Datasets:
xszheng2020
/
the_stack_dedup_python_hits_6

Dataset card Data Studio Files
xet
Community
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
f99ec542f5deddfce0972897f33d7ddd1f846d32
1,477
py
Python
src/Paths.py
00anupam00/comparative-analysis
e7c630e9706a9be1144dd484340c1c826ded0d65
[ "Apache-2.0" ]
null
null
null
src/Paths.py
00anupam00/comparative-analysis
e7c630e9706a9be1144dd484340c1c826ded0d65
[ "Apache-2.0" ]
null
null
null
src/Paths.py
00anupam00/comparative-analysis
e7c630e9706a9be1144dd484340c1c826ded0d65
[ "Apache-2.0" ]
null
null
null
import os basePath = os.path.abspath(os.getcwd()) # #ssl_reneg_pcap = basePath + "/input/ssl/dataset.pcap" #ssl_reneg_dataset = basePath + "/input/ssl/dataset.csv" #ssl_reneg_labels = basePath + "/input/ssl/labels.csv" #arp_spoof_pcap = basePath + "/input/arp/dataset.pcap" #arp_spoof_dataset = basePath + "/input/arp/dataset.csv" #arp_spoof_labels = basePath + "/input/arp/labels.csv" #syn_dos_pcap = basePath + "/input/syn/dataset.pcap" #syn_dos_dataset = basePath + "/input/syn/dataset.csv" #syn_dos_labels = basePath + "/input/syn/labels.csv" ssl_reneg_pcap = basePath + "/comparative-analysis/input/ssl/dataset.pcap" ssl_reneg_dataset = basePath + "/comparative-analysis/input/ssl/dataset.csv" ssl_reneg_labels = basePath + "/comparative-analysis/input/ssl/labels.csv" arp_spoof_pcap = basePath + "/comparative-analysis/input/arp/dataset.pcap" arp_spoof_dataset = basePath + "/comparative-analysis/input/arp/dataset.csv" arp_spoof_labels = basePath + "/comparative-analysis/input/arp/labels.csv" syn_dos_pcap = basePath + "/comparative-analysis/input/syn/dataset.pcap" syn_dos_dataset = basePath + "/comparative-analysis/input/syn/dataset.csv" syn_dos_labels = basePath + "/comparative-analysis/input/syn/labels.csv" ## staging paths arp_vec_path = basePath + "/comparative-analysis/input/arp/dataset_vec.csv" ssl_vec_path = basePath + "/comparative-analysis/input/ssl/dataset_vec.csv" syn_vec_path = basePath + "/comparative-analysis/input/syn/dataset_vec.csv"
41.027778
76
0.772512
205
1,477
5.346341
0.107317
0.208029
0.29562
0.350365
0.881387
0.818431
0.572993
0.572993
0
0
0
0
0.083954
1,477
35
77
42.2
0.810052
0.332431
0
0
0
0
0.542094
0.542094
0
0
0
0
0
1
0
false
0
0.071429
0
0.071429
0
0
0
0
null
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
null
0
0
0
0
0
0
0
0
0
0
0
0
0
6
f9c9c65efc6dcd6267b2a5cdf96187d00b9cb2ed
11,005
py
Python
misc/seed_simgan.py
roddtalebi/ezCGP
a93df7ae91fd5905df368661b86ae653c3d08869
[ "MIT" ]
null
null
null
misc/seed_simgan.py
roddtalebi/ezCGP
a93df7ae91fd5905df368661b86ae653c3d08869
[ "MIT" ]
null
null
null
misc/seed_simgan.py
roddtalebi/ezCGP
a93df7ae91fd5905df368661b86ae653c3d08869
[ "MIT" ]
null
null
null
''' use utilities/lisp_generator.py to seed simgan refiner and discriminator blocks ''' ### packages import numpy as np import torch ### sys relative to root dir import sys from os.path import dirname, realpath sys.path.append(dirname(dirname(realpath(__file__)))) ### absolute imports wrt root from codes.utilities import lisp_generator import codes.block_definitions.utilities.operators_pytorch as opPytorch import codes.block_definitions.utilities.operators_simgan_train_config as opTrainConfig from codes.block_definitions.shapemeta.block_shapemeta import (BlockShapeMeta_SimGAN_Network, BlockShapeMeta_SimGAN_Train_Config, BlockShapeMeta_SimGAN_Train_Config) from codes.block_definitions.evaluate.block_evaluate_pytorch import (BlockEvaluate_SimGAN_Refiner, BlockEvaluate_SimGAN_Discriminator, BlockEvaluate_SimGAN_Train_Config) # to be used later for writing the seed block_seed_info = [] ### REFINER # CONSTANTS in_channels = 5 nb_channels = 10 kernel = 5 padding = 2 use_leaky_relu = True num_blocks = 2 shapemeta_refiner = BlockShapeMeta_SimGAN_Network() args = [] genome = [None]*shapemeta_refiner.genome_count this_args = [nb_channels, kernel, 1, padding, None] genome[0] = {"ftn": opPytorch.conv1d_layer, "inputs": [-1], "args": list(np.arange(len(this_args))+len(args))} args+=this_args for i in range(num_blocks): this_args = [nb_channels, kernel, use_leaky_relu] genome[i+1] = {"ftn": opPytorch.resnet, "inputs": [i], "args": list(np.arange(len(this_args))+len(args))} args+=this_args genome[shapemeta_refiner.main_count] = i+1 material = lisp_generator.FakeMaterial(genome, args, "poop") definition = lisp_generator.FakeDefinition(shapemeta_refiner.input_count, shapemeta_refiner.main_count, shapemeta_refiner.output_count) definition.get_lisp(material) block_seed_info.append([genome, args, shapemeta_refiner.input_count, shapemeta_refiner.main_count, shapemeta_refiner.output_count, "RefinerBlock"]) ''' Verify lisp worked... print(material.lisp) # now try evaluating to see that evaluate_def works and graph builds evaluate_def = BlockEvaluate_SimGAN_Refiner() fake_data = torch.randn(500,1,92) evaluate_def.evaluate(material, definition, [fake_data], None, []) print("Built graph!") output = material.graph(fake_data) print("\n...go to discrim\n") ''' ### DISCRIM # CONSTANTS pixel_length = 6 mbd_kernel_dims = 5 shapemeta_discrim = BlockShapeMeta_SimGAN_Network() args = [] genome = [None]*shapemeta_discrim.genome_count this_args = [64, 5, 2, 2, torch.nn.LeakyReLU(0.1)] genome[0] = {"ftn": opPytorch.conv1d_layer, "inputs": [-1], "args": list(np.arange(len(this_args))+len(args))} args+=this_args this_args = [32, 5, 2, 2, torch.nn.LeakyReLU(0.1)] genome[1] = {"ftn": opPytorch.conv1d_layer, "inputs": [0], "args": list(np.arange(len(this_args))+len(args))} args+=this_args this_args = [] genome[2] = {"ftn": opPytorch.batch_normalization, "inputs": [1], "args": list(np.arange(len(this_args))+len(args))} args+=this_args this_args = [3, 1, 1] genome[3] = {"ftn": opPytorch.avg_pool, "inputs": [2], "args": list(np.arange(len(this_args))+len(args))} args+=this_args this_args = [16, 1, 2, 0, torch.nn.LeakyReLU(0.1)] genome[4] = {"ftn": opPytorch.conv1d_layer, "inputs": [3], "args": list(np.arange(len(this_args))+len(args))} args+=this_args this_args = [] genome[5] = {"ftn": opPytorch.batch_normalization, "inputs": [4], "args": list(np.arange(len(this_args))+len(args))} args+=this_args this_args = [8, 1, 2, 0, torch.nn.LeakyReLU(0.1)] genome[6] = {"ftn": opPytorch.conv1d_layer, "inputs": [5], "args": list(np.arange(len(this_args))+len(args))} args+=this_args this_args = [] genome[7] = {"ftn": opPytorch.flatten_layer, "inputs": [6], "args": list(np.arange(len(this_args))+len(args))} args+=this_args this_args = [4*pixel_length, mbd_kernel_dims] genome[8] = {"ftn": opPytorch.minibatch_discrimination, "inputs": [7], "args": list(np.arange(len(this_args))+len(args))} args+=this_args this_args = [] genome[9] = {"ftn": opPytorch.feature_extraction, "inputs": [-1], "args": list(np.arange(len(this_args))+len(args))} args+=this_args this_args = [1] genome[10] = {"ftn": opPytorch.pytorch_concat, "inputs": [7, 8], "args": list(np.arange(len(this_args))+len(args))} args+=this_args this_args = [1] genome[11] = {"ftn": opPytorch.pytorch_concat, "inputs": [10, 9], "args": list(np.arange(len(this_args))+len(args))} args+=this_args this_args = [0.5] genome[12] = {"ftn": opPytorch.dropout, "inputs": [11], "args": list(np.arange(len(this_args))+len(args))} args+=this_args genome[shapemeta_discrim.main_count] = 12 material = lisp_generator.FakeMaterial(genome, args, "poop") definition = lisp_generator.FakeDefinition(shapemeta_discrim.input_count, shapemeta_discrim.main_count, shapemeta_discrim.output_count) definition.get_lisp(material) block_seed_info.append([genome, args, shapemeta_discrim.input_count, shapemeta_discrim.main_count, shapemeta_discrim.output_count, "DiscriminatorBlock"]) '''# Verify that the lisp works... print(material.lisp) # now try evaluating to see that evaluate_def works and graph builds evaluate_def = BlockEvaluate_SimGAN_Discriminator() fake_data = torch.randn(500,1,92) evaluate_def.evaluate(material, definition, [fake_data], None, []) print("Built graph!") output = material.graph(fake_data) ''' ### Train Config shapemeta_trainconfig = BlockShapeMeta_SimGAN_Train_Config() args = [] genome = [None]*shapemeta_trainconfig.genome_count this_args = [5000, 500, 400, 1, 2, 0.001, 0.0001, 0.0001, True, 0, True, 4] genome[0] = {"ftn": opTrainConfig.simgan_train_config, "inputs": [-1], "args": list(np.arange(len(this_args))+len(args))} args+=this_args genome[shapemeta_trainconfig.main_count] = 0 material = lisp_generator.FakeMaterial(genome, args, "poop") definition = lisp_generator.FakeDefinition(shapemeta_trainconfig.input_count, shapemeta_trainconfig.main_count, shapemeta_trainconfig.output_count) material.evaluated = [None]*definition.genome_count material.dead = False definition.input_dtypes = [dict] definition.get_lisp(material) block_seed_info.append([genome, args, shapemeta_trainconfig.input_count, shapemeta_trainconfig.main_count, shapemeta_trainconfig.output_count, "ConfigBlock"]) ''' # Verify lisp working print(material.lisp) # now try evaluating to see that evaluate_def works and graph builds evaluate_def = BlockEvaluate_SimGAN_Train_Config() fake_data = torch.randn(500,1,92) evaluate_def.evaluate(material, definition, [fake_data], None, []) print("Built graph!") config = material.output[-1][0] # get from supplements ''' # COMPILE AND SAVE! lisp_generator.generate_individual_seed(list_of_info=block_seed_info, individual_name="SimGAN_Seed0") ### Train Config for ECG shapemeta_trainconfig = BlockShapeMeta_SimGAN_Train_Config() args = [] genome = [None]*shapemeta_trainconfig.genome_count this_args = [3000, 500, 400, 1, 2, 0.001, 0.0001, 0.01, True, 0, True, 40] genome[0] = {"ftn": opTrainConfig.simgan_train_config_ecg, "inputs": [-1], "args": list(np.arange(len(this_args))+len(args))} args+=this_args genome[shapemeta_trainconfig.main_count] = 0 material = lisp_generator.FakeMaterial(genome, args, "poop") definition = lisp_generator.FakeDefinition(shapemeta_trainconfig.input_count, shapemeta_trainconfig.main_count, shapemeta_trainconfig.output_count) material.evaluated = [None]*definition.genome_count material.dead = False definition.input_dtypes = [dict] definition.get_lisp(material) block_seed_info = [] block_seed_info.append([genome, args, shapemeta_trainconfig.input_count, shapemeta_trainconfig.main_count, shapemeta_trainconfig.output_count, "ConfigBlock"]) ''' # Verify lisp working print(material.lisp) # now try evaluating to see that evaluate_def works and graph builds evaluate_def = BlockEvaluate_SimGAN_Train_Config() fake_data = torch.randn(500,1,92) evaluate_def.evaluate(material, definition, [fake_data], None, []) print("Built graph!") config = material.output[-1][0] # get from supplements ''' # COMPILE AND SAVE! lisp_generator.generate_individual_seed(list_of_info=block_seed_info, individual_name="SimGAN_ECG_Seed0") ### Train Config for Transform shapemeta_trainconfig = BlockShapeMeta_SimGAN_Train_Config() args = [] genome = [None]*shapemeta_trainconfig.genome_count this_args = [2000, 500, 400, 1, 2, 0.001, 0.0001, 0.01, True, 0, True, 4] genome[0] = {"ftn": opTrainConfig.simgan_train_config, "inputs": [-1], "args": list(np.arange(len(this_args))+len(args))} args+=this_args genome[shapemeta_trainconfig.main_count] = 0 material = lisp_generator.FakeMaterial(genome, args, "poop") definition = lisp_generator.FakeDefinition(shapemeta_trainconfig.input_count, shapemeta_trainconfig.main_count, shapemeta_trainconfig.output_count) material.evaluated = [None]*definition.genome_count material.dead = False definition.input_dtypes = [dict] definition.get_lisp(material) block_seed_info = [] block_seed_info.append([genome, args, shapemeta_trainconfig.input_count, shapemeta_trainconfig.main_count, shapemeta_trainconfig.output_count, "ConfigBlock"]) ''' # Verify lisp working print(material.lisp) # now try evaluating to see that evaluate_def works and graph builds evaluate_def = BlockEvaluate_SimGAN_Train_Config() fake_data = torch.randn(500,1,92) evaluate_def.evaluate(material, definition, [fake_data], None, []) print("Built graph!") config = material.output[-1][0] # get from supplements ''' # COMPILE AND SAVE! lisp_generator.generate_individual_seed(list_of_info=block_seed_info, individual_name="SimGAN_Transform_Seed0")
33.966049
147
0.666879
1,344
11,005
5.218006
0.125744
0.0616
0.051333
0.041067
0.801369
0.763867
0.751034
0.731071
0.731071
0.71396
0
0.025
0.211268
11,005
323
148
34.071207
0.782949
0.029896
0
0.591837
0
0
0.041217
0.002471
0
0
0
0
0
1
0
false
0
0.045918
0
0.045918
0
0
0
0
null
0
0
0
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
6
f9d37785c165c5ea0c5d0079c01875f999b43eb3
4,494
py
Python
lcclassifier/results/borrar/tables (copy).py
opimentel-github/astro-lightcurves-classifier
80d0e02b95625e89f250086fa8d4a09688c9cbf6
[ "MIT" ]
null
null
null
lcclassifier/results/borrar/tables (copy).py
opimentel-github/astro-lightcurves-classifier
80d0e02b95625e89f250086fa8d4a09688c9cbf6
[ "MIT" ]
null
null
null
lcclassifier/results/borrar/tables (copy).py
opimentel-github/astro-lightcurves-classifier
80d0e02b95625e89f250086fa8d4a09688c9cbf6
[ "MIT" ]
null
null
null
from __future__ import print_function from __future__ import division from . import _C import numpy as np from fuzzytools.files import search_for_filedirs, load_pickle import fuzzytools.strings as strings import fuzzytools.datascience.statistics as dstats from fuzzytools.dataframes import DFBuilder from . import utils as utils import pandas as pd ################################################################################################################################################### def get_column_query_df_table(rootdir, cfilename, kf, lcset_name, model_names, metric_names, query_dict, day_to_metric=None, mode='fine-tuning', arch_modes=['Parallel', 'Serial'], ): info_df = DFBuilder() for arch_mode in arch_modes: for query_value in query_values: info_df[f'{query_value} [{arch_mode}]'] = [] for kmn,model_name in enumerate(model_names): new_rootdir = f'{rootdir}/{mode}/{model_name}' new_rootdir = new_rootdir.replace('mode=pre-training', f'mode={mode}') # patch new_rootdir = new_rootdir.replace('mode=fine-tuning', f'mode={mode}') # patch filedirs = search_for_filedirs(new_rootdir, fext=fext, verbose=0) print(f'[{kmn}][{len(filedirs)}#] {model_name}') mn_dict = strings.get_dict_from_string(model_name) rsc = mn_dict['rsc'] mdl = mn_dict['mdl'] is_parallel = 'Parallel' in mdl arch_mode = 'Parallel' if is_parallel else 'Serial' if arch_mode in arch_modes: for km,metric_name in enumerate(metric_names): day_metric = [] day_metric_avg = [] for filedir in filedirs: rdict = load_pickle(filedir, verbose=0) #model_name = rdict['model_name'] days = rdict['days'] survey = rdict['survey'] band_names = ''.join(rdict['band_names']) class_names = rdict['class_names'] v, vs, _ = utils.get_metric_along_day(days, rdict, metric_name, day_to_metric) day_metric += [v] day_metric_avg += [vs.mean()] xe_day_metric = dstats.XError(day_metric, 0) xe_day_metric_avg = dstats.XError(day_metric_avg, 0) key = f'{mn_dict[query_key]} [{arch_mode}]' info_df[key] += [xe_day_metric] info_df[key] += [xe_day_metric_avg] key = f'metric={utils.get_mday_str(metric_name, day_to_metric)}' if not key in index_df: index_df += [key] index_df += [f'metric={utils.get_mday_avg_str(metric_name, day_to_metric)}'] info_df = pd.DataFrame.from_dict(info_df) info_df.index = index_df return info_df ################################################################################################################################################### def get_df_table(rootdir, metric_names, model_names, day_to_metric, format_f, fext='metrics', mode='fine-tuning', arch_modes=['Parallel', 'Serial'], ): index_df = [] info_df = {} for arch_mode in arch_modes: for model_name in model_names: info_df[f'{format_f(model_name)} [{arch_mode}]'] = [] for kmn,model_name in enumerate(model_names): new_rootdir = f'{rootdir}/{mode}/{model_name}' new_rootdir = new_rootdir.replace('mode=pre-training', f'mode={mode}') # patch new_rootdir = new_rootdir.replace('mode=fine-tuning', f'mode={mode}') # patch filedirs = search_for_filedirs(new_rootdir, fext=fext, verbose=0) print(f'[{kmn}][{len(filedirs)}#] {model_name}') mn_dict = strings.get_dict_from_string(model_name) rsc = mn_dict['rsc'] mdl = mn_dict['mdl'] is_parallel = 'Parallel' in mdl arch_mode = 'Parallel' if is_parallel else 'Serial' if arch_mode in arch_modes: for km,metric_name in enumerate(metric_names): day_metric = [] day_metric_avg = [] for filedir in filedirs: rdict = load_pickle(filedir, verbose=0) #model_name = rdict['model_name'] days = rdict['days'] survey = rdict['survey'] band_names = ''.join(rdict['band_names']) class_names = rdict['class_names'] v, vs, _ = utils.get_metric_along_day(days, rdict, metric_name, day_to_metric) day_metric += [v] day_metric_avg += [vs.mean()] xe_day_metric = dstats.XError(day_metric, 0) xe_day_metric_avg = dstats.XError(day_metric_avg, 0) key = f'{format_f(model_name)} [{arch_mode}]' info_df[key] += [xe_day_metric] info_df[key] += [xe_day_metric_avg] key = f'metric={utils.get_mday_str(metric_name, day_to_metric)}' if not key in index_df: index_df += [key] index_df += [f'metric={utils.get_mday_avg_str(metric_name, day_to_metric)}'] info_df = pd.DataFrame.from_dict(info_df) info_df.index = index_df return info_df
37.140496
147
0.662439
646
4,494
4.270898
0.151703
0.065241
0.043494
0.032621
0.797028
0.797028
0.797028
0.740123
0.740123
0.740123
0
0.002091
0.148643
4,494
121
148
37.140496
0.719028
0.019359
0
0.772277
0
0
0.18992
0.076942
0
0
0
0
0
1
0.019802
false
0
0.09901
0
0.138614
0.029703
0
0
0
null
0
0
0
0
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
6
ddaeb0a76122b61978cacbc71919e07a91cceed1
35
py
Python
Code/odooerp/odoo-8.0/openerp/addons/hr_expense/tests/__init__.py
zhupangithub/WEBERP
714512082ec5c6db07cbf6af0238ceefe2d2c1a5
[ "MIT" ]
1
2019-12-29T11:53:56.000Z
2019-12-29T11:53:56.000Z
odoo/addons/hr_expense/tests/__init__.py
tuanquanghpvn/odoo8-tutorial
52d25f1ca5f233c431cb9d3b24b79c3b4fb5127e
[ "MIT" ]
null
null
null
odoo/addons/hr_expense/tests/__init__.py
tuanquanghpvn/odoo8-tutorial
52d25f1ca5f233c431cb9d3b24b79c3b4fb5127e
[ "MIT" ]
3
2020-10-08T14:42:10.000Z
2022-01-28T14:12:29.000Z
from . import test_journal_entries
17.5
34
0.857143
5
35
5.6
1
0
0
0
0
0
0
0
0
0
0
0
0.114286
35
1
35
35
0.903226
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
1
0
1
0
1
1
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
6
dddc2db85082b163957a875af9fb3ddfcfbfc620
36
py
Python
adform/reporting/__init__.py
dutkiewicz/adform-api
5b670ea971c261565d1fe4cf7c18b2e109f8449d
[ "MIT" ]
null
null
null
adform/reporting/__init__.py
dutkiewicz/adform-api
5b670ea971c261565d1fe4cf7c18b2e109f8449d
[ "MIT" ]
6
2019-11-29T04:53:15.000Z
2020-06-29T04:41:24.000Z
adform/reporting/__init__.py
dutkiewicz/adform-api
5b670ea971c261565d1fe4cf7c18b2e109f8449d
[ "MIT" ]
null
null
null
from . import metadata, stats_async
18
35
0.805556
5
36
5.6
1
0
0
0
0
0
0
0
0
0
0
0
0.138889
36
1
36
36
0.903226
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
1
0
1
0
1
1
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
6
fb313aecfebd997ef0dde13bd6a1570b125adfce
116
py
Python
kafkaSchemaManager/implementation/kafka/__init__.py
YendiyarovSV/kafka-avro-producer-topkrabbensteam
d7a318b465ff38897150a4a4db267309793373bc
[ "Apache-2.0" ]
null
null
null
kafkaSchemaManager/implementation/kafka/__init__.py
YendiyarovSV/kafka-avro-producer-topkrabbensteam
d7a318b465ff38897150a4a4db267309793373bc
[ "Apache-2.0" ]
null
null
null
kafkaSchemaManager/implementation/kafka/__init__.py
YendiyarovSV/kafka-avro-producer-topkrabbensteam
d7a318b465ff38897150a4a4db267309793373bc
[ "Apache-2.0" ]
null
null
null
from .KafkaAvroProducer import KafkaAvroProducer from .KafkaSchemaRegistryUpdater import KafkaSchemaRegistryUpdater
38.666667
66
0.913793
8
116
13.25
0.5
0
0
0
0
0
0
0
0
0
0
0
0.068966
116
2
67
58
0.981481
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
1
0
1
0
1
0
1
null
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
6
34b1eb5cadefb2fcebf40ceca2c60914230b2755
214
py
Python
ievv_opensource/demo/project/test/settings.py
appressoas/ievv_opensource
63e87827952ddc8f6f86145b79478ef21d6a0990
[ "BSD-3-Clause" ]
null
null
null
ievv_opensource/demo/project/test/settings.py
appressoas/ievv_opensource
63e87827952ddc8f6f86145b79478ef21d6a0990
[ "BSD-3-Clause" ]
37
2015-10-26T09:14:12.000Z
2022-02-10T10:35:33.000Z
ievv_opensource/demo/project/test/settings.py
appressoas/ievv_opensource
63e87827952ddc8f6f86145b79478ef21d6a0990
[ "BSD-3-Clause" ]
1
2015-11-06T07:56:34.000Z
2015-11-06T07:56:34.000Z
from ievv_opensource.demo.project.default.settings import * # noqa ROOT_URLCONF = 'ievv_opensource.demo.project.test.urls' INSTALLED_APPS += [ 'ievv_opensource.ievv_i18n_url.tests.ievv_i18n_url_testapp', ]
23.777778
67
0.78972
29
214
5.482759
0.655172
0.264151
0.226415
0.314465
0
0
0
0
0
0
0
0.020833
0.102804
214
8
68
26.75
0.807292
0.018692
0
0
0
0
0.456731
0.456731
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
1
null
0
0
0
0
0
0
0
0
0
0
0
0
0
6
34fdc42a2596ea04ce366653308e75dd9f432dec
20
py
Python
rstoolbox/tests/__init__.py
sesterhe/RosettaSilentToolbox
010941b9b20974c61a86858bfb73d5913afc6849
[ "MIT" ]
14
2019-01-22T15:56:58.000Z
2022-02-07T23:49:50.000Z
rstoolbox/tests/__init__.py
sesterhe/RosettaSilentToolbox
010941b9b20974c61a86858bfb73d5913afc6849
[ "MIT" ]
null
null
null
rstoolbox/tests/__init__.py
sesterhe/RosettaSilentToolbox
010941b9b20974c61a86858bfb73d5913afc6849
[ "MIT" ]
2
2020-05-23T20:39:15.000Z
2022-02-07T23:49:57.000Z
from . import helper
20
20
0.8
3
20
5.333333
1
0
0
0
0
0
0
0
0
0
0
0
0.15
20
1
20
20
0.941176
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
1
0
1
0
1
1
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
6
9b33a51c99ab438a53dc2f4aeb5f1d92e1bed9e5
1,133
py
Python
tests/y2021/test_2021_d4.py
ErikThorsell/advent-of-code-python
8afb3d2dd731b77a421eff9dbd33d1f6a9dfbee3
[ "MIT" ]
2
2021-12-03T16:17:13.000Z
2022-01-27T12:29:45.000Z
tests/y2021/test_2021_d4.py
ErikThorsell/advent-of-code-python
8afb3d2dd731b77a421eff9dbd33d1f6a9dfbee3
[ "MIT" ]
null
null
null
tests/y2021/test_2021_d4.py
ErikThorsell/advent-of-code-python
8afb3d2dd731b77a421eff9dbd33d1f6a9dfbee3
[ "MIT" ]
1
2021-12-29T20:38:38.000Z
2021-12-29T20:38:38.000Z
"""TEST MODULE TEMPLATE""" from advent_of_code.y2021.d4 import solution_1 from advent_of_code.y2021.d4 import solution_2 from advent_of_code.utils.parse import parse_bingo def test_solution_1(): example_input = """7,4,9,5,11,17,23,2,0,14,21,24,10,16,13,6,15,25,12,22,18,20,8,19,3,26,1 22 13 17 11 0 8 2 23 4 24 21 9 14 16 7 6 10 3 18 5 1 12 20 15 19 3 15 0 2 22 9 18 13 17 5 19 8 7 25 23 20 11 10 24 4 14 21 16 12 6 14 21 17 24 4 10 16 15 9 19 18 8 23 26 20 22 11 13 6 5 2 0 12 3 7""" example_result = 4512 orders, boards = parse_bingo(example_input) assert solution_1(orders, boards) == example_result def test_solution_2(): example_input = """7,4,9,5,11,17,23,2,0,14,21,24,10,16,13,6,15,25,12,22,18,20,8,19,3,26,1 22 13 17 11 0 8 2 23 4 24 21 9 14 16 7 6 10 3 18 5 1 12 20 15 19 3 15 0 2 22 9 18 13 17 5 19 8 7 25 23 20 11 10 24 4 14 21 16 12 6 14 21 17 24 4 10 16 15 9 19 18 8 23 26 20 22 11 13 6 5 2 0 12 3 7""" example_result = 1924 orders, boards = parse_bingo(example_input) assert solution_2(orders, boards) == example_result
20.232143
93
0.64519
278
1,133
2.539568
0.179856
0.033994
0.050992
0.067989
0.78187
0.78187
0.78187
0.78187
0.541076
0.541076
0
0.426347
0.263019
1,133
55
94
20.6
0.419162
0.017652
0
0.790698
0
0.046512
0.538392
0.126468
0
0
0
0
0.046512
1
0.046512
false
0
0.069767
0
0.116279
0
0
0
0
null
0
0
0
0
1
1
1
0
0
0
1
0
0
0
0
0
1
0
0
1
0
0
0
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
6
9b4a6944d5a8013da4a6c6a8b7c36266abc31826
200
py
Python
tests/test_namespace.py
najafimh/openvpn-api
e892de4197da22e889f56ab167d74a948bfea493
[ "MIT" ]
28
2019-09-27T15:46:13.000Z
2022-02-11T11:54:18.000Z
tests/test_namespace.py
najafimh/openvpn-api
e892de4197da22e889f56ab167d74a948bfea493
[ "MIT" ]
25
2019-11-12T18:38:23.000Z
2021-04-01T14:29:43.000Z
tests/test_namespace.py
najafimh/openvpn-api
e892de4197da22e889f56ab167d74a948bfea493
[ "MIT" ]
11
2019-09-28T01:13:35.000Z
2022-01-15T14:23:07.000Z
import unittest class TestNamespace(unittest.TestCase): def test_import(self): from openvpn_api import VPN from openvpn_api import VPNType from openvpn_api import errors
22.222222
39
0.725
25
200
5.64
0.56
0.234043
0.297872
0.425532
0
0
0
0
0
0
0
0
0.24
200
8
40
25
0.927632
0
0
0
0
0
0
0
0
0
0
0
0
1
0.166667
false
0
0.833333
0
1.166667
0
1
0
0
null
1
1
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
0
0
1
0
0
0
0
6
9b7c8d80477e4948228666b8df0e9a19f0c226d5
23
py
Python
luckhole/__init__.py
Floozutter/luck-be-a-loophole
47fc17958c51aee982ed27c13ced273accc782e8
[ "Unlicense" ]
null
null
null
luckhole/__init__.py
Floozutter/luck-be-a-loophole
47fc17958c51aee982ed27c13ced273accc782e8
[ "Unlicense" ]
null
null
null
luckhole/__init__.py
Floozutter/luck-be-a-loophole
47fc17958c51aee982ed27c13ced273accc782e8
[ "Unlicense" ]
null
null
null
from .save import Save
11.5
22
0.782609
4
23
4.5
0.75
0
0
0
0
0
0
0
0
0
0
0
0.173913
23
1
23
23
0.947368
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
1
0
1
0
1
1
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
6
32f613ac1608cfd813c99fbc48e47c66779279cf
96
py
Python
venv/lib/python3.8/site-packages/numpy/core/tests/_locales.py
Retraces/UkraineBot
3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71
[ "MIT" ]
2
2022-03-13T01:58:52.000Z
2022-03-31T06:07:54.000Z
venv/lib/python3.8/site-packages/numpy/core/tests/_locales.py
DesmoSearch/Desmobot
b70b45df3485351f471080deb5c785c4bc5c4beb
[ "MIT" ]
19
2021-11-20T04:09:18.000Z
2022-03-23T15:05:55.000Z
venv/lib/python3.8/site-packages/numpy/core/tests/_locales.py
DesmoSearch/Desmobot
b70b45df3485351f471080deb5c785c4bc5c4beb
[ "MIT" ]
null
null
null
/home/runner/.cache/pip/pool/47/6b/4d/06207363a87bcc7a9ec691acb8cd5035dff6091f897ea117738a455d82
96
96
0.895833
9
96
9.555556
1
0
0
0
0
0
0
0
0
0
0
0.4375
0
96
1
96
96
0.458333
0
0
0
0
0
0
0
0
1
0
0
0
0
null
null
0
0
null
null
0
1
0
0
null
0
0
0
0
0
0
0
0
0
0
1
0
0
1
0
0
1
0
0
0
0
0
0
0
null
1
0
0
0
1
0
0
0
0
0
0
0
0
6
fd38933fbcdae16ffda5ed54eb3034a20498c13b
2,111
py
Python
tests/test_layer_utils.py
flaviusfetean/visualkeras
dabfc8f8680d538670b25c6e557c3fa094a95825
[ "MIT" ]
148
2020-10-05T14:26:35.000Z
2022-03-31T20:44:19.000Z
tests/test_layer_utils.py
flaviusfetean/visualkeras
dabfc8f8680d538670b25c6e557c3fa094a95825
[ "MIT" ]
21
2020-10-05T18:18:10.000Z
2022-02-27T03:43:23.000Z
tests/test_layer_utils.py
flaviusfetean/visualkeras
dabfc8f8680d538670b25c6e557c3fa094a95825
[ "MIT" ]
23
2021-01-02T23:04:18.000Z
2022-02-24T08:50:49.000Z
from visualkeras.layer_utils import get_incoming_layers, \ get_outgoing_layers, find_layer_by_id, find_input_layers, find_output_layers, find_layer_by_name, is_internal_input def test_get_incoming_layers(functional_model): assert list(get_incoming_layers(functional_model.get_layer('input_1'))) == [] assert list(get_incoming_layers(functional_model.get_layer('layer_1_1'))) == [functional_model.get_layer('input_1')] assert list(get_incoming_layers(functional_model.get_layer('concat'))) == \ [functional_model.get_layer('layer_1_2'), functional_model.get_layer('layer_2_2'), functional_model.get_layer('layer_3_2'), functional_model.get_layer('input_2')] def test_get_outgoing_layers(functional_model): assert len(list(get_outgoing_layers(functional_model.get_layer('dense_4')))) == 0 assert list(get_outgoing_layers(functional_model.get_layer('input_1'))) == \ [functional_model.get_layer('layer_1_1'), functional_model.get_layer('layer_2_1'), functional_model.get_layer('layer_3_1')] assert list(get_outgoing_layers(functional_model.get_layer('concat'))) == \ [functional_model.get_layer('flatten')] def test_find_layer_by_id(functional_model): assert find_layer_by_id(functional_model, 0) == None layer = functional_model.get_layer('dense_1') assert find_layer_by_id(functional_model, id(layer)) == layer def test_find_layer_by_name(functional_model): assert find_layer_by_name(functional_model, 'input_1') == functional_model.get_layer('input_1') def test_find_input_layers(functional_model): assert list(find_input_layers(functional_model)) == [functional_model.get_layer('input_1'), functional_model.get_layer('input_2')] def test_find_output_layers(functional_model): assert list(find_output_layers(functional_model)) == [functional_model.get_layer('dense_4'), functional_model.get_layer('concat')] def test_is_internal_input(model): assert is_internal_input(model.get_layer('dense_1')) is False assert is_internal_input(model._layers[0]) is True
43.979167
144
0.76883
302
2,111
4.877483
0.115894
0.325866
0.194162
0.327902
0.837067
0.678887
0.490156
0.376103
0.312967
0.26205
0
0.015566
0.11748
2,111
47
145
44.914894
0.775094
0
0
0
0
0
0.081478
0
0
0
0
0
0.481481
1
0.259259
false
0
0.037037
0
0.296296
0
0
0
0
null
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
null
0
0
0
1
0
1
0
0
0
0
0
0
0
6
fd394a2cad40e43e547945c006e4209aea249e67
13,423
py
Python
trajectory/OutputFiles/XlsxOutputFile.py
RobertPastor/flight-profile
bdc3bb9defeb347db26f96f7accd4d06cad1e33b
[ "MIT" ]
null
null
null
trajectory/OutputFiles/XlsxOutputFile.py
RobertPastor/flight-profile
bdc3bb9defeb347db26f96f7accd4d06cad1e33b
[ "MIT" ]
null
null
null
trajectory/OutputFiles/XlsxOutputFile.py
RobertPastor/flight-profile
bdc3bb9defeb347db26f96f7accd4d06cad1e33b
[ "MIT" ]
null
null
null
''' Created on 12 juil. 2014 @author: PASTOR Robert Written By: Robert PASTOR @Email: < robert [--DOT--] pastor0691 (--AT--) orange [--DOT--] fr > @http://trajectoire-predict.monsite-orange.fr/ @copyright: Copyright 2015 Robert PASTOR This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>. @note: create an Xlsx file ''' from datetime import datetime import xlsxwriter import os import unittest class XlsxOutput(): FileName = "" workbook = None worksheet = None RowIndex = 0 def __init__(self, fileName, sheetName="Results"): self.className = self.__class__.__name__ self.RowIndex = 0 self.filePath = fileName self.FilesFolder = os.path.dirname(__file__) print ( self.className + ': file folder= {0}'.format(self.FilesFolder) ) self.filePath = os.path.abspath(self.FilesFolder + os.path.sep + ".." + os.path.sep + "ResultsFiles" + os.path.sep + self.filePath) print ( self.className + ': file path= {0}'.format(self.filePath) ) self.filePath = self.filePath + '-{0}.xlsx'.format(datetime.now().strftime("%d-%b-%Y-%Hh%Mm%S")) print ( self.className + ': file path= {0}'.format(self.filePath) ) self.workbook = xlsxwriter.Workbook(self.filePath) self.worksheet = self.workbook.add_worksheet(sheetName) def writeHeaders(self, Headers): assert isinstance(Headers, list) self.RowIndex = 0 ColumnIndex = 0 for header in Headers: self.worksheet.write(self.RowIndex, ColumnIndex, header) ColumnIndex = ColumnIndex + 1 self.RowIndex += 1 def writeOneFloatValue(self, time, floatValue ): ColumnIndex = 0 self.worksheet.write(self.RowIndex, ColumnIndex, time) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, floatValue) self.RowIndex += 1 def writeTwoFloatValues(self, time, firstFloatValue, secondFloatValue): ColumnIndex = 0 self.worksheet.write(self.RowIndex, ColumnIndex, time) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, firstFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, secondFloatValue) self.RowIndex += 1 def writeFourFloatValues(self, time, firstFloatValue, secondFloatValue, thirdFloatValue, fourthFloatValue): ColumnIndex = 0 self.worksheet.write(self.RowIndex, ColumnIndex, time) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, firstFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, secondFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, thirdFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, fourthFloatValue) self.RowIndex += 1 def writeSixFloatValues(self, time, firstFloatValue, secondFloatValue, thirdFloatValue, fourthFloatValue, fifthFloatValue, sixthFloatValue): ColumnIndex = 0 self.worksheet.write(self.RowIndex, ColumnIndex, time) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, firstFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, secondFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, thirdFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, fourthFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, fifthFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, sixthFloatValue) self.RowIndex += 1 def writeSevenFloatValues(self, time, firstFloatValue, secondFloatValue, thirdFloatValue, fourthFloatValue, fifthFloatValue, sixthFloatValue, seventhFloatValue): ColumnIndex = 0 self.worksheet.write(self.RowIndex, ColumnIndex, time) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, firstFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, secondFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, thirdFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, fourthFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, fifthFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, sixthFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, seventhFloatValue) self.RowIndex += 1 def writeNineFloatValues(self, elapsedTimeSeconds, firstFloatValue, secondFloatValue, thirdFloatValue, fourthFloatValue, fifthFloatValue, sixthFloatValue, seventhFloatValue, eighthFloatValue, ninethFloatValue): ColumnIndex = 0 self.worksheet.write(self.RowIndex, ColumnIndex, elapsedTimeSeconds) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, firstFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, secondFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, thirdFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, fourthFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, fifthFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, sixthFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, seventhFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, eighthFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, ninethFloatValue) self.RowIndex += 1 def writeTenFloatValues(self, elapsedTimeSeconds, firstFloatValue, secondFloatValue, thirdFloatValue, fourthFloatValue, fifthFloatValue, sixthFloatValue, seventhFloatValue, eighthFloatValue, ninethFloatValue, tenthFloatValue): ColumnIndex = 0 self.worksheet.write(self.RowIndex, ColumnIndex, elapsedTimeSeconds) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, firstFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, secondFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, thirdFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, fourthFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, fifthFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, sixthFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, seventhFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, eighthFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, ninethFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, tenthFloatValue) self.RowIndex += 1 def writeElevenFloatValues(self, elapsedTimeSeconds, firstFloatValue, secondFloatValue, thirdFloatValue, fourthFloatValue, fifthFloatValue, sixthFloatValue, seventhFloatValue, eighthFloatValue, ninethFloatValue, tenthFloatValue, EleventhFloatValue): ColumnIndex = 0 self.worksheet.write(self.RowIndex, ColumnIndex, elapsedTimeSeconds) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, firstFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, secondFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, thirdFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, fourthFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, fifthFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, sixthFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, seventhFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, eighthFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, ninethFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, tenthFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, EleventhFloatValue) self.RowIndex += 1 def writeFifteenFloatValues(self, elapsedTimeSeconds, firstFloatValue, secondFloatValue, thirdFloatValue, fourthFloatValue, fifthFloatValue, sixthFloatValue, seventhFloatValue, eighthFloatValue, ninethFloatValue, tenthFloatValue, eleventhFloatValue, twelvethFloatValue, thirdteenFloatValue, fourteenFloatValue , fifteenFloatValue, endOfSimulation): ColumnIndex = 0 self.worksheet.write(self.RowIndex, ColumnIndex, elapsedTimeSeconds) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, firstFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, secondFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, thirdFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, fourthFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, fifthFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, sixthFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, seventhFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, eighthFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, ninethFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, tenthFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, eleventhFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, twelvethFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, thirdteenFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, fourteenFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, fifteenFloatValue) ColumnIndex += 1 self.worksheet.write(self.RowIndex, ColumnIndex, str(endOfSimulation)) self.RowIndex += 1 def close(self): self.workbook.close()
44.44702
139
0.604932
1,098
13,423
7.379781
0.153005
0.130322
0.168826
0.206343
0.770949
0.76774
0.747131
0.745033
0.687647
0.687647
0
0.011905
0.317887
13,423
302
140
44.44702
0.873089
0.076361
0
0.756522
0
0
0.007829
0
0
0
0
0
0.004348
1
0.052174
false
0
0.017391
0
0.091304
0.013043
0
0
0
null
0
0
1
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
6
b5cb712da13f2464dcc7bde346b99d18e5490fad
26
py
Python
__init__.py
grayondream/algorithm-forth
c2c21419ec7c42b10f229ab41ac10edf7afbe956
[ "Apache-2.0" ]
null
null
null
__init__.py
grayondream/algorithm-forth
c2c21419ec7c42b10f229ab41ac10edf7afbe956
[ "Apache-2.0" ]
null
null
null
__init__.py
grayondream/algorithm-forth
c2c21419ec7c42b10f229ab41ac10edf7afbe956
[ "Apache-2.0" ]
null
null
null
import src import tools
8.666667
13
0.769231
4
26
5
0.75
0
0
0
0
0
0
0
0
0
0
0
0.230769
26
2
14
13
1
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
1
0
1
0
1
1
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
6
bd22a08671b1052f0bdabfa08f57aa91b77bfc79
534
py
Python
ex109/moeda.py
paulo-caixeta/Exercicios_Curso_Python
3b77925499c174ea9ff81dec65d6319125219b9a
[ "MIT" ]
null
null
null
ex109/moeda.py
paulo-caixeta/Exercicios_Curso_Python
3b77925499c174ea9ff81dec65d6319125219b9a
[ "MIT" ]
null
null
null
ex109/moeda.py
paulo-caixeta/Exercicios_Curso_Python
3b77925499c174ea9ff81dec65d6319125219b9a
[ "MIT" ]
null
null
null
def moeda(p = 0, moeda = 'R$'): return (f'{moeda}{p:.2f}'.replace('.',',')) def metade(p = 0, formato=False): res = p/2 return res if formato is False else moeda(res) def dobro(p = 0, formato=False): res = p*2 return res if formato is False else moeda(res) def aumentar(p = 0, taxa = 0, formato=False): res = p * (1+taxa/100) return res if formato is False else moeda(res) def diminuir(p = 0, taxa = 0, formato=False): res = p - (p * taxa/100) return res if formato is False else moeda(res)
24.272727
50
0.608614
92
534
3.532609
0.25
0.030769
0.16
0.196923
0.784615
0.784615
0.784615
0.784615
0.643077
0.643077
0
0.041872
0.2397
534
22
51
24.272727
0.758621
0
0
0.285714
0
0
0.033645
0
0
0
0
0
0
1
0.357143
false
0
0
0.071429
0.714286
0
0
0
0
null
0
0
1
0
1
1
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
1
0
0
0
0
0
0
0
6
1f9c59d82bd0202261a25060d72f670da784e6a9
7,467
py
Python
swipt/people/patxi.py
stoneworksolutions/swipt
3dfd0f1b6ba9b0f2cdba85c92098483c5d9cdd94
[ "Unlicense" ]
null
null
null
swipt/people/patxi.py
stoneworksolutions/swipt
3dfd0f1b6ba9b0f2cdba85c92098483c5d9cdd94
[ "Unlicense" ]
null
null
null
swipt/people/patxi.py
stoneworksolutions/swipt
3dfd0f1b6ba9b0f2cdba85c92098483c5d9cdd94
[ "Unlicense" ]
null
null
null
def patxi(): tip = raw_input("Don't forget to use your new company WC as soon as possible, It's important....") kk = """ @@X @@@@@' +@'+@@; @X''+@@ X@''''@@ @+'''''X@ @@'''''''X@ @@X'''''''''@@ `@@@''''''''''''@X +@@@'''''''''''''''@ +@@@+''''''''''+''''''@@ `X@@@+'''''''''''''''''''''@ .@@@@+''''''''''''''''''''''''@@ X@@@''''''''''''''''''''''''''+''@ @@''''''''''''''''''''''''''''+++'@. @@''''''''''''''''''''''''''''+++++@@ @X''''''''''''''''+'''''''''''+++++++@ '@'''''''''''''''''''''''''''++++++++'@ @+''+'''''''''''''''''''''''+++++++++'@ @+'''''''''''''''''''''''++++++++++++'@ @+'''''''''''''''''''''++++++++++++++'@ '@'''''''''''''''''++++++++++++X+++++'@ @+'''''''''''++++++++++++++++++++++'+@ :@''''''+++++++++++++++++++++++++++'@@ X@'''''+++++++++++++++++++++++++''+@@@@@` `X@@@@@+'''''''''+++++++++++''''''''''+''''@@@ X@@@X''''''''''''''''''''''''''''''''''''''''''@@ ;@@+''''''''''''''''''''''''''''''''''''''''''''''@X @@+'''''''''''''''''''''''''''''''''''''''''''''''''@ @@'''''''''''''''''''''''''''''''''''''''''''''''''''@@ :@'''''''''''''''''''''''''''''''''''''''''''''''''''''@ @+'''''''''''''''''''''''''''''''''''''''''''''''''''''@ +@''''''''''''''''''''''''''''''''''''''''''''''''+++'''@, @+'''''''''''''+''''''''''''''''''''''''+'''''''++++++''@' @''''''''''''''''''''''''''''''''''''''''''''+++++++++''@X ;@'''''''''''''''''''''''''''''''''''''''''++++++++++++''@+ @@'''''''''''''''''''''''''''''''''''''++++++++++++++++''@; @X'''''''''''''''''''''''''''''+'''++++++++++++++++++++''@. @X''''''''''''''''''''''''''''+++++++++++++++++++++++++''@ @@''''''''''''''''''''''+++++++++++++++++++++++++++++++'+@ ;@'''''''''''''''++++++++++++++++++++++++++++'++++++++''@' @''''''''++++++++++++++++++++++++++++++++++++++++++++'+@ @+''''++++++++++++++++++++++++++++++++++++++++++++++''@' ;@'''''''''+++++++++++++++++++++++++++++++++++++'''''@@@@@+` `'@@'''''''''''''''''''''''''''''''''''''''''''''''''++++'X@@@: '@@@X''''''''''''''''''''''''''''''''''''''''''''''''''''''''''X@@ `@@+'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''@@ ,@X'''''''''''+''''''''''''''''''''''''''''''''''''''''''''''''''''''@@ `@+''''''''''''''''''''''''''''''''''''''''+'''''''''''''''''''''''''''@X @X'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''+++''@ ,@''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''+++''@@ @X''''''''''''''''''''''''''''''''''''''''''''''''''''++'''''''''''+++++''@ @'''''''''''''''''''''''''''''''''''''''''''''''''''''+''''''''''''+++++''@+ @'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''++++++''+@ @'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''++++++++'''@ @'''''''''''''+''''''''''''''''++''''''''''''''''''''''''''''''++++++++++''@` @''''''''''''''''''''''''''''''++''''''''''''''''''''''''''''++++++++++++''@' @''''''''''''''''''''''''''''''''''''''''''''''''''''''''''++++++++++++++''@@ @''''''''''''''''''''''''''''''''''''''''''''''''''''''''++++++++++++++++''X@ @X''''''''''''''''''''''''''''''''''''''''''''''''''''++++++++++++++++++'''X@ ;@'''''''''''''''''''''''''''''''''''''''++'''''''++++++++++++++++''++++'''X@ @'''''''''''''''''''''''''''''''''''''''''''+++++++++++++++++++++++++++'''@X @X'''''''''''''''''''''''''''''''''''''+++++++++++++++++++++++++++++++''''@' ,@'''''''''''''''''''''''''''''+++++++++++++++++++++++++++++++++++++++''''@,,` `,,@@'''''''''''''''''''++++++++++++++++++++++++++++++++++++++++++++++'''''+@,,,, ,,,,@X'''''''''''++++++++++++++++++++++++++++++++++++++++++++++++++++''''''@X,,,,` ,,,,,:@X''''''''''''++++++++++++++++++++++++++++++++++++++++++'''''''''''''@@,,,,,, ,,,,,,,@@@@+''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''+@@@,,,,,,, ,,,,,,,,,'@@@@@X+''''''''''''''''''''''''''''''''''''''''''''''''''+@@@@@X:,,,,,,,, `,,,,,,,,,,,,;X@@@@@@@X++''''''''''''''''''''''''''''''''''+X@@@@@@@@',,,,,,,,,,,,, ,,,,,,,,,,,,,,,,,,,'@@@@@@@@@@@@@@@@@XXXXXXX@@@@@@@@@@@@@@@@@+:,,,,,,,,,,,,,,,,,, .,,,,,,,,,,,,,,,,,,,,,,,,,,,:;''+XX@@@@@@@@@@@@X+':,,,,,,,,,,,,,,,,,,,,,,,,,,,, ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,` `,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,. ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,` `,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,` .,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,.` `..,,,,,,,,,,,,,,,,,,,,,,,,,,,,.` """ print('\033[0;33m{0}'.format(kk)) print('\033[0m') patxi()
86.825581
103
0.022365
76
7,467
2.184211
0.394737
0.46988
0.686747
0.891566
0.240964
0.240964
0.240964
0.240964
0.240964
0.240964
0
0.002489
0.408196
7,467
85
104
87.847059
0.035076
0
0
0
0
0
0.986318
0.563127
0
0
0
0
0
1
0.011905
false
0
0.011905
0
0.02381
0.02381
0
0
0
null
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
1
1
null
0
0
0
0
0
0
0
0
0
0
0
0
0
6
1fa6a78eadaf77d31016ac987aceafe2f1dceefa
35
py
Python
jiautohc/__init__.py
tc-imba/ji-auto-hc
5915a6a0f69015bc2a9aec83420c772abcef2c0a
[ "Apache-2.0" ]
4
2019-02-28T01:26:16.000Z
2019-07-23T16:57:27.000Z
jiautohc/__init__.py
tc-imba/ji-auto-hc
5915a6a0f69015bc2a9aec83420c772abcef2c0a
[ "Apache-2.0" ]
null
null
null
jiautohc/__init__.py
tc-imba/ji-auto-hc
5915a6a0f69015bc2a9aec83420c772abcef2c0a
[ "Apache-2.0" ]
null
null
null
from jiautohc.__main__ import main
17.5
34
0.857143
5
35
5.2
0.8
0
0
0
0
0
0
0
0
0
0
0
0.114286
35
1
35
35
0.83871
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
1
0
1
0
1
1
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
6
1faf2a8d315bf9c88b08ba282339896d7c6d45dc
17,865
py
Python
demos/b64.py
Muller7/Face-
81346109174d5ed59d01f2771ce785fa3b357038
[ "Apache-2.0" ]
null
null
null
demos/b64.py
Muller7/Face-
81346109174d5ed59d01f2771ce785fa3b357038
[ "Apache-2.0" ]
null
null
null
demos/b64.py
Muller7/Face-
81346109174d5ed59d01f2771ce785fa3b357038
[ "Apache-2.0" ]
null
null
null
import os,base64 from stringcut import cutstr strs='''''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\n\n\n\n\n \n\n\n''' # base64 decode def b64decode(strs): imgdata=base64.b64decode(cutstr(strs,',')) file1=open('./static/real_time.png','wb') file=open('real_time.png','wb') file1.write(imgdata) file.write(imgdata) file1.close() file.close() if __name__ == '__main__': b64decode(strs)
893.25
17,499
0.965743
509
17,865
33.876228
0.927308
0.000812
0.001044
0.00116
0.000464
0.000464
0.000464
0
0
0
0
0.159348
0.004478
17,865
19
17,500
940.263158
0.810177
0.000728
0
0
0
0.076923
0.982353
0.980504
0
1
0
0
0
1
0.076923
false
0
0.153846
0
0.230769
0
0
0
1
null
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
1
1
null
1
0
0
0
0
0
0
0
0
0
0
0
0
6
951fe79fe1accde582fbf0dcad23268d4cf823b1
31
py
Python
giovannicuriel/disguise/main.py
giovannicuriel/disguise
a99124a03c30c484f32ef5fe611524faf883da6e
[ "BSD-3-Clause" ]
null
null
null
giovannicuriel/disguise/main.py
giovannicuriel/disguise
a99124a03c30c484f32ef5fe611524faf883da6e
[ "BSD-3-Clause" ]
3
2021-06-02T02:21:37.000Z
2021-08-20T13:59:05.000Z
giovannicuriel/disguise/main.py
giovannicuriel/disguise
a99124a03c30c484f32ef5fe611524faf883da6e
[ "BSD-3-Clause" ]
null
null
null
def main(): print('Olá')
6.2
16
0.483871
4
31
3.75
1
0
0
0
0
0
0
0
0
0
0
0
0.290323
31
5
16
6.2
0.681818
0
0
0
0
0
0.103448
0
0
0
0
0
0
1
0.5
true
0
0
0
0.5
0.5
1
1
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
1
1
0
0
0
0
1
0
6
1f4458f5635e23cfc7cd3dc4a2899a2d0ce744e8
551
py
Python
fifty_off/api/serializers/__init__.py
DanielSalazar1/50off
e39c8709ea8ac81b39c02060517353ed03b60074
[ "BSD-3-Clause" ]
null
null
null
fifty_off/api/serializers/__init__.py
DanielSalazar1/50off
e39c8709ea8ac81b39c02060517353ed03b60074
[ "BSD-3-Clause" ]
10
2020-06-05T20:15:38.000Z
2022-01-13T01:58:11.000Z
fifty_off/api/serializers/__init__.py
DanielSalazar1/50off
e39c8709ea8ac81b39c02060517353ed03b60074
[ "BSD-3-Clause" ]
null
null
null
# from api.serializers.DashboardSerializer import from api.serializers.gateway.login_serializer import LoginSerializer from api.serializers.gateway.register_serializer import RegisterSerializer from api.serializers.item.category_serializer import CategorySerializer from api.serializers.item.item_serializer import ItemSerializer from api.serializers.item.image_serializer import ImagesSerializer from api.serializers.homepage.homepage_serializer import UserLocationSerializer from api.serializers.item.favorites_serializer import FavoritesSerializer
61.222222
79
0.898367
61
551
8
0.344262
0.114754
0.295082
0.180328
0
0
0
0
0
0
0
0
0.058076
551
8
80
68.875
0.94027
0.085299
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
1
0
1
0
0
0
0
null
0
1
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
6
1f65224bdae3b2ec25572f1b6bdb5aadcd927125
26
py
Python
cornac/models/bpr/__init__.py
GuoJingyao/cornac
e7529990ec1dfa586c4af3de98e4b3e00a786578
[ "Apache-2.0" ]
null
null
null
cornac/models/bpr/__init__.py
GuoJingyao/cornac
e7529990ec1dfa586c4af3de98e4b3e00a786578
[ "Apache-2.0" ]
null
null
null
cornac/models/bpr/__init__.py
GuoJingyao/cornac
e7529990ec1dfa586c4af3de98e4b3e00a786578
[ "Apache-2.0" ]
null
null
null
from .recom_bpr import BPR
26
26
0.846154
5
26
4.2
0.8
0
0
0
0
0
0
0
0
0
0
0
0.115385
26
1
26
26
0.913043
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
1
0
1
0
1
1
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
6
1f8b1d028d9d94949dfb8e413c9f8a343860b57d
12,398
py
Python
SimModel_Python_API/simmodel_swig/Release/SimWall_Wall_Interior.py
EnEff-BIM/EnEffBIM-Framework
6328d39b498dc4065a60b5cc9370b8c2a9a1cddf
[ "MIT" ]
3
2016-05-30T15:12:16.000Z
2022-03-22T08:11:13.000Z
SimModel_Python_API/simmodel_swig/Release/SimWall_Wall_Interior.py
EnEff-BIM/EnEffBIM-Framework
6328d39b498dc4065a60b5cc9370b8c2a9a1cddf
[ "MIT" ]
21
2016-06-13T11:33:45.000Z
2017-05-23T09:46:52.000Z
SimModel_Python_API/simmodel_swig/Release/SimWall_Wall_Interior.py
EnEff-BIM/EnEffBIM-Framework
6328d39b498dc4065a60b5cc9370b8c2a9a1cddf
[ "MIT" ]
null
null
null
# This file was automatically generated by SWIG (http://www.swig.org). # Version 3.0.7 # # Do not make changes to this file unless you know what you are doing--modify # the SWIG interface file instead. from sys import version_info if version_info >= (2, 6, 0): def swig_import_helper(): from os.path import dirname import imp fp = None try: fp, pathname, description = imp.find_module('_SimWall_Wall_Interior', [dirname(__file__)]) except ImportError: import _SimWall_Wall_Interior return _SimWall_Wall_Interior if fp is not None: try: _mod = imp.load_module('_SimWall_Wall_Interior', fp, pathname, description) finally: fp.close() return _mod _SimWall_Wall_Interior = swig_import_helper() del swig_import_helper else: import _SimWall_Wall_Interior del version_info try: _swig_property = property except NameError: pass # Python < 2.2 doesn't have 'property'. def _swig_setattr_nondynamic(self, class_type, name, value, static=1): if (name == "thisown"): return self.this.own(value) if (name == "this"): if type(value).__name__ == 'SwigPyObject': self.__dict__[name] = value return method = class_type.__swig_setmethods__.get(name, None) if method: return method(self, value) if (not static): if _newclass: object.__setattr__(self, name, value) else: self.__dict__[name] = value else: raise AttributeError("You cannot add attributes to %s" % self) def _swig_setattr(self, class_type, name, value): return _swig_setattr_nondynamic(self, class_type, name, value, 0) def _swig_getattr_nondynamic(self, class_type, name, static=1): if (name == "thisown"): return self.this.own() method = class_type.__swig_getmethods__.get(name, None) if method: return method(self) if (not static): return object.__getattr__(self, name) else: raise AttributeError(name) def _swig_getattr(self, class_type, name): return _swig_getattr_nondynamic(self, class_type, name, 0) def _swig_repr(self): try: strthis = "proxy of " + self.this.__repr__() except: strthis = "" return "<%s.%s; %s >" % (self.__class__.__module__, self.__class__.__name__, strthis,) try: _object = object _newclass = 1 except AttributeError: class _object: pass _newclass = 0 try: import weakref weakref_proxy = weakref.proxy except: weakref_proxy = lambda x: x import SimWall_Wall_Default import base class SimWall_Wall_Interior(SimWall_Wall_Default.SimWall_Wall): __swig_setmethods__ = {} for _s in [SimWall_Wall_Default.SimWall_Wall]: __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {})) __setattr__ = lambda self, name, value: _swig_setattr(self, SimWall_Wall_Interior, name, value) __swig_getmethods__ = {} for _s in [SimWall_Wall_Default.SimWall_Wall]: __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {})) __getattr__ = lambda self, name: _swig_getattr(self, SimWall_Wall_Interior, name) __repr__ = _swig_repr def ContainedBldgElementArrays(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_ContainedBldgElementArrays(self, *args) def Name(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_Name(self, *args) def Representation(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_Representation(self, *args) def ConstructionType(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_ConstructionType(self, *args) def WallIsExternal(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_WallIsExternal(self, *args) def CompositeThermalTrans(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_CompositeThermalTrans(self, *args) def PhotoVotaicArrayOnElement(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_PhotoVotaicArrayOnElement(self, *args) def WallHeight(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_WallHeight(self, *args) def WallLength(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_WallLength(self, *args) def WallThickness(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_WallThickness(self, *args) def WallGrossSideArea(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_WallGrossSideArea(self, *args) def WallNetSideArea(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_WallNetSideArea(self, *args) def WallGrossVolume(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_WallGrossVolume(self, *args) def WallNetVolume(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_WallNetVolume(self, *args) def ClassRef_UniFormat(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_ClassRef_UniFormat(self, *args) def MaterialLayerSet(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_MaterialLayerSet(self, *args) def ConnectedSlabs(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_ConnectedSlabs(self, *args) def ConnectedWalls(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_ConnectedWalls(self, *args) def SimWall_Name(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_SimWall_Name(self, *args) def SimWall_SurfType(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_SimWall_SurfType(self, *args) def SimWall_ConstructionName(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_SimWall_ConstructionName(self, *args) def SimWall_ZoneName(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_SimWall_ZoneName(self, *args) def SimWall_OutsdBndCond(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_SimWall_OutsdBndCond(self, *args) def SimWall_OutsdBndCondObject(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_SimWall_OutsdBndCondObject(self, *args) def SimWall_SunExposure(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_SimWall_SunExposure(self, *args) def SimWall_WindExposure(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_SimWall_WindExposure(self, *args) def SimWall_ViewFactToGnd(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_SimWall_ViewFactToGnd(self, *args) def SimWall_NumbVerts(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_SimWall_NumbVerts(self, *args) def SimWall_Vertex_1_120_X_Coord(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_SimWall_Vertex_1_120_X_Coord(self, *args) def SimWall_Vertex_1_120_Y_Coord(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_SimWall_Vertex_1_120_Y_Coord(self, *args) def SimWall_Vertex_1_120_Z_Coord(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_SimWall_Vertex_1_120_Z_Coord(self, *args) def SurfaceProperty_SolarIncidentInside_Name(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_SurfaceProperty_SolarIncidentInside_Name(self, *args) def SurfaceProperty_SolarIncidentInside_SurfName(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_SurfaceProperty_SolarIncidentInside_SurfName(self, *args) def SurfaceProperty_SolarIncidentInside_ConstructionName(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_SurfaceProperty_SolarIncidentInside_ConstructionName(self, *args) def SurfaceProperty_SolarIncidentInside_InsideSurfaceIncidentSunSolarRadSchedName(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_SurfaceProperty_SolarIncidentInside_InsideSurfaceIncidentSunSolarRadSchedName(self, *args) def SurfProp_HeatTransAlg_MultSurf_Name(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_SurfProp_HeatTransAlg_MultSurf_Name(self, *args) def SurfProp_HeatTransAlg_MultSurf_SurfType(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_SurfProp_HeatTransAlg_MultSurf_SurfType(self, *args) def SurfProp_HeatTransAlg_MultSurf_Algorithm(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_SurfProp_HeatTransAlg_MultSurf_Algorithm(self, *args) def T24ConstructStatus3(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_T24ConstructStatus3(self, *args) def __init__(self, *args): this = _SimWall_Wall_Interior.new_SimWall_Wall_Interior(*args) try: self.this.append(this) except: self.this = this def _clone(self, f=0, c=None): return _SimWall_Wall_Interior.SimWall_Wall_Interior__clone(self, f, c) __swig_destroy__ = _SimWall_Wall_Interior.delete_SimWall_Wall_Interior __del__ = lambda self: None SimWall_Wall_Interior_swigregister = _SimWall_Wall_Interior.SimWall_Wall_Interior_swigregister SimWall_Wall_Interior_swigregister(SimWall_Wall_Interior) class SimWall_Wall_Interior_sequence(base.sequence_common): __swig_setmethods__ = {} for _s in [base.sequence_common]: __swig_setmethods__.update(getattr(_s, '__swig_setmethods__', {})) __setattr__ = lambda self, name, value: _swig_setattr(self, SimWall_Wall_Interior_sequence, name, value) __swig_getmethods__ = {} for _s in [base.sequence_common]: __swig_getmethods__.update(getattr(_s, '__swig_getmethods__', {})) __getattr__ = lambda self, name: _swig_getattr(self, SimWall_Wall_Interior_sequence, name) __repr__ = _swig_repr def __init__(self, *args): this = _SimWall_Wall_Interior.new_SimWall_Wall_Interior_sequence(*args) try: self.this.append(this) except: self.this = this def assign(self, n, x): return _SimWall_Wall_Interior.SimWall_Wall_Interior_sequence_assign(self, n, x) def begin(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_sequence_begin(self, *args) def end(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_sequence_end(self, *args) def rbegin(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_sequence_rbegin(self, *args) def rend(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_sequence_rend(self, *args) def at(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_sequence_at(self, *args) def front(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_sequence_front(self, *args) def back(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_sequence_back(self, *args) def push_back(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_sequence_push_back(self, *args) def pop_back(self): return _SimWall_Wall_Interior.SimWall_Wall_Interior_sequence_pop_back(self) def detach_back(self, pop=True): return _SimWall_Wall_Interior.SimWall_Wall_Interior_sequence_detach_back(self, pop) def insert(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_sequence_insert(self, *args) def erase(self, *args): return _SimWall_Wall_Interior.SimWall_Wall_Interior_sequence_erase(self, *args) def detach(self, position, r, erase=True): return _SimWall_Wall_Interior.SimWall_Wall_Interior_sequence_detach(self, position, r, erase) def swap(self, x): return _SimWall_Wall_Interior.SimWall_Wall_Interior_sequence_swap(self, x) __swig_destroy__ = _SimWall_Wall_Interior.delete_SimWall_Wall_Interior_sequence __del__ = lambda self: None SimWall_Wall_Interior_sequence_swigregister = _SimWall_Wall_Interior.SimWall_Wall_Interior_sequence_swigregister SimWall_Wall_Interior_sequence_swigregister(SimWall_Wall_Interior_sequence) # This file is compatible with both classic and new-style classes.
