_on(
self.right(p1, q2),
self.chord_to_directrix(p1, q2)
))
self.play(*[ShowCreation(e) for e in \
[k2, k2_label, p2q1, p1q2]])
k1f = Line()
k1f.add_updater(lambda m:\
m.put_start_and_end_on(
k1.get_center(),
focus.get_center()
))
k2f = Line()
k2f.add_updater(lambda m:\
m.put_start_and_end_on(
k2.get_center(),
focus.get_center()
))
explain = TexMobject('K_1F \\perp K_2F')
explain.to_edge(RIGHT)
self.wait(2)
self.play(ShowCreation(k1f), ShowCreation(k2f))
self.wait(3)
self.play(Write(explain))
self.wait(5)
self.play(ApplyMethod(q1_y.set_value, 0.5),
ApplyMethod(p1_y.set_value, -3))
self.wait(3)
self.play(ApplyMethod(q1_y.set_value, 3),
ApplyMethod(p1_y.set_value, -9))
self.wait(10)
class Prob4(Parabola):
CONFIG = {
'focus': 3,
'x_min': -10
}
def construct(self):
self.adjust_x_range()
graph = self.get_graph(color=LIGHT_BROWN)
directrix = self.get_directrix()
focus = Dot().move_to(self.get_focus())
focus.set_fill(DARK_BROWN)
focus.plot_depth = 1
focusLabel = TexMobject('F').scale(0.5)
focusLabel.next_to(focus, RIGHT)
self.play(*[ShowCreation(e) for\
e in [graph, directrix, focus, focusLabel]])
a = Dot()
a.set_fill(DARK_BROWN)
a.move_to(self.coords_to_point(0, 0))
a.plot_depth = 1
a_label = TexMobject('A').scale(0.5)
a_label.next_to(a, RIGHT)
self.play(*[ShowCreation(e) for e in [a, a_label]])
y_val = ValueTracker(8)
m = Dot()
m.set_fill(DARK_BLUE)
m.plot_depth = 1
m.add_updater(lambda m:\
m.move_to(self.coords_to_point(
-self.focus, y_val.get_value()
)))
m_label = TexMobject('M').scale(0.5)
m_label.add_updater(lambda l:\
l.next_to(m, LEFT))
p = Dot()
p.set_fill(DARK_BLUE)
p.plot_depth = 1
p.add_updater(lambda m:\
m.move_to(self.coords_to_point(
self.func(y_val.get_value()),
y_val.get_value()
)))
p_label = TexMobject('P').scale(0.5)
p_label.add_updater(lambda m:\
m.next_to(p, RIGHT))
self.play(*[ShowCreation(e) for e in\
[m, m_label, p, p_label]])
k = Dot()
k.set_fill(DARK_BLUE)
k.plot_depth = 1
k.add_updater(lambda m:\
m.move_to(self.chord_to_directrix(
p, a
)))
k_label = TexMobject('K').scale(0.5)
k_label.add_updater(lambda m:\
m.next_to(k, LEFT))
pk = Line()
pk.add_updater(lambda l:\
l.put_start_and_end_on(
p.get_center(),
self.chord_to_directrix(p, a)
))
mp = Line()
mp.add_updater(lambda l:\
l.put_start_and_end_on(
m.get_center(),
p.get_center()
))
self.play(*[ShowCreation(e) for e in\
[k, k_label, pk, mp]])
kf = Line()
kf.add_updater(lambda l:\
l.put_start_and_end_on(
k.get_center(),
focus.get_center()
))
mf = Line()
mf.add_updater(lambda l:\
l.put_start_and_end_on(
m.get_center(),
focus.get_center()
))
self.play(ShowCreation(kf), ShowCreation(mf))
form = TexMobject('KF \\perp MF')
form.scale(0.7)
form.to_edge(RIGHT)
self.play(Write(form))
af = DashedLine(a.get_center(), focus.get_center())
pf = DashedLine()
def get_pf_extent():
vec = focus.get_center() - p.get_center()
vec = normalize(vec)
return focus.get_center() + 2 * vec
pf.add_updater(lambda m:\
m.put_start_and_end_on(
p.get_center(),
get_pf_extent()
))
self.play(ShowCreation(af), ShowCreation(pf))
self.wait(3)
self.play(ApplyMethod(y_val.set_value, 2))
self.wait(3)
self.play(ApplyMethod(y_val.set_value, -2))
self.wait(3)
self.play(ApplyMethod(y_val.set_value, -8))
self.wait(10)
class Prob5(Parabola):
CONFIG = {
'focus': 3,
'x_min': -10
}
def construct(self):
self.adjust_x_range()
graph = self.get_graph(color=LIGHT_BROWN)
directrix = self.get_directrix()
focus = Dot().move_to(self.get_focus())
focus.set_fill(DARK_BROWN)
focus.plot_depth = 1
focusLabel = TexMobject('F').scale(0.5)
focusLabel.next_to(focus, RIGHT + UP)
self.play(*[ShowCreation(e) for\
e in [graph, directrix, focus, focusLabel]])
h_line = self.get_horizontal()
x = Dot()
x.set_fill(DARK_BROWN)
x.plot_depth = 1
x.move_to(self.coords_to_point(-self.focus, 0))
x_label = TexMobject('X').scale(0.5)
x_label.next_to(x, LEFT + UP)
self.play(ShowCreation(h_line))
self.play(ShowCreation(x), ShowCreation(x_label))
y_val = ValueTracker(8)
p = Dot()
p.set_fill(DARK_BLUE)
p.plot_depth = 1
p.add_updater(lambda m:\
m.move_to(self.coords_to_point(
self.func(y_val.get_value()),
y_val.get_value()
)))
q = Dot()
q.set_fill(DARK_BLUE)
q.plot_depth = 1
q.add_updater(lambda m:\
m.move_to(self.coords_to_point(
self.func(-y_val.get_value()),
-y_val.get_value()
)))
t = Dot()
t.set_fill(DARK_BLUE)
t.plot_depth = 1
t.add_updater(lambda m:\
m.move_to(self.coords_to_point(
self.func(y_val.get_value()), 0
)))
p_label = TexMobject('P').scale(0.5)
p_label.add_updater(lambda m:\
m.next_to(p, RIGHT))
q_label = TexMobject('Q').scale(0.5)
q_label.add_updater(lambda m:\
m.next_to(q, RIGHT))
t_label = TexMobject('T').scale(0.5)
t_label.add_updater(lambda m:\
m.next_to(t, RIGHT + UP))
pq = Line()
pq.add_updater(lambda m:\
m.put_start_and_end_on(
p.get_center(),
self.coords_to_point(
self.func(-y_val.get_value()),
-y_val.get_value()
)))
pt = Line()
pt.add_updater(lambda m:\
m.put_start_and_end_on(
p.get_center(),
self.coords_to_point(
self.func(y_val.get_value()), 0
)))
self.play(ShowCreation(p), ShowCreation(p_label))
self.play(ShowCreation(pt))
self.play(ShowCreation(t), ShowCreation(t_label))
label1 = CText('纵标线').scale(0.3)\
.next_to(pt, RIGHT)
self.play(ShowCreation(label1))
self.wait()
self.play(FadeOut(label1))
self.play(ShowCreation(pq))
self.remove(pt)
self.play(ShowCreation(q), ShowCreation(q_label))
label2 = CText('双纵标线').scale(0.3)\
.next_to(t, RIGHT+DOWN)
self.play(ShowCreation(label2))
self.wait()
self.play(FadeOut(label2))
self.wait()
inter = Dot()
inter.set_fill(DARK_BLUE)
inter.plot_depth = 1
inter.add_updater(lambda m:\
m.move_to(
self.coords_to_point(
4*(self.focus**3)/(y_val.get_value()**2),
4*self.focus**2/y_val.get_value()
) if y_val.get_value() != 0 else
self.coords_to_point(0, 0)
))
inter_label = TexMobject("P'").scale(0.5)
inter_label.add_updater(lambda m:\
m.next_to(inter, LEFT + UP, buff=SMALL_BUFF))
px = Line()
px.add_updater(lambda m:\
m.put_start_and_end_on(
self.right(p, inter),
x.get_center()
))
self.play(ShowCreation(px))
self.play(ShowCreation(inter),
ShowCreation(inter_label))
self.wait()
form = CText("P'Q经过焦点").shift(UP)
form.scale(0.5)
form.to_edge(RIGHT)
self.play(Write(form))
interq = Line()
interq.add_updater(lambda m:\
m.put_start_and_end_on(
inter.get_center(),
q.get_center()
))
self.play(ShowCreation(interq))
self.wait(2)
self.play(ApplyMethod(y_val.set_value, 4))
self.wait(2)
self.play(ApplyMethod(y_val.set_value, -4))
self.wait(2)
self.play(ApplyMethod(y_val.set_value, -9))
self.wait(2)
self.play(ApplyMethod(y_val.set_value, 9))
self.wait(10)
| 29.027027
| 73
| 0.524652
| 2,958
| 22,554
| 3.756254
| 0.0524
| 0.069301
| 0.110881
| 0.110161
| 0.815768
| 0.767978
| 0.747727
| 0.745297
| 0.701737
| 0.694897
| 0
| 0.037219
| 0.349561
| 22,554
| 777
| 74
| 29.027027
| 0.720177
| 0
| 0
| 0.697205
| 0
| 0
| 0.007892
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.009317
| false
| 0
| 0.004658
| 0
| 0.031056
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
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| 0
| null | 0
| 0
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| 0
| 0
| 0
| 0
|
0
| 6
|
c8128a8dd438ad2d493df254ee45c6a1a57355fd
| 95,273
|
py
|
Python
|
cottonformation/res/batch.py
|
MacHu-GWU/cottonformation-project
|
23e28c08cfb5a7cc0db6dbfdb1d7e1585c773f3b
|
[
"BSD-2-Clause"
] | 5
|
2021-07-22T03:45:59.000Z
|
2021-12-17T21:07:14.000Z
|
cottonformation/res/batch.py
|
MacHu-GWU/cottonformation-project
|
23e28c08cfb5a7cc0db6dbfdb1d7e1585c773f3b
|
[
"BSD-2-Clause"
] | 1
|
2021-06-25T18:01:31.000Z
|
2021-06-25T18:01:31.000Z
|
cottonformation/res/batch.py
|
MacHu-GWU/cottonformation-project
|
23e28c08cfb5a7cc0db6dbfdb1d7e1585c773f3b
|
[
"BSD-2-Clause"
] | 2
|
2021-06-27T03:08:21.000Z
|
2021-06-28T22:15:51.000Z
|
# -*- coding: utf-8 -*-
"""
This module
"""
import attr
import typing
from ..core.model import (
Property, Resource, Tag, GetAtt, TypeHint, TypeCheck,
)
from ..core.constant import AttrMeta
#--- Property declaration ---
@attr.s
class PropJobDefinitionAuthorizationConfig(Property):
"""
AWS Object Type = "AWS::Batch::JobDefinition.AuthorizationConfig"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-authorizationconfig.html
Property Document:
- ``p_AccessPointId``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-authorizationconfig.html#cfn-batch-jobdefinition-authorizationconfig-accesspointid
- ``p_Iam``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-authorizationconfig.html#cfn-batch-jobdefinition-authorizationconfig-iam
"""
AWS_OBJECT_TYPE = "AWS::Batch::JobDefinition.AuthorizationConfig"
p_AccessPointId: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "AccessPointId"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-authorizationconfig.html#cfn-batch-jobdefinition-authorizationconfig-accesspointid"""
p_Iam: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "Iam"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-authorizationconfig.html#cfn-batch-jobdefinition-authorizationconfig-iam"""
@attr.s
class PropJobDefinitionResourceRequirement(Property):
"""
AWS Object Type = "AWS::Batch::JobDefinition.ResourceRequirement"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-resourcerequirement.html
Property Document:
- ``p_Type``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-resourcerequirement.html#cfn-batch-jobdefinition-resourcerequirement-type
- ``p_Value``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-resourcerequirement.html#cfn-batch-jobdefinition-resourcerequirement-value
"""
AWS_OBJECT_TYPE = "AWS::Batch::JobDefinition.ResourceRequirement"
p_Type: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "Type"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-resourcerequirement.html#cfn-batch-jobdefinition-resourcerequirement-type"""
p_Value: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "Value"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-resourcerequirement.html#cfn-batch-jobdefinition-resourcerequirement-value"""
@attr.s
class PropJobDefinitionEnvironment(Property):
"""
AWS Object Type = "AWS::Batch::JobDefinition.Environment"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-environment.html
Property Document:
- ``p_Name``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-environment.html#cfn-batch-jobdefinition-environment-name
- ``p_Value``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-environment.html#cfn-batch-jobdefinition-environment-value
"""
AWS_OBJECT_TYPE = "AWS::Batch::JobDefinition.Environment"
p_Name: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "Name"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-environment.html#cfn-batch-jobdefinition-environment-name"""
p_Value: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "Value"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-environment.html#cfn-batch-jobdefinition-environment-value"""
@attr.s
class PropJobDefinitionVolumesHost(Property):
