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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0a3175f67f662288eea8df62bf6eeb547b34b249
| 167
|
py
|
Python
|
form/__init__.py
|
gregbugaj/form-processor
|
0c803de43a98b4a02efa956803e64793995256ff
|
[
"MIT"
] | null | null | null |
form/__init__.py
|
gregbugaj/form-processor
|
0c803de43a98b4a02efa956803e64793995256ff
|
[
"MIT"
] | 1
|
2021-11-09T11:11:32.000Z
|
2021-11-09T11:11:32.000Z
|
form/__init__.py
|
gregbugaj/form-processor
|
0c803de43a98b4a02efa956803e64793995256ff
|
[
"MIT"
] | null | null | null |
"""
Name : __init__.py.py
"""
from __future__ import absolute_import
import os, sys
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '.')))
| 20.875
| 81
| 0.718563
| 26
| 167
| 4.115385
| 0.615385
| 0.168224
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.006623
| 0.095808
| 167
| 8
| 81
| 20.875
| 0.701987
| 0.125749
| 0
| 0
| 0
| 0
| 0.007194
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.666667
| 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
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
0a3f2fe8a14d10c97891725feb02412d1c2d506f
| 189
|
py
|
Python
|
trax/trax/templatetags/__init__.py
|
christianlupus/trax
|
85af6f908cbf55584f74856207ae3f6530728ccb
|
[
"MIT"
] | 4
|
2021-01-19T16:12:24.000Z
|
2021-08-05T07:25:44.000Z
|
trax/trax/templatetags/__init__.py
|
christianlupus/trax
|
85af6f908cbf55584f74856207ae3f6530728ccb
|
[
"MIT"
] | 1
|
2021-03-18T20:44:01.000Z
|
2021-03-18T20:44:01.000Z
|
trax/trax/templatetags/__init__.py
|
christianlupus/trax
|
85af6f908cbf55584f74856207ae3f6530728ccb
|
[
"MIT"
] | 1
|
2021-08-16T01:10:52.000Z
|
2021-08-16T01:10:52.000Z
|
from django import template
from trax.trax import utils
register = template.Library()
@register.filter(name='humanize_timedelta')
def d(value):
return utils.humanize_timedelta(value)
| 21
| 43
| 0.78836
| 25
| 189
| 5.88
| 0.64
| 0.231293
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.116402
| 189
| 8
| 44
| 23.625
| 0.88024
| 0
| 0
| 0
| 0
| 0
| 0.095238
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.166667
| false
| 0
| 0.333333
| 0.166667
| 0.666667
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 1
| 0
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| null | 0
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| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
|
0
| 5
|
0a42eddb2ead3b195437e25c8c36327ff95fd608
| 226
|
py
|
Python
|
src/controllers/product_controller.py
|
Serious-and-Pro-Gamers/SPG-Backend
|
567a068619867ce5579b75f8d39a2c36fdb8a737
|
[
"MIT"
] | null | null | null |
src/controllers/product_controller.py
|
Serious-and-Pro-Gamers/SPG-Backend
|
567a068619867ce5579b75f8d39a2c36fdb8a737
|
[
"MIT"
] | null | null | null |
src/controllers/product_controller.py
|
Serious-and-Pro-Gamers/SPG-Backend
|
567a068619867ce5579b75f8d39a2c36fdb8a737
|
[
"MIT"
] | null | null | null |
from ..services.product_service import get_products
from flask import Blueprint
product_api = Blueprint("product_api", __name__)
@product_api.route("/product", methods=["GET"])
def get_product():
return get_products()
| 20.545455
| 51
| 0.765487
| 29
| 226
| 5.586207
| 0.517241
| 0.185185
| 0.234568
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.115044
| 226
| 10
| 52
| 22.6
| 0.81
| 0
| 0
| 0
| 0
| 0
| 0.097345
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.166667
| false
| 0
| 0.333333
| 0.166667
| 0.666667
| 0.333333
| 1
| 0
| 0
| null | 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
|
0
| 5
|
0a47c1fdefbae54af218ff059c4893cde7a0674f
| 79,976
|
py
|
Python
|
src/v71.py
|
numb3r33/Kaggle_Home_Credit
|
f8f56a0514b928d7ed4b8f38c6edc53b67bab32d
|
[
"MIT"
] | null | null | null |
src/v71.py
|
numb3r33/Kaggle_Home_Credit
|
f8f56a0514b928d7ed4b8f38c6edc53b67bab32d
|
[
"MIT"
] | 14
|
2020-01-28T22:02:01.000Z
|
2022-03-11T23:33:08.000Z
|
src/v71.py
|
numb3r33/Kaggle_Home_Credit
|
f8f56a0514b928d7ed4b8f38c6edc53b67bab32d
|
[
"MIT"
] | null | null | null |
import pandas as pd
import numpy as np
import scipy as sp
import argparse
import os
import gc
import time
from base import *
from features import *
from datetime import datetime
from sklearn.externals import joblib
from sklearn.model_selection import cross_val_score, StratifiedKFold
basepath = os.path.expanduser('../')
SEED = 1231
np.random.seed(SEED)
#############################################################################################################
# EXPERIMENT PARAMETERS #
#############################################################################################################
COLS_TO_REMOVE = ['SK_ID_CURR',
'TARGET',
'OCCUPATION_TYPE__5',
'OCCUPATION_TYPE__-1',
'OCCUPATION_TYPE__11',
'OCCUPATION_TYPE__15',
'ORGANIZATION_TYPE__29',
'ORGANIZATION_TYPE__5',
'FLAG_OWN_REALTY',
'FLAG_DOCUMENT_21',
'ORGANIZATION_TYPE__21',
'FLAG_DOCUMENT_14',
'ORGANIZATION_TYPE__17',
'ORGANIZATION_TYPE__27',
'ORGANIZATION_TYPE__32',
'FLAG_DOCUMENT_16',
'ORGANIZATION_TYPE__47',
'FLAG_DOCUMENT_13',
'FLAG_DOCUMENT_11',
'ORGANIZATION_TYPE__40',
'ORGANIZATION_TYPE__23',
'ORGANIZATION_TYPE__14',
'diff_max_min_credit_term',
'ORGANIZATION_TYPE__1',
'ORGANIZATION_TYPE__9',
'OCCUPATION_TYPE__nan',
'ORGANIZATION_TYPE__41',
'OCCUPATION_TYPE__7',
'FLAG_MOBIL',
'ORGANIZATION_TYPE__18',
'ORGANIZATION_TYPE__38',
'ORGANIZATION_TYPE__44',
'FLAG_DOCUMENT_12',
'ORGANIZATION_TYPE__0',
'FLAG_DOCUMENT_2',
'ORGANIZATION_TYPE__13',
'OCCUPATION_TYPE__0',
'FLAG_DOCUMENT_4',
'OCCUPATION_TYPE__16',
'ORGANIZATION_TYPE__49',
'FLAG_DOCUMENT_6',
'FLAG_DOCUMENT_9',
'ORGANIZATION_TYPE__nan',
'OCCUPATION_TYPE__12',
'ORGANIZATION_TYPE__20',
'FLAG_CONT_MOBILE',
'ORGANIZATION_TYPE__37',
'ORGANIZATION_TYPE__45',
'FLAG_EMP_PHONE',
'FLAG_DOCUMENT_17',
'LIVE_REGION_NOT_WORK_REGION',
'OCCUPATION_TYPE__17',
'NAME_TYPE_SUITE',
'ORGANIZATION_TYPE__15',
'REG_REGION_NOT_LIVE_REGION',
'FLAG_DOCUMENT_10',
'ORGANIZATION_TYPE__3',
'OCCUPATION_TYPE__2',
'ORGANIZATION_TYPE__19',
'FLAG_DOCUMENT_19',
'AMT_REQ_CREDIT_BUREAU_DAY',
'credits_ended_bureau',
'ORGANIZATION_TYPE__8',
'ORGANIZATION_TYPE__16',
'FLAG_DOCUMENT_8',
'ORGANIZATION_TYPE__25',
'OCCUPATION_TYPE__6',
'NUM_NULLS_EXT_SCORES',
'ORGANIZATION_TYPE__48',
'ORGANIZATION_TYPE__53',
'ORGANIZATION_TYPE__10',
'FLAG_DOCUMENT_7',
'ORGANIZATION_TYPE__55',
'ORGANIZATION_TYPE__24',
'NAME_EDUCATION_TYPE__0',
'ORGANIZATION_TYPE__46',
'ELEVATORS_MODE',
'NAME_EDUCATION_TYPE__nan',
'ORGANIZATION_TYPE__22',
'ORGANIZATION_TYPE__50',
'REG_REGION_NOT_WORK_REGION',
'ORGANIZATION_TYPE__56',
'FLAG_DOCUMENT_5',
'FLAG_DOCUMENT_20',
'ORGANIZATION_TYPE__2',
'ORGANIZATION_TYPE__6',
'OCCUPATION_TYPE__13',
'ORGANIZATION_TYPE__52',
'FLAG_DOCUMENT_15',
'ORGANIZATION_TYPE__43',
'AMT_REQ_CREDIT_BUREAU_HOUR',
'NAME_HOUSING_TYPE',
'ORGANIZATION_TYPE__11',
'HOUSETYPE_MODE',
'EMERGENCYSTATE_MODE',
'ORGANIZATION_TYPE__28',
'NAME_EDUCATION_TYPE__2',
'ORGANIZATION_TYPE__4',
'OCCUPATION_TYPE__14',
'ORGANIZATION_TYPE__35',
'LIVE_CITY_NOT_WORK_CITY',
'num_diff_credits',
'ORGANIZATION_TYPE__51',
'REG_CITY_NOT_WORK_CITY',
'FLAG_EMAIL',
'ORGANIZATION_TYPE__57',
'NAME_HOUSING_TYPE__0',
'NAME_INCOME_TYPE__2',
'NAME_INCOME_TYPE__5',
'NAME_HOUSING_TYPE__nan',
'NAME_INCOME_TYPE__nan',
'NAME_INCOME_TYPE__0',
'NAME_INCOME_TYPE__6',
'NAME_CONTRACT_STATUS_3',
'NAME_INCOME_TYPE__3',
'diff_balance_curr_credit',
'ratio_min_installment_balance',
'NAME_HOUSING_TYPE__4',
'CODE_REJECT_REASON_5',
'CODE_REJECT_REASON_8',
'ORGANIZATION_TYPE__33',
'CODE_REJECT_REASON_0',
'OCCUPATION_TYPE__1',
'NAME_HOUSING_TYPE__5',
'sum_num_times_prolonged',
'NAME_GOODS_CATEGORY_13',
'NAME_GOODS_CATEGORY_4',
'NAME_GOODS_CATEGORY_26',
'PRODUCT_COMBINATION_-1',
'NAME_GOODS_CATEGORY_24',
'NAME_GOODS_CATEGORY_15',
'NAME_GOODS_CATEGORY_20',
'NAME_GOODS_CATEGORY_9',
'CODE_REJECT_REASON_6',
'NAME_GOODS_CATEGORY_6',
'NAME_GOODS_CATEGORY_0',
'num_high_int_no_info_loans',
'NAME_HOUSING_TYPE__2',
'NAME_GOODS_CATEGORY_14',
'NAME_GOODS_CATEGORY_17',
'PRODUCT_COMBINATION_16',
'PRODUCT_COMBINATION_15',
'OCCUPATION_TYPE__10',
'PRODUCT_COMBINATION_14',
'NAME_GOODS_CATEGORY_1',
'NAME_GOODS_CATEGORY_12',
'NAME_GOODS_CATEGORY_21',
'NAME_GOODS_CATEGORY_25',
'OCCUPATION_TYPE__9',
'NAME_GOODS_CATEGORY_10',
'NAME_GOODS_CATEGORY_16',
'NAME_GOODS_CATEGORY_8',
'mean_CODE_GENDER_ORGANIZATION_TYPE_DAYS_REGISTRATION',
'FLAG_DOCUMENT_18',
'NAME_GOODS_CATEGORY_18',
'ORGANIZATION_TYPE__30',
'sum_CODE_GENDER_NAME_EDUCATION_TYPE_OWN_CAR_AGE',
'ORGANIZATION_TYPE__12',
'NAME_EDUCATION_TYPE__3',
'ORGANIZATION_TYPE__36',
'ORGANIZATION_TYPE__34'
]
PARAMS = {
'num_boost_round': 5000,
'early_stopping_rounds': 200,
'boosting_type': 'gbdt',
'objective': 'binary',
'learning_rate': .02,
'metric': 'auc',
'max_depth': 8,
'num_leaves': 35,
'sub_feature': .1,
'feature_fraction_seed': SEED,
'min_data_in_leaf': 100,
'max_bin': 300,
'lambda_l2': 100,
'nthread': 4,
'verbose': -1,
'seed': SEED
}
MODEL_FILENAME = 'v71'
SAMPLE_SIZE = .5
# NOTE: column in frequency encoded columns
# cannot be in ohe cols.
FREQ_ENCODING_COLS = ['ORGANIZATION_OCCUPATION',
'age_emp_categorical',
'age_occupation'
]
OHE_COLS = [
'ORGANIZATION_TYPE',
'OCCUPATION_TYPE',
'NAME_EDUCATION_TYPE',
'NAME_HOUSING_TYPE',
'NAME_INCOME_TYPE'
]
class Modelv71(BaseModel):
def __init__(self, **params):
self.params = params
self.n_train = 307511 # TODO: find a way to remove this constant
def load_data(self, filenames):
dfs = []
for filename in filenames:
dfs.append(pd.read_csv(filename, parse_dates=True, keep_date_col=True))
df = pd.concat(dfs)
df.index = np.arange(len(df))
df = super(Modelv71, self).reduce_mem_usage(df)
return df
def preprocess(self):
tr = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'application_train.pkl'))
te = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'application_test.pkl'))
ntrain = len(tr)
data = pd.concat((tr, te))
del tr, te
gc.collect()
if not os.path.exists(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'current_application_train.pkl')):
print('Generating features based on current application ....')
t0 = time.clock()
data, FEATURE_NAMES = current_application_features(data)
data.index = np.arange(len(data))
# fill infrequent values
data = super(Modelv71, self).fill_infrequent_values(data)
data.iloc[:ntrain].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'current_application_train.pkl'))
data.iloc[ntrain:].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'current_application_test.pkl'))
print('\nTook: {} seconds'.format(time.clock() - t0))
else:
print('Already generated features based on current application')
if not os.path.exists(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'bureau_train.pkl')):
bureau = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + 'bureau.pkl'))
for col in bureau.select_dtypes(include=['category']).columns:
bureau.loc[:, col] = bureau.loc[:, col].cat.codes
print('Generating features based on credits reported to bureau ....')
t0 = time.clock()
data, FEATURE_NAMES = bureau_features(bureau, data)
data.index = np.arange(len(data))
# fill infrequent values
data = super(Modelv71, self).fill_infrequent_values(data)
data.iloc[:ntrain].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'bureau_train.pkl'))
data.iloc[ntrain:].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'bureau_test.pkl'))
print('\nTook: {} seconds'.format(time.clock() - t0))
del bureau
gc.collect()
else:
print('Already generated features based on bureau application')
if not os.path.exists(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'bureau_bal_train.pkl')):
bureau = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + 'bureau.pkl'))
bureau_bal = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + 'bureau_balance.pkl'))
for col in bureau.select_dtypes(include=['category']).columns:
bureau.loc[:, col] = bureau.loc[:, col].cat.codes
for col in bureau_bal.select_dtypes(include=['category']).columns:
bureau_bal.loc[:, col] = bureau_bal.loc[:, col].cat.codes
print('Generating features based on credits reported to bureau and bureau balance ....')
t0 = time.clock()
data, FEATURE_NAMES = bureau_and_balance(bureau, bureau_bal, data)
data.index = np.arange(len(data))
# fill infrequent values
data = super(Modelv71, self).fill_infrequent_values(data)
data.iloc[:ntrain].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'bureau_bal_train.pkl'))
data.iloc[ntrain:].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'bureau_bal_test.pkl'))
print('\nTook: {} seconds'.format(time.clock() - t0))
else:
print('Already generated features based on bureau and balance')
if not os.path.exists(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'prev_app_train.pkl')):
prev_app = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + 'previous_application.pkl'))
for col in prev_app.select_dtypes(include=['category']).columns:
prev_app.loc[:, col] = prev_app.loc[:, col].cat.codes
print('Generating features based on previous application ....')
t0 = time.clock()
data, FEATURE_NAMES = prev_app_features(prev_app, data)
data.index = np.arange(len(data))
# fill infrequent values
data = super(Modelv71, self).fill_infrequent_values(data)
del prev_app
gc.collect()
data.iloc[:ntrain].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'prev_app_train.pkl'))
data.iloc[ntrain:].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'prev_app_test.pkl'))
print('\nTook: {} seconds'.format(time.clock() - t0))
else:
print('Already generated features based on previous application')
if not os.path.exists(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'pos_cash_train.pkl')):
pos_cash = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + 'POS_CASH_balance.pkl'))
for col in pos_cash.select_dtypes(include=['category']).columns:
pos_cash.loc[:, col] = pos_cash.loc[:, col].cat.codes
print('Generating features based on pos cash ....')
t0 = time.clock()
data, FEATURE_NAMES = pos_cash_features(pos_cash, data)
data.index = np.arange(len(data))
# fill infrequent values
data = super(Modelv71, self).fill_infrequent_values(data)
del pos_cash
gc.collect()
data.iloc[:ntrain].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'pos_cash_train.pkl'))
data.iloc[ntrain:].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'pos_cash_test.pkl'))
print('\nTook: {} seconds'.format(time.clock() - t0))
else:
print('Already generated features based on pos cash')
if not os.path.exists(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'credit_train.pkl')):
credit_bal = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + 'credit_card_balance.pkl'))
for col in credit_bal.select_dtypes(include=['category']).columns:
credit_bal.loc[:, col] = credit_bal.loc[:, col].cat.codes
print('Generating features based on Credit Card ....')
t0 = time.clock()
data, FEATURE_NAMES = credit_card_features(credit_bal, data)
data.index = np.arange(len(data))
# fill infrequent values
data = super(Modelv71, self).fill_infrequent_values(data)
del credit_bal
gc.collect()
data.iloc[:ntrain].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'credit_train.pkl'))
data.iloc[ntrain:].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'credit_test.pkl'))
print('\nTook: {} seconds'.format(time.clock() - t0))
else:
print('Already generated features based on Credit Card')
if not os.path.exists(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'installments_train.pkl')):
installments = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + 'installments_payments.pkl'))
for col in installments.select_dtypes(include=['category']).columns:
installments.loc[:, col] = installments.loc[:, col].cat.codes
print('Generating features based on Installments ....')
t0 = time.clock()
data, FEATURE_NAMES = get_installment_features(installments, data)
data.index = np.arange(len(data))
# fill infrequent values
data = super(Modelv71, self).fill_infrequent_values(data)
del installments
gc.collect()
data.iloc[:ntrain].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'installments_train.pkl'))
data.iloc[ntrain:].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'installments_test.pkl'))
print('\nTook: {} seconds'.format(time.clock() - t0))
else:
print('Already generated features based on Installments')
if not os.path.exists(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'prev_app_bureau_train.pkl')):
prev_app = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + 'previous_application.pkl'))
bureau = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + 'bureau.pkl'))
for col in prev_app.select_dtypes(include=['category']).columns:
prev_app.loc[:, col] = prev_app.loc[:, col].cat.codes
for col in bureau.select_dtypes(include=['category']).columns:
bureau.loc[:, col] = bureau.loc[:, col].cat.codes
print('Generating features based on Previous Applications and Bureau Applications....')
t0 = time.clock()
data, FEATURE_NAMES = prev_app_bureau(prev_app, bureau, data)
data.index = np.arange(len(data))
# fill infrequent values
data = super(Modelv71, self).fill_infrequent_values(data)
del bureau, prev_app
gc.collect()
data.iloc[:ntrain].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'prev_app_bureau_train.pkl'))
data.iloc[ntrain:].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'prev_app_bureau_test.pkl'))
print('\nTook: {} seconds'.format(time.clock() - t0))
else:
print('Already generated features based on Previous application and Bureau Applications')
if not os.path.exists(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'prev_app_credit_train.pkl')):
prev_app = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + 'previous_application.pkl'))
credit_bal = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + 'credit_card_balance.pkl'))
for col in prev_app.select_dtypes(include=['category']).columns:
prev_app.loc[:, col] = prev_app.loc[:, col].cat.codes
for col in credit_bal.select_dtypes(include=['category']).columns:
credit_bal.loc[:, col] = credit_bal.loc[:, col].cat.codes
print('Generating features based on Previous Applications and Credit card balance ....')
t0 = time.clock()
data, FEATURE_NAMES = prev_app_credit_card(prev_app, credit_bal, data)
data.index = np.arange(len(data))
# fill infrequent values
data = super(Modelv71, self).fill_infrequent_values(data)
del credit_bal, prev_app
gc.collect()
data.iloc[:ntrain].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'prev_app_credit_train.pkl'))
data.iloc[ntrain:].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'prev_app_credit_test.pkl'))
print('\nTook: {} seconds'.format(time.clock() - t0))
else:
print('Already generated features based on Previous application and Credit card balance')
if not os.path.exists(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'prev_app_installments_train.pkl')):
prev_app = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + 'previous_application.pkl'))
installments = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + 'installments_payments.pkl'))
for col in prev_app.select_dtypes(include=['category']).columns:
prev_app.loc[:, col] = prev_app.loc[:, col].cat.codes
for col in installments.select_dtypes(include=['category']).columns:
installments.loc[:, col] = installments.loc[:, col].cat.codes
print('Generating features based on Previous Applications and Installment Payments ....')
t0 = time.clock()
data, FEATURE_NAMES = prev_app_installments(prev_app, installments, data)
data.index = np.arange(len(data))
# fill infrequent values
data = super(Modelv71, self).fill_infrequent_values(data)
del installments, prev_app
gc.collect()
data.iloc[:ntrain].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'prev_app_installments_train.pkl'))
data.iloc[ntrain:].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'prev_app_installments_test.pkl'))
print('\nTook: {} seconds'.format(time.clock() - t0))
else:
print('Already generated features based on Previous application and Installment Payments.')
if not os.path.exists(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'loan_stacking_train.pkl')):
bureau = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + 'bureau.pkl'))
prev_app = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + 'previous_application.pkl'))
credit_bal = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + 'credit_card_balance.pkl'))
for col in bureau.select_dtypes(include=['category']).columns:
bureau.loc[:, col] = bureau.loc[:, col].cat.codes
for col in prev_app.select_dtypes(include=['category']).columns:
prev_app.loc[:, col] = prev_app.loc[:, col].cat.codes
for col in credit_bal.select_dtypes(include=['category']).columns:
credit_bal.loc[:, col] = credit_bal.loc[:, col].cat.codes
print('Generating features based on loan stacking ....')
t0 = time.clock()
data, FEATURE_NAMES = loan_stacking(bureau, prev_app, credit_bal, data)
data.index = np.arange(len(data))
# fill infrequent values
data = super(Modelv71, self).fill_infrequent_values(data)
data.iloc[:ntrain].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'loan_stacking_train.pkl'))
data.iloc[ntrain:].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'loan_stacking_test.pkl'))
print('\nTook: {} seconds'.format(time.clock() - t0))
del bureau
gc.collect()
else:
print('Already generated features based on loan stacking.')
if not os.path.exists(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'feature_groups_train.pkl')):
print('Generating features based on feature groups ....')
t0 = time.clock()
data, FEATURE_NAMES = feature_groups(data)
data.index = np.arange(len(data))
# fill infrequent values
data = super(Modelv71, self).fill_infrequent_values(data)
data.iloc[:ntrain].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'feature_groups_train.pkl'))
data.iloc[ntrain:].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'feature_groups_test.pkl'))
print('\nTook: {} seconds'.format(time.clock() - t0))
else:
print('Already generated features based on feature groups.')
if not os.path.exists(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'prev_app_pos_cash_train.pkl')):
print('Generating features based on previous application and pos cash ....')
prev_app = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + 'previous_application.pkl'))
pos_cash = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + 'POS_CASH_balance.pkl'))
for col in prev_app.select_dtypes(include=['category']).columns:
prev_app.loc[:, col] = prev_app.loc[:, col].cat.codes
for col in pos_cash.select_dtypes(include=['category']).columns:
pos_cash.loc[:, col] = pos_cash.loc[:, col].cat.codes
t0 = time.clock()
data, FEATURE_NAMES = prev_app_pos(prev_app, pos_cash, data)
data.index = np.arange(len(data))
# fill infrequent values
data = super(Modelv71, self).fill_infrequent_values(data)
data.iloc[:ntrain].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'prev_app_pos_cash_train.pkl'))
data.iloc[ntrain:].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'prev_app_pos_cash_test.pkl'))
print('\nTook: {} seconds'.format(time.clock() - t0))
else:
print('Already generated features based on previous application and pos cash.')
if not os.path.exists(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'prev_app_pos_cash_credit_bal_train.pkl')):
print('Generating features based on previous application, pos cash and credit card balance ....')
prev_app = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + 'previous_application.pkl'))
pos_cash = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + 'POS_CASH_balance.pkl'))
credit_bal = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + 'credit_card_balance.pkl'))
for col in prev_app.select_dtypes(include=['category']).columns:
prev_app.loc[:, col] = prev_app.loc[:, col].cat.codes
for col in pos_cash.select_dtypes(include=['category']).columns:
pos_cash.loc[:, col] = pos_cash.loc[:, col].cat.codes
for col in credit_bal.select_dtypes(include=['category']).columns:
credit_bal.loc[:, col] = credit_bal.loc[:, col].cat.codes
t0 = time.time()
data, FEATURE_NAMES = prev_app_pos_credit(prev_app, pos_cash, credit_bal, data)
data.index = np.arange(len(data))
# fill infrequent values
data = super(Modelv71, self).fill_infrequent_values(data)
data.iloc[:ntrain].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'prev_app_pos_cash_credit_bal_train.pkl'))
data.iloc[ntrain:].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'prev_app_pos_cash_credit_bal_test.pkl'))
print('\nTook: {} seconds'.format(time.time() - t0))
else:
print('Already generated features based on previous application, pos cash and credit card balance.')
if not os.path.exists(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'prev_app_ohe_train.pkl')):
print('Generating features based on previous application one hot encoded features ....')
prev_app = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + 'previous_application.pkl'))
for col in prev_app.select_dtypes(include=['category']).columns:
prev_app.loc[:, col] = prev_app.loc[:, col].cat.codes
t0 = time.time()
data, FEATURE_NAMES = prev_app_ohe(prev_app, data)
data.index = np.arange(len(data))
# fill infrequent values
data = super(Modelv71, self).fill_infrequent_values(data)
data.iloc[:ntrain].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'prev_app_ohe_train.pkl'))
data.iloc[ntrain:].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'prev_app_ohe_test.pkl'))
print('\nTook: {} seconds'.format(time.time() - t0))
else:
print('Already generated features based on previous application one hot encode features.')
def prepare_features(self):
tr = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'application_train.pkl'))
te = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'application_test.pkl'))
ntrain = len(tr)
data = pd.concat((tr, te))
del tr, te
gc.collect()
if not os.path.exists(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'current_application_train.pkl')):
print('Generating features based on current application ....')
t0 = time.clock()
data, FEATURE_NAMES = current_application_features(data)
data.index = np.arange(len(data))
# fill infrequent values
data = super(Modelv71, self).fill_infrequent_values(data)
data.iloc[:ntrain].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'current_application_train.pkl'))
data.iloc[ntrain:].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'current_application_test.pkl'))
print('\nTook: {} seconds'.format(time.clock() - t0))
else:
print('Already generated features based on current application')
if not os.path.exists(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'bureau_train.pkl')):
bureau = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + 'bureau.pkl'))
for col in bureau.select_dtypes(include=['category']).columns:
bureau.loc[:, col] = bureau.loc[:, col].cat.codes
print('Generating features based on credits reported to bureau ....')
t0 = time.clock()
data, FEATURE_NAMES = bureau_features(bureau, data)
data.index = np.arange(len(data))
# fill infrequent values
data = super(Modelv71, self).fill_infrequent_values(data)
data.iloc[:ntrain].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'bureau_train.pkl'))
data.iloc[ntrain:].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'bureau_test.pkl'))
print('\nTook: {} seconds'.format(time.clock() - t0))
del bureau
gc.collect()
else:
print('Already generated features based on bureau application')
if not os.path.exists(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'bureau_bal_train.pkl')):
bureau = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + 'bureau.pkl'))
bureau_bal = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + 'bureau_balance.pkl'))
for col in bureau.select_dtypes(include=['category']).columns:
bureau.loc[:, col] = bureau.loc[:, col].cat.codes
for col in bureau_bal.select_dtypes(include=['category']).columns:
bureau_bal.loc[:, col] = bureau_bal.loc[:, col].cat.codes
print('Generating features based on credits reported to bureau and bureau balance ....')
t0 = time.clock()
data, FEATURE_NAMES = bureau_and_balance(bureau, bureau_bal, data)
data.index = np.arange(len(data))
# fill infrequent values
data = super(Modelv71, self).fill_infrequent_values(data)
data.iloc[:ntrain].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'bureau_bal_train.pkl'))
data.iloc[ntrain:].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'bureau_bal_test.pkl'))
print('\nTook: {} seconds'.format(time.clock() - t0))
else:
print('Already generated features based on bureau and balance')
if not os.path.exists(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'prev_app_train.pkl')):
prev_app = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + 'previous_application.pkl'))
for col in prev_app.select_dtypes(include=['category']).columns:
prev_app.loc[:, col] = prev_app.loc[:, col].cat.codes
print('Generating features based on previous application ....')
t0 = time.clock()
data, FEATURE_NAMES = prev_app_features(prev_app, data)
data.index = np.arange(len(data))
# fill infrequent values
data = super(Modelv71, self).fill_infrequent_values(data)
del prev_app
gc.collect()
data.iloc[:ntrain].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'prev_app_train.pkl'))
data.iloc[ntrain:].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'prev_app_test.pkl'))
print('\nTook: {} seconds'.format(time.clock() - t0))
else:
print('Already generated features based on previous application')
if not os.path.exists(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'pos_cash_train.pkl')):
pos_cash = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + 'POS_CASH_balance.pkl'))
for col in pos_cash.select_dtypes(include=['category']).columns:
pos_cash.loc[:, col] = pos_cash.loc[:, col].cat.codes
print('Generating features based on pos cash ....')
t0 = time.clock()
data, FEATURE_NAMES = pos_cash_features(pos_cash, data)
data.index = np.arange(len(data))
# fill infrequent values
data = super(Modelv71, self).fill_infrequent_values(data)
del pos_cash
gc.collect()
data.iloc[:ntrain].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'pos_cash_train.pkl'))
data.iloc[ntrain:].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'pos_cash_test.pkl'))
print('\nTook: {} seconds'.format(time.clock() - t0))
else:
print('Already generated features based on pos cash')
if not os.path.exists(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'credit_train.pkl')):
credit_bal = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + 'credit_card_balance.pkl'))
for col in credit_bal.select_dtypes(include=['category']).columns:
credit_bal.loc[:, col] = credit_bal.loc[:, col].cat.codes
print('Generating features based on Credit Card ....')
t0 = time.clock()
data, FEATURE_NAMES = credit_card_features(credit_bal, data)
data.index = np.arange(len(data))
# fill infrequent values
data = super(Modelv71, self).fill_infrequent_values(data)
del credit_bal
gc.collect()
data.iloc[:ntrain].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'credit_train.pkl'))
data.iloc[ntrain:].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'credit_test.pkl'))
print('\nTook: {} seconds'.format(time.clock() - t0))
else:
print('Already generated features based on Credit Card')
if not os.path.exists(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'installments_train.pkl')):
installments = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + 'installments_payments.pkl'))
for col in installments.select_dtypes(include=['category']).columns:
installments.loc[:, col] = installments.loc[:, col].cat.codes
print('Generating features based on Installments ....')
t0 = time.clock()
data, FEATURE_NAMES = get_installment_features(installments, data)
data.index = np.arange(len(data))
# fill infrequent values
data = super(Modelv71, self).fill_infrequent_values(data)
del installments
gc.collect()
data.iloc[:ntrain].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'installments_train.pkl'))
data.iloc[ntrain:].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'installments_test.pkl'))
print('\nTook: {} seconds'.format(time.clock() - t0))
else:
print('Already generated features based on Installments')
if not os.path.exists(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'prev_app_bureau_train.pkl')):
prev_app = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + 'previous_application.pkl'))
bureau = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + 'bureau.pkl'))
for col in prev_app.select_dtypes(include=['category']).columns:
prev_app.loc[:, col] = prev_app.loc[:, col].cat.codes
for col in bureau.select_dtypes(include=['category']).columns:
bureau.loc[:, col] = bureau.loc[:, col].cat.codes
print('Generating features based on Previous Applications and Bureau Applications....')
t0 = time.clock()
data, FEATURE_NAMES = prev_app_bureau(prev_app, bureau, data)
data.index = np.arange(len(data))
# fill infrequent values
data = super(Modelv71, self).fill_infrequent_values(data)
del bureau, prev_app
gc.collect()
data.iloc[:ntrain].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'prev_app_bureau_train.pkl'))
data.iloc[ntrain:].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'prev_app_bureau_test.pkl'))
print('\nTook: {} seconds'.format(time.clock() - t0))
else:
print('Already generated features based on Previous application and Bureau Applications')
if not os.path.exists(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'prev_app_credit_train.pkl')):
prev_app = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + 'previous_application.pkl'))
credit_bal = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + 'credit_card_balance.pkl'))
for col in prev_app.select_dtypes(include=['category']).columns:
prev_app.loc[:, col] = prev_app.loc[:, col].cat.codes
for col in credit_bal.select_dtypes(include=['category']).columns:
credit_bal.loc[:, col] = credit_bal.loc[:, col].cat.codes
print('Generating features based on Previous Applications and Credit card balance ....')
t0 = time.clock()
data, FEATURE_NAMES = prev_app_credit_card(prev_app, credit_bal, data)
data.index = np.arange(len(data))
# fill infrequent values
data = super(Modelv71, self).fill_infrequent_values(data)
del credit_bal, prev_app
gc.collect()
data.iloc[:ntrain].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'prev_app_credit_train.pkl'))
data.iloc[ntrain:].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'prev_app_credit_test.pkl'))
print('\nTook: {} seconds'.format(time.clock() - t0))
else:
print('Already generated features based on Previous application and Credit card balance')
if not os.path.exists(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'prev_app_installments_train.pkl')):
prev_app = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + 'previous_application.pkl'))
installments = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + 'installments_payments.pkl'))
for col in prev_app.select_dtypes(include=['category']).columns:
prev_app.loc[:, col] = prev_app.loc[:, col].cat.codes
for col in installments.select_dtypes(include=['category']).columns:
installments.loc[:, col] = installments.loc[:, col].cat.codes
print('Generating features based on Previous Applications and Installment Payments ....')
t0 = time.clock()
data, FEATURE_NAMES = prev_app_installments(prev_app, installments, data)
data.index = np.arange(len(data))
# fill infrequent values
data = super(Modelv71, self).fill_infrequent_values(data)
del installments, prev_app
gc.collect()
data.iloc[:ntrain].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'prev_app_installments_train.pkl'))
data.iloc[ntrain:].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'prev_app_installments_test.pkl'))
print('\nTook: {} seconds'.format(time.clock() - t0))
else:
print('Already generated features based on Previous application and Installment Payments.')
if not os.path.exists(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'loan_stacking_train.pkl')):
bureau = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + 'bureau.pkl'))
prev_app = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + 'previous_application.pkl'))
credit_bal = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + 'credit_card_balance.pkl'))
for col in bureau.select_dtypes(include=['category']).columns:
bureau.loc[:, col] = bureau.loc[:, col].cat.codes
for col in prev_app.select_dtypes(include=['category']).columns:
prev_app.loc[:, col] = prev_app.loc[:, col].cat.codes
for col in credit_bal.select_dtypes(include=['category']).columns:
credit_bal.loc[:, col] = credit_bal.loc[:, col].cat.codes
print('Generating features based on loan stacking ....')
t0 = time.clock()
data, FEATURE_NAMES = loan_stacking(bureau, prev_app, credit_bal, data)
data.index = np.arange(len(data))
# fill infrequent values
data = super(Modelv71, self).fill_infrequent_values(data)
data.iloc[:ntrain].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'loan_stacking_train.pkl'))
data.iloc[ntrain:].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'loan_stacking_test.pkl'))
print('\nTook: {} seconds'.format(time.clock() - t0))
del bureau
gc.collect()
else:
print('Already generated features based on loan stacking.')
if not os.path.exists(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'feature_groups_train.pkl')):
print('Generating features based on feature groups ....')
t0 = time.clock()
data, FEATURE_NAMES = feature_groups(data)
data.index = np.arange(len(data))
# fill infrequent values
data = super(Modelv71, self).fill_infrequent_values(data)
data.iloc[:ntrain].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'feature_groups_train.pkl'))
data.iloc[ntrain:].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'feature_groups_test.pkl'))
print('\nTook: {} seconds'.format(time.clock() - t0))
else:
print('Already generated features based on feature groups.')
if not os.path.exists(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'prev_app_pos_cash_train.pkl')):
print('Generating features based on previous application and pos cash ....')
prev_app = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + 'previous_application.pkl'))
pos_cash = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + 'POS_CASH_balance.pkl'))
for col in prev_app.select_dtypes(include=['category']).columns:
prev_app.loc[:, col] = prev_app.loc[:, col].cat.codes
for col in pos_cash.select_dtypes(include=['category']).columns:
pos_cash.loc[:, col] = pos_cash.loc[:, col].cat.codes
t0 = time.clock()
data, FEATURE_NAMES = prev_app_pos(prev_app, pos_cash, data)
data.index = np.arange(len(data))
# fill infrequent values
data = super(Modelv71, self).fill_infrequent_values(data)
data.iloc[:ntrain].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'prev_app_pos_cash_train.pkl'))
data.iloc[ntrain:].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'prev_app_pos_cash_test.pkl'))
print('\nTook: {} seconds'.format(time.clock() - t0))
else:
print('Already generated features based on previous application and pos cash.')
if not os.path.exists(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'prev_app_pos_cash_credit_bal_train.pkl')):
print('Generating features based on previous application, pos cash and credit card balance ....')
prev_app = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + 'previous_application.pkl'))
pos_cash = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + 'POS_CASH_balance.pkl'))
credit_bal = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + 'credit_card_balance.pkl'))
for col in prev_app.select_dtypes(include=['category']).columns:
prev_app.loc[:, col] = prev_app.loc[:, col].cat.codes
for col in pos_cash.select_dtypes(include=['category']).columns:
pos_cash.loc[:, col] = pos_cash.loc[:, col].cat.codes
for col in credit_bal.select_dtypes(include=['category']).columns:
credit_bal.loc[:, col] = credit_bal.loc[:, col].cat.codes
t0 = time.time()
data, FEATURE_NAMES = prev_app_pos_credit(prev_app, pos_cash, credit_bal, data)
data.index = np.arange(len(data))
# fill infrequent values
data = super(Modelv71, self).fill_infrequent_values(data)
data.iloc[:ntrain].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'prev_app_pos_cash_credit_bal_train.pkl'))
data.iloc[ntrain:].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'prev_app_pos_cash_credit_bal_test.pkl'))
print('\nTook: {} seconds'.format(time.time() - t0))
else:
print('Already generated features based on previous application, pos cash and credit card balance.')
if not os.path.exists(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'prev_app_ohe_train.pkl')):
print('Generating features based on previous application one hot encoded features ....')
prev_app = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + 'previous_application.pkl'))
for col in prev_app.select_dtypes(include=['category']).columns:
prev_app.loc[:, col] = prev_app.loc[:, col].cat.codes
t0 = time.time()
data, FEATURE_NAMES = prev_app_ohe(prev_app, data)
data.index = np.arange(len(data))
# fill infrequent values
data = super(Modelv71, self).fill_infrequent_values(data)
data.iloc[:ntrain].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'prev_app_ohe_train.pkl'))
data.iloc[ntrain:].loc[:, FEATURE_NAMES].to_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'prev_app_ohe_test.pkl'))
print('\nTook: {} seconds'.format(time.time() - t0))
else:
print('Already generated features based on previous application one hot encode features.')
# This method currently takes care of loading engineered features from disk
# and merging train and test to report back a dataframe (data) which can be used by
# other layers.
def merge_datasets(self):
def get_filenames():
filenames = [f'application_',
f'current_application_',
f'bureau_',
f'bureau_bal_',
f'prev_app_',
f'pos_cash_',
f'credit_',
f'installments_',
f'prev_app_bureau_',
f'prev_app_credit_',
f'prev_app_installments_',
f'loan_stacking_',
f'feature_groups_',
f'prev_app_pos_cash_',
f'prev_app_pos_cash_credit_bal_',
f'prev_app_ohe_'
]
return filenames
train = []
test = []
filenames = get_filenames()
for filename_ in filenames:
tmp = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'{filename_}train.pkl'))
tmp.index = np.arange(len(tmp))
train.append(tmp)
for filename_ in filenames:
tmp = pd.read_pickle(os.path.join(basepath, self.params['output_path'] + 'feature_groups/' + f'{filename_}test.pkl'))
tmp.index = np.arange(len(tmp))
test.append(tmp)
return pd.concat(train, axis=1), pd.concat(test, axis=1)
def feature_interaction(self, data, key, agg_feature, agg_func, agg_func_name):
key_name = '_'.join(key)
tmp = data.groupby(key)[agg_feature].apply(agg_func)\
.reset_index()\
.rename(columns={agg_feature: f'{agg_func_name}_{key_name}_{agg_feature}'})
data.loc[:, f'{agg_func_name}_{key_name}_{agg_feature}'] = data.loc[:, key]\
.merge(tmp, on=key, how='left')[f'{agg_func_name}_{key_name}_{agg_feature}']
return data
def get_features(self, train, test, compute_ohe):
data = pd.concat((train, test))
data.index = np.arange(len(data))
for col in data.select_dtypes(include=['category']).columns:
data[col] = data[col].cat.codes
# TODO: not very happy with the way we are computing interactions
# because if we omit any of this feature from pipeline it would
# still work but would most likely be a feature of all null values.
# concatenate OCCUPATION TYPE AND ORGANIZATION TYPE
data.loc[:, 'ORGANIZATION_OCCUPATION'] = pd.factorize(data.ORGANIZATION_TYPE.astype(np.str) +\
data.OCCUPATION_TYPE.astype(np.str)
)[0]
# interaction between total debt to income and (annuity / credit)
data.loc[:, 'debt_income_to_annuity_credit'] = data.total_debt_to_income / data.ratio_annuity_credit
# interaction between days birth and ratio of annuity to credit
data.loc[:, 'add_days_birth_annuity_credit'] = data.DAYS_BIRTH + data.ratio_annuity_credit
# interaction between ratio of annuity to credit with external source 2 score
data.loc[:, 'mult_annuity_credit_ext_source_2'] = data.ratio_annuity_credit * data.EXT_SOURCE_2
data.loc[:, 'ratio_annuity_credit_ext_source_2'] = data.ratio_annuity_credit / data.EXT_SOURCE_2.map(np.log1p)
data.loc[:, 'mult_annuity_credit_ext_source_1'] = data.ratio_annuity_credit * data.EXT_SOURCE_1
data.loc[:, 'ratio_annuity_credit_ext_source_1'] = data.ratio_annuity_credit / data.EXT_SOURCE_1.map(np.log1p)
data.loc[:, 'mult_annuity_credit_ext_source_3'] = data.ratio_annuity_credit * data.EXT_SOURCE_3
data.loc[:, 'ratio_annuity_credit_ext_source_3'] = data.ratio_annuity_credit / data.EXT_SOURCE_3.map(np.log1p)
# interaction between ratio of annuity to credit with total amount paid in installments
data.loc[:, 'mult_annuity_credit_amt_payment_sum'] = data.ratio_annuity_credit * data.AMT_PAYMENT_sum
# interaction between total amount paid in installments and delay in installments
data.loc[:, 'mult_amt_payment_sum_delay_installment'] = data.AMT_PAYMENT_sum * data.delay_in_installment_payments
# interaction between credit / annuity and age
data.loc[:, 'diff_credit_annuity_age'] = (data.AMT_CREDIT / data.AMT_ANNUITY) - (-data.DAYS_BIRTH / 365)
# interaction between ext_3 and age
data.loc[:, 'ext_3_age'] = data.EXT_SOURCE_3 * (-data.DAYS_BIRTH / 365)
# interaction between ext_2 and age
data.loc[:, 'ext_2_age'] = data.EXT_SOURCE_2 * (-data.DAYS_BIRTH / 365)
# interaction between rate and external source 2
data.loc[:, 'add_rate_ext_2'] = (data.AMT_CREDIT / data.AMT_ANNUITY) + data.EXT_SOURCE_2
# interaction between rate and age
data.loc[:, 'add_rate_age'] = (data.AMT_CREDIT / data.AMT_ANNUITY) + (-data.DAYS_BIRTH / 365)
# interaction between age and employed and external score 2
data.loc[:, 'add_mult_age_employed_ext_2'] = ((-data.DAYS_BIRTH / 365) +\
(-data.DAYS_EMPLOYED.replace({365243: np.nan}))) *\
(data.EXT_SOURCE_2)
# combine ratio annuity credit, region populative relative and ext source 2
data.loc[:, 'rate_annuity_region_ext_source_2'] = data.ratio_annuity_credit * data.REGION_POPULATION_RELATIVE * data.EXT_SOURCE_2
data.loc[:, 'region_ext_source_3'] = data.REGION_POPULATION_RELATIVE * data.EXT_SOURCE_3
# Relationship between AMT_REQ_CREDIT_BUREAU_HOUR and AMT_REQ_CREDIT_BUREAU_YEAR
data.loc[:, 'ratio_check_hour_to_year'] = data.AMT_REQ_CREDIT_BUREAU_HOUR.div(data.AMT_REQ_CREDIT_BUREAU_YEAR)
# Relationship between Income and ratio annuity credit
data.loc[:, 'mult_ratio_income'] = (data.ratio_annuity_credit * data.AMT_INCOME_TOTAL).map(np.log1p)
data.loc[:, 'div_ratio_income'] = (data.AMT_INCOME_TOTAL / data.ratio_annuity_credit).map(np.log1p)
# Gender, Education and other features
data = self.feature_interaction(data, ['CODE_GENDER', 'NAME_EDUCATION_TYPE'], 'EXT_SOURCE_2', np.mean, 'mean')
data = self.feature_interaction(data, ['CODE_GENDER', 'NAME_EDUCATION_TYPE'], 'EXT_SOURCE_2', np.var, 'var')
data = self.feature_interaction(data, ['CODE_GENDER', 'NAME_EDUCATION_TYPE'], 'EXT_SOURCE_1', np.mean, 'mean')
data = self.feature_interaction(data, ['CODE_GENDER', 'NAME_EDUCATION_TYPE'], 'AMT_CREDIT', np.mean, 'mean')
data = self.feature_interaction(data, ['CODE_GENDER', 'NAME_EDUCATION_TYPE'], 'AMT_ANNUITY', np.mean, 'mean')
data = self.feature_interaction(data, ['CODE_GENDER', 'NAME_EDUCATION_TYPE'], 'OWN_CAR_AGE', np.max, 'max')
data = self.feature_interaction(data, ['CODE_GENDER', 'NAME_EDUCATION_TYPE'], 'OWN_CAR_AGE', np.sum, 'sum')
# Gender, Occupation and Ext scores
data = self.feature_interaction(data, ['CODE_GENDER', 'OCCUPATION_TYPE'], 'EXT_SOURCE_2', np.mean, 'mean')
# Gender, Organization and other features
data = self.feature_interaction(data, ['CODE_GENDER', 'ORGANIZATION_TYPE'], 'EXT_SOURCE_2', np.mean, 'mean')
data = self.feature_interaction(data, ['CODE_GENDER', 'ORGANIZATION_TYPE'], 'AMT_ANNUITY', np.mean, 'mean')
data = self.feature_interaction(data, ['CODE_GENDER', 'ORGANIZATION_TYPE'], 'AMT_INCOME_TOTAL', np.mean, 'mean')
data = self.feature_interaction(data, ['CODE_GENDER', 'ORGANIZATION_TYPE'], 'DAYS_REGISTRATION', np.mean, 'mean')
data = self.feature_interaction(data, ['CODE_GENDER', 'ORGANIZATION_TYPE'], 'EXT_SOURCE_1', np.mean, 'mean')
# Gender, Reg city not work city and other fatures
data = self.feature_interaction(data, ['CODE_GENDER', 'REG_CITY_NOT_WORK_CITY'], 'AMT_ANNUITY', np.mean, 'mean')
data = self.feature_interaction(data, ['CODE_GENDER', 'REG_CITY_NOT_WORK_CITY'], 'CNT_CHILDREN', np.mean, 'mean')
data = self.feature_interaction(data, ['CODE_GENDER', 'REG_CITY_NOT_WORK_CITY'], 'DAYS_ID_PUBLISH', np.mean, 'mean')
# Income, Occupation and Ext Score
data = self.feature_interaction(data, ['NAME_INCOME_TYPE', 'OCCUPATION_TYPE'], 'EXT_SOURCE_2', np.mean, 'mean')
# Occupation and Organization and Ext Score
data = self.feature_interaction(data, ['OCCUPATION_TYPE', 'ORGANIZATION_TYPE'], 'EXT_SOURCE_2', np.mean, 'mean')
# Income, Education and Ext score
data = self.feature_interaction(data, ['NAME_INCOME_TYPE', 'NAME_EDUCATION_TYPE'], 'EXT_SOURCE_2', np.mean, 'mean')
# Education and Occupation and other features
data = self.feature_interaction(data, ['NAME_EDUCATION_TYPE', 'OCCUPATION_TYPE'], 'AMT_CREDIT', np.mean, 'mean')
data = self.feature_interaction(data, ['NAME_EDUCATION_TYPE', 'OCCUPATION_TYPE'], 'EXT_SOURCE_1', np.mean, 'mean')
data = self.feature_interaction(data, ['NAME_EDUCATION_TYPE', 'OCCUPATION_TYPE'], 'EXT_SOURCE_2', np.mean, 'mean')
data = self.feature_interaction(data, ['NAME_EDUCATION_TYPE', 'OCCUPATION_TYPE'], 'EXT_SOURCE_3', np.mean, 'mean')
data = self.feature_interaction(data, ['NAME_EDUCATION_TYPE', 'OCCUPATION_TYPE'], 'OWN_CAR_AGE', np.mean, 'mean')
# Education, Occupation, Reg city not work city and other features
data = self.feature_interaction(data, ['NAME_EDUCATION_TYPE', 'OCCUPATION_TYPE', 'REG_CITY_NOT_WORK_CITY'], 'EXT_SOURCE_2', np.mean, 'mean')
# Occupation and other features
data = self.feature_interaction(data, ['OCCUPATION_TYPE'], 'AMT_ANNUITY', np.mean, 'mean')
data = self.feature_interaction(data, ['OCCUPATION_TYPE'], 'CNT_CHILDREN', np.mean, 'mean')
data = self.feature_interaction(data, ['OCCUPATION_TYPE'], 'CNT_FAM_MEMBERS', np.mean, 'mean')
data = self.feature_interaction(data, ['OCCUPATION_TYPE'], 'DAYS_BIRTH', np.mean, 'mean')
data = self.feature_interaction(data, ['OCCUPATION_TYPE'], 'DAYS_EMPLOYED', np.mean, 'mean')
data = self.feature_interaction(data, ['OCCUPATION_TYPE'], 'EXT_SOURCE_2', np.mean, 'mean')
data = self.feature_interaction(data, ['OCCUPATION_TYPE'], 'EXT_SOURCE_3', np.mean, 'mean')
# frequency encoding of some of the categorical variables.
data = frequency_encoding(data, FREQ_ENCODING_COLS)
# one hot encoding of some of the categorical variables controlled by a flag
# if flag is True then one hot encoding else do frequency encoding.
if compute_ohe:
data = super(Modelv71, self).prepare_ohe(data, OHE_COLS, drop_col=True)
else:
data = frequency_encoding(data, OHE_COLS)
return data
# This method would perform feature engineering on merged datasets.
def fe(self, train, test, compute_ohe=True):
original_train = train.copy()
data = self.get_features(original_train, test, compute_ohe)
train = data.iloc[:len(train)]
test = data.iloc[len(train):]
del data, original_train
gc.collect()
return train, test
# This method just calls the base class with X,y, Xte and yte in the right format
# to train and returns a trained model which could be dumped on disk for further use.
# TODO: Find out why we are not able to load back model from disk and generate correct predictions
# there seems to be some issue in it right now.
def train(self, train, test, feature_list, is_eval, TARGET_NAME='TARGET', **params):
X = train.loc[:, feature_list]
y = train.loc[:, TARGET_NAME]
Xte = test.loc[:, feature_list]
yte = []
if is_eval:
yte = test.loc[:, TARGET_NAME]
return super(Modelv71, self).train_lgb(X, y, Xte, yte, **params)
# This method just takes in a model and test dataset and returns predictions
# prints out AUC on the test dataset as well in the process.
def evaluate(self, test, feature_list, is_eval, model, TARGET_NAME='TARGET'):
Xte = test.loc[:, feature_list]
yte = []
if is_eval:
yte = test.loc[:, TARGET_NAME]
return super(Modelv71, self).evaluate_lgb(Xte, yte, model)
def cross_validate(self, train, feature_list, params, TARGET_NAME='TARGET'):
Xtr = train.loc[:, feature_list]
ytr = train.loc[:, TARGET_NAME]
return super(Modelv71, self).cross_validate(Xtr, ytr, params)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Home Credit Default Risk Solution')
parser.add_argument('-input_path', help='Path to input directory') # path to raw files
parser.add_argument('-output_path', help='Path to output directory') # path to working data folder
parser.add_argument('-data_folder', help='Folder name of the dataset') # dataset folder name
parser.add_argument('-p', type=bool, help='Preprocess')
parser.add_argument('-cv', type=bool, help='Cross Validation')
parser.add_argument('-v', type=str, help='Validation')
parser.add_argument('-features', type=bool, help='Generate Features')
parser.add_argument('-s', type=bool, help='Whether to work on a sample or not.')
parser.add_argument('-seed', type=int, help='Random SEED')
parser.add_argument('-cv_seed', type=int, help='CV SEED')
parser.add_argument('-t', type=bool, help='Full Training Loop.')
parser.add_argument('-ensemble', type=bool , help='Average out predictions.')
args = parser.parse_args()
if args.p:
print('Preprocessing ...')
input_path = args.input_path
output_path = args.output_path
params = {
'input_path': input_path,
'output_path': output_path
}
m = Modelv71(**params)
m.preprocess()
elif args.features:
print('Generating features ...')
print()
input_path = args.input_path
output_path = args.output_path
params = {
'input_path': input_path,
'output_path': output_path,
}
m = Modelv71(**params)
m.prepare_features()
elif args.v is not None and len(args.v):
print('Train and generate predictions on a fold')
input_path = args.input_path
output_path = args.output_path
data_folder = args.data_folder
fold_indicator = args.v
is_sample = args.s
cv_seed = args.cv_seed
SEED = int(args.seed)
print('*' * 100)
print('SEED FOUND: {}'.format(SEED))
params = {
'input_path': input_path,
'output_path': output_path
}
PARAMS = joblib.load(os.path.join(basepath, output_path + f'{data_folder}{MODEL_FILENAME}_{cv_seed}_params.pkl'))
# Set seed to Params
PARAMS['seed'] = SEED
PARAMS['feature_fraction_seed'] = SEED
PARAMS['bagging_seed'] = SEED
print('*' * 100)
print('PARAMS: {}'.format(PARAMS))
m = Modelv71(**params)
if os.path.exists(os.path.join(basepath, output_path + f'{data_folder}data.h5')):
print('Loading dataset from disk ...')
data = pd.read_hdf(os.path.join(basepath, output_path + f'{data_folder}data.h5'), format='table', key='data')
else:
print('Merge feature groups and save them to disk ...')
train, test = m.merge_datasets()
train, test = m.fe(train, test)
data = pd.concat((train, test))
data.to_hdf(os.path.join(basepath, output_path + f'{data_folder}data.h5'), format='table', key='data')
del train, test
gc.collect()
itr = pd.read_csv(os.path.join(basepath, input_path + 'cv_idx.csv'), usecols=[fold_indicator])[fold_indicator].values
print('Shape of fold indices ', len(itr))
ite = np.array(list(set(np.arange(m.n_train)) - set(itr)))
train = data.iloc[:m.n_train].iloc[itr]
test = data.iloc[:m.n_train].iloc[ite]
del data
gc.collect()
if is_sample:
print('*' * 100)
print('Take a random sample of the training data ...')
train = train.sample(frac=SAMPLE_SIZE)
# check to see if feature list exists on disk or not for a particular model
if os.path.exists(os.path.join(basepath, output_path + f'{data_folder}{MODEL_FILENAME}_features.npy')):
feature_list = np.load(os.path.join(basepath, output_path + f'{data_folder}{MODEL_FILENAME}_features.npy'))
else:
feature_list = train.columns.tolist()
feature_list = list(set(feature_list) - set(COLS_TO_REMOVE))
np.save(os.path.join(basepath, output_path + f'{data_folder}{MODEL_FILENAME}_features.npy'), feature_list)
# print features with null percentage
print('Top-5 features with highest percentage of null values ...\n')
print((train.loc[:, feature_list].isnull().sum() / len(train)).sort_values(ascending=False).iloc[:5])
# print number of features explored in the experiment
print('*' * 100)
print('Number of features: {}'.format(len(feature_list)))
print('*' * 100)
model_identifier = f'{data_folder}{MODEL_FILENAME}_{fold_indicator}_{SEED}'
if os.path.exists(os.path.join(basepath, output_path + f'{model_identifier}_model.txt')):
print('Loading model from disk ...')
model = lgb.Booster(model_file=os.path.join(basepath, output_path + f'{model_identifier}_model.txt'))
yhold = test.TARGET
hold_preds = np.array(model.predict(test.loc[:, feature_list]))
print('AUC score: {}'.format(roc_auc_score(yhold, hold_preds)))
else:
print('Saving model to disk ...')