39.484076
150
0.751573
1,483
12,398
5.759946
0.124747
0.1893
0.311402
0.216109
0.663428
0.612386
0.591899
0.541208
0.498127
0.334114
0
0.004376
0.170511
12,398
313
151
39.610224
0.826235
0.023714
0
0.220264
1
0
0.016703
0.003638
0
0
0
0
0
1
0.277533
false
0.008811
0.052863
0.251101
0.696035
0
0
0
0
null
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
null
0
0
0
0
0
1
0
0
0
1
1
0
0
6
2f0588e77f53ac55121d8c8751f6e3ef496d59e0
88
py
Python
table_2.py
I8PI/Blueberry
4b21fa170614cc15810e5aff6e8f6c0520ade078
[ "MIT" ]
1
2020-02-27T09:41:33.000Z
2020-02-27T09:41:33.000Z
table_2.py
I8PI/Blueberry
4b21fa170614cc15810e5aff6e8f6c0520ade078
[ "MIT" ]
null
null
null
table_2.py
I8PI/Blueberry
4b21fa170614cc15810e5aff6e8f6c0520ade078
[ "MIT" ]
null
null
null
for i in range(1,5): for j in range(1,5): print(j,end=" ") print( )
17.6
25
0.454545
16
88
2.5
0.5625
0.35
0.4
0.45
0
0
0
0
0
0
0
0.071429
0.363636
88
4
26
22
0.642857
0
0
0
0
0
0.011905
0
0
0
0
0
0
1
0
false
0
0
0
0
0.5
1
0
0
null
1
1
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
0
0
0
0
0
1
0
6
2f30717b855261ce25ba7d2d28b752b2b85fe552
162
py
Python
evaluation/__init__.py
Abirami-mygithub/InjectTFParallel
0f3d545ef8e4ea8cdffd0d23cb0ea6e30cdc302e
[ "MIT" ]
null
null
null
evaluation/__init__.py
Abirami-mygithub/InjectTFParallel
0f3d545ef8e4ea8cdffd0d23cb0ea6e30cdc302e
[ "MIT" ]
null
null
null
evaluation/__init__.py
Abirami-mygithub/InjectTFParallel
0f3d545ef8e4ea8cdffd0d23cb0ea6e30cdc302e
[ "MIT" ]
null
null
null
from tensorflow.keras.utils import plot_model # plot model architecture def plot_model_arch(model, name): plot_model(model, show_shapes=True, to_file=name)
23.142857
53
0.796296
25
162
4.92
0.64
0.292683
0
0
0
0
0
0
0
0
0
0
0.123457
162
6
54
27
0.866197
0.141975
0
0
0
0
0
0
0
0
0
0
0
1
0.333333
false
0
0.333333
0
0.666667
0
1
0
0
null
1
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
1
0
0
1
0
1
0
0
6
2f33c6487ba4be5102d30c538544f2ab9ec3c0be
84
py
Python
lib/__init__.py
TomLXXVI/pipe-network-sim
c2307aba3138bc87ebb24f48e5299149db893ea9
[ "MIT" ]
null
null
null
lib/__init__.py
TomLXXVI/pipe-network-sim
c2307aba3138bc87ebb24f48e5299149db893ea9
[ "MIT" ]
null
null
null
lib/__init__.py
TomLXXVI/pipe-network-sim
c2307aba3138bc87ebb24f48e5299149db893ea9
[ "MIT" ]
1
2022-01-19T20:27:43.000Z
2022-01-19T20:27:43.000Z
from lib.pypeflow.analysis import Analyzer from lib.pypeflow.design import Designer
28
42
0.857143
12
84
6
0.666667
0.194444
0.416667
0
0
0
0
0
0
0
0
0
0.095238
84
2
43
42
0.947368
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
1
0
0
6
2f34d7b77b7e0618e7c3f5dfc7c1e01d4877f454
111
py
Python
backend/embeddings/__init__.py
juananpe/forum40
e527318ac7724be2b6c831e786b2905a100e8425
[ "Apache-2.0" ]
null
null
null
backend/embeddings/__init__.py
juananpe/forum40
e527318ac7724be2b6c831e786b2905a100e8425
[ "Apache-2.0" ]
null
null
null
backend/embeddings/__init__.py
juananpe/forum40
e527318ac7724be2b6c831e786b2905a100e8425
[ "Apache-2.0" ]
null
null
null
from embeddings.embed import embed from embeddings.index import index from embeddings.retrieve import retrieve
27.75
40
0.864865
15
111
6.4
0.4
0.4375
0
0
0
0
0
0
0
0
0
0
0.108108
111
3
41
37
0.969697
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
1
0
1
0
1
0
0
null
1
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
6
2f78ae4c57a2754ae5d691cbca4ad58ed73f732e
42
py
Python
src/main.py
FagnerLuan/Ola-Mundo
aff75e9bc014a346dfe3cdf5e7a14aa7625c41ac
[ "MIT" ]
null
null
null
src/main.py
FagnerLuan/Ola-Mundo
aff75e9bc014a346dfe3cdf5e7a14aa7625c41ac
[ "MIT" ]
null
null
null
src/main.py
FagnerLuan/Ola-Mundo
aff75e9bc014a346dfe3cdf5e7a14aa7625c41ac
[ "MIT" ]
null
null
null
print('Olá, Mundo no Curso Git e github')
21
41
0.714286
8
42
3.75
1
0
0
0
0
0
0
0
0
0
0
0
0.166667
42
1
42
42
0.857143
0
0
0
0
0
0.761905
0
0
0
0
0
0
1
0
true
0
0
0
0
1
1
1
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
1
0
null
0
0
0
0
0
0
1
0
0
0
0
1
0
6
85fba0abae022b1ee712d446edaa8acb5273cda4
3,694
py
Python
AwsCvs2ParquetGlue.py
press0/csv2parquet
444889100a2099c13c9bae5b8e03e948a5dc4354
[ "Apache-2.0" ]
null
null
null
AwsCvs2ParquetGlue.py
press0/csv2parquet
444889100a2099c13c9bae5b8e03e948a5dc4354
[ "Apache-2.0" ]
null
null
null
AwsCvs2ParquetGlue.py
press0/csv2parquet
444889100a2099c13c9bae5b8e03e948a5dc4354
[ "Apache-2.0" ]
null
null
null
import sys from awsglue.transforms import * from awsglue.utils import getResolvedOptions from pyspark.context import SparkContext from awsglue.context import GlueContext from awsglue.job import Job ## @params: [JOB_NAME] args = getResolvedOptions(sys.argv, ['JOB_NAME']) sc = SparkContext() glueContext = GlueContext(sc) spark = glueContext.spark_session job = Job(glueContext) job.init(args['JOB_NAME'], args) ## @type: DataSource ## @args: [database = "csv2parquet", table_name = "csv", transformation_ctx = "datasource0"] ## @return: datasource0 ## @inputs: [] datasource0 = glueContext.create_dynamic_frame.from_catalog(database = "csv2parquet", table_name = "csv", transformation_ctx = "datasource0") ## @type: ApplyMapping ## @args: [mapping = [("cusip", "string", "cusip", "string"), ("price", "double", "price", "double"), ("security_type", "long", "security_type", "long"), ("trade_date", "string", "trade_date", "string")], transformation_ctx = "applymapping1"] ## @return: applymapping1 ## @inputs: [frame = datasource0] applymapping1 = ApplyMapping.apply(frame = datasource0, mappings = [("cusip", "string", "cusip", "string"), ("price", "double", "price", "double"), ("security_type", "long", "security_type", "long"), ("trade_date", "string", "trade_date", "string")], transformation_ctx = "applymapping1") ## @type: ResolveChoice ## @args: [choice = "make_struct", transformation_ctx = "resolvechoice2"] ## @return: resolvechoice2 ## @inputs: [frame = applymapping1] resolvechoice2 = ResolveChoice.apply(frame = applymapping1, choice = "make_struct", transformation_ctx = "resolvechoice2") ## @type: DropNullFields ## @args: [transformation_ctx = "dropnullfields3"] ## @return: dropnullfields3 ## @inputs: [frame = resolvechoice2] dropnullfields3 = DropNullFields.apply(frame = resolvechoice2, transformation_ctx = "dropnullfields3") ## @type: DataSink ## @args: [connection_type = "s3", connection_options = {"path": "s3://press0-test/parquet"}, format = "parquet", transformation_ctx = "datasink4"] ## @return: datasink4 ## @inputs: [frame = dropnullfields3] datasink4 = glueContext.write_dynamic_frame.from_options(frame = dropnullfields3, connection_type = "s3", connection_options = {"path": "s3://press0-test/parquet"}, format = "parquet", transformation_ctx = "datasink4") job.commit() from awsglue.transforms import * from awsglue.utils import getResolvedOptions from pyspark.context import SparkContext from awsglue.context import GlueContext from awsglue.job import Job spark.sql("show databases").show() glueContext = GlueContext(SparkContext.getOrCreate()) prices_DyF = glueContext.create_dynamic_frame.from_catalog(database="csv2parquet", table_name="csv") print ("Count: ", prices_DyF.count()) prices_DyF.printSchema() job = Job(glueContext) job.init("csv2parquet") datasource0 = glueContext.create_dynamic_frame.from_catalog(database = "csv2parquet", table_name = "csv", transformation_ctx = "datasource0") applymapping1 = ApplyMapping.apply(frame = datasource0, mappings = [("cusip", "string", "cusip", "string"), ("price", "double", "price", "double"), ("security_type", "long", "security_type", "long"), ("trade_date", "string", "trade_date", "string")], transformation_ctx = "applymapping1") resolvechoice2 = ResolveChoice.apply(frame = applymapping1, choice = "make_struct", transformation_ctx = "resolvechoice2") dropnullfields3 = DropNullFields.apply(frame = resolvechoice2, transformation_ctx = "dropnullfields3") datasink4 = glueContext.write_dynamic_frame.from_options(frame = dropnullfields3, connection_type = "s3", connection_options = {"path": "s3://press0-test/parquet"}, format = "parquet", transformation_ctx = "datasink4") job.commit()
55.134328
288
0.744992
385
3,694
6.987013
0.197403
0.094796
0.035688
0.041636
0.795167
0.777323
0.759851
0.759851
0.737918
0.675093
0
0.016777
0.096373
3,694
66
289
55.969697
0.789095
0.261776
0
0.666667
0
0
0.197913
0.01789
0
0
0
0
0
1
0
false
0
0.305556
0
0.305556
0.055556
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
1
0
0
0
0
6
c82289e0920b7383c954bdebc71cd62f1d5efd9b
110
py
Python
calamari_ocr/ocr/dataset/imageprocessors/__init__.py
jacektl/calamari
980477aefe4e56f7fc373119c1b38649798d8686
[ "Apache-2.0" ]
922
2018-07-06T05:18:22.000Z
2022-03-22T12:38:32.000Z
calamari_ocr/ocr/dataset/imageprocessors/__init__.py
jacektl/calamari
980477aefe4e56f7fc373119c1b38649798d8686
[ "Apache-2.0" ]
267
2018-07-14T22:10:41.000Z
2022-03-28T18:38:43.000Z
calamari_ocr/ocr/dataset/imageprocessors/__init__.py
jacektl/calamari
980477aefe4e56f7fc373119c1b38649798d8686
[ "Apache-2.0" ]
227
2018-07-06T07:42:16.000Z
2022-02-27T05:29:59.000Z
from .augmentation import AugmentationProcessorParams from .preparesample import PrepareSampleProcessorParams
36.666667
55
0.909091
8
110
12.5
0.75
0
0
0
0
0
0
0
0
0
0
0
0.072727
110
2
56
55
0.980392
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
1
0
1
0
1
0
1
null
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
6
c864a430a9fbd3ec7048c413fd4902e0890802fe
43
py
Python
trisicell/tl/cna/__init__.py
faridrashidi/trisicell
4db89edd44c03ccb6c7d3477beff0079c3ff8035
[ "BSD-3-Clause" ]
2
2021-07-02T13:53:15.000Z
2021-11-16T03:14:36.000Z
trisicell/tl/cna/__init__.py
faridrashidi/trisicell
4db89edd44c03ccb6c7d3477beff0079c3ff8035
[ "BSD-3-Clause" ]
58
2021-06-14T17:14:39.000Z
2022-03-11T19:32:54.000Z
trisicell/tl/cna/__init__.py
faridrashidi/trisicell
4db89edd44c03ccb6c7d3477beff0079c3ff8035
[ "BSD-3-Clause" ]
null
null
null
from trisicell.tl.cna._cna import infercna
21.5
42
0.837209
7
43
5
0.857143
0
0
0
0
0
0
0
0
0
0
0
0.093023
43
1
43
43
0.897436
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
1
0
1
0
1
1
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
6
c0d33f9b7d333297e0a37fd694cb0176bcef7c63
40
py
Python
app/template_db/template_engine/connectors/docx_publiposting/__init__.py
Plawn/petit_publipost_gateway
e0a09207ae5bcad1623f8e7662e004ad9b59ffbe
[ "Apache-2.0" ]
null
null
null
app/template_db/template_engine/connectors/docx_publiposting/__init__.py
Plawn/petit_publipost_gateway
e0a09207ae5bcad1623f8e7662e004ad9b59ffbe
[ "Apache-2.0" ]
7
2021-06-22T09:48:59.000Z
2022-01-10T16:08:00.000Z
app/template_db/template_engine/connectors/docx_publiposting/__init__.py
Plawn/petit_publiposter
e0a09207ae5bcad1623f8e7662e004ad9b59ffbe
[ "Apache-2.0" ]
null
null
null
from .docx_template import DocxTemplator
40
40
0.9
5
40
7
1
0
0
0
0
0
0
0
0
0
0
0
0.075
40
1
40
40
0.945946
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
1
0
1
0
1
1
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
6
c0ebc4f999ac60ff18f2965790402ca449dfa4e4
9,766
py
Python
test/preprocess_tests/convert_fastqs_to_unmapped_bam_test.py
YosefLab/SingleCellLineageTracing
d9133fc80c8314e7935fde037dd86111cac47447
[ "MIT" ]
52
2019-05-14T02:06:24.000Z
2022-03-27T05:22:56.000Z
test/preprocess_tests/convert_fastqs_to_unmapped_bam_test.py
sbradford2/Cassiopeia
010072b307f7eadbf10dc4af8b2165e48f1736a7
[ "MIT" ]
88
2019-06-07T15:07:45.000Z
2022-03-22T14:40:03.000Z
test/preprocess_tests/convert_fastqs_to_unmapped_bam_test.py
sbradford2/Cassiopeia
010072b307f7eadbf10dc4af8b2165e48f1736a7
[ "MIT" ]
17
2019-05-17T00:46:16.000Z
2022-03-25T00:39:18.000Z
""" Tests for converting FASTQs to an unmapped BAM in pipeline.py """ import os import unittest import tempfile import pysam import ngs_tools as ngs from cassiopeia.preprocess import pipeline class TestConvertFastqsToUnmappedBam(unittest.TestCase): def setUp(self): dir_path = os.path.dirname(os.path.realpath(__file__)) test_files_path = os.path.join(dir_path, "test_files") self.fastq_10xv3_fps = [ os.path.join(test_files_path, "10xv3_1.fastq.gz"), os.path.join(test_files_path, "10xv3_2.fastq.gz"), ] self.fastq_indropsv3_fps = [ os.path.join(test_files_path, "indropsv3_1.fastq.gz"), os.path.join(test_files_path, "indropsv3_2.fastq.gz"), os.path.join(test_files_path, "indropsv3_3.fastq.gz"), ] self.fastq_slideseq2_fps = [ os.path.join(test_files_path, "slideseq2_1.fastq.gz"), os.path.join(test_files_path, "slideseq2_2.fastq.gz"), ] def test_dropseq(self): # NOTE: using 10xv3 fastqs just for testing bam_fp = pipeline.convert_fastqs_to_unmapped_bam( self.fastq_10xv3_fps, "dropseq", tempfile.mkdtemp(), name="test" ) with pysam.AlignmentFile(bam_fp, "rb", check_sq=False) as f: alignments = list(f.fetch(until_eof=True)) self.assertEqual(2, len(alignments)) self.assertEqual( [ "M03718:773:000000000-JKHP3:1:1101:18272:1693", "M03718:773:000000000-JKHP3:1:1101:17963:1710", ], [al.query_name for al in alignments], ) self.assertEqual( [ read.sequence for read in ngs.fastq.Fastq(self.fastq_10xv3_fps[1]) ], [al.query_sequence for al in alignments], ) self.assertEqual( [ read.qualities.string for read in ngs.fastq.Fastq(self.fastq_10xv3_fps[1]) ], [ pysam.array_to_qualitystring(al.query_qualities) for al in alignments ], ) self.assertEqual( { ("UR", "TACGCCAA"), ("UY", "GGFECEE0"), ("CR", "TACGTCATCTCC"), ("CY", "1111AFAFFFBF"), ("RG", "test"), }, set(alignments[0].get_tags()), ) self.assertEqual( { ("UR", "AAACATTC"), ("UY", "FFGGBFGF"), ("CR", "TTAGATCGTTAG"), ("CY", "1>>11DFAFAAA"), ("RG", "test"), }, set(alignments[1].get_tags()), ) def test_10xv2(self): # NOTE: using 10xv3 fastqs just for testing bam_fp = pipeline.convert_fastqs_to_unmapped_bam( self.fastq_10xv3_fps, "10xv2", tempfile.mkdtemp(), name="test" ) with pysam.AlignmentFile(bam_fp, "rb", check_sq=False) as f: alignments = list(f.fetch(until_eof=True)) self.assertEqual(2, len(alignments)) self.assertEqual( [ "M03718:773:000000000-JKHP3:1:1101:18272:1693", "M03718:773:000000000-JKHP3:1:1101:17963:1710", ], [al.query_name for al in alignments], ) self.assertEqual( [ read.sequence for read in ngs.fastq.Fastq(self.fastq_10xv3_fps[1]) ], [al.query_sequence for al in alignments], ) self.assertEqual( [ read.qualities.string for read in ngs.fastq.Fastq(self.fastq_10xv3_fps[1]) ], [ pysam.array_to_qualitystring(al.query_qualities) for al in alignments ], ) self.assertEqual( { ("UR", "CCAAAACAGT"), ("UY", "CEE0C0BA0D"), ("CR", "TACGTCATCTCCTACG"), ("CY", "1111AFAFFFBFGGFE"), ("RG", "test"), }, set(alignments[0].get_tags()), ) self.assertEqual( { ("UR", "ATTCCTGAGT"), ("UY", "BFGFGFF10F"), ("CR", "TTAGATCGTTAGAAAC"), ("CY", "1>>11DFAFAAAFFGG"), ("RG", "test"), }, set(alignments[1].get_tags()), ) def test_10xv3(self): bam_fp = pipeline.convert_fastqs_to_unmapped_bam( self.fastq_10xv3_fps, "10xv3", tempfile.mkdtemp(), name="test" ) with pysam.AlignmentFile(bam_fp, "rb", check_sq=False) as f: alignments = list(f.fetch(until_eof=True)) self.assertEqual(2, len(alignments)) self.assertEqual( [ "M03718:773:000000000-JKHP3:1:1101:18272:1693", "M03718:773:000000000-JKHP3:1:1101:17963:1710", ], [al.query_name for al in alignments], ) self.assertEqual( [ read.sequence for read in ngs.fastq.Fastq(self.fastq_10xv3_fps[1]) ], [al.query_sequence for al in alignments], ) self.assertEqual( [ read.qualities.string for read in ngs.fastq.Fastq(self.fastq_10xv3_fps[1]) ], [ pysam.array_to_qualitystring(al.query_qualities) for al in alignments ], ) self.assertEqual( { ("UR", "CCAAAACAGTTT"), ("UY", "CEE0C0BA0DFG"), ("CR", "TACGTCATCTCCTACG"), ("CY", "1111AFAFFFBFGGFE"), ("RG", "test"), }, set(alignments[0].get_tags()), ) self.assertEqual( { ("UR", "ATTCCTGAGTCA"), ("UY", "BFGFGFF10FG1"), ("CR", "TTAGATCGTTAGAAAC"), ("CY", "1>>11DFAFAAAFFGG"), ("RG", "test"), }, set(alignments[1].get_tags()), ) def test_indropsv3(self): bam_fp = pipeline.convert_fastqs_to_unmapped_bam( self.fastq_indropsv3_fps, "indropsv3", tempfile.mkdtemp(), name="test", ) with pysam.AlignmentFile(bam_fp, "rb", check_sq=False) as f: alignments = list(f.fetch(until_eof=True)) self.assertEqual(2, len(alignments)) self.assertEqual( [ "M03718:773:000000000-JKHP3:1:1101:18272:1693", "M03718:773:000000000-JKHP3:1:1101:17963:1710", ], [al.query_name for al in alignments], ) self.assertEqual( [ read.sequence for read in ngs.fastq.Fastq(self.fastq_10xv3_fps[1]) ], [al.query_sequence for al in alignments], ) self.assertEqual( [ read.qualities.string for read in ngs.fastq.Fastq(self.fastq_10xv3_fps[1]) ], [ pysam.array_to_qualitystring(al.query_qualities) for al in alignments ], ) self.assertEqual( { ("UR", "CCAAAA"), ("UY", "FFBFGG"), ("CR", "TACGTCATCTCCTACG"), ("CY", "1111AFAF1111AFAF"), ("RG", "test"), }, set(alignments[0].get_tags()), ) self.assertEqual( { ("UR", "TTAGAA"), ("UY", "FAAAFF"), ("CR", "TTAGATCGTTAGATCG"), ("CY", "1>>11DFA1>>11DFA"), ("RG", "test"), }, set(alignments[1].get_tags()), ) def test_slideseq2(self): bam_fp = pipeline.convert_fastqs_to_unmapped_bam( self.fastq_slideseq2_fps, "slideseq2", tempfile.mkdtemp(), name="test", ) with pysam.AlignmentFile(bam_fp, "rb", check_sq=False) as f: alignments = list(f.fetch(until_eof=True)) self.assertEqual(2, len(alignments)) self.assertEqual( [ "NB501583:801:H7JLTBGXH:1:11101:20912:1050", "NB501583:801:H7JLTBGXH:1:11101:8670:1050", ], [al.query_name for al in alignments], ) self.assertEqual( [ read.sequence for read in ngs.fastq.Fastq(self.fastq_slideseq2_fps[1]) ], [al.query_sequence for al in alignments], ) self.assertEqual( [ read.qualities.string for read in ngs.fastq.Fastq(self.fastq_slideseq2_fps[1]) ], [ pysam.array_to_qualitystring(al.query_qualities) for al in alignments ], ) self.assertEqual( { ("UR", "TTTTTTTTT"), ("UY", "EEEEEEEEE"), ("CR", "CTTTGNTCAATGTT"), ("CY", "AAAAA#EEAEEEEE"), ("RG", "test"), }, set(alignments[0].get_tags()), ) self.assertEqual( { ("UR", "AGTGTCTCA"), ("UY", "EAEAEAEEE"), ("CR", "CTCTTNATCCTCAT"), ("CY", "AAAAA#EEE/EAE/"), ("RG", "test"), }, set(alignments[1].get_tags()), ) if __name__ == "__main__": unittest.main()
32.125
76
0.473991
922
9,766
4.852495
0.155098
0.100581
0.111757
0.056996
0.800626
0.789003
0.789003
0.757264
0.757264
0.71055
0
0.078274
0.399549
9,766
303
77
32.231023
0.684686
0.01495
0
0.51049
0
0
0.131516
0.045053
0
0
0
0
0.104895
1
0.020979
false
0
0.020979
0
0.045455
0
0
0
0
null
0
0
0
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
6
c0f27cf0707f3bb4be0aa28831410788f2261da3
174
py
Python
django_test/hmdb/subviews/reg_param.py
wolframowy/mgr
9d61cef8d135e255f724f57ba55a0dc8c4269219
[ "MIT" ]
null
null
null
django_test/hmdb/subviews/reg_param.py
wolframowy/mgr
9d61cef8d135e255f724f57ba55a0dc8c4269219
[ "MIT" ]
null
null
null
django_test/hmdb/subviews/reg_param.py
wolframowy/mgr
9d61cef8d135e255f724f57ba55a0dc8c4269219
[ "MIT" ]
null
null
null
from django.shortcuts import render def reg_param(request): return reg_parm_get(request) def reg_parm_get(request): return render(request, 'hmdb/reg_param.html')
17.4
49
0.764368
26
174
4.884615
0.538462
0.094488
0.15748
0.267717
0
0
0
0
0
0
0
0
0.143678
174
9
50
19.333333
0.852349
0
0
0
0
0
0.109195
0
0
0
0
0
0
1
0.4
false
0
0.2
0.4
1
0
1
0
0
null
0
0
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
1
0
0
0
1
1
0
0
6
2390a5279ad34fa21e3cc7230f5aa1800516f15d
101
py
Python
sklearn_test/do_train.py
lostfish/nlp_test
b6ca6ba86e265ba0a9c3913007e2e945a9441303
[ "MIT" ]
1
2018-04-17T11:08:36.000Z
2018-04-17T11:08:36.000Z
sklearn_test/do_train.py
lostfish/nlp_test
b6ca6ba86e265ba0a9c3913007e2e945a9441303
[ "MIT" ]
null
null
null
sklearn_test/do_train.py
lostfish/nlp_test
b6ca6ba86e265ba0a9c3913007e2e945a9441303
[ "MIT" ]
null
null
null
#! /usr/bin/env python #encoding: utf-8 from do_clf import train_and_validate train_and_validate()
14.428571
37
0.782178
17
101
4.352941
0.823529
0.216216
0.432432
0
0
0
0
0
0
0
0
0.011236
0.118812
101
6
38
16.833333
0.820225
0.356436
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
0.5
0
0.5
0
1
0
0
null
1
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
0
0
0
6
23a32e59ae47a5e30b968732c353926fbd65b407
121
py
Python
irida_uploader_cl/parsers/__init__.py
duanjunhyq/irida_uploader_cl
d0e5d404c5b5b10c3411ded71a20f5ab062aabba
[ "MIT" ]
null
null
null
irida_uploader_cl/parsers/__init__.py
duanjunhyq/irida_uploader_cl
d0e5d404c5b5b10c3411ded71a20f5ab062aabba
[ "MIT" ]
null
null
null
irida_uploader_cl/parsers/__init__.py
duanjunhyq/irida_uploader_cl
d0e5d404c5b5b10c3411ded71a20f5ab062aabba
[ "MIT" ]
null
null
null
from irida_uploader_cl.parsers.parsers import Parser, supported_parsers from irida_uploader_cl.parsers import exceptions
40.333333
71
0.892562
17
121
6.058824
0.529412
0.174757
0.330097
0.368932
0.504854
0
0
0
0
0
0
0
0.07438
121
2
72
60.5
0.919643
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
1
0
1
0
1
0
0
null
0
1
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
0
0
0
6
f199e0dea5b18dc9b21587c302f235a6b334c5a6
4,817
py
Python
IO_xyz.py
aztan2/LatticeGreenFunction_new
383868a61c91eb956c4a9fa45fafe3f64cd7ef4f
[ "MIT" ]
null
null
null
IO_xyz.py
aztan2/LatticeGreenFunction_new
383868a61c91eb956c4a9fa45fafe3f64cd7ef4f
[ "MIT" ]
null
null
null
IO_xyz.py
aztan2/LatticeGreenFunction_new
383868a61c91eb956c4a9fa45fafe3f64cd7ef4f
[ "MIT" ]
null
null
null
import numpy as np from collections import namedtuple atominfo = namedtuple('atom',['ind','reg','m','n','t','basis']) def grid_from_xyz(s,atomtypes,a0=1.0): """ Read from a string containing the data from an xyz file. Parameters ---------- s : string containing the data from an xyz file. atomtypes: list of name labels for each basis atom type a0 : lattice constant in Angstroms (default a0=1.0 means do not scale coords out by a0) Returns ------- grid : list of [atom index,region,m-coord,n-coord,t-coord,basis] for each atom in the geometry ** coordinates are scaled out by a factor of a0 !! """ grid = [] for line in s.splitlines()[2:]: if line != '': entries = line.split() i = int(len(grid)) reg = 0 m,n,t = float(entries[1])/a0,float(entries[2])/a0,float(entries[3])/a0 basis = atomtypes.index(entries[0]) grid.append(atominfo(i,reg,m,n,t,basis)) return grid def grid_from_xyz_reg(s,atomtypes,a0=1.0): """ Read from a string containing the data from an xyz file and label atoms by regions. The atoms in the xyz file must already be listed in order by regions and the second line of the file contains the size_1,size_12,size_123,size_in info. Parameters ---------- s : string containing the data from the anisotropic dislocation geometry setup file atomtypes: list of name labels for each basis atom type a0 : lattice constant in Angstroms (default a0=1.0 means do not scale coords out by a0) Returns ------- grid : list of [atom index,region,m-coord,n-coord,t-coord,basis] for each atom in the geometry ** coordinates are scaled out by a factor of a0 !! sizes : numbers of atoms in reg1, 1+2, 1+2+3, 1+2+3+buffer """ size_1,size_12,size_123,size_in = [int(i) for i in (s.splitlines()[1]).split()[:4]] grid = [] for line in s.splitlines()[2:]: if line != '': entries = line.split() i = int(len(grid)) if i < size_1: reg = 1 elif i < size_12: reg = 2 elif i < size_123: reg = 3 elif i < size_in: reg = 4 else: reg = 5 m,n,t = float(entries[1])/a0,float(entries[2])/a0,float(entries[3])/a0 basis = atomtypes.index(entries[0]) grid.append(atominfo(i,reg,m,n,t,basis)) return grid,[size_1,size_12,size_123,size_in] def grid_to_xyz(grid,atomtypes,a0,header): """ Create a string that will be written to a xyz file, e.g. anisotropic code geometry file. Parameters ---------- grid : list of [atom index,region,m-coord,n-coord,t-coord,basis] for each atom in the geometry ** coordinates are scaled out by a factor of a0 !! atomtypes: list of name labels for each basis atom type a0 : lattice constant in Angstroms header : comment string for 2nd line of xyz file Returns ------- s : string containing the data for the xyz file """ s = "{numatoms}\n".format(numatoms=len(grid)) s += header + "\n" for atom in grid: # print atom index, mnt coords s += "{atomtype} {mcoord:20.15f} {ncoord:20.15f} {tcoord:20.15f}\n".format( atomtype=atomtypes[atom[5]],mcoord=a0*atom[2],ncoord=a0*atom[3],tcoord=a0*atom[4]) return s def grid_to_xyz_reg(grid,sizes,atomtypes,a0): """ Create a string that will be written to a xyz file. The atoms in the xyz file are listed in order by regions and the second line of the file contains the size_1,size_12,size_123,size_in info. Parameters ---------- grid : list of [atom index,region,m-coord,n-coord,t-coord,basis] for each atom in the geometry ** coordinates are scaled out by a factor of a0 !! sizes : list of [size_1,size_12,size_123,size_in] atomtypes: list of name labels for each basis atom type a0 : lattice constant in Angstroms Returns ------- s : string containing the data for the xyz file """ [size_1,size_12,size_123,size_in] = sizes s = "{numatoms}\n".format(numatoms=len(grid)) s += "{size_1} {size_12} {size_123} {size_in}\n".format(size_1=size_1,size_12=size_12, size_123=size_123,size_in=size_in) for atom in grid: # print atom index, mnt coords s += "{atomtype} {mcoord:20.15f} {ncoord:20.15f} {tcoord:20.15f}\n".format( atomtype=atomtypes[atom[5]],mcoord=a0*atom[2],ncoord=a0*atom[3],tcoord=a0*atom[4]) return s
33.22069
98
0.591239
730
4,817
3.832877
0.164384
0.025018
0.028949
0.031451
0.824875
0.810936
0.79664
0.782702
0.705504
0.705504
0
0.045681
0.291053
4,817
144
99
33.451389
0.773646
0.480382
0
0.565217
0
0.043478
0.092718
0
0
0
0
0
0
1
0.086957
false
0
0.043478
0
0.217391
0
0
0
0
null
0
0
0
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
6
f1b0a38fa240e21a83758025b22f68c287620903
82
py
Python
utils/__init__.py
nrcan-eodms-sgdot-rncan/eodms-rapi-orderdownload
cdaab69a35186aece87a054fafdb53780e58c0a6
[ "MIT" ]
2
2021-09-22T16:20:46.000Z
2021-11-19T17:01:01.000Z
utils/__init__.py
nrcan-eodms-sgdot-rncan/eodms-rapi-orderdownload
cdaab69a35186aece87a054fafdb53780e58c0a6
[ "MIT" ]
7
2021-06-29T21:00:37.000Z
2021-09-09T17:20:30.000Z
utils/__init__.py
nrcan-eodms-sgdot-rncan/eodms-rapi-orderdownload
cdaab69a35186aece87a054fafdb53780e58c0a6
[ "MIT" ]
5
2021-04-14T19:18:29.000Z
2021-09-22T17:12:01.000Z
from . import spatial from . import csv_util from . import image from . import eod
20.5
22
0.768293
13
82
4.769231
0.538462
0.645161
0
0
0
0
0
0
0
0
0
0
0.182927
82
4
23
20.5
0.925373
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
1
0
1
0
1
0
0
null
1
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
6
7b08a72e568997bf00285087ed8721b789ed57af
3,432
py
Python
analysis/migrations/0001_initial.py
truemrwalker/mads-app
79481293af2c0ce5533ab9ebd24868965c3c0031
[ "MIT" ]
null
null
null
analysis/migrations/0001_initial.py
truemrwalker/mads-app
79481293af2c0ce5533ab9ebd24868965c3c0031
[ "MIT" ]
2
2021-04-22T06:57:27.000Z
2021-08-06T03:19:42.000Z
analysis/migrations/0001_initial.py
truemrwalker/mads-app
79481293af2c0ce5533ab9ebd24868965c3c0031
[ "MIT" ]
2
2021-02-12T01:19:44.000Z
2021-05-14T06:54:34.000Z
# -*- coding: utf-8 -*- # Generated by Django 1.11.12 on 2018-05-11 05:54 from __future__ import unicode_literals from django.conf import settings from django.db import migrations, models import django.db.models.deletion import django.utils.timezone import jsonfield.fields import model_utils.fields import uuid class Migration(migrations.Migration): initial = True dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ] operations = [ migrations.CreateModel( name='ComponentInstance', fields=[ ('created', model_utils.fields.AutoCreatedField(db_index=True, default=django.utils.timezone.now, editable=False, verbose_name='created')), ('modified', model_utils.fields.AutoLastModifiedField(db_index=True, default=django.utils.timezone.now, editable=False, verbose_name='modified')), ('id', models.UUIDField(db_index=True, default=uuid.uuid4, editable=False, help_text='Unique ID for this particular workspace', primary_key=True, serialize=False)), ('name', models.CharField(help_text='Enter the name of the workspace', max_length=200)), ('contents', models.BinaryField()), ('owner', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to=settings.AUTH_USER_MODEL)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='VisComponent', fields=[ ('created', model_utils.fields.AutoCreatedField(db_index=True, default=django.utils.timezone.now, editable=False, verbose_name='created')), ('modified', model_utils.fields.AutoLastModifiedField(db_index=True, default=django.utils.timezone.now, editable=False, verbose_name='modified')), ('id', models.UUIDField(db_index=True, default=uuid.uuid4, editable=False, help_text='Unique ID for this particular workspace', primary_key=True, serialize=False)), ('name', models.CharField(help_text='Enter the name of the workspace', max_length=200)), ('contents', jsonfield.fields.JSONField()), ('owner', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to=settings.AUTH_USER_MODEL)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='Workspace', fields=[ ('created', model_utils.fields.AutoCreatedField(db_index=True, default=django.utils.timezone.now, editable=False, verbose_name='created')), ('modified', model_utils.fields.AutoLastModifiedField(db_index=True, default=django.utils.timezone.now, editable=False, verbose_name='modified')), ('id', models.UUIDField(db_index=True, default=uuid.uuid4, editable=False, help_text='Unique ID for this particular workspace', primary_key=True, serialize=False)), ('name', models.CharField(help_text='Enter the name of the workspace', max_length=200)), ('contents', jsonfield.fields.JSONField()), ('owner', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to=settings.AUTH_USER_MODEL)), ], options={ 'abstract': False, }, ), ]
52
180
0.646562
375
3,432
5.773333
0.224
0.029099
0.045727
0.074827
0.796305
0.796305
0.796305
0.796305
0.796305
0.796305
0
0.011325
0.228147
3,432
65
181
52.8
0.805965
0.020105
0
0.614035
1
0
0.124702
0
0
0
0
0
0
1
0
false
0
0.140351
0
0.210526
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
1
0
0
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
6
9e3e28f40aa0326cb0b7b2fcf4294ef46edfd0ac
8,166
py
Python
src/app/tests/test_api.py
agustin380/book-rest-example
365ed7d4c28c361eea4e900400bf4914630468d0
[ "MIT" ]
null
null
null
src/app/tests/test_api.py
agustin380/book-rest-example
365ed7d4c28c361eea4e900400bf4914630468d0
[ "MIT" ]
null
null
null
src/app/tests/test_api.py
agustin380/book-rest-example
365ed7d4c28c361eea4e900400bf4914630468d0
[ "MIT" ]
null
null
null
import unittest import json from ..app import app from .. import settings from ..models import db, Book, Chapter class ApiTestCase(unittest.TestCase): """Base API test case.""" def setUp(self): """Create model tables""" app.config['TESTING'] = True self.app = app.test_client() db.create_all() def tearDown(self): db.drop_all() class TestBookApi(ApiTestCase): def test_get_ok(self): """Performing a GET request for an existing book returns a JSON representation of the book. """ book = Book.create('title', 'author') r = self.app.get('/api/books/{}/'.format(book.id)) self.assertEqual(r.status_code, 200) response = json.loads(r.data.decode('utf-8')) self.assertEqual(response, book.to_dict()) def test_get_not_found(self): """Performing a GET request for a non-existing book returns a 404 status code. """ r = self.app.get('/api/books/{}/'.format(1)) self.assertEqual(r.status_code, 404) def test_put_ok(self): """Performing a PUT request for an existing book updates said book. """ book = Book.create('title', 'author') book_data = book.to_dict() data = { 'title': 'title_2', 'author': 'author_2', } r = self.app.put( '/api/books/{}/'.format(1), content_type='application/json', data=json.dumps(data) ) self.assertEqual(r.status_code, 201) book_data.update(data) response = json.loads(r.data.decode('utf-8')) self.assertEqual(response, book_data) def test_put_not_found(self): """Performing a PUT request for a non-existing book returns a 404 status code. """ r = self.app.put('/api/books/{}/'.format(1)) self.assertEqual(r.status_code, 404) def test_delete_ok(self): """Performing a DELETE request for an existing book deletes it. """ book = Book.create('title', 'author') r = self.app.delete('/api/books/{}/'.format(1)) self.assertEqual(r.status_code, 204) self.assertIsNone(Book.query.first()) def test_delete_not_found(self): """Performing a DELETE request for a non-existing book returns a 404 status code. """ r = self.app.delete('/api/books/{}/'.format(1)) self.assertEqual(r.status_code, 404) class TestBookListApi(ApiTestCase): def test_get_ok(self): """Performing a GET request returns a list of the existing books. """ book = Book.create('title', 'author') book_2 = Book.create('title_2', 'author_2') r = self.app.get('/api/books/') self.assertEqual(r.status_code, 200) response = json.loads(r.data.decode('utf-8')) expected = [ {'id': 1, 'title': 'title', 'author': 'author', 'chapters': []}, {'id': 2, 'title': 'title_2', 'author': 'author_2', 'chapters': []}, ] self.assertEqual(response, expected) def test_post_ok(self): """Performing a POST request creates a new book.""" data = { 'title': 'title', 'author': 'author', } r = self.app.post( '/api/books/', content_type='application/json', data=json.dumps(data) ) self.assertEqual(r.status_code, 201) book = Book.query.get(1) response = json.loads(r.data.decode('utf-8')) self.assertEqual(response, book.to_dict()) def test_post_error(self): """Performing a POST request with missing parameters returns a 400 status code. """ data = { 'title': 'title', } r = self.app.post( '/api/books/', content_type='application/json', data=json.dumps(data) ) self.assertEqual(r.status_code, 400) response = json.loads(r.data.decode('utf-8')) self.assertEqual(response, {'message': 'Missing parameters'}) class TestChapterListApi(ApiTestCase): def test_get_ok(self): """Performing a GET request returns a list of a book's chapters. """ book = Book.create('title', 'author') chapter_1 = Chapter.create(name='chapter_1', book=book) chapter_2 = Chapter.create(name='chapter_2', book=book) r = self.app.get('/api/books/1/chapters/') self.assertEqual(r.status_code, 200) response = json.loads(r.data.decode('utf-8')) expected = [ {'id': 1, 'name': 'chapter_1', 'book': 1}, {'id': 2, 'name': 'chapter_2', 'book': 1}, ] self.assertEqual(response, expected) def test_post_ok(self): """Performing a POST request creates a new chapter.""" book = Book.create('title', 'author') data = { 'name': 'chapter_1', } r = self.app.post( '/api/books/1/chapters/', content_type='application/json', data=json.dumps(data) ) self.assertEqual(r.status_code, 201) chapter = Chapter.query.get(1) response = json.loads(r.data.decode('utf-8')) self.assertEqual(response, chapter.to_dict()) def test_post_error(self): """Performing a POST request with missing parameters returns a 400 status code. """ book = Book.create('title', 'author') data = {} r = self.app.post( '/api/books/1/chapters/', content_type='application/json', data=json.dumps(data) ) self.assertEqual(r.status_code, 400) response = json.loads(r.data.decode('utf-8')) self.assertEqual(response, {'message': 'Missing parameters'}) class TestChapterApi(ApiTestCase): def test_get_ok(self): """Performing a GET request for an existing chapter returns a JSON representation of the chapter. """ book = Book.create('title', 'author') chapter = Chapter.create(name='chapter', book=book) r = self.app.get('/api/chapters/{}/'.format(book.id)) self.assertEqual(r.status_code, 200) response = json.loads(r.data.decode('utf-8')) self.assertEqual(response, chapter.to_dict()) def test_get_not_found(self): """Performing a GET request for a non-existing chapter returns a 404 status code. """ r = self.app.get('/api/chapters/{}/'.format(1)) self.assertEqual(r.status_code, 404) def test_put_ok(self): """Performing a PUT request for an existing chapter updates said chapter. """ book = Book.create('title', 'author') chapter = Chapter.create(name='chapter', book=book) chapter_data = chapter.to_dict() data = { 'name': 'chapter_2', } r = self.app.put( '/api/chapters/{}/'.format(1), content_type='application/json', data=json.dumps(data) ) self.assertEqual(r.status_code, 201) chapter_data.update(data) response = json.loads(r.data.decode('utf-8')) self.assertEqual(response, chapter_data) def test_put_not_found(self): """Performing a PUT request for a non-existing chapter returns a 404 status code. """ r = self.app.put('/api/chapters/{}/'.format(1)) self.assertEqual(r.status_code, 404) def test_delete_ok(self): """Performing a DELETE request for an existing chapter deletes it. """ book = Book.create('title', 'author') chapter = Chapter.create(name='chapter', book=book) r = self.app.delete('/api/chapters/{}/'.format(1)) self.assertEqual(r.status_code, 204) self.assertEqual(len(Chapter.query.all()), 0) def test_delete_not_found(self): """Performing a DELETE request for a non-existing chapter returns a 404 status code. """ r = self.app.delete('/api/chapters/{}/'.format(1)) self.assertEqual(r.status_code, 404)
33.060729
81
0.57568
999
8,166
4.60961
0.102102
0.094463
0.058632
0.085993
0.846037
0.837351
0.772638
0.758523
0.737025
0.733985
0
0.021313
0.281778
8,166
246
82
33.195122
0.763853
0.168259
0
0.621951
0
0
0.1293
0.010135
0
0
0
0
0.182927
1
0.121951
false
0
0.030488
0
0.182927
0
0
0
0
null
0
0
0
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
6
9e408518c31129445b5172a16abc6561e6761e4f
112
py
Python
Flask backend/app/routes/__init__.py
MuhamedAbdalla/e-commerce
9e06e699e696d50d7739df355f0bc8708195cb35
[ "MIT" ]
1
2021-04-26T00:17:12.000Z
2021-04-26T00:17:12.000Z
Flask backend/app/routes/__init__.py
MuhamedAbdalla/e-commerce
9e06e699e696d50d7739df355f0bc8708195cb35
[ "MIT" ]
null
null
null
Flask backend/app/routes/__init__.py
MuhamedAbdalla/e-commerce
9e06e699e696d50d7739df355f0bc8708195cb35
[ "MIT" ]
null
null
null
from .auth import * from .category import * from .customer import * from .order import * from .product import *
18.666667
23
0.732143
15
112
5.466667
0.466667
0.487805
0
0
0
0
0
0
0
0
0
0
0.178571
112
5
24
22.4
0.891304
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
1
0
1
0
1
0
0
null
1
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
6
9e6ac6371defd94998e24c6e0447f7f9a23d8bd8
10,035
py
Python
backend/api/views/ReviewViewSet.py
kukiamarilla/polijira
510dbc1473db973ac71fc68fa5a9b758b90a780b
[ "MIT" ]
1
2022-03-02T02:28:49.000Z
2022-03-02T02:28:49.000Z
backend/api/views/ReviewViewSet.py
kukiamarilla/polijira
510dbc1473db973ac71fc68fa5a9b758b90a780b
[ "MIT" ]
22
2021-09-01T17:44:25.000Z
2021-10-07T19:39:09.000Z
backend/api/views/ReviewViewSet.py
kukiamarilla/polijira
510dbc1473db973ac71fc68fa5a9b758b90a780b
[ "MIT" ]
null
null
null
from rest_framework import viewsets, status from rest_framework.response import Response from backend.api.models import Miembro, Usuario, Review, UserStory, SprintBacklog from backend.api.serializers import ReviewSerializer from backend.api.decorators import FormValidator from backend.api.forms import CreateReviewForm, UpdateReviewForm import datetime from django.db import transaction class ReviewViewSet(viewsets.ViewSet): """ ReviewViewSet View para el modelo Review Args: viewsets (ViewSet): View del módulo rest_framework """ def retrieve(self, request, pk=None): """ retrieve Obtiene un review especificado Args: request (Any): request pk (int, optional): Primary key. Defaults to None. Returns: JSON: Review obtenido """ try: usuario_request = Usuario.objects.get(user=request.user) review = Review.objects.get(pk=pk) miembro = Miembro.objects.get(usuario=usuario_request, proyecto=review.user_story.proyecto) if not miembro.tiene_permiso("ver_user_stories"): response = { "message": "No tiene permiso para realizar esta acción", "permission_required": ["ver_user_stories"], "error": "forbidden" } return Response(response, status=status.HTTP_403_FORBIDDEN) reviews = Review.objects.get(pk=pk) serializer = ReviewSerializer(reviews, many=False) return Response(serializer.data) except Review.DoesNotExist: response = { "message": "No existe review del User Story", "error": "not_found" } return Response(response, status=status.HTTP_404_NOT_FOUND) except Miembro.DoesNotExist: response = { "message": "Usted no es miembro de este Proyecto", "error": "forbidden" } return Response(response, status=status.HTTP_403_FORBIDDEN) @transaction.atomic @FormValidator(form=CreateReviewForm) def create(self, request): """ create Crea un review para un user story Args: request (Any): request Returns: JSON: Review creado """ try: usuario_request = Usuario.objects.get(user=request.user) user_story = UserStory.objects.get(pk=request.data["user_story"]) miembro = Miembro.objects.get(usuario=usuario_request, proyecto=user_story.proyecto) if not miembro.tiene_permiso("ver_user_stories") or not miembro.tiene_permiso("crear_reviews"): response = { "message": "No tiene permiso para realizar esta acción", "permission_required": [ "ver_user_stories", "crear_reviews" ], "error": "forbidden" } return Response(response, status=status.HTTP_403_FORBIDDEN) if not user_story.estado == 'P': response = { "message": "No se puede crear review en el estado actual del User Story", "error": "forbidden" } return Response(response, status=status.HTTP_403_FORBIDDEN) sprint_backlog = SprintBacklog.objects.filter(user_story=user_story, sprint__estado="A") if not len(sprint_backlog): response = { "message": "No se puede crear review si el user story no está en un sprint activo", "error": "forbidden" } return Response(response, status=status.HTTP_403_FORBIDDEN) observacion = request.data["observacion"] review = Review.objects.create( user_story=user_story, observacion=observacion, fecha_creacion=datetime.date.today(), autor=usuario_request ) serializer = ReviewSerializer(review, many=False) return Response(serializer.data) except Miembro.DoesNotExist: response = { "message": "Usted no es miembro de este Proyecto", "error": "forbidden" } return Response(response, status=status.HTTP_403_FORBIDDEN) @transaction.atomic @FormValidator(form=UpdateReviewForm) def update(self, request, pk=None): """ update Modificar un review Args: request (Any): request pk (int, optional): Primary key. Defaults to None. Returns: JSON: Detalles del review modificado """ try: usuario_request = Usuario.objects.get(user=request.user) review = Review.objects.get(pk=pk) miembro_request = Miembro.objects.get( usuario=usuario_request, proyecto=review.user_story.proyecto) if not usuario_request == review.autor: response = { "message": "Usted no el autor de este review", "error": "forbidden" } return Response(response, status=status.HTTP_403_FORBIDDEN) if not miembro_request.tiene_permiso("ver_user_stories") or \ not miembro_request.tiene_permiso("modificar_reviews"): response = { "message": "No tiene permiso para realizar esta acción", "permission_required": [ "ver_user_stories", "modificar_reviews" ], "error": "forbidden" } return Response(response, status=status.HTTP_403_FORBIDDEN) if not review.user_story.estado == 'P': response = { "message": "No se puede modificar review en el estado actual del User Story", "error": "forbidden" } return Response(response, status=status.HTTP_403_FORBIDDEN) sprint_backlog = SprintBacklog.objects.filter(user_story=review.user_story, sprint__estado="A") if not len(sprint_backlog): response = { "message": "No se puede modificar review si el user story no está en un sprint activo", "error": "forbidden" } return Response(response, status=status.HTTP_403_FORBIDDEN) review.observacion = request.data["observacion"] review.save() serializer = ReviewSerializer(review, many=False) return Response(serializer.data) except Review.DoesNotExist: response = { "message": "No existe el review especificado", "error": "not_found" } return Response(response, status=status.HTTP_404_NOT_FOUND) except Miembro.DoesNotExist: response = { "message": "Usted no es miembro de este Proyecto", "error": "forbidden" } return Response(response, status=status.HTTP_403_FORBIDDEN) def destroy(self, request, pk=None): """ destroy Elimina un review especificado Args: request (Any): request pk (int, optional): Primary key. Defaults to None. """ try: usuario_request = Usuario.objects.get(user=request.user) review = Review.objects.get(pk=pk) miembro_request = Miembro.objects.get( usuario=usuario_request, proyecto=review.user_story.proyecto) if not usuario_request == review.autor: response = { "message": "Usted no el autor de este review", "error": "forbidden" } return Response(response, status=status.HTTP_403_FORBIDDEN) if not miembro_request.tiene_permiso("ver_user_stories") or \ not miembro_request.tiene_permiso("eliminar_reviews"): response = { "message": "No tiene permiso para realizar esta acción", "permission_required": [ "ver_user_stories", "eliminar_reviews" ], "error": "forbidden" } return Response(response, status=status.HTTP_403_FORBIDDEN) if not review.user_story.estado == 'P': response = { "message": "No se puede eliminar review en el estado actual del User Story", "error": "forbidden" } return Response(response, status=status.HTTP_403_FORBIDDEN) sprint_backlog = SprintBacklog.objects.filter(user_story=review.user_story, sprint__estado="A") if not len(sprint_backlog): response = { "message": "No se puede eliminar review si el user story no está en un sprint activo", "error": "forbidden" } return Response(response, status=status.HTTP_403_FORBIDDEN) review.delete() response = {"message": "Review eliminado"} return Response(response) except Review.DoesNotExist: response = { "message": "No existe el review especificado", "error": "not_found" } return Response(response, status=status.HTTP_404_NOT_FOUND) except Miembro.DoesNotExist: response = { "message": "Usted no es miembro de este Proyecto", "error": "forbidden" } return Response(response, status=status.HTTP_403_FORBIDDEN)
41.8125
107
0.556652
948
10,035
5.758439
0.137131
0.041216
0.080601
0.097454
0.801429
0.783477
0.783477
0.775417
0.76351
0.747206
0
0.008885
0.360737
10,035
239
108
41.987448
0.842089
0.066866
0
0.634409
0
0
0.179044
0
0
0
0
0
0
1
0.021505
false
0
0.043011
0
0.193548
0.053763
0
0
0
null
0
0
0
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
6
7b6e89fe170718bf24d35430dba263194269d2cd
5,669
py
Python
trec2015/cuttsum/l2s/_simple.py
kedz/cuttsum
992c21192af03fd2ef863f5ab7d10752f75580fa
[ "Apache-2.0" ]
6
2015-09-10T02:22:21.000Z
2021-10-01T16:36:46.000Z
trec2015/cuttsum/l2s/_simple.py
kedz/cuttsum
992c21192af03fd2ef863f5ab7d10752f75580fa
[ "Apache-2.0" ]
null
null
null
trec2015/cuttsum/l2s/_simple.py
kedz/cuttsum
992c21192af03fd2ef863f5ab7d10752f75580fa
[ "Apache-2.0" ]
2
2018-04-04T10:44:32.000Z
2021-10-01T16:37:26.000Z
import pyvw from cuttsum.l2s._base import _SearchBase import pandas as pd class SelectBasicNextBias(_SearchBase): def setup_cache(self): return None def basic_cols(self): return [ "BASIC length", "BASIC char length", "BASIC doc position", "BASIC all caps ratio", "BASIC upper ratio", "BASIC lower ratio", "BASIC punc ratio", "BASIC person ratio", "BASIC organization ratio", "BASIC date ratio", "BASIC time ratio", "BASIC duration ratio", "BASIC number ratio", "BASIC ordinal ratio", "BASIC percent ratio", "BASIC money ratio", "BASIC set ratio", "BASIC misc ratio"] def update_cache(self, pred, sents, df, cache): return cache def make_select_example(self, sent, sents, df, cache): return self.example(lambda: { "b": [x for x in df.iloc[sent][self.basic_cols()].iteritems()],}, labelType=self.vw.lCostSensitive) def make_next_example(self, sents, df, cache, is_oracle): return self.example(lambda: {"n": ["bias"],}, labelType=self.vw.lCostSensitive) def get_feature_weights(self, dataframes): ex = self.vw.example( {"b": self.basic_cols(), "n": ["bias"], }, labelType=self.vw.lCostSensitive) fw = [] for i, feat in enumerate(self.basic_cols()): w = self.vw.get_weight(ex.feature("b", i)) fw.append(("b:" + feat, w)) fw.append(("n:bias", self.vw.get_weight(ex.feature("n", 0)))) fw.sort(key=lambda x: x[1]) return fw class SelectBasicNextBiasDocAvg(_SearchBase): def __init__(self, vw, sch, num_actions): pyvw.SearchTask.__init__(self, vw, sch, num_actions) sch.set_options( sch.IS_LDF ) self._with_scores = False def setup_cache(self): return pd.DataFrame(columns=self.basic_cols()) def basic_cols(self): return [ "BASIC length", "BASIC char length", "BASIC doc position", "BASIC all caps ratio", "BASIC upper ratio", "BASIC lower ratio", "BASIC punc ratio", "BASIC person ratio", "BASIC organization ratio", "BASIC date ratio", "BASIC time ratio", "BASIC duration ratio", "BASIC number ratio", "BASIC ordinal ratio", "BASIC percent ratio", "BASIC money ratio", "BASIC set ratio", "BASIC misc ratio", "LM domain avg lp", "LM gw avg lp"] def update_cache(self, pred, sents, df, cache): series = df.iloc[pred][self.basic_cols()] cache = cache.append(series, ignore_index=True) return cache def make_select_example(self, sent, sents, df, cache): if len(cache) > 0: return self.example(lambda: { "a": [x for x in df.iloc[sent][self.basic_cols()].iteritems()], "b": [x for x in df.iloc[sents][self.basic_cols()].mean().iteritems()], "c": [x for x in cache.mean().iteritems()] }, labelType=self.vw.lCostSensitive) else: return self.example(lambda: { "a": [x for x in df.iloc[sent][self.basic_cols()].iteritems()], "b": [x for x in df.iloc[sents][self.basic_cols()].mean().iteritems()], }, labelType=self.vw.lCostSensitive) def make_next_example(self, sents, df, cache, is_oracle): if len(sents) > 0 and len(cache) > 0: return self.example(lambda: { "d": ["bias"], "e": [x for x in df.iloc[sents][ self.basic_cols()].mean().iteritems()], "f": [x for x in cache.mean().iteritems()] }, labelType=self.vw.lCostSensitive) elif len(sents) > 0 and len(cache) == 0: return self.example(lambda: { "d": ["bias"], "e": [x for x in df.iloc[sents][ self.basic_cols()].mean().iteritems()], }, labelType=self.vw.lCostSensitive) elif len(sents) == 0 and len(cache) > 0: return self.example(lambda: { "d": ["bias"], "f": [x for x in cache.mean().iteritems()] }, labelType=self.vw.lCostSensitive) else: return self.example(lambda: { "d": ["bias"], }, labelType=self.vw.lCostSensitive) def get_feature_weights(self, dataframes): ex = self.vw.example( {"a": self.basic_cols(), "b": self.basic_cols(), "c": self.basic_cols(), "d": ["bias"], "e": self.basic_cols(), "f": self.basic_cols(), }, labelType=self.vw.lCostSensitive) fw = [] for i, feat in enumerate(self.basic_cols()): w = self.vw.get_weight(ex.feature("a", i)) fw.append(("a:" + feat, w)) for i, feat in enumerate(self.basic_cols()): w = self.vw.get_weight(ex.feature("b", i)) fw.append(("b:" + feat, w)) for i, feat in enumerate(self.basic_cols()): w = self.vw.get_weight(ex.feature("c", i)) fw.append(("c:" + feat, w)) for i, feat in enumerate(self.basic_cols()): w = self.vw.get_weight(ex.feature("e", i)) fw.append(("e:" + feat, w)) for i, feat in enumerate(self.basic_cols()): w = self.vw.get_weight(ex.feature("f", i)) fw.append(("f:" + feat, w)) fw.append(("d:bias", self.vw.get_weight(ex.feature("d", 0)))) fw.sort(key=lambda x: x[1]) return fw
