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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
fc807cc925ac7f44f1a008e0fed384f213e58b2c
| 45,543
|
py
|
Python
|
database.py
|
cflewis/aspect-oriented-commentator
|
341eefe7111116a8a938a33b930da5a85f1320e8
|
[
"AFL-3.0"
] | 1
|
2016-05-08T23:45:59.000Z
|
2016-05-08T23:45:59.000Z
|
database.py
|
cflewis/aspect-oriented-commentator
|
341eefe7111116a8a938a33b930da5a85f1320e8
|
[
"AFL-3.0"
] | null | null | null |
database.py
|
cflewis/aspect-oriented-commentator
|
341eefe7111116a8a938a33b930da5a85f1320e8
|
[
"AFL-3.0"
] | null | null | null |
#!/usr/bin/env python
# encoding: utf-8
"""
database.py
Created by Chris Lewis on 2008-11-03.
Copyright (c) 2008 Chris Lewis. All rights reserved.
"""
import sys
import os
import unittest
import sqlite3
import MySQLdb
import time
from springpython.aop import *
from commentator import *
#class Storage(object):
# def store(self, invocation, results):
# pass
class Database:
def __init__(self):
self.connection = sqlite3.connect("tag.db")
def reinitializeDB(self):
print "Restoring database"
cursor = self.connection.cursor()
cursor.executescript('''
DROP TABLE IF EXISTS `CHARACTER`;
CREATE TABLE `CHARACTER` (
`NAME` varchar(11) NOT NULL,
PRIMARY KEY (`NAME`)
);
INSERT INTO `CHARACTER` (`NAME`) VALUES ('Alice');
INSERT INTO `CHARACTER` (`NAME`) VALUES ('Bob');
INSERT INTO `CHARACTER` (`NAME`) VALUES ('Carol');
INSERT INTO `CHARACTER` (`NAME`) VALUES ('Dave');
INSERT INTO `CHARACTER` (`NAME`) VALUES ('Player');
DROP TABLE IF EXISTS `STATISTICS`;
CREATE TABLE `STATISTICS` (
`AVG_SPEED` double(11,2) default NULL,
`AVG_SPEED_SAMPLES` int(20) default NULL,
`NAME` varchar(11) NOT NULL default ''
);
INSERT INTO `STATISTICS` (`AVG_SPEED`,`AVG_SPEED_SAMPLES`,`NAME`) VALUES ('55.94','410','Alice');
INSERT INTO `STATISTICS` (`AVG_SPEED`,`AVG_SPEED_SAMPLES`,`NAME`) VALUES ('55.87','410','Bob');
INSERT INTO `STATISTICS` (`AVG_SPEED`,`AVG_SPEED_SAMPLES`,`NAME`) VALUES ('48.63','410','Carol');
INSERT INTO `STATISTICS` (`AVG_SPEED`,`AVG_SPEED_SAMPLES`,`NAME`) VALUES ('68.69','410','Dave');
INSERT INTO `STATISTICS` (`AVG_SPEED`,`AVG_SPEED_SAMPLES`,`NAME`) VALUES ('75.80','410','Player');
DROP TABLE IF EXISTS `TAG`;
CREATE TABLE `TAG` (
`TAGGER_NAME` varchar(11) NOT NULL default '',
`TAGGED_NAME` varchar(11) NOT NULL default '',
`TIME_TO_TAG` int(20) NOT NULL default '0',
`TAG_ID` int(11),
`TIME_OF_TAG` timestamp,
PRIMARY KEY (`TAG_ID`)
);
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Dave','8','16','2008-11-11 18:40:02');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Dave','Player','3','17','2008-11-11 18:40:05');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Dave','4','18','2008-11-11 18:40:09');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Bob','8','19','2008-11-11 18:46:23');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Bob','Player','3','20','2008-11-11 18:46:26');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Alice','9','21','2008-11-16 16:00:54');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Alice','Dave','5','22','2008-11-16 16:00:59');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Alice','11','23','2008-11-16 16:01:24');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Dave','13','24','2008-11-18 17:15:35');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Alice','4','25','2008-11-18 17:17:30');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Bob','30','26','2008-11-18 17:38:44');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Bob','Carol','12','27','2008-11-18 17:38:56');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Carol','Alice','5','28','2008-11-18 17:39:00');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Alice','Player','6','29','2008-11-18 17:39:06');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Alice','4','30','2008-11-18 17:41:06');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Alice','Player','4','31','2008-11-18 17:41:10');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Alice','21','32','2008-11-18 17:41:31');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Alice','Dave','6','33','2008-11-18 17:41:37');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Dave','Alice','3','34','2008-11-18 17:41:40');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Alice','Dave','3','35','2008-11-18 17:41:43');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Dave','Player','12','36','2008-11-18 17:41:55');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Dave','6','37','2008-11-18 17:50:46');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Dave','Player','6','38','2008-11-18 17:50:52');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Dave','7','39','2008-11-18 17:51:44');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Bob','11','40','2008-11-22 15:43:22');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Bob','Player','7','41','2008-11-22 15:43:29');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Alice','6','42','2008-11-22 15:43:35');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Alice','Player','5','43','2008-11-22 15:43:40');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Alice','11','44','2008-11-22 15:43:51');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Alice','Player','5','45','2008-11-22 15:43:56');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Dave','5','46','2008-11-22 15:44:46');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Dave','Player','9','47','2008-11-22 15:44:54');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Bob','4','48','2008-11-22 15:44:59');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Bob','Dave','5','49','2008-11-22 15:45:04');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Dave','Player','9','50','2008-11-22 15:45:12');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Dave','11','51','2008-11-22 15:45:23');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Dave','Player','4','52','2008-11-22 15:45:27');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Alice','11','53','2008-11-22 15:50:00');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Alice','Player','3','54','2008-11-22 15:50:04');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Alice','7','55','2008-11-22 15:50:10');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Alice','Player','3','56','2008-11-22 15:50:13');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Carol','23','57','2008-11-22 15:50:37');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Carol','Player','9','58','2008-11-22 15:50:46');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Carol','4','59','2008-11-22 15:50:50');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Carol','Player','3','60','2008-11-22 15:50:53');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Alice','9','61','2008-11-22 15:51:02');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Alice','Player','3','62','2008-11-22 15:51:05');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Dave','16','63','2008-11-22 15:51:22');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Dave','Player','6','64','2008-11-22 15:51:28');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Dave','6','65','2008-11-22 15:51:34');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Dave','22','66','2008-11-22 16:08:13');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Dave','Player','3','67','2008-11-22 16:08:16');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Bob','8','68','2008-11-22 16:08:46');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Bob','Player','4','69','2008-11-22 16:08:50');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Bob','6','70','2008-11-22 16:08:56');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Bob','Alice','15','71','2008-11-22 16:09:11');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Alice','Bob','7','72','2008-11-22 16:09:18');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Bob','Player','19','73','2008-11-22 16:09:37');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Carol','25','74','2008-11-22 16:10:02');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Carol','Player','3','75','2008-11-22 16:10:05');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Bob','10','76','2008-11-22 16:13:12');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Bob','Carol','5','77','2008-11-22 16:13:18');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Carol','Player','5','78','2008-11-22 16:13:22');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Dave','13','79','2008-11-22 16:13:35');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Dave','Player','3','80','2008-11-22 16:13:38');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Alice','8','81','2008-11-22 16:13:47');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Alice','Player','3','82','2008-11-22 16:13:50');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Alice','13','83','2008-11-22 16:14:31');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Alice','Player','12','84','2008-11-22 16:14:43');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Alice','5','85','2008-11-22 16:14:48');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Alice','Player','3','86','2008-11-22 16:14:51');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Carol','14','87','2008-11-22 16:15:05');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Carol','Player','12','88','2008-11-22 16:15:16');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Dave','6','89','2008-11-22 16:29:09');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Dave','Player','3','90','2008-11-22 16:29:12');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Bob','16','91','2008-11-22 16:31:58');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Bob','Player','4','92','2008-11-22 16:32:02');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Carol','22','93','2008-11-22 16:32:23');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Carol','Player','3','94','2008-11-22 16:32:27');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Bob','5','95','2008-11-22 16:32:31');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Bob','Player','3','96','2008-11-22 16:32:35');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Bob','5','97','2008-11-22 16:58:30');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Bob','Dave','6','98','2008-11-22 16:58:36');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Carol','9','99','2008-11-22 16:59:39');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Carol','Alice','9','100','2008-11-22 16:59:48');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Dave','5','101','2008-11-22 17:00:29');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Dave','Player','12','102','2008-11-22 17:00:40');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Dave','5','103','2008-11-22 17:00:42');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Dave','Player','3','104','2008-11-22 17:00:47');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Dave','3','105','2008-11-22 17:00:52');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Dave','Player','11','106','2008-11-22 17:01:00');
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INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Alice','14','248','2008-12-01 16:09:24');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Alice','Player','9','249','2008-12-01 16:09:33');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Carol','4','250','2008-12-01 16:12:06');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Carol','Player','17','251','2008-12-01 16:12:22');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Alice','58','252','2008-12-01 16:13:20');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Alice','Player','5','253','2008-12-01 16:13:24');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Dave','4','254','2008-12-01 16:14:03');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Dave','Carol','6','255','2008-12-01 16:14:09');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Carol','Dave','5','256','2008-12-01 16:14:14');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Dave','8','257','2008-12-01 16:14:47');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Dave','Carol','13','258','2008-12-01 16:15:00');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Carol','Dave','4','259','2008-12-01 16:15:04');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Dave','Carol','3','260','2008-12-01 16:15:08');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Carol','Dave','5','261','2008-12-01 16:15:13');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Dave','Carol','3','262','2008-12-01 16:15:16');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Carol','Dave','5','263','2008-12-01 16:15:20');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Dave','Carol','6','264','2008-12-01 16:15:26');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Carol','Dave','4','265','2008-12-01 16:15:30');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Dave','Carol','3','266','2008-12-01 16:15:33');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Carol','Dave','4','267','2008-12-01 16:15:37');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Dave','Carol','4','268','2008-12-01 16:15:41');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Carol','Dave','5','269','2008-12-01 16:15:45');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Dave','Bob','5','270','2008-12-01 16:15:50');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Bob','Carol','4','271','2008-12-01 16:15:54');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Carol','Bob','3','272','2008-12-01 16:15:57');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Bob','Carol','5','273','2008-12-01 16:16:03');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Carol','Bob','4','274','2008-12-01 16:16:06');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Bob','Carol','5','275','2008-12-01 16:16:12');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Carol','Player','15','276','2008-12-01 16:16:27');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Carol','14','277','2008-12-01 17:35:40');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Carol','Player','6','278','2008-12-01 17:35:47');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Player','Carol','13','279','2008-12-01 17:35:59');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Carol','Alice','11','280','2008-12-01 17:36:10');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Alice','Carol','3','281','2008-12-01 17:36:13');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Carol','Alice','5','282','2008-12-01 17:36:18');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Alice','Carol','3','283','2008-12-01 17:36:21');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Carol','Alice','4','284','2008-12-01 17:36:25');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Alice','Carol','3','285','2008-12-01 17:36:29');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Carol','Alice','3','286','2008-12-01 17:36:32');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Alice','Carol','26','287','2008-12-01 17:36:58');
INSERT INTO `TAG` (`TAGGER_NAME`,`TAGGED_NAME`,`TIME_TO_TAG`,`TAG_ID`,`TIME_OF_TAG`) VALUES ('Carol','Player','4','288','2008-12-01 17:37:01');
''')
self.connection.commit()
cursor.close()
print "Database restored"
def get_cursor(self):
return self.connection.cursor()
def store_game_state(self, observer):
self.store_tag(observer)
def store_character(self, observer):
self.store_average_speed(observer)
def store_tag(self, observer):
if observer.last_tag:
self.execute_update("insert into TAG(TAGGER_NAME, TAGGED_NAME, TIME_TO_TAG) \
values(?,?,?)", \
(observer.last_tag["tagger"].getName(), \
observer.last_tag["tagged"].getName(), \
observer.time_to_tag), \
)
def store_average_speed(self, observer):
self.execute_update("update STATISTICS set AVG_SPEED = ?, AVG_SPEED_SAMPLES = ? where NAME = ?", \
(observer.average_speed, observer.ticks, observer.invocation.getName()))
def get_tag_number(self, tagger, tagged):
return self.get_single_value("select count(*) from TAG where TAGGER_NAME = ? \
and TAGGED_NAME = ?", (tagger, tagged))
def get_average_speed(self, name):
return self.get_single_value("select AVG_SPEED from STATISTICS where NAME = ?", (name,))
def get_single_value(self, sql_statement, bind_parameters = ()):
cursor = self.get_cursor()
cursor.execute(sql_statement, bind_parameters)
returnValue = cursor.fetchone()[0]
cursor.close()
return returnValue
def execute_update(self, sql_statement, bind_parameters = ()):
cursor = self.connection.cursor()
cursor.execute(sql_statement, bind_parameters)
self.connection.commit()
cursor.close()
def clean_up(self):
self.connection.close()
class databaseTests(unittest.TestCase):
def setUp(self):
pass
if __name__ == '__main__':
d = Database()
d.reinitializeDB()
| 113.8575
| 152
| 0.635729
| 7,576
| 45,543
| 3.557946
| 0.05491
| 0.105361
| 0.092154
| 0.193137
| 0.906251
| 0.779225
| 0.773994
| 0.767687
| 0.767687
| 0.767687
| 0
| 0.124253
| 0.118547
| 45,543
| 399
| 153
| 114.142857
| 0.547205
| 0.002437
| 0
| 0.033058
| 0
| 0.76584
| 0.947017
| 0.632073
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0.002755
| 0.022039
| null | null | 0.00551
| 0
| 0
| 0
| null | 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 12
|
fc810069ece4da3b66fe19aa3eb400884b249b78
| 2,970
|
py
|
Python
|
ex008.py
|
maurocesarj/Aulas-Senac-Python
|
422dcde9c4bfc899d79706df8949dae582dd6ad4
|
[
"MIT"
] | null | null | null |
ex008.py
|
maurocesarj/Aulas-Senac-Python
|
422dcde9c4bfc899d79706df8949dae582dd6ad4
|
[
"MIT"
] | null | null | null |
ex008.py
|
maurocesarj/Aulas-Senac-Python
|
422dcde9c4bfc899d79706df8949dae582dd6ad4
|
[
"MIT"
] | null | null | null |
print('-=-= iniciando calculadora -=-=')
op = int(input(' 1. SOMAR / 2. SUBTRAIR / 3. DIVIDIR / 4. MULTIPLICAR / 5. SAIR: '))
cont = 1
if op == 5:
print('Programa encerrado')
else:
# Soma
if op == 1:
print('Modo SOMA')
num1 = int(input(f'Digite o primeiro número: '))
num2 = int(input(f'Digite o segundo número: '))
confirmacao = int(input('Para exibir o resultado digite [0] '))
if confirmacao == 0:
soma = num2 + num1
print(f'{num1} + {num2} = {soma}')
else:
while confirmacao != 0:
num1 = int(input(f'Digite o primeiro número: '))
num2 = int(input(f'Digite o segundo número: '))
confirmacao = str(input('Para mostrar o resultado digite [0] '))
soma = num2 + num1
print(f'{num1} + {num2} = {soma}')
# Subtração
elif op == 2:
print('Modo SUBTRAÇÃO')
num1 = int(input(f'Digite o primeiro número: '))
num2 = int(input(f'Digite o segundo número: '))
confirmacao = int(input('Para exibir o resultado digite [0] '))
if confirmacao == 0:
sub = num1 - num2
print(f'{num1} - {num2} = {sub}')
else:
while confirmacao != 0:
num1 = int(input(f'Digite o primeiro número: '))
num2 = int(input(f'Digite o segundo número: '))
confirmacao = str(input('Para mostrar o resultado digite [0] '))
sub = num1 - num2
print(f'{num1} - {num2} = {sub}')
# Divisão
elif op == 3:
print('Modo DIVISÃO')
num1 = int(input(f'Digite o primeiro número: '))
num2 = int(input(f'Digite o segundo número: '))
confirmacao = int(input('Para exibir o resultado digite [0] '))
if confirmacao == 0:
div = num1 / num2
print(f'{num1} / {num2} = {div}')
else:
while confirmacao != 0:
num1 = int(input(f'Digite o primeiro número: '))
num2 = int(input(f'Digite o segundo número: '))
confirmacao = str(input('Para mostrar o resultado digite [0] '))
div = num1 / num2
print(f'{num1} / {num2} = {div}')
# Multiplicação
elif op == 4:
print('Modo MULTIPLICAÇÃO')
num1 = int(input(f'Digite o primeiro número: '))
num2 = int(input(f'Digite o segundo número: '))
confirmacao = int(input('Para exibir o resultado digite [0] '))
if confirmacao == 0:
mult = num1 * num2
print(f'{num1} x {num2} = {mult}')
else:
while confirmacao != 0:
num1 = int(input(f'Digite o primeiro número: '))
num2 = int(input(f'Digite o segundo número: '))
confirmacao = str(input('Para mostrar o resultado digite [0] '))
mult = num1 * num2
print(f'{num1} x {num2} = {mult}')
| 42.428571
| 86
| 0.512121
| 347
| 2,970
| 4.383285
| 0.135447
| 0.110454
| 0.094675
| 0.157791
| 0.842867
| 0.842867
| 0.842867
| 0.842867
| 0.842867
| 0.729783
| 0
| 0.03888
| 0.350505
| 2,970
| 70
| 87
| 42.428571
| 0.749611
| 0.012121
| 0
| 0.80303
| 0
| 0
| 0.35802
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.212121
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
fca86294d6c8654cd0ddb0f9af8aedfe3977df2a
| 4,122
|
py
|
Python
|
Python/phonenumbers/data/region_MX.py
|
skykisl/uberbruns2
|
26933efce04dba700d93cc75c7b74e069fb02d26
|
[
"Unlicense"
] | 5
|
2015-04-27T20:10:56.000Z
|
2018-06-14T18:19:09.000Z
|
python/phonenumbers/data/region_MX.py
|
vemel/python-phonenumbers
|
595c322bf12106a3b95e3f202e948a7c6b6c15b8
|
[
"Apache-2.0"
] | 2
|
2017-06-08T16:11:13.000Z
|
2018-05-07T11:50:13.000Z
|
python/phonenumbers/data/region_MX.py
|
vemel/python-phonenumbers
|
595c322bf12106a3b95e3f202e948a7c6b6c15b8
|
[
"Apache-2.0"
] | 6
|
2015-02-19T11:11:04.000Z
|
2022-03-15T19:38:31.000Z
|
"""Auto-generated file, do not edit by hand. MX metadata"""
from ..phonemetadata import NumberFormat, PhoneNumberDesc, PhoneMetadata
PHONE_METADATA_MX = PhoneMetadata(id='MX', country_code=52, international_prefix='0[09]',
general_desc=PhoneNumberDesc(national_number_pattern='[1-9]\\d{9,10}', possible_number_pattern='\\d{7,11}'),
fixed_line=PhoneNumberDesc(national_number_pattern='(?:33|55|81)\\d{8}|(?:2(?:2[2-9]|3[1-35-8]|4[13-9]|7[1-689]|8[1-578]|9[467])|3(?:1[1-79]|[2458][1-9]|7[1-8]|9[1-5])|4(?:1[1-57-9]|[24-6][1-9]|[37][1-8]|8[1-35-9]|9[2-689])|5(?:88|9[1-79])|6(?:1[2-68]|[234][1-9]|5[1-3689]|6[12457-9]|7[1-7]|8[67]|9[4-8])|7(?:[13467][1-9]|2[1-8]|5[13-9]|8[1-69]|9[17])|8(?:2[13-689]|3[1-6]|4[124-6]|6[1246-9]|7[1-378]|9[12479])|9(?:1[346-9]|2[1-4]|3[2-46-8]|5[1348]|[69][1-9]|7[12]|8[1-8]))\\d{7}', possible_number_pattern='\\d{7,10}', example_number='2221234567'),
mobile=PhoneNumberDesc(national_number_pattern='1(?:(?:33|55|81)\\d{8}|(?:2(?:2[2-9]|3[1-35-8]|4[13-9]|7[1-689]|8[1-578]|9[467])|3(?:1[1-79]|[2458][1-9]|7[1-8]|9[1-5])|4(?:1[1-57-9]|[24-6][1-9]|[37][1-8]|8[1-35-9]|9[2-689])|5(?:88|9[1-79])|6(?:1[2-68]|[2-4][1-9]|5[1-3689]|6[12457-9]|7[1-7]|8[67]|9[4-8])|7(?:[13467][1-9]|2[1-8]|5[13-9]|8[1-69]|9[17])|8(?:2[13-689]|3[1-6]|4[124-6]|6[1246-9]|7[1-378]|9[12479])|9(?:1[346-9]|2[1-4]|3[2-46-8]|5[1348]|[69][1-9]|7[12]|8[1-8]))\\d{7})', possible_number_pattern='\\d{11}', example_number='12221234567'),
toll_free=PhoneNumberDesc(national_number_pattern='800\\d{7}', possible_number_pattern='\\d{10}', example_number='8001234567'),
premium_rate=PhoneNumberDesc(national_number_pattern='900\\d{7}', possible_number_pattern='\\d{10}', example_number='9001234567'),
shared_cost=PhoneNumberDesc(national_number_pattern='NA', possible_number_pattern='NA'),
personal_number=PhoneNumberDesc(national_number_pattern='NA', possible_number_pattern='NA'),
voip=PhoneNumberDesc(national_number_pattern='NA', possible_number_pattern='NA'),
pager=PhoneNumberDesc(national_number_pattern='NA', possible_number_pattern='NA'),
uan=PhoneNumberDesc(national_number_pattern='NA', possible_number_pattern='NA'),
emergency=PhoneNumberDesc(national_number_pattern='06[568]|911', possible_number_pattern='\\d{3}', example_number='066'),
voicemail=PhoneNumberDesc(national_number_pattern='NA', possible_number_pattern='NA'),
no_international_dialling=PhoneNumberDesc(national_number_pattern='NA', possible_number_pattern='NA'),
national_prefix='01',
national_prefix_for_parsing='0[12]|04[45](\\d{10})',
national_prefix_transform_rule=u'1\\1',
number_format=[NumberFormat(pattern='([358]\\d)(\\d{4})(\\d{4})', format=u'\\1 \\2 \\3', leading_digits_pattern=['33|55|81'], national_prefix_formatting_rule=u'01 \\1', national_prefix_optional_when_formatting=True),
NumberFormat(pattern='(\\d{3})(\\d{3})(\\d{4})', format=u'\\1 \\2 \\3', leading_digits_pattern=['[2467]|3[12457-9]|5[89]|8[02-9]|9[0-35-9]'], national_prefix_formatting_rule=u'01 \\1', national_prefix_optional_when_formatting=True),
NumberFormat(pattern='(1)([358]\\d)(\\d{4})(\\d{4})', format=u'044 \\2 \\3 \\4', leading_digits_pattern=['1(?:33|55|81)'], national_prefix_formatting_rule=u'\\1', national_prefix_optional_when_formatting=True),
NumberFormat(pattern='(1)(\\d{3})(\\d{3})(\\d{4})', format=u'044 \\2 \\3 \\4', leading_digits_pattern=['1(?:[2467]|3[12457-9]|5[89]|8[2-9]|9[1-35-9])'], national_prefix_formatting_rule=u'\\1', national_prefix_optional_when_formatting=True)],
intl_number_format=[NumberFormat(pattern='([358]\\d)(\\d{4})(\\d{4})', format=u'\\1 \\2 \\3', leading_digits_pattern=['33|55|81']),
NumberFormat(pattern='(\\d{3})(\\d{3})(\\d{4})', format=u'\\1 \\2 \\3', leading_digits_pattern=['[2467]|3[12457-9]|5[89]|8[02-9]|9[0-35-9]']),
NumberFormat(pattern='(1)([358]\\d)(\\d{4})(\\d{4})', format=u'\\1 \\2 \\3 \\4', leading_digits_pattern=['1(?:33|55|81)']),
NumberFormat(pattern='(1)(\\d{3})(\\d{3})(\\d{4})', format=u'\\1 \\2 \\3 \\4', leading_digits_pattern=['1(?:[2467]|3[12457-9]|5[89]|8[2-9]|9[1-35-9])'])])
| 142.137931
| 552
| 0.657448
| 755
| 4,122
| 3.42649
| 0.161589
| 0.130653
| 0.081175
| 0.180905
| 0.745651
| 0.70777
| 0.70777
| 0.70777
| 0.70777
| 0.512563
| 0
| 0.176727
| 0.055556
| 4,122
| 28
| 553
| 147.214286
| 0.487799
| 0.012858
| 0
| 0
| 1
| 0.230769
| 0.394044
| 0.3111
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.038462
| 0
| 0.038462
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 9
|
5d8667de4a3057039d34d5a7b8ee13cd51277e3a
| 11,864
|
py
|
Python
|
utils/h36motion.py
|
705062791/Progressively-Generating-Better-Initial-Guesses-Towards-Next-Stages-forHigh-Quality-Human-Motion-Pre
|
0b9c95a7871dfab4663b84ae15b97087c2513188
|
[
"MIT"
] | 2
|
2022-03-09T06:18:58.000Z
|
2022-03-13T10:48:01.000Z
|
utils/h36motion.py
|
705062791/Progressively-Generating-Better-Initial-Guesses-Towards-Next-Stages-forHigh-Quality-Human-Motion-Pre
|
0b9c95a7871dfab4663b84ae15b97087c2513188
|
[
"MIT"
] | null | null | null |
utils/h36motion.py
|
705062791/Progressively-Generating-Better-Initial-Guesses-Towards-Next-Stages-forHigh-Quality-Human-Motion-Pre
|
0b9c95a7871dfab4663b84ae15b97087c2513188
|
[
"MIT"
] | null | null | null |
from torch.utils.data import Dataset
import numpy as np
from h5py import File
import scipy.io as sio
from utils import data_utils
from matplotlib import pyplot as plt
import torch
class Datasets(Dataset):
def __init__(self, opt, actions=None, split=0):
"""
:param path_to_data:
:param actions:
:param input_n:
:param output_n:
:param dct_used:
:param split: 0 train, 1 testing, 2 validation
:param sample_rate:
"""
#self.path_to_data = "./datasets/h3.6m/"
self.path_to_data = opt.data_dir
self.split = split
self.in_n = opt.input_n
self.out_n = opt.output_n
self.sample_rate = 2
self.seq = {}
self.data_idx = []
self.dimensions_to_use = np.array(
[6, 7, 8, 9, 12, 13, 14, 15, 21, 22, 23, 24, 27, 28, 29, 30, 36, 37, 38, 39, 40, 41, 42,
43, 44, 45, 46, 47, 51, 52, 53, 54, 55, 56, 57, 60, 61, 62, 75, 76, 77, 78, 79, 80, 81, 84, 85, 86])
self.dimensions_to_ignore = np.array(
[[0, 1, 2, 3, 4, 5, 10, 11, 16, 17, 18, 19, 20, 25, 26, 31, 32, 33, 34, 35, 48, 49, 50, 58,
59, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 82, 83, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97,
98]])
seq_len = self.in_n + self.out_n
subs = np.array([[1, 6, 7, 8, 9], [11], [5]])
# acts = data_utils.define_actions(actions)
if actions is None:
acts = ["walking", "eating", "smoking", "discussion", "directions",
"greeting", "phoning", "posing", "purchases", "sitting",
"sittingdown", "takingphoto", "waiting", "walkingdog",
"walkingtogether"]
else:
acts = actions
# subs = np.array([[1], [11], [5]])
# acts = ['walking']
subs = subs[split]
for subj in subs:
for action_idx in np.arange(len(acts)):
action = acts[action_idx]
if self.split <= 1 or opt.test_sample_num < 0:
for subact in [1, 2]: # subactions
print("Reading subject {0}, action {1}, subaction {2}".format(subj, action, subact))
filename = '{0}/S{1}/{2}_{3}.txt'.format(self.path_to_data, subj, action, subact)
the_sequence = data_utils.readCSVasFloat(filename)
n, d = the_sequence.shape
even_list = range(0, n, self.sample_rate)
num_frames = len(even_list)
the_sequence = np.array(the_sequence[even_list, :])
# the_sequence = torch.from_numpy(the_sequence).float().cuda()
# remove global rotation and translation
the_sequence[:, 0:6] = 0
# p3d = data_utils.expmap2xyz_torch(the_sequence)
self.seq[(subj, action, subact)] = the_sequence
valid_frames = np.arange(0, num_frames - seq_len + 1, opt.skip_rate)
tmp_data_idx_1 = [(subj, action, subact)] * len(valid_frames)
tmp_data_idx_2 = list(valid_frames)
self.data_idx.extend(zip(tmp_data_idx_1, tmp_data_idx_2))
else:
print("Reading subject {0}, action {1}, subaction {2}".format(subj, action, 1))
filename = '{0}/S{1}/{2}_{3}.txt'.format(self.path_to_data, subj, action, 1)
the_sequence1 = data_utils.readCSVasFloat(filename)
n, d = the_sequence1.shape
even_list = range(0, n, self.sample_rate)
num_frames1 = len(even_list)
the_sequence1 = np.array(the_sequence1[even_list, :])
# the_seq1 = torch.from_numpy(the_sequence1).float().cuda()
the_sequence1[:, 0:6] = 0
# p3d1 = data_utils.expmap2xyz_torch(the_seq1)
self.seq[(subj, action, 1)] = the_sequence1
print("Reading subject {0}, action {1}, subaction {2}".format(subj, action, 2))
filename = '{0}/S{1}/{2}_{3}.txt'.format(self.path_to_data, subj, action, 2)
the_sequence2 = data_utils.readCSVasFloat(filename)
n, d = the_sequence2.shape
even_list = range(0, n, self.sample_rate)
num_frames2 = len(even_list)
the_sequence2 = np.array(the_sequence2[even_list, :])
# the_seq2 = torch.from_numpy(the_sequence2).float().cuda()
the_sequence2[:, 0:6] = 0
# p3d2 = data_utils.expmap2xyz_torch(the_seq2)
self.seq[(subj, action, 2)] = the_sequence2
# fs_sel1, fs_sel2 = data_utils.find_indices_256(num_frames1, num_frames2, seq_len,
# input_n=self.in_n)
fs_sel1, fs_sel2 = data_utils.find_indices_srnn(num_frames1, num_frames2, seq_len,
input_n=self.in_n)
valid_frames = fs_sel1[:, 0]
tmp_data_idx_1 = [(subj, action, 1)] * len(valid_frames)
tmp_data_idx_2 = list(valid_frames)
self.data_idx.extend(zip(tmp_data_idx_1, tmp_data_idx_2))
valid_frames = fs_sel2[:, 0]
tmp_data_idx_1 = [(subj, action, 2)] * len(valid_frames)
tmp_data_idx_2 = list(valid_frames)
self.data_idx.extend(zip(tmp_data_idx_1, tmp_data_idx_2))
def __len__(self):
return np.shape(self.data_idx)[0]
def __getitem__(self, item):
key, start_frame = self.data_idx[item]
fs = np.arange(start_frame, start_frame + self.in_n + self.out_n)
return self.seq[key][fs]
class Datasets(Dataset):
def __init__(self, opt, actions=None, split=0):
"""
:param path_to_data:
:param actions:
:param input_n:
:param output_n:
:param dct_used:
:param split: 0 train, 1 testing, 2 validation
:param sample_rate:
"""
#self.path_to_data = "./datasets/h3.6m/"
self.path_to_data = opt.data_dir
self.split = split
self.in_n = opt.input_n
self.out_n = opt.output_n
self.sample_rate = 2
self.seq = {}
self.data_idx = []
self.dimensions_to_use = np.array(
[6, 7, 8, 9, 12, 13, 14, 15, 21, 22, 23, 24, 27, 28, 29, 30, 36, 37, 38, 39, 40, 41, 42,
43, 44, 45, 46, 47, 51, 52, 53, 54, 55, 56, 57, 60, 61, 62, 75, 76, 77, 78, 79, 80, 81, 84, 85, 86])
self.dimensions_to_ignore = np.array(
[[0, 1, 2, 3, 4, 5, 10, 11, 16, 17, 18, 19, 20, 25, 26, 31, 32, 33, 34, 35, 48, 49, 50, 58,
59, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 82, 83, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97,
98]])
seq_len = self.in_n + self.out_n
subs = np.array([[1, 6, 7, 8, 9], [11], [5]])
# acts = data_utils.define_actions(actions)
if actions is None:
acts = ["walking", "eating", "smoking", "discussion", "directions",
"greeting", "phoning", "posing", "purchases", "sitting",
"sittingdown", "takingphoto", "waiting", "walkingdog",
"walkingtogether"]
else:
acts = actions
# subs = np.array([[1], [11], [5]])
# acts = ['walking']
subs = subs[split]
for subj in subs:
for action_idx in np.arange(len(acts)):
action = acts[action_idx]
if self.split <= 1:
for subact in [1, 2]: # subactions
print("Reading subject {0}, action {1}, subaction {2}".format(subj, action, subact))
filename = '{0}/S{1}/{2}_{3}.txt'.format(self.path_to_data, subj, action, subact)
the_sequence = data_utils.readCSVasFloat(filename)
n, d = the_sequence.shape
even_list = range(0, n, self.sample_rate)
num_frames = len(even_list)
the_sequence = np.array(the_sequence[even_list, :])
# the_sequence = torch.from_numpy(the_sequence).float().cuda()
# remove global rotation and translation
the_sequence[:, 0:6] = 0
# p3d = data_utils.expmap2xyz_torch(the_sequence)
self.seq[(subj, action, subact)] = the_sequence
valid_frames = np.arange(0, num_frames - seq_len + 1, opt.skip_rate)
tmp_data_idx_1 = [(subj, action, subact)] * len(valid_frames)
tmp_data_idx_2 = list(valid_frames)
self.data_idx.extend(zip(tmp_data_idx_1, tmp_data_idx_2))
else:
print("Reading subject {0}, action {1}, subaction {2}".format(subj, action, 1))
filename = '{0}/S{1}/{2}_{3}.txt'.format(self.path_to_data, subj, action, 1)
the_sequence1 = data_utils.readCSVasFloat(filename)
n, d = the_sequence1.shape
even_list = range(0, n, self.sample_rate)
num_frames1 = len(even_list)
the_sequence1 = np.array(the_sequence1[even_list, :])
# the_seq1 = torch.from_numpy(the_sequence1).float().cuda()
the_sequence1[:, 0:6] = 0
# p3d1 = data_utils.expmap2xyz_torch(the_seq1)
self.seq[(subj, action, 1)] = the_sequence1
print("Reading subject {0}, action {1}, subaction {2}".format(subj, action, 2))
filename = '{0}/S{1}/{2}_{3}.txt'.format(self.path_to_data, subj, action, 2)
the_sequence2 = data_utils.readCSVasFloat(filename)
n, d = the_sequence2.shape
even_list = range(0, n, self.sample_rate)
num_frames2 = len(even_list)
the_sequence2 = np.array(the_sequence2[even_list, :])
# the_seq2 = torch.from_numpy(the_sequence2).float().cuda()
the_sequence2[:, 0:6] = 0
# p3d2 = data_utils.expmap2xyz_torch(the_seq2)
self.seq[(subj, action, 2)] = the_sequence2
# fs_sel1, fs_sel2 = data_utils.find_indices_256(num_frames1, num_frames2, seq_len,
# input_n=self.in_n)
fs_sel1, fs_sel2 = data_utils.find_indices_srnn(num_frames1, num_frames2, seq_len,
input_n=self.in_n)
valid_frames = fs_sel1[:, 0]
tmp_data_idx_1 = [(subj, action, 1)] * len(valid_frames)
tmp_data_idx_2 = list(valid_frames)
self.data_idx.extend(zip(tmp_data_idx_1, tmp_data_idx_2))
valid_frames = fs_sel2[:, 0]
tmp_data_idx_1 = [(subj, action, 2)] * len(valid_frames)
tmp_data_idx_2 = list(valid_frames)
self.data_idx.extend(zip(tmp_data_idx_1, tmp_data_idx_2))
def __len__(self):
return np.shape(self.data_idx)[0]
def __getitem__(self, item):
key, start_frame = self.data_idx[item]
fs = np.arange(start_frame, start_frame + self.in_n + self.out_n)
return self.seq[key][fs]
| 48.823045
| 117
| 0.513318
| 1,496
| 11,864
| 3.824198
| 0.15508
| 0.044048
| 0.041951
| 0.023073
| 0.970809
| 0.970809
| 0.970809
| 0.970809
| 0.970809
| 0.970809
| 0
| 0.082546
| 0.366908
| 11,864
| 242
| 118
| 49.024793
| 0.679137
| 0.135199
| 0
| 0.945455
| 0
| 0
| 0.065213
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.036364
| false
| 0
| 0.042424
| 0.012121
| 0.115152
| 0.036364
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
f8e6be148ab77c946d5266348c2f5820d36df3df
| 2,326
|
py
|
Python
|
tests/test_xs_Al_float.py
|
ornlneutronimaging/braggedgemodeling
|
6bf49b008cae5707f3c950e4a4d574211f201976
|
[
"MIT"
] | 1
|
2019-03-27T13:38:24.000Z
|
2019-03-27T13:38:24.000Z
|
tests/test_xs_Al_float.py
|
ornlneutronimaging/braggedgemodeling
|
6bf49b008cae5707f3c950e4a4d574211f201976
|
[
"MIT"
] | 32
|
2018-02-15T18:03:34.000Z
|
2022-02-09T14:59:55.000Z
|
tests/test_xs_Al_float.py
|
ornlneutronimaging/braggedgemodeling
|
6bf49b008cae5707f3c950e4a4d574211f201976
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python
# Jiao Lin <jiao.lin@gmail.com>
import os, numpy as np
from bem import xscalc, diffraction
from bem import xtaloriprobmodel as xopm
from bem.matter import fccAl
thisdir = os.path.dirname(__file__)
def test1():
lambdas = np.arange(0.05, 5.5, 0.005)
T = 300
# if max_diffraction_index is too small, the low wavelength portion will be a bit off
calc = xscalc.XSCalculator(fccAl, T, max_diffraction_index=8)
calc.xs_coh_el(1.5)
coh_el_xs = calc.xs_coh_el(lambdas)
inc_el_xs = calc.xs_inc_el(lambdas)
inel_xs = calc.xs_inel(lambdas)
abs_xs = calc.xs_abs(lambdas)
coh_inel_xs = calc.xs_coh_inel(lambdas)
inc_inel_xs = calc.xs_inc_inel(lambdas)
total = calc.xs(lambdas)
for i,l in enumerate(lambdas):
assert np.isclose(calc.xs_coh_el(l), coh_el_xs[i])
assert np.isclose(calc.xs_inc_el(l), inc_el_xs[i])
assert np.isclose(calc.xs_inel(l), inel_xs[i])
assert np.isclose(calc.xs_abs(l), abs_xs[i])
assert np.isclose(calc.xs_coh_inel(l), coh_inel_xs[i])
assert np.isclose(calc.xs_inc_inel(l), inc_inel_xs[i])
assert np.isclose(calc.xs(l), total[i])
continue
return
def test2():
lambdas = np.arange(0.05, 5.5, 0.005)
T = 300
texture_model = xopm.MarchDollase()
texture_model.r[(0,1,1)] = 2
texture_model.beta[(0,1,1)] = np.deg2rad(60.)
calc = xscalc.XSCalculator(fccAl, T, texture_model, max_diffraction_index=8)
calc.xs_coh_el(1.5)
coh_el_xs = calc.xs_coh_el(lambdas)
inc_el_xs = calc.xs_inc_el(lambdas)
inel_xs = calc.xs_inel(lambdas)
abs_xs = calc.xs_abs(lambdas)
coh_inel_xs = calc.xs_coh_inel(lambdas)
inc_inel_xs = calc.xs_inc_inel(lambdas)
total = calc.xs(lambdas)
for i,l in enumerate(lambdas):
assert np.isclose(calc.xs_coh_el(l), coh_el_xs[i])
assert np.isclose(calc.xs_inc_el(l), inc_el_xs[i])
assert np.isclose(calc.xs_inel(l), inel_xs[i])
assert np.isclose(calc.xs_abs(l), abs_xs[i])
assert np.isclose(calc.xs_coh_inel(l), coh_inel_xs[i])
assert np.isclose(calc.xs_inc_inel(l), inc_inel_xs[i])
assert np.isclose(calc.xs(l), total[i])
continue
return
def main():
test1()
test2()
return
if __name__ == '__main__': main()
# End of file
| 32.305556
| 89
| 0.671109
| 406
| 2,326
| 3.583744
| 0.204434
| 0.123711
| 0.14433
| 0.182818
| 0.740893
| 0.702406
| 0.702406
| 0.702406
| 0.702406
| 0.702406
| 0
| 0.023656
| 0.200344
| 2,326
| 71
| 90
| 32.760563
| 0.758602
| 0.062769
| 0
| 0.719298
| 0
| 0
| 0.003676
| 0
| 0
| 0
| 0
| 0
| 0.245614
| 1
| 0.052632
| false
| 0
| 0.070175
| 0
| 0.175439
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
f8f81011d22d4199a4714c62df839a3d1c7e4074
| 658
|
py
|
Python
|
tests/learnergy/visual/test_convergence.py
|
recogna-lab/recogners
|
711295f53b47c76246d57df24b75d18bc7ec62e8
|
[
"MIT"
] | 1
|
2019-07-04T19:36:42.000Z
|
2019-07-04T19:36:42.000Z
|
tests/learnergy/visual/test_convergence.py
|
recogna-lab/recogners
|
711295f53b47c76246d57df24b75d18bc7ec62e8
|
[
"MIT"
] | null | null | null |
tests/learnergy/visual/test_convergence.py
|
recogna-lab/recogners
|
711295f53b47c76246d57df24b75d18bc7ec62e8
|
[
"MIT"
] | 1
|
2019-12-09T16:18:51.000Z
|
2019-12-09T16:18:51.000Z
|
from learnergy.visual import convergence
def test_convergence_plot():
new_model = {"mse": [1, 2, 3], "pl": [1.5, 2, 2.5], "time": [0.1, 0.2, 0.3]}
try:
convergence.plot(new_model["mse"], new_model["pl"], new_model["time"], labels=1)
except:
convergence.plot(
new_model["mse"],
new_model["pl"],
new_model["time"],
labels=["MSE", "log-PL", "time (s)"],
)
try:
convergence.plot(
new_model["mse"], new_model["pl"], new_model["time"], labels=["MSE"]
)
except:
convergence.plot(new_model["mse"], new_model["pl"], new_model["time"])
| 28.608696
| 88
| 0.530395
| 86
| 658
| 3.883721
| 0.267442
| 0.311377
| 0.269461
| 0.344311
| 0.778443
| 0.700599
| 0.700599
| 0.700599
| 0.700599
| 0.700599
| 0
| 0.03125
| 0.270517
| 658
| 22
| 89
| 29.909091
| 0.664583
| 0
| 0
| 0.333333
| 0
| 0
| 0.098784
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.055556
| false
| 0
| 0.055556
| 0
| 0.111111
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
5d1f705fa11584c216d27baaac7efac7baa86e52
| 75,670
|
py
|
Python
|
airflow/providers/google/cloud/hooks/vertex_ai/auto_ml.py
|
arezamoosavi/airflow
|
c3c81c3144386d1de535c1c5e777270e727bb69e
|
[
"Apache-2.0"
] | 2
|
2016-08-23T14:22:15.000Z
|
2017-09-28T19:45:26.000Z
|
airflow/providers/google/cloud/hooks/vertex_ai/auto_ml.py
|
arezamoosavi/airflow
|
c3c81c3144386d1de535c1c5e777270e727bb69e
|
[
"Apache-2.0"
] | 4
|
2019-01-24T11:01:17.000Z
|
2022-02-28T04:28:07.000Z
|
airflow/providers/google/cloud/hooks/vertex_ai/auto_ml.py
|
arezamoosavi/airflow
|
c3c81c3144386d1de535c1c5e777270e727bb69e
|
[
"Apache-2.0"
] | 6
|
2018-04-09T07:46:05.000Z
|
2019-07-16T00:13:15.000Z
|
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
#
"""
This module contains a Google Cloud Vertex AI hook.
.. spelling::
aiplatform
au
codepoints
milli
mae
quantile
quantiles
Quantiles
rmse
rmsle
rmspe
wape
prc
roc
Jetson
forecasted
Struct
sentimentMax
TrainingPipeline
targetColumn
optimizationObjective
"""
from typing import Dict, List, Optional, Sequence, Tuple, Union
from google.api_core.operation import Operation
from google.api_core.retry import Retry
from google.cloud.aiplatform import (
AutoMLForecastingTrainingJob,
AutoMLImageTrainingJob,
AutoMLTabularTrainingJob,
AutoMLTextTrainingJob,
AutoMLVideoTrainingJob,
datasets,
models,
)
from google.cloud.aiplatform_v1 import JobServiceClient, PipelineServiceClient
from google.cloud.aiplatform_v1.services.pipeline_service.pagers import ListTrainingPipelinesPager
from google.cloud.aiplatform_v1.types import TrainingPipeline
from airflow import AirflowException
from airflow.providers.google.common.hooks.base_google import GoogleBaseHook
class AutoMLHook(GoogleBaseHook):
"""Hook for Google Cloud Vertex AI Auto ML APIs."""
def __init__(
self,
gcp_conn_id: str = "google_cloud_default",
delegate_to: Optional[str] = None,
impersonation_chain: Optional[Union[str, Sequence[str]]] = None,
) -> None:
super().__init__(
gcp_conn_id=gcp_conn_id,
delegate_to=delegate_to,
impersonation_chain=impersonation_chain,
)
self._job: Optional[
Union[
AutoMLForecastingTrainingJob,
AutoMLImageTrainingJob,
AutoMLTabularTrainingJob,
AutoMLTextTrainingJob,
AutoMLVideoTrainingJob,
]
] = None
def get_pipeline_service_client(
self,
region: Optional[str] = None,
) -> PipelineServiceClient:
"""Returns PipelineServiceClient."""
client_options = None
if region and region != 'global':
client_options = {'api_endpoint': f'{region}-aiplatform.googleapis.com:443'}
return PipelineServiceClient(
credentials=self._get_credentials(), client_info=self.client_info, client_options=client_options
)
def get_job_service_client(
self,
region: Optional[str] = None,
) -> JobServiceClient:
"""Returns JobServiceClient"""
client_options = None
if region and region != 'global':
client_options = {'api_endpoint': f'{region}-aiplatform.googleapis.com:443'}
return JobServiceClient(
credentials=self._get_credentials(), client_info=self.client_info, client_options=client_options
)
def get_auto_ml_tabular_training_job(
self,
display_name: str,
optimization_prediction_type: str,
optimization_objective: Optional[str] = None,
column_specs: Optional[Dict[str, str]] = None,
column_transformations: Optional[List[Dict[str, Dict[str, str]]]] = None,
optimization_objective_recall_value: Optional[float] = None,
optimization_objective_precision_value: Optional[float] = None,
project: Optional[str] = None,
location: Optional[str] = None,
labels: Optional[Dict[str, str]] = None,
training_encryption_spec_key_name: Optional[str] = None,
model_encryption_spec_key_name: Optional[str] = None,
) -> AutoMLTabularTrainingJob:
"""Returns AutoMLTabularTrainingJob object"""
return AutoMLTabularTrainingJob(
display_name=display_name,
optimization_prediction_type=optimization_prediction_type,
optimization_objective=optimization_objective,
column_specs=column_specs,
column_transformations=column_transformations,
optimization_objective_recall_value=optimization_objective_recall_value,
optimization_objective_precision_value=optimization_objective_precision_value,
project=project,
location=location,
credentials=self._get_credentials(),
labels=labels,
training_encryption_spec_key_name=training_encryption_spec_key_name,
model_encryption_spec_key_name=model_encryption_spec_key_name,
)
def get_auto_ml_forecasting_training_job(
self,
display_name: str,
optimization_objective: Optional[str] = None,
column_specs: Optional[Dict[str, str]] = None,
column_transformations: Optional[List[Dict[str, Dict[str, str]]]] = None,
project: Optional[str] = None,
location: Optional[str] = None,
labels: Optional[Dict[str, str]] = None,
training_encryption_spec_key_name: Optional[str] = None,
model_encryption_spec_key_name: Optional[str] = None,
) -> AutoMLForecastingTrainingJob:
"""Returns AutoMLForecastingTrainingJob object"""
return AutoMLForecastingTrainingJob(
display_name=display_name,
optimization_objective=optimization_objective,
column_specs=column_specs,
column_transformations=column_transformations,
project=project,
location=location,
credentials=self._get_credentials(),
labels=labels,
training_encryption_spec_key_name=training_encryption_spec_key_name,
model_encryption_spec_key_name=model_encryption_spec_key_name,
)
def get_auto_ml_image_training_job(
self,
display_name: str,
prediction_type: str = "classification",
multi_label: bool = False,
model_type: str = "CLOUD",
base_model: Optional[models.Model] = None,
project: Optional[str] = None,
location: Optional[str] = None,
labels: Optional[Dict[str, str]] = None,
training_encryption_spec_key_name: Optional[str] = None,
model_encryption_spec_key_name: Optional[str] = None,
) -> AutoMLImageTrainingJob:
"""Returns AutoMLImageTrainingJob object"""
return AutoMLImageTrainingJob(
display_name=display_name,
prediction_type=prediction_type,
multi_label=multi_label,
model_type=model_type,
base_model=base_model,
project=project,
location=location,
credentials=self._get_credentials(),
labels=labels,
training_encryption_spec_key_name=training_encryption_spec_key_name,
model_encryption_spec_key_name=model_encryption_spec_key_name,
)
def get_auto_ml_text_training_job(
self,
display_name: str,
prediction_type: str,
multi_label: bool = False,
sentiment_max: int = 10,
project: Optional[str] = None,
location: Optional[str] = None,
labels: Optional[Dict[str, str]] = None,
training_encryption_spec_key_name: Optional[str] = None,
model_encryption_spec_key_name: Optional[str] = None,
) -> AutoMLTextTrainingJob:
"""Returns AutoMLTextTrainingJob object"""
return AutoMLTextTrainingJob(
display_name=display_name,
prediction_type=prediction_type,
multi_label=multi_label,
sentiment_max=sentiment_max,
project=project,
location=location,
credentials=self._get_credentials(),
labels=labels,
training_encryption_spec_key_name=training_encryption_spec_key_name,
model_encryption_spec_key_name=model_encryption_spec_key_name,
)
def get_auto_ml_video_training_job(
self,
display_name: str,
prediction_type: str = "classification",
model_type: str = "CLOUD",
project: Optional[str] = None,
location: Optional[str] = None,
labels: Optional[Dict[str, str]] = None,
training_encryption_spec_key_name: Optional[str] = None,
model_encryption_spec_key_name: Optional[str] = None,
) -> AutoMLVideoTrainingJob:
"""Returns AutoMLVideoTrainingJob object"""
return AutoMLVideoTrainingJob(
display_name=display_name,
prediction_type=prediction_type,
model_type=model_type,
project=project,
location=location,
credentials=self._get_credentials(),
labels=labels,
training_encryption_spec_key_name=training_encryption_spec_key_name,
model_encryption_spec_key_name=model_encryption_spec_key_name,
)
@staticmethod
def extract_model_id(obj: Dict) -> str:
"""Returns unique id of the Model."""
return obj["name"].rpartition("/")[-1]
def wait_for_operation(self, operation: Operation, timeout: Optional[float] = None):
"""Waits for long-lasting operation to complete."""
try:
return operation.result(timeout=timeout)
except Exception:
error = operation.exception(timeout=timeout)
raise AirflowException(error)
def cancel_auto_ml_job(self) -> None:
"""Cancel Auto ML Job for training pipeline"""
if self._job:
self._job.cancel()
@GoogleBaseHook.fallback_to_default_project_id
def create_auto_ml_tabular_training_job(
self,
project_id: str,
region: str,
display_name: str,
dataset: datasets.TabularDataset,
target_column: str,
optimization_prediction_type: str,
optimization_objective: Optional[str] = None,
column_specs: Optional[Dict[str, str]] = None,
column_transformations: Optional[List[Dict[str, Dict[str, str]]]] = None,
optimization_objective_recall_value: Optional[float] = None,
optimization_objective_precision_value: Optional[float] = None,
labels: Optional[Dict[str, str]] = None,
training_encryption_spec_key_name: Optional[str] = None,
model_encryption_spec_key_name: Optional[str] = None,
training_fraction_split: Optional[float] = None,
validation_fraction_split: Optional[float] = None,
test_fraction_split: Optional[float] = None,
predefined_split_column_name: Optional[str] = None,
timestamp_split_column_name: Optional[str] = None,
weight_column: Optional[str] = None,
budget_milli_node_hours: int = 1000,
model_display_name: Optional[str] = None,
model_labels: Optional[Dict[str, str]] = None,
disable_early_stopping: bool = False,
export_evaluated_data_items: bool = False,
export_evaluated_data_items_bigquery_destination_uri: Optional[str] = None,
export_evaluated_data_items_override_destination: bool = False,
sync: bool = True,
) -> models.Model:
"""
Create an AutoML Tabular Training Job.
:param project_id: Required. Project to run training in.
:param region: Required. Location to run training in.
:param display_name: Required. The user-defined name of this TrainingPipeline.
:param dataset: Required. The dataset within the same Project from which data will be used to train
the Model. The Dataset must use schema compatible with Model being trained, and what is
compatible should be described in the used TrainingPipeline's [training_task_definition]
[google.cloud.aiplatform.v1beta1.TrainingPipeline.training_task_definition]. For tabular
Datasets, all their data is exported to training, to pick and choose from.
:param target_column: Required. The name of the column values of which the Model is to predict.
:param optimization_prediction_type: The type of prediction the Model is to produce.
"classification" - Predict one out of multiple target values is picked for each row.
"regression" - Predict a value based on its relation to other values. This type is available only
to columns that contain semantically numeric values, i.e. integers or floating point number, even
if stored as e.g. strings.
:param optimization_objective: Optional. Objective function the Model is to be optimized towards.
The training task creates a Model that maximizes/minimizes the value of the objective function
over the validation set.
The supported optimization objectives depend on the prediction type, and in the case of
classification also the number of distinct values in the target column (two distinct values
-> binary, 3 or more distinct values -> multi class). If the field is not set, the default
objective function is used.
Classification (binary):
"maximize-au-roc" (default) - Maximize the area under the receiver operating characteristic (ROC)
curve.
"minimize-log-loss" - Minimize log loss.
"maximize-au-prc" - Maximize the area under the precision-recall curve.
"maximize-precision-at-recall" - Maximize precision for a specified recall value.
"maximize-recall-at-precision" - Maximize recall for a specified precision value.
Classification (multi class):
"minimize-log-loss" (default) - Minimize log loss.
Regression:
"minimize-rmse" (default) - Minimize root-mean-squared error (RMSE).
"minimize-mae" - Minimize mean-absolute error (MAE).
"minimize-rmsle" - Minimize root-mean-squared log error (RMSLE).
:param column_specs: Optional. Alternative to column_transformations where the keys of the dict are
column names and their respective values are one of AutoMLTabularTrainingJob.column_data_types.
When creating transformation for BigQuery Struct column, the column should be flattened using "."
as the delimiter. Only columns with no child should have a transformation. If an input column has
no transformations on it, such a column is ignored by the training, except for the targetColumn,
which should have no transformations defined on. Only one of column_transformations or
column_specs should be passed.
:param column_transformations: Optional. Transformations to apply to the input columns (i.e. columns
other than the targetColumn). Each transformation may produce multiple result values from the
column's value, and all are used for training. When creating transformation for BigQuery Struct
column, the column should be flattened using "." as the delimiter. Only columns with no child
should have a transformation. If an input column has no transformations on it, such a column is
ignored by the training, except for the targetColumn, which should have no transformations
defined on. Only one of column_transformations or column_specs should be passed. Consider using
column_specs as column_transformations will be deprecated eventually.
:param optimization_objective_recall_value: Optional. Required when maximize-precision-at-recall
optimizationObjective was picked, represents the recall value at which the optimization is done.
The minimum value is 0 and the maximum is 1.0.
:param optimization_objective_precision_value: Optional. Required when maximize-recall-at-precision
optimizationObjective was picked, represents the precision value at which the optimization is
done.
The minimum value is 0 and the maximum is 1.0.
:param labels: Optional. The labels with user-defined metadata to organize TrainingPipelines. Label
keys and values can be no longer than 64 characters (Unicode codepoints), can only contain
lowercase letters, numeric characters, underscores and dashes. International characters are
allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
:param training_encryption_spec_key_name: Optional. The Cloud KMS resource identifier of the customer
managed encryption key used to protect the training pipeline. Has the form:
``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``. The key needs to be
in the same region as where the compute resource is created. If set, this TrainingPipeline will
be secured by this key.
Note: Model trained by this TrainingPipeline is also secured by this key if ``model_to_upload``
is not set separately.
:param model_encryption_spec_key_name: Optional. The Cloud KMS resource identifier of the customer
managed encryption key used to protect the model. Has the form:
``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``. The key needs to be
in the same region as where the compute resource is created. If set, the trained Model will be
secured by this key.
:param training_fraction_split: Optional. The fraction of the input data that is to be used to train
the Model. This is ignored if Dataset is not provided.
:param validation_fraction_split: Optional. The fraction of the input data that is to be used to
validate the Model. This is ignored if Dataset is not provided.
:param test_fraction_split: Optional. The fraction of the input data that is to be used to evaluate
the Model. This is ignored if Dataset is not provided.
:param predefined_split_column_name: Optional. The key is a name of one of the Dataset's data
columns. The value of the key (either the label's value or value in the column) must be one of
{``training``, ``validation``, ``test``}, and it defines to which set the given piece of data is
assigned. If for a piece of data the key is not present or has an invalid value, that piece is
ignored by the pipeline. Supported only for tabular and time series Datasets.
:param timestamp_split_column_name: Optional. The key is a name of one of the Dataset's data columns.
The value of the key values of the key (the values in the column) must be in RFC 3339 `date-time`
format, where `time-offset` = `"Z"` (e.g. 1985-04-12T23:20:50.52Z). If for a piece of data the
key is not present or has an invalid value, that piece is ignored by the pipeline. Supported only
for tabular and time series Datasets. This parameter must be used with training_fraction_split,
validation_fraction_split and test_fraction_split.
:param weight_column: Optional. Name of the column that should be used as the weight column. Higher
values in this column give more importance to the row during Model training. The column must have
numeric values between 0 and 10000 inclusively, and 0 value means that the row is ignored. If the
weight column field is not set, then all rows are assumed to have equal weight of 1.
:param budget_milli_node_hours (int): Optional. The train budget of creating this Model, expressed in
milli node hours i.e. 1,000 value in this field means 1 node hour. The training cost of the model
will not exceed this budget. The final cost will be attempted to be close to the budget, though
may end up being (even) noticeably smaller - at the backend's discretion. This especially may
happen when further model training ceases to provide any improvements. If the budget is set to a
value known to be insufficient to train a Model for the given training set, the training won't be
attempted and will error. The minimum value is 1000 and the maximum is 72000.
:param model_display_name: Optional. If the script produces a managed Vertex AI Model. The display
name of the Model. The name can be up to 128 characters long and can be consist of any UTF-8
characters. If not provided upon creation, the job's display_name is used.
:param model_labels: Optional. The labels with user-defined metadata to organize your Models. Label
keys and values can be no longer than 64 characters (Unicode codepoints), can only contain
lowercase letters, numeric characters, underscores and dashes. International characters are
allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
:param disable_early_stopping: Required. If true, the entire budget is used. This disables the early
stopping feature. By default, the early stopping feature is enabled, which means that training
might stop before the entire training budget has been used, if further training does no longer
brings significant improvement to the model.
:param export_evaluated_data_items: Whether to export the test set predictions to a BigQuery table.
If False, then the export is not performed.
:param export_evaluated_data_items_bigquery_destination_uri: Optional. URI of desired destination
BigQuery table for exported test set predictions.
Expected format: ``bq://<project_id>:<dataset_id>:<table>``
If not specified, then results are exported to the following auto-created BigQuery table:
``<project_id>:export_evaluated_examples_<model_name>_<yyyy_MM_dd'T'HH_mm_ss_SSS'Z'>
.evaluated_examples``
Applies only if [export_evaluated_data_items] is True.
:param export_evaluated_data_items_override_destination: Whether to override the contents of
[export_evaluated_data_items_bigquery_destination_uri], if the table exists, for exported test
set predictions. If False, and the table exists, then the training job will fail. Applies only if
[export_evaluated_data_items] is True and [export_evaluated_data_items_bigquery_destination_uri]
is specified.
:param sync: Whether to execute this method synchronously. If False, this method will be executed in
concurrent Future and any downstream object will be immediately returned and synced when the
Future has completed.
"""
self._job = self.get_auto_ml_tabular_training_job(
project=project_id,
location=region,
display_name=display_name,
optimization_prediction_type=optimization_prediction_type,
optimization_objective=optimization_objective,
column_specs=column_specs,
column_transformations=column_transformations,
optimization_objective_recall_value=optimization_objective_recall_value,
optimization_objective_precision_value=optimization_objective_precision_value,
labels=labels,
training_encryption_spec_key_name=training_encryption_spec_key_name,
model_encryption_spec_key_name=model_encryption_spec_key_name,
)
if not self._job:
raise AirflowException("AutoMLTabularTrainingJob was not created")
model = self._job.run(
dataset=dataset,
target_column=target_column,
training_fraction_split=training_fraction_split,
validation_fraction_split=validation_fraction_split,
test_fraction_split=test_fraction_split,
predefined_split_column_name=predefined_split_column_name,
timestamp_split_column_name=timestamp_split_column_name,
weight_column=weight_column,
budget_milli_node_hours=budget_milli_node_hours,
model_display_name=model_display_name,
model_labels=model_labels,
disable_early_stopping=disable_early_stopping,
export_evaluated_data_items=export_evaluated_data_items,
export_evaluated_data_items_bigquery_destination_uri=(
export_evaluated_data_items_bigquery_destination_uri
),
export_evaluated_data_items_override_destination=export_evaluated_data_items_override_destination,
sync=sync,
)
model.wait()
return model
@GoogleBaseHook.fallback_to_default_project_id
def create_auto_ml_forecasting_training_job(
self,
project_id: str,
region: str,
display_name: str,
dataset: datasets.TimeSeriesDataset,
target_column: str,
time_column: str,
time_series_identifier_column: str,
unavailable_at_forecast_columns: List[str],
available_at_forecast_columns: List[str],
forecast_horizon: int,
data_granularity_unit: str,
data_granularity_count: int,
optimization_objective: Optional[str] = None,
column_specs: Optional[Dict[str, str]] = None,
column_transformations: Optional[List[Dict[str, Dict[str, str]]]] = None,
labels: Optional[Dict[str, str]] = None,
training_encryption_spec_key_name: Optional[str] = None,
model_encryption_spec_key_name: Optional[str] = None,
training_fraction_split: Optional[float] = None,
validation_fraction_split: Optional[float] = None,
test_fraction_split: Optional[float] = None,
predefined_split_column_name: Optional[str] = None,
weight_column: Optional[str] = None,
time_series_attribute_columns: Optional[List[str]] = None,
context_window: Optional[int] = None,
export_evaluated_data_items: bool = False,
export_evaluated_data_items_bigquery_destination_uri: Optional[str] = None,
export_evaluated_data_items_override_destination: bool = False,
quantiles: Optional[List[float]] = None,
validation_options: Optional[str] = None,
budget_milli_node_hours: int = 1000,
model_display_name: Optional[str] = None,
model_labels: Optional[Dict[str, str]] = None,
sync: bool = True,
) -> models.Model:
"""
Create an AutoML Forecasting Training Job.
:param project_id: Required. Project to run training in.
:param region: Required. Location to run training in.
:param display_name: Required. The user-defined name of this TrainingPipeline.
:param dataset: Required. The dataset within the same Project from which data will be used to train
the Model. The Dataset must use schema compatible with Model being trained, and what is
compatible should be described in the used TrainingPipeline's [training_task_definition]
[google.cloud.aiplatform.v1beta1.TrainingPipeline.training_task_definition]. For time series
Datasets, all their data is exported to training, to pick and choose from.
:param target_column: Required. Name of the column that the Model is to predict values for.
:param time_column: Required. Name of the column that identifies time order in the time series.
:param time_series_identifier_column: Required. Name of the column that identifies the time series.
:param unavailable_at_forecast_columns: Required. Column names of columns that are unavailable at
forecast. Each column contains information for the given entity (identified by the
[time_series_identifier_column]) that is unknown before the forecast (e.g. population of a city
in a given year, or weather on a given day).
:param available_at_forecast_columns: Required. Column names of columns that are available at
forecast. Each column contains information for the given entity (identified by the
[time_series_identifier_column]) that is known at forecast.
:param forecast_horizon: Required. The amount of time into the future for which forecasted values for
the target are returned. Expressed in number of units defined by the [data_granularity_unit] and
[data_granularity_count] field. Inclusive.
:param data_granularity_unit: Required. The data granularity unit. Accepted values are ``minute``,
``hour``, ``day``, ``week``, ``month``, ``year``.
:param data_granularity_count: Required. The number of data granularity units between data points in
the training data. If [data_granularity_unit] is `minute`, can be 1, 5, 10, 15, or 30. For all
other values of [data_granularity_unit], must be 1.
:param optimization_objective: Optional. Objective function the model is to be optimized towards. The
training process creates a Model that optimizes the value of the objective function over the
validation set. The supported optimization objectives:
"minimize-rmse" (default) - Minimize root-mean-squared error (RMSE).
"minimize-mae" - Minimize mean-absolute error (MAE).
"minimize-rmsle" - Minimize root-mean-squared log error (RMSLE).
"minimize-rmspe" - Minimize root-mean-squared percentage error (RMSPE).
"minimize-wape-mae" - Minimize the combination of weighted absolute percentage error (WAPE) and
mean-absolute-error (MAE).
"minimize-quantile-loss" - Minimize the quantile loss at the defined quantiles. (Set this
objective to build quantile forecasts.)
:param column_specs: Optional. Alternative to column_transformations where the keys of the dict are
column names and their respective values are one of AutoMLTabularTrainingJob.column_data_types.
When creating transformation for BigQuery Struct column, the column should be flattened using "."
as the delimiter. Only columns with no child should have a transformation. If an input column has
no transformations on it, such a column is ignored by the training, except for the targetColumn,
which should have no transformations defined on. Only one of column_transformations or
column_specs should be passed.
:param column_transformations: Optional. Transformations to apply to the input columns (i.e. columns
other than the targetColumn). Each transformation may produce multiple result values from the
column's value, and all are used for training. When creating transformation for BigQuery Struct
column, the column should be flattened using "." as the delimiter. Only columns with no child
should have a transformation. If an input column has no transformations on it, such a column is
ignored by the training, except for the targetColumn, which should have no transformations
defined on. Only one of column_transformations or column_specs should be passed. Consider using
column_specs as column_transformations will be deprecated eventually.
:param labels: Optional. The labels with user-defined metadata to organize TrainingPipelines. Label
keys and values can be no longer than 64 characters (Unicode codepoints), can only contain
lowercase letters, numeric characters, underscores and dashes. International characters are
allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
:param training_encryption_spec_key_name: Optional. The Cloud KMS resource identifier of the customer
managed encryption key used to protect the training pipeline. Has the form:
``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``. The key needs to be
in the same region as where the compute resource is created. If set, this TrainingPipeline will
be secured by this key.
Note: Model trained by this TrainingPipeline is also secured by this key if ``model_to_upload``
is not set separately.
:param model_encryption_spec_key_name: Optional. The Cloud KMS resource identifier of the customer
managed encryption key used to protect the model. Has the form:
``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``. The key needs to be
in the same region as where the compute resource is created.
If set, the trained Model will be secured by this key.
:param training_fraction_split: Optional. The fraction of the input data that is to be used to train
the Model. This is ignored if Dataset is not provided.
:param validation_fraction_split: Optional. The fraction of the input data that is to be used to
validate the Model. This is ignored if Dataset is not provided.
:param test_fraction_split: Optional. The fraction of the input data that is to be used to evaluate
the Model. This is ignored if Dataset is not provided.
:param predefined_split_column_name: Optional. The key is a name of one of the Dataset's data
columns. The value of the key (either the label's value or value in the column) must be one of
{``TRAIN``, ``VALIDATE``, ``TEST``}, and it defines to which set the given piece of data is
assigned. If for a piece of data the key is not present or has an invalid value, that piece is
ignored by the pipeline.
Supported only for tabular and time series Datasets.
:param weight_column: Optional. Name of the column that should be used as the weight column. Higher
values in this column give more importance to the row during Model training. The column must have
numeric values between 0 and 10000 inclusively, and 0 value means that the row is ignored. If the
weight column field is not set, then all rows are assumed to have equal weight of 1.
:param time_series_attribute_columns: Optional. Column names that should be used as attribute
columns. Each column is constant within a time series.
:param context_window: Optional. The amount of time into the past training and prediction data is
used for model training and prediction respectively. Expressed in number of units defined by the
[data_granularity_unit] and [data_granularity_count] fields. When not provided uses the default
value of 0 which means the model sets each series context window to be 0 (also known as "cold
start"). Inclusive.
:param export_evaluated_data_items: Whether to export the test set predictions to a BigQuery table.
If False, then the export is not performed.
:param export_evaluated_data_items_bigquery_destination_uri: Optional. URI of desired destination
BigQuery table for exported test set predictions. Expected format:
``bq://<project_id>:<dataset_id>:<table>``
If not specified, then results are exported to the following auto-created BigQuery table:
``<project_id>:export_evaluated_examples_<model_name>_<yyyy_MM_dd'T'HH_mm_ss_SSS'Z'>
.evaluated_examples``
Applies only if [export_evaluated_data_items] is True.
:param export_evaluated_data_items_override_destination: Whether to override the contents of
[export_evaluated_data_items_bigquery_destination_uri], if the table exists, for exported test
set predictions. If False, and the table exists, then the training job will fail.
Applies only if [export_evaluated_data_items] is True and
[export_evaluated_data_items_bigquery_destination_uri] is specified.
:param quantiles: Quantiles to use for the `minizmize-quantile-loss`
[AutoMLForecastingTrainingJob.optimization_objective]. This argument is required in this case.
Accepts up to 5 quantiles in the form of a double from 0 to 1, exclusive. Each quantile must be
unique.