"""
AWS Object Type = "AWS::Batch::JobDefinition.VolumesHost"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-volumeshost.html
Property Document:
- ``p_SourcePath``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-volumeshost.html#cfn-batch-jobdefinition-volumeshost-sourcepath
"""
AWS_OBJECT_TYPE = "AWS::Batch::JobDefinition.VolumesHost"
p_SourcePath: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "SourcePath"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-volumeshost.html#cfn-batch-jobdefinition-volumeshost-sourcepath"""
@attr.s
class PropJobQueueComputeEnvironmentOrder(Property):
"""
AWS Object Type = "AWS::Batch::JobQueue.ComputeEnvironmentOrder"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobqueue-computeenvironmentorder.html
Property Document:
- ``rp_ComputeEnvironment``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobqueue-computeenvironmentorder.html#cfn-batch-jobqueue-computeenvironmentorder-computeenvironment
- ``rp_Order``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobqueue-computeenvironmentorder.html#cfn-batch-jobqueue-computeenvironmentorder-order
"""
AWS_OBJECT_TYPE = "AWS::Batch::JobQueue.ComputeEnvironmentOrder"
rp_ComputeEnvironment: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type),
metadata={AttrMeta.PROPERTY_NAME: "ComputeEnvironment"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobqueue-computeenvironmentorder.html#cfn-batch-jobqueue-computeenvironmentorder-computeenvironment"""
rp_Order: int = attr.ib(
default=None,
validator=attr.validators.instance_of(int),
metadata={AttrMeta.PROPERTY_NAME: "Order"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobqueue-computeenvironmentorder.html#cfn-batch-jobqueue-computeenvironmentorder-order"""
@attr.s
class PropJobDefinitionSecret(Property):
"""
AWS Object Type = "AWS::Batch::JobDefinition.Secret"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-secret.html
Property Document:
- ``rp_Name``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-secret.html#cfn-batch-jobdefinition-secret-name
- ``rp_ValueFrom``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-secret.html#cfn-batch-jobdefinition-secret-valuefrom
"""
AWS_OBJECT_TYPE = "AWS::Batch::JobDefinition.Secret"
rp_Name: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type),
metadata={AttrMeta.PROPERTY_NAME: "Name"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-secret.html#cfn-batch-jobdefinition-secret-name"""
rp_ValueFrom: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type),
metadata={AttrMeta.PROPERTY_NAME: "ValueFrom"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-secret.html#cfn-batch-jobdefinition-secret-valuefrom"""
@attr.s
class PropJobDefinitionNetworkConfiguration(Property):
"""
AWS Object Type = "AWS::Batch::JobDefinition.NetworkConfiguration"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties-networkconfiguration.html
Property Document:
- ``p_AssignPublicIp``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties-networkconfiguration.html#cfn-batch-jobdefinition-containerproperties-networkconfiguration-assignpublicip
"""
AWS_OBJECT_TYPE = "AWS::Batch::JobDefinition.NetworkConfiguration"
p_AssignPublicIp: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "AssignPublicIp"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties-networkconfiguration.html#cfn-batch-jobdefinition-containerproperties-networkconfiguration-assignpublicip"""
@attr.s
class PropJobDefinitionLogConfiguration(Property):
"""
AWS Object Type = "AWS::Batch::JobDefinition.LogConfiguration"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties-logconfiguration.html
Property Document:
- ``rp_LogDriver``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties-logconfiguration.html#cfn-batch-jobdefinition-containerproperties-logconfiguration-logdriver
- ``p_Options``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties-logconfiguration.html#cfn-batch-jobdefinition-containerproperties-logconfiguration-options
- ``p_SecretOptions``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties-logconfiguration.html#cfn-batch-jobdefinition-containerproperties-logconfiguration-secretoptions
"""
AWS_OBJECT_TYPE = "AWS::Batch::JobDefinition.LogConfiguration"
rp_LogDriver: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type),
metadata={AttrMeta.PROPERTY_NAME: "LogDriver"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties-logconfiguration.html#cfn-batch-jobdefinition-containerproperties-logconfiguration-logdriver"""
p_Options: dict = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(dict)),
metadata={AttrMeta.PROPERTY_NAME: "Options"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties-logconfiguration.html#cfn-batch-jobdefinition-containerproperties-logconfiguration-options"""
p_SecretOptions: typing.List[typing.Union['PropJobDefinitionSecret', dict]] = attr.ib(
default=None,
converter=PropJobDefinitionSecret.from_list,
validator=attr.validators.optional(attr.validators.deep_iterable(member_validator=attr.validators.instance_of(PropJobDefinitionSecret), iterable_validator=attr.validators.instance_of(list))),
metadata={AttrMeta.PROPERTY_NAME: "SecretOptions"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties-logconfiguration.html#cfn-batch-jobdefinition-containerproperties-logconfiguration-secretoptions"""
@attr.s
class PropComputeEnvironmentLaunchTemplateSpecification(Property):
"""
AWS Object Type = "AWS::Batch::ComputeEnvironment.LaunchTemplateSpecification"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-launchtemplatespecification.html
Property Document:
- ``p_LaunchTemplateId``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-launchtemplatespecification.html#cfn-batch-computeenvironment-launchtemplatespecification-launchtemplateid
- ``p_LaunchTemplateName``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-launchtemplatespecification.html#cfn-batch-computeenvironment-launchtemplatespecification-launchtemplatename
- ``p_Version``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-launchtemplatespecification.html#cfn-batch-computeenvironment-launchtemplatespecification-version
"""
AWS_OBJECT_TYPE = "AWS::Batch::ComputeEnvironment.LaunchTemplateSpecification"
p_LaunchTemplateId: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "LaunchTemplateId"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-launchtemplatespecification.html#cfn-batch-computeenvironment-launchtemplatespecification-launchtemplateid"""
p_LaunchTemplateName: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "LaunchTemplateName"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-launchtemplatespecification.html#cfn-batch-computeenvironment-launchtemplatespecification-launchtemplatename"""
p_Version: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "Version"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-launchtemplatespecification.html#cfn-batch-computeenvironment-launchtemplatespecification-version"""
@attr.s
class PropJobDefinitionMountPoints(Property):
"""
AWS Object Type = "AWS::Batch::JobDefinition.MountPoints"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-mountpoints.html
Property Document:
- ``p_ContainerPath``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-mountpoints.html#cfn-batch-jobdefinition-mountpoints-containerpath
- ``p_ReadOnly``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-mountpoints.html#cfn-batch-jobdefinition-mountpoints-readonly
- ``p_SourceVolume``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-mountpoints.html#cfn-batch-jobdefinition-mountpoints-sourcevolume
"""
AWS_OBJECT_TYPE = "AWS::Batch::JobDefinition.MountPoints"
p_ContainerPath: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "ContainerPath"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-mountpoints.html#cfn-batch-jobdefinition-mountpoints-containerpath"""
p_ReadOnly: bool = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(bool)),
metadata={AttrMeta.PROPERTY_NAME: "ReadOnly"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-mountpoints.html#cfn-batch-jobdefinition-mountpoints-readonly"""
p_SourceVolume: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "SourceVolume"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-mountpoints.html#cfn-batch-jobdefinition-mountpoints-sourcevolume"""
@attr.s
class PropSchedulingPolicyShareAttributes(Property):
"""
AWS Object Type = "AWS::Batch::SchedulingPolicy.ShareAttributes"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-schedulingpolicy-shareattributes.html
Property Document:
- ``p_ShareIdentifier``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-schedulingpolicy-shareattributes.html#cfn-batch-schedulingpolicy-shareattributes-shareidentifier
- ``p_WeightFactor``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-schedulingpolicy-shareattributes.html#cfn-batch-schedulingpolicy-shareattributes-weightfactor
"""
AWS_OBJECT_TYPE = "AWS::Batch::SchedulingPolicy.ShareAttributes"
p_ShareIdentifier: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "ShareIdentifier"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-schedulingpolicy-shareattributes.html#cfn-batch-schedulingpolicy-shareattributes-shareidentifier"""
p_WeightFactor: float = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(float)),
metadata={AttrMeta.PROPERTY_NAME: "WeightFactor"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-schedulingpolicy-shareattributes.html#cfn-batch-schedulingpolicy-shareattributes-weightfactor"""
@attr.s
class PropJobDefinitionEvaluateOnExit(Property):
"""
AWS Object Type = "AWS::Batch::JobDefinition.EvaluateOnExit"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-evaluateonexit.html
Property Document:
- ``rp_Action``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-evaluateonexit.html#cfn-batch-jobdefinition-evaluateonexit-action
- ``p_OnExitCode``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-evaluateonexit.html#cfn-batch-jobdefinition-evaluateonexit-onexitcode
- ``p_OnReason``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-evaluateonexit.html#cfn-batch-jobdefinition-evaluateonexit-onreason
- ``p_OnStatusReason``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-evaluateonexit.html#cfn-batch-jobdefinition-evaluateonexit-onstatusreason
"""
AWS_OBJECT_TYPE = "AWS::Batch::JobDefinition.EvaluateOnExit"
rp_Action: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type),
metadata={AttrMeta.PROPERTY_NAME: "Action"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-evaluateonexit.html#cfn-batch-jobdefinition-evaluateonexit-action"""
p_OnExitCode: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "OnExitCode"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-evaluateonexit.html#cfn-batch-jobdefinition-evaluateonexit-onexitcode"""