38.564626
87
0.540307
706
5,669
4.24221
0.160057
0.093489
0.091152
0.023372
0.825376
0.810017
0.792321
0.770284
0.747579
0.747579
0
0.00307
0.31046
5,669
146
88
38.828767
0.763111
0
0
0.619048
0
0
0.130711
0
0
0
0
0
0
1
0.103175
false
0
0.02381
0.055556
0.269841
0
0
0
0
null
0
0
0
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
6
7ba9b9b87624253529ee8bce1d05de9bc848f1a6
90
py
Python
debianbts/__init__.py
santosh653/python-debianbts
4bba6adb7695e273250639f1eb8b3c813cbddedc
[ "MIT" ]
7
2015-02-22T19:47:26.000Z
2021-09-18T18:50:44.000Z
debianbts/__init__.py
santosh653/python-debianbts
4bba6adb7695e273250639f1eb8b3c813cbddedc
[ "MIT" ]
31
2015-04-24T03:41:19.000Z
2021-08-22T13:11:36.000Z
debianbts/__init__.py
santosh653/python-debianbts
4bba6adb7695e273250639f1eb8b3c813cbddedc
[ "MIT" ]
18
2015-01-20T09:44:35.000Z
2021-09-18T18:50:46.000Z
from debianbts.debianbts import * # noqa from debianbts.version import __version__ # noqa
30
48
0.811111
11
90
6.272727
0.454545
0.376812
0
0
0
0
0
0
0
0
0
0
0.133333
90
2
49
45
0.884615
0.1
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
1
0
1
0
1
0
0
null
1
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
6
7bc7a13b7a018611c7fc52c8a3be437f23f3fcc2
15,154
py
Python
occurrence/migrations/0033_auto_20190506_1347.py
ropable/wastd
295c60760548d177859de9c0bebdae93342767d0
[ "MIT" ]
3
2020-07-23T06:37:43.000Z
2022-01-27T09:40:40.000Z
occurrence/migrations/0033_auto_20190506_1347.py
ropable/wastd
295c60760548d177859de9c0bebdae93342767d0
[ "MIT" ]
337
2018-07-12T05:56:29.000Z
2022-03-30T02:40:41.000Z
occurrence/migrations/0033_auto_20190506_1347.py
ropable/wastd
295c60760548d177859de9c0bebdae93342767d0
[ "MIT" ]
2
2020-02-24T00:05:46.000Z
2020-07-15T07:02:29.000Z
# Generated by Django 2.1.7 on 2019-05-06 05:47 from django.db import migrations, models import django.db.models.deletion import uuid class Migration(migrations.Migration): dependencies = [ ('occurrence', '0032_auto_20190501_1749'), ] operations = [ migrations.CreateModel( name='AnimalHealth', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('code', models.SlugField(help_text='A unique, url-safe code.', max_length=500, unique=True, verbose_name='Code')), ('label', models.CharField(blank=True, help_text='A human-readable, self-explanatory label.', max_length=500, null=True, verbose_name='Label')), ('description', models.TextField(blank=True, help_text='A comprehensive description.', null=True, verbose_name='Description')), ], options={ 'ordering': ['code'], 'abstract': False, }, ), migrations.CreateModel( name='CauseOfDeath', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('code', models.SlugField(help_text='A unique, url-safe code.', max_length=500, unique=True, verbose_name='Code')), ('label', models.CharField(blank=True, help_text='A human-readable, self-explanatory label.', max_length=500, null=True, verbose_name='Label')), ('description', models.TextField(blank=True, help_text='A comprehensive description.', null=True, verbose_name='Description')), ], options={ 'ordering': ['code'], 'abstract': False, }, ), migrations.CreateModel( name='Confidence', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('code', models.SlugField(help_text='A unique, url-safe code.', max_length=500, unique=True, verbose_name='Code')), ('label', models.CharField(blank=True, help_text='A human-readable, self-explanatory label.', max_length=500, null=True, verbose_name='Label')), ('description', models.TextField(blank=True, help_text='A comprehensive description.', null=True, verbose_name='Description')), ], options={ 'ordering': ['code'], 'abstract': False, }, ), migrations.CreateModel( name='DetectionMethod', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('code', models.SlugField(help_text='A unique, url-safe code.', max_length=500, unique=True, verbose_name='Code')), ('label', models.CharField(blank=True, help_text='A human-readable, self-explanatory label.', max_length=500, null=True, verbose_name='Label')), ('description', models.TextField(blank=True, help_text='A comprehensive description.', null=True, verbose_name='Description')), ], options={ 'ordering': ['code'], 'abstract': False, }, ), migrations.CreateModel( name='PermitType', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('code', models.SlugField(help_text='A unique, url-safe code.', max_length=500, unique=True, verbose_name='Code')), ('label', models.CharField(blank=True, help_text='A human-readable, self-explanatory label.', max_length=500, null=True, verbose_name='Label')), ('description', models.TextField(blank=True, help_text='A comprehensive description.', null=True, verbose_name='Description')), ], options={ 'ordering': ['code'], 'abstract': False, }, ), migrations.CreateModel( name='ReproductiveMaturity', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('code', models.SlugField(help_text='A unique, url-safe code.', max_length=500, unique=True, verbose_name='Code')), ('label', models.CharField(blank=True, help_text='A human-readable, self-explanatory label.', max_length=500, null=True, verbose_name='Label')), ('description', models.TextField(blank=True, help_text='A comprehensive description.', null=True, verbose_name='Description')), ], options={ 'ordering': ['code'], 'abstract': False, }, ), migrations.CreateModel( name='SampleDestination', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('code', models.SlugField(help_text='A unique, url-safe code.', max_length=500, unique=True, verbose_name='Code')), ('label', models.CharField(blank=True, help_text='A human-readable, self-explanatory label.', max_length=500, null=True, verbose_name='Label')), ('description', models.TextField(blank=True, help_text='A comprehensive description.', null=True, verbose_name='Description')), ], options={ 'ordering': ['code'], 'abstract': False, }, ), migrations.CreateModel( name='SampleType', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('code', models.SlugField(help_text='A unique, url-safe code.', max_length=500, unique=True, verbose_name='Code')), ('label', models.CharField(blank=True, help_text='A human-readable, self-explanatory label.', max_length=500, null=True, verbose_name='Label')), ('description', models.TextField(blank=True, help_text='A comprehensive description.', null=True, verbose_name='Description')), ], options={ 'ordering': ['code'], 'abstract': False, }, ), migrations.CreateModel( name='SecondarySigns', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('code', models.SlugField(help_text='A unique, url-safe code.', max_length=500, unique=True, verbose_name='Code')), ('label', models.CharField(blank=True, help_text='A human-readable, self-explanatory label.', max_length=500, null=True, verbose_name='Label')), ('description', models.TextField(blank=True, help_text='A comprehensive description.', null=True, verbose_name='Description')), ], options={ 'ordering': ['code'], 'abstract': False, }, ), migrations.AddField( model_name='animalobservation', name='actions_required', field=models.TextField(blank=True, help_text='Any actions required, if applicable.', null=True, verbose_name='Actions required'), ), migrations.AddField( model_name='animalobservation', name='actions_taken', field=models.TextField(blank=True, help_text='Any actions taken, if applicable.', null=True, verbose_name='Actions taken'), ), migrations.AddField( model_name='animalobservation', name='distinctive_features', field=models.TextField(blank=True, help_text='Distinctive features of the primary observed animal. Include injuries if applicable.', null=True, verbose_name='Distinctive features'), ), migrations.AddField( model_name='animalobservation', name='no_adult_female', field=models.PositiveIntegerField(blank=True, help_text='Including the primary observed animal.', null=True, verbose_name='Number of adult females'), ), migrations.AddField( model_name='animalobservation', name='no_adult_male', field=models.PositiveIntegerField(blank=True, help_text='Including the primary observed animal.', null=True, verbose_name='Number of adult males'), ), migrations.AddField( model_name='animalobservation', name='no_adult_unknown', field=models.PositiveIntegerField(blank=True, help_text='Including the primary observed animal.', null=True, verbose_name='Number of adult unknown'), ), migrations.AddField( model_name='animalobservation', name='no_dependent_young_female', field=models.PositiveIntegerField(blank=True, help_text='Including the primary observed animal.', null=True, verbose_name='Number of dependent_young females'), ), migrations.AddField( model_name='animalobservation', name='no_dependent_young_male', field=models.PositiveIntegerField(blank=True, help_text='Including the primary observed animal.', null=True, verbose_name='Number of dependent_young males'), ), migrations.AddField( model_name='animalobservation', name='no_dependent_young_unknown', field=models.PositiveIntegerField(blank=True, help_text='Including the primary observed animal.', null=True, verbose_name='Number of dependent_young unknown'), ), migrations.AddField( model_name='animalobservation', name='no_juvenile_female', field=models.PositiveIntegerField(blank=True, help_text='Including the primary observed animal.', null=True, verbose_name='Number of juvenile females'), ), migrations.AddField( model_name='animalobservation', name='no_juvenile_male', field=models.PositiveIntegerField(blank=True, help_text='Including the primary observed animal.', null=True, verbose_name='Number of juvenile males'), ), migrations.AddField( model_name='animalobservation', name='no_juvenile_unknown', field=models.PositiveIntegerField(blank=True, help_text='Including the primary observed animal.', null=True, verbose_name='Number of juvenile unknown'), ), migrations.AddField( model_name='animalobservation', name='observation_details', field=models.TextField(blank=True, help_text='Any relevant details of the observation.', null=True, verbose_name='Observation details'), ), migrations.AddField( model_name='physicalsample', name='collector_id', field=models.TextField(blank=True, help_text='The unique collector ID.', null=True, verbose_name='Collector ID'), ), migrations.AddField( model_name='physicalsample', name='permit_id', field=models.TextField(blank=True, help_text='The unique permit ID.', null=True, verbose_name='Permit ID'), ), migrations.AddField( model_name='physicalsample', name='sample_label', field=models.TextField(blank=True, help_text='The label must be unique within the sample type.', null=True, verbose_name='Sample Label'), ), migrations.AlterField( model_name='areaencounter', name='source_id', field=models.CharField(default=uuid.UUID('6d456822-6fc2-11e9-a870-ecf4bb19b5fc'), help_text='The ID of the record in the original source, if available.', max_length=1000, verbose_name='Source ID'), ), migrations.AddField( model_name='animalobservation', name='cause_of_death', field=models.ForeignKey(blank=True, help_text='The cause of death of the primary observed animal, if applicable.', null=True, on_delete=django.db.models.deletion.CASCADE, to='occurrence.CauseOfDeath', verbose_name='Cause of Death'), ), migrations.AddField( model_name='animalobservation', name='detection_method', field=models.ForeignKey(blank=True, help_text='What brought the human observer to the Encounter?', null=True, on_delete=django.db.models.deletion.CASCADE, to='occurrence.DetectionMethod', verbose_name='Detection Method'), ), migrations.AddField( model_name='animalobservation', name='health', field=models.ForeignKey(blank=True, help_text='The health status of the primary observed animal.', null=True, on_delete=django.db.models.deletion.CASCADE, to='occurrence.AnimalHealth', verbose_name='Animal Health'), ), migrations.AddField( model_name='animalobservation', name='maturity', field=models.ForeignKey(blank=True, help_text='Reproductive Maturity of the primary observed animal.', null=True, on_delete=django.db.models.deletion.CASCADE, to='occurrence.ReproductiveMaturity', verbose_name='Reproductive Maturity'), ), migrations.AddField( model_name='animalobservation', name='secondary_signs', field=models.ManyToManyField(blank=True, help_text='Any observed secondary signs of the animal.', to='occurrence.SecondarySigns', verbose_name='Secondary Signs'), ), migrations.AddField( model_name='animalobservation', name='species_id_confidence', field=models.ForeignKey(blank=True, help_text='How correct is the species ID according to the observer?', null=True, on_delete=django.db.models.deletion.CASCADE, to='occurrence.Confidence', verbose_name='Species ID Confidence'), ), migrations.AddField( model_name='physicalsample', name='permit_type', field=models.ForeignKey(blank=True, help_text='Add missing values through the data curation portal.', null=True, on_delete=django.db.models.deletion.CASCADE, to='occurrence.PermitType', verbose_name='Permit Type'), ), migrations.AddField( model_name='physicalsample', name='sample_destination', field=models.ForeignKey(blank=True, help_text='Add missing values through the data curation portal.', null=True, on_delete=django.db.models.deletion.CASCADE, to='occurrence.SampleDestination', verbose_name='Sample Destination'), ), migrations.AddField( model_name='physicalsample', name='sample_type', field=models.ForeignKey(blank=True, help_text='Add missing values through the data curation portal.', null=True, on_delete=django.db.models.deletion.CASCADE, to='occurrence.SampleType', verbose_name='Sample Type'), ), ]
57.619772
247
0.625907
1,582
15,154
5.85019
0.10177
0.07369
0.069692
0.078984
0.838466
0.825932
0.781415
0.712156
0.65262
0.621718
0
0.00946
0.246602
15,154
262
248
57.839695
0.801174
0.00297
0
0.683594
1
0
0.283908
0.024691
0
0
0
0
0
1
0
false
0
0.011719
0
0.023438
0
0
0
0
null
0
0
0
1
1
1
1
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
6
c8bcc2d271fc92221e512f3eeaf02b76b2a63733
189
py
Python
myapp/views.py
koatse/heroku_helloworld
3ad9afabb5c7225f2a1ecf6407d4f8e861b51e78
[ "MIT" ]
null
null
null
myapp/views.py
koatse/heroku_helloworld
3ad9afabb5c7225f2a1ecf6407d4f8e861b51e78
[ "MIT" ]
null
null
null
myapp/views.py
koatse/heroku_helloworld
3ad9afabb5c7225f2a1ecf6407d4f8e861b51e78
[ "MIT" ]
null
null
null
from django.shortcuts import render from django.http import HttpResponse, HttpResponseRedirect # Create your views here. def test(request): return HttpResponse("THis is a test page")
23.625
58
0.78836
25
189
5.96
0.8
0.134228
0
0
0
0
0
0
0
0
0
0
0.148148
189
7
59
27
0.925466
0.121693
0
0
0
0
0.115854
0
0
0
0
0
0
1
0.25
false
0
0.5
0.25
1
0
1
0
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
1
0
0
1
1
1
0
0
6
cde93e6cc2687937662cac178e4a217441ec2cde
9,410
py
Python
venv/include/stock.py
SpereShelde/pals_helper
4994ac393d30003d703fb33cc02fe4981a261a9a
[ "MIT" ]
null
null
null
venv/include/stock.py
SpereShelde/pals_helper
4994ac393d30003d703fb33cc02fe4981a261a9a
[ "MIT" ]
1
2021-06-02T00:34:51.000Z
2021-06-02T00:34:51.000Z
venv/include/stock.py
SpereShelde/pals_helper
4994ac393d30003d703fb33cc02fe4981a261a9a
[ "MIT" ]
null
null
null
from telegram.ext import Updater, CommandHandler, MessageHandler, Filters import sqlite3 import configparser config = configparser.ConfigParser() config.read('config.ini') db_address = config.get('global','DB_ADDRESS') chat_id = config.get('global','CHAT_ID') def stock(update, context): connection = sqlite3.connect(db_address) cursor = connection.cursor() cursor.execute('SELECT * FROM storage') storage = cursor.fetchall() message = "Here is the list of stock." for food in storage: # print(food) message += "\n/"+str(food[1]).replace(" ", "_")+":\t"+str(food[3])+" left" connection.close() context.bot.send_message(chat_id=chat_id, text=message) def update_stock_database(name, operation): #operation = -1 or 1 connection = sqlite3.connect(db_address) cursor = connection.cursor() cursor.execute("UPDATE storage set stock=stock+? WHERE name=?", [operation, name]) connection.commit() cursor.execute("SELECT stock FROM storage WHERE name=?", [name]) stock = cursor.fetchone()[0] connection.close() return str(stock) def chicken_wings(update, context): message = "Need edit stock?\n/add_chicken_wings: add 1\n/redu_chicken_wings: reduce 1" print(message) context.bot.send_message(chat_id=chat_id, text=message) def add_chicken_wings(update, context): stock = update_stock_database("chicken wings", 1) message = "Success!\nChicken wings: " + stock + " left" context.bot.send_message(chat_id=chat_id, text=message) def redu_chicken_wings(update, context): stock = update_stock_database("chicken wings", -1) message = "Success!\nChicken wings: " + stock + " left" context.bot.send_message(chat_id=chat_id, text=message) def chicken_thigh(update, context): message = "Need edit stock?\n/add_chicken_thigh: add 1\n/redu_chicken_thigh: reduce 1" print(message) context.bot.send_message(chat_id=chat_id, text=message) def add_chicken_thigh(update, context): stock = update_stock_database("chicken thigh", 1) message = "Success!\nChicken thigh: " + stock + " left" context.bot.send_message(chat_id=chat_id, text=message) def redu_chicken_thigh(update, context): stock = update_stock_database("chicken thigh", -1) message = "Success!\nChicken thigh: " + stock + " left" context.bot.send_message(chat_id=chat_id, text=message) def chicken_breast(update, context): message = "Need edit stock?\n/add_chicken_breast: add 1\n/redu_chicken_breast: reduce 1" print(message) context.bot.send_message(chat_id=chat_id, text=message) def add_chicken_breast(update, context): stock = update_stock_database("chicken breast", 1) message = "Success!\nChicken breast: " + stock + " left" context.bot.send_message(chat_id=chat_id, text=message) def redu_chicken_breast(update, context): stock = update_stock_database("chicken breast", -1) message = "Success!\nChicken breast: " + stock + " left" context.bot.send_message(chat_id=chat_id, text=message) def pork_belly_strip(update, context): message = "Need edit stock?\n/add_pork_belly_strip: add 1\n/redu_pork_belly_strip: reduce 1" print(message) context.bot.send_message(chat_id=chat_id, text=message) def add_pork_belly_strip(update, context): stock = update_stock_database("pork belly strip", 1) message = "Success!\nPork belly strip: " + stock + " left" context.bot.send_message(chat_id=chat_id, text=message) def redu_pork_belly_strip(update, context): stock = update_stock_database("pork belly strip", -1) message = "Success!\nPork belly strip: " + stock + " left" context.bot.send_message(chat_id=chat_id, text=message) def pork_belly_slices(update, context): message = "Need edit stock?\n/add_pork_belly_slices: add 1\n/redu_pork_belly_slices: reduce 1" print(message) context.bot.send_message(chat_id=chat_id, text=message) def add_pork_belly_slices(update, context): stock = update_stock_database("pork belly slices", 1) message = "Success!\nPork belly slices: " + stock + " left" context.bot.send_message(chat_id=chat_id, text=message) def redu_pork_belly_slices(update, context): stock = update_stock_database("pork belly slices", -1) message = "Success!\nPork belly slices: " + stock + " left" context.bot.send_message(chat_id=chat_id, text=message) def pork_loin(update, context): message = "Need edit stock?\n/add_pork_loin: add 1\n/redu_pork_loin: reduce 1" print(message) context.bot.send_message(chat_id=chat_id, text=message) def add_pork_loin(update, context): stock = update_stock_database("pork loin", 1) message = "Success!\nPork loin: " + stock + " left" context.bot.send_message(chat_id=chat_id, text=message) def redu_pork_loin(update, context): stock = update_stock_database("pork loin", -11) message = "Success!\nPork loin: " + stock + " left" context.bot.send_message(chat_id=chat_id, text=message) def pork_ribs(update, context): message = "Need edit stock?\n/add_pork_ribs: add 1\n/redu_pork_ribs: reduce 1" print(message) context.bot.send_message(chat_id=chat_id, text=message) def add_pork_ribs(update, context): stock = update_stock_database("pork ribs", 1) message = "Success!\nPork ribs: " + stock + " left" context.bot.send_message(chat_id=chat_id, text=message) def redu_pork_ribs(update, context): stock = update_stock_database("pork ribs", - 1) message = "Success!\nPork ribs: " + stock + " left" context.bot.send_message(chat_id=chat_id, text=message) def lamp_chops(update, context): message = "Need edit stock?\n/add_lamp_chops: add 1\n/redu_lamp_chops: reduce 1" print(message) context.bot.send_message(chat_id=chat_id, text=message) def add_lamp_chops(update, context): stock = update_stock_database("lamp chops", 1) message = "Success!\nLamp chops: " + stock + " left" context.bot.send_message(chat_id=chat_id, text=message) def redu_lamp_chops(update, context): stock = update_stock_database("lamp chops", -1) message = "Success!\nLamp chops: " + stock + " left" context.bot.send_message(chat_id=chat_id, text=message) def lamp(update, context): message = "Need edit stock?\n/add_lamp: add 1\n/redu_lamp: reduce 1" print(message) context.bot.send_message(chat_id=chat_id, text=message) def add_lamp(update, context): stock = update_stock_database("lamp", 1) message = "Success!\nLamp: " + stock + " left" context.bot.send_message(chat_id=chat_id, text=message) def redu_lamp(update, context): stock = update_stock_database("lamp", -1) message = "Success!\nLamp: " + stock + " left" context.bot.send_message(chat_id=chat_id, text=message) def beef_tenderloin(update, context): message = "Need edit stock?\n/add_beef_tenderloin: add 1\n/redu_beef_tenderloin: reduce 1" print(message) context.bot.send_message(chat_id=chat_id, text=message) def add_beef_tenderloin(update, context): stock = update_stock_database("beef tenderloin", 1) message = "Success!\nBeef tenderloin: " + stock + " left" context.bot.send_message(chat_id=chat_id, text=message) def redu_beef_tenderloin(update, context): stock = update_stock_database("beef tenderloin", -1) message = "Success!\nBeef tenderloin: " + stock + " left" context.bot.send_message(chat_id=chat_id, text=message) def beef(update, context): message = "Need edit stock?\n/add_beef: add 1\n/redu_beef: reduce 1" print(message) context.bot.send_message(chat_id=chat_id, text=message) def add_beef(update, context): stock = update_stock_database("beef", 1) message = "Success!\nBeef: " + stock + " left" context.bot.send_message(chat_id=chat_id, text=message) def redu_beef(update, context): stock = update_stock_database("beef", -1) message = "Success!\nBeef: " + stock + " left" context.bot.send_message(chat_id=chat_id, text=message) def add_stock(update, context): connection = sqlite3.connect(db_address) cursor = connection.cursor() cursor.execute('SELECT * FROM storage') storage = cursor.fetchall() message = "What kind of food stock need add?" for food in storage: message +="\n/add_"+str(food[1]).replace(" ","_")+":\t"+str(food[3])+" left" connection.close() context.bot.send_message(chat_id=chat_id, text=message)
38.884298
98
0.639426
1,196
9,410
4.812709
0.073579
0.075052
0.085129
0.127693
0.8836
0.859798
0.853544
0.853544
0.853544
0.787874
0
0.007886
0.245377
9,410
242
99
38.884298
0.802704
0.003188
0
0.581281
0
0
0.202495
0.041587
0
0
0
0
0
1
0.17734
false
0
0.014778
0
0.197044
0.054187
0
0
0
null
0
0
0
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
6
a82be0462a06615c319882dc37faf69954d5086a
232
py
Python
meregistro/apps/backend/models/__init__.py
MERegistro/meregistro
6cde3cab2bd1a8e3084fa38147de377d229391e3
[ "BSD-3-Clause" ]
null
null
null
meregistro/apps/backend/models/__init__.py
MERegistro/meregistro
6cde3cab2bd1a8e3084fa38147de377d229391e3
[ "BSD-3-Clause" ]
null
null
null
meregistro/apps/backend/models/__init__.py
MERegistro/meregistro
6cde3cab2bd1a8e3084fa38147de377d229391e3
[ "BSD-3-Clause" ]
null
null
null
from ConfiguracionSolapasExtensionAulica import ConfiguracionSolapasExtensionAulica from ConfiguracionSolapasEstablecimiento import ConfiguracionSolapasEstablecimiento from ConfiguracionSolapasAnexo import ConfiguracionSolapasAnexo
58
83
0.948276
12
232
18.333333
0.416667
0
0
0
0
0
0
0
0
0
0
0
0.051724
232
3
84
77.333333
1
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
1
0
1
0
1
0
1
null
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
6
b522cd68a8b3af98b4aab4f0931ba56c1add037b
26
py
Python
esse3api/__init__.py
hpsc-smartlab/esse3api
416c52149f28c886cab72671b20b209b40857edf
[ "MIT" ]
2
2018-04-04T15:56:40.000Z
2018-05-23T11:46:06.000Z
esse3api/__init__.py
hpsc-smartlab/esse3api
416c52149f28c886cab72671b20b209b40857edf
[ "MIT" ]
null
null
null
esse3api/__init__.py
hpsc-smartlab/esse3api
416c52149f28c886cab72671b20b209b40857edf
[ "MIT" ]
null
null
null
from .esse3api import app
13
25
0.807692
4
26
5.25
1
0
0
0
0
0
0
0
0
0
0
0.045455
0.153846
26
1
26
26
0.909091
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
1
0
1
0
1
1
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
6
b555edcc844a79e9e7e2c4e63fbcf0cc3b1a3175
8,127
py
Python
tests/processing_components/test_image_gather_scatter.py
SKA-ScienceDataProcessor/rascil
bd3b47f779e18e184781e2928ad1539d1fdc1c9b
[ "Apache-2.0" ]
7
2019-12-14T13:42:33.000Z
2022-01-28T03:31:45.000Z
tests/processing_components/test_image_gather_scatter.py
SKA-ScienceDataProcessor/rascil
bd3b47f779e18e184781e2928ad1539d1fdc1c9b
[ "Apache-2.0" ]
6
2020-01-08T09:40:08.000Z
2020-06-11T14:56:13.000Z
tests/processing_components/test_image_gather_scatter.py
SKA-ScienceDataProcessor/rascil
bd3b47f779e18e184781e2928ad1539d1fdc1c9b
[ "Apache-2.0" ]
3
2020-01-14T11:14:16.000Z
2020-09-15T05:21:06.000Z
"""Unit tests for image iteration """ import os import logging import unittest import numpy from rascil.data_models.polarisation import PolarisationFrame from rascil.processing_components.image.operations import export_image_to_fits from rascil.processing_components.image.operations import create_empty_image_like from rascil.processing_components.image.gather_scatter import image_gather_facets, image_scatter_facets, image_gather_channels, \ image_scatter_channels from rascil.processing_components.simulation import create_test_image log = logging.getLogger('logger') log.setLevel(logging.WARNING) class TestImageGatherScatters(unittest.TestCase): def setUp(self): from rascil.data_models.parameters import rascil_path, rascil_data_path self.dir = rascil_path('test_results') self.persist = os.getenv("RASCIL_PERSIST", False) def test_scatter_gather_facet(self): m31original = create_test_image(polarisation_frame=PolarisationFrame('stokesI')) assert numpy.max(numpy.abs(m31original.data)), "Original is empty" for nraster in [1, 4, 8]: m31model = create_test_image(polarisation_frame=PolarisationFrame('stokesI')) image_list = image_scatter_facets(m31model, facets=nraster) for patch in image_list: assert patch.data.shape[3] == (m31model.data.shape[3] // nraster), \ "Number of pixels in each patch: %d not as expected: %d" % (patch.data.shape[3], (m31model.data.shape[3] // nraster)) assert patch.data.shape[2] == (m31model.data.shape[2] // nraster), \ "Number of pixels in each patch: %d not as expected: %d" % (patch.data.shape[2], (m31model.data.shape[2] // nraster)) patch.data[...] = 1.0 m31reconstructed = create_empty_image_like(m31model) m31reconstructed = image_gather_facets(image_list, m31reconstructed, facets=nraster) flat = image_gather_facets(image_list, m31reconstructed, facets=nraster, return_flat=True) assert numpy.max(numpy.abs(flat.data)), "Flat is empty for %d" % nraster assert numpy.max(numpy.abs(m31reconstructed.data)), "Raster is empty for %d" % nraster def test_scatter_gather_facet_overlap(self): m31original = create_test_image(polarisation_frame=PolarisationFrame('stokesI')) assert numpy.max(numpy.abs(m31original.data)), "Original is empty" for nraster, overlap in [(1, 0), (4, 8), (8, 16)]: m31model = create_test_image(polarisation_frame=PolarisationFrame('stokesI')) image_list = image_scatter_facets(m31model, facets=nraster, overlap=overlap) for patch in image_list: assert patch.data.shape[3] == (2 * overlap + m31model.data.shape[3] // nraster), \ "Number of pixels in each patch: %d not as expected: %d" % (patch.data.shape[3], (2 * overlap + m31model.data.shape[3] // nraster)) assert patch.data.shape[2] == (2 * overlap + m31model.data.shape[2] // nraster), \ "Number of pixels in each patch: %d not as expected: %d" % (patch.data.shape[2], (2 * overlap + m31model.data.shape[2] // nraster)) patch.data[...] = 1.0 m31reconstructed = create_empty_image_like(m31model) m31reconstructed = image_gather_facets(image_list, m31reconstructed, facets=nraster, overlap=overlap) flat = image_gather_facets(image_list, m31reconstructed, facets=nraster, overlap=overlap, return_flat=True) assert numpy.max(numpy.abs(flat.data)), "Flat is empty for %d" % nraster assert numpy.max(numpy.abs(m31reconstructed.data)), "Raster is empty for %d" % nraster def test_scatter_gather_facet_overlap_taper(self): m31original = create_test_image(polarisation_frame=PolarisationFrame('stokesI')) assert numpy.max(numpy.abs(m31original.data)), "Original is empty" for taper in ['linear', None]: for nraster, overlap in [(1, 0), (4, 8), (8, 8), (8, 16)]: m31model = create_test_image(polarisation_frame=PolarisationFrame('stokesI')) image_list = image_scatter_facets(m31model, facets=nraster, overlap=overlap, taper=taper) for patch in image_list: assert patch.data.shape[3] == (2 * overlap + m31model.data.shape[3] // nraster), \ "Number of pixels in each patch: %d not as expected: %d" % (patch.data.shape[3], (2 * overlap + m31model.data.shape[3] // nraster)) assert patch.data.shape[2] == (2 * overlap + m31model.data.shape[2] // nraster), \ "Number of pixels in each patch: %d not as expected: %d" % (patch.data.shape[2], (2 * overlap + m31model.data.shape[2] // nraster)) m31reconstructed = create_empty_image_like(m31model) m31reconstructed = image_gather_facets(image_list, m31reconstructed, facets=nraster, overlap=overlap, taper=taper) flat = image_gather_facets(image_list, m31reconstructed, facets=nraster, overlap=overlap, taper=taper, return_flat=True) if self.persist: export_image_to_fits(m31reconstructed, "%s/test_image_gather_scatter_%dnraster_%doverlap_%s_reconstructed.fits" % (self.dir, nraster, overlap, taper)) if self.persist: export_image_to_fits(flat, "%s/test_image_gather_scatter_%dnraster_%doverlap_%s_flat.fits" % (self.dir, nraster, overlap, taper)) assert numpy.max(numpy.abs(flat.data)), "Flat is empty for %d" % nraster assert numpy.max(numpy.abs(m31reconstructed.data)), "Raster is empty for %d" % nraster def test_scatter_gather_channel(self): for nchan in [128, 16]: m31cube = create_test_image(polarisation_frame=PolarisationFrame('stokesI'), frequency=numpy.linspace(1e8, 1.1e8, nchan)) for subimages in [16, 8, 2, 1]: image_list = image_scatter_channels(m31cube, subimages=subimages) m31cuberec = image_gather_channels(image_list, m31cube, subimages=subimages) diff = m31cube.data - m31cuberec.data assert numpy.max(numpy.abs(diff)) == 0.0, "Scatter gather failed for %d" % subimages def test_gather_channel(self): for nchan in [128, 16]: m31cube = create_test_image(polarisation_frame=PolarisationFrame('stokesI'), frequency=numpy.linspace(1e8, 1.1e8, nchan)) image_list = image_scatter_channels(m31cube, subimages=nchan) m31cuberec = image_gather_channels(image_list, None, subimages=nchan) assert m31cube.shape == m31cuberec.shape diff = m31cube.data - m31cuberec.data assert numpy.max(numpy.abs(diff)) == 0.0, "Scatter gather failed for %d" % nchan if __name__ == '__main__': unittest.main()
60.2
129
0.571429
848
8,127
5.29717
0.125
0.048085
0.0374
0.046527
0.826581
0.813224
0.780721
0.724622
0.706812
0.685663
0
0.036202
0.333826
8,127
134
130
60.649254
0.793498
0.003691
0
0.481132
0
0
0.097676
0.016197
0
0
0
0
0.169811
1
0.056604
false
0
0.09434
0
0.160377
0
0
0
0
null
0
0
0
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
6
a92314c8de3b3a27bca1c5e5e017fa7d6fbe1a3a
70
py
Python
pepperbot/models/GroupInfo.py
SSmJaE/PepperBot
0f34c90fc8f6d90fd8881193992d0dde756c2dde
[ "MIT" ]
27
2021-03-26T16:17:38.000Z
2022-03-30T21:39:07.000Z
pepperbot/models/GroupInfo.py
SSmJaE/PepperBot
0f34c90fc8f6d90fd8881193992d0dde756c2dde
[ "MIT" ]
null
null
null
pepperbot/models/GroupInfo.py
SSmJaE/PepperBot
0f34c90fc8f6d90fd8881193992d0dde756c2dde
[ "MIT" ]
7
2021-05-27T17:28:37.000Z
2021-12-22T11:22:08.000Z
from pydantic import BaseModel class GroupInfo(BaseModel): pass
11.666667
30
0.771429
8
70
6.75
0.875
0
0
0
0
0
0
0
0
0
0
0
0.185714
70
5
31
14
0.947368
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
1
0
0
6
a94805e131159f821ffe0ef9c76a0cfd02ee98dd
43
py
Python
wepppy/nodb/mods/locations/wepppy_locations_portland/livneh_daily_observed/__init__.py
hwbeeson/wepppy
6358552df99853c75be8911e7ef943108ae6923e
[ "BSD-3-Clause" ]
null
null
null
wepppy/nodb/mods/locations/wepppy_locations_portland/livneh_daily_observed/__init__.py
hwbeeson/wepppy
6358552df99853c75be8911e7ef943108ae6923e
[ "BSD-3-Clause" ]
null
null
null
wepppy/nodb/mods/locations/wepppy_locations_portland/livneh_daily_observed/__init__.py
hwbeeson/wepppy
6358552df99853c75be8911e7ef943108ae6923e
[ "BSD-3-Clause" ]
null
null
null
from .data_manager import LivnehDataManager
43
43
0.906977
5
43
7.6
1
0
0
0
0
0
0
0
0
0
0
0
0.069767
43
1
43
43
0.95
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
1
0
1
0
1
1
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
6
8d476d1cf4b8de11e2f848539a97bebceb4d9fc3
83,446
py
Python
parser/team23/grammar/parsetab.py
18SebastianVC/tytus
2b22f4339356b6cf46e3235a5219f68e5ba5573b
[ "MIT" ]
null
null
null
parser/team23/grammar/parsetab.py
18SebastianVC/tytus
2b22f4339356b6cf46e3235a5219f68e5ba5573b
[ "MIT" ]
null
null
null
parser/team23/grammar/parsetab.py
18SebastianVC/tytus
2b22f4339356b6cf46e3235a5219f68e5ba5573b
[ "MIT" ]
null
null
null
# parsetab.py # This file is automatically generated. Do not edit. # pylint: disable=W,C,R _tabversion = '3.10' _lr_method = 'LALR' _lr_signature = 'leftPAR_ABREPAR_CIERRArightIGUALleftORleftANDleftNO_IGUALnonassocMAYORMENORMAYOR_IGUALMENOR_IGUALleftMASMENOSleftASTERISCODIVISIONMODULOleftPOTENCIArightNOTleftLLAVE_ABRELLAVE_CIERRAABS ADD ALL ALTER AND AS ASC ASTERISCO AVG BETWEEN BIGINT BOOLEAN BY CADENA CASE CASTEO CBRT CEIL CEILING CHAR CHARACTER CHECK COLUMN COMA CONSTRAINT CORCHE_ABRE CORCHE_CIERRA COUNT CREATE CURRENT_USER DATABASE DATABASES DATE DAY DECIMAL DECIMAL_NUM DEFAULT DEGREES DELETE DESC DIFERENTE DISTINCT DIV DIVISION DOUBLE DROP ELSE END ENTERO ENUM EXISTS EXP FACTORIAL FALSE FIELDS FIRST FLOOR FOREIGN FROM FULL GCD GREATEST GROUP HAVING HOUR ID IF IGUAL ILIKE IN INHERITS INNER INSERT INTEGER INTERSECT INTERVAL INTO IS ISNULL JOIN KEY LAST LEAST LEFT LIKE LIMIT LLAVE_ABRE LLAVE_CIERRA LN LOG MAS MAX MAYOR MAYOR_IGUAL MENOR MENOR_IGUAL MENOS MIN MINUTE MOD MODE MODULO MONEY MONTH NOT NOTNULL NO_IGUAL NULL NULLS NUMERIC OFFSET OR ORDER OUTER OWNER PAR_ABRE PAR_CIERRA PI POTENCIA POWER PRECISION PRIMARY PUNTO PUNTOCOMA RADIANS REAL REFERENCE REFERENCES RENAME REPLACE RIGHT ROUND SECOND SELECT SESSION_USER SET SHOW SIMILAR SMALLINT SUBSTRING SUM SYMMETRIC TABLE TEXT THEN TIME TIMESTAMP TO TRUE TYPE UNION UNIQUE UNKNOWN UPDATE USE VALUES VARCHAR VARYING WHEN WHERE WITH WITHOUT YEAR ZONEinit : instruccionesinstrucciones : instrucciones instruccioninstrucciones : instruccion instruccion : crear_statement PUNTOCOMA\n | alter_statement PUNTOCOMA\n | drop_statement PUNTOCOMA\n | seleccionar PUNTOCOMAinstruccion : SHOW DATABASES PUNTOCOMA\n | INSERT INTO ID VALUES PAR_ABRE list_val PAR_CIERRA PUNTOCOMA\n | UPDATE ID SET ID IGUAL expression where PUNTOCOMA\n | DELETE FROM ID WHERE ID IGUAL expression PUNTOCOMA\n | USE DATABASE ID PUNTOCOMAcrear_statement : CREATE TABLE ID PAR_ABRE contenido_tabla PAR_CIERRA inherits_statementcrear_statement : CREATE or_replace DATABASE if_not_exists ID owner_ mode_or_replace : OR REPLACE\n | if_not_exists : IF NOT EXISTS\n | owner_ : OWNER IGUAL ID\n | mode_ : MODE IGUAL ENTERO\n | alter_statement : ALTER DATABASE ID rename_owneralter_statement : ALTER TABLE ID alter_oprename_owner : RENAME TO ID\n | OWNER TO LLAVE_ABRE ow_op LLAVE_CIERRAow_op : ID\n | CURRENT_USER\n | SESSION_USERdrop_statement : DROP DATABASE if_exists IDdrop_statement : DROP TABLE IDif_exists : IF EXISTS\n | contenido_tabla : contenido_tabla COMA manejo_tablacontenido_tabla : manejo_tablamanejo_tabla : declaracion_columna\n | condition_columndeclaracion_columna : ID type_column condition_column_rowdeclaracion_columna : ID type_columntype_column : SMALLINT\n | INTEGER\n\t | BIGINT\n\t | DECIMAL\n\t | NUMERIC\n\t | REAL\n\t | DOUBLE PRECISION\n\t | MONEY\n\t | VARCHAR PAR_ABRE ENTERO PAR_CIERRA\n | CHAR PAR_ABRE ENTERO PAR_CIERRA\n | CHARACTER PAR_ABRE ENTERO PAR_CIERRA\n | CHARACTER VARYING PAR_ABRE ENTERO PAR_CIERRA\n \t | TEXT\n\t | DATE\n | TIMESTAMP\n | TIMEcondition_column_row : condition_column_row condition_columncondition_column_row : condition_columncondition_column : constraint UNIQUE op_unique\n | constraint CHECK PAR_ABRE expression PAR_CIERRA\n | key_tablecondition_column : DEFAULT expression\n | NULL\n | NOT NULL\n\t | REFERENCE ID\n\t\t | CONSTRAINT ID key_table\n \t\t | constraint : CONSTRAINT ID\n | op_unique : PAR_ABRE list_id PAR_CIERRA\n | constraint CHECK PAR_ABRE expression PAR_CIERRA\n | list_id : list_id COMA aliaslist_id : aliasalias : IDkey_table : PRIMARY KEY list_key\n\t | FOREIGN KEY PAR_ABRE list_id PAR_CIERRA REFERENCES ID PAR_ABRE list_id PAR_CIERRAlist_key : PAR_ABRE list_id PAR_CIERRA\n\t | alter_op : ADD op_add\n\t | ALTER COLUMN ID alter_col_op\n\t | DROP alter_drop IDalter_drop : CONSTRAINT\n\t | COLUMN op_add : CHECK PAR_ABRE ID DIFERENTE CADENA PAR_CIERRA\n | CONSTRAINT ID UNIQUE PAR_ABRE ID PAR_CIERRA\n | key_table REFERENCES PAR_ABRE list_id PAR_CIERRAalter_col_op : SET NOT NULL\n | TYPE type_columninherits_statement : INHERITS PAR_ABRE ID PAR_CIERRA\n | list_val : list_val COMA expressionlist_val : expressionwhere : WHERE ID IGUAL expression\n | seleccionar : SELECT distinto select_list FROM table_expression list_fin_selectseleccionar : SELECT GREATEST expressiones\n | SELECT LEAST expressioneslist_fin_select : list_fin_select fin_selectlist_fin_select : fin_selectfin_select : group_by \n\t | donde\n\t | order_by\n\t | group_having\n\t | limite\n \t| expressiones : PAR_ABRE list_expression PAR_CIERRAexpressiones : list_expressiondistinto : DISTINCT\n\t | select_list : ASTERISCO\n\t | expressiones table_expression : expressionesdonde : WHERE expressionesgroup_by : GROUP BY expressiones order_by : ORDER BY expressiones asc_desc nulls_f_lgroup_having : HAVING expressiones asc_desc : ASC\n\t | DESCnulls_f_l : NULLS LAST\n\t | NULLS FIRST\n\t | limite : LIMIT ENTERO\n\t | LIMIT ALL\n\t | OFFSET ENTEROlist_expression : list_expression COMA expressionlist_expression : expressionexpression : SUBSTRING PAR_ABRE expression COMA expression COMA expression PAR_CIERRAexpression : expression NOT BETWEEN SYMMETRIC expression AND expressionexpression : expression NOT BETWEEN expression AND expression\n | expression BETWEEN SYMMETRIC expression AND expressionexpression : expression BETWEEN expression AND expressionexpression : expression IS DISTINCT FROM expressionexpression : expression IS NOT DISTINCT FROM expressionexpression : ID PUNTO IDexpression : expression IS NOT NULL\n | expression IS NOT TRUE\n | expression IS NOT FALSE\n | expression IS NOT UNKNOWNexpression : expression IS NULL\n | expression IS TRUE\n | expression IS FALSE\n | expression IS UNKNOWNexpression : expression ISNULL\n | expression NOTNULLexpression : SUM PAR_ABRE expression PAR_CIERRA\n | COUNT PAR_ABRE expression PAR_CIERRA\n | AVG PAR_ABRE expression PAR_CIERRA\n | MAX PAR_ABRE expression PAR_CIERRA\n | MIN PAR_ABRE expression PAR_CIERRA\n | ABS PAR_ABRE expression PAR_CIERRA\n | CBRT PAR_ABRE expression PAR_CIERRA\n | CEIL PAR_ABRE expression PAR_CIERRA\n | CEILING PAR_ABRE expression PAR_CIERRA \n | DEGREES PAR_ABRE expression PAR_CIERRA\n | DIV PAR_ABRE expression PAR_CIERRA\n | EXP PAR_ABRE expression PAR_CIERRA\n | FACTORIAL PAR_ABRE expression PAR_CIERRA \n | FLOOR PAR_ABRE expression PAR_CIERRA\n | GCD PAR_ABRE expression PAR_CIERRA\n | LN PAR_ABRE expression PAR_CIERRA\n | LOG PAR_ABRE expression PAR_CIERRA\n | MOD PAR_ABRE expression PAR_CIERRA\n | PI PAR_ABRE expression PAR_CIERRA\n | POWER PAR_ABRE expression PAR_CIERRA\n | RADIANS PAR_ABRE expression PAR_CIERRA\n | ROUND PAR_ABRE expression PAR_CIERRAexpression : seleccionarexpression : PAR_ABRE expression PAR_CIERRAexpression : expression MAYOR expressionexpression : expression MENOR expressionexpression : expression MAYOR_IGUAL expressionexpression : expression MENOR_IGUAL expressionexpression : expression IGUAL expressionexpression : expression NO_IGUAL expressionexpression : expression DIFERENTE expressionexpression : expression AND expressionexpression : expression OR expressionexpression : NOT expressionexpression : ID\n | ASTERISCOexpression : ENTEROexpression : DECIMAL_NUMexpression : CADENA' _lr_action_items = {'SHOW':([0,2,3,17,18,19,20,21,38,92,364,366,368,],[8,8,-3,-2,-4,-5,-6,-7,-8,-12,-9,-10,-11,]),'INSERT':([0,2,3,17,18,19,20,21,38,92,364,366,368,],[9,9,-3,-2,-4,-5,-6,-7,-8,-12,-9,-10,-11,]),'UPDATE':([0,2,3,17,18,19,20,21,38,92,364,366,368,],[10,10,-3,-2,-4,-5,-6,-7,-8,-12,-9,-10,-11,]),'DELETE':([0,2,3,17,18,19,20,21,38,92,364,366,368,],[11,11,-3,-2,-4,-5,-6,-7,-8,-12,-9,-10,-11,]),'USE':([0,2,3,17,18,19,20,21,38,92,364,366,368,],[12,12,-3,-2,-4,-5,-6,-7,-8,-12,-9,-10,-11,]),'CREATE':([0,2,3,17,18,19,20,21,38,92,364,366,368,],[13,13,-3,-2,-4,-5,-6,-7,-8,-12,-9,-10,-11,]),'ALTER':([0,2,3,17,18,19,20,21,38,47,92,364,366,368,],[14,14,-3,-2,-4,-5,-6,-7,-8,99,-12,-9,-10,-11,]),'DROP':([0,2,3,17,18,19,20,21,38,47,92,364,366,368,],[15,15,-3,-2,-4,-5,-6,-7,-8,102,-12,-9,-10,-11,]),'SELECT':([0,2,3,16,17,18,19,20,21,34,35,36,37,38,54,58,92,105,106,109,111,112,116,117,118,119,120,121,122,123,124,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,159,184,186,229,274,276,280,281,283,289,313,331,350,352,357,360,362,364,366,368,394,397,398,404,],[16,16,-3,-109,-2,-4,-5,-6,-7,16,16,16,-108,-8,16,16,-12,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,-9,-10,-11,16,16,16,16,]),'$end':([1,2,3,17,18,19,20,21,38,92,364,366,368,],[0,-1,-3,-2,-4,-5,-6,-7,-8,-12,-9,-10,-11,]),'PUNTOCOMA':([4,5,6,7,22,42,50,55,56,59,82,83,84,85,86,87,88,96,100,103,114,115,125,166,171,178,179,181,182,183,187,190,191,192,193,194,195,196,197,198,199,200,201,203,228,231,232,233,234,235,236,238,242,243,244,245,246,256,259,265,266,267,268,269,270,271,272,285,286,287,288,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,314,316,319,324,336,343,349,351,353,354,355,356,359,361,384,385,387,391,393,395,396,399,400,401,410,411,414,415,416,417,418,420,421,422,426,427,428,430,433,434,],[18,19,20,21,38,92,-31,-107,-126,-179,-167,-181,-182,-183,-96,-180,-97,-23,-24,-30,-143,-144,-178,-20,-79,-105,-112,-106,-168,-125,-176,-139,-140,-141,-142,-169,-170,-171,-172,-173,-174,-175,-177,-134,-94,-40,-41,-42,-43,-44,-45,-47,-52,-53,-54,-55,-90,-22,-25,-81,-95,-99,-100,-101,-102,-103,-104,-135,-136,-137,-138,-145,-146,-147,-148,-149,-150,-151,-152,-153,-154,-155,-156,-157,-158,-159,-160,-161,-162,-163,-164,-165,-166,364,366,368,-46,-13,-14,-80,-98,-113,-116,-122,-123,-124,-131,-132,-19,-26,-88,-114,-129,-130,-133,-48,-49,-50,-21,-87,-86,-121,-117,-118,-128,-93,-51,-89,-84,-85,-115,-127,-119,-120,]),'DATABASES':([8,],[22,]),'INTO':([9,],[23,]),'ID':([10,16,23,25,26,27,30,31,32,33,34,35,36,37,40,44,48,54,58,91,93,94,104,105,106,109,111,112,116,117,118,119,120,121,122,123,124,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,159,162,163,168,170,173,175,176,177,184,186,229,247,258,260,262,274,276,280,281,283,289,313,315,329,330,331,334,335,338,348,350,352,357,360,362,374,389,394,397,398,404,406,425,435,],[24,-109,39,41,42,43,46,47,-33,50,59,59,59,-108,90,-18,103,59,59,151,152,166,-32,59,59,59,59,59,59,59,59,59,59,59,59,59,59,203,59,59,59,59,59,59,59,59,59,59,59,59,59,59,59,59,59,59,59,59,59,59,59,59,59,252,253,259,261,263,265,-82,-83,59,59,59,152,-17,340,346,59,59,59,59,59,59,59,367,378,379,59,378,378,384,378,59,59,59,59,59,403,413,59,59,59,59,378,432,378,]),'FROM':([11,51,52,53,55,56,59,82,83,84,85,86,87,88,114,115,125,178,179,181,182,183,187,188,190,191,192,193,194,195,196,197,198,199,200,201,203,266,267,268,269,270,271,272,284,285,286,287,288,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,349,351,353,354,355,356,359,361,391,393,395,396,415,416,417,418,428,430,433,434,],[25,105,-110,-111,-107,-126,-179,-167,-181,-182,-183,-96,-180,-97,-143,-144,-178,-105,-112,-106,-168,-125,-176,283,-139,-140,-141,-142,-169,-170,-171,-172,-173,-174,-175,-177,-134,-95,-99,-100,-101,-102,-103,-104,362,-135,-136,-137,-138,-145,-146,-147,-148,-149,-150,-151,-152,-153,-154,-155,-156,-157,-158,-159,-160,-161,-162,-163,-164,-165,-166,-98,-113,-116,-122,-123,-124,-131,-132,-114,-129,-130,-133,-121,-117,-118,-128,-115,-127,-119,-120,]),'DATABASE':([12,13,14,15,28,45,],[26,-16,30,32,44,-15,]),'TABLE':([13,14,15,],[27,31,33,]),'OR':([13,52,55,56,59,82,83,84,85,86,87,88,108,114,115,125,178,179,180,181,182,183,185,187,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,227,228,250,266,267,268,269,270,271,272,279,282,285,286,287,288,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,316,349,351,353,354,355,356,358,359,361,363,365,380,391,393,395,396,415,416,417,418,419,420,423,428,430,433,434,],[29,-180,-107,123,-179,-167,-181,-182,-183,-96,-180,-97,123,-143,-144,-178,-105,-112,123,-106,-168,123,123,-176,-139,-140,-141,-142,-169,-170,-171,-172,123,-174,123,-177,123,-134,123,123,123,123,123,123,123,123,123,123,123,123,123,123,123,123,123,123,123,123,123,123,123,123,123,-95,-99,-100,-101,-102,-103,-104,123,123,-135,-136,-137,-138,-145,-146,-147,-148,-149,-150,-151,-152,-153,-154,-155,-156,-157,-158,-159,-160,-161,-162,-163,-164,-165,-166,123,-98,-113,-116,-122,-123,-124,123,-131,123,123,123,123,-114,-129,-130,123,-121,-117,-118,-128,123,123,123,-115,-127,-119,-120,]),'GREATEST':([16,],[35,]),'LEAST':([16,],[36,]),'DISTINCT':([16,113,189,],[37,188,284,]),'ASTERISCO':([16,34,35,36,37,54,58,105,106,109,111,112,116,117,118,119,120,121,122,123,124,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,159,184,186,229,274,276,280,281,283,289,313,331,350,352,357,360,362,394,397,398,404,],[-109,52,87,87,-108,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,87,]),'PAR_ABRE':([16,34,35,36,37,43,54,57,58,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,89,105,106,109,111,112,116,117,118,119,120,121,122,123,124,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,159,172,184,186,229,239,240,241,248,249,254,255,264,274,276,280,281,283,289,313,323,325,331,347,350,352,357,360,362,375,394,397,398,404,432,],[-109,54,54,54,-108,93,106,124,106,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,54,106,106,106,106,106,106,106,106,106,106,106,106,106,106,106,106,106,106,106,106,106,106,106,106,106,106,106,106,106,106,106,106,106,106,106,106,106,106,262,106,106,106,320,321,322,329,331,334,335,348,54,54,106,106,106,106,106,373,374,106,389,54,54,106,106,106,404,106,106,106,106,435,]),'SUBSTRING':([16,34,35,36,37,54,58,105,106,109,111,112,116,117,118,119,120,121,122,123,124,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,159,184,186,229,274,276,280,281,283,289,313,331,350,352,357,360,362,394,397,398,404,],[-109,57,57,57,-108,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,57,]),'SUM':([16,34,35,36,37,54,58,105,106,109,111,112,116,117,118,119,120,121,122,123,124,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,159,184,186,229,274,276,280,281,283,289,313,331,350,352,357,360,362,394,397,398,404,],[-109,60,60,60,-108,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,60,]),'COUNT':([16,34,35,36,37,54,58,105,106,109,111,112,116,117,118,119,120,121,122,123,124,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,159,184,186,229,274,276,280,281,283,289,313,331,350,352,357,360,362,394,397,398,404,],[-109,61,61,61,-108,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,61,]),'AVG':([16,34,35,36,37,54,58,105,106,109,111,112,116,117,118,119,120,121,122,123,124,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,159,184,186,229,274,276,280,281,283,289,313,331,350,352,357,360,362,394,397,398,404,],[-109,62,62,62,-108,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,62,]),'MAX':([16,34,35,36,37,54,58,105,106,109,111,112,116,117,118,119,120,121,122,123,124,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,159,184,186,229,274,276,280,281,283,289,313,331,350,352,357,360,362,394,397,398,404,],[-109,63,63,63,-108,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,63,]),'MIN':([16,34,35,36,37,54,58,105,106,109,111,112,116,117,118,119,120,121,122,123,124,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,159,184,186,229,274,276,280,281,283,289,313,331,350,352,357,360,362,394,397,398,404,],[-109,64,64,64,-108,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,64,]),'ABS':([16,34,35,36,37,54,58,105,106,109,111,112,116,117,118,119,120,121,122,123,124,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,159,184,186,229,274,276,280,281,283,289,313,331,350,352,357,360,362,394,397,398,404,],[-109,65,65,65,-108,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,65,]),'CBRT':([16,34,35,36,37,54,58,105,106,109,111,112,116,117,118,119,120,121,122,123,124,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,159,184,186,229,274,276,280,281,283,289,313,331,350,352,357,360,362,394,397,398,404,],[-109,66,66,66,-108,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,66,]),'CEIL':([16,34,35,36,37,54,58,105,106,109,111,112,116,117,118,119,120,121,122,123,124,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,159,184,186,229,274,276,280,281,283,289,313,331,350,352,357,360,362,394,397,398,404,],[-109,67,67,67,-108,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,67,]),'CEILING':([16,34,35,36,37,54,58,105,106,109,111,112,116,117,118,119,120,121,122,123,124,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,159,184,186,229,274,276,280,281,283,289,313,331,350,352,357,360,362,394,397,398,404,],[-109,68,68,68,-108,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,68,]),'DEGREES':([16,34,35,36,37,54,58,105,106,109,111,112,116,117,118,119,120,121,122,123,124,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,159,184,186,229,274,276,280,281,283,289,313,331,350,352,357,360,362,394,397,398,404,],[-109,69,69,69,-108,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,69,]),'DIV':([16,34,35,36,37,54,58,105,106,109,111,112,116,117,118,119,120,121,122,123,124,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,159,184,186,229,274,276,280,281,283,289,313,331,350,352,357,360,362,394,397,398,404,],[-109,70,70,70,-108,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,70,]),'EXP':([16,34,35,36,37,54,58,105,106,109,111,112,116,117,118,119,120,121,122,123,124,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,159,184,186,229,274,276,280,281,283,289,313,331,350,352,357,360,362,394,397,398,404,],[-109,71,71,71,-108,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,71,]),'FACTORIAL':([16,34,35,36,37,54,58,105,106,109,111,112,116,117,118,119,120,121,122,123,124,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,159,184,186,229,274,276,280,281,283,289,313,331,350,352,357,360,362,394,397,398,404,],[-109,72,72,72,-108,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,72,]),'FLOOR':([16,34,35,36,37,54,58,105,106,109,111,112,116,117,118,119,120,121,122,123,124,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,159,184,186,229,274,276,280,281,283,289,313,331,350,352,357,360,362,394,397,398,404,],[-109,73,73,73,-108,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,73,]),'GCD':([16,34,35,36,37,54,58,105,106,109,111,112,116,117,118,119,120,121,122,123,124,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,159,184,186,229,274,276,280,281,283,289,313,331,350,352,357,360,362,394,397,398,404,],[-109,74,74,74,-108,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,74,]),'LN':([16,34,35,36,37,54,58,105,106,109,111,112,116,117,118,119,120,121,122,123,124,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,159,184,186,229,274,276,280,281,283,289,313,331,350,352,357,360,362,394,397,398,404,],[-109,75,75,75,-108,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,75,]),'LOG':([16,34,35,36,37,54,58,105,106,109,111,112,116,117,118,119,120,121,122,123,124,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,159,184,186,229,274,276,280,281,283,289,313,331,350,352,357,360,362,394,397,398,404,],[-109,76,76,76,-108,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,76,]),'MOD':([16,34,35,36,37,54,58,105,106,109,111,112,116,117,118,119,120,121,122,123,124,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,159,184,186,229,274,276,280,281,283,289,313,331,350,352,357,360,362,394,397,398,404,],[-109,77,77,77,-108,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,77,]),'PI':([16,34,35,36,37,54,58,105,106,109,111,112,116,117,118,119,120,121,122,123,124,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,159,184,186,229,274,276,280,281,283,289,313,331,350,352,357,360,362,394,397,398,404,],[-109,78,78,78,-108,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,78,]),'POWER':([16,34,35,36,37,54,58,105,106,109,111,112,116,117,118,119,120,121,122,123,124,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,159,184,186,229,274,276,280,281,283,289,313,331,350,352,357,360,362,394,397,398,404,],[-109,79,79,79,-108,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,79,]),'RADIANS':([16,34,35,36,37,54,58,105,106,109,111,112,116,117,118,119,120,121,122,123,124,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,159,184,186,229,274,276,280,281,283,289,313,331,350,352,357,360,362,394,397,398,404,],[-109,80,80,80,-108,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,80,]),'ROUND':([16,34,35,36,37,54,58,105,106,109,111,112,116,117,118,119,120,121,122,123,124,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,159,184,186,229,274,276,280,281,283,289,313,331,350,352,357,360,362,394,397,398,404,],[-109,81,81,81,-108,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,81,]),'NOT':([16,34,35,36,37,52,54,55,56,58,59,82,83,84,85,86,87,88,93,95,105,106,108,109,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,158,159,160,178,179,180,181,182,183,184,185,186,187,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,227,228,229,230,231,232,233,234,235,236,238,242,243,244,245,247,248,250,251,252,254,266,267,268,269,270,271,272,274,276,279,280,281,282,283,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,313,316,317,318,319,328,331,332,333,344,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,365,369,380,391,393,394,395,396,397,398,399,400,401,404,405,407,408,415,416,417,418,419,420,421,423,428,430,431,433,434,437,],[-109,58,58,58,-108,-180,58,-107,110,58,-179,-167,-181,-182,-183,-96,-180,-97,161,167,58,58,110,58,58,58,189,-143,-144,58,58,58,58,58,58,58,58,58,110,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,58,-60,58,-62,-105,-112,110,-106,-168,110,58,110,58,110,-139,-140,-141,-142,110,110,110,110,110,110,110,110,110,-134,110,110,110,110,110,110,110,110,110,110,110,110,110,110,110,110,110,110,110,110,110,110,110,110,58,161,-40,-41,-42,-43,-44,-45,-47,-52,-53,-54,-55,161,-71,110,-63,-64,-78,-95,-99,-100,-101,-102,-103,-104,58,58,110,58,58,110,58,-135,-136,-137,-138,58,-145,-146,-147,-148,-149,-150,-151,-152,-153,-154,-155,-156,-157,-158,-159,-160,-161,-162,-163,-164,-165,-166,58,110,161,-57,-46,-58,58,-65,-75,386,-98,58,-113,58,-116,-122,-123,-124,58,110,110,58,110,58,110,110,-56,110,-114,110,58,110,110,58,58,-48,-49,-50,58,-69,-59,-77,-121,-117,-118,110,110,110,-51,110,-115,-127,-70,-119,-120,-76,]),'ENTERO':([16,34,35,36,37,54,58,105,106,109,111,112,116,117,118,119,120,121,122,123,124,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,159,184,186,229,274,276,277,278,280,281,283,289,313,320,321,322,331,350,352,357,360,362,373,383,394,397,398,404,],[-109,83,83,83,-108,83,83,83,83,83,83,83,83,83,83,83,83,83,83,83,83,83,83,83,83,83,83,83,83,83,83,83,83,83,83,83,83,83,83,83,83,83,83,83,83,83,83,83,83,83,83,354,356,83,83,83,83,83,370,371,372,83,83,83,83,83,83,402,410,83,83,83,83,]),'DECIMAL_NUM':([16,34,35,36,37,54,58,105,106,109,111,112,116,117,118,119,120,121,122,123,124,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,159,184,186,229,274,276,280,281,283,289,313,331,350,352,357,360,362,394,397,398,404,],[-109,84,84,84,-108,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,84,]),'CADENA':([16,34,35,36,37,54,58,105,106,109,111,112,116,117,118,119,120,121,122,123,124,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,159,184,186,229,274,276,280,281,283,289,313,331,350,352,357,360,362,388,394,397,398,404,],[-109,85,85,85,-108,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,85,412,85,85,85,85,]),'SET':([24,261,],[40,344,]),'REPLACE':([29,],[45,]),'IF':([32,44,],[49,95,]),'VALUES':([39,],[89,]),'WHERE':([41,55,56,59,82,83,84,85,86,87,88,114,115,125,178,179,181,182,183,187,190,191,192,193,194,195,196,197,198,199,200,201,203,228,266,267,268,269,270,271,272,285,286,287,288,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,349,351,353,354,355,356,359,361,391,393,395,396,415,416,417,418,428,430,433,434,],[91,-107,-126,-179,-167,-181,-182,-183,-96,-180,-97,-143,-144,-178,274,-112,-106,-168,-125,-176,-139,-140,-141,-142,-169,-170,-171,-172,-173,-174,-175,-177,-134,315,274,-99,-100,-101,-102,-103,-104,-135,-136,-137,-138,-145,-146,-147,-148,-149,-150,-151,-152,-153,-154,-155,-156,-157,-158,-159,-160,-161,-162,-163,-164,-165,-166,-98,-113,-116,-122,-123,-124,-131,-132,-114,-129,-130,-133,-121,-117,-118,-128,-115,-127,-119,-120,]),'RENAME':([46,],[97,]),'OWNER':([46,166,],[98,257,]),'ADD':([47,],[101,]),'EXISTS':([49,167,],[104,258,]),'BETWEEN':([52,55,56,59,82,83,84,85,86,87,88,108,110,114,115,125,178,179,180,181,182,183,185,187,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,227,228,250,266,267,268,269,270,271,272,279,282,285,286,287,288,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,316,349,351,353,354,355,356,358,359,361,363,365,380,391,393,395,396,415,416,417,418,419,420,423,428,430,433,434,],[-180,-107,111,-179,-167,-181,-182,-183,-96,-180,-97,111,184,-143,-144,-178,-105,-112,111,-106,-168,111,111,-176,-139,-140,-141,-142,-169,-170,-171,-172,-173,-174,111,-177,111,-134,111,111,111,111,111,111,111,111,111,111,111,111,111,111,111,111,111,111,111,111,111,111,111,111,111,-95,-99,-100,-101,-102,-103,-104,111,111,-135,-136,-137,-138,-145,-146,-147,-148,-149,-150,-151,-152,-153,-154,-155,-156,-157,-158,-159,-160,-161,-162,-163,-164,-165,-166,111,-98,-113,-116,-122,-123,-124,111,-131,111,111,111,111,-114,-129,-130,111,-121,-117,-118,-128,111,111,111,-115,-127,-119,-120,]),'IS':([52,55,56,59,82,83,84,85,86,87,88,108,114,115,125,178,179,180,181,182,183,185,187,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,227,228,250,266,267,268,269,270,271,272,279,282,285,286,287,288,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,316,349,351,353,354,355,356,358,359,361,363,365,380,391,393,395,396,415,416,417,418,419,420,423,428,430,433,434,],[-180,-107,113,-179,-167,-181,-182,-183,-96,-180,-97,113,-143,-144,-178,-105,-112,113,-106,-168,113,113,-176,-139,-140,-141,-142,-169,-170,-171,-172,-173,-174,113,-177,113,-134,113,113,113,113,113,113,113,113,113,113,113,113,113,113,113,113,113,113,113,113,113,113,113,113,113,-95,-99,-100,-101,-102,-103,-104,113,113,-135,-136,-137,-138,-145,-146,-147,-148,-149,-150,-151,-152,-153,-154,-155,-156,-157,-158,-159,-160,-161,-162,-163,-164,-165,-166,113,-98,-113,-116,-122,-123,-124,113,-131,113,113,113,113,-114,-129,-130,113,-121,-117,-118,-128,113,113,113,-115,-127,-119,-120,]),'ISNULL':([52,55,56,59,82,83,84,85,86,87,88,108,114,115,125,178,179,180,181,182,183,185,187,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,227,228,250,266,267,268,269,270,271,272,279,282,285,286,287,288,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,316,349,351,353,354,355,356,358,359,361,363,365,380,391,393,395,396,415,416,417,418,419,420,423,428,430,433,434,],[-180,-107,114,-179,-167,-181,-182,-183,-96,-180,-97,114,-143,-144,-178,-105,-112,114,-106,-168,114,114,-176,-139,-140,-141,-142,-169,-170,-171,-172,-173,-174,114,-177,114,-134,114,114,114,114,114,114,114,114,114,114,114,114,114,114,114,114,114,114,114,114,114,114,114,114,114,-95,-99,-100,-101,-102,-103,-104,114,114,-135,-136,-137,-138,-145,-146,-147,-148,-149,-150,-151,-152,-153,-154,-155,-156,-157,-158,-159,-160,-161,-162,-163,-164,-165,-166,114,-98,-113,-116,-122,-123,-124,114,-131,114,114,114,114,-114,-129,-130,114,-121,-117,-118,-128,114,114,114,-115,-127,-119,-120,]),'NOTNULL':([52,55,56,59,82,83,84,85,86,87,88,108,114,115,125,178,179,180,181,182,183,185,187,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,227,228,250,266,267,268,269,270,271,272,279,282,285,286,287,288,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,316,349,351,353,354,355,356,358,359,361,363,365,380,391,393,395,396,415,416,417,418,419,420,423,428,430,433,434,],[-180,-107,115,-179,-167,-181,-182,-183,-96,-180,-97,115,-143,-144,-178,-105,-112,115,-106,-168,115,115,-176,-139,-140,-141,-142,-169,-170,-171,-172,-173,-174,115,-177,115,-134,115,115,115,115,115,115,115,115,115,115,115,115,115,115,115,115,115,115,115,115,115,115,115,115,115,-95,-99,-100,-101,-102,-103,-104,115,115,-135,-136,-137,-138,-145,-146,-147,-148,-149,-150,-151,-152,-153,-154,-155,-156,-157,-158,-159,-160,-161,-162,-163,-164,-165,-166,115,-98,-113,-116,-122,-123,-124,115,-131,115,115,115,115,-114,-129,-130,115,-121,-117,-118,-128,115,115,115,-115,-127,-119,-120,]),'MAYOR':([52,55,56,59,82,83,84,85,86,87,88,108,114,115,125,178,179,180,181,182,183,185,187,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,227,228,250,266,267,268,269,270,271,272,279,282,285,286,287,288,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,316,349,351,353,354,355,356,358,359,361,363,365,380,391,393,395,396,415,416,417,418,419,420,423,428,430,433,434,],[-180,-107,116,-179,-167,-181,-182,-183,-96,-180,-97,116,-143,-144,-178,-105,-112,116,-106,-168,116,116,116,-139,-140,-141,-142,None,None,None,None,116,116,116,116,116,-134,116,116,116,116,116,116,116,116,116,116,116,116,116,116,116,116,116,116,116,116,116,116,116,116,116,-95,-99,-100,-101,-102,-103,-104,116,116,-135,-136,-137,-138,-145,-146,-147,-148,-149,-150,-151,-152,-153,-154,-155,-156,-157,-158,-159,-160,-161,-162,-163,-164,-165,-166,116,-98,-113,-116,-122,-123,-124,116,116,116,116,116,116,-114,116,116,116,-121,-117,-118,116,116,116,116,-115,-127,-119,-120,]),'MENOR':([52,55,56,59,82,83,84,85,86,87,88,108,114,115,125,178,179,180,181,182,183,185,187,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,227,228,250,266,267,268,269,270,271,272,279,282,285,286,287,288,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,316,349,351,353,354,355,356,358,359,361,363,365,380,391,393,395,396,415,416,417,418,419,420,423,428,430,433,434,],[-180,-107,117,-179,-167,-181,-182,-183,-96,-180,-97,117,-143,-144,-178,-105,-112,117,-106,-168,117,117,117,-139,-140,-141,-142,None,None,None,None,117,117,117,117,117,-134,117,117,117,117,117,117,117,117,117,117,117,117,117,117,117,117,117,117,117,117,117,117,117,117,117,-95,-99,-100,-101,-102,-103,-104,117,117,-135,-136,-137,-138,-145,-146,-147,-148,-149,-150,-151,-152,-153,-154,-155,-156,-157,-158,-159,-160,-161,-162,-163,-164,-165,-166,117,-98,-113,-116,-122,-123,-124,117,117,117,117,117,117,-114,117,117,117,-121,-117,-118,117,117,117,117,-115,-127,-119,-120,]),'MAYOR_IGUAL':([52,55,56,59,82,83,84,85,86,87,88,108,114,115,125,178,179,180,181,182,183,185,187,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,227,228,250,266,267,268,269,270,271,272,279,282,285,286,287,288,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,316,349,351,353,354,355,356,358,359,361,363,365,380,391,393,395,396,415,416,417,418,419,420,423,428,430,433,434,],[-180,-107,118,-179,-167,-181,-182,-183,-96,-180,-97,118,-143,-144,-178,-105,-112,118,-106,-168,118,118,118,-139,-140,-141,-142,None,None,None,None,118,118,118,118,118,-134,118,118,118,118,118,118,118,118,118,118,118,118,118,118,118,118,118,118,118,118,118,118,118,118,118,-95,-99,-100,-101,-102,-103,-104,118,118,-135,-136,-137,-138,-145,-146,-147,-148,-149,-150,-151,-152,-153,-154,-155,-156,-157,-158,-159,-160,-161,-162,-163,-164,-165,-166,118,-98,-113,-116,-122,-123,-124,118,118,118,118,118,118,-114,118,118,118,-121,-117,-118,118,118,118,118,-115,-127,-119,-120,]),'MENOR_IGUAL':([52,55,56,59,82,83,84,85,86,87,88,108,114,115,125,178,179,180,181,182,183,185,187,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,227,228,250,266,267,268,269,270,271,272,279,282,285,286,287,288,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,316,349,351,353,354,355,356,358,359,361,363,365,380,391,393,395,396,415,416,417,418,419,420,423,428,430,433,434,],[-180,-107,119,-179,-167,-181,-182,-183,-96,-180,-97,119,-143,-144,-178,-105,-112,119,-106,-168,119,119,119,-139,-140,-141,-142,None,None,None,None,119,119,119,119,119,-134,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,119,-95,-99,-100,-101,-102,-103,-104,119,119,-135,-136,-137,-138,-145,-146,-147,-148,-149,-150,-151,-152,-153,-154,-155,-156,-157,-158,-159,-160,-161,-162,-163,-164,-165,-166,119,-98,-113,-116,-122,-123,-124,119,119,119,119,119,119,-114,119,119,119,-121,-117,-118,119,119,119,119,-115,-127,-119,-120,]),'IGUAL':([52,55,56,59,82,83,84,85,86,87,88,90,108,114,115,125,151,178,179,180,181,182,183,185,187,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,227,228,250,257,266,267,268,269,270,271,272,279,282,285,286,287,288,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,316,337,349,351,353,354,355,356,358,359,361,363,365,367,380,391,393,395,396,415,416,417,418,419,420,423,428,430,433,434,],[-180,-107,120,-179,-167,-181,-182,-183,-96,-180,-97,150,120,-143,-144,-178,229,-105,-112,120,-106,-168,120,120,-176,-139,-140,-141,-142,-169,-170,-171,-172,120,-174,120,-177,120,-134,120,120,120,120,120,120,120,120,120,120,120,120,120,120,120,120,120,120,120,120,120,120,120,120,120,338,-95,-99,-100,-101,-102,-103,-104,120,120,-135,-136,-137,-138,-145,-146,-147,-148,-149,-150,-151,-152,-153,-154,-155,-156,-157,-158,-159,-160,-161,-162,-163,-164,-165,-166,120,383,-98,-113,-116,-122,-123,-124,120,-131,120,120,120,398,120,-114,-129,-130,120,-121,-117,-118,-128,120,120,120,-115,-127,-119,-120,]),'NO_IGUAL':([52,55,56,59,82,83,84,85,86,87,88,108,114,115,125,178,179,180,181,182,183,185,187,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,227,228,250,266,267,268,269,270,271,272,279,282,285,286,287,288,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,316,349,351,353,354,355,356,358,359,361,363,365,380,391,393,395,396,415,416,417,418,419,420,423,428,430,433,434,],[-180,-107,121,-179,-167,-181,-182,-183,-96,-180,-97,121,-143,-144,-178,-105,-112,121,-106,-168,121,121,121,-139,-140,-141,-142,-169,-170,-171,-172,121,-174,121,121,121,-134,121,121,121,121,121,121,121,121,121,121,121,121,121,121,121,121,121,121,121,121,121,121,121,121,121,-95,-99,-100,-101,-102,-103,-104,121,121,-135,-136,-137,-138,-145,-146,-147,-148,-149,-150,-151,-152,-153,-154,-155,-156,-157,-158,-159,-160,-161,-162,-163,-164,-165,-166,121,-98,-113,-116,-122,-123,-124,121,121,121,121,121,121,-114,121,121,121,-121,-117,-118,121,121,121,121,-115,-127,-119,-120,]),'DIFERENTE':([52,55,56,59,82,83,84,85,86,87,88,108,114,115,125,178,179,180,181,182,183,185,187,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,227,228,250,266,267,268,269,270,271,272,279,282,285,286,287,288,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,316,346,349,351,353,354,355,356,358,359,361,363,365,380,391,393,395,396,415,416,417,418,419,420,423,428,430,433,434,],[-180,-107,122,-179,-167,-181,-182,-183,-96,-180,-97,122,-143,-144,-178,-105,-112,122,-106,-168,122,122,-176,-139,-140,-141,-142,-169,-170,-171,-172,-173,-174,122,-177,122,-134,122,122,122,122,122,122,122,122,122,122,122,122,122,122,122,122,122,122,122,122,122,122,122,122,122,-95,-99,-100,-101,-102,-103,-104,122,122,-135,-136,-137,-138,-145,-146,-147,-148,-149,-150,-151,-152,-153,-154,-155,-156,-157,-158,-159,-160,-161,-162,-163,-164,-165,-166,122,388,-98,-113,-116,-122,-123,-124,122,-131,122,122,122,122,-114,-129,-130,122,-121,-117,-118,-128,122,122,122,-115,-127,-119,-120,]),'AND':([52,55,56,59,82,83,84,85,86,87,88,108,114,115,125,178,179,180,181,182,183,185,187,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,227,228,250,266,267,268,269,270,271,272,279,282,285,286,287,288,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,316,349,351,353,354,355,356,358,359,361,363,365,380,391,393,395,396,415,416,417,418,419,420,423,428,430,433,434,],[-180,-107,112,-179,-167,-181,-182,-183,-96,-180,-97,112,-143,-144,-178,-105,-112,112,-106,-168,112,281,-176,-139,-140,-141,-142,-169,-170,-171,-172,112,-174,112,112,112,-134,112,112,112,112,112,112,112,112,112,112,112,112,112,112,112,112,112,112,112,112,112,112,112,112,112,-95,-99,-100,-101,-102,-103,-104,357,360,-135,-136,-137,-138,-145,-146,-147,-148,-149,-150,-151,-152,-153,-154,-155,-156,-157,-158,-159,-160,-161,-162,-163,-164,-165,-166,112,-98,-113,-116,-122,-123,-124,394,-131,112,112,112,112,-114,-129,-130,112,-121,-117,-118,-128,112,112,112,-115,-127,-119,-120,]),'COMA':([52,55,56,59,82,83,84,85,86,87,88,93,107,108,114,115,125,153,154,155,156,158,160,178,179,181,182,183,187,190,191,192,193,194,195,196,197,198,199,200,201,202,203,226,227,230,231,232,233,234,235,236,238,242,243,244,245,247,248,250,251,252,254,266,267,268,269,270,271,272,285,286,287,288,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,317,318,319,326,328,332,333,349,351,353,354,355,356,359,361,363,365,369,376,377,378,381,382,390,391,393,395,396,399,400,401,405,407,408,415,416,417,418,421,424,428,430,431,433,434,436,437,],[-180,109,-126,-179,-167,-181,-182,-183,-96,-180,-97,-66,109,-126,-143,-144,-178,247,-35,-36,-37,-60,-62,-105,-112,-106,-168,-125,-176,-139,-140,-141,-142,-169,-170,-171,-172,-173,-174,-175,-177,289,-134,313,-92,-39,-40,-41,-42,-43,-44,-45,-47,-52,-53,-54,-55,-66,-71,-61,-63,-64,-78,-95,-99,-100,-101,-102,-103,-104,-135,-136,-137,-138,-145,-146,-147,-148,-149,-150,-151,-152,-153,-154,-155,-156,-157,-158,-159,-160,-161,-162,-163,-164,-165,-166,-38,-57,-46,-34,-58,-65,-75,-98,-113,-116,-122,-123,-124,-131,-132,397,-91,-56,406,-73,-74,406,406,406,-114,-129,-130,-133,-48,-49,-50,-69,-59,-77,-121,-117,-118,-128,-51,-72,-115,-127,-70,-119,-120,406,-76,]),'PAR_CIERRA':([55,56,59,82,83,84,85,86,87,88,93,107,108,114,115,125,153,154,155,156,158,160,178,179,180,181,182,183,187,190,191,192,193,194,195,196,197,198,199,200,201,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,230,231,232,233,234,235,236,238,242,243,244,245,247,248,250,251,252,254,266,267,268,269,270,271,272,285,286,287,288,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,317,318,319,326,328,332,333,349,351,353,354,355,356,359,361,365,369,370,371,372,376,377,378,380,381,382,390,391,393,395,396,399,400,401,402,403,405,407,408,412,413,415,416,417,418,419,421,423,424,428,430,431,433,434,436,437,],[-107,-126,-179,-167,-181,-182,-183,-96,-180,-97,-66,181,182,-143,-144,-178,246,-35,-36,-37,-60,-62,-105,-112,182,-106,-168,-125,-176,-139,-140,-141,-142,-169,-170,-171,-172,-173,-174,-175,-177,-134,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,-92,-39,-40,-41,-42,-43,-44,-45,-47,-52,-53,-54,-55,-66,-71,-61,-63,-64,-78,-95,-99,-100,-101,-102,-103,-104,-135,-136,-137,-138,-145,-146,-147,-148,-149,-150,-151,-152,-153,-154,-155,-156,-157,-158,-159,-160,-161,-162,-163,-164,-165,-166,-38,-57,-46,-34,-58,-65,-75,-98,-113,-116,-122,-123,-124,-131,-132,-91,-56,399,400,401,405,-73,-74,407,408,409,414,-114,-129,-130,-133,-48,-49,-50,421,422,-69,-59,-77,426,427,-121,-117,-118,-128,430,-51,431,-72,-115,-127,-70,-119,-120,437,-76,]),'GROUP':([55,56,59,82,83,84,85,86,87,88,114,115,125,178,179,181,182,183,187,190,191,192,193,194,195,196,197,198,199,200,201,203,266,267,268,269,270,271,272,285,286,287,288,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,349,351,353,354,355,356,359,361,391,393,395,396,415,416,417,418,428,430,433,434,],[-107,-126,-179,-167,-181,-182,-183,-96,-180,-97,-143,-144,-178,273,-112,-106,-168,-125,-176,-139,-140,-141,-142,-169,-170,-171,-172,-173,-174,-175,-177,-134,273,-99,-100,-101,-102,-103,-104,-135,-136,-137,-138,-145,-146,-147,-148,-149,-150,-151,-152,-153,-154,-155,-156,-157,-158,-159,-160,-161,-162,-163,-164,-165,-166,-98,-113,-116,-122,-123,-124,-131,-132,-114,-129,-130,-133,-121,-117,-118,-128,-115,-127,-119,-120,]),'ORDER':([55,56,59,82,83,84,85,86,87,88,114,115,125,178,179,181,182,183,187,190,191,192,193,194,195,196,197,198,199,200,201,203,266,267,268,269,270,271,272,285,286,287,288,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,349,351,353,354,355,356,359,361,391,393,395,396,415,416,417,418,428,430,433,434,],[-107,-126,-179,-167,-181,-182,-183,-96,-180,-97,-143,-144,-178,275,-112,-106,-168,-125,-176,-139,-140,-141,-142,-169,-170,-171,-172,-173,-174,-175,-177,-134,275,-99,-100,-101,-102,-103,-104,-135,-136,-137,-138,-145,-146,-147,-148,-149,-150,-151,-152,-153,-154,-155,-156,-157,-158,-159,-160,-161,-162,-163,-164,-165,-166,-98,-113,-116,-122,-123,-124,-131,-132,-114,-129,-130,-133,-121,-117,-118,-128,-115,-127,-119,-120,]),'HAVING':([55,56,59,82,83,84,85,86,87,88,114,115,125,178,179,181,182,183,187,190,191,192,193,194,195,196,197,198,199,200,201,203,266,267,268,269,270,271,272,285,286,287,288,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,349,351,353,354,355,356,359,361,391,393,395,396,415,416,417,418,428,430,433,434,],[-107,-126,-179,-167,-181,-182,-183,-96,-180,-97,-143,-144,-178,276,-112,-106,-168,-125,-176,-139,-140,-141,-142,-169,-170,-171,-172,-173,-174,-175,-177,-134,276,-99,-100,-101,-102,-103,-104,-135,-136,-137,-138,-145,-146,-147,-148,-149,-150,-151,-152,-153,-154,-155,-156,-157,-158,-159,-160,-161,-162,-163,-164,-165,-166,-98,-113,-116,-122,-123,-124,-131,-132,-114,-129,-130,-133,-121,-117,-118,-128,-115,-127,-119,-120,]),'LIMIT':([55,56,59,82,83,84,85,86,87,88,114,115,125,178,179,181,182,183,187,190,191,192,193,194,195,196,197,198,199,200,201,203,266,267,268,269,270,271,272,285,286,287,288,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,349,351,353,354,355,356,359,361,391,393,395,396,415,416,417,418,428,430,433,434,],[-107,-126,-179,-167,-181,-182,-183,-96,-180,-97,-143,-144,-178,277,-112,-106,-168,-125,-176,-139,-140,-141,-142,-169,-170,-171,-172,-173,-174,-175,-177,-134,277,-99,-100,-101,-102,-103,-104,-135,-136,-137,-138,-145,-146,-147,-148,-149,-150,-151,-152,-153,-154,-155,-156,-157,-158,-159,-160,-161,-162,-163,-164,-165,-166,-98,-113,-116,-122,-123,-124,-131,-132,-114,-129,-130,-133,-121,-117,-118,-128,-115,-127,-119,-120,]),'OFFSET':([55,56,59,82,83,84,85,86,87,88,114,115,125,178,179,181,182,183,187,190,191,192,193,194,195,196,197,198,199,200,201,203,266,267,268,269,270,271,272,285,286,287,288,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,349,351,353,354,355,356,359,361,391,393,395,396,415,416,417,418,428,430,433,434,],[-107,-126,-179,-167,-181,-182,-183,-96,-180,-97,-143,-144,-178,278,-112,-106,-168,-125,-176,-139,-140,-141,-142,-169,-170,-171,-172,-173,-174,-175,-177,-134,278,-99,-100,-101,-102,-103,-104,-135,-136,-137,-138,-145,-146,-147,-148,-149,-150,-151,-152,-153,-154,-155,-156,-157,-158,-159,-160,-161,-162,-163,-164,-165,-166,-98,-113,-116,-122,-123,-124,-131,-132,-114,-129,-130,-133,-121,-117,-118,-128,-115,-127,-119,-120,]),'DEFAULT':([55,56,59,82,83,84,85,86,87,88,93,114,115,125,158,160,178,179,181,182,183,187,190,191,192,193,194,195,196,197,198,199,200,201,203,230,231,232,233,234,235,236,238,242,243,244,245,247,248,250,251,252,254,266,267,268,269,270,271,272,285,286,287,288,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,317,318,319,328,332,333,349,351,353,354,355,356,359,361,369,391,393,395,396,399,400,401,405,407,408,415,416,417,418,421,428,430,431,433,434,437,],[-107,-126,-179,-167,-181,-182,-183,-96,-180,-97,159,-143,-144,-178,-60,-62,-105,-112,-106,-168,-125,-176,-139,-140,-141,-142,-169,-170,-171,-172,-173,-174,-175,-177,-134,159,-40,-41,-42,-43,-44,-45,-47,-52,-53,-54,-55,159,-71,-61,-63,-64,-78,-95,-99,-100,-101,-102,-103,-104,-135,-136,-137,-138,-145,-146,-147,-148,-149,-150,-151,-152,-153,-154,-155,-156,-157,-158,-159,-160,-161,-162,-163,-164,-165,-166,159,-57,-46,-58,-65,-75,-98,-113,-116,-122,-123,-124,-131,-132,-56,-114,-129,-130,-133,-48,-49,-50,-69,-59,-77,-121,-117,-118,-128,-51,-115,-127,-70,-119,-120,-76,]),'NULL':([55,56,59,82,83,84,85,86,87,88,93,113,114,115,125,158,160,161,178,179,181,182,183,187,189,190,191,192,193,194,195,196,197,198,199,200,201,203,230,231,232,233,234,235,236,238,242,243,244,245,247,248,250,251,252,254,266,267,268,269,270,271,272,285,286,287,288,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,317,318,319,328,332,333,349,351,353,354,355,356,359,361,369,386,391,393,395,396,399,400,401,405,407,408,415,416,417,418,421,428,430,431,433,434,437,],[-107,-126,-179,-167,-181,-182,-183,-96,-180,-97,160,190,-143,-144,-178,-60,-62,251,-105,-112,-106,-168,-125,-176,285,-139,-140,-141,-142,-169,-170,-171,-172,-173,-174,-175,-177,-134,160,-40,-41,-42,-43,-44,-45,-47,-52,-53,-54,-55,160,-71,-61,-63,-64,-78,-95,-99,-100,-101,-102,-103,-104,-135,-136,-137,-138,-145,-146,-147,-148,-149,-150,-151,-152,-153,-154,-155,-156,-157,-158,-159,-160,-161,-162,-163,-164,-165,-166,160,-57,-46,-58,-65,-75,-98,-113,-116,-122,-123,-124,-131,-132,-56,411,-114,-129,-130,-133,-48,-49,-50,-69,-59,-77,-121,-117,-118,-128,-51,-115,-127,-70,-119,-120,-76,]),'REFERENCE':([55,56,59,82,83,84,85,86,87,88,93,114,115,125,158,160,178,179,181,182,183,187,190,191,192,193,194,195,196,197,198,199,200,201,203,230,231,232,233,234,235,236,238,242,243,244,245,247,248,250,251,252,254,266,267,268,269,270,271,272,285,286,287,288,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,317,318,319,328,332,333,349,351,353,354,355,356,359,361,369,391,393,395,396,399,400,401,405,407,408,415,416,417,418,421,428,430,431,433,434,437,],[-107,-126,-179,-167,-181,-182,-183,-96,-180,-97,162,-143,-144,-178,-60,-62,-105,-112,-106,-168,-125,-176,-139,-140,-141,-142,-169,-170,-171,-172,-173,-174,-175,-177,-134,162,-40,-41,-42,-43,-44,-45,-47,-52,-53,-54,-55,162,-71,-61,-63,-64,-78,-95,-99,-100,-101,-102,-103,-104,-135,-136,-137,-138,-145,-146,-147,-148,-149,-150,-151,-152,-153,-154,-155,-156,-157,-158,-159,-160,-161,-162,-163,-164,-165,-166,162,-57,-46,-58,-65,-75,-98,-113,-116,-122,-123,-124,-131,-132,-56,-114,-129,-130,-133,-48,-49,-50,-69,-59,-77,-121,-117,-118,-128,-51,-115,-127,-70,-119,-120,-76,]),'CONSTRAINT':([55,56,59,82,83,84,85,86,87,88,93,101,102,114,115,125,158,160,178,179,181,182,183,187,190,191,192,193,194,195,196,197,198,199,200,201,203,230,231,232,233,234,235,236,238,242,243,244,245,247,248,250,251,252,254,266,267,268,269,270,271,272,285,286,287,288,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,317,318,319,328,332,333,349,351,353,354,355,356,359,361,369,391,393,395,396,399,400,401,405,407,408,415,416,417,418,421,428,430,431,433,434,437,],[-107,-126,-179,-167,-181,-182,-183,-96,-180,-97,163,173,176,-143,-144,-178,-60,-62,-105,-112,-106,-168,-125,-176,-139,-140,-141,-142,-169,-170,-171,-172,-173,-174,-175,-177,-134,163,-40,-41,-42,-43,-44,-45,-47,-52,-53,-54,-55,163,330,-61,-63,-64,-78,-95,-99,-100,-101,-102,-103,-104,-135,-136,-137,-138,-145,-146,-147,-148,-149,-150,-151,-152,-153,-154,-155,-156,-157,-158,-159,-160,-161,-162,-163,-164,-165,-166,163,-57,-46,-58,-65,-75,-98,-113,-116,-122,-123,-124,-131,-132,-56,-114,-129,-130,-133,-48,-49,-50,-69,-59,-77,-121,-117,-118,-128,-51,-115,-127,-70,-119,-120,-76,]),'PRIMARY':([55,56,59,82,83,84,85,86,87,88,93,101,114,115,125,158,160,178,179,181,182,183,187,190,191,192,193,194,195,196,197,198,199,200,201,203,230,231,232,233,234,235,236,238,242,243,244,245,247,248,250,251,252,253,254,266,267,268,269,270,271,272,285,286,287,288,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,317,318,319,328,332,333,349,351,353,354,355,356,359,361,369,391,393,395,396,399,400,401,405,407,408,415,416,417,418,421,428,430,431,433,434,437,],[-107,-126,-179,-167,-181,-182,-183,-96,-180,-97,164,164,-143,-144,-178,-60,-62,-105,-112,-106,-168,-125,-176,-139,-140,-141,-142,-169,-170,-171,-172,-173,-174,-175,-177,-134,164,-40,-41,-42,-43,-44,-45,-47,-52,-53,-54,-55,164,-71,-61,-63,-64,164,-78,-95,-99,-100,-101,-102,-103,-104,-135,-136,-137,-138,-145,-146,-147,-148,-149,-150,-151,-152,-153,-154,-155,-156,-157,-158,-159,-160,-161,-162,-163,-164,-165,-166,164,-57,-46,-58,-65,-75,-98,-113,-116,-122,-123,-124,-131,-132,-56,-114,-129,-130,-133,-48,-49,-50,-69,-59,-77,-121,-117,-118,-128,-51,-115,-127,-70,-119,-120,-76,]),'FOREIGN':([55,56,59,82,83,84,85,86,87,88,93,101,114,115,125,158,160,178,179,181,182,183,187,190,191,192,193,194,195,196,197,198,199,200,201,203,230,231,232,233,234,235,236,238,242,243,244,245,247,248,250,251,252,253,254,266,267,268,269,270,271,272,285,286,287,288,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,317,318,319,328,332,333,349,351,353,354,355,356,359,361,369,391,393,395,396,399,400,401,405,407,408,415,416,417,418,421,428,430,431,433,434,437,],[-107,-126,-179,-167,-181,-182,-183,-96,-180,-97,165,165,-143,-144,-178,-60,-62,-105,-112,-106,-168,-125,-176,-139,-140,-141,-142,-169,-170,-171,-172,-173,-174,-175,-177,-134,165,-40,-41,-42,-43,-44,-45,-47,-52,-53,-54,-55,165,-71,-61,-63,-64,165,-78,-95,-99,-100,-101,-102,-103,-104,-135,-136,-137,-138,-145,-146,-147,-148,-149,-150,-151,-152,-153,-154,-155,-156,-157,-158,-159,-160,-161,-162,-163,-164,-165,-166,165,-57,-46,-58,-65,-75,-98,-113,-116,-122,-123,-124,-131,-132,-56,-114,-129,-130,-133,-48,-49,-50,-69,-59,-77,-121,-117,-118,-128,-51,-115,-127,-70,-119,-120,-76,]),'UNIQUE':([55,56,59,82,83,84,85,86,87,88,93,114,115,125,157,158,160,178,179,181,182,183,187,190,191,192,193,194,195,196,197,198,199,200,201,203,230,231,232,233,234,235,236,238,242,243,244,245,247,248,250,251,252,253,254,263,266,267,268,269,270,271,272,285,286,287,288,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,317,318,319,328,332,333,349,351,353,354,355,356,359,361,369,391,393,395,396,399,400,401,405,407,408,415,416,417,418,421,428,430,431,433,434,437,],[-107,-126,-179,-167,-181,-182,-183,-96,-180,-97,-68,-143,-144,-178,248,-60,-62,-105,-112,-106,-168,-125,-176,-139,-140,-141,-142,-169,-170,-171,-172,-173,-174,-175,-177,-134,-66,-40,-41,-42,-43,-44,-45,-47,-52,-53,-54,-55,-68,-71,-61,-63,-64,-67,-78,347,-95,-99,-100,-101,-102,-103,-104,-135,-136,-137,-138,-145,-146,-147,-148,-149,-150,-151,-152,-153,-154,-155,-156,-157,-158,-159,-160,-161,-162,-163,-164,-165,-166,-66,-57,-46,-58,-65,-75,-98,-113,-116,-122,-123,-124,-131,-132,-56,-114,-129,-130,-133,-48,-49,-50,-69,-59,-77,-121,-117,-118,-128,-51,-115,-127,-70,-119,-120,-76,]),'CHECK':([55,56,59,82,83,84,85,86,87,88,93,101,114,115,125,157,158,160,178,179,181,182,183,187,190,191,192,193,194,195,196,197,198,199,200,201,203,230,231,232,233,234,235,236,238,242,243,244,245,247,248,250,251,252,253,254,266,267,268,269,270,271,272,285,286,287,288,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,317,318,319,327,328,332,333,349,351,353,354,355,356,359,361,369,379,391,393,395,396,399,400,401,405,407,408,415,416,417,418,421,428,430,431,433,434,437,],[-107,-126,-179,-167,-181,-182,-183,-96,-180,-97,-68,172,-143,-144,-178,249,-60,-62,-105,-112,-106,-168,-125,-176,-139,-140,-141,-142,-169,-170,-171,-172,-173,-174,-175,-177,-134,-66,-40,-41,-42,-43,-44,-45,-47,-52,-53,-54,-55,-68,-68,-61,-63,-64,-67,-78,-95,-99,-100,-101,-102,-103,-104,-135,-136,-137,-138,-145,-146,-147,-148,-149,-150,-151,-152,-153,-154,-155,-156,-157,-158,-159,-160,-161,-162,-163,-164,-165,-166,-66,-57,-46,375,-58,-65,-75,-98,-113,-116,-122,-123,-124,-131,-132,-56,-67,-114,-129,-130,-133,-48,-49,-50,-69,-59,-77,-121,-117,-118,-128,-51,-115,-127,-70,-119,-120,-76,]),'ASC':([55,56,59,82,83,84,85,86,87,88,114,115,125,178,179,181,182,183,187,190,191,192,193,194,195,196,197,198,199,200,201,203,266,267,268,269,270,271,272,285,286,287,288,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,349,351,353,354,355,356,359,361,391,392,393,395,396,415,416,417,418,428,430,433,434,],[-107,-126,-179,-167,-181,-182,-183,-96,-180,-97,-143,-144,-178,-105,-112,-106,-168,-125,-176,-139,-140,-141,-142,-169,-170,-171,-172,-173,-174,-175,-177,-134,-95,-99,-100,-101,-102,-103,-104,-135,-136,-137,-138,-145,-146,-147,-148,-149,-150,-151,-152,-153,-154,-155,-156,-157,-158,-159,-160,-161,-162,-163,-164,-165,-166,-98,-113,-116,-122,-123,-124,-131,-132,-114,416,-129,-130,-133,-121,-117,-118,-128,-115,-127,-119,-120,]),'DESC':([55,56,59,82,83,84,85,86,87,88,114,115,125,178,179,181,182,183,187,190,191,192,193,194,195,196,197,198,199,200,201,203,266,267,268,269,270,271,272,285,286,287,288,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,349,351,353,354,355,356,359,361,391,392,393,395,396,415,416,417,418,428,430,433,434,],[-107,-126,-179,-167,-181,-182,-183,-96,-180,-97,-143,-144,-178,-105,-112,-106,-168,-125,-176,-139,-140,-141,-142,-169,-170,-171,-172,-173,-174,-175,-177,-134,-95,-99,-100,-101,-102,-103,-104,-135,-136,-137,-138,-145,-146,-147,-148,-149,-150,-151,-152,-153,-154,-155,-156,-157,-158,-159,-160,-161,-162,-163,-164,-165,-166,-98,-113,-116,-122,-123,-124,-131,-132,-114,417,-129,-130,-133,-121,-117,-118,-128,-115,-127,-119,-120,]),'PUNTO':([59,],[126,]),'TO':([97,98,],[168,169,]),'COLUMN':([99,102,],[170,177,]),'SYMMETRIC':([111,184,],[186,280,]),'TRUE':([113,189,],[191,286,]),'FALSE':([113,189,],[192,287,]),'UNKNOWN':([113,189,],[193,288,]),'SMALLINT':([152,345,],[231,231,]),'INTEGER':([152,345,],[232,232,]),'BIGINT':([152,345,],[233,233,]),'DECIMAL':([152,345,],[234,234,]),'NUMERIC':([152,345,],[235,235,]),'REAL':([152,345,],[236,236,]),'DOUBLE':([152,345,],[237,237,]),'MONEY':([152,345,],[238,238,]),'VARCHAR':([152,345,],[239,239,]),'CHAR':([152,345,],[240,240,]),'CHARACTER':([152,345,],[241,241,]),'TEXT':([152,345,],[242,242,]),'DATE':([152,345,],[243,243,]),'TIMESTAMP':([152,345,],[244,244,]),'TIME':([152,345,],[245,245,]),'KEY':([164,165,],[254,255,]),'MODE':([166,256,384,],[-20,337,-19,]),'LLAVE_ABRE':([169,],[260,]),'REFERENCES':([174,254,333,408,409,437,],[264,-78,-75,-77,425,-76,]),'PRECISION':([237,],[319,]),'VARYING':([241,],[323,]),'INHERITS':([246,],[325,]),'CURRENT_USER':([260,],[341,]),'SESSION_USER':([260,],[342,]),'TYPE':([261,],[345,]),'BY':([273,275,],[350,352,]),'ALL':([277,],[355,]),'LLAVE_CIERRA':([339,340,341,342,],[385,-27,-28,-29,]),'NULLS':([415,416,417,],[429,-117,-118,]),'LAST':([429,],[433,]),'FIRST':([429,],[434,]),} _lr_action = {} for _k, _v in _lr_action_items.items(): for _x,_y in zip(_v[0],_v[1]): if not _x in _lr_action: _lr_action[_x] = {} _lr_action[_x][_k] = _y del _lr_action_items _lr_goto_items = {'init':([0,],[1,]),'instrucciones':([0,],[2,]),'instruccion':([0,2,],[3,17,]),'crear_statement':([0,2,],[4,4,]),'alter_statement':([0,2,],[5,5,]),'drop_statement':([0,2,],[6,6,]),'seleccionar':([0,2,34,35,36,54,58,105,106,109,111,112,116,117,118,119,120,121,122,123,124,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,159,184,186,229,274,276,280,281,283,289,313,331,350,352,357,360,362,394,397,398,404,],[7,7,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,82,]),'or_replace':([13,],[28,]),'distinto':([16,],[34,]),'if_exists':([32,],[48,]),'select_list':([34,],[51,]),'expressiones':([34,35,36,105,274,276,350,352,],[53,86,88,179,351,353,391,392,]),'list_expression':([34,35,36,54,105,274,276,350,352,],[55,55,55,107,55,55,55,55,55,]),'expression':([34,35,36,54,58,105,106,109,111,112,116,117,118,119,120,121,122,123,124,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,159,184,186,229,274,276,280,281,283,289,313,331,350,352,357,360,362,394,397,398,404,],[56,56,56,108,125,56,180,183,185,187,194,195,196,197,198,199,200,201,202,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,227,228,250,279,282,316,56,56,358,359,361,363,365,380,56,56,393,395,396,418,419,420,423,]),'if_not_exists':([44,],[94,]),'rename_owner':([46,],[96,]),'alter_op':([47,],[100,]),'contenido_tabla':([93,],[153,]),'manejo_tabla':([93,247,],[154,326,]),'declaracion_columna':([93,247,],[155,155,]),'condition_column':([93,230,247,317,],[156,318,156,369,]),'constraint':([93,230,247,248,317,],[157,157,157,327,157,]),'key_table':([93,101,230,247,253,317,],[158,174,158,158,332,158,]),'op_add':([101,],[171,]),'alter_drop':([102,],[175,]),'table_expression':([105,],[178,]),'list_val':([149,],[226,]),'type_column':([152,345,],[230,387,]),'owner_':([166,],[256,]),'list_fin_select':([178,],[266,]),'fin_select':([178,266,],[267,349,]),'group_by':([178,266,],[268,268,]),'donde':([178,266,],[269,269,]),'order_by':([178,266,],[270,270,]),'group_having':([178,266,],[271,271,]),'limite':([178,266,],[272,272,]),'where':([228,],[314,]),'condition_column_row':([230,],[317,]),'inherits_statement':([246,],[324,]),'op_unique':([248,],[328,]),'list_key':([254,],[333,]),'mode_':([256,],[336,]),'ow_op':([260,],[339,]),'alter_col_op':([261,],[343,]),'list_id':([329,334,335,348,435,],[376,381,382,390,436,]),'alias':([329,334,335,348,406,435,],[377,377,377,377,424,377,]),'asc_desc':([392,],[415,]),'nulls_f_l':([415,],[428,]),} _lr_goto = {} for _k, _v in _lr_goto_items.items(): for _x, _y in zip(_v[0], _v[1]): if not _x in _lr_goto: _lr_goto[_x] = {} _lr_goto[_x][_k] = _y del _lr_goto_items _lr_productions = [ ("S' -> init","S'",1,None,None,None), ('init -> instrucciones','init',1,'p_init','sql_grammar.py',324), ('instrucciones -> instrucciones instruccion','instrucciones',2,'p_instrucciones_lista','sql_grammar.py',328), ('instrucciones -> instruccion','instrucciones',1,'p_instrucciones_instruccion','sql_grammar.py',333), ('instruccion -> crear_statement PUNTOCOMA','instruccion',2,'p_instruccion','sql_grammar.py',337), ('instruccion -> alter_statement PUNTOCOMA','instruccion',2,'p_instruccion','sql_grammar.py',338), ('instruccion -> drop_statement PUNTOCOMA','instruccion',2,'p_instruccion','sql_grammar.py',339), ('instruccion -> seleccionar PUNTOCOMA','instruccion',2,'p_instruccion','sql_grammar.py',340), ('instruccion -> SHOW DATABASES PUNTOCOMA','instruccion',3,'p_aux_instruccion','sql_grammar.py',344), ('instruccion -> INSERT INTO ID VALUES PAR_ABRE list_val PAR_CIERRA PUNTOCOMA','instruccion',8,'p_aux_instruccion','sql_grammar.py',345), ('instruccion -> UPDATE ID SET ID IGUAL expression where PUNTOCOMA','instruccion',8,'p_aux_instruccion','sql_grammar.py',346), ('instruccion -> DELETE FROM ID WHERE ID IGUAL expression PUNTOCOMA','instruccion',8,'p_aux_instruccion','sql_grammar.py',347), ('instruccion -> USE DATABASE ID PUNTOCOMA','instruccion',4,'p_aux_instruccion','sql_grammar.py',348), ('crear_statement -> CREATE TABLE ID PAR_ABRE contenido_tabla PAR_CIERRA inherits_statement','crear_statement',7,'p_crear_statement_tbl','sql_grammar.py',367), ('crear_statement -> CREATE or_replace DATABASE if_not_exists ID owner_ mode_','crear_statement',7,'p_crear_statement_db','sql_grammar.py',373), ('or_replace -> OR REPLACE','or_replace',2,'p_or_replace_db','sql_grammar.py',379), ('or_replace -> <empty>','or_replace',0,'p_or_replace_db','sql_grammar.py',380), ('if_not_exists -> IF NOT EXISTS','if_not_exists',3,'p_if_not_exists_db','sql_grammar.py',388), ('if_not_exists -> <empty>','if_not_exists',0,'p_if_not_exists_db','sql_grammar.py',389), ('owner_ -> OWNER IGUAL ID','owner_',3,'p_owner_db','sql_grammar.py',397), ('owner_ -> <empty>','owner_',0,'p_owner_db','sql_grammar.py',398), ('mode_ -> MODE IGUAL ENTERO','mode_',3,'p_mode_db','sql_grammar.py',408), ('mode_ -> <empty>','mode_',0,'p_mode_db','sql_grammar.py',409), ('alter_statement -> ALTER DATABASE ID rename_owner','alter_statement',4,'p_alter_db','sql_grammar.py',419), ('alter_statement -> ALTER TABLE ID alter_op','alter_statement',4,'p_alter_tbl','sql_grammar.py',425), ('rename_owner -> RENAME TO ID','rename_owner',3,'p_rename_owner_db','sql_grammar.py',432), ('rename_owner -> OWNER TO LLAVE_ABRE ow_op LLAVE_CIERRA','rename_owner',5,'p_rename_owner_db','sql_grammar.py',433), ('ow_op -> ID','ow_op',1,'p_ow_op_db','sql_grammar.py',443), ('ow_op -> CURRENT_USER','ow_op',1,'p_ow_op_db','sql_grammar.py',444), ('ow_op -> SESSION_USER','ow_op',1,'p_ow_op_db','sql_grammar.py',445), ('drop_statement -> DROP DATABASE if_exists ID','drop_statement',4,'p_drop_db','sql_grammar.py',449), ('drop_statement -> DROP TABLE ID','drop_statement',3,'p_drop_tbl','sql_grammar.py',458), ('if_exists -> IF EXISTS','if_exists',2,'p_if_exists_db','sql_grammar.py',464), ('if_exists -> <empty>','if_exists',0,'p_if_exists_db','sql_grammar.py',465), ('contenido_tabla -> contenido_tabla COMA manejo_tabla','contenido_tabla',3,'p_contenido_tabla','sql_grammar.py',472), ('contenido_tabla -> manejo_tabla','contenido_tabla',1,'p_aux_contenido_table','sql_grammar.py',477), ('manejo_tabla -> declaracion_columna','manejo_tabla',1,'p_manejo_tabla','sql_grammar.py',481), ('manejo_tabla -> condition_column','manejo_tabla',1,'p_manejo_tabla','sql_grammar.py',482), ('declaracion_columna -> ID type_column condition_column_row','declaracion_columna',3,'p_aux_declaracion_columna','sql_grammar.py',486), ('declaracion_columna -> ID type_column','declaracion_columna',2,'p_declaracion_columna','sql_grammar.py',492), ('type_column -> SMALLINT','type_column',1,'p_type_column','sql_grammar.py',498), ('type_column -> INTEGER','type_column',1,'p_type_column','sql_grammar.py',499), ('type_column -> BIGINT','type_column',1,'p_type_column','sql_grammar.py',500), ('type_column -> DECIMAL','type_column',1,'p_type_column','sql_grammar.py',501), ('type_column -> NUMERIC','type_column',1,'p_type_column','sql_grammar.py',502), ('type_column -> REAL','type_column',1,'p_type_column','sql_grammar.py',503), ('type_column -> DOUBLE PRECISION','type_column',2,'p_type_column','sql_grammar.py',504), ('type_column -> MONEY','type_column',1,'p_type_column','sql_grammar.py',505), ('type_column -> VARCHAR PAR_ABRE ENTERO PAR_CIERRA','type_column',4,'p_type_column','sql_grammar.py',506), ('type_column -> CHAR PAR_ABRE ENTERO PAR_CIERRA','type_column',4,'p_type_column','sql_grammar.py',507), ('type_column -> CHARACTER PAR_ABRE ENTERO PAR_CIERRA','type_column',4,'p_type_column','sql_grammar.py',508), ('type_column -> CHARACTER VARYING PAR_ABRE ENTERO PAR_CIERRA','type_column',5,'p_type_column','sql_grammar.py',509), ('type_column -> TEXT','type_column',1,'p_type_column','sql_grammar.py',510), ('type_column -> DATE','type_column',1,'p_type_column','sql_grammar.py',511), ('type_column -> TIMESTAMP','type_column',1,'p_type_column','sql_grammar.py',512), ('type_column -> TIME','type_column',1,'p_type_column','sql_grammar.py',513), ('condition_column_row -> condition_column_row condition_column','condition_column_row',2,'p_condition_column_row','sql_grammar.py',547), ('condition_column_row -> condition_column','condition_column_row',1,'p_aux_condition_column_row','sql_grammar.py',552), ('condition_column -> constraint UNIQUE op_unique','condition_column',3,'p_condition_column','sql_grammar.py',556), ('condition_column -> constraint CHECK PAR_ABRE expression PAR_CIERRA','condition_column',5,'p_condition_column','sql_grammar.py',557), ('condition_column -> key_table','condition_column',1,'p_condition_column','sql_grammar.py',558), ('condition_column -> DEFAULT expression','condition_column',2,'p_aux_condition_column','sql_grammar.py',573), ('condition_column -> NULL','condition_column',1,'p_aux_condition_column','sql_grammar.py',574), ('condition_column -> NOT NULL','condition_column',2,'p_aux_condition_column','sql_grammar.py',575), ('condition_column -> REFERENCE ID','condition_column',2,'p_aux_condition_column','sql_grammar.py',576), ('condition_column -> CONSTRAINT ID key_table','condition_column',3,'p_aux_condition_column','sql_grammar.py',577), ('condition_column -> <empty>','condition_column',0,'p_aux_condition_column','sql_grammar.py',578), ('constraint -> CONSTRAINT ID','constraint',2,'p_constraint','sql_grammar.py',600), ('constraint -> <empty>','constraint',0,'p_constraint','sql_grammar.py',601), ('op_unique -> PAR_ABRE list_id PAR_CIERRA','op_unique',3,'p_op_unique','sql_grammar.py',611), ('op_unique -> constraint CHECK PAR_ABRE expression PAR_CIERRA','op_unique',5,'p_op_unique','sql_grammar.py',612), ('op_unique -> <empty>','op_unique',0,'p_op_unique','sql_grammar.py',613), ('list_id -> list_id COMA alias','list_id',3,'p_list_id','sql_grammar.py',624), ('list_id -> alias','list_id',1,'p_aux_list_id','sql_grammar.py',629), ('alias -> ID','alias',1,'p_alias','sql_grammar.py',633), ('key_table -> PRIMARY KEY list_key','key_table',3,'p_key_table','sql_grammar.py',639), ('key_table -> FOREIGN KEY PAR_ABRE list_id PAR_CIERRA REFERENCES ID PAR_ABRE list_id PAR_CIERRA','key_table',10,'p_key_table','sql_grammar.py',640), ('list_key -> PAR_ABRE list_id PAR_CIERRA','list_key',3,'p_list_key','sql_grammar.py',653), ('list_key -> <empty>','list_key',0,'p_list_key','sql_grammar.py',654), ('alter_op -> ADD op_add','alter_op',2,'p_alter_op','sql_grammar.py',661), ('alter_op -> ALTER COLUMN ID alter_col_op','alter_op',4,'p_alter_op','sql_grammar.py',662), ('alter_op -> DROP alter_drop ID','alter_op',3,'p_alter_op','sql_grammar.py',663), ('alter_drop -> CONSTRAINT','alter_drop',1,'p_aux_alter_op','sql_grammar.py',678), ('alter_drop -> COLUMN','alter_drop',1,'p_aux_alter_op','sql_grammar.py',679), ('op_add -> CHECK PAR_ABRE ID DIFERENTE CADENA PAR_CIERRA','op_add',6,'p_op_add','sql_grammar.py',683), ('op_add -> CONSTRAINT ID UNIQUE PAR_ABRE ID PAR_CIERRA','op_add',6,'p_op_add','sql_grammar.py',684), ('op_add -> key_table REFERENCES PAR_ABRE list_id PAR_CIERRA','op_add',5,'p_op_add','sql_grammar.py',685), ('alter_col_op -> SET NOT NULL','alter_col_op',3,'p_alter_col_op','sql_grammar.py',698), ('alter_col_op -> TYPE type_column','alter_col_op',2,'p_alter_col_op','sql_grammar.py',699), ('inherits_statement -> INHERITS PAR_ABRE ID PAR_CIERRA','inherits_statement',4,'p_inherits_tbl','sql_grammar.py',709), ('inherits_statement -> <empty>','inherits_statement',0,'p_inherits_tbl','sql_grammar.py',710), ('list_val -> list_val COMA expression','list_val',3,'p_list_val','sql_grammar.py',719), ('list_val -> expression','list_val',1,'p_aux_list_val','sql_grammar.py',724), ('where -> WHERE ID IGUAL expression','where',4,'p_where','sql_grammar.py',728), ('where -> <empty>','where',0,'p_where','sql_grammar.py',729), ('seleccionar -> SELECT distinto select_list FROM table_expression list_fin_select','seleccionar',6,'p_seleccionar','sql_grammar.py',739), ('seleccionar -> SELECT GREATEST expressiones','seleccionar',3,'p_aux_seleccionar','sql_grammar.py',749), ('seleccionar -> SELECT LEAST expressiones','seleccionar',3,'p_aux_seleccionar','sql_grammar.py',750), ('list_fin_select -> list_fin_select fin_select','list_fin_select',2,'p_list_fin_select','sql_grammar.py',756), ('list_fin_select -> fin_select','list_fin_select',1,'p_aux_list_fin_select','sql_grammar.py',761), ('fin_select -> group_by','fin_select',1,'p_fin_select','sql_grammar.py',765), ('fin_select -> donde','fin_select',1,'p_fin_select','sql_grammar.py',766), ('fin_select -> order_by','fin_select',1,'p_fin_select','sql_grammar.py',767), ('fin_select -> group_having','fin_select',1,'p_fin_select','sql_grammar.py',768), ('fin_select -> limite','fin_select',1,'p_fin_select','sql_grammar.py',769), ('fin_select -> <empty>','fin_select',0,'p_fin_select','sql_grammar.py',770), ('expressiones -> PAR_ABRE list_expression PAR_CIERRA','expressiones',3,'p_expressiones','sql_grammar.py',777), ('expressiones -> list_expression','expressiones',1,'p_aux_expressiones','sql_grammar.py',781), ('distinto -> DISTINCT','distinto',1,'p_distinto','sql_grammar.py',785), ('distinto -> <empty>','distinto',0,'p_distinto','sql_grammar.py',786), ('select_list -> ASTERISCO','select_list',1,'p_select_list','sql_grammar.py',793), ('select_list -> expressiones','select_list',1,'p_select_list','sql_grammar.py',794), ('table_expression -> expressiones','table_expression',1,'p_table_expression','sql_grammar.py',798), ('donde -> WHERE expressiones','donde',2,'p_donde','sql_grammar.py',802), ('group_by -> GROUP BY expressiones','group_by',3,'p_group_by','sql_grammar.py',811), ('order_by -> ORDER BY expressiones asc_desc nulls_f_l','order_by',5,'p_order_by','sql_grammar.py',820), ('group_having -> HAVING expressiones','group_having',2,'p_group_having','sql_grammar.py',829), ('asc_desc -> ASC','asc_desc',1,'p_asc_desc','sql_grammar.py',838), ('asc_desc -> DESC','asc_desc',1,'p_asc_desc','sql_grammar.py',839), ('nulls_f_l -> NULLS LAST','nulls_f_l',2,'p_nulls_f_l','sql_grammar.py',843), ('nulls_f_l -> NULLS FIRST','nulls_f_l',2,'p_nulls_f_l','sql_grammar.py',844), ('nulls_f_l -> <empty>','nulls_f_l',0,'p_nulls_f_l','sql_grammar.py',845), ('limite -> LIMIT ENTERO','limite',2,'p_limite','sql_grammar.py',852), ('limite -> LIMIT ALL','limite',2,'p_limite','sql_grammar.py',853), ('limite -> OFFSET ENTERO','limite',2,'p_limite','sql_grammar.py',854), ('list_expression -> list_expression COMA expression','list_expression',3,'p_list_expression','sql_grammar.py',863), ('list_expression -> expression','list_expression',1,'p_aux_list_expression','sql_grammar.py',868), ('expression -> SUBSTRING PAR_ABRE expression COMA expression COMA expression PAR_CIERRA','expression',8,'p_expression','sql_grammar.py',872), ('expression -> expression NOT BETWEEN SYMMETRIC expression AND expression','expression',7,'p_expression_between3','sql_grammar.py',881), ('expression -> expression NOT BETWEEN expression AND expression','expression',6,'p_expression_between2','sql_grammar.py',890), ('expression -> expression BETWEEN SYMMETRIC expression AND expression','expression',6,'p_expression_between2','sql_grammar.py',891), ('expression -> expression BETWEEN expression AND expression','expression',5,'p_expression_between','sql_grammar.py',900), ('expression -> expression IS DISTINCT FROM expression','expression',5,'p_expression_Distinct','sql_grammar.py',911), ('expression -> expression IS NOT DISTINCT FROM expression','expression',6,'p_expression_not_Distinct','sql_grammar.py',920), ('expression -> ID PUNTO ID','expression',3,'p_expression_puntoId','sql_grammar.py',929), ('expression -> expression IS NOT NULL','expression',4,'p_expression_null3','sql_grammar.py',938), ('expression -> expression IS NOT TRUE','expression',4,'p_expression_null3','sql_grammar.py',939), ('expression -> expression IS NOT FALSE','expression',4,'p_expression_null3','sql_grammar.py',940), ('expression -> expression IS NOT UNKNOWN','expression',4,'p_expression_null3','sql_grammar.py',941), ('expression -> expression IS NULL','expression',3,'p_expression_null2','sql_grammar.py',950), ('expression -> expression IS TRUE','expression',3,'p_expression_null2','sql_grammar.py',951), ('expression -> expression IS FALSE','expression',3,'p_expression_null2','sql_grammar.py',952), ('expression -> expression IS UNKNOWN','expression',3,'p_expression_null2','sql_grammar.py',953), ('expression -> expression ISNULL','expression',2,'p_expression_null','sql_grammar.py',962), ('expression -> expression NOTNULL','expression',2,'p_expression_null','sql_grammar.py',963), ('expression -> SUM PAR_ABRE expression PAR_CIERRA','expression',4,'p_expression_agrupar','sql_grammar.py',972), ('expression -> COUNT PAR_ABRE expression PAR_CIERRA','expression',4,'p_expression_agrupar','sql_grammar.py',973), ('expression -> AVG PAR_ABRE expression PAR_CIERRA','expression',4,'p_expression_agrupar','sql_grammar.py',974), ('expression -> MAX PAR_ABRE expression PAR_CIERRA','expression',4,'p_expression_agrupar','sql_grammar.py',975), ('expression -> MIN PAR_ABRE expression PAR_CIERRA','expression',4,'p_expression_agrupar','sql_grammar.py',976), ('expression -> ABS PAR_ABRE expression PAR_CIERRA','expression',4,'p_expression_agrupar','sql_grammar.py',977), ('expression -> CBRT PAR_ABRE expression PAR_CIERRA','expression',4,'p_expression_agrupar','sql_grammar.py',978), ('expression -> CEIL PAR_ABRE expression PAR_CIERRA','expression',4,'p_expression_agrupar','sql_grammar.py',979), ('expression -> CEILING PAR_ABRE expression PAR_CIERRA','expression',4,'p_expression_agrupar','sql_grammar.py',980), ('expression -> DEGREES PAR_ABRE expression PAR_CIERRA','expression',4,'p_expression_agrupar','sql_grammar.py',981), ('expression -> DIV PAR_ABRE expression PAR_CIERRA','expression',4,'p_expression_agrupar','sql_grammar.py',982), ('expression -> EXP PAR_ABRE expression PAR_CIERRA','expression',4,'p_expression_agrupar','sql_grammar.py',983), ('expression -> FACTORIAL PAR_ABRE expression PAR_CIERRA','expression',4,'p_expression_agrupar','sql_grammar.py',984), ('expression -> FLOOR PAR_ABRE expression PAR_CIERRA','expression',4,'p_expression_agrupar','sql_grammar.py',985), ('expression -> GCD PAR_ABRE expression PAR_CIERRA','expression',4,'p_expression_agrupar','sql_grammar.py',986), ('expression -> LN PAR_ABRE expression PAR_CIERRA','expression',4,'p_expression_agrupar','sql_grammar.py',987), ('expression -> LOG PAR_ABRE expression PAR_CIERRA','expression',4,'p_expression_agrupar','sql_grammar.py',988), ('expression -> MOD PAR_ABRE expression PAR_CIERRA','expression',4,'p_expression_agrupar','sql_grammar.py',989), ('expression -> PI PAR_ABRE expression PAR_CIERRA','expression',4,'p_expression_agrupar','sql_grammar.py',990), ('expression -> POWER PAR_ABRE expression PAR_CIERRA','expression',4,'p_expression_agrupar','sql_grammar.py',991), ('expression -> RADIANS PAR_ABRE expression PAR_CIERRA','expression',4,'p_expression_agrupar','sql_grammar.py',992), ('expression -> ROUND PAR_ABRE expression PAR_CIERRA','expression',4,'p_expression_agrupar','sql_grammar.py',993), ('expression -> seleccionar','expression',1,'p_expression_select','sql_grammar.py',1003), ('expression -> PAR_ABRE expression PAR_CIERRA','expression',3,'p_expression_ss','sql_grammar.py',1007), ('expression -> expression MAYOR expression','expression',3,'p_expression_relacional_aux_mayor','sql_grammar.py',1011), ('expression -> expression MENOR expression','expression',3,'p_expression_relacional_aux_menor','sql_grammar.py',1017), ('expression -> expression MAYOR_IGUAL expression','expression',3,'p_expression_relacional_aux_mayorigual','sql_grammar.py',1023), ('expression -> expression MENOR_IGUAL expression','expression',3,'p_expression_relacional_aux_menorigual','sql_grammar.py',1029), ('expression -> expression IGUAL expression','expression',3,'p_expression_relacional_aux_igual','sql_grammar.py',1035), ('expression -> expression NO_IGUAL expression','expression',3,'p_expression_relacional_aux_noigual','sql_grammar.py',1041), ('expression -> expression DIFERENTE expression','expression',3,'p_expression_relacional_aux_diferente','sql_grammar.py',1047), ('expression -> expression AND expression','expression',3,'p_expression_logica_and__and','sql_grammar.py',1053), ('expression -> expression OR expression','expression',3,'p_expression_logica_or','sql_grammar.py',1059), ('expression -> NOT expression','expression',2,'p_expression_logica_not','sql_grammar.py',1065), ('expression -> ID','expression',1,'p_solouno_expression','sql_grammar.py',1071), ('expression -> ASTERISCO','expression',1,'p_solouno_expression','sql_grammar.py',1072), ('expression -> ENTERO','expression',1,'p_expression_entero','sql_grammar.py',1082), ('expression -> DECIMAL_NUM','expression',1,'p_expression_decimal','sql_grammar.py',1094), ('expression -> CADENA','expression',1,'p_expression_cadena','sql_grammar.py',1115), ]
389.934579
52,986
0.684598
17,314
83,446
3.2245
0.051635
0.032779
0.039334
0.014616
0.744326
0.712784
0.660875
0.637393
0.603683
0.576081
0
0.460021
0.046629
83,446
213
52,987
391.765258
0.241745
0.001007
0
0.009852
1
0.004926
0.29021
0.020394
0
0
0
0
0
1
0
false
0
0
0
0
0
0
0
0
null
0
0
0
0
1
0
0
0
0
0
1
0
0
0
1
1
1
0
0
0
1
0
0
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
6
8d657a9489297a09ec3346ea7991e20903d249ec
132
py
Python
client/py_client/common/__init__.py
thefstock/FirstockPy
09b4dcf3470f83de991b43213958d2c6783f997b
[ "MIT" ]
1
2022-03-29T06:56:06.000Z
2022-03-29T06:56:06.000Z
client/py_client/common/__init__.py
thefstock/FirstockPy
09b4dcf3470f83de991b43213958d2c6783f997b
[ "MIT" ]
3
2022-01-17T09:31:21.000Z
2022-03-11T12:12:08.000Z
client/py_client/common/__init__.py
thefstock/FirstockPy
09b4dcf3470f83de991b43213958d2c6783f997b
[ "MIT" ]
null
null
null
""" The commonly used datastructures, methods, classes etc. """ from .enums import * from .exceptions import * from .models import *
22
55
0.734848
16
132
6.0625
0.75
0.206186
0
0
0
0
0
0
0
0
0
0
0.151515
132
6
56
22
0.866071
0.416667
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
1
0
1
0
1
0
0
null
1
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
6
a5cff97dc261f730b61994c4a5aee0b785be4ae1
167
py
Python
src/langumo_ko/__init__.py
affjljoo3581/langumo-ko
84cb82635a2ff16dc31b36cb9d00474057577aa3
[ "Apache-2.0" ]
6
2020-09-28T04:04:45.000Z
2022-01-13T12:24:10.000Z
src/langumo_ko/__init__.py
affjljoo3581/langumo-ko
84cb82635a2ff16dc31b36cb9d00474057577aa3
[ "Apache-2.0" ]
null
null
null
src/langumo_ko/__init__.py
affjljoo3581/langumo-ko
84cb82635a2ff16dc31b36cb9d00474057577aa3
[ "Apache-2.0" ]
1
2020-12-06T11:25:08.000Z
2020-12-06T11:25:08.000Z
from langumo_ko.namuwiki import NamuWikiParser from langumo_ko.moducorpus import (ModuNewsParser, ModuWebParser, ModuWrittenParser)
41.75
65
0.694611
14
167
8.142857
0.714286
0.192982
0.22807
0
0
0
0
0
0
0
0
0
0.269461
167
3
66
55.666667
0.934426
0
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
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
6
a5daea132a271a0ae4a0064a9614e352383e369e
19
py
Python
src/vpn/__init__.py
sandbox-pokhara/openvpn-tools
0afdf105f08311be70d547dbd247743fc61c6ece
[ "MIT" ]
2
2021-12-30T15:38:27.000Z
2022-02-21T17:23:13.000Z
src/vpn/__init__.py
sandbox-pokhara/openvpn-tools
0afdf105f08311be70d547dbd247743fc61c6ece
[ "MIT" ]
4
2021-02-05T13:46:51.000Z
2022-02-27T21:34:26.000Z
src/vpn/__init__.py
sandbox-pokhara/openvpn-tools
0afdf105f08311be70d547dbd247743fc61c6ece
[ "MIT" ]
7
2021-01-14T21:18:58.000Z
2022-02-02T14:54:29.000Z
from .all import *
9.5
18
0.684211
3
19
4.333333
1
0
0
0
0
0
0
0
0
0
0
0
0.210526
19
1
19
19
0.866667
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
1
0
1
0
1
1
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
6
571170e2ea431cdff14aedc90699d2b424708401
112
py
Python
models/__init__.py
bilginfurkan/Anonimce
7d73c13ae8d5c873b6863878370ad83ec9ee5acc
[ "Apache-2.0" ]
2
2021-02-15T12:56:58.000Z
2021-02-21T12:38:47.000Z
models/__init__.py
bilginfurkan/Anonimce
7d73c13ae8d5c873b6863878370ad83ec9ee5acc
[ "Apache-2.0" ]
null
null
null
models/__init__.py
bilginfurkan/Anonimce
7d73c13ae8d5c873b6863878370ad83ec9ee5acc
[ "Apache-2.0" ]
null
null
null
from .files import * from .users import * from .posts import * from .permissions import * from .reports import *
22.4
26
0.741071
15
112
5.533333
0.466667
0.481928
0
0
0
0
0
0
0
0
0
0
0.169643
112
5
27
22.4
0.892473
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
1
0
1
0
1
0
0
null
1
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
6
93ed6481d8f407091d12839f74adf02ab7ad673f
29
py
Python
featureflagtech/__init__.py
featureflagtech/featureflagtechpython
fb48ca01c4d6ef81680d34b6626cdfe8908ada09
[ "Apache-2.0" ]
1
2021-06-10T23:27:21.000Z
2021-06-10T23:27:21.000Z
featureflagtech/__init__.py
featureflagtech/featureflagtechpython
fb48ca01c4d6ef81680d34b6626cdfe8908ada09
[ "Apache-2.0" ]
null
null
null
featureflagtech/__init__.py
featureflagtech/featureflagtechpython
fb48ca01c4d6ef81680d34b6626cdfe8908ada09
[ "Apache-2.0" ]
null
null
null
from .main import FeatureFlag
29
29
0.862069
4
29
6.25
1
0
0
0
0
0
0
0
0
0
0
0
0.103448
29
1
29
29
0.961538
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
1
0
1
0
1
1
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
6
9e0473fda96400aae84f98c3df61a3855935f159
133,763
py
Python
dimcli/core/dsl_grammar_dict.py
dottinf/dimcli
708e83675afa6279424487c5b7417f5393c480bb
[ "MIT" ]
1
2020-04-15T06:16:33.000Z
2020-04-15T06:16:33.000Z
dimcli/core/dsl_grammar_dict.py
dottinf/dimcli
708e83675afa6279424487c5b7417f5393c480bb
[ "MIT" ]
null
null
null
dimcli/core/dsl_grammar_dict.py
dottinf/dimcli
708e83675afa6279424487c5b7417f5393c480bb
[ "MIT" ]
null
null
null
#!/usr/bin/python # -*- coding: utf-8 -*- # # # SYNTAX_DICT is a dictionary representation of operators and other constants of the DSL language # # SYNTAX_DICT = { 'allowed_starts_special_commands': { 'help' : [], '.docs' : [], 'quit' : [], '.show' : [], '.json_compact' : [], '.json_full' : [], '.export_as_html' : [], '.export_as_csv' : [], '.export_as_json' : [], '.record_notebook' : [], '.url' : [], }, 'allowed_starts_dsl_query': { 'search': [], 'describe': [ 'version', 'source', 'entity', 'schema'], 'check_researcher_ids': [], 'classify': [], 'extract_grants': [], 'extract_concepts': [], 'extract_affiliations': [], }, 'dimensions_urls' : { 'publications' : 'https://app.dimensions.ai/details/publication/', 'grants' : 'https://app.dimensions.ai/details/grant/', 'patents' : 'https://app.dimensions.ai/details/patent/', 'policy_documents' : 'https://app.dimensions.ai/details/policy_documents/', 'clinical_trials' : 'https://app.dimensions.ai/details/clinical_trial/', 'datasets' : 'https://app.dimensions.ai/details/data_set/', 'researchers' : 'https://app.dimensions.ai/discover/publication?and_facet_researcher=', 'organizations' : 'https://app.dimensions.ai/discover/publication?and_facet_research_org=', }, 'dimensions_object_id_patterns' : { 'publications' : 'pub.', 'grants' : 'grant.', # 'patents' : 'not available', 'policy_documents' : 'policy.', # 'clinical_trials' : 'not available', # 'datasets' : 'not available' 'researchers' : 'ur.', 'organizations' : 'grid.', }, 'lang_all': [ 'search', 'return', 'for', 'where', 'in', 'limit', 'skip', 'aggregate', '=', # filter operators https://docs.dimensions.ai/dsl/language.html#simple-filters '!=', '>', '<', '>=', '<=', '~', 'is empty', 'is not empty', "count", # https://docs.dimensions.ai/dsl/language.html#filter-functions 'sort by', 'asc', 'desc', "AND", # boolean operators https://docs.dimensions.ai/dsl/language.html#id6 "OR", "NOT", "&&", "!", "||", "+", "-", ], 'lang_after_search' : ['in', 'where', 'for', 'return'], 'lang_after_filter' : ['and', 'or', 'not', 'return', ], 'lang_after_for_text' : ['and', 'or', 'not', 'return', 'where' ], 'lang_after_return' : ['sort by', 'aggregate', 'limit',], 'lang_after_sort_by' : ['asc', 'desc', 'limit', ], 'lang_after_limit' : ['skip' ], 'lang_filter_operators' : ['=', '!=', '>', '<', '>=', '<=', '~', 'is empty', 'is not empty'], 'lang_text_operators' : ['AND', 'OR', 'NOT', '&&', '!', '||', '+', '-', '?', '*', '~'], } # # GRAMMAR_DICT is a dictionary rendering of the DSL grammar JSON # which can be obtained with the query `describe schema` # # last updated: v 1.19 2019-09-05 # # how to create: # # In [1]: import dimcli # In [2]: dimcli.login() # In [3]: dsl = dimcli.Dsl() # In [4]: dsl.query("describe schema").json # # then save to a py file, reformat and save the results in GRAMMAR_DICT symbol # # GRAMMAR_DICT = { 'sources': { 'publications': { 'fields': { 'category_rcdc': { 'type': 'categories', 'description': '`Research, Condition, and Disease Categorization <https://app.dimensions.ai/browse/publication/rcdc>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'category_hrcs_hc': { 'type': 'categories', 'description': '`HRCS - Health Categories <https://app.dimensions.ai/browse/publication/hrcs_hc>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'proceedings_title': { 'type': 'string', 'description': 'Title of the conference proceedings volume associated to a publication.', 'long_description': None, 'is_entity': False, 'is_filter': False, 'is_facet': False }, 'book_series_title': { 'type': 'string', 'description': 'The title of the book series book, belong to.', 'long_description': None, 'is_entity': False, 'is_filter': False, 'is_facet': False }, 'id': { 'type': 'string', 'description': 'Dimensions publication ID.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'research_orgs': { 'type': 'organizations', 'description': 'GRID organisations associated to a publication. Identifiers are automatically extracted from author affiliations text, so they can be missing in some cases (note: this field supports :ref:`filter-functions`: ``count``).', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'times_cited': { 'type': 'integer', 'description': 'Number of citations (note: does not support emptiness filters).', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': True }, 'journal': { 'type': 'journals', 'description': 'The journal a publication belongs to.', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'book_title': { 'type': 'string', 'description': 'The title of the book a chapter belongs to (note: this field is available only for chapters).', 'long_description': None, 'is_entity': False, 'is_filter': False, 'is_facet': False }, 'category_sdg': { 'type': 'categories', 'description': 'SDG - Sustainable Development Goals', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'category_hra': { 'type': 'categories', 'description': '`Health Research Areas <https://app.dimensions.ai/browse/publication/health_research_areas>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'category_for': { 'type': 'categories', 'description': '`ANZSRC Fields of Research classification <https://app.dimensions.ai/browse/publication/for>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'category_hrcs_rac': { 'type': 'categories', 'description': '`HRCS – Research Activity Codes <https://app.dimensions.ai/browse/publication/hrcs_rac>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'pmcid': { 'type': 'string', 'description': 'PubMed Central ID.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'relative_citation_ratio': { 'type': 'float', 'description': 'Relative citation performance of an article when compared to others in its area of research (note: does not support emptiness filters).', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'pages': { 'type': 'string', 'description': 'The pages of the publication, as they would appear in a citation record.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'volume': { 'type': 'string', 'description': 'Publication volume.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'publisher': { 'type': 'string', 'description': 'Name of the publisher as a string.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': True }, 'date': { 'type': 'date', 'description': 'The publication date of a document, eg "2018-01-01" (note: dates can sometimes be incomplete and include only the month or the year).', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'mesh_terms': { 'type': 'string', 'description': 'Medical Subject Heading terms as used in PubMed.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': True }, 'open_access_categories': { 'type': 'open_access', 'description': 'Open Access categories for publications. See below for more examples.', 'long_description': 'Open Access category data for publications values:\n\n * `oa_all`: Article is freely available\n * `gold_pure`: Version Of Record (VOR) is free under an open licence from a full OA journal\n * `gold_hybrid`: Version Of Record (VOR) is free under an open licence in a paid-access journal\n * `gold_bronze`: Freely available on publisher page, but without an open licence\n * `green_pub`: Free copy of published version in an OA repository\n * `green_acc`: Free copy of accepted version in an OA repository\n * `green_sub`: Free copy of submitted version, or where version is unknown, in an OA repository\n * `closed`: No freely available copy has been identified', 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'category_bra': { 'type': 'categories', 'description': '`Broad Research Areas <https://app.dimensions.ai/browse/publication/broad_research_areas>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'altmetric': { 'type': 'float', 'description': 'Altmetric attention score.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'research_org_cities': { 'type': 'cities', 'description': 'City of the organisations authors are affiliated to, expressed as GeoNames ID and name.', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'concepts': { 'type': 'string', 'description': 'Concepts describing the main topics of a publication (note: automatically derived from the publication text using machine learning).', 'long_description': None, 'is_entity': False, 'is_filter': False, 'is_facet': False }, 'year': { 'type': 'integer', 'description': 'The year of publication (note: when the `date` field is available, this is equal to the year part of the full date).', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': True }, 'pmid': { 'type': 'string', 'description': 'PubMed ID.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'funder_countries': { 'type': 'countries', 'description': 'The country of the organisations funding this publication.', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'research_org_countries': { 'type': 'countries', 'description': 'Country of the organisations authors are affiliated to, identified using GeoNames codes (note: this field supports :ref:`filter-functions`: ``count``).', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'altmetric_id': { 'type': 'integer', 'description': 'AltMetric Publication ID', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'field_citation_ratio': { 'type': 'float', 'description': 'Relative citation performance of article when compared to similarly aged articles in its area of research (note: does not support emptiness filters).', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'category_icrp_ct': { 'type': 'categories', 'description': '`ICRP Cancer Types <https://app.dimensions.ai/browse/publication/cancer_types>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'resulting_publication_doi': { 'type': 'string', 'description': 'For preprints, the DOIs of the resulting full publications.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'supporting_grant_ids': { 'type': 'string', 'description': 'Grants supporting a publication, returned as a list of dimensions grants IDs (see also: :ref:`publications_model` section).', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'research_org_state_codes': { 'type': 'states', 'description': 'State of the organisations authors are affiliated to, expressed as GeoNames codes (ISO\u200c-3166-2).', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'authors': { 'type': 'json', 'description': 'Ordered list of authors names and their affiliations, as they appear in the original publication. The list can include researcher and organization identifiers, when available (note: in order to search for disambiguated authors, use the `in researchers` syntax).', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'doi': { 'type': 'string', 'description': 'Digital object identifier.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'research_org_state_names': { 'type': 'string', 'description': 'State name of the organisations authors are affiliated to, as a string.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'issn': { 'type': 'string', 'description': 'International Standard Serial Number', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'issue': { 'type': 'string', 'description': 'The issue number of a publication.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'title': { 'type': 'string', 'description': 'Title of a publication.', 'long_description': None, 'is_entity': False, 'is_filter': False, 'is_facet': False }, 'funders': { 'type': 'organizations', 'description': 'The GRID organisation funding this publication.', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'journal_lists': { 'type': 'string', 'description': "Independent grouping of journals outside of Dimensions, e.g. 'ERA 2015' or 'Norwegian register level 1'.", 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': True }, 'category_icrp_cso': { 'type': 'categories', 'description': '`ICRP Common Scientific Outline <https://app.dimensions.ai/browse/publication/cso>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'category_uoa': { 'type': 'categories', 'description': '`Units of Assessment <https://app.dimensions.ai/browse/publication/uoa>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'linkout': { 'type': 'string', 'description': 'Original URL for a publication full text.', 'long_description': None, 'is_entity': False, 'is_filter': False, 'is_facet': False }, 'research_org_country_names': { 'type': 'string', 'description': 'Country name of the organisations authors are affiliated to, as a string.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'date_inserted': { 'type': 'date', 'description': "Date when the record was inserted into Dimensions (note: this field does not support exact match on the data, only range filters e.g. `<=` or `>=').", 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'reference_ids': { 'type': 'string', 'description': 'Dimensions publication ID for publications in the references list, i.e. outgoing citations (see also: :ref:`publications_model` section).', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'book_doi': { 'type': 'string', 'description': 'The DOI of the book a chapter belongs to (note: this field is available only for chapters).', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'type': { 'type': 'string', 'description': 'Publication type (one of: article, chapter, proceeding, monograph, preprint or book).', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': True }, 'researchers': { 'type': 'researchers', 'description': "Researcher IDs matched to the publication's authors list. (note: this returns only the disambiguated authors of a publication; in order to get the full authors list, the field `authors` should be used). This field supports :ref:`filter-functions`: ``count``.", 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'recent_citations': { 'type': 'integer', 'description': 'Number of citations received in the last two years. Does not support emptiness filters', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False } }, 'fieldsets': ['all', 'basics', 'extras', 'book', 'categories'], 'metrics': { 'count': { 'name': 'count', 'description': 'Total count' }, 'altmetric_median': { 'name': 'altmetric_median', 'description': 'Median Altmetric attention score' }, 'altmetric_avg': { 'name': 'altmetric_avg', 'description': 'Altmetric attention score mean' }, 'citations_total': { 'name': 'citations_total', 'description': 'Aggregated number of citations' }, 'citations_avg': { 'name': 'citations_avg', 'description': 'Arithmetic mean of citations' }, 'citations_median': { 'name': 'citations_median', 'description': 'Median of citations' }, 'recent_citations_total': { 'name': 'recent_citations_total', 'description': 'For a given article, in a given year, the number of citations accrued in the last two year period. Single value stored per document, year window rolls over in July.' }, 'rcr_avg': { 'name': 'rcr_avg', 'description': 'Arithmetic mean of `relative_citation_ratio` field.' }, 'fcr_gavg': { 'name': 'fcr_gavg', 'description': 'Geometric mean of `field_citation_ratio` field (note: This field cannot be used for sorting results).' } }, 'search_fields': [ 'full_data_exact', 'concepts', 'full_data', 'title_only', 'authors', 'title_abstract_only' ] }, 'grants': { 'fields': { 'category_rcdc': { 'type': 'categories', 'description': '`Research, Condition, and Disease Categorization <https://app.dimensions.ai/browse/publication/rcdc>`_ .', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'category_hrcs_hc': { 'type': 'categories', 'description': '`HRCS - Health Categories <https://app.dimensions.ai/browse/publication/hrcs_hc>`_ .', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'funding_currency': { 'type': 'string', 'description': 'Original funding currency.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': True }, 'id': { 'type': 'string', 'description': 'Dimensions grant ID.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'research_orgs': { 'type': 'organizations', 'description': 'GRID organisations receiving the grant (note: identifiers are automatically extracted from the source text and can be missing in some cases).', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'language': { 'type': 'string', 'description': 'Grant original language, as ISO 639-1 language codes.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': True }, 'funding_nzd': { 'type': 'float', 'description': 'Funding amount awarded in NZD.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'start_date': { 'type': 'date', 'description': "Date when the grant starts, in the format 'YYYY-MM-DD'.", 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'active_year': { 'type': 'integer', 'description': 'List of active years for a grant.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': True }, 'funding_cad': { 'type': 'float', 'description': 'Funding amount awarded in CAD.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'category_for': { 'type': 'categories', 'description': '`ANZSRC Fields of Research classification <https://app.dimensions.ai/browse/publication/for>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'funding_org_city': { 'type': 'string', 'description': 'City name for funding organisation.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': True }, 'funding_jpy': { 'type': 'float', 'description': 'Funding amount awarded in JPY.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'funding_gbp': { 'type': 'float', 'description': 'Funding amount awarded in GBP.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'category_hrcs_rac': { 'type': 'categories', 'description': '`HRCS – Research Activity Codes <https://app.dimensions.ai/browse/publication/hrcs_rac>`_ .', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'category_hra': { 'type': 'categories', 'description': '`Health Research Areas <https://app.dimensions.ai/browse/publication/health_research_areas?redirect_path=/discover/publication>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'start_year': { 'type': 'integer', 'description': 'Year when the grant starts.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': True }, 'funding_org_acronym': { 'type': 'string', 'description': 'Acronym for funding organisation.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': True }, 'category_bra': { 'type': 'categories', 'description': '`Broad Research Areas <https://app.dimensions.ai/browse/publication/broad_research_areas?redirect_path=/discover/publication>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'abstract': { 'type': 'string', 'description': 'Abstract or summary from a grant proposal.', 'long_description': None, 'is_entity': False, 'is_filter': False, 'is_facet': False }, 'research_org_cities': { 'type': 'cities', 'description': 'City of the research organisations receiving the grant, expressed as GeoNames id and name.', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'concepts': { 'type': 'string', 'description': 'Concepts describing the main topics of a grant (note: automatically derived from the grant text using machine learning).', 'long_description': None, 'is_entity': False, 'is_filter': False, 'is_facet': False }, 'end_date': { 'type': 'date', 'description': 'Date when the grant ends.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'funder_countries': { 'type': 'countries', 'description': 'The country linked to the organisation funding the grant, expressed as GeoNames codes.', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'original_title': { 'type': 'string', 'description': 'Title of the grant in its original language.', 'long_description': None, 'is_entity': False, 'is_filter': False, 'is_facet': False }, 'research_org_countries': { 'type': 'countries', 'description': 'Country of the research organisations receiving the grant, expressed as GeoNames code and name.', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'funding_aud': { 'type': 'float', 'description': 'Funding amount awarded in AUD.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'funding_eur': { 'type': 'float', 'description': 'Funding amount awarded in EUR.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'research_org_state_codes': { 'type': 'states', 'description': 'State of the organisations receiving the grant, expressed as GeoNames codes (ISO\u200c-3166-2).', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'funding_org_name': { 'type': 'string', 'description': 'Name of funding organisation.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': True }, 'investigator_details': { 'type': 'json', 'description': "Additional details about investigators, including affiliations and roles e.g. 'PI' or 'Co-PI' (note: if the investigator has a Dimensions researcher ID, that is returned as well).", 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'title': { 'type': 'string', 'description': 'Title of the grant in English (if the grant language is not English, this field contains a translation of the title).', 'long_description': None, 'is_entity': False, 'is_filter': False, 'is_facet': False }, 'funders': { 'type': 'organizations', 'description': 'The organisation funding the grant. This is normally a GRID organisation, but in very few cases a Dimensions funder ID is used.', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'grant_number': { 'type': 'string', 'description': 'Grant identifier, as provided by the source (e.g., funder, aggregator) the grant was derived from.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'funding_usd': { 'type': 'float', 'description': 'Funding amount awarded in USD.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'linkout': { 'type': 'string', 'description': 'Original URL for the grant.', 'long_description': None, 'is_entity': False, 'is_filter': False, 'is_facet': False }, 'funding_chf': { 'type': 'float', 'description': 'Funding amount awarded in CHF.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'category_icrp_cso': { 'type': 'categories', 'description': '`ICRP Common Scientific Outline <https://app.dimensions.ai/browse/publication/cso>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'foa_number': { 'type': 'string', 'description': 'The funding opportunity announcement (FOA) number, where available e.g. for grants from the US National Institute of Health (NIH) or from the National Science Foundation (NSF).', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'date_inserted': { 'type': 'date', 'description': 'Date when the record was inserted into Dimensions (note: this field does not support exact match on the data, only range filters e.g. `<=` or `>=`).', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'category_icrp_ct': { 'type': 'categories', 'description': '`ICRP Cancer Types <https://app.dimensions.ai/browse/publication/cancer_types>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'researchers': { 'type': 'researchers', 'description': 'Dimensions researchers IDs associated to the grant.', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'language_title': { 'type': 'string', 'description': 'ISO 639-1 language code for the original grant title.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': True } }, 'fieldsets': ['all', 'basics', 'extras', 'categories'], 'metrics': { 'count': { 'name': 'count', 'description': 'Total count' }, 'funding': { 'name': 'funding', 'description': 'Total funding amount, in USD.' } }, 'search_fields': [ 'concepts', 'full_data', 'title_only', 'investigators', 'title_abstract_only' ] }, 'patents': { 'fields': { 'category_rcdc': { 'type': 'categories', 'description': '`Research, Condition, and Disease Categorization <https://app.dimensions.ai/browse/publication/rcdc>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'category_hrcs_hc': { 'type': 'categories', 'description': '`HRCS - Health Categories <https://app.dimensions.ai/browse/publication/hrcs_hc>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'additional_filters': { 'type': 'string', 'description': "Additional filters describing the patents, e.g. whether it's about a 'Research Organisation', or it is part of the 'Orange Book'.", 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'assignee_countries': { 'type': 'countries', 'description': 'Country of the assignees of the patent, expressed as GeoNames code and name.', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'id': { 'type': 'string', 'description': 'Dimensions patent ID', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'assignee_state_names': { 'type': 'string', 'description': 'State name of the assignee, as a string.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': True }, 'times_cited': { 'type': 'integer', 'description': 'The number of times the patent has been cited by other patents.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': True }, 'filing_status': { 'type': 'string', 'description': "Filing Status of the patent e.g. 'Application' or 'Grant'.", 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'ipcr': { 'type': 'string', 'description': '`International Patent Classification Reform Categorization <https://www.wipo.int/classifications/ipc/en/faq/>`_.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': True }, 'category_hra': { 'type': 'categories', 'description': '`Health Research Areas <https://app.dimensions.ai/browse/publication/health_research_areas?redirect_path=/discover/publication>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'publication_date': { 'type': 'date', 'description': 'Date of publication of a patent.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'category_for': { 'type': 'categories', 'description': '`ANZSRC Fields of Research classification <https://app.dimensions.ai/browse/publication/for>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'category_hrcs_rac': { 'type': 'categories', 'description': '`HRCS – Research Activity Codes <https://app.dimensions.ai/browse/publication/hrcs_rac>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'inventor_names': { 'type': 'string', 'description': 'Names of the people who invented the patent.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'original_assignees': { 'type': 'organizations', 'description': 'GRID organisations that first owned the patent.', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'assignee_state_codes': { 'type': 'states', 'description': 'State of the assignee, expressed using GeoNames (ISO\u200c-3166-2) codes.', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'date': { 'type': 'date', 'description': 'Date when the patent was filed.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'category_bra': { 'type': 'categories', 'description': '`Broad Research Areas <https://app.dimensions.ai/browse/publication/broad_research_areas?redirect_path=/discover/publication>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'priority_year': { 'type': 'integer', 'description': 'The filing year of the earliest application of which priority is claimed.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': True }, 'abstract': { 'type': 'string', 'description': 'Abstract or description of the patent.', 'long_description': None, 'is_entity': False, 'is_filter': False, 'is_facet': False }, 'year': { 'type': 'integer', 'description': 'The year the patent was filed.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': True }, 'assignee_cities': { 'type': 'cities', 'description': 'City of the assignees of the patent, expressed as GeoNames ID and name.', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'granted_date': { 'type': 'date', 'description': 'The date on which the official body grants the patent.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'current_assignees': { 'type': 'organizations', 'description': 'GRID organisations currenlty owning the patent.', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'legal_status': { 'type': 'string', 'description': "The legal status of the patent, e.g. 'Granted', 'Active', 'Abandoned' etc..", 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'priority_date': { 'type': 'date', 'description': 'The earliest filing date in a family of patent applications.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'current_assignee_names': { 'type': 'string', 'description': 'Names of the GRID organisations currently holding the patent.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'associated_grant_ids': { 'type': 'string', 'description': 'Dimensions IDs of the grants associated to the patent (see also: :ref:`patents_model` section).', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'category_icrp_ct': { 'type': 'categories', 'description': '`ICRP Cancer Types <https://app.dimensions.ai/browse/publication/cancer_types>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'publication_ids': { 'type': 'string', 'description': 'Dimensions IDs of the publications related to this patent (see also: :ref:`patents_model` section).', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'assignees': { 'type': 'organizations', 'description': 'GRID organisations who own or have owned the rights of a patent (note: this is a combination of `current_assignees` and `original_assigness` fields).', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'original_assignee_names': { 'type': 'string', 'description': 'Name of the GRID organisation that first owned the patent.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'title': { 'type': 'string', 'description': 'The title of the patent.', 'long_description': None, 'is_entity': False, 'is_filter': False, 'is_facet': False }, 'publication_year': { 'type': 'integer', 'description': 'Year of publication of a patent.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': True }, 'funders': { 'type': 'organizations', 'description': 'GRID organisations funding the patent.', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'category_icrp_cso': { 'type': 'categories', 'description': '`ICRP Common Scientific Outline <https://app.dimensions.ai/browse/publication/cso>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'expiration_date': { 'type': 'date', 'description': 'Date when the patent expires.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'date_inserted': { 'type': 'date', 'description': 'Date when the record was inserted into Dimensions (note: this field does not support exact match on the data, only range filters e.g. `<=` or `>=`).', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'cpc': { 'type': 'string', 'description': '`Cooperative Patent Classification Categorization <https://www.epo.org/searching-for-patents/helpful-resources/first-time-here/classification/cpc.html>`_.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': True }, 'cited_by_ids': { 'type': 'string', 'description': 'Dimensions IDs of the patents that cite this patent (see also: :ref:`patents_model` section).', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'reference_ids': { 'type': 'string', 'description': 'Dimensions IDs of the patents which are cited by this patent (see also: :ref:`patents_model` section).', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'granted_year': { 'type': 'integer', 'description': 'The year on which the official body grants the patent.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': True }, 'jurisdiction': { 'type': 'string', 'description': "The jurisdiction where the patent was granted, e.g. 'US', 'DE', 'EP'...", 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': True }, 'researchers': { 'type': 'researchers', 'description': "Researcher IDs matched to the patent's inventors list. (note: this returns only the disambiguated inventors of a patent; in order to get the full list of inventors, the field `inventors` should be used).", 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'assignee_names': { 'type': 'string', 'description': 'Name of the GRID assignees of the patent.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False } }, 'fieldsets': ['all', 'basics', 'extras', 'categories'], 'metrics': { 'count': { 'name': 'count', 'description': 'Total count' } }, 'search_fields': ['inventors', 'full_data', 'title_only', 'title_abstract_only'] }, 'clinical_trials': { 'fields': { 'category_rcdc': { 'type': 'categories', 'description': '`Research, Condition, and Disease Categorization <https://app.dimensions.ai/browse/publication/rcdc>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'category_hrcs_hc': { 'type': 'categories', 'description': '`HRCS - Health Categories <https://app.dimensions.ai/browse/publication/hrcs_hc>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'conditions': { 'type': 'string', 'description': "List of medical conditions names, e.g. 'Breast cancer' or 'Obesity'.", 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'id': { 'type': 'string', 'description': 'Dimensions clinical trial ID', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'research_orgs': { 'type': 'organizations', 'description': 'GRID organizations involved, e.g. as sponsors or collaborators.', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'active_years': { 'type': 'integer', 'description': 'List of active years for a clinical trial.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': True }, 'phase': { 'type': 'string', 'description': 'Phase of the clinical trial, as a string.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'category_hra': { 'type': 'categories', 'description': '`Health Research Areas <https://app.dimensions.ai/browse/publication/health_research_areas?redirect_path=/discover/publication>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'gender': { 'type': 'string', 'description': "The gender of the clinical trial subjects e.g. 