# train model
model, feat_df = m.train(train, test, feature_list, is_eval=True, **PARAMS)
if not is_sample:
model.save_model(os.path.join(basepath, output_path + f'{model_identifier}_model.txt'))
if not os.path.exists(os.path.join(basepath, output_path + f'{data_folder}{MODEL_FILENAME}_true_holdout.npy')):
np.save(os.path.join(basepath, output_path + f'{data_folder}{MODEL_FILENAME}_true_holdout.npy'), test.TARGET)
hold_preds = model.predict(test.loc[:, feature_list])
np.save(os.path.join(basepath, output_path + f'{model_identifier}_preds_holdout.npy'), hold_preds)
feat_df.to_csv(os.path.join(basepath, output_path + f'{model_identifier}_feat_imp.csv'), index=False)
elif args.cv:
print('Cross validation on training and store parameters and cv score on disk ...')
input_path = args.input_path
output_path = args.output_path
data_folder = args.data_folder
is_sample = args.s
SEED = args.seed
params = {
'input_path': input_path,
'output_path': output_path
}
m = Modelv71(**params)
if os.path.exists(os.path.join(basepath, output_path + f'{data_folder}data.h5')):
print('Loading dataset from disk ...')
data = pd.read_hdf(os.path.join(basepath, output_path + f'{data_folder}data.h5'), format='table', key='data')
else:
print('Merge feature groups and save them to disk ...')
train, test = m.merge_datasets()
train, test = m.fe(train, test)
data = pd.concat((train, test))
data.to_hdf(os.path.join(basepath, output_path + f'{data_folder}data.h5'), format='table', key='data')
del train, test
gc.collect()
train = data.iloc[:m.n_train]
del data
gc.collect()
if is_sample:
print('*' * 100)
print('Take a random sample of the training data ...')
train = train.sample(frac=SAMPLE_SIZE)
# check to see if feature list exists on disk or not for a particular model
if os.path.exists(os.path.join(basepath, output_path + f'{data_folder}{MODEL_FILENAME}_features.npy')):
feature_list = np.load(os.path.join(basepath, output_path + f'{data_folder}{MODEL_FILENAME}_features.npy'))
else:
feature_list = train.columns.tolist()
feature_list = list(set(feature_list) - set(COLS_TO_REMOVE))
np.save(os.path.join(basepath, output_path + f'{data_folder}{MODEL_FILENAME}_features.npy'), feature_list)
PARAMS['seed'] = SEED
PARAMS['feature_fraction_seed'] = SEED
PARAMS['bagging_seed'] = SEED
cv_history = m.cross_validate(train, feature_list, PARAMS.copy())
cv_score = str(cv_history.iloc[-1]['auc-mean']) + '_' + str(cv_history.iloc[-1]['auc-stdv'])
PARAMS['num_boost_round'] = len(cv_history)
print('*' * 100)
print('Best AUC: {}'.format(cv_score))
joblib.dump(PARAMS, os.path.join(basepath, output_path + f'{data_folder}{MODEL_FILENAME}_{SEED}_params.pkl'))
joblib.dump(cv_score, os.path.join(basepath, output_path + f'{data_folder}{MODEL_FILENAME}_{SEED}_cv.pkl'))
elif args.t:
print('Full Training')
input_path = args.input_path
output_path = args.output_path
data_folder = args.data_folder
SEED = int(args.seed)
params = {
'input_path': input_path,
'output_path': output_path
}
m = Modelv71(**params)
# Load or save data from/ on disk
if os.path.exists(os.path.join(basepath, output_path + f'{data_folder}data.h5')):
print('Loading dataset from disk ...')
data = pd.read_hdf(os.path.join(basepath, output_path + f'{data_folder}data.h5'), format='table', key='data')
else:
print('Merge feature groups and save them to disk ...')
train, test = m.merge_datasets()
train, test = m.fe(train, test)
data = pd.concat((train, test))
data.to_hdf(os.path.join(basepath, output_path + f'{data_folder}data.h5'), format='table', key='data')
del train, test
gc.collect()
# separate out training and test set.
train = data.iloc[:m.n_train]
test = data.iloc[m.n_train:]
# check to see if feature list exists on disk or not for a particular model
if os.path.exists(os.path.join(basepath, output_path + f'{data_folder}{MODEL_FILENAME}_features.npy')):
feature_list = np.load(os.path.join(basepath, output_path + f'{data_folder}{MODEL_FILENAME}_features.npy'))
else:
feature_list = train.columns.tolist()
feature_list = list(set(feature_list) - set(COLS_TO_REMOVE))
np.save(os.path.join(basepath, output_path + f'{data_folder}{MODEL_FILENAME}_features.npy'), feature_list)
# Load params and holdout score from disk.
PARAMS = joblib.load(os.path.join(basepath, output_path + f'{data_folder}{MODEL_FILENAME}_{SEED}_params.pkl'))
HOLDOUT_SCORE = joblib.load(os.path.join(basepath, output_path + f'{data_folder}{MODEL_FILENAME}_{SEED}_cv.pkl'))
PARAMS['num_boost_round'] = int(1.2 * PARAMS['num_boost_round'])
PARAMS['learning_rate'] /= 1.2
PARAMS['seed'] = SEED
PARAMS['feature_fraction_seed'] = SEED
PARAMS['bagging_seed'] = SEED
print('*' * 100)
print('PARAMS are: {}'.format(PARAMS))
# train model
model, feat_df = m.train(train, test, feature_list, is_eval=False, **PARAMS)
# evaluation part
preds, score = m.evaluate(test, feature_list, is_eval=False, model=model)
sub_identifier = "%s-%s-%s-%s" % (datetime.now().strftime('%Y%m%d-%H%M'), MODEL_FILENAME, HOLDOUT_SCORE, SEED)
sub = pd.read_csv(os.path.join(basepath, 'data/raw/sample_submission.csv.zip'))
sub['TARGET'] = preds
sub.to_csv(os.path.join(basepath, 'submissions/%s.csv'%(sub_identifier)), index=False)
elif args.ensemble:
output_files = []
ensemble_preds = 0
for f in output_files:
sub = pd.read_csv(f)['TARGET'].values
ensemble_preds += sub
ensemble_preds /= len(output_files)
HOLDOUT_SCORE = .79479
sub_identifier = "%s-%s-%s" % (datetime.now().strftime('%Y%m%d-%H%M'), MODEL_FILENAME, HOLDOUT_SCORE)
sub = pd.read_csv(os.path.join(basepath, 'data/raw/sample_submission.csv.zip'))
sub['TARGET'] = ensemble_preds
sub.to_csv(os.path.join(basepath, 'submissions/ensemble_%s.csv'%(sub_identifier)), index=False)
| 50.141693
| 178
| 0.600455
| 9,567
| 79,976
| 4.769625
| 0.056653
| 0.028139
| 0.038351
| 0.069032
| 0.799698
| 0.785256
| 0.77226
| 0.760032
| 0.752471
| 0.745458
| 0
| 0.008701
| 0.271406
| 79,976
| 1,595
| 179
| 50.141693
| 0.774395
| 0.047827
| 0
| 0.602041
| 0
| 0
| 0.264049
| 0.080563
| 0
| 0
| 0
| 0.000627
| 0
| 1
| 0.011132
| false
| 0
| 0.011132
| 0
| 0.03154
| 0.1141
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
0a6d632b1772539f47faa55339818c01ae3cdee5
| 23
|
py
|
Python
|
Introduction/Scripts/hello.py
|
inakleinbottle/Do-Something-Different
|
d0c23abf1033061d5ed48aa0b9f6ca57e4ad0b56
|
[
"MIT"
] | 1
|
2019-02-19T13:46:25.000Z
|
2019-02-19T13:46:25.000Z
|
Introduction/Scripts/hello.py
|
inakleinbottle/Do-Something-Different
|
d0c23abf1033061d5ed48aa0b9f6ca57e4ad0b56
|
[
"MIT"
] | null | null | null |
Introduction/Scripts/hello.py
|
inakleinbottle/Do-Something-Different
|
d0c23abf1033061d5ed48aa0b9f6ca57e4ad0b56
|
[
"MIT"
] | null | null | null |
print('Hello world!')
| 7.666667
| 21
| 0.652174
| 3
| 23
| 5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.130435
| 23
| 2
| 22
| 11.5
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0.545455
| 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
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 5
|
6a661407fe35d4066a656242612db0cbd7f56c5e
| 519
|
py
|
Python
|
atest/testdata/standard_libraries/builtin/FailUntilSucceeds.py
|
rdagum/robotframework
|
b7069d505374e9f09a140ed5a9727d2a40716446
|
[
"ECL-2.0",
"Apache-2.0"
] | 7,073
|
2015-01-01T17:19:16.000Z
|
2022-03-31T22:01:29.000Z
|
atest/testdata/standard_libraries/builtin/FailUntilSucceeds.py
|
imust6226/robotframework
|
08c56fef2ebc64d682c7f99acd77c480d8d0e028
|
[
"ECL-2.0",
"Apache-2.0"
] | 2,412
|
2015-01-02T09:29:05.000Z
|
2022-03-31T13:10:46.000Z
|
atest/testdata/standard_libraries/builtin/FailUntilSucceeds.py
|
rticau/robotframework
|
33ee46dfacd5173c0a38d89c1a60abf6a747c8c0
|
[
"ECL-2.0",
"Apache-2.0"
] | 2,298
|
2015-01-03T02:47:15.000Z
|
2022-03-31T02:00:16.000Z
|
import time
class FailUntilSucceeds:
ROBOT_LIBRARY_SCOPE = 'TESTCASE'
def __init__(self, times_to_fail=0):
self.times_to_fail = int(times_to_fail)
def set_times_to_fail(self, times_to_fail):
self.__init__(times_to_fail)
def fail_until_retried_often_enough(self, message="Hello", sleep=0):
self.times_to_fail -= 1
time.sleep(sleep)
if self.times_to_fail >= 0:
raise Exception('Still %d times to fail!' % self.times_to_fail)
return message
| 27.315789
| 75
| 0.678227
| 75
| 519
| 4.253333
| 0.413333
| 0.219436
| 0.344828
| 0.282132
| 0.360502
| 0.163009
| 0.163009
| 0
| 0
| 0
| 0
| 0.010025
| 0.231214
| 519
| 18
| 76
| 28.833333
| 0.789474
| 0
| 0
| 0
| 0
| 0
| 0.069364
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.230769
| false
| 0
| 0.076923
| 0
| 0.538462
| 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
| 0
| 1
| 0
|
0
| 5
|
6a68c2764f755abc87cb291a4f895c97a7cdf46f
| 5,518
|
py
|
Python
|
tests/test_inventory_service_interface.py
|
msager27/drift-backend
|
0994431a8ff4e7a6ae067794cf2b6d2a82f3481c
|
[
"Apache-2.0"
] | 6
|
2019-02-18T17:04:01.000Z
|
2020-08-17T16:35:00.000Z
|
tests/test_inventory_service_interface.py
|
msager27/drift-backend
|
0994431a8ff4e7a6ae067794cf2b6d2a82f3481c
|
[
"Apache-2.0"
] | 223
|
2019-01-25T03:14:47.000Z
|
2022-02-15T14:51:06.000Z
|
tests/test_inventory_service_interface.py
|
msager27/drift-backend
|
0994431a8ff4e7a6ae067794cf2b6d2a82f3481c
|
[
"Apache-2.0"
] | 12
|
2019-01-24T01:45:01.000Z
|
2021-06-17T18:22:10.000Z
|
import unittest
import mock
import requests
import responses
from kerlescan import inventory_service_interface
from kerlescan.exceptions import ItemNotReturned, ServiceError
from drift import app
from . import fixtures
class InventoryServiceTests(unittest.TestCase):
def setUp(self):
test_connexion_app = app.create_app()
test_flask_app = test_connexion_app.app
self.client = test_flask_app.test_client()
self.mock_logger = mock.Mock()
self.mock_counters = {
"systems_compared_no_sysprofile": mock.MagicMock(),
"inventory_service_requests": mock.MagicMock(),
"inventory_service_exceptions": mock.MagicMock(),
}
def _create_response_for_systems(self, service_hostname, system_uuids):
url_template = "http://%s/api/inventory/v1/hosts/%s"
responses.add(
responses.GET,
url_template % (service_hostname, system_uuids),
body=fixtures.FETCH_SYSTEMS_INV_SVC,
status=requests.codes.ok,
content_type="application/json",
)
def _create_response_for_system_tags(self, service_hostname, system_uuids):
url_template = "http://%s/api/inventory/v1/hosts/%s/tags"
responses.add(
responses.GET,
url_template % (service_hostname, system_uuids),
body=fixtures.FETCH_SYSTEM_TAGS,
status=requests.codes.ok,
content_type="application/json",
)
def _create_response_for_system_profiles(self, service_hostname, system_uuids):
url_template = "http://%s/api/inventory/v1/hosts/%s/system_profile"
responses.add(
responses.GET,
url_template % (service_hostname, system_uuids),
body=fixtures.FETCH_SYSTEM_PROFILES_INV_SVC,
status=requests.codes.ok,
content_type="application/json",
)
def _create_500_response_for_systems(self, service_hostname, system_uuids):
url_template = "http://%s/api/inventory/v1/hosts/%s"
responses.add(
responses.GET,
url_template % (service_hostname, system_uuids),
body="I am error",
status=requests.codes.INTERNAL_SERVER_ERROR,
content_type="application/json",
)
def _create_500_response_for_system_profiles(self, service_hostname, system_uuids):
url_template = "http://%s/api/inventory/v1/hosts/%s/system_profile"
responses.add(
responses.GET,
url_template % (service_hostname, system_uuids),
body="I am error",
status=requests.codes.INTERNAL_SERVER_ERROR,
content_type="application/json",
)
@responses.activate
def test_fetch_systems_with_profiles(self):
systems_to_fetch = [
"243926fa-262f-11e9-a632-c85b761454fa",
"264fb5b2-262f-11e9-9b12-c85b761454fa",
]
self._create_response_for_systems(
"inventory_svc_url_is_not_set", ",".join(systems_to_fetch)
)
self._create_response_for_system_profiles(
"inventory_svc_url_is_not_set", ",".join(systems_to_fetch)
)
self._create_response_for_system_tags(
"inventory_svc_url_is_not_set", ",".join(systems_to_fetch)
)
systems = inventory_service_interface.fetch_systems_with_profiles(
systems_to_fetch, "my-auth-key", self.mock_logger, self.mock_counters
)
found_system_ids = {system["id"] for system in systems}
self.assertSetEqual(found_system_ids, set(systems_to_fetch))
@responses.activate
def test_fetch_systems_missing_system(self):
systems_to_fetch = [
"243926fa-262f-11e9-a632-c85b761454fa",
"264fb5b2-262f-11e9-9b12-c85b761454fa",
"269a3da8-262f-11e9-8ee5-c85b761454fa",
]
self._create_response_for_systems(
"inventory_svc_url_is_not_set", ",".join(systems_to_fetch)
)
self._create_response_for_system_profiles(
"inventory_svc_url_is_not_set", ",".join(systems_to_fetch)
)
self._create_response_for_system_tags(
"inventory_svc_url_is_not_set", ",".join(systems_to_fetch)
)
with self.assertRaises(ItemNotReturned) as cm:
inventory_service_interface.fetch_systems_with_profiles(
systems_to_fetch, "my-auth-key", self.mock_logger, self.mock_counters
)
self.assertEqual(
cm.exception.message,
"ids [269a3da8-262f-11e9-8ee5-c85b761454fa] not available to display",
)
@responses.activate
def test_fetch_systems_backend_service_error(self):
systems_to_fetch = [
"243926fa-262f-11e9-a632-c85b761454fa",
"264fb5b2-262f-11e9-9b12-c85b761454fa",
"269a3da8-262f-11e9-8ee5-c85b761454fa",
]
self._create_500_response_for_systems(
"inventory_svc_url_is_not_set", ",".join(systems_to_fetch)
)
self._create_500_response_for_system_profiles(
"inventory_svc_url_is_not_set", ",".join(systems_to_fetch)
)
with self.assertRaises(ServiceError) as cm:
inventory_service_interface.fetch_systems_with_profiles(
systems_to_fetch, "my-auth-key", self.mock_logger, self.mock_counters
)
self.assertEqual(cm.exception.message, "Error received from backend service")
| 36.065359
| 87
| 0.655672
| 620
| 5,518
| 5.443548
| 0.166129
| 0.04
| 0.062222
| 0.077037
| 0.780148
| 0.768593
| 0.735704
| 0.735704
| 0.735704
| 0.731259
| 0
| 0.052046
| 0.251359
| 5,518
| 152
| 88
| 36.302632
| 0.764948
| 0
| 0
| 0.507937
| 0
| 0
| 0.190468
| 0.114897
| 0
| 0
| 0
| 0
| 0.039683
| 1
| 0.071429
| false
| 0
| 0.063492
| 0
| 0.142857
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
6a6fee1eedd26e6438c7a0af1969d96b21509de4
| 157
|
py
|
Python
|
classes/LinguisticSystem.py
|
Jollyfant/EPOS-TURTLE
|
8848a83e513b0bc9aacd96e290fc9f235bed687a
|
[
"MIT"
] | 1
|
2020-10-06T13:28:54.000Z
|
2020-10-06T13:28:54.000Z
|
classes/LinguisticSystem.py
|
Jollyfant/EPOS-TURTLE
|
8848a83e513b0bc9aacd96e290fc9f235bed687a
|
[
"MIT"
] | null | null | null |
classes/LinguisticSystem.py
|
Jollyfant/EPOS-TURTLE
|
8848a83e513b0bc9aacd96e290fc9f235bed687a
|
[
"MIT"
] | null | null | null |
from EPOS import Node
class LinguisticSystem(Node):
def __init__(self, *args):
self.type = self.dct.LinguisticSystem
Node.__init__(self, args)
| 19.625
| 41
| 0.719745
| 20
| 157
| 5.25
| 0.6
| 0.380952
| 0.228571
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.178344
| 157
| 7
| 42
| 22.428571
| 0.813953
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.2
| false
| 0
| 0.2
| 0
| 0.6
| 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
| 0
| 0
| 1
| 0
|
0
| 5
|
6a796f2e592eb70fe0bb1212a4df589ebd774dc2
| 169
|
py
|
Python
|
pineboolib/__init__.py
|
juanjosepablos/pineboo
|
f6ce515aec6e0139821bb9c1d62536d9fb50dae4
|
[
"MIT"
] | 2
|
2017-12-10T23:06:16.000Z
|
2017-12-10T23:06:23.000Z
|
pineboolib/__init__.py
|
Aulla/pineboo
|
3ad6412d365a6ad65c3bb2bdc03f5798d7c37004
|
[
"MIT"
] | 36
|
2017-11-05T21:13:47.000Z
|
2020-08-26T15:56:15.000Z
|
pineboolib/__init__.py
|
deavid/pineboo
|
acc96ab6d5b8bb182990af6dea4bf0986af15549
|
[
"MIT"
] | 9
|
2015-01-15T18:15:42.000Z
|
2019-05-05T18:53:00.000Z
|
# -*- coding: utf-8 -*-
"""
Base module for the execution of pineboo.
Library oriented to emulate Eneboo from python.
"""
from .core.utils import logging # noqa: F401
| 21.125
| 47
| 0.698225
| 24
| 169
| 4.916667
| 0.958333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.028777
| 0.177515
| 169
| 7
| 48
| 24.142857
| 0.820144
| 0.733728
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 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
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
6aa5e398ccb0e0d605636bc1418ef34aa7ce7655
| 100
|
py
|
Python
|
neural_network/__init__.py
|
fu11zero/neural-network-implementation
|
e82b7464afe8d8b0b47be7bd316f18869e3268c4
|
[
"MIT"
] | null | null | null |
neural_network/__init__.py
|
fu11zero/neural-network-implementation
|
e82b7464afe8d8b0b47be7bd316f18869e3268c4
|
[
"MIT"
] | null | null | null |
neural_network/__init__.py
|
fu11zero/neural-network-implementation
|
e82b7464afe8d8b0b47be7bd316f18869e3268c4
|
[
"MIT"
] | 1
|
2021-03-02T10:08:42.000Z
|
2021-03-02T10:08:42.000Z
|
"""
A package with everything you need to build a simple neural network
"""
# todo - add unit tests
| 20
| 67
| 0.72
| 16
| 100
| 4.5
| 0.9375
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.2
| 100
| 4
| 68
| 25
| 0.9
| 0.9
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0.25
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 0
| 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
| 5
|
6aa8ab1fbf9e5793bcb8b9974e799ca525e35fd2
| 77
|
py
|
Python
|
test/client/test_kafka_producer_client.py
|
DebasishMaji/PI
|
e293982cae8f8755d28d7b3de22966dc74759b90
|
[
"Apache-2.0"
] | null | null | null |
test/client/test_kafka_producer_client.py
|
DebasishMaji/PI
|
e293982cae8f8755d28d7b3de22966dc74759b90
|
[
"Apache-2.0"
] | null | null | null |
test/client/test_kafka_producer_client.py
|
DebasishMaji/PI
|
e293982cae8f8755d28d7b3de22966dc74759b90
|
[
"Apache-2.0"
] | null | null | null |
import unittest
class TestKafkaProducerClient(unittest.TestCase):
pass
| 12.833333
| 49
| 0.805195
| 7
| 77
| 8.857143
| 0.857143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.142857
| 77
| 5
| 50
| 15.4
| 0.939394
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.333333
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
|
0
| 5
|
6ab5705a374e662435d1a634c130bfcefbf95015
| 36
|
py
|
Python
|
GrowBeanSprouts/boot.py
|
cocoaswifty/ESP8266
|
eb6d4111586ff2841077909ca4a07a997dc30176
|
[
"MIT"
] | null | null | null |
GrowBeanSprouts/boot.py
|
cocoaswifty/ESP8266
|
eb6d4111586ff2841077909ca4a07a997dc30176
|
[
"MIT"
] | null | null | null |
GrowBeanSprouts/boot.py
|
cocoaswifty/ESP8266
|
eb6d4111586ff2841077909ca4a07a997dc30176
|
[
"MIT"
] | null | null | null |
import gc
gc.collect() # 記憶體回收功能
| 12
| 24
| 0.666667
| 5
| 36
| 4.8
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.222222
| 36
| 2
| 25
| 18
| 0.857143
| 0.194444
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 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
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
6ac8e2a069847b75a2eda1594503fc41190c9cb2
| 214
|
py
|
Python
|
python/testData/refactoring/move/referenceToClassWithNewInMovedSymbol/before/src/classFile.py
|
jnthn/intellij-community
|
8fa7c8a3ace62400c838e0d5926a7be106aa8557
|
[
"Apache-2.0"
] | 2
|
2019-04-28T07:48:50.000Z
|
2020-12-11T14:18:08.000Z
|
python/testData/refactoring/move/referenceToClassWithNewInMovedSymbol/before/src/classFile.py
|
Cyril-lamirand/intellij-community
|
60ab6c61b82fc761dd68363eca7d9d69663cfa39
|
[
"Apache-2.0"
] | 173
|
2018-07-05T13:59:39.000Z
|
2018-08-09T01:12:03.000Z
|
python/testData/refactoring/move/referenceToClassWithNewInMovedSymbol/before/src/classFile.py
|
Cyril-lamirand/intellij-community
|
60ab6c61b82fc761dd68363eca7d9d69663cfa39
|
[
"Apache-2.0"
] | 2
|
2020-03-15T08:57:37.000Z
|
2020-04-07T04:48:14.000Z
|
from collections import namedtuple
class Pipeline(namedtuple('_Pipeline', 'name')):
def __new__(cls, name):
return super(Pipeline, cls).__new__(cls, name)
def __init__(self, name):
pass
| 19.454545
| 54
| 0.668224
| 25
| 214
| 5.2
| 0.6
| 0.107692
| 0.153846
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.214953
| 214
| 10
| 55
| 21.4
| 0.77381
| 0
| 0
| 0
| 0
| 0
| 0.060748
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0.166667
| 0.166667
| 0.166667
| 0.833333
| 0
| 1
| 0
| 0
| 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 | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
|
0
| 5
|
6ad14d8f72edf2be1f8751f8d3a8d81bb3487b43
| 2,090
|
py
|
Python
|
playground/control/attribs/event.py
|
phlax/playground
|
ca661f7adcc2c3502f63e630c96e87e31aa9309a
|
[
"Apache-2.0"
] | 8
|
2020-11-23T21:08:32.000Z
|
2021-12-18T10:37:25.000Z
|
playground/control/attribs/event.py
|
phlax/playground
|
ca661f7adcc2c3502f63e630c96e87e31aa9309a
|
[
"Apache-2.0"
] | 273
|
2020-11-23T19:27:06.000Z
|
2020-12-21T17:34:49.000Z
|
playground/control/attribs/event.py
|
phlax/playground
|
ca661f7adcc2c3502f63e630c96e87e31aa9309a
|
[
"Apache-2.0"
] | 2
|
2020-11-24T09:49:29.000Z
|
2020-12-30T10:39:10.000Z
|
# -*- coding: utf-8 -*-
import attr
from playground.control.attribs.base import ValidatingAttribs
@attr.s(kw_only=True)
class ContainerEventAttribs(ValidatingAttribs):
id = attr.ib(type=str)
name = attr.ib(type=str)
status = attr.ib(type=str)
action = attr.ib(type=str)
attributes = attr.ib(type=dict)
logs = attr.ib(type=list, default=[])
image = attr.ib(type=str, default='')
@attr.s(kw_only=True)
class ProxyEventAttribs(ContainerEventAttribs):
logs = attr.ib(type=list, default=[])
port_mappings = attr.ib(type=list, default=[])
build_from = attr.ib(type=str, default='')
@attr.s(kw_only=True)
class ServiceEventAttribs(ContainerEventAttribs):
service_type = attr.ib(type=str, default='')
@attr.s(kw_only=True)
class ImageEventAttribs(ValidatingAttribs):
action = attr.ib(type=str)
image = attr.ib(type=str)
@attr.s(kw_only=True)
class NetworkEventAttribs(ValidatingAttribs):
id = attr.ib(type=str)
action = attr.ib(type=str)
name = attr.ib(type=str)
proxy = attr.ib(type=str, default='')
service = attr.ib(type=str, default='')
containers = attr.ib(type=list, default=[])
@attr.s(kw_only=True)
class NetworkTransmitAttribs(ValidatingAttribs):
id = attr.ib(type=str)
action = attr.ib(type=str)
name = attr.ib(type=str)
networks = attr.ib(type=dict, default={})
service = attr.ib(type=str, default='')
proxy = attr.ib(type=str, default='')
@attr.s(kw_only=True)
class ServiceTransmitAttribs(ValidatingAttribs):
id = attr.ib(type=str)
name = attr.ib(type=str)
status = attr.ib(type=str)
image = attr.ib(type=str, default='')
service_type = attr.ib(type=str, default='')
logs = attr.ib(type=list, default=[])
@attr.s(kw_only=True)
class ProxyTransmitAttribs(ValidatingAttribs):
id = attr.ib(type=str)
name = attr.ib(type=str)
status = attr.ib(type=str)
image = attr.ib(type=str, default='')
logs = attr.ib(type=list, default=[])
port_mappings = attr.ib(type=list, default=[])
build_from = attr.ib(type=str, default='')
| 27.5
| 61
| 0.674641
| 291
| 2,090
| 4.797251
| 0.151203
| 0.163324
| 0.272206
| 0.270057
| 0.751433
| 0.747135
| 0.680516
| 0.616046
| 0.616046
| 0.580229
| 0
| 0.000571
| 0.161722
| 2,090
| 75
| 62
| 27.866667
| 0.796233
| 0.010048
| 0
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.035714
| 0
| 0.857143
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 5
|
6adac87e8a130f81d3591d0c9d2d2e0bbf307813
| 113
|
py
|
Python
|
python/demo_queries/volumes/treatments_by_volume.py
|
jocelynpender/fna-query
|
e538563f63eaea7b4bc84b7446e7ed7b53001774
|
[
"MIT"
] | null | null | null |
python/demo_queries/volumes/treatments_by_volume.py
|
jocelynpender/fna-query
|
e538563f63eaea7b4bc84b7446e7ed7b53001774
|
[
"MIT"
] | 6
|
2020-01-30T16:52:47.000Z
|
2021-06-02T00:59:48.000Z
|
python/demo_queries/volumes/treatments_by_volume.py
|
jocelynpender/fna-query
|
e538563f63eaea7b4bc84b7446e7ed7b53001774
|
[
"MIT"
] | null | null | null |
from src.query import *
if __name__ == '__main__':
ask_query("[[Volume::Volume 17]]", "taxa_volume_17.csv")
| 22.6
| 60
| 0.672566
| 16
| 113
| 4.0625
| 0.75
| 0.246154
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.041237
| 0.141593
| 113
| 4
| 61
| 28.25
| 0.628866
| 0
| 0
| 0
| 0
| 0
| 0.415929
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.333333
| 0
| 0.333333
| 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
| 0
| 0
|
0
| 5
|
6ae77bdac90f207a6df929bff6e8975c356091fa
| 78
|
py
|
Python
|
tasks/ALL_IN_ONE.py
|
link-money/distribution_robot-master
|
4c35d80b8b74b6549529d147277981d593a24402
|
[
"MIT"
] | null | null | null |
tasks/ALL_IN_ONE.py
|
link-money/distribution_robot-master
|
4c35d80b8b74b6549529d147277981d593a24402
|
[
"MIT"
] | null | null | null |
tasks/ALL_IN_ONE.py
|
link-money/distribution_robot-master
|
4c35d80b8b74b6549529d147277981d593a24402
|
[
"MIT"
] | null | null | null |
# -*- coding: UTF-8 -*-
import distribute_link_to_wechat
import send_orders
| 13
| 32
| 0.74359
| 11
| 78
| 4.909091
| 0.909091
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.014925
| 0.141026
| 78
| 5
| 33
| 15.6
| 0.791045
| 0.269231
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 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
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
0a8de6d50ce7847f3ea6adec89a2c1548126ecc7
| 50
|
py
|
Python
|
aula03/exec10.py
|
miguelviladev/programming-fundamentals
|
1a4cad5fabca00acdb4fc4eaf8ed637c54e57d3b
|
[
"CC0-1.0"
] | 6
|
2021-11-21T13:12:57.000Z
|
2022-01-13T00:39:40.000Z
|
aula03/exec10.py
|
miguelviladev/programming-fundamentals
|
1a4cad5fabca00acdb4fc4eaf8ed637c54e57d3b
|
[
"CC0-1.0"
] | null | null | null |
aula03/exec10.py
|
miguelviladev/programming-fundamentals
|
1a4cad5fabca00acdb4fc4eaf8ed637c54e57d3b
|
[
"CC0-1.0"
] | null | null | null |
def hms2sec(h, m, s):
return h*3600 + m*60 + s
| 25
| 28
| 0.56
| 11
| 50
| 2.545455
| 0.727273
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.189189
| 0.26
| 50
| 2
| 28
| 25
| 0.567568
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| false
| 0
| 0
| 0.5
| 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
| 0
| 1
| 1
| 0
|
0
| 5
|
0aa146e803a41d2261e3b0d3cfb9aacc62201c2f
| 107
|
py
|
Python
|
examples/shapes/shapes/__init__.py
|
SerpentAI/offshoot
|
7906a95aeee511ca4ecd431ed5de1f779d2a13ab
|
[
"Apache-2.0"
] | 42
|
2017-01-23T22:36:03.000Z
|
2021-11-14T21:22:17.000Z
|
examples/shapes/shapes/__init__.py
|
SerpentAI/offshoot
|
7906a95aeee511ca4ecd431ed5de1f779d2a13ab
|
[
"Apache-2.0"
] | 1
|
2017-09-15T18:37:10.000Z
|
2017-09-15T18:37:10.000Z
|
examples/shapes/shapes/__init__.py
|
SerpentAI/offshoot
|
7906a95aeee511ca4ecd431ed5de1f779d2a13ab
|
[
"Apache-2.0"
] | 6
|
2017-04-14T13:07:27.000Z
|
2020-06-17T06:24:18.000Z
|
from shapes.square import Square
# Discover plugins
import offshoot
offshoot.discover("Shape", globals())
| 17.833333
| 37
| 0.794393
| 13
| 107
| 6.538462
| 0.692308
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.11215
| 107
| 5
| 38
| 21.4
| 0.894737
| 0.149533
| 0
| 0
| 0
| 0
| 0.05618
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.666667
| 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
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
0aaccaad5f9315b65819d287404e78255877baf8
| 35
|
py
|
Python
|
__init__.py
|
gabrielhuang/TheNumericsOfGANs
|
d7208f40f36634add9aa7b2e3bf09b4b60c8b37f
|
[
"MIT"
] | 46
|
2017-11-02T02:52:22.000Z
|
2021-12-18T17:41:23.000Z
|
__init__.py
|
gabrielhuang/TheNumericsOfGANs
|
d7208f40f36634add9aa7b2e3bf09b4b60c8b37f
|
[
"MIT"
] | 2
|
2017-10-31T14:35:30.000Z
|
2021-11-18T03:30:29.000Z
|
__init__.py
|
gabrielhuang/TheNumericsOfGANs
|
d7208f40f36634add9aa7b2e3bf09b4b60c8b37f
|
[
"MIT"
] | 13
|
2017-10-17T06:51:30.000Z
|
2020-03-05T03:43:23.000Z
|
from autoencoders import avae, vae
| 17.5
| 34
| 0.828571
| 5
| 35
| 5.8
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.142857
| 35
| 1
| 35
| 35
| 0.966667
| 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
| 0
| 0
|
0
| 5
|
0ab26892713b6e953e4d711a6c999e7cf5c0a743
| 102
|
py
|
Python
|
bng_latlon/__init__.py
|
fmalina/bng_to_latlon
|
3c94bdf7160ce98ff9829f4dc86265438730de0c
|
[
"MIT"
] | 4
|
2020-11-11T03:20:29.000Z
|
2021-06-09T15:50:09.000Z
|
bng_latlon/__init__.py
|
fmalina/bng_to_latlon
|
3c94bdf7160ce98ff9829f4dc86265438730de0c
|
[
"MIT"
] | 3
|
2020-11-12T14:23:03.000Z
|
2022-01-06T11:55:22.000Z
|
bng_latlon/__init__.py
|
fmalina/bng_to_latlon
|
3c94bdf7160ce98ff9829f4dc86265438730de0c
|
[
"MIT"
] | 1
|
2021-10-01T00:12:18.000Z
|
2021-10-01T00:12:18.000Z
|
from bng_latlon.bng_to_latlon import OSGB36toWGS84
from bng_latlon.latlon_to_bng import WGS84toOSGB36
| 34
| 50
| 0.901961
| 16
| 102
| 5.375
| 0.4375
| 0.162791
| 0.302326
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.085106
| 0.078431
| 102
| 2
| 51
| 51
| 0.829787
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 1
| 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
| 5
|
0ae4b6b242822fece70f0c84a52e2006c19f9e83
| 405
|
py
|
Python
|
dialogs/__init__.py
|
swprojects/Serial-Sequence-Creator
|
cf468a3db777d6b4348fd53d1daa8432f6889f11
|
[
"MIT"
] | 1
|
2018-10-29T20:10:43.000Z
|
2018-10-29T20:10:43.000Z
|
dialogs/__init__.py
|
swprojects/Serial-Sequence-Creator
|
cf468a3db777d6b4348fd53d1daa8432f6889f11
|
[
"MIT"
] | null | null | null |
dialogs/__init__.py
|
swprojects/Serial-Sequence-Creator
|
cf468a3db777d6b4348fd53d1daa8432f6889f11
|
[
"MIT"
] | 1
|
2018-10-29T20:11:31.000Z
|
2018-10-29T20:11:31.000Z
|
from dialogs import delay
from dialogs import initialiser
from dialogs import setvoltage
from dialogs import stepvoltage
from dialogs import readvoltage
from dialogs import sendreceive
from dialogs import test
from dialogs import setup
from dialogs import addfunction
from dialogs import callfunction
from dialogs import integercompare
from dialogs import floatcompare
from dialogs import stringcompare
| 27
| 34
| 0.866667
| 52
| 405
| 6.75
| 0.307692
| 0.407407
| 0.62963
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.133333
| 405
| 15
| 35
| 27
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
0ae58fb4f91c2cb2633e875c66734245d753b08d
| 396,975
|
py
|
Python
|
Base.py
|
okxjd/processing_ng
|
accc304d2a2606f1fa09872d5020bde11389299c
|
[
"MIT"
] | null | null | null |
Base.py
|
okxjd/processing_ng
|
accc304d2a2606f1fa09872d5020bde11389299c
|
[
"MIT"
] | null | null | null |
Base.py
|
okxjd/processing_ng
|
accc304d2a2606f1fa09872d5020bde11389299c
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import logging
TPL_FORMAT = {\
'ten': [
{'1': ('kn', '66:0')}, {'1': ('kn', '66:1')}, {'1': ('kn', '66:2')}, {'1': ('kn', '66:3')},
{'1': ('kn', '66:4')}, {'1': ('kn', '66:5')}, {'1': ('kn', '66:6')}, {'1': ('kn', '66:7')}
],
'district': [
{'1': ('kn', '66:00')}, {'1': ('kn', '66:01')}, {'1': ('kn', '66:02')}, {'1': ('kn', '66:03')},
{'1': ('kn', '66:04')}, {'1': ('kn', '66:05')}, {'1': ('kn', '66:06')}, {'1': ('kn', '66:07')},
{'1': ('kn', '66:08')}, {'1': ('kn', '66:09')}, {'1': ('kn', '66:10')}, {'1': ('kn', '66:11')},
{'1': ('kn', '66:12')}, {'1': ('kn', '66:13')}, {'1': ('kn', '66:14')}, {'1': ('kn', '66:15')},
{'1': ('kn', '66:16')}, {'1': ('kn', '66:17')}, {'1': ('kn', '66:18')}, {'1': ('kn', '66:19')},
{'1': ('kn', '66:20')}, {'1': ('kn', '66:21')}, {'1': ('kn', '66:22')}, {'1': ('kn', '66:23')},
{'1': ('kn', '66:24')}, {'1': ('kn', '66:25')}, {'1': ('kn', '66:26')}, {'1': ('kn', '66:27')},
{'1': ('kn', '66:28')}, {'1': ('kn', '66:29')}, {'1': ('kn', '66:30')}, {'1': ('kn', '66:31')},
{'1': ('kn', '66:32')}, {'1': ('kn', '66:33')}, {'1': ('kn', '66:34')}, {'1': ('kn', '66:35')},
{'1': ('kn', '66:36')}, {'1': ('kn', '66:37')}, {'1': ('kn', '66:38')}, {'1': ('kn', '66:39')},
{'1': ('kn', '66:40')}, {'1': ('kn', '66:42')}, {'1': ('kn', '66:43')}, {'1': ('kn', '66:44')},
{'1': ('kn', '66:45')}, {'1': ('kn', '66:46')}, {'1': ('kn', '66:47')}, {'1': ('kn', '66:48')},
{'1': ('kn', '66:49')}, {'1': ('kn', '66:50')}, {'1': ('kn', '66:51')}, {'1': ('kn', '66:52')},
{'1': ('kn', '66:53')}, {'1': ('kn', '66:54')}, {'1': ('kn', '66:55')}, {'1': ('kn', '66:57')},
{'1': ('kn', '66:58')}, {'1': ('kn', '66:59')}, {'1': ('kn', '66:60')}, {'1': ('kn', '66:61')},
{'1': ('kn', '66:62')}, {'1': ('kn', '66:63')}, {'1': ('kn', '66:64')}, {'1': ('kn', '66:65')},
{'1': ('kn', '66:66')}, {'1': ('kn', '66:67')}, {'1': ('kn', '66:68')}, {'1': ('kn', '66:69')},
{'1': ('kn', '66:70')}, {'1': ('kn', '66:71')}, {'1': ('kn', '66:72')}, {'1': ('kn', '66:73')},
{'1': ('kn', '66:41:00')}, {'1': ('kn', '66:41:01')}, {'1': ('kn', '66:41:02')}, {'1': ('kn', '66:41:03')},
{'1': ('kn', '66:41:04')}, {'1': ('kn', '66:41:05')}, {'1': ('kn', '66:41:06')}, {'1': ('kn', '66:41:07')},
{'1': ('kn', '66:41:08')}, {'1': ('kn', '66:41:09')}, {'1': ('kn', '66:41:1')}, {'1': ('kn', '66:41:2')},
{'1': ('kn', '66:41:3')}, {'1': ('kn', '66:41:4')}, {'1': ('kn', '66:41:5')}, {'1': ('kn', '66:41:6')},
{'1': ('kn', '66:41:7')}, {'1': ('kn', '66:41:8')}, {'1': ('kn', '66:41:9')},
{'1': ('kn', '66:56:00')}, {'1': ('kn', '66:56:01')}, {'1': ('kn', '66:56:02')}, {'1': ('kn', '66:56:03')},
{'1': ('kn', '66:56:04')}, {'1': ('kn', '66:56:05')}, {'1': ('kn', '66:56:06')}, {'1': ('kn', '66:56:07')},
{'1': ('kn', '66:56:08')}, {'1': ('kn', '66:56:09')}, {'1': ('kn', '66:56:1')}, {'1': ('kn', '66:56:2')},
{'1': ('kn', '66:56:3')}, {'1': ('kn', '66:56:4')}, {'1': ('kn', '66:56:5')}, {'1': ('kn', '66:56:6')},
{'1': ('kn', '66:56:7')}, {'1': ('kn', '66:56:8')}, {'1': ('kn', '66:56:9')}
],
'block': [
{'1': ('kn', '66:00:00')},
{'1': ('kn', '66:00:01')},
{'1': ('kn', '66:00:02')},
{'1': ('kn', '66:00:03')},
{'1': ('kn', '66:00:04')},
{'1': ('kn', '66:00:05')},
{'1': ('kn', '66:00:06')},
{'1': ('kn', '66:00:07')},
{'1': ('kn', '66:00:08')},
{'1': ('kn', '66:00:09')},
{'1': ('kn', '66:00:10')},
{'1': ('kn', '66:00:11')},
{'1': ('kn', '66:00:12')},
{'1': ('kn', '66:00:13')},
{'1': ('kn', '66:00:14')},
{'1': ('kn', '66:00:15')},
{'1': ('kn', '66:00:16')},
{'1': ('kn', '66:00:17')},
{'1': ('kn', '66:00:18')},
{'1': ('kn', '66:00:19')},
{'1': ('kn', '66:00:20')},
{'1': ('kn', '66:00:21')},
{'1': ('kn', '66:00:22')},
{'1': ('kn', '66:00:23')},
{'1': ('kn', '66:00:24')},
{'1': ('kn', '66:00:25')},
{'1': ('kn', '66:00:26')},
{'1': ('kn', '66:00:27')},
{'1': ('kn', '66:00:28')},
{'1': ('kn', '66:00:29')},
{'1': ('kn', '66:00:30')},
{'1': ('kn', '66:00:31')},
{'1': ('kn', '66:00:32')},
{'1': ('kn', '66:00:33')},
{'1': ('kn', '66:00:34')},
{'1': ('kn', '66:00:35')},
{'1': ('kn', '66:00:36')},
{'1': ('kn', '66:00:37')},
{'1': ('kn', '66:00:38')},
{'1': ('kn', '66:00:39')},
{'1': ('kn', '66:00:40')},
{'1': ('kn', '66:00:41')},
{'1': ('kn', '66:00:42')},
{'1': ('kn', '66:00:43')},
{'1': ('kn', '66:00:44')},
{'1': ('kn', '66:00:45')},
{'1': ('kn', '66:00:46')},
{'1': ('kn', '66:00:47')},
{'1': ('kn', '66:00:48')},
{'1': ('kn', '66:00:49')},
{'1': ('kn', '66:00:50')},
{'1': ('kn', '66:00:51')},
{'1': ('kn', '66:00:52')},
{'1': ('kn', '66:00:53')},
{'1': ('kn', '66:00:54')},
{'1': ('kn', '66:00:55')},
{'1': ('kn', '66:00:56')},
{'1': ('kn', '66:00:57')},
{'1': ('kn', '66:00:58')},
{'1': ('kn', '66:00:59')},
{'1': ('kn', '66:00:60')},
{'1': ('kn', '66:00:61')},
{'1': ('kn', '66:00:62')},
{'1': ('kn', '66:00:63')},
{'1': ('kn', '66:00:64')},
{'1': ('kn', '66:00:65')},
{'1': ('kn', '66:00:66')},
{'1': ('kn', '66:00:67')},
{'1': ('kn', '66:00:68')},
{'1': ('kn', '66:00:69')},
{'1': ('kn', '66:00:70')},
{'1': ('kn', '66:00:71')},
{'1': ('kn', '66:00:72')},
{'1': ('kn', '66:00:73')},
{'1': ('kn', '66:00:74')},
{'1': ('kn', '66:00:75')},
{'1': ('kn', '66:00:76')},
{'1': ('kn', '66:00:77')},
{'1': ('kn', '66:00:78')},
{'1': ('kn', '66:00:79')},
{'1': ('kn', '66:00:80')},
{'1': ('kn', '66:00:81')},
{'1': ('kn', '66:00:82')},
{'1': ('kn', '66:00:83')},
{'1': ('kn', '66:00:84')},
{'1': ('kn', '66:00:85')},
{'1': ('kn', '66:00:86')},
{'1': ('kn', '66:00:87')},
{'1': ('kn', '66:00:88')},
{'1': ('kn', '66:00:89')},
{'1': ('kn', '66:00:90')},
{'1': ('kn', '66:00:91')},
{'1': ('kn', '66:00:92')},
{'1': ('kn', '66:00:93')},
{'1': ('kn', '66:00:94')},
{'1': ('kn', '66:00:95')},
{'1': ('kn', '66:00:96')},
{'1': ('kn', '66:00:97')},
{'1': ('kn', '66:00:98')},
{'1': ('kn', '66:00:99')},
{'1': ('kn', '66:01:00')},
{'1': ('kn', '66:01:01')},
{'1': ('kn', '66:01:02')},
{'1': ('kn', '66:01:03')},
{'1': ('kn', '66:01:04')},
{'1': ('kn', '66:01:05')},
{'1': ('kn', '66:01:06')},
{'1': ('kn', '66:01:07')},
{'1': ('kn', '66:01:08')},
{'1': ('kn', '66:01:09')},
{'1': ('kn', '66:01:10')},
{'1': ('kn', '66:01:11')},
{'1': ('kn', '66:01:12')},
{'1': ('kn', '66:01:13')},
{'1': ('kn', '66:01:14')},
{'1': ('kn', '66:01:15')},
{'1': ('kn', '66:01:16')},
{'1': ('kn', '66:01:17')},
{'1': ('kn', '66:01:18')},
{'1': ('kn', '66:01:19')},
{'1': ('kn', '66:01:20')},
{'1': ('kn', '66:01:21')},
{'1': ('kn', '66:01:22')},
{'1': ('kn', '66:01:23')},
{'1': ('kn', '66:01:24')},
{'1': ('kn', '66:01:25')},
{'1': ('kn', '66:01:26')},
{'1': ('kn', '66:01:27')},
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| 116
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| 0.33815
| 396,975
| 14,869
| 117
| 26.698164
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|
0
| 5
|
0ae706c9954ff13c7bd95dfdd68d959873e24001
| 132
|
py
|
Python
|
countryInfo/landingParser.py
|
berylxzhang/Covid-Web
|
4a2e74545270928047952eafc5c465dab876f914
|
[
"MIT"
] | null | null | null |
countryInfo/landingParser.py
|
berylxzhang/Covid-Web
|
4a2e74545270928047952eafc5c465dab876f914
|
[
"MIT"
] | null | null | null |
countryInfo/landingParser.py
|
berylxzhang/Covid-Web
|
4a2e74545270928047952eafc5c465dab876f914
|
[
"MIT"
] | null | null | null |
from bs4 import BeautifulSoup
from datetime import datetime, timedelta
import requests
import json
import math
import csv
import re
| 16.5
| 40
| 0.848485
| 19
| 132
| 5.894737
| 0.578947
| 0
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| 0.00885
| 0.143939
| 132
| 8
| 41
| 16.5
| 0.982301
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|
0
| 5
|
0ae753b4e63dac2dea159634308b941339a985b9
| 42
|
py
|
Python
|
run.py
|
julianparismorgan/duke-information-bot
|
cd631f1e4d8dae9708ef15564892814870ed9ddf
|
[
"Apache-2.0"
] | null | null | null |
run.py
|
julianparismorgan/duke-information-bot
|
cd631f1e4d8dae9708ef15564892814870ed9ddf
|
[
"Apache-2.0"
] | null | null | null |
run.py
|
julianparismorgan/duke-information-bot
|
cd631f1e4d8dae9708ef15564892814870ed9ddf
|
[
"Apache-2.0"
] | null | null | null |
from main import app
app.run(debug=True)
| 10.5
| 20
| 0.761905
| 8
| 42
| 4
| 0.875
| 0
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| 0.142857
| 42
| 3
| 21
| 14
| 0.888889
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| 0
|
0
| 5
|
0af2fd5e8c4ff4fc61d64c49d91851fe8597f21b
| 1,902
|
py
|
Python
|
imagersite/imager_images/models.py
|
famavott/django-imager
|
a9867656af7a665f81574c1be5d50a2a703b4af4
|
[
"MIT"
] | null | null | null |
imagersite/imager_images/models.py
|
famavott/django-imager
|
a9867656af7a665f81574c1be5d50a2a703b4af4
|
[
"MIT"
] | 1
|
2017-11-27T05:32:39.000Z
|
2017-11-27T05:32:39.000Z
|
imagersite/imager_images/models.py
|
famavott/django-imager
|
a9867656af7a665f81574c1be5d50a2a703b4af4
|
[
"MIT"
] | null | null | null |
"""Models for imager_images app."""