:param validation_options: Validation options for the data validation component. The available
options are: "fail-pipeline" - (default), will validate against the validation and fail the
pipeline if it fails. "ignore-validation" - ignore the results of the validation and continue the
pipeline
:param budget_milli_node_hours: Optional. The train budget of creating this Model, expressed in milli
node hours i.e. 1,000 value in this field means 1 node hour. The training cost of the model will
not exceed this budget. The final cost will be attempted to be close to the budget, though may
end up being (even) noticeably smaller - at the backend's discretion. This especially may happen
when further model training ceases to provide any improvements. If the budget is set to a value
known to be insufficient to train a Model for the given training set, the training won't be
attempted and will error. The minimum value is 1000 and the maximum is 72000.
:param model_display_name: Optional. If the script produces a managed Vertex AI Model. The display
name of the Model. The name can be up to 128 characters long and can be consist of any UTF-8
characters. If not provided upon creation, the job's display_name is used.
:param model_labels: Optional. The labels with user-defined metadata to organize your Models. Label
keys and values can be no longer than 64 characters (Unicode codepoints), can only contain
lowercase letters, numeric characters, underscores and dashes. International characters are
allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
:param sync: Whether to execute this method synchronously. If False, this method will be executed in
concurrent Future and any downstream object will be immediately returned and synced when the
Future has completed.
"""
self._job = self.get_auto_ml_forecasting_training_job(
project=project_id,
location=region,
display_name=display_name,
optimization_objective=optimization_objective,
column_specs=column_specs,
column_transformations=column_transformations,
labels=labels,
training_encryption_spec_key_name=training_encryption_spec_key_name,
model_encryption_spec_key_name=model_encryption_spec_key_name,
)
if not self._job:
raise AirflowException("AutoMLForecastingTrainingJob was not created")
model = self._job.run(
dataset=dataset,
target_column=target_column,
time_column=time_column,
time_series_identifier_column=time_series_identifier_column,
unavailable_at_forecast_columns=unavailable_at_forecast_columns,
available_at_forecast_columns=available_at_forecast_columns,
forecast_horizon=forecast_horizon,
data_granularity_unit=data_granularity_unit,
data_granularity_count=data_granularity_count,
training_fraction_split=training_fraction_split,
validation_fraction_split=validation_fraction_split,
test_fraction_split=test_fraction_split,
predefined_split_column_name=predefined_split_column_name,
weight_column=weight_column,
time_series_attribute_columns=time_series_attribute_columns,
context_window=context_window,
export_evaluated_data_items=export_evaluated_data_items,
export_evaluated_data_items_bigquery_destination_uri=(
export_evaluated_data_items_bigquery_destination_uri
),
export_evaluated_data_items_override_destination=export_evaluated_data_items_override_destination,
quantiles=quantiles,
validation_options=validation_options,
budget_milli_node_hours=budget_milli_node_hours,
model_display_name=model_display_name,
model_labels=model_labels,
sync=sync,
)
model.wait()
return model
@GoogleBaseHook.fallback_to_default_project_id
def create_auto_ml_image_training_job(
self,
project_id: str,
region: str,
display_name: str,
dataset: datasets.ImageDataset,
prediction_type: str = "classification",
multi_label: bool = False,
model_type: str = "CLOUD",
base_model: Optional[models.Model] = None,
labels: Optional[Dict[str, str]] = None,
training_encryption_spec_key_name: Optional[str] = None,
model_encryption_spec_key_name: Optional[str] = None,
training_fraction_split: Optional[float] = None,
validation_fraction_split: Optional[float] = None,
test_fraction_split: Optional[float] = None,
training_filter_split: Optional[str] = None,
validation_filter_split: Optional[str] = None,
test_filter_split: Optional[str] = None,
budget_milli_node_hours: Optional[int] = None,
model_display_name: Optional[str] = None,
model_labels: Optional[Dict[str, str]] = None,
disable_early_stopping: bool = False,
sync: bool = True,
) -> models.Model:
"""
Create an AutoML Image Training Job.
:param project_id: Required. Project to run training in.
:param region: Required. Location to run training in.
:param display_name: Required. The user-defined name of this TrainingPipeline.
:param dataset: Required. The dataset within the same Project from which data will be used to train
the Model. The Dataset must use schema compatible with Model being trained, and what is
compatible should be described in the used TrainingPipeline's [training_task_definition]
[google.cloud.aiplatform.v1beta1.TrainingPipeline.training_task_definition]. For tabular
Datasets, all their data is exported to training, to pick and choose from.
:param prediction_type: The type of prediction the Model is to produce, one of:
"classification" - Predict one out of multiple target values is picked for each row.
"object_detection" - Predict a value based on its relation to other values. This type is
available only to columns that contain semantically numeric values, i.e. integers or floating
point number, even if stored as e.g. strings.
:param multi_label: Required. Default is False. If false, a single-label (multi-class) Model will be
trained (i.e. assuming that for each image just up to one annotation may be applicable). If true,
a multi-label Model will be trained (i.e. assuming that for each image multiple annotations may
be applicable).
This is only applicable for the "classification" prediction_type and will be ignored otherwise.
:param model_type: Required. One of the following:
"CLOUD" - Default for Image Classification. A Model best tailored to be used within Google Cloud,
and which cannot be exported.
"CLOUD_HIGH_ACCURACY_1" - Default for Image Object Detection. A model best tailored to be used
within Google Cloud, and which cannot be exported. Expected to have a higher latency, but should
also have a higher prediction quality than other cloud models.
"CLOUD_LOW_LATENCY_1" - A model best tailored to be used within Google Cloud, and which cannot be
exported. Expected to have a low latency, but may have lower prediction quality than other cloud
models.
"MOBILE_TF_LOW_LATENCY_1" - A model that, in addition to being available within Google Cloud, can
also be exported as TensorFlow or Core ML model and used on a mobile or edge device afterwards.
Expected to have low latency, but may have lower prediction quality than other mobile models.
"MOBILE_TF_VERSATILE_1" - A model that, in addition to being available within Google Cloud, can
also be exported as TensorFlow or Core ML model and used on a mobile or edge device with
afterwards.
"MOBILE_TF_HIGH_ACCURACY_1" - A model that, in addition to being available within Google Cloud,
can also be exported as TensorFlow or Core ML model and used on a mobile or edge device
afterwards. Expected to have a higher latency, but should also have a higher prediction quality
than other mobile models.
:param base_model: Optional. Only permitted for Image Classification models. If it is specified, the
new model will be trained based on the `base` model. Otherwise, the new model will be trained
from scratch. The `base` model must be in the same Project and Location as the new Model to
train, and have the same model_type.
:param labels: Optional. The labels with user-defined metadata to organize TrainingPipelines. Label
keys and values can be no longer than 64 characters (Unicode codepoints), can only contain
lowercase letters, numeric characters, underscores and dashes. International characters are
allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
:param training_encryption_spec_key_name: Optional. The Cloud KMS resource identifier of the customer
managed encryption key used to protect the training pipeline. Has the form:
``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``. The key needs to be
in the same region as where the compute resource is created. If set, this TrainingPipeline will
be secured by this key.
Note: Model trained by this TrainingPipeline is also secured by this key if ``model_to_upload``
is not set separately.
:param model_encryption_spec_key_name: Optional. The Cloud KMS resource identifier of the customer
managed encryption key used to protect the model. Has the form:
``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``.
The key needs to be in the same region as where the compute resource is created.
If set, the trained Model will be secured by this key.
:param training_fraction_split: Optional. The fraction of the input data that is to be used to train
the Model. This is ignored if Dataset is not provided.
:param validation_fraction_split: Optional. The fraction of the input data that is to be used to
validate the Model. This is ignored if Dataset is not provided.
:param test_fraction_split: Optional. The fraction of the input data that is to be used to evaluate
the Model. This is ignored if Dataset is not provided.
:param training_filter_split: Optional. A filter on DataItems of the Dataset. DataItems that match
this filter are used to train the Model. A filter with same syntax as the one used in
DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the
FilterSplit filters, then it is assigned to the first set that applies to it in the training,
validation, test order. This is ignored if Dataset is not provided.
:param validation_filter_split: Optional. A filter on DataItems of the Dataset. DataItems that match
this filter are used to validate the Model. A filter with same syntax as the one used in
DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the
FilterSplit filters, then it is assigned to the first set that applies to it in the training,
validation, test order. This is ignored if Dataset is not provided.
:param test_filter_split: Optional. A filter on DataItems of the Dataset. DataItems that match this
filter are used to test the Model. A filter with same syntax as the one used in
DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the
FilterSplit filters, then it is assigned to the first set that applies to it in the training,
validation, test order. This is ignored if Dataset is not provided.
:param budget_milli_node_hours: Optional. The train budget of creating this Model, expressed in milli
node hours i.e. 1,000 value in this field means 1 node hour.
Defaults by `prediction_type`:
`classification` - For Cloud models the budget must be: 8,000 - 800,000 milli node hours
(inclusive). The default value is 192,000 which represents one day in wall time, assuming 8 nodes
are used.
`object_detection` - For Cloud models the budget must be: 20,000 - 900,000 milli node hours
(inclusive). The default value is 216,000 which represents one day in wall time, assuming 9 nodes
are used.
The training cost of the model will not exceed this budget. The final cost will be attempted to
be close to the budget, though may end up being (even) noticeably smaller - at the backend's
discretion. This especially may happen when further model training ceases to provide any
improvements. If the budget is set to a value known to be insufficient to train a Model for the
given training set, the training won't be attempted and will error.
:param model_display_name: Optional. The display name of the managed Vertex AI Model. The name can be
up to 128 characters long and can be consist of any UTF-8 characters. If not provided upon
creation, the job's display_name is used.
:param model_labels: Optional. The labels with user-defined metadata to organize your Models. Label
keys and values can be no longer than 64 characters (Unicode codepoints), can only contain
lowercase letters, numeric characters, underscores and dashes. International characters are
allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
:param disable_early_stopping: Required. If true, the entire budget is used. This disables the early
stopping feature. By default, the early stopping feature is enabled, which means that training
might stop before the entire training budget has been used, if further training does no longer
brings significant improvement to the model.
:param sync: Whether to execute this method synchronously. If False, this method will be executed in
concurrent Future and any downstream object will be immediately returned and synced when the
Future has completed.
"""
self._job = self.get_auto_ml_image_training_job(
project=project_id,
location=region,
display_name=display_name,
prediction_type=prediction_type,
multi_label=multi_label,
model_type=model_type,
base_model=base_model,
labels=labels,
training_encryption_spec_key_name=training_encryption_spec_key_name,
model_encryption_spec_key_name=model_encryption_spec_key_name,
)
if not self._job:
raise AirflowException("AutoMLImageTrainingJob was not created")
model = self._job.run(
dataset=dataset,
training_fraction_split=training_fraction_split,
validation_fraction_split=validation_fraction_split,
test_fraction_split=test_fraction_split,
training_filter_split=training_filter_split,
validation_filter_split=validation_filter_split,
test_filter_split=test_filter_split,
budget_milli_node_hours=budget_milli_node_hours,
model_display_name=model_display_name,
model_labels=model_labels,
disable_early_stopping=disable_early_stopping,
sync=sync,
)
model.wait()
return model
@GoogleBaseHook.fallback_to_default_project_id
def create_auto_ml_text_training_job(
self,
project_id: str,
region: str,
display_name: str,
dataset: datasets.TextDataset,
prediction_type: str,
multi_label: bool = False,
sentiment_max: int = 10,
labels: Optional[Dict[str, str]] = None,
training_encryption_spec_key_name: Optional[str] = None,
model_encryption_spec_key_name: Optional[str] = None,
training_fraction_split: Optional[float] = None,
validation_fraction_split: Optional[float] = None,
test_fraction_split: Optional[float] = None,
training_filter_split: Optional[str] = None,
validation_filter_split: Optional[str] = None,
test_filter_split: Optional[str] = None,
model_display_name: Optional[str] = None,
model_labels: Optional[Dict[str, str]] = None,
sync: bool = True,
) -> models.Model:
"""
Create an AutoML Text Training Job.
:param project_id: Required. Project to run training in.
:param region: Required. Location to run training in.
:param display_name: Required. The user-defined name of this TrainingPipeline.
:param dataset: Required. The dataset within the same Project from which data will be used to train
the Model. The Dataset must use schema compatible with Model being trained, and what is
compatible should be described in the used TrainingPipeline's [training_task_definition]
[google.cloud.aiplatform.v1beta1.TrainingPipeline.training_task_definition].
:param prediction_type: The type of prediction the Model is to produce, one of:
"classification" - A classification model analyzes text data and returns a list of categories
that apply to the text found in the data. Vertex AI offers both single-label and multi-label text
classification models.
"extraction" - An entity extraction model inspects text data for known entities referenced in the
data and labels those entities in the text.
"sentiment" - A sentiment analysis model inspects text data and identifies the prevailing
emotional opinion within it, especially to determine a writer's attitude as positive, negative,
or neutral.
:param multi_label: Required and only applicable for text classification task. If false, a
single-label (multi-class) Model will be trained (i.e. assuming that for each text snippet just
up to one annotation may be applicable). If true, a multi-label Model will be trained (i.e.
assuming that for each text snippet multiple annotations may be applicable).
:param sentiment_max: Required and only applicable for sentiment task. A sentiment is expressed as an
integer ordinal, where higher value means a more positive sentiment. The range of sentiments that
will be used is between 0 and sentimentMax (inclusive on both ends), and all the values in the
range must be represented in the dataset before a model can be created. Only the Annotations with
this sentimentMax will be used for training. sentimentMax value must be between 1 and 10
(inclusive).
:param labels: Optional. The labels with user-defined metadata to organize TrainingPipelines. Label
keys and values can be no longer than 64 characters (Unicode codepoints), can only contain
lowercase letters, numeric characters, underscores and dashes. International characters are
allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
:param training_encryption_spec_key_name: Optional. The Cloud KMS resource identifier of the customer
managed encryption key used to protect the training pipeline. Has the form:
``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``.
The key needs to be in the same region as where the compute resource is created.
If set, this TrainingPipeline will be secured by this key.
Note: Model trained by this TrainingPipeline is also secured by this key if ``model_to_upload``
is not set separately.
:param model_encryption_spec_key_name: Optional. The Cloud KMS resource identifier of the customer
managed encryption key used to protect the model. Has the form:
``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``.
The key needs to be in the same region as where the compute resource is created.
If set, the trained Model will be secured by this key.
:param training_fraction_split: Optional. The fraction of the input data that is to be used to train
the Model. This is ignored if Dataset is not provided.
:param validation_fraction_split: Optional. The fraction of the input data that is to be used to
validate the Model. This is ignored if Dataset is not provided.
:param test_fraction_split: Optional. The fraction of the input data that is to be used to evaluate
the Model. This is ignored if Dataset is not provided.
:param training_filter_split: Optional. A filter on DataItems of the Dataset. DataItems that match
this filter are used to train the Model. A filter with same syntax as the one used in
DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the
FilterSplit filters, then it is assigned to the first set that applies to it in the training,
validation, test order. This is ignored if Dataset is not provided.
:param validation_filter_split: Optional. A filter on DataItems of the Dataset. DataItems that match
this filter are used to validate the Model. A filter with same syntax as the one used in
DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the
FilterSplit filters, then it is assigned to the first set that applies to it in the training,
validation, test order. This is ignored if Dataset is not provided.
:param test_filter_split: Optional. A filter on DataItems of the Dataset. DataItems that match this
filter are used to test the Model. A filter with same syntax as the one used in
DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the
FilterSplit filters, then it is assigned to the first set that applies to it in the training,
validation, test order. This is ignored if Dataset is not provided.
:param model_display_name: Optional. The display name of the managed Vertex AI Model. The name can be
up to 128 characters long and can consist of any UTF-8 characters.
If not provided upon creation, the job's display_name is used.
:param model_labels: Optional. The labels with user-defined metadata to organize your Models. Label
keys and values can be no longer than 64 characters (Unicode codepoints), can only contain
lowercase letters, numeric characters, underscores and dashes. International characters are
allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
:param sync: Whether to execute this method synchronously. If False, this method will be executed in
concurrent Future and any downstream object will be immediately returned and synced when the
Future has completed.
"""
self._job = self.get_auto_ml_text_training_job(
project=project_id,
location=region,
display_name=display_name,
prediction_type=prediction_type,
multi_label=multi_label,
sentiment_max=sentiment_max,
labels=labels,
training_encryption_spec_key_name=training_encryption_spec_key_name,
model_encryption_spec_key_name=model_encryption_spec_key_name,
)
if not self._job:
raise AirflowException("AutoMLTextTrainingJob was not created")
model = self._job.run(
dataset=dataset,
training_fraction_split=training_fraction_split,
validation_fraction_split=validation_fraction_split,
test_fraction_split=test_fraction_split,
training_filter_split=training_filter_split,
validation_filter_split=validation_filter_split,
test_filter_split=test_filter_split,
model_display_name=model_display_name,
model_labels=model_labels,
sync=sync,
)
model.wait()
return model
@GoogleBaseHook.fallback_to_default_project_id
def create_auto_ml_video_training_job(
self,
project_id: str,
region: str,
display_name: str,
dataset: datasets.VideoDataset,
prediction_type: str = "classification",
model_type: str = "CLOUD",
labels: Optional[Dict[str, str]] = None,
training_encryption_spec_key_name: Optional[str] = None,
model_encryption_spec_key_name: Optional[str] = None,
training_fraction_split: Optional[float] = None,
test_fraction_split: Optional[float] = None,
training_filter_split: Optional[str] = None,
test_filter_split: Optional[str] = None,
model_display_name: Optional[str] = None,
model_labels: Optional[Dict[str, str]] = None,
sync: bool = True,
) -> models.Model:
"""
Create an AutoML Video Training Job.
:param project_id: Required. Project to run training in.
:param region: Required. Location to run training in.
:param display_name: Required. The user-defined name of this TrainingPipeline.
:param dataset: Required. The dataset within the same Project from which data will be used to train
the Model. The Dataset must use schema compatible with Model being trained, and what is
compatible should be described in the used TrainingPipeline's [training_task_definition]
[google.cloud.aiplatform.v1beta1.TrainingPipeline.training_task_definition]. For tabular
Datasets, all their data is exported to training, to pick and choose from.
:param prediction_type: The type of prediction the Model is to produce, one of:
"classification" - A video classification model classifies shots and segments in your videos
according to your own defined labels.
"object_tracking" - A video object tracking model detects and tracks multiple objects in shots
and segments. You can use these models to track objects in your videos according to your own
pre-defined, custom labels.
"action_recognition" - A video action recognition model pinpoints the location of actions with
short temporal durations (~1 second).
:param model_type: Required. One of the following:
"CLOUD" - available for "classification", "object_tracking" and "action_recognition" A Model best
tailored to be used within Google Cloud, and which cannot be exported.
"MOBILE_VERSATILE_1" - available for "classification", "object_tracking" and "action_recognition"
A model that, in addition to being available within Google Cloud, can also be exported (see
ModelService.ExportModel) as a TensorFlow or TensorFlow Lite model and used on a mobile or edge
device with afterwards.
"MOBILE_CORAL_VERSATILE_1" - available only for "object_tracking" A versatile model that is meant
to be exported (see ModelService.ExportModel) and used on a Google Coral device.
"MOBILE_CORAL_LOW_LATENCY_1" - available only for "object_tracking" A model that trades off
quality for low latency, to be exported (see ModelService.ExportModel) and used on a Google Coral
device.
"MOBILE_JETSON_VERSATILE_1" - available only for "object_tracking" A versatile model that is
meant to be exported (see ModelService.ExportModel) and used on an NVIDIA Jetson device.
"MOBILE_JETSON_LOW_LATENCY_1" - available only for "object_tracking" A model that trades off
quality for low latency, to be exported (see ModelService.ExportModel) and used on an NVIDIA
Jetson device.
:param labels: Optional. The labels with user-defined metadata to organize TrainingPipelines. Label
keys and values can be no longer than 64 characters (Unicode codepoints), can only contain
lowercase letters, numeric characters, underscores and dashes. International characters are
allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
:param training_encryption_spec_key_name: Optional. The Cloud KMS resource identifier of the customer
managed encryption key used to protect the training pipeline. Has the form:
``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``.
The key needs to be in the same region as where the compute resource is created.
If set, this TrainingPipeline will be secured by this key.
Note: Model trained by this TrainingPipeline is also secured by this key if ``model_to_upload``
is not set separately.
:param model_encryption_spec_key_name: Optional. The Cloud KMS resource identifier of the customer
managed encryption key used to protect the model. Has the form:
``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``.
The key needs to be in the same region as where the compute resource is created.
If set, the trained Model will be secured by this key.
:param training_fraction_split: Optional. The fraction of the input data that is to be used to train
the Model. This is ignored if Dataset is not provided.
:param test_fraction_split: Optional. The fraction of the input data that is to be used to evaluate
the Model. This is ignored if Dataset is not provided.
:param training_filter_split: Optional. A filter on DataItems of the Dataset. DataItems that match
this filter are used to train the Model. A filter with same syntax as the one used in
DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the
FilterSplit filters, then it is assigned to the first set that applies to it in the training,
validation, test order. This is ignored if Dataset is not provided.
:param test_filter_split: Optional. A filter on DataItems of the Dataset. DataItems that match this
filter are used to test the Model. A filter with same syntax as the one used in
DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the
FilterSplit filters, then it is assigned to the first set that applies to it in the training,
validation, test order. This is ignored if Dataset is not provided.
:param model_display_name: Optional. The display name of the managed Vertex AI Model. The name can be
up to 128 characters long and can be consist of any UTF-8 characters. If not provided upon
creation, the job's display_name is used.
:param model_labels: Optional. The labels with user-defined metadata to organize your Models. Label
keys and values can be no longer than 64 characters (Unicode codepoints), can only contain
lowercase letters, numeric characters, underscores and dashes. International characters are
allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
:param sync: Whether to execute this method synchronously. If False, this method will be executed in
concurrent Future and any downstream object will be immediately returned and synced when the
Future has completed.
"""
self._job = self.get_auto_ml_video_training_job(
project=project_id,
location=region,
display_name=display_name,
prediction_type=prediction_type,
model_type=model_type,
labels=labels,
training_encryption_spec_key_name=training_encryption_spec_key_name,
model_encryption_spec_key_name=model_encryption_spec_key_name,
)
if not self._job:
raise AirflowException("AutoMLVideoTrainingJob was not created")
model = self._job.run(
dataset=dataset,
training_fraction_split=training_fraction_split,
test_fraction_split=test_fraction_split,
training_filter_split=training_filter_split,
test_filter_split=test_filter_split,
model_display_name=model_display_name,
model_labels=model_labels,
sync=sync,
)
model.wait()
return model
@GoogleBaseHook.fallback_to_default_project_id
def delete_training_pipeline(
self,
project_id: str,
region: str,
training_pipeline: str,
retry: Optional[Retry] = None,
timeout: Optional[float] = None,
metadata: Sequence[Tuple[str, str]] = (),
) -> Operation:
"""
Deletes a TrainingPipeline.
:param project_id: Required. The ID of the Google Cloud project that the service belongs to.
:param region: Required. The ID of the Google Cloud region that the service belongs to.
:param training_pipeline: Required. The name of the TrainingPipeline resource to be deleted.
:param retry: Designation of what errors, if any, should be retried.
:param timeout: The timeout for this request.
:param metadata: Strings which should be sent along with the request as metadata.
"""
client = self.get_pipeline_service_client(region)
name = client.training_pipeline_path(project_id, region, training_pipeline)
result = client.delete_training_pipeline(
request={
'name': name,
},
retry=retry,
timeout=timeout,
metadata=metadata,
)
return result
@GoogleBaseHook.fallback_to_default_project_id
def get_training_pipeline(
self,
project_id: str,
region: str,
training_pipeline: str,
retry: Optional[Retry] = None,
timeout: Optional[float] = None,
metadata: Sequence[Tuple[str, str]] = (),
) -> TrainingPipeline:
"""
Gets a TrainingPipeline.
:param project_id: Required. The ID of the Google Cloud project that the service belongs to.
:param region: Required. The ID of the Google Cloud region that the service belongs to.
:param training_pipeline: Required. The name of the TrainingPipeline resource.
:param retry: Designation of what errors, if any, should be retried.
:param timeout: The timeout for this request.
:param metadata: Strings which should be sent along with the request as metadata.
"""
client = self.get_pipeline_service_client(region)
name = client.training_pipeline_path(project_id, region, training_pipeline)
result = client.get_training_pipeline(
request={
'name': name,
},
retry=retry,
timeout=timeout,
metadata=metadata,
)
return result
@GoogleBaseHook.fallback_to_default_project_id
def list_training_pipelines(
self,
project_id: str,
region: str,
page_size: Optional[int] = None,
page_token: Optional[str] = None,
filter: Optional[str] = None,
read_mask: Optional[str] = None,
retry: Optional[Retry] = None,
timeout: Optional[float] = None,
metadata: Sequence[Tuple[str, str]] = (),
) -> ListTrainingPipelinesPager:
"""
Lists TrainingPipelines in a Location.
:param project_id: Required. The ID of the Google Cloud project that the service belongs to.
:param region: Required. The ID of the Google Cloud region that the service belongs to.
:param filter: Optional. The standard list filter. Supported fields:
- ``display_name`` supports = and !=.
- ``state`` supports = and !=.
Some examples of using the filter are:
- ``state="PIPELINE_STATE_SUCCEEDED" AND display_name="my_pipeline"``
- ``state="PIPELINE_STATE_RUNNING" OR display_name="my_pipeline"``
- ``NOT display_name="my_pipeline"``
- ``state="PIPELINE_STATE_FAILED"``
:param page_size: Optional. The standard list page size.
:param page_token: Optional. The standard list page token. Typically obtained via
[ListTrainingPipelinesResponse.next_page_token][google.cloud.aiplatform.v1.ListTrainingPipelinesResponse.next_page_token]
of the previous
[PipelineService.ListTrainingPipelines][google.cloud.aiplatform.v1.PipelineService.ListTrainingPipelines]
call.
:param read_mask: Optional. Mask specifying which fields to read.
:param retry: Designation of what errors, if any, should be retried.
:param timeout: The timeout for this request.
:param metadata: Strings which should be sent along with the request as metadata.
"""
client = self.get_pipeline_service_client(region)
parent = client.common_location_path(project_id, region)
result = client.list_training_pipelines(
request={
'parent': parent,
'page_size': page_size,
'page_token': page_token,
'filter': filter,
'read_mask': read_mask,
},
retry=retry,
timeout=timeout,
metadata=metadata,
)
return result
| 60.198886
| 133
| 0.691833
| 9,699
| 75,670
| 5.245077
| 0.075059
| 0.011834
| 0.023392
| 0.028896
| 0.836393
| 0.813041
| 0.806967
| 0.796588
| 0.792597
| 0.786956
| 0
| 0.003869
| 0.255465
| 75,670
| 1,256
| 134
| 60.246815
| 0.899093
| 0.610189
| 0
| 0.741197
| 0
| 0
| 0.018396
| 0.007752
| 0
| 0
| 0
| 0
| 0
| 1
| 0.033451
| false
| 0
| 0.015845
| 0
| 0.080986
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
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| 0
| 0
|
0
| 7
|
53a778849d0043c3d52e474d23fa4e439e8dc8e2
| 42,430
|
py
|
Python
|
isi_sdk_9_0_0/isi_sdk_9_0_0/api/ipmi_api.py
|
mohitjain97/isilon_sdk_python
|
a371f438f542568edb8cda35e929e6b300b1177c
|
[
"Unlicense"
] | 24
|
2018-06-22T14:13:23.000Z
|
2022-03-23T01:21:26.000Z
|
isi_sdk_9_0_0/isi_sdk_9_0_0/api/ipmi_api.py
|
mohitjain97/isilon_sdk_python
|
a371f438f542568edb8cda35e929e6b300b1177c
|
[
"Unlicense"
] | 46
|
2018-04-30T13:28:22.000Z
|
2022-03-21T21:11:07.000Z
|
isi_sdk_9_0_0/isi_sdk_9_0_0/api/ipmi_api.py
|
mohitjain97/isilon_sdk_python
|
a371f438f542568edb8cda35e929e6b300b1177c
|
[
"Unlicense"
] | 29
|
2018-06-19T00:14:04.000Z
|
2022-02-08T17:51:19.000Z
|
# coding: utf-8
"""
Isilon SDK
Isilon SDK - Language bindings for the OneFS API # noqa: E501
OpenAPI spec version: 10
Contact: sdk@isilon.com
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
import re # noqa: F401
# python 2 and python 3 compatibility library
import six
from isi_sdk_9_0_0.api_client import ApiClient
class IpmiApi(object):
"""NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
Ref: https://github.com/swagger-api/swagger-codegen
"""
def __init__(self, api_client=None):
if api_client is None:
api_client = ApiClient()
self.api_client = api_client
def get_config_feature(self, config_feature_id, **kwargs): # noqa: E501
"""get_config_feature # noqa: E501
Retrieve the Remote IPMI Management feature configuration. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_config_feature(config_feature_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str config_feature_id: Retrieve the Remote IPMI Management feature configuration. (required)
:return: ConfigFeatures
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_config_feature_with_http_info(config_feature_id, **kwargs) # noqa: E501
else:
(data) = self.get_config_feature_with_http_info(config_feature_id, **kwargs) # noqa: E501
return data
def get_config_feature_with_http_info(self, config_feature_id, **kwargs): # noqa: E501
"""get_config_feature # noqa: E501
Retrieve the Remote IPMI Management feature configuration. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_config_feature_with_http_info(config_feature_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str config_feature_id: Retrieve the Remote IPMI Management feature configuration. (required)
:return: ConfigFeatures
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['config_feature_id'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_config_feature" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'config_feature_id' is set
if ('config_feature_id' not in params or
params['config_feature_id'] is None):
raise ValueError("Missing the required parameter `config_feature_id` when calling `get_config_feature`") # noqa: E501
collection_formats = {}
path_params = {}
if 'config_feature_id' in params:
path_params['ConfigFeatureId'] = params['config_feature_id'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['basicAuth'] # noqa: E501
return self.api_client.call_api(
'/platform/10/ipmi/config/features/{ConfigFeatureId}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ConfigFeatures', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def get_config_features(self, **kwargs): # noqa: E501
"""get_config_features # noqa: E501
Get detailed information for all remote IPMI features. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_config_features(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: ConfigFeaturesExtended
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_config_features_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.get_config_features_with_http_info(**kwargs) # noqa: E501
return data
def get_config_features_with_http_info(self, **kwargs): # noqa: E501
"""get_config_features # noqa: E501
Get detailed information for all remote IPMI features. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_config_features_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: ConfigFeaturesExtended
If the method is called asynchronously,
returns the request thread.
"""
all_params = [] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_config_features" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['basicAuth'] # noqa: E501
return self.api_client.call_api(
'/platform/10/ipmi/config/features', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ConfigFeaturesExtended', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def get_config_network(self, **kwargs): # noqa: E501
"""get_config_network # noqa: E501
Retrieve the Remote IPMI Management static network configuration settings. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_config_network(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: ConfigNetwork
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_config_network_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.get_config_network_with_http_info(**kwargs) # noqa: E501
return data
def get_config_network_with_http_info(self, **kwargs): # noqa: E501
"""get_config_network # noqa: E501
Retrieve the Remote IPMI Management static network configuration settings. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_config_network_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: ConfigNetwork
If the method is called asynchronously,
returns the request thread.
"""
all_params = [] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_config_network" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['basicAuth'] # noqa: E501
return self.api_client.call_api(
'/platform/10/ipmi/config/network', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ConfigNetwork', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def get_config_node(self, config_node_id, **kwargs): # noqa: E501
"""get_config_node # noqa: E501
Retrieve the Remote IPMI Management node configuration. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_config_node(config_node_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param int config_node_id: Retrieve the Remote IPMI Management node configuration. (required)
:return: ConfigNodes
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_config_node_with_http_info(config_node_id, **kwargs) # noqa: E501
else:
(data) = self.get_config_node_with_http_info(config_node_id, **kwargs) # noqa: E501
return data
def get_config_node_with_http_info(self, config_node_id, **kwargs): # noqa: E501
"""get_config_node # noqa: E501
Retrieve the Remote IPMI Management node configuration. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_config_node_with_http_info(config_node_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param int config_node_id: Retrieve the Remote IPMI Management node configuration. (required)
:return: ConfigNodes
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['config_node_id'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_config_node" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'config_node_id' is set
if ('config_node_id' not in params or
params['config_node_id'] is None):
raise ValueError("Missing the required parameter `config_node_id` when calling `get_config_node`") # noqa: E501
collection_formats = {}
path_params = {}
if 'config_node_id' in params:
path_params['ConfigNodeId'] = params['config_node_id'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['basicAuth'] # noqa: E501
return self.api_client.call_api(
'/platform/10/ipmi/config/nodes/{ConfigNodeId}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ConfigNodes', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def get_config_nodes(self, **kwargs): # noqa: E501
"""get_config_nodes # noqa: E501
Retrieve the Remote IPMI Management nodes configuration. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_config_nodes(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: ConfigNodesExtended
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_config_nodes_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.get_config_nodes_with_http_info(**kwargs) # noqa: E501
return data
def get_config_nodes_with_http_info(self, **kwargs): # noqa: E501
"""get_config_nodes # noqa: E501
Retrieve the Remote IPMI Management nodes configuration. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_config_nodes_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: ConfigNodesExtended
If the method is called asynchronously,
returns the request thread.
"""
all_params = [] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_config_nodes" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['basicAuth'] # noqa: E501
return self.api_client.call_api(
'/platform/10/ipmi/config/nodes', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ConfigNodesExtended', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def get_config_settings(self, **kwargs): # noqa: E501
"""get_config_settings # noqa: E501
View the Remote IPMI Management configuration settings. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_config_settings(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: ConfigSettings
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_config_settings_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.get_config_settings_with_http_info(**kwargs) # noqa: E501
return data
def get_config_settings_with_http_info(self, **kwargs): # noqa: E501
"""get_config_settings # noqa: E501
View the Remote IPMI Management configuration settings. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_config_settings_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: ConfigSettings
If the method is called asynchronously,
returns the request thread.
"""
all_params = [] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_config_settings" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['basicAuth'] # noqa: E501
return self.api_client.call_api(
'/platform/10/ipmi/config/settings', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ConfigSettings', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def get_config_user(self, **kwargs): # noqa: E501
"""get_config_user # noqa: E501
View the Remote IPMI Management user. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_config_user(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: ConfigUser
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_config_user_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.get_config_user_with_http_info(**kwargs) # noqa: E501
return data
def get_config_user_with_http_info(self, **kwargs): # noqa: E501
"""get_config_user # noqa: E501
View the Remote IPMI Management user. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_config_user_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: ConfigUser
If the method is called asynchronously,
returns the request thread.
"""
all_params = [] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_config_user" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['basicAuth'] # noqa: E501
return self.api_client.call_api(
'/platform/10/ipmi/config/user', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ConfigUser', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def update_config_feature(self, config_feature, config_feature_id, **kwargs): # noqa: E501
"""update_config_feature # noqa: E501
Modify remote IPMI Management feature settings # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.update_config_feature(config_feature, config_feature_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param ConfigFeature config_feature: (required)
:param str config_feature_id: Modify remote IPMI Management feature settings (required)
:return: None
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.update_config_feature_with_http_info(config_feature, config_feature_id, **kwargs) # noqa: E501
else:
(data) = self.update_config_feature_with_http_info(config_feature, config_feature_id, **kwargs) # noqa: E501
return data
def update_config_feature_with_http_info(self, config_feature, config_feature_id, **kwargs): # noqa: E501
"""update_config_feature # noqa: E501
Modify remote IPMI Management feature settings # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.update_config_feature_with_http_info(config_feature, config_feature_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param ConfigFeature config_feature: (required)
:param str config_feature_id: Modify remote IPMI Management feature settings (required)
:return: None
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['config_feature', 'config_feature_id'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method update_config_feature" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'config_feature' is set
if ('config_feature' not in params or
params['config_feature'] is None):
raise ValueError("Missing the required parameter `config_feature` when calling `update_config_feature`") # noqa: E501
# verify the required parameter 'config_feature_id' is set
if ('config_feature_id' not in params or
params['config_feature_id'] is None):
raise ValueError("Missing the required parameter `config_feature_id` when calling `update_config_feature`") # noqa: E501
collection_formats = {}
path_params = {}
if 'config_feature_id' in params:
path_params['ConfigFeatureId'] = params['config_feature_id'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'config_feature' in params:
body_params = params['config_feature']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['basicAuth'] # noqa: E501
return self.api_client.call_api(
'/platform/10/ipmi/config/features/{ConfigFeatureId}', 'PUT',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type=None, # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def update_config_network(self, config_network, **kwargs): # noqa: E501
"""update_config_network # noqa: E501
Modify the remote IPMI Management static network configuration settings # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.update_config_network(config_network, async_req=True)
>>> result = thread.get()
:param async_req bool
:param ConfigNetworkExtended config_network: (required)
:return: None
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.update_config_network_with_http_info(config_network, **kwargs) # noqa: E501
else:
(data) = self.update_config_network_with_http_info(config_network, **kwargs) # noqa: E501
return data
def update_config_network_with_http_info(self, config_network, **kwargs): # noqa: E501
"""update_config_network # noqa: E501
Modify the remote IPMI Management static network configuration settings # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.update_config_network_with_http_info(config_network, async_req=True)
>>> result = thread.get()
:param async_req bool
:param ConfigNetworkExtended config_network: (required)
:return: None
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['config_network'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method update_config_network" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'config_network' is set
if ('config_network' not in params or
params['config_network'] is None):
raise ValueError("Missing the required parameter `config_network` when calling `update_config_network`") # noqa: E501
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'config_network' in params:
body_params = params['config_network']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['basicAuth'] # noqa: E501
return self.api_client.call_api(
'/platform/10/ipmi/config/network', 'PUT',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type=None, # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def update_config_settings(self, config_settings, **kwargs): # noqa: E501
"""update_config_settings # noqa: E501
Modify remote IPMI Management configuration settings # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.update_config_settings(config_settings, async_req=True)
>>> result = thread.get()
:param async_req bool
:param ConfigSettingsSettings config_settings: (required)
:return: None
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.update_config_settings_with_http_info(config_settings, **kwargs) # noqa: E501
else:
(data) = self.update_config_settings_with_http_info(config_settings, **kwargs) # noqa: E501
return data
def update_config_settings_with_http_info(self, config_settings, **kwargs): # noqa: E501
"""update_config_settings # noqa: E501
Modify remote IPMI Management configuration settings # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.update_config_settings_with_http_info(config_settings, async_req=True)
>>> result = thread.get()
:param async_req bool
:param ConfigSettingsSettings config_settings: (required)
:return: None
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['config_settings'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method update_config_settings" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'config_settings' is set
if ('config_settings' not in params or
params['config_settings'] is None):
raise ValueError("Missing the required parameter `config_settings` when calling `update_config_settings`") # noqa: E501
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'config_settings' in params:
body_params = params['config_settings']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['basicAuth'] # noqa: E501
return self.api_client.call_api(
'/platform/10/ipmi/config/settings', 'PUT',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type=None, # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def update_config_user(self, config_user, **kwargs): # noqa: E501
"""update_config_user # noqa: E501
Modify remote IPMI Management user # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.update_config_user(config_user, async_req=True)
>>> result = thread.get()
:param async_req bool
:param ConfigUserUser config_user: (required)
:return: None
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.update_config_user_with_http_info(config_user, **kwargs) # noqa: E501
else:
(data) = self.update_config_user_with_http_info(config_user, **kwargs) # noqa: E501
return data
def update_config_user_with_http_info(self, config_user, **kwargs): # noqa: E501
"""update_config_user # noqa: E501
Modify remote IPMI Management user # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.update_config_user_with_http_info(config_user, async_req=True)
>>> result = thread.get()
:param async_req bool
:param ConfigUserUser config_user: (required)
:return: None
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['config_user'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method update_config_user" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'config_user' is set
if ('config_user' not in params or
params['config_user'] is None):
raise ValueError("Missing the required parameter `config_user` when calling `update_config_user`") # noqa: E501
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'config_user' in params:
body_params = params['config_user']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['basicAuth'] # noqa: E501
return self.api_client.call_api(
'/platform/10/ipmi/config/user', 'PUT',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type=None, # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
| 38.855311
| 133
| 0.61737
| 4,792
| 42,430
| 5.181135
| 0.03798
| 0.053166
| 0.024811
| 0.031899
| 0.966449
| 0.958676
| 0.945384
| 0.938175
| 0.928991
| 0.91429
| 0
| 0.017668
| 0.295687
| 42,430
| 1,091
| 134
| 38.890926
| 0.813144
| 0.325524
| 0
| 0.783877
| 1
| 0
| 0.186586
| 0.049345
| 0
| 0
| 0
| 0
| 0
| 1
| 0.039451
| false
| 0
| 0.006861
| 0
| 0.104631
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
54e953446ece9f6c5972ca602ad93f66f5b2cb46
| 1,162
|
py
|
Python
|
methods/black_scholes.py
|
o1i/hull
|
2f2c37fbcb8571e992031ba1b5ff23bf85023d44
|
[
"MIT"
] | null | null | null |
methods/black_scholes.py
|
o1i/hull
|
2f2c37fbcb8571e992031ba1b5ff23bf85023d44
|
[
"MIT"
] | null | null | null |
methods/black_scholes.py
|
o1i/hull
|
2f2c37fbcb8571e992031ba1b5ff23bf85023d44
|
[
"MIT"
] | null | null | null |
import math
from scipy.stats import norm
def put_eur_bs(s: float, k: float, r: float, sig: float, t: float) -> float:
"""
Price of a european put option according to the Black-Scholes-Merton model.
Source: Chapter 15
:param s: Price of the underlying at time 0
:param k: Strike
:param r: Risk free rate
:param sig: volatility (non-negative)
:param t: Time to maturity
:return: option price
"""
d1 = (math.log(s / k) + (r + sig ** 2 / 2) * t) / (sig * math.sqrt(t))
return k * math.exp(-r * t) * norm.cdf(-d1 + sig * math.sqrt(t)) - s * norm.cdf(-d1)
def call_eur_bs(s: float, k: float, r: float, sig: float, t: float) -> float:
"""
Price of a european call option according to the Black-Scholes-Merton model.
Source: Chapter 15
:param s: Price of the underlying at time 0
:param k: Strike
:param r: Risk free rate
:param sig: volatility (non-negative)
:param t: Time to maturity
:return: option price
"""
d1 = (math.log(s / k) + (r + sig ** 2 / 2) * t) / (sig * math.sqrt(t))
return - k * math.exp(-r * t) * norm.cdf(d1 - sig * math.sqrt(t)) + s * norm.cdf(d1)
| 34.176471
| 88
| 0.604131
| 191
| 1,162
| 3.65445
| 0.272251
| 0.040115
| 0.063037
| 0.068768
| 0.922636
| 0.922636
| 0.922636
| 0.922636
| 0.922636
| 0.922636
| 0
| 0.018433
| 0.253012
| 1,162
| 33
| 89
| 35.212121
| 0.785714
| 0.461274
| 0
| 0.25
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0.25
| 0
| 0.75
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
54ee917ac978a4c97046566c8c39af862ec94cbd
| 56,480
|
py
|
Python
|
tests/test_quota.py
|
RCOSDP/osf.io
|
e7d08f9786868471ffba5320e5cf44a900a2b56b
|
[
"Apache-2.0"
] | null | null | null |
tests/test_quota.py
|
RCOSDP/osf.io
|
e7d08f9786868471ffba5320e5cf44a900a2b56b
|
[
"Apache-2.0"
] | null | null | null |
tests/test_quota.py
|
RCOSDP/osf.io
|
e7d08f9786868471ffba5320e5cf44a900a2b56b
|
[
"Apache-2.0"
] | null | null | null |
# -*- coding: utf-8 -*-
import datetime
import mock
from nose.tools import * # noqa (PEP8 asserts)
import pytest
from addons.osfstorage.models import OsfStorageFileNode
from api.base import settings as api_settings
from framework.auth import signing
from tests.base import OsfTestCase
from osf.models import (
FileLog, FileInfo, TrashedFileNode, TrashedFolder, UserQuota, ProjectStorageType, BaseFileNode
)
from osf_tests.factories import (
AuthUserFactory, ProjectFactory, UserFactory, InstitutionFactory, RegionFactory
)
from website.util import web_url_for, quota
from api.base import settings as api_settings
@pytest.mark.enable_implicit_clean
@pytest.mark.enable_quickfiles_creation
class TestQuotaProfileView(OsfTestCase):
def setUp(self):
super(TestQuotaProfileView, self).setUp()
self.user = AuthUserFactory()
self.quota_text = '{}%, {}[{}] / {}[GB]'
def tearDown(self):
super(TestQuotaProfileView, self).tearDown()
@mock.patch('website.util.quota.used_quota')
def test_default_quota(self, mock_usedquota):
mock_usedquota.return_value = 0
response = self.app.get(
web_url_for('profile_view_id', uid=self.user._id),
auth=self.user.auth
)
expected = self.quota_text.format(0.0, 0, 'B', api_settings.DEFAULT_MAX_QUOTA)
assert_in(expected, response.body.decode())
assert_in('Usage of NII storage', response.body.decode())
def test_custom_quota(self):
UserQuota.objects.create(
storage_type=UserQuota.NII_STORAGE,
user=self.user,
max_quota=200,
used=0
)
response = self.app.get(
web_url_for('profile_view_id', uid=self.user._id),
auth=self.user.auth
)
assert_in(self.quota_text.format(0.0, 0, 'B', 200), response.body.decode())
assert_in('Usage of NII storage', response.body.decode())
@mock.patch('website.util.quota.used_quota')
def test_institution_default_quota(self, mock_usedquota):
mock_usedquota.return_value = 0
institution = InstitutionFactory()
self.user.affiliated_institutions.add(institution)
RegionFactory(_id=institution._id)
response = self.app.get(
web_url_for('profile_view_id', uid=self.user._id),
auth=self.user.auth
)
expected = self.quota_text.format(0.0, 0, 'B', api_settings.DEFAULT_MAX_QUOTA)
assert_in(expected, response.body.decode())
assert_in('Usage of Institutional storage', response.body.decode())
def test_institution_custom_quota(self):
institution = InstitutionFactory()
self.user.affiliated_institutions.add(institution)
RegionFactory(_id=institution._id)
UserQuota.objects.create(
storage_type=UserQuota.CUSTOM_STORAGE,
user=self.user,
max_quota=200,
used=100 * api_settings.SIZE_UNIT_GB
)
response = self.app.get(
web_url_for('profile_view_id', uid=self.user._id),
auth=self.user.auth
)
assert_in(self.quota_text.format(50.0, 100.0, 'GB', 200), response.body.decode())
assert_in('Usage of Institutional storage', response.body.decode())
def test_used_quota_bytes(self):
UserQuota.objects.create(user=self.user, max_quota=100, used=560)
response = self.app.get(
web_url_for('profile_view_id', uid=self.user._id),
auth=self.user.auth
)
assert_in(self.quota_text.format(0.0, 560, 'B', 100), response.body.decode())
def test_used_quota_giga(self):
UserQuota.objects.create(user=self.user, max_quota=100, used=5.2 * api_settings.SIZE_UNIT_GB)
response = self.app.get(
web_url_for('profile_view_id', uid=self.user._id),
auth=self.user.auth
)
assert_in(self.quota_text.format(5.2, 5.2, 'GB', 100), response.body.decode())
def test_used_quota_storage_icon_ok(self):
UserQuota.objects.create(user=self.user, max_quota=100, used=0)
response = self.app.get(
web_url_for('profile_view_id', uid=self.user._id),
auth=self.user.auth
)
assert_in('storage_ok.png', response.body.decode())
def test_used_quota_storage_icon_warning(self):
UserQuota.objects.create(user=self.user, max_quota=100, used=95 * api_settings.SIZE_UNIT_GB)
response = self.app.get(
web_url_for('profile_view_id', uid=self.user._id),
auth=self.user.auth
)
assert_in('storage_warning.png', response.body.decode())
def test_used_quota_storage_icon_error(self):
UserQuota.objects.create(user=self.user, max_quota=100, used=105 * api_settings.SIZE_UNIT_GB)
response = self.app.get(
web_url_for('profile_view_id', uid=self.user._id),
auth=self.user.auth
)
assert_in('storage_error.png', response.body.decode())
class TestAbbreviateSize(OsfTestCase):
def setUp(self):
super(TestAbbreviateSize, self).setUp()
def tearDown(self):
super(TestAbbreviateSize, self).tearDown()
def test_abbreviate_byte(self):
abbr_size = quota.abbreviate_size(512)
assert_equal(abbr_size[0], 512)
assert_equal(abbr_size[1], 'B')
def test_abbreviate_kilobyte(self):
abbr_size = quota.abbreviate_size(512 * api_settings.BASE_FOR_METRIC_PREFIX)
assert_equal(abbr_size[0], 512)
assert_equal(abbr_size[1], 'KB')
def test_abbreviate_megabyte(self):
abbr_size = quota.abbreviate_size(512 * api_settings.BASE_FOR_METRIC_PREFIX ** 2)
assert_equal(abbr_size[0], 512)
assert_equal(abbr_size[1], 'MB')
def test_abbreviate_gigabyte(self):
abbr_size = quota.abbreviate_size(512 * api_settings.BASE_FOR_METRIC_PREFIX ** 3)
assert_equal(abbr_size[0], 512)
assert_equal(abbr_size[1], 'GB')
def test_abbreviate_terabyte(self):
abbr_size = quota.abbreviate_size(512 * api_settings.BASE_FOR_METRIC_PREFIX ** 4)
assert_equal(abbr_size[0], 512)
assert_equal(abbr_size[1], 'TB')
class TestUsedQuota(OsfTestCase):
def setUp(self):
super(TestUsedQuota, self).setUp()
self.user = UserFactory()
self.node = [
ProjectFactory(creator=self.user),
ProjectFactory(creator=self.user)
]
def tearDown(self):
super(TestUsedQuota, self).tearDown()
def test_calculate_used_quota(self):
file_list = []
# No files
assert_equal(quota.used_quota(self.user._id, storage_type=UserQuota.NII_STORAGE), 0)
assert_equal(quota.used_quota(self.user._id, storage_type=UserQuota.CUSTOM_STORAGE), 0)
# Add a file to node[0]
file_list.append(OsfStorageFileNode.create(
target=self.node[0],
name='file0'
))
file_list[0].save()
FileInfo.objects.create(file=file_list[0], file_size=500)
assert_equal(quota.used_quota(self.user._id, storage_type=UserQuota.NII_STORAGE), 500)
assert_equal(quota.used_quota(self.user._id, storage_type=UserQuota.CUSTOM_STORAGE), 0)
# Add a file to node[1]
file_list.append(OsfStorageFileNode.create(
target=self.node[1],
name='file1'
))
file_list[1].save()
FileInfo.objects.create(file=file_list[1], file_size=1000)
assert_equal(quota.used_quota(self.user._id, storage_type=UserQuota.NII_STORAGE), 1500)
assert_equal(quota.used_quota(self.user._id, storage_type=UserQuota.CUSTOM_STORAGE), 0)
def test_calculate_used_quota_custom_storage(self):
file_list = []
institution = InstitutionFactory()
self.user.affiliated_institutions.add(institution)
RegionFactory(_id=institution._id)
ProjectStorageType.objects.filter(node__in=[self.node[0], self.node[1]]).update(
storage_type=ProjectStorageType.CUSTOM_STORAGE
)
# No files
assert_equal(quota.used_quota(self.user._id, storage_type=UserQuota.NII_STORAGE), 0)
assert_equal(quota.used_quota(self.user._id, storage_type=UserQuota.CUSTOM_STORAGE), 0)
# Add a file to node[0]
file_list.append(OsfStorageFileNode.create(
target=self.node[0],
name='file0'
))
file_list[0].save()
FileInfo.objects.create(file=file_list[0], file_size=500)
assert_equal(quota.used_quota(self.user._id, storage_type=UserQuota.NII_STORAGE), 0)
assert_equal(quota.used_quota(self.user._id, storage_type=UserQuota.CUSTOM_STORAGE), 500)
# Add a file to node[1]
file_list.append(OsfStorageFileNode.create(
target=self.node[1],
name='file1'
))
file_list[1].save()
FileInfo.objects.create(file=file_list[1], file_size=1000)
assert_equal(quota.used_quota(self.user._id, storage_type=UserQuota.NII_STORAGE), 0)
assert_equal(quota.used_quota(self.user._id, storage_type=UserQuota.CUSTOM_STORAGE), 1500)
def test_calculate_used_quota_deleted_file(self):
# Add a (deleted) file to node[0]
file_node = OsfStorageFileNode.create(
target=self.node[0],
name='file0',
deleted_on=datetime.datetime.now(),
deleted_by=self.user
)
file_node.save()
FileInfo.objects.create(file=file_node, file_size=500)
assert_equal(quota.used_quota(self.user._id, storage_type=UserQuota.NII_STORAGE), 0)
assert_equal(quota.used_quota(self.user._id, storage_type=UserQuota.CUSTOM_STORAGE), 0)
class TestSaveFileInfo(OsfTestCase):
def setUp(self):
super(TestSaveFileInfo, self).setUp()
self.user = UserFactory()
self.project_creator = UserFactory()
self.node = ProjectFactory(creator=self.project_creator)
self.file = OsfStorageFileNode.create(
target=self.node,
path='/testfile',
_id='testfile',
name='testfile',
materialized_path='/testfile'
)
self.file.save()
def test_add_file_info(self):
file_info_query = FileInfo.objects.filter(file=self.file)
assert_false(file_info_query.exists())
quota.update_used_quota(
self=None,
target=self.node,
user=self.user,
event_type=FileLog.FILE_ADDED,
payload={
'provider': 'osfstorage',
'metadata': {
'provider': 'osfstorage',
'name': 'testfile',
'materialized': '/filename',
'path': '/' + self.file._id,
'kind': 'file',
'size': 1000,
'created_utc': '',
'modified_utc': '',
'extra': {'version': '1'}
}
}
)
file_info_list = FileInfo.objects.filter(file=self.file).all()
assert_equal(file_info_list.count(), 1)
file_info = file_info_list.first()
assert_equal(file_info.file_size, 1000)
def test_update_file_info(self):
file_info = FileInfo(file=self.file, file_size=1000)
file_info.save()
quota.update_used_quota(
self=None,
target=self.node,
user=self.user,
event_type=FileLog.FILE_UPDATED,
payload={
'provider': 'osfstorage',
'metadata': {
'provider': 'osfstorage',
'name': 'testfile',
'materialized': '/filename',
'path': self.file._id,
'kind': 'file',
'size': 2500,
'created_utc': '',
'modified_utc': '',
'extra': {'version': '2'}
}
}
)
file_info = FileInfo.objects.get(file=self.file)
assert_equal(file_info.file_size, 2500)
def test_file_info_when_not_osfstorage(self):
file_info_query = FileInfo.objects.filter(file=self.file)
assert_false(file_info_query.exists())
quota.update_used_quota(
self=None,
target=self.node,
user=self.user,
event_type=FileLog.FILE_ADDED,
payload={
'provider': 'github',
'metadata': {
'provider': 'github',
'name': 'testfile',
'materialized': '/filename',
'path': '/' + self.file._id,
'kind': 'file',
'size': 1000,
'created_utc': '',
'modified_utc': '',
'extra': {'version': '1'}
}
}
)
assert_false(file_info_query.exists())
class TestSaveUsedQuota(OsfTestCase):
def setUp(self):
super(TestSaveUsedQuota, self).setUp()
self.user = UserFactory()
self.project_creator = UserFactory()
self.node = ProjectFactory(creator=self.project_creator)
self.file = OsfStorageFileNode.create(
target=self.node,
path='/testfile',
_id='testfile',
name='testfile',
materialized_path='/testfile'
)
self.file.save()
self.base_file_node = BaseFileNode(type='osf.s3file', provider='s3', _path='/testfile',
_materialized_path='/testfile', target_object_id=self.node.id, target_content_type_id=2)
self.base_folder_node = BaseFileNode(type='osf.s3folder', provider='s3', _path='/testfolder',
_materialized_path='/testfolder', target_object_id=self.node.id, target_content_type_id=2)
def test_add_first_file(self):
assert_false(UserQuota.objects.filter(user=self.project_creator).exists())
quota.update_used_quota(
self=None,
target=self.node,
user=self.user,
event_type=FileLog.FILE_ADDED,
payload={
'provider': 'osfstorage',
'metadata': {
'provider': 'osfstorage',
'name': 'testfile',
'materialized': '/filename',
'path': '/' + self.file._id,
'kind': 'file',
'size': 1000,
'created_utc': '',
'modified_utc': '',
'extra': {'version': '1'}
}
}
)
user_quota = UserQuota.objects.filter(
storage_type=UserQuota.NII_STORAGE,
user=self.project_creator
).all()
assert_equal(len(user_quota), 1)
user_quota = user_quota[0]
assert_equal(user_quota.used, 1000)
def test_add_first_file_custom_storage(self):
assert_false(UserQuota.objects.filter(user=self.project_creator).exists())
institution = InstitutionFactory()
self.user.affiliated_institutions.add(institution)
RegionFactory(_id=institution._id)
ProjectStorageType.objects.filter(node=self.node).update(
storage_type=ProjectStorageType.CUSTOM_STORAGE
)
quota.update_used_quota(
self=None,
target=self.node,
user=self.user,
event_type=FileLog.FILE_ADDED,
payload={
'provider': 'osfstorage',
'metadata': {
'provider': 'osfstorage',
'name': 'testfile',
'materialized': '/filename',
'path': '/' + self.file._id,
'kind': 'file',
'size': 1200,
'created_utc': '',
'modified_utc': '',
'extra': {'version': '1'}
}
}
)
user_quota = UserQuota.objects.filter(
storage_type=UserQuota.CUSTOM_STORAGE,
user=self.project_creator
).all()
assert_equal(len(user_quota), 1)
user_quota = user_quota[0]
assert_equal(user_quota.used, 1200)
def test_add_file(self):
UserQuota.objects.create(
user=self.project_creator,
storage_type=UserQuota.NII_STORAGE,
max_quota=api_settings.DEFAULT_MAX_QUOTA,
used=5500
)
quota.update_used_quota(
self=None,
target=self.node,
user=self.user,
event_type=FileLog.FILE_ADDED,
payload={
'provider': 'osfstorage',
'metadata': {
'provider': 'osfstorage',
'name': 'testfile',
'materialized': '/filename',
'path': self.file._id,
'kind': 'file',
'size': 1000,
'created_utc': '',
'modified_utc': '',
'extra': {'version': '1'}
}
}
)
user_quota = UserQuota.objects.filter(
storage_type=UserQuota.NII_STORAGE,
user=self.project_creator
).all()
assert_equal(len(user_quota), 1)
user_quota = user_quota[0]
assert_equal(user_quota.used, 6500)
def test_add_file_custom_storage(self):
UserQuota.objects.create(
user=self.project_creator,
storage_type=UserQuota.CUSTOM_STORAGE,
max_quota=api_settings.DEFAULT_MAX_QUOTA,
used=5500
)
institution = InstitutionFactory()
self.user.affiliated_institutions.add(institution)
RegionFactory(_id=institution._id)
ProjectStorageType.objects.filter(node=self.node).update(
storage_type=ProjectStorageType.CUSTOM_STORAGE
)
quota.update_used_quota(
self=None,
target=self.node,
user=self.user,
event_type=FileLog.FILE_ADDED,
payload={
'provider': 'osfstorage',
'metadata': {
'provider': 'osfstorage',
'name': 'testfile',
'materialized': '/filename',
'path': self.file._id,
'kind': 'file',
'size': 1200,
'created_utc': '',
'modified_utc': '',
'extra': {'version': '1'}
}
}
)
user_quota = UserQuota.objects.filter(
storage_type=UserQuota.CUSTOM_STORAGE,
user=self.project_creator
).all()
assert_equal(len(user_quota), 1)
user_quota = user_quota[0]
assert_equal(user_quota.used, 6700)
def test_add_file_negative_size(self):
quota.update_used_quota(
self=None,
target=self.node,
user=self.user,
event_type=FileLog.FILE_ADDED,
payload={
'provider': 'osfstorage',
'metadata': {
'provider': 'osfstorage',
'name': 'testfile',
'materialized': '/filename',
'path': self.file._id,
'kind': 'file',
'size': -1000,
'created_utc': '',
'modified_utc': '',
'extra': {'version': '1'}
}
}
)
assert_false(UserQuota.objects.filter(user=self.project_creator).exists())
def test_delete_file(self):
UserQuota.objects.create(
user=self.project_creator,
storage_type=UserQuota.NII_STORAGE,
max_quota=api_settings.DEFAULT_MAX_QUOTA,
used=5500
)
FileInfo.objects.create(file=self.file, file_size=1000)
self.file.deleted_on = datetime.datetime.now()
self.file.deleted_by = self.user
self.file.type = 'osf.trashedfile'
self.file.save()
quota.update_used_quota(
self=None,
target=self.node,
user=self.user,
event_type=FileLog.FILE_REMOVED,
payload={
'provider': 'osfstorage',
'metadata': {
'provider': 'osfstorage',
'name': 'testfile',
'materialized': '/filename',
'path': self.file._id,
'kind': 'file',
'extra': {}
}
}
)
user_quota = UserQuota.objects.get(
storage_type=UserQuota.NII_STORAGE,
user=self.project_creator
)
assert_equal(user_quota.used, 4500)
def test_delete_file_custom_storage(self):
UserQuota.objects.create(
user=self.project_creator,
storage_type=UserQuota.CUSTOM_STORAGE,
max_quota=api_settings.DEFAULT_MAX_QUOTA,
used=5500
)
FileInfo.objects.create(file=self.file, file_size=1200)
institution = InstitutionFactory()
self.user.affiliated_institutions.add(institution)
RegionFactory(_id=institution._id)
ProjectStorageType.objects.filter(node=self.node).update(
storage_type=ProjectStorageType.CUSTOM_STORAGE
)
self.file.deleted_on = datetime.datetime.now()
self.file.deleted_by = self.user
self.file.type = 'osf.trashedfile'
self.file.save()
quota.update_used_quota(
self=None,
target=self.node,
user=self.user,
event_type=FileLog.FILE_REMOVED,
payload={
'provider': 'osfstorage',
'metadata': {
'provider': 'osfstorage',
'name': 'testfile',
'materialized': '/filename',
'path': self.file._id,
'kind': 'file',
'extra': {}
}
}
)
user_quota = UserQuota.objects.get(
storage_type=UserQuota.CUSTOM_STORAGE,
user=self.project_creator
)
assert_equal(user_quota.used, 4300)
def test_delete_file_lower_used_quota(self):
UserQuota.objects.create(
user=self.project_creator,
storage_type=UserQuota.NII_STORAGE,
max_quota=api_settings.DEFAULT_MAX_QUOTA,
used=500
)
FileInfo.objects.create(file=self.file, file_size=1000)
self.file.deleted_on = datetime.datetime.now()
self.file.deleted_by = self.user
self.file.type = 'osf.trashedfile'
self.file.save()
quota.update_used_quota(
self=None,
target=self.node,
user=self.user,
event_type=FileLog.FILE_REMOVED,
payload={
'provider': 'osfstorage',
'metadata': {
'provider': 'osfstorage',
'name': 'testfile',
'materialized': '/filename',
'path': self.file._id,
'kind': 'file',
'extra': {}
}
}
)
user_quota = UserQuota.objects.get(
storage_type=UserQuota.NII_STORAGE,
user=self.project_creator
)
assert_equal(user_quota.used, 0)
@mock.patch('website.util.quota.logging')
def test_delete_file_invalid_file(self, mock_logging):
UserQuota.objects.create(
user=self.project_creator,
storage_type=UserQuota.NII_STORAGE,
max_quota=api_settings.DEFAULT_MAX_QUOTA,
used=5500
)
quota.update_used_quota(
self=None,
target=self.node,
user=self.user,
event_type=FileLog.FILE_REMOVED,
payload={
'provider': 'osfstorage',
'metadata': {
'provider': 'osfstorage',
'name': 'testfile',
'materialized': '/filename',
'path': 'malicioususereditedthis',
'kind': 'file',
'extra': {}
}
}
)
user_quota = UserQuota.objects.get(
storage_type=UserQuota.NII_STORAGE,
user=self.project_creator
)
assert_equal(user_quota.used, 5500)
mock_logging.error.assert_called_with('FileNode not found, cannot update used quota!')
@mock.patch('website.util.quota.logging')
def test_delete_file_without_fileinfo(self, mock_logging):
UserQuota.objects.create(
user=self.project_creator,
storage_type=UserQuota.NII_STORAGE,
max_quota=api_settings.DEFAULT_MAX_QUOTA,
used=5500
)
self.file.deleted_on = datetime.datetime.now()
self.file.deleted_by = self.user
self.file.type = 'osf.trashedfile'
self.file.save()
quota.update_used_quota(
self=None,
target=self.node,
user=self.user,
event_type=FileLog.FILE_REMOVED,
payload={
'provider': 'osfstorage',
'metadata': {
'provider': 'osfstorage',
'name': 'testfile',
'materialized': '/filename',
'path': self.file._id,
'kind': 'file',
'extra': {}
}
}
)
user_quota = UserQuota.objects.get(
storage_type=UserQuota.NII_STORAGE,
user=self.project_creator
)
assert_equal(user_quota.used, 5500)
mock_logging.error.assert_called_with('FileInfo not found, cannot update used quota!')