'Male', 'Female' or 'All'.", 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'category_for': { 'type': 'categories', 'description': '`ANZSRC Fields of Research classification <https://app.dimensions.ai/browse/publication/for>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'acronym': { 'type': 'string', 'description': 'Acronym of the clinical trial.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'category_hrcs_rac': { 'type': 'categories', 'description': '`HRCS – Research Activity Codes <https://app.dimensions.ai/browse/publication/hrcs_rac>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'brief_title': { 'type': 'string', 'description': 'Brief title of the clinical trial.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'registry': { 'type': 'string', 'description': "The platform where the clinical trial has been registered, e.g. 'ClinicalTrials.gov' or 'EU-CTR'.", 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'date': { 'type': 'date', 'description': 'Start date of a clinical trial.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'category_bra': { 'type': 'categories', 'description': '`Broad Research Areas <https://app.dimensions.ai/browse/publication/broad_research_areas?redirect_path=/discover/publication>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'abstract': { 'type': 'string', 'description': 'Abstract or description of the clinical trial.', 'long_description': None, 'is_entity': False, 'is_filter': False, 'is_facet': False }, 'funder_countries': { 'type': 'countries', 'description': 'The country group the funding organisations.', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'associated_grant_ids': { 'type': 'string', 'description': 'Dimensions IDs of the grants associated to the clinical trial (see also: :ref:`clinical_trials_model` section).', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'publication_ids': { 'type': 'string', 'description': 'Dimensions IDs of the publications related to this clinical trial (see also: :ref:`clinical_trials_model` section).', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'investigator_details': { 'type': 'json', 'description': 'Additional details about investigators, including affiliations and roles.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'title': { 'type': 'string', 'description': 'The title of the clinical trial.', 'long_description': None, 'is_entity': False, 'is_filter': False, 'is_facet': False }, 'funders': { 'type': 'organizations', 'description': 'GRID funding organisations that are involved with the clinical trial.', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'linkout': { 'type': 'string', 'description': 'Original URL for the clinical trial.', 'long_description': None, 'is_entity': False, 'is_filter': False, 'is_facet': False }, 'category_icrp_cso': { 'type': 'categories', 'description': '`ICRP Common Scientific Outline <https://app.dimensions.ai/browse/publication/cso>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'interventions': { 'type': 'json', 'description': "Structured JSON object containing information about the clinical trial's interventions according to the research plan or protocol created by the investigators.", 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'date_inserted': { 'type': 'date', 'description': "Date when the record was inserted into Dimensions (note: this field does not support exact match on the data, only range filters e.g. `<=` or `>=').", 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'category_icrp_ct': { 'type': 'categories', 'description': '`ICRP Cancer Types <https://app.dimensions.ai/browse/publication/cancer_types>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'researchers': { 'type': 'researchers', 'description': 'Dimensions researchers IDs associated to the clinical trial.', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True } }, 'fieldsets': ['all', 'basics', 'extras', 'categories'], 'metrics': { 'count': { 'name': 'count', 'description': 'Total count' } }, 'search_fields': [ 'investigators', 'full_data', 'title_only', 'title_abstract_only' ] }, 'policy_documents': { 'fields': { 'category_rcdc': { 'type': 'categories', 'description': '`Research, Condition, and Disease Categorization <https://app.dimensions.ai/browse/publication/rcdc>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'category_hrcs_hc': { 'type': 'categories', 'description': '`HRCS - Health Categories <https://app.dimensions.ai/browse/publication/hrcs_hc>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'id': { 'type': 'string', 'description': 'Dimensions policy document ID', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'publisher_org_country': { 'type': 'countries', 'description': 'Country of the organization publishing the policy document.', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'publisher_org_state': { 'type': 'states', 'description': 'State of the organization publishing the policy document.', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'publisher_org': { 'type': 'organizations', 'description': 'GRID organization publishing the policy document.', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'category_hra': { 'type': 'categories', 'description': '`Health Research Areas <https://app.dimensions.ai/browse/publication/health_research_areas?redirect_path=/discover/publication>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'category_for': { 'type': 'categories', 'description': '`ANZSRC Fields of Research classification <https://app.dimensions.ai/browse/publication/for>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'category_hrcs_rac': { 'type': 'categories', 'description': '`HRCS – Research Activity Codes <https://app.dimensions.ai/browse/publication/hrcs_rac>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'category_bra': { 'type': 'categories', 'description': '`Broad Research Areas <https://app.dimensions.ai/browse/publication/broad_research_areas?redirect_path=/discover/publication>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'year': { 'type': 'integer', 'description': 'Year of publication of the policy document.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': True }, 'publication_ids': { 'type': 'string', 'description': 'Dimensions IDs of the publications related to this policy document (see also: :ref:`policy_documents_model` section).', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'title': { 'type': 'string', 'description': 'Title of the policy document.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'linkout': { 'type': 'string', 'description': 'Original URL for the policy document.', 'long_description': None, 'is_entity': False, 'is_filter': False, 'is_facet': False }, 'category_icrp_cso': { 'type': 'categories', 'description': '`ICRP Common Scientific Outline <https://app.dimensions.ai/browse/publication/cso>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'date_inserted': { 'type': 'date', 'description': 'Date when the record was inserted into Dimensions (note: this field does not support exact match on the data, only range filters e.g. `<=` or `>=`).', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'publisher_org_city': { 'type': 'cities', 'description': 'City of the organization publishing the policy document.', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'category_icrp_ct': { 'type': 'categories', 'description': '`ICRP Cancer Types <https://app.dimensions.ai/browse/publication/cancer_types>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True } }, 'fieldsets': ['all', 'basics', 'categories'], 'metrics': { 'count': { 'name': 'count', 'description': 'Total count' } }, 'search_fields': ['full_data', 'title_only'] }, 'researchers': { 'fields': { 'current_research_org': { 'type': 'organizations', 'description': 'The most recent research organization linked to the researcher.', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'first_name': { 'type': 'string', 'description': 'First Name.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'total_grants': { 'type': 'integer', 'description': 'Total grants count.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': True }, 'last_grant_year': { 'type': 'integer', 'description': 'Last year the researcher was awarded a grant.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': True }, 'nih_ppid': { 'type': 'string', 'description': 'The PI Profile ID (i.e., ppid) is a Researcher ID from the US National Institute of Health (NIH).', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'id': { 'type': 'string', 'description': 'Dimensions researcher ID.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'research_orgs': { 'type': 'organizations', 'description': 'All research organizations linked to the researcher.', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'last_publication_year': { 'type': 'integer', 'description': None, 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': True }, 'total_publications': { 'type': 'integer', 'description': 'Total publications count.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': True }, 'orcid_id': { 'type': 'string', 'description': '`ORCID <https://orcid.org/>`_ ID.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'first_publication_year': { 'type': 'integer', 'description': None, 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': True }, 'last_name': { 'type': 'string', 'description': 'Last Name.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'obsolete': { 'type': 'integer', 'description': 'Indicates researcher ID status. 0 means that the researcher ID is still active, 1 means that the researcher ID is no longer valid. See the `redirect` field for further information on invalid researcher IDs.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'redirect': { 'type': 'string', 'description': 'Indicates status of a researcher ID marked as obsolete. Empty means that the researcher ID was deleted. Otherwise ID provided means that is the new ID into which the obsolete one was redirected. If multiple values are available, it means that the original researcher ID was split into multiple IDs.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'first_grant_year': { 'type': 'integer', 'description': 'First year the researcher was awarded a grant.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': True } }, 'fieldsets': ['all', 'basics', 'extras'], 'metrics': { 'count': { 'name': 'count', 'description': 'Total count' } }, 'search_fields': ['researcher'] }, 'organizations': { 'fields': { 'ukprn_ids': { 'type': 'string', 'description': 'UKPRN IDs for this organization', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'longitude': { 'type': 'float', 'description': None, 'long_description': None, 'is_entity': False, 'is_filter': False, 'is_facet': False }, 'id': { 'type': 'string', 'description': 'GRID ID of the organization. E.g., "grid.26999.3d".', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'cnrs_ids': { 'type': 'string', 'description': 'CNRS IDs for this organization', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'organization_parent_ids': { 'type': 'string', 'description': 'Parent organization IDs', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'wikidata_ids': { 'type': 'string', 'description': 'WikiData IDs for this organization', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'established': { 'type': 'integer', 'description': 'Year when the organization was estabilished', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'acronym': { 'type': 'string', 'description': 'GRID acronym of the organization. E.g., "UT" for `grid.26999.3d <https://grid.ac/institutes/grid.26999.3d>`_', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'city_name': { 'type': 'string', 'description': 'GRID name of the organization country. E.g., "Bethesda" for `grid.419635.c <https://grid.ac/institutes/grid.419635.c>`_', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': True }, 'country_name': { 'type': 'string', 'description': 'GRID name of the organization country. E.g., "Japan" for `grid.26999.3d <https://grid.ac/institutes/grid.26999.3d>`_', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': True }, 'ucas_ids': { 'type': 'string', 'description': 'UCAS IDs for this organization', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'isni_ids': { 'type': 'string', 'description': 'ISNI IDs for this organization', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'wikipedia_url': { 'type': 'string', 'description': 'Wikipedia URL', 'long_description': None, 'is_entity': False, 'is_filter': False, 'is_facet': False }, 'hesa_ids': { 'type': 'string', 'description': 'HESA IDs for this organization', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'types': { 'type': 'string', 'description': 'Type of an organization. Available types include: ``Company``, ``Education``, ``Healthcare``, ``Nonprofit``, ``Facility``, ``Other``, ``Government``, ``Archive``, ``Education,Company``, ``Education,Facility``, ``Education,Healthcare``, ``Education,Other``, ``Archive,Nonprofit``. Furhter explanation is on the `GRID <https://www.grid.ac/pages/policies>`_ website.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': True }, 'organization_related_ids': { 'type': 'string', 'description': 'Related organization IDs', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'orgref_ids': { 'type': 'string', 'description': 'OrgRef IDs for this organization', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'name': { 'type': 'string', 'description': 'GRID name of the organization. E.g., "University of Tokyo" for `grid.26999.3d <https://grid.ac/institutes/grid.26999.3d>`_', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'external_ids_fundref': { 'type': 'string', 'description': 'Fundref IDs for this organization', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'organization_child_ids': { 'type': 'string', 'description': 'Child organization IDs', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'linkout': { 'type': 'string', 'description': None, 'long_description': None, 'is_entity': False, 'is_filter': False, 'is_facet': False }, 'state_name': { 'type': 'string', 'description': 'GRID name of the organization country. E.g., "Maryland" for `grid.419635.c <https://grid.ac/institutes/grid.419635.c>`_', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': True }, 'latitude': { 'type': 'float', 'description': None, 'long_description': None, 'is_entity': False, 'is_filter': False, 'is_facet': False } }, 'fieldsets': ['all', 'basics'], 'metrics': { 'count': { 'name': 'count', 'description': 'Total count' } }, 'search_fields': ['full_data'] }, 'datasets': { 'fields': { 'category_rcdc': { 'type': 'categories', 'description': '`Research, Condition, and Disease Categorization <https: //app.dimensions.ai/browse/publication/rcdc>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'category_hrcs_hc': { 'type': 'categories', 'description': '`HRCS - Health Categories <https: //app.dimensions.ai/browse/publication/hrcs_hc>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'repository_id': { 'type': 'string', 'description': 'The ID of the repository of the dataset.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': True }, 'id': { 'type': 'string', 'description': 'Dimensions dataset ID.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'research_orgs': { 'type': 'organizations', 'description': 'GRID organisations linked to the publication associated to the dataset (note: this field supports count: count).', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'research_org_states': { 'type': 'states', 'description': 'State of the organisations the publication authors are affiliated to, expressed as GeoNames codes (ISO\u200c-3166-2).', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'journal': { 'type': 'journals', 'description': 'The journal a data set belongs to.', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'category_hra': { 'type': 'categories', 'description': '`Health Research Areas <https: //app.dimensions.ai/browse/publication/health_research_areas>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'category_for': { 'type': 'categories', 'description': '`ANZSRC Fields of Research classification <https: //app.dimensions.ai/browse/publication/for>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'date_embargo': { 'type': 'date', 'description': 'The embargo date of the dataset.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'category_hrcs_rac': { 'type': 'categories', 'description': '`HRCS – Research Activity Codes <https: //app.dimensions.ai/browse/publication/hrcs_rac>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'date': { 'type': 'date', 'description': 'The publication date of the dataset, eg "2018-01-01".', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'description': { 'type': 'string', 'description': 'Description of the dataset.', 'long_description': None, 'is_entity': False, 'is_filter': False, 'is_facet': False }, 'category_bra': { 'type': 'categories', 'description': '`Broad Research Areas <https: //app.dimensions.ai/browse/publication/broad_research_areas>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'research_org_cities': { 'type': 'cities', 'description': 'City of the organisations the publication authors are affiliated to, expressed as GeoNames ID and name.', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'year': { 'type': 'integer', 'description': 'Year of publication of the dataset.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': True }, 'date_created': { 'type': 'date', 'description': 'The creation date of the dataset.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'funder_countries': { 'type': 'countries', 'description': 'The country linked to the organisation funding the grant, expressed as GeoNames codes.', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'associated_publication_id': { 'type': 'string', 'description': 'The Dimensions ID of the publication linked to the dataset (single value).', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'research_org_countries': { 'type': 'countries', 'description': 'Country of the organisations the publication authors are affiliated to, identified using GeoNames codes (note: this field supports count: count). (note: this field supports count: count).', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'associated_grant_ids': { 'type': 'string', 'description': 'Dimensions IDs of the grants associated to the dataset (see also: Cross Source Links section).', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'category_icrp_ct': { 'type': 'categories', 'description': '`ICRP Cancer Types <https: //app.dimensions.ai/browse/publication/cancer_types>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'publication_ids': { 'type': 'string', 'description': 'The Dimensions IDs of the publications the dataset cites.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'authors': { 'type': 'json', 'description': 'Ordered list of the dataset authors. ORCIDs are included if available.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'doi': { 'type': 'string', 'description': 'Dataset DOI.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'license': { 'type': 'json', 'description': 'The dataset licence, as a structured JSON containing the license name, URL, and value.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'title': { 'type': 'string', 'description': 'Title of the dataset.', 'long_description': None, 'is_entity': False, 'is_filter': False, 'is_facet': False }, 'figshare_url': { 'type': 'string', 'description': 'Figshare URL for the dataset.', 'long_description': None, 'is_entity': False, 'is_filter': False, 'is_facet': False }, 'funders': { 'type': 'organizations', 'description': 'The GRID organisations funding the dataset.', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'date_modified': { 'type': 'date', 'description': 'The last modification date of the dataset.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'keywords': { 'type': 'string', 'description': 'Keywords used to describe the dataset (from authors).', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': True }, 'category_icrp_cso': { 'type': 'categories', 'description': '`ICRP Common Scientific Outline <https: //app.dimensions.ai/browse/publication/cso>`_', 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'date_inserted': { 'type': 'date', 'description': "Date when the record was inserted into Dimensions (note: this field does not support exact match on the data, only range filters e.g. `<=` or `>=').", 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': False }, 'language_desc': { 'type': 'string', 'description': 'Dataset title language, as ISO 639-1 language codes.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': True }, 'researchers': { 'type': 'researchers', 'description': "Dimensions researchers IDs associated to the dataset's associated publication. Note: in most cases, these would be the same as the dataset authors.", 'long_description': None, 'is_entity': True, 'is_filter': True, 'is_facet': True }, 'language_title': { 'type': 'string', 'description': 'Dataset title language, as ISO 639-1 language codes.', 'long_description': None, 'is_entity': False, 'is_filter': True, 'is_facet': True } }, 'fieldsets': ['all', 'basics', 'categories'], 'metrics': { 'count': { 'name': 'count', 'description': 'Total count' } }, 'search_fields': ['full_data', 'title_only', 'title_abstract_only'] } }, 'entities': { 'categories': { 'fields': { 'id': { 'name': 'string', 'type': 'string', 'description': 'Dimensions ID of the category.', 'long_description': None, 'is_filter': True }, 'name': { 'name': 'string', 'type': 'string', 'description': "Name of the category. Note: this may include an identifier from the original source. E.g., '2.1 Biological and endogenous factors' (HRCS_RAC codes) or '1103 Clinical Sciences' (FOR codes).", 'long_description': None, 'is_filter': True } }, 'fieldsets': ['all', 'basics'] }, 'cities': { 'fields': { 'id': { 'name': 'string', 'type': 'string', 'description': "GeoNames city ID (eg '5391811' for `geonames:5391811 <http://www.geonames.org/5391811>`_ )", 'long_description': None, 'is_filter': True }, 'name': { 'name': 'string', 'type': 'string', 'description': 'GeoNames city name.', 'long_description': None, 'is_filter': True } }, 'fieldsets': ['all', 'basics'] }, 'countries': { 'fields': { 'id': { 'name': 'string', 'type': 'string', 'description': "GeoNames country code (eg 'US' for `geonames:6252001 <http://www.geonames.org/6252001>`_ )", 'long_description': None, 'is_filter': True }, 'name': { 'name': 'string', 'type': 'string', 'description': 'GeoNames country name.', 'long_description': None, 'is_filter': True } }, 'fieldsets': ['all', 'basics'] }, 'journals': { 'fields': { 'id': { 'name': 'string', 'type': 'string', 'description': 'Dimensions ID of a journal. E.g., `jour.1016355 <https://app.dimensions.ai/discover/publication?and_facet_source_title=jour.1016355>`_ or `jour.1077219 <https://app.dimensions.ai/discover/publication?and_facet_source_title=jour.1077219>`_ .', 'long_description': None, 'is_filter': True }, 'title': { 'name': 'string', 'type': 'string', 'description': "Title of a journal publication. E.g. 'Nature' or 'The Lancet'", 'long_description': None, 'is_filter': True } }, 'fieldsets': ['all', 'basics'] }, 'org_groups': { 'fields': { 'id': { 'name': 'string', 'type': 'string', 'description': 'Dimensions ID of the organization group.', 'long_description': None, 'is_filter': True }, 'name': { 'name': 'string', 'type': 'string', 'description': "Name of the organization group. E.g., 'NIH' or 'ICRP'.", 'long_description': None, 'is_filter': True } }, 'fieldsets': ['all', 'basics'] }, 'states': { 'fields': { 'id': { 'name': 'string', 'type': 'string', 'description': "GeoNames state code (ISO\u200c-3166-2). E.g., 'US.CA' for `geonames:5332921 <http://www.geonames.org/5332921>`_ .", 'long_description': None, 'is_filter': True }, 'name': { 'name': 'string', 'type': 'string', 'description': 'GeoNames state name (ISO\u200c-3166-2).', 'long_description': None, 'is_filter': True } }, 'fieldsets': ['all', 'basics'] }, 'open_access': { 'fields': { 'id': { 'name': 'string', 'type': 'string', 'description': "Dimensions ID of the open access category. E.g., one of 'closed', 'oa_all', 'gold_bronze', 'gold_pure', 'green_sub', 'gold_hybrid', 'green_pub', 'green_acc'. (see also the :ref:`publications` field ``open_access``).", 'long_description': None, 'is_filter': True }, 'name': { 'name': 'string', 'type': 'string', 'description': "Name of the open access category. E.g., 'Closed' or 'Pure Gold'.", 'long_description': None, 'is_filter': True }, 'description': { 'name': 'string', 'type': 'string', 'description': 'Description of the open access category.', 'long_description': None, 'is_filter': False } }, 'fieldsets': ['all', 'basics'] } } }
37.521178
740
0.3492
8,572
133,763
5.263299
0.076878
0.043886
0.116231
0.128466
0.769865
0.746348
0.7264
0.704413
0.689474
0.668639
0
0.004278
0.55792
133,763
3,564
741
37.531706
0.758582
0.005898
0
0.767903
0
0.028871
0.353297
0.010125
0
0
0
0
0
1
0
false
0
0
0
0
0.000566
0
0
0
null
0
0
0
0
1
1
1
0
1
0
0
1
0
0
0
0
0
0
0
1
0
0
0
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
6
f550c87993538bf758ea112b913b3e3c4f129954
152
py
Python
10_testing_and_logging/spam_10.py
varshashivhare/Mastering-Python
6101fa7855e57d0bbd194e936084bd64d9d38d76
[ "MIT" ]
30
2016-10-28T18:14:15.000Z
2021-08-29T15:20:56.000Z
10_testing_and_logging/spam_10.py
varshashivhare/Mastering-Python
6101fa7855e57d0bbd194e936084bd64d9d38d76
[ "MIT" ]
null
null
null
10_testing_and_logging/spam_10.py
varshashivhare/Mastering-Python
6101fa7855e57d0bbd194e936084bd64d9d38d76
[ "MIT" ]
31
2016-09-10T22:47:12.000Z
2022-03-13T04:50:35.000Z
class Spam(object): def __init__(self, count): self.count = count def __eq__(self, other): return self.count == other.count
15.2
40
0.605263
19
152
4.421053
0.526316
0.321429
0
0
0
0
0
0
0
0
0
0
0.282895
152
9
41
16.888889
0.770642
0
0
0
0
0
0
0
0
0
0
0
0
1
0.4
false
0
0
0.2
0.8
0
1
0
0
null
1
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
1
0
0
0
1
1
0
0
6
f57531af2ac36b695768b5d040c941224636f3d3
39
py
Python
ais_code.py
climate-x/alien_invasive_species
d0780872c1f1d27dacefefeea991e61d8ccf4901
[ "MIT" ]
1
2020-11-29T10:41:51.000Z
2020-11-29T10:41:51.000Z
ais_code.py
saquib-mehmood/biodiversity_alien_invasive_species-
d0780872c1f1d27dacefefeea991e61d8ccf4901
[ "MIT" ]
null
null
null
ais_code.py
saquib-mehmood/biodiversity_alien_invasive_species-
d0780872c1f1d27dacefefeea991e61d8ccf4901
[ "MIT" ]
null
null
null
"Project Code: Alien Invasive Species"
19.5
38
0.794872
5
39
6.2
1
0
0
0
0
0
0
0
0
0
0
0
0.128205
39
1
39
39
0.911765
0.923077
0
0
0
0
0.923077
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
1
0
0
0
0
1
0
null
0
0
0
0
0
0
1
0
0
0
0
0
0
6
f598c807f1bb13a746ba894f83a1c87adf3c128a
26
py
Python
urlcounter/__init__.py
lingeringcode/urlcounter
b858c0366f2e7a0907a112de8ed8446ac82edc9c
[ "BSD-3-Clause" ]
null
null
null
urlcounter/__init__.py
lingeringcode/urlcounter
b858c0366f2e7a0907a112de8ed8446ac82edc9c
[ "BSD-3-Clause" ]
null
null
null
urlcounter/__init__.py
lingeringcode/urlcounter
b858c0366f2e7a0907a112de8ed8446ac82edc9c
[ "BSD-3-Clause" ]
null
null
null
from .urlcounter import *
13
25
0.769231
3
26
6.666667
1
0
0
0
0
0
0
0
0
0
0
0
0.153846
26
1
26
26
0.909091
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
1
0
1
0
1
1
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
6
191f297fccc7ec0e2f28d4823e3f6426b35beb36
127
py
Python
aarms/evaluation/metrics/__init__.py
eldrin/aarms
bdd5455ac8dcfc1fe91a12fdd132b74e6c37609d
[ "MIT" ]
null
null
null
aarms/evaluation/metrics/__init__.py
eldrin/aarms
bdd5455ac8dcfc1fe91a12fdd132b74e6c37609d
[ "MIT" ]
3
2020-11-05T08:44:46.000Z
2020-11-10T17:25:15.000Z
aarms/evaluation/metrics/__init__.py
eldrin/aarms
bdd5455ac8dcfc1fe91a12fdd132b74e6c37609d
[ "MIT" ]
null
null
null
from .metrics import NDCG, Precision, Recall, AveragePrecision __all__ = ["NDCG", "Precision", "Recall", "AveragePrecision"]
25.4
62
0.740157
12
127
7.5
0.666667
0.288889
0.422222
0.777778
0
0
0
0
0
0
0
0
0.11811
127
4
63
31.75
0.803571
0
0
0
0
0
0.275591
0
0
0
0
0
0
1
0
false
0
0.5
0
0.5
0
1
0
0
null
1
1
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
0
0
1
0
0
0
0
6
1941febbd2b63c2b30a9e08d7cf7200b3ff00838
8,747
py
Python
graf_5_2.py
campovski/prstki
d7099b9936aa64aa4cf178d8c211a921b1542b53
[ "MIT" ]
null
null
null
graf_5_2.py
campovski/prstki
d7099b9936aa64aa4cf178d8c211a921b1542b53
[ "MIT" ]
8
2016-02-29T08:20:38.000Z
2016-04-23T12:56:47.000Z
graf_5_2.py
campovski/prstki
d7099b9936aa64aa4cf178d8c211a921b1542b53
[ "MIT" ]
null
null
null
################################################################################################################### ## Risanje grafa # # Program narisi_graf_5_2.py naredi datoteko graf_5_2.dot, ki predstavlja graf vseh možnih potez in pozicij v igri. # # POZOR! Datoteka graf_5_2.dot se odpira približno 30 minut. # ################################################################################################################### def najdi_vozlisca(stevilo_potez, roke=2, prsti=5): '''Funkcija vrne množico vozlišč, ketere elementi so trojice (trenutno_vozlisce, igralec, novo_vozlisce).''' mnozica_vozlisc = set() novi = [((1,1),(1,1))] prvi_na_vrsti = True for poteza in range(stevilo_potez): if prvi_na_vrsti == True: for ((L,D),(l,d)) in novi: seznam = [] # Udari z levo: if L != 0: if l != 0: seznam.append(((L,D),(min((l+L)%prsti,d),max((l+L)%prsti,d)))) if d != 0: seznam.append(((L,D),(min(l,(d+L)%prsti),max(l,(d+L)%prsti)))) # Udari z desno: if D != 0: if l != 0: seznam.append(((L,D),(min((l+D)%prsti,d),max((l+D)%prsti,d)))) if d != 0: seznam.append(((L,D),(min(l,(d+D)%prsti),max(l,(d+D)%prsti)))) for element in seznam: mnozica_vozlisc.add((((L,D),(l,d)),1,element)) prvi_na_vrsti = False novi = seznam else: for ((L,D),(l,d)) in novi: seznam = [] # Udari z levo: if l != 0: if L != 0: seznam.append(((min((L+l)%prsti,D),max((L+l)%prsti,D)),(l,d))) if D != 0: seznam.append(((min(L,(D+l)%prsti),max(L,(D+l)%prsti)),(l,d))) # Udari z desno: if d != 0: if L != 0: seznam.append(((min((L+d)%prsti,D),max((L+d)%prsti,D)),(l,d))) if D != 0: seznam.append(((min(L,(D+d)%prsti),max(L,(D+d)%prsti)),(l,d))) for element in seznam: mnozica_vozlisc.add((((L,D),(l,d)),2,element)) prvi_na_vrsti = True novi = seznam return mnozica_vozlisc def delitev_prvega(seznam_vozlisc, prsti=5, roke=2): ''' Funkcija vrne seznam parov, kjer prva komponenta predstavlja vozlišče pred opravljeno delitvijo, druga pa vozlišče po delitvi. Velja za prvega igralca. ''' seznam_novih = [] for ((L,D),(l,d)) in seznam_vozlisc: staro_vozlisce = ((L,D),(l,d)) if (L == 0 and D == 0): pass elif (L == 0 and D%roke == 0): novo_vozlisce = ((D//roke,D//roke),(l,d)) seznam_novih.append((staro_vozlisce,novo_vozlisce)) elif (D == 0 and L%roke == 0): novo_vozlisce = ((L//roke,L//roke),(l,d)) seznam_novih.append((staro_vozlisce,novo_vozlisce)) return(seznam_novih) def delitev_drugega(seznam_vozlisc, prsti=5, roke=2): ''' Funkcija vrne seznam parov, kjer prva komponenta predstavlja vozlišče pred opravljeno delitvijo, druga pa vozlišče po delitvi. Velja za drugega igralca.''' seznam_novih = [] for ((L,D),(l,d)) in seznam_vozlisc: staro_vozlisce = ((L,D),(l,d)) if (L == 0 and D == 0): pass elif (l == 0 and d%roke == 0): novo_vozlisce = ((L,D),(d//roke,d//roke)) seznam_novih.append((staro_vozlisce,novo_vozlisce)) elif (d == 0 and l%roke == 0): novo_vozlisce = ((L,D),(l//roke,l//roke)) seznam_novih.append((staro_vozlisce,novo_vozlisce)) return(seznam_novih) def poteza_prvega_z_delitvijo(seznam_vozlisc, prsti=5): ''' Funkcija vrne seznam vseh potencialnih vozlišč, ki nastanejo po opravljeni potezi prvega igralca in množico vseh potencialnih potez, katere elementi so trojice (trenutno_vozlisce, igralec, novo_vozlisce). ''' mnozica_potez = set() seznam_novih = [] for ((L,D),(l,d)) in seznam_vozlisc: novo_vozlisce = None ### CE NI DELITVE: # Udari z levo: if L != 0: if l != 0: novo_vozlisce_1 = ((L,D),(min((l+L)%prsti,d),max((l+L)%prsti,d))) if novo_vozlisce_1 not in seznam_novih: seznam_novih.append(novo_vozlisce_1) mnozica_potez.add((((L,D),(l,d)),1,novo_vozlisce_1)) if d != 0: novo_vozlisce_2 = ((L,D),(min(l,(d+L)%prsti),max(l,(d+L)%prsti))) if novo_vozlisce_2 not in seznam_novih: seznam_novih.append(novo_vozlisce_2) mnozica_potez.add((((L,D),(l,d)),1,novo_vozlisce_2)) # Udari z desno: if D != 0: if l != 0: novo_vozlisce_3 = ((L,D),(min((l+D)%prsti,d),max((l+D)%prsti,d))) if novo_vozlisce_3 not in seznam_novih: seznam_novih.append(novo_vozlisce_3) mnozica_potez.add((((L,D),(l,d)),1,novo_vozlisce_3)) if d != 0: novo_vozlisce_4 = ((L,D),(min(l,(d+D)%prsti),max(l,(d+D)%prsti))) if novo_vozlisce_4 not in seznam_novih: seznam_novih.append(novo_vozlisce_4) mnozica_potez.add((((L,D),(l,d)),1,novo_vozlisce_4)) ### CE JE DELITEV: for (staro,novo) in delitev_prvega(seznam_vozlisc): ((L,D),(l,d)) = staro ((A,B),(l,d)) = novo if l != 0: novo_vozlisce_1 = ((A,B),(min((l+A)%prsti,d),max((l+A)%prsti,d))) if novo_vozlisce_1 not in seznam_novih: seznam_novih.append(novo_vozlisce_1) mnozica_potez.add((((L,D),(l,d)),1,novo_vozlisce_1)) if d != 0: novo_vozlisce_2 = ((A,B),(min(l,(d+A)%prsti),max(l,(d+A)%prsti))) if novo_vozlisce_2 not in seznam_novih: seznam_novih.append(novo_vozlisce_2) mnozica_potez.add((((L,D),(l,d)),1,novo_vozlisce_2)) return(seznam_novih, mnozica_potez) def poteza_drugega_z_delitvijo(seznam_vozlisc, prsti=5): ''' Funkcija vrne seznam vseh potencialnih vozlišč, ki nastanejo po opravljeni potezi drugega igralca in množico vseh potencialnih potez, katere elementi so trojice (trenutno_vozlisce, igralec, novo_vozlisce). ''' mnozica_potez = set() seznam_novih = [] for ((L,D),(l,d)) in seznam_vozlisc: novo_vozlisce = None ### CE NI DELITVE: # Udari z levo: if l != 0: if L != 0: novo_vozlisce_1 = ((min((L+l)%prsti,D),max((L+l)%prsti,D)),(l,d)) if novo_vozlisce_1 not in seznam_novih: seznam_novih.append(novo_vozlisce_1) mnozica_potez.add((((L,D),(l,d)),2,novo_vozlisce_1)) if D != 0: novo_vozlisce_2 = ((min(L,(D+l)%prsti),max(L,(D+l)%prsti)),(l,d)) if novo_vozlisce_2 not in seznam_novih: seznam_novih.append(novo_vozlisce_2) mnozica_potez.add((((L,D),(l,d)),2,novo_vozlisce_2)) # Udari z desno: if d != 0: if L != 0: novo_vozlisce_3 = ((min((L+d)%prsti,D),max((L+d)%prsti,D)),(l,d)) if novo_vozlisce_3 not in seznam_novih: seznam_novih.append(novo_vozlisce_3) mnozica_potez.add((((L,D),(l,d)),2,novo_vozlisce_3)) if D != 0: novo_vozlisce_4 = ((min(L,(D+d)%prsti),max(L,(D+d)%prsti)),(l,d)) if novo_vozlisce_4 not in seznam_novih: seznam_novih.append(novo_vozlisce_4) mnozica_potez.add((((L,D),(l,d)),2,novo_vozlisce_4)) ### CE JE DELITEV: for (staro,novo) in delitev_drugega(seznam_vozlisc): ((L,D),(l,d)) = staro ((L,D),(a,b)) = novo if L != 0: novo_vozlisce_1 = ((min((L+a)%prsti,D),max((L+a)%prsti,D)),(a,b)) if novo_vozlisce_1 not in seznam_novih: seznam_novih.append(novo_vozlisce_1) mnozica_potez.add((((L,D),(l,d)),2,novo_vozlisce_1)) if D != 0: novo_vozlisce_2 = ((min(L,(D+a)%prsti),max(L,(D+a)%prsti)),(a,b)) if novo_vozlisce_2 not in seznam_novih: seznam_novih.append(novo_vozlisce_2) mnozica_potez.add((((L,D),(l,d)),2,novo_vozlisce_2)) return(seznam_novih, mnozica_potez) zacetek = najdi_vozlisca(2) # NE ZBRISI!! def vrni_mnozico_potez(mnozica=set(), seznam_zacetnih=[((1,1),(1,1))], k=0): ''' Mnozica vseh potez (trojic), ki jih lahko opravimo v igri. ''' k += 1 if k > 2: return mnozica.union(zacetek) else: return vrni_mnozico_potez((poteza_prvega_z_delitvijo(seznam_zacetnih))[1].union(poteza_drugega_z_delitvijo((poteza_prvega_z_delitvijo(seznam_zacetnih))[0])[1]),(poteza_drugega_z_delitvijo((poteza_prvega_z_delitvijo(seznam_zacetnih))[0]))[0], k) def naredi_dot(roke=2, prsti=5): ''' Funkcija vse trojice iz vrni_mnozico_potez zapise v datoteko graf_5_2.dot. ''' try: os.remove("graf_5_2.dot") except: pass izhod = open("graf_5_2.dot", "w") izhod.write("digraph {\n node [style = filled];\n") for (((L,D),(l,d)), poteza, ((A,B),(a,b))) in vrni_mnozico_potez(): if ((L,D),(l,d)) == ((A,B),(a,b)): pass else: izhod.write(" \""+str(L)+","+str(D)+" "+str(l)+","+str(d)+"\" -> \""+str(A)+","+str(B)+" "+str(a)+","+str(b)+"\" [label ="+str(poteza)+"];\n") if (A,B) == (0,0) or (a,b) == (0,0): izhod.write(" \""+str(A)+","+str(B)+" "+str(a)+","+str(b)+"\" [color=coral];\n") if (L,D) == (1,1) and (l,d) == (1,1): izhod.write(" \""+str(L)+","+str(D)+" "+str(l)+","+str(d)+"\" [color=darkseagreen];\n") izhod.write(" }") naredi_dot()
47.026882
246
0.595633
1,419
8,747
3.496829
0.102889
0.041919
0.021161
0.020959
0.818622
0.792825
0.784563
0.770657
0.747481
0.728335
0
0.021208
0.186007
8,747
185
247
47.281081
0.675702
0.152509
0
0.533784
0
0
0.03964
0.003233
0
0
0
0
0
1
0.047297
false
0.027027
0
0
0.067568
0
0
0
0
null
0
0
0
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
6
5ff14205d9afb029d0f48514f045f82e82785810
24
py
Python
tests/underflow/__init__.py
plewis/phycas
9f5a4d9b2342dab907d14a46eb91f92ad80a5605
[ "MIT" ]
3
2015-09-24T23:12:57.000Z
2021-04-12T07:07:01.000Z
tests/underflow/__init__.py
plewis/phycas
9f5a4d9b2342dab907d14a46eb91f92ad80a5605
[ "MIT" ]
null
null
null
tests/underflow/__init__.py
plewis/phycas
9f5a4d9b2342dab907d14a46eb91f92ad80a5605
[ "MIT" ]
1
2015-11-23T10:35:43.000Z
2015-11-23T10:35:43.000Z
from underflow import *
12
23
0.791667
3
24
6.333333
1
0
0
0
0
0
0
0
0
0
0
0
0.166667
24
1
24
24
0.95
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
1
0
1
0
1
1
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
6
fd7087aa4a285dd67bcef07619f089434978974d
108
py
Python
pages/main_page.py
YegorKehl/selenium_course_final_project
c3ee84f987d4c0eb37f8ffae4d3080994e6541ad
[ "Unlicense" ]
null
null
null
pages/main_page.py
YegorKehl/selenium_course_final_project
c3ee84f987d4c0eb37f8ffae4d3080994e6541ad
[ "Unlicense" ]
null
null
null
pages/main_page.py
YegorKehl/selenium_course_final_project
c3ee84f987d4c0eb37f8ffae4d3080994e6541ad
[ "Unlicense" ]
null
null
null
from .base_page import BasePage from .locators import MainPageLocators class MainPage(BasePage): pass
15.428571
38
0.796296
13
108
6.538462
0.769231
0
0
0
0
0
0
0
0
0
0
0
0.157407
108
6
39
18
0.934066
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0.25
0.5
0
0.75
0
1
0
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
1
1
0
1
0
0
6
8bf73ec091f3087c31ce7a2979cfc3da93be1f70
190
py
Python
net2grid/__init__.py
PariseC/net2grid
99070d7635d7544aa885d8adbebbf2431c43c204
[ "Apache-2.0" ]
null
null
null
net2grid/__init__.py
PariseC/net2grid
99070d7635d7544aa885d8adbebbf2431c43c204
[ "Apache-2.0" ]
null
null
null
net2grid/__init__.py
PariseC/net2grid
99070d7635d7544aa885d8adbebbf2431c43c204
[ "Apache-2.0" ]
null
null
null
from .readfiles import read_gmns_network_from_csv from .network import partition_grid from .writer import save_network __all__=['read_gmns_network_from_csv','partition_grid','save_network']
38
70
0.857895
28
190
5.25
0.428571
0.108844
0.204082
0.258503
0.29932
0
0
0
0
0
0
0
0.068421
190
5
70
38
0.830508
0
0
0
0
0
0.272251
0.136126
0
0
0
0
0
1
0
false
0
0.75
0
0.75
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
1
0
1
0
0
6
e3246bd29475e5eeb6b7db68b745ea93cba5024d
8,249
py
Python
pirates/leveleditor/worldData/shipNavyWarship1.py
itsyaboyrocket/pirates
6ca1e7d571c670b0d976f65e608235707b5737e3
[ "BSD-3-Clause" ]
3
2021-02-25T06:38:13.000Z
2022-03-22T07:00:15.000Z
pirates/leveleditor/worldData/shipNavyWarship1.py
itsyaboyrocket/pirates
6ca1e7d571c670b0d976f65e608235707b5737e3
[ "BSD-3-Clause" ]
null
null
null
pirates/leveleditor/worldData/shipNavyWarship1.py
itsyaboyrocket/pirates
6ca1e7d571c670b0d976f65e608235707b5737e3
[ "BSD-3-Clause" ]
1
2021-02-25T06:38:17.000Z
2021-02-25T06:38:17.000Z
# uncompyle6 version 3.2.0 # Python bytecode 2.4 (62061) # Decompiled from: Python 2.7.14 (v2.7.14:84471935ed, Sep 16 2017, 20:19:30) [MSC v.1500 32 bit (Intel)] # Embedded file name: pirates.leveleditor.worldData.shipNavyWarship1 from pandac.PandaModules import Point3, VBase3, Vec4 objectStruct = {'Objects': {'1189041615.92gjeon': {'Type': 'Ship Part', 'Name': 'shipNavyWarship1', 'Category': '21: Light Frigate', 'File': '', 'Flagship': True, 'Objects': {'1189041831.52gjeon': {'Type': 'Spawn Node', 'Aggro Radius': '12.0000', 'AnimSet': 'default', 'Hpr': Point3(0.0, 0.0, 0.0), 'Min Population': '1', 'Patrol Radius': '12.0000', 'Pause Chance': '100', 'Pause Duration': '30', 'Pos': Point3(0.486, 6.352, 14.945), 'Scale': VBase3(1.0, 1.0, 1.0), 'Spawnables': 'Area', 'Start State': 'Patrol', 'Team': 'default', 'Visual': {'Color': (0, 0, 0.65, 1), 'Model': 'models/misc/smiley'}}, '1189041887.11gjeon': {'Type': 'Movement Node', 'Hpr': Point3(0.0, 0.0, 0.0), 'Pause Chance': '100', 'Pause Duration': '30', 'Pos': Point3(-13.623, 14.112, 14.944), 'Scale': VBase3(1.0, 1.0, 1.0), 'Visual': {'Color': (0.65, 0, 0, 1), 'Model': 'models/misc/smiley'}}, '1189041891.25gjeon': {'Type': 'Movement Node', 'Hpr': Point3(0.0, 0.0, 0.0), 'Pause Chance': '100', 'Pause Duration': '30', 'Pos': Point3(13.63, 14.416, 14.944), 'Scale': VBase3(1.0, 1.0, 1.0), 'Visual': {'Color': (0.65, 0, 0, 1), 'Model': 'models/misc/smiley'}}, '1189041896.81gjeon': {'Type': 'Movement Node', 'Hpr': Point3(0.0, 0.0, 0.0), 'Pause Chance': '100', 'Pause Duration': '30', 'Pos': Point3(-14.612, 35.063, 26.805), 'Scale': VBase3(1.0, 1.0, 1.0), 'Visual': {'Color': (0.65, 0, 0, 1), 'Model': 'models/misc/smiley'}}, '1189041899.17gjeon': {'Type': 'Movement Node', 'Hpr': Point3(0.0, 0.0, 0.0), 'Pause Chance': '100', 'Pause Duration': '30', 'Pos': Point3(15.142, 35.717, 26.805), 'Scale': VBase3(1.0, 1.0, 1.0), 'Visual': {'Color': (0.65, 0, 0, 1), 'Model': 'models/misc/smiley'}}, '1189041904.69gjeon': {'Type': 'Movement Node', 'Hpr': Point3(0.0, 0.0, 0.0), 'Pause Chance': '100', 'Pause Duration': '30', 'Pos': Point3(-14.865, 48.227, 30.65), 'Scale': VBase3(1.0, 1.0, 1.0), 'Visual': {'Color': (0.65, 0, 0, 1), 'Model': 'models/misc/smiley'}}, '1189041907.03gjeon': {'Type': 'Movement Node', 'Hpr': Point3(0.0, 0.0, 0.0), 'Pause Chance': '100', 'Pause Duration': '30', 'Pos': Point3(14.455, 48.255, 30.65), 'Scale': VBase3(1.0, 1.0, 1.0), 'Visual': {'Color': (0.65, 0, 0, 1), 'Model': 'models/misc/smiley'}}, '1189041919.69gjeon': {'Type': 'Movement Node', 'Hpr': Point3(0.0, 0.0, 0.0), 'Pause Chance': '100', 'Pause Duration': '30', 'Pos': Point3(-16.7, 61.161, 30.624), 'Scale': VBase3(1.0, 1.0, 1.0), 'Visual': {'Color': (0.65, 0, 0, 1), 'Model': 'models/misc/smiley'}}, '1189041922.19gjeon': {'Type': 'Movement Node', 'Hpr': Point3(0.0, 0.0, 0.0), 'Pause Chance': '100', 'Pause Duration': '30', 'Pos': Point3(17.571, 61.67, 30.623), 'Scale': VBase3(1.0, 1.0, 1.0), 'Visual': {'Color': (0.65, 0, 0, 1), 'Model': 'models/misc/smiley'}}, '1189041926.47gjeon': {'Type': 'Movement Node', 'Hpr': Point3(0.0, 0.0, 0.0), 'Pause Chance': '100', 'Pause Duration': '30', 'Pos': Point3(0.105, 33.781, 26.804), 'Scale': VBase3(1.0, 1.0, 1.0), 'Visual': {'Color': (0.65, 0, 0, 1), 'Model': 'models/misc/smiley'}}, '1189041938.34gjeon': {'Type': 'Movement Node', 'Hpr': Point3(0.0, 0.0, 0.0), 'Pause Chance': 100, 'Pause Duration': 30, 'Pos': Point3(0.494, -25.058, 29.274), 'Scale': VBase3(1.0, 1.0, 1.0), 'Visual': {'Color': (0.65, 0, 0, 1), 'Model': 'models/misc/smiley'}}, '1189041947.16gjeon': {'Type': 'Movement Node', 'Hpr': Point3(0.0, 0.0, 0.0), 'Pause Chance': '100', 'Pause Duration': '30', 'Pos': Point3(18.51, -15.632, 14.788), 'Scale': VBase3(1.0, 1.0, 1.0), 'Visual': {'Color': (0.65, 0, 0, 1), 'Model': 'models/misc/smiley'}}, '1189041954.84gjeon': {'Type': 'Movement Node', 'Hpr': Point3(0.0, 0.0, 0.0), 'Pause Chance': '100', 'Pause Duration': '30', 'Pos': Point3(-20.088, -17.149, 14.728), 'Scale': VBase3(1.0, 1.0, 1.0), 'Visual': {'Color': (0.65, 0, 0, 1), 'Model': 'models/misc/smiley'}}, '1189041960.7gjeon': {'Type': 'Movement Node', 'Hpr': Point3(0.0, 0.0, 0.0), 'Pause Chance': '100', 'Pause Duration': '30', 'Pos': Point3(-17.314, -26.611, 11.281), 'Scale': VBase3(1.0, 1.0, 1.0), 'Visual': {'Color': (0.65, 0, 0, 1), 'Model': 'models/misc/smiley'}}, '1189041962.84gjeon': {'Type': 'Movement Node', 'Hpr': Point3(0.0, 0.0, 0.0), 'Pause Chance': '100', 'Pause Duration': '30', 'Pos': Point3(16.974, -26.086, 11.251), 'Scale': VBase3(1.0, 1.0, 1.0), 'Visual': {'Color': (0.65, 0, 0, 1), 'Model': 'models/misc/smiley'}}, '1189041973.92gjeon': {'Type': 'Movement Node', 'Hpr': Point3(0.0, 0.0, 0.0), 'Pause Chance': 100, 'Pause Duration': 30, 'Pos': Point3(-9.721, -47.545, 12.719), 'Scale': VBase3(1.0, 1.0, 1.0), 'Visual': {'Color': (0.65, 0, 0, 1), 'Model': 'models/misc/smiley'}}, '1189041975.97gjeon': {'Type': 'Movement Node', 'Hpr': Point3(0.0, 0.0, 0.0), 'Pause Chance': 100, 'Pause Duration': 30, 'Pos': Point3(8.083, -48.061, 12.747), 'Scale': VBase3(1.0, 1.0, 1.0), 'Visual': {'Color': (0.65, 0, 0, 1), 'Model': 'models/misc/smiley'}}}, 'Respawns': True, 'Team': 'EvilNavy', 'Visual': {'Model': ['models/shipparts/warshipL1-geometry_High', 'models/shipparts/warshipL1-collisions', 'models/shipparts/warCabinAL1-collisions', 'models/shipparts/warCabinAL1-geometry_High']}}}, 'Node Links': [['1189041831.52gjeon', '1189041891.25gjeon', 'Bi-directional'], ['1189041831.52gjeon', '1189041887.11gjeon', 'Bi-directional'], ['1189041831.52gjeon', '1189041938.34gjeon', 'Bi-directional'], ['1189041896.81gjeon', '1189041887.11gjeon', 'Bi-directional'], ['1189041904.69gjeon', '1189041896.81gjeon', 'Bi-directional'], ['1189041904.69gjeon', '1189041919.69gjeon', 'Bi-directional'], ['1189041922.19gjeon', '1189041919.69gjeon', 'Bi-directional'], ['1189041922.19gjeon', '1189041907.03gjeon', 'Bi-directional'], ['1189041904.69gjeon', '1189041907.03gjeon', 'Bi-directional'], ['1189041907.03gjeon', '1189041899.17gjeon', 'Bi-directional'], ['1189041899.17gjeon', '1189041926.47gjeon', 'Bi-directional'], ['1189041896.81gjeon', '1189041926.47gjeon', 'Bi-directional'], ['1189041899.17gjeon', '1189041891.25gjeon', 'Bi-directional'], ['1189041947.16gjeon', '1189041891.25gjeon', 'Bi-directional'], ['1189041947.16gjeon', '1189041962.84gjeon', 'Bi-directional'], ['1189041975.97gjeon', '1189041962.84gjeon', 'Bi-directional'], ['1189041960.7gjeon', '1189041973.92gjeon', 'Bi-directional'], ['1189041954.84gjeon', '1189041960.7gjeon', 'Bi-directional'], ['1189041954.84gjeon', '1189041887.11gjeon', 'Bi-directional']], 'Layers': {}, 'ObjectIds': {'1189041615.92gjeon': '["Objects"]["1189041615.92gjeon"]', '1189041831.52gjeon': '["Objects"]["1189041615.92gjeon"]["Objects"]["1189041831.52gjeon"]', '1189041887.11gjeon': '["Objects"]["1189041615.92gjeon"]["Objects"]["1189041887.11gjeon"]', '1189041891.25gjeon': '["Objects"]["1189041615.92gjeon"]["Objects"]["1189041891.25gjeon"]', '1189041896.81gjeon': '["Objects"]["1189041615.92gjeon"]["Objects"]["1189041896.81gjeon"]', '1189041899.17gjeon': '["Objects"]["1189041615.92gjeon"]["Objects"]["1189041899.17gjeon"]', '1189041904.69gjeon': '["Objects"]["1189041615.92gjeon"]["Objects"]["1189041904.69gjeon"]', '1189041907.03gjeon': '["Objects"]["1189041615.92gjeon"]["Objects"]["1189041907.03gjeon"]', '1189041919.69gjeon': '["Objects"]["1189041615.92gjeon"]["Objects"]["1189041919.69gjeon"]', '1189041922.19gjeon': '["Objects"]["1189041615.92gjeon"]["Objects"]["1189041922.19gjeon"]', '1189041926.47gjeon': '["Objects"]["1189041615.92gjeon"]["Objects"]["1189041926.47gjeon"]', '1189041938.34gjeon': '["Objects"]["1189041615.92gjeon"]["Objects"]["1189041938.34gjeon"]', '1189041947.16gjeon': '["Objects"]["1189041615.92gjeon"]["Objects"]["1189041947.16gjeon"]', '1189041954.84gjeon': '["Objects"]["1189041615.92gjeon"]["Objects"]["1189041954.84gjeon"]', '1189041960.7gjeon': '["Objects"]["1189041615.92gjeon"]["Objects"]["1189041960.7gjeon"]', '1189041962.84gjeon': '["Objects"]["1189041615.92gjeon"]["Objects"]["1189041962.84gjeon"]', '1189041973.92gjeon': '["Objects"]["1189041615.92gjeon"]["Objects"]["1189041973.92gjeon"]', '1189041975.97gjeon': '["Objects"]["1189041615.92gjeon"]["Objects"]["1189041975.97gjeon"]'}}
1,374.833333
7,965
0.643472
1,148
8,249
4.621951
0.165505
0.038824
0.039012
0.038447
0.469845
0.437052
0.401621
0.401621
0.39559
0.38824
0
0.271922
0.083404
8,249
6
7,965
1,374.833333
0.429837
0.026912
0
0
0
0
0.588184
0.16353
0
0
0
0
0
1
0
false
0
0.5
0
0.5
0
0
0
0
null
0
0
0
0
0
0
0
0
0
0
1
0
0
1
1
1
1
0
0
0
0
0
0
0
null
0
0
0
0
0
0
0
0
1
0
0
0
0
6
8b5f563e6a893272164379efdc93f782f3f39a9c
120
py
Python
batchglm/api/models/tf2/glm_beta.py
le-ander/batchglm
31b905b99b6baa7c94b82550d6a74f00d81966ea
[ "BSD-3-Clause" ]
null
null
null
batchglm/api/models/tf2/glm_beta.py
le-ander/batchglm
31b905b99b6baa7c94b82550d6a74f00d81966ea
[ "BSD-3-Clause" ]
null
null
null
batchglm/api/models/tf2/glm_beta.py
le-ander/batchglm
31b905b99b6baa7c94b82550d6a74f00d81966ea
[ "BSD-3-Clause" ]
null
null
null
#from batchglm.models.glm_beta import InputDataGLM, Model, Simulator #from batchglm.train.tf2.glm_beta import Estimator
40
68
0.841667
17
120
5.823529
0.705882
0.242424
0.262626
0
0
0
0
0
0
0
0
0.009091
0.083333
120
2
69
60
0.890909
0.966667
0
null
0
null
0
0
null
0
0
0
null
1
null
true
0
0
null
null
null
1
0