p_OnReason: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "OnReason"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-evaluateonexit.html#cfn-batch-jobdefinition-evaluateonexit-onreason"""
p_OnStatusReason: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "OnStatusReason"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-evaluateonexit.html#cfn-batch-jobdefinition-evaluateonexit-onstatusreason"""
@attr.s
class PropJobDefinitionUlimit(Property):
"""
AWS Object Type = "AWS::Batch::JobDefinition.Ulimit"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-ulimit.html
Property Document:
- ``rp_HardLimit``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-ulimit.html#cfn-batch-jobdefinition-ulimit-hardlimit
- ``rp_Name``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-ulimit.html#cfn-batch-jobdefinition-ulimit-name
- ``rp_SoftLimit``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-ulimit.html#cfn-batch-jobdefinition-ulimit-softlimit
"""
AWS_OBJECT_TYPE = "AWS::Batch::JobDefinition.Ulimit"
rp_HardLimit: int = attr.ib(
default=None,
validator=attr.validators.instance_of(int),
metadata={AttrMeta.PROPERTY_NAME: "HardLimit"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-ulimit.html#cfn-batch-jobdefinition-ulimit-hardlimit"""
rp_Name: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type),
metadata={AttrMeta.PROPERTY_NAME: "Name"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-ulimit.html#cfn-batch-jobdefinition-ulimit-name"""
rp_SoftLimit: int = attr.ib(
default=None,
validator=attr.validators.instance_of(int),
metadata={AttrMeta.PROPERTY_NAME: "SoftLimit"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-ulimit.html#cfn-batch-jobdefinition-ulimit-softlimit"""
@attr.s
class PropJobDefinitionFargatePlatformConfiguration(Property):
"""
AWS Object Type = "AWS::Batch::JobDefinition.FargatePlatformConfiguration"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties-fargateplatformconfiguration.html
Property Document:
- ``p_PlatformVersion``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties-fargateplatformconfiguration.html#cfn-batch-jobdefinition-containerproperties-fargateplatformconfiguration-platformversion
"""
AWS_OBJECT_TYPE = "AWS::Batch::JobDefinition.FargatePlatformConfiguration"
p_PlatformVersion: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "PlatformVersion"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties-fargateplatformconfiguration.html#cfn-batch-jobdefinition-containerproperties-fargateplatformconfiguration-platformversion"""
@attr.s
class PropJobDefinitionTimeout(Property):
"""
AWS Object Type = "AWS::Batch::JobDefinition.Timeout"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-timeout.html
Property Document:
- ``p_AttemptDurationSeconds``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-timeout.html#cfn-batch-jobdefinition-timeout-attemptdurationseconds
"""
AWS_OBJECT_TYPE = "AWS::Batch::JobDefinition.Timeout"
p_AttemptDurationSeconds: int = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(int)),
metadata={AttrMeta.PROPERTY_NAME: "AttemptDurationSeconds"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-timeout.html#cfn-batch-jobdefinition-timeout-attemptdurationseconds"""
@attr.s
class PropJobDefinitionTmpfs(Property):
"""
AWS Object Type = "AWS::Batch::JobDefinition.Tmpfs"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-tmpfs.html
Property Document:
- ``rp_ContainerPath``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-tmpfs.html#cfn-batch-jobdefinition-tmpfs-containerpath
- ``rp_Size``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-tmpfs.html#cfn-batch-jobdefinition-tmpfs-size
- ``p_MountOptions``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-tmpfs.html#cfn-batch-jobdefinition-tmpfs-mountoptions
"""
AWS_OBJECT_TYPE = "AWS::Batch::JobDefinition.Tmpfs"
rp_ContainerPath: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type),
metadata={AttrMeta.PROPERTY_NAME: "ContainerPath"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-tmpfs.html#cfn-batch-jobdefinition-tmpfs-containerpath"""
rp_Size: int = attr.ib(
default=None,
validator=attr.validators.instance_of(int),
metadata={AttrMeta.PROPERTY_NAME: "Size"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-tmpfs.html#cfn-batch-jobdefinition-tmpfs-size"""
p_MountOptions: typing.List[TypeHint.intrinsic_str] = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.deep_iterable(member_validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), iterable_validator=attr.validators.instance_of(list))),
metadata={AttrMeta.PROPERTY_NAME: "MountOptions"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-tmpfs.html#cfn-batch-jobdefinition-tmpfs-mountoptions"""
@attr.s
class PropJobDefinitionEfsVolumeConfiguration(Property):
"""
AWS Object Type = "AWS::Batch::JobDefinition.EfsVolumeConfiguration"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-efsvolumeconfiguration.html
Property Document:
- ``rp_FileSystemId``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-efsvolumeconfiguration.html#cfn-batch-jobdefinition-efsvolumeconfiguration-filesystemid
- ``p_AuthorizationConfig``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-efsvolumeconfiguration.html#cfn-batch-jobdefinition-efsvolumeconfiguration-authorizationconfig
- ``p_RootDirectory``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-efsvolumeconfiguration.html#cfn-batch-jobdefinition-efsvolumeconfiguration-rootdirectory
- ``p_TransitEncryption``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-efsvolumeconfiguration.html#cfn-batch-jobdefinition-efsvolumeconfiguration-transitencryption
- ``p_TransitEncryptionPort``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-efsvolumeconfiguration.html#cfn-batch-jobdefinition-efsvolumeconfiguration-transitencryptionport
"""
AWS_OBJECT_TYPE = "AWS::Batch::JobDefinition.EfsVolumeConfiguration"
rp_FileSystemId: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type),
metadata={AttrMeta.PROPERTY_NAME: "FileSystemId"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-efsvolumeconfiguration.html#cfn-batch-jobdefinition-efsvolumeconfiguration-filesystemid"""
p_AuthorizationConfig: typing.Union['PropJobDefinitionAuthorizationConfig', dict] = attr.ib(
default=None,
converter=PropJobDefinitionAuthorizationConfig.from_dict,
validator=attr.validators.optional(attr.validators.instance_of(PropJobDefinitionAuthorizationConfig)),
metadata={AttrMeta.PROPERTY_NAME: "AuthorizationConfig"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-efsvolumeconfiguration.html#cfn-batch-jobdefinition-efsvolumeconfiguration-authorizationconfig"""
p_RootDirectory: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "RootDirectory"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-efsvolumeconfiguration.html#cfn-batch-jobdefinition-efsvolumeconfiguration-rootdirectory"""
p_TransitEncryption: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "TransitEncryption"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-efsvolumeconfiguration.html#cfn-batch-jobdefinition-efsvolumeconfiguration-transitencryption"""
p_TransitEncryptionPort: int = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(int)),
metadata={AttrMeta.PROPERTY_NAME: "TransitEncryptionPort"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-efsvolumeconfiguration.html#cfn-batch-jobdefinition-efsvolumeconfiguration-transitencryptionport"""
@attr.s
class PropJobDefinitionDevice(Property):
"""
AWS Object Type = "AWS::Batch::JobDefinition.Device"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-device.html
Property Document:
- ``p_ContainerPath``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-device.html#cfn-batch-jobdefinition-device-containerpath
- ``p_HostPath``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-device.html#cfn-batch-jobdefinition-device-hostpath
- ``p_Permissions``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-device.html#cfn-batch-jobdefinition-device-permissions
"""
AWS_OBJECT_TYPE = "AWS::Batch::JobDefinition.Device"
p_ContainerPath: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "ContainerPath"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-device.html#cfn-batch-jobdefinition-device-containerpath"""
p_HostPath: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "HostPath"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-device.html#cfn-batch-jobdefinition-device-hostpath"""
p_Permissions: typing.List[TypeHint.intrinsic_str] = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.deep_iterable(member_validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), iterable_validator=attr.validators.instance_of(list))),
metadata={AttrMeta.PROPERTY_NAME: "Permissions"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-device.html#cfn-batch-jobdefinition-device-permissions"""
@attr.s
class PropComputeEnvironmentEc2ConfigurationObject(Property):
"""
AWS Object Type = "AWS::Batch::ComputeEnvironment.Ec2ConfigurationObject"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-ec2configurationobject.html
Property Document:
- ``rp_ImageType``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-ec2configurationobject.html#cfn-batch-computeenvironment-ec2configurationobject-imagetype
- ``p_ImageIdOverride``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-ec2configurationobject.html#cfn-batch-computeenvironment-ec2configurationobject-imageidoverride
"""
AWS_OBJECT_TYPE = "AWS::Batch::ComputeEnvironment.Ec2ConfigurationObject"
rp_ImageType: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type),
metadata={AttrMeta.PROPERTY_NAME: "ImageType"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-ec2configurationobject.html#cfn-batch-computeenvironment-ec2configurationobject-imagetype"""
p_ImageIdOverride: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "ImageIdOverride"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-ec2configurationobject.html#cfn-batch-computeenvironment-ec2configurationobject-imageidoverride"""
@attr.s
class PropJobDefinitionVolumes(Property):
"""
AWS Object Type = "AWS::Batch::JobDefinition.Volumes"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-volumes.html
Property Document:
- ``p_EfsVolumeConfiguration``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-volumes.html#cfn-batch-jobdefinition-volumes-efsvolumeconfiguration
- ``p_Host``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-volumes.html#cfn-batch-jobdefinition-volumes-host
- ``p_Name``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-volumes.html#cfn-batch-jobdefinition-volumes-name
"""
AWS_OBJECT_TYPE = "AWS::Batch::JobDefinition.Volumes"
p_EfsVolumeConfiguration: typing.Union['PropJobDefinitionEfsVolumeConfiguration', dict] = attr.ib(
default=None,
converter=PropJobDefinitionEfsVolumeConfiguration.from_dict,
validator=attr.validators.optional(attr.validators.instance_of(PropJobDefinitionEfsVolumeConfiguration)),