from django.db import models
from imager_profile.models import ImagerProfile
class Photo(models.Model):
"""Create a photo model."""
user = models.ForeignKey(ImagerProfile, related_name='photo', on_delete=models.CASCADE)
imgfile = models.ImageField(upload_to='images')
title = models.CharField(max_length=50, blank=True, null=True)
description = models.TextField(blank=True, null=True)
date_uploaded = models.DateTimeField(auto_now_add=True)
date_modified = models.DateTimeField(auto_now=True)
date_published = models.DateTimeField(auto_now_add=True)
PUBLISHED = [
('PRIVATE', 'Private'),
('SHARED', 'Shared'),
('PUBLIC', 'Public')
]
published = models.CharField(
max_length=10,
choices=PUBLISHED,
blank=True,
default='PUBLIC'
)
def __str__(self):
"""Return name for object."""
return str(self.title)
class Album(models.Model):
"""Create an album model."""
user = models.ForeignKey(ImagerProfile, related_name='album', on_delete=models.CASCADE)
photo = models.ManyToManyField(Photo, related_name='album')
title = models.CharField(max_length=50, blank=True, null=True)
description = models.TextField(blank=True, null=True)
cover = models.ImageField(upload_to='images', blank=True, null=True)
date_uploaded = models.DateTimeField(auto_now_add=True)
date_modified = models.DateTimeField(auto_now=True)
date_published = models.DateTimeField(auto_now_add=True)
PUBLISHED = [
('PRIVATE', 'Private'),
('SHARED', 'Shared'),
('PUBLIC', 'Public')
]
published = models.CharField(
max_length=10,
choices=PUBLISHED,
blank=True,
default='PUBLIC'
)
def __str__(self):
"""Return name for object."""
return str(self.title)
| 31.7
| 91
| 0.663512
| 217
| 1,902
| 5.645161
| 0.276498
| 0.051429
| 0.112653
| 0.127347
| 0.781224
| 0.732245
| 0.732245
| 0.652245
| 0.652245
| 0.652245
| 0
| 0.005309
| 0.207676
| 1,902
| 59
| 92
| 32.237288
| 0.807565
| 0.064143
| 0
| 0.711111
| 0
| 0
| 0.065564
| 0
| 0
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| 0
| 0
| 1
| 0.044444
| false
| 0
| 0.044444
| 0
| 0.6
| 0
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| 0
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| 1
| 1
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| 1
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| 0
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| 0
| null | 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 5
|
e401774efdc497b57e8ff72b264698f27ca77e3a
| 184
|
py
|
Python
|
moni-moni/server/server/apps/catalogue/managers.py
|
amal-thundiyil/fundrize-dapp
|
7dceb0239f15c7348ab4ad2bed189c90bf54dde6
|
[
"MIT"
] | null | null | null |
moni-moni/server/server/apps/catalogue/managers.py
|
amal-thundiyil/fundrize-dapp
|
7dceb0239f15c7348ab4ad2bed189c90bf54dde6
|
[
"MIT"
] | null | null | null |
moni-moni/server/server/apps/catalogue/managers.py
|
amal-thundiyil/fundrize-dapp
|
7dceb0239f15c7348ab4ad2bed189c90bf54dde6
|
[
"MIT"
] | 2
|
2022-02-26T18:30:01.000Z
|
2022-02-27T05:17:49.000Z
|
from django.db import models
class FundraiserManager(models.Manager):
def get_queryset(self):
return super(FundraiserManager, self).get_queryset().filter(is_active=True)
| 26.285714
| 83
| 0.766304
| 23
| 184
| 6
| 0.782609
| 0.15942
| 0
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| 0
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| 0
| 0
| 0
| 0.13587
| 184
| 6
| 84
| 30.666667
| 0.867925
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| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 5
|
7c57539ca9311ec330adf347094948d8ab994ff5
| 165
|
py
|
Python
|
inst/python/src/inference/ewsNET_generic.py
|
duncanobrien/EWSmethods
|
7f33c3288e141512da95d5e8a5add3d7e8e4cc54
|
[
"MIT"
] | null | null | null |
inst/python/src/inference/ewsNET_generic.py
|
duncanobrien/EWSmethods
|
7f33c3288e141512da95d5e8a5add3d7e8e4cc54
|
[
"MIT"
] | null | null | null |
inst/python/src/inference/ewsNET_generic.py
|
duncanobrien/EWSmethods
|
7f33c3288e141512da95d5e8a5add3d7e8e4cc54
|
[
"MIT"
] | null | null | null |
import os
import numpy as np
from numpy.random import seed
seed(1)
from tensorflow.random import set_seed
set_seed(2)
from python.src.inference.ewsnet import EWSNet
| 20.625
| 46
| 0.824242
| 29
| 165
| 4.62069
| 0.551724
| 0.179104
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.013793
| 0.121212
| 165
| 7
| 47
| 23.571429
| 0.910345
| 0
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| 0
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| 0
| 0
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| 1
| 0
| true
| 0
| 0.714286
| 0
| 0.714286
| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 1
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| 0
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| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
7ccc2ebf71221ce918ed40f146f65cd481363fe1
| 8,812
|
py
|
Python
|
tftk/image/dataset/classification.py
|
kitfactory/tftk
|
5cae0a96b99eecb6f64922068c162f973eebba71
|
[
"MIT"
] | 6
|
2020-03-09T00:18:08.000Z
|
2021-11-08T09:27:19.000Z
|
tftk/image/dataset/classification.py
|
kitfactory/tftk
|
5cae0a96b99eecb6f64922068c162f973eebba71
|
[
"MIT"
] | 2
|
2022-02-10T07:34:22.000Z
|
2022-03-12T01:10:05.000Z
|
tftk/image/dataset/classification.py
|
kitfactory/tftk
|
5cae0a96b99eecb6f64922068c162f973eebba71
|
[
"MIT"
] | null | null | null |
import os
import glob
from typing import List,Dict
import shutil
import tensorflow as tf
import tensorflow_datasets as tfds
from icrawler.builtin import BingImageCrawler
from tftk.image.dataset import BaseDataset
from typing import Tuple
class Mnist(BaseDataset):
@classmethod
def get_train_dataset(cls,**kwargs)->Tuple[tf.data.Dataset, int]:
ds, info = tfds.load(name="mnist", split="train",with_info=True)
return ds, info.splits["train"].num_examples
@classmethod
def get_validation_dataset(cls, **kwargs)->Tuple[tf.data.Dataset, int]:
return (None, -1)
@classmethod
def get_test_dataset(cls,**kwargs)->Tuple[tf.data.Dataset, int]:
ds, info = tfds.load(name="mnist", split="test",with_info=True)
return ds, info.splits["test"].num_examples
class Cifar10(BaseDataset):
@classmethod
def get_train_dataset(cls)->Tuple[tf.data.Dataset, int]:
ds, info = tfds.load(name="cifar10", split="train",with_info=True)
return ds, info.splits["train"].num_examples
@classmethod
def get_validation_dataset(cls, **kwargs)->Tuple[tf.data.Dataset, int]:
return (None, -1)
@classmethod
def get_test_dataset(cls)->Tuple[tf.data.Dataset, int]:
ds, info = tfds.load(name="cifar10", split="test",with_info=True)
return ds, info.splits["test"].num_examples
class ImageCrawler():
@classmethod
def crawl_keywords_save_folder(cls, name:str, keywords:List[str], base_dir:str="tmp", filters:Dict={"size":"large"}, max_num:int= 10000, train_ratio:float=0.8):
"""キーワードでクロールしてデータセットを作成する。
Arguments:
name {str} -- データセット名
keywords {List[str]} -- キーワード
filter {[type]} -- [description]
Keyword Arguments:
dest_base_dir {str} -- [description] (default: {"tmp/crawl"})
train_ratio {float} -- [description] (default: {0.8})
"""
download_base = base_dir + os.path.sep + name
for k in keywords:
download_dir = download_base + os.path.sep + "train" + os.path.sep + k
if os.path.exists(download_dir) != True:
os.makedirs(download_dir)
storage={"root_dir": download_dir }
print( "keyword:",k, " dir",download_dir)
crawler = BingImageCrawler(storage=storage)
crawler.crawl(keyword=k, filters=filters, max_num=max_num, file_idx_offset=0)
move_dir = download_base + os.path.sep + "test" + os.path.sep + k
if os.path.exists(move_dir) != True:
os.makedirs(move_dir)
file_list = glob.glob(download_dir+os.path.sep+"*.jpg")
move_num:int = int(len(file_list) * train_ratio)
move_list = file_list[move_num:]
for f in move_list:
shutil.move(f,move_dir)
class ImageLabelFolderDataset():
@classmethod
def get_train_dataset(cls, name="dataset", manual_dir="./", **kwargs)->Tuple[tf.data.Dataset, int]:
print(name,manual_dir)
builder = tfds.image.ImageLabelFolder(name)
dl_config = tfds.download.DownloadConfig(manual_dir=manual_dir)
builder.download_and_prepare(download_config=dl_config)
ds = builder.as_dataset(split='train', shuffle_files=False)
len = builder.info.splits['train'].num_examples # Splits, num examples,... automatically extracted
return ds, len
@classmethod
def get_validation_dataset(cls, name="dataset", dir="./", **kwargs)->Tuple[tf.data.Dataset, int]:
"""validation用データセットを取得する。
Arguments:
int {[type]} -- [description]
Returns:
[type] -- [description]
"""
builder = tfds.image.ImageLabelFolder(name)
dl_config = tfds.download.DownloadConfig(manual_dir=dir)
builder.download_and_prepare(download_config=dl_config)
ds = builder.as_dataset(split='validation', shuffle_files=False)
len = builder.info.splits['validation'].num_examples # Splits, num examples,... automatically extracted
return ds, len
@classmethod
def get_test_dataset(cls, name="dataset", dir="./", **kwargs)->Tuple[tf.data.Dataset, int]:
"""test用データセットを取得する。
Arguments:
int {[type]} -- [description]
Returns:
[type] -- [description]
"""
builder = tfds.image.ImageLabelFolder(name)
dl_config = tfds.download.DownloadConfig(manual_dir=dir)
builder.download_and_prepare(download_config=dl_config)
ds = builder.as_dataset(split='test', shuffle_files=False)
len = builder.info.splits['test'].num_examples # Splits, num examples,... automatically extracted
return ds, len
class Place365Small(BaseDataset):
@classmethod
def get_train_dataset(cls,**kwargs)->Tuple[tf.data.Dataset, int]:
ds, info = tfds.load(name="places365_small", split="train",with_info=True)
return ds, info.splits["train"].num_examples
@classmethod
def get_validation_dataset(cls,**kwargs)->Tuple[tf.data.Dataset, int]:
ds, info = tfds.load(name="places365_small", split="validation",with_info=True)
return ds, info.splits["validation"].num_examples
@classmethod
def get_test_dataset(cls,**kwargs)->Tuple[tf.data.Dataset, int]:
ds, info = tfds.load(name="places365_small", split="test",with_info=True)
return ds, info.splits["test"].num_examples
class Food101(BaseDataset):
@classmethod
def get_train_dataset(cls,**kwargs)->Tuple[tf.data.Dataset, int]:
ds, info = tfds.load(name="food101", split="train",with_info=True)
return ds, info.splits["train"].num_examples
@classmethod
def get_validation_dataset(cls,**kwargs)->Tuple[tf.data.Dataset, int]:
ds, info = tfds.load(name="food101", split="validation",with_info=True)
return ds, info.splits["validation"].num_examples
@classmethod
def get_test_dataset(cls,**kwargs)->Tuple[tf.data.Dataset, int]:
return (None,-1)
class PatchCamelyon(BaseDataset):
@classmethod
def get_train_dataset(cls,**kwargs)->Tuple[tf.data.Dataset, int]:
ds, info = tfds.load(name="patch_camelyon", split="train",with_info=True)
return ds, info.splits["train"].num_examples
@classmethod
def get_validation_dataset(cls,**kwargs)->Tuple[tf.data.Dataset, int]:
return (None,-1)
@classmethod
def get_test_dataset(cls,**kwargs)->Tuple[tf.data.Dataset, int]:
ds, info = tfds.load(name="patch_camelyon", split="test",with_info=True)
return ds, info.splits["test"].num_examples
class ImageNetResized(BaseDataset):
"""ImageNetリサイズ画像
Arguments:
BaseDataset {[type]} -- [description]
Returns:
[type] -- [description]
"""
@classmethod
def get_train_dataset(cls,**kwargs)->Tuple[tf.data.Dataset, int]:
ds, info = tfds.load(name="imagenet_resized/64x64", split="train",with_info=True)
return ds, info.splits["train"].num_examples
@classmethod
def get_test_dataset(cls,**kwargs)->Tuple[tf.data.Dataset, int]:
ds, info = tfds.load(name="imagenet_resized/64x64", split="validation",with_info=True)
return ds, info.splits["validation"].num_examples
@classmethod
def get_test_dataset(cls,**kwargs)->Tuple[tf.data.Dataset, int]:
return (None,-1)
# bellows are working...
class ImageNet2012(BaseDataset):
@classmethod
def get_train_dataset(cls)->Tuple[tf.data.Dataset, int]:
ds, info = tfds.load(name="imagenet2012", split="train",with_info=True,data_dir="D:\\imagenet")
return ds, 1281167
@classmethod
def get_test_dataset(cls)->Tuple[tf.data.Dataset, int]:
ds, info = tfds.load(name="imagenet2012", split="ttest",with_info=True,data_dir="D:\\imagenet")
return ds, info.splits["test"].num_examples
class CatsVsDogs(BaseDataset):
@classmethod
def get_train_dataset(cls)->Tuple[tf.data.Dataset, int]:
ds, info = tfds.load(name="cats_vs_dogs", split="train",with_info=True)
return ds, info.splits["train"].num_examples
@classmethod
def get_test_dataset(cls)->Tuple[tf.data.Dataset, int]:
raise Exception("No test data")
class RockPaperScissors(BaseDataset):
@classmethod
def get_train_dataset(cls)->Tuple[tf.data.Dataset, int]:
ds, info = tfds.load(name="rock_paper_scissors", split="train",with_info=True)
return ds, info.splits["train"].num_examples
@classmethod
def get_test_dataset(cls)->Tuple[tf.data.Dataset, int]:
ds, info = tfds.load(name="rock_paper_scissors", split="test",with_info=True)
return ds, info.splits["test"].num_examples
| 36.263374
| 164
| 0.655356
| 1,102
| 8,812
| 5.085299
| 0.128857
| 0.037473
| 0.081906
| 0.086724
| 0.7707
| 0.759814
| 0.743041
| 0.71788
| 0.703961
| 0.691113
| 0
| 0.00987
| 0.20665
| 8,812
| 242
| 165
| 36.413223
| 0.791732
| 0.087494
| 0
| 0.551948
| 0
| 0
| 0.072993
| 0.005635
| 0
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| 0
| 0
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| 1
| 0.181818
| false
| 0
| 0.058442
| 0.032468
| 0.480519
| 0.012987
| 0
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| null | 0
| 0
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| 1
| 1
| 1
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| null | 0
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| 0
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| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
7ccfef191ddea4faf4194ff51501b0efc66436c8
| 27
|
py
|
Python
|
pathtree/__init__.py
|
skylerlee/pathtree
|
7c76f0748562ff51da20de369cf178f13a5edb22
|
[
"MIT"
] | null | null | null |
pathtree/__init__.py
|
skylerlee/pathtree
|
7c76f0748562ff51da20de369cf178f13a5edb22
|
[
"MIT"
] | null | null | null |
pathtree/__init__.py
|
skylerlee/pathtree
|
7c76f0748562ff51da20de369cf178f13a5edb22
|
[
"MIT"
] | null | null | null |
from .pathtree import Tree
| 13.5
| 26
| 0.814815
| 4
| 27
| 5.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0.148148
| 27
| 1
| 27
| 27
| 0.956522
| 0
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| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
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| 1
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| 1
| 1
| 0
| null | 0
| 0
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| 0
| 0
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| 0
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| 0
| 0
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| 1
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
7cfa284a426257bcc1c640df564aa82fd0a787ed
| 278
|
py
|
Python
|
home/views.py
|
mamad-azimi-jozani/django
|
23bf60265116bcaa91c873f96f80537b2d763990
|
[
"MIT"
] | null | null | null |
home/views.py
|
mamad-azimi-jozani/django
|
23bf60265116bcaa91c873f96f80537b2d763990
|
[
"MIT"
] | null | null | null |
home/views.py
|
mamad-azimi-jozani/django
|
23bf60265116bcaa91c873f96f80537b2d763990
|
[
"MIT"
] | null | null | null |
from django.shortcuts import render
# Create your views here.
def index_view(request):
return render(request,"home/index.html")
def about_view(request):
return render(request,"home/about.html")
def contact_view(request):
return render(request,"home/contact.html")
| 25.272727
| 46
| 0.755396
| 39
| 278
| 5.307692
| 0.461538
| 0.15942
| 0.246377
| 0.333333
| 0.492754
| 0.492754
| 0
| 0
| 0
| 0
| 0
| 0
| 0.125899
| 278
| 11
| 46
| 25.272727
| 0.851852
| 0.082734
| 0
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| 0
| 0
| 0.185039
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.428571
| false
| 0
| 0.142857
| 0.428571
| 1
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 5
|
7cfdcbf20b4d4b5e94ffd6aa3cd7c0e6517fd25e
| 54
|
py
|
Python
|
theisle/__init__.py
|
gquarles/theisle
|
5170ebcbfbe1ec58d63c44385f008b830be037e7
|
[
"MIT"
] | null | null | null |
theisle/__init__.py
|
gquarles/theisle
|
5170ebcbfbe1ec58d63c44385f008b830be037e7
|
[
"MIT"
] | null | null | null |
theisle/__init__.py
|
gquarles/theisle
|
5170ebcbfbe1ec58d63c44385f008b830be037e7
|
[
"MIT"
] | null | null | null |
from .Server import Server
from .Player import Player
| 18
| 26
| 0.814815
| 8
| 54
| 5.5
| 0.5
| 0
| 0
| 0
| 0
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| 0.148148
| 54
| 2
| 27
| 27
| 0.956522
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| true
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| null | 0
| 0
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| 0
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| null | 0
| 0
| 0
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| 0
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| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
6b34fdfad2cd1e7890829c56bb4ef4ab59026aca
| 11,347
|
py
|
Python
|
tests/text/test_bertscore.py
|
bpkwee/metrics
|
3aba057ad9ff87183aaaf5988b8ccfdab81b2095
|
[
"Apache-2.0"
] | 1
|
2022-03-22T08:49:04.000Z
|
2022-03-22T08:49:04.000Z
|
tests/text/test_bertscore.py
|
bpkwee/metrics
|
3aba057ad9ff87183aaaf5988b8ccfdab81b2095
|
[
"Apache-2.0"
] | null | null | null |
tests/text/test_bertscore.py
|
bpkwee/metrics
|
3aba057ad9ff87183aaaf5988b8ccfdab81b2095
|
[
"Apache-2.0"
] | null | null | null |
import os
from typing import Any, Dict, List
import numpy as np
import pytest
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
from torchmetrics.functional.text.bert import bert_score as metrics_bert_score
from torchmetrics.text.bert import BERTScore
from torchmetrics.utilities.imports import _BERTSCORE_AVAILABLE
if _BERTSCORE_AVAILABLE:
from bert_score import score as original_bert_score
os.environ["TOKENIZERS_PARALLELISM"] = "1"
# Examples and expected values taken from:
# https://github.com/Tiiiger/bert_score/blob/master/tests/test_scorer.py
preds = [
"28-year-old chef found dead in San Francisco mall",
"A 28-year-old chef who recently moved to San Francisco was "
"found dead in the staircase of a local shopping center.",
"The victim's brother said he cannot imagine anyone who would want to harm him,\"Finally, it went uphill again at "
'him."',
]
targets = [
"28-Year-Old Chef Found Dead at San Francisco Mall",
"A 28-year-old chef who had recently moved to San Francisco was found dead in the stairwell of a local mall this "
"week.",
"But the victim's brother says he can't think of anyone who would want to hurt him, saying, \"Things were finally "
'going well for him."',
]
_METRICS = ["precision", "recall", "f1"]
MODEL_NAME = "albert-base-v2"
def _assert_list(preds: Any, targets: Any, threshold: float = 1e-8):
"""Assert two lists are equal."""
assert np.allclose(preds, targets, atol=threshold, equal_nan=True)
def _parse_original_bert_score(score: torch.Tensor) -> Dict[str, List[float]]:
"""Parse the BERT score returned by the original `bert-score` package."""
score_dict = {metric: value.tolist() for metric, value in zip(_METRICS, score)}
return score_dict
preds_batched = [preds[0:2], preds[2:]]
targets_batched = [targets[0:2], targets[2:]]
@pytest.mark.parametrize(
"preds,targets",
[(preds, targets)],
)
@pytest.mark.skipif(not _BERTSCORE_AVAILABLE, reason="test requires bert_score")
def test_score_fn(preds, targets):
"""Tests for functional."""
original_score = original_bert_score(preds, targets, model_type=MODEL_NAME, num_layers=8, idf=False, batch_size=3)
original_score = _parse_original_bert_score(original_score)
metrics_score = metrics_bert_score(
preds, targets, model_name_or_path=MODEL_NAME, num_layers=8, idf=False, batch_size=3
)
for metric in _METRICS:
_assert_list(metrics_score[metric], original_score[metric])
@pytest.mark.parametrize(
"preds,targets",
[(preds, targets)],
)
@pytest.mark.skipif(not _BERTSCORE_AVAILABLE, reason="test requires bert_score")
def test_score_fn_with_idf(preds, targets):
"""Tests for functional with IDF rescaling."""
original_score = original_bert_score(preds, targets, model_type=MODEL_NAME, num_layers=12, idf=True, batch_size=3)
original_score = _parse_original_bert_score(original_score)
metrics_score = metrics_bert_score(
preds, targets, model_name_or_path=MODEL_NAME, num_layers=12, idf=True, batch_size=3
)
for metric in _METRICS:
_assert_list(metrics_score[metric], original_score[metric])
@pytest.mark.parametrize(
"preds,targets",
[(preds, targets)],
)
@pytest.mark.skipif(not _BERTSCORE_AVAILABLE, reason="test requires bert_score")
def test_score_fn_all_layers(preds, targets):
"""Tests for functional and all layers."""
original_score = original_bert_score(
preds, targets, model_type=MODEL_NAME, all_layers=True, idf=False, batch_size=3
)
original_score = _parse_original_bert_score(original_score)
metrics_score = metrics_bert_score(
preds, targets, model_name_or_path=MODEL_NAME, all_layers=True, idf=False, batch_size=3
)
for metric in _METRICS:
_assert_list(metrics_score[metric], original_score[metric])
@pytest.mark.parametrize(
"preds,targets",
[(preds, targets)],
)
@pytest.mark.skipif(not _BERTSCORE_AVAILABLE, reason="test requires bert_score")
def test_score_fn_all_layers_with_idf(preds, targets):
"""Tests for functional and all layers with IDF rescaling."""
original_score = original_bert_score(preds, targets, model_type=MODEL_NAME, all_layers=True, idf=True, batch_size=3)
original_score = _parse_original_bert_score(original_score)
metrics_score = metrics_bert_score(
preds, targets, model_name_or_path=MODEL_NAME, all_layers=True, idf=True, batch_size=3
)
for metric in _METRICS:
_assert_list(metrics_score[metric], original_score[metric])
@pytest.mark.parametrize(
"preds,targets",
[(preds, targets)],
)
@pytest.mark.skipif(not _BERTSCORE_AVAILABLE, reason="test requires bert_score")
def test_score_fn_all_layers_rescale_with_baseline(preds, targets):
"""Tests for functional with baseline rescaling."""
original_score = original_bert_score(
preds,
targets,
model_type=MODEL_NAME,
lang="en",
num_layers=8,
idf=False,
batch_size=3,
rescale_with_baseline=True,
)
original_score = _parse_original_bert_score(original_score)
metrics_score = metrics_bert_score(
preds,
targets,
model_name_or_path=MODEL_NAME,
lang="en",
num_layers=8,
idf=False,
batch_size=3,
rescale_with_baseline=True,
)
for metric in _METRICS:
_assert_list(metrics_score[metric], original_score[metric])
@pytest.mark.parametrize(
"preds,targets",
[(preds, targets)],
)
@pytest.mark.skipif(not _BERTSCORE_AVAILABLE, reason="test requires bert_score")
def test_score_fn_rescale_with_baseline(preds, targets):
"""Tests for functional with baseline rescaling with all layers."""
original_score = original_bert_score(
preds,
targets,
model_type=MODEL_NAME,
lang="en",
all_layers=True,
idf=False,
batch_size=3,
rescale_with_baseline=True,
)
original_score = _parse_original_bert_score(original_score)
metrics_score = metrics_bert_score(
preds,
targets,
model_name_or_path=MODEL_NAME,
lang="en",
all_layers=True,
idf=False,
batch_size=3,
rescale_with_baseline=True,
)
for metric in _METRICS:
_assert_list(metrics_score[metric], original_score[metric])
@pytest.mark.parametrize(
"preds,targets",
[(preds, targets)],
)
@pytest.mark.skipif(not _BERTSCORE_AVAILABLE, reason="test requires bert_score")
def test_score(preds, targets):
"""Tests for metric."""
original_score = original_bert_score(preds, targets, model_type=MODEL_NAME, num_layers=8, idf=False, batch_size=3)
original_score = _parse_original_bert_score(original_score)
scorer = BERTScore(model_name_or_path=MODEL_NAME, num_layers=8, idf=False, batch_size=3)
scorer.update(preds=preds, target=targets)
metrics_score = scorer.compute()
for metric in _METRICS:
_assert_list(metrics_score[metric], original_score[metric])
@pytest.mark.parametrize(
"preds,targets",
[(preds, targets)],
)
@pytest.mark.skipif(not _BERTSCORE_AVAILABLE, reason="test requires bert_score")
def test_score_with_idf(preds, targets):
"""Tests for metric with IDF rescaling."""
original_score = original_bert_score(preds, targets, model_type=MODEL_NAME, num_layers=8, idf=True, batch_size=3)
original_score = _parse_original_bert_score(original_score)
scorer = BERTScore(model_name_or_path=MODEL_NAME, num_layers=8, idf=True, batch_size=3)
scorer.update(preds=preds, target=targets)
metrics_score = scorer.compute()
for metric in _METRICS:
_assert_list(metrics_score[metric], original_score[metric])
@pytest.mark.parametrize(
"preds,targets",
[(preds, targets)],
)
@pytest.mark.skipif(not _BERTSCORE_AVAILABLE, reason="test requires bert_score")
def test_score_all_layers(preds, targets):
"""Tests for metric and all layers."""
original_score = original_bert_score(
preds, targets, model_type=MODEL_NAME, all_layers=True, idf=False, batch_size=3
)
original_score = _parse_original_bert_score(original_score)
scorer = BERTScore(model_name_or_path=MODEL_NAME, all_layers=True, idf=False, batch_size=3)
scorer.update(preds=preds, target=targets)
metrics_score = scorer.compute()
for metric in _METRICS:
_assert_list(metrics_score[metric], original_score[metric])
@pytest.mark.parametrize(
"preds,targets",
[(preds, targets)],
)
@pytest.mark.skipif(not _BERTSCORE_AVAILABLE, reason="test requires bert_score")
def test_score_all_layers_with_idf(preds, targets):
"""Tests for metric and all layers with IDF rescaling."""
original_score = original_bert_score(preds, targets, model_type=MODEL_NAME, all_layers=True, idf=True, batch_size=3)
original_score = _parse_original_bert_score(original_score)
scorer = BERTScore(model_name_or_path=MODEL_NAME, all_layers=True, idf=True, batch_size=3)
scorer.update(preds=preds, target=targets)
metrics_score = scorer.compute()
for metric in _METRICS:
_assert_list(metrics_score[metric], original_score[metric])
@pytest.mark.parametrize(
"preds,targets",
[(preds_batched, targets_batched)],
)
@pytest.mark.skipif(not _BERTSCORE_AVAILABLE, reason="test requires bert_score")
def test_accumulation(preds, targets):
"""Tests for metric works with accumulation."""
original_score = original_bert_score(
sum(preds, []), sum(targets, []), model_type=MODEL_NAME, num_layers=8, idf=False, batch_size=3
)
original_score = _parse_original_bert_score(original_score)
scorer = BERTScore(model_name_or_path=MODEL_NAME, num_layers=8, idf=False, batch_size=3)
for p, r in zip(preds, targets):
scorer.update(preds=p, target=r)
metrics_score = scorer.compute()
for metric in _METRICS:
_assert_list(metrics_score[metric], original_score[metric])
def _bert_score_ddp(rank, world_size, preds, targets, original_score):
"""Define a DDP process for BERTScore."""
os.environ["MASTER_ADDR"] = "localhost"
os.environ["MASTER_PORT"] = "12355"
dist.init_process_group("gloo", rank=rank, world_size=world_size)
scorer = BERTScore(model_name_or_path=MODEL_NAME, num_layers=8, idf=False, batch_size=3, max_length=128)
scorer.update(preds, targets)
metrics_score = scorer.compute()
for metric in _METRICS:
_assert_list(metrics_score[metric], original_score[metric])
dist.destroy_process_group()
def _test_score_ddp_fn(rank, world_size, preds, targets):
"""Core functionality for the `test_score_ddp` test."""
original_score = original_bert_score(preds, targets, model_type=MODEL_NAME, num_layers=8, idf=False, batch_size=3)
original_score = _parse_original_bert_score(original_score)
_bert_score_ddp(rank, world_size, preds, targets, original_score)
@pytest.mark.parametrize(
"preds,targets",
[(preds, targets)],
)
@pytest.mark.skipif(not (_BERTSCORE_AVAILABLE and dist.is_available()), reason="test requires bert_score")
def test_score_ddp(preds, targets):
"""Tests for metric using DDP."""
world_size = 2
mp.spawn(_test_score_ddp_fn, args=(world_size, preds, targets), nprocs=world_size, join=False)
| 35.130031
| 120
| 0.728827
| 1,549
| 11,347
| 5.040671
| 0.127824
| 0.090676
| 0.058786
| 0.045722
| 0.79854
| 0.772541
| 0.766906
| 0.757941
| 0.741803
| 0.727203
| 0
| 0.007182
| 0.165594
| 11,347
| 322
| 121
| 35.23913
| 0.81749
| 0.067683
| 0
| 0.6
| 0
| 0.041667
| 0.102029
| 0.002096
| 0
| 0
| 0
| 0
| 0.058333
| 1
| 0.066667
| false
| 0
| 0.045833
| 0
| 0.116667
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
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0
| 5
|
860dcae6d737c1948ed120f2d6f0dc219223b867
| 14,228
|
py
|
Python
|
autotest/t012_test.py
|
Gael-de-Sailly/flopy
|
4104cf5e6a35e2a1fd6183442962ae5cb258fa7a
|
[
"CC0-1.0",
"BSD-3-Clause"
] | null | null | null |
autotest/t012_test.py
|
Gael-de-Sailly/flopy
|
4104cf5e6a35e2a1fd6183442962ae5cb258fa7a
|
[
"CC0-1.0",
"BSD-3-Clause"
] | null | null | null |
autotest/t012_test.py
|
Gael-de-Sailly/flopy
|
4104cf5e6a35e2a1fd6183442962ae5cb258fa7a
|
[
"CC0-1.0",
"BSD-3-Clause"
] | null | null | null |
# Test loading of MODFLOW and MT3D models that come with MT3D distribution
import os
import sys
import flopy
pthtest = os.path.join('..', 'examples', 'data', 'mt3d_test')
pth2005 = os.path.join(pthtest, 'mf2005mt3d')
pth2000 = os.path.join(pthtest, 'mf2kmt3d')
pthNWT = os.path.join(pthtest, 'mfnwt_mt3dusgs')
newpth = os.path.join('.', 'temp', 't012')
mf2k_exe = 'mf2000'
mf2005_exe = 'mf2005'
mfnwt_exe = 'mfnwt'
mt3d_exe = 'mt3dms'
mt3d_usgs_exe = 'mt3dusgs'
ismf2k = flopy.which(mf2k_exe)
ismf2005 = flopy.which(mf2005_exe)
ismfnwt = flopy.which(mfnwt_exe)
ismt3d = flopy.which(mt3d_exe)
ismt3dusgs = flopy.which(mt3d_usgs_exe)
def test_mf2005_p07():
pth = os.path.join(pth2005, 'P07')
namfile = 'p7mf2005.nam'
mf = flopy.modflow.Modflow.load(namfile, model_ws=pth, verbose=True,
exe_name=mf2005_exe)
cpth = os.path.join(newpth, 'P07')
mf.model_ws = cpth
mf.write_input()
if ismf2005 is not None:
success, buff = mf.run_model(silent=False)
assert success, '{} did not run'.format(mf.name)
namfile = 'p7mt.nam'
mt = flopy.mt3d.mt.Mt3dms.load(namfile, model_ws=pth, verbose=True,
exe_name=mt3d_exe)
# Optional keyword line is absent in this example, ensure defaults are kept
assert mt.btn.DRYCell is False
assert mt.btn.Legacy99Stor is False
assert mt.btn.MFStyleArr is False
assert mt.btn.AltWTSorb is False
mt.model_ws = cpth
ftlfile = 'p7.ftl'
mt.ftlfilename = ftlfile
mt.write_input()
if ismt3d is not None and ismf2005 is not None:
success, buff = mt.run_model(silent=False,
normal_msg='program completed.')
assert success, '{} did not run'.format(mt.name)
os.remove(os.path.join(cpth, ftlfile))
return
def test_mf2000_p07():
pth = os.path.join(pth2000, 'P07')
namfile = 'p7mf2k.nam'
mf = flopy.modflow.Modflow.load(namfile, model_ws=pth,
verbose=True,
exe_name=mf2k_exe)
cpth = os.path.join(newpth, 'P07_2K')
mf.model_ws = cpth
mf.write_input()
if ismf2k is not None:
success, buff = mf.run_model(silent=True)
assert success, '{} did not run'.format(mf.name)
namfile = 'p7mt.nam'
mt = flopy.mt3d.mt.Mt3dms.load(namfile, model_ws=pth, verbose=True,
exe_name=mt3d_exe)
mt.model_ws = cpth
ftlfile = 'p7.ftl'
mt.ftlfilename = ftlfile
mt.write_input()
if ismt3d is not None and ismf2k is not None:
success, buff = mt.run_model(silent=False,
normal_msg='program completed.')
assert success, '{} did not run'.format(mt.name)
os.remove(os.path.join(cpth, ftlfile))
return
def test_mf2000_HSSTest():
pth = os.path.join(pth2000, 'HSSTest')
namfile = 'hsstest_mf2k.nam'
mf = flopy.modflow.Modflow.load(namfile, model_ws=pth,
version='mf2k', verbose=True,
exe_name=mf2k_exe)
cpth = os.path.join(newpth, 'HSSTest')
mf.model_ws = cpth
mf.write_input()
if ismf2k is not None:
success, buff = mf.run_model(silent=True)
assert success, '{} did not run'.format(mf.name)
namfile = 'hsstest_mt.nam'
mt = flopy.mt3d.mt.Mt3dms.load(namfile, model_ws=pth, verbose=True,
exe_name=mt3d_exe)
mt.model_ws = cpth
ftlfile = 'hsstest.FTL'
mt.ftlfilename = ftlfile
mt.write_input()
if ismt3d is not None and ismf2k is not None:
success, buff = mt.run_model(silent=False,
normal_msg='program completed.')
assert success, '{} did not run'.format(mt.name)
os.remove(os.path.join(cpth, ftlfile))
return
# cannot run this model because it uses mnw1 and there is no load for mnw1
# this model includes block format data in the btn file
def test_mf2000_mnw():
pth = os.path.join(pth2000, 'mnw')
namfile = 't5mf2k.nam'
mf = flopy.modflow.Modflow.load(namfile, model_ws=pth, verbose=True)
cpth = os.path.join(newpth, 'MNW')
mf.model_ws = cpth
namfile = 't5mt.nam'
mt = flopy.mt3d.mt.Mt3dms.load(namfile, model_ws=pth, verbose=True)
mt.change_model_ws(cpth)
mt.write_input()
return
def test_mf2000_MultiDiffusion():
pth = os.path.join(pth2000, 'MultiDiffusion')
namfile = 'p7mf2k.nam'
mf = flopy.modflow.Modflow.load(namfile, model_ws=pth,
version='mf2k', verbose=True,
exe_name=mf2k_exe)
cpth = os.path.join(newpth, 'MultiDiffusion')
mf.model_ws = cpth
mf.write_input()
if ismf2k is not None:
success, buff = mf.run_model(silent=True)
assert success, '{} did not run'.format(mf.name)
namfile = 'P7MT.NAM'
mt = flopy.mt3d.mt.Mt3dms.load(namfile, model_ws=pth, verbose=True,
exe_name=mt3d_exe)
mt.model_ws = cpth
ftlfile = 'p7.ftl'
mt.ftlfilename = ftlfile
mt.write_input()
if ismt3d is not None and ismf2k is not None:
success, buff = mt.run_model(silent=False,
normal_msg='program completed.')
assert success, '{} did not run'.format(mt.name)
os.remove(os.path.join(cpth, ftlfile))
return
def test_mf2000_reinject():
pth = os.path.join(pth2000, 'reinject')
namfile = 'p3mf2k.nam'
mf = flopy.modflow.Modflow.load(namfile, model_ws=pth,
version='mf2k', verbose=True,
exe_name=mf2k_exe)
cpth = os.path.join(newpth, 'reinject')
mf.model_ws = cpth
mf.write_input()
if ismf2k is not None:
success, buff = mf.run_model(silent=True)
assert success, '{} did not run'.format(mf.name)
namfile = 'p3mt.nam'
mt = flopy.mt3d.mt.Mt3dms.load(namfile, model_ws=pth, verbose=True,
exe_name=mt3d_exe)
mt.model_ws = cpth
ftlfile = 'p3.ftl'
mt.ftlfilename = ftlfile
mt.write_input()
if ismt3d is not None and ismf2k is not None:
success, buff = mt.run_model(silent=False,
normal_msg='program completed.')
assert success, '{} did not run'.format(mt.name)
os.remove(os.path.join(cpth, ftlfile))
return
def test_mf2000_SState():
pth = os.path.join(pth2000, 'SState')
namfile = 'SState_mf2k.nam'
mf = flopy.modflow.Modflow.load(namfile, model_ws=pth,
version='mf2k', verbose=True,
exe_name=mf2k_exe)
cpth = os.path.join(newpth, 'SState')
mf.model_ws = cpth
mf.write_input()
if ismf2k is not None:
success, buff = mf.run_model(silent=True)
assert success, '{} did not run'.format(mf.name)
namfile = 'SState_mt.nam'
mt = flopy.mt3d.mt.Mt3dms.load(namfile, model_ws=pth, verbose=True,
exe_name=mt3d_exe)
mt.model_ws = cpth
ftlfile = 'SState.ftl'
mt.ftlfilename = ftlfile
mt.write_input()
if ismt3d is not None and ismf2k is not None:
success, buff = mt.run_model(silent=False,
normal_msg='program completed.')
assert success, '{} did not run'.format(mt.name)
os.remove(os.path.join(cpth, ftlfile))
return
def test_mf2000_tob():
pth = os.path.join(pth2000, 'tob')
namfile = 'p7mf2k.nam'
mf = flopy.modflow.Modflow.load(namfile, model_ws=pth,
version='mf2k', verbose=True,
exe_name=mf2k_exe)
cpth = os.path.join(newpth, 'tob')
mf.model_ws = cpth
mf.lmt6.output_file_header = 'extended'
mf.lmt6.output_file_format = 'formatted'
mf.write_input()
if ismf2k is not None:
success, buff = mf.run_model(silent=True)
assert success, '{} did not run'.format(mf.name)
namfile = 'p7mt.nam'
mt = flopy.mt3d.mt.Mt3dms.load(namfile, model_ws=pth, verbose=True,
exe_name=mt3d_exe, forgive=True)
mt.model_ws = cpth
ftlfile = 'p7.ftl'
mt.ftlfilename = ftlfile
mt.write_input()
if ismt3d is not None and ismf2k is not None:
success, buff = mt.run_model(silent=False,
normal_msg='program completed.')
assert success, '{} did not run'.format(mt.name)
os.remove(os.path.join(cpth, ftlfile))
return
def test_mf2000_zeroth():
pth = os.path.join(pth2000, 'zeroth')
namfile = 'z0mf2k.nam'
mf = flopy.modflow.Modflow.load(namfile, model_ws=pth,
version='mf2k', verbose=True,
exe_name=mf2k_exe)
cpth = os.path.join(newpth, 'zeroth')
mf.model_ws = cpth
mf.write_input()
if ismf2k is not None:
success, buff = mf.run_model(silent=True)
assert success, '{} did not run'.format(mf.name)
namfile = 'z0mt.nam'
mt = flopy.mt3d.mt.Mt3dms.load(namfile, model_ws=pth, verbose=True,
exe_name=mt3d_exe)
mt.model_ws = cpth
ftlfile = 'zeroth.ftl'
mt.ftlfilename = ftlfile
mt.write_input()
if ismt3d is not None and ismf2k is not None:
success, buff = mt.run_model(silent=False,
normal_msg='program completed.')
assert success, '{} did not run'.format(mt.name)
os.remove(os.path.join(cpth, ftlfile))
return
def test_mfnwt_CrnkNic():
# fix for CI failures on GitHub actions - remove once fixed in MT3D-USGS
runTest = True
if 'CI' in os.environ:
if sys.platform.lower() in ("win32", "darwin"):
runTest = False
if runTest:
pth = os.path.join(pthNWT, 'sft_crnkNic')
namefile = 'CrnkNic.nam'
mf = flopy.modflow.Modflow.load(namefile, model_ws=pth,
version='mfnwt', verbose=True,
exe_name=mfnwt_exe)
cpth = os.path.join(newpth, 'SFT_CRNKNIC')
mf.model_ws = cpth
mf.write_input()
if ismfnwt is not None:
success, buff = mf.run_model(silent=False)
assert success, '{} did not run'.format(mf.name)
namefile = 'CrnkNic.mtnam'
mt = flopy.mt3d.mt.Mt3dms.load(namefile, model_ws=pth, verbose=True,
version='mt3d-usgs',
exe_name=mt3d_usgs_exe)
mt.model_ws = cpth
ftlfile = 'CrnkNic.ftl'
mt.ftlfilename = ftlfile
mt.ftlfree = True
mt.write_input()
if ismt3dusgs is not None and ismfnwt is not None:
success, buff = mt.run_model(silent=False,
normal_msg='program completed.')
assert success, '{} did not run'.format(mt.name)
os.remove(os.path.join(cpth, ftlfile))
return
def test_mfnwt_LKT():
pth = os.path.join(pthNWT, 'lkt')
namefile = 'lkt_mf.nam'
mf = flopy.modflow.Modflow.load(namefile, model_ws=pth,
version='mfnwt', verbose=True,
forgive=False,
exe_name=mfnwt_exe)
assert not mf.load_fail, 'MODFLOW model did not load'
cpth = os.path.join(newpth, 'LKT')
mf.model_ws = cpth
# write modflow-nwt files
mf.write_input()
success = False
if ismfnwt is not None:
success, buff = mf.run_model(silent=False)
assert success, '{} did not run'.format(mf.name)
namefile = 'lkt_mt.nam'
mt = flopy.mt3d.mt.Mt3dms.load(namefile, model_ws=pth, verbose=True,
version='mt3d-usgs', exe_name=mt3d_usgs_exe,
modflowmodel=mf)
mt.model_ws = cpth
ftlfile = 'lkt.ftl'
mt.ftlfilename = ftlfile
mt.ftlfree = True
# write mt3d files
mt.write_input()
if ismt3dusgs is not None and ismfnwt is not None and success:
success, buff = mt.run_model(silent=False,
normal_msg='program completed.')
assert success, '{} did not run'.format(mt.name)
os.remove(os.path.join(cpth, ftlfile))
return
def test_mfnwt_keat_uzf():
pth = os.path.join(pthNWT, 'keat_uzf')
namefile = 'Keat_UZF_mf.nam'
mf = flopy.modflow.Modflow.load(namefile, model_ws=pth,
version='mfnwt', verbose=True,
exe_name=mfnwt_exe)
cpth = os.path.join(newpth, 'KEAT_UZF')
mf.model_ws = cpth
mf.write_input()
if ismfnwt is not None:
success, buff = mf.run_model(silent=True)
assert success, '{} did not run'.format(mf.name)
namefile = 'Keat_UZF_mt.nam'
mt = flopy.mt3d.mt.Mt3dms.load(namefile, model_ws=pth, verbose=True,
version='mt3d-usgs', exe_name=mt3d_usgs_exe)
# Check a few options specified as optional keywords on line 3
assert mt.btn.DRYCell is True
assert mt.btn.Legacy99Stor is False
assert mt.btn.MFStyleArr is True
assert mt.btn.AltWTSorb is False
mt.model_ws = cpth
ftlfile = 'Keat_UZF.ftl'
mt.ftlfilename = ftlfile
mt.ftlfree = True
mt.write_input()
if ismt3dusgs is not None and ismfnwt is not None:
success, buff = mt.run_model(silent=False,
normal_msg='program completed.')
assert success, '{} did not run'.format(mt.name)
os.remove(os.path.join(cpth, ftlfile))
return
if __name__ == '__main__':
# test_mf2000_mnw()
# test_mf2005_p07()
# test_mf2000_p07()
# test_mf2000_HSSTest()
# test_mf2000_MultiDiffusion()
# test_mf2000_reinject()
# test_mf2000_SState()
# test_mf2000_tob()
# test_mf2000_zeroth()
test_mfnwt_CrnkNic()
# test_mfnwt_LKT()
# test_mfnwt_keat_uzf()
| 33.477647
| 79
| 0.586379
| 1,843
| 14,228
| 4.393923
| 0.091698
| 0.041492
| 0.049395
| 0.051618
| 0.783527
| 0.73796
| 0.733514
| 0.725488
| 0.722154
| 0.721166
| 0
| 0.031319
| 0.304329
| 14,228
| 424
| 80
| 33.556604
| 0.786826
| 0.047301
| 0
| 0.664634
| 0
| 0
| 0.091245
| 0
| 0
| 0
| 0
| 0
| 0.094512
| 1
| 0.036585
| false
| 0
| 0.009146
| 0
| 0.082317
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
861c79202d3018270e1e3c3e4e86d5e7b41a9e20
| 42
|
py
|
Python
|
src/ekpmeasure/control/instruments/tektronix3252/__init__.py
|
cjfinnell/ekpmeasure
|
e6611c053cad28e06f4f8a94764ebe3805cddb15
|
[
"MIT"
] | null | null | null |
src/ekpmeasure/control/instruments/tektronix3252/__init__.py
|
cjfinnell/ekpmeasure
|
e6611c053cad28e06f4f8a94764ebe3805cddb15
|
[
"MIT"
] | null | null | null |
src/ekpmeasure/control/instruments/tektronix3252/__init__.py
|
cjfinnell/ekpmeasure
|
e6611c053cad28e06f4f8a94764ebe3805cddb15
|
[
"MIT"
] | null | null | null |
from .core import *
from ._utils import *
| 14
| 21
| 0.714286
| 6
| 42
| 4.833333
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.190476
| 42
| 2
| 22
| 21
| 0.852941
| 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
| 0
| 0
|
0
| 5
|
864f345087aa477a3bbf139a5b5e9d286a743963
| 206
|
py
|
Python
|
src/grokcore/view/tests/base/view/templatedirectory_with_path_sep_fixture.py
|
zopefoundation/grokcore.view
|
c574c0d041130ac607c95feb610a2b75bfc30abf
|
[
"ZPL-2.1"
] | null | null | null |
src/grokcore/view/tests/base/view/templatedirectory_with_path_sep_fixture.py
|
zopefoundation/grokcore.view
|
c574c0d041130ac607c95feb610a2b75bfc30abf
|
[
"ZPL-2.1"
] | 8
|
2016-02-02T13:42:20.000Z
|
2022-02-16T07:06:52.000Z
|
src/grokcore/view/tests/base/view/templatedirectory_with_path_sep_fixture.py
|
zopefoundation/grokcore.view
|
c574c0d041130ac607c95feb610a2b75bfc30abf
|
[
"ZPL-2.1"
] | 5
|
2015-04-03T05:01:45.000Z
|
2018-06-13T08:41:30.000Z
|
"""
This should fail because you can not use path separator in templatedir
directive.