@mock.patch('website.util.quota.logging')
def test_delete_file_not_trashed(self, mock_logging):
UserQuota.objects.create(
user=self.project_creator,
storage_type=UserQuota.NII_STORAGE,
max_quota=api_settings.DEFAULT_MAX_QUOTA,
used=5500
)
FileInfo.objects.create(file=self.file, file_size=1000)
self.file.deleted_on = datetime.datetime.now()
self.file.deleted_by = self.user
self.file.save()
quota.update_used_quota(
self=None,
target=self.node,
user=self.user,
event_type=FileLog.FILE_REMOVED,
payload={
'provider': 'osfstorage',
'metadata': {
'provider': 'osfstorage',
'name': 'testfile',
'materialized': '/filename',
'path': self.file._id,
'kind': 'file',
'extra': {}
}
}
)
user_quota = UserQuota.objects.get(
storage_type=UserQuota.NII_STORAGE,
user=self.project_creator
)
assert_equal(user_quota.used, 5500)
mock_logging.error.assert_called_with('FileNode is not trashed, cannot update used quota!')
def test_delete_file_without_userquota(self):
FileInfo.objects.create(file=self.file, file_size=1000)
self.file.deleted_on = datetime.datetime.now()
self.file.deleted_by = self.user
self.file.type = 'osf.trashedfile'
self.file.save()
quota.update_used_quota(
self=None,
target=self.node,
user=self.user,
event_type=FileLog.FILE_REMOVED,
payload={
'provider': 'osfstorage',
'metadata': {
'provider': 'osfstorage',
'name': 'testfile',
'materialized': '/filename',
'path': self.file._id,
'kind': 'file',
'extra': {}
}
}
)
assert_false(UserQuota.objects.filter(user=self.project_creator).exists())
def test_delete_folder(self):
UserQuota.objects.create(
user=self.project_creator,
storage_type=UserQuota.NII_STORAGE,
max_quota=api_settings.DEFAULT_MAX_QUOTA,
used=5500
)
folder1 = TrashedFolder(
target=self.node,
name='testfolder',
deleted_on=datetime.datetime.now(),
deleted_by=self.user
)
folder1.save()
folder2 = TrashedFolder(
target=self.node,
name='testfolder',
parent_id=folder1.id,
deleted_on=datetime.datetime.now(),
deleted_by=self.user
)
folder2.save()
file1 = TrashedFileNode.create(
target=self.node,
name='testfile1',
parent_id=folder1.id,
deleted_on=datetime.datetime.now(),
deleted_by=self.user
)
file1.provider = 'osfstorage'
file1.save()
file2 = TrashedFileNode.create(
target=self.node,
name='testfile2',
parent_id=folder2.id,
deleted_on=datetime.datetime.now(),
deleted_by=self.user
)
file2.provider = 'osfstorage'
file2.save()
file1_info = FileInfo(file=file1, file_size=2000)
file1_info.save()
file2_info = FileInfo(file=file2, file_size=3000)
file2_info.save()
quota.update_used_quota(
self=None,
target=self.node,
user=self.user,
event_type=FileLog.FILE_REMOVED,
payload={
'provider': 'osfstorage',
'metadata': {
'provider': 'osfstorage',
'name': 'testfolder',
'materialized': '/testfolder',
'path': '{}/'.format(folder1._id),
'kind': 'folder',
'extra': {}
}
}
)
user_quota = UserQuota.objects.get(
storage_type=UserQuota.NII_STORAGE,
user=self.project_creator
)
assert_equal(user_quota.used, 500)
def test_edit_file(self):
UserQuota.objects.create(
user=self.project_creator,
storage_type=UserQuota.NII_STORAGE,
max_quota=api_settings.DEFAULT_MAX_QUOTA,
used=5500
)
FileInfo.objects.create(file=self.file, file_size=1000)
quota.update_used_quota(
self=None,
target=self.node,
user=self.user,
event_type=FileLog.FILE_UPDATED,
payload={
'provider': 'osfstorage',
'metadata': {
'provider': 'osfstorage',
'name': 'testfile',
'materialized': '/filename',
'path': self.file._id,
'kind': 'file',
'size': 1500,
'created_utc': '',
'modified_utc': '',
'extra': {'version': '2'}
}
}
)
user_quota = UserQuota.objects.get(
storage_type=UserQuota.NII_STORAGE,
user=self.project_creator
)
assert_equal(user_quota.used, 6000)
def test_edit_file_custom_storage(self):
UserQuota.objects.create(
user=self.project_creator,
storage_type=UserQuota.CUSTOM_STORAGE,
max_quota=api_settings.DEFAULT_MAX_QUOTA,
used=5500
)
FileInfo.objects.create(file=self.file, file_size=1000)
institution = InstitutionFactory()
self.user.affiliated_institutions.add(institution)
RegionFactory(_id=institution._id)
ProjectStorageType.objects.filter(node=self.node).update(
storage_type=ProjectStorageType.CUSTOM_STORAGE
)
quota.update_used_quota(
self=None,
target=self.node,
user=self.user,
event_type=FileLog.FILE_UPDATED,
payload={
'provider': 'osfstorage',
'metadata': {
'provider': 'osfstorage',
'name': 'testfile',
'materialized': '/filename',
'path': self.file._id,
'kind': 'file',
'size': 1700,
'created_utc': '',
'modified_utc': '',
'extra': {'version': '2'}
}
}
)
user_quota = UserQuota.objects.get(
storage_type=UserQuota.CUSTOM_STORAGE,
user=self.project_creator
)
assert_equal(user_quota.used, 6200)
def test_edit_file_negative_size(self):
UserQuota.objects.create(
user=self.project_creator,
storage_type=UserQuota.NII_STORAGE,
max_quota=api_settings.DEFAULT_MAX_QUOTA,
used=5500
)
FileInfo.objects.create(file=self.file, file_size=1000)
quota.update_used_quota(
self=None,
target=self.node,
user=self.user,
event_type=FileLog.FILE_UPDATED,
payload={
'provider': 'osfstorage',
'metadata': {
'provider': 'osfstorage',
'name': 'testfile',
'materialized': '/filename',
'path': self.file._id,
'kind': 'file',
'size': -1500,
'created_utc': '',
'modified_utc': '',
'extra': {'version': '2'}
}
}
)
user_quota = UserQuota.objects.get(
storage_type=UserQuota.NII_STORAGE,
user=self.project_creator
)
assert_equal(user_quota.used, 5500)
def test_edit_file_without_fileinfo(self):
UserQuota.objects.create(
user=self.project_creator,
storage_type=UserQuota.NII_STORAGE,
max_quota=api_settings.DEFAULT_MAX_QUOTA,
used=5500
)
quota.update_used_quota(
self=None,
target=self.node,
user=self.user,
event_type=FileLog.FILE_UPDATED,
payload={
'provider': 'osfstorage',
'metadata': {
'provider': 'osfstorage',
'name': 'testfile',
'materialized': '/filename',
'path': self.file._id,
'kind': 'file',
'size': 1500,
'created_utc': '',
'modified_utc': '',
'extra': {'version': '2'}
}
}
)
user_quota = UserQuota.objects.get(
storage_type=UserQuota.NII_STORAGE,
user=self.project_creator
)
assert_equal(user_quota.used, 7000)
def test_edit_file_lower_used_quota(self):
UserQuota.objects.create(
user=self.project_creator,
storage_type=UserQuota.NII_STORAGE,
max_quota=api_settings.DEFAULT_MAX_QUOTA,
used=500
)
FileInfo.objects.create(file=self.file, file_size=3000)
quota.update_used_quota(
self=None,
target=self.node,
user=self.user,
event_type=FileLog.FILE_UPDATED,
payload={
'provider': 'osfstorage',
'metadata': {
'provider': 'osfstorage',
'name': 'testfile',
'materialized': '/filename',
'path': self.file._id,
'kind': 'file',
'size': 2000,
'created_utc': '',
'modified_utc': '',
'extra': {'version': '2'}
}
}
)
user_quota = UserQuota.objects.get(
storage_type=UserQuota.NII_STORAGE,
user=self.project_creator
)
assert_equal(user_quota.used, 0)
def test_add_file_when_not_osfstorage(self):
UserQuota.objects.create(
user=self.project_creator,
storage_type=UserQuota.NII_STORAGE,
max_quota=api_settings.DEFAULT_MAX_QUOTA,
used=5500
)
quota.update_used_quota(
self=None,
target=self.node,
user=self.user,
event_type=FileLog.FILE_ADDED,
payload={
'provider': 'github',
'metadata': {
'provider': 'github',
'name': 'testfile',
'materialized': '/filename',
'path': self.file._id,
'kind': 'file',
'size': 1000,
'created_utc': '',
'modified_utc': '',
'extra': {'version': '1'}
}
}
)
user_quota = UserQuota.objects.get(
storage_type=UserQuota.NII_STORAGE,
user=self.project_creator
)
assert_equal(user_quota.used, 5500)
def test_move_file(self):
quota.update_used_quota(
self=None,
target=self.node,
user=self.user,
event_type=FileLog.FILE_ADDED,
payload={
'metadata': {
'created_utc': '',
'modified_utc': '',
'extra': {'version': '1'}
},
'source': {
'provider': 'osfstorage',
'name': 'testfile',
'materialized': '/filename',
'path': self.file._id,
'kind': 'file',
'size': 1000,
'extra': {'version': '1'}
},
'destination': {
'provider': 'osfstorage',
'name': 'testfile',
'materialized': '/filename',
'path': self.file._id,
'kind': 'file',
'size': 1000,
'extra': {'version': '1'}
}
}
)
def test_rename_folder_with_AmazonS3(self):
mock_base_file_node = mock.MagicMock()
mock_base_file_node_orderby = mock.MagicMock()
mock_base_file_node.objects.filter.return_value = [BaseFileNode(type='osf.s3folder', provider='s3', _path='/newfoldername',
_materialized_path='/newfoldername', target_object_id=self.node.id, target_content_type_id=2)]
mock_base_file_node_orderby.filter.return_value.order_by.return_value.first.return_value = BaseFileNode(type='osf.s3folder', provider='s3', _path='/newfoldername',
_materialized_path='/newfoldername', target_object_id=self.node.id, target_content_type_id=2)
with mock.patch('website.util.quota.BaseFileNode', mock_base_file_node):
with mock.patch('website.util.quota.BaseFileNode', mock_base_file_node_orderby):
quota.update_used_quota(
self=None,
target=self.node,
user=self.user,
event_type=FileLog.FILE_RENAMED,
payload={
'destination': {
'provider': 's3',
'path': '/newfoldername',
'kind': 'folder',
},
'source': {
'provider': 's3',
'path': '/prefolderename',
'kind': 'folder',
},
}
)
def test_rename_file_with_AmazonS3(self):
mock_base_file_node = mock.MagicMock()
mock_base_file_node_orderby = mock.MagicMock()
mock_base_file_node.objects.filter.return_value = [BaseFileNode(type='osf.s3file', provider='s3', _path='/newfilename',
_materialized_path='/newfilename', target_object_id=self.node.id, target_content_type_id=2)]
mock_base_file_node_orderby.filter.return_value.order_by.return_value.first.return_value = BaseFileNode(type='osf.s3file', provider='s3', _path='/newfilename',
_materialized_path='/newfilename', target_object_id=self.node.id, target_content_type_id=2)
with mock.patch('website.util.quota.BaseFileNode', mock_base_file_node):
with mock.patch('website.util.quota.BaseFileNode', mock_base_file_node_orderby):
quota.update_used_quota(
self=None,
target=self.node,
user=self.user,
event_type=FileLog.FILE_RENAMED,
payload={
'destination': {
'provider': 's3',
'path': '/newfilename',
'kind': 'file',
},
'source': {
'provider': 's3',
'path': '/prefilename',
'kind': 'file',
},
}
)
def test_upload_file_with_Amazon_S3(self):
UserQuota.objects.create(
user=self.project_creator,
storage_type=UserQuota.CUSTOM_STORAGE,
max_quota=api_settings.DEFAULT_MAX_QUOTA,
used=5000
)
institution = InstitutionFactory()
self.user.affiliated_institutions.add(institution)
RegionFactory(_id=institution._id)
ProjectStorageType.objects.filter(node=self.node).update(
storage_type=ProjectStorageType.CUSTOM_STORAGE
)
mock_base_file_node = mock.MagicMock()
mock_file_info = mock.MagicMock()
mock_base_file_node.objects.filter.return_value.order_by.return_value.first.return_value = BaseFileNode(type='osf.s3file', provider='s3', _path='/testfile',
_materialized_path='/testfile', parent_id=self.node.id, target_object_id=self.node.id, target_content_type_id=2)
mock_file_info.objects.create.return_value = None
with mock.patch('website.util.quota.BaseFileNode', mock_base_file_node):
with mock.patch('website.util.quota.FileInfo', mock_file_info):
quota.update_used_quota(
self=None,
target=self.node,
user=self.user,
event_type=FileLog.FILE_ADDED,
payload={
'provider': 's3',
'metadata': {
'provider': 's3',
'name': 'testfile',
'materialized': '/testfile',
'path': '/testfile',
'kind': 'file',
'size': 2000,
}
}
)
user_quota = UserQuota.objects.filter(
storage_type=UserQuota.CUSTOM_STORAGE,
user=self.project_creator
).all()
assert_equal(len(user_quota), 1)
user_quota = user_quota[0]
assert_equal(user_quota.used, 7000)
def test_add_folder_with_Amazon_S3(self):
UserQuota.objects.create(
user=self.project_creator,
storage_type=UserQuota.CUSTOM_STORAGE,
max_quota=api_settings.DEFAULT_MAX_QUOTA,
used=5000
)
institution = InstitutionFactory()
self.user.affiliated_institutions.add(institution)
RegionFactory(_id=institution._id)
ProjectStorageType.objects.filter(node=self.node).update(
storage_type=ProjectStorageType.CUSTOM_STORAGE
)
mock_base_file_node = mock.MagicMock()
mock_file_info = mock.MagicMock()
mock_base_file_node.return_value = BaseFileNode(type='osf.s3folder', provider='s3', _path='/testfolder',
_materialized_path='/testfolder', target_object_id=self.node.id, target_content_type_id=2)
mock_base_file_node.objects.filter.return_value.order_by.return_value.first.return_value = BaseFileNode(type='osf.s3folder', provider='s3', _path='/testfolder',
_materialized_path='/testfolder', target_object_id=self.node.id, target_content_type_id=2)
mock_file_info.objects.create.return_value = None
with mock.patch('website.util.quota.BaseFileNode', mock_base_file_node):
with mock.patch('website.util.quota.FileInfo', mock_file_info):
quota.update_used_quota(
self=None,
target=self.node,
user=self.user,
event_type=FileLog.FILE_ADDED,
payload={
'provider': 's3',
'action': 'create_folder',
'metadata': {
'provider': 's3',
'name': '/testfolder',
'materialized': '/testfolder',
'path': '/testfolder',
'kind': 'folder',
'size': 0,
}
}
)
user_quota = UserQuota.objects.filter(
storage_type=UserQuota.CUSTOM_STORAGE,
user=self.project_creator
).all()
assert_equal(len(user_quota), 1)
user_quota = user_quota[0]
assert_equal(user_quota.used, 5000)
def test_delete_file_with_Amazon_S3(self):
mock_base_file_node = mock.MagicMock()
mock_file_info = mock.MagicMock()
mock_user_quota = mock.MagicMock()
mock_base_file_node.objects.filter.return_value.order_by.return_value.first.return_value = self.base_file_node
mock_file_info.objects.get.return_value = FileInfo(file=self.base_file_node, file_size=1000)
mock_user_quota.objects.filter.return_value.first.return_value = UserQuota(
user=self.project_creator,
storage_type=UserQuota.CUSTOM_STORAGE,
max_quota=api_settings.DEFAULT_MAX_QUOTA,
used=5500
)
with mock.patch('website.util.quota.BaseFileNode', mock_base_file_node):
with mock.patch('website.util.quota.FileInfo', mock_file_info):
with mock.patch('website.util.quota.UserQuota', mock_user_quota):
quota.update_used_quota(
self=None,
target=self.node,
user=self.user,
event_type=FileLog.FILE_REMOVED,
payload={
'provider': 's3',
'metadata': {
'provider': 's3',
'name': 'testfile',
'materialized': '/filename',
'path': '/filename',
'kind': 'file'
}
}
)
user_quota = UserQuota.objects.get(
storage_type=UserQuota.CUSTOM_STORAGE,
user=self.project_creator
)
assert_equal(user_quota.used, 4500)
def test_delete_folder_with_Amazon_S3(self):
mock_base_file_node = mock.MagicMock()
mock_file_info = mock.MagicMock()
mock_user_quota = mock.MagicMock()
mock_base_file_node.objects.filter.return_value.order_by.return_value.first.return_value = self.base_folder_node
folder_element = BaseFileNode(type='osf.s3folder', provider='s3', _path='/testfolder/foldername',
_materialized_path='/testfolder/foldername', target_object_id=self.node.id, target_content_type_id=2)
mock_base_file_node.objects.filter.return_value.all.return_value = [self.base_file_node, folder_element]
mock_file_info.objects.get.return_value = FileInfo(file=self.base_file_node, file_size=1500)
mock_user_quota.objects.filter.return_value.first.return_value = UserQuota(
user=self.project_creator,
storage_type=UserQuota.CUSTOM_STORAGE,
max_quota=api_settings.DEFAULT_MAX_QUOTA,
used=5500
)
with mock.patch('website.util.quota.BaseFileNode', mock_base_file_node):
with mock.patch('website.util.quota.FileInfo', mock_file_info):
with mock.patch('website.util.quota.UserQuota', mock_user_quota):
quota.update_used_quota(
self=None,
target=self.node,
user=self.user,
event_type=FileLog.FILE_REMOVED,
payload={
'provider': 's3',
'metadata': {
'provider': 's3',
'name': 'testfolder',
'materialized': '/testfolder',
'path': '/testfolder',
'kind': 'folder'
}
}
)
user_quota = UserQuota.objects.get(
storage_type=UserQuota.CUSTOM_STORAGE,
user=self.project_creator
)
assert_equal(user_quota.used, 4000)
class TestUpdateUserUsedQuota(OsfTestCase):
def setUp(self):
super(TestUpdateUserUsedQuota, self).setUp()
self.user = UserFactory()
self.user.save()
self.user_quota = UserQuota.objects.create(user=self.user, storage_type=UserQuota.NII_STORAGE, max_quota=200,
used=1000)
@mock.patch.object(UserQuota, 'save')
@mock.patch('website.util.quota.used_quota')
def test_update_user_used_quota_method_with_user_quota_exist(self, mock_used, mock_user_quota_save):
mock_used.return_value = 500
quota.update_user_used_quota(
user=self.user,
storage_type=UserQuota.NII_STORAGE
)
mock_user_quota_save.assert_called()
@mock.patch('website.util.quota.used_quota')
def test_update_user_used_quota_method_with_user_quota_not_exist(self, mock_used):
another_user = UserFactory()
mock_used.return_value = 500
quota.update_user_used_quota(
user=another_user,
storage_type=UserQuota.NII_STORAGE
)
user_quota = UserQuota.objects.filter(
storage_type=UserQuota.NII_STORAGE,
).all()
assert_equal(len(user_quota), 2)
user_quota = user_quota.filter(user=another_user)
assert_equal(len(user_quota), 1)
user_quota = user_quota[0]
assert_equal(user_quota.used, 500)
class TestQuotaApiWaterbutler(OsfTestCase):
def setUp(self):
super(TestQuotaApiWaterbutler, self).setUp()
self.user = AuthUserFactory()
self.node = ProjectFactory(creator=self.user)
def test_default_values(self):
ProjectStorageType.objects.filter(node=self.node).delete()
response = self.app.get(
'{}?payload={payload}&signature={signature}'.format(
self.node.api_url_for('waterbutler_creator_quota'),
**signing.sign_data(signing.default_signer, {})
)
)
assert_equal(response.status_code, 200)
assert_equal(response.json['max'], api_settings.DEFAULT_MAX_QUOTA * api_settings.SIZE_UNIT_GB)
assert_equal(response.json['used'], 0)
def test_used_half_custom_quota(self):
UserQuota.objects.create(
storage_type=UserQuota.NII_STORAGE,
user=self.user,
max_quota=200,
used=100 * api_settings.SIZE_UNIT_GB
)
response = self.app.get(
'{}?payload={payload}&signature={signature}'.format(
self.node.api_url_for('waterbutler_creator_quota'),
**signing.sign_data(signing.default_signer, {})
)
)
assert_equal(response.status_code, 200)
assert_equal(response.json['max'], 200 * api_settings.SIZE_UNIT_GB)
assert_equal(response.json['used'], 100 * api_settings.SIZE_UNIT_GB)
def test_used_half_custom_institution_quota(self):
UserQuota.objects.create(
storage_type=UserQuota.NII_STORAGE,
user=self.user,
max_quota=150,
used=0
)
UserQuota.objects.create(
storage_type=UserQuota.CUSTOM_STORAGE,
user=self.user,
max_quota=200,
used=100 * api_settings.SIZE_UNIT_GB
)
institution = InstitutionFactory()
self.user.affiliated_institutions.add(institution)
RegionFactory(_id=institution._id)
ProjectStorageType.objects.filter(node=self.node).update(
storage_type=ProjectStorageType.CUSTOM_STORAGE
)
response = self.app.get(
'{}?payload={payload}&signature={signature}'.format(
self.node.api_url_for('waterbutler_creator_quota'),
**signing.sign_data(signing.default_signer, {})
)
)
assert_equal(response.status_code, 200)
assert_equal(response.json['max'], 200 * api_settings.SIZE_UNIT_GB)
assert_equal(response.json['used'], 100 * api_settings.SIZE_UNIT_GB)
class TestQuotaApiBrowser(OsfTestCase):
def setUp(self):
super(TestQuotaApiBrowser, self).setUp()
self.user = AuthUserFactory()
self.node = ProjectFactory(creator=self.user)
def test_default_values(self):
ProjectStorageType.objects.filter(node=self.node).delete()
response = self.app.get(
self.node.api_url_for('get_creator_quota'),
auth=self.user.auth
)
assert_equal(response.status_code, 200)
assert_equal(response.json['max'], api_settings.DEFAULT_MAX_QUOTA * api_settings.SIZE_UNIT_GB)
assert_equal(response.json['used'], 0)
def test_used_half_custom_quota(self):
UserQuota.objects.create(
storage_type=UserQuota.NII_STORAGE,
user=self.user,
max_quota=200,
used=100 * api_settings.SIZE_UNIT_GB
)
response = self.app.get(
self.node.api_url_for('get_creator_quota'),
auth=self.user.auth
)
assert_equal(response.status_code, 200)
assert_equal(response.json['max'], 200 * api_settings.SIZE_UNIT_GB)
assert_equal(response.json['used'], 100 * api_settings.SIZE_UNIT_GB)
def test_used_half_custom_institution_quota(self):
UserQuota.objects.create(
storage_type=UserQuota.NII_STORAGE,
user=self.user,
max_quota=150,
used=0
)
UserQuota.objects.create(
storage_type=UserQuota.CUSTOM_STORAGE,
user=self.user,
max_quota=200,
used=100 * api_settings.SIZE_UNIT_GB
)
institution = InstitutionFactory()
self.user.affiliated_institutions.add(institution)
RegionFactory(_id=institution._id)
ProjectStorageType.objects.filter(node=self.node).update(
storage_type=ProjectStorageType.CUSTOM_STORAGE
)
response = self.app.get(
self.node.api_url_for('get_creator_quota'),
auth=self.user.auth
)
assert_equal(response.status_code, 200)
assert_equal(response.json['max'], 200 * api_settings.SIZE_UNIT_GB)
assert_equal(response.json['used'], 100 * api_settings.SIZE_UNIT_GB)
| 36.651525
| 171
| 0.549309
| 5,584
| 56,480
| 5.290115
| 0.048173
| 0.030873
| 0.044685
| 0.032769
| 0.905552
| 0.887847
| 0.866385
| 0.862356
| 0.851354
| 0.841909
| 0
| 0.017951
| 0.344086
| 56,480
| 1,540
| 172
| 36.675325
| 0.779436
| 0.003169
| 0
| 0.725748
| 0
| 0
| 0.09458
| 0.015526
| 0
| 0
| 0
| 0
| 0.07221
| 1
| 0.047411
| false
| 0
| 0.008753
| 0
| 0.061999
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
071aa000e99aad1a70d05aa2c7c64c12e0c0d75a
| 1,174
|
py
|
Python
|
Backend/src/reports/tests.py
|
Valle1806/EnergyCorp
|
aba09105eedcb7dc694b201e50953e19e4e2936b
|
[
"MIT"
] | 1
|
2021-01-21T08:30:57.000Z
|
2021-01-21T08:30:57.000Z
|
Backend/src/reports/tests.py
|
ChristianTaborda/Energycorp
|
2447b5af211501450177b0b60852dcb31d6ca12d
|
[
"MIT"
] | null | null | null |
Backend/src/reports/tests.py
|
ChristianTaborda/Energycorp
|
2447b5af211501450177b0b60852dcb31d6ca12d
|
[
"MIT"
] | null | null | null |
from django.test import TestCase
# Create your tests here.
"""
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"""
| 6.051546
| 32
| 0.821976
| 162
| 1,174
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| 1.890155
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| 3.73057
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| 194
| 33
| 6.051546
| 0.99076
| 0.019591
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| true
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| null | 1
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0
| 12
|
07248dd26e933bfe6039ad16bd28c6b6916376c6
| 50,606
|
py
|
Python
|
src/test_vectors.py
|
de-centralized-systems/sssmp
|
8e2d143b8eb1c354c077f0667a774325fe55d262
|
[
"CC-BY-4.0"
] | null | null | null |
src/test_vectors.py
|
de-centralized-systems/sssmp
|
8e2d143b8eb1c354c077f0667a774325fe55d262
|
[
"CC-BY-4.0"
] | null | null | null |
src/test_vectors.py
|
de-centralized-systems/sssmp
|
8e2d143b8eb1c354c077f0667a774325fe55d262
|
[
"CC-BY-4.0"
] | 1
|
2021-05-07T09:29:48.000Z
|
2021-05-07T09:29:48.000Z
|
# fmt: off
TEST_VECTORS = [
{
"s": bytes.fromhex("243f6a8885a308d313198a2e03707344"),
"n": 1,
"t": 1,
"c": [
bytes.fromhex("243f6a8885a308d313198a2e03707344"),
],
"shares": [
(1, bytes.fromhex("243f6a8885a308d313198a2e03707344")),
],
},
{
"s": bytes.fromhex("243f6a8885a308d313198a2e03707344"),
"n": 2,
"t": 1,
"c": [
bytes.fromhex("243f6a8885a308d313198a2e03707344"),
],
"shares": [
(1, bytes.fromhex("243f6a8885a308d313198a2e03707344")),
(2, bytes.fromhex("243f6a8885a308d313198a2e03707344")),
],
},
{
"s": bytes.fromhex("243f6a8885a308d313198a2e03707344"),
"n": 2,
"t": 2,
"c": [
bytes.fromhex("243f6a8885a308d313198a2e03707344"),
bytes.fromhex("b7e151628aed2a6ace7500d3cfa9f92a"),
],
"shares": [
(1, bytes.fromhex("93de3bea0f4e22b9dd6c8afdccd98a6e")),
(2, bytes.fromhex("51e6c84c8a625c0794f38a9386399a10")),
],
},
{
"s": bytes.fromhex("243f6a8885a308d313198a2e03707344"),
"n": 3,
"t": 1,
"c": [
bytes.fromhex("243f6a8885a308d313198a2e03707344"),
],
"shares": [
(1, bytes.fromhex("243f6a8885a308d313198a2e03707344")),
(2, bytes.fromhex("243f6a8885a308d313198a2e03707344")),
(3, bytes.fromhex("243f6a8885a308d313198a2e03707344")),
],
},
{
"s": bytes.fromhex("243f6a8885a308d313198a2e03707344"),
"n": 3,
"t": 2,
"c": [
bytes.fromhex("243f6a8885a308d313198a2e03707344"),
bytes.fromhex("b7e151628aed2a6ace7500d3cfa9f92a"),
],
"shares": [
(1, bytes.fromhex("93de3bea0f4e22b9dd6c8afdccd98a6e")),
(2, bytes.fromhex("51e6c84c8a625c0794f38a9386399a10")),
(3, bytes.fromhex("e607992e008f766d5a868a404990633a")),
],
},
{
"s": bytes.fromhex("243f6a8885a308d313198a2e03707344"),
"n": 3,
"t": 3,
"c": [
bytes.fromhex("243f6a8885a308d313198a2e03707344"),
bytes.fromhex("b7e151628aed2a6abf7158809cf4f3c7"),
bytes.fromhex("62e7160f38b4da5624cecd28f4ddb05a"),
],
"shares": [
(1, bytes.fromhex("f1392de537faf8ef88a61f866b5930d9")),
(2, bytes.fromhex("c25790706a841944e6ee2395ddda78a2")),
(3, bytes.fromhex("1751d71dd8dde9787d51b63db5f33b3f")),
],
},
{
"s": bytes.fromhex("243f6a8885a308d313198a2e03707344"),
"n": 4,
"t": 1,
"c": [
bytes.fromhex("243f6a8885a308d313198a2e03707344"),
],
"shares": [
(1, bytes.fromhex("243f6a8885a308d313198a2e03707344")),
(2, bytes.fromhex("243f6a8885a308d313198a2e03707344")),
(3, bytes.fromhex("243f6a8885a308d313198a2e03707344")),
(4, bytes.fromhex("243f6a8885a308d313198a2e03707344")),
],
},
{
"s": bytes.fromhex("243f6a8885a308d313198a2e03707344"),
"n": 4,
"t": 2,
"c": [
bytes.fromhex("243f6a8885a308d313198a2e03707344"),
bytes.fromhex("b7e151628aed2a6ace7500d3cfa9f92a"),
],
"shares": [
(1, bytes.fromhex("93de3bea0f4e22b9dd6c8afdccd98a6e")),
(2, bytes.fromhex("51e6c84c8a625c0794f38a9386399a10")),
(3, bytes.fromhex("e607992e008f766d5a868a404990633a")),
(4, bytes.fromhex("ce96351b9b3aa06006d68a4f12e2baec")),
],
},
{
"s": bytes.fromhex("243f6a8885a308d313198a2e03707344"),
"n": 4,
"t": 3,
"c": [
bytes.fromhex("243f6a8885a308d313198a2e03707344"),
bytes.fromhex("b7e151628aed2a6abf7158809cf4f3c7"),
bytes.fromhex("62e7160f38b4da5624cecd28f4ddb05a"),
],
"shares": [
(1, bytes.fromhex("f1392de537faf8ef88a61f866b5930d9")),
(2, bytes.fromhex("c25790706a841944e6ee2395ddda78a2")),
(3, bytes.fromhex("1751d71dd8dde9787d51b63db5f33b3f")),
(4, bytes.fromhex("b4644eeb368faf77af9295ae9cf267a2")),
],
},
{
"s": bytes.fromhex("243f6a8885a308d313198a2e03707344"),
"n": 4,
"t": 4,
"c": [
bytes.fromhex("243f6a8885a308d313198a2e03707344"),
bytes.fromhex("b7e151628aed2a6abf7158809cf4f3c7"),
bytes.fromhex("62e7160f38b4da56a784d9045190cfef"),
bytes.fromhex("324e7738926cfbe5e5f9f878118a1712"),
],
"shares": [
(1, bytes.fromhex("c3775adda596030aee15f3d2df9e587e")),
(2, bytes.fromhex("491105ab96c9802db554f2c8f7c927d0")),
(3, bytes.fromhex("02e61c6ef78fa79c2078ad3f4d367e86")),
(4, bytes.fromhex("80628a6997d10b12227aaa710eb561eb")),
],
},
{
"s": bytes.fromhex("243f6a8885a308d313198a2e03707344"),
"n": 5,
"t": 1,
"c": [
bytes.fromhex("243f6a8885a308d313198a2e03707344"),
],
"shares": [
(1, bytes.fromhex("243f6a8885a308d313198a2e03707344")),
(2, bytes.fromhex("243f6a8885a308d313198a2e03707344")),
(3, bytes.fromhex("243f6a8885a308d313198a2e03707344")),
(4, bytes.fromhex("243f6a8885a308d313198a2e03707344")),
(5, bytes.fromhex("243f6a8885a308d313198a2e03707344")),
],
},
{
"s": bytes.fromhex("243f6a8885a308d313198a2e03707344"),
"n": 5,
"t": 2,
"c": [
bytes.fromhex("243f6a8885a308d313198a2e03707344"),
bytes.fromhex("b7e151628aed2a6ace7500d3cfa9f92a"),
],
"shares": [
(1, bytes.fromhex("93de3bea0f4e22b9dd6c8afdccd98a6e")),
(2, bytes.fromhex("51e6c84c8a625c0794f38a9386399a10")),
(3, bytes.fromhex("e607992e008f766d5a868a404990633a")),
(4, bytes.fromhex("ce96351b9b3aa06006d68a4f12e2baec")),
(5, bytes.fromhex("7977647911d78a0ac8a38a9cdd4b43c6")),
],
},
{
"s": bytes.fromhex("243f6a8885a308d313198a2e03707344"),
"n": 5,
"t": 3,
"c": [
bytes.fromhex("243f6a8885a308d313198a2e03707344"),
bytes.fromhex("b7e151628aed2a6abf7158809cf4f3c7"),
bytes.fromhex("62e7160f38b4da5624cecd28f4ddb05a"),
],
"shares": [
(1, bytes.fromhex("f1392de537faf8ef88a61f866b5930d9")),
(2, bytes.fromhex("c25790706a841944e6ee2395ddda78a2")),
(3, bytes.fromhex("1751d71dd8dde9787d51b63db5f33b3f")),
(4, bytes.fromhex("b4644eeb368faf77af9295ae9cf267a2")),
(5, bytes.fromhex("6162098684d65f4b342d0006f4db243f")),
],
},
{
"s": bytes.fromhex("243f6a8885a308d313198a2e03707344"),
"n": 5,
"t": 4,
"c": [
bytes.fromhex("243f6a8885a308d313198a2e03707344"),
bytes.fromhex("b7e151628aed2a6abf7158809cf4f3c7"),
bytes.fromhex("62e7160f38b4da56a784d9045190cfef"),
bytes.fromhex("324e7738926cfbe5e5f9f878118a1712"),
],
"shares": [
(1, bytes.fromhex("c3775adda596030aee15f3d2df9e587e")),
(2, bytes.fromhex("491105ab96c9802db554f2c8f7c927d0")),
(3, bytes.fromhex("02e61c6ef78fa79c2078ad3f4d367e86")),
(4, bytes.fromhex("80628a6997d10b12227aaa710eb561eb")),
(5, bytes.fromhex("a2854c712ad5e8a0b4b607b79d3d7da2")),
],
},
{
"s": bytes.fromhex("243f6a8885a308d313198a2e03707344"),
"n": 5,
"t": 5,
"c": [
bytes.fromhex("243f6a8885a308d313198a2e03707344"),
bytes.fromhex("b7e151628aed2a6abf7158809cf4f3c7"),
bytes.fromhex("62e7160f38b4da56a784d9045190cfef"),
bytes.fromhex("324e7738926cfbe5f4bf8d8d8c31d763"),
bytes.fromhex("da06c80abb1185eb645fd38c7831e461"),
],
"shares": [
(1, bytes.fromhex("197192d71e8786e19b0c55ab3a147c6e")),
(2, bytes.fromhex("4671310bd3c2081f27d5e839b25bbf3f")),
(3, bytes.fromhex("d780e0c40995aa45a16f34bf8e997444")),
(4, bytes.fromhex("7038e787ab61531fb5e2dde3e6471378")),
(5, bytes.fromhex("88d9e995ad7435461938086cc1dab2af")),
],
},
{
"s": bytes.fromhex("243f6a8885a308d313198a2e03707344a4093822"),
"n": 1,
"t": 1,
"c": [
bytes.fromhex("243f6a8885a308d313198a2e03707344a4093822"),
],
"shares": [
(1, bytes.fromhex("243f6a8885a308d313198a2e03707344a4093822")),
],
},
{
"s": bytes.fromhex("243f6a8885a308d313198a2e03707344a4093822"),
"n": 2,
"t": 1,
"c": [
bytes.fromhex("243f6a8885a308d313198a2e03707344a4093822"),
],
"shares": [
(1, bytes.fromhex("243f6a8885a308d313198a2e03707344a4093822")),
(2, bytes.fromhex("243f6a8885a308d313198a2e03707344a4093822")),
],
},
{
"s": bytes.fromhex("243f6a8885a308d313198a2e03707344a4093822"),
"n": 2,
"t": 2,
"c": [
bytes.fromhex("243f6a8885a308d313198a2e03707344a4093822"),
bytes.fromhex("b7e151628aed2a6abf7158802933783373a292e3"),
],
"shares": [
(1, bytes.fromhex("93de3bea0f4e22b9ac68d2ae2a430b77d7abaac1")),
(2, bytes.fromhex("51e6c84c8a625c0776fb3a3551168322425607ff")),
],
},
{
"s": bytes.fromhex("243f6a8885a308d313198a2e03707344a4093822"),
"n": 3,
"t": 1,
"c": [
bytes.fromhex("243f6a8885a308d313198a2e03707344a4093822"),
],
"shares": [
(1, bytes.fromhex("243f6a8885a308d313198a2e03707344a4093822")),
(2, bytes.fromhex("243f6a8885a308d313198a2e03707344a4093822")),
(3, bytes.fromhex("243f6a8885a308d313198a2e03707344a4093822")),
],
},
{
"s": bytes.fromhex("243f6a8885a308d313198a2e03707344a4093822"),
"n": 3,
"t": 2,
"c": [
bytes.fromhex("243f6a8885a308d313198a2e03707344a4093822"),
bytes.fromhex("b7e151628aed2a6abf7158802933783373a292e3"),
],
"shares": [
(1, bytes.fromhex("93de3bea0f4e22b9ac68d2ae2a430b77d7abaac1")),
(2, bytes.fromhex("51e6c84c8a625c0776fb3a3551168322425607ff")),
(3, bytes.fromhex("e607992e008f766dc98a62b57825fb1131f4951c")),
],
},
{
"s": bytes.fromhex("243f6a8885a308d313198a2e03707344a4093822"),
"n": 3,
"t": 3,
"c": [
bytes.fromhex("243f6a8885a308d313198a2e03707344a4093822"),
bytes.fromhex("b7e151628aed2a6abf7158809cf4f3c762e7160f"),
bytes.fromhex("38b4da56a784d9045190cfefa00c8fe35cf55b72"),
],
"shares": [
(1, bytes.fromhex("ab6ae1bca8cafbbdfdf81d413f880f609a1b755f")),
(2, bytes.fromhex("b1008d0f20441517298d2ba496b384700b2563ef")),
(3, bytes.fromhex("3e55063b0d2de679c76cbccbaa4bf85435372e92")),
],
},
{
"s": bytes.fromhex("243f6a8885a308d313198a2e03707344a4093822"),
"n": 4,
"t": 1,
"c": [
bytes.fromhex("243f6a8885a308d313198a2e03707344a4093822"),
],
"shares": [
(1, bytes.fromhex("243f6a8885a308d313198a2e03707344a4093822")),
(2, bytes.fromhex("243f6a8885a308d313198a2e03707344a4093822")),
(3, bytes.fromhex("243f6a8885a308d313198a2e03707344a4093822")),
(4, bytes.fromhex("243f6a8885a308d313198a2e03707344a4093822")),
],
},
{
"s": bytes.fromhex("243f6a8885a308d313198a2e03707344a4093822"),
"n": 4,
"t": 2,
"c": [
bytes.fromhex("243f6a8885a308d313198a2e03707344a4093822"),
bytes.fromhex("b7e151628aed2a6abf7158802933783373a292e3"),
],
"shares": [
(1, bytes.fromhex("93de3bea0f4e22b9ac68d2ae2a430b77d7abaac1")),
(2, bytes.fromhex("51e6c84c8a625c0776fb3a3551168322425607ff")),
(3, bytes.fromhex("e607992e008f766dc98a62b57825fb1131f4951c")),
(4, bytes.fromhex("ce96351b9b3aa060d9c6f118a7bc888873b74683")),
],
},
{
"s": bytes.fromhex("243f6a8885a308d313198a2e03707344a4093822"),
"n": 4,
"t": 3,
"c": [
bytes.fromhex("243f6a8885a308d313198a2e03707344a4093822"),
bytes.fromhex("b7e151628aed2a6abf7158809cf4f3c762e7160f"),
bytes.fromhex("38b4da56a784d9045190cfefa00c8fe35cf55b72"),
],
"shares": [
(1, bytes.fromhex("ab6ae1bca8cafbbdfdf81d413f880f609a1b755f")),
(2, bytes.fromhex("b1008d0f20441517298d2ba496b384700b2563ef")),
(3, bytes.fromhex("3e55063b0d2de679c76cbccbaa4bf85435372e92")),
(4, bytes.fromhex("63233a0c05a29f20be05b56aab4dbac78071a77f")),
],
},
{
"s": bytes.fromhex("243f6a8885a308d313198a2e03707344a4093822"),
"n": 4,
"t": 4,
"c": [
bytes.fromhex("243f6a8885a308d313198a2e03707344a4093822"),
bytes.fromhex("b7e151628aed2a6abf7158809cf4f3c762e7160f"),
bytes.fromhex("38b4da56a784d9045190cfef324e7738926cfbe5"),
bytes.fromhex("f4bf8d8d8c31d763da06c80aa76e97088bcd17bf"),
],
"shares": [
(1, bytes.fromhex("5fd56c3124fb2cde27fed54b0aa460b3df4fc277")),
(2, bytes.fromhex("508f890b2cd7f722a3bd31f4a7fd9d712a456d0a")),
(3, bytes.fromhex("25ca8cb18829177e7d4ee8ad5156d1b6469be5f0")),
(4, bytes.fromhex("2a371a2c6556ce93829e65dc06df59ee6fa9fed8")),
],
},
{
"s": bytes.fromhex("243f6a8885a308d313198a2e03707344a4093822"),
"n": 5,
"t": 1,
"c": [
bytes.fromhex("243f6a8885a308d313198a2e03707344a4093822"),
],
"shares": [
(1, bytes.fromhex("243f6a8885a308d313198a2e03707344a4093822")),
(2, bytes.fromhex("243f6a8885a308d313198a2e03707344a4093822")),
(3, bytes.fromhex("243f6a8885a308d313198a2e03707344a4093822")),
(4, bytes.fromhex("243f6a8885a308d313198a2e03707344a4093822")),
(5, bytes.fromhex("243f6a8885a308d313198a2e03707344a4093822")),
],
},
{
"s": bytes.fromhex("243f6a8885a308d313198a2e03707344a4093822"),
"n": 5,
"t": 2,
"c": [
bytes.fromhex("243f6a8885a308d313198a2e03707344a4093822"),
bytes.fromhex("b7e151628aed2a6abf7158802933783373a292e3"),
],
"shares": [
(1, bytes.fromhex("93de3bea0f4e22b9ac68d2ae2a430b77d7abaac1")),
(2, bytes.fromhex("51e6c84c8a625c0776fb3a3551168322425607ff")),
(3, bytes.fromhex("e607992e008f766dc98a62b57825fb1131f4951c")),
(4, bytes.fromhex("ce96351b9b3aa060d9c6f118a7bc888873b74683")),
(5, bytes.fromhex("7977647911d78a0a66b7a9988e8ff0bb0015d460")),
],
},
{
"s": bytes.fromhex("243f6a8885a308d313198a2e03707344a4093822"),
"n": 5,
"t": 3,
"c": [
bytes.fromhex("243f6a8885a308d313198a2e03707344a4093822"),
bytes.fromhex("b7e151628aed2a6abf7158809cf4f3c762e7160f"),
bytes.fromhex("38b4da56a784d9045190cfefa00c8fe35cf55b72"),
],
"shares": [
(1, bytes.fromhex("ab6ae1bca8cafbbdfdf81d413f880f609a1b755f")),
(2, bytes.fromhex("b1008d0f20441517298d2ba496b384700b2563ef")),
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0
| 11
|
073bf7c68487ccfe049150cac5ba9e659b7333ba
| 38,096
|
py
|
Python
|
hospital/migrations/0006_auto_20211129_1757.py
|
romsha28/hospital_python
|
1bb86266223df5084321917169156aaec1c5e318
|
[
"Apache-2.0"
] | null | null | null |
hospital/migrations/0006_auto_20211129_1757.py
|
romsha28/hospital_python
|
1bb86266223df5084321917169156aaec1c5e318
|
[
"Apache-2.0"
] | 1
|
2021-10-18T08:56:11.000Z
|
2021-10-18T08:56:11.000Z
|
hospital/migrations/0006_auto_20211129_1757.py
|
romsha28/hospital_python
|
1bb86266223df5084321917169156aaec1c5e318
|
[
"Apache-2.0"
] | null | null | null |
# Generated by Django 3.2.8 on 2021-11-29 12:27
from django.db import migrations, models
import phonenumber_field.modelfields
class Migration(migrations.Migration):
dependencies = [
('hospital', '0005_alter_appointments_patient_id'),
]
operations = [
migrations.AlterField(
model_name='appmodels',
name='created_by',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='appmodels',
name='deleted_at',
field=models.DateTimeField(blank=True, null=True),
),
migrations.AlterField(
model_name='appmodels',
name='deleted_by',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='appmodels',
name='description',
field=models.TextField(blank=True, null=True),
),
migrations.AlterField(
model_name='appmodels',
name='name',
field=models.CharField(blank=True, default='heading text', max_length=50, null=True),
),
migrations.AlterField(
model_name='appmodels',
name='primary_image',
field=models.CharField(blank=True, max_length=150, null=True),
),
migrations.AlterField(
model_name='appmodels',
name='slug',
field=models.CharField(blank=True, max_length=100, null=True),
),
migrations.AlterField(
model_name='appmodels',
name='sort_id',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='appmodels',
name='updated_at',
field=models.DateTimeField(blank=True, null=True),
),
migrations.AlterField(
model_name='appmodels',
name='updated_by',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='appointments',
name='address',
field=models.CharField(blank=True, max_length=100, null=True),
),
migrations.AlterField(
model_name='appointments',
name='address2',
field=models.CharField(blank=True, max_length=100, null=True),
),
migrations.AlterField(
model_name='appointments',
name='appointment_id',
field=models.CharField(blank=True, max_length=100, null=True),
),
migrations.AlterField(
model_name='appointments',
name='blood_group',
field=models.CharField(blank=True, max_length=50, null=True),
),
migrations.AlterField(
model_name='appointments',
name='city',
field=models.CharField(blank=True, max_length=100, null=True),
),
migrations.AlterField(
model_name='appointments',
name='country',
field=models.CharField(blank=True, max_length=100, null=True),
),
migrations.AlterField(
model_name='appointments',
name='created_by',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='appointments',
name='deleted_at',
field=models.DateTimeField(blank=True, null=True),
),
migrations.AlterField(
model_name='appointments',
name='deleted_by',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='appointments',
name='description',
field=models.TextField(blank=True, null=True),
),
migrations.AlterField(
model_name='appointments',
name='dob',
field=models.DateField(blank=True, null=True),
),
migrations.AlterField(
model_name='appointments',
name='doctor_appointment_at',
field=models.DateTimeField(blank=True, null=True),
),
migrations.AlterField(
model_name='appointments',
name='doctor_id',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='appointments',
name='email',
field=models.EmailField(blank=True, max_length=254, null=True),
),
migrations.AlterField(
model_name='appointments',
name='illness_information',
field=models.CharField(blank=True, max_length=100, null=True),
),
migrations.AlterField(
model_name='appointments',
name='locality',
field=models.CharField(blank=True, max_length=100, null=True),
),
migrations.AlterField(
model_name='appointments',
name='mobile',
field=models.CharField(blank=True, max_length=20, null=True),
),
migrations.AlterField(
model_name='appointments',
name='mobile1',
field=models.CharField(blank=True, max_length=20, null=True),
),
migrations.AlterField(
model_name='appointments',
name='pincode',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='appointments',
name='state',
field=models.CharField(blank=True, max_length=100, null=True),
),
migrations.AlterField(
model_name='appointments',
name='title',
field=models.CharField(blank=True, default='General user as patient', max_length=120, null=True),
),
migrations.AlterField(
model_name='appointments',
name='translation_id',
field=models.CharField(blank=True, max_length=100, null=True),
),
migrations.AlterField(
model_name='appointments',
name='updated_at',
field=models.DateTimeField(blank=True, null=True),
),
migrations.AlterField(
model_name='appointments',
name='updated_by',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='banners',
name='created_by',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='banners',
name='deleted_at',
field=models.DateTimeField(blank=True, null=True),
),
migrations.AlterField(
model_name='banners',
name='deleted_by',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='banners',
name='description',
field=models.TextField(blank=True, null=True),
),
migrations.AlterField(
model_name='banners',
name='long_description',
field=models.TextField(blank=True, null=True),
),
migrations.AlterField(
model_name='banners',
name='primary_image',
field=models.CharField(blank=True, max_length=150, null=True),
),
migrations.AlterField(
model_name='banners',
name='slug',
field=models.CharField(blank=True, max_length=150, null=True),
),
migrations.AlterField(
model_name='banners',
name='sort_id',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='banners',
name='title',
field=models.CharField(blank=True, default='heading text', max_length=200, null=True),
),
migrations.AlterField(
model_name='banners',
name='updated_at',
field=models.DateTimeField(blank=True, null=True),
),
migrations.AlterField(
model_name='banners',
name='updated_by',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='blogs',
name='created_by',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='blogs',
name='deleted_at',
field=models.DateTimeField(blank=True, null=True),
),
migrations.AlterField(
model_name='blogs',
name='deleted_by',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='blogs',
name='description',
field=models.TextField(blank=True, null=True),
),
migrations.AlterField(
model_name='blogs',
name='long_description',
field=models.TextField(blank=True, null=True),
),
migrations.AlterField(
model_name='blogs',
name='primary_image',
field=models.CharField(blank=True, max_length=150, null=True),
),
migrations.AlterField(
model_name='blogs',
name='slug',
field=models.CharField(blank=True, max_length=150, null=True),
),
migrations.AlterField(
model_name='blogs',
name='sort_id',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='blogs',
name='title',
field=models.CharField(blank=True, default='heading text', max_length=200, null=True),
),
migrations.AlterField(
model_name='blogs',
name='updated_at',
field=models.DateTimeField(blank=True, null=True),
),
migrations.AlterField(
model_name='blogs',
name='updated_by',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='categories',
name='created_by',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='categories',
name='deleted_at',
field=models.DateTimeField(blank=True, null=True),
),
migrations.AlterField(
model_name='categories',
name='deleted_by',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='categories',
name='description',
field=models.TextField(blank=True, null=True),
),
migrations.AlterField(
model_name='categories',
name='name',
field=models.CharField(blank=True, default='heading text', max_length=50, null=True),
),
migrations.AlterField(
model_name='categories',
name='primary_image',
field=models.CharField(blank=True, max_length=150, null=True),
),
migrations.AlterField(
model_name='categories',
name='slug',
field=models.CharField(blank=True, max_length=100, null=True),
),
migrations.AlterField(
model_name='categories',
name='sort_id',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='categories',
name='updated_at',
field=models.DateTimeField(blank=True, null=True),
),
migrations.AlterField(
model_name='categories',
name='updated_by',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='contacts',
name='created_by',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='contacts',
name='deleted_at',
field=models.DateTimeField(blank=True, null=True),
),
migrations.AlterField(
model_name='contacts',
name='deleted_by',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='contacts',
name='email',
field=models.EmailField(blank=True, max_length=150, null=True),
),
migrations.AlterField(
model_name='contacts',
name='message',
field=models.TextField(blank=True, null=True),
),
migrations.AlterField(
model_name='contacts',
name='subject',
field=models.CharField(blank=True, default='Contact For Best Service', max_length=150, null=True),
),
migrations.AlterField(
model_name='contacts',
name='updated_at',
field=models.DateTimeField(blank=True, null=True),
),
migrations.AlterField(
model_name='contacts',
name='updated_by',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='historylogs',
name='about',
field=models.TextField(blank=True, null=True),
),
migrations.AlterField(
model_name='historylogs',
name='address',
field=models.TextField(blank=True, null=True),
),
migrations.AlterField(
model_name='historylogs',
name='browser',
field=models.CharField(blank=True, max_length=50, null=True),
),
migrations.AlterField(
model_name='historylogs',
name='city',
field=models.CharField(blank=True, max_length=50, null=True),
),
migrations.AlterField(
model_name='historylogs',
name='country',
field=models.CharField(blank=True, max_length=50, null=True),
),
migrations.AlterField(
model_name='historylogs',
name='created_by',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='historylogs',
name='device',
field=models.CharField(blank=True, max_length=50, null=True),
),
migrations.AlterField(
model_name='historylogs',
name='email',
field=models.EmailField(blank=True, max_length=150, null=True),
),
migrations.AlterField(
model_name='historylogs',
name='ip_address',
field=models.CharField(blank=True, max_length=50, null=True),
),
migrations.AlterField(
model_name='historylogs',
name='mobile',
field=phonenumber_field.modelfields.PhoneNumberField(blank=True, max_length=128, null=True, region='IN'),
),
migrations.AlterField(
model_name='historylogs',
name='model_name',
field=models.CharField(blank=True, default='heading text', max_length=50, null=True),
),
migrations.AlterField(
model_name='historylogs',
name='name',
field=models.CharField(blank=True, default='heading text', max_length=50, null=True),
),
migrations.AlterField(
model_name='historylogs',
name='os',
field=models.CharField(blank=True, max_length=50, null=True),
),
migrations.AlterField(
model_name='historylogs',
name='primary_image',
field=models.CharField(blank=True, max_length=150, null=True),
),
migrations.AlterField(
model_name='historylogs',
name='slug',
field=models.CharField(blank=True, max_length=100, null=True),
),
migrations.AlterField(
model_name='historylogs',
name='sort_id',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='historylogs',
name='user_name',
field=models.CharField(blank=True, default='heading text', max_length=50, null=True),
),
migrations.AlterField(
model_name='paymentgetways',
name='created_by',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='paymentgetways',
name='deleted_at',
field=models.DateTimeField(blank=True, null=True),
),
migrations.AlterField(
model_name='paymentgetways',
name='deleted_by',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='paymentgetways',
name='description',
field=models.TextField(blank=True, null=True),
),
migrations.AlterField(
model_name='paymentgetways',
name='name',
field=models.CharField(blank=True, default='heading text', max_length=50, null=True),
),
migrations.AlterField(
model_name='paymentgetways',
name='primary_image',
field=models.CharField(blank=True, max_length=150, null=True),
),
migrations.AlterField(
model_name='paymentgetways',
name='slug',
field=models.CharField(blank=True, max_length=100, null=True),
),
migrations.AlterField(
model_name='paymentgetways',
name='sort_id',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='paymentgetways',
name='updated_at',
field=models.DateTimeField(blank=True, null=True),
),
migrations.AlterField(
model_name='paymentgetways',
name='updated_by',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='paymentmodes',
name='created_by',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='paymentmodes',
name='deleted_at',
field=models.DateTimeField(blank=True, null=True),
),
migrations.AlterField(
model_name='paymentmodes',
name='deleted_by',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='paymentmodes',
name='description',
field=models.TextField(blank=True, null=True),
),
migrations.AlterField(
model_name='paymentmodes',
name='name',
field=models.CharField(blank=True, default='heading text', max_length=50, null=True),
),
migrations.AlterField(
model_name='paymentmodes',
name='primary_image',
field=models.CharField(blank=True, max_length=150, null=True),
),
migrations.AlterField(
model_name='paymentmodes',
name='slug',
field=models.CharField(blank=True, max_length=100, null=True),
),
migrations.AlterField(
model_name='paymentmodes',
name='sort_id',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='paymentmodes',
name='updated_at',
field=models.DateTimeField(blank=True, null=True),
),
migrations.AlterField(
model_name='paymentmodes',
name='updated_by',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='subcategories',
name='Category_id',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='subcategories',
name='created_by',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='subcategories',
name='deleted_at',
field=models.DateTimeField(blank=True, null=True),
),
migrations.AlterField(
model_name='subcategories',
name='deleted_by',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='subcategories',
name='description',
field=models.TextField(blank=True, null=True),
),
migrations.AlterField(
model_name='subcategories',
name='name',
field=models.CharField(blank=True, default='heading text', max_length=50, null=True),
),
migrations.AlterField(
model_name='subcategories',
name='primary_image',
field=models.CharField(blank=True, max_length=150, null=True),
),
migrations.AlterField(
model_name='subcategories',
name='slug',
field=models.CharField(blank=True, max_length=100, null=True),
),
migrations.AlterField(
model_name='subcategories',
name='sort_id',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='subcategories',
name='updated_at',
field=models.DateTimeField(blank=True, null=True),
),
migrations.AlterField(
model_name='subcategories',
name='updated_by',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='subscriptionhistory',
name='break_at',
field=models.DateTimeField(blank=True, null=True),
),
migrations.AlterField(
model_name='subscriptionhistory',
name='completed_appointments',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='subscriptionhistory',
name='created_by',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='subscriptionhistory',
name='deleted_at',
field=models.DateTimeField(blank=True, null=True),
),
migrations.AlterField(
model_name='subscriptionhistory',
name='deleted_by',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='subscriptionhistory',
name='doctor_id',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='subscriptionhistory',
name='expiry_at',
field=models.DateTimeField(blank=True, null=True),
),
migrations.AlterField(
model_name='subscriptionhistory',
name='expiry_in_months',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='subscriptionhistory',
name='join_at',
field=models.DateTimeField(blank=True, null=True),
),
migrations.AlterField(
model_name='subscriptionhistory',
name='joiner_id',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='subscriptionhistory',
name='order_id',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='subscriptionhistory',
name='payment_status',
field=models.CharField(blank=True, max_length=50, null=True),
),
migrations.AlterField(
model_name='subscriptionhistory',
name='plan_discount',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='subscriptionhistory',
name='plan_id',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='subscriptionhistory',
name='remarks',
field=models.TextField(blank=True, null=True),
),
migrations.AlterField(
model_name='subscriptionhistory',
name='sort_id',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='subscriptionhistory',
name='total_appointments',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='subscriptionhistory',
name='updated_at',
field=models.DateTimeField(blank=True, null=True),
),
migrations.AlterField(
model_name='subscriptionhistory',
name='updated_by',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='subscriptionplans',
name='created_by',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='subscriptionplans',
name='day_appointments',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='subscriptionplans',
name='deleted_at',
field=models.DateTimeField(blank=True, null=True),
),
migrations.AlterField(
model_name='subscriptionplans',
name='deleted_by',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='subscriptionplans',
name='description',
field=models.TextField(blank=True, null=True),
),
migrations.AlterField(
model_name='subscriptionplans',
name='discount',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='subscriptionplans',
name='doctor_id',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='subscriptionplans',
name='expiry_in_months',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='subscriptionplans',
name='name',
field=models.CharField(blank=True, default='Basic Panal', max_length=100, null=True),
),
migrations.AlterField(
model_name='subscriptionplans',
name='plan_key',
field=models.CharField(blank=True, max_length=50, null=True),
),
migrations.AlterField(
model_name='subscriptionplans',
name='primary_image',
field=models.CharField(blank=True, max_length=150, null=True),
),
migrations.AlterField(
model_name='subscriptionplans',
name='slug',
field=models.CharField(blank=True, max_length=100, null=True),
),
migrations.AlterField(
model_name='subscriptionplans',
name='sort_id',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='subscriptionplans',
name='start_at',
field=models.DateTimeField(blank=True, null=True),
),
migrations.AlterField(
model_name='subscriptionplans',
name='stop_at',
field=models.DateTimeField(blank=True, null=True),
),
migrations.AlterField(
model_name='subscriptionplans',
name='title',
field=models.CharField(blank=True, max_length=150, null=True),
),
migrations.AlterField(
model_name='subscriptionplans',
name='total_appointments',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='subscriptionplans',
name='updated_at',
field=models.DateTimeField(blank=True, null=True),
),
migrations.AlterField(
model_name='subscriptionplans',
name='updated_by',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='subscriptionplans',
name='weekly_appointments',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='translations',
name='created_by',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='translations',
name='deleted_at',
field=models.DateTimeField(blank=True, null=True),
),
migrations.AlterField(
model_name='translations',
name='deleted_by',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='translations',
name='description',
field=models.TextField(blank=True, null=True),
),
migrations.AlterField(
model_name='translations',
name='error_code',
field=models.CharField(blank=True, max_length=50, null=True),
),
migrations.AlterField(
model_name='translations',
name='payment_getway',
field=models.CharField(blank=True, max_length=50, null=True),
),
migrations.AlterField(
model_name='translations',
name='payment_method',
field=models.CharField(blank=True, max_length=50, null=True),
),
migrations.AlterField(
model_name='translations',
name='payment_mode',
field=models.CharField(blank=True, max_length=50, null=True),
),
migrations.AlterField(
model_name='translations',
name='payment_note',
field=models.CharField(blank=True, max_length=200, null=True),
),
migrations.AlterField(
model_name='translations',
name='payment_status',
field=models.CharField(blank=True, max_length=50, null=True),
),
migrations.AlterField(
model_name='translations',
name='recever_email',
field=models.EmailField(blank=True, max_length=100, null=True),
),
migrations.AlterField(
model_name='translations',
name='recever_name',
field=models.CharField(blank=True, max_length=100, null=True),
),
migrations.AlterField(
model_name='translations',
name='recever_user_id',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='translations',
name='reference_code',
field=models.CharField(blank=True, max_length=100, null=True),
),
migrations.AlterField(
model_name='translations',
name='sender_email',
field=models.EmailField(blank=True, max_length=100, null=True),
),
migrations.AlterField(
model_name='translations',
name='sender_name',
field=models.CharField(blank=True, max_length=100, null=True),
),
migrations.AlterField(
model_name='translations',
name='sender_user_id',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='translations',
name='updated_at',
field=models.DateTimeField(blank=True, null=True),
),
migrations.AlterField(
model_name='translations',
name='updated_by',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='treatmentcategories',
name='created_by',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='treatmentcategories',
name='deleted_at',
field=models.DateTimeField(blank=True, null=True),
),
migrations.AlterField(
model_name='treatmentcategories',
name='deleted_by',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='treatmentcategories',
name='description',
field=models.TextField(blank=True, null=True),
),
migrations.AlterField(
model_name='treatmentcategories',
name='name',
field=models.CharField(blank=True, default='heading text', max_length=50, null=True),
),
migrations.AlterField(
model_name='treatmentcategories',
name='primary_image',
field=models.CharField(blank=True, max_length=150, null=True),
),
migrations.AlterField(
model_name='treatmentcategories',
name='slug',
field=models.CharField(blank=True, max_length=100, null=True),
),
migrations.AlterField(
model_name='treatmentcategories',
name='sort_id',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='treatmentcategories',
name='treatment_id',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='treatmentcategories',
name='updated_at',
field=models.DateTimeField(blank=True, null=True),
),
migrations.AlterField(
model_name='treatmentcategories',
name='updated_by',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='treatments',
name='created_by',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='treatments',
name='deleted_at',
field=models.DateTimeField(blank=True, null=True),
),
migrations.AlterField(
model_name='treatments',
name='deleted_by',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='treatments',
name='description',
field=models.TextField(blank=True, null=True),
),
migrations.AlterField(
model_name='treatments',
name='name',
field=models.CharField(blank=True, default='heading text', max_length=50, null=True),
),
migrations.AlterField(
model_name='treatments',
name='primary_image',
field=models.FileField(blank=True, null=True, upload_to='treatments/', verbose_name='primary_image'),
),
migrations.AlterField(
model_name='treatments',
name='slug',
field=models.CharField(blank=True, max_length=100, null=True),
),
migrations.AlterField(
model_name='treatments',
name='sort_id',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='treatments',
name='updated_at',
field=models.DateTimeField(blank=True, null=True),
),
migrations.AlterField(
model_name='treatments',
name='updated_by',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='userslogs',
name='browser',
field=models.CharField(blank=True, max_length=50, null=True),
),
migrations.AlterField(
model_name='userslogs',
name='created_by',
field=models.IntegerField(blank=True, null=True),
),
migrations.AlterField(
model_name='userslogs',
name='device',
field=models.CharField(blank=True, max_length=50, null=True),
),
migrations.AlterField(
model_name='userslogs',
name='email',
field=models.EmailField(blank=True, max_length=150, null=True),
),
migrations.AlterField(
model_name='userslogs',
name='ip_address',
field=models.CharField(blank=True, max_length=50, null=True),
),
migrations.AlterField(
model_name='userslogs',
name='mobile',
field=phonenumber_field.modelfields.PhoneNumberField(blank=True, max_length=128, null=True, region='IN'),
),
migrations.AlterField(
model_name='userslogs',
name='name',
field=models.CharField(blank=True, default='heading text', max_length=50, null=True),
),
migrations.AlterField(
model_name='userslogs',
name='os',
field=models.CharField(blank=True, max_length=50, null=True),
),
]
| 35.939623
| 117
| 0.560453
| 3,411
| 38,096
| 6.128994
| 0.044269
| 0.090405
| 0.249928
| 0.289917
| 0.971348
| 0.970248
| 0.963695
| 0.962068
| 0.938534
| 0.929877
| 0
| 0.008799
| 0.32279
| 38,096
| 1,059
| 118
| 35.97356
| 0.801543
| 0.001181
| 0
| 0.94302
| 1
| 0
| 0.126337
| 0.002024
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.001899
| 0
| 0.004748
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 10
|
4ad4a22f31a992de5f9dcb0367d551b11f26c07d
| 7,873
|
py
|
Python
|
tests/test_probabilities.py
|
alercebroker/ztf-api-apf
|
916f02009a07e43d6ed26bdb1e2b0ec7f41321f1
|
[
"Apache-2.0"
] | null | null | null |
tests/test_probabilities.py
|
alercebroker/ztf-api-apf
|
916f02009a07e43d6ed26bdb1e2b0ec7f41321f1
|
[
"Apache-2.0"
] | null | null | null |
tests/test_probabilities.py
|
alercebroker/ztf-api-apf
|
916f02009a07e43d6ed26bdb1e2b0ec7f41321f1
|
[
"Apache-2.0"
] | null | null | null |
from api.resources.probabilities.probabilities import Probabilities
from db_plugins.db.sql.models import Probability, Taxonomy
taxonomy = [
Taxonomy(
classifier_name="lc_classifier",
classes=[
"SNIa",
"SNIbc",
"SNII",
"SLSN",
"QSO",
"AGN",
"Blazar",
"CV/Nova",
"YSO",
"LPV",
"E",
"DSCT",
"RRL",
"CEP",
"Periodic-Other",
],
),
Taxonomy(
classifier_name="lc_classifier_top",
classes=["Transient", "Stochastic", "Periodic"],
),
Taxonomy(
classifier_name="lc_classifier_transient",
classes=["SNIa", "SNIbc", "SNII", "SLSN"],
),
Taxonomy(
classifier_name="lc_classifier_stochastic",
classes=["QSO", "AGN", "Blazar", "CV/Nova", "YSO"],
),
Taxonomy(
classifier_name="lc_classifier_periodic",
classes=["LPV", "E", "DSCT", "RRL", "CEP", "Periodic-Other"],
),
Taxonomy(
classifier_name="stamp_classifier",
classes=["NS", "ANG", "SV", "dioretsa", "sugob"],
),
Taxonomy(
classifier_name="stamp_classifier",
classes=["SN", "AGN", "VS", "asteroid", "bogus"],
),
]
def test_get_probabilities(client):
r = client.get("objects/ZTF1/probabilities")
assert r.status_code == 200
assert isinstance(r.json, list)
def test_get_probabilities_not_found(client):
r = client.get("objects/ZTF2/probabilities")
assert r.status_code == 404
def test_order_probs():
probs = [
Probability(classifier_name="lc_classifier", class_name="AGN"),
Probability(classifier_name="lc_classifier", class_name="Blazar"),
Probability(classifier_name="lc_classifier", class_name="CEP"),
Probability(classifier_name="lc_classifier", class_name="CV/Nova"),
Probability(classifier_name="lc_classifier", class_name="DSCT"),
Probability(classifier_name="lc_classifier", class_name="E"),
Probability(classifier_name="lc_classifier", class_name="LPV"),
Probability(
classifier_name="lc_classifier", class_name="Periodic-Other"
),
Probability(classifier_name="lc_classifier", class_name="QSO"),
Probability(classifier_name="lc_classifier", class_name="RRL"),
Probability(classifier_name="lc_classifier", class_name="SLSN"),
Probability(classifier_name="lc_classifier", class_name="SNIa"),
Probability(classifier_name="lc_classifier", class_name="SNIbc"),
Probability(classifier_name="lc_classifier", class_name="SNII"),
Probability(classifier_name="lc_classifier", class_name="YSO"),
Probability(
classifier_name="lc_classifier_periodic", class_name="CEP"
),
Probability(
classifier_name="lc_classifier_periodic", class_name="DSCT"
),
Probability(classifier_name="lc_classifier_periodic", class_name="E"),
Probability(
classifier_name="lc_classifier_periodic", class_name="LPV"
),
Probability(
classifier_name="lc_classifier_periodic",
class_name="Periodic-Other",
),
Probability(
classifier_name="lc_classifier_periodic", class_name="RRL"
),
Probability(
classifier_name="lc_classifier_stochastic", class_name="AGN"
),
Probability(
classifier_name="lc_classifier_stochastic", class_name="Blazar"
),
Probability(
classifier_name="lc_classifier_stochastic", class_name="CV/Nova"
),
Probability(
classifier_name="lc_classifier_stochastic", class_name="QSO"
),
Probability(
classifier_name="lc_classifier_stochastic", class_name="YSO"
),
Probability(
classifier_name="lc_classifier_top", class_name="Periodic"
),
Probability(
classifier_name="lc_classifier_top", class_name="Stochastic"
),
Probability(
classifier_name="lc_classifier_top", class_name="Transient"
),
Probability(classifier_name="stamp_classifier", class_name="AGN"),
Probability(classifier_name="stamp_classifier", class_name="asteroid"),
Probability(classifier_name="stamp_classifier", class_name="bogus"),
Probability(classifier_name="stamp_classifier", class_name="SN"),
Probability(classifier_name="stamp_classifier", class_name="VS"),
]
probabilities_resource = Probabilities()
ordered = probabilities_resource.order_probs(probs, taxonomy)
expected = [
Probability(classifier_name="lc_classifier", class_name="SNIa"),
Probability(
classifier_name="lc_classifier_periodic", class_name="LPV"
),
Probability(
classifier_name="lc_classifier_stochastic", class_name="QSO"
),
Probability(
classifier_name="lc_classifier_top", class_name="Transient"
),
Probability(classifier_name="stamp_classifier", class_name="SN"),
Probability(classifier_name="lc_classifier", class_name="SNIbc"),
Probability(classifier_name="lc_classifier_periodic", class_name="E"),
Probability(
classifier_name="lc_classifier_stochastic", class_name="AGN"
),
Probability(
classifier_name="lc_classifier_top", class_name="Stochastic"
),
Probability(classifier_name="stamp_classifier", class_name="AGN"),
Probability(classifier_name="lc_classifier", class_name="SNII"),
Probability(
classifier_name="lc_classifier_periodic", class_name="DSCT"
),
Probability(
classifier_name="lc_classifier_stochastic", class_name="Blazar"
),
Probability(
classifier_name="lc_classifier_top", class_name="Periodic"
),
Probability(classifier_name="stamp_classifier", class_name="VS"),
Probability(classifier_name="lc_classifier", class_name="SLSN"),
Probability(
classifier_name="lc_classifier_periodic", class_name="RRL"
),
Probability(
classifier_name="lc_classifier_stochastic", class_name="CV/Nova"
),
Probability(classifier_name="stamp_classifier", class_name="asteroid"),
Probability(classifier_name="lc_classifier", class_name="QSO"),
Probability(
classifier_name="lc_classifier_periodic", class_name="CEP"
),
Probability(
classifier_name="lc_classifier_stochastic", class_name="YSO"
),
Probability(classifier_name="stamp_classifier", class_name="bogus"),
Probability(classifier_name="lc_classifier", class_name="AGN"),
Probability(
classifier_name="lc_classifier_periodic",
class_name="Periodic-Other",
),
Probability(classifier_name="lc_classifier", class_name="Blazar"),
Probability(classifier_name="lc_classifier", class_name="CV/Nova"),
Probability(classifier_name="lc_classifier", class_name="YSO"),
Probability(classifier_name="lc_classifier", class_name="LPV"),
Probability(classifier_name="lc_classifier", class_name="E"),
Probability(classifier_name="lc_classifier", class_name="DSCT"),
Probability(classifier_name="lc_classifier", class_name="RRL"),
Probability(classifier_name="lc_classifier", class_name="CEP"),
Probability(
classifier_name="lc_classifier", class_name="Periodic-Other"
),
]
for i, proba in enumerate(ordered):
assert proba.class_name == expected[i].class_name
assert proba.classifier_name == expected[i].classifier_name
| 39.169154
| 79
| 0.638257
| 767
| 7,873
| 6.202086
| 0.09648
| 0.226613
| 0.357368
| 0.344335
| 0.887324
| 0.819424
| 0.796721
| 0.796721
| 0.785789
| 0.783057
| 0
| 0.001335
| 0.238664
| 7,873
| 200
| 80
| 39.365
| 0.792292
| 0
| 0
| 0.748691
| 0
| 0
| 0.237267
| 0.079385
| 0
| 0
| 0
| 0
| 0.026178
| 1
| 0.015707
| false
| 0
| 0.010471
| 0
| 0.026178
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 10
|
ab0592ec650115a828de5f164f69779c6cdcdada
| 63,910
|
py
|
Python
|
nnunet/network_architecture/generic_MedT.py
|
Magnety/nnUNet
|
f07e6fdf191377550c57bcdc8859798486f60443
|
[
"Apache-2.0"
] | null | null | null |
nnunet/network_architecture/generic_MedT.py
|
Magnety/nnUNet
|
f07e6fdf191377550c57bcdc8859798486f60443
|
[
"Apache-2.0"
] | null | null | null |
nnunet/network_architecture/generic_MedT.py
|
Magnety/nnUNet
|
f07e6fdf191377550c57bcdc8859798486f60443
|
[
"Apache-2.0"
] | null | null | null |
from torch.nn import CrossEntropyLoss, Dropout, Softmax, Linear, Conv3d, LayerNorm
import math
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torch
import numpy as np
import copy
import ml_collections
from nnunet.network_architecture.initialization import InitWeights_He
from nnunet.utilities.nd_softmax import softmax_helper
from nnunet.network_architecture.neural_network import SegmentationNetwork
class qkv_transform(nn.Conv1d):
"""Conv1d for qkv_transform"""
def conv1x1x1(in_planes, out_planes, stride=1):
"""1x1 convolution"""
return nn.Conv3d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
class AxialAttention(nn.Module):
def __init__(self, in_planes, out_planes, groups=4, kernel_size=(8, 16, 32),
stride=(1, 1, 1), bias=False, width=0):
# in_planes = width =64
# out_planes = width = 64
# groups = 8
assert (in_planes % groups == 0) and (out_planes % groups == 0)
super(AxialAttention, self).__init__()
self.in_planes = in_planes # planes
self.out_planes = out_planes # planes
self.groups = groups # 8
self.group_planes = out_planes // groups # 8
self.kernel_size = kernel_size
self.stride = stride
self.bias = bias
self.width = width
# Multi-head self attention
self.qkv_transform = qkv_transform(in_planes, out_planes * 2, kernel_size=1, stride=1, padding=0, bias=False)
self.bn_qkv = nn.BatchNorm1d(out_planes * 2)
self.bn_similarity = nn.BatchNorm2d(groups * 3)
self.bn_output = nn.BatchNorm1d(out_planes * 2)
# Position embedding
self.relative = nn.Parameter(torch.randn(self.group_planes * 2, kernel_size[2 - self.width] * 2 - 1),
requires_grad=True)
query_index = torch.arange(kernel_size[2 - self.width]).unsqueeze(0)
key_index = torch.arange(kernel_size[2 - self.width]).unsqueeze(1)
relative_index = key_index - query_index + kernel_size[2 - self.width] - 1
self.register_buffer('flatten_index', relative_index.view(-1))
if stride != (1, 1, 1):
self.pooling = nn.AvgPool3d(stride, stride=stride)
self.reset_parameters()
def forward(self, x):
# pdb.set_trace()
# N C L H W
# ##print("x.shape:",x.shape)
if self.width == 0: # length
x = x.permute(0, 2, 3, 1, 4) # N, L, H ,C, W
elif self.width == 1: # height
x = x.permute(0, 4, 2, 1, 3) # N, W, L, C, H
else: # width
x = x.permute(0, 3, 4, 1, 2) # N, H, W, C, L
N, W, L, C, H = x.shape
x = x.contiguous().view(N * W * L, C, H)
# ##print("x.shape:",x.shape)
# Transformations
qkv = self.bn_qkv(self.qkv_transform(x))
# ##print("qkv.shape:",qkv.shape)
q, k, v = torch.split(qkv.reshape(N * W * L, self.groups, self.group_planes * 2, H),
[self.group_planes // 2, self.group_planes // 2, self.group_planes], dim=2)
# ##print("q.shape:",q.shape)
# ##print("k.shape:",k.shape)
# ##print("v.shape:",v.shape)
# Calculate position embedding
all_embeddings = torch.index_select(self.relative, 1, self.flatten_index).view(self.group_planes * 2,
self.kernel_size[2 - self.width],
self.kernel_size[2 - self.width])
# ##print("all_beddings.shape:",all_embeddings.shape)
q_embedding, k_embedding, v_embedding = torch.split(all_embeddings,
[self.group_planes // 2, self.group_planes // 2,
self.group_planes], dim=0)
# ##print("q_bedding.shape:", q_embedding.shape)
# ##print("k_bedding.shape:", k_embedding.shape)
# ##print("v_bedding.shape:", v_embedding.shape)
qr = torch.einsum('bgci,cij->bgij', q, q_embedding)
kr = torch.einsum('bgci,cij->bgij', k, k_embedding).transpose(2, 3)
qk = torch.einsum('bgci, bgcj->bgij', q, k)
# ##print("qr.shape:", qr.shape)
# ##print("kr.shape:", kr.shape)
# ##print("qk.shape:", qk.shape)
stacked_similarity = torch.cat([qk, qr, kr], dim=1)
# ##print("stacked_similarity.shape:", stacked_similarity.shape)
stacked_similarity = self.bn_similarity(stacked_similarity).view(N * W * L, 3, self.groups, H, H)
# ##print("stacked_similarity.shape:", stacked_similarity.shape)
stacked_similarity = stacked_similarity.sum(dim=1)
# ##print("stacked_similarity.shape:", stacked_similarity.shape)
# stacked_similarity = self.bn_qr(qr) + self.bn_kr(kr) + self.bn_qk(qk)
# (N, groups, H, H, W)
similarity = F.softmax(stacked_similarity, dim=3)
# ##print("similarity.shape:", similarity.shape)
sv = torch.einsum('bgij,bgcj->bgci', similarity, v)
# ##print("sv.shape:", sv.shape)
sve = torch.einsum('bgij,cij->bgci', similarity, v_embedding)
# ##print("sve.shape:", sve.shape)
stacked_output = torch.cat([sv, sve], dim=-1).view(N * W * L, self.out_planes * 2, H)
# ##print("stacked_similarity.shape:", stacked_output.shape)
output = self.bn_output(stacked_output).view(N, W, L, self.out_planes, 2, H).sum(dim=-2)
# ##print("output.shape:", output.shape)
if self.width == 0: # length
output = output.permute(0, 3, 1, 2, 4) # N, L, H ,C, W
elif self.width == 1: # height
output = output.permute(0, 3, 2, 4, 1) # N, W, L, C, H
else: # width
output = output.permute(0, 3, 4, 1, 2) # N, H, W, C, L
# ##print("output.shape:", output.shape)
if self.stride != (1, 1, 1):
output = self.pooling(output)
return output
def reset_parameters(self):
self.qkv_transform.weight.data.normal_(0, math.sqrt(1. / self.in_planes))
# nn.init.uniform_(self.relative, -0.1, 0.1)
nn.init.normal_(self.relative, 0., math.sqrt(1. / self.group_planes))
class AxialAttention_dynamic(nn.Module):
def __init__(self, in_planes, out_planes, groups=4, kernel_size=(8, 16, 32),
stride=(1, 1, 1), bias=False, width=0):
assert (in_planes % groups == 0) and (out_planes % groups == 0)
super(AxialAttention_dynamic, self).__init__()
self.in_planes = in_planes
self.out_planes = out_planes
self.groups = groups
self.group_planes = out_planes // groups
self.kernel_size = kernel_size
self.stride = stride
self.bias = bias
self.width = width
# Multi-head self attention
self.qkv_transform = qkv_transform(in_planes, out_planes * 2, kernel_size=1, stride=1,
padding=0, bias=False)
self.bn_qkv = nn.BatchNorm1d(out_planes * 2)
self.bn_similarity = nn.BatchNorm2d(groups * 3)
self.bn_output = nn.BatchNorm1d(out_planes * 2)
# Priority on encoding
## Initial values
self.f_qr = nn.Parameter(torch.tensor(0.1), requires_grad=False)
self.f_kr = nn.Parameter(torch.tensor(0.1), requires_grad=False)
self.f_sve = nn.Parameter(torch.tensor(0.1), requires_grad=False)
self.f_sv = nn.Parameter(torch.tensor(1.0), requires_grad=False)
# Position embedding
self.relative = nn.Parameter(torch.randn(self.group_planes * 2, kernel_size[2 - self.width] * 2 - 1),
requires_grad=True)
query_index = torch.arange(kernel_size[2 - self.width]).unsqueeze(0)
key_index = torch.arange(kernel_size[2 - self.width]).unsqueeze(1)
relative_index = key_index - query_index + kernel_size[2 - self.width] - 1
self.register_buffer('flatten_index', relative_index.view(-1))
if stride != (1, 1, 1):
self.pooling = nn.AvgPool3d(stride, stride=stride)
self.reset_parameters()
# self.print_para()
def forward(self, x):
# ##print("x.shape:",x.shape)
if self.width == 0: # length
x = x.permute(0, 2, 3, 1, 4) # N, L, H ,C, W
elif self.width == 1: # height
x = x.permute(0, 4, 2, 1, 3) # N, W, L, C, H
else: # width
x = x.permute(0, 3, 4, 1, 2) # N, H, W, C, L
# ##print("x.shape:",x.shape)
N, W, L, C, H = x.shape
x = x.contiguous().view(N * W * L, C, H)
# ##print("x.shape:",x.shape)
# Transformations
qkv = self.bn_qkv(self.qkv_transform(x))
# ##print("qkv.shape:",qkv.shape)
q, k, v = torch.split(qkv.reshape(N * W * L, self.groups, self.group_planes * 2, H),
[self.group_planes // 2, self.group_planes // 2, self.group_planes], dim=2)
# ##print("q.shape:",q.shape)
# ##print("k.shape:",k.shape)
# ##print("v.shape:",v.shape)
# Calculate position embedding
all_embeddings = torch.index_select(self.relative, 1, self.flatten_index).view(self.group_planes * 2,
self.kernel_size[2 - self.width],
self.kernel_size[2 - self.width])
# ##print("all_beddings.shape:",all_embeddings.shape)
q_embedding, k_embedding, v_embedding = torch.split(all_embeddings,
[self.group_planes // 2, self.group_planes // 2,
self.group_planes], dim=0)
# ##print("q_bedding.shape:", q_embedding.shape)
# ##print("k_bedding.shape:", k_embedding.shape)
# ##print("v_bedding.shape:", v_embedding.shape)
qr = torch.einsum('bgci,cij->bgij', q, q_embedding)
# ##print("qr.shape:", qr.shape)
kr = torch.einsum('bgci,cij->bgij', k, k_embedding).transpose(2, 3)
# ##print("kr.shape:", kr.shape)
qk = torch.einsum('bgci, bgcj->bgij', q, k)
# ##print("qk.shape:", qk.shape)
# multiply by factors
qr = torch.mul(qr, self.f_qr)
# ##print("qr.shape:", qr.shape)
kr = torch.mul(kr, self.f_kr)
# ##print("kr.shape:", kr.shape)
stacked_similarity = torch.cat([qk, qr, kr], dim=1)
# ##print("stacked_similarity.shape:", stacked_similarity.shape)
stacked_similarity = self.bn_similarity(stacked_similarity).view(N * W * L, 3, self.groups, H, H).sum(dim=1)
# stacked_similarity = self.bn_qr(qr) + self.bn_kr(kr) + self.bn_qk(qk)
# ##print("stacked_similarity.shape:", stacked_similarity.shape)
# (N, groups, H, H, W)
similarity = F.softmax(stacked_similarity, dim=3)
# ##print("similarity.shape:", similarity.shape)
sv = torch.einsum('bgij,bgcj->bgci', similarity, v)
# ##print("sv.shape:", sv.shape)
sve = torch.einsum('bgij,cij->bgci', similarity, v_embedding)
# ##print("sve.shape:", sve.shape)
# multiply by factors
sv = torch.mul(sv, self.f_sv)
# ##print("sv.shape:", sv.shape)
sve = torch.mul(sve, self.f_sve)
# ##print("sve.shape:", sve.shape)
stacked_output = torch.cat([sv, sve], dim=-1).view(N * W * L, self.out_planes * 2, H)
# ##print("stacked_output.shape:", stacked_output.shape)
output = self.bn_output(stacked_output).view(N, W, L, self.out_planes, 2, H).sum(dim=-2)
# ##print("output.shape:", output.shape)
if self.width == 0: # length
output = output.permute(0, 3, 1, 2, 4) # N, L, H ,C, W
elif self.width == 1: # height
output = output.permute(0, 3, 2, 4, 1) # N, W, L, C, H
else: # width
output = output.permute(0, 3, 4, 1, 2) # N, H, W, C, L
# ##print("output.shape:", output.shape)
if self.stride != (1, 1, 1):
output = self.pooling(output)
return output
def reset_parameters(self):
self.qkv_transform.weight.data.normal_(0, math.sqrt(1. / self.in_planes))
# nn.init.uniform_(self.relative, -0.1, 0.1)
nn.init.normal_(self.relative, 0., math.sqrt(1. / self.group_planes))
class AxialAttention_wopos(nn.Module):
def __init__(self, in_planes, out_planes, groups=4, kernel_size=(8, 16, 32),
stride=(1, 1, 1), bias=False, width=0):
assert (in_planes % groups == 0) and (out_planes % groups == 0)
super(AxialAttention_wopos, self).__init__()
self.in_planes = in_planes
self.out_planes = out_planes
self.groups = groups
self.group_planes = out_planes // groups
self.kernel_size = kernel_size
self.stride = stride
self.bias = bias
self.width = width
# Multi-head self attention
self.qkv_transform = qkv_transform(in_planes, out_planes * 2, kernel_size=1, stride=1,
padding=0, bias=False)
self.bn_qkv = nn.BatchNorm1d(out_planes * 2)
self.bn_similarity = nn.BatchNorm2d(groups)
self.bn_output = nn.BatchNorm1d(out_planes * 1)
if stride != (1, 1, 1):
self.pooling = nn.AvgPool3d(stride, stride=stride)
self.reset_parameters()
def forward(self, x):
# ##print("aaw_x.shape",x.shape)
if self.width == 0: # length
x = x.permute(0, 2, 3, 1, 4) # N, L, H ,C, W
elif self.width == 1: # height
x = x.permute(0, 4, 2, 1, 3) # N, W, L, C, H
else: # width
x = x.permute(0, 3, 4, 1, 2) # N, H, W, C, L
# ##print("aaw_x.shape",x.shape)
N, W, L, C, H = x.shape
x = x.contiguous().view(N * W * L, C, H)
# ##print("aaw_x.shape",x.shape)
# Transformations
qkv = self.bn_qkv(self.qkv_transform(x))
# ##print("aaw_qkv.shape:",qkv.shape)
q, k, v = torch.split(qkv.reshape(N * W * L, self.groups, self.group_planes * 2, H),
[self.group_planes // 2, self.group_planes // 2, self.group_planes], dim=2)
# ##print("aaw_q.shape",q.shape)
# ##print("aaw_k.shape",k.shape)
# ##print("aaw_v.shape",v.shape)
qk = torch.einsum('bgci, bgcj->bgij', q, k)
# ##print("aaw_qk.shape",qk.shape)
stacked_similarity = self.bn_similarity(qk).reshape(N * W * L, 1, self.groups, H, H).sum(dim=1).contiguous()
# ##print("aaw_stacked_similarity.shape",stacked_similarity.shape)
similarity = F.softmax(stacked_similarity, dim=3)
# ##print("aaw_similarity.shape",similarity.shape)
sv = torch.einsum('bgij,bgcj->bgci', similarity, v)
# ##print("aaw_sv.shape",sv.shape)
sv = sv.reshape(N * W * L, self.out_planes * 1, H).contiguous()
# ##print("aaw_sv.shape",sv.shape)
output = self.bn_output(sv).reshape(N, W, L, self.out_planes, 1, H).sum(dim=-2).contiguous()
# ##print("aaw_output.shape",output.shape)
if self.width == 0: # length
output = output.permute(0, 3, 1, 2, 4) # N, L, H ,C, W
elif self.width == 1: # height
output = output.permute(0, 3, 2, 4, 1) # N, W, L, C, H
else: # width
output = output.permute(0, 3, 4, 1, 2) # N, H, W, C, L
if self.stride != (1, 1, 1):
output = self.pooling(output)
return output
def reset_parameters(self):
self.qkv_transform.weight.data.normal_(0, math.sqrt(1. / self.in_planes))
# nn.init.uniform_(self.relative, -0.1, 0.1)
# nn.init.normal_(self.relative, 0., math.sqrt(1. / self.group_planes))
class AxialBlock(nn.Module):
expansion = 2
def __init__(self, conv_op, norm_op, nonlin, inplanes, planes, stride=(1, 1, 1), downsample=None, groups=1, # 8
base_width=64, dilation=1, norm_layer=None, kernel_size=(8, 16, 32)):
super(AxialBlock, self).__init__()
if norm_layer is None:
norm_layer = norm_op
width = int(planes * (base_width / 64.)) # width = planes
# Both self.conv2 and self.downsample layers downsample the input when stride != 1
self.conv_down = conv1x1x1(inplanes, width)
self.bn1 = norm_layer(width)
self.length_block = AxialAttention(width, width, groups=groups, kernel_size=kernel_size, width=0)
self.hight_block = AxialAttention(width, width, groups=groups, kernel_size=kernel_size, width=1)
self.width_block = AxialAttention(width, width, groups=groups, stride=stride, kernel_size=kernel_size, width=2)
self.conv_up = conv1x1x1(width, planes * self.expansion)
self.bn2 = norm_layer(planes * self.expansion)
self.relu = nonlin()
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = x
out = self.conv_down(x)
out = self.bn1(out)
out = self.relu(out)
# ##print(out.shape)
out = self.length_block(out)
out = self.hight_block(out)
out = self.width_block(out)
out = self.relu(out)
out = self.conv_up(out)
out = self.bn2(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class AxialBlock_dynamic(nn.Module):
expansion = 2
def __init__(self, conv_op, norm_op, nonlin, inplanes, planes, stride=(1, 1, 1), downsample=None, groups=1,
base_width=64, dilation=1, norm_layer=None, kernel_size=(128, 128, 128)):
super(AxialBlock_dynamic, self).__init__()
if norm_layer is None:
norm_layer = norm_op
width = int(planes * (base_width / 64.))