metadata={AttrMeta.PROPERTY_NAME: "EfsVolumeConfiguration"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-volumes.html#cfn-batch-jobdefinition-volumes-efsvolumeconfiguration"""
p_Host: typing.Union['PropJobDefinitionVolumesHost', dict] = attr.ib(
default=None,
converter=PropJobDefinitionVolumesHost.from_dict,
validator=attr.validators.optional(attr.validators.instance_of(PropJobDefinitionVolumesHost)),
metadata={AttrMeta.PROPERTY_NAME: "Host"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-volumes.html#cfn-batch-jobdefinition-volumes-host"""
p_Name: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "Name"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-volumes.html#cfn-batch-jobdefinition-volumes-name"""
@attr.s
class PropSchedulingPolicyFairsharePolicy(Property):
"""
AWS Object Type = "AWS::Batch::SchedulingPolicy.FairsharePolicy"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-schedulingpolicy-fairsharepolicy.html
Property Document:
- ``p_ComputeReservation``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-schedulingpolicy-fairsharepolicy.html#cfn-batch-schedulingpolicy-fairsharepolicy-computereservation
- ``p_ShareDecaySeconds``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-schedulingpolicy-fairsharepolicy.html#cfn-batch-schedulingpolicy-fairsharepolicy-sharedecayseconds
- ``p_ShareDistribution``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-schedulingpolicy-fairsharepolicy.html#cfn-batch-schedulingpolicy-fairsharepolicy-sharedistribution
"""
AWS_OBJECT_TYPE = "AWS::Batch::SchedulingPolicy.FairsharePolicy"
p_ComputeReservation: float = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(float)),
metadata={AttrMeta.PROPERTY_NAME: "ComputeReservation"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-schedulingpolicy-fairsharepolicy.html#cfn-batch-schedulingpolicy-fairsharepolicy-computereservation"""
p_ShareDecaySeconds: float = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(float)),
metadata={AttrMeta.PROPERTY_NAME: "ShareDecaySeconds"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-schedulingpolicy-fairsharepolicy.html#cfn-batch-schedulingpolicy-fairsharepolicy-sharedecayseconds"""
p_ShareDistribution: typing.List[typing.Union['PropSchedulingPolicyShareAttributes', dict]] = attr.ib(
default=None,
converter=PropSchedulingPolicyShareAttributes.from_list,
validator=attr.validators.optional(attr.validators.deep_iterable(member_validator=attr.validators.instance_of(PropSchedulingPolicyShareAttributes), iterable_validator=attr.validators.instance_of(list))),
metadata={AttrMeta.PROPERTY_NAME: "ShareDistribution"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-schedulingpolicy-fairsharepolicy.html#cfn-batch-schedulingpolicy-fairsharepolicy-sharedistribution"""
@attr.s
class PropComputeEnvironmentComputeResources(Property):
"""
AWS Object Type = "AWS::Batch::ComputeEnvironment.ComputeResources"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-computeresources.html
Property Document:
- ``rp_MaxvCpus``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-computeresources.html#cfn-batch-computeenvironment-computeresources-maxvcpus
- ``rp_Subnets``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-computeresources.html#cfn-batch-computeenvironment-computeresources-subnets
- ``rp_Type``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-computeresources.html#cfn-batch-computeenvironment-computeresources-type
- ``p_AllocationStrategy``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-computeresources.html#cfn-batch-computeenvironment-computeresources-allocationstrategy
- ``p_BidPercentage``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-computeresources.html#cfn-batch-computeenvironment-computeresources-bidpercentage
- ``p_DesiredvCpus``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-computeresources.html#cfn-batch-computeenvironment-computeresources-desiredvcpus
- ``p_Ec2Configuration``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-computeresources.html#cfn-batch-computeenvironment-computeresources-ec2configuration
- ``p_Ec2KeyPair``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-computeresources.html#cfn-batch-computeenvironment-computeresources-ec2keypair
- ``p_ImageId``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-computeresources.html#cfn-batch-computeenvironment-computeresources-imageid
- ``p_InstanceRole``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-computeresources.html#cfn-batch-computeenvironment-computeresources-instancerole
- ``p_InstanceTypes``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-computeresources.html#cfn-batch-computeenvironment-computeresources-instancetypes
- ``p_LaunchTemplate``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-computeresources.html#cfn-batch-computeenvironment-computeresources-launchtemplate
- ``p_MinvCpus``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-computeresources.html#cfn-batch-computeenvironment-computeresources-minvcpus
- ``p_PlacementGroup``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-computeresources.html#cfn-batch-computeenvironment-computeresources-placementgroup
- ``p_SecurityGroupIds``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-computeresources.html#cfn-batch-computeenvironment-computeresources-securitygroupids
- ``p_SpotIamFleetRole``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-computeresources.html#cfn-batch-computeenvironment-computeresources-spotiamfleetrole
- ``p_Tags``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-computeresources.html#cfn-batch-computeenvironment-computeresources-tags
"""
AWS_OBJECT_TYPE = "AWS::Batch::ComputeEnvironment.ComputeResources"
rp_MaxvCpus: int = attr.ib(
default=None,
validator=attr.validators.instance_of(int),
metadata={AttrMeta.PROPERTY_NAME: "MaxvCpus"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-computeresources.html#cfn-batch-computeenvironment-computeresources-maxvcpus"""
rp_Subnets: typing.List[TypeHint.intrinsic_str] = attr.ib(
default=None,
validator=attr.validators.deep_iterable(member_validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), iterable_validator=attr.validators.instance_of(list)),
metadata={AttrMeta.PROPERTY_NAME: "Subnets"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-computeresources.html#cfn-batch-computeenvironment-computeresources-subnets"""
rp_Type: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type),
metadata={AttrMeta.PROPERTY_NAME: "Type"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-computeresources.html#cfn-batch-computeenvironment-computeresources-type"""
p_AllocationStrategy: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "AllocationStrategy"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-computeresources.html#cfn-batch-computeenvironment-computeresources-allocationstrategy"""
p_BidPercentage: int = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(int)),
metadata={AttrMeta.PROPERTY_NAME: "BidPercentage"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-computeresources.html#cfn-batch-computeenvironment-computeresources-bidpercentage"""
p_DesiredvCpus: int = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(int)),
metadata={AttrMeta.PROPERTY_NAME: "DesiredvCpus"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-computeresources.html#cfn-batch-computeenvironment-computeresources-desiredvcpus"""
p_Ec2Configuration: typing.List[typing.Union['PropComputeEnvironmentEc2ConfigurationObject', dict]] = attr.ib(
default=None,
converter=PropComputeEnvironmentEc2ConfigurationObject.from_list,
validator=attr.validators.optional(attr.validators.deep_iterable(member_validator=attr.validators.instance_of(PropComputeEnvironmentEc2ConfigurationObject), iterable_validator=attr.validators.instance_of(list))),
metadata={AttrMeta.PROPERTY_NAME: "Ec2Configuration"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-computeresources.html#cfn-batch-computeenvironment-computeresources-ec2configuration"""
p_Ec2KeyPair: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "Ec2KeyPair"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-computeresources.html#cfn-batch-computeenvironment-computeresources-ec2keypair"""
p_ImageId: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "ImageId"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-computeresources.html#cfn-batch-computeenvironment-computeresources-imageid"""
p_InstanceRole: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "InstanceRole"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-computeresources.html#cfn-batch-computeenvironment-computeresources-instancerole"""
p_InstanceTypes: typing.List[TypeHint.intrinsic_str] = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.deep_iterable(member_validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), iterable_validator=attr.validators.instance_of(list))),
metadata={AttrMeta.PROPERTY_NAME: "InstanceTypes"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-computeresources.html#cfn-batch-computeenvironment-computeresources-instancetypes"""
p_LaunchTemplate: typing.Union['PropComputeEnvironmentLaunchTemplateSpecification', dict] = attr.ib(
default=None,
converter=PropComputeEnvironmentLaunchTemplateSpecification.from_dict,
validator=attr.validators.optional(attr.validators.instance_of(PropComputeEnvironmentLaunchTemplateSpecification)),
metadata={AttrMeta.PROPERTY_NAME: "LaunchTemplate"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-computeresources.html#cfn-batch-computeenvironment-computeresources-launchtemplate"""
p_MinvCpus: int = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(int)),
metadata={AttrMeta.PROPERTY_NAME: "MinvCpus"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-computeresources.html#cfn-batch-computeenvironment-computeresources-minvcpus"""
p_PlacementGroup: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "PlacementGroup"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-computeresources.html#cfn-batch-computeenvironment-computeresources-placementgroup"""
p_SecurityGroupIds: typing.List[TypeHint.intrinsic_str] = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.deep_iterable(member_validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), iterable_validator=attr.validators.instance_of(list))),
metadata={AttrMeta.PROPERTY_NAME: "SecurityGroupIds"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-computeresources.html#cfn-batch-computeenvironment-computeresources-securitygroupids"""
p_SpotIamFleetRole: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "SpotIamFleetRole"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-computeresources.html#cfn-batch-computeenvironment-computeresources-spotiamfleetrole"""
p_Tags: dict = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(dict)),