"""
import grokcore.view as grok
import os.path
grok.templatedir('templatedirectoryname' + os.path.sep + 'subdirname')
| 22.888889
| 70
| 0.771845
| 28
| 206
| 5.678571
| 0.785714
| 0.075472
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.131068
| 206
| 8
| 71
| 25.75
| 0.888268
| 0.393204
| 0
| 0
| 0
| 0
| 0.264957
| 0.179487
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.666667
| 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
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
8676563efd35f8154e999e7ec58bf767944bf5da
| 214
|
py
|
Python
|
poop/hfdp/command/simpleremote/light_off_command.py
|
cassiobotaro/poop
|
fc218fbf638c50da8ea98dab7de26ad2a52e83f5
|
[
"MIT"
] | 37
|
2020-12-27T00:13:07.000Z
|
2022-01-31T19:30:18.000Z
|
poop/hfdp/command/simpleremote/light_off_command.py
|
cassiobotaro/poop
|
fc218fbf638c50da8ea98dab7de26ad2a52e83f5
|
[
"MIT"
] | null | null | null |
poop/hfdp/command/simpleremote/light_off_command.py
|
cassiobotaro/poop
|
fc218fbf638c50da8ea98dab7de26ad2a52e83f5
|
[
"MIT"
] | 7
|
2020-12-26T22:33:47.000Z
|
2021-11-07T01:29:59.000Z
|
from poop.hfdp.command.simpleremote.light import Light
class LightOffCommand:
def __init__(self, light: Light) -> None:
self.__light = light
def execute(self) -> None:
self.__light.off()
| 21.4
| 54
| 0.672897
| 26
| 214
| 5.230769
| 0.576923
| 0.198529
| 0.205882
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.219626
| 214
| 9
| 55
| 23.777778
| 0.814371
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0.166667
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 5
|
86bee91e705904cece9972c4535be3dc34e3f158
| 72
|
py
|
Python
|
tests/integration/__init__.py
|
DataScienceHobbyGroup/nacho-b
|
e4cfc62f2daa45cb939bb544491cdb1c1a7294ef
|
[
"MIT"
] | null | null | null |
tests/integration/__init__.py
|
DataScienceHobbyGroup/nacho-b
|
e4cfc62f2daa45cb939bb544491cdb1c1a7294ef
|
[
"MIT"
] | 1
|
2021-04-30T22:09:21.000Z
|
2021-04-30T22:09:21.000Z
|
tests/integration/__init__.py
|
DataScienceHobbyGroup/nacho-b
|
e4cfc62f2daa45cb939bb544491cdb1c1a7294ef
|
[
"MIT"
] | null | null | null |
"""
TODO: Add description.
Date: 2021-05-27
Author: Vitali Lupusor
"""
| 10.285714
| 22
| 0.680556
| 10
| 72
| 4.9
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.131148
| 0.152778
| 72
| 6
| 23
| 12
| 0.672131
| 0.875
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0.166667
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 0
| 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
| 5
|
86e47d1023749a947a6d43382a8b11cc309ebba4
| 95
|
py
|
Python
|
Language Proficiency (Python)/ginortS.py
|
Muntaha-Islam0019/HackerRank-Solutions
|
caa687aab67461aba69026d3bdc44b62c1dec1c9
|
[
"MIT"
] | null | null | null |
Language Proficiency (Python)/ginortS.py
|
Muntaha-Islam0019/HackerRank-Solutions
|
caa687aab67461aba69026d3bdc44b62c1dec1c9
|
[
"MIT"
] | null | null | null |
Language Proficiency (Python)/ginortS.py
|
Muntaha-Islam0019/HackerRank-Solutions
|
caa687aab67461aba69026d3bdc44b62c1dec1c9
|
[
"MIT"
] | null | null | null |
import string
print(*sorted(input(), key=(string.ascii_letters + '1357902468').index), sep='')
| 31.666667
| 80
| 0.715789
| 12
| 95
| 5.583333
| 0.916667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.113636
| 0.073684
| 95
| 2
| 81
| 47.5
| 0.647727
| 0
| 0
| 0
| 0
| 0
| 0.105263
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0.5
| 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
| 0
| 1
| 0
| 0
| 1
|
0
| 5
|
86f6e059547de8768008c98f04bc75f520548260
| 128
|
py
|
Python
|
skimage/filters/rank/tests/__init__.py
|
thewtex/scikit-image
|
22bb6b94698b8889cbdf26b25d9e4fdb8b968d97
|
[
"BSD-3-Clause"
] | 5
|
2022-01-05T00:41:46.000Z
|
2022-03-21T07:22:58.000Z
|
skimage/filters/rank/tests/__init__.py
|
thewtex/scikit-image
|
22bb6b94698b8889cbdf26b25d9e4fdb8b968d97
|
[
"BSD-3-Clause"
] | 30
|
2020-04-15T19:37:40.000Z
|
2020-04-22T21:19:35.000Z
|
skimage/filters/rank/tests/__init__.py
|
thewtex/scikit-image
|
22bb6b94698b8889cbdf26b25d9e4fdb8b968d97
|
[
"BSD-3-Clause"
] | 20
|
2021-11-07T13:55:56.000Z
|
2021-12-02T10:54:01.000Z
|
from ...._shared.testing import setup_test, teardown_test
def setup():
setup_test()
def teardown():
teardown_test()
| 12.8
| 57
| 0.695313
| 16
| 128
| 5.25
| 0.5
| 0.214286
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.179688
| 128
| 9
| 58
| 14.222222
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.4
| true
| 0
| 0.2
| 0
| 0.6
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
813fb3904d56b7b51a21d2597806e43861a00f8a
| 472
|
py
|
Python
|
cloudless/providers/aws_mock/__init__.py
|
SYU15/cloudless
|
4a68c6d0b29dee997aadc89136918d75f374635f
|
[
"Apache-2.0"
] | null | null | null |
cloudless/providers/aws_mock/__init__.py
|
SYU15/cloudless
|
4a68c6d0b29dee997aadc89136918d75f374635f
|
[
"Apache-2.0"
] | null | null | null |
cloudless/providers/aws_mock/__init__.py
|
SYU15/cloudless
|
4a68c6d0b29dee997aadc89136918d75f374635f
|
[
"Apache-2.0"
] | null | null | null |
"""
The Mock AWS provider will provision resources using a library called `moto`, which is a mock client
for Amazon Web Services. This means that no resources will get provisioned, but cloudless will see
what you create for the duration of your session.
You should not use this directly, but instead pass in the string "mock-aws" as the "provider" in the
top level `cloudless.Client` object.
"""
from cloudless.providers.aws_mock import (network, service, paths, image)
| 47.2
| 100
| 0.779661
| 76
| 472
| 4.828947
| 0.710526
| 0.038147
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.158898
| 472
| 9
| 101
| 52.444444
| 0.924433
| 0.824153
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 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
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
d4ec94e07f6b492c5816a95f3f4b265e263dc30b
| 53
|
py
|
Python
|
examples/hello_world/hello.py
|
syegulalp/pypacker
|
6b8da415747e0cc1eb6f2af70bf005c911d15620
|
[
"MIT"
] | 8
|
2021-10-19T12:59:54.000Z
|
2022-03-15T18:37:32.000Z
|
examples/hello_world/hello.py
|
syegulalp/pypacker
|
6b8da415747e0cc1eb6f2af70bf005c911d15620
|
[
"MIT"
] | 1
|
2022-03-15T01:13:12.000Z
|
2022-03-18T13:58:50.000Z
|
examples/hello_world/hello.py
|
syegulalp/pypacker
|
6b8da415747e0cc1eb6f2af70bf005c911d15620
|
[
"MIT"
] | 2
|
2021-12-25T00:10:59.000Z
|
2022-02-09T04:29:53.000Z
|
print("Hello world")
input("Press enter to continue")
| 26.5
| 32
| 0.754717
| 8
| 53
| 5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.09434
| 53
| 2
| 32
| 26.5
| 0.833333
| 0
| 0
| 0
| 0
| 0
| 0.62963
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 0.5
| 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
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 5
|
d4f5bf2f5d934f120c8dc4bba933acee387e133c
| 26,577
|
py
|
Python
|
webmd/webmd/spiders/webmd_spider.py
|
27pirateking/panacea-scraper
|
19c6876eff4fe709fde40f9673156d39f7efd340
|
[
"MIT"
] | null | null | null |
webmd/webmd/spiders/webmd_spider.py
|
27pirateking/panacea-scraper
|
19c6876eff4fe709fde40f9673156d39f7efd340
|
[
"MIT"
] | null | null | null |
webmd/webmd/spiders/webmd_spider.py
|
27pirateking/panacea-scraper
|
19c6876eff4fe709fde40f9673156d39f7efd340
|
[
"MIT"
] | null | null | null |
from scrapy import Spider, Request
from scrapy.selector import Selector
from webmd.items import WebmdItem
import urllib
import re
headers = {'User-Agent': 'Chrome/56.0.2924.87', 'enc_data': 'OXYIMo2UzzqFUzYszFv4lWP6aDP0r+h4AOC2fYVQIl8=', 'timestamp': 'Thu, 09 Feb 2017 02:11:34 GMT', 'client_id': '3454df96-c7a5-47bb-a74e-890fb3c30a0d'}
class WebmdSpider(Spider):
name = "webmd_spider"
allowed_urls = ['http://www.webmd.com/']
start_urls = ['http://www.webmd.com/drugs/index-drugs.aspx?show=conditions']
def parse(self, response):
# follow links to next alphabet page
atoz = response.xpath('//*[@id="drugs_view"]/li/a/@href').extract()
print("parsing...")
for i in range(2, len(atoz)):
yield Request(response.urljoin(atoz[i]), callback = self.parse_az, dont_filter= True)
def parse_az(self, response):
# follow links to condition
Aa = response.xpath('//*[@id="showAsubNav"]/ul/li').extract()
print("selecting alphabet...")
for i in range(len(Aa)):
yield Request(response.urljoin(response.xpath('//*[@id="showAsubNav"]/ul/li//a/@href').extract()[i]), \
callback = self.parse_condition,\
dont_filter= True)
def parse_condition(self, response):
# follow links to drugs
table = response.xpath('//*[@id="az-box"]/div//a').extract()
print("scraping condition and following link to drugs...")
for i in range(len(table)):
Condition = response.xpath('//*[@id="az-box"]/div//a/text()').extract()[i]
yield Request(response.urljoin(response.xpath('//*[@id="az-box"]/div//a/@href').extract()[i]), \
callback = self.parse_drug, meta = {'Condition' : Condition},\
dont_filter= True)
def parse_drug(self, response):
# following links to drug details
Condition = response.meta['Condition']
print("scraping drug info and following link to details...")
if re.search('Please select a condition below to view a list', response.body):
yield Request(response.urljoin(response.xpath('//*[@id="fdbSearchResults"]/ul/li[1]/a//@href').extract()[0]),\
callback = self.parse_drug, meta = {'Condition': Condition},\
dont_filter= True)
else:
rows = response.xpath('//*[@id="vit_drugsContent"]/div/div/table[2]/tr').extract()
for i in range(len(rows)):
Drug = response.xpath('//*[@id="vit_drugsContent"]/div/div/table[2]/tr/td[1]/a/text()').extract()[i]
Indication = response.xpath('//*[@id="vit_drugsContent"]/div/div/table[2]/tr/td[2]/@class').extract()[i].replace('drug_ind_fmt', '')
Type = response.xpath('//*[@id="vit_drugsContent"]/div/div/table[2]/tr/td[3]/@class').extract()[i].replace('drug_type_fmt', '')
Review = response.xpath('//*[@id="vit_drugsContent"]/div/div/table[2]/tr/td[4]/a/text()').extract()[i].replace('\r\n', '')
aspx_index = response.xpath('//*[@id="vit_drugsContent"]/div/div/table[2]/tr/td[1]/a/@href').extract()[i].find('aspx') + 4
yield Request(response.urljoin(response.xpath('//*[@id="vit_drugsContent"]/div/div/table[2]/tr/td[1]/a//@href').extract()[i][:aspx_index]),\
callback = self.parse_details, meta = {'Condition': Condition, 'Drug': Drug, 'Indication': Indication, 'Type': Type, 'Review': Review},\
dont_filter= True)
def parse_details(self, response):
Condition = response.meta['Condition']
Drug = response.meta['Drug']
Indication = response.meta['Indication']
Type = response.meta['Type']
Review = response.meta['Review']
print("scraping details and following link to contraindications...")
if re.search('The medication you searched for has more', response.body):
yield Request(response.urljoin(response.xpath('//*[@id="ContentPane28"]/div/section/p[1]/a//@href').extract()[0]), \
callback = self.parse_details, meta = {'Condition': Condition, 'Drug': Drug, 'Indication': Indication, 'Type': Type, 'Review': Review},\
dont_filter= True)
else:
Use = ' '.join(response.xpath('//*[@id="ContentPane28"]/div/div/div/div[3]/div[1]/div[1]/h3/preceding-sibling::p//text()').extract())
HowtoUse = ' '.join(response.xpath('//*[@id="ContentPane28"]/div/div/div/div[3]/div[1]/div[1]/h3/following-sibling::p//text()').extract())
Sides = ' '.join(response.xpath('//*[@id="ContentPane28"]/div/div/div/div[3]/div[2]/div/p[1]//text()').extract()).replace('\r\n', '')
Precautions = ' '.join(response.xpath('//*[@id="ContentPane28"]/div/div/div/div[3]/div[3]/div/p//text()').extract())
Interactions = ' '.join(response.xpath('//*[@id="ContentPane28"]/div/div/div/div[3]/div[4]/div[1]/p[2]//text()').extract())
revurl = response.xpath('//*[@id="ContentPane28"]/div/div/div/div[2]/nav/ul/li[7]/a//@href').extract()[0]
if re.search('(rx/)(\d+)',response.xpath('//*[@id="ContentPane28"]/div/div/div/div[4]/div[1]/div/a/@href').extract()[0]):
priceid = re.search('(rx/)(\d+)',response.xpath('//*[@id="ContentPane28"]/div/div/div/div[4]/div[1]/div/a/@href').extract()[0]).group(2)
else:
priceid = ''
if not Use:
Use = ' '
if not Sides:
Sides = ' '
if not Interactions:
Interactions = ' '
if not Precautions:
Precautions = ' '
if not HowtoUse:
HowtoUse = ' '
if re.search('COMMON BRAND NAME', response.body):
BrandName = ', '.join(response.xpath('//*[@id="ContentPane28"]/div/header/section/section[1]/p/a/text()').extract())
GenName = response.xpath('//*[@id="ContentPane28"]/div/header/section/section[2]/p/text()').extract()[0]
if not BrandName:
BrandName = ' '
if not GenName:
GenName = ' '
elif re.search('GENERIC NAME', response.body):
BrandName = ' '
GenName = response.xpath('//*[@id="ContentPane28"]/div/header/section/section[1]/p/text()').extract()[0]
if not GenName:
GenName = ' '
else:
GenName = ' '
BrandName = ' '
yield Request(response.urljoin(response.url + '/list-contraindications'),\
callback = self.parse_avoid, meta = {'Condition': Condition, 'Drug': Drug, 'Indication': Indication, 'Type': Type, 'Review': Review,\
'Use': Use, \
'HowtoUse': HowtoUse, \
'Sides': Sides,\
'Precautions': Precautions,\
'Interactions': Interactions,\
'BrandName': BrandName,\
'GenName': GenName,\
'revurl': revurl,\
'priceid': priceid},\
dont_filter= True)
def parse_avoid(self, response):
Condition = response.meta['Condition']
Drug = response.meta['Drug']
Indication = response.meta['Indication']
Type = response.meta['Type']
Review = response.meta['Review']
Use = response.meta['Use']
HowtoUse = response.meta['HowtoUse']
Sides = response.meta['Sides']
Precautions = response.meta['Precautions']
Interactions = response.meta['Interactions']
BrandName = response.meta['BrandName']
GenName = response.meta['GenName']
revurl = response.meta['revurl']
priceid = response.meta['priceid']
print("scraping avoid use cases...")
if re.search("We\'re sorry, but we couldn\'t find the page you tried", response.body):
AvoidUse = ' '
Allergies = ' '
elif re.search('Conditions:', response.body):
AvoidUse = ' '.join(response.xpath('//*[@id="ContentPane28"]/div/article/section/p[2]/text()').extract())
Allergies = ' '.join(response.xpath('//*[@id="ContentPane28"]/div/article/section/p[3]/text()').extract())
elif re.search('Allergies:', response.body):
AvoidUse = ' '
Allergies = ' '.join(response.xpath('//*[@id="ContentPane28"]/div/article/section/p[2]/text()').extract())
else:
AvoidUse = ' '
Allergies = ' '
if not AvoidUse:
AvoidUse = ' '
if not Allergies:
Allergies = ' '
yield Request(response.urljoin(revurl), \
callback=self.parse_reviews,
meta={'Condition': Condition, 'Drug': Drug, 'Indication': Indication, 'Type': Type,
'Review': Review, \
'Use': Use, \
'HowtoUse': HowtoUse, \
'Sides': Sides, \
'Precautions': Precautions, \
'Interactions': Interactions, \
'BrandName': BrandName, \
'GenName': GenName, \
'AvoidUse': AvoidUse,\
'Allergies': Allergies,\
'priceid': priceid}, \
dont_filter=True)
def parse_reviews(self, response):
Condition = response.meta['Condition']
Drug = response.meta['Drug']
Indication = response.meta['Indication']
Type = response.meta['Type']
Review = response.meta['Review']
Use = response.meta['Use']
HowtoUse = response.meta['HowtoUse']
Sides = response.meta['Sides']
Precautions = response.meta['Precautions']
Interactions = response.meta['Interactions']
BrandName = response.meta['BrandName']
GenName = response.meta['GenName']
AvoidUse = response.meta['AvoidUse']
Allergies = response.meta['Allergies']
priceid = response.meta['priceid']
if re.search('Rate this treatment and share your opinion', response.body):
Effectiveness = ' '
EaseofUse = ' '
Satisfaction = ' '
yield Request('http://www.webmd.com/search/2/api/rx/forms/v2/' + priceid, \
method='GET', headers=headers, \
callback=self.parse_prices, \
meta={'Condition': Condition, 'Drug': Drug, 'Indication': Indication, 'Type': Type,
'Review': Review, \
'Use': Use, \
'HowtoUse': HowtoUse, \
'Sides': Sides, \
'Precautions': Precautions, \
'Interactions': Interactions, \
'BrandName': BrandName, \
'GenName': GenName, \
'AvoidUse': AvoidUse, \
'Allergies': Allergies,
'Effectiveness': Effectiveness, \
'EaseofUse': EaseofUse, \
'Satisfaction': Satisfaction}, \
dont_filter=True)
elif re.search('Be the first to share your experience with this treatment', response.body):
Effectiveness = ' '
EaseofUse = ' '
Satisfaction = ' '
yield Request('http://www.webmd.com/search/2/api/rx/forms/v2/' + priceid, \
method='GET', headers=headers, \
callback=self.parse_prices, \
meta={'Condition': Condition, 'Drug': Drug, 'Indication': Indication, 'Type': Type,
'Review': Review, \
'Use': Use, \
'HowtoUse': HowtoUse, \
'Sides': Sides, \
'Precautions': Precautions, \
'Interactions': Interactions, \
'BrandName': BrandName, \
'GenName': GenName, \
'AvoidUse': AvoidUse, \
'Allergies': Allergies,
'Effectiveness': Effectiveness, \
'EaseofUse': EaseofUse, \
'Satisfaction': Satisfaction}, \
dont_filter=True)
else:
url = 'http://www.webmd.com/drugs/service/UserRatingService.asmx/GetUserReviewSummary?repositoryId=1&primaryId=' # 6007&secondaryId=-1&secondaryIdValue='
url2 = '&secondaryId=-1&secondaryIdValue='
id = re.search('(drugid=)(\d+)', response.url).group(2)
id2 = urllib.quote(re.sub("\s+", " ", response.xpath('//option[@value = -1]//text()').extract()[0]).strip())
yield Request(url + id + url2 + id2,\
callback= self.parse_ratings, \
meta={'Condition': Condition, 'Drug': Drug, 'Indication': Indication, 'Type': Type,
'Review': Review, \
'Use': Use, \
'HowtoUse': HowtoUse, \
'Sides': Sides, \
'Precautions': Precautions, \
'Interactions': Interactions, \
'BrandName': BrandName, \
'GenName': GenName, \
'AvoidUse': AvoidUse, \
'Allergies': Allergies, \
'priceid': priceid}, \
dont_filter=True)
def parse_ratings(self, response):
Condition = response.meta['Condition']
Drug = response.meta['Drug']
Indication = response.meta['Indication']
Type = response.meta['Type']
Review = response.meta['Review']
Use = response.meta['Use']
HowtoUse = response.meta['HowtoUse']
Sides = response.meta['Sides']
Precautions = response.meta['Precautions']
Interactions = response.meta['Interactions']
BrandName = response.meta['BrandName']
GenName = response.meta['GenName']
AvoidUse = response.meta['AvoidUse']
Allergies = response.meta['Allergies']
priceid = response.meta['priceid']
if re.search('("xsd:string">)(\d+.\d+)',response.xpath('//*/*').extract()[3]):
Effectiveness = re.search('("xsd:string">)(\d+.\d+)',response.xpath('//*/*').extract()[3]).group(2)
else:
Effectiveness = re.search('("xsd:string">)(\d+)',response.xpath('//*/*').extract()[3]).group(2)
if re.search('("xsd:string">)(\d+.\d+)',response.xpath('//*/*').extract()[4]):
EaseofUse = re.search('("xsd:string">)(\d+.\d+)',response.xpath('//*/*').extract()[4]).group(2)
else:
EaseofUse = re.search('("xsd:string">)(\d+)',response.xpath('//*/*').extract()[4]).group(2)
if re.search('("xsd:string">)(\d+.\d+)',response.xpath('//*/*').extract()[5]):
Satisfaction = re.search('("xsd:string">)(\d+.\d+)',response.xpath('//*/*').extract()[5]).group(2)
else:
Satisfaction = re.search('("xsd:string">)(\d+)',response.xpath('//*/*').extract()[5]).group(2)
if priceid != '':
yield Request('http://www.webmd.com/search/2/api/rx/forms/v2/'+priceid,\
method='GET', headers=headers, \
callback=self.parse_prices, \
meta={'Condition': Condition, 'Drug': Drug, 'Indication': Indication, 'Type': Type,
'Review': Review, \
'Use': Use, \
'HowtoUse': HowtoUse, \
'Sides': Sides, \
'Precautions': Precautions, \
'Interactions': Interactions, \
'BrandName': BrandName, \
'GenName': GenName, \
'AvoidUse': AvoidUse, \
'Allergies': Allergies,
'Effectiveness': Effectiveness,\
'EaseofUse': EaseofUse,\
'Satisfaction': Satisfaction}, \
dont_filter=True)
else:
strength = ' '
form = ' '
val = ' '
EstimatedPrice = ' '
item = WebmdItem()
item['AvoidUse'] = AvoidUse
item['Allergies'] = Allergies
item['Use'] = Use
item['HowtoUse'] = HowtoUse
item['Precautions'] = Precautions
item['Interactions'] = Interactions
item['Sides'] = Sides
item['Condition'] = Condition
item['Drug'] = Drug
item['Indication'] = Indication
item['Type'] = Type
item['Review'] = Review
item['BrandName'] = BrandName
item['GenName'] = GenName
item['Effectiveness'] = Effectiveness
item['EaseofUse'] = EaseofUse
item['Satisfaction'] = Satisfaction
item['EstimatedPrice'] = EstimatedPrice
item['Dosage'] = strength
item['PkgCount'] = val
item['Form'] = form
yield item
def parse_prices(self, response):
Condition = response.meta['Condition']
Drug = response.meta['Drug']
Indication = response.meta['Indication']
Type = response.meta['Type']
Review = response.meta['Review']
Use = response.meta['Use']
HowtoUse = response.meta['HowtoUse']
Sides = response.meta['Sides']
Precautions = response.meta['Precautions']
Interactions = response.meta['Interactions']
BrandName = response.meta['BrandName']
GenName = response.meta['GenName']
AvoidUse = response.meta['AvoidUse']
Allergies = response.meta['Allergies']
Effectiveness = response.meta['Effectiveness']
EaseofUse = response.meta['EaseofUse']
Satisfaction = response.meta['Satisfaction']
if re.search('("NDC":\[")(\d+)', response.body):
if re.search('("value":)(\d+)', response.body).group(2):
ndc = re.search('("NDC":\[")(\d+)', response.body).group(2)
val = re.search('("value":)(\d+)', response.body).group(2)
if re.search('("form":")(\w+)', response.body):
form = re.search('("form":")(\w+)', response.body).group(2)
else:
form = ' '
if re.search('("strength":")(\d+\s+\w+)', response.body):
strength = re.search('("strength":")(\d+\s+\w+)', response.body).group(2)
else:
strength = ' '
urlp = 'http://www.webmd.com/search/2/api/rx/pricing/ndc/'
urlp2 = '00000?lat=40.7466&lng=-73.9098&rad=5&rollup=true&pgroup='
yield Request(urlp + ndc + '/' + val + '/' + urlp2, \
method='GET',
headers=headers,
callback=self.parse_estprice,
meta={'Condition': Condition, 'Drug': Drug, 'Indication': Indication, 'Type': Type,
'Review': Review, \
'Use': Use, \
'HowtoUse': HowtoUse, \
'Sides': Sides, \
'Precautions': Precautions, \
'Interactions': Interactions, \
'BrandName': BrandName, \
'GenName': GenName, \
'AvoidUse': AvoidUse, \
'Allergies': Allergies,
'Effectiveness': Effectiveness, \
'EaseofUse': EaseofUse, \
'Satisfaction': Satisfaction,\
'strength': strength,\
'val': val,\
'form': form}, \
dont_filter=True)
else:
strength = ' '
form = ' '
val= ' '
EstimatedPrice = ' '
item = WebmdItem()
item['AvoidUse'] = AvoidUse
item['Allergies'] = Allergies
item['Use'] = Use
item['HowtoUse'] = HowtoUse
item['Precautions'] = Precautions
item['Interactions'] = Interactions
item['Sides'] = Sides
item['Condition'] = Condition
item['Drug'] = Drug
item['Indication'] = Indication
item['Type'] = Type
item['Review'] = Review
item['BrandName'] = BrandName
item['GenName'] = GenName
item['Effectiveness'] = Effectiveness
item['EaseofUse'] = EaseofUse
item['Satisfaction'] = Satisfaction
item['EstimatedPrice'] = EstimatedPrice
item['Dosage'] = strength
item['PkgCount'] = val
item['Form'] = form
yield item
def parse_estprice(self,response):
Condition = response.meta['Condition']
Drug = response.meta['Drug']
Indication = response.meta['Indication']
Type = response.meta['Type']
Review = response.meta['Review']
Use = response.meta['Use']
HowtoUse = response.meta['HowtoUse']
Sides = response.meta['Sides']
Precautions = response.meta['Precautions']
Interactions = response.meta['Interactions']
BrandName = response.meta['BrandName']
GenName = response.meta['GenName']
AvoidUse = response.meta['AvoidUse']
Allergies = response.meta['Allergies']
Effectiveness = response.meta['Effectiveness']
EaseofUse = response.meta['EaseofUse']
Satisfaction = response.meta['Satisfaction']
strength = response.meta['strength']
val = response.meta['val']
form = response.meta['form']
if re.search('("PharmacyGroupMinPrice":)(\d+.\d+)', response.body):
EstimatedPrice = re.search('("PharmacyGroupMinPrice":)(\d+.\d+)', response.body).group(2)
item = WebmdItem()
item['AvoidUse'] = AvoidUse
item['Allergies'] = Allergies
item['Use'] = Use
item['HowtoUse'] = HowtoUse
item['Precautions'] = Precautions
item['Interactions'] = Interactions
item['Sides'] = Sides
item['Condition'] = Condition
item['Drug'] = Drug
item['Indication'] = Indication
item['Type'] = Type
item['Review'] = Review
item['BrandName'] = BrandName
item['GenName'] = GenName
item['Effectiveness'] = Effectiveness
item['EaseofUse'] = EaseofUse
item['Satisfaction'] = Satisfaction
item['EstimatedPrice'] = EstimatedPrice
item['Dosage'] = strength
item['PkgCount'] = val
item['Form'] = form
yield item
elif re.search('("PharmacyGroupMinPrice":)(\d+)', response.body):
EstimatedPrice = re.search('("PharmacyGroupMinPrice":)(\d+)', response.body).group(2)
item = WebmdItem()
item['AvoidUse'] = AvoidUse
item['Allergies'] = Allergies
item['Use'] = Use
item['HowtoUse'] = HowtoUse
item['Precautions'] = Precautions
item['Interactions'] = Interactions
item['Sides'] = Sides
item['Condition'] = Condition
item['Drug'] = Drug
item['Indication'] = Indication
item['Type'] = Type
item['Review'] = Review
item['BrandName'] = BrandName
item['GenName'] = GenName
item['Effectiveness'] = Effectiveness
item['EaseofUse'] = EaseofUse
item['Satisfaction'] = Satisfaction
item['EstimatedPrice'] = EstimatedPrice
item['Dosage'] = strength
item['PkgCount'] = val
item['Form'] = form
yield item
else:
EstimatedPrice = ' '
item = WebmdItem()
item['AvoidUse'] = AvoidUse
item['Allergies'] = Allergies
item['Use'] = Use
item['HowtoUse'] = HowtoUse
item['Precautions'] = Precautions
item['Interactions'] = Interactions
item['Sides'] = Sides
item['Condition'] = Condition
item['Drug'] = Drug
item['Indication'] = Indication
item['Type'] = Type
item['Review'] = Review
item['BrandName'] = BrandName
item['GenName'] = GenName
item['Effectiveness'] = Effectiveness
item['EaseofUse'] = EaseofUse
item['Satisfaction'] = Satisfaction
item['EstimatedPrice'] = EstimatedPrice
item['Dosage'] = strength
item['PkgCount'] = val
item['Form'] = form
yield item
| 45.664948
| 206
| 0.483877
| 2,256
| 26,577
| 5.676418
| 0.106383
| 0.081524
| 0.033968
| 0.032797
| 0.794081
| 0.769327
| 0.742933
| 0.716149
| 0.681165
| 0.667656
| 0
| 0.011511
| 0.365843
| 26,577
| 582
| 207
| 45.664948
| 0.748309
| 0.005757
| 0
| 0.731006
| 0
| 0.030801
| 0.225747
| 0.080851
| 0
| 0
| 0
| 0
| 0
| 1
| 0.020534
| false
| 0
| 0.010267
| 0
| 0.039014
| 0.01232
| 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
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
be29a53e86adde4c085c3e6261fff6293a949e87
| 3,968
|
py
|
Python
|
model.py
|
mengyuethu/myRL_pytorch
|
870d5a801af85f2d43230f193b95c19dd7bb4ee7
|
[
"MIT"
] | null | null | null |
model.py
|
mengyuethu/myRL_pytorch
|
870d5a801af85f2d43230f193b95c19dd7bb4ee7
|
[
"MIT"
] | null | null | null |
model.py
|
mengyuethu/myRL_pytorch
|
870d5a801af85f2d43230f193b95c19dd7bb4ee7
|
[
"MIT"
] | null | null | null |
import numpy as np
import torch
import torch.nn as nn
class Double_DQN(nn.Module):
def __init__(self, input_img_size, input_channels, output_channels):
super(Double_DQN, self).__init__()
self.input_img_size = input_img_size
self.in_features_linear = int(pow(((((input_img_size - 8)/4+1)-4)/2+1)-2, 2)*64)
self.input_channels = np.array([input_channels, 32, 64, self.in_features_linear, 512])
self.output_channels = np.array([32, 64, 64, 512, output_channels])
self.conv1 = nn.Conv2d(self.input_channels[0], self.output_channels[0], kernel_size=8, stride=4)
self.conv2 = nn.Conv2d(self.input_channels[1], self.output_channels[1], kernel_size=4, stride=2)
self.conv3 = nn.Conv2d(self.input_channels[2], self.output_channels[2], kernel_size=3, stride=1)
self.fc1 = nn.Linear(self.input_channels[3], self.output_channels[3])
self.fc2 = nn.Linear(self.input_channels[4], self.output_channels[4])
def forward(self, x):
x = nn.functional.relu(self.conv1(x))
x = nn.functional.relu(self.conv2(x))
x = nn.functional.relu(self.conv3(x))
x = x.view(-1, self.input_channels[3])
x = nn.functional.relu(self.fc1(x))
x = self.fc2(x)
return x
class Dueling_DQN(nn.Module):
def __init__(self, input_img_size, input_channels, output_channels):
super(Dueling_DQN, self).__init__()
self.input_img_size = input_img_size
self.in_features_linear = int(pow(((((input_img_size - 8)/4+1)-4)/2+1)-2, 2)*64)
self.input_channels = np.array([input_channels, 32, 64, self.in_features_linear, 512])
self.output_channels = np.array([32, 64, 64, 512, output_channels])
self.conv1 = nn.Conv2d(self.input_channels[0], self.output_channels[0], kernel_size=8, stride=4)
self.conv2 = nn.Conv2d(self.input_channels[1], self.output_channels[1], kernel_size=4, stride=2)
self.conv3 = nn.Conv2d(self.input_channels[2], self.output_channels[2], kernel_size=3, stride=1)
self.fc11 = nn.Linear(self.input_channels[3], self.output_channels[3])
self.fc12 = nn.Linear(self.input_channels[3], self.output_channels[3])
self.fc21 = nn.Linear(self.input_channels[4], self.output_channels[4])
self.fc22 = nn.Linear(self.input_channels[4], 1)
def forward(self, x):
x = nn.functional.relu(self.conv1(x))
x = nn.functional.relu(self.conv2(x))
x = nn.functional.relu(self.conv3(x))
x = x.view(-1, self.input_channels[3])
x1 = nn.functional.relu(self.fc11(x))
x2 = nn.functional.relu(self.fc12(x))
x1 = self.fc21(x1)
x2 = self.fc22(x2)
x = x2 + x1 - x1.mean(dim=1).reshape(x2.shape)
return x
class DDPG_Actor(nn.Module):
def __init__(self, state_dim, action_dim, action_max):
super(DDPG_Actor, self).__init__()
self.fc1 = nn.Linear(state_dim, 400)
self.fc2 = nn.Linear(400, 300)
self.fc3 = nn.Linear(300, action_dim)
self.bn1 = nn.BatchNorm1d(state_dim)
self.bn2 = nn.BatchNorm1d(400)
self.bn3 = nn.BatchNorm1d(300)
self.action_max = action_max
def forward(self, s):
x = nn.functional.relu(self.fc1(self.bn1(s)))
x = nn.functional.relu(self.fc2(self.bn2(x)))
x = self.action_max * torch.tanh(self.fc3(self.bn3(x)))
return x
class DDPG_Critic(nn.Module):
def __init__(self, state_dim, action_dim):
super(DDPG_Critic, self).__init__()
self.fc1 = nn.Linear(state_dim, 400 - action_dim)
self.fc2 = nn.Linear(400, 300)
self.fc3 = nn.Linear(300, 1)
self.bn1 = nn.BatchNorm1d(state_dim)
self.bn2 = nn.BatchNorm1d(400 - action_dim)
def forward(self, s, a):
x = nn.functional.relu(self.fc1(self.bn1(s)))
x = nn.functional.relu(self.fc2(torch.cat([self.bn2(x), a], dim=1)))
x = self.fc3(x)
return x
| 41.768421
| 104
| 0.642893
| 615
| 3,968
| 3.949594
| 0.123577
| 0.074105
| 0.11198
| 0.10704
| 0.795389
| 0.795389
| 0.774393
| 0.774393
| 0.774393
| 0.716756
| 0
| 0.06467
| 0.208921
| 3,968
| 94
| 105
| 42.212766
| 0.709143
| 0
| 0
| 0.473684
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.105263
| false
| 0
| 0.039474
| 0
| 0.25
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
be2aa077fccc6bd941001b058be6fa7c9f1d0141
| 1,012
|
py
|
Python
|
input_files/dag_20_input.py
|
harkabeeparolus/kodkalender-2020
|
ad6ca9c6e067ad206c54854771c8c6bb1bf27cfa
|
[
"MIT"
] | null | null | null |
input_files/dag_20_input.py
|
harkabeeparolus/kodkalender-2020
|
ad6ca9c6e067ad206c54854771c8c6bb1bf27cfa
|
[
"MIT"
] | null | null | null |
input_files/dag_20_input.py
|
harkabeeparolus/kodkalender-2020
|
ad6ca9c6e067ad206c54854771c8c6bb1bf27cfa
|
[
"MIT"
] | null | null | null |
instruktioner = ['s', 's', 's', 'w', 's', 's', 'a', 'a', 'w', 's', 's', 's', 'a', 'd', 'd', 's', 'a', 's', 'a', 'd', 'd', 'w', 'a', 'a', 'a', 'w', 'a', 'a', 's', 'w', 's', 's', 'w', 's', 'd', 'd', 'd', 'w', 'd', 's', 's', 's', 'w', 's', 's', 'd', 'w', 's', 'a', 'd', 's', 's', 's', 'd', 'w', 'd', 'a', 's', 'w', 'a', 'd', 's', 'w', 'w', 'a', 'd', 's', 's', 'a', 's', 'a', 'w', 's', 'a', 'd', 'w', 'a', 'd', 'w', 'w', 'd', 's', 'd', 'w', 'd', 'w', 's', 'd', 'd', 'w', 'w', 'w', 's', 's', 'a', 'd', 's', 'd', 'w', 'w', 'd', 'w', 'a', 'w', 'd', 'd', 'd', 'a', 'a', 'w', 's', 'a', 'd', 'a', 'w', 's', 'a', 'w', 's', 'a', 'd', 'w', 'a', 'w', 'a', 'w', 'd', 'a', 'w', 's', 'd', 'w', 'd', 'a', 'a', 'a', 'w', 'd', 'w', 'd', 'a', 'd', 'd', 's', 'd', 'a', 'd', 'w', 'a', 's', 'w', 'w', 'w', 'w', 'w', 'a', 'w', 'a', 'w', 'a', 's', 's', 'a', 'w', 'w', 'a', 'a', 'w', 'w', 'a', 'a', 'a', 'w', 's', 'a', 'd', 'a', 'w', 'a', 'd', 'a', 'd', 'w', 'a', 'w', 's', 'd', 'd', 's', 'd', 'w', 'd', 's', 'w', 'd', 'a', 'd', 'd', 'w']
| 506
| 1,011
| 0.209486
| 200
| 1,012
| 1.06
| 0.025
| 0.169811
| 0.113208
| 0.09434
| 0.5
| 0.207547
| 0.150943
| 0.150943
| 0
| 0
| 0
| 0
| 0.198617
| 1,012
| 1
| 1,012
| 1,012
| 0.261406
| 0
| 0
| 0
| 0
| 0
| 0.19664
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
078b388e0f29d996c31e941e2dcf511a28cd971a
| 59
|
py
|
Python
|
Game21/modules/sprites/__init__.py
|
ttkaixin1998/pikachupythongames
|
609a3a5a2be3f5a187c332c7980bb5bb14548f02
|
[
"MIT"
] | 4,013
|
2018-06-16T08:00:02.000Z
|
2022-03-30T11:48:14.000Z
|
Game21/modules/sprites/__init__.py
|
pigbearcat/Games
|
b8c47ef1bcce9a9db3f3730c162e6e8e08b508a2
|
[
"MIT"
] | 22
|
2018-10-18T00:15:50.000Z
|
2022-01-13T08:16:15.000Z
|
Game21/modules/sprites/__init__.py
|
pigbearcat/Games
|
b8c47ef1bcce9a9db3f3730c162e6e8e08b508a2
|
[
"MIT"
] | 2,172
|
2018-07-20T04:03:14.000Z
|
2022-03-31T14:18:29.000Z
|
'''初始化'''
from .mole import Mole
from .hammer import Hammer
| 19.666667
| 26
| 0.728814
| 9
| 59
| 4.777778
| 0.555556
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.135593
| 59
| 3
| 26
| 19.666667
| 0.843137
| 0.050847
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 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
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
07ac4ca68d139d5d26c9ab59386078c6ca71bf26
| 16,319
|
py
|
Python
|
fastestimator/util/util_test.py
|
rajesh1226/fastestimator
|
0765c7478c0889cf4e2841d51a35c9a06a406472
|
[
"Apache-2.0"
] | 1
|
2019-12-17T22:43:08.000Z
|
2019-12-17T22:43:08.000Z
|
fastestimator/util/util_test.py
|
rajesh1226/fastestimator
|
0765c7478c0889cf4e2841d51a35c9a06a406472
|
[
"Apache-2.0"
] | null | null | null |
fastestimator/util/util_test.py
|
rajesh1226/fastestimator
|
0765c7478c0889cf4e2841d51a35c9a06a406472
|
[
"Apache-2.0"
] | null | null | null |
# Copyright 2019 The FastEstimator Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
import copy
from unittest import TestCase
from fastestimator.cli.cli_util import parse_cli_to_dictionary
from .util import (get_num_devices, parse_string_to_python, prettify_metric_name, remove_blacklist_keys, strip_suffix)
class TestUtil(TestCase):
mock_good_parsed_result = {
"train_loss": [[0, 12.027558], [100, 2.565781], [200, 0.824913], [300, 0.561318], [400, 0.427389],
[500, 0.528405], [600, 0.686736]],
"lr": [[0, 0.0002], [100, 0.0002], [200, 0.0002], [300, 0.0002], [400, 0.0002], [500, 0.0002], [600, 0.0002]],
"example/sec": [[0, 0.0], [100, 44.738688], [200, 45.086421], [300, 44.689092], [400, 44.799198],
[500, 44.523727], [600, 45.055799]],
"val_loss": [[281, 0.725258], [562, 4.125795]],
"min_val_loss": [[281, 0.725258], [562, 0.725258]],
"since_best": [[281, 0.0], [562, 1.0]],
"val_mask_raw_loss": [[281, -0.007752625846316934], [562, -0.15434368319272632]],
"val_image_labels_loss": [[281, 0.7330105359355609], [562, 4.280138591821823]],
"val_mask_raw_conditionalDice": [[281, 0.007752625846316934], [562, 0.15434368319272632]],
"val_image_labels_my_binary_accuracy": [[281, 0.5194444588075081], [562, 0.5662698552840286]]
}
# -------------------------------------------------------------------------------------------------------- #
# ---------------------------------------------- GPU Count ----------------------------------------------- #
# -------------------------------------------------------------------------------------------------------- #
def test_get_num_devices(self):
try:
result = subprocess.run(['nvidia-smi', '-q'], stdout=subprocess.PIPE).stdout.decode('utf-8')
lines = [line.split() for line in result.splitlines() if line.startswith("Attached GPUs")]
devices = int(lines[0][-1])
except:
devices = 1
assert devices == get_num_devices()
# -------------------------------------------------------------------------------------------------------- #
# ------------------------------------------- KEY Blacklisting ------------------------------------------- #
# -------------------------------------------------------------------------------------------------------- #
def test_remove_blacklist_keys_success(self):
expected = {
"train_loss": [[0, 12.027558], [100, 2.565781], [200, 0.824913], [300, 0.561318], [400, 0.427389],
[500, 0.528405], [600, 0.686736]],
"lr": [[0, 0.0002], [100, 0.0002], [200, 0.0002], [300, 0.0002], [400, 0.0002], [500, 0.0002],
[600, 0.0002]],
"val_image_labels_loss": [[281, 0.7330105359355609], [562, 4.280138591821823]]
}
blacklist = [
"val_loss",
"since_best",
"example/sec",
"min_val_loss",
"val_mask_raw_loss",
"val_mask_raw_conditionalDice",
"val_image_labels_my_binary_accuracy"
]
actual = copy.deepcopy(self.mock_good_parsed_result)
remove_blacklist_keys(actual, blacklist)
self.assertDictEqual(actual, expected)
def test_remove_blacklist_keys_none(self):
expected = self.mock_good_parsed_result
blacklist = None
actual = copy.deepcopy(self.mock_good_parsed_result)
remove_blacklist_keys(actual, blacklist)
self.assertDictEqual(actual, expected)
def test_remove_blacklist_keys_empty_list(self):
expected = self.mock_good_parsed_result
blacklist = []
actual = copy.deepcopy(self.mock_good_parsed_result)
remove_blacklist_keys(actual, blacklist)
self.assertDictEqual(actual, expected)
def test_remove_blacklist_keys_empty_set(self):
expected = self.mock_good_parsed_result
blacklist = {}
actual = copy.deepcopy(self.mock_good_parsed_result)
remove_blacklist_keys(actual, blacklist)
self.assertDictEqual(actual, expected)
def test_remove_blacklist_keys_empty(self):
expected = {}
blacklist = ["FAKE_KEY"]
actual = {}
remove_blacklist_keys(actual, blacklist)
self.assertDictEqual(actual, expected)
def test_remove_blacklist_keys_missing(self):
expected = {
"train_loss": [[0, 12.027558], [100, 2.565781], [200, 0.824913], [300, 0.561318], [400, 0.427389],
[500, 0.528405], [600, 0.686736]],
"lr": [[0, 0.0002], [100, 0.0002], [200, 0.0002], [300, 0.0002], [400, 0.0002], [500, 0.0002],
[600, 0.0002]],
"val_image_labels_loss": [[281, 0.7330105359355609], [562, 4.280138591821823]]
}
blacklist = [
"val_loss",
"since_best",
"example/sec",
"min_val_loss",
"val_mask_raw_loss",
"val_mask_raw_conditionalDice",
"val_image_labels_my_binary_accuracy",
"FAKE_KEY"
]
actual = copy.deepcopy(self.mock_good_parsed_result)
remove_blacklist_keys(actual, blacklist)
self.assertDictEqual(actual, expected)
# -------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------ Suffix ------------------------------------------------ #
# -------------------------------------------------------------------------------------------------------- #
def test_strip_suffix(self):
base = "ImageNet.txt"
suffix = ".txt"
expected = "ImageNet"
actual = strip_suffix(base, suffix)
self.assertEqual(actual, expected)
def test_strip_suffix_empty(self):
base = "ImageNet.txt"
suffix = ""
expected = "ImageNet.txt"
actual = strip_suffix(base, suffix)
self.assertEqual(actual, expected)
def test_strip_suffix_none(self):
base = "ImageNet.txt"
suffix = None
expected = "ImageNet.txt"
actual = strip_suffix(base, suffix)
self.assertEqual(actual, expected)
def test_strip_suffix_full(self):
base = "ImageNet.txt"
suffix = "ImageNet.txt"
expected = ""
actual = strip_suffix(base, suffix)
self.assertEqual(actual, expected)
def test_strip_suffix_wrong_suffix(self):
base = "ImageNet.txt"
suffix = ".tzt"
expected = "ImageNet.txt"
actual = strip_suffix(base, suffix)
self.assertEqual(actual, expected)
def test_strip_suffix_super_suffix(self):
base = "ImageNet.txt"
suffix = "MImageNet.txt"
expected = "ImageNet.txt"
actual = strip_suffix(base, suffix)
self.assertEqual(actual, expected)
def test_strip_suffix_empty_base(self):
base = ""
suffix = ".txt"
expected = ""
actual = strip_suffix(base, suffix)
self.assertEqual(actual, expected)
def test_strip_suffix_no_base(self):
base = None
suffix = ".txt"
expected = None
actual = strip_suffix(base, suffix)
self.assertEqual(actual, expected)
# -------------------------------------------------------------------------------------------------------- #
# ----------------------------------------------- Prettify ----------------------------------------------- #
# -------------------------------------------------------------------------------------------------------- #
def test_prettify_metric_name(self):
base = "val_mask_raw_conditionalDice"
expected = "Val Mask Raw Conditional Dice"
actual = prettify_metric_name(base)
self.assertEqual(actual, expected)
# -------------------------------------------------------------------------------------------------------- #
# --------------------------------------------- Parse String --------------------------------------------- #
# -------------------------------------------------------------------------------------------------------- #
def test_parse_string_to_python_none(self):
input_string = None
expected = ""
actual = parse_string_to_python(input_string)
self.assertEqual(actual, expected)
def test_parse_string_to_python(self):
input_string = ""
expected = ""
actual = parse_string_to_python(input_string)
self.assertEqual(actual, expected)
def test_parse_string_to_python_array(self):
input_string = '[]'
expected = []
actual = parse_string_to_python(input_string)
self.assertListEqual(actual, expected)
def test_parse_string_to_python_tuple(self):
input_string = '()'
expected = ()
actual = parse_string_to_python(input_string)
self.assertTupleEqual(actual, expected)
def test_parse_string_to_python_dict(self):
input_string = '{}'
expected = {}
actual = parse_string_to_python(input_string)
self.assertDictEqual(actual, expected)
def test_parse_string_to_python_true(self):
input_string = "true"
expected = True
actual = parse_string_to_python(input_string)
self.assertEqual(actual, expected)
def test_parse_string_to_python_true2(self):
input_string = "True"
expected = True
actual = parse_string_to_python(input_string)
self.assertEqual(actual, expected)
def test_parse_string_to_python_false(self):
input_string = "false"
expected = False
actual = parse_string_to_python(input_string)
self.assertEqual(actual, expected)
def test_parse_string_to_python_false2(self):