# Both self.conv2 and self.downsample layers downsample the input when stride != 1
self.conv_down = conv1x1x1(inplanes, width)
self.bn1 = norm_layer(width)
self.length_block = AxialAttention_dynamic(width, width, groups=groups, kernel_size=kernel_size, width=0)
self.hight_block = AxialAttention_dynamic(width, width, groups=groups, kernel_size=kernel_size, width=1)
self.width_block = AxialAttention_dynamic(width, width, groups=groups, stride=stride, kernel_size=kernel_size,
width=2)
self.conv_up = conv1x1x1(width, planes * self.expansion)
self.bn2 = norm_layer(planes * self.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = x
out = self.conv_down(x)
out = self.bn1(out)
out = self.relu(out)
out = self.length_block(out)
out = self.hight_block(out)
out = self.width_block(out)
out = self.relu(out)
out = self.conv_up(out)
out = self.bn2(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class AxialBlock_wopos(nn.Module):
expansion = 2
def __init__(self, conv_op, norm_op, nonlin, inplanes, planes, stride=(1, 1, 1), downsample=None, groups=1,
base_width=64, dilation=1, norm_layer=None, kernel_size=(128, 128, 128)):
super(AxialBlock_wopos, self).__init__()
if norm_layer is None:
norm_layer = norm_op
# ##print(kernel_size)
width = int(planes * (base_width / 64.))
# Both self.conv2 and self.downsample layers downsample the input when stride != 1
self.conv_down = conv1x1x1(inplanes, width)
self.conv1 = conv_op(width, width, kernel_size=1)
self.bn1 = norm_layer(width)
self.length_block = AxialAttention_wopos(width, width, groups=groups, kernel_size=kernel_size, width=0)
self.hight_block = AxialAttention_wopos(width, width, groups=groups, kernel_size=kernel_size, width=1)
self.width_block = AxialAttention_wopos(width, width, groups=groups, stride=stride, kernel_size=kernel_size,
width=2)
self.conv_up = conv1x1x1(width, planes * self.expansion)
self.bn2 = norm_layer(planes * self.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = x
# pdb.set_trace()
out = self.conv_down(x)
out = self.bn1(out)
out = self.relu(out)
# ##print("aaw_out.shape",out.shape)
out = self.length_block(out)
# ##print("aaw_out.shape",out.shape)
out = self.hight_block(out)
out = self.width_block(out)
out = self.relu(out)
out = self.conv_up(out)
out = self.bn2(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class ResAxialAttentionUNet(SegmentationNetwork):
DEFAULT_BATCH_SIZE_3D = 2
DEFAULT_PATCH_SIZE_3D = (64, 192, 160)
SPACING_FACTOR_BETWEEN_STAGES = 2
BASE_NUM_FEATURES_3D = 30
MAX_NUMPOOL_3D = 999
MAX_NUM_FILTERS_3D = 320
DEFAULT_PATCH_SIZE_2D = (256, 256)
BASE_NUM_FEATURES_2D = 30
DEFAULT_BATCH_SIZE_2D = 50
MAX_NUMPOOL_2D = 999
MAX_FILTERS_2D = 480
use_this_for_batch_size_computation_2D = 19739648
use_this_for_batch_size_computation_3D = 520000000 # 505789440
def __init__(self, layers, input_channels, num_classes, num_pool, num_conv_per_stage=2,
feat_map_mul_on_downscale=2, conv_op=nn.Conv2d,
norm_op=nn.BatchNorm2d, norm_op_kwargs=None,
dropout_op=nn.Dropout2d, dropout_op_kwargs=None,
nonlin=nn.LeakyReLU, nonlin_kwargs=None, dropout_in_localization=False,
final_nonlin=softmax_helper, weightInitializer=InitWeights_He(1e-2), pool_op_kernel_sizes=None,
conv_kernel_sizes=None,
upscale_logits=False, convolutional_pooling=False, convolutional_upsampling=False,
max_num_features=None,
seg_output_use_bias=False, deep_supervision=True,
groups=2, width_per_group=64, replace_stride_with_dilation=None,
norm_layer=None, s=0.25, img_size=(128, 128, 128), ):
super(ResAxialAttentionUNet, self).__init__()
self.convolutional_upsampling = convolutional_upsampling # True
self.convolutional_pooling = convolutional_pooling # True
self.upscale_logits = upscale_logits # False
if nonlin_kwargs is None:
nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True} #
if dropout_op_kwargs is None:
dropout_op_kwargs = {'p': 0.5, 'inplace': True}
if norm_op_kwargs is None:
norm_op_kwargs = {'eps': 1e-5, 'affine': True, 'momentum': 0.1}
self.conv_kwargs = {'stride': 1, 'dilation': 1, 'bias': True}
self.nonlin = nonlin # nn.LeakyReLU
self.nonlin_kwargs = nonlin_kwargs # {'negative_slope': 1e-2, 'inplace': True}
self.dropout_op_kwargs = dropout_op_kwargs # {'p': 0, 'inplace': True}
self.norm_op_kwargs = norm_op_kwargs # {'eps': 1e-5, 'affine': True}
self.weightInitializer = weightInitializer # InitWeights_He(1e-2)
self.conv_op = conv_op # nn.Conv3d
self.norm_op = norm_op # nn.InstanceNorm3d
self.dropout_op = dropout_op # nn.Dropout3d
self.num_classes = num_classes
self.final_nonlin = final_nonlin
self._deep_supervision = deep_supervision
self.do_ds = deep_supervision
if conv_op == nn.Conv2d:
# pool_op = nn.MaxPool2d
if pool_op_kernel_sizes is None:
pool_op_kernel_sizes = [(2, 2)] * num_pool
if conv_kernel_sizes is None:
conv_kernel_sizes = [(3, 3)] * (num_pool + 1)
elif conv_op == nn.Conv3d:
pool_op = nn.AvgPool3d
if pool_op_kernel_sizes is None:
pool_op_kernel_sizes = [(2, 2, 2)] * num_pool
if conv_kernel_sizes is None:
conv_kernel_sizes = [(3, 3, 3)] * (num_pool + 1)
else:
raise ValueError("unknown convolution dimensionality, conv op: %s" % str(conv_op))
self.input_shape_must_be_divisible_by = np.prod(pool_op_kernel_sizes, 0, dtype=np.int64)
self.pool_op_kernel_sizes = pool_op_kernel_sizes
self.conv_kernel_sizes = conv_kernel_sizes
self.conv_pad_sizes = []
for krnl in self.conv_kernel_sizes:
self.conv_pad_sizes.append([1 if i == 3 else 0 for i in krnl])
if max_num_features is None:
if self.conv_op == nn.Conv3d:
self.max_num_features = self.MAX_NUM_FILTERS_3D
else:
self.max_num_features = self.MAX_FILTERS_2D
else:
self.max_num_features = max_num_features
if self.weightInitializer is not None:
self.apply(self.weightInitializer)
if norm_layer is None:
norm_layer = self.norm_op
self._norm_layer = norm_layer
self.inplanes = int(64 * s)
self.dilation = 1
if replace_stride_with_dilation is None:
replace_stride_with_dilation = [False, False, False]
if len(replace_stride_with_dilation) != 3:
raise ValueError("replace_stride_with_dilation should be None "
"or a 3-element tuple, got {}".format(replace_stride_with_dilation))
self.groups = groups
self.base_width = width_per_group
self.conv1 = self.conv_op(input_channels, self.inplanes, kernel_size=7, stride=(1, 2, 2), padding=3, bias=False)
self.conv2 = self.conv_op(self.inplanes, 64, kernel_size=3, stride=1, padding=1, bias=False)
self.conv3 = self.conv_op(64, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = norm_layer(self.inplanes)
self.bn2 = norm_layer(64)
self.bn3 = norm_layer(self.inplanes)
self.relu = self.nonlin()
block = AxialBlock
# self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(self.conv_op, self.norm_op, self.nonlin, block, int(64 * s), layers[0],
kernel_size=(img_size[0], img_size[1] // 2, img_size[2] // 2))
self.layer2 = self._make_layer(self.conv_op, self.norm_op, self.nonlin, block, int(128 * s), layers[1],
stride=(1, 2, 2), kernel_size=(img_size[0], img_size[1] // 2, img_size[2] // 2),
dilate=replace_stride_with_dilation[0])
self.layer3 = self._make_layer(self.conv_op, self.norm_op, self.nonlin, block, int(256 * s), layers[2],
stride=(1, 2, 2), kernel_size=(img_size[0], img_size[1] // 4, img_size[2] // 4),
dilate=replace_stride_with_dilation[1])
self.layer4 = self._make_layer(self.conv_op, self.norm_op, self.nonlin, block, int(512 * s), layers[3],
stride=(1, 2, 2), kernel_size=(img_size[0], img_size[1] // 8, img_size[2] // 8),
dilate=replace_stride_with_dilation[2])
# Decoder
self.decoder1 = self.conv_op(int(512 * 2 * s), int(512 * 2 * s), kernel_size=3, stride=2, padding=1)
self.decoder2 = self.conv_op(int(512 * 2 * s), int(512 * s), kernel_size=3, stride=1, padding=1)
self.decoder3 = self.conv_op(int(512 * s), int(256 * s), kernel_size=3, stride=1, padding=1)
self.decoder4 = self.conv_op(int(256 * s), int(128 * s), kernel_size=3, stride=1, padding=1)
self.decoder5 = self.conv_op(int(128 * s), int(64 * s), kernel_size=3, stride=1, padding=1)
self.adjust = self.conv_op(int(64 * s), num_classes, kernel_size=1, stride=1, padding=0)
self.soft = nn.Softmax(dim=1)
def _make_layer(self, conv_op, norm_op, nonlin, block, planes, blocks, kernel_size=(8, 16, 32), stride=(1, 1, 1),
dilate=False):
norm_layer = norm_op
downsample = None
previous_dilation = self.dilation
if dilate:
self.dilation *= stride
stride = 1
if stride != (1, 1, 1) or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
conv1x1x1(self.inplanes, planes * block.expansion, stride),
norm_layer(planes * block.expansion),
)
layers = []
layers.append(block(conv_op, norm_op, nonlin, self.inplanes, planes, stride, downsample, groups=self.groups,
base_width=self.base_width, dilation=previous_dilation,
norm_layer=norm_layer, kernel_size=kernel_size))
self.inplanes = planes * block.expansion
if stride != (1, 1, 1):
kernel_size = (kernel_size[0] // 2, kernel_size[1] // 2, kernel_size[2] // 2)
for _ in range(1, blocks):
layers.append(block(conv_op, norm_op, nonlin, self.inplanes, planes, groups=self.groups,
base_width=self.base_width, dilation=self.dilation,
norm_layer=norm_layer, kernel_size=kernel_size))
return nn.Sequential(*layers)
def _forward_impl(self, x):
# AxialAttention Encoder
# pdb.set_trace()
# ##print("x.shape:",x.shape)
x = self.conv1(x)
# ##print("x1.shape:",x.shape)
x = self.bn1(x)
# ##print("x2.shape:",x.shape)
x = self.relu(x)
# ##print("x3.shape:",x.shape)
x = self.conv2(x)
# ##print("x4.shape:",x.shape)
x = self.bn2(x)
# ##print("x5.shape:",x.shape)
x = self.relu(x)
# ##print("x6.shape:",x.shape)
x = self.conv3(x)
# ##print("x7.shape:",x.shape)
x = self.bn3(x)
# ##print("x8.shape:",x.shape)
x = self.relu(x)
# ##print("x9.shape:",x.shape)
x1 = self.layer1(x)
# ##print("x11.shape:",x1.shape)
x2 = self.layer2(x1)
# ##print("x12.shape:",x2.shape)
# ##print(x2.shape)
x3 = self.layer3(x2)
# ##print("x13.shape:",x3.shape)
# ##print(x3.shape)
x4 = self.layer4(x3)
# ##print("x14.shape:",x4.shape)
x = F.relu(F.interpolate(self.decoder1(x4), scale_factor=(2, 2, 2), mode='trilinear'))
x = torch.add(x, x4)
x = F.relu(F.interpolate(self.decoder2(x), scale_factor=(1, 2, 2), mode='trilinear'))
x = torch.add(x, x3)
x = F.relu(F.interpolate(self.decoder3(x), scale_factor=(1, 2, 2), mode='trilinear'))
x = torch.add(x, x2)
x = F.relu(F.interpolate(self.decoder4(x), scale_factor=(1, 2, 2), mode='trilinear'))
x = torch.add(x, x1)
x = F.relu(F.interpolate(self.decoder5(x), scale_factor=(1, 2, 2), mode='trilinear'))
x = self.adjust(F.relu(x))
# pdb.set_trace()
# out = self.active(out)
return x
def forward(self, x):
seg_outputs = []
seg_outputs.append(self._forward_impl(x))
if self._deep_supervision and self.do_ds:
return tuple([seg_outputs[-1]])
else:
return seg_outputs[-1]
@staticmethod
def compute_approx_vram_consumption(patch_size, num_pool_per_axis, base_num_features, max_num_features,
num_modalities, num_classes, pool_op_kernel_sizes, deep_supervision=False,
conv_per_stage=2):
"""
This only applies for num_conv_per_stage and convolutional_upsampling=True
not real vram consumption. just a constant term to which the vram consumption will be approx proportional
(+ offset for parameter storage)
:param deep_supervision:
:param patch_size:
:param num_pool_per_axis:
:param base_num_features:
:param max_num_features:
:param num_modalities:
:param num_classes:
:param pool_op_kernel_sizes:
:return:
"""
if not isinstance(num_pool_per_axis, np.ndarray):
num_pool_per_axis = np.array(num_pool_per_axis)
npool = len(pool_op_kernel_sizes)
map_size = np.array(patch_size)
tmp = np.int64((conv_per_stage * 2 + 1) * np.prod(map_size, dtype=np.int64) * base_num_features +
num_modalities * np.prod(map_size, dtype=np.int64) +
num_classes * np.prod(map_size, dtype=np.int64))
num_feat = base_num_features
for p in range(npool):
for pi in range(len(num_pool_per_axis)):
map_size[pi] /= pool_op_kernel_sizes[p][pi]
num_feat = min(num_feat * 2, max_num_features)
num_blocks = 10 # conv_per_stage + conv_per_stage for the convs of encode/decode and 1 for transposed conv
tmp += num_blocks * np.prod(map_size, dtype=np.int64) * num_feat
if deep_supervision and p < (npool - 2):
tmp += np.prod(map_size, dtype=np.int64) * num_classes
# ##print(p, map_size, num_feat, tmp)
return tmp
class medt_net(SegmentationNetwork):
DEFAULT_BATCH_SIZE_3D = 2
DEFAULT_PATCH_SIZE_3D = (64, 192, 160)
SPACING_FACTOR_BETWEEN_STAGES = 2
BASE_NUM_FEATURES_3D = 30
MAX_NUMPOOL_3D = 999
MAX_NUM_FILTERS_3D = 320
DEFAULT_PATCH_SIZE_2D = (256, 256)
BASE_NUM_FEATURES_2D = 30
DEFAULT_BATCH_SIZE_2D = 50
MAX_NUMPOOL_2D = 999
MAX_FILTERS_2D = 480
use_this_for_batch_size_computation_2D = 19739648
use_this_for_batch_size_computation_3D = 520000000 # 505789440
def __init__(self, layers, input_channels, num_classes, num_pool, num_conv_per_stage=2,
feat_map_mul_on_downscale=2, conv_op=nn.Conv2d,
norm_op=nn.BatchNorm2d, norm_op_kwargs=None,
dropout_op=nn.Dropout2d, dropout_op_kwargs=None,
nonlin=nn.LeakyReLU, nonlin_kwargs=None, dropout_in_localization=False,
final_nonlin=softmax_helper, weightInitializer=InitWeights_He(1e-2), pool_op_kernel_sizes=None,
conv_kernel_sizes=None,
upscale_logits=False, convolutional_pooling=False, convolutional_upsampling=False,
max_num_features=None,
seg_output_use_bias=False, deep_supervision=True,
groups=8, width_per_group=64, replace_stride_with_dilation=None,
norm_layer=None, s=0.25, img_size=(128, 128, 128), ):
super(medt_net, self).__init__()
self.convolutional_upsampling = convolutional_upsampling # True
self.convolutional_pooling = convolutional_pooling # True
self.upscale_logits = upscale_logits # False
if nonlin_kwargs is None:
nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True} #
if dropout_op_kwargs is None:
dropout_op_kwargs = {'p': 0.5, 'inplace': True}
if norm_op_kwargs is None:
norm_op_kwargs = {'eps': 1e-5, 'affine': True, 'momentum': 0.1}
self.conv_kwargs = {'stride': 1, 'dilation': 1, 'bias': True}
self.nonlin = nonlin # nn.LeakyReLU
self.nonlin_kwargs = nonlin_kwargs # {'negative_slope': 1e-2, 'inplace': True}
self.dropout_op_kwargs = dropout_op_kwargs # {'p': 0, 'inplace': True}
self.norm_op_kwargs = norm_op_kwargs # {'eps': 1e-5, 'affine': True}
self.weightInitializer = weightInitializer # InitWeights_He(1e-2)
self.conv_op = conv_op # nn.Conv3d
self.norm_op = norm_op # nn.InstanceNorm3d
self.dropout_op = dropout_op # nn.Dropout3d
self.num_classes = num_classes
self.final_nonlin = final_nonlin
self._deep_supervision = deep_supervision
self.do_ds = deep_supervision
if conv_op == nn.Conv2d:
# pool_op = nn.MaxPool2d
if pool_op_kernel_sizes is None:
pool_op_kernel_sizes = [(2, 2)] * num_pool
if conv_kernel_sizes is None:
conv_kernel_sizes = [(3, 3)] * (num_pool + 1)
elif conv_op == nn.Conv3d:
pool_op = nn.AvgPool3d
if pool_op_kernel_sizes is None:
pool_op_kernel_sizes = [(2, 2, 2)] * num_pool
if conv_kernel_sizes is None:
conv_kernel_sizes = [(3, 3, 3)] * (num_pool + 1)
else:
raise ValueError("unknown convolution dimensionality, conv op: %s" % str(conv_op))
self.input_shape_must_be_divisible_by = np.prod(pool_op_kernel_sizes, 0, dtype=np.int64)
self.pool_op_kernel_sizes = pool_op_kernel_sizes
self.conv_kernel_sizes = conv_kernel_sizes
self.conv_pad_sizes = []
for krnl in self.conv_kernel_sizes:
self.conv_pad_sizes.append([1 if i == 3 else 0 for i in krnl])
if max_num_features is None:
if self.conv_op == nn.Conv3d:
self.max_num_features = self.MAX_NUM_FILTERS_3D
else:
self.max_num_features = self.MAX_FILTERS_2D
else:
self.max_num_features = max_num_features
if self.weightInitializer is not None:
self.apply(self.weightInitializer)
block = AxialBlock_dynamic
block_2 = AxialBlock_wopos
if norm_layer is None:
norm_layer = self.norm_op
self._norm_layer = norm_layer
self.inplanes = int(64 * s)
self.dilation = 1
if replace_stride_with_dilation is None:
replace_stride_with_dilation = [False, False, False]
if len(replace_stride_with_dilation) != 3:
raise ValueError("replace_stride_with_dilation should be None "
"or a 3-element tuple, got {}".format(replace_stride_with_dilation))
self.groups = groups
self.base_width = width_per_group
self.conv1 = self.conv_op(input_channels, self.inplanes, kernel_size=7, stride=(1, 2, 2), padding=3,
bias=False)
self.conv2 = self.conv_op(self.inplanes, 128, kernel_size=3, stride=1, padding=1, bias=False)
self.conv3 = self.conv_op(128, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = norm_layer(self.inplanes)
self.bn2 = norm_layer(128)
self.bn3 = norm_layer(self.inplanes)
# self.conv1 = nn.Conv2d(1, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = norm_layer(self.inplanes)
self.relu = nn.ReLU(inplace=True)
# self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(self.conv_op, self.norm_op, self.nonlin, block, int(128 * s), layers[0],
kernel_size=(img_size[0], img_size[1] // 2, img_size[2] // 2))
self.layer2 = self._make_layer(self.conv_op, self.norm_op, self.nonlin, block, int(256 * s), layers[1],
stride=(1, 2, 2), kernel_size=(img_size[0], img_size[1] // 2, img_size[2] // 2),
dilate=replace_stride_with_dilation[0])
# self.layer3 = self._make_layer(block, int(512 * s), layers[2], stride=2, kernel_size=(img_size//4),
# dilate=replace_stride_with_dilation[1])
# self.layer4 = self._make_layer(block, int(1024 * s), layers[3], stride=2, kernel_size=(img_size//8),
# dilate=replace_stride_with_dilation[2])
# Decoder
# self.decoder1 = nn.Conv2d(int(1024 *2*s) , int(1024*2*s), kernel_size=3, stride=2, padding=1)
# self.decoder2 = nn.Conv2d(int(1024 *2*s) , int(1024*s), kernel_size=3, stride=1, padding=1)
# self.decoder3 = nn.Conv2d(int(1024*s), int(512*s), kernel_size=3, stride=1, padding=1)
self.decoder4 = self.conv_op(int(512 * s), int(256 * s), kernel_size=3, stride=1, padding=1)
self.decoder5 = self.conv_op(int(256 * s), int(128 * s), kernel_size=3, stride=1, padding=1)
self.adjust = self.conv_op(int(128 * s), num_classes, kernel_size=1, stride=1, padding=0)
self.soft = nn.Softmax(dim=1)
self.conv1_p = self.conv_op(input_channels, self.inplanes, kernel_size=7, stride=(1, 2, 2), padding=3,
bias=False)
self.conv2_p = self.conv_op(self.inplanes, 128, kernel_size=3, stride=1, padding=1,
bias=False)
self.conv3_p = self.conv_op(128, self.inplanes, kernel_size=3, stride=1, padding=1,
bias=False)
# self.conv1 = nn.Conv2d(1, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1_p = norm_layer(self.inplanes)
self.bn2_p = norm_layer(128)
self.bn3_p = norm_layer(self.inplanes)
self.relu_p = self.nonlin
img_size_p = (img_size[0], img_size[1] // 4, img_size[2] // 4)
self.layer1_p = self._make_layer(self.conv_op, self.norm_op, self.nonlin, block_2, int(128 * s), layers[0],
kernel_size=(img_size_p[0], img_size_p[1] // 2, img_size_p[2] // 2))
self.layer2_p = self._make_layer(self.conv_op, self.norm_op, self.nonlin, block_2, int(256 * s), layers[1],
stride=(1, 2, 2),
kernel_size=(img_size_p[0], img_size_p[1] // 2, img_size_p[2] // 2),
dilate=replace_stride_with_dilation[0])
self.layer3_p = self._make_layer(self.conv_op, self.norm_op, self.nonlin, block_2, int(512 * s), layers[2],
stride=(1, 2, 2),
kernel_size=(img_size_p[0], img_size_p[1] // 4, img_size_p[2] // 4),
dilate=replace_stride_with_dilation[1])
self.layer4_p = self._make_layer(self.conv_op, self.norm_op, self.nonlin, block_2, int(1024 * s), layers[3],
stride=(1, 2, 2),
kernel_size=(img_size_p[0], img_size_p[1] // 8, img_size_p[2] // 8),
dilate=replace_stride_with_dilation[2])
# Decoder
self.decoder1_p = self.conv_op(int(1024 * 2 * s), int(1024 * 2 * s), kernel_size=3, stride=2, padding=1)
self.decoder2_p = self.conv_op(int(1024 * 2 * s), int(1024 * s), kernel_size=3, stride=1, padding=1)
self.decoder3_p = self.conv_op(int(1024 * s), int(512 * s), kernel_size=3, stride=1, padding=1)
self.decoder4_p = self.conv_op(int(512 * s), int(256 * s), kernel_size=3, stride=1, padding=1)
self.decoder5_p = self.conv_op(int(256 * s), int(128 * s), kernel_size=3, stride=1, padding=1)
self.decoderf = self.conv_op(int(128 * s), int(128 * s), kernel_size=3, stride=1, padding=1)
self.adjust_p = self.conv_op(int(128 * s), num_classes, kernel_size=1, stride=1, padding=0)
self.soft_p = nn.Softmax(dim=1)
def _make_layer(self, conv_op, norm_op, nonlin, block, planes, blocks, kernel_size=(128, 128, 128),
stride=(1, 1, 1), dilate=False):
norm_layer = self._norm_layer
downsample = None
previous_dilation = self.dilation
if dilate:
self.dilation *= stride
stride = 1
if stride != (1, 1, 1) or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
conv1x1x1(self.inplanes, planes * block.expansion, stride),
norm_layer(planes * block.expansion),
)
layers = []
layers.append(block(conv_op, norm_op, nonlin, self.inplanes, planes, stride, downsample, groups=self.groups,
base_width=self.base_width, dilation=previous_dilation,
norm_layer=norm_layer, kernel_size=kernel_size))
self.inplanes = planes * block.expansion
if stride != (1, 1, 1):
kernel_size = (kernel_size[0] // 1, kernel_size[1] // 2, kernel_size[2] // 2)
for _ in range(1, blocks):
layers.append(block(conv_op, norm_op, nonlin, self.inplanes, planes, groups=self.groups,
base_width=self.base_width, dilation=self.dilation,
norm_layer=norm_layer, kernel_size=kernel_size))
return nn.Sequential(*layers)
def _forward_impl(self, x):
xin = x.clone()
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.relu(x)
x = self.conv3(x)
x = self.bn3(x)
# x = F.max_pool2d(x,2,2)
x = self.relu(x)
# x = self.maxpool(x)
# pdb.set_trace()
x1 = self.layer1(x)
# ##print("x1.shape",x1.shape)
x2 = self.layer2(x1)
# ##print("x2.shape",x2.shape)
# x3 = self.layer3(x2)
# ###print(x3.shape)
# x4 = self.layer4(x3)
# ###print(x4.shape)
# x = F.relu(F.interpolate(self.decoder1(x4), scale_factor=(2,2), mode ='trilinear'))
# x = torch.add(x, x4)
# x = F.relu(F.interpolate(self.decoder2(x4) , scale_factor=(2,2), mode ='trilinear'))
# x = torch.add(x, x3)
# x = F.relu(F.interpolate(self.decoder3(x3) , scale_factor=(2,2), mode ='trilinear'))
# x = torch.add(x, x2)
x = F.relu(F.interpolate(self.decoder4(x2), scale_factor=(1, 2, 2), mode='trilinear'))
# ##print("x.shape",x.shape)
x = torch.add(x, x1)
x = F.relu(F.interpolate(self.decoder5(x), scale_factor=(1, 2, 2), mode='trilinear'))
# ##print(x.shape)
# end of full image training
# y_out = torch.ones((1,2,128,128))
x_loc = x.clone()
# x = F.relu(F.interpolate(self.decoder5(x) , scale_factor=(2,2), mode ='trilinear'))
# start
for i in range(0, 4):
for j in range(0, 4):
x_p = xin[:, :, :, 32 * i:32 * (i + 1), 32 * j:32 * (j + 1)]
# begin patch wise
# ##print("x_p.shape",x_p.shape)
x_p = self.conv1_p(x_p)
x_p = self.bn1_p(x_p)
# x = F.max_pool2d(x,2,2)
x_p = self.relu(x_p)
x_p = self.conv2_p(x_p)
x_p = self.bn2_p(x_p)
# x = F.max_pool2d(x,2,2)
x_p = self.relu(x_p)
x_p = self.conv3_p(x_p)
x_p = self.bn3_p(x_p)
# x = F.max_pool2d(x,2,2)
x_p = self.relu(x_p)
# x = self.maxpool(x)
# pdb.set_trace()
# ##print("xp.shape",x_p.shape)
x1_p = self.layer1_p(x_p)
# ##print(x1.shape)
x2_p = self.layer2_p(x1_p)
# ##print(x2.shape)
x3_p = self.layer3_p(x2_p)
# ###print(x3.shape)
x4_p = self.layer4_p(x3_p)
x_p = F.relu(F.interpolate(self.decoder1_p(x4_p), scale_factor=(2, 2, 2), mode='trilinear'))
x_p = torch.add(x_p, x4_p)
x_p = F.relu(F.interpolate(self.decoder2_p(x_p), scale_factor=(1, 2, 2), mode='trilinear'))
x_p = torch.add(x_p, x3_p)
x_p = F.relu(F.interpolate(self.decoder3_p(x_p), scale_factor=(1, 2, 2), mode='trilinear'))
x_p = torch.add(x_p, x2_p)
x_p = F.relu(F.interpolate(self.decoder4_p(x_p), scale_factor=(1, 2, 2), mode='trilinear'))
x_p = torch.add(x_p, x1_p)
x_p = F.relu(F.interpolate(self.decoder5_p(x_p), scale_factor=(1, 2, 2), mode='trilinear'))
x_loc[:, :, :, 32 * i:32 * (i + 1), 32 * j:32 * (j + 1)] = x_p
x = torch.add(x, x_loc)
x = F.relu(self.decoderf(x))
x = self.adjust(F.relu(x))
# pdb.set_trace()
return x
def forward(self, x):
seg_outputs = []
seg_outputs.append(self._forward_impl(x))
if self._deep_supervision and self.do_ds:
return tuple([seg_outputs[-1]])
else:
return seg_outputs[-1]
class TUNet(SegmentationNetwork):
DEFAULT_BATCH_SIZE_3D = 2
DEFAULT_PATCH_SIZE_3D = (64, 192, 160)
SPACING_FACTOR_BETWEEN_STAGES = 2
BASE_NUM_FEATURES_3D = 30
MAX_NUMPOOL_3D = 999
MAX_NUM_FILTERS_3D = 320
DEFAULT_PATCH_SIZE_2D = (256, 256)
BASE_NUM_FEATURES_2D = 30
DEFAULT_BATCH_SIZE_2D = 50
MAX_NUMPOOL_2D = 999
MAX_FILTERS_2D = 480
use_this_for_batch_size_computation_2D = 19739648
use_this_for_batch_size_computation_3D = 520000000 # 505789440
def __init__(self, layers, input_channels, num_classes, num_pool, num_conv_per_stage=2,
feat_map_mul_on_downscale=2, conv_op=nn.Conv2d,
norm_op=nn.BatchNorm2d, norm_op_kwargs=None,
dropout_op=nn.Dropout2d, dropout_op_kwargs=None,
nonlin=nn.LeakyReLU, nonlin_kwargs=None, dropout_in_localization=False,
final_nonlin=softmax_helper, weightInitializer=InitWeights_He(1e-2), pool_op_kernel_sizes=None,
conv_kernel_sizes=None,
upscale_logits=False, convolutional_pooling=False, convolutional_upsampling=False,
max_num_features=None,
seg_output_use_bias=False, deep_supervision=True,
groups=8, width_per_group=64, replace_stride_with_dilation=None,
norm_layer=None, s=0.25, img_size=(128, 128, 128), ):
super(TUNet, self).__init__()
self.convolutional_upsampling = convolutional_upsampling # True
self.convolutional_pooling = convolutional_pooling # True
self.upscale_logits = upscale_logits # False
if nonlin_kwargs is None:
nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True} #
if dropout_op_kwargs is None:
dropout_op_kwargs = {'p': 0.5, 'inplace': True}
if norm_op_kwargs is None:
norm_op_kwargs = {'eps': 1e-5, 'affine': True, 'momentum': 0.1}
self.conv_kwargs = {'stride': 1, 'dilation': 1, 'bias': True}
self.nonlin = nonlin # nn.LeakyReLU
self.nonlin_kwargs = nonlin_kwargs # {'negative_slope': 1e-2, 'inplace': True}
self.dropout_op_kwargs = dropout_op_kwargs # {'p': 0, 'inplace': True}
self.norm_op_kwargs = norm_op_kwargs # {'eps': 1e-5, 'affine': True}
self.weightInitializer = weightInitializer # InitWeights_He(1e-2)
self.conv_op = conv_op # nn.Conv3d
self.norm_op = norm_op # nn.InstanceNorm3d
self.dropout_op = dropout_op # nn.Dropout3d
self.num_classes = num_classes
self.final_nonlin = final_nonlin
self._deep_supervision = deep_supervision
self.do_ds = deep_supervision
if conv_op == nn.Conv2d:
# pool_op = nn.MaxPool2d
if pool_op_kernel_sizes is None:
pool_op_kernel_sizes = [(2, 2)] * num_pool
if conv_kernel_sizes is None:
conv_kernel_sizes = [(3, 3)] * (num_pool + 1)
elif conv_op == nn.Conv3d:
pool_op = nn.AvgPool3d
if pool_op_kernel_sizes is None:
pool_op_kernel_sizes = [(2, 2, 2)] * num_pool
if conv_kernel_sizes is None:
conv_kernel_sizes = [(3, 3, 3)] * (num_pool + 1)
else:
raise ValueError("unknown convolution dimensionality, conv op: %s" % str(conv_op))
self.input_shape_must_be_divisible_by = np.prod(pool_op_kernel_sizes, 0, dtype=np.int64)
self.pool_op_kernel_sizes = pool_op_kernel_sizes
self.conv_kernel_sizes = conv_kernel_sizes
self.conv_pad_sizes = []
for krnl in self.conv_kernel_sizes:
self.conv_pad_sizes.append([1 if i == 3 else 0 for i in krnl])
if max_num_features is None:
if self.conv_op == nn.Conv3d:
self.max_num_features = self.MAX_NUM_FILTERS_3D
else:
self.max_num_features = self.MAX_FILTERS_2D
else:
self.max_num_features = max_num_features
if self.weightInitializer is not None:
self.apply(self.weightInitializer)
block = AxialBlock_dynamic
block_2 = AxialBlock_wopos
if norm_layer is None:
norm_layer = self.norm_op
self._norm_layer = norm_layer
self.inplanes = int(64 * s)
self.dilation = 1
if replace_stride_with_dilation is None:
replace_stride_with_dilation = [False, False, False]
if len(replace_stride_with_dilation) != 3:
raise ValueError("replace_stride_with_dilation should be None "
"or a 3-element tuple, got {}".format(replace_stride_with_dilation))
self.groups = groups
self.base_width = width_per_group
self.conv1_u = self.conv_op(input_channels, self.inplanes, kernel_size=7, stride=(2, 2, 2), padding=3,
bias=False)
self.conv2_u = nn.Sequential(
self.conv_op(self.inplanes, self.inplanes * 2, kernel_size=3, stride=(1,2,2), padding=1, bias=True),
nn.GroupNorm(16, self.inplanes * 2),
nn.PReLU(),
self.conv_op(self.inplanes * 2, self.inplanes * 2, kernel_size=3, stride=1, padding=1, bias=True),
nn.GroupNorm(16, self.inplanes * 2),
nn.PReLU(),
)
self.conv3_u = nn.Sequential(
self.conv_op(self.inplanes * 2, self.inplanes * 4, kernel_size=3, stride=(1,2,2), padding=1, bias=True),
nn.GroupNorm(16, self.inplanes * 4),
nn.PReLU(),
self.conv_op(self.inplanes * 4, self.inplanes * 4, kernel_size=3, stride=1, padding=1, bias=True),
nn.GroupNorm(16, self.inplanes * 4),
nn.PReLU(),
)
self.conv4_u = nn.Sequential(
self.conv_op(self.inplanes * 4, self.inplanes * 8, kernel_size=3, stride=(1,2,2), padding=1, bias=True),
nn.GroupNorm(16, self.inplanes * 8),
nn.PReLU(),
self.conv_op(self.inplanes * 8, self.inplanes * 8, kernel_size=3, stride=1, padding=1, bias=True),
nn.GroupNorm(16, self.inplanes * 8),
nn.PReLU(),
)
self.decoder1_u = self.conv_op(self.inplanes * 8, self.inplanes * 4, kernel_size=3, stride=1, padding=1)
self.decoder2_u = self.conv_op(self.inplanes * 4, self.inplanes * 2, kernel_size=3, stride=1, padding=1)
self.decoder3_u = self.conv_op(self.inplanes * 2, self.inplanes * 2, kernel_size=3, stride=1, padding=1)
# self.decoder4_u = self.conv_op(int(512 * s), int(256 * s), kernel_size=3, stride=1, padding=1)
# self.decoder5_u = self.conv_op(int(256 * s), int(128 * s), kernel_size=3, stride=1, padding=1)
self.conv1_p = self.conv_op(input_channels, self.inplanes, kernel_size=7, stride=(1, 2, 2), padding=3,
bias=False)
self.conv2_p = self.conv_op(self.inplanes, 128, kernel_size=3, stride=1, padding=1,
bias=False)
self.conv3_p = self.conv_op(128, self.inplanes, kernel_size=3, stride=1, padding=1,
bias=False)
# self.conv1 = nn.Conv2d(1, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1_p = norm_layer(self.inplanes)
self.bn2_p = norm_layer(128)
self.bn3_p = norm_layer(self.inplanes)
self.relu_p = self.nonlin
self.relu = nn.ReLU(inplace=True)
img_size_p = (img_size[0], img_size[1] // 4, img_size[2] // 4)
self.layer1_p = self._make_layer(self.conv_op, self.norm_op, self.nonlin, block, int(128 * s), layers[0],
kernel_size=(img_size_p[0], img_size_p[1] // 2, img_size_p[2] // 2))
self.layer2_p = self._make_layer(self.conv_op, self.norm_op, self.nonlin, block, int(256 * s), layers[1],
stride=(1, 2, 2),
kernel_size=(img_size_p[0], img_size_p[1] // 2, img_size_p[2] // 2),
dilate=replace_stride_with_dilation[0])
self.layer3_p = self._make_layer(self.conv_op, self.norm_op, self.nonlin, block, int(512 * s), layers[2],
stride=(1, 2, 2),
kernel_size=(img_size_p[0], img_size_p[1] // 4, img_size_p[2] // 4),
dilate=replace_stride_with_dilation[1])
self.layer4_p = self._make_layer(self.conv_op, self.norm_op, self.nonlin, block, int(1024 * s), layers[3],
stride=(1, 2, 2),
kernel_size=(img_size_p[0], img_size_p[1] // 8, img_size_p[2] // 8),
dilate=replace_stride_with_dilation[2])
# Decoder
self.decoder1_p = self.conv_op(int(1024 * 2 * s), int(1024 * 2 * s), kernel_size=3, stride=2, padding=1)
self.decoder2_p = self.conv_op(int(1024 * 2 * s), int(1024 * s), kernel_size=3, stride=1, padding=1)
self.decoder3_p = self.conv_op(int(1024 * s), int(512 * s), kernel_size=3, stride=1, padding=1)
self.decoder4_p = self.conv_op(int(512 * s), int(256 * s), kernel_size=3, stride=1, padding=1)
self.decoder5_p = self.conv_op(int(256 * s), int(128 * s), kernel_size=3, stride=1, padding=1)
self.decoderf = self.conv_op(int(128 * s), int(128 * s), kernel_size=3, stride=1, padding=1)
self.adjust_p = self.conv_op(int(128 * s), num_classes, kernel_size=1, stride=1, padding=0)
self.soft_p = nn.Softmax(dim=1)
def _make_layer(self, conv_op, norm_op, nonlin, block, planes, blocks, kernel_size=(128, 128, 128),
stride=(1, 1, 1), dilate=False):
norm_layer = self._norm_layer
downsample = None
previous_dilation = self.dilation
if dilate:
self.dilation *= stride
stride = 1
if stride != (1, 1, 1) or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
conv1x1x1(self.inplanes, planes * block.expansion, stride),
norm_layer(planes * block.expansion),
)
layers = []
layers.append(block(conv_op, norm_op, nonlin, self.inplanes, planes, stride, downsample, groups=self.groups,
base_width=self.base_width, dilation=previous_dilation,
norm_layer=norm_layer, kernel_size=kernel_size))
self.inplanes = planes * block.expansion
if stride != (1, 1, 1):
kernel_size = (kernel_size[0] // 1, kernel_size[1] // 2, kernel_size[2] // 2)
for _ in range(1, blocks):
layers.append(block(conv_op, norm_op, nonlin, self.inplanes, planes, groups=self.groups,
base_width=self.base_width, dilation=self.dilation,
norm_layer=norm_layer, kernel_size=kernel_size))
return nn.Sequential(*layers)
def _forward_impl(self, x):
xin = x.clone()
x = self.conv1_u(x)
##print(x.shape)
x = self.conv2_u(x)
##print(x.shape)
x = self.conv3_u(x)
##print(x.shape)
x = self.conv4_u(x)
x = F.relu(F.interpolate(x, scale_factor=(1, 2, 2), mode='trilinear',align_corners=True))
##print("de",x.shape)
x = F.relu(F.interpolate(self.decoder1_u(x), scale_factor=(1, 2, 2), mode='trilinear',align_corners=True))
##print("de",x.shape)
x = F.relu(F.interpolate(self.decoder2_u(x), scale_factor=(1, 2, 2), mode='trilinear',align_corners=True))
##print("de",x.shape)
x = F.relu(F.interpolate(self.decoder3_u(x), scale_factor=(2, 2, 2), mode='trilinear',align_corners=True))
##print("de",x.shape)
x_loc = x.clone()
# x = F.relu(F.interpolate(self.decoder5(x) , scale_factor=(2,2), mode ='trilinear'))
# start
for i in range(0, 4):
for j in range(0, 4):
x_p = xin[:, :, :, 32 * i:32 * (i + 1), 32 * j:32 * (j + 1)]
# begin patch wise
# ##print("x_p.shape",x_p.shape)
x_p = self.conv1_p(x_p)
x_p = self.bn1_p(x_p)
# x = F.max_pool2d(x,2,2)
x_p = self.relu(x_p)
x_p = self.conv2_p(x_p)
x_p = self.bn2_p(x_p)
# x = F.max_pool2d(x,2,2)
x_p = self.relu(x_p)
x_p = self.conv3_p(x_p)
x_p = self.bn3_p(x_p)
# x = F.max_pool2d(x,2,2)
x_p = self.relu(x_p)
# x = self.maxpool(x)
# pdb.set_trace()
# ##print("xp.shape",x_p.shape)
x1_p = self.layer1_p(x_p)
##print("layer1.shape",x1_p.shape)
# ##print(x1.shape)
x2_p = self.layer2_p(x1_p)
##print("layer2.shape",x2_p.shape)
# ##print(x2.shape)
x3_p = self.layer3_p(x2_p)
##print("layer3.shape",x3_p.shape)
# ###print(x3.shape)
x4_p = self.layer4_p(x3_p)
##print("layer4.shape",x4_p.shape)
x_p = F.relu(F.interpolate(self.decoder1_p(x4_p), scale_factor=(2, 2, 2), mode='trilinear',align_corners=True))
##print("_layer1.shape",x_p.shape)
x_p = torch.add(x_p, x4_p)
x_p = F.relu(F.interpolate(self.decoder2_p(x_p), scale_factor=(1, 2, 2), mode='trilinear',align_corners=True))
##print("_layer2.shape",x_p.shape)
x_p = torch.add(x_p, x3_p)
x_p = F.relu(F.interpolate(self.decoder3_p(x_p), scale_factor=(1, 2, 2), mode='trilinear',align_corners=True))
##print("_layer3.shape",x_p.shape)
x_p = torch.add(x_p, x2_p)
x_p = F.relu(F.interpolate(self.decoder4_p(x_p), scale_factor=(1, 2, 2), mode='trilinear',align_corners=True))
##print("_layer4.shape",x_p.shape)
x_p = torch.add(x_p, x1_p)
x_p = F.relu(F.interpolate(self.decoder5_p(x_p), scale_factor=(1, 2, 2), mode='trilinear',align_corners=True))
##print("_layer5.shape",x_p.shape)
x_loc[:, :, :, 32 * i:32 * (i + 1), 32 * j:32 * (j + 1)] = x_p
x = torch.add(x, x_loc)
x = F.relu(self.decoderf(x))
x = self.adjust_p(F.relu(x))
# pdb.set_trace()
return x
def forward(self, x):
seg_outputs = []
seg_outputs.append(self._forward_impl(x))
if self._deep_supervision and self.do_ds:
return tuple([seg_outputs[-1]])
else:
return seg_outputs[-1]
| 46.111111
| 127
| 0.578345
| 8,782
| 63,910
| 3.98941
| 0.044978
| 0.041673
| 0.020836
| 0.022321
| 0.9289
| 0.917197
| 0.907493
| 0.891023
| 0.874468
| 0.862623
| 0
| 0.044061
| 0.294023
| 63,910
| 1,386
| 128
| 46.111111
| 0.732441
| 0.132311
| 0
| 0.821616
| 0
| 0
| 0.017161
| 0.001532
| 0
| 0
| 0
| 0
| 0.003148
| 1
| 0.03043
| false
| 0
| 0.012592
| 0
| 0.118573
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
ab4a561c3abb8a22465b9d6e25b867437e3f97ec
| 158
|
py
|
Python
|
code/archs/segmentation/__init__.py
|
haasj22/Recycling_Segmentation
|
0420baf4c2ac2bd18b03791e123fb4b0ad7869b1
|
[
"MIT"
] | 1
|
2021-03-26T00:10:17.000Z
|
2021-03-26T00:10:17.000Z
|
code/archs/segmentation/__init__.py
|
haasj22/Recycling_Segmentation
|
0420baf4c2ac2bd18b03791e123fb4b0ad7869b1
|
[
"MIT"
] | null | null | null |
code/archs/segmentation/__init__.py
|
haasj22/Recycling_Segmentation
|
0420baf4c2ac2bd18b03791e123fb4b0ad7869b1
|
[
"MIT"
] | null | null | null |
from IIC.code.archs.segmentation.baselines import *
from IIC.code.archs.segmentation.net10a import *
from IIC.code.archs.segmentation.net10a_twohead import *
| 39.5
| 56
| 0.829114
| 22
| 158
| 5.909091
| 0.409091
| 0.161538
| 0.253846
| 0.369231
| 0.830769
| 0.615385
| 0.615385
| 0
| 0
| 0
| 0
| 0.027397
| 0.075949
| 158
| 3
| 57
| 52.666667
| 0.863014
| 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 8
|
db44a54bb26157747522ae9ef4e659d7f414bb68
| 20
|
py
|
Python
|
LuoguCodes/P3541.py
|
Anguei/OI-Codes
|
0ef271e9af0619d4c236e314cd6d8708d356536a
|
[
"MIT"
] | null | null | null |
LuoguCodes/P3541.py
|
Anguei/OI-Codes
|
0ef271e9af0619d4c236e314cd6d8708d356536a
|
[
"MIT"
] | null | null | null |
LuoguCodes/P3541.py
|
Anguei/OI-Codes
|
0ef271e9af0619d4c236e314cd6d8708d356536a
|
[
"MIT"
] | null | null | null |
print "756396726\n1"
| 20
| 20
| 0.8
| 3
| 20
| 5.333333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.526316
| 0.05
| 20
| 1
| 20
| 20
| 0.315789
| 0
| 0
| 0
| 0
| 0
| 0.571429
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 1
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 7
|
db4f390adef9cecbab2b2bd5946ed5a1f69f8807
| 2,479
|
py
|
Python
|
tests/test_fakeap.py
|
kmillerusaf/profiler
|
3fc19ef36ba0fe95fd9c0c7e775e5d1e5c26d253
|
[
"BSD-3-Clause"
] | 3
|
2020-04-11T14:50:10.000Z
|
2020-12-31T00:21:28.000Z
|
tests/test_fakeap.py
|
kmillerusaf/profiler
|
3fc19ef36ba0fe95fd9c0c7e775e5d1e5c26d253
|
[
"BSD-3-Clause"
] | 32
|
2020-05-05T15:37:38.000Z
|
2021-03-05T05:24:11.000Z
|
tests/test_fakeap.py
|
kmillerusaf/profiler
|
3fc19ef36ba0fe95fd9c0c7e775e5d1e5c26d253
|
[
"BSD-3-Clause"
] | 7
|
2020-06-12T00:49:38.000Z
|
2020-12-31T00:21:30.000Z
|
# -*- coding: utf-8 -*-
import pytest
import multiprocessing as mp
from profiler import fakeap
class TestFakeAP:
@pytest.mark.parametrize(
"seq,expected",
[(mp.Value("i", 1), 2), (mp.Value("i", 1969), 1970), (mp.Value("i", 4096), 1)],
)
def test_next_sequence_number(self, seq, expected):
assert fakeap._Utils.next_sequence_number(seq) == expected
def test_build_fake_frame_ies(self):
conf = {
"GENERAL": {
"ssid": "WLAN Pi",
"channel": 36,
"interface": "wlan1",
"files_path": "/var/www/html/profiler",
}
}
frame = fakeap._Utils.build_fake_frame_ies(conf)
frame_bytes = bytes(frame)
# 2.0.1
old = b"\x00\x07WLAN Pi\x01\x08\x8c\x12\x98$\xb0H`l\x03\x01$\x05\x06\x05\x04\x00\x03\x00\x00-\x1a\xef\x19\x1b\xff\xff\xff\x00\x00\x00\x00\x00\x00\x00\x00\x00 \x00\x00\x00\x00\x00\x00\x00\x00\x00\x000\x18\x01\x00\x00\x0f\xac\x04\x01\x00\x00\x0f\xac\x04\x02\x00\x00\x0f\xac\x02\x00\x0f\xac\x04\x8c\x00=\x16$\x00\x04\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x006\x03E\xc2\x00F\x05\x02\x00\x00\x00\x00\x7f\x08\x00\x00\x08\x00\x00\x00\x00@\xbf\x0c2\x00\x80\x03\xaa\xff\x00\x00\xaa\xff\x00\x00\xc0\x05\x00$\x00\x00\x00\xff##\t\x01\x00\x02@\x00\x04p\x0c\x80\x02\x03\x80\x04\x00\x00\x00\xaa\xff\xaa\xff{\x1c\xc7q\x1c\xc7q\x1c\xc7q\x1c\xc7q\xff\x07$\xf4?\x00\x19\xfc\xff\xdd\x18\x00P\xf2\x02\x01\x01\x8a\x00\x03\xa4\x00\x00'\xa4\x00\x00BC^\x00b2/\x00"
# 4ss HT, 8 ss HE, HE beamforming, etc
known = b"\x00\x07WLAN Pi\x01\x08\x8c\x12\x98$\xb0H`l\x03\x01$\x05\x06\x05\x04\x00\x03\x00\x00-\x1a\xef\x19\x1b\xff\xff\xff\xff\x00\x00\x00\x00\x00\x00\x00\x00 \x00\x00\x00\x00\x00\x00\x00\x00\x00\x000\x18\x01\x00\x00\x0f\xac\x04\x01\x00\x00\x0f\xac\x04\x02\x00\x00\x0f\xac\x02\x00\x0f\xac\x04\x8c\x00=\x16$\x00\x04\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x006\x03E\xc2\x00F\x05\x02\x00\x00\x00\x00\x7f\x08\x00\x00\x08\x00\x00\x00\x00@\xbf\x0c2\x00\x80\x03\xaa\xff\x00\x00\xaa\xff\x00\x00\xc0\x05\x00$\x00\x00\x00\xff##\r\x01\x00\x02@\x00\x04p\x0c\x89\x7f\x03\x80\x04\x00\x00\x00\xaa\xaa\xaa\xaa{\x1c\xc7q\x1c\xc7q\x1c\xc7q\x1c\xc7q\xff\x07$\xf4?\x00\x19\xfc\xff\xff\x03'\x05\x00\xff\x0e&\t\x03\xa4('\xa4(Bs(br(\xff\x03;\x00\x00\xdd\x18\x00P\xf2\x02\x01\x01\x8a\x00\x03\xa4\x00\x00'\xa4\x00\x00BC^\x00b2/\x00"
assert frame_bytes == known
| 75.121212
| 849
| 0.664784
| 486
| 2,479
| 3.355967
| 0.236626
| 0.389945
| 0.424893
| 0.478234
| 0.673207
| 0.673207
| 0.649908
| 0.624157
| 0.624157
| 0.624157
| 0
| 0.285192
| 0.117386
| 2,479
| 33
| 850
| 75.121212
| 0.460238
| 0.025817
| 0
| 0
| 0
| 0.083333
| 0.696517
| 0.658375
| 0
| 0
| 0
| 0
| 0.083333
| 1
| 0.083333
| false
| 0
| 0.125
| 0
| 0.25
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 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
| 8
|
db666e733eab6f4fd4d23a2a1f502f21e0988ff5
| 71
|
py
|
Python
|
archive_verify/redis_client.py
|
Molmed/snpseq-archive-verify
|
0e9fb9558108138187ab453cfedc7309d2a6cf13
|
[
"MIT"
] | null | null | null |
archive_verify/redis_client.py
|
Molmed/snpseq-archive-verify
|
0e9fb9558108138187ab453cfedc7309d2a6cf13
|
[
"MIT"
] | 1
|
2018-09-17T15:04:33.000Z
|
2018-09-21T14:05:54.000Z
|
archive_verify/redis_client.py
|
Molmed/snpseq-archive-verify
|
0e9fb9558108138187ab453cfedc7309d2a6cf13
|
[
"MIT"
] | 5
|
2018-01-17T20:32:47.000Z
|
2018-08-30T14:48:44.000Z
|
from redis import Redis
def get_redis_instance():
return Redis()
| 11.833333
| 25
| 0.732394
| 10
| 71
| 5
| 0.7
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.197183
| 71
| 5
| 26
| 14.2
| 0.877193
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| true
| 0
| 0.333333
| 0.333333
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 1
| 1
| 0
|
0
| 7
|
db704f380b6db26580a5e5cd060a86283335c124
| 9,162
|
py
|
Python
|
bin/gpio-pingroup-input-examples.py
|
ralph-mcardell/dibase-rpi-python
|
724c18d1f3c6745b3dddf582ea2272ed4e2df8ac
|
[
"BSD-3-Clause"
] | null | null | null |
bin/gpio-pingroup-input-examples.py
|
ralph-mcardell/dibase-rpi-python
|
724c18d1f3c6745b3dddf582ea2272ed4e2df8ac
|
[
"BSD-3-Clause"
] | null | null | null |
bin/gpio-pingroup-input-examples.py
|
ralph-mcardell/dibase-rpi-python
|
724c18d1f3c6745b3dddf582ea2272ed4e2df8ac
|
[
"BSD-3-Clause"
] | null | null | null |
#!/usr/bin/python
'''
Example uses of the dibase.rpi.gpio package modules.
Shows examples of using multiple pin IO objects to input from GPIO pins.
Developed by R.E. McArdell / Dibase Limited.
Copyright (c) 2012 Dibase Limited
License: dual: GPL or BSD.