0
null
1
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
1
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
0
0
0
0
0
6
8b8bdf63989016df673d30ad4c7a1959eca2072a
119
py
Python
social/tests/__init__.py
MadeInHaus/django-social
2d88760cbff07083ad3fbab8d60bf340b2a8eba0
[ "MIT" ]
null
null
null
social/tests/__init__.py
MadeInHaus/django-social
2d88760cbff07083ad3fbab8d60bf340b2a8eba0
[ "MIT" ]
1
2015-01-07T17:30:25.000Z
2015-01-07T17:30:25.000Z
social/tests/__init__.py
MadeInHaus/django-social
2d88760cbff07083ad3fbab8d60bf340b2a8eba0
[ "MIT" ]
null
null
null
from .test_facebook import FacebookTest from .test_twitter import TwitterTest from .test_instagram import InstagramTest
39.666667
41
0.882353
15
119
6.8
0.6
0.235294
0
0
0
0
0
0
0
0
0
0
0.092437
119
3
41
39.666667
0.944444
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
1
0
1
0
1
0
0
null
1
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
6
8bc6f755d7b09b3e489c3599cd1efde41e8cf321
11,250
py
Python
tests/test_unmap.py
spreaker/aws-cloud-unmap
b855a50db63f091295f04e54481a052e6d21e15d
[ "MIT" ]
9
2019-04-03T12:55:16.000Z
2021-09-16T11:55:13.000Z
tests/test_unmap.py
spreaker/aws-cloud-unmap
b855a50db63f091295f04e54481a052e6d21e15d
[ "MIT" ]
5
2019-03-29T11:26:40.000Z
2020-12-22T14:14:20.000Z
tests/test_unmap.py
spreaker/aws-cloud-unmap
b855a50db63f091295f04e54481a052e6d21e15d
[ "MIT" ]
2
2020-02-20T22:54:09.000Z
2021-04-14T21:20:45.000Z
import unittest import boto3 from unittest.mock import patch from botocore.stub import Stubber from cloudunmap.unmap import matchServiceInstanceInRunningInstances, unmapTerminatedInstancesFromService from .mocks import mockBotoClient, mockServiceInstance, mockEC2Instance class TestUnmap(unittest.TestCase): def setUp(self): self.ec2Client = boto3.client("ec2") self.sdClient = boto3.client("servicediscovery") self.sdStubber = Stubber(self.sdClient) self.sdStubber.activate() self.ec2Stubber = Stubber(self.ec2Client) self.ec2Stubber.activate() self.botoClientMock = mockBotoClient({"ec2": self.ec2Client, "servicediscovery": self.sdClient}) # # matchServiceInstanceInRunningInstances() # def testMatchServiceInstanceInRunningInstances(self): runningInstances = [ {"InstanceId": "i-1", "PrivateIpAddress": "172.0.0.1"}, {"InstanceId": "i-2", "PrivateIpAddress": "172.0.0.2", "PublicIpAddress": "2.2.2.2"} ] self.assertFalse(matchServiceInstanceInRunningInstances( {"Id": "i-1", "Attributes": {"AWS_INSTANCE_IPV4": "172.0.0.1"}}, [])) self.assertTrue(matchServiceInstanceInRunningInstances( {"Id": "i-1", "Attributes": {"AWS_INSTANCE_IPV4": "172.0.0.1"}}, runningInstances)) self.assertFalse(matchServiceInstanceInRunningInstances( {"Id": "i-x", "Attributes": {"AWS_INSTANCE_IPV4": "172.0.0.1"}}, runningInstances)) self.assertFalse(matchServiceInstanceInRunningInstances( {"Id": "i-1", "Attributes": {"AWS_INSTANCE_IPV4": "172.0.0.2"}}, runningInstances)) self.assertTrue(matchServiceInstanceInRunningInstances( {"Id": "i-2", "Attributes": {"AWS_INSTANCE_IPV4": "172.0.0.2"}}, runningInstances)) self.assertTrue(matchServiceInstanceInRunningInstances( {"Id": "i-2", "Attributes": {"AWS_INSTANCE_IPV4": "2.2.2.2"}}, runningInstances)) # # unmapTerminatedInstancesFromService() # def testUnmapTerminatedInstancesFromServiceShouldDoNothingIfRegisteredInstancesAreRunning(self): # Mock Cloud Map client self.sdStubber.add_response( "list_instances", {"Instances": [mockServiceInstance("i-1", "172.0.0.1"), mockServiceInstance("i-2", "2.2.2.2")]}, {"ServiceId": "srv-1", "MaxResults": 100}) # Mock EC2 client self.ec2Stubber.add_response( "describe_instances", {"Reservations": [{"Instances": [ mockEC2Instance("i-1", privateIp="172.0.0.1"), mockEC2Instance("i-2", privateIp="172.0.0.2", publicIp="2.2.2.2"), ]}]}, {"Filters": [{"Name": "instance-id", "Values": ["i-1", "i-2"]}], "MaxResults": 1000}) with patch("boto3.client", side_effect=self.botoClientMock): unmapTerminatedInstancesFromService(serviceId="srv-1", serviceRegion="eu-west-1", instancesRegions=["eu-west-1"]) self.ec2Stubber.assert_no_pending_responses() self.sdStubber.assert_no_pending_responses() def testUnmapTerminatedInstancesFromServiceShouldDeregisterInstancesNotFound(self): # Mock Cloud Map client self.sdStubber.add_response( "list_instances", {"Instances": [mockServiceInstance("i-1", "172.0.0.1"), mockServiceInstance("i-2", "2.2.2.2")]}, {"ServiceId": "srv-1", "MaxResults": 100}) self.sdStubber.add_response( "deregister_instance", {}, {"ServiceId": "srv-1", "InstanceId": "i-2"}) # Mock EC2 client self.ec2Stubber.add_response( "describe_instances", {"Reservations": [{"Instances": [ mockEC2Instance("i-1", privateIp="172.0.0.1"), ]}]}, {"Filters": [{"Name": "instance-id", "Values": ["i-1", "i-2"]}], "MaxResults": 1000}) with patch("boto3.client", side_effect=self.botoClientMock): unmapTerminatedInstancesFromService(serviceId="srv-1", serviceRegion="eu-west-1", instancesRegions=["eu-west-1"]) self.ec2Stubber.assert_no_pending_responses() self.sdStubber.assert_no_pending_responses() def testUnmapTerminatedInstancesFromServiceShouldDeregisterInstancesFoundButWithDifferentIp(self): # Mock Cloud Map client self.sdStubber.add_response( "list_instances", {"Instances": [mockServiceInstance("i-1", "172.0.0.1"), mockServiceInstance("i-2", "2.2.2.2")]}, {"ServiceId": "srv-1", "MaxResults": 100}) self.sdStubber.add_response( "deregister_instance", {}, {"ServiceId": "srv-1", "InstanceId": "i-2"}) # Mock EC2 client self.ec2Stubber.add_response( "describe_instances", {"Reservations": [{"Instances": [ mockEC2Instance("i-1", privateIp="172.0.0.1"), mockEC2Instance("i-2", publicIp="1.1.1.1"), ]}]}, {"Filters": [{"Name": "instance-id", "Values": ["i-1", "i-2"]}], "MaxResults": 1000}) with patch("boto3.client", side_effect=self.botoClientMock): unmapTerminatedInstancesFromService(serviceId="srv-1", serviceRegion="eu-west-1", instancesRegions=["eu-west-1"]) self.ec2Stubber.assert_no_pending_responses() self.sdStubber.assert_no_pending_responses() def testUnmapTerminatedInstancesFromServiceShouldDeregisterInstancesFoundButTerminating(self): # Mock Cloud Map client self.sdStubber.add_response( "list_instances", {"Instances": [mockServiceInstance("i-1", "172.0.0.1"), mockServiceInstance("i-2", "2.2.2.2")]}, {"ServiceId": "srv-1", "MaxResults": 100}) self.sdStubber.add_response( "deregister_instance", {}, {"ServiceId": "srv-1", "InstanceId": "i-2"}) # Mock EC2 client self.ec2Stubber.add_response( "describe_instances", {"Reservations": [{"Instances": [ mockEC2Instance("i-1", privateIp="172.0.0.1"), mockEC2Instance("i-2", privateIp="172.0.0.2", publicIp="2.2.2.2", state="shutting-down"), ]}]}, {"Filters": [{"Name": "instance-id", "Values": ["i-1", "i-2"]}], "MaxResults": 1000}) with patch("boto3.client", side_effect=self.botoClientMock): unmapTerminatedInstancesFromService(serviceId="srv-1", serviceRegion="eu-west-1", instancesRegions=["eu-west-1"]) self.ec2Stubber.assert_no_pending_responses() self.sdStubber.assert_no_pending_responses() def testUnmapTerminatedInstancesFromServiceShouldDeregisterInstancesFoundButTerminated(self): # Mock Cloud Map client self.sdStubber.add_response( "list_instances", {"Instances": [mockServiceInstance("i-1", "172.0.0.1"), mockServiceInstance("i-2", "2.2.2.2")]}, {"ServiceId": "srv-1", "MaxResults": 100}) self.sdStubber.add_response( "deregister_instance", {}, {"ServiceId": "srv-1", "InstanceId": "i-2"}) # Mock EC2 client self.ec2Stubber.add_response( "describe_instances", {"Reservations": [{"Instances": [ mockEC2Instance("i-1", privateIp="172.0.0.1"), mockEC2Instance("i-2", privateIp="172.0.0.2", publicIp="2.2.2.2", state="terminated"), ]}]}, {"Filters": [{"Name": "instance-id", "Values": ["i-1", "i-2"]}], "MaxResults": 1000}) with patch("boto3.client", side_effect=self.botoClientMock): unmapTerminatedInstancesFromService(serviceId="srv-1", serviceRegion="eu-west-1", instancesRegions=["eu-west-1"]) self.ec2Stubber.assert_no_pending_responses() self.sdStubber.assert_no_pending_responses() def testUnmapTerminatedInstancesFromServiceShouldSkipRegisteredInstancesWithoutIpv4Attribute(self): # Mock Cloud Map client self.sdStubber.add_response( "list_instances", {"Instances": [mockServiceInstance("i-1", "172.0.0.1"), mockServiceInstance("i-2", ipv4=None)]}, {"ServiceId": "srv-1", "MaxResults": 100}) # Mock EC2 client self.ec2Stubber.add_response( "describe_instances", {"Reservations": [{"Instances": [ mockEC2Instance("i-1", privateIp="172.0.0.1"), ]}]}, {"Filters": [{"Name": "instance-id", "Values": ["i-1"]}], "MaxResults": 1000}) with patch("boto3.client", side_effect=self.botoClientMock): unmapTerminatedInstancesFromService(serviceId="srv-1", serviceRegion="eu-west-1", instancesRegions=["eu-west-1"]) self.ec2Stubber.assert_no_pending_responses() self.sdStubber.assert_no_pending_responses() def testUnmapTerminatedInstancesFromServiceShouldDoNothingIfAllRegisteredInstancesWouldBeDeregistered(self): # Mock Cloud Map client self.sdStubber.add_response( "list_instances", {"Instances": [mockServiceInstance("i-1", "172.0.0.1"), mockServiceInstance("i-2", "2.2.2.2")]}, {"ServiceId": "srv-1", "MaxResults": 100}) # Mock EC2 client self.ec2Stubber.add_response( "describe_instances", {"Reservations": []}, {"Filters": [{"Name": "instance-id", "Values": ["i-1", "i-2"]}], "MaxResults": 1000}) with patch("boto3.client", side_effect=self.botoClientMock): unmapTerminatedInstancesFromService(serviceId="srv-1", serviceRegion="eu-west-1", instancesRegions=["eu-west-1"]) self.ec2Stubber.assert_no_pending_responses() self.sdStubber.assert_no_pending_responses() def testUnmapTerminatedInstancesFromServiceShouldSupportMultipleInstancesRegions(self): # Mock Cloud Map client self.sdStubber.add_response( "list_instances", {"Instances": [mockServiceInstance("i-1", "172.0.0.1"), mockServiceInstance("i-2", "2.2.2.2"), mockServiceInstance("i-3", "3.3.3.3")]}, {"ServiceId": "srv-1", "MaxResults": 100}) self.sdStubber.add_response( "deregister_instance", {}, {"ServiceId": "srv-1", "InstanceId": "i-3"}) # Mock EC2 client self.ec2Stubber.add_response( "describe_instances", {"Reservations": [{"Instances": [mockEC2Instance("i-1", privateIp="172.0.0.1")]}]}, {"Filters": [{"Name": "instance-id", "Values": ["i-1", "i-2", "i-3"]}], "MaxResults": 1000}) self.ec2Stubber.add_response( "describe_instances", {"Reservations": [{"Instances": [mockEC2Instance("i-2", publicIp="2.2.2.2")]}]}, {"Filters": [{"Name": "instance-id", "Values": ["i-1", "i-2", "i-3"]}], "MaxResults": 1000}) with patch("boto3.client", side_effect=self.botoClientMock): unmapTerminatedInstancesFromService(serviceId="srv-1", serviceRegion="eu-west-1", instancesRegions=["eu-west-1", "us-east-1"]) self.ec2Stubber.assert_no_pending_responses() self.sdStubber.assert_no_pending_responses()
45.918367
147
0.616444
1,084
11,250
6.29428
0.088561
0.013484
0.01451
0.011725
0.776198
0.774146
0.774146
0.774146
0.774146
0.774146
0
0.049276
0.220711
11,250
244
148
46.106557
0.728984
0.033956
0
0.738636
0
0
0.219364
0
0
0
0
0
0.125
1
0.056818
false
0
0.034091
0
0.096591
0
0
0
0
null
0
0
0
0
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
6
8be743836e562b47651e8ebdfed320c9d3c75dc9
2,113
py
Python
src/py/test_scope.py
progressive-identity/ref-python
f65cc21c707bcf0629b8b96de7d92074477b1231
[ "Apache-2.0" ]
null
null
null
src/py/test_scope.py
progressive-identity/ref-python
f65cc21c707bcf0629b8b96de7d92074477b1231
[ "Apache-2.0" ]
null
null
null
src/py/test_scope.py
progressive-identity/ref-python
f65cc21c707bcf0629b8b96de7d92074477b1231
[ "Apache-2.0" ]
null
null
null
import scope def test_scope(): provider, path, conds, fields = scope.parse("provider.path.to_resource[var1=value1].{field_a,field_b}") assert provider == "provider" assert path == "path.to_resource" assert conds[0].cond == "=" assert conds[0].var == "var1" assert conds[0].value == "value1" assert fields[0] == "field_a" assert fields[1] == "field_b" def test_scope_no_conds(): provider, path, conds, fields = scope.parse("provider.path.to_resource.{field_a,field_b}") assert provider == "provider" assert path == "path.to_resource" assert conds is None assert fields[0] == "field_a" assert fields[1] == "field_b" def test_scope_all_fields(): provider, path, conds, fields = scope.parse("provider.path.to_resource[var1=value1].*") assert provider == "provider" assert path == "path.to_resource" assert conds[0].cond == "=" assert conds[0].var == "var1" assert conds[0].value == "value1" assert fields == "*" def test_scope_no_conds_all_fields(): provider, path, conds, fields = scope.parse("provider.path.to_resource.*") assert provider == "provider" assert path == "path.to_resource" assert conds is None assert fields[0] == "*" def test_scope_no_path(): provider, path, conds, fields = scope.parse("provider[var1=value1].{field_a,field_b}") assert provider == "provider" assert path is None assert conds[0].cond == "=" assert conds[0].var == "var1" assert conds[0].value == "value1" assert fields[0] == "field_a" assert fields[1] == "field_b" def test_all_operator(): OPS = ["=", "<", "<=", ">", ">=", "!="] conds = ",".join(f"var{i}{op}value{i}" for i, op in enumerate(OPS)) s = f"provider.path.to_resource[{conds}].*" print(s) provider, path, conds, fields = scope.parse(s) assert provider == "provider" assert path == "path.to_resource" for i, (op, cond) in enumerate(zip(OPS, conds)): assert cond.cond == op assert cond.var == f"var{i}" assert cond.value == f"value{i}" assert fields == "*"
28.554054
107
0.625651
286
2,113
4.486014
0.132867
0.102884
0.109119
0.10756
0.813718
0.793453
0.767732
0.735776
0.699922
0.699922
0
0.016667
0.204922
2,113
73
108
28.945205
0.747024
0
0
0.566038
0
0
0.231425
0.114056
0
0
0
0
0.660377
1
0.113208
false
0
0.018868
0
0.132075
0.018868
0
0
0
null
0
0
0
1
1
1
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
null
0
0
0
1
0
0
0
0
0
0
0
0
0
6
4745058d0475229e1f3438a2b3eb91c1ceab6e3e
4,357
py
Python
tests/unit/test_aws_urn.py
CloudWanderer-io/CloudWanderer
bad89c771cebe931790347afb49aa3bd046f3467
[ "MIT" ]
16
2020-12-22T17:01:48.000Z
2022-01-21T10:37:14.000Z
tests/unit/test_aws_urn.py
CloudWanderer-io/CloudWanderer
bad89c771cebe931790347afb49aa3bd046f3467
[ "MIT" ]
110
2020-12-07T21:55:48.000Z
2022-01-11T12:10:49.000Z
tests/unit/test_aws_urn.py
CloudWanderer-io/CloudWanderer
bad89c771cebe931790347afb49aa3bd046f3467
[ "MIT" ]
2
2021-12-23T21:09:23.000Z
2021-12-23T22:25:24.000Z
import unittest from cloudwanderer import URN class TestURN(unittest.TestCase): def setUp(self): self.test_urn_subresource = URN( account_id="111111111111", region="us-east-1", service="iam", resource_type="role_policy", resource_id_parts=["test-role", "test-policy"], ) self.test_urn_resource = URN( account_id="111111111111", region="us-east-1", service="iam", resource_type="role", resource_id="test-role", ) def test_from_string(self): assert ( URN.from_string("urn:aws:111111111111:us-east-1:iam:role_policy:test-role/test-policy") == self.test_urn_subresource ) def test_from_string_with_multiple_ids(self): assert URN.from_string( "urn:aws:111111111111:us-east-1:iam:role_policy:test-role/test-policy/this/should/be/included" ) == URN( account_id="111111111111", region="us-east-1", service="iam", resource_type="role_policy", resource_id_parts=["test-role", "test-policy", "this", "should", "be", "included"], ) def test_str(self): assert str(self.test_urn_subresource) == "urn:aws:111111111111:us-east-1:iam:role_policy:test-role/test-policy" def test_repr(self): assert repr(self.test_urn_subresource) == str( "URN(" "account_id='111111111111', " "region='us-east-1', " "service='iam', " "resource_type='role_policy', " "resource_id_parts=['test-role', 'test-policy'])" ) def test_equality(self): assert URN( account_id="123456789012", region="us-east-1", service="iam", resource_type="role_policy", resource_id="test-role/test-role-policy", ) == URN( account_id="123456789012", region="us-east-1", service="iam", resource_type="role_policy", resource_id="test-role/test-role-policy", ) def test_resource_id_with_slashes(self): urn = URN( account_id="080863329876", region="eu-west-1", service="cloudwatch", resource_type="metric", resource_id="AWS/Logs/IncomingBytes", ) assert str(urn) == r"urn:aws:080863329876:eu-west-1:cloudwatch:metric:AWS\/Logs\/IncomingBytes" assert urn.resource_id == r"AWS\/Logs\/IncomingBytes" def test_resource_id_parts_with_slashes(self): urn = URN( account_id="080863329876", region="eu-west-1", service="cloudwatch", resource_type="metric", resource_id_parts=["AWS/Logs", "IncomingBytes"], ) assert str(urn) == r"urn:aws:080863329876:eu-west-1:cloudwatch:metric:AWS\/Logs/IncomingBytes" assert urn.resource_id_parts == [r"AWS/Logs", r"IncomingBytes"] assert urn.resource_id == r"AWS\/Logs/IncomingBytes" def test_resource_id_with_slashes_from_string(self): urn = URN.from_string(r"urn:aws:080863329876:eu-west-1:cloudwatch:metric:AWS\/Logs\/IncomingBytes") assert urn == URN( account_id="080863329876", region="eu-west-1", service="cloudwatch", resource_type="metric", resource_id="AWS/Logs/IncomingBytes", ) assert urn.resource_id_parts == ["AWS/Logs/IncomingBytes"] def test_from_string_errors_with_no_id(self): with self.assertRaisesRegex( ValueError, "Resource ID must be supplied as the 7th element in a colon separated string" ): URN.from_string(r"urn:aws:080863329876:eu-west-1:cloudwatch:metric") def test_resource_id_with_integer(self): urn = URN.from_string(r"urn:aws:080863329876:eu-west-1:lambda:layer_version:test_layer/1") assert urn == URN( account_id="080863329876", region="eu-west-1", service="lambda", resource_type="layer_version", resource_id_parts=["test_layer", "1"], ) assert urn.resource_id_parts == ["test_layer", "1"] assert urn.resource_id_parts_parsed == ["test_layer", 1]
35.422764
119
0.589626
506
4,357
4.859684
0.136364
0.085401
0.0488
0.03172
0.808865
0.775519
0.748272
0.739325
0.713705
0.713705
0
0.076212
0.280239
4,357
122
120
35.713115
0.707908
0
0
0.409524
0
0.057143
0.321781
0.185449
0
0
0
0
0.152381
1
0.104762
false
0
0.019048
0
0.133333
0
0
0
0
null
0
0
0
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
6
47acfb8ea7d66022b65eccfdc971c5fdd91bcb2a
11,738
py
Python
utils/dd_cookies.py
sunshunli/duodian
22c9f1bee84ca25718c28a13875fa4254d6927e8
[ "MIT" ]
64
2021-04-27T08:55:26.000Z
2021-07-07T09:36:25.000Z
utils/dd_cookies.py
sunshunli/duodian
22c9f1bee84ca25718c28a13875fa4254d6927e8
[ "MIT" ]
null
null
null
utils/dd_cookies.py
sunshunli/duodian
22c9f1bee84ca25718c28a13875fa4254d6927e8
[ "MIT" ]
null
null
null
import os def get_cookies(): cookies1 = "tempid=C957C764BD50000210E4171010C91D36; cookie_id=16218196209405beKr; stealTipCount=1; web_session_count=8; updateTime=1621953996000; store_id=150; vender_id=1; appVersion=5.0.4; dmall-locale=zh_CN; addr=%E5%8C%97%E4%BA%AC%E5%B8%82%E4%B8%9C%E5%9F%8E%E5%8C%BA%E5%88%A9%E7%94%9F%E5%86%99%E5%AD%97%E6%A5%BC; community=%E5%88%A9%E7%94%9F%E5%86%99%E5%AD%97%E6%A5%BC; session_id=e8770ce117c0412f88948726597a7a59; first_session_time=1623135206887; session_count=2; inited=true; console_mode=0; dmTenantId=1; device=HUAWEI%20HUAWEI%20YAL-AL00%20LMY47I; sysVersion=Android-5.1.1; screen=1920*1080; recommend=1; userId=395388955; token=ca23a209-c415-4e64-bbd3-5d03730b67e9; uuid=70c1430d8f180e33; apiVersion=5.0.4; dSource=; oaid=; env=app; tdc=; utmId=; androidId=70c1430d8f180e33; originBusinessFormat=1-2-4-8; channelId=dm010000000001; risk=1; areaId=110101; currentTime=1623135239284; abFlag=1-1-B; lastInstallTime=1619534455218; version=5.0.4; tpc=; storeGroupKey=1e42f776d48afdef6cf1fd1772f0f96c@MS0xNTAtMQ; firstInstallTime=1619534455218; networkType=1; deliveryLng=116.410543; deliveryLat=39.916615; cid=160a3797c8a8315f5aa; storeId=150; sessionId=e8770ce117c0412f88948726597a7a59; User-Agent=dmall/5.0.4%20Dalvik/2.1.0%20%28Linux%3B%20U%3B%20Android%205.1.1%3B%20YAL-AL00%20Build/LMY47I%29; xyz=ac; appName=com.wm.dmall; lng=116.410344; platform=ANDROID; smartLoading=1; ticketName=A514D41153123DCAE3DB465DAFE0FE1F4D4116123140B978113F668820EA821CDE306E5BA281687C853D43129DE1AC99FBBB3E48022897B31562185781D4EA342116FCDA4BA78559A415AF31A5FD26DB3BFA99EE0B382DA8733E1163A56D07A4860FC9A0B4716491E41E4FD157963152272396876117C4A3AA157C9E9C3A7EC9; utmSource=; appMode=online; venderId=1; wifiState=1; gatewayCache=; platformStoreGroupKey=d6c9d533f2183b99bd7375c2ecd37afa@MjAzMi04MDA4Mg; lat=39.916295; businessCode=10312; isOpenNotification=1" cookies2 = "tempid=C957C6D8F2D0000266C6F4E6E9AC7000; _utm_id=257568882; cookie_id=16219338173739yeh9; web_session_count=16; updateTime=1621953996000; inited=true; console_mode=0; store_id=150; vender_id=1; appVersion=5.0.6; dmall-locale=zh_CN; addr=%E5%8C%97%E4%BA%AC%E5%B8%82%E4%B8%9C%E5%9F%8E%E5%8C%BA%E5%88%A9%E7%94%9F%E5%86%99%E5%AD%97%E6%A5%BC; community=%E5%88%A9%E7%94%9F%E5%86%99%E5%AD%97%E6%A5%BC; session_id=b9a35fb70fdc4d6993059b02eb948529; first_session_time=1623134988893; session_count=1; dmTenantId=1; device=OPPO%20OPPO%20PCRT00%20LMY47I; sysVersion=Android-5.1.1; screen=1920*1080; recommend=1; userId=29071278; token=8bb39444-2884-44ef-ba73-15f8ee5fe389; uuid=52b30fc7470e5be3; apiVersion=5.0.6; dSource=; oaid=; env=app; tdc=; utmId=; androidId=52b30fc7470e5be3; originBusinessFormat=1-2-4-8; channelId=dm010000002006; risk=1; areaId=110101; currentTime=1623135046461; abFlag=1-1-B; lastInstallTime=1620090005685; version=5.0.6; tpc=; storeGroupKey=1e42f776d48afdef6cf1fd1772f0f96c@MS0xNTAtMQ; firstInstallTime=1619533954362; networkType=1; deliveryLng=116.410543; deliveryLat=39.916615; cid=18071adc03a741e5d6f; storeId=150; sessionId=8c60571e4d924bbfad6a675b9dbf9783; User-Agent=dmall/5.0.6%20Dalvik/2.1.0%20%28Linux%3B%20U%3B%20Android%205.1.1%3B%20PCRT00%20Build/LMY47I%29; xyz=ac; appName=com.wm.dmall; lng=116.410344; platform=ANDROID; smartLoading=1; ticketName=9C97E435377962F7854D77C984DAFAF826C9A2F2BD22CFAF22D1757D175D3E5CC50B5007C2DCDD0F9DDBE1F1A0FAF3B0E65CAC5C5063A07BF0CC4CFDC2FEB1F38CA50139B39629AE42FEA5248B9E53DA8FCF711C3A715DE6233904279DB7E31C58262110F339657E5A908245FC77F36C8F2CF0896F6B3B5EB241200FD77092E9; utmSource=; appMode=online; venderId=1; wifiState=1; gatewayCache=; sysVersionInt=22; platformStoreGroupKey=d6c9d533f2183b99bd7375c2ecd37afa@MjAzMi04MDA4Mg; lat=39.916295; businessCode=10312; isOpenNotification=1" cookies3 = "tempid=C957C7F37A600002AF6B1820EC5D1D34; cookie_id=1620880041609ZQDu1; web_session_count=9; updateTime=1621953996000; store_id=150; vender_id=1; appVersion=5.0.4; dmall-locale=zh_CN; addr=%E5%8C%97%E4%BA%AC%E5%B8%82%E4%B8%9C%E5%9F%8E%E5%8C%BA%E5%88%A9%E7%94%9F%E5%86%99%E5%AD%97%E6%A5%BC; community=%E5%88%A9%E7%94%9F%E5%86%99%E5%AD%97%E6%A5%BC; session_id=0f5c17ccdfee4b88b3e4a3d6b049a48e; first_session_time=1623135355061; session_count=2; inited=true; console_mode=0; dmTenantId=1; device=OPPO%20OPPO%20PCRT00%20LMY47I; sysVersion=Android-5.1.1; screen=1920*1080; recommend=1; userId=29073035; token=f25dc715-e04c-4e92-92e4-441fe2c036bf; uuid=6a5e26be1c08a75b; apiVersion=5.0.4; dSource=; oaid=; env=app; tdc=; utmId=; androidId=6a5e26be1c08a75b; originBusinessFormat=1-2-4-8; channelId=dm010000000001; risk=1; areaId=110101; currentTime=1623135387620; abFlag=1-1-B; lastInstallTime=1619535053759; version=5.0.4; tpc=; storeGroupKey=1e42f776d48afdef6cf1fd1772f0f96c@MS0xNTAtMQ; firstInstallTime=1619535053759; networkType=1; deliveryLng=116.410543; deliveryLat=39.916615; cid=1a0018970a0304150fd; storeId=150; sessionId=0f5c17ccdfee4b88b3e4a3d6b049a48e; User-Agent=dmall/5.0.4%20Dalvik/2.1.0%20%28Linux%3B%20U%3B%20Android%205.1.1%3B%20PCRT00%20Build/LMY47I%29; xyz=ac; appName=com.wm.dmall; lng=116.410344; platform=ANDROID; smartLoading=1; ticketName=83287047E571B0609396C081D5D6BC06A5652216A599E00F1193FD8BBBE06B849D66D2D7B77C8D4257AC640AD1606149F1A99A1A71E1E35EA3D31F812598069CF0399773BC4473F96057EFC7309D452367B59122E7450254A97F802EAE3286CBE38527BE50EFC761C3BC4C6802F24DA6430F9C7BF99E10917733B833A9EAE16F; utmSource=; appMode=online; venderId=1; wifiState=1; gatewayCache=; platformStoreGroupKey=d6c9d533f2183b99bd7375c2ecd37afa@MjAzMi04MDA4Mg; lat=39.916295; businessCode=10312; isOpenNotification=1" cookies4 = "tempid=C957C86B79100002BD7F3F9011B111DD; cookie_id=1620914508030TZ9tI; _utm_id=257568882; stealTipCount=1; lastBackGardenTime=1621820031569; updateTime=1621953996000; inited=true; console_mode=0; web_session_count=7; store_id=150; vender_id=1; appVersion=5.0.4; dmall-locale=zh_CN; addr=%E5%8C%97%E4%BA%AC%E5%B8%82%E4%B8%9C%E5%9F%8E%E5%8C%BA%E5%88%A9%E7%94%9F%E5%86%99%E5%AD%97%E6%A5%BC; community=%E5%88%A9%E7%94%9F%E5%86%99%E5%AD%97%E6%A5%BC; session_id=1b2c7ffc533e48a69b968ec3788ba493; first_session_time=1622001815919; session_count=3; dmTenantId=1; device=HUAWEI%20HUAWEI%20PCT-AL10%20LMY47I; sysVersion=Android-5.1.1; screen=1920*1080; recommend=1; userId=10130723; token=656b29d3-6519-4fa2-bab4-147f84cb3df9; uuid=905a0cf45280e50d; apiVersion=5.0.4; dSource=; oaid=; env=app; tdc=; utmId=; androidId=905a0cf45280e50d; originBusinessFormat=1-2-4-8; channelId=dm010000000001; risk=1; areaId=110101; currentTime=1623135480861; abFlag=1-1-B; lastInstallTime=1619535594008; version=5.0.4; tpc=; storeGroupKey=1e42f776d48afdef6cf1fd1772f0f96c@MS0xNTAtMQ; firstInstallTime=1619535594008; networkType=1; deliveryLng=116.410543; deliveryLat=39.916615; cid=18071adc03a69d321ad; storeId=150; sessionId=1b2c7ffc533e48a69b968ec3788ba493; User-Agent=dmall/5.0.4%20Dalvik/2.1.0%20%28Linux%3B%20U%3B%20Android%205.1.1%3B%20PCT-AL10%20Build/LMY47I%29; xyz=ac; appName=com.wm.dmall; lng=116.410344; platform=ANDROID; smartLoading=1; ticketName=099A8211EFB9341844E60CC70D933CE8F873B8D83F14FB551E6767AD8857C1C11DFB36F0D1711A090DC5AAB1F3E57477D5DA5BE15D1F6315E7B9CB4A0ED44D6EB86297E45A5B4A1CA6FF47BD382F205604226C99F4439EA92C56A4222DE3644607222E7BF11980CF7E7B1A0BC2200E242A00FB686C1D0F429036E6D3EF664FDE; utmSource=; appMode=online; venderId=1; wifiState=1; gatewayCache=; platformStoreGroupKey=d6c9d533f2183b99bd7375c2ecd37afa@MjAzMi04MDA4Mg; lat=39.916295; businessCode=10312; isOpenNotification=1" cookies5 = "tempid=C957C8E6D7400002556F637036001D98; lastBackGardenTime=1620877999105; stealTipCount=1; web_session_count=18; updateTime=1623135542922; abFlag=1-1-B; inited=true; console_mode=0; store_id=150; vender_id=1; appVersion=5.0.4; dmall-locale=zh_CN; addr=%E5%8C%97%E4%BA%AC%E5%B8%82%E4%B8%9C%E5%9F%8E%E5%8C%BA%E5%88%A9%E7%94%9F%E5%86%99%E5%AD%97%E6%A5%BC; community=%E5%88%A9%E7%94%9F%E5%86%99%E5%AD%97%E6%A5%BC; session_id=2de69ac95524481ba4a67cd271688fa1; first_session_time=1623164721122; session_count=2; dmTenantId=1; device=OnePlus%20OnePlus%20GM1900%20LMY47I; sysVersion=Android-5.1.1; screen=1920*1080; recommend=1; userId=401656357; token=d9608d70-73fb-4b7d-b58f-bb07e0a721ae; uuid=7f9abade877db4ed; apiVersion=5.0.4; dSource=; oaid=; env=app; tdc=; utmId=; androidId=7f9abade877db4ed; originBusinessFormat=1-2-4-8; channelId=dm010000000001; risk=1; areaId=110101; currentTime=1623164934468; lastInstallTime=1619536070832; version=5.0.4; tpc=; storeGroupKey=1e42f776d48afdef6cf1fd1772f0f96c@MS0xNTAtMQ; firstInstallTime=1619536070832; networkType=1; deliveryLng=116.410543; deliveryLat=39.916615; cid=140fe1da9e0470a8adb; storeId=150; sessionId=2de69ac95524481ba4a67cd271688fa1; User-Agent=dmall/5.0.4%20Dalvik/2.1.0%20%28Linux%3B%20U%3B%20Android%205.1.1%3B%20GM1900%20Build/LMY47I%29; xyz=ac; appName=com.wm.dmall; lng=116.41025143091747; platform=ANDROID; smartLoading=1; ticketName=30A1B7ECEE90F7990906B6FEFF9335758C73786B232EBEE6AFD0E8CD34ADD3049193442037066EBD52B685DBE3158C3E415319EF7A96242318BA2B6D8720213641FF245A0A99BE555DFCC5AE3FD527C32CE758179348562357BE2B549F2EE8590B88C3E62F3EA0718357710CDB56C25B015D7A5A42CDE527F9DA460952CC5814; utmSource=; appMode=online; venderId=1; wifiState=1; gatewayCache=; platformStoreGroupKey=d6c9d533f2183b99bd7375c2ecd37afa@MjAzMi04MDA4Mg; lat=39.91639643210142; businessCode=10312; isOpenNotification=1" cookies6 = "tempid=C957C8E6D7400002556F637036001D98; lastBackGardenTime=1620877999105; stealTipCount=1; web_session_count=19; updateTime=1623060822487; device=HUAWEI%20HUAWEI%20PCT-AL10%20LMY47I; sysVersion=Android-5.1.1; screen=1920*1080; apiVersion=5.0.4; dSource=; oaid=; tdc=; utmId=; androidId=0e3a208b2cf0b1c5; channelId=dm010000000001; currentTime=1623060823002; abFlag=1-1-B; lastInstallTime=1619536070832; version=5.0.4; tpc=home_10312; firstInstallTime=1619536070832; networkType=1; deliveryLng=116.410543; deliveryLat=39.916615; cid=140fe1da9e0470a8adb; sessionId=fad5037a201c4d5894a36fa02519ccca; User-Agent=dmall/5.0.4%20Dalvik/2.1.0%20%28Linux%3B%20U%3B%20Android%205.1.1%3B%20PCT-AL10%20Build/LMY47I%29; xyz=ac; appName=com.wm.dmall; smartLoading=1; utmSource=; wifiState=1; gatewayCache=; isOpenNotification=1; inited=true; console_mode=0; uuid=7f9abade877db4ed; store_id=150; storeId=150; vender_id=1; venderId=1; businessCode=10312; appMode=online; storeGroupKey=1e42f776d48afdef6cf1fd1772f0f96c@MS0xNTAtMQ; platformStoreGroupKey=d6c9d533f2183b99bd7375c2ecd37afa@MjAzMi04MDA4Mg; originBusinessFormat=1-2-4-8; appVersion=5.0.4; platform=ANDROID; dmall-locale=zh_CN; token=175dbcb8-3d9d-4265-808f-a87b005c38fc; ticketName=789AD8B9C9019C45AAE3F2276179CCD5533E42D59424DFE9E664B6E11734661D4A6708D62B31618B1059E12BBC32AD8D0D8DFF764E16F17C2DE84B25D4F64F2F37D6BA7530B3319FCD175C0CBF78F50763F6026720A8DAC255C4EC8FBFB2D611F60F9C18A11A665A47F93150800DC5A15B7E892D1CB998793732A7C0FEE7D5F5; userId=2310428; lat=39.916615; lng=116.410543; addr=%E5%8C%97%E4%BA%AC%E5%B8%82%E4%B8%9C%E5%9F%8E%E5%8C%BA%E5%88%A9%E7%94%9F%E5%86%99%E5%AD%97%E6%A5%BC; community=%E5%88%A9%E7%94%9F%E5%86%99%E5%AD%97%E6%A5%BC; areaId=110101; session_id=fad5037a201c4d5894a36fa02519ccca; env=app; first_session_time=1623059882872; session_count=2; recommend=1; dmTenantId=1; risk=1" cookiesList = [cookies4,cookies2, cookies3, cookies1, cookies5, cookies6] # 多账号准备 if "DD_GARDEN_COOKIE" in os.environ: """ 判断是否运行自GitHub action,"DD_GARDEN_COOKIE" 该参数与 repo里的Secrets的名称保持一致 """ print("执行自GitHub action") dd_garden_cookie = os.environ["DD_GARDEN_COOKIE"] cookiesList = [] # 重置cookiesList for line in dd_garden_cookie.split('\n'): if not line: continue cookiesList.append(line) return cookiesList
404.758621
1,903
0.827143
1,461
11,738
6.590691
0.195072
0.004985
0.006231
0.00997
0.538062
0.500571
0.494652
0.476893
0.471389
0.395368
0
0.331869
0.049668
11,738
28
1,904
419.214286
0.531331
0.001619
0
0
0
0.333333
0.956821
0.669018
0
0
0
0
0
1
0.055556
false
0
0.055556
0
0.166667
0.055556
0
0
0
null
0
0
0
0
0
0
0
0
0
0
1
0
0
0
1
1
0
0
0
0
0
1
1
1
null
0
0
0
0
0
0
0
0
0
0
0
0
0
6
47d19aee9446210f57b398c89782621fa591121d
50
py
Python
studentskaprehrana/__init__.py
drobilc/StudentskaPrehrana
01f6c9cd98cdf5e9588bf03dcc72bf476fd9af5b
[ "MIT" ]
1
2017-12-28T14:50:53.000Z
2017-12-28T14:50:53.000Z
studentskaprehrana/__init__.py
drobilc/StudentskaPrehrana
01f6c9cd98cdf5e9588bf03dcc72bf476fd9af5b
[ "MIT" ]
null
null
null
studentskaprehrana/__init__.py
drobilc/StudentskaPrehrana
01f6c9cd98cdf5e9588bf03dcc72bf476fd9af5b
[ "MIT" ]
null
null
null
from .studentskaprehrana import StudentskaPrehrana
50
50
0.92
4
50
11.5
0.75
0
0
0
0
0
0
0
0
0
0
0
0.06
50
1
50
50
0.978723
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
1
0
1
0
1
0
1
null
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
6
d004e219515148d340b6f356ad12ebb19cd28ad3
13,875
py
Python
src/validx/contracts.py
Cottonwood-Technology/ValidX
8ade8377e2bf6c5a7835d33a1e74552744ccdcdf
[ "BSD-2-Clause" ]
19
2019-11-08T20:22:15.000Z
2022-03-21T10:42:45.000Z
src/validx/contracts.py
Cottonwood-Technology/ValidX
8ade8377e2bf6c5a7835d33a1e74552744ccdcdf
[ "BSD-2-Clause" ]
7
2020-04-30T09:51:34.000Z
2021-10-05T13:11:28.000Z
src/validx/contracts.py
Cottonwood-Technology/ValidX
8ade8377e2bf6c5a7835d33a1e74552744ccdcdf
[ "BSD-2-Clause" ]
3
2019-09-25T03:44:21.000Z
2020-08-20T14:21:50.000Z
from collections.abc import Container, Sequence, Mapping, Callable from .types import chars, frozendict def expect( obj, attr, value, nullable=False, types=None, not_types=None, convert_to=None ): """ Check, whether the value satisfies expectations :param obj: an object, which will set the value to its attribute. It is used to make error messages more specific. :param str attr: name of an attribute of the object. It is used to make error messages more specific. :param value: checked value itself. :param bool nullable: accept ``None`` as a valid value. Default: ``False`` — does not accept ``None``. :param types: define acceptable types of the value. Default: ``None`` — accept any type. :type types: None, type or tuple :param not_types: define implicitly unacceptable types of the value. Default: ``None`` — accept any type. :type types: None, type or tuple :param type convert_to: convert the value to specified type. Default: ``None`` — does not convert the value. :raises TypeError: * if ``types is not None`` and ``not isinstance(value, types)``; * if ``not_types is not None`` and ``isinstance(value, not_types)``. """ if nullable and value is None: return value if types is not None and not isinstance(value, types): raise TypeError( "%s.%s.%s should be of type %r" % (obj.__class__.__module__, obj.__class__.__name__, attr, types) ) if not_types is not None and isinstance(value, not_types): raise TypeError( "%s.%s.%s should not be of type %r" % (obj.__class__.__module__, obj.__class__.__name__, attr, not_types) ) if convert_to is not None and not isinstance(value, convert_to): value = convert_to(value) return value def expect_flag(obj, attr, value): """ Check, whether the value satisfies expectations of boolean flag :param obj: an object, which will set the value to its attribute. It is used to make error messages more specific. :param str attr: name of an attribute of the object. It is used to make error messages more specific. :param value: checked value itself. :param bool nullable: accept ``None`` as a valid value. Default: ``False`` — does not accept ``None``. :raises TypeError: if ``not isinstance(value, (bool, int, type(None)))``. """ return expect(obj, attr, value, types=(bool, int, type(None)), convert_to=bool) def expect_length(obj, attr, value, nullable=False): """ Check, whether the value satisfies expectations of integer length :param obj: an object, which will set the value to its attribute. It is used to make error messages more specific. :param str attr: name of an attribute of the object. It is used to make error messages more specific. :param value: checked value itself. :param bool nullable: accept ``None`` as a valid value. Default: ``False`` — does not accept ``None``. :raises TypeError: if ``not isinstance(value, int)``. :raises ValueError: if ``value < 0``. """ value = expect(obj, attr, value, nullable=nullable, types=int) if value is not None: if value < 0: raise ValueError( "%s.%s.%s should not be negative number" % (obj.__class__.__module__, obj.__class__.__name__, attr) ) return value def expect_str(obj, attr, value, nullable=False): """ Check, whether the value satisfies expectations of base string :param obj: an object, which will set the value to its attribute. It is used to make error messages more specific. :param str attr: name of an attribute of the object. It is used to make error messages more specific. :param value: checked value itself. :param bool nullable: accept ``None`` as a valid value. Default: ``False`` — does not accept ``None``. :raises TypeError: if ``not isinstance(value, str)``. """ return expect(obj, attr, value, nullable=nullable, types=str) def expect_callable(obj, attr, value, nullable=False): """ Check, whether the value satisfies expectations of callable :param obj: an object, which will set the value to its attribute. It is used to make error messages more specific. :param str attr: name of an attribute of the object. It is used to make error messages more specific. :param value: checked value itself. :param bool nullable: accept ``None`` as a valid value. Default: ``False`` — does not accept ``None``. :raises TypeError: if ``not isinstance(value, collections.abc.Callable)``. """ return expect(obj, attr, value, nullable=nullable, types=Callable) def expect_container(obj, attr, value, nullable=False, empty=False, item_type=None): """ Check, whether the value satisfies expectations of container :param obj: an object, which will set the value to its attribute. It is used to make error messages more specific. :param str attr: name of an attribute of the object. It is used to make error messages more specific. :param value: checked value itself. :param bool nullable: accept ``None`` as a valid value. Default: ``False`` — does not accept ``None``. :param bool empty: accept empty container as a valid value. Default: ``False`` — does not accept empty container. :param type item_type: check, whether each item of the container has specified type. Default: ``None`` — does not check items. :raises TypeError: * if ``not isinstance(value, collections.abc.Container)``; * if ``isinstance(value, (str, bytes))``; * if ``item_type is not None`` and ``isinstance(item, item_type)``, ``for item in value``. :raises ValueError: if ``not empty`` and ``not value``. :returns: passed container converted to ``frozenset``, if items are hashable, otherwise to ``tuple``. """ value = expect( obj, attr, value, nullable=nullable, types=Container, not_types=chars ) if value is not None: if not isinstance(value, frozenset): try: value = frozenset(value) except TypeError: # Unhashable type, fallback to tuple value = tuple(value) if not value and not empty: raise ValueError( "%s.%s.%s should not be empty" % (obj.__class__.__module__, obj.__class__.__name__, attr) ) if item_type is not None: for item in value: if not isinstance(item, item_type): raise TypeError( "%s.%s.%s items should be of type %r, got %r" % ( obj.__class__.__module__, obj.__class__.__name__, attr, item_type, type(item), ) ) return value def expect_sequence(obj, attr, value, nullable=False, empty=False, item_type=None): """ Check, whether the value satisfies expectations of sequence :param obj: an object, which will set the value to its attribute. It is used to make error messages more specific. :param str attr: name of an attribute of the object. It is used to make error messages more specific. :param value: checked value itself. :param bool nullable: accept ``None`` as a valid value. Default: ``False`` — does not accept ``None``. :param bool empty: accept empty sequence as a valid value. Default: ``False`` — does not accept empty sequence. :param type item_type: check, whether each item of the sequence has specified type. Default: ``None`` — does not check items. :raises TypeError: * if ``not isinstance(value, collections.abc.Sequence)``; * if ``isinstance(value, (str, bytes))``; * if ``item_type is not None`` and ``isinstance(item, item_type)``, ``for item in value``. :raises ValueError: if ``not empty`` and ``not value``. :returns: passed sequence converted to ``tuple``. """ value = expect( obj, attr, value, nullable=nullable, types=Sequence, not_types=chars, convert_to=tuple, ) if value is not None: if not value and not empty: raise ValueError( "%s.%s.%s should not be empty" % (obj.__class__.__module__, obj.__class__.__name__, attr) ) if item_type is not None: for n, item in enumerate(value): if not isinstance(item, item_type): raise TypeError( "%s.%s.%s[%s] value should be of type %r" % ( obj.__class__.__module__, obj.__class__.__name__, attr, n, item_type, ) ) return value def expect_mapping(obj, attr, value, nullable=False, empty=False, value_type=None): """ Check, whether the value satisfies expectations of mapping :param obj: an object, which will set the value to its attribute. It is used to make error messages more specific. :param str attr: name of an attribute of the object. It is used to make error messages more specific. :param value: checked value itself. :param bool nullable: accept ``None`` as a valid value. Default: ``False`` — does not accept ``None``. :param bool empty: accept empty mapping as a valid value. Default: ``False`` — does not accept empty mapping. :param type value_type: check, whether each value of the mapping has specified type. Default: ``None`` — does not check items. :raises TypeError: * if ``not isinstance(value, collections.abc.Sequence)``; * if ``isinstance(value, (str, bytes))``; * if ``value_type is not None`` and ``isinstance(val, value_type)``, ``for key, val in value.items()``. :raises ValueError: if ``not empty`` and ``not value``. :returns: passed mapping converted to ``frozendict``. """ value = expect( obj, attr, value, nullable=nullable, types=Mapping, convert_to=frozendict ) if value is not None: if not value and not empty: raise ValueError( "%s.%s.%s should not be empty" % (obj.__class__.__module__, obj.__class__.__name__, attr) ) if value_type is not None: for key, val in value.items(): if not isinstance(val, value_type): raise TypeError( "%s.%s.%s[%r] value should be of type %r" % ( obj.__class__.__module__, obj.__class__.__name__, attr, key, value_type, ) ) return value def expect_tuple(obj, attr, value, struct, nullable=False): """ Check, whether the value satisfies expectations of tuple of specific structure :param obj: an object, which will set the value to its attribute. It is used to make error messages more specific. :param str attr: name of an attribute of the object. It is used to make error messages more specific. :param value: checked value itself. :param tuple struct: tuple of types. :param bool nullable: accept ``None`` as a valid value. Default: ``False`` — does not accept ``None``. :raises TypeError: * if ``not isinstance(value, collections.abc.Sequence)``; * if ``isinstance(value, (str, bytes))``; * if ``not isinstance(item, item_type)``, ``for item_type, item in zip(struct, value)``. :raises ValueError: if ``len(value) != len(struct)``. :returns: passed sequence converted to ``tuple``. """ value = expect( obj, attr, value, nullable=nullable, types=Sequence, not_types=chars, convert_to=tuple, ) if value is not None: if len(value) != len(struct): raise ValueError( "%s.%s.%s should be a tuple of %r" % (obj.__class__.__module__, obj.__class__.__name__, attr, struct) ) for n, (item_type, item) in enumerate(zip(struct, value)): if not isinstance(item, item_type): raise TypeError( "%s.%s.%s[%s] value should be of type %r" % ( obj.__class__.__module__, obj.__class__.__name__, attr, n, item_type, ) ) return value
30.097614
84
0.560505
1,655
13,875
4.571601
0.070695
0.006344
0.019033
0.023791
0.832805
0.814169
0.789188
0.752709
0.717156
0.703013
0
0.000221
0.348036
13,875
460
85
30.163043
0.834181
0.524685
0
0.537415
0
0
0.066221
0
0
0
0
0
0
1
0.061224
false
0
0.013605
0
0.142857
0
0
0
0
null
0
0
0
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
6
d04d82a239f725c730097dbfd171538ef22e4964
201
py
Python
metaopt/tests/unit/core/call/call.py
cigroup-ol/metaopt
6dfd5105d3c6eaf00f96670175cae16021069514
[ "BSD-3-Clause" ]
8
2015-02-02T21:42:23.000Z
2019-06-30T18:12:43.000Z
metaopt/tests/unit/core/call/call.py
cigroup-ol/metaopt
6dfd5105d3c6eaf00f96670175cae16021069514
[ "BSD-3-Clause" ]
4
2015-09-24T14:12:38.000Z
2021-12-08T22:42:52.000Z
metaopt/tests/unit/core/call/call.py
cigroup-ol/metaopt
6dfd5105d3c6eaf00f96670175cae16021069514
[ "BSD-3-Clause" ]
6
2015-02-27T12:35:33.000Z
2020-10-15T21:04:02.000Z
""" TODO Write unit tests for call. Note that there are already integration tests. """ # Future from __future__ import absolute_import, division, print_function, \ unicode_literals, with_statement
28.714286
78
0.781095
26
201
5.730769
0.884615
0
0
0
0
0
0
0
0
0
0
0
0.149254
201
6
79
33.5
0.871345
0.427861
0
0
0
0
0
0
0
0
0
0.166667
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
1
0
0
0
1
0
1
0
0
1
0
6
d09e1c5ffb65bf8fbd288b45f06d263d46a5e8f1
35
py
Python
src/leader/secret20/__init__.py
Leibniz137/EthereumBridge
4b82a68cdc09e5ea79ec2fbf87aa065a2a3a5ffa
[ "MIT" ]
14
2020-09-20T02:06:58.000Z
2021-10-12T12:16:28.000Z
src/leader/secret20/__init__.py
scrtlabs/EthereumBridge
e585c060c11dd264df46bed1f477f139deb1b37c
[ "MIT" ]
5
2020-11-17T05:39:48.000Z
2020-12-15T13:41:12.000Z
src/leader/secret20/__init__.py
scrtlabs/EthereumBridge
e585c060c11dd264df46bed1f477f139deb1b37c
[ "MIT" ]
7
2020-10-19T15:52:56.000Z
2021-09-19T08:57:28.000Z
from .leader import Secret20Leader
17.5
34
0.857143
4
35
7.5
1
0
0
0
0
0
0
0
0
0
0
0.064516
0.114286
35
1
35
35
0.903226
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
1
0
1
0
1
1
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
6
d0aa1d8358e51e6e37881ccb1312a31665e8b1d2
24
py
Python
centermask/model_zoo/__init__.py
MiXaiLL76/centermask2
612fa5f02b09c4167e14031be50c6e5e4e58ea77
[ "Apache-2.0" ]
13
2019-12-02T14:46:56.000Z
2021-12-14T09:15:30.000Z
centermask/model_zoo/__init__.py
MiXaiLL76/centermask2
612fa5f02b09c4167e14031be50c6e5e4e58ea77
[ "Apache-2.0" ]
2
2020-11-13T18:14:19.000Z
2021-10-12T23:01:47.000Z
centermask/model_zoo/__init__.py
MiXaiLL76/centermask2
612fa5f02b09c4167e14031be50c6e5e4e58ea77
[ "Apache-2.0" ]
1
2021-03-24T15:46:53.000Z
2021-03-24T15:46:53.000Z
from .model_zoo import *
24
24
0.791667
4
24
4.5
1
0
0
0
0
0
0
0
0
0
0
0
0.125
24
1
24
24
0.857143
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
1
0
1
0
1
1
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
6
d0c971ed12330a125aa9104b392c1be96f8e728e
116
py
Python
koroba/__init__.py
sergevkim/koroba
91d433f6bc7ecf47c62f7be4ddcdc8b38e5b27b7
[ "MIT" ]
null
null
null
koroba/__init__.py
sergevkim/koroba
91d433f6bc7ecf47c62f7be4ddcdc8b38e5b27b7
[ "MIT" ]
1
2021-05-16T07:05:19.000Z
2021-05-16T07:05:19.000Z
koroba/__init__.py
sergevkim/koroba
91d433f6bc7ecf47c62f7be4ddcdc8b38e5b27b7
[ "MIT" ]
null
null
null
from .datamodules import * from .loggers import * from .losses import * from .runner import * from .utils import *
16.571429
26
0.732759
15
116
5.666667
0.466667
0.470588
0
0
0
0
0
0
0
0
0
0
0.181034
116
6
27
19.333333
0.894737
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
1
0
1
0
1
0
0
null
1
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
6
4beacc496c05f263cf0c50d7f161e87710989284
76
py
Python
np/reference/ch7code/interestrate.py
focusunsink/study_python
322326642db54df8725793d70a95d21ac40b6507
[ "MIT" ]
null
null
null
np/reference/ch7code/interestrate.py
focusunsink/study_python
322326642db54df8725793d70a95d21ac40b6507
[ "MIT" ]
null
null
null
np/reference/ch7code/interestrate.py
focusunsink/study_python
322326642db54df8725793d70a95d21ac40b6507
[ "MIT" ]
null
null
null
import numpy as np print "Interest rate", 12 * np.rate(167, -100, 9000, 0)
19
55
0.671053
14
76
3.642857
0.857143
0
0
0
0
0
0
0
0
0
0
0.209677
0.184211
76
3
56
25.333333
0.612903
0
0
0
0
0
0.171053
0
0
0
0
0
0
0
null
null
0
0.5
null
null
0.5
1
0
0
null
0
0
0
0
0
0
0
0
0
0
1
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
1
0
0
0
1
0
0
1
0
6
4befb56fe72859ac12cd0bceec866489be42540d
6,048
py
Python
SCFInitialGuess/construction/utilities.py
jcartus/SCFInitialGuess
e4a9280e8cbabb126946e47affa652243b74753c
[ "MIT" ]
1
2020-03-02T02:36:59.000Z
2020-03-02T02:36:59.000Z
SCFInitialGuess/construction/utilities.py
jcartus/SCFInitialGuess
e4a9280e8cbabb126946e47affa652243b74753c
[ "MIT" ]
null
null
null
SCFInitialGuess/construction/utilities.py
jcartus/SCFInitialGuess
e4a9280e8cbabb126946e47affa652243b74753c
[ "MIT" ]
null
null
null