input_string = "False"
expected = False
actual = parse_string_to_python(input_string)
self.assertEqual(actual, expected)
def test_parse_string_to_python_boolean_array(self):
input_string = '[true, false, true, false]'
expected = [True, False, True, False]
actual = parse_string_to_python(input_string)
self.assertListEqual(actual, expected)
def test_parse_string_to_python_boolean_array2(self):
input_string = '[True, False, True, False]'
expected = [True, False, True, False]
actual = parse_string_to_python(input_string)
self.assertListEqual(actual, expected)
def test_parse_string_to_python_boolean_tuple(self):
input_string = '(True, False, True, False)'
expected = (True, False, True, False)
actual = parse_string_to_python(input_string)
self.assertTupleEqual(actual, expected)
def test_parse_string_to_python_int(self):
input_string = "7"
expected = 7
actual = parse_string_to_python(input_string)
self.assertEqual(actual, expected)
def test_parse_string_to_python_int_array(self):
input_string = '[0, -2, 4, 8]'
expected = [0, -2, 4, 8]
actual = parse_string_to_python(input_string)
self.assertListEqual(actual, expected)
def test_parse_string_to_python_int_tuple(self):
input_string = '(0, -2, 4, 8)'
expected = (0, -2, 4, 8)
actual = parse_string_to_python(input_string)
self.assertTupleEqual(actual, expected)
def test_parse_string_to_python_float(self):
input_string = "7.5"
expected = 7.5
actual = parse_string_to_python(input_string)
self.assertEqual(actual, expected)
def test_parse_string_to_python_float_array(self):
input_string = '[0.5, -2.1, 4, 8.89]'
expected = [0.5, -2.1, 4, 8.89]
actual = parse_string_to_python(input_string)
self.assertListEqual(actual, expected)
def test_parse_string_to_python_float_tuple(self):
input_string = '(0.5, -2.1, 4, 8.89)'
expected = (0.5, -2.1, 4, 8.89)
actual = parse_string_to_python(input_string)
self.assertTupleEqual(actual, expected)
def test_parse_string_to_python_string(self):
input_string = "random string"
expected = "random string"
actual = parse_string_to_python(input_string)
self.assertEqual(actual, expected)
def test_parse_string_to_python_string_array(self):
input_string = '["random", "string"]'
expected = ['random', 'string']
actual = parse_string_to_python(input_string)
self.assertListEqual(actual, expected)
def test_parse_string_to_python_string_tuple(self):
input_string = '("random", "string")'
expected = ('random', 'string')
actual = parse_string_to_python(input_string)
self.assertTupleEqual(actual, expected)
def test_parse_string_to_python_string_tuple2(self):
input_string = "('random', 'string')"
expected = ('random', 'string')
actual = parse_string_to_python(input_string)
self.assertTupleEqual(actual, expected)
def test_parse_string_to_python_nested(self):
input_string = '("random", ["string1", 10], (True, 7.5, "string2"))'
expected = ('random', ["string1", 10], (True, 7.5, "string2"))
actual = parse_string_to_python(input_string)
self.assertTupleEqual(actual, expected)
def test_parse_string_to_python_string_dict(self):
input_string = '{"key1":"val1","key2":"val2"}'
expected = {"key1": "val1", "key2": "val2"}
actual = parse_string_to_python(input_string)
self.assertDictEqual(actual, expected)
# -------------------------------------------------------------------------------------------------------- #
# ----------------------------------------------- Parse CLI ---------------------------------------------- #
# -------------------------------------------------------------------------------------------------------- #
def test_parse_cli_to_dictionary_none(self):
input_list = None
expected = {}
actual = parse_cli_to_dictionary(input_list)
self.assertDictEqual(actual, expected)
def test_parse_cli_to_dictionary(self):
input_list = []
expected = {}
actual = parse_cli_to_dictionary(input_list)
self.assertDictEqual(actual, expected)
def test_parse_cli_to_dictionary_no_key(self):
input_list = ["thing1", "thing2", "True", "(0,", "1)"]
expected = {}
actual = parse_cli_to_dictionary(input_list)
self.assertDictEqual(actual, expected)
def test_parse_cli_to_dictionary_one_key_string(self):
input_list = ["thing1", "--key1", "True", "(0,", "1)"]
expected = {"key1": 'True(0,1)'}
actual = parse_cli_to_dictionary(input_list)
self.assertDictEqual(actual, expected)
def test_parse_cli_to_dictionary_one_key_string2(self):
input_list = ["--key1", "True"]
expected = {"key1": True}
actual = parse_cli_to_dictionary(input_list)
self.assertDictEqual(actual, expected)
def test_parse_cli_to_dictionary_one_key_tuple(self):
input_list = ["thing1", "--key1", "(0,", "1)"]
expected = {"key1": (0, 1)}
actual = parse_cli_to_dictionary(input_list)
self.assertDictEqual(actual, expected)
def test_parse_cli_to_dictionary_two_key_tuple(self):
input_list = ["--key1", "(0,", "1)", "--key2", "[True,", "False,", "'args']"]
expected = {"key1": (0, 1), "key2": [True, False, 'args']}
actual = parse_cli_to_dictionary(input_list)
self.assertDictEqual(actual, expected)
| 42.497396
| 118
| 0.559654
| 1,719
| 16,319
| 5.013962
| 0.132054
| 0.063813
| 0.073906
| 0.108017
| 0.778861
| 0.756468
| 0.733032
| 0.721197
| 0.71296
| 0.689871
| 0
| 0.066892
| 0.221337
| 16,319
| 383
| 119
| 42.608355
| 0.611395
| 0.157485
| 0
| 0.523333
| 0
| 0
| 0.093597
| 0.02256
| 0
| 0
| 0
| 0
| 0.156667
| 1
| 0.156667
| false
| 0
| 0.013333
| 0
| 0.176667
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
07c880c2634ed3737e2a088598c572507782f3ed
| 3,712
|
py
|
Python
|
silo/benchmarks/results/istc3-8-1-13_compress.py
|
anshsarkar/TailBench
|
25845756aee9a892229c25b681051591c94daafd
|
[
"MIT"
] | 274
|
2015-01-23T16:24:09.000Z
|
2022-02-22T03:16:14.000Z
|
silo/benchmarks/results/istc3-8-1-13_compress.py
|
anshsarkar/TailBench
|
25845756aee9a892229c25b681051591c94daafd
|
[
"MIT"
] | 3
|
2015-03-17T11:52:36.000Z
|
2019-07-22T23:04:25.000Z
|
silo/benchmarks/results/istc3-8-1-13_compress.py
|
anshsarkar/TailBench
|
25845756aee9a892229c25b681051591c94daafd
|
[
"MIT"
] | 94
|
2015-01-07T06:55:36.000Z
|
2022-01-22T08:14:15.000Z
|
RESULTS = [({'scale_factor': 1, 'db': 'ndb-proto2', 'par_load': False, 'threads': 1, 'log_compress': True, 'bench_opts': '', 'log_fake_writes': False, 'retry': False, 'log_nofsync': False, 'name': 'scale_tpcc', 'bench': 'tpcc', 'numa_memory': '4G', 'persist': True}, [(26529.8, 26529.8, 0.0375845, 86.9875, 0.0), (26590.3, 26590.3, 0.0374976, 96.7505, 0.0), (25907.2, 25907.2, 0.0384877, 89.7372, 0.0)]), ({'scale_factor': 4, 'db': 'ndb-proto2', 'par_load': False, 'threads': 4, 'log_compress': True, 'bench_opts': '', 'log_fake_writes': False, 'retry': False, 'log_nofsync': False, 'name': 'scale_tpcc', 'bench': 'tpcc', 'numa_memory': '16G', 'persist': True}, [(94923.0, 94923.0, 0.0420244, 118.105, 3.9137), (96772.3, 96772.3, 0.0412222, 99.4943, 3.66408), (97558.4, 97558.4, 0.040879, 98.773, 3.61314)]), ({'scale_factor': 8, 'db': 'ndb-proto2', 'par_load': False, 'threads': 8, 'log_compress': True, 'bench_opts': '', 'log_fake_writes': False, 'retry': False, 'log_nofsync': False, 'name': 'scale_tpcc', 'bench': 'tpcc', 'numa_memory': '32G', 'persist': True}, [(180752.0, 180752.0, 0.0441143, 155.911, 7.25652), (183287.0, 183287.0, 0.0435169, 135.895, 7.32585), (183258.0, 183258.0, 0.0435234, 196.72, 7.24199)]), ({'scale_factor': 12, 'db': 'ndb-proto2', 'par_load': False, 'threads': 12, 'log_compress': True, 'bench_opts': '', 'log_fake_writes': False, 'retry': False, 'log_nofsync': False, 'name': 'scale_tpcc', 'bench': 'tpcc', 'numa_memory': '48G', 'persist': True}, [(277963.0, 277963.0, 0.0430299, 165.512, 10.3201), (275547.0, 275547.0, 0.0434273, 155.312, 11.0243), (275690.0, 275690.0, 0.0434118, 117.571, 10.8426)]), ({'scale_factor': 16, 'db': 'ndb-proto2', 'par_load': False, 'threads': 16, 'log_compress': True, 'bench_opts': '', 'log_fake_writes': False, 'retry': False, 'log_nofsync': False, 'name': 'scale_tpcc', 'bench': 'tpcc', 'numa_memory': '64G', 'persist': True}, [(363296.0, 363296.0, 0.0439195, 139.954, 13.9389), (365580.0, 365580.0, 0.0436271, 124.046, 14.7982), (367573.0, 367573.0, 0.0433953, 124.731, 14.1017)]), ({'scale_factor': 20, 'db': 'ndb-proto2', 'par_load': False, 'threads': 20, 'log_compress': True, 'bench_opts': '', 'log_fake_writes': False, 'retry': False, 'log_nofsync': False, 'name': 'scale_tpcc', 'bench': 'tpcc', 'numa_memory': '80G', 'persist': True}, [(454856.0, 454856.0, 0.0438369, 148.817, 17.5135), (457039.0, 457039.0, 0.0436352, 164.887, 18.0665), (458381.0, 458381.0, 0.0435093, 111.431, 17.9343)]), ({'scale_factor': 24, 'db': 'ndb-proto2', 'par_load': False, 'threads': 24, 'log_compress': True, 'bench_opts': '', 'log_fake_writes': False, 'retry': False, 'log_nofsync': False, 'name': 'scale_tpcc', 'bench': 'tpcc', 'numa_memory': '96G', 'persist': True}, [(543068.0, 543068.0, 0.0440688, 135.805, 19.8644), (547803.0, 547803.0, 0.0436806, 225.355, 21.0608), (547088.0, 547088.0, 0.043648, 175.741, 21.4165)]), ({'scale_factor': 28, 'db': 'ndb-proto2', 'par_load': False, 'threads': 28, 'log_compress': True, 'bench_opts': '', 'log_fake_writes': False, 'retry': False, 'log_nofsync': False, 'name': 'scale_tpcc', 'bench': 'tpcc', 'numa_memory': '112G', 'persist': True}, [(627272.0, 627272.0, 0.0445038, 143.691, 25.3725), (627820.0, 627820.0, 0.0444822, 115.447, 24.7481), (626374.0, 626374.0, 0.0445666, 139.218, 23.8384)]), ({'scale_factor': 32, 'db': 'ndb-proto2', 'par_load': False, 'threads': 32, 'log_compress': True, 'bench_opts': '', 'log_fake_writes': False, 'retry': False, 'log_nofsync': False, 'name': 'scale_tpcc', 'bench': 'tpcc', 'numa_memory': '128G', 'persist': True}, [(668864.0, 668864.0, 0.0476782, 298.516, 24.8858), (659859.0, 659859.0, 0.0483394, 298.576, 24.3908), (654704.0, 654704.0, 0.0487128, 326.656, 25.9193)])]
| 1,856
| 3,711
| 0.644935
| 586
| 3,712
| 3.947099
| 0.312287
| 0.021617
| 0.042802
| 0.054475
| 0.501946
| 0.501946
| 0.501946
| 0.385214
| 0.385214
| 0.385214
| 0
| 0.283448
| 0.099946
| 3,712
| 1
| 3,712
| 3,712
| 0.40886
| 0
| 0
| 0
| 0
| 0
| 0.330011
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
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| 0
| 0
| 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
| 0
| 0
| 0
| 0
|
0
| 5
|
07d862d023abe68c0a3a2328cad0b4ac0de855f1
| 183
|
py
|
Python
|
src/elementary_flask/components/_component/__init__.py
|
xaled/flaskup
|
265d410e01fd3bd50afa4f6f925e981ad1b13307
|
[
"MIT"
] | null | null | null |
src/elementary_flask/components/_component/__init__.py
|
xaled/flaskup
|
265d410e01fd3bd50afa4f6f925e981ad1b13307
|
[
"MIT"
] | null | null | null |
src/elementary_flask/components/_component/__init__.py
|
xaled/flaskup
|
265d410e01fd3bd50afa4f6f925e981ad1b13307
|
[
"MIT"
] | null | null | null |
from .component import *
from .container import *
# from .markup_plus import *
from .weak_component import *
__all__ = component.__all__ + weak_component.__all__ + container.__all__
| 26.142857
| 72
| 0.781421
| 22
| 183
| 5.636364
| 0.363636
| 0.241935
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.136612
| 183
| 6
| 73
| 30.5
| 0.78481
| 0.142077
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.75
| 0
| 0.75
| 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
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
07deb7df52173f5e68b8ecbb4666117121189519
| 784
|
py
|
Python
|
test_phile/__init__.py
|
BoniLindsley/phile
|
87982e74491d20a6af723702d540ed9608b83df0
|
[
"MIT"
] | null | null | null |
test_phile/__init__.py
|
BoniLindsley/phile
|
87982e74491d20a6af723702d540ed9608b83df0
|
[
"MIT"
] | 21
|
2020-10-07T12:54:58.000Z
|
2021-05-27T13:40:37.000Z
|
test_phile/__init__.py
|
BoniLindsley/phile
|
87982e74491d20a6af723702d540ed9608b83df0
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python3
"""
.. automodule:: test_phile.test_PySide2
.. automodule:: test_phile.test_asyncio
.. automodule:: test_phile.test_builtins
.. automodule:: test_phile.test_cmd
.. automodule:: test_phile.test_configuration
.. automodule:: test_phile.test_data
.. automodule:: test_phile.test_datetime
.. automodule:: test_phile.test_imapclient
.. automodule:: test_phile.test_keyring
.. automodule:: test_phile.test_launcher
.. automodule:: test_phile.test_main
.. automodule:: test_phile.test_notify
.. automodule:: test_phile.test_os
.. automodule:: test_phile.test_tmux
.. automodule:: test_phile.test_tray
.. automodule:: test_phile.test_trigger
.. automodule:: test_phile.test_unittest
.. automodule:: test_phile.test_watchdog
.. automodule:: test_phile.threaded_mock
"""
| 34.086957
| 45
| 0.78699
| 99
| 784
| 5.848485
| 0.272727
| 0.459413
| 0.623489
| 0.715026
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.002766
| 0.077806
| 784
| 22
| 46
| 35.636364
| 0.798064
| 0.987245
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
6af24111a3df028e88a7bcbf2d1d5a8d7652f637
| 43
|
py
|
Python
|
api/src/opentrons/hardware_control/scripts/__init__.py
|
anuwrag/opentrons
|
28c8d76a19e367c6bd38f5290faaa32abf378715
|
[
"Apache-2.0"
] | 2
|
2015-11-10T17:49:51.000Z
|
2016-01-15T04:43:37.000Z
|
api/src/opentrons/hardware_control/scripts/__init__.py
|
anuwrag/opentrons
|
28c8d76a19e367c6bd38f5290faaa32abf378715
|
[
"Apache-2.0"
] | null | null | null |
api/src/opentrons/hardware_control/scripts/__init__.py
|
anuwrag/opentrons
|
28c8d76a19e367c6bd38f5290faaa32abf378715
|
[
"Apache-2.0"
] | null | null | null |
"""Scripts for the hardware controller."""
| 21.5
| 42
| 0.72093
| 5
| 43
| 6.2
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.116279
| 43
| 1
| 43
| 43
| 0.815789
| 0.837209
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| 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
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
ed08b378cc18f7df1a52a79a9fa053726a52db1c
| 110
|
py
|
Python
|
example/plugins/helloworld2.py
|
stevencnix/Plugin_Manager
|
b9014ae0a8b8a4c6e379860d63b69184053f7ff5
|
[
"MIT"
] | null | null | null |
example/plugins/helloworld2.py
|
stevencnix/Plugin_Manager
|
b9014ae0a8b8a4c6e379860d63b69184053f7ff5
|
[
"MIT"
] | null | null | null |
example/plugins/helloworld2.py
|
stevencnix/Plugin_Manager
|
b9014ae0a8b8a4c6e379860d63b69184053f7ff5
|
[
"MIT"
] | null | null | null |
class Plugin:
def __init__(self):
pass
def execute(self):
print("HELLO WORLD 2. :D")
| 15.714286
| 34
| 0.554545
| 14
| 110
| 4.071429
| 0.857143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.013514
| 0.327273
| 110
| 6
| 35
| 18.333333
| 0.756757
| 0
| 0
| 0
| 0
| 0
| 0.154545
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.4
| false
| 0.2
| 0
| 0
| 0.6
| 0.2
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 1
| 0
|
0
| 5
|
ed0aef10711622fe84757a602fa5f5474b8c8bd3
| 9,223
|
py
|
Python
|
tests/test_reassembler.py
|
Kyle-Kyle/angr
|
345b2131a7a67e3a6ffc7d9fd475146a3e12f837
|
[
"BSD-2-Clause"
] | 6,132
|
2015-08-06T23:24:47.000Z
|
2022-03-31T21:49:34.000Z
|
tests/test_reassembler.py
|
Kyle-Kyle/angr
|
345b2131a7a67e3a6ffc7d9fd475146a3e12f837
|
[
"BSD-2-Clause"
] | 2,272
|
2015-08-10T08:40:07.000Z
|
2022-03-31T23:46:44.000Z
|
tests/test_reassembler.py
|
Kyle-Kyle/angr
|
345b2131a7a67e3a6ffc7d9fd475146a3e12f837
|
[
"BSD-2-Clause"
] | 1,155
|
2015-08-06T23:37:39.000Z
|
2022-03-31T05:54:11.000Z
|
import sys
import platform
import os
import tempfile
import subprocess
import shutil
import angr
test_location = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..', '..', 'binaries', 'tests')
# Note: Reassembler is intensively tested by Patcherex test cases on CGC binaries.
def is_linux_x64():
return sys.platform.startswith("linux") and platform.machine().endswith("64")
def test_data_reference_collection_in_add():
# Issue reported and test binary provided by Antonio F. Montoya
# Fixed in https://github.com/angr/pyvex/pull/192
p = angr.Project(os.path.join(test_location, "x86_64", "df_gcc_-O1"), auto_load_libs=False)
vexblock_opt0 = p.factory.block(0x402431, opt_level=0).vex
vexblock_opt1 = p.factory.block(0x402431, opt_level=1).vex
vexblock_opt1_nostmt = p.factory.block(0x402431, opt_level=1, collect_data_refs=True).vex_nostmt
cfg = p.analyses.CFG()
cfg._model.memory_data = {}
cfg._collect_data_references(vexblock_opt0, 0x402431)
memory_data_opt0 = cfg._model.memory_data
cfg._model.memory_data = {}
# bypass the IRSB unoptimization step
cfg._collect_data_references_by_scanning_stmts(vexblock_opt1, 0x402431)
memory_data_opt1 = cfg._model.memory_data
cfg._model.memory_data = {}
cfg._collect_data_references(vexblock_opt1_nostmt, 0x402431)
memory_data_opt1_nostmt = cfg._model.memory_data
assert memory_data_opt0.keys() == memory_data_opt1.keys()
assert memory_data_opt0.keys() == memory_data_opt1_nostmt.keys()
def test_ln_gcc_O2():
# Issue reported and test binary provided by Antonio F. Montoya
p = angr.Project(os.path.join(test_location, "x86_64", "ln_gcc_-O2"), auto_load_libs=False)
r = p.analyses.Reassembler(syntax="at&t")
r.symbolize()
r.remove_unnecessary_stuff()
assembly = r.assembly(comments=True, symbolized=True)
# There should be two symbols with the same name: file_name. Reassembler renames the second one to file_name_0.
# Test their existence.
assert "\nfile_name:" in assembly and "\nfile_name_0:" in assembly
if is_linux_x64():
# we should be able to compile it and run it ... if we are running on x64 Linux
tempdir = tempfile.mkdtemp(prefix="angr_test_reassembler_")
asm_filename = "ln_gcc-O2.s"
bin_filename = "ln_gcc-O2"
asm_filepath = os.path.join(tempdir, asm_filename)
bin_filepath = os.path.join(tempdir, bin_filename)
with open(asm_filepath, "w") as f:
f.write(assembly)
# Call out to GCC, and it should return 0. Otherwise check_call() will raise an exception.
subprocess.check_call(["gcc", "-no-pie", asm_filepath, "-o", bin_filepath],
stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
# Run the generated binary file, and it should not crash (which is a pretty basic requirement, I know)
subprocess.check_call([bin_filepath, "--help"],
stdout=subprocess.DEVNULL)
# Pick up after ourselves
shutil.rmtree(tempdir)
def test_chmod_gcc_O1():
# Issue reported and test binary provided by Antonio F. Montoya
p = angr.Project(os.path.join(test_location, "x86_64", "chmod_gcc_-O1"), auto_load_libs=False)
r = p.analyses.Reassembler(syntax="at&t")
r.symbolize()
r.remove_unnecessary_stuff()
assembly = r.assembly(comments=True, symbolized=True)
if is_linux_x64():
# we should be able to compile it and run it ... if we are running on x64 Linux
tempdir = tempfile.mkdtemp(prefix="angr_test_reassembler_")
asm_filename = "chmod_gcc-O1.s"
bin_filename = "chmod_gcc-O1"
asm_filepath = os.path.join(tempdir, asm_filename)
bin_filepath = os.path.join(tempdir, bin_filename)
with open(asm_filepath, "w") as f:
f.write(assembly)
# Call out to GCC, and it should return 0. Otherwise check_call() will raise an exception.
subprocess.check_call(["gcc", "-no-pie", asm_filepath, "-o", bin_filepath],
stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
# Run the generated binary file, and it should not crash (which is a pretty basic requirement, I know)
subprocess.check_call([bin_filepath, "--help"],
stdout=subprocess.DEVNULL)
# Pick up after ourselves
shutil.rmtree(tempdir)
def test_ex_gpp():
# Issue reported and test binary provided by Antonio F. Montoya
p = angr.Project(os.path.join(test_location, "x86_64", "ex_g++"), auto_load_libs=False)
r = p.analyses.Reassembler(syntax="at&t")
r.symbolize()
r.remove_unnecessary_stuff()
assembly = r.assembly(comments=True, symbolized=True)
if is_linux_x64():
# we should be able to compile it and run it ... if we are running on x64 Linux
tempdir = tempfile.mkdtemp(prefix="angr_test_reassembler_")
asm_filename = "ex_g++.s"
bin_filename = "ex_g++"
asm_filepath = os.path.join(tempdir, asm_filename)
bin_filepath = os.path.join(tempdir, bin_filename)
with open(asm_filepath, "w") as f:
f.write(assembly)
# Call out to GCC, and it should return 0. Otherwise check_call() will raise an exception.
subprocess.check_call(["g++", "-no-pie", asm_filepath, "-o", bin_filepath],
stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
# Run the generated binary file and check the output
output = subprocess.check_output([bin_filepath])
assert output == b"A1\nA2\n"
# Pick up after ourselves
shutil.rmtree(tempdir)
def test_df_gcc_O1():
# Issue reported and test binary provided by Antonio F. Montoya
p = angr.Project(os.path.join(test_location, "x86_64", "df_gcc_-O1"), auto_load_libs=False)
r = p.analyses.Reassembler(syntax="at&t")
r.symbolize()
r.remove_unnecessary_stuff()
assembly = r.assembly(comments=True, symbolized=True)
if is_linux_x64():
# we should be able to compile it and run it ... if we are running on x64 Linux
tempdir = tempfile.mkdtemp(prefix="angr_test_reassembler_")
asm_filename = "df_gcc-O1.s"
bin_filename = "df_gcc-O1"
asm_filepath = os.path.join(tempdir, asm_filename)
bin_filepath = os.path.join(tempdir, bin_filename)
with open(asm_filepath, "w") as f:
f.write(assembly)
# Call out to GCC, and it should return 0. Otherwise check_call() will raise an exception.
subprocess.check_call(["gcc", "-no-pie", asm_filepath, "-o", bin_filepath],
stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
# Run the generated binary file, and it should not crash (which is a pretty basic requirement, I know)
subprocess.check_call([bin_filepath, "--help"],
stdout=subprocess.DEVNULL)
# Pick up after ourselves
shutil.rmtree(tempdir)
def test_dir_gcc_O0():
# Issue reported and test binary provided by Antonio F. Montoya
p = angr.Project(os.path.join(test_location, "x86_64", "dir_gcc_-O0"), auto_load_libs=False)
r = p.analyses.Reassembler(syntax="at&t")
r.symbolize()
r.remove_unnecessary_stuff()
assembly = r.assembly(comments=True, symbolized=True)
if is_linux_x64():
# we should be able to compile it and run it ... if we are running on x64 Linux
tempdir = tempfile.mkdtemp(prefix="angr_test_reassembler_")
asm_filename = "dir_gcc-O0.s"
bin_filename = "dir_gcc-O0"
asm_filepath = os.path.join(tempdir, asm_filename)
bin_filepath = os.path.join(tempdir, bin_filename)
with open(asm_filepath, "w") as f:
f.write(assembly)
# Call out to GCC, and it should return 0. Otherwise check_call() will raise an exception.
subprocess.check_call(["gcc", "-no-pie", asm_filepath, "-o", bin_filepath],
stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
# Run the generated binary file, and it should not crash (which is a pretty basic requirement, I know)
subprocess.check_call([bin_filepath, "--help"],
stdout=subprocess.DEVNULL)
subprocess.check_call([bin_filepath, "-la", "/"],
stdout=subprocess.DEVNULL)
# Pick up after ourselves
shutil.rmtree(tempdir)
def test_helloworld():
# Reassembler complains about TYPE_OTHER symbols, which is because it's trying to classify bytes inside the ELF
# header as pointers. We identify the ELF header in CFGFast to workaround this problem.
# https://github.com/angr/angr/issues/1630
p = angr.Project(os.path.join(test_location, "x86_64", "hello_world"), auto_load_libs=False)
r = p.analyses.Reassembler(syntax="at&t")
r.symbolize()
r.remove_unnecessary_stuff()
_ = r.assembly(comments=True, symbolized=True)
# No exception should have been raised
if __name__ == "__main__":
test_data_reference_collection_in_add()
test_ln_gcc_O2()
test_chmod_gcc_O1()
test_ex_gpp()
test_df_gcc_O1()
test_dir_gcc_O0()
test_helloworld()
| 40.991111
| 115
| 0.677762
| 1,286
| 9,223
| 4.650078
| 0.163297
| 0.020067
| 0.0301
| 0.0301
| 0.779933
| 0.769231
| 0.747826
| 0.737793
| 0.716054
| 0.691639
| 0
| 0.020357
| 0.217066
| 9,223
| 224
| 116
| 41.174107
| 0.807783
| 0.255015
| 0
| 0.582734
| 0
| 0
| 0.074334
| 0.016096
| 0
| 0
| 0.007024
| 0
| 0.028777
| 1
| 0.057554
| false
| 0
| 0.05036
| 0.007194
| 0.115108
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
ed6644716c4bca7de5c6d4aa548a7fce325f10b9
| 57
|
py
|
Python
|
setup/code_seq2seq/seq2seq/dataset/__init__.py
|
anonmyous-author/anonymous-code
|
3032e1b3fd41c57db6ca91ab86f39aebdf39bda8
|
[
"MIT"
] | null | null | null |
setup/code_seq2seq/seq2seq/dataset/__init__.py
|
anonmyous-author/anonymous-code
|
3032e1b3fd41c57db6ca91ab86f39aebdf39bda8
|
[
"MIT"
] | null | null | null |
setup/code_seq2seq/seq2seq/dataset/__init__.py
|
anonmyous-author/anonymous-code
|
3032e1b3fd41c57db6ca91ab86f39aebdf39bda8
|
[
"MIT"
] | 1
|
2021-06-09T12:53:39.000Z
|
2021-06-09T12:53:39.000Z
|
from .fields import SourceField, TargetField, FnameField
| 28.5
| 56
| 0.842105
| 6
| 57
| 8
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.105263
| 57
| 1
| 57
| 57
| 0.941176
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 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
| 5
|
71f3a14b9e0b9efbce586ce28d5c3be47aeb24b4
| 280
|
py
|
Python
|
RecoCTPPS/ProtonReconstruction/python/ctppsProtons_cff.py
|
bisnupriyasahu/cmssw
|
6cf37ca459246525be0e8a6f5172c6123637d259
|
[
"Apache-2.0"
] | 3
|
2018-08-24T19:10:26.000Z
|
2019-02-19T11:45:32.000Z
|
RecoCTPPS/ProtonReconstruction/python/ctppsProtons_cff.py
|
bisnupriyasahu/cmssw
|
6cf37ca459246525be0e8a6f5172c6123637d259
|
[
"Apache-2.0"
] | 3
|
2018-08-23T13:40:24.000Z
|
2019-12-05T21:16:03.000Z
|
RecoCTPPS/ProtonReconstruction/python/ctppsProtons_cff.py
|
bisnupriyasahu/cmssw
|
6cf37ca459246525be0e8a6f5172c6123637d259
|
[
"Apache-2.0"
] | 5
|
2018-08-21T16:37:52.000Z
|
2020-01-09T13:33:17.000Z
|
import FWCore.ParameterSet.Config as cms
from RecoCTPPS.ProtonReconstruction.ctppsProtons_cfi import *
# TODO: remove these lines once conditions data are available in DB
from CalibPPS.ESProducers.ctppsOpticalFunctions_cff import *
ctppsProtons.lhcInfoLabel = ctppsLHCInfoLabel
| 35
| 67
| 0.853571
| 32
| 280
| 7.40625
| 0.875
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.103571
| 280
| 7
| 68
| 40
| 0.944223
| 0.232143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.142857
| 0
| 1
| 0
| true
| 0
| 0.75
| 0
| 0.75
| 0
| 0
| 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
| 1
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
9c442c9d249a37582642bb79245c80c7e7e05bed
| 24
|
py
|
Python
|
config/custom_components/ble_monitor/test/__init__.py
|
Poeschl/home-assistant-config
|
380640bc46b14542866fbf8bbdc4218b2d58b55c
|
[
"MIT"
] | 820
|
2020-11-12T18:45:02.000Z
|
2022-03-31T19:58:58.000Z
|
custom_components/ble_monitor/test/__init__.py
|
scrambledleek/ha_hacs_ble_monitor
|
abafc7a34312e695667325dbe97c309f20dd1527
|
[
"MIT"
] | 541
|
2020-11-12T16:59:10.000Z
|
2022-03-31T20:41:44.000Z
|
custom_components/ble_monitor/test/__init__.py
|
scrambledleek/ha_hacs_ble_monitor
|
abafc7a34312e695667325dbe97c309f20dd1527
|
[
"MIT"
] | 152
|
2020-11-13T20:38:02.000Z
|
2022-03-31T09:58:36.000Z
|
# Tests for BLE monitor
| 12
| 23
| 0.75
| 4
| 24
| 4.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.208333
| 24
| 1
| 24
| 24
| 0.947368
| 0.875
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| 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
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
9c53902141ef96db2d208df9d41b7a583932f625
| 163
|
py
|
Python
|
predictor/apps.py
|
varungg/Music-Genre-Classification
|
bba9129939fb0fe496bca0540dab52d4449f70a0
|
[
"MIT"
] | 9
|
2021-04-27T03:59:27.000Z
|
2022-03-15T11:49:55.000Z
|
predictor/apps.py
|
varungg/Music-Genre-Classification
|
bba9129939fb0fe496bca0540dab52d4449f70a0
|
[
"MIT"
] | 5
|
2020-03-21T14:43:31.000Z
|
2022-02-10T12:11:48.000Z
|
predictor/apps.py
|
varungg/Music-Genre-Classification
|
bba9129939fb0fe496bca0540dab52d4449f70a0
|
[
"MIT"
] | 14
|
2021-05-10T11:16:31.000Z
|
2022-03-26T19:13:41.000Z
|
from django.apps import AppConfig
from django.conf import settings
import os
import pickle
class PredictorConfig(AppConfig):
# create path to models
pass
| 18.111111
| 33
| 0.785276
| 22
| 163
| 5.818182
| 0.727273
| 0.15625
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.177914
| 163
| 8
| 34
| 20.375
| 0.955224
| 0.128834
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.166667
| 0.666667
| 0
| 0.833333
| 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
| 0
| 0
|
0
| 5
|
9c67999c0edc46c604b07fa133fad236a54f2c5e
| 48
|
py
|
Python
|
envinorma/parametrization/exceptions.py
|
Envinorma/envinorma-data
|
85c00abc1af9a3b14912229b0789a0d1d5ae7b69
|
[
"MIT"
] | 4
|
2020-12-11T09:40:12.000Z
|
2022-03-08T13:43:35.000Z
|
envinorma/parametrization/exceptions.py
|
Envinorma/envinorma-data
|
85c00abc1af9a3b14912229b0789a0d1d5ae7b69
|
[
"MIT"
] | 104
|
2020-12-10T15:20:13.000Z
|
2021-09-30T13:05:00.000Z
|
envinorma/parametrization/exceptions.py
|
Envinorma/envinorma-data
|
85c00abc1af9a3b14912229b0789a0d1d5ae7b69
|
[
"MIT"
] | null | null | null |
class ParametrizationError(Exception):
pass
| 16
| 38
| 0.791667
| 4
| 48
| 9.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.145833
| 48
| 2
| 39
| 24
| 0.926829
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 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
| 0
| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 5
|
92c25494d0e3aee715c20b4d6c51563a7eb7ece3
| 1,535
|
py
|
Python
|
covid-19-timelapse/dashapps/term_frequency/config.py
|
dowjones/developer-platform
|
75a5805730bcfa427e174237c5b8dec5813cf7a8
|
[
"Apache-2.0"
] | 8
|
2020-03-07T14:57:29.000Z
|
2021-09-16T20:32:13.000Z
|
covid-19-timelapse/dashapps/term_frequency/config.py
|
dowjones/developer-platform
|
75a5805730bcfa427e174237c5b8dec5813cf7a8
|
[
"Apache-2.0"
] | null | null | null |
covid-19-timelapse/dashapps/term_frequency/config.py
|
dowjones/developer-platform
|
75a5805730bcfa427e174237c5b8dec5813cf7a8
|
[
"Apache-2.0"
] | 6
|
2020-03-31T15:08:50.000Z
|
2022-02-16T12:55:12.000Z
|
import os
TERMS_TO_REMOVE = ['covid 19', 'per cent' , 'tested positive', 'confirmed cases', 'hong kong', 'year old', 'prime minister', 'said the', 'united states', 'last week', 'new cases', 'coronavirus cases', 'spread coronavirus', 'world health', 'two weeks', 'he said', 'spread virus', 'health officials', 'death toll', 'related story', 'dow jones', 'around world', 'stay home', 'last year', 'i think', 'coronavirus the', 'cases coronavirus', 'health minister', 'the government', 'said would', 'health care', 'said statement', 'positive coronavirus', 'disease control', 'said we', 'health organization', '2020 et', 'health authorities', 'hubei province', 'due coronavirus', 'white house', 'health ministry', 'mainland china', 'jones newswires', 'social media', 'number cases', 'donald trump', 'the coronavirus', 'federal government', 'outside china', 'officials said', 'new year', 'read more', 'the company', 'across country', 'block time', 'also said', 'spread covid', 'impact coronavirus', 'lunar new', 'interest rates', 'vice president', 'president donald', '19 cases', 'last month', 'et gmt', 'next week', 'total number', '19 pandemic', 'people infected', 'p 500', 'positive covid', 'positive virus', 'control prevention', 'respiratory syndrome', 'forward looking', 's p', 'prime minister', '19 outbreak', '19 patients', '24 hours', 'cases covid', 'said i', 'the number', 'remain open', 'pandemic the', 'make sure', 'many people', 'looking statements', 'it also', 'long term']
USER_KEY = os.getenv('SNAPSHOT_USERKEY_VLAD')
| 255.833333
| 1,476
| 0.689902
| 194
| 1,535
| 5.43299
| 0.623711
| 0.024668
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.014179
| 0.127036
| 1,535
| 5
| 1,477
| 307
| 0.772388
| 0
| 0
| 0
| 0
| 0
| 0.723779
| 0.013681
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.333333
| 0
| 0.333333
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
1301c5efa980dd438e65d9aca9bef200719949e0
| 35
|
py
|
Python
|
django_yadt/__init__.py
|
lamby/django-yadt
|
3bd06cf8edcfed352ce5f4271ba7e8f7e88429e3
|
[
"BSD-3-Clause"
] | null | null | null |
django_yadt/__init__.py
|
lamby/django-yadt
|
3bd06cf8edcfed352ce5f4271ba7e8f7e88429e3
|
[
"BSD-3-Clause"
] | null | null | null |
django_yadt/__init__.py
|
lamby/django-yadt
|
3bd06cf8edcfed352ce5f4271ba7e8f7e88429e3
|
[
"BSD-3-Clause"
] | null | null | null |
from .fields import YADTImageField
| 17.5
| 34
| 0.857143
| 4
| 35
| 7.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.114286
| 35
| 1
| 35
| 35
| 0.967742
| 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
| 0
| 0
|
0
| 5
|
13143878dc5015557ffbaecd2cda1dcf84a94dba
| 48
|
py
|
Python
|
hello.py
|
wert321/git-sandbox
|
4efe25957a809bcf2cfc903e458909653a9615af
|
[
"MIT"
] | null | null | null |
hello.py
|
wert321/git-sandbox
|
4efe25957a809bcf2cfc903e458909653a9615af
|
[
"MIT"
] | null | null | null |
hello.py
|
wert321/git-sandbox
|
4efe25957a809bcf2cfc903e458909653a9615af
|
[
"MIT"
] | null | null | null |
for i in range(10):
print 'hell'
print 'ssss'
| 9.6
| 19
| 0.645833
| 9
| 48
| 3.444444
| 0.888889
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.052632
| 0.208333
| 48
| 4
| 20
| 12
| 0.763158
| 0
| 0
| 0
| 0
| 0
| 0.166667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 0.666667
| 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 5
|
13311479025312fc8c617a5919a658fb347fb683
| 50
|
py
|
Python
|
dynamic_selection/utils/__init__.py
|
Kthyeon/FINE
|
ae8a24a4a2514feafd9a9ed394af87f397708ccf
|
[
"MIT"
] | 2
|
2021-12-22T02:25:00.000Z
|
2022-01-06T09:33:11.000Z
|
dynamic_selection/utils/__init__.py
|
Kthyeon/FINE
|
ae8a24a4a2514feafd9a9ed394af87f397708ccf
|
[
"MIT"
] | null | null | null |
dynamic_selection/utils/__init__.py
|
Kthyeon/FINE
|
ae8a24a4a2514feafd9a9ed394af87f397708ccf
|
[
"MIT"
] | 2
|
2021-10-01T14:39:06.000Z
|
2022-01-06T09:33:12.000Z
|
# from .util import *
from .parse_config import *
| 16.666667
| 27
| 0.72
| 7
| 50
| 5
| 0.714286
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.18
| 50
| 2
| 28
| 25
| 0.853659
| 0.38
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 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
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
13415fca0e1da8180687fa068c92ee131f425da1
| 113
|
py
|
Python
|
links/admin.py
|
zengboi/FlipCart-Tracker
|
81750e2ab0e426362da1bd32226ea39bcff9157c
|
[
"Unlicense"
] | null | null | null |
links/admin.py
|
zengboi/FlipCart-Tracker
|
81750e2ab0e426362da1bd32226ea39bcff9157c
|
[
"Unlicense"
] | null | null | null |
links/admin.py
|
zengboi/FlipCart-Tracker
|
81750e2ab0e426362da1bd32226ea39bcff9157c
|
[
"Unlicense"
] | null | null | null |
from django.contrib import admin
from .models import Link
# Register your models here.
admin.site.register(Link)
| 22.6
| 32
| 0.80531
| 17
| 113
| 5.352941
| 0.647059
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.123894
| 113
| 5
| 33
| 22.6
| 0.919192
| 0.230089
| 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 | 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
| 5
|
135012644f9ab73d6abdef072f2a379421ad79e1
| 1,367
|
py
|
Python
|
TypeRacerStats/file_paths.py
|
GeoffreyXue/TypeRacerStats
|
b57b4aed16e057aa2bf41eb96c950d8425def629
|
[
"MIT"
] | 22
|
2020-11-22T20:36:05.000Z
|
2021-12-07T11:15:19.000Z
|
TypeRacerStats/file_paths.py
|
fiveoutofnine/TypeRacerStats
|
f86d971bdee5c48d2e9540c46a82f15d917804a7
|
[
"MIT"
] | 1
|
2021-12-12T13:53:34.000Z
|
2021-12-12T13:53:34.000Z
|
TypeRacerStats/file_paths.py
|
fiveoutofnine/TypeRacerStats
|
f86d971bdee5c48d2e9540c46a82f15d917804a7
|
[
"MIT"
] | 2
|
2021-11-03T19:10:52.000Z
|
2021-12-11T19:33:09.000Z
|
CONFIG_FILE_PATH = 'TypeRacerStats/src/config.json'
ACCOUNTS_FILE_PATH = 'TypeRacerStats/src/accounts.json'
ALIASES_FILE_PATH = 'TypeRacerStats/src/commands.json'
PREFIXES_FILE_PATH = 'TypeRacerStats/src/prefixes.json'
SUPPORTERS_FILE_PATH = 'TypeRacerStats/src/supporter_colors.json'
UNIVERSES_FILE_PATH = 'TypeRacerStats/src/universes.txt'
ART_JSON = 'TypeRacerStats/src/art.json'
CLIPS_JSON = 'TypeRacerStats/src/clips.json'
DATABASE_PATH = 'TypeRacerStats/src/data/typeracer.db'
TEMPORARY_DATABASE_PATH = 'TypeRacerStats/src/data/temp.db'
TEXTS_FILE_PATH = 'TypeRacerStats/src/data/texts'
TOPTENS_JSON_FILE_PATH = 'TypeRacerStats/src/data/texts/top_ten.json'
TOPTENS_FILE_PATH = 'TypeRacerStats/src/data/texts/player_top_tens.json'
TEXTS_FILE_PATH_CSV = 'TypeRacerStats/src/data/texts/texts.csv'
MAINTAIN_PLAYERS_TXT = 'TypeRacerStats/src/data/maintain_players.txt'
CSS_COLORS = 'TypeRacerStats/src/css_colors.json'
CMAPS = 'TypeRacerStats/src/cmaps.json'
TYPERACER_RECORDS_JSON = 'TypeRacerStats/src/data/typeracer_records.json'
COUNTRY_CODES = 'TypeRacerStats/src/country_codes.json'
TEXTS_LENGTHS = 'TypeRacerStats/src/data/texts/texts.json'
TEXTS_LARGE = 'TypeRacerStats/src/data/texts/texts_large.json'
CHANGELOG = 'TypeRacerStats/src/changelog.json'
KEYMAPS_SVG = 'TypeRacerStats/src/keymap_svg.txt'
BLANK_KEYMAP = 'TypeRacerStats/src/keymap_template.json'
| 54.68
| 73
| 0.831748
| 182
| 1,367
| 5.983516
| 0.247253
| 0.374656
| 0.212121
| 0.206612
| 0.239669
| 0.093664
| 0
| 0
| 0
| 0
| 0
| 0
| 0.05267
| 1,367
| 24
| 74
| 56.958333
| 0.840927
| 0
| 0
| 0
| 0
| 0
| 0.630578
| 0.630578
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 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
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
1387e701fedafc5d0333a9c9f2e9246e9866906e
| 41
|
py
|
Python
|
examples/str.encode/ex1.py
|
mcorne/python-by-example
|
15339c0909c84b51075587a6a66391100971c033
|
[
"MIT"
] | null | null | null |
examples/str.encode/ex1.py
|
mcorne/python-by-example
|
15339c0909c84b51075587a6a66391100971c033
|
[
"MIT"
] | null | null | null |
examples/str.encode/ex1.py
|
mcorne/python-by-example
|
15339c0909c84b51075587a6a66391100971c033
|
[
"MIT"
] | null | null | null |
print('À Bientôt Meßingenieur'.encode())
| 20.5
| 40
| 0.756098
| 5
| 41
| 6.2
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.073171
| 41
| 1
| 41
| 41
| 0.815789
| 0
| 0
| 0
| 0
| 0
| 0.536585
| 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
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 5
|
1388d10c3b0c75b9ae6e58bb40c71c5ac832719f
| 109
|
py
|
Python
|
Testing/Stage 1/EmulateKBM.py
|
Shanjiith-Pranov/2022-Computing-Coursework
|
ddb36b5229b7bb35239ddf874a7a394438fae6ec
|
[
"MIT"
] | 1
|
2022-01-18T14:58:14.000Z
|
2022-01-18T14:58:14.000Z
|
Testing/Stage 1/EmulateKBM.py
|
Shanjiith-Pranov/2022-Computing-Coursework
|
ddb36b5229b7bb35239ddf874a7a394438fae6ec
|
[
"MIT"
] | null | null | null |
Testing/Stage 1/EmulateKBM.py
|
Shanjiith-Pranov/2022-Computing-Coursework
|
ddb36b5229b7bb35239ddf874a7a394438fae6ec
|
[
"MIT"
] | null | null | null |
import pyautogui
import time
time.sleep(5)
pyautogui.keyDown('down')
time.sleep(2)
pyautogui.keyUp('down')
| 12.111111
| 25
| 0.761468
| 16
| 109
| 5.1875
| 0.5625
| 0.216867
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.020202
| 0.091743
| 109
| 8
| 26
| 13.625
| 0.818182
| 0
| 0
| 0
| 0
| 0
| 0.074074
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.333333
| 0
| 0.333333
| 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
| 0
| 0
|
0
| 5
|
13b10309443dff92cd5d4a274e015c65b1656e15
| 40
|
py
|
Python
|
olo007.py
|
olo007/CintaKamu
|
9285c04fad3b0c4e92b74d4223e430f76e88a80b
|
[
"MIT"
] | null | null | null |
olo007.py
|
olo007/CintaKamu
|
9285c04fad3b0c4e92b74d4223e430f76e88a80b
|
[
"MIT"
] | null | null | null |
olo007.py
|
olo007/CintaKamu
|
9285c04fad3b0c4e92b74d4223e430f76e88a80b
|
[
"MIT"
] | null | null | null |
#olo007
#Poland
print("Aku Cinta Kamu")
| 10
| 23
| 0.725
| 6
| 40
| 4.833333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.085714
| 0.125
| 40
| 3
| 24
| 13.333333
| 0.742857
| 0.3
| 0
| 0
| 0
| 0
| 0.538462
| 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
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 5
|
13caea0250ae2ceadedef71d58182471355a317c
| 201
|
py
|
Python
|
config.py
|
pdufter/simalign-demo
|
d435c2f714c12e4c8ec88e2c2572429e09fba40e
|
[
"MIT"
] | 1
|
2022-03-24T06:29:23.000Z
|
2022-03-24T06:29:23.000Z
|
config.py
|
pdufter/simalign-demo
|
d435c2f714c12e4c8ec88e2c2572429e09fba40e
|
[
"MIT"
] | null | null | null |
config.py
|
pdufter/simalign-demo
|
d435c2f714c12e4c8ec88e2c2572429e09fba40e
|
[
"MIT"
] | null | null | null |
import os
class Config(object):
SECRET_KEY = os.environ["FLASK_SECRET_KEY"]
RECAPTCHA_PUBLIC_KEY = os.environ["CAPTCHA_SITE_KEY"]
RECAPTCHA_PRIVATE_KEY = os.environ["CAPTCHA_SECRET_KEY"]
| 25.125
| 60
| 0.761194
| 28
| 201
| 5.071429
| 0.5
| 0.190141
| 0.253521
| 0.267606
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.134328
| 201
| 7
| 61
| 28.714286
| 0.816092
| 0
| 0
| 0
| 0
| 0
| 0.248756
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.2
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 5
|
b939290cc00959c9e7948dbc074fbfee5cda333c
| 40
|
py
|
Python
|
pycloser/__init__.py
|
arvinkulagin/pycloser
|
7be4aa5634176745d54e6640076bbb1b1ec5b3b9
|
[
"MIT"
] | null | null | null |
pycloser/__init__.py
|
arvinkulagin/pycloser
|
7be4aa5634176745d54e6640076bbb1b1ec5b3b9
|
[
"MIT"
] | null | null | null |
pycloser/__init__.py
|
arvinkulagin/pycloser
|
7be4aa5634176745d54e6640076bbb1b1ec5b3b9
|
[
"MIT"
] | null | null | null |
from .closer import defer, listen, close
| 40
| 40
| 0.8
| 6
| 40
| 5.333333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.125
| 40
| 1
| 40
| 40
| 0.914286
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 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
| 0
| 0
|
0
| 5
|
b95410cd27ecd2ee0a169f10f26139f2b88b4147
| 3,801
|
py
|
Python
|
core/utils/tensors.py
|
manzar96/st7
|
8dac6fa3497e5a3594766a232a9e8436120e9563
|
[
"MIT"
] | null | null | null |
core/utils/tensors.py
|
manzar96/st7
|
8dac6fa3497e5a3594766a232a9e8436120e9563
|
[
"MIT"
] | 1
|
2020-12-07T14:57:23.000Z
|
2020-12-07T14:57:23.000Z
|
core/utils/tensors.py
|
manzar96/st7
|
8dac6fa3497e5a3594766a232a9e8436120e9563
|
[
"MIT"
] | 1
|
2021-06-21T11:11:13.000Z
|
2021-06-21T11:11:13.000Z
|
from core.utils import mytypes
from typing import cast, Callable, Optional, Tuple
import torch
def t_(data: mytypes.NdTensor,
dtype: torch.dtype = torch.float,
device: Optional[mytypes.Device] = 'cpu',
requires_grad: bool = False) -> torch.Tensor:
"""Convert a list or numpy array to torch tensor. If a torch tensor
is passed it is cast to dtype, device and the requires_grad flag is
set IN PLACE.