'''
import time
import sys
if __name__ == '__main__':
sys.path.insert(0, './..')
import dibase.rpi.gpio.pingroup as pingroup
import dibase.rpi.gpio.gpioerror as error
def poll_GPIO_GEN6_GCLK_opened_non_blocking_integer():
INTERVAL = 1.0 # seconds
with pingroup.open_pingroup( [ pingroup.PinId.p1_gpio_gen6()
, pingroup.PinId.p1_gpio_gclk()
], 'rNI' ) as in_pins:
print "Start sampling GPIO_GEN6 and GPIO_GCLK (open mode rNI) ..."
time.sleep(INTERVAL)
first_sample = in_pins.read()
time.sleep(INTERVAL)
second_sample = in_pins.read()
time.sleep(INTERVAL)
third_sample = in_pins.read()
print "Read 3 samples from P1 GPIO_GEN6, GPIO_GCLK at approximately",\
INTERVAL, "second intervals:", first_sample, second_sample,\
third_sample
def poll_GPIO_GEN6_GCLK_opened_non_blocking_sequence():
INTERVAL = 1.0 # seconds
with pingroup.open_pingroup( [ pingroup.PinId.p1_gpio_gen6()
, pingroup.PinId.p1_gpio_gclk()
], 'rNS' ) as in_pins:
print "Start sampling GPIO_GEN6 and GPIO_GCLK (open mode rNS) ..."
time.sleep(INTERVAL)
first_sample = list(in_pins.read())
time.sleep(INTERVAL)
second_sample = list(in_pins.read())
time.sleep(INTERVAL)
third_sample = list(in_pins.read())
print "Read 3 samples from P1 GPIO_GEN6, GPIO_GCLK at approximately",\
INTERVAL, "second intervals:", first_sample, second_sample,\
third_sample
def poll_GPIO_GEN6_GCLK_opened_blocking_on_rising_edge_integer():
INTERVAL = 1.0 # seconds
with pingroup.open_pingroup( [ pingroup.PinId.p1_gpio_gen6()
, pingroup.PinId.p1_gpio_gclk()
], 'rRI' ) as in_pins:
print "Start sampling GPIO_GEN6 and GPIO_GCLK (open mode rRI) ..."
in_pins.read() # returns immediately with initial state of pins
time.sleep(INTERVAL)
first_sample = in_pins.read(0)
time.sleep(INTERVAL)
second_sample = in_pins.read(0)
time.sleep(INTERVAL)
third_sample = in_pins.read(0)
print "Read 3 samples from P1 GPIO_GEN6, GPIO_GCLK at approximately",\
INTERVAL, "second intervals:", first_sample, second_sample,\
third_sample
def poll_GPIO_GEN6_GCLK_opened_blocking_on_rising_edge_sequence():
INTERVAL = 1.0 # seconds
with pingroup.open_pingroup( [ pingroup.PinId.p1_gpio_gen6()
, pingroup.PinId.p1_gpio_gclk()
], 'rRS' ) as in_pins:
print "Start sampling GPIO_GEN6 and GPIO_GCLK (open mode rRS) ..."
in_pins.read() # returns immediately with initial state of pins
time.sleep(INTERVAL)
first_sample = list(in_pins.read(0))
time.sleep(INTERVAL)
second_sample = list(in_pins.read(0))
time.sleep(INTERVAL)
third_sample = list(in_pins.read(0))
print "Read 3 samples from P1 GPIO_GEN6, GPIO_GCLK at approximately",\
INTERVAL, "second intervals:", first_sample, second_sample,\
third_sample
def wait_on_rising_edge_GPIO_GEN6_GCLK_no_timeout_integer():
with pingroup.open_pingroup( [ pingroup.PinId.p1_gpio_gen6()
, pingroup.PinId.p1_gpio_gclk()
], 'rRI' ) as in_pins:
in_pins.read() # returns immediately with initial state of pins
print "Start sampling 3 transitions to HIGH state on P1 GPIO_GEN6 or GPIO_GCLK..."
in_pins.read()
print "High once"
in_pins.read()
print "High twice"
in_pins.read()
print "High thrice"
print "P1 GPIO_GEN6 or GPIO_GCLK gone high 3 times"
def wait_on_rising_edge_GPIO_GEN6_GCLK_no_timeout_sequence():
with pingroup.open_pingroup( [ pingroup.PinId.p1_gpio_gen6()
, pingroup.PinId.p1_gpio_gclk()
], 'rRS' ) as in_pins:
in_pins.read() # returns immediately with initial state of pins
print "Start sampling 3 transitions to HIGH state on P1 GPIO_GEN6 or GPIO_GCLK..."
first_sample = list(in_pins.read())
print "High once"
second_sample = list(in_pins.read())
print "High twice"
third_sample = list(in_pins.read())
print "High thrice"
print "P1 GPIO_GEN6 or GPIO_GCLK gone high 3 times, returned values were",\
first_sample, second_sample, third_sample
def wait_on_both_GPIO_GEN6_GCLK_no_timeout_integer():
with pingroup.open_pingroup( [ pingroup.PinId.p1_gpio_gen6()
, pingroup.PinId.p1_gpio_gclk()
], 'rBI' ) as in_pins:
in_pins.read() # returns immediately with initial state of pins
print "Start waiting for 3 state changes on P1 GPIO_GEN6 or GPIO_GCLK..."
first_sample = in_pins.read()
second_sample = in_pins.read()
third_sample = in_pins.read()
print "Read 3 samples from P1 GPIO_GEN6 and GPIO_GCLK ",\
first_sample, second_sample, third_sample
def wait_on_both_GPIO_GEN6_GCLK_no_timeout_sequence():
with pingroup.open_pingroup( [ pingroup.PinId.p1_gpio_gen6()
, pingroup.PinId.p1_gpio_gclk()
], 'rBS' ) as in_pins:
in_pins.read() # returns immediately with initial state of pins
print "Start waiting for 3 state changes on P1 GPIO_GEN6 or GPIO_GCLK..."
first_sample = list(in_pins.read())
second_sample = list(in_pins.read())
third_sample = list(in_pins.read())
print "Read 3 samples from P1 GPIO_GEN6 and GPIO_GCLK ",\
first_sample, second_sample, third_sample
def wait_on_both_GPIO_GEN6_GCLK_with_timeouts_integer():
with pingroup.open_pingroup( [ pingroup.PinId.p1_gpio_gen6()
, pingroup.PinId.p1_gpio_gclk()
], 'rBI' ) as in_pins:
in_pins.read() # returns immediately with initial state of pins
print "Waiting for 0.0001s for state change on P1 GPIO_GEN6 or GPIO_GCLK, timeout probable..."
value = in_pins.read( 0.0001 )
if value==None:
print "Wait for input change timed out..."
else:
print "Value read:", value
print "Waiting for 10000s for state change on P1 GPIO_GEN6 or GPIO_GCLK, timeout less probable..."
value = in_pins.read( 10000 )
if value==None:
print "Wait for input change timed out..."
else:
print "Value read:", value
def wait_on_both_GPIO_GEN6_GCLK_with_timeouts_sequence():
with pingroup.open_pingroup( [ pingroup.PinId.p1_gpio_gen6()
, pingroup.PinId.p1_gpio_gclk()
], 'rBS' ) as in_pins:
in_pins.read() # returns immediately with initial state of pins
print "Waiting for 0.0001s for state change on P1 GPIO_GEN6 or GPIO_GCLK, timeout probable..."
value = in_pins.read(0.0001)
if value==None:
print "Wait for input change timed out..."
else:
print "Value read:", value
print "Waiting for 10000s for state change on P1 GPIO_GEN6 or GPIO_GCLK, timeout less probable..."
value = in_pins.read(10000)
if value==None:
print "Wait for input change timed out..."
else:
print "Value read:", value
if __name__ == '__main__':
try:
INTERVAL = 0.3 # seconds
poll_GPIO_GEN6_GCLK_opened_non_blocking_integer()
time.sleep( INTERVAL )
poll_GPIO_GEN6_GCLK_opened_non_blocking_sequence()
time.sleep( INTERVAL )
poll_GPIO_GEN6_GCLK_opened_blocking_on_rising_edge_integer()
time.sleep( INTERVAL )
poll_GPIO_GEN6_GCLK_opened_blocking_on_rising_edge_sequence()
time.sleep( INTERVAL )
wait_on_rising_edge_GPIO_GEN6_GCLK_no_timeout_integer()
time.sleep( INTERVAL )
wait_on_rising_edge_GPIO_GEN6_GCLK_no_timeout_sequence()
time.sleep( INTERVAL )
wait_on_both_GPIO_GEN6_GCLK_no_timeout_integer()
time.sleep( INTERVAL )
wait_on_both_GPIO_GEN6_GCLK_no_timeout_sequence()
time.sleep( INTERVAL )
wait_on_both_GPIO_GEN6_GCLK_with_timeouts_integer()
time.sleep( INTERVAL )
wait_on_both_GPIO_GEN6_GCLK_with_timeouts_sequence()
except error.GPIOError, e:
print "Oops unexpected GPIO related error:",\
e.__class__.__name__,':', e
except ValueError:
print "Oops unexpected value error!"
| 43.837321
| 106
| 0.627156
| 1,180
| 9,162
| 4.559322
| 0.113559
| 0.074349
| 0.066915
| 0.070632
| 0.90948
| 0.903532
| 0.887175
| 0.877323
| 0.84145
| 0.771747
| 0
| 0.02445
| 0.29022
| 9,162
| 208
| 107
| 44.048077
| 0.80286
| 0.047151
| 0
| 0.741573
| 0
| 0
| 0.20414
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0.022472
| null | null | 0.202247
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
db71f48624d1b9f529aa05c213f033c4c1640396
| 223
|
py
|
Python
|
test_hello_package.py
|
raja073/hello-package
|
37d43948fb44cd9d99dd7903b396525fb5f4a86b
|
[
"MIT"
] | null | null | null |
test_hello_package.py
|
raja073/hello-package
|
37d43948fb44cd9d99dd7903b396525fb5f4a86b
|
[
"MIT"
] | null | null | null |
test_hello_package.py
|
raja073/hello-package
|
37d43948fb44cd9d99dd7903b396525fb5f4a86b
|
[
"MIT"
] | null | null | null |
from hello_package import hello_package
def test_hello_package_no_params():
assert hello_package() == "Hello, Package!"
def test_hello_package_with_params():
assert hello_package("Dinkan") == "Hello, Dinkan"
| 17.153846
| 53
| 0.748879
| 29
| 223
| 5.344828
| 0.37931
| 0.541935
| 0.193548
| 0.245161
| 0.4
| 0.4
| 0
| 0
| 0
| 0
| 0
| 0
| 0.147982
| 223
| 12
| 54
| 18.583333
| 0.815789
| 0
| 0
| 0
| 0
| 0
| 0.154545
| 0
| 0
| 0
| 0
| 0
| 0.4
| 1
| 0.4
| true
| 0
| 0.2
| 0
| 0.6
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 1
| 0
|
0
| 7
|
db94c87d2ecc6b6a29f266b1c678c56fe6ba1cd3
| 6,038
|
py
|
Python
|
landlab/components/erosion_deposition/tests/test_erodep_steady_state.py
|
vaga3461/landlab
|
a52de95e6895c93a7a0b7c9fcc05114e53d5bda6
|
[
"MIT"
] | null | null | null |
landlab/components/erosion_deposition/tests/test_erodep_steady_state.py
|
vaga3461/landlab
|
a52de95e6895c93a7a0b7c9fcc05114e53d5bda6
|
[
"MIT"
] | null | null | null |
landlab/components/erosion_deposition/tests/test_erodep_steady_state.py
|
vaga3461/landlab
|
a52de95e6895c93a7a0b7c9fcc05114e53d5bda6
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Thu Jul 27 14:23:25 2017
@author: gtucker
"""
import numpy as np
from numpy.testing import assert_equal
from landlab import RasterModelGrid
from landlab.components import ErosionDeposition, FlowAccumulator
def test_erodep_slope_area_small_vs():
"""Test steady state run with Vs << 1."""
# Set up a 5x5 grid with open boundaries and low initial elevations.
rg = RasterModelGrid((5, 5))
z = rg.add_zeros("node", "topographic__elevation")
z[:] = 0.01 * rg.x_of_node
# Create a D8 flow handler
fa = FlowAccumulator(rg, flow_director="FlowDirectorD8")
# Parameter values for test 1
K = 0.001
vs = 0.0001
U = 0.001
dt = 10.0
# Create the ErosionDeposition component...
ed = ErosionDeposition(
rg, K=K, phi=0.0, v_s=vs, m_sp=0.5, n_sp=1.0, solver="adaptive"
)
# ... and run it to steady state.
for i in range(1000):
fa.run_one_step()
ed.run_one_step(dt=dt)
z[rg.core_nodes] += U * dt
# Test the results
s = rg.at_node["topographic__steepest_slope"]
sa_factor = (1.0 + vs) * U / K
a11 = 2.0
a12 = 1.0
s = rg.at_node["topographic__steepest_slope"]
s11 = sa_factor * (a11 ** -0.5)
s12 = sa_factor * (a12 ** -0.5)
assert_equal(np.round(s[11], 3), np.round(s11, 3))
assert_equal(np.round(s[12], 3), np.round(s12, 3))
def test_erodep_slope_area_big_vs():
"""Test steady state run with Vs >> 1."""
# Set up a 5x5 grid with open boundaries and low initial elevations.
rg = RasterModelGrid((5, 5))
z = rg.add_zeros("node", "topographic__elevation")
z[:] = 0.01 * rg.x_of_node
# Create a D8 flow handler
fa = FlowAccumulator(rg, flow_director="FlowDirectorD8")
# Next test: big Vs
K = 1.0
vs = 1000.0
U = 0.001
dt = 10.0
# Create the ErosionDeposition component...
ed = ErosionDeposition(
rg, K=K, phi=0.0, v_s=vs, m_sp=0.5, n_sp=1.0, solver="adaptive"
)
# ... and run it to steady state.
for i in range(1000):
fa.run_one_step()
ed.run_one_step(dt=dt)
z[rg.core_nodes] += U * dt
# Test the results
s = rg.at_node["topographic__steepest_slope"]
sa_factor = (1.0 + vs) * U / K
a11 = 2.0
a12 = 1.0
s11 = sa_factor * (a11 ** -0.5)
s12 = sa_factor * (a12 ** -0.5)
assert_equal(np.round(s[11], 2), np.round(s11, 2))
assert_equal(np.round(s[12], 2), np.round(s12, 2))
def test_erodep_slope_area_with_vs_unity():
"""Test steady state run with Vs = 1."""
# Set up a 5x5 grid with open boundaries and low initial elevations.
rg = RasterModelGrid((5, 5))
z = rg.add_zeros("node", "topographic__elevation")
z[:] = 0.01 * rg.x_of_node
# Create a D8 flow handler
fa = FlowAccumulator(rg, flow_director="FlowDirectorD8")
# test: Vs = 1
K = 0.002
vs = 1.0
U = 0.001
dt = 10.0
# Create the ErosionDeposition component...
ed = ErosionDeposition(
rg, K=K, phi=0.0, v_s=vs, m_sp=0.5, n_sp=1.0, solver="adaptive"
)
# ... and run it to steady state.
for i in range(1000):
fa.run_one_step()
ed.run_one_step(dt=dt)
z[rg.core_nodes] += U * dt
# Test the results
s = rg.at_node["topographic__steepest_slope"]
sa_factor = (1.0 + vs) * U / K
a11 = 2.0
a12 = 1.0
s11 = sa_factor * (a11 ** -0.5)
s12 = sa_factor * (a12 ** -0.5)
assert_equal(np.round(s[11], 2), np.round(s11, 2))
assert_equal(np.round(s[12], 2), np.round(s12, 2))
def test_erodep_slope_area_shear_stress_scaling():
"""Test steady state run with m_sp = 0.33, n_sp=0.67, Vs = 1."""
# Set up a 5x5 grid with open boundaries and low initial elevations.
rg = RasterModelGrid((5, 5))
rg.set_closed_boundaries_at_grid_edges(True, True, True, False)
z = rg.add_zeros("node", "topographic__elevation")
z[:] = 0.01 * rg.x_of_node
# Create a D8 flow handler
fa = FlowAccumulator(rg, flow_director="FlowDirectorD8")
# test: Vs = 1
K = 0.002
vs = 1.0
U = 0.001
dt = 10.0
m_sp = 0.33
n_sp = 0.67
# Create the ErosionDeposition component...
ed = ErosionDeposition(
rg, K=K, phi=0.0, v_s=vs, m_sp=m_sp, n_sp=n_sp, solver="adaptive"
)
# ... and run it to steady state.
for i in range(1500):
fa.run_one_step()
ed.run_one_step(dt=dt)
z[rg.core_nodes] += U * dt
# Test the results
s = rg.at_node["topographic__steepest_slope"]
sa_factor = ((1.0 + vs) * U / K) ** (1.0 / n_sp)
a6 = rg.at_node["drainage_area"][6]
a8 = rg.at_node["drainage_area"][8]
s6 = sa_factor * (a6 ** -(m_sp / n_sp))
s8 = sa_factor * (a8 ** -(m_sp / n_sp))
assert_equal(np.round(s[6], 2), np.round(s6, 2))
assert_equal(np.round(s[8], 2), np.round(s8, 2))
def test_erodep_slope_area_with_threshold():
"""Test steady state run with Vs = 1 and wc = 0.00001."""
# Set up a 5x5 grid with open boundaries and low initial elevations.
rg = RasterModelGrid((5, 5))
z = rg.add_zeros("node", "topographic__elevation")
z[:] = 0.01 * rg.x_of_node
# Create a D8 flow handler
fa = FlowAccumulator(rg, flow_director="FlowDirectorD8")
# test: Vs = 1
K = 0.002
vs = 1.0
U = 0.001
dt = 10.0
wc = 0.0001
# Create the ErosionDeposition component...
ed = ErosionDeposition(
rg, K=K, phi=0.0, v_s=vs, m_sp=0.5, n_sp=1.0, sp_crit=wc, solver="adaptive"
)
# ... and run it to steady state.
for i in range(1000):
fa.run_one_step()
ed.run_one_step(dt=dt)
z[rg.core_nodes] += U * dt
# Test the results
s = rg.at_node["topographic__steepest_slope"]
sa_factor = ((1.0 + vs) * U + wc) / K # approximate sol'n
a11 = 2.0
a12 = 1.0
s11 = sa_factor * (a11 ** -0.5)
s12 = sa_factor * (a12 ** -0.5)
assert_equal(np.round(s[11], 2), np.round(s11, 2))
assert_equal(np.round(s[12], 2), np.round(s12, 2))
| 28.347418
| 83
| 0.602021
| 1,002
| 6,038
| 3.46008
| 0.142715
| 0.040381
| 0.028843
| 0.051918
| 0.854918
| 0.818864
| 0.807038
| 0.781367
| 0.774445
| 0.774445
| 0
| 0.080248
| 0.252898
| 6,038
| 212
| 84
| 28.481132
| 0.688317
| 0.220768
| 0
| 0.728682
| 0
| 0
| 0.092241
| 0.058621
| 0
| 0
| 0
| 0
| 0.085271
| 1
| 0.03876
| false
| 0
| 0.031008
| 0
| 0.069767
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| 0
| 0
|
0
| 7
|
dba4cff7cbb70da167c683a63c0533519e7e4eb5
| 323
|
py
|
Python
|
Validation/HcalHits/python/SimHitsValidationSequence_cff.py
|
pasmuss/cmssw
|
566f40c323beef46134485a45ea53349f59ae534
|
[
"Apache-2.0"
] | null | null | null |
Validation/HcalHits/python/SimHitsValidationSequence_cff.py
|
pasmuss/cmssw
|
566f40c323beef46134485a45ea53349f59ae534
|
[
"Apache-2.0"
] | null | null | null |
Validation/HcalHits/python/SimHitsValidationSequence_cff.py
|
pasmuss/cmssw
|
566f40c323beef46134485a45ea53349f59ae534
|
[
"Apache-2.0"
] | null | null | null |
import FWCore.ParameterSet.Config as cms
from Validation.HcalHits.SimHitsValidationHcal_cfi import *
import Validation.HcalHits.SimHitsValidationHcal_cfi
AllSimHitsValidation = Validation.HcalHits.SimHitsValidationHcal_cfi.simHitsValidationHcal.clone()
hcalSimHitsValidationSequence = cms.Sequence(AllSimHitsValidation)
| 35.888889
| 98
| 0.885449
| 28
| 323
| 10.107143
| 0.535714
| 0.190813
| 0.413428
| 0.44523
| 0
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| 0
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| 0
| 0
| 0.058824
| 323
| 8
| 99
| 40.375
| 0.930921
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| 0
| 1
| 0
|
0
| 7
|
dbb0a354440a4a5a0c82034933b6754263fd511d
| 148
|
py
|
Python
|
test/__init__.py
|
coatk1/oboyo
|
8df1acfbb250ffd15203b9d83d2d4b26efd3d0f0
|
[
"MIT"
] | null | null | null |
test/__init__.py
|
coatk1/oboyo
|
8df1acfbb250ffd15203b9d83d2d4b26efd3d0f0
|
[
"MIT"
] | null | null | null |
test/__init__.py
|
coatk1/oboyo
|
8df1acfbb250ffd15203b9d83d2d4b26efd3d0f0
|
[
"MIT"
] | null | null | null |
#from geo.calc import Calc
#from geo.calc import Distance
#from geo.geosp import Wt
#from geo.geosp import Gh
#from geo.files.csv_file import check
| 24.666667
| 37
| 0.790541
| 27
| 148
| 4.296296
| 0.444444
| 0.301724
| 0.189655
| 0.293103
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.135135
| 148
| 5
| 38
| 29.6
| 0.90625
| 0.932432
| 0
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| 0
| 0
| 0
| 0
|
0
| 7
|
dbd960eae87bf15eb1b434be84e18f6b7c92aac9
| 60
|
py
|
Python
|
devilry/devilry_message/models/__init__.py
|
aless80/devilry-django
|
416c262e75170d5662542f15e2d7fecf5ab84730
|
[
"BSD-3-Clause"
] | 29
|
2015-01-18T22:56:23.000Z
|
2020-11-10T21:28:27.000Z
|
devilry/devilry_message/models/__init__.py
|
aless80/devilry-django
|
416c262e75170d5662542f15e2d7fecf5ab84730
|
[
"BSD-3-Clause"
] | 786
|
2015-01-06T16:10:18.000Z
|
2022-03-16T11:10:50.000Z
|
devilry/devilry_message/models/__init__.py
|
aless80/devilry-django
|
416c262e75170d5662542f15e2d7fecf5ab84730
|
[
"BSD-3-Clause"
] | 15
|
2015-04-06T06:18:43.000Z
|
2021-02-24T12:28:30.000Z
|
from .base import Message
from .base import MessageReceiver
| 20
| 33
| 0.833333
| 8
| 60
| 6.25
| 0.625
| 0.32
| 0.56
| 0
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| 0
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| 0
| 0.133333
| 60
| 2
| 34
| 30
| 0.961538
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| 1
| 0
|
0
| 7
|
916f48848d0fa03f46aca02d215bb8a18d6748a1
| 172
|
py
|
Python
|
src/run.py
|
codedesk/flask-gae-boilerplate
|
0af770555acc3fc43d8b75bdce2b1dc3fff29ae3
|
[
"CNRI-Python"
] | null | null | null |
src/run.py
|
codedesk/flask-gae-boilerplate
|
0af770555acc3fc43d8b75bdce2b1dc3fff29ae3
|
[
"CNRI-Python"
] | null | null | null |
src/run.py
|
codedesk/flask-gae-boilerplate
|
0af770555acc3fc43d8b75bdce2b1dc3fff29ae3
|
[
"CNRI-Python"
] | null | null | null |
import os
import sys
sys.path.insert(1, os.path.join(os.path.abspath('.'), 'lib'))
sys.path.insert(1, os.path.join(os.path.abspath('.'), 'application'))
import application
| 28.666667
| 69
| 0.709302
| 28
| 172
| 4.357143
| 0.357143
| 0.196721
| 0.213115
| 0.229508
| 0.606557
| 0.606557
| 0.606557
| 0.606557
| 0.606557
| 0.606557
| 0
| 0.0125
| 0.069767
| 172
| 5
| 70
| 34.4
| 0.75
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| 0
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| 0
| 1
| 0
| 1
| 0
|
0
| 8
|
917e620085411914958ed371e568adc9f6d69c78
| 11,212
|
py
|
Python
|
01_conv_network.py
|
metataro/DirectFeedbackAlignment
|
7e2cbc3f001ac2290a15440628bb2b97d4ec52ab
|
[
"MIT"
] | 5
|
2020-04-30T11:36:46.000Z
|
2021-09-09T06:08:34.000Z
|
01_conv_network.py
|
metataro/DirectFeedbackAlignment
|
7e2cbc3f001ac2290a15440628bb2b97d4ec52ab
|
[
"MIT"
] | null | null | null |
01_conv_network.py
|
metataro/DirectFeedbackAlignment
|
7e2cbc3f001ac2290a15440628bb2b97d4ec52ab
|
[
"MIT"
] | 1
|
2021-01-07T03:10:32.000Z
|
2021-01-07T03:10:32.000Z
|
from multiprocessing import freeze_support
import matplotlib.pyplot as plt
import numpy as np
import scipy.interpolate
import scipy.ndimage.filters
import dataset.cifar10_dataset
from network import activation, weight_initializer
from network.layers.conv_to_fully_connected import ConvToFullyConnected
from network.layers.convolution_im2col import Convolution
from network.layers.dropout import Dropout
from network.layers.fully_connected import FullyConnected
from network.layers.max_pool import MaxPool
from network.model import Model
from network.optimizer import GDMomentumOptimizer
if __name__ == '__main__':
"""
------------------------------------
DFA
------------------------------------
Result:
------------------------------------
loss on test set: 1.5317771647628007
accuracy on test set: 0.4463
Train statisistics:
------------------------------------
time spend during forward pass: 621.903642654419
time spend during backward pass: 90.21121907234192
time spend during update pass: 1.5559537410736084
time spend in total: 788.1902389526367
------------------------------------
BP
------------------------------------
Result:
------------------------------------
loss on test set: 1.0581702546107525
accuracy on test set: 0.6258
Train statisistics:
------------------------------------
time spend during forward pass: 575.9978523254395
time spend during backward pass: 269.75944471359253
time spend during update pass: 1.299713134765625
time spend in total: 918.6962807178497
"""
freeze_support()
num_iteration = 30
data = dataset.cifar10_dataset.load()
# layers = [
# Convolution((32, 3, 3, 3), stride=1, padding=1, dropout_rate=0, activation=activation.tanh),
# Convolution((32, 32, 3, 3), stride=1, padding=1, dropout_rate=0, activation=activation.tanh),
# MaxPool(size=2, stride=2),
# Dropout(0.2),
# Convolution((64, 32, 3, 3), stride=1, padding=1, dropout_rate=0, activation=activation.tanh),
# MaxPool(size=2, stride=2),
# Dropout(0.3),
# Convolution((128, 64, 3, 3), stride=1, padding=1, dropout_rate=0, activation=activation.tanh),
# MaxPool(size=2, stride=2),
# Dropout(0.4),
# ConvToFullyConnected(),
# FullyConnected(size=512, activation=activation.tanh),
# FullyConnected(size=10, activation=None, last_layer=True)
# ]
layers = [
MaxPool(size=2, stride=2),
Convolution((16, 3, 3, 3), stride=1, padding=1, dropout_rate=0, activation=activation.tanh),
MaxPool(size=2, stride=2),
Convolution((16, 16, 3, 3), stride=1, padding=1, dropout_rate=0, activation=activation.tanh),
MaxPool(size=2, stride=2),
Convolution((32, 16, 3, 3), stride=1, padding=1, dropout_rate=0, activation=activation.tanh),
MaxPool(size=2, stride=2),
ConvToFullyConnected(),
FullyConnected(size=64, activation=activation.tanh),
FullyConnected(size=10, activation=None, last_layer=True)
]
layers = [
# MaxPool(size=2, stride=2),
Convolution((8, 3, 4, 4), stride=2, padding=2, dropout_rate=0, activation=activation.tanh),
#MaxPool(size=2, stride=2),
Convolution((16, 8, 3, 3), stride=2, padding=1, dropout_rate=0, activation=activation.tanh),
#MaxPool(size=2, stride=2),
Convolution((32, 16, 3, 3), stride=2, padding=1, dropout_rate=0, activation=activation.tanh),
#MaxPool(size=2, stride=2),
ConvToFullyConnected(),
FullyConnected(size=64, activation=activation.tanh),
FullyConnected(size=10, activation=None, last_layer=True)
]
# -------------------------------------------------------
# Train with BP
# -------------------------------------------------------
model = Model(
layers=layers,
num_classes=10,
optimizer=GDMomentumOptimizer(lr=1e-2, mu=0.9),
lr_decay=0.5,
lr_decay_interval=7
)
print("\nRun training:\n------------------------------------")
stats_bp = model.train(data_set=data, method='bp', num_passes=num_iteration, batch_size=64)
loss, accuracy = model.cost(*data.test_set())
print("\nResult:\n------------------------------------")
print('loss on test set: {}'.format(loss))
print('accuracy on test set: {}'.format(accuracy))
print("\nTrain statisistics:\n------------------------------------")
print("time spend during forward pass: {}".format(stats_bp['forward_time']))
print("time spend during backward pass: {}".format(stats_bp['backward_time']))
print("time spend during update pass: {}".format(stats_bp['update_time']))
print("time spend in total: {}".format(stats_bp['total_time']))
# plt.title('Loss function')
# plt.xlabel('epoch')
# plt.ylabel('loss')
# plt.plot(np.arange(len(stats_bp['train_loss'])), stats_bp['train_loss'])
# plt.plot(stats_bp['valid_step'], stats_bp['valid_loss'])
# plt.legend(['train loss bp', 'validation loss bp'], loc='upper right')
# plt.grid(True)
# plt.show()
# plt.title('Accuracy')
# plt.xlabel('epoch')
# plt.ylabel('accuracy')
# plt.plot(np.arange(len(stats_bp['train_accuracy'])), stats_bp['train_accuracy'])
# plt.plot(stats_bp['valid_step'], stats_bp['valid_accuracy'])
# plt.legend(['train accuracy bp', 'validation accuracy bp'], loc='lower right')
# plt.grid(True)
# plt.show()
# exit()
# -------------------------------------------------------
# Train with DFA
# -------------------------------------------------------
model = Model(
layers=layers,
num_classes=10,
optimizer=GDMomentumOptimizer(lr=1e-2, mu=0.9),
# regularization=0.001,
# lr_decay=0.5,
# lr_decay_interval=3
# layers=layers,
# num_classes=10,
# optimizer=GDMomentumOptimizer(lr=1e-3, mu=0.9),
# regularization=0.001,
lr_decay=0.5,
lr_decay_interval=7
)
print("\nRun training:\n------------------------------------")
stats_dfa = model.train(data_set=data, method='dfa', num_passes=num_iteration, batch_size=64)
loss, accuracy = model.cost(*data.test_set())
print("\nResult:\n------------------------------------")
print('loss on test set: {}'.format(loss))
print('accuracy on test set: {}'.format(accuracy))
print("\nTrain statisistics:\n------------------------------------")
print("time spend during forward pass: {}".format(stats_dfa['forward_time']))
print("time spend during backward pass: {}".format(stats_dfa['backward_time']))
print("time spend during update pass: {}".format(stats_dfa['update_time']))
print("time spend in total: {}".format(stats_dfa['total_time']))
# plt.title('Loss function')
# plt.xlabel('epoch')
# plt.ylabel('loss')
# plt.plot(np.arange(len(stats_dfa['train_loss'])), stats_dfa['train_loss'])
# plt.plot(stats_dfa['valid_step'], stats_dfa['valid_loss'])
# plt.legend(['train loss dfa', 'validation loss dfa'], loc='upper right')
# plt.grid(True)
# plt.show()
# plt.title('Accuracy')
# plt.xlabel('epoch')
# plt.ylabel('accuracy')
# plt.plot(np.arange(len(stats_dfa['train_accuracy'])), stats_dfa['train_accuracy'])
# plt.plot(stats_dfa['valid_step'], stats_dfa['valid_accuracy'])
# plt.legend(['train accuracy dfa', 'validation accuracy dfa'], loc='lower right')
# plt.grid(True)
# plt.show()
# exit()
plt.title('Loss function')
plt.xlabel('epoch')
plt.ylabel('loss')
dfa_train_loss = scipy.ndimage.filters.gaussian_filter1d(stats_dfa['train_loss'], sigma=10)
bp_train_loss = scipy.ndimage.filters.gaussian_filter1d(stats_bp['train_loss'], sigma=10)
plt.plot(np.arange(len(stats_dfa['train_loss'])), dfa_train_loss)
plt.plot(stats_dfa['valid_step'], stats_dfa['valid_loss'])
plt.plot(np.arange(len(stats_bp['train_loss'])), bp_train_loss)
plt.plot(stats_bp['valid_step'], stats_bp['valid_loss'])
plt.legend(['train loss dfa', 'validation loss dfa', 'train loss bp', 'validation loss bp'], loc='upper right')
plt.grid(True)
plt.show()
plt.title('Accuracy')
plt.xlabel('epoch')
plt.ylabel('accuracy')
dfa_train_accuracy = scipy.ndimage.filters.gaussian_filter1d(stats_dfa['train_accuracy'], sigma=10)
bp_train_accuracy = scipy.ndimage.filters.gaussian_filter1d(stats_bp['train_accuracy'], sigma=10)
plt.plot(np.arange(len(stats_dfa['train_accuracy'])), dfa_train_accuracy)
plt.plot(stats_dfa['valid_step'], stats_dfa['valid_accuracy'])
plt.plot(np.arange(len(stats_bp['train_accuracy'])), bp_train_accuracy)
plt.plot(stats_bp['valid_step'], stats_bp['valid_accuracy'])
plt.legend(['train accuracy dfa', 'validation accuracy dfa', 'train accuracy bp', 'validation accuracy bp'], loc='lower right')
plt.grid(True)
plt.show()
# Forward, regularization, update and validation passes are excactly the same operations for dfa and bp. Therefore
# they should take euqally long. To ensure that inequalities don't affect the result, we normalize the time here.
# The reference time is the one measured for bp.
total_time_bp = stats_bp['total_time']
total_time_dfa = total_time_bp - stats_bp['backward_time'] + stats_dfa['backward_time']
step_to_time_bp = total_time_bp / len(stats_bp['train_loss'])
step_to_time_dfa = step_to_time_bp * total_time_dfa / stats_bp['total_time']
plt.title('Loss vs time')
plt.xlabel('time')
plt.ylabel('loss')
dfa_train_loss = scipy.ndimage.filters.gaussian_filter1d(stats_dfa['train_loss'], sigma=10)
bp_train_loss = scipy.ndimage.filters.gaussian_filter1d(stats_bp['train_loss'], sigma=10)
plt.plot(np.arange(len(stats_dfa['train_loss'])) * step_to_time_dfa, dfa_train_loss)
plt.plot(np.asarray(stats_dfa['valid_step']) * step_to_time_dfa, stats_dfa['valid_loss'])
plt.plot(np.arange(len(stats_bp['train_loss'])) * step_to_time_bp, bp_train_loss)
plt.plot(np.asarray(stats_bp['valid_step']) * step_to_time_bp, stats_bp['valid_loss'])
plt.legend(['train loss dfa', 'validation loss dfa', 'train loss bp', 'validation loss bp'], loc='best')
plt.grid(True)
plt.show()
plt.title('Accuracy vs time')
plt.xlabel('time')
plt.ylabel('accuracy')
dfa_train_accuracy = scipy.ndimage.filters.gaussian_filter1d(stats_dfa['train_accuracy'], sigma=10)
bp_train_accuracy = scipy.ndimage.filters.gaussian_filter1d(stats_bp['train_accuracy'], sigma=10)
plt.plot(np.arange(len(stats_dfa['train_accuracy'])) * step_to_time_dfa, dfa_train_accuracy)
plt.plot(np.asarray(stats_dfa['valid_step']) * step_to_time_dfa, stats_dfa['valid_accuracy'])
plt.plot(np.arange(len(stats_bp['train_accuracy'])) * step_to_time_bp, bp_train_accuracy)
plt.plot(np.asarray(stats_bp['valid_step']) * step_to_time_bp, stats_bp['valid_accuracy'])
plt.legend(['train accuracy dfa', 'validation accuracy dfa', 'train accuracy bp', 'validation accuracy bp'], loc='lower right')
plt.grid(True)
plt.show()
| 42.957854
| 131
| 0.634231
| 1,443
| 11,212
| 4.746362
| 0.123354
| 0.033728
| 0.021025
| 0.026281
| 0.816032
| 0.797635
| 0.762447
| 0.733246
| 0.722587
| 0.692364
| 0
| 0.039543
| 0.165448
| 11,212
| 260
| 132
| 43.123077
| 0.692423
| 0.242776
| 0
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| 0
| 0
| 0.215652
| 0.039063
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| 0.063492
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| 0.142857
| 0
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| 1
| 1
| 1
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| 1
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| null | 0
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| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 7
|
91aa57b660ed6f7f1260a8e6ea7d464f797acd70
| 2,636
|
py
|
Python
|
directdm/dm_eft.py
|
DirectDM/directdm-py
|
9e940703bc4e5b2266ce2c93c27abee755c0cbaf
|
[
"MIT"
] | 5
|
2017-09-09T16:22:00.000Z
|
2021-11-17T07:31:11.000Z
|
directdm/dm_eft.py
|
DirectDM/directdm-py
|
9e940703bc4e5b2266ce2c93c27abee755c0cbaf
|
[
"MIT"
] | 2
|
2018-04-17T16:43:27.000Z
|
2018-04-19T12:34:54.000Z
|
directdm/dm_eft.py
|
DirectDM/directdm-py
|
9e940703bc4e5b2266ce2c93c27abee755c0cbaf
|
[
"MIT"
] | 2
|
2018-05-10T17:39:57.000Z
|
2018-09-19T16:40:07.000Z
|
#!/usr/bin/env python3
import sys
import warnings
from directdm.num.num_input import Num_input
from directdm import wilson_coefficients as wc
#--------------------------#
# Define the default input #
#--------------------------#
default_input = Num_input().input_parameters
#---------------------------------------#
# "Wrapper" classes to be used by users #
#---------------------------------------#
class WC_3f(wc.WC_3flavor):
def __init__(self, coeff_dict, DM_type=None, user_input_dict=None):
""" 'wrapper' class providing input for 5 flavor Wilson coefficients """
if user_input_dict is None:
self.ip = default_input
else:
#print("Updating the default input parameters...")
self.ip = Num_input(user_input_dict).input_parameters
if DM_type is None:
DM_type = "D"
else:
pass
wc.WC_3flavor.__init__(self, coeff_dict, DM_type, self.ip)
class WC_4f(wc.WC_4flavor):
def __init__(self, coeff_dict, DM_type=None, user_input_dict=None):
""" 'wrapper' class providing input for 5 flavor Wilson coefficients """
if user_input_dict is None:
self.ip = default_input
else:
#print("Updating the default input parameters...")
self.ip = Num_input(user_input_dict).input_parameters
if DM_type is None:
DM_type = "D"
else:
pass
wc.WC_4flavor.__init__(self, coeff_dict, DM_type, self.ip)
class WC_5f(wc.WC_5flavor):
def __init__(self, coeff_dict, DM_type=None, user_input_dict=None):
""" 'wrapper' class providing input for 5 flavor Wilson coefficients """
if user_input_dict is None:
self.ip = default_input
else:
#print("Updating the default input parameters...")
self.ip = Num_input(user_input_dict).input_parameters
if DM_type is None:
DM_type = "D"
else:
pass
wc.WC_5flavor.__init__(self, coeff_dict, DM_type, self.ip)
class WC_EW(wc.WilCo_EW):
def __init__(self, coeff_dict, Ychi, dchi, DM_type=None, user_input_dict=None):
""" 'wrapper' class providing input for 5 flavor Wilson coefficients """
if user_input_dict is None:
self.ip = default_input
else:
#print("Updating the default input parameters...")
self.ip = Num_input(user_input_dict).input_parameters
if DM_type is None:
DM_type = "D"
else:
pass
wc.WilCo_EW.__init__(self, coeff_dict, Ychi, dchi, DM_type, self.ip)
| 29.617978
| 83
| 0.597496
| 340
| 2,636
| 4.308824
| 0.170588
| 0.065529
| 0.106485
| 0.092833
| 0.800683
| 0.798635
| 0.798635
| 0.798635
| 0.76041
| 0.76041
| 0
| 0.007239
| 0.266313
| 2,636
| 88
| 84
| 29.954545
| 0.750259
| 0.256449
| 0
| 0.714286
| 0
| 0
| 0.002086
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.081633
| false
| 0.081633
| 0.081633
| 0
| 0.244898
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 8
|
91c376824625f49cc2bd8af9ed8eaf738a29dd79
| 87
|
py
|
Python
|
build/lib/browsers_detector/__init__.py
|
Emire1/TemplateMatchResearch
|
57f30d291b3af61ff4483fd34c5922e849f3eac6
|
[
"MIT"
] | null | null | null |
build/lib/browsers_detector/__init__.py
|
Emire1/TemplateMatchResearch
|
57f30d291b3af61ff4483fd34c5922e849f3eac6
|
[
"MIT"
] | null | null | null |
build/lib/browsers_detector/__init__.py
|
Emire1/TemplateMatchResearch
|
57f30d291b3af61ff4483fd34c5922e849f3eac6
|
[
"MIT"
] | 1
|
2021-05-14T06:23:33.000Z
|
2021-05-14T06:23:33.000Z
|
from browsers_detector.browsers_detector import *
from browsers_detector.data import *
| 29
| 49
| 0.862069
| 11
| 87
| 6.545455
| 0.454545
| 0.666667
| 0.555556
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.091954
| 87
| 2
| 50
| 43.5
| 0.911392
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
37e4123f26465bb96983966faa3be89a2d996764
| 48,885
|
py
|
Python
|
app/hackney_law_data_client/apis/related_link_api.py
|
tombull/hackneylawclassifier
|
54cea27f77ec37317ca60a678805a528a1fc5a88
|
[
"MIT"
] | null | null | null |
app/hackney_law_data_client/apis/related_link_api.py
|
tombull/hackneylawclassifier
|
54cea27f77ec37317ca60a678805a528a1fc5a88
|
[
"MIT"
] | null | null | null |
app/hackney_law_data_client/apis/related_link_api.py
|
tombull/hackneylawclassifier
|
54cea27f77ec37317ca60a678805a528a1fc5a88
|
[
"MIT"
] | null | null | null |
# coding: utf-8
"""
RelatedLinkApi.py
Copyright 2016 SmartBear Software
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
from __future__ import absolute_import
import sys
import os
# python 2 and python 3 compatibility library
from six import iteritems
from ..configuration import Configuration
from ..api_client import ApiClient
class RelatedLinkApi(object):
"""
NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
Ref: https://github.com/swagger-api/swagger-codegen
"""
def __init__(self, api_client=None):
config = Configuration()
if api_client:
self.api_client = api_client
else:
if not config.api_client:
config.api_client = ApiClient()
self.api_client = config.api_client
def create_related_link(self, document, **kwargs):
"""
Create some relatedLinks
Create one or more relatedLinks.
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.create_related_link(document, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param RelatedLink document: Create a document by sending the paths to be added in the request body. (required)
:param str select: Select which paths will be returned by the query. [doc](https://github.com/wprl/baucis/wiki/Query-String-Parameters#select)
:param str populate: Specify which paths to populate. [doc](https://github.com/wprl/baucis/wiki/Query-String-Parameters#populate)
:param str sort: Set the fields by which to sort. [doc](https://github.com/wprl/baucis/wiki/Query-String-Parameters#sort)
:return: RelatedLink
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['document', 'select', 'populate', 'sort']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method create_related_link" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'document' is set
if ('document' not in params) or (params['document'] is None):
raise ValueError("Missing the required parameter `document` when calling `create_related_link`")
resource_path = '/relatedLinks'.replace('{format}', 'json')
path_params = {}
query_params = {}
if 'select' in params:
query_params['select'] = params['select']
if 'populate' in params:
query_params['populate'] = params['populate']
if 'sort' in params:
query_params['sort'] = params['sort']
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'document' in params:
body_params = params['document']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json', 'text/html'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type(['application/json'])
# Authentication setting
auth_settings = ['apikey', 'basic']
response = self.api_client.call_api(resource_path, 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='RelatedLink',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def delete_by_ids(self, document, **kwargs):
"""
Delete all the objects matching the ids provided.
Delete a set of object in one shot.
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.delete_by_ids(document, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param list[str] document: Array of Ids to delete. (required)
:return: None
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['document']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method delete_by_ids" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'document' is set
if ('document' not in params) or (params['document'] is None):
raise ValueError("Missing the required parameter `document` when calling `delete_by_ids`")
resource_path = '/relatedLinks/deleteByIds'.replace('{format}', 'json')
path_params = {}
query_params = {}
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'document' in params:
body_params = params['document']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json', 'text/html'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type(['application/json'])
# Authentication setting
auth_settings = ['apikey', 'basic']
response = self.api_client.call_api(resource_path, 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type=None,
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def delete_related_link_by_id(self, id, **kwargs):
"""
Delete a relatedLink by its unique ID
Deletes an existing relatedLink by its ID.
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.delete_related_link_by_id(id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param str id: The identifier of the resource. (required)
:param str select: Select which paths will be returned by the query. [doc](https://github.com/wprl/baucis/wiki/Query-String-Parameters#select)
:param str populate: Specify which paths to populate. [doc](https://github.com/wprl/baucis/wiki/Query-String-Parameters#populate)
:return: RelatedLink
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['id', 'select', 'populate']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method delete_related_link_by_id" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'id' is set
if ('id' not in params) or (params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `delete_related_link_by_id`")
resource_path = '/relatedLinks/{id}'.replace('{format}', 'json')
path_params = {}
if 'id' in params:
path_params['id'] = params['id']
query_params = {}
if 'select' in params:
query_params['select'] = params['select']
if 'populate' in params:
query_params['populate'] = params['populate']
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json', 'text/html'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type(['application/json'])
# Authentication setting
auth_settings = ['apikey', 'basic']
response = self.api_client.call_api(resource_path, 'DELETE',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='RelatedLink',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def delete_related_link_by_query(self, **kwargs):
"""
Delete some relatedLinks by query
Delete all relatedLinks matching the specified query.
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.delete_related_link_by_query(callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param str select: Select which paths will be returned by the query. [doc](https://github.com/wprl/baucis/wiki/Query-String-Parameters#select)
:param str populate: Specify which paths to populate. [doc](https://github.com/wprl/baucis/wiki/Query-String-Parameters#populate)
:param str sort: Set the fields by which to sort. [doc](https://github.com/wprl/baucis/wiki/Query-String-Parameters#sort)
:param int skip: How many documents to skip. [doc](https://github.com/wprl/baucis/wiki/Query-String-Parameters#skip)
:param int limit: The maximum number of documents to send. [doc](https://github.com/wprl/baucis/wiki/Query-String-Parameters#limit)
:param str conditions: Set the conditions used to find or remove the document(s). [doc](https://github.com/wprl/baucis/wiki/Query-String-Parameters#conditions)
:param str distinct: Set to a path name to retrieve an array of distinct values. [doc](https://github.com/wprl/baucis/wiki/Query-String-Parameters#distinct)
:param str hint: Add an index hint to the query (must be enabled per controller). [doc](https://github.com/wprl/baucis/wiki/Query-String-Parameters#hint)
:param str comment: Add a comment to a query (must be enabled per controller). [doc](https://github.com/wprl/baucis/wiki/Query-String-Parameters#comment)
:return: list[RelatedLink]
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['select', 'populate', 'sort', 'skip', 'limit', 'conditions', 'distinct', 'hint', 'comment']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method delete_related_link_by_query" % key
)
params[key] = val
del params['kwargs']
resource_path = '/relatedLinks'.replace('{format}', 'json')
path_params = {}
query_params = {}
if 'select' in params:
query_params['select'] = params['select']
if 'populate' in params:
query_params['populate'] = params['populate']
if 'sort' in params:
query_params['sort'] = params['sort']
if 'skip' in params:
query_params['skip'] = params['skip']
if 'limit' in params:
query_params['limit'] = params['limit']
if 'conditions' in params:
query_params['conditions'] = params['conditions']
if 'distinct' in params:
query_params['distinct'] = params['distinct']
if 'hint' in params:
query_params['hint'] = params['hint']
if 'comment' in params:
query_params['comment'] = params['comment']
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json', 'text/html'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type(['application/json'])
# Authentication setting
auth_settings = ['apikey', 'basic']
response = self.api_client.call_api(resource_path, 'DELETE',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='list[RelatedLink]',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def get_related_link_by_id(self, id, **kwargs):
"""
Get a relatedLink by its unique ID
Retrieve a relatedLink by its ID.
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_related_link_by_id(id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param str id: The identifier of the resource. (required)
:param str select: Select which paths will be returned by the query. [doc](https://github.com/wprl/baucis/wiki/Query-String-Parameters#select)
:param str populate: Specify which paths to populate. [doc](https://github.com/wprl/baucis/wiki/Query-String-Parameters#populate)
:return: RelatedLink
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['id', 'select', 'populate']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_related_link_by_id" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'id' is set
if ('id' not in params) or (params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `get_related_link_by_id`")
resource_path = '/relatedLinks/{id}'.replace('{format}', 'json')
path_params = {}
if 'id' in params:
path_params['id'] = params['id']
query_params = {}
if 'select' in params:
query_params['select'] = params['select']
if 'populate' in params:
query_params['populate'] = params['populate']
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json', 'text/html'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type(['application/json'])
# Authentication setting
auth_settings = ['apikey', 'basic']
response = self.api_client.call_api(resource_path, 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='RelatedLink',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def get_related_link_classification(self, id, **kwargs):
"""
Retrieves the linked classification.
Retrieves the linked classification.
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_related_link_classification(id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param str id: The ID of a RelatedLink. (required)
:return: Classification
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['id']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_related_link_classification" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'id' is set
if ('id' not in params) or (params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `get_related_link_classification`")
resource_path = '/relatedLinks/{id}/classification'.replace('{format}', 'json')
path_params = {}
if 'id' in params:
path_params['id'] = params['id']
query_params = {}
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json', 'text/html'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type(['application/json'])
# Authentication setting
auth_settings = ['apikey', 'basic']
response = self.api_client.call_api(resource_path, 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='Classification',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def get_related_link_related_answer(self, id, **kwargs):
"""
Retrieves the linked relatedAnswer.
Retrieves the linked relatedAnswer.
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.get_related_link_related_answer(id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param str id: The ID of a RelatedLink. (required)
:return: PotentialAnswer
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['id']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_related_link_related_answer" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'id' is set
if ('id' not in params) or (params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `get_related_link_related_answer`")
resource_path = '/relatedLinks/{id}/relatedAnswer'.replace('{format}', 'json')
path_params = {}
if 'id' in params:
path_params['id'] = params['id']
query_params = {}
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json', 'text/html'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type(['application/json'])
# Authentication setting
auth_settings = ['apikey', 'basic']
response = self.api_client.call_api(resource_path, 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='PotentialAnswer',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def query_related_link(self, **kwargs):
"""
Query some relatedLinks
Query over relatedLinks.
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.query_related_link(callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param str select: Select which paths will be returned by the query. [doc](https://github.com/wprl/baucis/wiki/Query-String-Parameters#select)
:param str populate: Specify which paths to populate. [doc](https://github.com/wprl/baucis/wiki/Query-String-Parameters#populate)
:param str sort: Set the fields by which to sort. [doc](https://github.com/wprl/baucis/wiki/Query-String-Parameters#sort)
:param bool count: Set to true to return count instead of documents. [doc](https://github.com/wprl/baucis/wiki/Query-String-Parameters#count)
:param int skip: How many documents to skip. [doc](https://github.com/wprl/baucis/wiki/Query-String-Parameters#skip)
:param int limit: The maximum number of documents to send. [doc](https://github.com/wprl/baucis/wiki/Query-String-Parameters#limit)
:param str conditions: Set the conditions used to find or remove the document(s). [doc](https://github.com/wprl/baucis/wiki/Query-String-Parameters#conditions)
:param str distinct: Set to a path name to retrieve an array of distinct values. [doc](https://github.com/wprl/baucis/wiki/Query-String-Parameters#distinct)
:param str hint: Add an index hint to the query (must be enabled per controller). [doc](https://github.com/wprl/baucis/wiki/Query-String-Parameters#hint)
:param str comment: Add a comment to a query (must be enabled per controller). [doc](https://github.com/wprl/baucis/wiki/Query-String-Parameters#comment)
:return: list[RelatedLink]
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['select', 'populate', 'sort', 'count', 'skip', 'limit', 'conditions', 'distinct', 'hint', 'comment']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method query_related_link" % key
)
params[key] = val
del params['kwargs']
resource_path = '/relatedLinks'.replace('{format}', 'json')
path_params = {}
query_params = {}
if 'select' in params:
query_params['select'] = params['select']
if 'populate' in params:
query_params['populate'] = params['populate']
if 'sort' in params:
query_params['sort'] = params['sort']
if 'count' in params:
query_params['count'] = params['count']
if 'skip' in params:
query_params['skip'] = params['skip']
if 'limit' in params:
query_params['limit'] = params['limit']
if 'conditions' in params:
query_params['conditions'] = params['conditions']
if 'distinct' in params:
query_params['distinct'] = params['distinct']
if 'hint' in params:
query_params['hint'] = params['hint']
if 'comment' in params:
query_params['comment'] = params['comment']
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json', 'text/html'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type(['application/json'])
# Authentication setting
auth_settings = ['apikey', 'basic']
response = self.api_client.call_api(resource_path, 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='list[RelatedLink]',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def set_related_link_classification(self, id, document, **kwargs):
"""
Link Classification.
Link Classification.
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.set_related_link_classification(id, document, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param str id: The ID of a RelatedLink. (required)
:param BodyIdParameter document: The ID of a classification. (required)
:return: RelatedLink
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['id', 'document']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method set_related_link_classification" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'id' is set
if ('id' not in params) or (params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `set_related_link_classification`")
# verify the required parameter 'document' is set
if ('document' not in params) or (params['document'] is None):
raise ValueError("Missing the required parameter `document` when calling `set_related_link_classification`")
resource_path = '/relatedLinks/{id}/classification'.replace('{format}', 'json')
path_params = {}
if 'id' in params:
path_params['id'] = params['id']
query_params = {}
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'document' in params:
body_params = params['document']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json', 'text/html'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type(['application/json'])
# Authentication setting
auth_settings = ['apikey', 'basic']
response = self.api_client.call_api(resource_path, 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='RelatedLink',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def set_related_link_related_answer(self, id, document, **kwargs):
"""
Link PotentialAnswer.
Link PotentialAnswer.
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.set_related_link_related_answer(id, document, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param str id: The ID of a RelatedLink. (required)
:param BodyIdParameter document: The ID of a potentialAnswer. (required)
:return: RelatedLink
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['id', 'document']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method set_related_link_related_answer" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'id' is set
if ('id' not in params) or (params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `set_related_link_related_answer`")
# verify the required parameter 'document' is set
if ('document' not in params) or (params['document'] is None):
raise ValueError("Missing the required parameter `document` when calling `set_related_link_related_answer`")
resource_path = '/relatedLinks/{id}/relatedAnswer'.replace('{format}', 'json')
path_params = {}
if 'id' in params:
path_params['id'] = params['id']
query_params = {}
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'document' in params:
body_params = params['document']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json', 'text/html'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type(['application/json'])
# Authentication setting
auth_settings = ['apikey', 'basic']
response = self.api_client.call_api(resource_path, 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='RelatedLink',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def unlink_classification_from_related_link(self, id, classification_id, **kwargs):
"""
Unlink the specified Classification.
Unlink the specified Classification.
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.unlink_classification_from_related_link(id, classification_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param str id: The ID of a RelatedLink. (required)
:param str classification_id: The ID of a Classification. (required)
:return: RelatedLink
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['id', 'classification_id']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method unlink_classification_from_related_link" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'id' is set
if ('id' not in params) or (params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `unlink_classification_from_related_link`")
# verify the required parameter 'classification_id' is set
if ('classification_id' not in params) or (params['classification_id'] is None):
raise ValueError("Missing the required parameter `classification_id` when calling `unlink_classification_from_related_link`")
resource_path = '/relatedLinks/{id}/classification/{classificationId}'.replace('{format}', 'json')
path_params = {}
if 'id' in params:
path_params['id'] = params['id']
if 'classification_id' in params:
path_params['classificationId'] = params['classification_id']
query_params = {}
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json', 'text/html'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type(['application/json'])
# Authentication setting
auth_settings = ['apikey', 'basic']
response = self.api_client.call_api(resource_path, 'DELETE',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='RelatedLink',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def unlink_related_answer_from_related_link(self, id, potential_answer_id, **kwargs):
"""
Unlink the specified PotentialAnswer.
Unlink the specified PotentialAnswer.
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.unlink_related_answer_from_related_link(id, potential_answer_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param str id: The ID of a RelatedLink. (required)
:param str potential_answer_id: The ID of a PotentialAnswer. (required)
:return: RelatedLink
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['id', 'potential_answer_id']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method unlink_related_answer_from_related_link" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'id' is set
if ('id' not in params) or (params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `unlink_related_answer_from_related_link`")
# verify the required parameter 'potential_answer_id' is set
if ('potential_answer_id' not in params) or (params['potential_answer_id'] is None):
raise ValueError("Missing the required parameter `potential_answer_id` when calling `unlink_related_answer_from_related_link`")
resource_path = '/relatedLinks/{id}/relatedAnswer/{potentialAnswerId}'.replace('{format}', 'json')
path_params = {}
if 'id' in params:
path_params['id'] = params['id']
if 'potential_answer_id' in params:
path_params['potentialAnswerId'] = params['potential_answer_id']
query_params = {}
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json', 'text/html'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type(['application/json'])
# Authentication setting
auth_settings = ['apikey', 'basic']
response = self.api_client.call_api(resource_path, 'DELETE',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='RelatedLink',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
def update_related_link(self, id, document, **kwargs):
"""
Modify a relatedLink by its unique ID
Update an existing relatedLink by its ID.
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.update_related_link(id, document, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param str id: The identifier of the resource. (required)
:param RelatedLink document: Update a document by sending the paths to be updated in the request body. (required)
:param str select: Select which paths will be returned by the query. [doc](https://github.com/wprl/baucis/wiki/Query-String-Parameters#select)
:param str populate: Specify which paths to populate. [doc](https://github.com/wprl/baucis/wiki/Query-String-Parameters#populate)
:param str x_baucis_update_operator: **BYPASSES VALIDATION** May be used with PUT to update the document using $push, $pull, or $set. [doc](https://github.com/wprl/baucis/wiki/HTTP-Headers)
:return: RelatedLink
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['id', 'document', 'select', 'populate', 'x_baucis_update_operator']
all_params.append('callback')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method update_related_link" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'id' is set
if ('id' not in params) or (params['id'] is None):
raise ValueError("Missing the required parameter `id` when calling `update_related_link`")
# verify the required parameter 'document' is set
if ('document' not in params) or (params['document'] is None):
raise ValueError("Missing the required parameter `document` when calling `update_related_link`")
resource_path = '/relatedLinks/{id}'.replace('{format}', 'json')
path_params = {}
if 'id' in params:
path_params['id'] = params['id']
query_params = {}
if 'select' in params:
query_params['select'] = params['select']
if 'populate' in params:
query_params['populate'] = params['populate']
header_params = {}
if 'x_baucis_update_operator' in params:
header_params['X-Baucis-Update-Operator'] = params['x_baucis_update_operator']
form_params = []
local_var_files = {}
body_params = None
if 'document' in params:
body_params = params['document']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json', 'text/html'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type(['application/json'])
# Authentication setting
auth_settings = ['apikey', 'basic']
response = self.api_client.call_api(resource_path, 'PUT',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='RelatedLink',
auth_settings=auth_settings,
callback=params.get('callback'))
return response
| 42.398092
| 197
| 0.562586
| 5,024
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| 5.315685
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| 0
| 0.000344
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| 48,885
| 1,152
| 198
| 42.434896
| 0.835106
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| 0.847896
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| 0.184173
| 0.035838
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| 0
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| 1
| 0.022654
| false
| 0
| 0.009709
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| 0.055016
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| 0
| null | 0
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| 1
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0
| 7
|
534a52e47a1136810a84cb9c83462c7154221e61
| 4,623
|
py
|
Python
|
tests/test_data/test_builder.py
|
HXZhong1997/mmclassification
|
8144844007158b866abaab4e1111a80dd9703352
|
[
"Apache-2.0"
] | 1
|
2022-03-07T13:55:57.000Z
|
2022-03-07T13:55:57.000Z
|
tests/test_data/test_builder.py
|
HXZhong1997/mmclassification
|
8144844007158b866abaab4e1111a80dd9703352
|
[
"Apache-2.0"
] | 5
|
2022-03-02T02:58:56.000Z
|
2022-03-23T05:51:53.000Z
|
tests/test_data/test_builder.py
|
HXZhong1997/mmclassification
|
8144844007158b866abaab4e1111a80dd9703352
|
[
"Apache-2.0"
] | 1
|
2022-01-04T03:19:50.000Z
|
2022-01-04T03:19:50.000Z
|
from unittest.mock import patch
import torch
from mmcv.utils import digit_version
from mmcls.datasets import build_dataloader
class TestDataloaderBuilder():
@classmethod
def setup_class(cls):
cls.data = list(range(20))
cls.samples_per_gpu = 5
cls.workers_per_gpu = 1
@patch('mmcls.datasets.builder.get_dist_info', return_value=(0, 1))
def test_single_gpu(self, _):
common_cfg = dict(
dataset=self.data,
samples_per_gpu=self.samples_per_gpu,
workers_per_gpu=self.workers_per_gpu,
dist=False)
# Test default config
dataloader = build_dataloader(**common_cfg)
if digit_version(torch.__version__) >= digit_version('1.8.0'):
assert dataloader.persistent_workers
elif hasattr(dataloader, 'persistent_workers'):
assert not dataloader.persistent_workers
assert dataloader.batch_size == self.samples_per_gpu
assert dataloader.num_workers == self.workers_per_gpu
assert not all(
torch.cat(list(iter(dataloader))) == torch.tensor(self.data))
# Test without shuffle
dataloader = build_dataloader(**common_cfg, shuffle=False)
assert all(
torch.cat(list(iter(dataloader))) == torch.tensor(self.data))
# Test with custom sampler_cfg
dataloader = build_dataloader(
**common_cfg,
sampler_cfg=dict(type='RepeatAugSampler', selected_round=0),
shuffle=False)
expect = [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6]
assert all(torch.cat(list(iter(dataloader))) == torch.tensor(expect))
@patch('mmcls.datasets.builder.get_dist_info', return_value=(0, 1))
def test_multi_gpu(self, _):
common_cfg = dict(
dataset=self.data,
samples_per_gpu=self.samples_per_gpu,
workers_per_gpu=self.workers_per_gpu,
num_gpus=2,
dist=False)
# Test default config
dataloader = build_dataloader(**common_cfg)
if digit_version(torch.__version__) >= digit_version('1.8.0'):
assert dataloader.persistent_workers
elif hasattr(dataloader, 'persistent_workers'):
assert not dataloader.persistent_workers
assert dataloader.batch_size == self.samples_per_gpu * 2
assert dataloader.num_workers == self.workers_per_gpu * 2
assert not all(
torch.cat(list(iter(dataloader))) == torch.tensor(self.data))
# Test without shuffle
dataloader = build_dataloader(**common_cfg, shuffle=False)
assert all(
torch.cat(list(iter(dataloader))) == torch.tensor(self.data))
# Test with custom sampler_cfg
dataloader = build_dataloader(
**common_cfg,
sampler_cfg=dict(type='RepeatAugSampler', selected_round=0),
shuffle=False)
expect = torch.tensor(
[0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6])
assert all(torch.cat(list(iter(dataloader))) == expect)
@patch('mmcls.datasets.builder.get_dist_info', return_value=(1, 2))
def test_distributed(self, _):
common_cfg = dict(
dataset=self.data,
samples_per_gpu=self.samples_per_gpu,
workers_per_gpu=self.workers_per_gpu,
num_gpus=2, # num_gpus will be ignored in distributed environment.