metadata={AttrMeta.PROPERTY_NAME: "Tags"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-computeenvironment-computeresources.html#cfn-batch-computeenvironment-computeresources-tags"""
@attr.s
class PropJobDefinitionRetryStrategy(Property):
"""
AWS Object Type = "AWS::Batch::JobDefinition.RetryStrategy"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-retrystrategy.html
Property Document:
- ``p_Attempts``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-retrystrategy.html#cfn-batch-jobdefinition-retrystrategy-attempts
- ``p_EvaluateOnExit``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-retrystrategy.html#cfn-batch-jobdefinition-retrystrategy-evaluateonexit
"""
AWS_OBJECT_TYPE = "AWS::Batch::JobDefinition.RetryStrategy"
p_Attempts: int = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(int)),
metadata={AttrMeta.PROPERTY_NAME: "Attempts"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-retrystrategy.html#cfn-batch-jobdefinition-retrystrategy-attempts"""
p_EvaluateOnExit: typing.List[typing.Union['PropJobDefinitionEvaluateOnExit', dict]] = attr.ib(
default=None,
converter=PropJobDefinitionEvaluateOnExit.from_list,
validator=attr.validators.optional(attr.validators.deep_iterable(member_validator=attr.validators.instance_of(PropJobDefinitionEvaluateOnExit), iterable_validator=attr.validators.instance_of(list))),
metadata={AttrMeta.PROPERTY_NAME: "EvaluateOnExit"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-retrystrategy.html#cfn-batch-jobdefinition-retrystrategy-evaluateonexit"""
@attr.s
class PropJobDefinitionLinuxParameters(Property):
"""
AWS Object Type = "AWS::Batch::JobDefinition.LinuxParameters"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties-linuxparameters.html
Property Document:
- ``p_Devices``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties-linuxparameters.html#cfn-batch-jobdefinition-containerproperties-linuxparameters-devices
- ``p_InitProcessEnabled``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties-linuxparameters.html#cfn-batch-jobdefinition-containerproperties-linuxparameters-initprocessenabled
- ``p_MaxSwap``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties-linuxparameters.html#cfn-batch-jobdefinition-containerproperties-linuxparameters-maxswap
- ``p_SharedMemorySize``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties-linuxparameters.html#cfn-batch-jobdefinition-containerproperties-linuxparameters-sharedmemorysize
- ``p_Swappiness``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties-linuxparameters.html#cfn-batch-jobdefinition-containerproperties-linuxparameters-swappiness
- ``p_Tmpfs``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties-linuxparameters.html#cfn-batch-jobdefinition-containerproperties-linuxparameters-tmpfs
"""
AWS_OBJECT_TYPE = "AWS::Batch::JobDefinition.LinuxParameters"
p_Devices: typing.List[typing.Union['PropJobDefinitionDevice', dict]] = attr.ib(
default=None,
converter=PropJobDefinitionDevice.from_list,
validator=attr.validators.optional(attr.validators.deep_iterable(member_validator=attr.validators.instance_of(PropJobDefinitionDevice), iterable_validator=attr.validators.instance_of(list))),
metadata={AttrMeta.PROPERTY_NAME: "Devices"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties-linuxparameters.html#cfn-batch-jobdefinition-containerproperties-linuxparameters-devices"""
p_InitProcessEnabled: bool = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(bool)),
metadata={AttrMeta.PROPERTY_NAME: "InitProcessEnabled"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties-linuxparameters.html#cfn-batch-jobdefinition-containerproperties-linuxparameters-initprocessenabled"""
p_MaxSwap: int = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(int)),
metadata={AttrMeta.PROPERTY_NAME: "MaxSwap"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties-linuxparameters.html#cfn-batch-jobdefinition-containerproperties-linuxparameters-maxswap"""
p_SharedMemorySize: int = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(int)),
metadata={AttrMeta.PROPERTY_NAME: "SharedMemorySize"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties-linuxparameters.html#cfn-batch-jobdefinition-containerproperties-linuxparameters-sharedmemorysize"""
p_Swappiness: int = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(int)),
metadata={AttrMeta.PROPERTY_NAME: "Swappiness"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties-linuxparameters.html#cfn-batch-jobdefinition-containerproperties-linuxparameters-swappiness"""
p_Tmpfs: typing.List[typing.Union['PropJobDefinitionTmpfs', dict]] = attr.ib(
default=None,
converter=PropJobDefinitionTmpfs.from_list,
validator=attr.validators.optional(attr.validators.deep_iterable(member_validator=attr.validators.instance_of(PropJobDefinitionTmpfs), iterable_validator=attr.validators.instance_of(list))),
metadata={AttrMeta.PROPERTY_NAME: "Tmpfs"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties-linuxparameters.html#cfn-batch-jobdefinition-containerproperties-linuxparameters-tmpfs"""
@attr.s
class PropJobDefinitionContainerProperties(Property):
"""
AWS Object Type = "AWS::Batch::JobDefinition.ContainerProperties"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties.html
Property Document:
- ``rp_Image``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties.html#cfn-batch-jobdefinition-containerproperties-image
- ``p_Command``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties.html#cfn-batch-jobdefinition-containerproperties-command
- ``p_Environment``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties.html#cfn-batch-jobdefinition-containerproperties-environment
- ``p_ExecutionRoleArn``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties.html#cfn-batch-jobdefinition-containerproperties-executionrolearn
- ``p_FargatePlatformConfiguration``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties.html#cfn-batch-jobdefinition-containerproperties-fargateplatformconfiguration
- ``p_InstanceType``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties.html#cfn-batch-jobdefinition-containerproperties-instancetype
- ``p_JobRoleArn``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties.html#cfn-batch-jobdefinition-containerproperties-jobrolearn
- ``p_LinuxParameters``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties.html#cfn-batch-jobdefinition-containerproperties-linuxparameters
- ``p_LogConfiguration``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties.html#cfn-batch-jobdefinition-containerproperties-logconfiguration
- ``p_Memory``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties.html#cfn-batch-jobdefinition-containerproperties-memory
- ``p_MountPoints``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties.html#cfn-batch-jobdefinition-containerproperties-mountpoints
- ``p_NetworkConfiguration``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties.html#cfn-batch-jobdefinition-containerproperties-networkconfiguration
- ``p_Privileged``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties.html#cfn-batch-jobdefinition-containerproperties-privileged
- ``p_ReadonlyRootFilesystem``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties.html#cfn-batch-jobdefinition-containerproperties-readonlyrootfilesystem
- ``p_ResourceRequirements``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties.html#cfn-batch-jobdefinition-containerproperties-resourcerequirements
- ``p_Secrets``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties.html#cfn-batch-jobdefinition-containerproperties-secrets
- ``p_Ulimits``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties.html#cfn-batch-jobdefinition-containerproperties-ulimits
- ``p_User``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties.html#cfn-batch-jobdefinition-containerproperties-user
- ``p_Vcpus``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties.html#cfn-batch-jobdefinition-containerproperties-vcpus
- ``p_Volumes``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties.html#cfn-batch-jobdefinition-containerproperties-volumes
"""
AWS_OBJECT_TYPE = "AWS::Batch::JobDefinition.ContainerProperties"
rp_Image: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type),
metadata={AttrMeta.PROPERTY_NAME: "Image"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties.html#cfn-batch-jobdefinition-containerproperties-image"""
p_Command: typing.List[TypeHint.intrinsic_str] = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.deep_iterable(member_validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), iterable_validator=attr.validators.instance_of(list))),
metadata={AttrMeta.PROPERTY_NAME: "Command"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties.html#cfn-batch-jobdefinition-containerproperties-command"""
p_Environment: typing.List[typing.Union['PropJobDefinitionEnvironment', dict]] = attr.ib(
default=None,
converter=PropJobDefinitionEnvironment.from_list,
validator=attr.validators.optional(attr.validators.deep_iterable(member_validator=attr.validators.instance_of(PropJobDefinitionEnvironment), iterable_validator=attr.validators.instance_of(list))),
metadata={AttrMeta.PROPERTY_NAME: "Environment"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties.html#cfn-batch-jobdefinition-containerproperties-environment"""
p_ExecutionRoleArn: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "ExecutionRoleArn"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties.html#cfn-batch-jobdefinition-containerproperties-executionrolearn"""
p_FargatePlatformConfiguration: typing.Union['PropJobDefinitionFargatePlatformConfiguration', dict] = attr.ib(
default=None,
converter=PropJobDefinitionFargatePlatformConfiguration.from_dict,
validator=attr.validators.optional(attr.validators.instance_of(PropJobDefinitionFargatePlatformConfiguration)),
metadata={AttrMeta.PROPERTY_NAME: "FargatePlatformConfiguration"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties.html#cfn-batch-jobdefinition-containerproperties-fargateplatformconfiguration"""
p_InstanceType: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "InstanceType"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties.html#cfn-batch-jobdefinition-containerproperties-instancetype"""
p_JobRoleArn: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "JobRoleArn"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties.html#cfn-batch-jobdefinition-containerproperties-jobrolearn"""
p_LinuxParameters: typing.Union['PropJobDefinitionLinuxParameters', dict] = attr.ib(
default=None,
converter=PropJobDefinitionLinuxParameters.from_dict,
validator=attr.validators.optional(attr.validators.instance_of(PropJobDefinitionLinuxParameters)),
metadata={AttrMeta.PROPERTY_NAME: "LinuxParameters"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties.html#cfn-batch-jobdefinition-containerproperties-linuxparameters"""
p_LogConfiguration: typing.Union['PropJobDefinitionLogConfiguration', dict] = attr.ib(