Args:
data: (list, np.ndarray, torch.Tensor): Data to be converted to
torch tensor.
dtype: (torch.dtype): The type of the tensor elements
(Default value = torch.float)
device: (torch.device, str): Device where the tensor should be
(Default value = 'cpu')
requires_grad: bool): Trainable tensor or not? (Default value = False)
Returns:
(torch.Tensor): A tensor of appropriate dtype, device and
requires_grad containing data
"""
if isinstance(device, str):
device = torch.device(device)
tt = (torch.as_tensor(data, dtype=dtype, device=device)
.requires_grad_(requires_grad))
return tt
def t(data: mytypes.NdTensor,
dtype: torch.dtype = torch.float,
device: mytypes.Device = 'cpu',
requires_grad: bool = False) -> torch.Tensor:
"""Convert a list or numpy array to torch tensor. If a torch tensor
is passed it is cast to dtype, device and the requires_grad flag is
set. This always copies data.
Args:
data: (list, np.ndarray, torch.Tensor): Data to be converted to
torch tensor.
dtype: (torch.dtype): The type of the tensor elements
(Default value = torch.float)
device: (torch.device, str): Device where the tensor should be
(Default value = 'cpu')
requires_grad: (bool): Trainable tensor or not? (Default value = False)
Returns:
(torch.Tensor): A tensor of appropriate dtype, device and
requires_grad containing data
"""
tt = torch.tensor(data, dtype=dtype, device=device,
requires_grad=requires_grad)
return tt
def to_device(tt: torch.Tensor,
device: Optional[mytypes.Device] = 'cpu',
non_blocking: bool = False) -> torch.Tensor:
return tt.to(device, non_blocking=non_blocking)
def from_checkpoint(
checkpoint_file: Optional[str],
obj: mytypes.ModuleOrOptimizer,
map_location: Optional[mytypes.Device] = None) -> mytypes.ModuleOrOptimizer: # noqa: E501
if checkpoint_file is None:
return obj
def mktensor(data: mytypes.NdTensor,
dtype: torch.dtype = torch.float,
device: mytypes.Device = 'cpu',
requires_grad: bool = False,
copy: bool = True) -> torch.Tensor:
"""Convert a list or numpy array to torch tensor. If a torch tensor
is passed it is cast to dtype, device and the requires_grad flag is
set. This can copy data or make the operation in place.
Args:
data: (list, np.ndarray, torch.Tensor): Data to be converted to
torch tensor.
dtype: (torch.dtype): The type of the tensor elements
(Default value = torch.float)
device: (torch.device, str): Device where the tensor should be
(Default value = 'cpu')
requires_grad: (bool): Trainable tensor or not? (Default value = False)
copy: (bool): If false creates the tensor inplace else makes a copy
(Default value = True)
Returns:
(torch.Tensor): A tensor of appropriate dtype, device and
requires_grad containing data
"""
tensor_factory = t if copy else t_
return tensor_factory(
data, dtype=dtype, device=device, requires_grad=requires_grad)
| 40.43617
| 98
| 0.639042
| 493
| 3,801
| 4.862069
| 0.178499
| 0.09637
| 0.037547
| 0.047559
| 0.7597
| 0.743846
| 0.743846
| 0.743846
| 0.743846
| 0.722987
| 0
| 0.001089
| 0.274928
| 3,801
| 94
| 99
| 40.43617
| 0.86865
| 0.539595
| 0
| 0.297297
| 0
| 0
| 0.007712
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.135135
| false
| 0
| 0.081081
| 0.027027
| 0.351351
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
b981190058f7b0b4b8db9246e69601b18dfa229f
| 73
|
py
|
Python
|
yolo/data/__init__.py
|
ioangatop/yolo
|
c65a72337369572bc07090f39123e2bf6ff5f4a3
|
[
"MIT"
] | 1
|
2021-06-28T01:22:38.000Z
|
2021-06-28T01:22:38.000Z
|
yolo/data/__init__.py
|
ioangatop/yolo
|
c65a72337369572bc07090f39123e2bf6ff5f4a3
|
[
"MIT"
] | null | null | null |
yolo/data/__init__.py
|
ioangatop/yolo
|
c65a72337369572bc07090f39123e2bf6ff5f4a3
|
[
"MIT"
] | null | null | null |
from .datasets import create_dataloader
from .utils import load_data_cfg
| 24.333333
| 39
| 0.863014
| 11
| 73
| 5.454545
| 0.818182
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.109589
| 73
| 2
| 40
| 36.5
| 0.923077
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 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
| 5
|
b98227fb168aef780f09c757a4e1ed25a1367065
| 148
|
py
|
Python
|
core/configs/jwt.py
|
joshua-hashimoto/drf-docker-template
|
72cdda1f93060138dd3d76df5ce704cbdb2a8a4a
|
[
"MIT"
] | null | null | null |
core/configs/jwt.py
|
joshua-hashimoto/drf-docker-template
|
72cdda1f93060138dd3d76df5ce704cbdb2a8a4a
|
[
"MIT"
] | null | null | null |
core/configs/jwt.py
|
joshua-hashimoto/drf-docker-template
|
72cdda1f93060138dd3d76df5ce704cbdb2a8a4a
|
[
"MIT"
] | null | null | null |
from datetime import timedelta
SIMPLE_JWT = {
'ACCESS_TOKEN_LIFETIME': timedelta(days=30),
'REFRESH_TOKEN_LIFETIME': timedelta(days=90),
}
| 21.142857
| 49
| 0.743243
| 18
| 148
| 5.833333
| 0.722222
| 0.247619
| 0.419048
| 0.495238
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.031496
| 0.141892
| 148
| 6
| 50
| 24.666667
| 0.795276
| 0
| 0
| 0
| 0
| 0
| 0.290541
| 0.290541
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.2
| 0
| 0.2
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
b986b0e916b6969698c29b0ba733ca6cf1c961ed
| 41
|
py
|
Python
|
zetafold/util/constants.py
|
rickyHong/Zetafold-repl
|
7a325bb65f242d8951c5d257cafa351a789a6f37
|
[
"MIT"
] | 8
|
2018-11-14T05:18:56.000Z
|
2018-12-03T14:21:56.000Z
|
zetafold/util/constants.py
|
rickyHong/Zetafold-repl
|
7a325bb65f242d8951c5d257cafa351a789a6f37
|
[
"MIT"
] | 9
|
2019-01-02T22:17:33.000Z
|
2019-03-29T23:15:50.000Z
|
zetafold/util/constants.py
|
rickyHong/Zetafold-repl
|
7a325bb65f242d8951c5d257cafa351a789a6f37
|
[
"MIT"
] | 4
|
2020-02-08T02:43:01.000Z
|
2021-08-22T09:23:17.000Z
|
KT_IN_KCAL = 0.61633135471 # 37 Celsius
| 20.5
| 40
| 0.756098
| 7
| 41
| 4.142857
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.411765
| 0.170732
| 41
| 1
| 41
| 41
| 0.441176
| 0.243902
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
b995b6274b096afb742fe92030bb5368cac6b0ae
| 2,968
|
py
|
Python
|
jet_django/deps/rest_framework/documentation.py
|
lukejamison/jet-dasboard
|
5dce66b6ea2f107d7120e5e0256346d2d3bc8ed9
|
[
"MIT"
] | 193
|
2018-08-27T06:10:48.000Z
|
2022-03-08T13:04:55.000Z
|
jet_django/deps/rest_framework/documentation.py
|
lukejamison/jet-dasboard
|
5dce66b6ea2f107d7120e5e0256346d2d3bc8ed9
|
[
"MIT"
] | 23
|
2018-10-21T15:05:41.000Z
|
2020-12-20T15:18:58.000Z
|
jet_django/deps/rest_framework/documentation.py
|
lukejamison/jet-dasboard
|
5dce66b6ea2f107d7120e5e0256346d2d3bc8ed9
|
[
"MIT"
] | 38
|
2018-10-31T16:19:25.000Z
|
2022-02-10T05:08:24.000Z
|
from django.conf.urls import include, url
from jet_django.deps.rest_framework.renderers import (
CoreJSONRenderer, DocumentationRenderer, SchemaJSRenderer
)
from jet_django.deps.rest_framework.schemas import SchemaGenerator, get_schema_view
from jet_django.deps.rest_framework.settings import api_settings
def get_docs_view(
title=None, description=None, schema_url=None, public=True,
patterns=None, generator_class=SchemaGenerator,
authentication_classes=api_settings.DEFAULT_AUTHENTICATION_CLASSES,
permission_classes=api_settings.DEFAULT_PERMISSION_CLASSES,
renderer_classes=None):
if renderer_classes is None:
renderer_classes = [DocumentationRenderer, CoreJSONRenderer]
return get_schema_view(
title=title,
url=schema_url,
description=description,
renderer_classes=renderer_classes,
public=public,
patterns=patterns,
generator_class=generator_class,
authentication_classes=authentication_classes,
permission_classes=permission_classes,
)
def get_schemajs_view(
title=None, description=None, schema_url=None, public=True,
patterns=None, generator_class=SchemaGenerator,
authentication_classes=api_settings.DEFAULT_AUTHENTICATION_CLASSES,
permission_classes=api_settings.DEFAULT_PERMISSION_CLASSES):
renderer_classes = [SchemaJSRenderer]
return get_schema_view(
title=title,
url=schema_url,
description=description,
renderer_classes=renderer_classes,
public=public,
patterns=patterns,
generator_class=generator_class,
authentication_classes=authentication_classes,
permission_classes=permission_classes,
)
def include_docs_urls(
title=None, description=None, schema_url=None, public=True,
patterns=None, generator_class=SchemaGenerator,
authentication_classes=api_settings.DEFAULT_AUTHENTICATION_CLASSES,
permission_classes=api_settings.DEFAULT_PERMISSION_CLASSES,
renderer_classes=None):
docs_view = get_docs_view(
title=title,
description=description,
schema_url=schema_url,
public=public,
patterns=patterns,
generator_class=generator_class,
authentication_classes=authentication_classes,
renderer_classes=renderer_classes,
permission_classes=permission_classes,
)
schema_js_view = get_schemajs_view(
title=title,
description=description,
schema_url=schema_url,
public=public,
patterns=patterns,
generator_class=generator_class,
authentication_classes=authentication_classes,
permission_classes=permission_classes,
)
urls = [
url(r'^$', docs_view, name='docs-index'),
url(r'^schema.js$', schema_js_view, name='schema-js')
]
return include((urls, 'api-docs'), namespace='api-docs')
| 34.917647
| 83
| 0.723383
| 305
| 2,968
| 6.711475
| 0.15082
| 0.143625
| 0.128969
| 0.073278
| 0.774792
| 0.754763
| 0.710796
| 0.710796
| 0.710796
| 0.710796
| 0
| 0
| 0.205526
| 2,968
| 84
| 84
| 35.333333
| 0.868109
| 0
| 0
| 0.675676
| 0
| 0
| 0.016173
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.040541
| false
| 0
| 0.054054
| 0
| 0.135135
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
b9ba1f8ac58afda18eb8f3b4f92d26902e2b4272
| 129
|
py
|
Python
|
contests_atcoder/abc171/abc171_e.py
|
takelifetime/competitive-programming
|
e7cf8ef923ccefad39a1727ca94c610d650fcb76
|
[
"BSD-2-Clause"
] | null | null | null |
contests_atcoder/abc171/abc171_e.py
|
takelifetime/competitive-programming
|
e7cf8ef923ccefad39a1727ca94c610d650fcb76
|
[
"BSD-2-Clause"
] | 1
|
2021-01-02T06:36:51.000Z
|
2021-01-02T06:36:51.000Z
|
contests_atcoder/abc171/abc171_e.py
|
takelifetime/competitive-programming
|
e7cf8ef923ccefad39a1727ca94c610d650fcb76
|
[
"BSD-2-Clause"
] | null | null | null |
n = int(input())
a = list(map(int, input().split()))
xor = a[0]
for x in a[1:]:
xor ^= x
ans = print(*[xor ^ x for x in a])
| 16.125
| 35
| 0.511628
| 27
| 129
| 2.444444
| 0.555556
| 0.242424
| 0.181818
| 0.212121
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.020408
| 0.24031
| 129
| 8
| 36
| 16.125
| 0.653061
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.166667
| 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
| 0
|
0
| 5
|
b9c2af2160f4a46258b29f5f3cfade584c19c3f0
| 346
|
py
|
Python
|
cron/__init__.py
|
JohnRobards/Chaos
|
3f3a4700a1fc69ee6d5a68eb798bed10c020053d
|
[
"MIT"
] | null | null | null |
cron/__init__.py
|
JohnRobards/Chaos
|
3f3a4700a1fc69ee6d5a68eb798bed10c020053d
|
[
"MIT"
] | null | null | null |
cron/__init__.py
|
JohnRobards/Chaos
|
3f3a4700a1fc69ee6d5a68eb798bed10c020053d
|
[
"MIT"
] | null | null | null |
import schedule
import settings
from .poll_pull_requests import poll_pull_requests as poll_pull_requests
from .restart_homepage import restart_homepage as restart_homepage
def schedule_jobs():
schedule.every(settings.PULL_REQUEST_POLLING_INTERVAL_SECONDS).seconds.do(poll_pull_requests)
schedule.every(120).seconds.do(restart_homepage)
| 34.6
| 97
| 0.855491
| 48
| 346
| 5.8125
| 0.395833
| 0.114695
| 0.229391
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.009494
| 0.086705
| 346
| 9
| 98
| 38.444444
| 0.873418
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.142857
| true
| 0
| 0.571429
| 0
| 0.714286
| 0
| 0
| 0
| 0
| null | 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
b9c6d8be94dc523fa006d86f2e88d7a06f9a6ece
| 29,820
|
py
|
Python
|
scripts/NGS/temp.py
|
shivankurkapoor/moleculardating
|
4a72c3e92a09ab321e0d92840cc7619857bbab8a
|
[
"BSD-3-Clause"
] | 1
|
2018-04-24T04:38:33.000Z
|
2018-04-24T04:38:33.000Z
|
scripts/NGS/temp.py
|
shivankurkapoor/molecular-dating
|
4a72c3e92a09ab321e0d92840cc7619857bbab8a
|
[
"BSD-3-Clause"
] | null | null | null |
scripts/NGS/temp.py
|
shivankurkapoor/molecular-dating
|
4a72c3e92a09ab321e0d92840cc7619857bbab8a
|
[
"BSD-3-Clause"
] | null | null | null |
from Bio import SeqIO
import sys
#parameters
MATCH_REWARD = 1#10#1
MISMATCH_PENALTY = -1#-2#-1
GAP_OPENING_PENALTY = -5#-15#-3
GAP_EXTENSION_PENALTY = -2#-7
NEG_INF = float('-inf')
def get_current(current, M, Ix, Iy, from_m, from_x, from_y):
#print 'current in get_current: ', current
return {
from_m : M,
from_x : Ix,
from_y : Iy
}[current]
def needleman_using_affine_penalty(s1, s2):
if len(s1) == 0 or len(s2) == 0:
print 'Error: One of the sequences is empty'
return
m = len(s1)
n = len(s2)
'''
3 matrices required for affine penalty
M: best score given that s1[i] is aligned to s2[j]
Ix: best score given that s1[i] is aligned to a gap
Iy: best score given that s2[j] is aligned to a gap
'''
M = [[(0,0) for j in range(n+1)] for i in range(m+1)]
Ix = [[(0,0) for j in range(n+1)] for i in range(m+1)]
Iy = [[(0,0) for j in range(n+1)] for i in range(m+1)]
#M = [[0]*(n+1) for i in range(m+1)]
#Ix = [[0]*(n+1) for i in range(m+1)]
#Iy = [[0]*(n+1) for i in range(m+1)]
#initialization
from_m = 3
from_x = 2
from_y = 1
M[0][0] = (0, 0)
Ix[0][0] = (GAP_OPENING_PENALTY, 0)
Iy[0][0] = (GAP_OPENING_PENALTY, 0)
for i in range(1,m+1):
M[i][0] = (NEG_INF, 0)
Ix[i][0] = (GAP_OPENING_PENALTY + GAP_EXTENSION_PENALTY * i, from_x)
Iy[i][0] = (NEG_INF, 0)
for j in range(1,n+1):
M[0][j] = (NEG_INF, 0)
Ix[0][j] = (NEG_INF, 0)
Iy[0][j] = (GAP_OPENING_PENALTY + GAP_EXTENSION_PENALTY * j, from_y)
#operate
for i in range(1, m+1):
for j in range(1, n+1):
add_factor = MISMATCH_PENALTY
if s1[i-1] == s2[j-1]:
add_factor = MATCH_REWARD
M[i][j] = max(
(M[i-1][j-1][0] + add_factor, from_m),
(Ix[i-1][j-1][0] + add_factor, from_x),
(Iy[i-1][j-1][0] + add_factor, from_y)
)
Ix[i][j] = max(
(M[i-1][j][0] + GAP_OPENING_PENALTY + GAP_EXTENSION_PENALTY, from_m),
(Ix[i-1][j][0] + GAP_EXTENSION_PENALTY, from_x)
)
Iy[i][j] = max(
(M[i][j-1][0] + GAP_OPENING_PENALTY + GAP_EXTENSION_PENALTY, from_m),
(Iy[i][j-1][0] + GAP_EXTENSION_PENALTY, from_y)
)
'''
print 'M'
for i in range(len(M)):
print M[i]
print '\n'
print 'Ix'
for i in range(len(Ix)):
print Ix[i]
print '\n'
print 'Iy'
for i in range(len(Iy)):
print Iy[i]
'''
#traceback
alignseq1 = []
alignseq2 = []
if M[m][n][0] > Ix[m][n][0]:
if M[m][n][0] > Iy[m][n][0]:
current = M
else:
current = Iy
else:
if Ix[m][n][0] > Iy[m][n][0]:
current = Ix
else:
current = Iy
i = m
j = n
while i > 0 or j > 0:
#print '\ncurrent:'
#print current[i][j]
#print 'current matrix'
#print current
if current == M:
#print 'inside M'
#print current[i][j][0], '\n'
current = get_current(current[i][j][1], M, Ix, Iy, from_m, from_x, from_y)
i -= 1
j -= 1
alignseq1.append(s1[i])
alignseq2.append(s2[j])
elif current == Ix:
#print 'inside Ix'
#print current[i][j][0], '\n'
current = get_current(current[i][j][1], M, Ix, Iy, from_m, from_x, from_y)
i -= 1
alignseq1.append(s1[i])
alignseq2.append('-')
#current = get_current(current[i][j][1], M, Ix, Iy, from_m, from_x, from_y)
elif current == Iy:
#print 'inside Y'
#print current[i][j][0], '\n'
current = get_current(current[i][j][1], M, Ix, Iy, from_m, from_x, from_y)
j -= 1
alignseq1.append('-')
alignseq2.append(s2[j])
#current = get_current(current[i][j][1], M, Ix, Iy, from_m, from_x, from_y)
alignseq1.reverse()
alignseq2.reverse()
return ''.join(alignseq1), ''.join(alignseq2)
def hammingDistance(s1, s2, gapsIgnore):
matches, mismatches, insertionErrors, deletionErrors = 0, 0, 0, 0
#bases = {'A':0, 'C':0, 'G':0, 'T':0, 'N':0}
for i in range(len(s1)):
if i >= len(s2):
break
if s1[i] == '-' and s2[i] == '-' or s1[i] == 'N':
continue
if s1[i] == s2[i]:
matches += 1
'''
score = scores[i-deletionErrors]
if signalHistogram.has_key(score):
signalHistogram[score] += 1
else:
signalHistogram[score] = 1
'''
elif s1[i] == '-' or s1[i] == '_':
insertionErrors += 1
'''
score = scores[i-deletionErrors]
if errorHistogram.has_key(score):
errorHistogram[score] += 1
else:
errorHistogram[score] = 1
'''
elif s2[i] == '-' or s2[i] == '_':
#bases[s1[i]] += 1
deletionErrors += 1
else:
mismatches += 1
'''
score = scores[i-deletionErrors]
if errorHistogram.has_key(score):
errorHistogram[score] += 1
else:
errorHistogram[score] = 1
'''
#return matches, mismatches, insertionErrors, deletionErrors
#print 'mismatches: ', mismatches
#print 'insertionErrors: ', insertionErrors
#print 'deletionErrors: ', deletionErrors
#print bases
if gapsIgnore:
return mismatches + abs(len(s1)-len(s2))
return mismatches + insertionErrors + deletionErrors
inputfile = sys.argv[1]
gapsIgnore = sys.argv[2]
gapsIgnore = True if gapsIgnore == 'nogaps' else False
handle = open(inputfile)
records = SeqIO.parse(handle, 'fasta')
rec = next(records)
s1 = str(rec.seq).replace('-','').upper()
rec = next(records)
s2 = str(rec.seq).replace('-','').upper()
#s1 = '--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------T------A-----A---T---A---------------C----C---G-----C----A-----T----------------G-------------C------------Tg------------GT----G---C-----T-------C-----T-----T-----T---C---------A--C--------A-A---------------G---A-----C-----G-----G------A----T----C---AC---------TAA--T--ACcgcaaa------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------GT--G---C---T---T------G----A---G----G----A---G----------G---G---------------G--C---T----T----G----T-----C----T--------C----C---G-------A---------------T----T--------A-------G--C---T----------A---------------G------T----T---------G-----G-----T------G------G----G----------G-------------------------T---------A------A----------C---------------------------------------G-------G----------------------------C------C------T----A---------C-------C-----A------A--------------------------G---G---------C----G------A-------T-------G----A-----------------------------T--------------------------------C---G----G---------------T---------A------G-----C--------T----G----------G-----T---C----T-------------G---------A-------G---------A--------------------------------------G---G---A----T-------G------G--------T---------C-----A----------G-----C------------------------------------------------------C----A-----C------------------------------------------------------------A-----C------T-----G-----G-----G---A-------------------C---------T--------G-------------------------A--------G-------------------------------A-------C-----------------A--------------C------G-------------------------------------------------------------------------------G---C----C-----C-----A-----G------------------A----C------T-----C-----C--------T-----------A----------------C----------G------------------------------------------------------G-----G-------A------G--------G------C------A----------------------------------------------G---C-----A-------------------------G----C----A---A------------------------------------------------------------G---------G------------A-----A------------T----------------C---T--------T----G----G----G------------C-------------------------------------A-----A-----T----G-----G----G---C------------G-----------------------------A--------A------A----------------------------------------------------G---C----C-----T----G----A-------------C---C----C------------------------------A---------G-------C----G---------A--------------------------C-----G----C----------C----G----C----------G------------------------T-------G-----A-----G----G----------G---------A-----------------------------T---------------------G-----------------A----------C----G----G-----C---C-----------T---------------------------T-----------C----------------------------G------------------------G---G----T----T---------G------T-----------A--------A----A-------------C----C----T----C-----------------T---T-------T-----------T-----C-----T-----C----A----G---G-----G-----A---A--G---Aataa-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------TG-A-----C---G---G--T------A--------C----C-----T-----G----A-------G------G-------A------------A-----------T--------A-----------A------G-----T-------C-------T----C---------G-------------G--------C-----------T-----A-----------------A------C----T-----A-----C-----G--------T-------------G---------------C--------------------------C----------------A-------G-----C----------A---------------------------G-----------------C-------C------------------------------------------------------------G-----C-----G--------G------T---------A----------A--------T-------A---------------------------------------------------------------------------C-----G----T----A----G-----------------G------A------G-----G------C---G--------------------------------------------A----G-----C----G---------T----T---------------------------------------------A-----T---C---C-------------------------------------G---------------------------------------G------------A----------------------------------------------------------------------------------T-------------------------------------------------T-------------------------------------------------------------------T-------------------------------------------------------A----------------------------------------T--------------------------------------------------T------------------------------G-----G---G----C---------G------------------T-------------------------------------------------A------------------------------------------------------------A------------------------------------------------------------------------------------------A------G-----------------T--G----G-----G-----C------------------G---T---------------A-----G-------G----T-------------------------------G-------G---T-------C-----T----T---T---C---------------------------------------------------------A---A---G------T-------C---------G-------G--------A-----T----G---T--G-------------------------A-----------------------------------------------------------A---------------------------------A---------------------------------------T---C----T----C----C----------C-------G-----G-----------C-----------------T-----C---------A----------------------A------------------------------------------------------------C-----T-----G-----G---------G-------A---G---G--G------------------------------------------G--T-----C----A----T----C---------C----------G-------A-----T-----A-----------C-----T------------------------------------------G---T------T---G-------G-----A-------C---------T--------------------------------T----------------G------A-----G------T---A------C-------A---G----C---A---------G-------------G----G-----G---A----A-----A----A--T------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------G--G-------A-----A-----T----T-----C----C---------C---------G-----G----T--------------G-------------------------------------T-----------------A-------G----T---G----------G---------T--------------------G--------------------------------------------------------------A-----------------------------------------A--------A-------------------------------------------------------------T----G------C----G-----T------A-----G-----A--T---------------------------------------------------------A----T-----C-------G----------G------G------A----G-----G-----A------------------------------A---C--------A-----------C-----------------------------------------C----------------------------------------------------A---G---------A--------------------------------------------------------------------G---G---C---------------G------A--------------------------------A--------------------------------------G---------------------------------------------------------------------------G----C----------------------------------------------------------------G----------A----T---T----T---------T-----C-------C-----A-------------------------------------------------------------G----G-----C----T----G-----A----------------------------A---A-----------C------T------------G---A-----C-----------------------------------------------------------A-----C----T------------------------------------------------------------G--AG---------------------------------G---C---C-------C-----------------------------------------------------------------G---------------------------------------A------------------------------------------------A---------------------------------------------A-------G----------C-------G----------T----------G-----G------------G------------------------------------------------------------G------------------------------------A-----G---C-------G------------------------------------------A-----------------A------C----A------G----G---------------A-----------------T-------------T--------------------A---------------------------------G--------A-------------------------T---------A-----C---------------------------------------------C---C-------T-----G---------G--------T-----A--------------------------------G---T-----------------------C----C----------A----C------G-----C---------------C------------------T------------------------T-----------A----------------A---------A-------C--------T---------A----------T------G----G----G------T----A-----C--------T----------A----G---G----T---A----T---A---G--G-GAgtatcgaccc-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------TT-T---C---T----G----T----G----C----C--------G----------A----------A-----G------C----------T--------A----------A----C----------------G------C----------T----T------T-----A------A-----------------G------T-------A--------C----------C-----C---------------------------C----------G----C----C------------------T---------G----G----G---------G----------A--------------------G------------T-------A------------------------------------C--------G----G----------T-----T--------G--------------------------------C-----------------A---------------A-------------------------------------------------G------A-------C-----T-------A---------------A-----A--------A--------------------------------------------------C-----T-----C--------------A-------------------A---------------A-------------------------------------G---------------------------------G------------------A----------------------A-------------------------T------T---G---------------A---C------G--------------------------------------------------G--------G----G---------G------C-----------------C-----C-----G--------------------------------C-----------------A--C--------------A--------------------------------------------------------A------------G------------C-------A----G--------C-----------G---------------G------A---------G------C-------G--------------------T--------G----------T------G-------G-----T-----------------T------------------------------T---------A-----------------------A-------------T--------T-----T----G------------A---------T--------------------G-------C-----------------------------T----------A----------------C-------A----------------------------------C---G--------A-------A----------------G------------A--------A-------C------------C-----T------------C---------------------A----C----C---A-------A------G---G---C----------T-------------------T----------G------------------A---------------C------A---T--G-------T---T--A---G-Aag------------T-----A-G--T---G---A--A----C---C-----G------A-----A-----A---------G--G---G--G--A-ACgacctgtcaaatcagga-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------GC--T--A--------T-----C----A---C---------A------G---G---------T----G-----C---T----G-----------C-----------------A----T----G-----G----C--------------T--------G---T-------C---G----------T----------------C----------------A--------G------C----------T------C----G-------T-----G--------C--C-----------G------------------------T-------------G------A-----------------------G----G----T-----------------G--------------------------------T------A----T----G-----G-----T--------T--------A-----------A---------G---------T--------------------------C---C----T-----G-------C--------A-------------------A----C----------G------A------G-------C--------------------G--------C--------------------A--------A----------C----C----C----T----T------------A-----T----T---G-----C-------C---A----G---------T-------T-----A---------------TA----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------T-----T-----T----T--------C----T----G----G--------C-------G----A---------T----------A------C----T------G-------C------C---T----C--G---------C-----------------------A------A--------------A----A--------------------------------C--G---G-------G-------G------A-----G------G---------------------A---------A------G----G--T----------------------------G---G-----G----G------------A-----T-----G-----A--------------------------C----G------T-------C-------A------A--G------T------C------A-----G-------C---A--------------T---G-----G----C------------------------------------------------------------C---T--------T--------T----A--T----------------------G---C-----C-------T-------T------G------G------G------C--T-------------------------------------A----C-------A---------C----A-----C----------A-------C------G-------C-----T------A------C---------------------------A---------------------A-----------------------T-------G----G------G---------C----G-----G--C-----------A---------------------------C---------------------------------------------------------A-------------A---------------------------T-----G----G-----G----------T----------------------------------------T--------------G----C-------C------------A-----------------C----C---G--G---------G------------T----------G-------------------A-------------------------C---C------G---G------G-----A-----------------------------------G----C------T----A-----A--T---------------------------------------------------------------------------------------------------------------C---C----C-----C------------------A----A---A-------------------------------------A---C---C----G----C-------C-------C-------------------------------------------------------T------------C----------------------------------------------------------------------------------A------------G---------T----------------------------------------------------------T---C--------T-------------------------G-----------------A------T---C----G----C-------A----G----G-------C-------------T------G----------------------------A-----------A-------A---------C--------C---------C--------------G----C------C----T----G----C----G-----G--------G-------A----A----------------------G----------T----C-------G-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------'
#s1 = s1.replace('-','').upper().replace('N','')
print 'original hd: '
print hammingDistance(s1, s2, True)
align1, align2 = needleman_using_affine_penalty(s1, s2)
print len(align1)
print len(align2)
hd = hammingDistance(align1, align2, gapsIgnore)
print 'new hd: ', hd
f = open('output.fasta', 'w')
f.write('>rdp ' + str(hd) + '\n')
f.write(align1 + '\n')
f.write('>user\n')
f.write(align2 + '\n')
f.close()
| 136.164384
| 24,017
| 0.149933
| 2,011
| 29,820
| 2.177026
| 0.070114
| 0.053906
| 0.026725
| 0.012791
| 0.567154
| 0.4582
| 0.32572
| 0.235724
| 0.193924
| 0.143673
| 0
| 0.00669
| 0.047552
| 29,820
| 218
| 24,018
| 136.788991
| 0.147454
| 0.833736
| 0
| 0.179688
| 0
| 0
| 0.029359
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0.015625
| null | null | 0.046875
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
b9cd8c7f8dc3b07e32b4052d7b50b89a2779ee98
| 65
|
py
|
Python
|
learnwithpeople/formats/de/formats.py
|
p2pu/learning-circles
|
ccd94208ec18082f8fda6d7f21eacdd71bad6023
|
[
"MIT"
] | 10
|
2016-05-03T20:41:25.000Z
|
2021-09-17T18:42:01.000Z
|
learnwithpeople/formats/de/formats.py
|
p2pu/learning-circles
|
ccd94208ec18082f8fda6d7f21eacdd71bad6023
|
[
"MIT"
] | 655
|
2016-05-04T19:00:35.000Z
|
2022-03-28T13:09:20.000Z
|
learnwithpeople/formats/de/formats.py
|
p2pu/learning-circles
|
ccd94208ec18082f8fda6d7f21eacdd71bad6023
|
[
"MIT"
] | 8
|
2016-05-06T10:24:27.000Z
|
2020-10-21T00:56:59.000Z
|
DATE_FORMAT = "l, j. N"
MEETING_DATETIME_FORMAT = "l, j. N, H:i"
| 21.666667
| 40
| 0.646154
| 13
| 65
| 3
| 0.692308
| 0.358974
| 0.410256
| 0.461538
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.169231
| 65
| 2
| 41
| 32.5
| 0.722222
| 0
| 0
| 0
| 0
| 0
| 0.292308
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
b9ebafc730ce93263c2637db96fd58c6d0874343
| 63
|
py
|
Python
|
src/app/settings/general.py
|
RdevJ/headquater
|
2da2290560b030f7ce365e1b71affd637fb9cab4
|
[
"MIT"
] | null | null | null |
src/app/settings/general.py
|
RdevJ/headquater
|
2da2290560b030f7ce365e1b71affd637fb9cab4
|
[
"MIT"
] | null | null | null |
src/app/settings/general.py
|
RdevJ/headquater
|
2da2290560b030f7ce365e1b71affd637fb9cab4
|
[
"MIT"
] | null | null | null |
class GeneralSettings(object):
API_V1_STR: str = "/api/v1"
| 21
| 31
| 0.698413
| 9
| 63
| 4.666667
| 0.666667
| 0.238095
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.037736
| 0.15873
| 63
| 2
| 32
| 31.5
| 0.754717
| 0
| 0
| 0
| 0
| 0
| 0.111111
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 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
| 0
| 0
| 1
| 0
|
0
| 5
|
6a0d2f41db0aa4053b2d0f848afee7268e08e72d
| 123
|
py
|
Python
|
pypeln/task/__init__.py
|
sackh/pypeln
|
4bbfb23d8fb7581e9c7511fdf4316e34b7a2a075
|
[
"MIT"
] | 1
|
2020-07-22T18:19:21.000Z
|
2020-07-22T18:19:21.000Z
|
pypeln/task/__init__.py
|
sackh/pypeln
|
4bbfb23d8fb7581e9c7511fdf4316e34b7a2a075
|
[
"MIT"
] | null | null | null |
pypeln/task/__init__.py
|
sackh/pypeln
|
4bbfb23d8fb7581e9c7511fdf4316e34b7a2a075
|
[
"MIT"
] | null | null | null |
from .api import Stage, concat, each, filter, flat_map, from_iterable, map, run
from .utils import TaskPool, get_namespace
| 41
| 79
| 0.788618
| 19
| 123
| 4.947368
| 0.789474
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.130081
| 123
| 2
| 80
| 61.5
| 0.878505
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 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
| 5
|
6a3956020997f0bd4d2193bf135f02f549c97cc3
| 29
|
py
|
Python
|
promap/__main__.py
|
ervanalb/promap
|
173ee77da96ae3eacea057a8b21c26dee8350078
|
[
"MIT"
] | 8
|
2019-02-19T21:48:50.000Z
|
2021-06-01T15:43:48.000Z
|
promap/__main__.py
|
ervanalb/promap
|
173ee77da96ae3eacea057a8b21c26dee8350078
|
[
"MIT"
] | 1
|
2021-03-15T14:56:22.000Z
|
2021-03-15T14:56:22.000Z
|
promap/__main__.py
|
ervanalb/promap
|
173ee77da96ae3eacea057a8b21c26dee8350078
|
[
"MIT"
] | null | null | null |
import promap
promap.main()
| 7.25
| 13
| 0.758621
| 4
| 29
| 5.5
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.137931
| 29
| 3
| 14
| 9.666667
| 0.88
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 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
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
dbf7b1a310b3326cba08cf5a546144dcd65cbc4a
| 1,933
|
py
|
Python
|
TopQuarkAnalysis/TopPairBSM/python/RecoInput_QCD_800_cfi.py
|
SWuchterl/cmssw
|
769b4a7ef81796579af7d626da6039dfa0347b8e
|
[
"Apache-2.0"
] | 6
|
2017-09-08T14:12:56.000Z
|
2022-03-09T23:57:01.000Z
|
TopQuarkAnalysis/TopPairBSM/python/RecoInput_QCD_800_cfi.py
|
SWuchterl/cmssw
|
769b4a7ef81796579af7d626da6039dfa0347b8e
|
[
"Apache-2.0"
] | 545
|
2017-09-19T17:10:19.000Z
|
2022-03-07T16:55:27.000Z
|
TopQuarkAnalysis/TopPairBSM/python/RecoInput_QCD_800_cfi.py
|
SWuchterl/cmssw
|
769b4a7ef81796579af7d626da6039dfa0347b8e
|
[
"Apache-2.0"
] | 14
|
2017-10-04T09:47:21.000Z
|
2019-10-23T18:04:45.000Z
|
# Dataset path /QCDDiJetPt800to1000/Summer08_IDEAL_V9_v1/GEN-SIM-RECO
import FWCore.ParameterSet.Config as cms
def RecoInput() :
maxEvents = cms.untracked.PSet( input = cms.untracked.int32(-1) )
readFiles = cms.untracked.vstring()
secFiles = cms.untracked.vstring()
source = cms.Source ("PoolSource",fileNames = readFiles, secondaryFileNames = secFiles)
readFiles.extend( (
'/store/mc/Summer08/QCDDiJetPt800to1000/GEN-SIM-RECO/IDEAL_V9_v1/0006/08043F8E-24A0-DD11-8F7B-001EC9ED88D8.root',
'/store/mc/Summer08/QCDDiJetPt800to1000/GEN-SIM-RECO/IDEAL_V9_v1/0006/1849B37F-23A0-DD11-8597-00145ED6E7C8.root',
'/store/mc/Summer08/QCDDiJetPt800to1000/GEN-SIM-RECO/IDEAL_V9_v1/0006/3215635E-22A0-DD11-9996-0030487C1154.root',
'/store/mc/Summer08/QCDDiJetPt800to1000/GEN-SIM-RECO/IDEAL_V9_v1/0006/3E0B8639-27A0-DD11-990D-001EC9ED7E46.root',
'/store/mc/Summer08/QCDDiJetPt800to1000/GEN-SIM-RECO/IDEAL_V9_v1/0006/3ED29688-E6A0-DD11-A2A5-001EC9ED88D8.root',
'/store/mc/Summer08/QCDDiJetPt800to1000/GEN-SIM-RECO/IDEAL_V9_v1/0006/700A2B80-23A0-DD11-A73E-001EC9ED8F2B.root',
'/store/mc/Summer08/QCDDiJetPt800to1000/GEN-SIM-RECO/IDEAL_V9_v1/0006/ACA05E81-26A0-DD11-97F2-003048C26CB6.root',
'/store/mc/Summer08/QCDDiJetPt800to1000/GEN-SIM-RECO/IDEAL_V9_v1/0006/AE8F64DA-30A0-DD11-B95F-0015C5E5B335.root',
'/store/mc/Summer08/QCDDiJetPt800to1000/GEN-SIM-RECO/IDEAL_V9_v1/0006/D017F01E-2FA0-DD11-8F66-00192165CCB4.root',
'/store/mc/Summer08/QCDDiJetPt800to1000/GEN-SIM-RECO/IDEAL_V9_v1/0006/F2BF165E-22A0-DD11-B63E-0030487C1154.root',
'/store/mc/Summer08/QCDDiJetPt800to1000/GEN-SIM-RECO/IDEAL_V9_v1/0008/B4566DA1-05A2-DD11-976A-001D09645B69.root',
'/store/mc/Summer08/QCDDiJetPt800to1000/GEN-SIM-RECO/IDEAL_V9_v1/0009/8EC9BC10-8BA2-DD11-8CB5-001D09645A9D.root'
) );
secFiles.extend( (
) )
return source
| 64.433333
| 121
| 0.755303
| 253
| 1,933
| 5.664032
| 0.332016
| 0.063503
| 0.081647
| 0.284717
| 0.535939
| 0.535939
| 0.535939
| 0.535939
| 0.535939
| 0.535939
| 0
| 0.259259
| 0.106053
| 1,933
| 29
| 122
| 66.655172
| 0.570023
| 0.034661
| 0
| 0
| 0
| 0.521739
| 0.713519
| 0.708155
| 0
| 0
| 0
| 0
| 0
| 1
| 0.043478
| false
| 0
| 0.043478
| 0
| 0.130435
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
e011707e16e0b42cc4612ab06bbbca0784b4d98d
| 29
|
py
|
Python
|
ja/code_snippets/results/result.api-monitor-delete.py
|
quotecenter/documentation-1
|
f365703264761aa2b19d5d1d8ec55a3a6082ef4d
|
[
"BSD-3-Clause"
] | null | null | null |
ja/code_snippets/results/result.api-monitor-delete.py
|
quotecenter/documentation-1
|
f365703264761aa2b19d5d1d8ec55a3a6082ef4d
|
[
"BSD-3-Clause"
] | null | null | null |
ja/code_snippets/results/result.api-monitor-delete.py
|
quotecenter/documentation-1
|
f365703264761aa2b19d5d1d8ec55a3a6082ef4d
|
[
"BSD-3-Clause"
] | null | null | null |
{'deleted_monitor_id': 2081}
| 14.5
| 28
| 0.758621
| 4
| 29
| 5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.148148
| 0.068966
| 29
| 1
| 29
| 29
| 0.592593
| 0
| 0
| 0
| 0
| 0
| 0.62069
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 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
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
e017dd1ed107c137883fd5bdeae2b6b7c9dc41a5
| 214
|
py
|
Python
|
rockart_examples/exceptions.py
|
van-kalsing/rockart-examples
|
042f795bc02390167c0defb33bfa6611532704b0
|
[
"MIT"
] | null | null | null |
rockart_examples/exceptions.py
|
van-kalsing/rockart-examples
|
042f795bc02390167c0defb33bfa6611532704b0
|
[
"MIT"
] | 1
|
2020-05-04T05:57:30.000Z
|
2020-05-04T05:57:30.000Z
|
rockart_examples/exceptions.py
|
van-kalsing/rockart-examples
|
042f795bc02390167c0defb33bfa6611532704b0
|
[
"MIT"
] | null | null | null |
class RockartExamplesException(Exception):
pass
class RockartExamplesIndexError(RockartExamplesException, IndexError):
pass
class RockartExamplesValueError(RockartExamplesException, ValueError):
pass
| 23.777778
| 70
| 0.831776
| 14
| 214
| 12.714286
| 0.571429
| 0.101124
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.116822
| 214
| 8
| 71
| 26.75
| 0.941799
| 0
| 0
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.5
| 0
| 0
| 0.5
| 0
| 1
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 5
|
e02054dfec8d9efc75d1209e8a96e41673552eaf
| 270
|
py
|
Python
|
jmilkfansblog/api/hooks.py
|
xiaoyh121/program
|
6826f024cce7a4250a1dab8dba145c1f0d713286
|
[
"Apache-2.0"
] | 176
|
2016-12-11T03:24:41.000Z
|
2021-12-10T11:44:37.000Z
|
jmilkfansblog/api/hooks.py
|
xiaoyh121/program
|
6826f024cce7a4250a1dab8dba145c1f0d713286
|
[
"Apache-2.0"
] | 4
|
2018-02-07T03:31:13.000Z
|
2021-12-25T13:03:49.000Z
|
jmilkfansblog/api/hooks.py
|
xiaoyh121/program
|
6826f024cce7a4250a1dab8dba145c1f0d713286
|
[
"Apache-2.0"
] | 76
|
2016-11-13T08:57:38.000Z
|
2021-12-25T12:02:05.000Z
|
from pecan import hooks
# class DBHook(hooks.PecanHook):
# """Create a db connection instance."""
#
# def before(self, state):
# """Excute the DBHook.before() before handle the restful request."""
# state.request.db_conn = db_api.get_session()
| 27
| 77
| 0.651852
| 34
| 270
| 5.088235
| 0.735294
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.211111
| 270
| 9
| 78
| 30
| 0.812207
| 0.855556
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 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
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
e02ae0778b9aba1d0c3d265b5082533b201c3595
| 10,551
|
py
|
Python
|
fhir/resources/tests/test_substance.py
|
cstoltze/fhir.resources
|
52f99738935b7313089d89daf94d73ce7d167c9d
|
[
"BSD-3-Clause"
] | 144
|
2019-05-08T14:24:43.000Z
|
2022-03-30T02:37:11.000Z
|
fhir/resources/tests/test_substance.py
|
cstoltze/fhir.resources
|
52f99738935b7313089d89daf94d73ce7d167c9d
|
[
"BSD-3-Clause"
] | 82
|
2019-05-13T17:43:13.000Z
|
2022-03-30T16:45:17.000Z
|
fhir/resources/tests/test_substance.py
|
cstoltze/fhir.resources
|
52f99738935b7313089d89daf94d73ce7d167c9d
|
[
"BSD-3-Clause"
] | 48
|
2019-04-04T14:14:53.000Z
|
2022-03-30T06:07:31.000Z
|
# -*- coding: utf-8 -*-
"""
Profile: http://hl7.org/fhir/StructureDefinition/Substance
Release: R4
Version: 4.0.1
Build ID: 9346c8cc45
Last updated: 2019-11-01T09:29:23.356+11:00
"""
from pydantic.validators import bytes_validator # noqa: F401
from .. import fhirtypes # noqa: F401
from .. import substance
def impl_substance_1(inst):
assert inst.category[0].coding[0].code == "chemical"
assert inst.category[0].coding[0].display == "Chemical"
assert (
inst.category[0].coding[0].system
== "http://terminology.hl7.org/CodeSystem/substance-category"
)
assert inst.code.coding[0].code == "333346007"
assert inst.code.coding[0].display == "Silver nitrate 20% solution (product)"
assert inst.code.coding[0].system == "http://snomed.info/sct"
assert inst.description == "Solution for silver nitrate stain"
assert inst.id == "f204"
assert inst.identifier[0].system == "http://acme.org/identifiers/substances"
assert inst.identifier[0].value == "15970"
assert inst.instance[0].expiry == fhirtypes.DateTime.validate("2018-01-01")
assert (
inst.instance[0].identifier.system
== "http://acme.org/identifiers/substances/lot"
)
assert inst.instance[0].identifier.value == "AB94687"
assert inst.instance[0].quantity.code == "mL"
assert inst.instance[0].quantity.system == "http://unitsofmeasure.org"
assert inst.instance[0].quantity.unit == "mL"
assert float(inst.instance[0].quantity.value) == float(100)
assert inst.meta.tag[0].code == "HTEST"
assert inst.meta.tag[0].display == "test health data"
assert (
inst.meta.tag[0].system == "http://terminology.hl7.org/CodeSystem/v3-ActReason"
)
assert inst.text.status == "generated"
def test_substance_1(base_settings):
"""No. 1 tests collection for Substance.
Test File: substance-example-silver-nitrate-product.json
"""
filename = (
base_settings["unittest_data_dir"]
/ "substance-example-silver-nitrate-product.json"
)
inst = substance.Substance.parse_file(
filename, content_type="application/json", encoding="utf-8"
)
assert "Substance" == inst.resource_type
impl_substance_1(inst)
# testing reverse by generating data from itself and create again.
data = inst.dict()
assert "Substance" == data["resourceType"]
inst2 = substance.Substance(**data)
impl_substance_1(inst2)
def impl_substance_2(inst):
assert inst.category[0].coding[0].code == "drug"
assert inst.category[0].coding[0].display == "Drug or Medicament"
assert (
inst.category[0].coding[0].system
== "http://terminology.hl7.org/CodeSystem/substance-category"
)
assert inst.code.coding[0].code == "392259005"
assert inst.code.coding[0].display == (
"Amoxicillin + clavulanate potassium 875mg/125mg tablet " "(product)"
)
assert inst.code.coding[0].system == "http://snomed.info/sct"
assert inst.contained[0].id == "ingr1"
assert inst.contained[1].id == "ingr2"
assert inst.description == "Augmentin 875"
assert inst.id == "f205"
assert inst.ingredient[0].quantity.denominator.code == "mg"
assert inst.ingredient[0].quantity.denominator.system == "http://unitsofmeasure.org"
assert inst.ingredient[0].quantity.denominator.unit == "mg"
assert float(inst.ingredient[0].quantity.denominator.value) == float(1000)
assert inst.ingredient[0].quantity.numerator.code == "mg"
assert inst.ingredient[0].quantity.numerator.system == "http://unitsofmeasure.org"
assert inst.ingredient[0].quantity.numerator.unit == "mg"
assert float(inst.ingredient[0].quantity.numerator.value) == float(875)
assert inst.ingredient[0].substanceReference.reference == "#ingr1"
assert inst.ingredient[1].quantity.denominator.code == "mg"
assert inst.ingredient[1].quantity.denominator.system == "http://unitsofmeasure.org"
assert inst.ingredient[1].quantity.denominator.unit == "mg"
assert float(inst.ingredient[1].quantity.denominator.value) == float(1000)
assert inst.ingredient[1].quantity.numerator.code == "mg"
assert inst.ingredient[1].quantity.numerator.system == "http://unitsofmeasure.org"
assert inst.ingredient[1].quantity.numerator.unit == "mg"
assert float(inst.ingredient[1].quantity.numerator.value) == float(125)
assert inst.ingredient[1].substanceReference.reference == "#ingr2"
assert inst.meta.tag[0].code == "HTEST"
assert inst.meta.tag[0].display == "test health data"
assert (
inst.meta.tag[0].system == "http://terminology.hl7.org/CodeSystem/v3-ActReason"
)
assert inst.text.status == "generated"
def test_substance_2(base_settings):
"""No. 2 tests collection for Substance.
Test File: substance-example-amoxicillin-clavulanate.json
"""
filename = (
base_settings["unittest_data_dir"]
/ "substance-example-amoxicillin-clavulanate.json"
)
inst = substance.Substance.parse_file(
filename, content_type="application/json", encoding="utf-8"
)
assert "Substance" == inst.resource_type
impl_substance_2(inst)
# testing reverse by generating data from itself and create again.
data = inst.dict()
assert "Substance" == data["resourceType"]
inst2 = substance.Substance(**data)
impl_substance_2(inst2)
def impl_substance_3(inst):
assert inst.category[0].coding[0].code == "chemical"
assert inst.category[0].coding[0].display == "Chemical"
assert (
inst.category[0].coding[0].system
== "http://terminology.hl7.org/CodeSystem/substance-category"
)
assert inst.code.coding[0].code == "88480006"
assert inst.code.coding[0].display == "Potassium"
assert inst.code.coding[0].system == "http://snomed.info/sct"
assert inst.id == "f203"
assert inst.identifier[0].system == "http://acme.org/identifiers/substances"
assert inst.identifier[0].value == "1234"
assert inst.meta.tag[0].code == "HTEST"
assert inst.meta.tag[0].display == "test health data"
assert (
inst.meta.tag[0].system == "http://terminology.hl7.org/CodeSystem/v3-ActReason"
)
assert inst.text.status == "generated"
def test_substance_3(base_settings):
"""No. 3 tests collection for Substance.
Test File: substance-example-f203-potassium.json
"""
filename = (
base_settings["unittest_data_dir"] / "substance-example-f203-potassium.json"
)
inst = substance.Substance.parse_file(
filename, content_type="application/json", encoding="utf-8"
)
assert "Substance" == inst.resource_type
impl_substance_3(inst)
# testing reverse by generating data from itself and create again.
data = inst.dict()
assert "Substance" == data["resourceType"]
inst2 = substance.Substance(**data)
impl_substance_3(inst2)
def impl_substance_4(inst):
assert inst.code.coding[0].code == "406466009"
assert inst.code.coding[0].display == "House dust allergen"
assert inst.code.coding[0].system == "http://snomed.info/sct"
assert inst.id == "f201"
assert inst.meta.tag[0].code == "HTEST"
assert inst.meta.tag[0].display == "test health data"
assert (
inst.meta.tag[0].system == "http://terminology.hl7.org/CodeSystem/v3-ActReason"
)
assert inst.text.status == "generated"
def test_substance_4(base_settings):
"""No. 4 tests collection for Substance.