dist=True)
# Test default config
dataloader = build_dataloader(**common_cfg)
if digit_version(torch.__version__) >= digit_version('1.8.0'):
assert dataloader.persistent_workers
elif hasattr(dataloader, 'persistent_workers'):
assert not dataloader.persistent_workers
assert dataloader.batch_size == self.samples_per_gpu
assert dataloader.num_workers == self.workers_per_gpu
non_expect = torch.tensor(self.data[1::2])
assert not all(torch.cat(list(iter(dataloader))) == non_expect)
# Test without shuffle
dataloader = build_dataloader(**common_cfg, shuffle=False)
expect = torch.tensor(self.data[1::2])
assert all(torch.cat(list(iter(dataloader))) == expect)
# Test with custom sampler_cfg
dataloader = build_dataloader(
**common_cfg,
sampler_cfg=dict(type='RepeatAugSampler', selected_round=0),
shuffle=False)
expect = torch.tensor(
[0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6][1::2])
assert all(torch.cat(list(iter(dataloader))) == expect)
| 37.893443
| 79
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| 578
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| 0.147059
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0
| 7
|
534de52877260f8d5f8de13a54a861a02fcdec27
| 1,646
|
py
|
Python
|
naive_tokenizers/bert_tokenizer_test.py
|
naivenlp/naive-tokenizers
|
811e1eb7452e85a42e4646204a02f28e626c73f6
|
[
"Apache-2.0"
] | 1
|
2020-07-02T11:33:35.000Z
|
2020-07-02T11:33:35.000Z
|
naive_tokenizers/bert_tokenizer_test.py
|
naivenlp/naive-tokenizers
|
811e1eb7452e85a42e4646204a02f28e626c73f6
|
[
"Apache-2.0"
] | 1
|
2020-06-10T03:56:43.000Z
|
2020-06-21T11:43:57.000Z
|
naive_tokenizers/bert_tokenizer_test.py
|
naivenlp/naive-tokenizers
|
811e1eb7452e85a42e4646204a02f28e626c73f6
|
[
"Apache-2.0"
] | null | null | null |
import unittest
from .bert_tokenizer import BertTokenizer
class BertTokenizerTest(unittest.TestCase):
def testBertTokenizer(self):
tokenizer = BertTokenizer(
vocab_file='testdata/vocab_chinese.txt',
bos_token='<S>',
eos_token='<T>')
self.assertEqual(0, tokenizer.pad_id)
self.assertEqual(100, tokenizer.unk_id)
self.assertEqual(104, tokenizer.bos_id)
self.assertEqual(105, tokenizer.eos_id)
self.assertEqual(101, tokenizer.cls_id)
self.assertEqual(102, tokenizer.sep_id)
self.assertEqual(103, tokenizer.mask_id)
tokenizer = BertTokenizer(vocab_file='testdata/vocab_chinese.txt', bos_token='<S>', eos_token='</S>')
self.assertEqual(0, tokenizer.pad_id)
self.assertEqual(100, tokenizer.unk_id)
self.assertEqual(104, tokenizer.bos_id)
self.assertEqual(21127, tokenizer.eos_id)
self.assertEqual(101, tokenizer.cls_id)
self.assertEqual(102, tokenizer.sep_id)
self.assertEqual(103, tokenizer.mask_id)
tokenizer = BertTokenizer(
vocab_file='testdata/vocab_chinese.txt', bos_token='<S>', eos_token='</S>', xxx_token='XXX')
self.assertEqual(0, tokenizer.pad_id)
self.assertEqual(100, tokenizer.unk_id)
self.assertEqual(104, tokenizer.bos_id)
self.assertEqual(21127, tokenizer.eos_id)
self.assertEqual(101, tokenizer.cls_id)
self.assertEqual(102, tokenizer.sep_id)
self.assertEqual(103, tokenizer.mask_id)
self.assertEqual(21128, tokenizer.xxx_id)
if __name__ == "__main__":
unittest.main()
| 36.577778
| 109
| 0.675577
| 196
| 1,646
| 5.44898
| 0.214286
| 0.308989
| 0.302434
| 0.087079
| 0.828652
| 0.828652
| 0.828652
| 0.828652
| 0.828652
| 0.828652
| 0
| 0.050614
| 0.207776
| 1,646
| 44
| 110
| 37.409091
| 0.768405
| 0
| 0
| 0.628571
| 0
| 0
| 0.066221
| 0.047388
| 0
| 0
| 0
| 0
| 0.628571
| 1
| 0.028571
| false
| 0
| 0.057143
| 0
| 0.114286
| 0
| 0
| 0
| 0
| null | 1
| 1
| 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 9
|
5359cfde2a3d12ba9c98a461696b34298efd2eb0
| 20,232
|
py
|
Python
|
src/Python/Unittests/test_delete_face.py
|
rzoller/OpenMesh
|
f84bca0b26c61eab5f9335b2191962ca8545c5f6
|
[
"BSD-3-Clause"
] | 19
|
2020-08-13T05:15:09.000Z
|
2022-03-31T14:51:29.000Z
|
src/Python/Unittests/test_delete_face.py
|
ccopsey/OpenMesh
|
93e6e626c3f282bf4275521c33cd8da1ca559c7d
|
[
"BSD-3-Clause"
] | 2
|
2020-09-08T07:03:04.000Z
|
2021-08-04T05:43:27.000Z
|
src/Python/Unittests/test_delete_face.py
|
ccopsey/OpenMesh
|
93e6e626c3f282bf4275521c33cd8da1ca559c7d
|
[
"BSD-3-Clause"
] | 10
|
2020-08-06T02:37:46.000Z
|
2021-07-01T09:12:06.000Z
|
import unittest
import openmesh
class DeleteFaceTriangleMesh(unittest.TestCase):
def test_delete_half_triangle_mesh_cube_no_edge_status(self):
self.mesh = openmesh.TriMesh()
self.vhandle = []
# Add some vertices
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d(-1, -1, 1)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d( 1, -1, 1)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d( 1, 1, 1)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d(-1, 1, 1)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d(-1, -1, -1)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d( 1, -1, -1)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d( 1, 1, -1)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d(-1, 1, -1)))
# Add six faces to form a cube
face_vhandles = []
face_vhandles.append(self.vhandle[0])
face_vhandles.append(self.vhandle[1])
face_vhandles.append(self.vhandle[3])
self.mesh.add_face(face_vhandles)
face_vhandles = []
face_vhandles.append(self.vhandle[1])
face_vhandles.append(self.vhandle[2])
face_vhandles.append(self.vhandle[3])
self.mesh.add_face(face_vhandles)
#=======================
face_vhandles = []
face_vhandles.append(self.vhandle[7])
face_vhandles.append(self.vhandle[6])
face_vhandles.append(self.vhandle[5])
self.mesh.add_face(face_vhandles)
face_vhandles = []
face_vhandles.append(self.vhandle[7])
face_vhandles.append(self.vhandle[5])
face_vhandles.append(self.vhandle[4])
self.mesh.add_face(face_vhandles)
#=======================
face_vhandles = []
face_vhandles.append(self.vhandle[1])
face_vhandles.append(self.vhandle[0])
face_vhandles.append(self.vhandle[4])
self.mesh.add_face(face_vhandles)
face_vhandles = []
face_vhandles.append(self.vhandle[1])
face_vhandles.append(self.vhandle[4])
face_vhandles.append(self.vhandle[5])
self.mesh.add_face(face_vhandles)
#=======================
face_vhandles = []
face_vhandles.append(self.vhandle[2])
face_vhandles.append(self.vhandle[1])
face_vhandles.append(self.vhandle[5])
self.mesh.add_face(face_vhandles)
face_vhandles = []
face_vhandles.append(self.vhandle[2])
face_vhandles.append(self.vhandle[5])
face_vhandles.append(self.vhandle[6])
self.mesh.add_face(face_vhandles)
#=======================
face_vhandles = []
face_vhandles.append(self.vhandle[3])
face_vhandles.append(self.vhandle[2])
face_vhandles.append(self.vhandle[6])
self.mesh.add_face(face_vhandles)
face_vhandles = []
face_vhandles.append(self.vhandle[3])
face_vhandles.append(self.vhandle[6])
face_vhandles.append(self.vhandle[7])
self.mesh.add_face(face_vhandles)
#=======================
face_vhandles = []
face_vhandles.append(self.vhandle[0])
face_vhandles.append(self.vhandle[3])
face_vhandles.append(self.vhandle[7])
self.mesh.add_face(face_vhandles)
face_vhandles = []
face_vhandles.append(self.vhandle[0])
face_vhandles.append(self.vhandle[7])
face_vhandles.append(self.vhandle[4])
self.mesh.add_face(face_vhandles)
# Test setup:
#
# 3 ======== 2
# / /|
# / / | z
# 0 ======== 1 | |
# | | | | y
# | 7 | 6 | /
# | | / | /
# | |/ |/
# 4 ======== 5 -------> x
# Check setup
self.assertEqual(self.mesh.n_edges(), 18)
self.assertEqual(self.mesh.n_halfedges(), 36)
self.assertEqual(self.mesh.n_vertices(), 8)
self.assertEqual(self.mesh.n_faces(), 12)
# =====================================================
# Now we delete half of the mesh
# =====================================================
self.mesh.request_face_status()
self.mesh.request_vertex_status()
self.mesh.request_halfedge_status()
n_face_to_delete = self.mesh.n_faces() / 2
# Check the variable
self.assertEqual(n_face_to_delete, 6)
for i in range(int(n_face_to_delete)):
self.mesh.delete_face(self.mesh.face_handle(i))
# =====================================================
# Check config afterwards
# =====================================================
self.assertEqual(self.mesh.n_edges(), 18)
self.assertEqual(self.mesh.n_halfedges(), 36)
self.assertEqual(self.mesh.n_vertices(), 8)
self.assertEqual(self.mesh.n_faces(), 12)
# =====================================================
# Cleanup and recheck
# =====================================================
self.mesh.garbage_collection()
self.assertEqual(self.mesh.n_edges(), 18)
self.assertEqual(self.mesh.n_halfedges(), 36)
self.assertEqual(self.mesh.n_vertices(), 8)
self.assertEqual(self.mesh.n_faces(), 6)
def test_delete_half_triangle_mesh_cube_with_edge_status(self):
self.mesh = openmesh.TriMesh()
self.vhandle = []
# Add some vertices
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d(-1, -1, 1)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d( 1, -1, 1)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d( 1, 1, 1)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d(-1, 1, 1)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d(-1, -1, -1)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d( 1, -1, -1)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d( 1, 1, -1)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d(-1, 1, -1)))
# Add six faces to form a cube
face_vhandles = []
face_vhandles.append(self.vhandle[0])
face_vhandles.append(self.vhandle[1])
face_vhandles.append(self.vhandle[3])
self.mesh.add_face(face_vhandles)
face_vhandles = []
face_vhandles.append(self.vhandle[1])
face_vhandles.append(self.vhandle[2])
face_vhandles.append(self.vhandle[3])
self.mesh.add_face(face_vhandles)
#=======================
face_vhandles = []
face_vhandles.append(self.vhandle[7])
face_vhandles.append(self.vhandle[6])
face_vhandles.append(self.vhandle[5])
self.mesh.add_face(face_vhandles)
face_vhandles = []
face_vhandles.append(self.vhandle[7])
face_vhandles.append(self.vhandle[5])
face_vhandles.append(self.vhandle[4])
self.mesh.add_face(face_vhandles)
#=======================
face_vhandles = []
face_vhandles.append(self.vhandle[1])
face_vhandles.append(self.vhandle[0])
face_vhandles.append(self.vhandle[4])
self.mesh.add_face(face_vhandles)
face_vhandles = []
face_vhandles.append(self.vhandle[1])
face_vhandles.append(self.vhandle[4])
face_vhandles.append(self.vhandle[5])
self.mesh.add_face(face_vhandles)
#=======================
face_vhandles = []
face_vhandles.append(self.vhandle[2])
face_vhandles.append(self.vhandle[1])
face_vhandles.append(self.vhandle[5])
self.mesh.add_face(face_vhandles)
face_vhandles = []
face_vhandles.append(self.vhandle[2])
face_vhandles.append(self.vhandle[5])
face_vhandles.append(self.vhandle[6])
self.mesh.add_face(face_vhandles)
#=======================
face_vhandles = []
face_vhandles.append(self.vhandle[3])
face_vhandles.append(self.vhandle[2])
face_vhandles.append(self.vhandle[6])
self.mesh.add_face(face_vhandles)
face_vhandles = []
face_vhandles.append(self.vhandle[3])
face_vhandles.append(self.vhandle[6])
face_vhandles.append(self.vhandle[7])
self.mesh.add_face(face_vhandles)
#=======================
face_vhandles = []
face_vhandles.append(self.vhandle[0])
face_vhandles.append(self.vhandle[3])
face_vhandles.append(self.vhandle[7])
self.mesh.add_face(face_vhandles)
face_vhandles = []
face_vhandles.append(self.vhandle[0])
face_vhandles.append(self.vhandle[7])
face_vhandles.append(self.vhandle[4])
self.mesh.add_face(face_vhandles)
# Test setup:
#
# 3 ======== 2
# / /|
# / / | z
# 0 ======== 1 | |
# | | | | y
# | 7 | 6 | /
# | | / | /
# | |/ |/
# 4 ======== 5 -------> x
# Check setup
self.assertEqual(self.mesh.n_edges(), 18)
self.assertEqual(self.mesh.n_halfedges(), 36)
self.assertEqual(self.mesh.n_vertices(), 8)
self.assertEqual(self.mesh.n_faces(), 12)
# =====================================================
# Now we delete half of the mesh
# =====================================================
self.mesh.request_face_status()
self.mesh.request_vertex_status()
self.mesh.request_edge_status()
self.mesh.request_halfedge_status()
n_face_to_delete = self.mesh.n_faces() / 2
# Check the variable
self.assertEqual(n_face_to_delete, 6)
for i in range(int(n_face_to_delete)):
self.mesh.delete_face(self.mesh.face_handle(i))
# =====================================================
# Check config afterwards
# =====================================================
self.assertEqual(self.mesh.n_edges(), 18)
self.assertEqual(self.mesh.n_halfedges(), 36)
self.assertEqual(self.mesh.n_vertices(), 8)
self.assertEqual(self.mesh.n_faces(), 12)
# =====================================================
# Cleanup and recheck
# =====================================================
self.mesh.garbage_collection()
self.assertEqual(self.mesh.n_edges(), 13)
self.assertEqual(self.mesh.n_vertices(), 8)
self.assertEqual(self.mesh.n_faces(), 6)
def test_deletete_half_poly_mesh_cube_without_edge_status(self):
self.mesh = openmesh.PolyMesh()
self.vhandle = []
# Add some vertices
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d(-1, -1, 1)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d( 1, -1, 1)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d( 1, 1, 1)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d(-1, 1, 1)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d(-1, -1, -1)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d( 1, -1, -1)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d( 1, 1, -1)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d(-1, 1, -1)))
# Add six faces to form a cube
face_vhandles = []
face_vhandles.append(self.vhandle[0])
face_vhandles.append(self.vhandle[1])
face_vhandles.append(self.vhandle[2])
face_vhandles.append(self.vhandle[3])
self.mesh.add_face(face_vhandles)
face_vhandles = []
face_vhandles.append(self.vhandle[7])
face_vhandles.append(self.vhandle[6])
face_vhandles.append(self.vhandle[5])
face_vhandles.append(self.vhandle[4])
self.mesh.add_face(face_vhandles)
face_vhandles = []
face_vhandles.append(self.vhandle[1])
face_vhandles.append(self.vhandle[0])
face_vhandles.append(self.vhandle[4])
face_vhandles.append(self.vhandle[5])
self.mesh.add_face(face_vhandles)
face_vhandles = []
face_vhandles.append(self.vhandle[2])
face_vhandles.append(self.vhandle[1])
face_vhandles.append(self.vhandle[5])
face_vhandles.append(self.vhandle[6])
self.mesh.add_face(face_vhandles)
face_vhandles = []
face_vhandles.append(self.vhandle[3])
face_vhandles.append(self.vhandle[2])
face_vhandles.append(self.vhandle[6])
face_vhandles.append(self.vhandle[7])
self.mesh.add_face(face_vhandles)
face_vhandles = []
face_vhandles.append(self.vhandle[0])
face_vhandles.append(self.vhandle[3])
face_vhandles.append(self.vhandle[7])
face_vhandles.append(self.vhandle[4])
self.mesh.add_face(face_vhandles)
# Test setup:
#
# 3 ======== 2
# / /|
# / / | z
# 0 ======== 1 | |
# | | | | y
# | 7 | 6 | /
# | | / | /
# | |/ |/
# 4 ======== 5 -------> x
# Check setup
self.assertEqual(self.mesh.n_edges(), 12)
self.assertEqual(self.mesh.n_halfedges(), 24)
self.assertEqual(self.mesh.n_vertices(), 8)
self.assertEqual(self.mesh.n_faces(), 6)
# =====================================================
# Now we delete half of the mesh
# =====================================================
self.mesh.request_face_status()
self.mesh.request_vertex_status()
self.mesh.request_halfedge_status()
n_face_to_delete = self.mesh.n_faces() / 2
# Check the variable
self.assertEqual(n_face_to_delete, 3)
for i in range(int(n_face_to_delete)):
self.mesh.delete_face(self.mesh.face_handle(i))
# =====================================================
# Check config afterwards
# =====================================================
self.assertEqual(self.mesh.n_edges(), 12)
self.assertEqual(self.mesh.n_halfedges(), 24)
self.assertEqual(self.mesh.n_vertices(), 8)
self.assertEqual(self.mesh.n_faces(), 6)
# =====================================================
# Cleanup and recheck
# =====================================================
self.mesh.garbage_collection()
self.assertEqual(self.mesh.n_edges(), 12)
self.assertEqual(self.mesh.n_halfedges(), 24)
self.assertEqual(self.mesh.n_vertices(), 8)
self.assertEqual(self.mesh.n_faces(), 3)
def test_deletete_half_poly_mesh_cube_with_edge_status(self):
self.mesh = openmesh.PolyMesh()
self.vhandle = []
# Add some vertices
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d(-1, -1, 1)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d( 1, -1, 1)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d( 1, 1, 1)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d(-1, 1, 1)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d(-1, -1, -1)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d( 1, -1, -1)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d( 1, 1, -1)))
self.vhandle.append(self.mesh.add_vertex(openmesh.Vec3d(-1, 1, -1)))
# Add six faces to form a cube
face_vhandles = []
face_vhandles.append(self.vhandle[0])
face_vhandles.append(self.vhandle[1])
face_vhandles.append(self.vhandle[2])
face_vhandles.append(self.vhandle[3])
self.mesh.add_face(face_vhandles)
face_vhandles = []
face_vhandles.append(self.vhandle[7])
face_vhandles.append(self.vhandle[6])
face_vhandles.append(self.vhandle[5])
face_vhandles.append(self.vhandle[4])
self.mesh.add_face(face_vhandles)
face_vhandles = []
face_vhandles.append(self.vhandle[1])
face_vhandles.append(self.vhandle[0])
face_vhandles.append(self.vhandle[4])
face_vhandles.append(self.vhandle[5])
self.mesh.add_face(face_vhandles)
face_vhandles = []
face_vhandles.append(self.vhandle[2])
face_vhandles.append(self.vhandle[1])
face_vhandles.append(self.vhandle[5])
face_vhandles.append(self.vhandle[6])
self.mesh.add_face(face_vhandles)
face_vhandles = []
face_vhandles.append(self.vhandle[3])
face_vhandles.append(self.vhandle[2])
face_vhandles.append(self.vhandle[6])
face_vhandles.append(self.vhandle[7])
self.mesh.add_face(face_vhandles)
face_vhandles = []
face_vhandles.append(self.vhandle[0])
face_vhandles.append(self.vhandle[3])
face_vhandles.append(self.vhandle[7])
face_vhandles.append(self.vhandle[4])
self.mesh.add_face(face_vhandles)
# Test setup:
#
# 3 ======== 2
# / /|
# / / | z
# 0 ======== 1 | |
# | | | | y
# | 7 | 6 | /
# | | / | /
# | |/ |/
# 4 ======== 5 -------> x
# Check setup
self.assertEqual(self.mesh.n_edges(), 12)
self.assertEqual(self.mesh.n_halfedges(), 24)
self.assertEqual(self.mesh.n_vertices(), 8)
self.assertEqual(self.mesh.n_faces(), 6)
# =====================================================
# Now we delete half of the mesh
# =====================================================
self.mesh.request_face_status()
self.mesh.request_vertex_status()
self.mesh.request_edge_status()
self.mesh.request_halfedge_status()
n_face_to_delete = self.mesh.n_faces() / 2
# Check the variable
self.assertEqual(n_face_to_delete, 3)
for i in range(int(n_face_to_delete)):
self.mesh.delete_face(self.mesh.face_handle(i))
# =====================================================
# Check config afterwards
# =====================================================
self.assertEqual(self.mesh.n_edges(), 12)
self.assertEqual(self.mesh.n_halfedges(), 24)
self.assertEqual(self.mesh.n_vertices(), 8)
self.assertEqual(self.mesh.n_faces(), 6)
# =====================================================
# Cleanup and recheck
# =====================================================
self.mesh.garbage_collection()
self.assertEqual(self.mesh.n_edges(), 10)
self.assertEqual(self.mesh.n_halfedges(), 20)
self.assertEqual(self.mesh.n_vertices(), 8)
self.assertEqual(self.mesh.n_faces(), 3)
if __name__ == '__main__':
suite = unittest.TestLoader().loadTestsFromTestCase(DeleteFaceTriangleMesh)
unittest.TextTestRunner(verbosity=2).run(suite)
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| 79
| 0.524516
| 2,197
| 20,232
| 4.635867
| 0.04142
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| 0.259205
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| 0.980167
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| 0.012384
| false
| 0
| 0.006192
| 0
| 0.021672
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
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| 0
| 0
| 0
| 0
|
0
| 10
|
72c0fd79aaf7d68245a9f483d160cb444b44902b
| 101
|
py
|
Python
|
reki_data_tool/postprocess/station/winter/meso1km/config.py
|
perillaroc/reki-data-tool
|
047424a2f8a1f0e16684bffaeded4044366f63c0
|
[
"MIT"
] | null | null | null |
reki_data_tool/postprocess/station/winter/meso1km/config.py
|
perillaroc/reki-data-tool
|
047424a2f8a1f0e16684bffaeded4044366f63c0
|
[
"MIT"
] | null | null | null |
reki_data_tool/postprocess/station/winter/meso1km/config.py
|
perillaroc/reki-data-tool
|
047424a2f8a1f0e16684bffaeded4044366f63c0
|
[
"MIT"
] | null | null | null |
OUTPUT_DIRECTORY = "/g11/wangdp/project/work/data/playground/operation/winter/meso1km/station/output"
| 101
| 101
| 0.841584
| 13
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|
0
| 7
|
72db410ea189cc74c03c5b92c16848a506305d71
| 198
|
py
|
Python
|
barcode/models/__init__.py
|
progistack/odoo-progistack
|
c0aded728e4c85b913effabad1fbdb8ff81a8174
|
[
"MIT"
] | null | null | null |
barcode/models/__init__.py
|
progistack/odoo-progistack
|
c0aded728e4c85b913effabad1fbdb8ff81a8174
|
[
"MIT"
] | null | null | null |
barcode/models/__init__.py
|
progistack/odoo-progistack
|
c0aded728e4c85b913effabad1fbdb8ff81a8174
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
from . import models
from . import barcode
from . import stock_location
from . import barcode_location
from . import product_template_sequence
from . import product_barcode
| 22
| 39
| 0.772727
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|
0
| 7
|
f41d7f64ea5d8e1175797c6636720f8e822e2e1e
| 7,854
|
py
|
Python
|
tests/locals/vm_selection/test_algorithms.py
|
beloglazov/openstack-neat
|
a5a853ae2affb0cdc582e3ab641737f5ebd3d0a7
|
[
"Apache-2.0"
] | 34
|
2015-01-04T08:02:37.000Z
|
2022-02-19T14:43:47.000Z
|
tests/locals/vm_selection/test_algorithms.py
|
MisterPup/OpenStack-Neat-Ceilometer
|
4e6685ea1a9deb75d1186e60097a357251eaed8d
|
[
"Apache-2.0"
] | 3
|
2015-01-23T07:45:15.000Z
|
2019-07-03T11:16:27.000Z
|
tests/locals/vm_selection/test_algorithms.py
|
MisterPup/OpenStack-Neat-Ceilometer
|
4e6685ea1a9deb75d1186e60097a357251eaed8d
|
[
"Apache-2.0"
] | 22
|
2015-01-14T17:54:46.000Z
|
2021-08-09T06:09:17.000Z
|
# Copyright 2012 Anton Beloglazov
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from mocktest import *
from pyqcy import *
import neat.locals.vm_selection.algorithms as selection
import logging
logging.disable(logging.CRITICAL)
class Selection(TestCase):
@qc(10)
def minimum_migration_time_factory(
x=dict_(
keys=str_(of='abc123-', min_length=36, max_length=36),
values=int_(min=0, max=3000),
min_length=1, max_length=5
)
):
alg = selection.minimum_migration_time_factory(300, 20., dict())
values = x.values()
vm_index = values.index(min(values))
vm = x.keys()[vm_index]
assert alg(dict(), x) == ([vm], {})
@qc(10)
def minimum_utilization_factory(
x=dict_(
keys=str_(of='abc123-', min_length=36, max_length=36),
values=list_(of=int_(min=0, max=3000),
min_length=1, max_length=10),
min_length=1, max_length=5
)
):
alg = selection.minimum_utilization_factory(300, 20., dict())
last_utilization = []
for utilization in x.values():
last_utilization.append(utilization[-1])
vm_index = last_utilization.index(min(last_utilization))
vm = x.keys()[vm_index]
assert alg(x, dict()) == ([vm], {})
@qc(10)
def random_factory(
x=dict_(
keys=str_(of='abc123-', min_length=36, max_length=36),
values=list_(of=int_(min=0, max=3000),
min_length=0, max_length=10),
min_length=1, max_length=3
)
):
with MockTransaction:
alg = selection.random_factory(300, 20., dict())
vm = x.keys()[random.randrange(len(x))]
expect(selection).choice(x.keys()).and_return(vm).once()
assert alg(x, dict()) == ([vm], {})
@qc(10)
def minimum_migration_time_max_cpu_factory(
x=dict_(
keys=str_(of='abc123-', min_length=36, max_length=36),
values=tuple_(list_(of=int_(min=0, max=3000),
min_length=1, max_length=10),
int_(min=0, max=3000)),
min_length=1, max_length=5
),
last_n=int_(min=1, max=10)
):
alg = selection.minimum_migration_time_max_cpu_factory(
300, 20., {'last_n': last_n})
vms_cpu = dict((k, v[0]) for k, v in x.items())
vms_ram = dict((k, v[1]) for k, v in x.items())
min_ram = min(vms_ram.values())
min_ram_vms_cpu = dict((k, float(sum(v[-last_n:])) / len(v[-last_n:]))
for k, v in vms_cpu.items()
if vms_ram[k] == min_ram and len(v[-last_n:]) > 0)
values = min_ram_vms_cpu.values()
vm_index = values.index(max(values))
vm = min_ram_vms_cpu.keys()[vm_index]
assert alg(vms_cpu, vms_ram) == ([vm], {})
@qc(10)
def minimum_migration_time_max_cpu_factory_equal_ram(
vms_cpu=dict_(
keys=str_(of='abc123-', min_length=36, max_length=36),
values=list_(of=int_(min=0, max=3000),
min_length=1, max_length=10),
min_length=1, max_length=5
),
ram=int_(min=1000, max=3000),
last_n=int_(min=1, max=10)
):
alg = selection.minimum_migration_time_max_cpu_factory(
300, 20., {'last_n': last_n})
vms_ram = dict((k, ram) for k, _ in vms_cpu.items())
min_ram = min(vms_ram.values())
min_ram_vms_cpu = dict((k, float(sum(v[-last_n:])) / len(v[-last_n:]))
for k, v in vms_cpu.items()
if vms_ram[k] == min_ram and len(v[-last_n:]) > 0)
values = min_ram_vms_cpu.values()
vm_index = values.index(max(values))
vm = min_ram_vms_cpu.keys()[vm_index]
assert alg(vms_cpu, vms_ram) == ([vm], {})
@qc(10)
def minimum_migration_time(
x=dict_(
keys=str_(of='abc123-', min_length=36, max_length=36),
values=int_(min=0, max=3000),
min_length=1, max_length=5
)
):
values = x.values()
vm_index = values.index(min(values))
vm = x.keys()[vm_index]
assert selection.minimum_migration_time(x) == vm
@qc(10)
def minimum_utilization(
x=dict_(
keys=str_(of='abc123-', min_length=36, max_length=36),
values=list_(of=int_(min=0, max=3000),
min_length=1, max_length=10),
min_length=1, max_length=5
)
):
last_utilization = []
for utilization in x.values():
last_utilization.append(utilization[-1])
vm_index = last_utilization.index(min(last_utilization))
vm = x.keys()[vm_index]
assert selection.minimum_utilization(x) == vm
@qc(10)
def random(
x=dict_(
keys=str_(of='abc123-', min_length=36, max_length=36),
values=list_(of=int_(min=0, max=3000),
min_length=0, max_length=10),
min_length=1, max_length=3
)
):
with MockTransaction:
vm = x.keys()[random.randrange(len(x))]
expect(selection).choice(x.keys()).and_return(vm).once()
assert selection.random(x) == vm
@qc(10)
def minimum_migration_time_max_cpu(
x=dict_(
keys=str_(of='abc123-', min_length=36, max_length=36),
values=tuple_(list_(of=int_(min=0, max=3000),
min_length=1, max_length=10),
int_(min=0, max=3000)),
min_length=1, max_length=5
),
last_n=int_(min=1, max=10)
):
vms_cpu = dict((k, v[0]) for k, v in x.items())
vms_ram = dict((k, v[1]) for k, v in x.items())
min_ram = min(vms_ram.values())
min_ram_vms_cpu = dict((k, float(sum(v[-last_n:])) / len(v[-last_n:]))
for k, v in vms_cpu.items()
if vms_ram[k] == min_ram and len(v[-last_n:]) > 0)
values = min_ram_vms_cpu.values()
vm_index = values.index(max(values))
vm = min_ram_vms_cpu.keys()[vm_index]
assert selection.minimum_migration_time_max_cpu(
last_n, vms_cpu, vms_ram) == vm
@qc(10)
def minimum_migration_time_max_cpu_equal_ram(
vms_cpu=dict_(
keys=str_(of='abc123-', min_length=36, max_length=36),
values=list_(of=int_(min=0, max=3000),
min_length=1, max_length=10),
min_length=1, max_length=5
),
ram=int_(min=1000, max=3000),
last_n=int_(min=1, max=10)
):
vms_ram = dict((k, ram) for k, _ in vms_cpu.items())
min_ram = min(vms_ram.values())
min_ram_vms_cpu = dict((k, float(sum(v[-last_n:])) / len(v[-last_n:]))
for k, v in vms_cpu.items()
if vms_ram[k] == min_ram and len(v[-last_n:]) > 0)
values = min_ram_vms_cpu.values()
vm_index = values.index(max(values))
vm = min_ram_vms_cpu.keys()[vm_index]
assert selection.minimum_migration_time_max_cpu(
last_n, vms_cpu, vms_ram) == vm
| 38.126214
| 81
| 0.55755
| 1,093
| 7,854
| 3.743824
| 0.118024
| 0.061584
| 0.039101
| 0.050831
| 0.829912
| 0.821359
| 0.814516
| 0.800098
| 0.782991
| 0.765152
| 0
| 0.048785
| 0.308378
| 7,854
| 205
| 82
| 38.312195
| 0.704529
| 0.07041
| 0
| 0.836158
| 0
| 0
| 0.011253
| 0
| 0
| 0
| 0
| 0
| 0.056497
| 1
| 0.056497
| false
| 0
| 0.022599
| 0
| 0.084746
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
be9cb309b2b8e42be2d52399ef799c8d641b937d
| 110
|
py
|
Python
|
examples/example_built_interpolator.py
|
DuraMAT/pvarc
|
bdcd87165eae530b953cfa26afb7e09e80dc52a3
|
[
"BSD-3-Clause"
] | 1
|
2020-10-08T21:09:02.000Z
|
2020-10-08T21:09:02.000Z
|
examples/example_built_interpolator.py
|
DuraMAT/pvarc
|
bdcd87165eae530b953cfa26afb7e09e80dc52a3
|
[
"BSD-3-Clause"
] | 4
|
2020-12-23T22:57:46.000Z
|
2020-12-23T23:13:09.000Z
|
examples/example_built_interpolator.py
|
DuraMAT/pvarc
|
bdcd87165eae530b953cfa26afb7e09e80dc52a3
|
[
"BSD-3-Clause"
] | null | null | null |
from pvarc import build_arc_reflection_model_interpolator_data
build_arc_reflection_model_interpolator_data()
| 36.666667
| 62
| 0.936364
| 15
| 110
| 6.2
| 0.6
| 0.172043
| 0.387097
| 0.494624
| 0.83871
| 0.83871
| 0
| 0
| 0
| 0
| 0
| 0
| 0.045455
| 110
| 3
| 63
| 36.666667
| 0.885714
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
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| 0
| null | 0
| 1
| 1
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| null | 0
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| 1
| 0
| 0
| 0
|
0
| 8
|
fe4670c3ca31fd97bfa8b5d529288c1da198c559
| 68,618
|
py
|
Python
|
benchmarks/SimResults/combinations_spec_pinned/cmp_sjengpovrayomnetppmilc/power.py
|
TugberkArkose/MLScheduler
|
e493b6cbf7b9d29a2c9300d7dd6f0c2f102e4061
|
[
"Unlicense"
] | null | null | null |
benchmarks/SimResults/combinations_spec_pinned/cmp_sjengpovrayomnetppmilc/power.py
|
TugberkArkose/MLScheduler
|
e493b6cbf7b9d29a2c9300d7dd6f0c2f102e4061
|
[
"Unlicense"
] | null | null | null |
benchmarks/SimResults/combinations_spec_pinned/cmp_sjengpovrayomnetppmilc/power.py
|
TugberkArkose/MLScheduler
|
e493b6cbf7b9d29a2c9300d7dd6f0c2f102e4061
|
[
"Unlicense"
] | null | null | null |
power = {'BUSES': {'Area': 1.33155,
'Bus/Area': 1.33155,
'Bus/Gate Leakage': 0.00662954,
'Bus/Peak Dynamic': 0.0,
'Bus/Runtime Dynamic': 0.0,
'Bus/Subthreshold Leakage': 0.0691322,
'Bus/Subthreshold Leakage with power gating': 0.0259246,
'Gate Leakage': 0.00662954,
'Peak Dynamic': 0.0,
'Runtime Dynamic': 0.0,
'Subthreshold Leakage': 0.0691322,
'Subthreshold Leakage with power gating': 0.0259246},
'Core': [{'Area': 32.6082,
'Execution Unit/Area': 8.2042,
'Execution Unit/Complex ALUs/Area': 0.235435,
'Execution Unit/Complex ALUs/Gate Leakage': 0.0132646,
'Execution Unit/Complex ALUs/Peak Dynamic': 0.0821389,
'Execution Unit/Complex ALUs/Runtime Dynamic': 0.267204,
'Execution Unit/Complex ALUs/Subthreshold Leakage': 0.20111,
'Execution Unit/Complex ALUs/Subthreshold Leakage with power gating': 0.0754163,
'Execution Unit/Floating Point Units/Area': 4.6585,
'Execution Unit/Floating Point Units/Gate Leakage': 0.0656156,
'Execution Unit/Floating Point Units/Peak Dynamic': 0.433494,
'Execution Unit/Floating Point Units/Runtime Dynamic': 0.304033,
'Execution Unit/Floating Point Units/Subthreshold Leakage': 0.994829,
'Execution Unit/Floating Point Units/Subthreshold Leakage with power gating': 0.373061,
'Execution Unit/Gate Leakage': 0.122718,
'Execution Unit/Instruction Scheduler/Area': 2.17927,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Area': 0.328073,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Gate Leakage': 0.00115349,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Peak Dynamic': 1.20978,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Runtime Dynamic': 0.415706,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage': 0.017004,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage with power gating': 0.00962066,
'Execution Unit/Instruction Scheduler/Gate Leakage': 0.00730101,
'Execution Unit/Instruction Scheduler/Instruction Window/Area': 1.00996,
'Execution Unit/Instruction Scheduler/Instruction Window/Gate Leakage': 0.00529112,
'Execution Unit/Instruction Scheduler/Instruction Window/Peak Dynamic': 2.07911,
'Execution Unit/Instruction Scheduler/Instruction Window/Runtime Dynamic': 0.719852,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage': 0.0800117,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage with power gating': 0.0455351,
'Execution Unit/Instruction Scheduler/Peak Dynamic': 4.84781,
'Execution Unit/Instruction Scheduler/ROB/Area': 0.841232,
'Execution Unit/Instruction Scheduler/ROB/Gate Leakage': 0.000856399,
'Execution Unit/Instruction Scheduler/ROB/Peak Dynamic': 1.55892,
'Execution Unit/Instruction Scheduler/ROB/Runtime Dynamic': 0.412855,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage': 0.0178624,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage with power gating': 0.00897339,
'Execution Unit/Instruction Scheduler/Runtime Dynamic': 1.54841,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage': 0.114878,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage with power gating': 0.0641291,
'Execution Unit/Integer ALUs/Area': 0.47087,
'Execution Unit/Integer ALUs/Gate Leakage': 0.0265291,
'Execution Unit/Integer ALUs/Peak Dynamic': 0.344447,
'Execution Unit/Integer ALUs/Runtime Dynamic': 0.101344,
'Execution Unit/Integer ALUs/Subthreshold Leakage': 0.40222,
'Execution Unit/Integer ALUs/Subthreshold Leakage with power gating': 0.150833,
'Execution Unit/Peak Dynamic': 6.33796,
'Execution Unit/Register Files/Area': 0.570804,
'Execution Unit/Register Files/Floating Point RF/Area': 0.208131,
'Execution Unit/Register Files/Floating Point RF/Gate Leakage': 0.000232788,
'Execution Unit/Register Files/Floating Point RF/Peak Dynamic': 0.0818963,
'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.0150697,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage': 0.00399698,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage with power gating': 0.00176968,
'Execution Unit/Register Files/Gate Leakage': 0.000622708,
'Execution Unit/Register Files/Integer RF/Area': 0.362673,
'Execution Unit/Register Files/Integer RF/Gate Leakage': 0.00038992,
'Execution Unit/Register Files/Integer RF/Peak Dynamic': 0.140134,
'Execution Unit/Register Files/Integer RF/Runtime Dynamic': 0.111449,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage': 0.00614175,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage with power gating': 0.00246675,
'Execution Unit/Register Files/Peak Dynamic': 0.22203,
'Execution Unit/Register Files/Runtime Dynamic': 0.126519,
'Execution Unit/Register Files/Subthreshold Leakage': 0.0101387,
'Execution Unit/Register Files/Subthreshold Leakage with power gating': 0.00423643,
'Execution Unit/Results Broadcast Bus/Area Overhead': 0.0442632,
'Execution Unit/Results Broadcast Bus/Gate Leakage': 0.00607074,
'Execution Unit/Results Broadcast Bus/Peak Dynamic': 0.361124,
'Execution Unit/Results Broadcast Bus/Runtime Dynamic': 0.888768,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage': 0.0920413,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage with power gating': 0.0345155,
'Execution Unit/Runtime Dynamic': 3.23628,
'Execution Unit/Subthreshold Leakage': 1.83518,
'Execution Unit/Subthreshold Leakage with power gating': 0.709678,
'Gate Leakage': 0.372997,
'Instruction Fetch Unit/Area': 5.86007,
'Instruction Fetch Unit/Branch Predictor/Area': 0.138516,
'Instruction Fetch Unit/Branch Predictor/Chooser/Area': 0.0435221,
'Instruction Fetch Unit/Branch Predictor/Chooser/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Chooser/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Chooser/Runtime Dynamic': 0.00298816,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage': 0.00759719,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage with power gating': 0.0039236,
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'Instruction Fetch Unit/Branch Predictor/Global Predictor/Runtime Dynamic': 0.00298816,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage': 0.00759719,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage with power gating': 0.0039236,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Area': 0.0257064,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Gate Leakage': 0.000154548,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Peak Dynamic': 0.0142575,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Runtime Dynamic': 0.00260731,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage': 0.00384344,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage with power gating': 0.00198631,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Area': 0.0151917,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Gate Leakage': 8.00196e-05,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Peak Dynamic': 0.00527447,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Runtime Dynamic': 0.00101186,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage': 0.00181347,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage with power gating': 0.000957045,
'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838,
'Instruction Fetch Unit/Branch Predictor/RAS/Area': 0.0105732,
'Instruction Fetch Unit/Branch Predictor/RAS/Gate Leakage': 4.63858e-05,
'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602,
'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.00160098,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage': 0.000932505,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733,
'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.0101846,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282,
'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954,
'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758,
'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867,
'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.0284851,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357,
'Instruction Fetch Unit/Gate Leakage': 0.0590479,
'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323,
'Instruction Fetch Unit/Instruction Buffer/Gate Leakage': 6.83558e-05,
'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827,
'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.107139,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682,
'Instruction Fetch Unit/Instruction Cache/Area': 3.14635,
'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931,
'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 6.43323,
'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.36594,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386,
'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799,
'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493,
'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404,
'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.363893,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage with power gating': 0.166104,
'Instruction Fetch Unit/Peak Dynamic': 8.96874,
'Instruction Fetch Unit/Runtime Dynamic': 0.875641,
'Instruction Fetch Unit/Subthreshold Leakage': 0.932587,
'Instruction Fetch Unit/Subthreshold Leakage with power gating': 0.408542,
'L2/Area': 4.53318,
'L2/Gate Leakage': 0.015464,
'L2/Peak Dynamic': 0.113944,
'L2/Runtime Dynamic': 0.02483,
'L2/Subthreshold Leakage': 0.834142,
'L2/Subthreshold Leakage with power gating': 0.401066,
'Load Store Unit/Area': 8.80969,
'Load Store Unit/Data Cache/Area': 6.84535,
'Load Store Unit/Data Cache/Gate Leakage': 0.0279261,
'Load Store Unit/Data Cache/Peak Dynamic': 5.17825,
'Load Store Unit/Data Cache/Runtime Dynamic': 1.91243,
'Load Store Unit/Data Cache/Subthreshold Leakage': 0.527675,
'Load Store Unit/Data Cache/Subthreshold Leakage with power gating': 0.25085,
'Load Store Unit/Gate Leakage': 0.0351387,
'Load Store Unit/LoadQ/Area': 0.0836782,
'Load Store Unit/LoadQ/Gate Leakage': 0.00059896,
'Load Store Unit/LoadQ/Peak Dynamic': 0.127505,
'Load Store Unit/LoadQ/Runtime Dynamic': 0.127505,
'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961,
'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918,
'Load Store Unit/Peak Dynamic': 5.78281,
'Load Store Unit/Runtime Dynamic': 2.66874,
'Load Store Unit/StoreQ/Area': 0.322079,
'Load Store Unit/StoreQ/Gate Leakage': 0.00329971,
'Load Store Unit/StoreQ/Peak Dynamic': 0.314406,
'Load Store Unit/StoreQ/Runtime Dynamic': 0.628811,
'Load Store Unit/StoreQ/Subthreshold Leakage': 0.0345621,
'Load Store Unit/StoreQ/Subthreshold Leakage with power gating': 0.0197004,
'Load Store Unit/Subthreshold Leakage': 0.591622,
'Load Store Unit/Subthreshold Leakage with power gating': 0.283406,
'Memory Management Unit/Area': 0.434579,
'Memory Management Unit/Dtlb/Area': 0.0879726,
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'Memory Management Unit/Dtlb/Runtime Dynamic': 0.112907,
'Memory Management Unit/Dtlb/Subthreshold Leakage': 0.0155699,
'Memory Management Unit/Dtlb/Subthreshold Leakage with power gating': 0.00887485,
'Memory Management Unit/Gate Leakage': 0.00813591,
'Memory Management Unit/Itlb/Area': 0.301552,
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'Memory Management Unit/Itlb/Subthreshold Leakage': 0.0413758,
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'Memory Management Unit/Peak Dynamic': 0.7514,
'Memory Management Unit/Runtime Dynamic': 0.174047,
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'Memory Management Unit/Subthreshold Leakage with power gating': 0.0399462,
'Peak Dynamic': 26.5166,
'Renaming Unit/Area': 0.369768,
'Renaming Unit/FP Front End RAT/Area': 0.168486,
'Renaming Unit/FP Front End RAT/Gate Leakage': 0.00489731,
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'Renaming Unit/FP Front End RAT/Runtime Dynamic': 0.285718,
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'Renaming Unit/FP Front End RAT/Subthreshold Leakage with power gating': 0.024925,
'Renaming Unit/Free List/Area': 0.0414755,
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'Renaming Unit/Free List/Subthreshold Leakage': 0.000670426,
'Renaming Unit/Free List/Subthreshold Leakage with power gating': 0.000377987,
'Renaming Unit/Gate Leakage': 0.00863632,
'Renaming Unit/Int Front End RAT/Area': 0.114751,
'Renaming Unit/Int Front End RAT/Gate Leakage': 0.00038343,
'Renaming Unit/Int Front End RAT/Peak Dynamic': 0.86945,
'Renaming Unit/Int Front End RAT/Runtime Dynamic': 0.210235,
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'Renaming Unit/Int Front End RAT/Subthreshold Leakage with power gating': 0.00348781,
'Renaming Unit/Peak Dynamic': 4.56169,
'Renaming Unit/Runtime Dynamic': 0.520648,
'Renaming Unit/Subthreshold Leakage': 0.070483,
'Renaming Unit/Subthreshold Leakage with power gating': 0.0362779,
'Runtime Dynamic': 7.50019,
'Subthreshold Leakage': 6.21877,
'Subthreshold Leakage with power gating': 2.58311},
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'Execution Unit/Area': 7.68434,
'Execution Unit/Complex ALUs/Area': 0.235435,
'Execution Unit/Complex ALUs/Gate Leakage': 0.0132646,
'Execution Unit/Complex ALUs/Peak Dynamic': 0.0,
'Execution Unit/Complex ALUs/Runtime Dynamic': 0.202689,
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'Execution Unit/Complex ALUs/Subthreshold Leakage with power gating': 0.0754163,
'Execution Unit/Floating Point Units/Area': 4.6585,
'Execution Unit/Floating Point Units/Gate Leakage': 0.0656156,
'Execution Unit/Floating Point Units/Peak Dynamic': 0.0,
'Execution Unit/Floating Point Units/Runtime Dynamic': 0.304033,
'Execution Unit/Floating Point Units/Subthreshold Leakage': 0.994829,
'Execution Unit/Floating Point Units/Subthreshold Leakage with power gating': 0.373061,
'Execution Unit/Gate Leakage': 0.120359,
'Execution Unit/Instruction Scheduler/Area': 1.66526,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Area': 0.275653,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Gate Leakage': 0.000977433,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Peak Dynamic': 1.04181,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Runtime Dynamic': 0.158386,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage': 0.0143453,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage with power gating': 0.00810519,
'Execution Unit/Instruction Scheduler/Gate Leakage': 0.00568913,
'Execution Unit/Instruction Scheduler/Instruction Window/Area': 0.805223,
'Execution Unit/Instruction Scheduler/Instruction Window/Gate Leakage': 0.00414562,
'Execution Unit/Instruction Scheduler/Instruction Window/Peak Dynamic': 1.6763,
'Execution Unit/Instruction Scheduler/Instruction Window/Runtime Dynamic': 0.255471,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage': 0.0625755,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage with power gating': 0.0355964,
'Execution Unit/Instruction Scheduler/Peak Dynamic': 3.82262,
'Execution Unit/Instruction Scheduler/ROB/Area': 0.584388,
'Execution Unit/Instruction Scheduler/ROB/Gate Leakage': 0.00056608,
'Execution Unit/Instruction Scheduler/ROB/Peak Dynamic': 1.10451,
'Execution Unit/Instruction Scheduler/ROB/Runtime Dynamic': 0.128953,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage': 0.00906853,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage with power gating': 0.00364446,
'Execution Unit/Instruction Scheduler/Runtime Dynamic': 0.54281,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage': 0.0859892,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage with power gating': 0.047346,
'Execution Unit/Integer ALUs/Area': 0.47087,
'Execution Unit/Integer ALUs/Gate Leakage': 0.0265291,
'Execution Unit/Integer ALUs/Peak Dynamic': 0.181147,
'Execution Unit/Integer ALUs/Runtime Dynamic': 0.101344,
'Execution Unit/Integer ALUs/Subthreshold Leakage': 0.40222,
'Execution Unit/Integer ALUs/Subthreshold Leakage with power gating': 0.150833,
'Execution Unit/Peak Dynamic': 4.17732,
'Execution Unit/Register Files/Area': 0.570804,
'Execution Unit/Register Files/Floating Point RF/Area': 0.208131,
'Execution Unit/Register Files/Floating Point RF/Gate Leakage': 0.000232788,
'Execution Unit/Register Files/Floating Point RF/Peak Dynamic': 0.0,
'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.00664342,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage': 0.00399698,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage with power gating': 0.00176968,
'Execution Unit/Register Files/Gate Leakage': 0.000622708,
'Execution Unit/Register Files/Integer RF/Area': 0.362673,
'Execution Unit/Register Files/Integer RF/Gate Leakage': 0.00038992,
'Execution Unit/Register Files/Integer RF/Peak Dynamic': 0.0480401,
'Execution Unit/Register Files/Integer RF/Runtime Dynamic': 0.0491321,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage': 0.00614175,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage with power gating': 0.00246675,
'Execution Unit/Register Files/Peak Dynamic': 0.0480401,
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'Execution Unit/Register Files/Subthreshold Leakage': 0.0101387,
'Execution Unit/Register Files/Subthreshold Leakage with power gating': 0.00423643,
'Execution Unit/Results Broadcast Bus/Area Overhead': 0.0390912,
'Execution Unit/Results Broadcast Bus/Gate Leakage': 0.00537402,
'Execution Unit/Results Broadcast Bus/Peak Dynamic': 0.101207,
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'Execution Unit/Results Broadcast Bus/Subthreshold Leakage with power gating': 0.0305543,
'Execution Unit/Runtime Dynamic': 1.47181,
'Execution Unit/Subthreshold Leakage': 1.79543,
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'Instruction Fetch Unit/Area': 5.85939,
'Instruction Fetch Unit/Branch Predictor/Area': 0.138516,
'Instruction Fetch Unit/Branch Predictor/Chooser/Area': 0.0435221,
'Instruction Fetch Unit/Branch Predictor/Chooser/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Chooser/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Chooser/Runtime Dynamic': 0.00212235,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage': 0.00759719,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage with power gating': 0.0039236,
'Instruction Fetch Unit/Branch Predictor/Gate Leakage': 0.000757657,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Area': 0.0435221,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Runtime Dynamic': 0.00212235,
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'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage with power gating': 0.0039236,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Area': 0.0257064,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Gate Leakage': 0.000154548,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Peak Dynamic': 0.0142575,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Runtime Dynamic': 0.00192382,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage': 0.00384344,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage with power gating': 0.00198631,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Area': 0.0151917,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Gate Leakage': 8.00196e-05,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Peak Dynamic': 0.00527447,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Runtime Dynamic': 0.000785907,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage': 0.00181347,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage with power gating': 0.000957045,
'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838,
'Instruction Fetch Unit/Branch Predictor/RAS/Area': 0.0105732,
'Instruction Fetch Unit/Branch Predictor/RAS/Gate Leakage': 4.63858e-05,
'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602,
'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.000705787,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage': 0.000932505,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733,
'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.0068743,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282,
'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954,
'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758,
'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867,
'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.0176599,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357,
'Instruction Fetch Unit/Gate Leakage': 0.0589979,
'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323,
'Instruction Fetch Unit/Instruction Buffer/Gate Leakage': 6.83558e-05,
'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827,
'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.047232,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682,
'Instruction Fetch Unit/Instruction Cache/Area': 3.14635,
'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931,
'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 3.00436,
'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.159876,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386,
'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799,
'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493,
'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404,
'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.160421,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage with power gating': 0.166104,
'Instruction Fetch Unit/Peak Dynamic': 5.36868,
'Instruction Fetch Unit/Runtime Dynamic': 0.392063,
'Instruction Fetch Unit/Subthreshold Leakage': 0.932286,
'Instruction Fetch Unit/Subthreshold Leakage with power gating': 0.40843,
'L2/Area': 4.53318,
'L2/Gate Leakage': 0.015464,
'L2/Peak Dynamic': 0.00524707,
'L2/Runtime Dynamic': 0.00171266,
'L2/Subthreshold Leakage': 0.834142,
'L2/Subthreshold Leakage with power gating': 0.401066,
'Load Store Unit/Area': 8.80901,
'Load Store Unit/Data Cache/Area': 6.84535,
'Load Store Unit/Data Cache/Gate Leakage': 0.0279261,
'Load Store Unit/Data Cache/Peak Dynamic': 2.38132,
'Load Store Unit/Data Cache/Runtime Dynamic': 0.552293,
'Load Store Unit/Data Cache/Subthreshold Leakage': 0.527675,
'Load Store Unit/Data Cache/Subthreshold Leakage with power gating': 0.25085,
'Load Store Unit/Gate Leakage': 0.0350888,
'Load Store Unit/LoadQ/Area': 0.0836782,
'Load Store Unit/LoadQ/Gate Leakage': 0.00059896,
'Load Store Unit/LoadQ/Peak Dynamic': 0.0370176,
'Load Store Unit/LoadQ/Runtime Dynamic': 0.0370176,
'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961,
'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918,
'Load Store Unit/Peak Dynamic': 2.55613,
'Load Store Unit/Runtime Dynamic': 0.771869,
'Load Store Unit/StoreQ/Area': 0.322079,
'Load Store Unit/StoreQ/Gate Leakage': 0.00329971,
'Load Store Unit/StoreQ/Peak Dynamic': 0.0912791,
'Load Store Unit/StoreQ/Runtime Dynamic': 0.182558,
'Load Store Unit/StoreQ/Subthreshold Leakage': 0.0345621,
'Load Store Unit/StoreQ/Subthreshold Leakage with power gating': 0.0197004,
'Load Store Unit/Subthreshold Leakage': 0.591321,
'Load Store Unit/Subthreshold Leakage with power gating': 0.283293,
'Memory Management Unit/Area': 0.4339,
'Memory Management Unit/Dtlb/Area': 0.0879726,
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'Memory Management Unit/Dtlb/Runtime Dynamic': 0.0324506,
'Memory Management Unit/Dtlb/Subthreshold Leakage': 0.0155699,
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'Memory Management Unit/Gate Leakage': 0.00808595,
'Memory Management Unit/Itlb/Area': 0.301552,
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'Memory Management Unit/Peak Dynamic': 0.398558,
'Memory Management Unit/Runtime Dynamic': 0.058729,
'Memory Management Unit/Subthreshold Leakage': 0.0766103,
'Memory Management Unit/Subthreshold Leakage with power gating': 0.0398333,
'Peak Dynamic': 16.0954,
'Renaming Unit/Area': 0.303608,
'Renaming Unit/FP Front End RAT/Area': 0.131045,
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'Renaming Unit/FP Front End RAT/Peak Dynamic': 2.51468,
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'Renaming Unit/FP Front End RAT/Subthreshold Leakage': 0.0308571,
'Renaming Unit/FP Front End RAT/Subthreshold Leakage with power gating': 0.0175885,
'Renaming Unit/Free List/Area': 0.0340654,
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'Renaming Unit/Free List/Subthreshold Leakage': 0.000370144,
'Renaming Unit/Free List/Subthreshold Leakage with power gating': 0.000201064,
'Renaming Unit/Gate Leakage': 0.00708398,
'Renaming Unit/Int Front End RAT/Area': 0.0941223,
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'Renaming Unit/Int Front End RAT/Subthreshold Leakage': 0.00435488,
'Renaming Unit/Int Front End RAT/Subthreshold Leakage with power gating': 0.00248228,
'Renaming Unit/Peak Dynamic': 3.58947,
'Renaming Unit/Runtime Dynamic': 0.087986,
'Renaming Unit/Subthreshold Leakage': 0.0552466,
'Renaming Unit/Subthreshold Leakage with power gating': 0.0276461,
'Runtime Dynamic': 2.78417,
'Subthreshold Leakage': 6.16288,
'Subthreshold Leakage with power gating': 2.55328},
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'Execution Unit/Complex ALUs/Gate Leakage': 0.0132646,
'Execution Unit/Complex ALUs/Peak Dynamic': 5.85708e-05,
'Execution Unit/Complex ALUs/Runtime Dynamic': 0.202735,
'Execution Unit/Complex ALUs/Subthreshold Leakage': 0.20111,
'Execution Unit/Complex ALUs/Subthreshold Leakage with power gating': 0.0754163,
'Execution Unit/Floating Point Units/Area': 4.6585,
'Execution Unit/Floating Point Units/Gate Leakage': 0.0656156,
'Execution Unit/Floating Point Units/Peak Dynamic': 0.000799492,
'Execution Unit/Floating Point Units/Runtime Dynamic': 0.304033,
'Execution Unit/Floating Point Units/Subthreshold Leakage': 0.994829,
'Execution Unit/Floating Point Units/Subthreshold Leakage with power gating': 0.373061,
'Execution Unit/Gate Leakage': 0.120359,
'Execution Unit/Instruction Scheduler/Area': 1.66526,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Area': 0.275653,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Gate Leakage': 0.000977433,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Peak Dynamic': 1.04181,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Runtime Dynamic': 0.0652261,
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'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage with power gating': 0.00810519,
'Execution Unit/Instruction Scheduler/Gate Leakage': 0.00568913,
'Execution Unit/Instruction Scheduler/Instruction Window/Area': 0.805223,
'Execution Unit/Instruction Scheduler/Instruction Window/Gate Leakage': 0.00414562,
'Execution Unit/Instruction Scheduler/Instruction Window/Peak Dynamic': 1.6763,
'Execution Unit/Instruction Scheduler/Instruction Window/Runtime Dynamic': 0.105207,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage': 0.0625755,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage with power gating': 0.0355964,
'Execution Unit/Instruction Scheduler/Peak Dynamic': 3.82262,
'Execution Unit/Instruction Scheduler/ROB/Area': 0.584388,
'Execution Unit/Instruction Scheduler/ROB/Gate Leakage': 0.00056608,
'Execution Unit/Instruction Scheduler/ROB/Peak Dynamic': 1.10451,
'Execution Unit/Instruction Scheduler/ROB/Runtime Dynamic': 0.0531051,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage': 0.00906853,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage with power gating': 0.00364446,
'Execution Unit/Instruction Scheduler/Runtime Dynamic': 0.223539,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage': 0.0859892,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage with power gating': 0.047346,
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'Execution Unit/Integer ALUs/Gate Leakage': 0.0265291,
'Execution Unit/Integer ALUs/Peak Dynamic': 0.0744775,
'Execution Unit/Integer ALUs/Runtime Dynamic': 0.101344,
'Execution Unit/Integer ALUs/Subthreshold Leakage': 0.40222,
'Execution Unit/Integer ALUs/Subthreshold Leakage with power gating': 0.150833,
'Execution Unit/Peak Dynamic': 3.96958,
'Execution Unit/Register Files/Area': 0.570804,
'Execution Unit/Register Files/Floating Point RF/Area': 0.208131,
'Execution Unit/Register Files/Floating Point RF/Gate Leakage': 0.000232788,
'Execution Unit/Register Files/Floating Point RF/Peak Dynamic': 0.000151041,
'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.00273588,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage': 0.00399698,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage with power gating': 0.00176968,
'Execution Unit/Register Files/Gate Leakage': 0.000622708,
'Execution Unit/Register Files/Integer RF/Area': 0.362673,
'Execution Unit/Register Files/Integer RF/Gate Leakage': 0.00038992,
'Execution Unit/Register Files/Integer RF/Peak Dynamic': 0.0197862,
'Execution Unit/Register Files/Integer RF/Runtime Dynamic': 0.0202335,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage': 0.00614175,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage with power gating': 0.00246675,
'Execution Unit/Register Files/Peak Dynamic': 0.0199372,
'Execution Unit/Register Files/Runtime Dynamic': 0.0229694,
'Execution Unit/Register Files/Subthreshold Leakage': 0.0101387,
'Execution Unit/Register Files/Subthreshold Leakage with power gating': 0.00423643,
'Execution Unit/Results Broadcast Bus/Area Overhead': 0.0390912,
'Execution Unit/Results Broadcast Bus/Gate Leakage': 0.00537402,
'Execution Unit/Results Broadcast Bus/Peak Dynamic': 0.0416986,
'Execution Unit/Results Broadcast Bus/Runtime Dynamic': 0.109466,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage': 0.081478,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage with power gating': 0.0305543,
'Execution Unit/Runtime Dynamic': 0.964086,
'Execution Unit/Subthreshold Leakage': 1.79543,
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'Gate Leakage': 0.368936,
'Instruction Fetch Unit/Area': 5.85939,
'Instruction Fetch Unit/Branch Predictor/Area': 0.138516,
'Instruction Fetch Unit/Branch Predictor/Chooser/Area': 0.0435221,
'Instruction Fetch Unit/Branch Predictor/Chooser/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Chooser/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Chooser/Runtime Dynamic': 0.000949002,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage': 0.00759719,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage with power gating': 0.0039236,
'Instruction Fetch Unit/Branch Predictor/Gate Leakage': 0.000757657,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Area': 0.0435221,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Runtime Dynamic': 0.000949002,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage': 0.00759719,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage with power gating': 0.0039236,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Area': 0.0257064,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Gate Leakage': 0.000154548,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Peak Dynamic': 0.0142575,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Runtime Dynamic': 0.000853091,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage': 0.00384344,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage with power gating': 0.00198631,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Area': 0.0151917,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Gate Leakage': 8.00196e-05,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Peak Dynamic': 0.00527447,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Runtime Dynamic': 0.000344745,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage': 0.00181347,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage with power gating': 0.000957045,
'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838,
'Instruction Fetch Unit/Branch Predictor/RAS/Area': 0.0105732,
'Instruction Fetch Unit/Branch Predictor/RAS/Gate Leakage': 4.63858e-05,
'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602,
'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.000290656,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage': 0.000932505,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733,
'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.00304175,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282,
'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954,
'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758,
'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867,
'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.00815174,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357,
'Instruction Fetch Unit/Gate Leakage': 0.0589979,
'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323,
'Instruction Fetch Unit/Instruction Buffer/Gate Leakage': 6.83558e-05,
'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827,
'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.019451,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682,
'Instruction Fetch Unit/Instruction Cache/Area': 3.14635,
'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931,
'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 1.23725,
'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.0763943,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386,
'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799,
'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493,
'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404,
'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.0660642,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage with power gating': 0.166104,
'Instruction Fetch Unit/Peak Dynamic': 3.51581,
'Instruction Fetch Unit/Runtime Dynamic': 0.173103,
'Instruction Fetch Unit/Subthreshold Leakage': 0.932286,
'Instruction Fetch Unit/Subthreshold Leakage with power gating': 0.40843,
'L2/Area': 4.53318,
'L2/Gate Leakage': 0.015464,
'L2/Peak Dynamic': 0.0309073,
'L2/Runtime Dynamic': 0.00886697,
'L2/Subthreshold Leakage': 0.834142,
'L2/Subthreshold Leakage with power gating': 0.401066,
'Load Store Unit/Area': 8.80901,
'Load Store Unit/Data Cache/Area': 6.84535,
'Load Store Unit/Data Cache/Gate Leakage': 0.0279261,
'Load Store Unit/Data Cache/Peak Dynamic': 1.9302,
'Load Store Unit/Data Cache/Runtime Dynamic': 0.345932,
'Load Store Unit/Data Cache/Subthreshold Leakage': 0.527675,
'Load Store Unit/Data Cache/Subthreshold Leakage with power gating': 0.25085,
'Load Store Unit/Gate Leakage': 0.0350888,
'Load Store Unit/LoadQ/Area': 0.0836782,
'Load Store Unit/LoadQ/Gate Leakage': 0.00059896,
'Load Store Unit/LoadQ/Peak Dynamic': 0.0224229,
'Load Store Unit/LoadQ/Runtime Dynamic': 0.022423,
'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961,
'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918,
'Load Store Unit/Peak Dynamic': 2.03609,
'Load Store Unit/Runtime Dynamic': 0.478938,
'Load Store Unit/StoreQ/Area': 0.322079,
'Load Store Unit/StoreQ/Gate Leakage': 0.00329971,
'Load Store Unit/StoreQ/Peak Dynamic': 0.055291,
'Load Store Unit/StoreQ/Runtime Dynamic': 0.110582,
'Load Store Unit/StoreQ/Subthreshold Leakage': 0.0345621,
'Load Store Unit/StoreQ/Subthreshold Leakage with power gating': 0.0197004,
'Load Store Unit/Subthreshold Leakage': 0.591321,
'Load Store Unit/Subthreshold Leakage with power gating': 0.283293,
'Memory Management Unit/Area': 0.4339,
'Memory Management Unit/Dtlb/Area': 0.0879726,
'Memory Management Unit/Dtlb/Gate Leakage': 0.00088729,
'Memory Management Unit/Dtlb/Peak Dynamic': 0.019623,
'Memory Management Unit/Dtlb/Runtime Dynamic': 0.0200719,
'Memory Management Unit/Dtlb/Subthreshold Leakage': 0.0155699,
'Memory Management Unit/Dtlb/Subthreshold Leakage with power gating': 0.00887485,
'Memory Management Unit/Gate Leakage': 0.00808595,
'Memory Management Unit/Itlb/Area': 0.301552,
'Memory Management Unit/Itlb/Gate Leakage': 0.00393464,
'Memory Management Unit/Itlb/Peak Dynamic': 0.0769274,
'Memory Management Unit/Itlb/Runtime Dynamic': 0.0125693,
'Memory Management Unit/Itlb/Subthreshold Leakage': 0.0413758,
'Memory Management Unit/Itlb/Subthreshold Leakage with power gating': 0.0235842,
'Memory Management Unit/Peak Dynamic': 0.266745,
'Memory Management Unit/Runtime Dynamic': 0.0326412,
'Memory Management Unit/Subthreshold Leakage': 0.0766103,
'Memory Management Unit/Subthreshold Leakage with power gating': 0.0398333,
'Peak Dynamic': 13.4086,
'Renaming Unit/Area': 0.303608,
'Renaming Unit/FP Front End RAT/Area': 0.131045,
'Renaming Unit/FP Front End RAT/Gate Leakage': 0.00351123,
'Renaming Unit/FP Front End RAT/Peak Dynamic': 2.51468,
'Renaming Unit/FP Front End RAT/Runtime Dynamic': 0.000397238,
'Renaming Unit/FP Front End RAT/Subthreshold Leakage': 0.0308571,
'Renaming Unit/FP Front End RAT/Subthreshold Leakage with power gating': 0.0175885,
'Renaming Unit/Free List/Area': 0.0340654,
'Renaming Unit/Free List/Gate Leakage': 2.5481e-05,
'Renaming Unit/Free List/Peak Dynamic': 0.0306032,
'Renaming Unit/Free List/Runtime Dynamic': 0.00294766,
'Renaming Unit/Free List/Subthreshold Leakage': 0.000370144,
'Renaming Unit/Free List/Subthreshold Leakage with power gating': 0.000201064,
'Renaming Unit/Gate Leakage': 0.00708398,
'Renaming Unit/Int Front End RAT/Area': 0.0941223,
'Renaming Unit/Int Front End RAT/Gate Leakage': 0.000283242,
'Renaming Unit/Int Front End RAT/Peak Dynamic': 0.731965,
'Renaming Unit/Int Front End RAT/Runtime Dynamic': 0.0330468,
'Renaming Unit/Int Front End RAT/Subthreshold Leakage': 0.00435488,
'Renaming Unit/Int Front End RAT/Subthreshold Leakage with power gating': 0.00248228,
'Renaming Unit/Peak Dynamic': 3.58947,
'Renaming Unit/Runtime Dynamic': 0.0363917,
'Renaming Unit/Subthreshold Leakage': 0.0552466,
'Renaming Unit/Subthreshold Leakage with power gating': 0.0276461,
'Runtime Dynamic': 1.69403,
'Subthreshold Leakage': 6.16288,
'Subthreshold Leakage with power gating': 2.55328},
{'Area': 32.0201,
'Execution Unit/Area': 7.68434,
'Execution Unit/Complex ALUs/Area': 0.235435,
'Execution Unit/Complex ALUs/Gate Leakage': 0.0132646,
'Execution Unit/Complex ALUs/Peak Dynamic': 0.0848322,
'Execution Unit/Complex ALUs/Runtime Dynamic': 0.26932,
'Execution Unit/Complex ALUs/Subthreshold Leakage': 0.20111,
'Execution Unit/Complex ALUs/Subthreshold Leakage with power gating': 0.0754163,
'Execution Unit/Floating Point Units/Area': 4.6585,
'Execution Unit/Floating Point Units/Gate Leakage': 0.0656156,
'Execution Unit/Floating Point Units/Peak Dynamic': 0.419045,
'Execution Unit/Floating Point Units/Runtime Dynamic': 0.304033,
'Execution Unit/Floating Point Units/Subthreshold Leakage': 0.994829,
'Execution Unit/Floating Point Units/Subthreshold Leakage with power gating': 0.373061,
'Execution Unit/Gate Leakage': 0.120359,
'Execution Unit/Instruction Scheduler/Area': 1.66526,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Area': 0.275653,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Gate Leakage': 0.000977433,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Peak Dynamic': 1.04181,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Runtime Dynamic': 0.118061,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage': 0.0143453,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage with power gating': 0.00810519,
'Execution Unit/Instruction Scheduler/Gate Leakage': 0.00568913,
'Execution Unit/Instruction Scheduler/Instruction Window/Area': 0.805223,
'Execution Unit/Instruction Scheduler/Instruction Window/Gate Leakage': 0.00414562,
'Execution Unit/Instruction Scheduler/Instruction Window/Peak Dynamic': 1.6763,
'Execution Unit/Instruction Scheduler/Instruction Window/Runtime Dynamic': 0.190427,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage': 0.0625755,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage with power gating': 0.0355964,
'Execution Unit/Instruction Scheduler/Peak Dynamic': 3.82262,
'Execution Unit/Instruction Scheduler/ROB/Area': 0.584388,
'Execution Unit/Instruction Scheduler/ROB/Gate Leakage': 0.00056608,
'Execution Unit/Instruction Scheduler/ROB/Peak Dynamic': 1.10451,
'Execution Unit/Instruction Scheduler/ROB/Runtime Dynamic': 0.0961214,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage': 0.00906853,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage with power gating': 0.00364446,
'Execution Unit/Instruction Scheduler/Runtime Dynamic': 0.404609,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage': 0.0859892,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage with power gating': 0.047346,
'Execution Unit/Integer ALUs/Area': 0.47087,
'Execution Unit/Integer ALUs/Gate Leakage': 0.0265291,
'Execution Unit/Integer ALUs/Peak Dynamic': 0.0707816,
'Execution Unit/Integer ALUs/Runtime Dynamic': 0.101344,
'Execution Unit/Integer ALUs/Subthreshold Leakage': 0.40222,
'Execution Unit/Integer ALUs/Subthreshold Leakage with power gating': 0.150833,
'Execution Unit/Peak Dynamic': 4.72201,
'Execution Unit/Register Files/Area': 0.570804,
'Execution Unit/Register Files/Floating Point RF/Area': 0.208131,
'Execution Unit/Register Files/Floating Point RF/Gate Leakage': 0.000232788,
'Execution Unit/Register Files/Floating Point RF/Peak Dynamic': 0.0791666,
'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.00495199,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage': 0.00399698,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage with power gating': 0.00176968,
'Execution Unit/Register Files/Gate Leakage': 0.000622708,
'Execution Unit/Register Files/Integer RF/Area': 0.362673,
'Execution Unit/Register Files/Integer RF/Gate Leakage': 0.00038992,
'Execution Unit/Register Files/Integer RF/Peak Dynamic': 0.0691576,
'Execution Unit/Register Files/Integer RF/Runtime Dynamic': 0.036623,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage': 0.00614175,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage with power gating': 0.00246675,
'Execution Unit/Register Files/Peak Dynamic': 0.148324,
'Execution Unit/Register Files/Runtime Dynamic': 0.041575,
'Execution Unit/Register Files/Subthreshold Leakage': 0.0101387,
'Execution Unit/Register Files/Subthreshold Leakage with power gating': 0.00423643,
'Execution Unit/Results Broadcast Bus/Area Overhead': 0.0390912,
'Execution Unit/Results Broadcast Bus/Gate Leakage': 0.00537402,
'Execution Unit/Results Broadcast Bus/Peak Dynamic': 0.166909,
'Execution Unit/Results Broadcast Bus/Runtime Dynamic': 0.322688,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage': 0.081478,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage with power gating': 0.0305543,
'Execution Unit/Runtime Dynamic': 1.44357,
'Execution Unit/Subthreshold Leakage': 1.79543,
'Execution Unit/Subthreshold Leakage with power gating': 0.688821,
'Gate Leakage': 0.368936,
'Instruction Fetch Unit/Area': 5.85939,
'Instruction Fetch Unit/Branch Predictor/Area': 0.138516,
'Instruction Fetch Unit/Branch Predictor/Chooser/Area': 0.0435221,
'Instruction Fetch Unit/Branch Predictor/Chooser/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Chooser/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Chooser/Runtime Dynamic': 0.000138314,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage': 0.00759719,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage with power gating': 0.0039236,
'Instruction Fetch Unit/Branch Predictor/Gate Leakage': 0.000757657,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Area': 0.0435221,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Runtime Dynamic': 0.000138314,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage': 0.00759719,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage with power gating': 0.0039236,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Area': 0.0257064,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Gate Leakage': 0.000154548,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Peak Dynamic': 0.0142575,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Runtime Dynamic': 0.000124787,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage': 0.00384344,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage with power gating': 0.00198631,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Area': 0.0151917,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Gate Leakage': 8.00196e-05,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Peak Dynamic': 0.00527447,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Runtime Dynamic': 5.06676e-05,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage': 0.00181347,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage with power gating': 0.000957045,
'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838,
'Instruction Fetch Unit/Branch Predictor/RAS/Area': 0.0105732,
'Instruction Fetch Unit/Branch Predictor/RAS/Gate Leakage': 4.63858e-05,
'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602,
'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.000526093,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage': 0.000932505,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733,
'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.000927509,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282,
'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954,
'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758,
'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867,
'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.00117195,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357,
'Instruction Fetch Unit/Gate Leakage': 0.0589979,
'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323,
'Instruction Fetch Unit/Instruction Buffer/Gate Leakage': 6.83558e-05,
'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827,
'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.0352066,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682,
'Instruction Fetch Unit/Instruction Cache/Area': 3.14635,
'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931,
'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 2.23945,
'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.0723056,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386,
'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799,
'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493,
'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404,
'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.119578,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage with power gating': 0.166104,
'Instruction Fetch Unit/Peak Dynamic': 4.56665,
'Instruction Fetch Unit/Runtime Dynamic': 0.229189,
'Instruction Fetch Unit/Subthreshold Leakage': 0.932286,
'Instruction Fetch Unit/Subthreshold Leakage with power gating': 0.40843,
'L2/Area': 4.53318,
'L2/Gate Leakage': 0.015464,
'L2/Peak Dynamic': 0.0352892,
'L2/Runtime Dynamic': 0.011944,
'L2/Subthreshold Leakage': 0.834142,
'L2/Subthreshold Leakage with power gating': 0.401066,
'Load Store Unit/Area': 8.80901,
'Load Store Unit/Data Cache/Area': 6.84535,
'Load Store Unit/Data Cache/Gate Leakage': 0.0279261,
'Load Store Unit/Data Cache/Peak Dynamic': 2.19812,
'Load Store Unit/Data Cache/Runtime Dynamic': 0.480566,
'Load Store Unit/Data Cache/Subthreshold Leakage': 0.527675,
'Load Store Unit/Data Cache/Subthreshold Leakage with power gating': 0.25085,
'Load Store Unit/Gate Leakage': 0.0350888,
'Load Store Unit/LoadQ/Area': 0.0836782,
'Load Store Unit/LoadQ/Gate Leakage': 0.00059896,
'Load Store Unit/LoadQ/Peak Dynamic': 0.0310907,
'Load Store Unit/LoadQ/Runtime Dynamic': 0.0310908,
'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961,
'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918,
'Load Store Unit/Peak Dynamic': 2.34494,
'Load Store Unit/Runtime Dynamic': 0.664985,
'Load Store Unit/StoreQ/Area': 0.322079,
'Load Store Unit/StoreQ/Gate Leakage': 0.00329971,
'Load Store Unit/StoreQ/Peak Dynamic': 0.0766645,
'Load Store Unit/StoreQ/Runtime Dynamic': 0.153329,
'Load Store Unit/StoreQ/Subthreshold Leakage': 0.0345621,
'Load Store Unit/StoreQ/Subthreshold Leakage with power gating': 0.0197004,
'Load Store Unit/Subthreshold Leakage': 0.591321,
'Load Store Unit/Subthreshold Leakage with power gating': 0.283293,
'Memory Management Unit/Area': 0.4339,
'Memory Management Unit/Dtlb/Area': 0.0879726,
'Memory Management Unit/Dtlb/Gate Leakage': 0.00088729,
'Memory Management Unit/Dtlb/Peak Dynamic': 0.0272085,
'Memory Management Unit/Dtlb/Runtime Dynamic': 0.0277368,
'Memory Management Unit/Dtlb/Subthreshold Leakage': 0.0155699,
'Memory Management Unit/Dtlb/Subthreshold Leakage with power gating': 0.00887485,
'Memory Management Unit/Gate Leakage': 0.00808595,
'Memory Management Unit/Itlb/Area': 0.301552,
'Memory Management Unit/Itlb/Gate Leakage': 0.00393464,
'Memory Management Unit/Itlb/Peak Dynamic': 0.139241,
'Memory Management Unit/Itlb/Runtime Dynamic': 0.0118585,
'Memory Management Unit/Itlb/Subthreshold Leakage': 0.0413758,
'Memory Management Unit/Itlb/Subthreshold Leakage with power gating': 0.0235842,
'Memory Management Unit/Peak Dynamic': 0.342089,
'Memory Management Unit/Runtime Dynamic': 0.0395953,
'Memory Management Unit/Subthreshold Leakage': 0.0766103,
'Memory Management Unit/Subthreshold Leakage with power gating': 0.0398333,
'Peak Dynamic': 15.6004,
'Renaming Unit/Area': 0.303608,
'Renaming Unit/FP Front End RAT/Area': 0.131045,
'Renaming Unit/FP Front End RAT/Gate Leakage': 0.00351123,
'Renaming Unit/FP Front End RAT/Peak Dynamic': 2.51468,
'Renaming Unit/FP Front End RAT/Runtime Dynamic': 0.208252,
'Renaming Unit/FP Front End RAT/Subthreshold Leakage': 0.0308571,
'Renaming Unit/FP Front End RAT/Subthreshold Leakage with power gating': 0.0175885,
'Renaming Unit/Free List/Area': 0.0340654,
'Renaming Unit/Free List/Gate Leakage': 2.5481e-05,
'Renaming Unit/Free List/Peak Dynamic': 0.0306032,
'Renaming Unit/Free List/Runtime Dynamic': 0.00786095,
'Renaming Unit/Free List/Subthreshold Leakage': 0.000370144,
'Renaming Unit/Free List/Subthreshold Leakage with power gating': 0.000201064,
'Renaming Unit/Gate Leakage': 0.00708398,
'Renaming Unit/Int Front End RAT/Area': 0.0941223,
'Renaming Unit/Int Front End RAT/Gate Leakage': 0.000283242,
'Renaming Unit/Int Front End RAT/Peak Dynamic': 0.731965,
'Renaming Unit/Int Front End RAT/Runtime Dynamic': 0.0556902,
'Renaming Unit/Int Front End RAT/Subthreshold Leakage': 0.00435488,
'Renaming Unit/Int Front End RAT/Subthreshold Leakage with power gating': 0.00248228,
'Renaming Unit/Peak Dynamic': 3.58947,
'Renaming Unit/Runtime Dynamic': 0.271803,
'Renaming Unit/Subthreshold Leakage': 0.0552466,
'Renaming Unit/Subthreshold Leakage with power gating': 0.0276461,
'Runtime Dynamic': 2.66109,
'Subthreshold Leakage': 6.16288,
'Subthreshold Leakage with power gating': 2.55328}],
'DRAM': {'Area': 0,
'Gate Leakage': 0,
'Peak Dynamic': 2.969721439161115,
'Runtime Dynamic': 2.969721439161115,
'Subthreshold Leakage': 4.252,
'Subthreshold Leakage with power gating': 4.252},
'L3': [{'Area': 61.9075,
'Gate Leakage': 0.0484137,
'Peak Dynamic': 0.141769,
'Runtime Dynamic': 0.0578359,
'Subthreshold Leakage': 6.80085,
'Subthreshold Leakage with power gating': 3.32364}],
'Processor': {'Area': 191.908,
'Gate Leakage': 1.53485,
'Peak Dynamic': 71.7628,
'Peak Power': 104.875,
'Runtime Dynamic': 14.6973,
'Subthreshold Leakage': 31.5774,
'Subthreshold Leakage with power gating': 13.9484,
'Total Cores/Area': 128.669,
'Total Cores/Gate Leakage': 1.4798,
'Total Cores/Peak Dynamic': 71.621,
'Total Cores/Runtime Dynamic': 14.6395,
'Total Cores/Subthreshold Leakage': 24.7074,
'Total Cores/Subthreshold Leakage with power gating': 10.2429,
'Total L3s/Area': 61.9075,
'Total L3s/Gate Leakage': 0.0484137,
'Total L3s/Peak Dynamic': 0.141769,
'Total L3s/Runtime Dynamic': 0.0578359,
'Total L3s/Subthreshold Leakage': 6.80085,
'Total L3s/Subthreshold Leakage with power gating': 3.32364,
'Total Leakage': 33.1122,
'Total NoCs/Area': 1.33155,
'Total NoCs/Gate Leakage': 0.00662954,
'Total NoCs/Peak Dynamic': 0.0,
'Total NoCs/Runtime Dynamic': 0.0,
'Total NoCs/Subthreshold Leakage': 0.0691322,
'Total NoCs/Subthreshold Leakage with power gating': 0.0259246}}
| 75.074398
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0
| 7
|
fe51fe582c04c4f4e3a06284c5f178b8b2223b14
| 240,271
|
py
|
Python
|
awspricingfull.py
|
chrswt/awspricingfull
|
cc59aa3d42a1a9c38556d26adb5d48920317bf93
|
[
"MIT"
] | null | null | null |
awspricingfull.py
|
chrswt/awspricingfull
|
cc59aa3d42a1a9c38556d26adb5d48920317bf93
|
[
"MIT"
] | null | null | null |
awspricingfull.py
|
chrswt/awspricingfull
|
cc59aa3d42a1a9c38556d26adb5d48920317bf93
|
[
"MIT"
] | 1
|
2018-09-18T20:39:37.000Z
|
2018-09-18T20:39:37.000Z
|
#!/usr/bin/python
#
# Copyright (c) 2015 Ilia Semenov (ilya.v.semenov@gmail.com)
#
# Permission is hereby granted, free of charge, to any person obtaining
# a copy of this software and associated documentation files (the
# "Software"), to deal in the Software without restriction, including
# without limitation the rights to use, copy, modify, merge, publish,
# distribute, sublicense, and/or sell copies of the Software, and to
# permit persons to whom the Software is furnished to do so, subject to
# the following conditions:
#
# The above copyright notice and this permission notice shall be
# included in all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
# LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
# OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
# WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
#
"""AWS Instances (EC2, ElastiCache, RDS, Redshift, DynamoDB) pricing retrieval project.