default=None,
converter=PropJobDefinitionLogConfiguration.from_dict,
validator=attr.validators.optional(attr.validators.instance_of(PropJobDefinitionLogConfiguration)),
metadata={AttrMeta.PROPERTY_NAME: "LogConfiguration"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties.html#cfn-batch-jobdefinition-containerproperties-logconfiguration"""
p_Memory: int = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(int)),
metadata={AttrMeta.PROPERTY_NAME: "Memory"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties.html#cfn-batch-jobdefinition-containerproperties-memory"""
p_MountPoints: typing.List[typing.Union['PropJobDefinitionMountPoints', dict]] = attr.ib(
default=None,
converter=PropJobDefinitionMountPoints.from_list,
validator=attr.validators.optional(attr.validators.deep_iterable(member_validator=attr.validators.instance_of(PropJobDefinitionMountPoints), iterable_validator=attr.validators.instance_of(list))),
metadata={AttrMeta.PROPERTY_NAME: "MountPoints"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties.html#cfn-batch-jobdefinition-containerproperties-mountpoints"""
p_NetworkConfiguration: typing.Union['PropJobDefinitionNetworkConfiguration', dict] = attr.ib(
default=None,
converter=PropJobDefinitionNetworkConfiguration.from_dict,
validator=attr.validators.optional(attr.validators.instance_of(PropJobDefinitionNetworkConfiguration)),
metadata={AttrMeta.PROPERTY_NAME: "NetworkConfiguration"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties.html#cfn-batch-jobdefinition-containerproperties-networkconfiguration"""
p_Privileged: bool = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(bool)),
metadata={AttrMeta.PROPERTY_NAME: "Privileged"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties.html#cfn-batch-jobdefinition-containerproperties-privileged"""
p_ReadonlyRootFilesystem: bool = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(bool)),
metadata={AttrMeta.PROPERTY_NAME: "ReadonlyRootFilesystem"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties.html#cfn-batch-jobdefinition-containerproperties-readonlyrootfilesystem"""
p_ResourceRequirements: typing.List[typing.Union['PropJobDefinitionResourceRequirement', dict]] = attr.ib(
default=None,
converter=PropJobDefinitionResourceRequirement.from_list,
validator=attr.validators.optional(attr.validators.deep_iterable(member_validator=attr.validators.instance_of(PropJobDefinitionResourceRequirement), iterable_validator=attr.validators.instance_of(list))),
metadata={AttrMeta.PROPERTY_NAME: "ResourceRequirements"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties.html#cfn-batch-jobdefinition-containerproperties-resourcerequirements"""
p_Secrets: typing.List[typing.Union['PropJobDefinitionSecret', dict]] = attr.ib(
default=None,
converter=PropJobDefinitionSecret.from_list,
validator=attr.validators.optional(attr.validators.deep_iterable(member_validator=attr.validators.instance_of(PropJobDefinitionSecret), iterable_validator=attr.validators.instance_of(list))),
metadata={AttrMeta.PROPERTY_NAME: "Secrets"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties.html#cfn-batch-jobdefinition-containerproperties-secrets"""
p_Ulimits: typing.List[typing.Union['PropJobDefinitionUlimit', dict]] = attr.ib(
default=None,
converter=PropJobDefinitionUlimit.from_list,
validator=attr.validators.optional(attr.validators.deep_iterable(member_validator=attr.validators.instance_of(PropJobDefinitionUlimit), iterable_validator=attr.validators.instance_of(list))),
metadata={AttrMeta.PROPERTY_NAME: "Ulimits"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties.html#cfn-batch-jobdefinition-containerproperties-ulimits"""
p_User: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "User"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties.html#cfn-batch-jobdefinition-containerproperties-user"""
p_Vcpus: int = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(int)),
metadata={AttrMeta.PROPERTY_NAME: "Vcpus"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties.html#cfn-batch-jobdefinition-containerproperties-vcpus"""
p_Volumes: typing.List[typing.Union['PropJobDefinitionVolumes', dict]] = attr.ib(
default=None,
converter=PropJobDefinitionVolumes.from_list,
validator=attr.validators.optional(attr.validators.deep_iterable(member_validator=attr.validators.instance_of(PropJobDefinitionVolumes), iterable_validator=attr.validators.instance_of(list))),
metadata={AttrMeta.PROPERTY_NAME: "Volumes"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-containerproperties.html#cfn-batch-jobdefinition-containerproperties-volumes"""
@attr.s
class PropJobDefinitionNodeRangeProperty(Property):
"""
AWS Object Type = "AWS::Batch::JobDefinition.NodeRangeProperty"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-noderangeproperty.html
Property Document:
- ``rp_TargetNodes``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-noderangeproperty.html#cfn-batch-jobdefinition-noderangeproperty-targetnodes
- ``p_Container``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-noderangeproperty.html#cfn-batch-jobdefinition-noderangeproperty-container
"""
AWS_OBJECT_TYPE = "AWS::Batch::JobDefinition.NodeRangeProperty"
rp_TargetNodes: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type),
metadata={AttrMeta.PROPERTY_NAME: "TargetNodes"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-noderangeproperty.html#cfn-batch-jobdefinition-noderangeproperty-targetnodes"""
p_Container: typing.Union['PropJobDefinitionContainerProperties', dict] = attr.ib(
default=None,
converter=PropJobDefinitionContainerProperties.from_dict,
validator=attr.validators.optional(attr.validators.instance_of(PropJobDefinitionContainerProperties)),
metadata={AttrMeta.PROPERTY_NAME: "Container"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-noderangeproperty.html#cfn-batch-jobdefinition-noderangeproperty-container"""
@attr.s
class PropJobDefinitionNodeProperties(Property):
"""
AWS Object Type = "AWS::Batch::JobDefinition.NodeProperties"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-nodeproperties.html
Property Document:
- ``rp_MainNode``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-nodeproperties.html#cfn-batch-jobdefinition-nodeproperties-mainnode
- ``rp_NodeRangeProperties``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-nodeproperties.html#cfn-batch-jobdefinition-nodeproperties-noderangeproperties
- ``rp_NumNodes``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-nodeproperties.html#cfn-batch-jobdefinition-nodeproperties-numnodes
"""
AWS_OBJECT_TYPE = "AWS::Batch::JobDefinition.NodeProperties"
rp_MainNode: int = attr.ib(
default=None,
validator=attr.validators.instance_of(int),
metadata={AttrMeta.PROPERTY_NAME: "MainNode"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-nodeproperties.html#cfn-batch-jobdefinition-nodeproperties-mainnode"""
rp_NodeRangeProperties: typing.List[typing.Union['PropJobDefinitionNodeRangeProperty', dict]] = attr.ib(
default=None,
converter=PropJobDefinitionNodeRangeProperty.from_list,
validator=attr.validators.deep_iterable(member_validator=attr.validators.instance_of(PropJobDefinitionNodeRangeProperty), iterable_validator=attr.validators.instance_of(list)),
metadata={AttrMeta.PROPERTY_NAME: "NodeRangeProperties"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-nodeproperties.html#cfn-batch-jobdefinition-nodeproperties-noderangeproperties"""
rp_NumNodes: int = attr.ib(
default=None,
validator=attr.validators.instance_of(int),
metadata={AttrMeta.PROPERTY_NAME: "NumNodes"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-batch-jobdefinition-nodeproperties.html#cfn-batch-jobdefinition-nodeproperties-numnodes"""
#--- Resource declaration ---
@attr.s
class JobQueue(Resource):
"""
AWS Object Type = "AWS::Batch::JobQueue"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-jobqueue.html
Property Document:
- ``rp_ComputeEnvironmentOrder``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-jobqueue.html#cfn-batch-jobqueue-computeenvironmentorder
- ``rp_Priority``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-jobqueue.html#cfn-batch-jobqueue-priority
- ``p_JobQueueName``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-jobqueue.html#cfn-batch-jobqueue-jobqueuename
- ``p_SchedulingPolicyArn``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-jobqueue.html#cfn-batch-jobqueue-schedulingpolicyarn
- ``p_State``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-jobqueue.html#cfn-batch-jobqueue-state
- ``p_Tags``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-jobqueue.html#cfn-batch-jobqueue-tags
"""
AWS_OBJECT_TYPE = "AWS::Batch::JobQueue"
rp_ComputeEnvironmentOrder: typing.List[typing.Union['PropJobQueueComputeEnvironmentOrder', dict]] = attr.ib(
default=None,
converter=PropJobQueueComputeEnvironmentOrder.from_list,
validator=attr.validators.deep_iterable(member_validator=attr.validators.instance_of(PropJobQueueComputeEnvironmentOrder), iterable_validator=attr.validators.instance_of(list)),
metadata={AttrMeta.PROPERTY_NAME: "ComputeEnvironmentOrder"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-jobqueue.html#cfn-batch-jobqueue-computeenvironmentorder"""
rp_Priority: int = attr.ib(
default=None,
validator=attr.validators.instance_of(int),
metadata={AttrMeta.PROPERTY_NAME: "Priority"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-jobqueue.html#cfn-batch-jobqueue-priority"""
p_JobQueueName: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "JobQueueName"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-jobqueue.html#cfn-batch-jobqueue-jobqueuename"""
p_SchedulingPolicyArn: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "SchedulingPolicyArn"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-jobqueue.html#cfn-batch-jobqueue-schedulingpolicyarn"""
p_State: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "State"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-jobqueue.html#cfn-batch-jobqueue-state"""
p_Tags: dict = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(dict)),
metadata={AttrMeta.PROPERTY_NAME: "Tags"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-jobqueue.html#cfn-batch-jobqueue-tags"""
@attr.s
class JobDefinition(Resource):
"""
AWS Object Type = "AWS::Batch::JobDefinition"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-jobdefinition.html
Property Document:
- ``rp_Type``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-jobdefinition.html#cfn-batch-jobdefinition-type
- ``p_ContainerProperties``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-jobdefinition.html#cfn-batch-jobdefinition-containerproperties
- ``p_JobDefinitionName``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-jobdefinition.html#cfn-batch-jobdefinition-jobdefinitionname
- ``p_NodeProperties``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-jobdefinition.html#cfn-batch-jobdefinition-nodeproperties