Test File: substance-example-f201-dust.json
"""
filename = base_settings["unittest_data_dir"] / "substance-example-f201-dust.json"
inst = substance.Substance.parse_file(
filename, content_type="application/json", encoding="utf-8"
)
assert "Substance" == inst.resource_type
impl_substance_4(inst)
# testing reverse by generating data from itself and create again.
data = inst.dict()
assert "Substance" == data["resourceType"]
inst2 = substance.Substance(**data)
impl_substance_4(inst2)
def impl_substance_5(inst):
assert inst.category[0].coding[0].code == "allergen"
assert inst.category[0].coding[0].display == "Allergen"
assert (
inst.category[0].coding[0].system
== "http://terminology.hl7.org/CodeSystem/substance-category"
)
assert inst.code.text == "apitoxin (Honey Bee Venom)"
assert inst.id == "example"
assert inst.identifier[0].system == "http://acme.org/identifiers/substances"
assert inst.identifier[0].value == "1463"
assert inst.meta.tag[0].code == "HTEST"
assert inst.meta.tag[0].display == "test health data"
assert (
inst.meta.tag[0].system == "http://terminology.hl7.org/CodeSystem/v3-ActReason"
)
assert inst.status == "active"
assert inst.text.status == "generated"
def test_substance_5(base_settings):
"""No. 5 tests collection for Substance.
Test File: substance-example.json
"""
filename = base_settings["unittest_data_dir"] / "substance-example.json"
inst = substance.Substance.parse_file(
filename, content_type="application/json", encoding="utf-8"
)
assert "Substance" == inst.resource_type
impl_substance_5(inst)
# testing reverse by generating data from itself and create again.
data = inst.dict()
assert "Substance" == data["resourceType"]
inst2 = substance.Substance(**data)
impl_substance_5(inst2)
def impl_substance_6(inst):
assert inst.code.coding[0].code == "3092008"
assert inst.code.coding[0].display == "Staphylococcus Aureus"
assert inst.code.coding[0].system == "http://snomed.info/sct"
assert inst.id == "f202"
assert inst.meta.tag[0].code == "HTEST"
assert inst.meta.tag[0].display == "test health data"
assert (
inst.meta.tag[0].system == "http://terminology.hl7.org/CodeSystem/v3-ActReason"
)
assert inst.text.status == "generated"
def test_substance_6(base_settings):
"""No. 6 tests collection for Substance.
Test File: substance-example-f202-staphylococcus.json
"""
filename = (
base_settings["unittest_data_dir"]
/ "substance-example-f202-staphylococcus.json"
)
inst = substance.Substance.parse_file(
filename, content_type="application/json", encoding="utf-8"
)
assert "Substance" == inst.resource_type
impl_substance_6(inst)
# testing reverse by generating data from itself and create again.
data = inst.dict()
assert "Substance" == data["resourceType"]
inst2 = substance.Substance(**data)
impl_substance_6(inst2)
| 37.021053
| 88
| 0.681547
| 1,323
| 10,551
| 5.366591
| 0.125472
| 0.125352
| 0.027887
| 0.043099
| 0.840282
| 0.802394
| 0.760282
| 0.715211
| 0.615352
| 0.527183
| 0
| 0.037522
| 0.174012
| 10,551
| 284
| 89
| 37.151408
| 0.777166
| 0.105109
| 0
| 0.4689
| 0
| 0
| 0.231856
| 0.023978
| 0
| 0
| 0
| 0
| 0.507177
| 1
| 0.057416
| false
| 0
| 0.014354
| 0
| 0.07177
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
e06d445f20623b568c1633d7fa192c52dcaf9bfb
| 30,612
|
py
|
Python
|
tests/test_distgit/test_generic_distgit.py
|
vfreex/doozer
|
8ad0a1234120cdc30890afbbdda1bc40e4a4fc76
|
[
"Apache-2.0"
] | null | null | null |
tests/test_distgit/test_generic_distgit.py
|
vfreex/doozer
|
8ad0a1234120cdc30890afbbdda1bc40e4a4fc76
|
[
"Apache-2.0"
] | null | null | null |
tests/test_distgit/test_generic_distgit.py
|
vfreex/doozer
|
8ad0a1234120cdc30890afbbdda1bc40e4a4fc76
|
[
"Apache-2.0"
] | null | null | null |
from __future__ import absolute_import, print_function, unicode_literals
import errno
import os
import unittest
import flexmock
import mock
from doozerlib import distgit, model
from doozerlib.assembly import AssemblyTypes
from .support import MockConfig, MockMetadata, MockRuntime, TestDistgit
class TestGenericDistGit(TestDistgit):
def setUp(self):
super(TestGenericDistGit, self).setUp()
self.dg = distgit.DistGitRepo(self.md, autoclone=False)
self.dg.runtime.group_config = model.Model()
@staticmethod
def mock_runtime(**kwargs):
def flexmock_defaults(**inner_kwargs):
params = dict(**kwargs)
for k, v in inner_kwargs.items():
if k not in params:
params[k] = v
return flexmock(**params)
# Pass in a set of defaults to flexmock, but allow caller to override
# anything they want.
return flexmock_defaults(
group_config=flexmock(
urls=flexmock(brew_image_host="brew-img-host", brew_image_namespace="brew-img-ns"),
insecure_source=False,
),
resolve_brew_image_url=lambda *_, **__: '',
working_dir="my-working-dir",
branch="some-branch",
command="some-command",
add_record=lambda *_, **__: None,
assembly_type=AssemblyTypes.STANDARD,
)
def test_init(self):
"""
Ensure that init creates the object expected
"""
self.assertIsInstance(self.dg, distgit.DistGitRepo)
def test_init_with_branch_override(self):
metadata = flexmock(runtime=self.mock_runtime(branch="original-branch"),
config=flexmock(distgit=flexmock(branch=distgit.Missing)),
name="_irrelevant_",
logger="_irrelevant_")
repo = distgit.DistGitRepo(metadata, autoclone=False)
self.assertEqual("original-branch", repo.branch)
metadata.config.distgit.branch = "new-branch"
repo = distgit.DistGitRepo(metadata, autoclone=False)
self.assertEqual("new-branch", repo.branch)
def test_init_with_autoclone(self):
flexmock(distgit.DistGitRepo).should_receive("clone").once()
distgit.DistGitRepo(self.md)
def test_clone_already_cloned(self):
# preventing tests from interacting with the real filesystem
flexmock(distgit).should_receive("Dir").and_return(flexmock(__exit__=None))
# pretenting the directory exists (already cloned)
flexmock(distgit.os.path).should_receive("isdir").and_return(True)
expected_log_msg = ("Distgit directory already exists; "
"skipping clone: my-root-dir/my-namespace/my-distgit-key")
logger = flexmock()
logger.should_receive("info").with_args(expected_log_msg).once()
metadata = flexmock(namespace="my-namespace",
distgit_key="my-distgit-key",
runtime=self.mock_runtime(local=False, branch="_irrelevant_", upcycle=False),
config=MockConfig(),
logger=logger,
prevent_cloning=False,
name="_irrelevant_")
expected_cmd = ['git', '-C', 'my-root-dir/my-namespace/my-distgit-key', 'rev-parse', 'HEAD']
(flexmock(distgit.exectools)
.should_receive("cmd_assert")
.with_args(expected_cmd, strip=True)
.and_return("abcdefg", "")
.once())
distgit.DistGitRepo(metadata, autoclone=False).clone("my-root-dir", "my-branch")
def test_clone_fails_to_create_namespace_dir(self):
# preventing tests from interacting with the real filesystem
flexmock(distgit).should_receive("Dir").and_return(flexmock(__exit__=None))
# pretenting the directory doesn't exist (not yet cloned)
flexmock(distgit.os.path).should_receive("isdir").and_return(False)
metadata = flexmock(config=MockConfig(),
runtime=self.mock_runtime(local=True,
branch="_irrelevant_",
command="_irrelevant_",
rhpkg_config_lst=[]),
name="_irrelevant_",
logger="_irrelevant_",
namespace="_irrelevant_",
prevent_cloning=False,
distgit_key="_irrelevant_")
repo = distgit.DistGitRepo(metadata, autoclone=False)
# simulating a "File exists" error
(flexmock(distgit.os)
.should_receive("mkdir")
.and_raise(OSError(errno.EEXIST, os.strerror(errno.EEXIST))))
try:
repo.clone("my-root-dir", "my-branch")
except OSError:
self.fail("Should not have raised a \"dir already exists\" exception")
except:
pass # doesn't matter if something fails at a later point
# simulating any other OSError
(flexmock(distgit.os)
.should_receive("mkdir")
.and_raise(OSError))
self.assertRaises(OSError, repo.clone, "my-root-dir", "my-branch")
def test_clone_with_fake_distgit(self):
# preventing tests from interacting with the real filesystem
flexmock(distgit).should_receive("Dir").and_return(flexmock(__exit__=None))
flexmock(distgit.os).should_receive("mkdir").replace_with(lambda _: None)
# pretenting the directory doesn't exist (not yet cloned)
flexmock(distgit.os.path).should_receive("isdir").and_return(False)
expected_log_msg = ("Creating local build dir: "
"my-root-dir/my-namespace/my-distgit-key")
logger = flexmock()
logger.should_receive("info").with_args(expected_log_msg).once()
expected_cmd = ["mkdir", "-p", "my-root-dir/my-namespace/my-distgit-key"]
(flexmock(distgit.exectools)
.should_receive("cmd_assert")
.with_args(expected_cmd)
.once())
expected_cmd = ['git', '-C', 'my-root-dir/my-namespace/my-distgit-key', 'rev-parse', 'HEAD']
(flexmock(distgit.exectools)
.should_receive("cmd_assert")
.with_args(expected_cmd, strip=True)
.and_return("abcdefg", "")
.once())
metadata = flexmock(config=MockConfig(content="_irrelevant_"),
runtime=self.mock_runtime(local=True,
command="images:rebase",
branch="_irrelevant_",
rhpkg_config_lst=[]),
namespace="my-namespace",
distgit_key="my-distgit-key",
prevent_cloning=False,
logger=logger,
name="_irrelevant_")
distgit.DistGitRepo(metadata, autoclone=False).clone("my-root-dir", "my-branch")
def test_clone_images_build_command(self):
# preventing tests from interacting with the real filesystem
flexmock(distgit).should_receive("Dir").and_return(flexmock(__exit__=None))
flexmock(distgit.os).should_receive("mkdir").replace_with(lambda _: None)
# pretenting the directory doesn't exist (not yet cloned)
flexmock(distgit.os.path).should_receive("isdir").and_return(False)
expected_log_msg = ("Cloning distgit repository [branch:my-branch] "
"into: my-root-dir/my-namespace/my-distgit-key")
logger = flexmock()
logger.should_receive("info").with_args(expected_log_msg).once()
expected_cmd = [
"timeout", "999", "rhpkg", "clone", "my-qualified-name",
"my-root-dir/my-namespace/my-distgit-key", "--branch", "my-branch"]
(flexmock(distgit.exectools)
.should_receive("cmd_assert")
.with_args(expected_cmd, retries=3, set_env=object)
.once()
.and_return(None))
expected_cmd = ['git', '-C', 'my-root-dir/my-namespace/my-distgit-key', 'rev-parse', 'HEAD']
(flexmock(distgit.exectools)
.should_receive("cmd_assert")
.with_args(expected_cmd, strip=True)
.and_return("abcdefg", "")
.once())
expected_warning = ("Warning: images:rebase was skipped and "
"therefore your local build will be sourced "
"from the current dist-git contents and not "
"the typical GitHub source. ")
(flexmock(distgit)
.should_receive("yellow_print")
.with_args(expected_warning)
.once())
metadata = flexmock(config=MockConfig(content="_irrelevant_"),
runtime=self.mock_runtime(local=False,
command="images:build",
global_opts={"rhpkg_clone_timeout": 999},
user=None,
branch="_irrelevant_",
rhpkg_config_lst=[],
downstream_commitish_overrides={}),
namespace="my-namespace",
distgit_key="my-distgit-key",
qualified_name="my-qualified-name",
prevent_cloning=False,
logger=logger,
name="_irrelevant_")
distgit.DistGitRepo(metadata, autoclone=False).clone("my-root-dir", "my-branch")
def test_clone_cmd_with_user(self):
# preventing tests from interacting with the real filesystem
flexmock(distgit).should_receive("Dir").and_return(flexmock(__exit__=None))
flexmock(distgit.os).should_receive("mkdir").replace_with(lambda _: None)
# pretenting the directory doesn't exist (not yet cloned)
flexmock(distgit.os.path).should_receive("isdir").and_return(False)
# avoid warning print in the middle of the test progress report
flexmock(distgit).should_receive("yellow_print").replace_with(lambda _: None)
expected_cmd = [
"timeout", "999", "rhpkg", "--user=my-user", "clone", "my-qualified-name",
"my-root-dir/my-namespace/my-distgit-key", "--branch", "my-branch"]
(flexmock(distgit.exectools)
.should_receive("cmd_assert")
.with_args(expected_cmd, retries=3, set_env=object)
.once()
.and_return(None))
expected_cmd = ['git', '-C', 'my-root-dir/my-namespace/my-distgit-key', 'rev-parse', 'HEAD']
(flexmock(distgit.exectools)
.should_receive("cmd_assert")
.with_args(expected_cmd, strip=True)
.and_return("abcdefg", "")
.once())
metadata = flexmock(config=MockConfig(content="_irrelevant_"),
runtime=self.mock_runtime(local=False,
command="images:build",
global_opts={"rhpkg_clone_timeout": 999},
user="my-user",
branch="_irrelevant_",
rhpkg_config_lst=[],
downstream_commitish_overrides={}),
namespace="my-namespace",
distgit_key="my-distgit-key",
qualified_name="my-qualified-name",
logger=flexmock(info=lambda _: None),
prevent_cloning=False,
name="_irrelevant_", )
distgit.DistGitRepo(metadata, autoclone=False).clone("my-root-dir", "my-branch")
def test_merge_branch(self):
# pretenting there is no Dockerfile nor .oit directory
flexmock(distgit.os.path).should_receive("isfile").and_return(False)
flexmock(distgit.os.path).should_receive("isdir").and_return(False)
expected_1st_log_msg = "Switching to branch: my-target"
logger = flexmock()
logger.should_receive("info").with_args(expected_1st_log_msg).once().ordered()
expected_1st_cmd = ["rhpkg", "switch-branch", "my-target"]
(flexmock(distgit.exectools)
.should_receive("cmd_assert")
.with_args(expected_1st_cmd, retries=3)
.once()
.ordered())
expected_2nd_log_msg = "Merging source branch history over current branch"
logger.should_receive("info").with_args(expected_2nd_log_msg).once().ordered()
expected_2nd_cmd = [
"git", "merge", "--allow-unrelated-histories", "-m",
"Merge branch my-branch into my-target", "my-branch"]
expected_on_retry = ["git", "reset", "--hard", "my-target"]
(flexmock(distgit.exectools)
.should_receive("cmd_assert")
.with_args(expected_2nd_cmd, retries=3, on_retry=expected_on_retry)
.once()
.ordered())
metadata = flexmock(config=flexmock(distgit=flexmock(branch="my-branch")),
logger=logger,
runtime=self.mock_runtime(branch="_irrelevant_",
rhpkg_config_lst=[]),
name="_irrelevant_")
distgit.DistGitRepo(metadata, autoclone=False).merge_branch("my-target")
def test_merge_branch_allow_overwrite(self):
# pretenting there is no Dockerfile nor .oit directory
flexmock(distgit.os.path).should_receive("isfile").and_return(False)
flexmock(distgit.os.path).should_receive("isdir").and_return(False)
expected_1st_log_msg = "Switching to branch: my-target"
logger = flexmock()
logger.should_receive("info").with_args(expected_1st_log_msg).once().ordered()
expected_1st_cmd = ["rhpkg", "switch-branch", "my-target"]
(flexmock(distgit.exectools)
.should_receive("cmd_assert")
.with_args(expected_1st_cmd, retries=3)
.once()
.ordered())
expected_2nd_log_msg = "Merging source branch history over current branch"
logger.should_receive("info").with_args(expected_2nd_log_msg).once().ordered()
expected_2nd_cmd = [
"git", "merge", "--allow-unrelated-histories", "-m",
"Merge branch my-branch into my-target", "my-branch"]
expected_on_retry = ["git", "reset", "--hard", "my-target"]
(flexmock(distgit.exectools)
.should_receive("cmd_assert")
.with_args(expected_2nd_cmd, retries=3, on_retry=expected_on_retry)
.once()
.ordered())
metadata = flexmock(config=flexmock(distgit=flexmock(branch="my-branch")),
logger=logger,
runtime=self.mock_runtime(branch="_irrelevant_",
rhpkg_config_lst=[]),
name="_irrelevant_")
(distgit.DistGitRepo(metadata, autoclone=False)
.merge_branch("my-target", allow_overwrite=True))
def test_merge_branch_dockerfile_or_oit_dir_already_present(self):
# pretenting there is a Dockerfile present
flexmock(distgit.os.path).should_receive("isfile").and_return(True)
# avoid actually executing any command
(flexmock(distgit.exectools)
.should_receive("cmd_assert")
.replace_with(lambda *_, **__: None))
metadata = flexmock(config=flexmock(distgit=flexmock(branch="my-branch")),
runtime=self.mock_runtime(branch="_irrelevant_",
rhpkg_config_lst=[]),
logger=flexmock(info=lambda _: None),
name="_irrelevant_")
repo = distgit.DistGitRepo(metadata, autoclone=False)
try:
repo.merge_branch("my-target")
self.fail()
except IOError as e:
expected_msg = ("Unable to continue merge. "
"Dockerfile found in target branch. "
"Use --allow-overwrite to force.")
self.assertEqual(expected_msg, str(e))
def test_source_path(self):
# preventing tests from interacting with the real filesystem
flexmock(distgit).should_receive("Dir").and_return(flexmock(__exit__=None))
flexmock(distgit.os.path).should_receive("isdir").and_return(True)
metadata = flexmock(runtime=self.mock_runtime(resolve_source=lambda *_: "source-root",
branch="_irrelevant_"),
config=flexmock(content=flexmock(source=flexmock(path="sub-path")),
distgit=flexmock(branch="_irrelevant_")),
logger=flexmock(info=lambda _: None),
config_filename="_irrelevant_",
name="_irrelevant_")
repo = distgit.DistGitRepo(metadata, autoclone=False)
self.assertEqual("source-root/sub-path", repo.source_path())
def test_source_path_without_sub_path(self):
# preventing tests from interacting with the real filesystem
flexmock(distgit).should_receive("Dir").and_return(flexmock(__exit__=None))
flexmock(distgit.os.path).should_receive("isdir").and_return(True)
metadata = flexmock(runtime=self.mock_runtime(resolve_source=lambda *_: "source-root",
branch="_irrelevant_"),
config=flexmock(content=flexmock(source=flexmock(path=distgit.Missing)),
distgit=flexmock(branch="_irrelevant_")),
logger=flexmock(info=lambda _: None),
config_filename="_irrelevant_",
name="_irrelevant_")
repo = distgit.DistGitRepo(metadata, autoclone=False)
self.assertEqual("source-root", repo.source_path())
def test_commit_local(self):
flexmock(distgit.exectools).should_receive("cmd_assert").times(0)
metadata = flexmock(runtime=self.mock_runtime(local=True, branch="_irrelevant_"),
config=flexmock(distgit=flexmock(branch="_irrelevant_")),
logger=flexmock(info=lambda _: None),
name="_irrelevant_")
repo = distgit.DistGitRepo(metadata, autoclone=False)
self.assertEqual("", repo.commit("commit msg"))
def test_commit_log_diff_failed(self):
# preventing tests from interacting with the real filesystem
flexmock(distgit).should_receive("Dir").and_return(flexmock(__exit__=None))
# simulating a failure when running "git diff Dockerfile"
(flexmock(distgit.exectools)
.should_receive("cmd_gather")
.with_args(["git", "diff", "Dockerfile"])
.and_return((1, "stdout", "stderr")))
metadata = flexmock(runtime=self.mock_runtime(local=False, branch="_irrelevant_"),
config=flexmock(distgit=flexmock(branch="_irrelevant_")),
logger=flexmock(info=lambda _: None),
name="_irrelevant_")
(flexmock(distgit.DistGitRepo).should_receive("_get_diff").once().and_raise(ChildProcessError, "Command returned non-zero exit status: Failed fetching distgit diff"))
repo = distgit.DistGitRepo(metadata, autoclone=False)
try:
repo.commit("commit msg", log_diff=True)
self.fail()
except ChildProcessError as e:
expected_msg = ("Command returned non-zero exit status: "
"Failed fetching distgit diff")
self.assertEqual(expected_msg, str(e))
def test_commit_log_diff_succeeded(self):
# preventing tests from interacting with the real filesystem
flexmock(distgit).should_receive("Dir").and_return(flexmock(__exit__=None))
# avoid actually executing any command
(flexmock(distgit.exectools)
.should_receive("cmd_gather")
.and_return((0, "stdout", "stderr")))
metadata = flexmock(distgit_key="my-distgit-key",
runtime=self.mock_runtime(local=False, branch="_irrelevant_"),
config=flexmock(distgit=flexmock(branch="_irrelevant_")),
logger=flexmock(info=lambda _: None),
name="_irrelevant_")
(flexmock(metadata.runtime)
.should_receive("add_distgits_diff")
.with_args("my-distgit-key", "stdout")
.once()
.and_return(None))
(flexmock(distgit.DistGitRepo).should_receive("_get_diff").once().and_return("stdout"))
distgit.DistGitRepo(metadata, autoclone=False).commit("commit msg", log_diff=True)
def test_commit_with_source_sha(self):
# preventing tests from interacting with the real filesystem
flexmock(distgit).should_receive("Dir").and_return(flexmock(__exit__=None))
expected_1st_cmd = ["git", "add", "-A", "."]
(flexmock(distgit.exectools)
.should_receive("cmd_assert")
.with_args(expected_1st_cmd)
.once()
.ordered())
expected_2nd_cmd = [
"git", "commit", "--allow-empty", "-m",
"# commit msg\nMaxFileSize: 104857600\njenkins.url: null\n"]
(flexmock(distgit.exectools)
.should_receive("cmd_assert")
.with_args(expected_2nd_cmd)
.once()
.ordered())
(flexmock(distgit.exectools)
.should_receive("cmd_gather")
.with_args(["git", "rev-parse", "HEAD"])
.and_return((0, "sha-from-stdout", "")))
metadata = flexmock(distgit_key="my-distgit-key",
runtime=self.mock_runtime(local=False,
branch="_irrelevant_",
add_distgits_diff=lambda: None),
config=flexmock(distgit=flexmock(branch="_irrelevant_")),
logger=flexmock(info=lambda _: None),
name="_irrelevant_")
repo = distgit.DistGitRepo(metadata, autoclone=False)
# @TODO: find out how/when source_sha gets assigned
repo.source_sha = "my-source-sha"
self.assertEqual("sha-from-stdout", repo.commit("commit msg"))
def test_commit_without_source_sha(self):
# preventing tests from interacting with the real filesystem
flexmock(distgit).should_receive("Dir").and_return(flexmock(__exit__=None))
expected_1st_cmd = ["git", "add", "-A", "."]
(flexmock(distgit.exectools)
.should_receive("cmd_assert")
.with_args(expected_1st_cmd)
.once()
.ordered())
expected_2nd_cmd = [
"git", "commit", "--allow-empty", "-m",
"# commit msg\nMaxFileSize: 104857600\njenkins.url: null\n"]
(flexmock(distgit.exectools)
.should_receive("cmd_assert")
.with_args(expected_2nd_cmd)
.once()
.ordered())
(flexmock(distgit.exectools)
.should_receive("cmd_gather")
.with_args(["git", "rev-parse", "HEAD"])
.and_return((0, "sha-from-stdout", "")))
metadata = flexmock(distgit_key="my-distgit-key",
runtime=self.mock_runtime(local=False,
branch="_irrelevant_",
add_distgits_diff=lambda: None),
config=flexmock(distgit=flexmock(branch="_irrelevant_")),
logger=flexmock(info=lambda _: None),
name="_irrelevant_")
repo = distgit.DistGitRepo(metadata, autoclone=False)
self.assertEqual("sha-from-stdout", repo.commit("commit msg"))
def test_commit_failed_fetching_sha(self):
# preventing tests from interacting with the real filesystem
flexmock(distgit).should_receive("Dir").and_return(flexmock(__exit__=None))
# simulating a failure when fetching the commit sha
(flexmock(distgit.exectools)
.should_receive("cmd_gather")
.and_return((0, "", "")) # git add
.and_return((0, "", "")) # git commit
.and_return((1, "", ""))) # git rev-parse
metadata = flexmock(distgit_key="my-distgit-key",
runtime=self.mock_runtime(local=False,
branch="_irrelevant_",
add_distgits_diff=lambda: None),
config=flexmock(distgit=flexmock(branch="_irrelevant_")),
logger=flexmock(info=lambda _: None),
name="_irrelevant_")
repo = distgit.DistGitRepo(metadata, autoclone=False)
repo.distgit_dir = "my-distgit-dir"
try:
repo.commit("commit msg")
self.fail()
except IOError as e:
expected_msg = ("Command returned non-zero exit status: "
"Failure fetching commit SHA for my-distgit-dir")
self.assertEqual(expected_msg, str(e))
def test_tag_local(self):
flexmock(distgit.exectools).should_receive("cmd_gather").times(0)
metadata = flexmock(runtime=self.mock_runtime(local=True, branch="_irrelevant_"),
config=flexmock(distgit=flexmock(branch="_irrelevant_")),
logger=flexmock(info=lambda _: None),
name="_irrelevant_")
repo = distgit.DistGitRepo(metadata, autoclone=False)
self.assertEqual("", repo.tag("my-version", "my-release"))
def test_tag_no_version(self):
flexmock(distgit.exectools).should_receive("cmd_gather").times(0)
metadata = flexmock(runtime=self.mock_runtime(local=False, branch="_irrelevant_"),
config=flexmock(distgit=flexmock(branch="_irrelevant_")),
logger=flexmock(info=lambda _: None),
name="_irrelevant_")
repo = distgit.DistGitRepo(metadata, autoclone=False)
self.assertIsNone(repo.tag(None, "my-release"))
def test_tag_no_release(self):
# preventing tests from interacting with the real filesystem
flexmock(distgit).should_receive("Dir").and_return(flexmock(__exit__=None))
(flexmock(distgit.exectools)
.should_receive("cmd_gather")
.with_args(["git", "tag", "-f", "my-version", "-m", "my-version"])
.once()
.and_return(None))
expected_log_msg = "Adding tag to local repo: my-version"
logger = flexmock()
logger.should_receive("info").with_args(expected_log_msg).once()
metadata = flexmock(runtime=self.mock_runtime(local=False, branch="_irrelevant_"),
config=flexmock(distgit=flexmock(branch="_irrelevant_")),
logger=logger,
name="_irrelevant_")
distgit.DistGitRepo(metadata, autoclone=False).tag("my-version", None)
def test_tag_with_release(self):
# preventing tests from interacting with the real filesystem
flexmock(distgit).should_receive("Dir").and_return(flexmock(__exit__=None))
(flexmock(distgit.exectools)
.should_receive("cmd_gather")
.with_args(["git", "tag", "-f", "my-version-my-release", "-m", "my-version-my-release"])
.once()
.and_return(None))
expected_log_msg = "Adding tag to local repo: my-version-my-release"
logger = flexmock()
logger.should_receive("info").with_args(expected_log_msg).once()
metadata = flexmock(runtime=self.mock_runtime(local=False, branch="_irrelevant_"),
config=flexmock(distgit=flexmock(branch="_irrelevant_")),
logger=logger,
name="_irrelevant_")
distgit.DistGitRepo(metadata, autoclone=False).tag("my-version", "my-release")
def test_logging(self):
"""
Ensure that logs work
"""
msg = "Hey there!"
self.dg.logger.info(msg)
actual = self.stream.getvalue()
self.assertIn(msg, actual)
def test_add_missing_pkgs_succeed(self):
md = MockMetadata(MockRuntime(self.logger))
d = distgit.ImageDistGitRepo(md, autoclone=False)
d._add_missing_pkgs("haproxy")
self.assertEqual(1, len(d.runtime.missing_pkgs))
self.assertIn("distgit_key image is missing package haproxy", d.runtime.missing_pkgs)
@mock.patch("requests.head")
def test_cgit_file_available(self, mocked_head):
meta = MockMetadata(MockRuntime(self.logger))
cgit_url = "http://distgit.example.com/cgit/containers/foo/plain/some_path/some_file.txt?h=some-branch&id=abcdefg"
meta.cgit_file_url = lambda *args, **kwargs: cgit_url
dg = distgit.ImageDistGitRepo(meta, autoclone=False)
dg.sha = "abcdefg"
mocked_head.return_value.status_code = 404
existence, url = dg.cgit_file_available("some_path/some_file.txt")
self.assertEqual(url, cgit_url)
self.assertFalse(existence)
mocked_head.return_value.status_code = 200
existence, url = dg.cgit_file_available("some_path/some_file.txt")
self.assertEqual(url, cgit_url)
self.assertTrue(existence)
mocked_head.return_value.status_code = 500
mocked_head.return_value.raise_for_status.side_effect = IOError
with self.assertRaises(IOError):
dg.cgit_file_available("some_path/some_file.txt")
if __name__ == "__main__":
unittest.main()
| 43.793991
| 174
| 0.577747
| 3,078
| 30,612
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| 0.070872
| 0.036853
| 0.046067
| 0.783133
| 0.759686
| 0.743917
| 0.72927
| 0.70098
| 0.67151
| 0
| 0.003774
| 0.307494
| 30,612
| 698
| 175
| 43.856734
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| 0.001953
| 0.155031
| 0.021679
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| 0.076172
| 1
| 0.056641
| false
| 0.001953
| 0.017578
| 0
| 0.080078
| 0.005859
| 0
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| null | 0
| 0
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0
| 5
|
0ee9b1bd5a9632fd5268b5efb0952b1cb3851af2
| 55,157
|
py
|
Python
|
macapype/pipelines/full_pipelines.py
|
Macatools/macapype
|
50820e2ab948c91c5362771d51688edd09b72499
|
[
"BSD-3-Clause"
] | 7
|
2020-07-04T04:04:03.000Z
|
2022-03-24T14:35:45.000Z
|
macapype/pipelines/full_pipelines.py
|
Macatools/macapype
|
50820e2ab948c91c5362771d51688edd09b72499
|
[
"BSD-3-Clause"
] | 95
|
2020-01-02T16:41:20.000Z
|
2021-12-07T15:50:41.000Z
|
macapype/pipelines/full_pipelines.py
|
Macatools/macapype
|
50820e2ab948c91c5362771d51688edd09b72499
|
[
"BSD-3-Clause"
] | 9
|
2019-11-14T12:46:14.000Z
|
2022-01-26T09:44:21.000Z
|
import nipype.interfaces.utility as niu
import nipype.pipeline.engine as pe
from nipype.interfaces import fsl
from nipype.interfaces import ants
from ..utils.utils_nodes import NodeParams
from macapype.nodes.correct_bias import T1xT2BiasFieldCorrection
from macapype.nodes.register import IterREGBET
from macapype.nodes.prepare import padding_cropped_img
from .prepare import (create_short_preparation_pipe,
create_short_preparation_FLAIR_pipe,
create_short_preparation_MD_pipe,
create_short_preparation_T1_pipe,
create_long_multi_preparation_pipe,
create_long_single_preparation_pipe,)
from .segment import (create_old_segment_pipe,
create_native_old_segment_pipe,
create_segment_atropos_pipe,
create_segment_atropos_seg_pipe,
create_mask_from_seg_pipe,
create_5tt_pipe)
from .correct_bias import (create_masked_correct_bias_pipe,
create_correct_bias_pipe)
from .register import (create_register_NMT_pipe, create_reg_seg_pipe)
from .extract_brain import (create_extract_pipe,
create_extract_T1_pipe)
from .surface import create_nii_to_mesh_pipe, create_nii_to_mesh_fs_pipe
from macapype.utils.misc import parse_key, list_input_files
###############################################################################
# SPM based segmentation (from: Régis Trapeau)
# -soft SPM or SPM_T1
###############################################################################
def create_full_spm_subpipes(
params_template, params={}, name='full_spm_subpipes',
pad=False, space='template'):
""" Description: SPM based segmentation pipeline from T1w and T2w images
in template space
Processing steps:
- Data preparation (short, with betcrop or crop)
- debias using T1xT2BiasFieldCorrection (using mask is betcrop)
- registration to template file with IterREGBET
- SPM segmentation the old way (SPM8, not dartel based)
Params:
- short_data_preparation_pipe (see :class:`create_short_preparation_pipe \
<macapype.pipelines.prepare.create_short_preparation_pipe>`)
- debias (see :class:`T1xT2BiasFieldCorrection \
<macapype.nodes.correct_bias.T1xT2BiasFieldCorrection>`) - also available \
as :ref:`indiv_params <indiv_params>`
- reg (see :class:`IterREGBET <macapype.nodes.register.IterREGBET>`) - \
also available as :ref:`indiv_params <indiv_params>`
- native_old_segment_pipe (see :class:`create_native_old_segment_pipe \
<macapype.pipelines.segment.create_old_segment_pipe>`)
Inputs:
inputnode:
list_T1:
T1 file names
list_T2:
T2 file names
indiv_params (opt):
dict with individuals parameters for some nodes
arguments:
params_template:
dict of template files containing brain_template and priors \
(list of template based segmented tissues)
params:
dictionary of node sub-parameters (from a json file)
name:
pipeline name (default = "full_spm_subpipes")
Outputs:
old_segment_pipe.thresh_gm.out_file:
segmented grey matter in template space
old_segment_pipe.thresh_wm.out_file:
segmented white matter in template space
old_segment_pipe.thresh_csf.out_file:
segmented csf in template space
"""
print("Full pipeline name: ", name)
# Creating pipeline
seg_pipe = pe.Workflow(name=name)
# Creating input node
inputnode = pe.Node(
niu.IdentityInterface(fields=['list_T1', 'list_T2', 'indiv_params']),
name='inputnode'
)
# output node
outputnode = pe.Node(
niu.IdentityInterface(fields=['brain_mask', 'segmented_brain_mask']),
name='outputnode')
# preprocessing
if 'long_single_preparation_pipe' in params.keys():
data_preparation_pipe = create_long_single_preparation_pipe(
params=parse_key(params, "long_single_preparation_pipe"))
elif 'long_multi_preparation_pipe' in params.keys():
data_preparation_pipe = create_long_multi_preparation_pipe(
params=parse_key(params, "long_multi_preparation_pipe"))
elif 'short_preparation_pipe' in params.keys():
data_preparation_pipe = create_short_preparation_pipe(
params=parse_key(params, "short_preparation_pipe"))
else:
print("Error, short_preparation_pipe, long_single_preparation_pipe or\
long_multi_preparation_pipe was not found in params, skipping")
test_node = pe.Node(niu.Function(input_names=['list_T1', 'list_T2'],
output_names=[''],
function=list_input_files),
name="test_node")
seg_pipe.connect(inputnode, 'list_T1',
test_node, 'list_T1')
seg_pipe.connect(inputnode, 'list_T2',
test_node, 'list_T2')
return seg_pipe
seg_pipe.connect(inputnode, 'list_T1',
data_preparation_pipe, 'inputnode.list_T1')
seg_pipe.connect(inputnode, 'list_T2',
data_preparation_pipe, 'inputnode.list_T2')
seg_pipe.connect(inputnode, 'indiv_params',
data_preparation_pipe, 'inputnode.indiv_params')
# Bias correction of cropped images
debias = NodeParams(T1xT2BiasFieldCorrection(),
params=parse_key(params, "debias"),
name='debias')
seg_pipe.connect(data_preparation_pipe, 'outputnode.preproc_T1',
debias, 't1_file')
seg_pipe.connect(data_preparation_pipe, 'outputnode.preproc_T2',
debias, 't2_file')
seg_pipe.connect(inputnode, ('indiv_params', parse_key, "debias"),
debias, 'indiv_params')
if 'bet_crop' in parse_key(params, "short_preparation_pipe"):
seg_pipe.connect(data_preparation_pipe, 'bet_crop.mask_file',
debias, 'b')
else:
debias.inputs.bet = 1
if pad:
print("Padding mask in native space")
pad_mask = pe.Node(
niu.Function(
input_names=['cropped_img_file', 'orig_img_file',
'indiv_crop'],
output_names=['padded_img_file'],
function=padding_cropped_img),
name="pad_mask")
seg_pipe.connect(debias, 'debiased_mask_file',
pad_mask, "cropped_img_file")
seg_pipe.connect(data_preparation_pipe, "av_T1.avg_img",
pad_mask, "orig_img_file")
seg_pipe.connect(inputnode, "indiv_params", pad_mask, "indiv_crop")
seg_pipe.connect(pad_mask, "padded_img_file",
outputnode, "brain_mask")
else:
seg_pipe.connect(debias, 'debiased_mask_file',
outputnode, "brain_mask")
# Iterative registration to the INIA19 template
reg = NodeParams(IterREGBET(),
params=parse_key(params, "reg"),
name='reg')
reg.inputs.refb_file = params_template["template_brain"]
seg_pipe.connect(debias, 't1_debiased_file',
reg, 'inw_file')
seg_pipe.connect(debias, 't1_debiased_brain_file',
reg, 'inb_file')
seg_pipe.connect(inputnode, ('indiv_params', parse_key, "reg"),
reg, 'indiv_params')
# Compute brain mask using old_segment of SPM and postprocessing on
# tissues' masks
if "old_segment_pipe" not in params.keys():
print("No segmentation, skipping")
return seg_pipe
if space == "template":
old_segment_pipe = create_old_segment_pipe(
params_template, params=parse_key(params, "old_segment_pipe"))
seg_pipe.connect(reg, 'warp_file',
old_segment_pipe, 'inputnode.T1')
seg_pipe.connect(inputnode, 'indiv_params',
old_segment_pipe, 'inputnode.indiv_params')
elif space == "native":
old_segment_pipe = create_native_old_segment_pipe(
params_template, params=parse_key(params, "old_segment_pipe"))
seg_pipe.connect(reg, 'warp_file',
old_segment_pipe, 'inputnode.T1')
seg_pipe.connect(reg, 'inv_transfo_file',
old_segment_pipe, 'inputnode.inv_transfo_file')
seg_pipe.connect(debias, 't1_debiased_brain_file',
old_segment_pipe, 'inputnode.native_T1')
seg_pipe.connect(inputnode, 'indiv_params',
old_segment_pipe, 'inputnode.indiv_params')
else:
print("Error, space={}".format(space))
return seg_pipe
if "mask_from_seg_pipe" in params.keys():
mask_from_seg_pipe = create_mask_from_seg_pipe(
params=parse_key(params, "mask_from_seg_pipe"))
seg_pipe.connect(old_segment_pipe, 'outputnode.threshold_csf',
mask_from_seg_pipe, 'inputnode.mask_csf')
seg_pipe.connect(old_segment_pipe, 'outputnode.threshold_wm',
mask_from_seg_pipe, 'inputnode.mask_wm')
seg_pipe.connect(old_segment_pipe, 'outputnode.threshold_gm',
mask_from_seg_pipe, 'inputnode.mask_gm')
seg_pipe.connect(inputnode, 'indiv_params',
mask_from_seg_pipe, 'inputnode.indiv_params')
if pad and space == "native":
print("Padding seg mask in native space")
pad_seg_mask = pe.Node(
niu.Function(
input_names=['cropped_img_file', 'orig_img_file',
'indiv_crop'],
output_names=['padded_img_file'],
function=padding_cropped_img),
name="pad_seg_mask")
seg_pipe.connect(mask_from_seg_pipe,
'merge_indexed_mask.indexed_mask',
pad_seg_mask, "cropped_img_file")
seg_pipe.connect(data_preparation_pipe, "av_T1.avg_img",
pad_seg_mask, "orig_img_file")
seg_pipe.connect(inputnode, "indiv_params",
pad_seg_mask, "indiv_crop")
seg_pipe.connect(pad_seg_mask, "padded_img_file",
outputnode, "segmented_brain_mask")
else:
seg_pipe.connect(mask_from_seg_pipe,
'merge_indexed_mask.indexed_mask',
outputnode, 'segmented_brain_mask')
if space == 'template':
# not mandatory
if "nii_to_mesh_fs_pipe" in params.keys():
nii_to_mesh_fs_pipe = create_nii_to_mesh_fs_pipe(
params=parse_key(params, "nii_to_mesh_fs_pipe"))
seg_pipe.connect(reg, 'warp_file',
nii_to_mesh_fs_pipe, 'inputnode.reg_brain_file')
seg_pipe.connect(old_segment_pipe, 'outputnode.threshold_wm',
nii_to_mesh_fs_pipe, 'inputnode.wm_mask_file')
seg_pipe.connect(inputnode, 'indiv_params',
nii_to_mesh_fs_pipe, 'inputnode.indiv_params')
return seg_pipe
###############################################################################
# FLAIR after SPM based segmentation
# -soft SPM_FLAIR or SPM_T1_FLAIR
###############################################################################
def create_transfo_FLAIR_pipe(params_template, params={},
name='transfo_FLAIR_pipe'):
""" Description: apply tranformation to FLAIR, MD and FA if necssary
Processing steps:
- -coreg FA on T1
- apply coreg on MD
- debias using T1xT2BiasFieldCorrection (using mask is betcrop)
- registration to template file with IterREGBET
- SPM segmentation the old way (SPM8, not dartel based)
Params:
- short_data_preparation_pipe (see :class:`create_short_preparation_pipe \
<macapype.pipelines.prepare.create_short_preparation_pipe>`)
- debias (see :class:`T1xT2BiasFieldCorrection \
<macapype.nodes.correct_bias.T1xT2BiasFieldCorrection>`) - also available \
as :ref:`indiv_params <indiv_params>`
- reg (see :class:`IterREGBET <macapype.nodes.register.IterREGBET>`) - \
also available as :ref:`indiv_params <indiv_params>`
- old_segment_pipe (see :class:`create_old_segment_pipe \
<macapype.pipelines.segment.create_old_segment_pipe>`)
- nii_to_mesh_fs_pipe (see :class:`create_nii_to_mesh_fs_pipe \
<macapype.pipelines.surface.create_nii_to_mesh_fs_pipe>`)
Inputs:
inputnode:
SS_T1:
T1 file names
orig_T1:
T2 file names
FLAIR:
flair file name
transfo_file:
Transformation file between native to template
inv_transfo_file:
Transformation file between template and native
threshold_wm:
gm binary tissue in template space
indiv_params (opt):
dict with individuals parameters for some nodes
outputnode:
coreg_FLAIR:
FLAIR coregistered to T1
norm_FLAIR:
FLAIR normalised in template space
arguments:
params_template:
dict of template files containing brain_template and priors \
(list of template based segmented tissues)
params:
dictionary of node sub-parameters (from a json file)
name:
pipeline name (default = "full_spm_subpipes")
Outputs:
"""
print("Transfo FLAIR pipe name: ", name)
# Creating pipeline
transfo_pipe = pe.Workflow(name=name)
# Creating input node
inputnode = pe.Node(
niu.IdentityInterface(
fields=['orig_T1', 'FLAIR', 'lin_transfo_file']),
name='inputnode'
)
data_preparation_pipe = create_short_preparation_FLAIR_pipe(
params=parse_key(params, "short_preparation_pipe"))
transfo_pipe.connect(inputnode, 'orig_T1',
data_preparation_pipe, 'inputnode.orig_T1')
transfo_pipe.connect(inputnode, 'FLAIR',
data_preparation_pipe, 'inputnode.FLAIR')
# apply norm to FLAIR
norm_lin_FLAIR = pe.Node(fsl.ApplyXFM(), name="norm_lin_FLAIR")
norm_lin_FLAIR.inputs.reference = params_template["template_brain"]
transfo_pipe.connect(data_preparation_pipe, 'outputnode.coreg_FLAIR',
norm_lin_FLAIR, 'in_file')
transfo_pipe.connect(inputnode, 'lin_transfo_file',
norm_lin_FLAIR, 'in_matrix_file')
# Creating output node
outputnode = pe.Node(
niu.IdentityInterface(
fields=['coreg_FLAIR', 'norm_FLAIR']),
name='outputnode'
)
transfo_pipe.connect(data_preparation_pipe, 'outputnode.coreg_FLAIR',
outputnode, 'coreg_FLAIR')
transfo_pipe.connect(norm_lin_FLAIR, 'out_file',
outputnode, 'norm_FLAIR')
return transfo_pipe
# SPM with MD
def create_transfo_MD_pipe(params_template, params={},
name='transfo_MD_pipe'):
""" Description: apply tranformation to FLAIR, MD and FA if necssary
Processing steps:
- -coreg FA on T1
- apply coreg on MD
- debias using T1xT2BiasFieldCorrection (using mask is betcrop)
- registration to template file with IterREGBET
- SPM segmentation the old way (SPM8, not dartel based)
Params:
- short_data_preparation_pipe (see :class:`create_short_preparation_pipe \
<macapype.pipelines.prepare.create_short_preparation_pipe>`)
- debias (see :class:`T1xT2BiasFieldCorrection \
<macapype.nodes.correct_bias.T1xT2BiasFieldCorrection>`) - also available \
as :ref:`indiv_params <indiv_params>`
- reg (see :class:`IterREGBET <macapype.nodes.register.IterREGBET>`) - \
also available as :ref:`indiv_params <indiv_params>`
- old_segment_pipe (see :class:`create_old_segment_pipe \
<macapype.pipelines.segment.create_old_segment_pipe>`)
- nii_to_mesh_fs_pipe (see :class:`create_nii_to_mesh_fs_pipe \
<macapype.pipelines.surface.create_nii_to_mesh_fs_pipe>`)
Inputs:
inputnode:
SS_T1:
T1 file names
orig_T1:
T2 file names
FLAIR:
flair file name
transfo_file:
Transformation file between native to template
inv_transfo_file:
Transformation file between template and native
threshold_wm:
gm binary tissue in template space
indiv_params (opt):
dict with individuals parameters for some nodes
arguments:
params_template:
dict of template files containing brain_template and priors \
(list of template based segmented tissues)
params:
dictionary of node sub-parameters (from a json file)
name:
pipeline name (default = "full_spm_subpipes")
Outputs:
"""
print("Transfo MD pipe name: ", name)
# Creating pipeline
transfo_pipe = pe.Workflow(name=name)
# Creating input node
inputnode = pe.Node(
niu.IdentityInterface(
fields=['orig_T1', 'SS_T2', 'MD', 'b0mean',
'threshold_wm', 'lin_transfo_file',
'inv_lin_transfo_file']),
name='inputnode'
)
compute_native_wm = pe.Node(fsl.ApplyXFM(), name='compute_native_wm')
transfo_pipe.connect(inputnode, 'threshold_wm',
compute_native_wm, 'in_file')
transfo_pipe.connect(inputnode, 'orig_T1',
compute_native_wm, 'reference')
transfo_pipe.connect(inputnode, 'inv_lin_transfo_file',
compute_native_wm, 'in_matrix_file')
data_preparation_pipe = create_short_preparation_MD_pipe(
params=parse_key(params, "short_preparation_pipe"))
transfo_pipe.connect(inputnode, 'SS_T2',
data_preparation_pipe, 'inputnode.SS_T2')
transfo_pipe.connect(inputnode, 'MD',
data_preparation_pipe, 'inputnode.MD')
transfo_pipe.connect(inputnode, 'b0mean',
data_preparation_pipe, 'inputnode.b0mean')
transfo_pipe.connect(compute_native_wm, 'out_file',
data_preparation_pipe, 'inputnode.native_wm_mask')
# apply norm to coreg_MD
norm_lin_MD = pe.Node(fsl.ApplyXFM(), name="norm_lin_MD")
norm_lin_MD.inputs.reference = params_template["template_brain"]
transfo_pipe.connect(data_preparation_pipe, 'outputnode.coreg_MD',
norm_lin_MD, 'in_file')
transfo_pipe.connect(inputnode, 'lin_transfo_file',
norm_lin_MD, 'in_matrix_file')
# apply norm to coreg_better_MD
norm_lin_better_MD = pe.Node(fsl.ApplyXFM(), name="norm_lin_better_MD")
norm_lin_better_MD.inputs.reference = params_template["template_brain"]
transfo_pipe.connect(data_preparation_pipe, 'outputnode.coreg_better_MD',
norm_lin_better_MD, 'in_file')
transfo_pipe.connect(inputnode, 'lin_transfo_file',
norm_lin_better_MD, 'in_matrix_file')
return transfo_pipe
###############################################################################
# ANTS based segmentation (from Kepkee Loh / Julien Sein)
# (-soft ANTS)
###############################################################################
def create_brain_extraction_pipe(params_template, params={},
name="brain_extraction_pipe"):
""" Description: Brain extraction with atlas-brex after debiasing
Params:
- correct_bias_pipe (see :class:`create_correct_bias_pipe \
<macapype.pipelines.correct_bias.create_correct_bias_pipe>`)
- extract_pipe (see `create_extract_pipe <macapype.pipeline.\
extract_brain.create_extract_pipe>`)
Inputs:
inputnode:
preproc_T1:
preprocessed T1 file
preproc_T2:
preprocessed T2 file
indiv_params (opt):
dict with individuals parameters for some nodes
arguments:
params_template:
dictionary of template files
params:
dictionary of node sub-parameters (from a json file)
name:
pipeline name (default = "full_segment_pipe")
Outputs:
"""
# creating pipeline
brain_extraction_pipe = pe.Workflow(name=name)
# Creating input node
inputnode = pe.Node(
niu.IdentityInterface(fields=['preproc_T1', 'preproc_T2',
'indiv_params']),
name='inputnode')
# output node
outputnode = pe.Node(
niu.IdentityInterface(fields=['debiased_T1', 'debiased_T2',
"brain_mask"]),
name='outputnode')
assert not ("correct_bias_pipe" in params.keys() and "N4debias" in
params.keys()), "error, only one of correct_bias_pipe or N4debias \
should be present"
if "correct_bias_pipe" in params.keys():
# Correct_bias_T1_T2
correct_bias_pipe = create_correct_bias_pipe(
params=parse_key(params, "correct_bias_pipe"))
brain_extraction_pipe.connect(inputnode, 'preproc_T1',
correct_bias_pipe,
'inputnode.preproc_T1')
brain_extraction_pipe.connect(inputnode, 'preproc_T2',
correct_bias_pipe,
'inputnode.preproc_T2')
brain_extraction_pipe.connect(correct_bias_pipe,
"outputnode.debiased_T1",
outputnode, "debiased_T1")
brain_extraction_pipe.connect(correct_bias_pipe,
"outputnode.debiased_T2",
outputnode, "debiased_T2")
# brain extraction
extract_pipe = create_extract_pipe(
params_template=params_template,
params=parse_key(params, "extract_pipe"))
brain_extraction_pipe.connect(correct_bias_pipe,
"outputnode.debiased_T1",
extract_pipe, "inputnode.restore_T1")
brain_extraction_pipe.connect(correct_bias_pipe,
"outputnode.debiased_T2",
extract_pipe, "inputnode.restore_T2")
brain_extraction_pipe.connect(inputnode, "indiv_params",
extract_pipe, "inputnode.indiv_params")
brain_extraction_pipe.connect(extract_pipe,
"smooth_mask.out_file",
outputnode, "brain_mask")
else:
if "N4debias" not in params.keys():
params["N4debias"] = {
"dimension": 3,
"bspline_fitting_distance": 200,
"n_iterations": [50, 50, 40, 30],
"convergence_threshold": 0.00000001,
"shrink_factor": 2,
"args": "-r 0 --verbose 1"}
print("Using default bet_crop N4debias: {}".format(
params["N4debias"]))
else:
print("Found N4debias in params.json")
# N4 intensity normalization over T1
N4debias_T1 = NodeParams(ants.N4BiasFieldCorrection(),
params=parse_key(params, "N4debias"),
name='N4debias_T1')
brain_extraction_pipe.connect(inputnode, 'preproc_T1',
N4debias_T1, "input_image")
brain_extraction_pipe.connect(
inputnode, ('indiv_params', parse_key, "N4debias"),
N4debias_T1, "indiv_params")
brain_extraction_pipe.connect(N4debias_T1, "output_image",
outputnode, "debiased_T1")
# N4 intensity normalization over T2
N4debias_T2 = NodeParams(ants.N4BiasFieldCorrection(),
params=parse_key(params, "N4debias"),
name='N4debias_T2')
brain_extraction_pipe.connect(inputnode, 'preproc_T2',
N4debias_T2, "input_image")
brain_extraction_pipe.connect(
inputnode, ('indiv_params', parse_key, "N4debias"),
N4debias_T2, "indiv_params")
brain_extraction_pipe.connect(N4debias_T2, "output_image",
outputnode, "debiased_T2")
# brain extraction
extract_pipe = create_extract_pipe(
params_template=params_template,
params=parse_key(params, "extract_pipe"))
brain_extraction_pipe.connect(N4debias_T1, "output_image",
extract_pipe, "inputnode.restore_T1")
brain_extraction_pipe.connect(N4debias_T2, "output_image",
extract_pipe, "inputnode.restore_T2")
brain_extraction_pipe.connect(inputnode, "indiv_params",
extract_pipe, "inputnode.indiv_params")
brain_extraction_pipe.connect(extract_pipe,
"smooth_mask.out_file",
outputnode, "brain_mask")
return brain_extraction_pipe
def create_brain_segment_from_mask_pipe(
params_template, params={}, name="brain_segment_from_mask_pipe",
NMT_version="v1.3", space="native"):
""" Description: Segment T1 (using T2 for bias correction) and a previously
computed mask with NMT Atlas and atropos segment.