Project contains one module which is designed to retrieve the AWS prices for
five major AWS services that have reserved capacity involved: EC2, ElastiCache,
RDS, Redshift and DynamoDB. The prices either On-Demand or Reserved (specified by user) can
be retrieved to Command Line in JSON, Table (Prettytable) or CSV formats. CSV format
option also saves the csv file to the folder specified by user, which is the main
use case.
The undocumented AWS pricing APIs are used as the sources. The same APIs serve
the data to the AWS pricing pages.
Both current and previous generation instance prices are retrieved.
Update 2.0: New pricing scheme (noUpfront, allUpfront, PartialUpfront) compatibility for RDS and Redshift is added. Minor bugs fixed.
Update 3.0: DynamoDB throughput capacity pricing is added. MariaDB and Aurora are added to RDS. New schema for AllPrices table introduced
(DB and OS columns merged).
Created: Mar 26, 2015
Updated: Feb 14, 2017
@author: Ilia Semenov
@version: 3.2
"""
import requests
import csv
import re
try:
import simplejson as json
except ImportError:
import json
import os
class AWSPrices(object):
"""
Abstract class - base for the pricing retrieval classes for different
services.
Attributes:
DEFAULT_CURRENCY (str): USD Currency used in every AWS pricing by
default.
REGIONS (list of str): List of AWS regions.
JSON_NAME_TO_REGIONS_API (dict of str: str): Mapping of some internal
AWS region names used in few sources to the conventional ones.
"""
DEFAULT_CURRENCY = "USD"
REGIONS = [
"us-east-1",
"us-east-2",
"us-west-1",
"us-west-2",
"eu-west-1",
"ap-southeast-1",
"ap-southeast-2",
"ap-northeast-1",
"ap-northeast-2",
"sa-east-1",
"eu-central-1",
"us-gov-west-1",
"ap-south-1",
"ca-central-1",
"eu-west-2"
]
JSON_NAME_TO_REGIONS_API = {
"us-east" : "us-east-1",
"us-east-1" : "us-east-1",
"us-east-2" : "us-east-2",
"us-west" : "us-west-1",
"us-west-1" : "us-west-1",
"us-west-2" : "us-west-2",
"eu-ireland" : "eu-west-1",
"eu-west-1" : "eu-west-1",
"apac-sin" : "ap-southeast-1",
"ap-southeast-1" : "ap-southeast-1",
"ap-southeast-2" : "ap-southeast-2",
"apac-syd" : "ap-southeast-2",
"apac-tokyo" : "ap-northeast-1",
"apac-seoul" : "ap-northeast-2",
"ap-northeast-1" : "ap-northeast-1",
"ap-northeast-2" : "ap-northeast-2",
"sa-east-1" : "sa-east-1",
"eu-central-1":"eu-central-1",
"us-gov-west-1":"us-gov-west-1",
"eu-frankfurt":"eu-central-1",
"ap-south-1":"ap-south-1",
"apac-mumbai":"ap-south-1",
"ca-central-1":"ca-central-1",
"eu-west-2":"eu-west-2"
}
def load_data(self,url):
"""
Method for retrieving the pricing data in a clean dictionary format.
Args:
url: The pricing source url.
Returns:
data (dict of dict: dict): Pricing data in a dictionary format
"""
f = requests.get(url).text
f = re.sub("/\\*[^\x00]+\\*/", "", f, 0, re.M)
f = re.sub("([a-zA-Z0-9]+):", "\"\\1\":", f)
f = re.sub(";", "\n", f)
f = re.sub("callback\(", "", f)
f = re.sub("\)$", "", f)
data = json.loads(f)
return data
def none_as_string(self,v):
"""
Method for returning a blank string instead of None.
Args:
v: The value to be checked for None.
Returns:
v or "" is v is None.
"""
if not v:
return ""
else:
return v
def get_ondemand_instances_prices(self):
"""
Abstract method for getting On-Demand pricing.
Implemented in child classes.
Raises:
NotImplementedError: Abstract method is not implemented.
"""
raise NotImplementedError( "Should have implemented this" )
def get_reserved_instances_prices(self):
"""
Abstract method for getting Reserved pricing.
Implemented in child classes.
Raises:
NotImplementedError: Abstract method is not implemented.
"""
raise NotImplementedError( "Should have implemented this" )
def return_json(self,u):
"""
Method printing the pricing data in JSON format to Console.
Args:
u (str): Parameter specifying On-Demand ("ondemand") or
Reserved ("reserved") pricing option.
Returns:
str: Pricing data in JSON string format or an error message.
"""
if u not in ["ondemand","reserved"]:
print("Function requires 1 parameter. Possible values:"
"\"ondemand\" or \"reserved\".")
else:
if u == "ondemand":
data = self.get_ondemand_instances_prices()
elif u == "reserved":
data = self.get_reserved_instances_prices()
return (json.dumps(data))
class EC2Prices(AWSPrices):
"""
Class for retrieving the EC2 pricing. Child of :class:`awspricingfull.AWSPrices` class.
Attributes:
INSTANCES_RESERVED_LINUX_URL (str): Undocumented AWS Pricing
API URL - EC2 Linux Reserved, Current Generation.
INSTANCES_RESERVED_RHEL_URL (str): Undocumented AWS Pricing
API URL - EC2 RHEL Reserved, Current Generation.
INSTANCES_RESERVED_SLES_URL (str): Undocumented AWS Pricing
API URL - EC2 SLES Reserved, Current Generation.
INSTANCES_RESERVED_WINDOWS_URL (str): Undocumented AWS Pricing
API URL - EC2 Windows Reserved, Current Generation.
INSTANCES_RESERVED_WINSQL_URL (str): Undocumented AWS Pricing
API URL - EC2 Windows SQL Reserved, Current Generation.
INSTANCES_RESERVED_WINSQLWEB_URL (str): Undocumented AWS Pricing
API URL - EC2 Windows SQL Web Reserved, Current Generation.
PG_INSTANCES_RESERVED_LINUX_URL (str): Undocumented AWS Pricing
API URL - EC2 Linux Reserved, Previous Generation.
PG_INSTANCES_RESERVED_RHEL_URL (str): Undocumented AWS Pricing
API URL - EC2 RHEL Reserved, Previous Generation.
PG_INSTANCES_RESERVED_SLES_URL (str): Undocumented AWS Pricing
API URL - EC2 SLES Reserved, Previous Generation.
PG_INSTANCES_RESERVED_WINDOWS_URL (str): Undocumented AWS Pricing
API URL - EC2 Windows Reserved, Previous Generation.
PG_INSTANCES_RESERVED_WINSQL_URL (str): Undocumented AWS Pricing
API URL - EC2 Windows SQL Reserved, Previous Generation.
PG_INSTANCES_RESERVED_WINSQLWEB_URL (str): Undocumented AWS Pricing
API URL - EC2 Windows SQL Web Reserved, Previous Generation.
INSTANCES_ONDEMAND_LINUX_URL (str): Undocumented AWS Pricing
API URL - EC2 Linux On-Demand, Current Generation.
INSTANCES_ONDEMAND_RHEL_URL (str): Undocumented AWS Pricing
API URL - EC2 RHEL On-Demand, Current Generation.
INSTANCES_ONDEMAND_SLES_URL (str): Undocumented AWS Pricing
API URL - EC2 SLES On-Demand, Current Generation.
INSTANCES_ONDEMAND_WINDOWS_URL (str): Undocumented AWS Pricing
API URL - EC2 Windows On-Demand, Current Generation.
INSTANCES_ONDEMAND_WINSQL_URL (str): Undocumented AWS Pricing
API URL - EC2 Windows SQL On-Demand, Current Generation.
INSTANCES_ONDEMAND_WINSQLWEB_URL (str): Undocumented AWS Pricing
API URL - EC2 Windows SQL Web On-Demand, Current Generation.
PG_INSTANCES_ONDEMAND_LINUX_URL (str): Undocumented AWS Pricing
API URL - EC2 Linux On-Demand, Previous Generation.
PG_INSTANCES_ONDEMAND_RHEL_URL (str): Undocumented AWS Pricing
API URL - EC2 RHEL On-Demand, Previous Generation.
PG_INSTANCES_ONDEMAND_SLES_URL (str): Undocumented AWS Pricing
API URL - EC2 SLES On-Demand, Previous Generation.
PG_INSTANCES_ONDEMAND_WINDOWS_URL (str): Undocumented AWS Pricing
API URL - EC2 Windows On-Demand, Previous Generation.
PG_INSTANCES_ONDEMAND_WINSQL_URL (str): Undocumented AWS Pricing
API URL - EC2 Windows SQL On-Demand, Previous Generation.
PG_INSTANCES_ONDEMAND_WINSQLWEB_URL (str): Undocumented AWS Pricing
API URL - EC2 Windows SQL Web On-Demand, Previous Generation.
INSTANCES_ONDEMAND_OS_TYPE_BY_URL (dict of str: str): Mapping of
On-Dermand urls to OS types.
INSTANCES_RESERVED_OS_TYPE_BY_URL (dict of str: str): Mapping of
Reserved urls to OS types.
"""
INSTANCES_ON_DEMAND_LINUX_URL =("http://a0.awsstatic.com/pricing/1/ec2/"+
"linux-od.min.js")
INSTANCES_ON_DEMAND_RHEL_URL =("http://a0.awsstatic.com/pricing/1/ec2/"+
"rhel-od.min.js")
INSTANCES_ON_DEMAND_SLES_URL = ("http://a0.awsstatic.com/pricing/1/ec2/"+
"sles-od.min.js")
INSTANCES_ON_DEMAND_WINDOWS_URL = ("http://a0.awsstatic.com/pricing/1/ec2/"+
"mswin-od.min.js")
INSTANCES_ON_DEMAND_WINSQL_URL = ("http://a0.awsstatic.com/pricing/1/ec2/"+
"mswinSQL-od.min.js")
INSTANCES_ON_DEMAND_WINSQLWEB_URL = ("http://a0.awsstatic.com/pricing/1/ec2/"+
"mswinSQLWeb-od.min.js")
PG_INSTANCES_ON_DEMAND_LINUX_URL = ("http://a0.awsstatic.com/pricing/1/ec2/"+
"previous-generation/linux-od.min.js")
PG_INSTANCES_ON_DEMAND_RHEL_URL = ("http://a0.awsstatic.com/pricing/1/ec2/"+
"previous-generation/rhel-od.min.js")
PG_INSTANCES_ON_DEMAND_SLES_URL = ("http://a0.awsstatic.com/pricing/1/ec2/"+
"previous-generation/sles-od.min.js")
PG_INSTANCES_ON_DEMAND_WINDOWS_URL = ("http://a0.awsstatic.com/pricing/1/ec2/"+
"previous-generation/mswin-od.min.js")
PG_INSTANCES_ON_DEMAND_WINSQL_URL = ("http://a0.awsstatic.com/pricing/1/ec2/"+
"previous-generation/mswinSQL-od.min.js")
PG_INSTANCES_ON_DEMAND_WINSQLWEB_URL = ("http://a0.awsstatic.com/pricing/1/ec2/"+
"previous-generation/mswinSQLWeb-od.min.js")
INSTANCES_RESERVED_LINUX_URL = ("http://a0.awsstatic.com/pricing/1/ec2/"+
"ri-v2/linux-unix-shared.min.js")
INSTANCES_RESERVED_RHEL_URL = ("http://a0.awsstatic.com/pricing/1/ec2/"+
"ri-v2/red-hat-enterprise-linux-shared.min.js")
INSTANCES_RESERVED_SLES_URL = ("http://a0.awsstatic.com/pricing/1/ec2/"+
"ri-v2/suse-linux-shared.min.js")
INSTANCES_RESERVED_WINDOWS_URL = ("http://a0.awsstatic.com/pricing/1/ec2/"+
"ri-v2/windows-shared.min.js")
INSTANCES_RESERVED_WINSQL_URL = ("http://a0.awsstatic.com/pricing/1/ec2/"+
"ri-v2/windows-with-sql-server-standard-shared.min.js")
INSTANCES_RESERVED_WINSQLWEB_URL = ("http://a0.awsstatic.com/pricing/1/ec2/"+
"ri-v2/windows-with-sql-server-web-shared.min.js")
PG_INSTANCES_RESERVED_LINUX_URL = ("http://a0.awsstatic.com/pricing/1/ec2/"+
"previous-generation/ri-v2/linux-unix-shared.min.js")
PG_INSTANCES_RESERVED_RHEL_URL = ("http://a0.awsstatic.com/pricing/1/ec2/"+
"previous-generation/ri-v2/red-hat-enterprise-linux-shared.min.js")
PG_INSTANCES_RESERVED_SLES_URL = ("http://a0.awsstatic.com/pricing/1/ec2/"+
"previous-generation/ri-v2/suse-linux-shared.min.js")
PG_INSTANCES_RESERVED_WINDOWS_URL = ("http://a0.awsstatic.com/pricing/1/ec2/"+
"previous-generation/ri-v2/windows-shared.min.js")
PG_INSTANCES_RESERVED_WINSQL_URL = ("http://a0.awsstatic.com/pricing/1/ec2/"+
"previous-generation/ri-v2/windows-with-sql-server-standard-shared.min.js")
PG_INSTANCES_RESERVED_WINSQLWEB_URL = ("http://a0.awsstatic.com/pricing/1/ec2/"+
"previous-generation/ri-v2/windows-with-sql-server-web-shared.min.js")
INSTANCES_ONDEMAND_OS_TYPE_BY_URL = {
INSTANCES_ON_DEMAND_LINUX_URL : "linux",
INSTANCES_ON_DEMAND_RHEL_URL : "rhel",
INSTANCES_ON_DEMAND_SLES_URL : "sles",
INSTANCES_ON_DEMAND_WINDOWS_URL : "mswin",
INSTANCES_ON_DEMAND_WINSQL_URL : "mswinSQL",
INSTANCES_ON_DEMAND_WINSQLWEB_URL : "mswinSQLWeb",
PG_INSTANCES_ON_DEMAND_LINUX_URL : "linux",
PG_INSTANCES_ON_DEMAND_RHEL_URL : "rhel",
PG_INSTANCES_ON_DEMAND_SLES_URL : "sles",
PG_INSTANCES_ON_DEMAND_WINDOWS_URL : "mswin",
PG_INSTANCES_ON_DEMAND_WINSQL_URL : "mswinSQL",
PG_INSTANCES_ON_DEMAND_WINSQLWEB_URL : "mswinSQLWeb"
}
INSTANCES_RESERVED_OS_TYPE_BY_URL = {
INSTANCES_RESERVED_LINUX_URL : "linux",
INSTANCES_RESERVED_RHEL_URL : "rhel",
INSTANCES_RESERVED_SLES_URL : "sles",
INSTANCES_RESERVED_WINDOWS_URL : "mswin",
INSTANCES_RESERVED_WINSQL_URL : "mswinSQL",
INSTANCES_RESERVED_WINSQLWEB_URL : "mswinSQLWeb",
PG_INSTANCES_RESERVED_LINUX_URL : "linux",
PG_INSTANCES_RESERVED_RHEL_URL : "rhel",
PG_INSTANCES_RESERVED_SLES_URL : "sles",
PG_INSTANCES_RESERVED_WINDOWS_URL : "mswin",
PG_INSTANCES_RESERVED_WINSQL_URL : "mswinSQL",
PG_INSTANCES_RESERVED_WINSQLWEB_URL : "mswinSQLWeb"
}
def get_reserved_instances_prices(self):
"""
Implementation of method for getting EC2 Reserved pricing.
Returns:
result (dict of dict: dict): EC2 Reserved pricing in dictionary format.
"""
currency = self.DEFAULT_CURRENCY
urls = [
self.INSTANCES_RESERVED_LINUX_URL,
self.INSTANCES_RESERVED_RHEL_URL,
self.INSTANCES_RESERVED_SLES_URL,
self.INSTANCES_RESERVED_WINDOWS_URL,
self.INSTANCES_RESERVED_WINSQL_URL,
self.INSTANCES_RESERVED_WINSQLWEB_URL,
self.PG_INSTANCES_RESERVED_LINUX_URL,
self.PG_INSTANCES_RESERVED_RHEL_URL,
self.PG_INSTANCES_RESERVED_SLES_URL,
self.PG_INSTANCES_RESERVED_WINDOWS_URL,
self.PG_INSTANCES_RESERVED_WINSQL_URL,
self.PG_INSTANCES_RESERVED_WINSQLWEB_URL,
]
result_regions = []
result_regions_index = {}
result = {
"config" : {
"currency" : currency,
},
"regions" : result_regions
}
for u in urls:
os_type = self.INSTANCES_RESERVED_OS_TYPE_BY_URL[u]
data = self.load_data(u)
if ("config" in data and data["config"] and "regions"
in data["config"] and data["config"]["regions"]):
for r in data["config"]["regions"]:
if "region" in r and r["region"]:
region_name = r["region"]
if region_name in result_regions_index:
instance_types = result_regions_index[region_name]["instanceTypes"]
else:
instance_types = []
result_regions.append({
"region" : region_name,
"instanceTypes" : instance_types
})
result_regions_index[region_name] = result_regions[-1]
if "instanceTypes" in r:
for it in r["instanceTypes"]:
instance_type = it["type"]
prices = {
"1" : {
"noUpfront" : {
"hourly" : None,
"upfront" : None
},
"partialUpfront":{
"hourly" : None,
"upfront" : None
},
"allUpfront":{
"hourly" : None,
"upfront" : None
}
},
"3" : {
"noUpfront" : {
"hourly" : None,
"upfront" : None
},
"partialUpfront":{
"hourly" : None,
"upfront" : None
},
"allUpfront":{
"hourly" : None,
"upfront" : None
}
}
}
instance_types.append({
"type" : instance_type,
"os" : os_type,
"prices" : prices
})
if "terms" in it:
for s in it["terms"]:
term=s["term"]
for po_data in s["purchaseOptions"]:
po=po_data["purchaseOption"]
for price_data in po_data["valueColumns"]:
price = None
try:
price = float(re.sub("[^0-9\.]", "",
price_data["prices"][currency]))
except ValueError:
price = None
if term=="yrTerm1Standard":
if price_data["name"] == "upfront":
prices["1"][po]["upfront"] = price
elif price_data["name"] == "monthlyStar":
prices["1"][po]["hourly"] = price/730
elif term=="yrTerm3Standard":
if price_data["name"] == "upfront":
prices["3"][po]["upfront"] = price
elif price_data["name"] == "monthlyStar":
prices["3"][po]["hourly"] = price/730
return result
def get_ondemand_instances_prices(self):
"""
Implementation of method for getting EC2 On-Demand pricing.
Returns:
result (dict of dict: dict): EC2 On-Demand pricing in dictionary format.
"""
currency = self.DEFAULT_CURRENCY
urls = [
self.INSTANCES_ON_DEMAND_LINUX_URL,
self.INSTANCES_ON_DEMAND_RHEL_URL,
self.INSTANCES_ON_DEMAND_SLES_URL,
self.INSTANCES_ON_DEMAND_WINDOWS_URL,
self.INSTANCES_ON_DEMAND_WINSQL_URL,
self.INSTANCES_ON_DEMAND_WINSQLWEB_URL,
self.PG_INSTANCES_ON_DEMAND_LINUX_URL,
self.PG_INSTANCES_ON_DEMAND_RHEL_URL,
self.PG_INSTANCES_ON_DEMAND_SLES_URL,
self.PG_INSTANCES_ON_DEMAND_WINDOWS_URL,
self.PG_INSTANCES_ON_DEMAND_WINSQL_URL,
self.PG_INSTANCES_ON_DEMAND_WINSQLWEB_URL
]
result_regions = []
result = {
"config" : {
"currency" : currency,
"unit" : "perhr"
},
"regions" : result_regions
}
for u in urls:
data = self.load_data(u)
if ("config" in data and data["config"] and "regions"
in data["config"] and data["config"]["regions"]):
for r in data["config"]["regions"]:
if "region" in r and r["region"]:
region_name = r["region"]
instance_types = []
if "instanceTypes" in r:
for it in r["instanceTypes"]:
if "sizes" in it:
for s in it["sizes"]:
instance_type = s["size"]
for price_data in s["valueColumns"]:
price = None
try:
price =float(re.sub("[^0-9\.]", "",
price_data["prices"][currency]))
except:
price = None
_type = instance_type
instance_types.append({
"type" : _type,
"os" : price_data["name"],
"price" : price
})
result_regions.append({
"region" : region_name,
"instanceTypes" : instance_types
})
return result
def print_table(self,u):
"""
Method printing the EC2 pricing data to the console
in the Pretty Table format (requires Pretty Table
import).
Args:
u (str): Parameter specifying On-Demand ("ondemand") or
Reserved ("reserved") pricing option.
Returns:
Prints EC2 pricing in the Pretty Table format.
"""
try:
from prettytable import PrettyTable
except ImportError:
print ("ERROR: Please install 'prettytable' using pip:"+
"pip install prettytable")
data = None
if u not in ["ondemand","reserved"]:
print("Function requires 1 parameter. Possible values:"
"\"ondemand\" or \"reserved\".")
else:
if u == "ondemand":
data = self.get_ondemand_instances_prices()
x = PrettyTable()
try:
x.set_field_names(["region", "type", "os", "price"])
except AttributeError:
x.field_names = ["region", "type", "os", "price"]
try:
x.aligns[-1] = "l"
except AttributeError:
x.align["price"] = "l"
for r in data["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
x.add_row([region_name, it["type"], it["os"], self.none_as_string(it["price"])])
elif u == "reserved":
data = self.get_reserved_instances_prices()
x = PrettyTable()
try:
x.set_field_names(["region", "type", "os", "term", "payment_type", "price", "upfront"])
except AttributeError:
x.field_names = ["region", "type", "os", "term", "payment_type", "price", "upfront"]
try:
x.aligns[-1] = "l"
x.aligns[-2] = "l"
except AttributeError:
x.align["price"] = "l"
x.align["upfront"] = "l"
for r in data["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
for term in it["prices"]:
for po in it["prices"][term]:
x.add_row ([region_name,
it["type"],
it["os"],
term,
po,
self.none_as_string(it["prices"][term][po]["hourly"]),
self.none_as_string(it["prices"][term][po]["upfront"])])
print(x)
def save_csv(self,u,path=os.getcwd()+"\\",name=None):
"""
Method saving the EC2 pricing data in CSV format to the
cpecified location.
Args:
u (str): Parameter specifying On-Demand ("ondemand") or
Reserved ("reserved") pricing option.
path (str): System path for saving the data file. Current
directory is the the defauilt value.
name (str): The desired name of the file. The default
values are "EC2_reserved_pricing.csv" for Reserved
and "EC2_ondemand_pricing.csv" for On-Demand.
Returns:
Prints EC2 pricing in the CSV format (console).
"""
if u not in ["ondemand","reserved"]:
print("Function requires 1 parameter. Possible values:"
"\"ondemand\" or \"reserved\".")
elif u == "ondemand":
if name is None:
name="EC2_ondemand_pricing.csv"
data = self.get_ondemand_instances_prices()
writer = csv.writer(open(path+name, 'wb'))
print("region,type,os,price")
writer.writerow(["region","type","os","price"])
for r in data["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
writer.writerow([region_name,it["type"],it["os"],self.none_as_string(it["price"])])
print("%s,%s,%s,%s" % (region_name,
it["type"],
it["os"],
self.none_as_string(it["price"])))
elif u == "reserved":
if name is None:
name="EC2_reserved_pricing.csv"
data = self.get_reserved_instances_prices()
writer = csv.writer(open(path+name, 'wb'))
print("region,type,os,term,payment_type,price,upfront")
writer.writerow(["region","type","os","term","payment_type","price","upfront"])
for r in data["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
for term in it["prices"]:
for po in it["prices"][term]:
print("%s,%s,%s,%s,%s,%s,%s" % (region_name,
it["type"],
it["os"],
term,
po,
self.none_as_string(it["prices"][term][po]["hourly"]),
self.none_as_string(it["prices"][term][po]["upfront"])))
writer.writerow([region_name,
it["type"],
it["os"],
term,
po,
self.none_as_string(it["prices"][term][po]["hourly"]),
self.none_as_string(it["prices"][term][po]["upfront"])])
class ELCPrices(AWSPrices):
"""
Class for retrieving the ElastiCache pricing. Child of :class:`awspricingfull.AWSPrices` class.
Attributes:
INSTANCES_ON_DEMAND_URL (str): Undocumented AWS Pricing API URL -
On-Demand ELC Current Generation.
PG_INSTANCES_ON_DEMAND_URL (str): Undocumented AWS Pricing API
URL - On-Demand ELC Previous Generation.
INSTANCES_RESERVED_LIGHT_UTILIZATION_URL (str): Undocumented AWS
Pricing - Light Reserved ELC Current Generation.
INSTANCES_RESERVED_MEDIUM_UTILIZATION_URL (str): Undocumented AWS
Pricing- Medium Reserved ELC Current Generation.
INSTANCES_RESERVED_HEAVY_UTILIZATION_URL (str): Undocumented AWS
Pricing- Heavy Reserved ELC Current Generation.
PG_INSTANCES_RESERVED_LIGHT_UTILIZATION_URL (str): Undocumented AWS
Pricing- Light Reserved ELC Previous Generation.
PG_INSTANCES_RESERVED_MEDIUM_UTILIZATION_URL (str): Undocumented AWS
Pricing- Medium Reserved ELC Previous Generation.
PG_INSTANCES_RESERVED_HEAVY_UTILIZATION_URL (str): Undocumented AWS
Pricing- Heavy Reserved ELC Previous Generation.
INSTANCES_RESERVED_UTILIZATION_TYPE_BY_URL (dict of str: str): Mapping of
Reserved urls to Reservation types.
INSTANCE_TYPE_MAPPING (dict of str: str): Mapping of internal AWS ELC
Type names to the conventional analogs.
"""
INSTANCES_ON_DEMAND_URL=("http://a0.awsstatic.com/pricing/1/elasticache/"+
"pricing-standard-deployments-elasticache.min.js")
PG_INSTANCES_ON_DEMAND_URL=("http://a0.awsstatic.com/pricing/1/elasticache/"+
"previous-generation/pricing-standard-deployments-elasticache.min.js")
INSTANCES_RESERVED_LIGHT_UTILIZATION_URL=("http://a0.awsstatic.com/pricing/1/elasticache/"+
"pricing-elasticache-light-standard-deployments-elasticache.min.js")
INSTANCES_RESERVED_MEDIUM_UTILIZATION_URL=("http://a0.awsstatic.com/pricing/1/elasticache/"+
"pricing-elasticache-medium-standard-deployments.min.js")
INSTANCES_RESERVED_HEAVY_UTILIZATION_URL=("http://a0.awsstatic.com/pricing/1/elasticache/"+
"pricing-elasticache-heavy-standard-deployments.min.js")
PG_INSTANCES_RESERVED_LIGHT_UTILIZATION_URL=("http://a0.awsstatic.com/pricing/1/elasticache/"+
"previous-generation/pricing-elasticache-light-standard-deployments.min.js")
PG_INSTANCES_RESERVED_MEDIUM_UTILIZATION_URL=("http://a0.awsstatic.com/pricing/1/elasticache/"+
"previous-generation/pricing-elasticache-medium-standard-deployments.min.js")
PG_INSTANCES_RESERVED_HEAVY_UTILIZATION_URL=("http://a0.awsstatic.com/pricing/1/elasticache/"+
"previous-generation/pricing-elasticache-heavy-standard-deployments.min.js")
INSTANCES_RESERVED_UTILIZATION_TYPE_BY_URL = {
INSTANCES_RESERVED_LIGHT_UTILIZATION_URL : "light",
INSTANCES_RESERVED_MEDIUM_UTILIZATION_URL : "medium",
INSTANCES_RESERVED_HEAVY_UTILIZATION_URL : "heavy",
PG_INSTANCES_RESERVED_LIGHT_UTILIZATION_URL : "light",
PG_INSTANCES_RESERVED_MEDIUM_UTILIZATION_URL : "medium",
PG_INSTANCES_RESERVED_HEAVY_UTILIZATION_URL : "heavy"
}
INSTANCE_TYPE_MAPPING = {
"microInstClass.microInst": "cache.t1.micro",
"sCacheNode.sm" : "cache.m1.small",
"sCacheNode.medInst" : "cache.m1.medium",
"sCacheNode.lg" : "cache.m1.large",
"sCacheNode.xl" : "cache.m1.xlarge",
"hiMemCacheClass.xl" : "cache.m2.xlarge",
"hiMemCacheClass.xxl" : "cache.m2.2xlarge",
"hiMemCacheClass.xxxxl" : "cache.m2.4xlarge",
"hiCPUDBInstClass.hiCPUxlDBInst" : "cache.c1.xlarge",
"enInstClass2.xl" : "cache.m3.xlarge",
"enInstClass2.xxl" : "cache.m3.2xlarge",
#Reserved
"mic" : "cache.t1.micro",
"sm" : "cache.m1.small",
"medInst" : "cache.m1.medium",
"lg" : "cache.m1.large",
"xl" : "cache.m1.xlarge",
"xlHiMem" : "cache.m2.xlarge",
"xxlHiMem" : "cache.m2.2xlarge",
"xxxxlHiMem" : "cache.m2.4xlarge",
"xlHiCPU" : "cache.c1.xlarge",
"xlEn" : "cache.m3.xlarge",
"xxlEn" : "cache.m3.2xlarge",
}
def get_reserved_instances_prices(self):
"""
Implementation of method for getting ELC Reserved pricing.
Returns:
result (dict of dict: dict): ELC Reserved pricing in dictionary format.
"""
currency = self.DEFAULT_CURRENCY
urls = [
self.INSTANCES_RESERVED_LIGHT_UTILIZATION_URL,
self.INSTANCES_RESERVED_MEDIUM_UTILIZATION_URL,
self.INSTANCES_RESERVED_HEAVY_UTILIZATION_URL,
self.PG_INSTANCES_RESERVED_LIGHT_UTILIZATION_URL,
self.PG_INSTANCES_RESERVED_MEDIUM_UTILIZATION_URL,
self.PG_INSTANCES_RESERVED_HEAVY_UTILIZATION_URL
]
result_regions = []
result_regions_index = {}
result = {
"config" : {
"currency" : currency,
},
"regions" : result_regions
}
for u in urls:
utilization_type = self.INSTANCES_RESERVED_UTILIZATION_TYPE_BY_URL[u]
data = self.load_data(u)
if ("config" in data and data["config"] and "regions"
in data["config"] and data["config"]["regions"]):
for r in data["config"]["regions"]:
if "region" in r and r["region"]:
region_name = self.JSON_NAME_TO_REGIONS_API[r["region"]]
if region_name in result_regions_index:
instance_types = result_regions_index[region_name]["instanceTypes"]
else:
instance_types = []
result_regions.append({
"region" : region_name,
"instanceTypes" : instance_types
})
result_regions_index[region_name] = result_regions[-1]
if "instanceTypes" in r:
for it in r["instanceTypes"]:
if "tiers" in it:
for s in it["tiers"]:
if (u==self.INSTANCES_RESERVED_LIGHT_UTILIZATION_URL or
u==self.INSTANCES_RESERVED_MEDIUM_UTILIZATION_URL):
_type = self.INSTANCE_TYPE_MAPPING[s["size"]]
else:
_type = s["size"]
prices = {
"1" : {
"hourly" : None,
"upfront" : None
},
"3" : {
"hourly" : None,
"upfront" : None
}
}
instance_types.append({
"type" : _type,
"utilization" : utilization_type,
"prices" : prices
})
for price_data in s["valueColumns"]:
price = None
try:
price = float(re.sub("[^0-9\\.]", "",
price_data["prices"][currency]))
except ValueError:
price = None
if price_data["name"] == "yrTerm1":
prices["1"]["upfront"] = price
elif price_data["name"] == "yearTerm1Hourly":
prices["1"]["hourly"] = price
elif price_data["name"] == "yrTerm3":
prices["3"]["upfront"] = price
elif price_data["name"] == "yearTerm3Hourly":
prices["3"]["hourly"] = price
return result
def get_ondemand_instances_prices(self):
"""
Implementation of method for getting ELC On-Denand pricing.
Returns:
result (dict of dict: dict): ELC On-Denand pricing in dictionary format.
"""
urls = [
self.INSTANCES_ON_DEMAND_URL,
self.PG_INSTANCES_ON_DEMAND_URL
]
currency = self.DEFAULT_CURRENCY
result_regions = []
result = {
"config" : {
"currency" : currency,
"unit" : "perhr"
},
"regions" : result_regions
}
for u in urls:
data = self.load_data(u)
if ("config" in data and data["config"] and "regions"
in data["config"] and data["config"]["regions"]):
for r in data["config"]["regions"]:
if "region" in r and r["region"]:
region_name = self.JSON_NAME_TO_REGIONS_API[r["region"]]
instance_types = []
if "types" in r:
for it in r["types"]:
if "tiers" in it:
for s in it["tiers"]:
_type = s["name"]
price = None
try:
price = float(re.sub("[^0-9\\.]",
"", s["prices"][currency]))
except ValueError:
price = None
instance_types.append({
"type" : _type,
"price" : price
})
result_regions.append({
"region" : region_name,
"instanceTypes" : instance_types
})
return result
def print_table(self,u):
"""
Method printing the ELC pricing data to the console
in the Pretty Table format (requires Pretty Table
import).
Args:
u (str): Parameter specifying On-Demand ("ondemand") or
Reserved ("reserved") pricing option.
Returns:
Prints ELC pricing in the Pretty Table format.
"""
try:
from prettytable import PrettyTable
except ImportError:
print ("ERROR: Please install 'prettytable' using pip: "+
"pip install prettytable")
data = None
if u not in ["ondemand","reserved"]:
print("Function requires 1 parameter. Possible values:"
"\"ondemand\" or \"reserved\".")
else:
if u == "ondemand":
x = PrettyTable()
data = self.get_ondemand_instances_prices()
try:
x.set_field_names(["region", "type", "price"])
except AttributeError:
x.field_names = ["region", "type", "price"]
try:
x.aligns[-1] = "l"
except AttributeError:
x.align["price"] = "l"
for r in data["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
x.add_row([region_name, it["type"],
self.none_as_string(it["price"])])
elif u == "reserved":
x = PrettyTable()
data = self.get_reserved_instances_prices()
try:
x.set_field_names(["region", "type", "utilization", "term", "price", "upfront"])
except AttributeError:
x.field_names = ["region", "type", "utilization", "term", "price", "upfront"]
try:
x.aligns[-1] = "l"
x.aligns[-2] = "l"
except AttributeError:
x.align["price"] = "l"
x.align["upfront"] = "l"
for r in data["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
for term in it["prices"]:
x.add_row([region_name,
it["type"],
it["utilization"],
term,
self.none_as_string(it["prices"][term]["hourly"]),
self.none_as_string(it["prices"][term]["upfront"])])
print(x)
def save_csv(self,u,path=os.getcwd()+"\\",name=None):
"""
Method saving the ELC pricing data in CSV format to the
cpecified location.
Args:
u (str): Parameter specifying On-Demand ("ondemand") or
Reserved ("reserved") pricing option.
path (str): System path for saving the data file. Current
directory is the the defauilt value.
name (str): The desired name of the file. The default
values are "ELC_reserved_pricing.csv" for Reserved
and "ELC_ondemand_pricing.csv" for On-Demand.
Returns:
Prints ELC pricing in the CSV format (console).
"""
if u not in ["ondemand","reserved"]:
print("Function requires Reservation parameter at the first"+
"position. Possible values:"+
"\"ondemand\" or \"reserved\".")
elif u == "ondemand":
if name is None:
name="ELC_ondemand_pricing.csv"
data = self.get_ondemand_instances_prices()
writer = csv.writer(open(path+name, 'wb'))
print("region,type,price")
writer.writerow(["region","type","price"])
for r in data["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
writer.writerow([region_name,it["type"],
self.none_as_string(it["price"])])
print("%s,%s,%s" % (region_name, it["type"],
self.none_as_string(it["price"])))
elif u == "reserved":
if name is None:
name="ELC_reserved_pricing.csv"
data = self.get_reserved_instances_prices()
writer = csv.writer(open(path+name, 'wb'))
print("region,type,utilization,term,price,upfront")
writer.writerow(["region","type","utilization","term","price","upfront"])
for r in data["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
for term in it["prices"]:
print("%s,%s,%s,%s,%s,%s" % (region_name,
it["type"],
it["utilization"],
term,
self.none_as_string(it["prices"][term]["hourly"]),
self.none_as_string(it["prices"][term]["upfront"])))
writer.writerow([region_name,
it["type"],
it["utilization"],
term,
self.none_as_string(it["prices"][term]["hourly"]),
self.none_as_string(it["prices"][term]["upfront"])])
class RDSPrices(AWSPrices):
"""
Class for retrieving the RDS pricing. Child of :class:`awspricingfull.AWSPrices` class.
Attributes:
RDS_MYSQL_ON_DEMAND_URL (str): Undocumented AWS
API URL - RDS Single AZ MySQL GPL, On-Demand, Current Generation.
RDS_MYSQL_MULTIAZ_ON_DEMAND_URL (str): Undocumented AWS
API URL - RDS Multi AZ MySQL GPL, On-Demand, Current Generation.
RDS_ORACLE_LICENSED_ON_DEMAND_URL (str): Undocumented AWS
API URL - RDS Single AZ Oracle Licensed, On-Demand, Current Generation.
RDS_ORACLE_LICENSED_MULTIAZ_ON_DEMAND_URL (str): Undocumented AWS
API URL - RDS Multi AZ Oracle Licensed, On-Demand, Current Generation.
RDS_ORACLE_BYOL_ON_DEMAND_URL (str): Undocumented AWS
API URL - RDS Single AZ Oracle BYOL, On-Demand, Current Generation.
RDS_ORACLE_BYOL_MULTIAZ_ON_DEMAND_URL (str): Undocumented AWS
API URL - RDS Multi AZ Oracle BYOL, On-Demand, Current Generation.
RDS_MSSQL_LICENSED_EXPRESS_ON_DEMAND_URL (str): Undocumented AWS
API URL - RDS Single AZ MS SQL Server Express Licensed, On-Demand, Current Generation.
RDS_MSSQL_LICENSED_WEB_ON_DEMAND_URL (str): Undocumented AWS
API URL - RDS Single AZ MS SQL Server WEB Licensed, On-Demand, Current Generation.
RDS_MSSQL_LICENSED_STANDARD_ON_DEMAND_URL (str): Undocumented AWS
API URL - RDS Single AZ MS SQL Server SE Licensed, On-Demand, Current Generation.
RDS_MSSQL_LICENSED_STANDARD_MULTIAZ_ON_DEMAND_URL (str): Undocumented AWS
API URL - RDS Multi AZ MS SQL Server SE Licensed, On-Demand, Current Generation.
RDS_MSSQL_BYOL_ON_DEMAND_URL (str): Undocumented AWS
API URL - RDS Single AZ MS SQL Server BYOL, On-Demand, Current Generation.
RDS_MSSQL_BYOL_MULTIAZ_ON_DEMAND_URL (str): Undocumented AWS
API URL - RDS Multi AZ MS SQL Server BYOL, On-Demand, Current Generation.
RDS_POSTGRESQL_ON_DEMAND_URL (str): Undocumented AWS
API URL - RDS Single AZ Postgres On-Demand, Current Generation.
RDS_POSTGRESQL_MULTIAZ_ON_DEMAND_URL (str): Undocumented AWS
API URL - RDS Multi AZ Postgres, On-Demand, Current Generation.
PG_RDS_MYSQL_ON_DEMAND_URL (str): Undocumented AWS
API URL - RDS Single AZ MySQL GPL, On-Demand, Previous Generation.
PG_RDS_MYSQL_MULTIAZ_ON_DEMAND_URL (str): Undocumented AWS
API URL - RDS Multi AZ MySQL GPL, On-Demand, Previous Generation.
PG_RDS_ORACLE_LICENSED_ON_DEMAND_URL (str): Undocumented AWS
API URL - RDS Single AZ Oracle Licensed, On-Demand, Previous Generation.
PG_RDS_ORACLE_LICENSED_MULTIAZ_ON_DEMAND_URL (str): Undocumented AWS
API URL - RDS Multi AZ Oracle Licensed, On-Demand, Previous Generation.
PG_RDS_ORACLE_BYOL_ON_DEMAND_URL (str): Undocumented AWS
API URL - RDS Single AZ Oracle BYOL, On-Demand, Previous Generation.
PG_RDS_ORACLE_BYOL_MULTIAZ_ON_DEMAND_URL (str): Undocumented AWS
API URL - RDS Multi AZ Oracle BYOL, On-Demand, Previous Generation.
PG_RDS_MSSQL_LICENSED_EXPRESS_ON_DEMAND_URL (str): Undocumented AWS
API URL - RDS Single AZ MS SQL Server Express Licensed, On-Demand, Previous Generation.
PG_RDS_MSSQL_LICENSED_WEB_ON_DEMAND_URL (str): Undocumented AWS
API URL - RDS Single AZ MS SQL Server WEB Licensed, On-Demand, Previous Generation.
PG_RDS_MSSQL_LICENSED_STANDARD_ON_DEMAND_URL (str): Undocumented AWS
API URL - RDS Single AZ MS SQL Server SE Licensed, On-Demand, Previous Generation.
PG_RDS_MSSQL_LICENSED_STANDARD_MULTIAZ_ON_DEMAND_URL (str): Undocumented AWS
API URL - RDS Multi AZ MS SQL Server SE Licensed, On-Demand, Previous Generation.
PG_RDS_MSSQL_BYOL_ON_DEMAND_URL (str): Undocumented AWS
API URL - RDS Single AZ MS SQL Server BYOL, On-Demand, Previous Generation.
PG_RDS_MSSQL_BYOL_MULTIAZ_ON_DEMAND_URL (str): Undocumented AWS
API URL - RDS Multi AZ MS SQL Server BYOL, On-Demand, Previous Generation.
PG_RDS_POSTGRESQL_ON_DEMAND_URL (str): Undocumented AWS
API URL - RDS Single AZ Postgres On-Demand, Previous Generation.
PG_RDS_POSTGRESQL_MULTIAZ_ON_DEMAND_URL (str): Undocumented AWS
API URL - RDS Multi AZ Postgres, On-Demand, Previous Generation.
RDS_MYSQL_RESERVED_URL (str): Undocumented AWS
API URL - RDS Single AZ MySQL GPL, Reserved, Current Generation.
RDS_MYSQL_MULTIAZ_RESERVED_URL (str): Undocumented AWS
API URL - RDS Multi AZ MySQL GPL, Reserved, Current Generation.
RDS_ORACLE_LICENSED_RESERVED_URL (str): Undocumented AWS
API URL - RDS Single AZ Oracle Licensed, Reserved, Current Generation.
RDS_ORACLE_LICENSED_MULTIAZ_RESERVED_URL (str): Undocumented AWS
API URL - RDS Multi AZ Oracle Licensed, Reserved, Current Generation.
RDS_ORACLE_BYOL_RESERVED_URL (str): Undocumented AWS
API URL - RDS Single AZ Oracle BYOL, Reserved, Current Generation.
RDS_ORACLE_BYOL_MULTIAZ_RESERVED_URL (str): Undocumented AWS
API URL - RDS Multi AZ Oracle BYOL, Reserved, Current Generation.
RDS_MSSQL_LICENSED_EX_RESERVED_LIGHT_URL (str): Undocumented AWS
API URL - RDS MS SQL Server Express Licensed, Reserved Light, Current Generation.
RDS_MSSQL_LICENSED_EX_RESERVED_MEDIUM_URL (str): Undocumented AWS
API URL - RDS MS SQL Server Express Licensed, Reserved Medium, Current Generation.
RDS_MSSQL_LICENSED_EX_RESERVED_HEAVY_URL (str): Undocumented AWS
API URL - RDS MS SQL Server Express Licensed, Reserved Heavy, Current Generation.
RDS_MSSQL_LICENSED_WEB_RESERVED_LIGHT_URL (str): Undocumented AWS
API URL - RDS MS SQL Server WEB Licensed, Reserved Light, Current Generation.
RDS_MSSQL_LICENSED_WEB_RESERVED_MEDIUM_URL (str): Undocumented AWS
API URL - RDS MS SQL Server WEB Licensed, Reserved Medium, Current Generation.
RDS_MSSQL_LICENSED_WEB_RESERVED_HEAVY_URL (str): Undocumented AWS
API URL - RDS MS SQL Server WEB Licensed, Reserved Heavy, Current Generation.
RDS_MSSQL_LICENSED_STANDARD_RESERVED_LIGHT_URL (str): Undocumented AWS
API URL - RDS MS SQL Server SE Licensed, Reserved Light, Current Generation.
RDS_MSSQL_LICENSED_STANDARD_RESERVED_MEDIUM_URL (str): Undocumented AWS
API URL - RDS MS SQL Server SE Licensed, Reserved Medium, Current Generation.
RDS_MSSQL_LICENSED_STANDARD_RESERVED_HEAVY_URL (str): Undocumented AWS
API URL - RDS MS SQL Server SE Licensed, Reserved Heavy, Current Generation.
RDS_MSSQL_BYOL_RESERVED_URL (str): Undocumented AWS
API URL - RDS Single AZ MS SQL Server BYOL, Reserved, Current Generation.
RDS_MSSQL_BYOL_MULTIAZ_RESERVED_URL (str): Undocumented AWS
API URL - RDS Multi AZ MS SQL Server BYOL, Reserved, Current Generation.
RDS_POSTGRESQL_RESERVED_URL (str): Undocumented AWS
API URL - RDS Single AZ Postgres, Reserved, Current Generation.
RDS_POSTGRESQL_MULTIAZ_RESERVED_URL (str): Undocumented AWS
API URL - RDS Multi AZ Postgres, Reserved, Current Generation.
PG_RDS_MYSQL_RESERVED_URL (str): Undocumented AWS
API URL - RDS Single AZ MySQL GPL, Reserved, Previous Generation.
PG_RDS_MYSQL_MULTIAZ_RESERVED_URL (str): Undocumented AWS
API URL - RDS Multi AZ MySQL GPL, Reserved, Previous Generation.
PG_RDS_ORACLE_LICENSED_RESERVED_URL (str): Undocumented AWS
API URL - RDS Single AZ Oracle Licensed, Reserved, Previous Generation.
PG_RDS_ORACLE_LICENSED_MULTIAZ_RESERVED_URL (str): Undocumented AWS
API URL - RDS Multi AZ Oracle Licensed, Reserved, Previous Generation.
PG_RDS_ORACLE_BYOL_RESERVED_URL (str): Undocumented AWS
API URL - RDS Single AZ Oracle BYOL, Reserved, Previous Generation.
PG_RDS_ORACLE_BYOL_MULTIAZ_RESERVED_URL (str): Undocumented AWS
API URL - RDS Multi AZ Oracle BYOL, Reserved, Previous Generation.
PG_RDS_MSSQL_LICENSED_EX_RESERVED_LIGHT_URL (str): Undocumented AWS
API URL - RDS MS SQL Server Express Licensed, Reserved Light, Previous Generation.
PG_RDS_MSSQL_LICENSED_EX_RESERVED_MEDIUM_URL (str): Undocumented AWS
API URL - RDS MS SQL Server Express Licensed, Reserved Medium, Previous Generation.
PG_RDS_MSSQL_LICENSED_EX_RESERVED_HEAVY_URL (str): Undocumented AWS
API URL - RDS MS SQL Server Express Licensed, Reserved Heavy, Previous Generation.
PG_RDS_MSSQL_LICENSED_WEB_RESERVED_LIGHT_URL (str): Undocumented AWS
API URL - RDS MS SQL Server WEB Licensed, Reserved Light, Previous Generation.
PG_RDS_MSSQL_LICENSED_WEB_RESERVED_MEDIUM_URL (str): Undocumented AWS
API URL - RDS MS SQL Server WEB Licensed, Reserved Medium, Previous Generation.
PG_RDS_MSSQL_LICENSED_WEB_RESERVED_HEAVY_URL (str): Undocumented AWS
API URL - RDS MS SQL Server WEB Licensed, Reserved Heavy, Previous Generation.
PG_RDS_MSSQL_LICENSED_STANDARD_RESERVED_LIGHT_URL (str): Undocumented AWS
API URL - RDS MS SQL Server SE Licensed, Reserved Light, Previous Generation.
PG_RDS_MSSQL_LICENSED_STANDARD_RESERVED_MEDIUM_URL (str): Undocumented AWS
API URL - RDS MS SQL Server SE Licensed, Reserved Medium, Previous Generation.
PG_RDS_MSSQL_LICENSED_STANDARD_RESERVED_HEAVY_URL (str): Undocumented AWS
API URL - RDS MS SQL Server SE Licensed, Reserved Heavy, Previous Generation.
PG_RDS_MSSQL_BYOL_RESERVED_URL (str): Undocumented AWS
API URL - RDS Single AZ MS SQL Server BYOL, Reserved, Previous Generation.
PG_RDS_MSSQL_BYOL_MULTIAZ_RESERVED_URL (str): Undocumented AWS
API URL - RDS Multi AZ MS SQL Server BYOL, Reserved, Previous Generation.
PG_RDS_POSTGRESQL_RESERVED_URL (str): Undocumented AWS
API URL - RDS Single AZ Postgres, Reserved, Previous Generation.
PG_RDS_POSTGRESQL_MULTIAZ_RESERVED_URL (str): Undocumented AWS
API URL - RDS Multi AZ Postgres, Reserved, Previous Generation.
RDS_ENGINE_TYPES (list of str): List of RDS engines.
RDS_MULTIAZ_TYPES (list of str): List of RDS Multi-AZ options.
RDS_MULTIAZ_MAPPING (dict of str: str): Mapping of internal AWS RDS
Family names to the cvonventional Multi-AZ tag.
RDS_ONDEMAND_TYPE_BY_URL (dict of str: list of str): Mapping of AWS RDS URLs
(On-Demand, Single AZ) to corresponding engine and license type.
RDS_ONDEMAND_MULTIAZ_TYPE_BY_URL (dict of str: list of str): Mapping of AWS RDS URLs
(On-Demand, Multi AZ) to corresponding engine and license type.
RDS_RESERVED_TYPE_BY_URL_NEW (dict of str: list of str): Mapping of AWS RDS URLs
(Reserved, Single AZ, New Pricing Scheme) to corresponding engine and license type.
RDS_RESERVED_MULTIAZ_TYPE_BY_URL_NEW (dict of str: list of str): Mapping of AWS RDS URLs
(Reserved, Multi AZ, New Pricing Scheme) to corresponding engine and license type.
RDS_RESERVED_TYPE_BY_URL_OLD (dict of str: list of str): Mapping of AWS RDS URLs
(Reserved, Old Pricing Scheme) to corresponding engine and license type.
INSTANCE_TYPE_MAPPING (dict of str: str): Mapping of internal AWS RDS
Type names to the cvonventional analogs.
"""
RDS_ENGINE_TYPES = [
"mysql",
"postgres",
"oracle-se1",
"oracle",
"sqlserver-ex",
"sqlserver-web",
"sqlserver-se",
"sqlserver",
"aurora"
]
RDS_MYSQL_ON_DEMAND_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/mysql/pricing-standard-deployments.min.js")
RDS_MYSQL_MULTIAZ_ON_DEMAND_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/mysql/pricing-multiAZ-deployments.min.js")
RDS_ORACLE_LICENSED_ON_DEMAND_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/oracle/pricing-li-standard-deployments.min.js")
RDS_ORACLE_LICENSED_MULTIAZ_ON_DEMAND_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/oracle/pricing-li-multiAZ-deployments.min.js")
RDS_ORACLE_BYOL_ON_DEMAND_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/oracle/pricing-byol-standard-deployments.min.js")
RDS_ORACLE_BYOL_MULTIAZ_ON_DEMAND_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/oracle/pricing-byol-multiAZ-deployments.min.js")
RDS_MSSQL_LICENSED_EXPRESS_ON_DEMAND_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/sqlserver/sqlserver-li-ex-ondemand.min.js")
RDS_MSSQL_LICENSED_WEB_ON_DEMAND_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/sqlserver/sqlserver-li-web-ondemand.min.js")
RDS_MSSQL_LICENSED_STANDARD_ON_DEMAND_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/sqlserver/sqlserver-li-se-ondemand.min.js")
RDS_MSSQL_LICENSED_STANDARD_MULTIAZ_ON_DEMAND_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/sqlserver/sqlserver-li-se-ondemand-maz.min.js")
RDS_MSSQL_BYOL_ON_DEMAND_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/sqlserver/sqlserver-byol-ondemand.min.js")
RDS_MSSQL_BYOL_MULTIAZ_ON_DEMAND_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/sqlserver/sqlserver-byol-ondemand-maz.min.js")
RDS_POSTGRESQL_ON_DEMAND_URL=("http://a0.awsstatic.com/pricing/1/"+
"rds/postgresql/pricing-standard-deployments.min.js")
RDS_POSTGRESQL_MULTIAZ_ON_DEMAND_URL=("http://a0.awsstatic.com/pricing/1/"+
"rds/postgresql/pricing-multiAZ-deployments.min.js")
RDS_AURORA_MULTIAZ_ON_DEMAND_URL=("http://a0.awsstatic.com/pricing/1/"+
"rds/aurora/pricing-multiAZ-deployments.min.js")
RDS_MARIADB_ON_DEMAND_URL=("http://a0.awsstatic.com/pricing/1/"+
"rds/mariadb/pricing-standard-deployments.min.js")
RDS_MARIADB_MULTIAZ_ON_DEMAND_URL=("http://a0.awsstatic.com/pricing/1/"+
"rds/mariadb/pricing-multiAZ-deployments.min.js")
PG_RDS_MYSQL_ON_DEMAND_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/mysql/previous-generation/pricing-standard-deployments.min.js")
PG_RDS_MYSQL_MULTIAZ_ON_DEMAND_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/mysql/previous-generation/pricing-multiAZ-deployments.min.js")
PG_RDS_ORACLE_LICENSED_ON_DEMAND_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/oracle/previous-generation/pricing-li-standard-deployments.min.js")
PG_RDS_ORACLE_LICENSED_MULTIAZ_ON_DEMAND_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/oracle/previous-generation/pricing-li-multiAZ-deployments.min.js")
PG_RDS_ORACLE_BYOL_ON_DEMAND_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/oracle/previous-generation/pricing-byol-standard-deployments.min.js")
PG_RDS_ORACLE_BYOL_MULTIAZ_ON_DEMAND_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/oracle/previous-generation/pricing-byol-multiAZ-deployments.min.js")
PG_RDS_MSSQL_LICENSED_EXPRESS_ON_DEMAND_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/sqlserver/previous-generation/sqlserver-li-ex-ondemand.min.js")
PG_RDS_MSSQL_LICENSED_WEB_ON_DEMAND_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/sqlserver/previous-generation/sqlserver-li-web-ondemand.min.js")
PG_RDS_MSSQL_LICENSED_STANDARD_ON_DEMAND_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/sqlserver/previous-generation/sqlserver-li-se-ondemand.min.js")
PG_RDS_MSSQL_LICENSED_STANDARD_MULTIAZ_ON_DEMAND_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/sqlserver/previous-generation/sqlserver-li-se-ondemand-maz.min.js")
PG_RDS_MSSQL_BYOL_ON_DEMAND_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/sqlserver/previous-generation/sqlserver-byol-ondemand.min.js")
PG_RDS_MSSQL_BYOL_MULTIAZ_ON_DEMAND_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/sqlserver/previous-generation/sqlserver-byol-ondemand-maz.min.js")
PG_RDS_POSTGRESQL_ON_DEMAND_URL=("http://a0.awsstatic.com/pricing/1/"+
"rds/postgresql/previous-generation/pricing-standard-deployments.min.js")
PG_RDS_POSTGRESQL_MULTIAZ_ON_DEMAND_URL=("http://a0.awsstatic.com/pricing/1/"+
"rds/postgresql/previous-generation/pricing-multiAZ-deployments.min.js")
RDS_MYSQL_RESERVED_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/reserved-instances/mysql-standard.min.js")
RDS_MYSQL_MULTIAZ_RESERVED_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/reserved-instances/mysql-multiAZ.min.js")
RDS_ORACLE_LICENSED_RESERVED_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/reserved-instances/oracle-se1-license-included-standard.min.js")
RDS_ORACLE_LICENSED_MULTIAZ_RESERVED_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/reserved-instances/oracle-se1-license-included-multiAZ.min.js")
RDS_ORACLE_BYOL_RESERVED_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/reserved-instances/oracle-se-byol-standard.min.js")
RDS_ORACLE_BYOL_MULTIAZ_RESERVED_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/reserved-instances/oracle-se-byol-multiAZ.min.js")
RDS_MSSQL_LICENSED_EX_RESERVED_LIGHT_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/sqlserver/sqlserver-li-ex-light-ri.min.js")
RDS_MSSQL_LICENSED_EX_RESERVED_MEDIUM_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/sqlserver/sqlserver-li-ex-medium-ri.min.js")
RDS_MSSQL_LICENSED_EX_RESERVED_HEAVY_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/sqlserver/sqlserver-li-ex-heavy-ri.min.js")
RDS_MSSQL_LICENSED_WEB_RESERVED_LIGHT_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/sqlserver/sqlserver-li-web-light-ri.min.js")
RDS_MSSQL_LICENSED_WEB_RESERVED_MEDIUM_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/sqlserver/sqlserver-li-web-medium-ri.min.js")
RDS_MSSQL_LICENSED_WEB_RESERVED_HEAVY_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/sqlserver/sqlserver-li-web-heavy-ri.min.js")
RDS_MSSQL_LICENSED_STANDARD_RESERVED_LIGHT_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/sqlserver/sqlserver-li-se-light-ri.min.js")
RDS_MSSQL_LICENSED_STANDARD_RESERVED_MEDIUM_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/sqlserver/sqlserver-li-se-medium-ri.min.js")
RDS_MSSQL_LICENSED_STANDARD_RESERVED_HEAVY_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/sqlserver/sqlserver-li-se-heavy-ri.min.js")
RDS_MSSQL_BYOL_RESERVED_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/reserved-instances/sql-server-se-byol-standard.min.js")
RDS_MSSQL_BYOL_MULTIAZ_RESERVED_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/reserved-instances/sql-server-se-byol-multiAZ.min.js")
RDS_POSTGRESQL_RESERVED_URL=("http://a0.awsstatic.com/pricing/1/"+
"rds/reserved-instances/postgresql-standard.min.js")
RDS_POSTGRESQL_MULTIAZ_RESERVED_URL=("http://a0.awsstatic.com/pricing/1/"+
"rds/reserved-instances/postgresql-multiAZ.min.js")
RDS_AURORA_MULTIAZ_RESERVED_URL=("http://a0.awsstatic.com/pricing/1/"+
"rds/reserved-instances/aurora-standard.min.js")
RDS_MARIADB_RESERVED_URL=("http://a0.awsstatic.com/pricing/1/"+
"rds/reserved-instances/mariadb-standard.min.js")
RDS_MARIADB_MULTIAZ_RESERVED_URL=("http://a0.awsstatic.com/pricing/1/"+
"rds/reserved-instances/mariadb-multiAZ.min.js")
PG_RDS_MYSQL_RESERVED_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/previous-generation/reserved-instances/mysql-standard.min.js")
PG_RDS_MYSQL_MULTIAZ_RESERVED_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/previous-generation/reserved-instances/mysql-multiAZ.min.js")
PG_RDS_ORACLE_LICENSED_RESERVED_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/previous-generation/reserved-instances/oracle-se1-license-included-standard.min.js")
PG_RDS_ORACLE_LICENSED_MULTIAZ_RESERVED_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/previous-generation/reserved-instances/oracle-se1-license-included-multiAZ.min.js")
PG_RDS_ORACLE_BYOL_RESERVED_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/previous-generation/reserved-instances/oracle-se-byol-standard.min.js")
PG_RDS_ORACLE_BYOL_MULTIAZ_RESERVED_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/previous-generation/reserved-instances/oracle-se-byol-multiAZ.min.js")
PG_RDS_MSSQL_LICENSED_EX_RESERVED_LIGHT_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/sqlserver/previous-generation/sqlserver-li-ex-light-ri.min.js")
PG_RDS_MSSQL_LICENSED_EX_RESERVED_MEDIUM_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/sqlserver/previous-generation/sqlserver-li-ex-medium-ri.min.js")
PG_RDS_MSSQL_LICENSED_EX_RESERVED_HEAVY_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/sqlserver/previous-generation/sqlserver-li-ex-heavy-ri.min.js")
PG_RDS_MSSQL_LICENSED_WEB_RESERVED_LIGHT_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/sqlserver/previous-generation/sqlserver-li-web-light-ri.min.js")
PG_RDS_MSSQL_LICENSED_WEB_RESERVED_MEDIUM_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/sqlserver/previous-generation/sqlserver-li-web-medium-ri.min.js")
PG_RDS_MSSQL_LICENSED_WEB_RESERVED_HEAVY_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/sqlserver/previous-generation/sqlserver-li-web-heavy-ri.min.js")
PG_RDS_MSSQL_LICENSED_STANDARD_RESERVED_LIGHT_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/sqlserver/previous-generation/sqlserver-li-se-light-ri.min.js")
PG_RDS_MSSQL_LICENSED_STANDARD_RESERVED_MEDIUM_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/sqlserver/previous-generation/sqlserver-li-se-medium-ri.min.js")
PG_RDS_MSSQL_LICENSED_STANDARD_RESERVED_HEAVY_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/sqlserver/previous-generation/sqlserver-li-se-heavy-ri.min.js")
PG_RDS_MSSQL_BYOL_RESERVED_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/previous-generation/reserved-instances/sql-server-se-byol-standard.min.js")
PG_RDS_MSSQL_BYOL_MULTIAZ_RESERVED_URL = ("http://a0.awsstatic.com/pricing/1/"+
"rds/previous-generation/reserved-instances/sql-server-se-byol-multiAZ.min.js")
PG_RDS_POSTGRESQL_RESERVED_URL=("http://a0.awsstatic.com/pricing/1/"+
"rds/previous-generation/reserved-instances/postgresql-standard.min.js")
PG_RDS_POSTGRESQL_MULTIAZ_RESERVED_URL=("http://a0.awsstatic.com/pricing/1/"+
"rds/previous-generation/reserved-instances/postgresql-multiAZ.min.js")
RDS_MULTIAZ_TYPES = [
"single",
"multi-az"
]
RDS_MULTIAZ_MAPPING = {
"Micro and Small Instances - Current Generation - Single-AZ" : "single",
"Standard Instances - Current Generation - Single-AZ" : "single",
"Micro and Small Instances - Current Generation - Multi-AZ" : "multi-az",
"Standard Instances - Current Generation - Multi-AZ" : "multi-az",
"Memory Optimized Instances - Current Generation - Single-AZ" : "single",
"Memory Optimized Instances - Current Generation - Multi-AZ" : "multi-az",
"Micro Instances - Previous Generation - Single-AZ" : "single",
"Micro Instances - Previous Generation - Multi-AZ" : "multi-az",
"Standard Instances - Previous Generation - Single-AZ" : "single",
"Standard Instances - Previous Generation - Multi-AZ" : "multi-az",
"Micro and Small Instances - Previous Generation - Single-AZ" : "single",
"Standard Instances - Previous Generation - Single-AZ" : "single",
"Micro and Small Instances - Previous Generation - Multi-AZ" : "multi-az",
"Standard Instances - Previous Generation - Multi-AZ" : "multi-az",
"Memory Optimized Instances - Previous Generation - Single-AZ" : "single",
"Memory Optimized Instances - Previous Generation - Multi-AZ" : "multi-az",
"Micro Instances - Previous Generation - Single-AZ" : "single",
"Micro Instances - Previous Generation - Multi-AZ" : "multi-az",
"Standard Instances - Previous Generation - Single-AZ" : "single",
"Standard Instances - Previous Generation - Multi-AZ" : "multi-az"
}
RDS_ONDEMAND_TYPE_BY_URL = {
RDS_MYSQL_ON_DEMAND_URL : ["gpl","mysql"],
RDS_ORACLE_LICENSED_ON_DEMAND_URL : ["included","oracle-se1"],
RDS_ORACLE_BYOL_ON_DEMAND_URL : ["byol","oracle"],
RDS_MSSQL_LICENSED_EXPRESS_ON_DEMAND_URL : ["included","sqlserver-ex"],
RDS_MSSQL_LICENSED_WEB_ON_DEMAND_URL : ["included","sqlserver-web"],
RDS_MSSQL_LICENSED_STANDARD_ON_DEMAND_URL : ["included","sqlserver-se"],
RDS_MSSQL_BYOL_ON_DEMAND_URL : ["byol","sqlserver"],
RDS_POSTGRESQL_ON_DEMAND_URL : ["postgresql","postgres"],
RDS_MARIADB_ON_DEMAND_URL : ["gpl","mariadb"],
PG_RDS_MYSQL_ON_DEMAND_URL : ["gpl","mysql"],
PG_RDS_ORACLE_LICENSED_ON_DEMAND_URL : ["included","oracle-se1"],
PG_RDS_ORACLE_BYOL_ON_DEMAND_URL : ["byol","oracle"],
PG_RDS_MSSQL_LICENSED_EXPRESS_ON_DEMAND_URL : ["included","sqlserver-ex"],
PG_RDS_MSSQL_LICENSED_WEB_ON_DEMAND_URL : ["included","sqlserver-web"],
PG_RDS_MSSQL_LICENSED_STANDARD_ON_DEMAND_URL : ["included","sqlserver-se"],
PG_RDS_MSSQL_BYOL_ON_DEMAND_URL : ["byol","sqlserver"],
PG_RDS_POSTGRESQL_ON_DEMAND_URL : ["postgresql","postgres"]
}
RDS_ONDEMAND_MULTIAZ_TYPE_BY_URL = {
RDS_MYSQL_MULTIAZ_ON_DEMAND_URL : ["gpl","mysql"],
RDS_ORACLE_LICENSED_MULTIAZ_ON_DEMAND_URL: ["included","oracle-se1"],
RDS_ORACLE_BYOL_MULTIAZ_ON_DEMAND_URL : ["byol","oracle"],
RDS_MSSQL_LICENSED_STANDARD_MULTIAZ_ON_DEMAND_URL : ["included","sqlserver-se"],
RDS_MSSQL_BYOL_MULTIAZ_ON_DEMAND_URL : ["byol","sqlserver"],
RDS_POSTGRESQL_MULTIAZ_ON_DEMAND_URL : ["postgresql","postgres"],
RDS_AURORA_MULTIAZ_ON_DEMAND_URL : ["aurora","aurora"],
RDS_MARIADB_MULTIAZ_ON_DEMAND_URL : ["gpl","mariadb"],
PG_RDS_MYSQL_MULTIAZ_ON_DEMAND_URL : ["gpl","mysql"],
PG_RDS_ORACLE_LICENSED_MULTIAZ_ON_DEMAND_URL: ["included","oracle-se1"],
PG_RDS_ORACLE_BYOL_MULTIAZ_ON_DEMAND_URL : ["byol","oracle"],
PG_RDS_MSSQL_LICENSED_STANDARD_MULTIAZ_ON_DEMAND_URL : ["included","sqlserver-se"],
PG_RDS_MSSQL_BYOL_MULTIAZ_ON_DEMAND_URL : ["byol","sqlserver"],
PG_RDS_POSTGRESQL_MULTIAZ_ON_DEMAND_URL : ["postgresql","postgres"]
}
RDS_RESERVED_TYPE_BY_URL_NEW = {
RDS_MYSQL_RESERVED_URL : ["gpl","mysql"],
RDS_ORACLE_LICENSED_RESERVED_URL : ["included","oracle-se1"],
RDS_ORACLE_BYOL_RESERVED_URL : ["byol","oracle"],
RDS_MSSQL_BYOL_RESERVED_URL : ["byol","sqlserver"],
RDS_POSTGRESQL_RESERVED_URL : ["postgresql","postgres"],
RDS_MARIADB_RESERVED_URL : ["gpl","mariadb"],
PG_RDS_MYSQL_RESERVED_URL : ["gpl","mysql"],
PG_RDS_ORACLE_LICENSED_RESERVED_URL : ["included","oracle-se1"],
PG_RDS_ORACLE_BYOL_RESERVED_URL : ["byol","oracle"],
PG_RDS_MSSQL_BYOL_RESERVED_URL : ["byol","sqlserver"],
PG_RDS_POSTGRESQL_RESERVED_URL : ["postgresql","postgres"]
}
RDS_RESERVED_MULTIAZ_TYPE_BY_URL_NEW = {
RDS_MYSQL_MULTIAZ_RESERVED_URL : ["gpl","mysql"],
RDS_ORACLE_LICENSED_MULTIAZ_RESERVED_URL : ["included","oracle-se1"],
RDS_ORACLE_BYOL_MULTIAZ_RESERVED_URL : ["byol","oracle"],
RDS_MSSQL_BYOL_MULTIAZ_RESERVED_URL : ["byol","sqlserver"],
RDS_POSTGRESQL_MULTIAZ_RESERVED_URL : ["postgresql","postgres"],
RDS_AURORA_MULTIAZ_RESERVED_URL : ["aurora","aurora"],
RDS_MARIADB_MULTIAZ_RESERVED_URL : ["gpl","mariadb"],
PG_RDS_MYSQL_MULTIAZ_RESERVED_URL : ["gpl","mysql"],
PG_RDS_ORACLE_LICENSED_MULTIAZ_RESERVED_URL : ["included","oracle-se1"],
PG_RDS_ORACLE_BYOL_MULTIAZ_RESERVED_URL : ["byol","oracle"],
PG_RDS_MSSQL_BYOL_MULTIAZ_RESERVED_URL : ["byol","sqlserver"],
PG_RDS_POSTGRESQL_MULTIAZ_RESERVED_URL : ["postgresql","postgres"]
}
RDS_RESERVED_TYPE_BY_URL_OLD = {
RDS_MSSQL_LICENSED_EX_RESERVED_LIGHT_URL : ["included","sqlserver-ex","light"],
RDS_MSSQL_LICENSED_EX_RESERVED_MEDIUM_URL : ["included","sqlserver-ex","medium"],
RDS_MSSQL_LICENSED_EX_RESERVED_HEAVY_URL : ["included","sqlserver-ex","heavy"],
RDS_MSSQL_LICENSED_WEB_RESERVED_LIGHT_URL : ["included","sqlserver-web","light"],
RDS_MSSQL_LICENSED_WEB_RESERVED_MEDIUM_URL : ["included","sqlserver-web","medium"],
RDS_MSSQL_LICENSED_WEB_RESERVED_HEAVY_URL : ["included","sqlserver-web","heavy"],
RDS_MSSQL_LICENSED_STANDARD_RESERVED_LIGHT_URL : ["included","sqlserver-se","light"],
RDS_MSSQL_LICENSED_STANDARD_RESERVED_MEDIUM_URL : ["included","sqlserver-se","medium"],
RDS_MSSQL_LICENSED_STANDARD_RESERVED_HEAVY_URL : ["included","sqlserver-se","heavy"],
PG_RDS_MSSQL_LICENSED_EX_RESERVED_LIGHT_URL : ["included","sqlserver-ex","light"],
PG_RDS_MSSQL_LICENSED_EX_RESERVED_MEDIUM_URL : ["included","sqlserver-ex","medium"],
PG_RDS_MSSQL_LICENSED_EX_RESERVED_HEAVY_URL : ["included","sqlserver-ex","heavy"],
PG_RDS_MSSQL_LICENSED_WEB_RESERVED_LIGHT_URL : ["included","sqlserver-web","light"],
PG_RDS_MSSQL_LICENSED_WEB_RESERVED_MEDIUM_URL : ["included","sqlserver-web","medium"],
PG_RDS_MSSQL_LICENSED_WEB_RESERVED_HEAVY_URL : ["included","sqlserver-web","heavy"],
PG_RDS_MSSQL_LICENSED_STANDARD_RESERVED_LIGHT_URL : ["included","sqlserver-se","light"],
PG_RDS_MSSQL_LICENSED_STANDARD_RESERVED_MEDIUM_URL : ["included","sqlserver-se","medium"],
PG_RDS_MSSQL_LICENSED_STANDARD_RESERVED_HEAVY_URL : ["included","sqlserver-se","heavy"]
}
INSTANCE_TYPE_MAPPING = {
"udbInstClass.uDBInst" : "db.t1.micro",
"dbInstClass.uDBInst" : "db.t1.micro",
"dbInstClass.db.t1.micro" : "db.t1.micro",
"dbInstClass.db.m3.medium" : "db.m3.medium",
"dbInstClass.db.m3.large" : "db.m3.large",
"dbInstClass.db.m3.xlarge" : "db.m3.xlarge",
"dbInstClass.db.m3.2xlarge" : "db.m3.2xlarge",
"dbInstClass.smDBInst" : "db.m1.small",
"dbInstClass.db.m1.small" : "db.m1.small",
"dbInstClass.medDBInst" : "db.m1.medium",
"dbInstClass.db.m1.medium" : "db.m1.medium",
"dbInstClass.lgDBInst" : "db.m1.large",
"dbInstClass.db.m1.large" : "db.m1.large",
"dbInstClass.xlDBInst" : "db.m1.xlarge",
"dbInstClass.db.m1.xlarge" : "db.m1.xlarge",
"hiMemDBInstClass.xlDBInst" : "db.m2.xlarge",
"memDBCurrentGen.db.m2.xlarge" : "db.m2.xlarge",
"hiMemDBInstClass.xxlDBInst" : "db.m2.2xlarge",
"memDBCurrentGen.db.m2.2xlarge" : "db.m2.2xlarge",
"hiMemDBInstClass.xxxxDBInst" : "db.m2.4xlarge",
"memDBCurrentGen.db.m2.4xlarge" : "db.m2.4xlarge",
"clusterHiMemDB.xxxxxxxxl" : "db.cr1.8xlarge",
"memDBCurrentGen.db.cr1.8xl": "db.cr1.8xlarge",
# Multiaz instances
"multiAZDBInstClass.uDBInst" : "db.t1.micro",
"multiAZDBInstClass.smDBInst" : "db.m1.small",
"multiAZDBInstClass.medDBInst" : "db.m1.medium",
"multiAZDBInstClass.lgDBInst" : "db.m1.large",
"multiAZDBInstClass.xlDBInst" : "db.m1.xlarge",
"multiAZDBInstClass.db.t1.micro" : "db.t1.micro",
"multiAZDBInstClass.db.m1.small" : "db.m1.small",
"multiAZDBInstClass.db.m1.medium" : "db.m1.medium",
"multiAZDBInstClass.db.m1.large" : "db.m1.large",
"multiAZDBInstClass.db.m1.xlarge" : "db.m1.xlarge",
"multiAZDBInstClass.db.m3.medium" : "db.m3.medium",
"multiAZDBInstClass.db.m3.large" : "db.m3.large",
"multiAZDBInstClass.db.m3.xlarge" : "db.m3.xlarge",
"multiAZDBInstClass.db.m3.2xlarge" : "db.m3.2xlarge",
"multiAZHiMemInstClass.xlDBInst" : "db.m2.xlarge",
"multiAZHiMemInstClass.xxlDBInst" : "db.m2.2xlarge",
"multiAZHiMemInstClass.xxxxDBInst" : "db.m2.4xlarge",
"multiAZClusterHiMemDB.xxxxxxxxl" : "db.cr1.8xlarge",
#Reserved
"stdDeployRes.u" : "db.t1.micro",
"stdDeployRes.micro" : "db.t1.micro",
"stdDeployRes.sm" : "db.m1.small",
"stdDeployRes.med" : "db.m1.medium",
"stdDeployRes.lg" : "db.m1.large",
"stdDeployRes.xl" : "db.m1.xlarge",
"stdDeployRes.xlHiMem" : "db.m2.xlarge",
"stdDeployRes.xxlHiMem" : "db.m2.2xlarge",
"stdDeployRes.xxxxlHiMem" : "db.m2.4xlarge",
"stdDeployRes.xxxxxxxxl" : "db.cr1.8xlarge",
#Reserved multi az
"multiAZdeployRes.u" : "db.t1.micro",
"multiAZdeployRes.sm" : "db.m1.small",
"multiAZdeployRes.med" : "db.m1.medium",
"multiAZdeployRes.lg" : "db.m1.large",
"multiAZdeployRes.xl" : "db.m1.xlarge",
"multiAZdeployRes.xlHiMem" : "db.m2.xlarge",
"multiAZdeployRes.xxlHiMem" : "db.m2.2xlarge",
"multiAZdeployRes.xxxxlHiMem" : "db.m2.4xlarge",
"multiAZdeployRes.xxxxxxxxl" : "db.cr1.8xlarge"
}
def get_reserved_instances_prices(self):
"""
Implementation of method for getting RDS Reserved pricing.