- ``p_Parameters``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-jobdefinition.html#cfn-batch-jobdefinition-parameters
- ``p_PlatformCapabilities``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-jobdefinition.html#cfn-batch-jobdefinition-platformcapabilities
- ``p_PropagateTags``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-jobdefinition.html#cfn-batch-jobdefinition-propagatetags
- ``p_RetryStrategy``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-jobdefinition.html#cfn-batch-jobdefinition-retrystrategy
- ``p_SchedulingPriority``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-jobdefinition.html#cfn-batch-jobdefinition-schedulingpriority
- ``p_Timeout``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-jobdefinition.html#cfn-batch-jobdefinition-timeout
- ``p_Tags``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-jobdefinition.html#cfn-batch-jobdefinition-tags
"""
AWS_OBJECT_TYPE = "AWS::Batch::JobDefinition"
rp_Type: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type),
metadata={AttrMeta.PROPERTY_NAME: "Type"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-jobdefinition.html#cfn-batch-jobdefinition-type"""
p_ContainerProperties: typing.Union['PropJobDefinitionContainerProperties', dict] = attr.ib(
default=None,
converter=PropJobDefinitionContainerProperties.from_dict,
validator=attr.validators.optional(attr.validators.instance_of(PropJobDefinitionContainerProperties)),
metadata={AttrMeta.PROPERTY_NAME: "ContainerProperties"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-jobdefinition.html#cfn-batch-jobdefinition-containerproperties"""
p_JobDefinitionName: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "JobDefinitionName"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-jobdefinition.html#cfn-batch-jobdefinition-jobdefinitionname"""
p_NodeProperties: typing.Union['PropJobDefinitionNodeProperties', dict] = attr.ib(
default=None,
converter=PropJobDefinitionNodeProperties.from_dict,
validator=attr.validators.optional(attr.validators.instance_of(PropJobDefinitionNodeProperties)),
metadata={AttrMeta.PROPERTY_NAME: "NodeProperties"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-jobdefinition.html#cfn-batch-jobdefinition-nodeproperties"""
p_Parameters: dict = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(dict)),
metadata={AttrMeta.PROPERTY_NAME: "Parameters"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-jobdefinition.html#cfn-batch-jobdefinition-parameters"""
p_PlatformCapabilities: typing.List[TypeHint.intrinsic_str] = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.deep_iterable(member_validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type), iterable_validator=attr.validators.instance_of(list))),
metadata={AttrMeta.PROPERTY_NAME: "PlatformCapabilities"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-jobdefinition.html#cfn-batch-jobdefinition-platformcapabilities"""
p_PropagateTags: bool = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(bool)),
metadata={AttrMeta.PROPERTY_NAME: "PropagateTags"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-jobdefinition.html#cfn-batch-jobdefinition-propagatetags"""
p_RetryStrategy: typing.Union['PropJobDefinitionRetryStrategy', dict] = attr.ib(
default=None,
converter=PropJobDefinitionRetryStrategy.from_dict,
validator=attr.validators.optional(attr.validators.instance_of(PropJobDefinitionRetryStrategy)),
metadata={AttrMeta.PROPERTY_NAME: "RetryStrategy"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-jobdefinition.html#cfn-batch-jobdefinition-retrystrategy"""
p_SchedulingPriority: int = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(int)),
metadata={AttrMeta.PROPERTY_NAME: "SchedulingPriority"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-jobdefinition.html#cfn-batch-jobdefinition-schedulingpriority"""
p_Timeout: typing.Union['PropJobDefinitionTimeout', dict] = attr.ib(
default=None,
converter=PropJobDefinitionTimeout.from_dict,
validator=attr.validators.optional(attr.validators.instance_of(PropJobDefinitionTimeout)),
metadata={AttrMeta.PROPERTY_NAME: "Timeout"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-jobdefinition.html#cfn-batch-jobdefinition-timeout"""
p_Tags: dict = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(dict)),
metadata={AttrMeta.PROPERTY_NAME: "Tags"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-jobdefinition.html#cfn-batch-jobdefinition-tags"""
@attr.s
class SchedulingPolicy(Resource):
"""
AWS Object Type = "AWS::Batch::SchedulingPolicy"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-schedulingpolicy.html
Property Document:
- ``p_FairsharePolicy``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-schedulingpolicy.html#cfn-batch-schedulingpolicy-fairsharepolicy
- ``p_Name``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-schedulingpolicy.html#cfn-batch-schedulingpolicy-name
- ``p_Tags``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-schedulingpolicy.html#cfn-batch-schedulingpolicy-tags
"""
AWS_OBJECT_TYPE = "AWS::Batch::SchedulingPolicy"
p_FairsharePolicy: typing.Union['PropSchedulingPolicyFairsharePolicy', dict] = attr.ib(
default=None,
converter=PropSchedulingPolicyFairsharePolicy.from_dict,
validator=attr.validators.optional(attr.validators.instance_of(PropSchedulingPolicyFairsharePolicy)),
metadata={AttrMeta.PROPERTY_NAME: "FairsharePolicy"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-schedulingpolicy.html#cfn-batch-schedulingpolicy-fairsharepolicy"""
p_Name: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "Name"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-schedulingpolicy.html#cfn-batch-schedulingpolicy-name"""
p_Tags: typing.Dict[str, TypeHint.intrinsic_str] = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.deep_mapping(key_validator=attr.validators.instance_of(str), value_validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type))),
metadata={AttrMeta.PROPERTY_NAME: "Tags"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-schedulingpolicy.html#cfn-batch-schedulingpolicy-tags"""
@property
def rv_Arn(self) -> GetAtt:
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-schedulingpolicy.html#aws-resource-batch-schedulingpolicy-return-values"""
return GetAtt(resource=self, attr_name="Arn")
@attr.s
class ComputeEnvironment(Resource):
"""
AWS Object Type = "AWS::Batch::ComputeEnvironment"
Resource Document: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-computeenvironment.html
Property Document:
- ``rp_Type``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-computeenvironment.html#cfn-batch-computeenvironment-type
- ``p_ComputeEnvironmentName``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-computeenvironment.html#cfn-batch-computeenvironment-computeenvironmentname
- ``p_ComputeResources``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-computeenvironment.html#cfn-batch-computeenvironment-computeresources
- ``p_ServiceRole``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-computeenvironment.html#cfn-batch-computeenvironment-servicerole
- ``p_State``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-computeenvironment.html#cfn-batch-computeenvironment-state
- ``p_UnmanagedvCpus``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-computeenvironment.html#cfn-batch-computeenvironment-unmanagedvcpus
- ``p_Tags``: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-computeenvironment.html#cfn-batch-computeenvironment-tags
"""
AWS_OBJECT_TYPE = "AWS::Batch::ComputeEnvironment"
rp_Type: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.instance_of(TypeCheck.intrinsic_str_type),
metadata={AttrMeta.PROPERTY_NAME: "Type"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-computeenvironment.html#cfn-batch-computeenvironment-type"""
p_ComputeEnvironmentName: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "ComputeEnvironmentName"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-computeenvironment.html#cfn-batch-computeenvironment-computeenvironmentname"""
p_ComputeResources: typing.Union['PropComputeEnvironmentComputeResources', dict] = attr.ib(
default=None,
converter=PropComputeEnvironmentComputeResources.from_dict,
validator=attr.validators.optional(attr.validators.instance_of(PropComputeEnvironmentComputeResources)),
metadata={AttrMeta.PROPERTY_NAME: "ComputeResources"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-computeenvironment.html#cfn-batch-computeenvironment-computeresources"""
p_ServiceRole: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "ServiceRole"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-computeenvironment.html#cfn-batch-computeenvironment-servicerole"""
p_State: TypeHint.intrinsic_str = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(TypeCheck.intrinsic_str_type)),
metadata={AttrMeta.PROPERTY_NAME: "State"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-computeenvironment.html#cfn-batch-computeenvironment-state"""
p_UnmanagedvCpus: int = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(int)),
metadata={AttrMeta.PROPERTY_NAME: "UnmanagedvCpus"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-computeenvironment.html#cfn-batch-computeenvironment-unmanagedvcpus"""
p_Tags: dict = attr.ib(
default=None,
validator=attr.validators.optional(attr.validators.instance_of(dict)),
metadata={AttrMeta.PROPERTY_NAME: "Tags"},
)
"""Doc: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-batch-computeenvironment.html#cfn-batch-computeenvironment-tags"""
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| false
| 0
| 0.005188
| 0
| 0.254215
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
c8454b6a7b2868452100a1d7f2609db3537a2db9
| 149
|
py
|
Python
|
treecompare/__init__.py
|
mx-pycoder/treecompare
|
784676039a6961366c92d131bc5496e10140200b
|
[
"MIT"
] | null | null | null |
treecompare/__init__.py
|
mx-pycoder/treecompare
|
784676039a6961366c92d131bc5496e10140200b
|
[
"MIT"
] | null | null | null |
treecompare/__init__.py
|
mx-pycoder/treecompare
|
784676039a6961366c92d131bc5496e10140200b
|
[
"MIT"
] | null | null | null |
# API
from ._treecompare import namecomp
from ._treecompare import treedups
from ._treecompare import treepurge
from ._treecompare import duplicate
| 21.285714
| 35
| 0.838926
| 17
| 149
| 7.117647
| 0.470588
| 0.495868
| 0.694215
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.127517
| 149
| 6
| 36
| 24.833333
| 0.930769
| 0.020134
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
c0b39ad9484f5f8d4a92121a2e472c16c9f3a7e6
| 49
|
py
|
Python
|
raspberrypi/python/python1_test.py
|
dambergn/programing-examples
|
d1086047caa52c7cc6d2e7877cbbbebd2a1cbee0
|
[
"MIT"
] | null | null | null |
raspberrypi/python/python1_test.py
|
dambergn/programing-examples
|
d1086047caa52c7cc6d2e7877cbbbebd2a1cbee0
|
[
"MIT"