Params:
- masked_correct_bias_pipe (see :class:`create_masked_correct_bias_pipe \
<macapype.pipelines.correct_bias.create_masked_correct_bias_pipe>`)
- register_NMT_pipe (see :class:`create_register_NMT_pipe \
<macapype.pipelines.register.create_register_NMT_pipe>`)
- segment_atropos_pipe (see :class:`create_segment_atropos_pipe \
<macapype.pipelines.segment.create_segment_atropos_pipe>`)
Inputs:
inputnode:
preproc_T1:
preprocessed T1 file name
preproc_T2:
preprocessed T2 file name
brain_mask:
a mask computed for the same T1/T2 images
indiv_params (opt):
dict with individuals parameters for some nodes
arguments:
params_template:
dictionary of template files
params:
dictionary of node sub-parameters (from a json file)
name:
pipeline name (default = "full_segment_pipe")
Outputs:
"""
# creating pipeline
brain_segment_pipe = pe.Workflow(name=name)
# creating inputnode
inputnode = pe.Node(
niu.IdentityInterface(
fields=['preproc_T1', 'preproc_T2', 'brain_mask', 'indiv_params']),
name='inputnode')
# correcting for bias T1/T2, but this time with a mask
masked_correct_bias_pipe = create_masked_correct_bias_pipe(
params=parse_key(params, "masked_correct_bias_pipe"))
brain_segment_pipe.connect(
inputnode, 'preproc_T1',
masked_correct_bias_pipe, "inputnode.preproc_T1")
brain_segment_pipe.connect(
inputnode, 'preproc_T2',
masked_correct_bias_pipe, "inputnode.preproc_T2")
brain_segment_pipe.connect(
inputnode, 'brain_mask',
masked_correct_bias_pipe, "inputnode.brain_mask")
# register NMT template, template mask and priors to subject T1
register_NMT_pipe = create_register_NMT_pipe(
params_template=params_template,
params=parse_key(params, "register_NMT_pipe"), NMT_version=NMT_version)
brain_segment_pipe.connect(
masked_correct_bias_pipe, 'outputnode.mask_debiased_T1',
register_NMT_pipe, "inputnode.T1")
brain_segment_pipe.connect(
inputnode, 'indiv_params',
register_NMT_pipe, "inputnode.indiv_params")
# ants Atropos
if NMT_version == "v2.0":
segment_atropos_pipe = create_segment_atropos_seg_pipe(
params=parse_key(params, "segment_atropos_pipe"))
brain_segment_pipe.connect(
register_NMT_pipe, 'align_seg.out_file', segment_atropos_pipe,
"inputnode.seg_file")
else:
segment_atropos_pipe = create_segment_atropos_pipe(
params=parse_key(params, "segment_atropos_pipe"))
if "use_priors" in params["segment_atropos_pipe"].keys():
brain_segment_pipe.connect(
register_NMT_pipe, 'align_seg_csf.out_file',
segment_atropos_pipe, "inputnode.csf_prior_file")
brain_segment_pipe.connect(
register_NMT_pipe, 'align_seg_gm.out_file',
segment_atropos_pipe, "inputnode.gm_prior_file")
brain_segment_pipe.connect(
register_NMT_pipe, 'align_seg_wm.out_file',
segment_atropos_pipe, "inputnode.wm_prior_file")
brain_segment_pipe.connect(
register_NMT_pipe, 'norm_intensity.output_image',
segment_atropos_pipe, "inputnode.brain_file")
if "export_5tt_pipe" in params.keys():
export_5tt_pipe = create_5tt_pipe(
params=parse_key(params, "export_5tt_pipe"))
brain_segment_pipe.connect(segment_atropos_pipe,
'outputnode.threshold_gm',
export_5tt_pipe, 'inputnode.gm_file')
brain_segment_pipe.connect(segment_atropos_pipe,
'outputnode.threshold_wm',
export_5tt_pipe, 'inputnode.wm_file')
brain_segment_pipe.connect(segment_atropos_pipe,
'outputnode.threshold_csf',
export_5tt_pipe, 'inputnode.csf_file')
# output
outputnode = pe.Node(
niu.IdentityInterface(
fields=["segmented_file", "threshold_gm", "threshold_wm",
"threshold_csf"]),
name='outputnode')
if space == 'native':
brain_segment_pipe.connect(segment_atropos_pipe,
'outputnode.segmented_file',
outputnode, 'segmented_file')
brain_segment_pipe.connect(segment_atropos_pipe,
'outputnode.threshold_gm',
outputnode, 'threshold_gm')
brain_segment_pipe.connect(segment_atropos_pipe,
'outputnode.threshold_wm',
outputnode, 'threshold_wm')
brain_segment_pipe.connect(segment_atropos_pipe,
'outputnode.threshold_csf',
outputnode, 'threshold_csf')
else:
reg_seg_pipe = create_reg_seg_pipe()
brain_segment_pipe.connect(segment_atropos_pipe,
'outputnode.segmented_file', reg_seg_pipe,
'inputnode.native_segmented_file')
brain_segment_pipe.connect(register_NMT_pipe,
'NMT_subject_align.transfo_file',
reg_seg_pipe, 'inputnode.transfo_file')
reg_seg_pipe.inputs.inputnode.ref_image = \
params_template['template_head']
brain_segment_pipe.connect(reg_seg_pipe, 'outputnode.norm_seg',
outputnode, 'segmented_file')
brain_segment_pipe.connect(reg_seg_pipe, 'outputnode.norm_gm',
outputnode, 'threshold_gm')
brain_segment_pipe.connect(reg_seg_pipe, 'outputnode.norm_wm',
outputnode, 'threshold_wm')
brain_segment_pipe.connect(reg_seg_pipe, 'outputnode.norm_csf',
outputnode, 'threshold_csf')
return brain_segment_pipe
def create_full_ants_subpipes(
params_template, params={}, name="full_ants_subpipes", mask_file=None,
space="native", pad=False):
"""Description: Segment T1 (using T2 for bias correction) .
Params:
- short_data_preparation_pipe (see :class:`create_short_preparation_pipe \
<macapype.pipelines.prepare.create_short_preparation_pipe>`) or \
long_single_preparation_pipe \
(see :class:`create_long_single_preparation_pipe \
<macapype.pipelines.prepare.create_long_single_preparation_pipe>`) or \
long_multi_preparation_pipe \
(see :class:`create_long_multi_preparation_pipe \
<macapype.pipelines.prepare.create_long_multi_preparation_pipe>`)
- brain_extraction_pipe (see :class:`create_brain_extraction_pipe \
<macapype.pipelines.full_pipelines.create_brain_extraction_pipe>`)
- brain_segment_pipe (see :class:`create_brain_segment_from_mask_pipe\
<macapype.pipelines.full_pipelines.create_brain_segment_from_mask_pipe>`)
- nii_to_mesh_pipe (see :class:`create_nii_to_mesh_pipe\
<macapype.pipelines.surface.create_nii_to_mesh_pipe>`)
Inputs:
inputnode:
list_T1:
preprocessed T1 file name
list_T2:
preprocessed T2 file name
indiv_params (opt):
dict with individuals parameters for some nodes
arguments:
params_template:
dictionary of template files
params:
dictionary of node sub-parameters (from a json file)
name:
pipeline name (default = "full_segment_pipe")
Outputs:
brain_mask
segmented_brain_mask:
indexed with tissue classes
"""
# creating pipeline
seg_pipe = pe.Workflow(name=name)
# Creating input node
inputnode = pe.Node(
niu.IdentityInterface(fields=['list_T1', 'list_T2', 'indiv_params']),
name='inputnode'
)
# output node
outputnode = pe.Node(
niu.IdentityInterface(fields=['brain_mask', 'segmented_brain_mask']),
name='outputnode')
# preprocessing
if 'long_single_preparation_pipe' in params.keys():
data_preparation_pipe = create_long_single_preparation_pipe(
params=parse_key(params, "long_single_preparation_pipe"))
elif 'long_multi_preparation_pipe' in params.keys():
data_preparation_pipe = create_long_multi_preparation_pipe(
params=parse_key(params, "long_multi_preparation_pipe"))
elif 'short_preparation_pipe' in params.keys():
data_preparation_pipe = create_short_preparation_pipe(
params=parse_key(params, "short_preparation_pipe"))
else:
print("Error, short_preparation_pipe, long_single_preparation_pipe or\
long_multi_preparation_pipe was not found in params, skipping")
test_node = pe.Node(niu.Function(input_names=['list_T1', 'list_T2'],
output_names=[''],
function=list_input_files),
name="test_node")
seg_pipe.connect(inputnode, 'list_T1',
test_node, 'list_T1')
seg_pipe.connect(inputnode, 'list_T2',
test_node, 'list_T2')
return seg_pipe
seg_pipe.connect(inputnode, 'list_T1',
data_preparation_pipe, 'inputnode.list_T1')
seg_pipe.connect(inputnode, 'list_T2',
data_preparation_pipe, 'inputnode.list_T2')
seg_pipe.connect(inputnode, 'indiv_params',
data_preparation_pipe, 'inputnode.indiv_params')
if mask_file is None:
# full extract brain pipeline (correct_bias, denoising, extract brain)
if "brain_extraction_pipe" not in params.keys():
return seg_pipe
brain_extraction_pipe = create_brain_extraction_pipe(
params=parse_key(params, "brain_extraction_pipe"),
params_template=params_template)
seg_pipe.connect(data_preparation_pipe, 'outputnode.preproc_T1',
brain_extraction_pipe, 'inputnode.preproc_T1')
seg_pipe.connect(data_preparation_pipe, 'outputnode.preproc_T2',
brain_extraction_pipe, 'inputnode.preproc_T2')
seg_pipe.connect(inputnode, 'indiv_params',
brain_extraction_pipe, 'inputnode.indiv_params')
if pad and space == "native":
print("Padding mask in native space")
pad_mask = pe.Node(
niu.Function(
input_names=['cropped_img_file', 'orig_img_file',
'indiv_crop'],
output_names=['padded_img_file'],
function=padding_cropped_img),
name="pad_mask")
seg_pipe.connect(brain_extraction_pipe, "outputnode.brain_mask",
pad_mask, "cropped_img_file")
seg_pipe.connect(data_preparation_pipe, "av_T1.avg_img",
pad_mask, "orig_img_file")
seg_pipe.connect(inputnode, "indiv_params", pad_mask, "indiv_crop")
seg_pipe.connect(pad_mask, "padded_img_file",
outputnode, "brain_mask")
else:
seg_pipe.connect(brain_extraction_pipe, "outputnode.brain_mask",
outputnode, "brain_mask")
# full_segment (restarting from the avg_align files)
if "brain_segment_pipe" not in params.keys():
return seg_pipe
if params["general"]["template_name"].split("_")[0] == "NMT":
print("found NMT template")
NMT_version = params["general"]["template_name"].split("_")[1]
else:
print("Not NMT template, NMT version used by default for processing")
NMT_version = "v1.3"
print("NMT_version:", NMT_version)
brain_segment_pipe = create_brain_segment_from_mask_pipe(
params_template=params_template,
params=parse_key(params, "brain_segment_pipe"),
NMT_version=NMT_version, space=space)
seg_pipe.connect(data_preparation_pipe, 'outputnode.preproc_T1',
brain_segment_pipe, 'inputnode.preproc_T1')
seg_pipe.connect(data_preparation_pipe, 'outputnode.preproc_T2',
brain_segment_pipe, 'inputnode.preproc_T2')
if mask_file is None:
seg_pipe.connect(brain_extraction_pipe,
"outputnode.brain_mask",
brain_segment_pipe, "inputnode.brain_mask")
else:
brain_segment_pipe.inputs.inputnode.brain_mask = mask_file
seg_pipe.connect(inputnode, 'indiv_params',
brain_segment_pipe, 'inputnode.indiv_params')
if pad and space == "native":
print("Padding seg mask in native space")
pad_seg_mask = pe.Node(
niu.Function(
input_names=['cropped_img_file', 'orig_img_file',
'indiv_crop'],
output_names=['padded_img_file'],
function=padding_cropped_img),
name="pad_seg_mask")
seg_pipe.connect(brain_segment_pipe, 'outputnode.segmented_file',
pad_seg_mask, "cropped_img_file")
seg_pipe.connect(data_preparation_pipe, "av_T1.avg_img",
pad_seg_mask, "orig_img_file")
seg_pipe.connect(inputnode, "indiv_params", pad_seg_mask, "indiv_crop")
seg_pipe.connect(pad_seg_mask, "padded_img_file",
outputnode, "segmented_brain_mask")
else:
seg_pipe.connect(brain_segment_pipe, 'outputnode.segmented_file',
outputnode, 'segmented_brain_mask')
if 'nii_to_mesh_pipe' in params.keys():
nii_to_mesh_pipe = create_nii_to_mesh_pipe(
params_template=params_template,
params=parse_key(params, "nii_to_mesh_pipe"))
# from data_preparation_pipe
seg_pipe.connect(data_preparation_pipe, 'outputnode.preproc_T1',
nii_to_mesh_pipe, 'inputnode.t1_ref_file')
# from brain_segment_pipe
seg_pipe.connect(brain_segment_pipe,
'register_NMT_pipe.NMT_subject_align.warpinv_file',
nii_to_mesh_pipe, 'inputnode.warpinv_file')
seg_pipe.connect(
brain_segment_pipe,
'register_NMT_pipe.NMT_subject_align.inv_transfo_file',
nii_to_mesh_pipe, 'inputnode.inv_transfo_file')
seg_pipe.connect(brain_segment_pipe,
'register_NMT_pipe.NMT_subject_align.aff_file',
nii_to_mesh_pipe, 'inputnode.aff_file')
seg_pipe.connect(brain_segment_pipe,
'segment_atropos_pipe.outputnode.segmented_file',
nii_to_mesh_pipe, "inputnode.segmented_file")
elif "nii_to_mesh_fs_pipe" in params.keys():
nii_to_mesh_fs_pipe = create_nii_to_mesh_fs_pipe(
params=parse_key(params, "nii_to_mesh_fs_pipe"))
seg_pipe.connect(brain_extraction_pipe,
'outputnode.debiased_T1',
nii_to_mesh_fs_pipe, 'inputnode.reg_brain_file')
seg_pipe.connect(brain_segment_pipe,
'segment_atropos_pipe.outputnode.threshold_wm',
nii_to_mesh_fs_pipe, 'inputnode.wm_mask_file')
seg_pipe.connect(inputnode, 'indiv_params',
nii_to_mesh_fs_pipe, 'inputnode.indiv_params')
return seg_pipe
###############################################################################
# ANTS based segmentation for adrien baboons (T1 without T2)
# -soft ANTS_T1
###############################################################################
# same as above, but replacing biascorrection with N4biascorrection
# in brain extraction and brain segmentation
def create_brain_extraction_T1_pipe(params_template, params={},
name="brain_extraction_T1_pipe"):
"""
Description: Brain extraction with only T1 images.
- extract_T1_pipe (see `create_extract_T1_pipe <macapype.pipeline.\
extract_brain.create_extract_T1_pipe>`)
Inputs:
inputnode:
preproc_T1:
preprocessed T1 file
indiv_params (opt):
dict with individuals parameters for some nodes
arguments:
params_template:
dictionary of template files
params:
dictionary of node sub-parameters (from a json file)
name:
pipeline name (default = "full_segment_pipe")
Outputs:
"""
# creating pipeline
brain_extraction_pipe = pe.Workflow(name=name)
# Creating input node
inputnode = pe.Node(
niu.IdentityInterface(fields=['preproc_T1', 'indiv_params']),
name='inputnode')
# brain extraction (with atlasbrex)
extract_T1_pipe = create_extract_T1_pipe(
params_template=params_template,
params=parse_key(params, "extract_pipe"))
brain_extraction_pipe.connect(inputnode, "preproc_T1",
extract_T1_pipe, "inputnode.restore_T1")
brain_extraction_pipe.connect(inputnode, "indiv_params",
extract_T1_pipe, "inputnode.indiv_params")
return brain_extraction_pipe
def create_brain_segment_from_mask_T1_pipe(
params_template, params={}, name="brain_segment_from_mask_T1_pipe",
space="native"):
"""
Description: Segment T1 from a previously computed mask.
Params:
- register_NMT_pipe (see :class:`create_register_NMT_pipe \
<macapype.pipelines.register.create_register_NMT_pipe>`)
- segment_atropos_pipe (see :class:`create_segment_atropos_pipe \
<macapype.pipelines.segment.create_segment_atropos_pipe>`)
Inputs:
inputnode:
preproc_T1:
preprocessed T1 file name
brain_mask:
a mask computed for the same T1/T2 images
indiv_params (opt):
dict with individuals parameters for some nodes
arguments:
params_template:
dictionary of template files
params:
dictionary of node sub-parameters (from a json file)
name:
pipeline name (default = "full_segment_pipe")
Outputs:
"""
# creating pipeline
brain_segment_pipe = pe.Workflow(name=name)
# creating inputnode
inputnode = pe.Node(
niu.IdentityInterface(
fields=['preproc_T1', 'brain_mask', 'indiv_params']),
name='inputnode')
# creating outputnode
outputnode = pe.Node(
niu.IdentityInterface(
fields=["segmented_file", "threshold_gm", "threshold_wm",
"threshold_csf"]),
name='outputnode')
# mask T1 using brain mask and perform N4 bias correction
# restore_mask_T1
restore_mask_T1 = pe.Node(fsl.ApplyMask(), name='restore_mask_T1')
brain_segment_pipe.connect(inputnode, 'preproc_T1',
restore_mask_T1, 'in_file')
brain_segment_pipe.connect(inputnode, 'brain_mask',
restore_mask_T1, 'mask_file')
NMT_version = "v1.3"
print("NMT_version:", NMT_version)
# register NMT template, template mask and priors to subject T1
register_NMT_pipe = create_register_NMT_pipe(
params_template=params_template,
params=parse_key(params, "register_NMT_pipe"), NMT_version=NMT_version)
brain_segment_pipe.connect(
restore_mask_T1, 'out_file',
register_NMT_pipe, "inputnode.T1")
brain_segment_pipe.connect(
inputnode, 'indiv_params',
register_NMT_pipe, "inputnode.indiv_params")
# ants Atropos
segment_atropos_pipe = create_segment_atropos_pipe(
params=parse_key(params, "segment_atropos_pipe"))
brain_segment_pipe.connect(
register_NMT_pipe, 'norm_intensity.output_image',
segment_atropos_pipe, "inputnode.brain_file")
if "use_priors" in params["segment_atropos_pipe"].keys():
brain_segment_pipe.connect(register_NMT_pipe, 'align_seg_csf.out_file',
segment_atropos_pipe,
"inputnode.csf_prior_file")
brain_segment_pipe.connect(register_NMT_pipe, 'align_seg_gm.out_file',
segment_atropos_pipe,
"inputnode.gm_prior_file")
brain_segment_pipe.connect(register_NMT_pipe, 'align_seg_wm.out_file',
segment_atropos_pipe,
"inputnode.wm_prior_file")
if space == 'native':
brain_segment_pipe.connect(segment_atropos_pipe,
'outputnode.segmented_file',
outputnode, 'segmented_file')
brain_segment_pipe.connect(segment_atropos_pipe,
'outputnode.threshold_gm',
outputnode, 'threshold_gm')
brain_segment_pipe.connect(segment_atropos_pipe,
'outputnode.threshold_wm',
outputnode, 'threshold_wm')
brain_segment_pipe.connect(segment_atropos_pipe,
'outputnode.threshold_csf',
outputnode, 'threshold_csf')
else:
reg_seg_pipe = create_reg_seg_pipe()
brain_segment_pipe.connect(segment_atropos_pipe,
'outputnode.segmented_file', reg_seg_pipe,
'inputnode.native_segmented_file')
brain_segment_pipe.connect(register_NMT_pipe,
'NMT_subject_align.transfo_file',
reg_seg_pipe, 'inputnode.transfo_file')
reg_seg_pipe.inputs.inputnode.ref_image = \
params_template['template_head']
brain_segment_pipe.connect(reg_seg_pipe, 'outputnode.norm_seg',
outputnode, 'segmented_file')
brain_segment_pipe.connect(reg_seg_pipe, 'outputnode.norm_gm',
outputnode, 'threshold_gm')
brain_segment_pipe.connect(reg_seg_pipe, 'outputnode.norm_wm',
outputnode, 'threshold_wm')
brain_segment_pipe.connect(reg_seg_pipe, 'outputnode.norm_csf',
outputnode, 'threshold_csf')
return brain_segment_pipe
def create_full_T1_ants_subpipes(params_template, params={},
name="full_T1_ants_subpipes", space="native",
pad=False):
"""Description: Full pipeline to segment T1 (with no T2).
Params:
- short_data_preparation_pipe (see :class:`create_short_preparation_pipe <\
macapype.pipelines.prepare.create_short_preparation_pipe>`
- brain_extraction_T1_pipe (see :class:`create_brain_extraction_T1_pipe \
<macapype.pipelines.full_pipelines.create_brain_extraction_T1_pipe>`)
- brain_segment_T1_pipe (see \
:class:`create_brain_segment_from_mask_T1_pipe \
<macapype.pipelines.full_pipelines.create_brain_segment_from_mask_T1_pipe>`)
Inputs:
inputnode:
list_T1:
preprocessed T1 file name
indiv_params (opt):
dict with individuals parameters for some nodes
arguments:
params_template:
dictionary of template files
params:
dictionary of node sub-parameters (from a json file)
name:
pipeline name (default = "full_segment_pipe")
Outputs:
"""
# creating pipeline
seg_pipe = pe.Workflow(name=name)
# Creating input node (grab only T1 files)
inputnode = pe.Node(
niu.IdentityInterface(fields=['list_T1', 'indiv_params']),
name='inputnode'
)
# output node
outputnode = pe.Node(
niu.IdentityInterface(fields=['brain_mask', 'segmented_brain_mask']),
name='outputnode')
# preprocessing (perform preparation pipe with only T1)
if 'short_preparation_pipe' in params.keys():
data_preparation_pipe = create_short_preparation_T1_pipe(
params=parse_key(params, "short_preparation_pipe"))
else:
print("Error, short_preparation_pipe was not found in params, \
skipping")
return seg_pipe
seg_pipe.connect(inputnode, 'list_T1',
data_preparation_pipe, 'inputnode.list_T1')
seg_pipe.connect(inputnode, 'indiv_params',
data_preparation_pipe, 'inputnode.indiv_params')
# full extract brain pipeline (correct_bias, denoising, extract brain)
if "brain_extraction_pipe" not in params.keys():
print("Error, brain_extraction_pipe was not found in params, \
skipping")
return seg_pipe
brain_extraction_pipe = create_brain_extraction_T1_pipe(
params=parse_key(params, "brain_extraction_pipe"),
params_template=params_template)
seg_pipe.connect(data_preparation_pipe, 'outputnode.preproc_T1',
brain_extraction_pipe, 'inputnode.preproc_T1')
seg_pipe.connect(inputnode, 'indiv_params',
brain_extraction_pipe, 'inputnode.indiv_params')
seg_pipe.connect(brain_extraction_pipe,
"extract_T1_pipe.smooth_mask.out_file",
outputnode, "brain_mask")
# full_segment (restarting from the avg_align files)
if "brain_segment_pipe" not in params.keys():
print("Error, brain_segment_pipe was not found in params, \
skipping")
return seg_pipe
brain_segment_pipe = create_brain_segment_from_mask_T1_pipe(
params_template=params_template,
params=parse_key(params, "brain_segment_pipe"), space=space)
seg_pipe.connect(data_preparation_pipe, 'outputnode.preproc_T1',
brain_segment_pipe, 'inputnode.preproc_T1')
seg_pipe.connect(brain_extraction_pipe,
"extract_T1_pipe.smooth_mask.out_file",
brain_segment_pipe, "inputnode.brain_mask")
seg_pipe.connect(inputnode, 'indiv_params',
brain_segment_pipe, 'inputnode.indiv_params')
if pad and space == "native":
print("Padding seg mask in native space")
pad_seg_mask = pe.Node(
niu.Function(
input_names=['cropped_img_file', 'orig_img_file',
'indiv_crop'],
output_names=['padded_img_file'],
function=padding_cropped_img),
name="pad_seg_mask")
seg_pipe.connect(brain_segment_pipe, 'outputnode.segmented_file',
pad_seg_mask, "cropped_img_file")
seg_pipe.connect(data_preparation_pipe, "av_T1.avg_img",
pad_seg_mask, "orig_img_file")
seg_pipe.connect(inputnode, "indiv_params", pad_seg_mask, "indiv_crop")
seg_pipe.connect(pad_seg_mask, "padded_img_file",
outputnode, "segmented_brain_mask")
else:
seg_pipe.connect(brain_segment_pipe, 'outputnode.segmented_file',
outputnode, 'segmented_brain_mask')
if "nii_to_mesh_fs_pipe" in params.keys():
nii_to_mesh_fs_pipe = create_nii_to_mesh_fs_pipe(
params=parse_key(params, "nii_to_mesh_fs_pipe"))
seg_pipe.connect(data_preparation_pipe, 'outputnode.preproc_T1',
nii_to_mesh_fs_pipe, 'inputnode.reg_brain_file')
seg_pipe.connect(brain_segment_pipe,
'segment_atropos_pipe.outputnode.threshold_wm',
nii_to_mesh_fs_pipe, 'inputnode.wm_mask_file')
seg_pipe.connect(inputnode, 'indiv_params',
nii_to_mesh_fs_pipe, 'inputnode.indiv_params')
return seg_pipe
| 36.192257
| 83
| 0.608409
| 5,890
| 55,157
| 5.314601
| 0.048048
| 0.055873
| 0.036227
| 0.030125
| 0.85733
| 0.822381
| 0.788199
| 0.743124
| 0.694885
| 0.660512
| 0
| 0.00806
| 0.300415
| 55,157
| 1,523
| 84
| 36.216021
| 0.803172
| 0.235365
| 0
| 0.659121
| 0
| 0
| 0.215919
| 0.08175
| 0
| 0
| 0
| 0
| 0.001332
| 1
| 0.011984
| false
| 0
| 0.019973
| 0
| 0.055925
| 0.027963
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
1605b9b1a75d92c333ed5a6d148dd5f4c30a4977
| 1,876
|
py
|
Python
|
pretrain/PyTorch/configuration.py
|
cgouttham/microsoft-hackathon
|
7e50981e0f165543676504592ad26818db13432f
|
[
"MIT"
] | null | null | null |
pretrain/PyTorch/configuration.py
|
cgouttham/microsoft-hackathon
|
7e50981e0f165543676504592ad26818db13432f
|
[
"MIT"
] | null | null | null |
pretrain/PyTorch/configuration.py
|
cgouttham/microsoft-hackathon
|
7e50981e0f165543676504592ad26818db13432f
|
[
"MIT"
] | null | null | null |
import json
# TODO better json handling
class BertJobConfiguration:
def __init__(self, config_file_path):
self.config = json.load(open(config_file_path, 'r', encoding='utf-8'))
# TODO improve this implementation
def replace_path_placeholders(self, files_location):
self.config['data']['datasets'] = {key: value.replace('placeholder/', files_location)
for (key, value) in self.config['data']['datasets'].items()}
self.config['validation']['path'] = self.config['validation']['path'].replace('placeholder/', files_location)
def get_name(self):
return self.config['name']
def get_token_file_type(self):
return self.config["bert_token_file"]
def get_model_file_type(self):
return self.config["bert_model_file"]
def get_learning_rate(self):
return self.config["training"]["learning_rate"]
def get_warmup_proportion(self):
return self.config["training"]["warmup_proportion"]
def get_total_training_steps(self):
return self.config["training"]["total_training_steps"]
def get_total_epoch_count(self):
return self.config["training"]["num_epochs"]
def get_num_workers(self):
return self.config['training']['num_workers']
def get_validation_folder_path(self):
return self.config['validation']['path']
def get_wiki_pretrain_dataset_path(self):
return self.config["data"]["datasets"]['wiki_pretrain_dataset']
def get_book_corpus_pretrain_dataset_path(self):
return self.config["data"]["datasets"]['bc_pretrain_dataset']
def get_decay_rate(self):
return self.config["training"]["decay_rate"]
def get_decay_step(self):
return self.config["training"]["decay_step"]
def get_model_config(self):
return self.config["bert_model_config"]
| 33.5
| 117
| 0.679638
| 233
| 1,876
| 5.180258
| 0.270386
| 0.1657
| 0.162386
| 0.23198
| 0.367854
| 0.301574
| 0.137531
| 0.084507
| 0.084507
| 0
| 0
| 0.000657
| 0.188699
| 1,876
| 55
| 118
| 34.109091
| 0.792378
| 0.030917
| 0
| 0
| 0
| 0
| 0.197245
| 0.01157
| 0
| 0
| 0
| 0.018182
| 0
| 1
| 0.444444
| false
| 0
| 0.027778
| 0.388889
| 0.888889
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 5
|
1613ce1d902036fdc6d945741303c064dc4b601e
| 138
|
py
|
Python
|
django_countries/tests/apps.py
|
shntar0kun/django-countries
|
54872318bd5166953bf61f4eb2fc06bf154f7c0b
|
[
"MIT"
] | null | null | null |
django_countries/tests/apps.py
|
shntar0kun/django-countries
|
54872318bd5166953bf61f4eb2fc06bf154f7c0b
|
[
"MIT"
] | 5
|
2020-03-24T16:37:25.000Z
|
2021-06-10T21:24:54.000Z
|
django_countries/tests/apps.py
|
shntar0kun/django-countries
|
54872318bd5166953bf61f4eb2fc06bf154f7c0b
|
[
"MIT"
] | null | null | null |
from django.apps import AppConfig
class TestConfig(AppConfig):
name = 'django_countries.tests'
label = 'django_countries_tests'
| 19.714286
| 36
| 0.76087
| 16
| 138
| 6.375
| 0.6875
| 0.294118
| 0.392157
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.15942
| 138
| 6
| 37
| 23
| 0.87931
| 0
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| 0
| 0.318841
| 0.318841
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| 0
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| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 5
|
1682b82ec44c127bf0c20b6cbe9b5765660e7ca2
| 87
|
py
|
Python
|
postie/__init__.py
|
itsnauman/Postie
|
0ae0c3b2482caa249b103a06547997ff1995f6e7
|
[
"MIT"
] | 89
|
2015-06-19T18:09:27.000Z
|
2015-07-21T19:29:41.000Z
|
postie/__init__.py
|
itsnauman/postie
|
0ae0c3b2482caa249b103a06547997ff1995f6e7
|
[
"MIT"
] | 4
|
2015-06-20T08:07:18.000Z
|
2015-07-19T10:14:19.000Z
|
postie/__init__.py
|
itsnauman/Postie
|
0ae0c3b2482caa249b103a06547997ff1995f6e7
|
[
"MIT"
] | 9
|
2015-06-19T18:26:15.000Z
|
2015-06-28T01:02:15.000Z
|
# -*- coding: utf-8 -*-
from .main import Postie
from .cli import create_parser, main
| 17.4
| 36
| 0.689655
| 13
| 87
| 4.538462
| 0.769231
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0.013889
| 0.172414
| 87
| 4
| 37
| 21.75
| 0.805556
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| 1
| 0
|
0
| 5
|
16894b8d03fb8f5ec9dc59a4116277eac5189b7f
| 76
|
py
|
Python
|
code/mypackage/my_module4.py
|
lungben/python_tutorial
|
b5cab0bee30cdebe6db2d671cce0c9230896b402
|
[
"MIT"
] | null | null | null |
code/mypackage/my_module4.py
|
lungben/python_tutorial
|
b5cab0bee30cdebe6db2d671cce0c9230896b402
|
[
"MIT"
] | 1
|
2019-07-30T16:59:31.000Z
|
2019-07-30T16:59:31.000Z
|
code/mypackage/my_module4.py
|
lungben/python_tutorial
|
b5cab0bee30cdebe6db2d671cce0c9230896b402
|
[
"MIT"
] | 1
|
2019-12-25T14:41:27.000Z
|
2019-12-25T14:41:27.000Z
|
# example file for submodule imports
def divide_me_by_2(x):
return x/2
| 15.2
| 36
| 0.736842
| 14
| 76
| 3.785714
| 0.857143
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.032787
| 0.197368
| 76
| 4
| 37
| 19
| 0.836066
| 0.447368
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| 0.5
| false
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| null | 0
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| 0
| 0
| 1
| 1
| 0
|
0
| 5
|
16ab6fccb8b0c35cbdefc0baeee86bc69b9e9c16
| 53
|
py
|
Python
|
automatagen/__init__.py
|
PetarPeychev/automata-terrain-gen
|
2062a79fe18d0d2359ab0245618383d0738471c8
|
[
"Unlicense"
] | null | null | null |
automatagen/__init__.py
|
PetarPeychev/automata-terrain-gen
|
2062a79fe18d0d2359ab0245618383d0738471c8
|
[
"Unlicense"
] | null | null | null |
automatagen/__init__.py
|
PetarPeychev/automata-terrain-gen
|
2062a79fe18d0d2359ab0245618383d0738471c8
|
[
"Unlicense"
] | 1
|
2021-03-12T09:15:53.000Z
|
2021-03-12T09:15:53.000Z
|
from automatagen.automatagen import TerrainGenerator
| 26.5
| 52
| 0.90566
| 5
| 53
| 9.6
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.075472
| 53
| 1
| 53
| 53
| 0.979592
| 0
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| true
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| null | 0
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| 0
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| 0
| 0
| 0
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| 1
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| null | 0
| 0
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| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
16c6715b023edb427f0e781aab325a2a0cd68328
| 208
|
py
|
Python
|
zounds/util/__init__.py
|
FelixAbrahamsson/zounds
|
197c358acf3bea4252cfc2561da70cbe799e2c75
|
[
"MIT"
] | 20
|
2016-06-04T05:44:28.000Z
|
2021-05-26T02:26:08.000Z
|
zounds/util/__init__.py
|
FelixAbrahamsson/zounds
|
197c358acf3bea4252cfc2561da70cbe799e2c75
|
[
"MIT"
] | 53
|
2016-08-07T15:11:38.000Z
|
2019-05-21T15:56:40.000Z
|
zounds/util/__init__.py
|
FelixAbrahamsson/zounds
|
197c358acf3bea4252cfc2561da70cbe799e2c75
|
[
"MIT"
] | 7
|
2016-08-14T15:50:33.000Z
|
2020-12-22T13:34:23.000Z
|
from .persistence import \
simple_lmdb_settings, simple_in_memory_settings, \
simple_object_storage_settings
from .handy import tuplify
from .midi import note_to_midi, midi_to_note, midi_instrument
| 26
| 61
| 0.822115
| 29
| 208
| 5.448276
| 0.551724
| 0.177215
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.134615
| 208
| 7
| 62
| 29.714286
| 0.877778
| 0
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| 0
| true
| 0
| 0.6
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| null | 0
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| null | 0
| 0
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| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
bc8b1a6f400efe49a436a74585a3409c7598e53e
| 205
|
py
|
Python
|
paraloop/__init__.py
|
jneeven/paraloop
|
61fbaa608cd20b4345c46f3458199f3b77689d8f
|
[
"MIT"
] | 6
|
2021-03-21T17:29:18.000Z
|
2021-06-05T04:56:56.000Z
|
paraloop/__init__.py
|
jneeven/paraloop
|
61fbaa608cd20b4345c46f3458199f3b77689d8f
|
[
"MIT"
] | null | null | null |
paraloop/__init__.py
|
jneeven/paraloop
|
61fbaa608cd20b4345c46f3458199f3b77689d8f
|
[
"MIT"
] | null | null | null |
import paraloop.aggregation_strategies as aggregation_strategies
from paraloop.paraloop import ParaLoop
from paraloop.variable import Variable
__all__ = ["aggregation_strategies", "ParaLoop", "Variable"]
| 34.166667
| 64
| 0.843902
| 22
| 205
| 7.545455
| 0.363636
| 0.379518
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.087805
| 205
| 5
| 65
| 41
| 0.887701
| 0
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| 0
| 0.185366
| 0.107317
| 0
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| false
| 0
| 0.75
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
bc9b3b380f47184fee405c04ce766266910f64d9
| 197
|
py
|
Python
|
tests/admin_scripts/app_raising_warning/models.py
|
ni-ning/django
|
2e7ba6057cfc82a15a22b6021cd60cf307152e2d
|
[
"CNRI-Python-GPL-Compatible",
"BSD-3-Clause"
] | 61,676
|
2015-01-01T00:05:13.000Z
|
2022-03-31T20:37:54.000Z
|
tests/admin_scripts/app_raising_warning/models.py
|
ni-ning/django
|
2e7ba6057cfc82a15a22b6021cd60cf307152e2d
|
[
"CNRI-Python-GPL-Compatible",
"BSD-3-Clause"
] | 8,884
|
2015-01-01T00:12:05.000Z
|
2022-03-31T19:53:11.000Z
|
tests/admin_scripts/app_raising_warning/models.py
|
mustafa0x/django
|
d7394cfa13a4d1a02356e3a83e10ec100fbb9948
|
[
"BSD-3-Clause",
"0BSD"
] | 33,143
|
2015-01-01T02:04:52.000Z
|
2022-03-31T19:42:46.000Z
|
from django.core import checks
from django.db import models
class ModelRaisingMessages(models.Model):
@classmethod
def check(self, **kwargs):
return [checks.Warning('A warning')]
| 21.888889
| 44
| 0.720812
| 24
| 197
| 5.916667
| 0.75
| 0.140845
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.177665
| 197
| 8
| 45
| 24.625
| 0.876543
| 0
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| 0
| 0
| 0
| 0.045685
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.166667
| false
| 0
| 0.333333
| 0.166667
| 0.833333
| 0
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| null | 0
| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
|
0
| 5
|
bcc01c06f7a77edb163cb58e270e4dc351bb515f
| 57
|
py
|
Python
|
Reading Data/lesson-18-get-fifa-players-from-the-web/tests/test_fifa_1.py
|
danielgarm/Data-Science-and-Machine-Learning
|
fa3e85cc42eb2e9f964ab5abb34d1c93e16d1cd9
|
[
"MIT"
] | null | null | null |
Reading Data/lesson-18-get-fifa-players-from-the-web/tests/test_fifa_1.py
|
danielgarm/Data-Science-and-Machine-Learning
|
fa3e85cc42eb2e9f964ab5abb34d1c93e16d1cd9
|
[
"MIT"
] | 2
|
2022-01-11T21:04:51.000Z
|
2022-01-11T21:05:05.000Z
|
Reading Data/lesson-18-get-fifa-players-from-the-web/tests/test_fifa_1.py
|
danielgarm/Data-Science-and-Machine-Learning
|
fa3e85cc42eb2e9f964ab5abb34d1c93e16d1cd9
|
[
"MIT"
] | null | null | null |
def test_fifa_1():
assert fifa_df.shape == (30, 5)
| 19
| 36
| 0.614035
| 10
| 57
| 3.2
| 0.9
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.090909
| 0.22807
| 57
| 2
| 37
| 28.5
| 0.636364
| 0
| 0
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| 0
| 0
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| 0
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| 0
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| 0.5
| 1
| 0.5
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| null | 0
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| 1
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
bcc490dd53330f1423aa63c3251883dd156ac3fb
| 1,778
|
py
|
Python
|
models.py
|
dist-uniparthenope/UniParthenopeAPI
|
92515b5a3e55da3afb5b18dcb748f394060c4f2c
|
[
"Apache-2.0"
] | null | null | null |
models.py
|
dist-uniparthenope/UniParthenopeAPI
|
92515b5a3e55da3afb5b18dcb748f394060c4f2c
|
[
"Apache-2.0"
] | null | null | null |
models.py
|
dist-uniparthenope/UniParthenopeAPI
|
92515b5a3e55da3afb5b18dcb748f394060c4f2c
|
[
"Apache-2.0"
] | null | null | null |
from app import db
from datetime import datetime
from werkzeug.security import generate_password_hash, check_password_hash
class User(db.Model):
id = db.Column(db.Integer, primary_key=True)
token = db.Column(db.String(80), index=True, unique=True)
username = db.Column(db.String(64), index=True, unique=True)
email = db.Column(db.String(120), index=True, unique=True)
password_hash = db.Column(db.String(128))
nome_bar = db.Column(db.String(120), index=True, unique=True)
def __repr__(self):
return '<User {}>'.format(self.username) + '<Id {}>'.format(self.id) + '<Email {}>'.format(self.email) + '<Password {}>'.format(self.password_hash)
def set_password(self, password):
self.password_hash = generate_password_hash(password)
def check_password(self, password):
return check_password_hash(self.password_hash, password)
class Food(db.Model):
id = db.Column(db.Integer, primary_key=True)
nome = db.Column(db.String(120))
image = db.Column(db.BLOB)
tipologia = db.Column(db.String(120))
descrizione = db.Column(db.String(120))
prezzo = db.Column(db.Integer)
sempre_attivo = db.Column(db.Boolean)
data = db.Column(db.DateTime, index=True, default=datetime.utcnow)
nome_food = db.Column(db.String(120), db.ForeignKey('user.nome_bar'))
def __repr__(self):
return '<Primo piatto {}>'.format(self.nome) + '<Id {}>'.format(self.id)
class Building(db.Model):
id_corso = db.Column(db.Integer, primary_key=True)
struttura_des = db.Column(db.String(120))
struttura_id = db.Column(db.String(10))
struttura_ga_id = db.Column(db.Integer)
corso_ga_id = db.Column(db.String(10))
def __repr__(self):
return '<Id Corso {}>'.format(self.id_corso)
| 37.829787
| 155
| 0.687852
| 257
| 1,778
| 4.599222
| 0.229572
| 0.135364
| 0.169205
| 0.162437
| 0.291878
| 0.192047
| 0.158206
| 0.13198
| 0.13198
| 0.067682
| 0
| 0.021419
| 0.15973
| 1,778
| 47
| 156
| 37.829787
| 0.769746
| 0
| 0
| 0.138889
| 1
| 0
| 0.050028
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.138889
| false
| 0.194444
| 0.083333
| 0.111111
| 0.972222
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 1
| 0
|
0
| 5
|
4c4fe3888cec4d2abcda85d185742b31a9cb394e
| 2,617
|
py
|
Python
|
favicons/_constants.py
|
checktheroads/favicons
|
108725598fd5d30cd10beab2b37bdccf9b79ad97
|
[
"BSD-3-Clause-Clear"
] | 7
|
2020-11-20T16:43:30.000Z
|
2021-05-15T06:10:26.000Z
|
favicons/_constants.py
|
checktheroads/favicons
|
108725598fd5d30cd10beab2b37bdccf9b79ad97
|
[
"BSD-3-Clause-Clear"
] | 1
|
2021-09-27T00:00:47.000Z
|
2021-09-27T00:00:47.000Z
|
favicons/_constants.py
|
thatmattlove/favicons
|
108725598fd5d30cd10beab2b37bdccf9b79ad97
|
[
"BSD-3-Clause-Clear"
] | 1
|
2022-02-20T13:14:05.000Z
|
2022-02-20T13:14:05.000Z
|
"""Static values for one way import."""
SUPPORTED_FORMATS = (".svg", ".jpeg", ".jpg", ".png", ".tiff", ".tif")
HTML_LINK = '<link rel="{rel}" type="{type}" href="{href}" />'
ICON_TYPES = (
{"image_fmt": "ico", "rel": None, "dimensions": (64, 64), "prefix": "favicon"},
{"image_fmt": "png", "rel": "icon", "dimensions": (16, 16), "prefix": "favicon"},
{"image_fmt": "png", "rel": "icon", "dimensions": (32, 32), "prefix": "favicon"},
{"image_fmt": "png", "rel": "icon", "dimensions": (64, 64), "prefix": "favicon"},
{"image_fmt": "png", "rel": "icon", "dimensions": (96, 96), "prefix": "favicon"},
{"image_fmt": "png", "rel": "icon", "dimensions": (180, 180), "prefix": "favicon"},
{
"image_fmt": "png",
"rel": "apple-touch-icon",
"dimensions": (57, 57),
"prefix": "apple-touch-icon",
},
{
"image_fmt": "png",
"rel": "apple-touch-icon",
"dimensions": (60, 60),
"prefix": "apple-touch-icon",
},
{
"image_fmt": "png",
"rel": "apple-touch-icon",
"dimensions": (72, 72),
"prefix": "apple-touch-icon",
},
{
"image_fmt": "png",
"rel": "apple-touch-icon",
"dimensions": (76, 76),
"prefix": "apple-touch-icon",
},
{
"image_fmt": "png",
"rel": "apple-touch-icon",
"dimensions": (114, 114),
"prefix": "apple-touch-icon",
},
{
"image_fmt": "png",
"rel": "apple-touch-icon",
"dimensions": (120, 120),
"prefix": "apple-touch-icon",
},
{
"image_fmt": "png",
"rel": "apple-touch-icon",
"dimensions": (144, 144),
"prefix": "apple-touch-icon",
},
{
"image_fmt": "png",
"rel": "apple-touch-icon",
"dimensions": (152, 152),
"prefix": "apple-touch-icon",
},
{
"image_fmt": "png",
"rel": "apple-touch-icon",
"dimensions": (167, 167),
"prefix": "apple-touch-icon",
},
{
"image_fmt": "png",
"rel": "apple-touch-icon",
"dimensions": (180, 180),
"prefix": "apple-touch-icon",
},
{"image_fmt": "png", "rel": None, "dimensions": (70, 70), "prefix": "mstile"},
{"image_fmt": "png", "rel": None, "dimensions": (270, 270), "prefix": "mstile"},
{"image_fmt": "png", "rel": None, "dimensions": (310, 310), "prefix": "mstile"},
{"image_fmt": "png", "rel": None, "dimensions": (310, 150), "prefix": "mstile"},
{"image_fmt": "png", "rel": "shortcut icon", "dimensions": (196, 196), "prefix": "favicon"},
)
| 32.7125
| 96
| 0.485671
| 276
| 2,617
| 4.518116
| 0.184783
| 0.134723
| 0.176423
| 0.224539
| 0.80834
| 0.798717
| 0.756215
| 0.756215
| 0.575782
| 0.506816
| 0
| 0.05478
| 0.260604
| 2,617
| 79
| 97
| 33.126582
| 0.589664
| 0.01261
| 0
| 0.4
| 0
| 0
| 0.44647
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
d5bc13504b86a4fdea95d3cdc295d7a5cfd7eb06
| 158
|
py
|
Python
|
django_tiles_gl/templatetags/tiles_gl_tags.py
|
kleingeist/django-tiles-gl
|
479203eb94737db7ec141035887350d1e39df2a0
|
[
"CC-BY-4.0"
] | null | null | null |
django_tiles_gl/templatetags/tiles_gl_tags.py
|
kleingeist/django-tiles-gl
|
479203eb94737db7ec141035887350d1e39df2a0
|
[
"CC-BY-4.0"
] | null | null | null |
django_tiles_gl/templatetags/tiles_gl_tags.py
|
kleingeist/django-tiles-gl
|
479203eb94737db7ec141035887350d1e39df2a0
|
[
"CC-BY-4.0"
] | null | null | null |
from django import template
register = template.Library()
@register.inclusion_tag("django_tiles_gl/maplibre_head.html")
def maplibre_head():
return {}
| 17.555556
| 61
| 0.772152
| 20
| 158
| 5.85
| 0.75
| 0.205128
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.120253
| 158
| 8
| 62
| 19.75
| 0.841727
| 0
| 0
| 0
| 0
| 0
| 0.21519
| 0.21519
| 0
| 0
| 0
| 0
| 0
| 1
| 0.2
| false
| 0
| 0.2
| 0.2
| 0.6
| 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
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 5
|
91426a51792969517dcdb39ce1c5b70f00ba6377
| 65
|
py
|
Python
|
codes/handlers/__init__.py
|
igormq/aes-lac-2018
|
5d99831ab9d1063308176d82c23c4fc21c0f89cd
|
[
"MIT"
] | 22
|
2018-06-11T21:33:11.000Z
|
2021-06-18T21:33:51.000Z
|
codes/handlers/__init__.py
|
igormq/aes-lac-2018
|
5d99831ab9d1063308176d82c23c4fc21c0f89cd
|
[
"MIT"
] | 7
|
2018-11-01T01:28:33.000Z
|
2019-12-10T01:51:52.000Z
|
codes/handlers/__init__.py
|
igormq/aes-lac-2018
|
5d99831ab9d1063308176d82c23c4fc21c0f89cd
|
[
"MIT"
] | 12
|
2018-07-09T18:38:44.000Z
|
2021-04-13T21:15:03.000Z
|
from .tensorboardx import TensorboardX
from .visdom import Visdom
| 32.5
| 38
| 0.861538
| 8
| 65
| 7
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.107692
| 65
| 2
| 39
| 32.5
| 0.965517
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 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
| 5
|
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