"""Consists of all the utilites used in construction of matrices. Author: - Johannes Cartus, TU Graz """ import numpy as np from SCFInitialGuess.utilities.constants import \ number_of_basis_functions as N_BASIS def embed(x, y, mask): """Embed a square matrix x with y where mask is true. Args: x <np.array>: to be embedded matrix y <np.array>: elements that are embedded into x. Same size as x. mask <np.array<bool>>: marks where to embed. Same size as x and y. """ p = x.copy() p[mask] = (y.copy())[mask] return p def embed_batch(X, Y, mask): """Embed a square matrix x with y where mask is true. Args: x <list<np.array>>: set of to be embedded matrices y <list<np.array>>: set of elements that are embedded into elements of X. Same size as X. mask <np.array<bool>>: marks where to embed. """ f_embedded = [] for (x, y) in zip(X, Y): f_embedded.append(embed(x, y, mask)) return np.array(f_embedded) def make_center_mask(mol): """Create a boolean matrix that is true for center block elements, and false else. mol <SCFInitialGuess.utilities.dataset.Molecule>: molecule that determines basis set and composition. """ dim = mol.dim mask = np.zeros((dim, dim)) current_dim = 0 for atom in mol.species: # calculate block range index_start = current_dim current_dim += N_BASIS[mol.basis][atom] index_end = current_dim # calculate logical vector L = np.arange(dim) L = np.logical_and(index_start <= L, L < index_end) m = np.logical_and.outer(L, L) mask = np.logical_or(mask, m) return mask def make_homo_mask(mol): """Create a boolean matrix that is true for all off-diagonal overlaps of atoms that are of the same element. mol <SCFInitialGuess.utilities.dataset.Molecule>: molecule that determines basis set and composition. """ dim = mol.dim mask = np.zeros((dim, dim)) current_dim_i = 0 for i, atom_i in enumerate(mol.species): # calculate block range index_start_i = current_dim_i current_dim_i += N_BASIS[mol.basis][atom_i] index_end_i = current_dim_i # calculate logical vector L_i = np.arange(dim) L_i = np.logical_and(index_start_i <= L_i, L_i < index_end_i) current_dim_j = 0 for j, atom_j in enumerate(mol.species): # calculate block range index_start_j = current_dim_j current_dim_j += N_BASIS[mol.basis][atom_j] index_end_j = current_dim_j if i == j: continue if atom_i == atom_j: # calculate logical vector L_j = np.arange(dim) L_j = np.logical_and(index_start_j <= L_j, L_j < index_end_j) m = np.logical_and.outer(L_i, L_j) mask = np.logical_or(mask, m) return mask def make_hetero_mask(mol): """Create a boolean matrix that is true for all off-diagonal overlaps of atoms that are NOT of the same element. mol <SCFInitialGuess.utilities.dataset.Molecule>: molecule that determines basis set and composition. """ dim = mol.dim mask = np.zeros((dim, dim)) current_dim_i = 0 for i, atom_i in enumerate(mol.species): # calculate block range index_start_i = current_dim_i current_dim_i += N_BASIS[mol.basis][atom_i] index_end_i = current_dim_i # calculate logical vector L_i = np.arange(dim) L_i = np.logical_and(index_start_i <= L_i, L_i < index_end_i) current_dim_j = 0 for j, atom_j in enumerate(mol.species): # calculate block range index_start_j = current_dim_j current_dim_j += N_BASIS[mol.basis][atom_j] index_end_j = current_dim_j if i == j: continue if atom_i != atom_j: # calculate logical vector L_j = np.arange(dim) L_j = np.logical_and(index_start_j <= L_j, L_j < index_end_j) m = np.logical_and.outer(L_i, L_j) mask = np.logical_or(mask, m) return mask def make_atom_pair_mask(mol, index_i , index_j): """Create the mask that corresponds to the atom pair (index_i, index_j). E.g. (0,0) would be the self-overlap of the first atom in the molecule. mol <SCFInitialGuess.utilities.dataset.Molecule>: molecule that determines basis set and composition. """ dim = mol.dim current_dim_i = 0 for i, atom_i in enumerate(mol.species): # calculate block range index_start_i = current_dim_i current_dim_i += N_BASIS[mol.basis][atom_i] index_end_i = current_dim_i if i < index_i: continue else: current_dim_j = 0 for j, atom_j in enumerate(mol.species): # calculate block range index_start_j = current_dim_j current_dim_j += N_BASIS[mol.basis][atom_j] index_end_j = current_dim_j if j < index_j: continue else: # calculate logical vector L_i = np.arange(dim) L_i = np.logical_and(index_start_i <= L_i, L_i < index_end_i) # calculate logical vector L_j = np.arange(dim) L_j = np.logical_and(index_start_j <= L_j, L_j < index_end_j) mask = np.logical_and.outer(L_i, L_j) break break return mask
27.743119
82
0.5625
840
6,048
3.819048
0.135714
0.087282
0.041147
0.033666
0.77899
0.739713
0.733791
0.721633
0.715087
0.715087
0
0.002318
0.35797
6,048
218
83
27.743119
0.823848
0.298611
0
0.701031
0
0
0
0
0
0
0
0
0
1
0.061856
false
0
0.020619
0
0.14433
0
0
0
0
null
0
0
0
0
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
6
ef0b3ba0ba7abb85be8b7b149b5aaa3b42e4da64
96
py
Python
venv/lib/python3.8/site-packages/rope/base/fscommands.py
GiulianaPola/select_repeats
17a0d053d4f874e42cf654dd142168c2ec8fbd11
[ "MIT" ]
2
2022-03-13T01:58:52.000Z
2022-03-31T06:07:54.000Z
venv/lib/python3.8/site-packages/rope/base/fscommands.py
DesmoSearch/Desmobot
b70b45df3485351f471080deb5c785c4bc5c4beb
[ "MIT" ]
19
2021-11-20T04:09:18.000Z
2022-03-23T15:05:55.000Z
venv/lib/python3.8/site-packages/rope/base/fscommands.py
DesmoSearch/Desmobot
b70b45df3485351f471080deb5c785c4bc5c4beb
[ "MIT" ]
null
null
null
/home/runner/.cache/pip/pool/1f/25/29/96266bdb681f6a20eae8a895c4d0785df90bfbe7af62a169fbb690708a
96
96
0.895833
9
96
9.555556
1
0
0
0
0
0
0
0
0
0
0
0.40625
0
96
1
96
96
0.489583
0
0
0
0
0
0
0
0
1
0
0
0
0
null
null
0
0
null
null
0
1
0
0
null
0
0
0
0
0
0
0
0
0
0
1
0
0
1
0
0
1
0
0
0
0
0
0
0
null
1
0
0
0
1
0
0
0
0
0
0
0
0
6
32baf2e1abf0a42333106e19614ab7a00581e471
1,174
py
Python
src/tests/test_series.py
Woojgh/data-structure
f44bcb5950f26b5e098f1e25d11ad6e19cfb0eb1
[ "MIT" ]
null
null
null
src/tests/test_series.py
Woojgh/data-structure
f44bcb5950f26b5e098f1e25d11ad6e19cfb0eb1
[ "MIT" ]
null
null
null
src/tests/test_series.py
Woojgh/data-structure
f44bcb5950f26b5e098f1e25d11ad6e19cfb0eb1
[ "MIT" ]
null
null
null
import pytest from series import fibonacci, lucas, sum_series @pytest.mark.parametrize("test_input, expected", [(0, 0), (1, 1), (2, 1), (3, 2), (4, 3), (5, 5), (6, 8)]) def test_fib(test_input, expected): actual = fibonacci(test_input) assert actual == expected @pytest.mark.parametrize("test_input, expected", [(0, 2), (1, 1), (2, 3), (3, 4), (4, 7), (5, 11), (6, 18)]) def test_lucas(test_input, expected): actual = lucas(test_input) assert actual == expected @pytest.mark.parametrize("test_input, expected", [(0, 0), (1, 1), (2, 1), (3, 2), (4, 3), (5, 5), (6, 8)]) def test_sum_series_fib(test_input, expected): actual = sum_series(test_input) assert actual == expected @pytest.mark.parametrize("test_input, expected", [(0, 2), (1, 1), (2, 3), (3, 4), (4, 7), (5, 11), (6, 18)]) def test_sum_series_lucas(test_input, expected): actual = sum_series(test_input, 2) assert actual == expected @pytest.mark.parametrize("test_input, expected", [(0, 10), (1, 11), (2, 21), (3, 32), (4, 53), (5, 85), (6, 138)]) def test_sum_series_custom(test_input, expected): actual = sum_series(test_input, 10, 11) assert actual == expected
35.575758
114
0.632879
186
1,174
3.833333
0.182796
0.189341
0.238429
0.175316
0.788219
0.701262
0.701262
0.701262
0.541374
0.541374
0
0.088505
0.162692
1,174
32
115
36.6875
0.636826
0
0
0.409091
0
0
0.085179
0
0
0
0
0
0.227273
1
0.227273
false
0
0.090909
0
0.318182
0
0
0
0
null
0
1
1
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
1
0
0
0
0
0
0
0
6
08ae64ebaa1e29c0cdbcf44d39700a32bf2d5aa9
13,428
py
Python
biserici_inlemnite/biserici/migrations/0002_auto_20210729_1649.py
ck-tm/biserici-inlemnite
c9d12127b92f25d3ab2fcc7b4c386419fe308a4e
[ "MIT" ]
null
null
null
biserici_inlemnite/biserici/migrations/0002_auto_20210729_1649.py
ck-tm/biserici-inlemnite
c9d12127b92f25d3ab2fcc7b4c386419fe308a4e
[ "MIT" ]
null
null
null
biserici_inlemnite/biserici/migrations/0002_auto_20210729_1649.py
ck-tm/biserici-inlemnite
c9d12127b92f25d3ab2fcc7b4c386419fe308a4e
[ "MIT" ]
null
null
null
# Generated by Django 3.1.13 on 2021-07-29 13:49 from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('biserici', '0001_initial'), ('nomenclatoare', '0001_initial'), ] operations = [ migrations.AddField( model_name='istoric', name='ctitori', field=models.ManyToManyField(related_name='ctitor', through='nomenclatoare.CtitorBiserica', to='nomenclatoare.Persoana'), ), migrations.AddField( model_name='istoric', name='datare_secol', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='biserici', to='nomenclatoare.secol'), ), migrations.AddField( model_name='istoric', name='evenimente', field=models.ManyToManyField(through='nomenclatoare.EvenimentBiserica', to='nomenclatoare.Eveniment'), ), migrations.AddField( model_name='istoric', name='mesteri', field=models.ManyToManyField(related_name='mester', through='nomenclatoare.MesterBiserica', to='nomenclatoare.Persoana'), ), migrations.AddField( model_name='istoric', name='mutari_biserica', field=models.ManyToManyField(through='nomenclatoare.MutareBiserica', to='nomenclatoare.Localitate'), ), migrations.AddField( model_name='istoric', name='personalitati', field=models.ManyToManyField(related_name='personalitate', through='nomenclatoare.PersonalitateBiserica', to='nomenclatoare.Persoana'), ), migrations.AddField( model_name='istoric', name='studiu_dendocronologic', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to='nomenclatoare.studiudendocronologic'), ), migrations.AddField( model_name='istoric', name='sursa_datare', field=models.ManyToManyField(blank=True, related_name='biserici', to='nomenclatoare.SursaDatare'), ), migrations.AddField( model_name='istoric', name='zugravi', field=models.ManyToManyField(related_name='zugrav', through='nomenclatoare.ZugravBiserica', to='nomenclatoare.Persoana'), ), migrations.AddField( model_name='identificare', name='biserica', field=models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, to='biserici.biserica'), ), migrations.AddField( model_name='identificare', name='cult', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='biserici', to='nomenclatoare.cultbiserica'), ), migrations.AddField( model_name='identificare', name='functiune', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='biserici', to='nomenclatoare.functiunebiserica'), ), migrations.AddField( model_name='identificare', name='functiune_initiala', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='biserici_initiale', to='nomenclatoare.functiunebiserica'), ), migrations.AddField( model_name='identificare', name='judet', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='biserici', to='nomenclatoare.judet'), ), migrations.AddField( model_name='identificare', name='localitate', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='biserici', to='nomenclatoare.localitate'), ), migrations.AddField( model_name='identificare', name='proprietate_actuala', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='biserici_initiale', to='nomenclatoare.proprietatebiserica'), ), migrations.AddField( model_name='identificare', name='singularitate', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='biserici', to='nomenclatoare.singularitatebiserica'), ), migrations.AddField( model_name='identificare', name='statut', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='biserici', to='nomenclatoare.statutbiserica'), ), migrations.AddField( model_name='identificare', name='utilizare', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='biserici', to='nomenclatoare.utilizarebiserica'), ), migrations.AddField( model_name='historicaluser', name='history_user', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to=settings.AUTH_USER_MODEL), ), migrations.AddField( model_name='historicalpatrimoniu', name='biserica', field=models.ForeignKey(blank=True, db_constraint=False, null=True, on_delete=django.db.models.deletion.DO_NOTHING, related_name='+', to='biserici.biserica'), ), migrations.AddField( model_name='historicalpatrimoniu', name='history_user', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to=settings.AUTH_USER_MODEL), ), migrations.AddField( model_name='historicalistoric', name='biserica', field=models.ForeignKey(blank=True, db_constraint=False, null=True, on_delete=django.db.models.deletion.DO_NOTHING, related_name='+', to='biserici.biserica'), ), migrations.AddField( model_name='historicalistoric', name='datare_secol', field=models.ForeignKey(blank=True, db_constraint=False, null=True, on_delete=django.db.models.deletion.DO_NOTHING, related_name='+', to='nomenclatoare.secol'), ), migrations.AddField( model_name='historicalistoric', name='history_user', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to=settings.AUTH_USER_MODEL), ), migrations.AddField( model_name='historicalistoric', name='studiu_dendocronologic', field=models.ForeignKey(blank=True, db_constraint=False, null=True, on_delete=django.db.models.deletion.DO_NOTHING, related_name='+', to='nomenclatoare.studiudendocronologic'), ), migrations.AddField( model_name='historicalidentificare', name='biserica', field=models.ForeignKey(blank=True, db_constraint=False, null=True, on_delete=django.db.models.deletion.DO_NOTHING, related_name='+', to='biserici.biserica'), ), migrations.AddField( model_name='historicalidentificare', name='cult', field=models.ForeignKey(blank=True, db_constraint=False, null=True, on_delete=django.db.models.deletion.DO_NOTHING, related_name='+', to='nomenclatoare.cultbiserica'), ), migrations.AddField( model_name='historicalidentificare', name='functiune', field=models.ForeignKey(blank=True, db_constraint=False, null=True, on_delete=django.db.models.deletion.DO_NOTHING, related_name='+', to='nomenclatoare.functiunebiserica'), ), migrations.AddField( model_name='historicalidentificare', name='functiune_initiala', field=models.ForeignKey(blank=True, db_constraint=False, null=True, on_delete=django.db.models.deletion.DO_NOTHING, related_name='+', to='nomenclatoare.functiunebiserica'), ), migrations.AddField( model_name='historicalidentificare', name='history_user', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to=settings.AUTH_USER_MODEL), ), migrations.AddField( model_name='historicalidentificare', name='judet', field=models.ForeignKey(blank=True, db_constraint=False, null=True, on_delete=django.db.models.deletion.DO_NOTHING, related_name='+', to='nomenclatoare.judet'), ), migrations.AddField( model_name='historicalidentificare', name='localitate', field=models.ForeignKey(blank=True, db_constraint=False, null=True, on_delete=django.db.models.deletion.DO_NOTHING, related_name='+', to='nomenclatoare.localitate'), ), migrations.AddField( model_name='historicalidentificare', name='proprietate_actuala', field=models.ForeignKey(blank=True, db_constraint=False, null=True, on_delete=django.db.models.deletion.DO_NOTHING, related_name='+', to='nomenclatoare.proprietatebiserica'), ), migrations.AddField( model_name='historicalidentificare', name='singularitate', field=models.ForeignKey(blank=True, db_constraint=False, null=True, on_delete=django.db.models.deletion.DO_NOTHING, related_name='+', to='nomenclatoare.singularitatebiserica'), ), migrations.AddField( model_name='historicalidentificare', name='statut', field=models.ForeignKey(blank=True, db_constraint=False, null=True, on_delete=django.db.models.deletion.DO_NOTHING, related_name='+', to='nomenclatoare.statutbiserica'), ), migrations.AddField( model_name='historicalidentificare', name='utilizare', field=models.ForeignKey(blank=True, db_constraint=False, null=True, on_delete=django.db.models.deletion.DO_NOTHING, related_name='+', to='nomenclatoare.utilizarebiserica'), ), migrations.AddField( model_name='historicaldescriere', name='amplasament', field=models.ForeignKey(blank=True, db_constraint=False, null=True, on_delete=django.db.models.deletion.DO_NOTHING, related_name='+', to='nomenclatoare.amplasamentbiserica'), ), migrations.AddField( model_name='historicaldescriere', name='biserica', field=models.ForeignKey(blank=True, db_constraint=False, null=True, on_delete=django.db.models.deletion.DO_NOTHING, related_name='+', to='biserici.biserica'), ), migrations.AddField( model_name='historicaldescriere', name='history_user', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to=settings.AUTH_USER_MODEL), ), migrations.AddField( model_name='historicaldescriere', name='topografie', field=models.ForeignKey(blank=True, db_constraint=False, null=True, on_delete=django.db.models.deletion.DO_NOTHING, related_name='+', to='nomenclatoare.topografiebiserica'), ), migrations.AddField( model_name='historicalconservare', name='biserica', field=models.ForeignKey(blank=True, db_constraint=False, null=True, on_delete=django.db.models.deletion.DO_NOTHING, related_name='+', to='biserici.biserica'), ), migrations.AddField( model_name='historicalconservare', name='history_user', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to=settings.AUTH_USER_MODEL), ), migrations.AddField( model_name='historicalbiserica', name='history_user', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to=settings.AUTH_USER_MODEL), ), migrations.AddField( model_name='descriere', name='amplasament', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to='nomenclatoare.amplasamentbiserica'), ), migrations.AddField( model_name='descriere', name='biserica', field=models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, to='biserici.biserica'), ), migrations.AddField( model_name='descriere', name='topografie', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to='nomenclatoare.topografiebiserica'), ), migrations.AddField( model_name='conservare', name='biserica', field=models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, to='biserici.biserica'), ), ]
51.646154
188
0.651996
1,338
13,428
6.376682
0.082212
0.101266
0.129395
0.151899
0.921472
0.889358
0.816221
0.666901
0.649789
0.614276
0
0.002301
0.223265
13,428
259
189
51.84556
0.815724
0.003426
0
0.789683
1
0
0.199402
0.097309
0
0
0
0
0
1
0
false
0
0.011905
0
0.027778
0
0
0
0
null
0
0
0
1
1
1
0
0
1
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
6
3ef8ffe1b12442c072e91b8248d73a57cd617118
1,752
py
Python
blogdashboard/blogs/forms.py
StephenTao/blog-dashboard
a6f55e005b86b8334a8b19a9bf03a313f5e814ca
[ "Apache-2.0" ]
null
null
null
blogdashboard/blogs/forms.py
StephenTao/blog-dashboard
a6f55e005b86b8334a8b19a9bf03a313f5e814ca
[ "Apache-2.0" ]
null
null
null
blogdashboard/blogs/forms.py
StephenTao/blog-dashboard
a6f55e005b86b8334a8b19a9bf03a313f5e814ca
[ "Apache-2.0" ]
null
null
null
from django.utils.translation import ugettext_lazy as _ from django.core.urlresolvers import reverse from horizon import exceptions from horizon import forms from horizon import messages from blogdashboard import api class UpdateForm(forms.SelfHandlingForm): title = forms.CharField( label=_("title"), required=False, widget=forms.TextInput() ) content = forms.CharField( label=_("content"), required=False, widget=forms.widgets.Textarea() ) def handle(self, request, data): try: ex = api.blogclient(request).blogs.update(data) msg = _('Execution has been created with id "%s".') % ex.id messages.success(request, msg) return True except Exception: msg = _('Failed to execute workflow "%s".') % data['title'] redirect = reverse('horizon:blogs:blogs:index') exceptions.handle(request, msg, redirect=redirect) class CreateForm(forms.SelfHandlingForm): title = forms.CharField( label=_("title"), required=False, widget=forms.TextInput() ) content = forms.CharField( label=_("content"), required=False, widget=forms.widgets.Textarea() ) def handle(self, request, data): try: ex = api.blogclient(request).blogs.create(data) msg = _('Execution has been created with id "%s".') % ex.id messages.success(request, msg) return True except Exception: msg = _('Failed to execute workflow "%s".') % data['title'] redirect = reverse('horizon:blogs:blogs:index') exceptions.handle(request, msg, redirect=redirect)
26.545455
71
0.609018
182
1,752
5.807692
0.335165
0.05298
0.071902
0.090823
0.785241
0.785241
0.785241
0.785241
0.785241
0.785241
0
0
0.280251
1,752
65
72
26.953846
0.838224
0
0
0.708333
0
0
0.130137
0.028539
0
0
0
0
0
1
0.041667
false
0
0.125
0
0.333333
0
0
0
0
null
0
0
0
0
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
6
f5f065bda0c474e7745267e4e54af3a890fb4f47
150
py
Python
ravegen/Decorators/__init__.py
mytab0r/RaveGen-Telegram-bot-generator
4b42ae622554c4b2442b35b1181f8f09886215d2
[ "MIT" ]
1
2020-06-13T17:16:57.000Z
2020-06-13T17:16:57.000Z
ravegen/Decorators/__init__.py
NICK-FTW/RaveGen-Telegram-bot-generator
269b36333a31cadb697f3c1250c6bf118cdc7fcc
[ "MIT" ]
5
2019-04-03T19:10:54.000Z
2019-06-14T17:21:14.000Z
ravegen/Decorators/__init__.py
NICK-FTW/RaveGen-Telegram-bot-generator
269b36333a31cadb697f3c1250c6bf118cdc7fcc
[ "MIT" ]
2
2019-03-19T19:45:05.000Z
2021-02-07T18:04:33.000Z
from RaveGen import * from Text import * from CommandHandler import * from Error import * from FunctionHandler import * from CallBackHandler import *
21.428571
29
0.8
18
150
6.666667
0.444444
0.416667
0
0
0
0
0
0
0
0
0
0
0.16
150
6
30
25
0.952381
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
1
0
1
0
1
0
0
null
1
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
6
f5f67cf52fcb1f167dfe08ad702162042a484a36
23
py
Python
muarch/funcs/__init__.py
DanielBok/muarch
c9bf60e3e029a443646fa35479ca2ed0dd23c31e
[ "MIT" ]
14
2019-03-14T10:10:17.000Z
2022-01-31T19:44:24.000Z
muarch/funcs/__init__.py
DanielBok/muarch
c9bf60e3e029a443646fa35479ca2ed0dd23c31e
[ "MIT" ]
2
2020-04-30T13:35:42.000Z
2021-08-12T07:52:06.000Z
muarch/funcs/__init__.py
DanielBok/muarch
c9bf60e3e029a443646fa35479ca2ed0dd23c31e
[ "MIT" ]
11
2019-05-27T15:55:10.000Z
2021-06-25T16:59:32.000Z
from .moments import *
11.5
22
0.73913
3
23
5.666667
1
0
0
0
0
0
0
0
0
0
0
0
0.173913
23
1
23
23
0.894737
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
1
0
1
0
1
1
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
6
eb34948ace701fd7f7b9a7bc3978ee35c67b405f
197
py
Python
test/context.py
woo-lang/woolang-project-generator
99b156e98e81545c839e8a4b73fba94085d56f19
[ "MIT" ]
1
2021-03-08T04:19:50.000Z
2021-03-08T04:19:50.000Z
test/context.py
woo-lang/woolang-project-generator
99b156e98e81545c839e8a4b73fba94085d56f19
[ "MIT" ]
null
null
null
test/context.py
woo-lang/woolang-project-generator
99b156e98e81545c839e8a4b73fba94085d56f19
[ "MIT" ]
null
null
null
import sys import os sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) import generate from generate.project import Project, ProjectFiles
21.888889
80
0.675127
26
197
4.961538
0.538462
0.139535
0
0
0
0
0
0
0
0
0
0.006369
0.203046
197
9
81
21.888889
0.815287
0
0
0
1
0
0.010526
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
1
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
6
de1f63a00d7dceab5cd03c2ead4dc7bba7b8a027
173
py
Python
autolens/pipeline/phase/interferometer/__init__.py
PyJedi/PyAutoLens
bcfb2e7b447aa24508fc648d60b6fd9b4fd852e7
[ "MIT" ]
1
2020-04-06T20:07:56.000Z
2020-04-06T20:07:56.000Z
autolens/pipeline/phase/interferometer/__init__.py
PyJedi/PyAutoLens
bcfb2e7b447aa24508fc648d60b6fd9b4fd852e7
[ "MIT" ]
null
null
null
autolens/pipeline/phase/interferometer/__init__.py
PyJedi/PyAutoLens
bcfb2e7b447aa24508fc648d60b6fd9b4fd852e7
[ "MIT" ]
null
null
null
from .phase import PhaseInterferometer from autolens.pipeline.phase.interferometer.result import Result from autolens.pipeline.phase.interferometer.analysis import Analysis
43.25
68
0.878613
20
173
7.6
0.45
0.157895
0.263158
0.328947
0.513158
0
0
0
0
0
0
0
0.069364
173
3
69
57.666667
0.944099
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
1
0
1
0
1
0
0
null
0
1
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
0
0
0
6
de535470746c7f3a308932abb8a6da6fc437ef07
34
py
Python
client/__init__.py
aanania/PyXTBClient
f0d70e03ea0a57e6f57fdd8d2ed1e596e732a1a3
[ "MIT" ]
11
2018-09-21T21:30:42.000Z
2021-03-11T08:46:35.000Z
client/__init__.py
aanania/PyXTBClient
f0d70e03ea0a57e6f57fdd8d2ed1e596e732a1a3
[ "MIT" ]
1
2020-04-10T10:47:26.000Z
2020-04-10T10:47:26.000Z
client/__init__.py
aanania/PyXTBClient
f0d70e03ea0a57e6f57fdd8d2ed1e596e732a1a3
[ "MIT" ]
3
2019-03-07T14:07:25.000Z
2020-04-10T15:28:09.000Z
from .xtb_client import XTBClient
17
33
0.852941
5
34
5.6
1
0
0
0
0
0
0
0
0
0
0
0
0.117647
34
1
34
34
0.933333
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
1
0
1
0
1
1
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
6
dee4a7495c7a2787f3c1d73eacb145c38e12816c
25,020
py
Python
model.py
ishine/MISOnet
88c0c27d89d0aa860b56060e80ff7794b8fff5a8
[ "MIT" ]
21
2021-11-19T15:31:27.000Z
2022-03-25T01:35:20.000Z
model.py
Xia-yuan/MISOnet
88c0c27d89d0aa860b56060e80ff7794b8fff5a8
[ "MIT" ]
4
2021-11-30T15:02:26.000Z
2022-03-29T07:14:32.000Z
model.py
Xia-yuan/MISOnet
88c0c27d89d0aa860b56060e80ff7794b8fff5a8
[ "MIT" ]
5
2021-07-27T05:58:27.000Z
2021-11-23T02:00:52.000Z
import torch import torch.nn as nn import torch.nn.functional as f import pdb import math EPS = 1e-8 class MISO_1(nn.Module): def __init__(self,num_spks, num_ch, num_bottleneck,en_bottleneck_channels,de_bottleneck_channels,norm_type): super(MISO_1,self).__init__() #init# # ch = 8 -> real + imag = 16 # en_bottleneck_channels = [2*Ch,24,32,32,32,32,64,128,384] # de_bottleneck_channels = [384,128,64,32,32,32,32,24,2*Spk] en_bottleneck_channels.insert(0,2*num_ch) de_bottleneck_channels.append(2*num_spks) # block_length = len(en_bottleneck_channels) """ num_bottleneck : number of bottleneck """ self.num_bottleneck = num_bottleneck self.encoders = nn.ModuleList() self.decoders = nn.ModuleList() for n_b in range(num_bottleneck): block = self.en_make_layer(n_b,en_bottleneck_channels[n_b], en_bottleneck_channels[n_b+1]) self.encoders.append(block) # self.TCN = TemporalConvNet(2,7,384,384,384,norm_type) self.TCN = TemporalConvNet(2,7,128,128,128,norm_type) for n_b in range(num_bottleneck): block = self.de_make_layer(n_b,2*de_bottleneck_channels[n_b],de_bottleneck_channels[n_b+1]) self.decoders.append(block) self.sigmoid = nn.Sigmoid() def en_make_layer(self,block_idx,in_channels, out_channels): layers = [] if block_idx < 5: if block_idx == 0: layers.append(init_Conv2d_(in_channels,out_channels,kernel_size=(3,3), stride=(1,1),padding=(1,0))) layers.append(DenseBlock(out_channels,out_channels,out_channels)) else: layers.append(Conv2d_(in_channels,out_channels,kernel_size=(3,3), stride=(1,2),padding=(1,0))) layers.append(DenseBlock(out_channels,out_channels,out_channels)) elif block_idx == 6: layers.append(Conv2d_(in_channels,out_channels,kernel_size=(3,3), stride=(1,1),padding=(1,0))) else: layers.append(Conv2d_(in_channels,out_channels,kernel_size=(3,3), stride=(1,2),padding=(1,0))) return nn.Sequential(*layers) def de_make_layer(self,block_idx,in_channels, out_channels): """ in_channels : input + skip-connection """ layers = [] if block_idx >= 2: if block_idx == 6: layers.append(DenseBlock(in_channels,in_channels//2,in_channels)) layers.append(last_Deconv2d_(in_channels,out_channels,kernel_size=(3,3), stride=(1,1), padding=(1,0))) else: layers.append(DenseBlock(in_channels,in_channels//2,in_channels)) layers.append(DeConv2d_(in_channels,out_channels,kernel_size=(3,3), stride=(1,2),padding=(1,0))) elif block_idx == 0: layers.append(DeConv2d_(in_channels,out_channels,kernel_size=(3,3),stride=(1,1),padding=(1,0))) else: layers.append(DeConv2d_(in_channels,out_channels,kernel_size=(3,3),stride=(1,2),padding=(1,0))) return nn.Sequential(*layers) def forward(self,mixture): real_spec = mixture.real.float() # [B,C,T,F] imag_spec = mixture.imag.float() # [B,C,T,F] #reference mic -> circular shift 고려해야 됨. x = torch.cat((real_spec,imag_spec),dim=1) xs = [] for i, encoder in enumerate(self.encoders): # print(i) x = encoder(x) xs.append(x) # print(x.shape) #Reshape [B,384, T ,1] -> [B,384,T] x = torch.squeeze(x) #[B,384,T] -> [B,384,T] tcn_out = self.TCN(x) de_x =tcn_out #Reshape [B,384,T] -> [B,384,T,1] de_x = torch.unsqueeze(de_x,dim=-1) for i, decoder in enumerate(self.decoders): #[B,C,T,F] -> [B,2C,T,F] de_x = torch.cat((de_x, xs[self.num_bottleneck-1-i]), dim=1) de_x = decoder(de_x) #[B,2*Spks,T,257] B,Spk_realimag,T,F = de_x.size() #[B,2*Spks,T,257] -> [B,Spk,T,257] o_real_spec = de_x[:,0:Spk_realimag//2,:,:] o_imag_spec = de_x[:,Spk_realimag//2:Spk_realimag,:,:] #[B,Spk,T,257] -> [B,Spk,T,257] # separate = torch.complex(o_real_spec,o_imag_spec) if True in torch.isnan(o_real_spec) or True in torch.isnan(o_imag_spec): pdb.set_trace() return torch.complex(o_real_spec, o_imag_spec) # #Mask # separate_real = self.sigmoid(o_real_spec) # separate_imag = self.sigmoid(o_imag_spec) # out_real_s1 = torch.unsqueeze(real_spec[:,0,:,:] * separate_real[:,0,:,:],dim=1) # out_real_s2 = torch.unsqueeze(real_spec[:,0,:,:] * separate_real[:,1,:,:],dim=1) # out_imag_s1 = torch.unsqueeze(imag_spec[:,0,:,:] * separate_imag[:,0,:,:],dim=1) # out_imag_s2 = torch.unsqueeze(imag_spec[:,0,:,:] * separate_imag[:,1,:,:],dim=1) # out_real = torch.cat((out_real_s1, out_real_s2), dim = 1) # out_imag = torch.cat((out_imag_s1, out_imag_s2), dim = 1) # separate = torch.complex(out_real,out_imag) # return separate # Mask Based # def forward(self,mixture): # real_spec = mixture.real.float() # [B,C,F,T] # imag_spec = mixture.imag.float() # [B,C,F,T] # #reference mic -> circular shift 고려해야 됨. # x = torch.cat((real_spec,imag_spec),dim=1) # xs = [] # for i, encoder in enumerate(self.encoders): # # print(i) # x = encoder(x) # xs.append(x) # #Reshape [B,384, T ,1] -> [B,384,T] # x = torch.squeeze(x) # #[B,384,T] -> [B,384,T] # tcn_out = self.TCN(x) # de_x = x * self.sigmoid(tcn_out) # #Reshape [B,384,T] -> [B,384,T,1] # de_x = torch.unsqueeze(de_x,dim=-1) # for i, decoder in enumerate(self.decoders): # #[B,C,T,F] -> [B,2C,T,F] # de_x = torch.cat((de_x, xs[self.num_bottleneck-1-i]), dim=1) # de_x = decoder(de_x) # #[B,2*Spks,T,257] # B,Spk_realimag,T,F = de_x.size() # #[B,2*Spks,T,257] -> [B,Spk,T,257] # o_real_spec = de_x[:,0:Spk_realimag//2,:,:] # o_imag_spec = de_x[:,Spk_realimag//2:Spk_realimag,:,:] # #[B,Spk,T,257] -> [B,Spk,T,257] # # separate = torch.complex(o_real_spec,o_imag_spec) # if True in torch.isnan(o_real_spec) or True in torch.isnan(o_imag_spec): # pdb.set_trace() # return torch.complex(o_real_spec, o_imag_spec) class MISO_2(nn.Module): def __init__(self,num_spks, num_ch, num_bottleneck,en_bottleneck_channels,de_bottleneck_channels,norm_type): super(MISO_2,self).__init__() #init# # ch = 8 -> real + imag = 16 # en_bottleneck_channels = [2*Ch,24,32,32,32,32,64,128,384] # de_bottleneck_channels = [384,128,64,32,32,32,32,24,2*Spk] en_bottleneck_channels.insert(0,2*(num_ch + 4)) # mixture 6ch + MISO1 1ch + BF 1ch de_bottleneck_channels.append(2*num_spks) # block_length = len(en_bottleneck_channels) """ num_bottleneck : number of bottleneck """ self.num_bottleneck = num_bottleneck self.encoders = nn.ModuleList() self.decoders = nn.ModuleList() for n_b in range(num_bottleneck): block = self.en_make_layer(n_b,en_bottleneck_channels[n_b], en_bottleneck_channels[n_b+1]) self.encoders.append(block) # self.TCN = TemporalConvNet(2,7,384,384,384,norm_type) self.TCN = TemporalConvNet(2,7,128,128,128,norm_type) for n_b in range(num_bottleneck): block = self.de_make_layer(n_b,2*de_bottleneck_channels[n_b],de_bottleneck_channels[n_b+1]) self.decoders.append(block) self.sigmoid = nn.Sigmoid() def en_make_layer(self,block_idx,in_channels, out_channels): layers = [] if block_idx < 5: if block_idx == 0: layers.append(init_Conv2d_(in_channels,out_channels,kernel_size=(3,3), stride=(1,1),padding=(1,0))) layers.append(DenseBlock(out_channels,out_channels,out_channels)) else: layers.append(Conv2d_(in_channels,out_channels,kernel_size=(3,3), stride=(1,2),padding=(1,0))) layers.append(DenseBlock(out_channels,out_channels,out_channels)) elif block_idx == 6: layers.append(Conv2d_(in_channels,out_channels,kernel_size=(3,3), stride=(1,1),padding=(1,0))) else: layers.append(Conv2d_(in_channels,out_channels,kernel_size=(3,3), stride=(1,2),padding=(1,0))) return nn.Sequential(*layers) def de_make_layer(self,block_idx,in_channels, out_channels): """ in_channels : input + skip-connection """ layers = [] if block_idx >= 2: if block_idx == 6: layers.append(DenseBlock(in_channels,in_channels//2,in_channels)) layers.append(last_Deconv2d_(in_channels,out_channels,kernel_size=(3,3), stride=(1,1), padding=(1,0))) else: layers.append(DenseBlock(in_channels,in_channels//2,in_channels)) layers.append(DeConv2d_(in_channels,out_channels,kernel_size=(3,3), stride=(1,2),padding=(1,0))) elif block_idx == 0: layers.append(DeConv2d_(in_channels,out_channels,kernel_size=(3,3),stride=(1,1),padding=(1,0))) else: layers.append(DeConv2d_(in_channels,out_channels,kernel_size=(3,3),stride=(1,2),padding=(1,0))) return nn.Sequential(*layers) def forward(self,mixture,MISO1,BF): mixture_real_spec = mixture.real.float() # [B,C,T,F] mixture_imag_spec = mixture.imag.float() # [B,C,T,F] MISO1_real_spec = MISO1.real.float() MISO1_imag_spec = MISO1.imag.float() BF_real_spec = BF.real.float() BF_imag_spec = BF.imag.float() real_spec = torch.cat((mixture_real_spec, MISO1_real_spec, BF_real_spec), dim= 1) imag_spec = torch.cat((mixture_imag_spec, MISO1_imag_spec, BF_imag_spec), dim= 1) #reference mic -> circular shift 고려해야 됨. x = torch.cat((real_spec,imag_spec),dim=1) xs = [] for i, encoder in enumerate(self.encoders): # print(i) x = encoder(x) xs.append(x) # print(x.shape) #Reshape [B,384, T ,1] -> [B,384,T] x = torch.squeeze(x) #[B,384,T] -> [B,384,T] tcn_out = self.TCN(x) de_x =tcn_out #Reshape [B,384,T] -> [B,384,T,1] de_x = torch.unsqueeze(de_x,dim=-1) for i, decoder in enumerate(self.decoders): #[B,C,T,F] -> [B,2C,T,F] de_x = torch.cat((de_x, xs[self.num_bottleneck-1-i]), dim=1) de_x = decoder(de_x) #[B,2*Spks,T,257] B,Spk_realimag,T,F = de_x.size() #[B,2*Spks,T,257] -> [B,Spk,T,257] o_real_spec = de_x[:,0:Spk_realimag//2,:,:] o_imag_spec = de_x[:,Spk_realimag//2:Spk_realimag,:,:] #[B,Spk,T,257] -> [B,Spk,T,257] # separate = torch.complex(o_real_spec,o_imag_spec) if True in torch.isnan(o_real_spec) or True in torch.isnan(o_imag_spec): pdb.set_trace() return torch.complex(o_real_spec, o_imag_spec) class MISO_3(nn.Module): def __init__(self,num_spks, num_ch, num_bottleneck,en_bottleneck_channels,de_bottleneck_channels,norm_type): super(MISO_3,self).__init__() #init# # ch = 8 -> real + imag = 16 # en_bottleneck_channels = [2*Ch,24,32,32,32,32,64,128,384] # de_bottleneck_channels = [384,128,64,32,32,32,32,24,2*Spk] en_bottleneck_channels.insert(0,2*(num_ch + 2)) # mixture 6ch + MISO1 1ch + BF 1ch de_bottleneck_channels.append(2*num_spks) # block_length = len(en_bottleneck_channels) """ num_bottleneck : number of bottleneck """ self.num_bottleneck = num_bottleneck self.encoders = nn.ModuleList() self.decoders = nn.ModuleList() for n_b in range(num_bottleneck): block = self.en_make_layer(n_b,en_bottleneck_channels[n_b], en_bottleneck_channels[n_b+1]) self.encoders.append(block) # self.TCN = TemporalConvNet(2,7,384,384,384,norm_type) self.TCN = TemporalConvNet(2,7,128,128,128,norm_type) for n_b in range(num_bottleneck): block = self.de_make_layer(n_b,2*de_bottleneck_channels[n_b],de_bottleneck_channels[n_b+1]) self.decoders.append(block) self.sigmoid = nn.Sigmoid() def en_make_layer(self,block_idx,in_channels, out_channels): layers = [] if block_idx < 5: if block_idx == 0: layers.append(init_Conv2d_(in_channels,out_channels,kernel_size=(3,3), stride=(1,1),padding=(1,0))) layers.append(DenseBlock(out_channels,out_channels,out_channels)) else: layers.append(Conv2d_(in_channels,out_channels,kernel_size=(3,3), stride=(1,2),padding=(1,0))) layers.append(DenseBlock(out_channels,out_channels,out_channels)) elif block_idx == 6: layers.append(Conv2d_(in_channels,out_channels,kernel_size=(3,3), stride=(1,1),padding=(1,0))) else: layers.append(Conv2d_(in_channels,out_channels,kernel_size=(3,3), stride=(1,2),padding=(1,0))) return nn.Sequential(*layers) def de_make_layer(self,block_idx,in_channels, out_channels): """ in_channels : input + skip-connection """ layers = [] if block_idx >= 2: if block_idx == 6: layers.append(DenseBlock(in_channels,in_channels//2,in_channels)) layers.append(last_Deconv2d_(in_channels,out_channels,kernel_size=(3,3), stride=(1,1), padding=(1,0))) else: layers.append(DenseBlock(in_channels,in_channels//2,in_channels)) layers.append(DeConv2d_(in_channels,out_channels,kernel_size=(3,3), stride=(1,2),padding=(1,0))) elif block_idx == 0: layers.append(DeConv2d_(in_channels,out_channels,kernel_size=(3,3),stride=(1,1),padding=(1,0))) else: layers.append(DeConv2d_(in_channels,out_channels,kernel_size=(3,3),stride=(1,2),padding=(1,0))) return nn.Sequential(*layers) def forward(self,mixture,MISO1,BF): mixture_real_spec = mixture.real.float() # [B,C,T,F] mixture_imag_spec = mixture.imag.float() # [B,C,T,F] MISO1_real_spec = MISO1.real.float() MISO1_imag_spec = MISO1.imag.float() BF_real_spec = BF.real.float() BF_imag_spec = BF.imag.float() real_spec = torch.cat((mixture_real_spec, MISO1_real_spec, BF_real_spec), dim= 1) imag_spec = torch.cat((mixture_imag_spec, MISO1_imag_spec, BF_imag_spec), dim= 1) #reference mic -> circular shift 고려해야 됨. x = torch.cat((real_spec,imag_spec),dim=1) xs = [] for i, encoder in enumerate(self.encoders): # print(i) x = encoder(x) xs.append(x) # print(x.shape) #Reshape [B,384, T ,1] -> [B,384,T] x = torch.squeeze(x) #[B,384,T] -> [B,384,T] tcn_out = self.TCN(x) de_x =tcn_out #Reshape [B,384,T] -> [B,384,T,1] de_x = torch.unsqueeze(de_x,dim=-1) for i, decoder in enumerate(self.decoders): #[B,C,T,F] -> [B,2C,T,F] de_x = torch.cat((de_x, xs[self.num_bottleneck-1-i]), dim=1) de_x = decoder(de_x) #[B,2*Spks,T,257] B,Spk_realimag,T,F = de_x.size() #[B,2*Spks,T,257] -> [B,Spk,T,257] o_real_spec = de_x[:,0:Spk_realimag//2,:,:] o_imag_spec = de_x[:,Spk_realimag//2:Spk_realimag,:,:] #[B,Spk,T,257] -> [B,Spk,T,257] # separate = torch.complex(o_real_spec,o_imag_spec) if True in torch.isnan(o_real_spec) or True in torch.isnan(o_imag_spec): pdb.set_trace() return torch.complex(o_real_spec, o_imag_spec) class init_Conv2d_(nn.Module): def __init__(self,in_channels, out_channels, kernel_size=(3,3),stride=(1,1),padding=(1,0)): super(init_Conv2d_, self).__init__() self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size,stride=stride, padding=padding) def forward(self,x): return self.conv2d(x) class Conv2d_(nn.Module): def __init__(self,in_channels, out_channels, kernel_size=(3,3),stride=(1,2),padding=(1,0), norm_type="IN"): super(Conv2d_,self).__init__() conv2d = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding) elu = nn.ELU() norm = nn.InstanceNorm2d(out_channels,affine=False) # 384 self.net = nn.Sequential(conv2d,elu,norm) def forward(self,x): return self.net(x) class last_Deconv2d_(nn.Module): def __init__(self,in_channels,out_channels, kernel_size=(3,3), stride=(1,1), padding=(1,0)): super(last_Deconv2d_,self).__init__() self.deconv2d = nn.ConvTranspose2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding) def forward(self,x): return self.deconv2d(x) class DeConv2d_(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, stride, padding, norm_type="IN"): super(DeConv2d_,self).__init__() deconv2d = nn.ConvTranspose2d(in_channels,out_channels,kernel_size=kernel_size, stride=stride, padding=padding) elu = nn.ELU() norm = nn.InstanceNorm2d(out_channels,affine=False) self.net = nn.Sequential(deconv2d,elu,norm) def forward(self,x): return self.net(x) class DenseBlock(nn.Module): def __init__(self,init_ch, g1, g2): super(DenseBlock,self).__init__() self.conv1 = nn.Sequential( nn.Conv2d(init_ch,g1, kernel_size=(3,3),stride=(1,1),padding=(1,1)), nn.ELU(), nn.InstanceNorm2d(g1,affine=False) ) self.conv2 = nn.Sequential( nn.Conv2d(init_ch+g1,g1, kernel_size=(3,3),stride=(1,1),padding=(1,1)), nn.ELU(), nn.InstanceNorm2d(g1,affine=False) ) self.conv3 = nn.Sequential( nn.Conv2d(init_ch+2*g1,g1, kernel_size=(3,3),stride=(1,1),padding=(1,1)), nn.ELU(), nn.InstanceNorm2d(g1,affine=False) ) self.conv4 = nn.Sequential( nn.Conv2d(init_ch+3*g1,g1, kernel_size=(3,3),stride=(1,1),padding=(1,1)), nn.ELU(), nn.InstanceNorm2d(g1,affine=False) ) self.conv5 = nn.Sequential( nn.Conv2d(init_ch+4*g1,g2, kernel_size=(3,3),stride=(1,1),padding=(1,1)), nn.ELU(), nn.InstanceNorm2d(g2,affine=False) ) def forward(self,x): y0 = self.conv1(x) y0_x = torch.cat((x,y0),dim=1) y1 = self.conv2(y0_x) y1_0_x = torch.cat((x,y0,y1),dim=1) y2 = self.conv3(y1_0_x) y2_1_0_x = torch.cat((x,y0,y1,y2),dim=1) y3 = self.conv4(y2_1_0_x) y3_2_1_0_x = torch.cat((x,y0,y1,y2,y3),dim=1) y4 = self.conv5(y3_2_1_0_x) return y4 class TemporalConvNet(nn.Module): def __init__(self, R, X, C_in, C_hidden, C_out, norm_type = "IN"): """ R : Number of repeats R = 2 X : Number of convolutional blocks in each repeat X = 7 C_in : Number of channels in input C_hidden : Number of channels in first conv block output C_out : Number of channels in output """ super(TemporalConvNet,self).__init__() repeats = [] for r in range(R): blocks = [] for x in range(X): dilation = 2**x # 0,2,4,8,16,32,64 # kernel(P) 3 stride 1 padding d dilation d featuremap 384 padding = 2**x blocks += [TemporalBlock(C_in,C_hidden,C_out, kernel_size= 3, stride = 1, padding=padding, dilation=dilation, norm_type = norm_type)] repeats += [nn.Sequential(*blocks)] self.temporal_conv_net = nn.Sequential(*repeats) def forward(self,x): """ Input : [B,C,T] Output : [B,C,T] """ return self.temporal_conv_net(x) class TemporalBlock(nn.Module): def __init__(self,in_channels,hidden_channels,out_channels,kernel_size, stride,padding,dilation,norm_type="IN"): """ in_channels : 384 out_channels : 384 kernel_size : 3 stride : 1 padding : d dilation : d featuremap : 384 """ super(TemporalBlock,self).__init__() norm_1 = chose_norm(norm_type, in_channels) # 384 elu_1 = nn.ELU() # [B,C,T] -> [B,C,T] dsconv_1 = DepthwiseSeparableConv(in_channels,hidden_channels,kernel_size,stride,padding,dilation,norm_type="gLN") norm_2 = chose_norm(norm_type, hidden_channels) # 384 elu_2 = nn.ELU() dsconv_2 = DepthwiseSeparableConv(hidden_channels,out_channels,kernel_size,stride,padding,dilation,norm_type="gLN") self.net = nn.Sequential(norm_1, elu_1, dsconv_1, norm_2, elu_2, dsconv_2) def forward(self,x): """ Input : [B,C,T] Output : [B,C,T] """ if x.dim() == 2: x = torch.unsqueeze(x,dim=0) residual = x out = self.net(x) return out + residual class DepthwiseSeparableConv(nn.Module): def __init__(self,in_channels,out_channels,kernel_size,stride,padding,dilation,norm_type="gLN"): super(DepthwiseSeparableConv,self).__init__() depthwise_conv = nn.Conv1d(in_channels,in_channels,kernel_size,stride=stride, padding=padding,dilation=dilation,groups=in_channels,bias=False) prelu = nn.PReLU() norm = chose_norm(norm_type,in_channels) pointwise_conv = nn.Conv1d(in_channels,out_channels,1,bias=False) self.net = nn.Sequential(depthwise_conv, prelu, norm, pointwise_conv) def forward(self,x): """ Input : [B,C_in,T] output : [B,C_out,T] """ return self.net(x) def chose_norm(norm_type, channel_size): """ input : [B, C, T] """ if norm_type=="gLN": return GlobalLayerNorm(channel_size) elif norm_type == "cLN": return ChannelwiseLayerNorm(channel_size) elif norm_type == "IN": return nn.InstanceNorm1d(channel_size,affine=False) else: return nn.BatchNorm1d(channel_size) class ChannelwiseLayerNorm(nn.Module): """Channel-wise Layer Normalization (cLN)""" def __init__(self, channel_size): super(ChannelwiseLayerNorm, self).__init__() self.gamma = nn.Parameter(torch.Tensor(1, channel_size, 1)) # [1, N, 1] self.beta = nn.Parameter(torch.Tensor(1, channel_size,1 )) # [1, N, 1] self.reset_parameters() def reset_parameters(self): self.gamma.data.fill_(1) self.beta.data.zero_() def forward(self, y): """ Args: y: [M, N, K], M is batch size, N is channel size, K is length Returns: cLN_y: [M, N, K] """ mean = torch.mean(y, dim=1, keepdim=True) # [M, 1, K] var = torch.var(y, dim=1, keepdim=True, unbiased=False) # [M, 1, K] cLN_y = self.gamma * (y - mean) / torch.pow(var + EPS, 0.5) + self.beta return cLN_y class GlobalLayerNorm(nn.Module): """Global Layer Normalization (gLN)""" def __init__(self, channel_size): super(GlobalLayerNorm, self).__init__() self.gamma = nn.Parameter(torch.Tensor(1, channel_size, 1)) # [1, N, 1] self.beta = nn.Parameter(torch.Tensor(1, channel_size,1 )) # [1, N, 1] self.reset_parameters() def reset_parameters(self): self.gamma.data.fill_(1) self.beta.data.zero_() def forward(self, y): """ Args: y: [M, N, K], M is batch size, N is channel size, K is length Returns: gLN_y: [M, N, K] """ # TODO: in torch 1.0, torch.mean() support dim list mean = y.mean(dim=1, keepdim=True).mean(dim=2, keepdim=True) #[M, 1, 1] var = (torch.pow(y-mean, 2)).mean(dim=1, keepdim=True).mean(dim=2, keepdim=True) gLN_y = self.gamma * (y - mean) / torch.pow(var + EPS, 0.5) + self.beta return gLN_y if __name__ == "__main__": input = torch.randn(10,8,150,257, dtype=torch.cfloat) model = MISO_1(8,8,2,"IN") output = model(input) pdb.set_trace()
39.032761
126
0.593405
3,591
25,020
3.894458
0.059315
0.049339
0.073364
0.060064
0.835109
0.818806
0.798427
0.778119
0.778119
0.764533
0
0.0485
0.263269
25,020
640
127
39.09375
0.710194
0.186571
0
0.649457
0
0
0.001783
0
0
0
0
0.001563
0
1
0.095109
false
0
0.013587
0.01087
0.206522
0
0
0
0
null
0
0
0
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
6
7247e22fa0497c945b8e18d5294c40daad7112fc
38
py
Python
CommonFiles/__init__.py
PesyCorm/AutomationFiles
3afe7cd28e6b472bd822c0974386591408f0d62d
[ "MIT" ]
null
null
null
CommonFiles/__init__.py
PesyCorm/AutomationFiles
3afe7cd28e6b472bd822c0974386591408f0d62d
[ "MIT" ]
null
null
null
CommonFiles/__init__.py
PesyCorm/AutomationFiles
3afe7cd28e6b472bd822c0974386591408f0d62d
[ "MIT" ]
null
null
null
from .DriverStart import DriverStarter
38
38
0.894737
4
38
8.5
1
0
0
0
0
0
0
0
0
0
0
0
0.078947
38
1
38
38
0.971429
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
1
0
1
0
1
1
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
6
a0ebad2df042355ad852e1ad45627c5cac1701f2
55
py
Python
vital_site/vital/forms/__init__.py
vital2/OLD-vital-development
d37fb5f715a9b5d0488af412496b415ba50957a1
[ "MIT" ]
15
2016-07-19T17:11:24.000Z
2019-10-22T16:54:08.000Z
vital_site/vital/forms/__init__.py
pdv8883/OLD-vital-development
d37fb5f715a9b5d0488af412496b415ba50957a1
[ "MIT" ]
27
2019-11-20T16:27:25.000Z
2021-09-07T23:44:15.000Z
vital_site/vital/forms/__init__.py
pdv8883/OLD-vital-development
d37fb5f715a9b5d0488af412496b415ba50957a1
[ "MIT" ]
13
2016-07-20T19:41:41.000Z
2019-06-04T17:04:24.000Z
from security_form import * from student_form import *
18.333333
27
0.818182
8
55
5.375
0.625
0.465116
0
0
0
0
0
0
0
0
0
0
0.145455
55
2
28
27.5
0.914894
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
1
0
1
0
1
0
0
null
1
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
6
19c46f95614df21c7ac6d034295f5086a1ffd068
31
py
Python
keycloak_admin_aio/_resources/clients/by_id/__init__.py
V-Mann-Nick/keycloak-admin-aio
83ac1af910e492a5864eb369aacfc0512e5c8c45
[ "Apache-2.0" ]
12
2021-11-08T18:03:09.000Z
2022-03-17T16:34:06.000Z
keycloak_admin_aio/_resources/clients/by_id/__init__.py
V-Mann-Nick/keycloak-admin-aio
83ac1af910e492a5864eb369aacfc0512e5c8c45
[ "Apache-2.0" ]
null
null
null
keycloak_admin_aio/_resources/clients/by_id/__init__.py
V-Mann-Nick/keycloak-admin-aio
83ac1af910e492a5864eb369aacfc0512e5c8c45
[ "Apache-2.0" ]
1
2021-11-14T13:55:30.000Z
2021-11-14T13:55:30.000Z
from .by_id import ClientsById
15.5
30
0.83871
5
31
5
1
0
0
0
0
0
0
0
0
0
0
0
0.129032
31
1
31
31
0.925926
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
1
0
1
0
1
1
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
6
19d77db3c6d85fa692bf3de934484ccede0a5f6d
46
py
Python
py_circuit_breaker/__init__.py
mdgreenwald/py-circuit-breaker
0624d2063d77b4e9064bec1fc0fd934005535564
[ "Apache-2.0" ]
1
2021-09-16T19:52:36.000Z
2021-09-16T19:52:36.000Z
py_circuit_breaker/__init__.py
mdgreenwald/py-circuit-breaker
0624d2063d77b4e9064bec1fc0fd934005535564
[ "Apache-2.0" ]
null
null
null
py_circuit_breaker/__init__.py
mdgreenwald/py-circuit-breaker
0624d2063d77b4e9064bec1fc0fd934005535564
[ "Apache-2.0" ]
null
null
null
from .py_circuit_breaker import CircuitBreaker
46
46
0.913043
6
46
6.666667
1
0
0
0
0
0
0
0
0
0
0
0
0.065217
46
1
46
46
0.930233
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
1
0
1
0
1
1
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
6
19fe88af7c415846f00ef274b9bcac4a53d8bd14
37
py
Python
mysql-rpc/service/__init__.py
Evanyok/PNblog
c1e133eadeb0e8db9b32bd46b04850ba03fd2c68
[ "MIT" ]
1
2019-04-29T05:36:20.000Z
2019-04-29T05:36:20.000Z
mysql-rpc/service/__init__.py
Evanyok/PNblog
c1e133eadeb0e8db9b32bd46b04850ba03fd2c68
[ "MIT" ]
null
null
null
mysql-rpc/service/__init__.py
Evanyok/PNblog
c1e133eadeb0e8db9b32bd46b04850ba03fd2c68
[ "MIT" ]
null
null
null
from .user_service import UserService
37
37
0.891892
5
37
6.4
1
0
0
0
0
0
0
0
0
0
0
0
0.081081
37
1
37
37
0.941176
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
1
0
1
0
1
1
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
6
c224d41f3d31ad72bc04dd076b64d3ea7c2d6329
182
py
Python
Session-3/Strings/S3SS15.py
saianuragpeddu/python-assignemts
a6bb192f2c0ef8ea86531c1a98f1b76150fa474b
[ "MIT" ]
null
null
null
Session-3/Strings/S3SS15.py
saianuragpeddu/python-assignemts
a6bb192f2c0ef8ea86531c1a98f1b76150fa474b
[ "MIT" ]
null
null
null
Session-3/Strings/S3SS15.py
saianuragpeddu/python-assignemts
a6bb192f2c0ef8ea86531c1a98f1b76150fa474b
[ "MIT" ]
1
2019-07-06T02:37:58.000Z
2019-07-06T02:37:58.000Z
def getCommonLetters(word1, word2): return ''.join(sorted(set(word1).intersection(set(word2)))) print(getCommonLetters('apple', 'strw')) print(getCommonLetters('sing', 'song'))
30.333333
63
0.725275
20
182
6.6
0.7
0.318182
0
0
0
0
0
0
0
0
0
0.02381
0.076923
182
5
64
36.4
0.761905
0
0
0
0
0
0.093407
0
0
0
0
0
0
1
0.25
false
0
0
0.25
0.5
0.5
1
0
0
null
1
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
1
0
0
0
1
0
1
0
6
c236a6a57a6e06c0941e840869122f14da6641de
36
py
Python
indexor/__init__.py
kpedrozag/myfirstpkg
554b293a9b1c555350cdea2861da09b8d54dbd08
[ "MIT" ]
null
null
null
indexor/__init__.py
kpedrozag/myfirstpkg
554b293a9b1c555350cdea2861da09b8d54dbd08
[ "MIT" ]
null
null
null
indexor/__init__.py
kpedrozag/myfirstpkg
554b293a9b1c555350cdea2861da09b8d54dbd08
[ "MIT" ]
null
null
null
from indexor.indexor import Indexor
18
35
0.861111
5
36
6.2
0.6
0
0
0
0
0
0
0
0
0
0
0
0.111111
36
1
36
36
0.96875
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
1
0
1
0
1
1
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
1
0
0
6
dfad9d168fe9db7089bd92bc4de7dd447caab3ec
41
py
Python
json_test.py
XtherDevTeam/XmediaCenter
398f3a70503643c0a1ef2d9874eb123122704053
[ "MIT" ]
1
2022-01-20T11:30:48.000Z
2022-01-20T11:30:48.000Z
json_test.py
leadsoft-ware/XmediaCenter
398f3a70503643c0a1ef2d9874eb123122704053
[ "MIT" ]
null
null
null
json_test.py
leadsoft-ware/XmediaCenter
398f3a70503643c0a1ef2d9874eb123122704053
[ "MIT" ]
null
null
null
import json,sys print(json.dumps( { } ))
13.666667
24
0.658537
6
41
4.5
0.833333
0
0
0
0
0
0
0
0
0
0
0
0.146341
41
3
24
13.666667
0.771429
0
0
0
0
0
0
0
0
0
0
0
0
1
0
true
0
0.5
0
0.5
0.5
1
1
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
1
0
1
0
0
1
0
6