] | null | null | null |
raspberrypi/python/python1_test.py
|
dambergn/programing-examples
|
d1086047caa52c7cc6d2e7877cbbbebd2a1cbee0
|
[
"MIT"
] | null | null | null |
#!/usr/bin/python
print 'Python1 Test Sucessfull'
| 24.5
| 31
| 0.77551
| 7
| 49
| 5.428571
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.022222
| 0.081633
| 49
| 2
| 31
| 24.5
| 0.822222
| 0.326531
| 0
| 0
| 0
| 0
| 0.69697
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 1
| 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
| 1
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 6
|
c0b4119e86cb14bc5067185a43c030a53cc5f2a5
| 26
|
py
|
Python
|
Python/pyworkout/modules_and_packages/menu/__init__.py
|
honchardev/Fun
|
ca7c0076e9bb3017c5d7e89aa7d5bd54a83c8ecc
|
[
"MIT"
] | null | null | null |
Python/pyworkout/modules_and_packages/menu/__init__.py
|
honchardev/Fun
|
ca7c0076e9bb3017c5d7e89aa7d5bd54a83c8ecc
|
[
"MIT"
] | 3
|
2020-03-24T16:26:35.000Z
|
2020-04-15T19:40:41.000Z
|
Python/pyworkout/modules_and_packages/menu/__init__.py
|
honchardev/Fun
|
ca7c0076e9bb3017c5d7e89aa7d5bd54a83c8ecc
|
[
"MIT"
] | null | null | null |
from menu.menu import menu
| 26
| 26
| 0.846154
| 5
| 26
| 4.4
| 0.6
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.115385
| 26
| 1
| 26
| 26
| 0.956522
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
c0c3a69cdf407b5a520a7aaf6817c943a7e8ec5f
| 120
|
py
|
Python
|
tensortrade/base/__init__.py
|
bwcknr/tensortrade
|
376f5e4cc4ad7df271774088884fbe88f8feb7d8
|
[
"Apache-2.0"
] | 34
|
2020-06-05T22:39:53.000Z
|
2022-01-09T03:09:12.000Z
|
tensortrade/base/__init__.py
|
bwcknr/tensortrade
|
376f5e4cc4ad7df271774088884fbe88f8feb7d8
|
[
"Apache-2.0"
] | 4
|
2020-11-13T18:48:52.000Z
|
2022-02-10T01:29:47.000Z
|
tensortrade/base/__init__.py
|
bwcknr/tensortrade
|
376f5e4cc4ad7df271774088884fbe88f8feb7d8
|
[
"Apache-2.0"
] | 8
|
2020-06-01T12:09:53.000Z
|
2022-01-18T14:45:29.000Z
|
from .clock import Clock
from .component import *
from .context import *
from .core import *
from .exceptions import *
| 17.142857
| 25
| 0.75
| 16
| 120
| 5.625
| 0.4375
| 0.333333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.175
| 120
| 6
| 26
| 20
| 0.909091
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
8d0cdb46fef6be7ec32f19ff100a24890613d302
| 4,205
|
py
|
Python
|
tests/test_htmlreflector.py
|
christabor/codeReflector
|
21c38ebaa6a418402b9bf97cc1d1a140b10d38e6
|
[
"Apache-2.0"
] | 3
|
2015-07-12T04:41:36.000Z
|
2015-09-18T02:28:35.000Z
|
tests/test_htmlreflector.py
|
christabor/codeReflector
|
21c38ebaa6a418402b9bf97cc1d1a140b10d38e6
|
[
"Apache-2.0"
] | null | null | null |
tests/test_htmlreflector.py
|
christabor/codeReflector
|
21c38ebaa6a418402b9bf97cc1d1a140b10d38e6
|
[
"Apache-2.0"
] | null | null | null |
# -*- coding: utf-8 -*-
__author__ = """Chris Tabor (dxdstudio@gmail.com)"""
import unittest
from code_reflector import html_reflector
class SelectorOutputTestCase(unittest.TestCase):
def setUp(self):
self.ref = html_reflector.HTMLReflector()
def test_single_class(self):
res = self.ref.process_string('.foo {}').extract().make_html(
save_as_string=True)
self.assertEqual(res, '<div class="foo"></div>')
def test_single_id(self):
res = self.ref.process_string('#foo {}').extract().make_html(
save_as_string=True)
self.assertEqual(res, '<div id="foo"></div>')
def test_pseudoselector(self):
res = self.ref.process_string('#foo:hover {}').extract().make_html(
save_as_string=True)
self.assertEqual(res, '')
def test_pseudoselector_mixed(self):
res = self.ref.process_string(
'#foo:hover {} #bar {}').extract().make_html(
save_as_string=True)
self.assertEqual(res, '<div id="bar"></div>')
def test_nested_id(self):
res = self.ref.process_string('#foo #bar #bim {}').extract().make_html(
save_as_string=True)
expected = ('<div id="foo"><div id="bar"><div id="bim">'
'</div></div></div>')
self.assertEqual(res, expected)
def test_nested_class(self):
res = self.ref.process_string('.foo .bar .bim {}').extract().make_html(
save_as_string=True)
expected = ('<div class="foo"><div class="bar"><div class="bim">'
'</div></div></div>')
self.assertEqual(res, expected)
def test_compound_class_id(self):
res = self.ref.process_string('.foo#bar {}').extract().make_html(
save_as_string=True)
expected = ('<div id="bar" class="foo"></div>')
self.assertEqual(res, expected)
def test_compound_multiclass(self):
res = self.ref.process_string('.foo.bar.bim {}').extract().make_html(
save_as_string=True)
expected = ('<div class="foo bar bim"></div>')
self.assertEqual(res, expected)
def test_compound_id_multiclass(self):
res = self.ref.process_string('#foo.bar.bim {}').extract().make_html(
save_as_string=True)
expected = ('<div id="foo" class="bar bim"></div>')
self.assertEqual(res, expected)
def test_compound_id_class(self):
res = self.ref.process_string('#foo.bar {}').extract().make_html(
save_as_string=True)
expected = ('<div id="foo" class="bar"></div>')
self.assertEqual(res, expected)
def test_nested_simple_class(self):
res = self.ref.process_string('.foo>.bar {}').extract().make_html(
save_as_string=True)
expected = ('<div class="foo"><div class="bar"></div></div>')
self.assertEqual(res, expected)
def test_nested_simple_id(self):
res = self.ref.process_string('#foo>#bar {}').extract().make_html(
save_as_string=True)
expected = ('<div id="foo"><div id="bar"></div></div>')
self.assertEqual(res, expected)
def test_nested_simple_id_spaces(self):
res = self.ref.process_string('#foo > #bar {}').extract().make_html(
save_as_string=True)
expected = ('<div id="foo"><div id="bar"></div></div>')
self.assertEqual(res, expected)
def test_nested_multiid_multiclass_tag(self):
res = self.ref.process_string(
'.foo > .bar > section#bam section.quux {}').extract().make_html(
save_as_string=True)
expected = ('<div class="foo"><div class="bar"><section id="bam">'
'<section class="quux"></section></section></div></div>')
self.assertEqual(res, expected)
def test_nested_multiid_multiclass_tag_mixedspaces(self):
res = self.ref.process_string(
'.foo > .bar>section#bam section.quux {}').extract().make_html(
save_as_string=True)
expected = ('<div class="foo"><div class="bar"><section id="bam">'
'<section class="quux"></section></section></div></div>')
self.assertEqual(res, expected)
| 39.669811
| 79
| 0.597146
| 520
| 4,205
| 4.621154
| 0.103846
| 0.046608
| 0.068664
| 0.087391
| 0.862672
| 0.862672
| 0.862672
| 0.862672
| 0.80774
| 0.782355
| 0
| 0.00031
| 0.231867
| 4,205
| 105
| 80
| 40.047619
| 0.743653
| 0.004994
| 0
| 0.511905
| 0
| 0
| 0.226208
| 0.042324
| 0
| 0
| 0
| 0
| 0.178571
| 1
| 0.190476
| false
| 0
| 0.02381
| 0
| 0.22619
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
23e314e87d1ce2b38f17668f757e05eb0bc939b5
| 529
|
py
|
Python
|
running_modes/enums/__init__.py
|
marco-foscato/Lib-INVENT
|
fe6a65ab7165abd87b25752a6b4208c8703d11f7
|
[
"Apache-2.0"
] | 26
|
2021-04-30T23:21:17.000Z
|
2022-03-10T06:33:11.000Z
|
running_modes/enums/__init__.py
|
marco-foscato/Lib-INVENT
|
fe6a65ab7165abd87b25752a6b4208c8703d11f7
|
[
"Apache-2.0"
] | 6
|
2021-10-03T08:35:48.000Z
|
2022-03-24T09:57:39.000Z
|
running_modes/enums/__init__.py
|
marco-foscato/Lib-INVENT
|
fe6a65ab7165abd87b25752a6b4208c8703d11f7
|
[
"Apache-2.0"
] | 10
|
2021-04-28T14:08:17.000Z
|
2022-03-04T04:18:13.000Z
|
from running_modes.enums.diversity_filter_enum import DiversityFilterEnum
from running_modes.enums.learning_strategy_enum import LearningStrategyEnum
from running_modes.enums.logging_mode_enum import LoggingModeEnum
from running_modes.enums.running_mode_enum import RunningModeEnum
from running_modes.enums.generative_model_regime import GenerativeModelRegimeEnum
from running_modes.enums.generative_model_parameters import GenerativeModelParametersEnum
from running_modes.enums.scoring_strategy_enum import ScoringStrategyEnum
| 58.777778
| 89
| 0.918715
| 63
| 529
| 7.380952
| 0.380952
| 0.165591
| 0.24086
| 0.316129
| 0.154839
| 0.154839
| 0
| 0
| 0
| 0
| 0
| 0
| 0.05482
| 529
| 8
| 90
| 66.125
| 0.93
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 1
| 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
| 6
|
23fa09c32208a388fee77f1efd42018f79e4deb4
| 9,971
|
py
|
Python
|
wechat.py
|
TANG617/2021-C-Homework
|
2056ae75c927ad1b5f96ef9c60e5f81af5910213
|
[
"MIT"
] | null | null | null |
wechat.py
|
TANG617/2021-C-Homework
|
2056ae75c927ad1b5f96ef9c60e5f81af5910213
|
[
"MIT"
] | null | null | null |
wechat.py
|
TANG617/2021-C-Homework
|
2056ae75c927ad1b5f96ef9c60e5f81af5910213
|
[
"MIT"
] | null | null | null |
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
this.src='../images/defaultHeader.jpg'
/images/small_white.png
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| 0
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| 0.002708
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| null | null | 0
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| null | 0
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| 1
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0
| 6
|
f1d4a924e8a5953f6e4d0b1a281f439de7754890
| 106
|
py
|
Python
|
netpbmfile/__init__.py
|
cgohlke/netpbmfile
|
4c84636d5535c44aa2e91fbc315b9a5282ecdf20
|
[
"BSD-3-Clause"
] | 4
|
2020-02-23T20:18:01.000Z
|
2022-03-05T09:47:55.000Z
|
netpbmfile/__init__.py
|
cgohlke/netpbmfile
|
4c84636d5535c44aa2e91fbc315b9a5282ecdf20
|
[
"BSD-3-Clause"
] | null | null | null |
netpbmfile/__init__.py
|
cgohlke/netpbmfile
|
4c84636d5535c44aa2e91fbc315b9a5282ecdf20
|
[
"BSD-3-Clause"
] | null | null | null |
# netpbmfile/__init__.py
from .netpbmfile import __doc__, __all__, __version__
from .netpbmfile import *
| 21.2
| 53
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| 12
| 106
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| 0.666667
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| 0
| 0
| 0.122642
| 106
| 4
| 54
| 26.5
| 0.741935
| 0.207547
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
f1e01161020bd19cd408163fd68e8137d22d090d
| 121
|
py
|
Python
|
ezotv/cache_tools/__init__.py
|
marcsello/ezotv-frontend
|
405c440a567e8a0f1577f10d45385f3171398afe
|
[
"CC0-1.0"
] | null | null | null |
ezotv/cache_tools/__init__.py
|
marcsello/ezotv-frontend
|
405c440a567e8a0f1577f10d45385f3171398afe
|
[
"CC0-1.0"
] | 7
|
2020-01-23T00:50:39.000Z
|
2020-04-18T20:34:40.000Z
|
ezotv/cache_tools/__init__.py
|
marcsello/ezotv-frontend
|
405c440a567e8a0f1577f10d45385f3171398afe
|
[
"CC0-1.0"
] | null | null | null |
#!/usr/bin/env python3
from .redis_client import redis_client
from .cached_base_http_session import CachedBaseHttpSession
| 40.333333
| 59
| 0.867769
| 17
| 121
| 5.882353
| 0.764706
| 0.22
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.008929
| 0.07438
| 121
| 3
| 59
| 40.333333
| 0.883929
| 0.173554
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
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