Returns:
result (dict of dict: dict): RDS Reserved pricing in dictionary format.
"""
currency = self.DEFAULT_CURRENCY
urls_new = [
self.RDS_MYSQL_RESERVED_URL,
self.RDS_MYSQL_MULTIAZ_RESERVED_URL,
self.RDS_ORACLE_LICENSED_RESERVED_URL,
self.RDS_ORACLE_LICENSED_MULTIAZ_RESERVED_URL,
self.RDS_ORACLE_BYOL_RESERVED_URL,
self.RDS_ORACLE_BYOL_MULTIAZ_RESERVED_URL,
self.RDS_MSSQL_BYOL_RESERVED_URL,
self.RDS_MSSQL_BYOL_MULTIAZ_RESERVED_URL,
self.RDS_POSTGRESQL_RESERVED_URL,
self.RDS_POSTGRESQL_MULTIAZ_RESERVED_URL,
self.RDS_AURORA_MULTIAZ_RESERVED_URL,
self.RDS_MARIADB_RESERVED_URL,
self.RDS_MARIADB_MULTIAZ_RESERVED_URL,
self.PG_RDS_MYSQL_RESERVED_URL,
self.PG_RDS_MYSQL_MULTIAZ_RESERVED_URL,
self.PG_RDS_ORACLE_LICENSED_RESERVED_URL,
self.PG_RDS_ORACLE_LICENSED_MULTIAZ_RESERVED_URL,
self.PG_RDS_ORACLE_BYOL_RESERVED_URL,
self.PG_RDS_ORACLE_BYOL_MULTIAZ_RESERVED_URL,
self.PG_RDS_MSSQL_BYOL_RESERVED_URL,
self.PG_RDS_MSSQL_BYOL_MULTIAZ_RESERVED_URL,
self.PG_RDS_POSTGRESQL_RESERVED_URL,
self.PG_RDS_POSTGRESQL_MULTIAZ_RESERVED_URL
]
urls_old = [
self.RDS_MSSQL_LICENSED_EX_RESERVED_LIGHT_URL,
self.RDS_MSSQL_LICENSED_EX_RESERVED_MEDIUM_URL,
self.RDS_MSSQL_LICENSED_EX_RESERVED_HEAVY_URL,
self.RDS_MSSQL_LICENSED_WEB_RESERVED_LIGHT_URL,
self.RDS_MSSQL_LICENSED_WEB_RESERVED_MEDIUM_URL,
self.RDS_MSSQL_LICENSED_WEB_RESERVED_HEAVY_URL,
self.RDS_MSSQL_LICENSED_STANDARD_RESERVED_LIGHT_URL,
self.RDS_MSSQL_LICENSED_STANDARD_RESERVED_MEDIUM_URL,
self.RDS_MSSQL_LICENSED_STANDARD_RESERVED_HEAVY_URL,
self.PG_RDS_MSSQL_LICENSED_EX_RESERVED_LIGHT_URL,
self.PG_RDS_MSSQL_LICENSED_EX_RESERVED_MEDIUM_URL,
self.PG_RDS_MSSQL_LICENSED_EX_RESERVED_HEAVY_URL,
self.PG_RDS_MSSQL_LICENSED_WEB_RESERVED_LIGHT_URL,
self.PG_RDS_MSSQL_LICENSED_WEB_RESERVED_MEDIUM_URL,
self.PG_RDS_MSSQL_LICENSED_WEB_RESERVED_HEAVY_URL,
self.PG_RDS_MSSQL_LICENSED_STANDARD_RESERVED_LIGHT_URL,
self.PG_RDS_MSSQL_LICENSED_STANDARD_RESERVED_MEDIUM_URL,
self.PG_RDS_MSSQL_LICENSED_STANDARD_RESERVED_HEAVY_URL
]
result_regions = []
result_regions_index = {}
result = {
"config" : {
"currency" : currency,
},
"regions" : result_regions
}
for u in urls_old:
info = self.RDS_RESERVED_TYPE_BY_URL_OLD[u]
dblicense = info[0]
db = info[1]
utilization_type = info[2]
data = self.load_data(u)
if ("config" in data and data["config"] and "regions"
in data["config"] and data["config"]["regions"]):
for r in data["config"]["regions"]:
if "region" in r and r["region"]:
region_name = self.JSON_NAME_TO_REGIONS_API[r["region"]]
if region_name in result_regions_index:
instance_types = result_regions_index[region_name]["instanceTypes"]
else:
instance_types = []
result_regions.append({
"region" : region_name,
"instanceTypes" : instance_types
})
result_regions_index[region_name] = result_regions[-1]
if "instanceTypes" in r:
for it in r["instanceTypes"]:
multiaz = self.RDS_MULTIAZ_MAPPING[it["type"]]
if "tiers" in it:
for s in it["tiers"]:
_type = s["size"]
prices = {
"1" : {
"hourly" : None,
"upfront" : None
},
"3" : {
"hourly" : None,
"upfront" : None
}
}
instance_types.append({
"type" : _type,
"multiaz" : multiaz,
"license" : dblicense,
"db" : db,
"utilization" : utilization_type,
"prices" : prices
})
for price_data in s["valueColumns"]:
price = None
try:
price = float(re.sub("[^0-9\.]", "",
price_data["prices"][currency]))
except ValueError:
price = None
if price_data["name"] == "yrTerm1":
prices["1"]["upfront"] = price
elif price_data["name"] == "yrTerm1Hourly":
prices["1"]["hourly"] = price
elif price_data["name"] == "yearTerm1Hourly":
prices["1"]["hourly"] = price
elif price_data["name"] == "yrTerm3":
prices["3"]["upfront"] = price
elif price_data["name"] == "yrTerm3Hourly":
prices["3"]["hourly"] = price
elif price_data["name"] == "yearTerm3Hourly":
prices["3"]["hourly"] = price
for u in urls_new:
if u in self.RDS_RESERVED_TYPE_BY_URL_NEW:
licensedb = self.RDS_RESERVED_TYPE_BY_URL_NEW[u]
multiaz = "single";
else:
licensedb = self.RDS_RESERVED_MULTIAZ_TYPE_BY_URL_NEW[u];
multiaz = "multi-az";
data = self.load_data(u)
if ("config" in data and data["config"] and "regions"
in data["config"] and data["config"]["regions"]):
for r in data["config"]["regions"]:
if "region" in r and r["region"]:
region_name = r["region"]
if region_name in result_regions_index:
instance_types = result_regions_index[region_name]["instanceTypes"]
else:
instance_types = []
result_regions.append({
"region" : region_name,
"instanceTypes" : instance_types
})
result_regions_index[region_name] = result_regions[-1]
if "instanceTypes" in r:
for it in r["instanceTypes"]:
instance_type = it["type"]
prices = {
"1" : {
"noUpfront" : {
"hourly" : None,
"upfront" : None
},
"partialUpfront":{
"hourly" : None,
"upfront" : None
},
"allUpfront":{
"hourly" : None,
"upfront" : None
}
},
"3" : {
"noUpfront" : {
"hourly" : None,
"upfront" : None
},
"partialUpfront":{
"hourly" : None,
"upfront" : None
},
"allUpfront":{
"hourly" : None,
"upfront" : None
}
}
}
instance_types.append({
"type" : instance_type,
"multiaz" : multiaz,
"license" : licensedb[0],
"db" : licensedb[1],
"utilization" : "heavy",
"prices" : prices
})
if "terms" in it:
for s in it["terms"]:
term=s["term"]
for po_data in s["purchaseOptions"]:
po=po_data["purchaseOption"]
for price_data in po_data["valueColumns"]:
price = None
try:
price = float(re.sub("[^0-9\.]", "",
price_data["prices"][currency]))
except ValueError:
price = None
if term=="yrTerm1":
if price_data["name"] == "upfront":
prices["1"][po]["upfront"] = price
elif price_data["name"] == "monthlyStar":
if price==None:
prices["1"][po]["hourly"] = 0
else:
prices["1"][po]["hourly"] = price/730
elif term=="yrTerm3":
if price_data["name"] == "upfront":
prices["3"][po]["upfront"] = price
elif price_data["name"] == "monthlyStar":
if price==None:
prices["3"][po]["hourly"] = 0
else:
prices["3"][po]["hourly"] = price/730
return result
def get_ondemand_instances_prices(self):
"""
Implementation of method for getting RDS On-Denand pricing.
Returns:
result (dict of dict: dict): RDS On-Denand pricing in dictionary format.
"""
currency = self.DEFAULT_CURRENCY
result_regions = []
result = {
"config" : {
"currency" : currency,
"unit" : "perhr"
},
"regions" : result_regions
}
urls = [
self.RDS_MYSQL_ON_DEMAND_URL,
self.RDS_MYSQL_MULTIAZ_ON_DEMAND_URL,
self.RDS_ORACLE_LICENSED_ON_DEMAND_URL,
self.RDS_ORACLE_LICENSED_MULTIAZ_ON_DEMAND_URL,
self.RDS_ORACLE_BYOL_ON_DEMAND_URL,
self.RDS_ORACLE_BYOL_MULTIAZ_ON_DEMAND_URL,
self.RDS_MSSQL_LICENSED_EXPRESS_ON_DEMAND_URL,
self.RDS_MSSQL_LICENSED_WEB_ON_DEMAND_URL,
self.RDS_MSSQL_LICENSED_STANDARD_ON_DEMAND_URL,
self.RDS_MSSQL_LICENSED_STANDARD_MULTIAZ_ON_DEMAND_URL,
self.RDS_MSSQL_BYOL_ON_DEMAND_URL,
self.RDS_MSSQL_BYOL_MULTIAZ_ON_DEMAND_URL,
self.RDS_POSTGRESQL_ON_DEMAND_URL,
self.RDS_POSTGRESQL_MULTIAZ_ON_DEMAND_URL,
self.RDS_AURORA_MULTIAZ_ON_DEMAND_URL,
self.RDS_MARIADB_ON_DEMAND_URL,
self.RDS_MARIADB_MULTIAZ_ON_DEMAND_URL,
self.PG_RDS_MYSQL_ON_DEMAND_URL,
self.PG_RDS_MYSQL_MULTIAZ_ON_DEMAND_URL,
self.PG_RDS_ORACLE_LICENSED_ON_DEMAND_URL,
self.PG_RDS_ORACLE_LICENSED_MULTIAZ_ON_DEMAND_URL,
self.PG_RDS_ORACLE_BYOL_ON_DEMAND_URL,
self.PG_RDS_ORACLE_BYOL_MULTIAZ_ON_DEMAND_URL,
self.PG_RDS_MSSQL_LICENSED_EXPRESS_ON_DEMAND_URL,
self.PG_RDS_MSSQL_LICENSED_WEB_ON_DEMAND_URL,
self.PG_RDS_MSSQL_LICENSED_STANDARD_ON_DEMAND_URL,
self.PG_RDS_MSSQL_LICENSED_STANDARD_MULTIAZ_ON_DEMAND_URL,
self.PG_RDS_MSSQL_BYOL_ON_DEMAND_URL,
self.PG_RDS_MSSQL_BYOL_MULTIAZ_ON_DEMAND_URL,
self.PG_RDS_POSTGRESQL_ON_DEMAND_URL,
self.PG_RDS_POSTGRESQL_MULTIAZ_ON_DEMAND_URL
]
for u in urls:
if u in self.RDS_ONDEMAND_TYPE_BY_URL:
licensedb = self.RDS_ONDEMAND_TYPE_BY_URL[u]
multiaz = "single";
else:
licensedb = self.RDS_ONDEMAND_MULTIAZ_TYPE_BY_URL[u];
multiaz = "multi-az";
data = self.load_data(u)
if ("config" in data and data["config"] and "regions"
in data["config"] and data["config"]["regions"]):
for r in data["config"]["regions"]:
if "region" in r and r["region"]:
region_name = self.JSON_NAME_TO_REGIONS_API[r["region"]]
instance_types = []
if "types" in r:
for it in r["types"]:
if "tiers" in it:
for s in it["tiers"]:
_type = s["name"]
price = None
try:
price = float(re.sub("[^0-9\.]", "",
s["prices"][currency]))
except ValueError:
price = None
instance_types.append({
"type" : _type,
"multiaz" : multiaz,
"license" : licensedb[0],
"db" : licensedb[1],
"price" : price
})
result_regions.append({
"region" : region_name,
"instanceTypes" : instance_types
})
return result
def print_table(self,u):
"""
Method printing the RDS pricing data to the console
in the Pretty Table format (requires Pretty Table
import).
Args:
u (str): Parameter specifying On-Demand ("ondemand") or
Reserved ("reserved") pricing option.
Returns:
Prints RDS pricing in the Pretty Table format.
"""
try:
from prettytable import PrettyTable
except ImportError:
print("ERROR: Please install 'prettytable' using pip: pip install prettytable")
data = None
if u not in ["ondemand","reserved"]:
print("Function requires Reservation parameter at the first"+
"position. Possible values:"+
"\"ondemand\" or \"reserved\".")
else:
if u == "ondemand":
data = self.get_ondemand_instances_prices()
x = PrettyTable()
try:
x.set_field_names(["region",
"type",
"multiaz",
"license",
"db",
"price"])
except AttributeError:
x.field_names = ["region",
"type",
"multiaz",
"license",
"db",
"price"]
try:
x.aligns[-1] = "l"
except AttributeError:
x.align["price"] = "l"
for r in data["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
x.add_row([region_name,
it["type"],
it["multiaz"],
it["license"],
it["db"],
self.none_as_string(it["price"])])
elif u == "reserved":
data = self.get_reserved_instances_prices()
x = PrettyTable()
try:
x.set_field_names(["region",
"type",
"multiaz",
"license",
"db",
"utilization",
"term",
"payment_type",
"price",
"upfront"])
except AttributeError:
x.field_names = ["region",
"type",
"multiaz",
"license",
"db",
"utilization",
"term",
"payment_type",
"price",
"upfront"]
try:
x.aligns[-1] = "l"
x.aligns[-2] = "l"
except AttributeError:
x.align["price"] = "l"
x.align["upfront"] = "l"
for r in data["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
for term in it["prices"]:
if "noUpfront" in it["prices"][term] or "partialUpfront" in it["prices"][term] or "allUpfront" in it["prices"][term]:
for po in it["prices"][term]:
x.add_row([region_name,
it["type"],
it["multiaz"],
it["license"],
it["db"],
it["utilization"],
term,
po,
self.none_as_string(it["prices"][term][po]["hourly"]),
self.none_as_string(it["prices"][term][po]["upfront"])])
else:
x.add_row([region_name,
it["type"],
it["multiaz"],
it["license"],
it["db"],
it["utilization"],
term,
"",
self.none_as_string(it["prices"][term]["hourly"]),
self.none_as_string(it["prices"][term]["upfront"])])
print(x)
def save_csv(self,u,path=os.getcwd()+"\\",name=None):
"""
Method saving the RDS pricing data in CSV format to the
cpecified location.
Args:
u (str): Parameter specifying On-Demand ("ondemand") or
Reserved ("reserved") pricing option.
path (str): System path for saving the data file. Current
directory is the the defauilt value.
name (str): The desired name of the file. The default
values are "RDS_reserved_pricing.csv" for Reserved
and "RDS_ondemand_pricing.csv" for On-Demand.
Returns:
Prints RDS pricing in the CSV format (console).
"""
data = None
if u not in ["ondemand","reserved"]:
print("Function requires Reservation parameter at the first"+
"position. Possible values:"+
"\"ondemand\" or \"reserved\".")
elif u == "ondemand":
if name is None:
name="RDS_ondemand_pricing.csv"
data = self.get_ondemand_instances_prices()
writer = csv.writer(open(path+name, 'wb'))
print("region,type,multiaz,license,db,price")
writer.writerow(["region",
"type",
"multiaz",
"license",
"db",
"price"])
for r in data["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
print("%s,%s,%s,%s,%s,%s" % (region_name,
it["type"],
it["multiaz"],
it["license"],
it["db"],
self.none_as_string(it["price"])))
writer.writerow([region_name,
it["type"],
it["multiaz"],
it["license"],
it["db"],
self.none_as_string(it["price"])])
elif u == "reserved":
if name is None:
name="RDS_reserved_pricing.csv"
data = self.get_reserved_instances_prices()
writer = csv.writer(open(path+name, 'wb'))
print("region,type,multiaz,license,db,utilization,term,payment_type,price,upfront")
writer.writerow(["region",
"type",
"multiaz",
"license",
"db",
"utilization",
"term",
"payment_type",
"price",
"upfront"])
for r in data["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
for term in it["prices"]:
if "noUpfront" in it["prices"][term] or "partialUpfront" in it["prices"][term] or "allUpfront" in it["prices"][term]:
for po in it["prices"][term]:
print("%s,%s,%s,%s,%s,%s,%s,%s,%s,%s" % (region_name,
it["type"],
it["multiaz"],
it["license"],
it["db"],
it["utilization"],
term,
po,
self.none_as_string(it["prices"][term][po]["hourly"]),
self.none_as_string(it["prices"][term][po]["upfront"])))
writer.writerow([region_name,
it["type"],
it["multiaz"],
it["license"],
it["db"],
it["utilization"],
term,
po,
self.none_as_string(it["prices"][term][po]["hourly"]),
self.none_as_string(it["prices"][term][po]["upfront"])])
else:
print("%s,%s,%s,%s,%s,%s,%s,%s,%s,%s" % (region_name,
it["type"],
it["multiaz"],
it["license"],
it["db"],
it["utilization"],
term,
"",
self.none_as_string(it["prices"][term]["hourly"]),
self.none_as_string(it["prices"][term]["upfront"])))
writer.writerow([region_name,
it["type"],
it["multiaz"],
it["license"],
it["db"],
it["utilization"],
term,
"",
self.none_as_string(it["prices"][term]["hourly"]),
self.none_as_string(it["prices"][term]["upfront"])])
class RSPrices(AWSPrices):
"""
Class for retrieving the Redshift pricing. Child of :class:`awspricingfull.AWSPrices` class.
Attributes:
RS_ON_DEMAND_URL (str): Undocumented AWS Pricing API
URL - On-Demand Redshift Nodes
PG_RS_ON_DEMAND_URL (str): Undocumented AWS Pricing API
URL - Reserved Redshift Nodes, Previous Generation
RS_RESERVED_URL (str): Undocumented AWS Pricing API
URL - Reserved Redshift Nodes
PG_RS_RESERVED_URL (str): Undocumented AWS Pricing API
URL - Reserved Redshift Nodes, Previous Generation
"""
RS_ON_DEMAND_URL=("http://a0.awsstatic.com/pricing/1/redshift/"+
"pricing-on-demand-redshift-instances.min.js")
PG_RS_ON_DEMAND_URL=("http://a0.awsstatic.com/pricing/1/redshift/"+
"previous-generation/pricing-on-demand-redshift-instances.min.js")
RS_RESERVED_URL=("http://a0.awsstatic.com/pricing/1/redshift/"+
"pricing-reserved-redshift-instances.min.js")
PG_RS_RESERVED_URL=("http://a0.awsstatic.com/pricing/1/redshift/"+
"previous-generation/pricing-reserved-redshift-instances.min.js")
def get_reserved_instances_prices(self):
"""
Implementation of method for getting Redshift Reserved pricing.
Returns:
result (dict of dict: dict): Redshift Reserved pricing in dictionary format.
"""
currency = self.DEFAULT_CURRENCY
urls = [
self.RS_RESERVED_URL,
self.PG_RS_RESERVED_URL
]
result_regions = []
result_regions_index = {}
result = {
"config" : {
"currency" : currency,
},
"regions" : result_regions
}
for u in urls:
data = self.load_data(u)
if ("config" in data and data["config"] and "regions"
in data["config"] and data["config"]["regions"]):
for r in data["config"]["regions"]:
if "region" in r and r["region"]:
region_name = r["region"]
if region_name in result_regions_index:
instance_types = result_regions_index[region_name]["instanceTypes"]
else:
instance_types = []
result_regions.append({
"region" : region_name,
"instanceTypes" : instance_types
})
result_regions_index[region_name] = result_regions[-1]
if "instanceTypes" in r:
for it in r["instanceTypes"]:
instance_type = it["type"]
prices = {
"1" : {
"noUpfront" : {
"hourly" : None,
"upfront" : None
},
"partialUpfront":{
"hourly" : None,
"upfront" : None
},
"allUpfront":{
"hourly" : None,
"upfront" : None
}
},
"3" : {
"noUpfront" : {
"hourly" : None,
"upfront" : None
},
"partialUpfront":{
"hourly" : None,
"upfront" : None
},
"allUpfront":{
"hourly" : None,
"upfront" : None
}
}
}
instance_types.append({
"type" : instance_type,
"prices" : prices
})
if "terms" in it:
for s in it["terms"]:
term=s["term"]
for po_data in s["purchaseOptions"]:
po=po_data["purchaseOption"]
for price_data in po_data["valueColumns"]:
price = None
try:
price = float(re.sub("[^0-9\.]", "",
price_data["prices"][currency]))
except ValueError:
price = None
if term=="yrTerm1":
if price_data["name"] == "upfront":
prices["1"][po]["upfront"] = price
elif price_data["name"] == "monthlyStar":
prices["1"][po]["hourly"] = price/730
elif term=="yrTerm3":
if price_data["name"] == "upfront":
prices["3"][po]["upfront"] = price
elif price_data["name"] == "monthlyStar":
prices["3"][po]["hourly"] = price/730
return result
def get_ondemand_instances_prices(self):
"""
Implementation of method for getting Redshift On-Denand pricing.
Returns:
result (dict of dict: dict): Redshift On-Denand pricing in dictionary format.
"""
currency = self.DEFAULT_CURRENCY
urls = [
self.RS_ON_DEMAND_URL,
self.PG_RS_ON_DEMAND_URL
]
result_regions = []
result = {
"config" : {
"currency" : currency,
"unit" : "perhr"
},
"regions" : result_regions
}
for u in urls:
data = self.load_data(u)
if ("config" in data and data["config"] and "regions"
in data["config"] and data["config"]["regions"]):
for r in data["config"]["regions"]:
if "region" in r and r["region"]:
region_name = r["region"]
instance_types = []
if "instanceTypes" in r:
for it in r["instanceTypes"]:
if "tiers" in it:
for s in it["tiers"]:
instance_type = s["size"]
for price_data in s["valueColumns"]:
price = None
try:
price =float(re.sub("[^0-9\.]", "",
price_data["prices"][currency]))
except:
price = None
_type = instance_type
instance_types.append({
"type" : _type,
"price" : price
})
result_regions.append({
"region" : region_name,
"instanceTypes" : instance_types
})
return result
def print_table(self,u):
"""
Method printing the Redshift pricing data to the console
in the Pretty Table format (requires Pretty Table
import).
Args:
u (str): Parameter specifying On-Demand ("ondemand") or
Reserved ("reserved") pricing option.
Returns:
Prints Redshift pricing in the Pretty Table format.
"""
try:
from prettytable import PrettyTable
except ImportError:
print("ERROR: Please install 'prettytable' using pip: pip install prettytable")
data = None
if u not in ["ondemand","reserved"]:
print("Function requires Reservation parameter at the first"+
"position. Possible values:"+
"\"ondemand\" or \"reserved\".")
else:
if u == "ondemand":
data = self.get_ondemand_instances_prices()
x = PrettyTable()
try:
x.set_field_names(["region", "type", "price"])
except AttributeError:
x.field_names = ["region", "type", "price"]
try:
x.aligns[-1] = "l"
except AttributeError:
x.align["price"] = "l"
for r in data["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
x.add_row([region_name, it["type"], self.none_as_string(it["price"])])
elif u == "reserved":
data = self.get_reserved_instances_prices()
x = PrettyTable()
try:
x.set_field_names(["region", "type", "term","payment_type" "price", "upfront"])
except AttributeError:
x.field_names = ["region", "type", "term", "payment_type","price", "upfront"]
try:
x.aligns[-1] = "l"
x.aligns[-2] = "l"
except AttributeError:
x.align["price"] = "l"
x.align["upfront"] = "l"
for r in data["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
for term in it["prices"]:
for po in it["prices"][term]:
x.add_row([region_name,
it["type"],
term,
po,
self.none_as_string(it["prices"][term][po]["hourly"]),
self.none_as_string(it["prices"][term][po]["upfront"])])
print(x)
def save_csv(self,u,path=os.getcwd()+"\\",name=None):
"""
Method saving the Redshift pricing data in CSV format to the
cpecified location.
Args:
u (str): Parameter specifying On-Demand ("ondemand") or
Reserved ("reserved") pricing option.
path (str): System path for saving the data file. Current
directory is the the defauilt value.
name (str): The desired name of the file. The default
values are "Redshift_reserved_pricing.csv" for Reserved
and "Redshift_ondemand_pricing.csv" for On-Demand.
Returns:
Prints Redshift pricing in the CSV format (console).
"""
if u not in ["ondemand","reserved"]:
print("Function requires Reservation parameter at the first"+
"position. Possible values:"+
"\"ondemand\" or \"reserved\".")
elif u == "ondemand":
if name is None:
name="RS_ondemand_pricing.csv"
data = self.get_ondemand_instances_prices()
writer = csv.writer(open(path+name, 'wb'))
print("region,type,price")
writer.writerow(["region","type","price"])
for r in data["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
writer.writerow([region_name,it["type"],self.none_as_string(it["price"])])
print("%s,%s,%s" % (region_name,
it["type"],
self.none_as_string(it["price"])))
elif u == "reserved":
if name is None:
name="RS_reserved_pricing.csv"
data = self.get_reserved_instances_prices()
writer = csv.writer(open(path+name, 'wb'))
print("region,type,term,payment_type,price,upfront")
writer.writerow(["region","type","term","payment_type","price","upfront"])
for r in data["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
for term in it["prices"]:
for po in it["prices"][term]:
print("%s,%s,%s,%s,%s,%s" % (region_name,
it["type"],
term,
po,
self.none_as_string(it["prices"][term][po]["hourly"]),
self.none_as_string(it["prices"][term][po]["upfront"])))
writer.writerow([region_name,
it["type"],
term,
po,
self.none_as_string(it["prices"][term][po]["hourly"]),
self.none_as_string(it["prices"][term][po]["upfront"])])
class DDBPrices(AWSPrices):
"""
Class for retrieving the DynamoDB pricing. Child of :class:`awspricingfull.AWSPrices` class.
Attributes:
DDB_ON_DEMAND_URL (str): Undocumented AWS Pricing API
URL - On-Demand DynamoDB Nodes
DDB_RESERVED_URL (str): Undocumented AWS Pricing API
URL - Reserved DynamoDB Nodes
INSTANCE_TYPE_MAPPING (dict of str: str): Mapping of DynamoDB throughput types (read/write)
to instance-type-like names (ddb.read/ddb.write)
"""
DDB_ON_DEMAND_URL=("http://a0.awsstatic.com/pricing/1/dynamodb/"+
"pricing-data-throughput.min.js")
DDB_RESERVED_URL=("http://a0.awsstatic.com/pricing/1/dynamodb/"+
"pricing-reserved-capacity.min.js")
INSTANCE_TYPE_MAPPING = {
"per50Reads": "ddb.read",
"per10Writes" : "ddb.write",
#Reserved
"readCapacity100" : "ddb.read",
"writeCapacity100" : "ddb.write"
}
def get_reserved_instances_prices(self):
"""
Implementation of method for getting DynamoDB Reserved pricing.
Returns:
result (dict of dict: dict): DynamoDB Reserved pricing in dictionary format.
"""
currency = self.DEFAULT_CURRENCY
urls = [
self.DDB_RESERVED_URL
]
result_regions = []
result_regions_index = {}
result = {
"config" : {
"currency" : currency,
},
"regions" : result_regions
}
for u in urls:
data = self.load_data(u)
if ("config" in data and data["config"] and "regions"
in data["config"] and data["config"]["regions"]):
for r in data["config"]["regions"]:
if "region" in r and r["region"]:
region_name = r["region"]
if region_name in result_regions_index:
instance_types = result_regions_index[region_name]["instanceTypes"]
else:
instance_types = []
result_regions.append({
"region" : region_name,
"instanceTypes" : instance_types
})
result_regions_index[region_name] = result_regions[-1]
if "instanceTypes" in r:
for it in r["instanceTypes"]:
for s in it["sizes"]:
instance_type = self.INSTANCE_TYPE_MAPPING[s["size"]]
prices = {
"1" : {
"partialUpfront":{
"hourly" : None,
"upfront" : None
}
},
"3" : {
"partialUpfront":{
"hourly" : None,
"upfront" : None
}
}
}
instance_types.append({
"type" : instance_type,
"prices" : prices
})
if "valueColumns" in s:
for v in s["valueColumns"]:
term = v["name"]
price = None
try:
price = float(re.sub("[^0-9\.]", "",
v["prices"][currency]))
except ValueError:
price = None
if term == "yrTerm1":
if price==None:
prices["1"]["partialUpfront"]["upfront"] = 0
else:
prices["1"]["partialUpfront"]["upfront"] = price / 100
elif term == "yrTerm1Hourly":
if price==None:
prices["1"]["partialUpfront"]["hourly"] = 0
else:
prices["1"]["partialUpfront"]["hourly"] = price / 100
elif term == "yrTerm3":
if price==None:
prices["3"]["partialUpfront"]["upfront"] = 0
else:
prices["3"]["partialUpfront"]["upfront"] = price / 100
elif term == "yrTerm3Hourly":
if price==None:
prices["3"]["partialUpfront"]["hourly"] = 0
else:
prices["3"]["partialUpfront"]["hourly"] = price / 100
return result
def get_ondemand_instances_prices(self):
"""
Implementation of method for getting DynamoDB On-Denand pricing.
Returns:
result (dict of dict: dict): DynamoDB On-Denand pricing in dictionary format.
"""
currency = self.DEFAULT_CURRENCY
urls = [
self.DDB_ON_DEMAND_URL
]
result_regions = []
result = {
"config" : {
"currency" : currency,
"unit" : "perhr"
},
"regions" : result_regions
}
for u in urls:
data = self.load_data(u)
if ("config" in data and data["config"] and "regions"
in data["config"] and data["config"]["regions"]):
for r in data["config"]["regions"]:
if "region" in r and r["region"]:
region_name = r["region"]
instance_types = []
if "values" in r:
instance_type = self.INSTANCE_TYPE_MAPPING[r["values"]["writes"]["rate"]]
price = None
try:
price =float(re.sub("[^0-9\.]", "",
r["values"]["writes"]["prices"][currency])) / 10
except:
price = None
_type = instance_type
instance_types.append({
"type" : _type,
"price" : price
})
instance_type = self.INSTANCE_TYPE_MAPPING[r["values"]["reads"]["rate"]]
price = None
try:
price = float(re.sub("[^0-9\.]", "",
r["values"]["reads"]["prices"][currency])) / 50
except:
price = None
_type = instance_type
instance_types.append({
"type" : _type,
"price" : price
})
result_regions.append({
"region" : region_name,
"instanceTypes" : instance_types
})
return result
def print_table(self,u):
"""
Method printing the DynamoDB pricing data to the console
in the Pretty Table format (requires Pretty Table
import).
Args:
u (str): Parameter specifying On-Demand ("ondemand") or
Reserved ("reserved") pricing option.
Returns:
Prints DynamoDB pricing in the Pretty Table format.
"""
try:
from prettytable import PrettyTable
except ImportError:
print("ERROR: Please install 'prettytable' using pip: pip install prettytable")
data = None
if u not in ["ondemand","reserved"]:
print("Function requires Reservation parameter at the first"+
"position. Possible values:"+
"\"ondemand\" or \"reserved\".")
else:
if u == "ondemand":
data = self.get_ondemand_instances_prices()
x = PrettyTable()
try:
x.set_field_names(["region", "type", "price"])
except AttributeError:
x.field_names = ["region", "type", "price"]
try:
x.aligns[-1] = "l"
except AttributeError:
x.align["price"] = "l"
for r in data["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
x.add_row([region_name, it["type"], self.none_as_string(it["price"])])
elif u == "reserved":
data = self.get_reserved_instances_prices()
x = PrettyTable()
try:
x.set_field_names(["region", "type", "term","payment_type" "price", "upfront"])
except AttributeError:
x.field_names = ["region", "type", "term", "payment_type","price", "upfront"]
try:
x.aligns[-1] = "l"
x.aligns[-2] = "l"
except AttributeError:
x.align["price"] = "l"
x.align["upfront"] = "l"
for r in data["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
for term in it["prices"]:
for po in it["prices"][term]:
x.add_row([region_name,
it["type"],
term,
po,
self.none_as_string(it["prices"][term][po]["hourly"]),
self.none_as_string(it["prices"][term][po]["upfront"])])
print(x)
def save_csv(self,u,path=os.getcwd()+"\\",name=None):
"""
Method saving the DynamoDB pricing data in CSV format to the
cpecified location.
Args:
u (str): Parameter specifying On-Demand ("ondemand") or
Reserved ("reserved") pricing option.
path (str): System path for saving the data file. Current
directory is the the defauilt value.
name (str): The desired name of the file. The default
values are "DDB_reserved_pricing.csv" for Reserved
and "DDB_ondemand_pricing.csv" for On-Demand.
Returns:
Prints DynamoDB pricing in the CSV format (console).
"""
if u not in ["ondemand","reserved"]:
print("Function requires Reservation parameter at the first"+
"position. Possible values:"+
"\"ondemand\" or \"reserved\".")
elif u == "ondemand":
if name is None:
name="DDB_ondemand_pricing.csv"
data = self.get_ondemand_instances_prices()
writer = csv.writer(open(path+name, 'wb'))
print("region,type,price")
writer.writerow(["region","type","price"])
for r in data["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
writer.writerow([region_name,it["type"],self.none_as_string(it["price"])])
print("%s,%s,%s" % (region_name,
it["type"],
self.none_as_string(it["price"])))
elif u == "reserved":
if name is None:
name="DDB_reserved_pricing.csv"
data = self.get_reserved_instances_prices()
writer = csv.writer(open(path+name, 'wb'))
print("region,type,term,payment_type,price,upfront")
writer.writerow(["region","type","term","payment_type","price","upfront"])
for r in data["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
for term in it["prices"]:
for po in it["prices"][term]:
print("%s,%s,%s,%s,%s,%s" % (region_name,
it["type"],
term,
po,
self.none_as_string(it["prices"][term][po]["hourly"]),
self.none_as_string(it["prices"][term][po]["upfront"])))
writer.writerow([region_name,
it["type"],
term,
po,
self.none_as_string(it["prices"][term][po]["hourly"]),
self.none_as_string(it["prices"][term][po]["upfront"])])
class AllAWSPrices(AWSPrices):
"""
Class for retrieving the instance pricing for 5 AWS Services:
EC2, RDS, ElastiCache, Redshift and DynamoDB. Child of :class:`awspricingfull.AWSPrices` class.
Attributes:
ec2 (EC2Prices): instance of :class:`awspricingfull.EC2Prices` class containing methods
for EC2 pricing retrieval.
elc (ELCPrices): instance of :class:`awspricingfull.ELCPrices` class containing methods
for ElastiCache pricing retrieval.
rds (RDSPrices): instance of :class:`awspricingfull.RDSPrices` class containing methods
for RDS pricing retrieval.
rs (RSPrices): instance of :class:`awspricingfull.RSPrices` class containing methods
for Redshift pricing retrieval.
ddb (DDBPrices): instance of :class:`awspricingfull.DDBPrices` class containing methods
for DynamoDB pricing retrieval.
"""
ec2=EC2Prices()
elc=ELCPrices()
rds=RDSPrices()
rs=RSPrices()
ddb=DDBPrices()
def return_json(self,u):
"""
Method printing the pricing data in JSON format to Console.
Args:
u (str): Parameter specifying On-Demand ("ondemand"),
Reserved ("reserved") or both ("all") pricing option.
Returns:
str: Pricing data in JSON string format or an error message.
"""
if u not in ["ondemand","reserved", "all"]:
print("Function requires Reservation parameter at the first"+
"position. Possible values:"+
"\"ondemand\" or \"reserved\" or \"all\".")
else:
if u=="ondemand":
ec2_data=self.ec2.get_ondemand_instances_prices()
elc_data=self.elc.get_ondemand_instances_prices()
rds_data=self.rds.get_ondemand_instances_prices()
rs_data=self.rs.get_ondemand_instances_prices()
ddb_data=self.ddb.get_ondemand_instances_prices()
res={
"ec2":ec2_data,
"elasticache":elc_data,
"rds":rds_data,
"redshift":rs_data,
"dynamodb":ddb_data
}
elif u=="reserved":
ec2_data=self.ec2.get_reserved_instances_prices()
elc_data=self.elc.get_reserved_instances_prices()
rds_data=self.rds.get_reserved_instances_prices()
rs_data=self.rs.get_reserved_instances_prices()
ddb_data=self.ddb.get_reserved_instances_prices()
res={
"ec2":ec2_data,
"elasticache":elc_data,
"rds":rds_data,
"redshift":rs_data,
"dynamodb":ddb_data
}
elif u=="all":
ec2_data_od=self.ec2.get_ondemand_instances_prices()
elc_data_od=self.elc.get_ondemand_instances_prices()
rds_data_od=self.rds.get_ondemand_instances_prices()
rs_data_od=self.rs.get_ondemand_instances_prices()
ddb_data_od=self.ddb.get_ondemand_instances_prices()
ec2_data_r=self.ec2.get_reserved_instances_prices()
elc_data_r=self.elc.get_reserved_instances_prices()
rds_data_r=self.rds.get_reserved_instances_prices()
rs_data_r=self.rs.get_reserved_instances_prices()
ddb_data_r=self.ddb.get_reserved_instances_prices()
res={
"ondemand":{
"ec2":ec2_data_od,
"elasticache":elc_data_od,
"rds":rds_data_od,
"redshift":rs_data_od,
"dynamodb":ddb_data_od},
"reserved":{
"ec2":ec2_data_r,
"elasticache":elc_data_r,
"rds":rds_data_r,
"redshift":rs_data_r,
"dynamodb":ddb_data_r}
}
return json.dumps(res)
def save_csv(self,u,path=os.getcwd()+"\\",name=None):
"""
Method saving the full pricing data in CSV format to the
cpecified location.
Args:
u (str): Parameter specifying On-Demand ("ondemand"),
Reserved ("reserved") or both ("all") pricing option.
path (str): System path for saving the data file. Current
directory is the the defauilt value.
name (str): The desired name of the file. The default
values are "FULL_reserved_pricing.csv" for Reserved,
"FULL_ondemand_pricing.csv" for On-Demand and
"FULL_all_pricing.csv" for both.
Returns:
Prints Full pricing in the CSV format (console).
"""
if u not in ["ondemand","reserved","all"]:
print("Function requires Reservation parameter at the first"+
"position. Possible values:"+
"\"ondemand\" or \"reserved\" or \"all\".")
elif u=="ondemand":
if name is None:
name="FULL_ondemand_pricing.csv"
ec2_data=self.ec2.get_ondemand_instances_prices()
elc_data=self.elc.get_ondemand_instances_prices()
rds_data=self.rds.get_ondemand_instances_prices()
rs_data=self.rs.get_ondemand_instances_prices()
ddb_data=self.ddb.get_ondemand_instances_prices()
writer = csv.writer(open(path+name, 'wb'))
print("service,region,type,multiaz,license,db,os,price")
writer.writerow(["service",
"region",
"type",
"multiaz",
"license",
"db",
"os",
"price"])
for r in ec2_data["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
writer.writerow(["ec2",
region_name,
it["type"],
"",
"",
"",
it["os"],
self.none_as_string(it["price"])])
print("%s,%s,%s,%s,%s,%s,%s,%s" % ("ec2",
region_name,
it["type"],
"",
"",
"",
it["os"],
self.none_as_string(it["price"])))
for r in elc_data["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
writer.writerow(["elasticache",
region_name,
it["type"],
"",
"",
"",
"",
self.none_as_string(it["price"])])
print("%s,%s,%s,%s,%s,%s,%s,%s" % ("elasticache",
region_name,
it["type"],
"",
"",
"",
"",
self.none_as_string(it["price"])))
for r in rds_data["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
print("%s,%s,%s,%s,%s,%s,%s,%s" % ("rds",
region_name,
it["type"],
it["multiaz"],
it["license"],
it["db"],
"",
self.none_as_string(it["price"])))
writer.writerow(["rds",
region_name,
it["type"],
it["multiaz"],
it["license"],
it["db"],
"",
self.none_as_string(it["price"])])
for r in rs_data["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
writer.writerow(["redshift",
region_name,
it["type"],
"",
"",
"",
"",
self.none_as_string(it["price"])])
print("%s,%s,%s,%s,%s,%s,%s,%s" % ("redshift",
region_name,
it["type"],
"",
"",
"",
"",
self.none_as_string(it["price"])))
for r in ddb_data["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
writer.writerow(["dynamodb",
region_name,
it["type"],
"",
"",
"",
"",
self.none_as_string(it["price"])])
print("%s,%s,%s,%s,%s,%s,%s,%s" % ("dynamodb",
region_name,
it["type"],
"",
"",
"",
"",
self.none_as_string(it["price"])))
elif u=="reserved":
if name is None:
name="FULL_reserved_pricing.csv"
ec2_data=self.ec2.get_reserved_instances_prices()
elc_data=self.elc.get_reserved_instances_prices()
rds_data=self.rds.get_reserved_instances_prices()
rs_data=self.rs.get_reserved_instances_prices()
ddb_data=self.ddb.get_reserved_instances_prices()
writer = csv.writer(open(path+name, 'wb'))
print("service,region,type,multiaz,license,db,os,utilization,term,payment_type,price,upfront")
writer.writerow(["service",
"region",
"type",
"multiaz",
"license",
"db",
"os",
"utilization",
"term",
"payment_type",
"price",
"upfront"])
for r in ec2_data["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
for term in it["prices"]:
for po in it["prices"][term]:
print("%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s" % ("ec2",
region_name,
it["type"],
"",
"",
"",
it["os"],
"heavy",
term,
po,
self.none_as_string(it["prices"][term][po]["hourly"]),
self.none_as_string(it["prices"][term][po]["upfront"])))
writer.writerow(["ec2",
region_name,
it["type"],
"",
"",
"",
it["os"],
"heavy",
term,
po,
self.none_as_string(it["prices"][term][po]["hourly"]),
self.none_as_string(it["prices"][term][po]["upfront"])])
for r in elc_data["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
for term in it["prices"]:
print("%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s" % ("elasticache",
region_name,
it["type"],
"",
"",
"",
"",
it["utilization"],
term,
"",
self.none_as_string(it["prices"][term]["hourly"]),
self.none_as_string(it["prices"][term]["upfront"])))
writer.writerow(["elasticache",
region_name,
it["type"],
"",
"",
"",
"",
it["utilization"],
term,
"",
self.none_as_string(it["prices"][term]["hourly"]),
self.none_as_string(it["prices"][term]["upfront"])])
for r in rds_data["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
for term in it["prices"]:
if "noUpfront" in it["prices"][term] or "partialUpfront" in it["prices"][term] or "allUpfront" in it["prices"][term]:
for po in it["prices"][term]:
print("%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s" % ("rds",
region_name,
it["type"],
it["multiaz"],
it["license"],
it["db"],
"",
it["utilization"],
term,
po,
self.none_as_string(it["prices"][term][po]["hourly"]),
self.none_as_string(it["prices"][term][po]["upfront"])))
writer.writerow(["rds",
region_name,
it["type"],
it["multiaz"],
it["license"],
it["db"],
"",
it["utilization"],
term,
po,
self.none_as_string(it["prices"][term][po]["hourly"]),
self.none_as_string(it["prices"][term][po]["upfront"])])
else:
print("%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s" % ("rds",
region_name,
it["type"],
it["multiaz"],
it["license"],
it["db"],
"",
it["utilization"],
term,
"",
self.none_as_string(it["prices"][term]["hourly"]),
self.none_as_string(it["prices"][term]["upfront"])))
writer.writerow(["rds",
region_name,
it["type"],
it["multiaz"],
it["license"],
it["db"],
"",
it["utilization"],
term,
"",
self.none_as_string(it["prices"][term]["hourly"]),
self.none_as_string(it["prices"][term]["upfront"])])
for r in rs_data["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
for term in it["prices"]:
for po in it["prices"][term]:
print("%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s" % ("redshift",
region_name,
it["type"],
"",
"",
"",
"",
"heavy",
term,
po,
self.none_as_string(it["prices"][term][po]["hourly"]),
self.none_as_string(it["prices"][term][po]["upfront"])))
writer.writerow(["redshift",
region_name,
it["type"],
"",
"",
"",
"",
"heavy",
term,
po,
self.none_as_string(it["prices"][term][po]["hourly"]),
self.none_as_string(it["prices"][term][po]["upfront"])])
for r in ddb_data["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
for term in it["prices"]:
for po in it["prices"][term]:
print("%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s" % ("dynamodb",
region_name,
it["type"],
"",
"",
"",
"",
"heavy",
term,
po,
self.none_as_string(it["prices"][term][po]["hourly"]),
self.none_as_string(it["prices"][term][po]["upfront"])))
writer.writerow(["dynamodb",
region_name,
it["type"],
"",
"",
"",
"",
"heavy",
term,
po,
self.none_as_string(it["prices"][term][po]["hourly"]),
self.none_as_string(it["prices"][term][po]["upfront"])])
elif u=="all":
if name is None:
name="FULL_all_pricing.csv"
ec2_data_od=self.ec2.get_ondemand_instances_prices()
elc_data_od=self.elc.get_ondemand_instances_prices()
rds_data_od=self.rds.get_ondemand_instances_prices()
rs_data_od=self.rs.get_ondemand_instances_prices()
ddb_data_od=self.ddb.get_ondemand_instances_prices()
ec2_data_r=self.ec2.get_reserved_instances_prices()
elc_data_r=self.elc.get_reserved_instances_prices()
rds_data_r=self.rds.get_reserved_instances_prices()
rs_data_r=self.rs.get_reserved_instances_prices()
ddb_data_r=self.ddb.get_reserved_instances_prices()
writer = csv.writer(open(path+name, 'wb'))
print("reserved_od,service,region,type,multiaz,license,db,os,utilization,term,payment_type,price,upfront")
writer.writerow(["reserved_od",
"service",
"region",
"type",
"multiaz",
"license",
"db",
"os",
"utilization",
"term",
"payment_type",
"price",
"upfront"])
for r in ec2_data_od["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
writer.writerow(["ondemand",
"ec2",
region_name,
it["type"],
"",
"",
"",
it["os"],
"",
"",
"",
self.none_as_string(it["price"]),
""])
print("%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s" % ("ondemand",
"ec2",
region_name,
it["type"],
"",
"",
"",
it["os"],
"",
"",
"",
self.none_as_string(it["price"]),
""))
for r in elc_data_od["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
print("%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s" % ("ondemand",
"elasticache",
region_name,
it["type"],
"",
"",
"",
"",
"",
"",
"",
self.none_as_string(it["price"]),
""))
writer.writerow(["ondemand",
"elasticache",
region_name,
it["type"],
"",
"",
"",
"",
"",
"",
"",
self.none_as_string(it["price"]),
""])
for r in rds_data_od["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
print("%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s" % ("ondemand",
"rds",
region_name,
it["type"],
it["multiaz"],
it["license"],
it["db"],
"",
"",
"",
"",
self.none_as_string(it["price"]),
""))
writer.writerow(["ondemand",
"rds",
region_name,
it["type"],
it["multiaz"],
it["license"],
it["db"],
"",
"",
"",
"",
self.none_as_string(it["price"]),
""])
for r in rs_data_od["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
writer.writerow(["ondemand",
"redshift",
region_name,
it["type"],
"",
"",
"",
"",
"",
"",
"",
self.none_as_string(it["price"]),
""])
print("%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s" % ("ondemand",
"redshift",
region_name,
it["type"],
"",
"",
"",
"",
"",
"",
"",
self.none_as_string(it["price"]),
""))
for r in ddb_data_od["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
writer.writerow(["ondemand",
"dynamodb",
region_name,
it["type"],
"",
"",
"",
"",
"",
"",
"",
self.none_as_string(it["price"]),
""])
print("%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s" % ("ondemand",
"dynamodb",
region_name,
it["type"],
"",
"",
"",
"",
"",
"",
"",
self.none_as_string(it["price"]),
""))
for r in ec2_data_r["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
for term in it["prices"]:
for po in it["prices"][term]:
print("%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s" % ("reserved",
"ec2",
region_name,
it["type"],
"",
"",
"",
it["os"],
"heavy",
term,
po,
self.none_as_string(it["prices"][term][po]["hourly"]),
self.none_as_string(it["prices"][term][po]["upfront"])))
writer.writerow(["reserved",
"ec2",
region_name,
it["type"],
"",
"",
"",
it["os"],
"heavy",
term,
po,
self.none_as_string(it["prices"][term][po]["hourly"]),
self.none_as_string(it["prices"][term][po]["upfront"])])
for r in elc_data_r["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
for term in it["prices"]:
print("%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s" % ("reserved",
"elasticache",
region_name,
it["type"],
"",
"",
"",
"",
it["utilization"],
term,
"",
self.none_as_string(it["prices"][term]["hourly"]),
self.none_as_string(it["prices"][term]["upfront"])))
writer.writerow(["reserved",
"elasticache",
region_name,
it["type"],
"",
"",
"",
"",
it["utilization"],
term,
"",
self.none_as_string(it["prices"][term]["hourly"]),
self.none_as_string(it["prices"][term]["upfront"])])
for r in rds_data_r["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
for term in it["prices"]:
if "noUpfront" in it["prices"][term] or "partialUpfront" in it["prices"][term] or "allUpfront" in it["prices"][term]:
for po in it["prices"][term]:
print("%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s" % ( "reserved",
"rds",
region_name,
it["type"],
it["multiaz"],
it["license"],
it["db"],
"",
it["utilization"],
term,
po,
self.none_as_string(it["prices"][term][po]["hourly"]),
self.none_as_string(it["prices"][term][po]["upfront"])))
writer.writerow(["reserved",
"rds",
region_name,
it["type"],
it["multiaz"],
it["license"],
it["db"],
"",
it["utilization"],
term,
po,
self.none_as_string(it["prices"][term][po]["hourly"]),
self.none_as_string(it["prices"][term][po]["upfront"])])
else:
print("%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s" % ( "reserved",
"rds",
region_name,
it["type"],
it["multiaz"],
it["license"],
it["db"],
"",
it["utilization"],
term,
"",
self.none_as_string(it["prices"][term]["hourly"]),
self.none_as_string(it["prices"][term]["upfront"])))
writer.writerow(["reserved",
"rds",
region_name,
it["type"],
it["multiaz"],
it["license"],
it["db"],
"",
it["utilization"],
term,
"",
self.none_as_string(it["prices"][term]["hourly"]),
self.none_as_string(it["prices"][term]["upfront"])])
for r in rs_data_r["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
for term in it["prices"]:
for po in it["prices"][term]:
print("%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s" % ("reserved",
"redshift",
region_name,
it["type"],
"",
"",
"",
"",
"heavy",
term,
po,
self.none_as_string(it["prices"][term][po]["hourly"]),
self.none_as_string(it["prices"][term][po]["upfront"])))
writer.writerow(["reserved",
"redshift",
region_name,
it["type"],
"",
"",
"",
"",
"heavy",
term,
po,
self.none_as_string(it["prices"][term][po]["hourly"]),
self.none_as_string(it["prices"][term][po]["upfront"])])
for r in ddb_data_r["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
for term in it["prices"]:
for po in it["prices"][term]:
print("%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s" % ("reserved",
"dynamodb",
region_name,
it["type"],
"",
"",
"",
"",
"heavy",
term,
po,
self.none_as_string(it["prices"][term][po]["hourly"]),
self.none_as_string(it["prices"][term][po]["upfront"])))
writer.writerow(["reserved",
"dynamodb",
region_name,
it["type"],
"",
"",
"",
"",
"heavy",
term,
po,
self.none_as_string(it["prices"][term][po]["hourly"]),
self.none_as_string(it["prices"][term][po]["upfront"])])
class AllAWSPrices2(AWSPrices):
"""
Class for retrieving the instance pricing for 5 AWS Services:
EC2, RDS, ElastiCache, Redshift and DynamoDB. Child of :class:`awspricingfull.AWSPrices` class.
Attributes:
ec2 (EC2Prices): instance of :class:`awspricingfull.EC2Prices` class containing methods
for EC2 pricing retrieval.
elc (ELCPrices): instance of :class:`awspricingfull.ELCPrices` class containing methods
for ElastiCache pricing retrieval.
rds (RDSPrices): instance of :class:`awspricingfull.RDSPrices` class containing methods
for RDS pricing retrieval.
rs (RSPrices): instance of :class:`awspricingfull.RSPrices` class containing methods
for Redshift pricing retrieval.
ddb (DDBPrices): instance of :class:`awspricingfull.DDBPrices` class containing methods
for DynamoDB pricing retrieval.
"""
ec2=EC2Prices()
elc=ELCPrices()
rds=RDSPrices()
rs=RSPrices()
ddb=DDBPrices()
def return_json(self,u):
"""
Method printing the pricing data in JSON format to Console.
Args:
u (str): Parameter specifying On-Demand ("ondemand"),
Reserved ("reserved") or both ("all") pricing option.
Returns:
str: Pricing data in JSON string format or an error message.
"""
if u not in ["ondemand","reserved", "all"]:
print("Function requires Reservation parameter at the first"+
"position. Possible values:"+
"\"ondemand\" or \"reserved\" or \"all\".")
else:
if u=="ondemand":
ec2_data=self.ec2.get_ondemand_instances_prices()
elc_data=self.elc.get_ondemand_instances_prices()
rds_data=self.rds.get_ondemand_instances_prices()
rs_data=self.rs.get_ondemand_instances_prices()
ddb_data=self.ddb.get_ondemand_instances_prices()
res={
"ec2":ec2_data,
"elasticache":elc_data,
"rds":rds_data,
"redshift":rs_data,
"dynamodb":ddb_data
}
elif u=="reserved":
ec2_data=self.ec2.get_reserved_instances_prices()
elc_data=self.elc.get_reserved_instances_prices()
rds_data=self.rds.get_reserved_instances_prices()
rs_data=self.rs.get_reserved_instances_prices()
ddb_data=self.ddb.get_reserved_instances_prices()
res={
"ec2":ec2_data,
"elasticache":elc_data,
"rds":rds_data,
"redshift":rs_data,
"dynamodb":ddb_data
}
elif u=="all":
ec2_data_od=self.ec2.get_ondemand_instances_prices()
elc_data_od=self.elc.get_ondemand_instances_prices()
rds_data_od=self.rds.get_ondemand_instances_prices()
rs_data_od=self.rs.get_ondemand_instances_prices()
ddb_data_od=self.ddb.get_ondemand_instances_prices()
ec2_data_r=self.ec2.get_reserved_instances_prices()
elc_data_r=self.elc.get_reserved_instances_prices()
rds_data_r=self.rds.get_reserved_instances_prices()
rs_data_r=self.rs.get_reserved_instances_prices()
ddb_data_r=self.ddb.get_reserved_instances_prices()
res={
"ondemand":{
"ec2":ec2_data_od,
"elasticache":elc_data_od,
"rds":rds_data_od,
"redshift":rs_data_od,
"dynamodb":ddb_data_od},
"reserved":{
"ec2":ec2_data_r,
"elasticache":elc_data_r,
"rds":rds_data_r,
"redshift":rs_data_r,
"dynamodb":ddb_data_r}
}
return json.dumps(res)
def save_csv(self,u,path=os.getcwd()+"\\",name=None):
"""
Method saving the full pricing data in CSV format to the
cpecified location.
Args:
u (str): Parameter specifying On-Demand ("ondemand"),
Reserved ("reserved") or both ("all") pricing option.
path (str): System path for saving the data file. Current
directory is the the defauilt value.
name (str): The desired name of the file. The default
values are "FULL_reserved_pricing.csv" for Reserved,
"FULL_ondemand_pricing.csv" for On-Demand and
"FULL_all_pricing.csv" for both.
Returns:
Prints Full pricing in the CSV format (console).
"""
if u not in ["ondemand","reserved","all"]:
print("Function requires Reservation parameter at the first"+
"position. Possible values:"+
"\"ondemand\" or \"reserved\" or \"all\".")
elif u=="ondemand":
if name is None:
name="FULL_ondemand_pricing.csv"
ec2_data=self.ec2.get_ondemand_instances_prices()
elc_data=self.elc.get_ondemand_instances_prices()
rds_data=self.rds.get_ondemand_instances_prices()
rs_data=self.rs.get_ondemand_instances_prices()
ddb_data=self.ddb.get_ondemand_instances_prices()
writer = csv.writer(open(path+name, 'wb'))
print("service,region,type,multiaz,license,os/db,price")
writer.writerow(["service",
"region",
"type",
"multiaz",
"license",
"os/db",
"price"])
for r in ec2_data["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
writer.writerow(["ec2",
region_name,
it["type"],
"",
"",
it["os"],
self.none_as_string(it["price"])])
print("%s,%s,%s,%s,%s,%s,%s" % ("ec2",
region_name,
it["type"],
"",
"",
it["os"],
self.none_as_string(it["price"])))
for r in elc_data["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
writer.writerow(["elasticache",
region_name,
it["type"],
"",
"",
"",
self.none_as_string(it["price"])])
print("%s,%s,%s,%s,%s,%s,%s" % ("elasticache",
region_name,
it["type"],
"",
"",
"",
self.none_as_string(it["price"])))
for r in rds_data["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
print("%s,%s,%s,%s,%s,%s,%s" % ("rds",
region_name,
it["type"],
it["multiaz"],
it["license"],
it["db"],
self.none_as_string(it["price"])))
writer.writerow(["rds",
region_name,
it["type"],
it["multiaz"],
it["license"],
it["db"],
self.none_as_string(it["price"])])
for r in rs_data["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
writer.writerow(["redshift",
region_name,
it["type"],
"",
"",
"",
self.none_as_string(it["price"])])
print("%s,%s,%s,%s,%s,%s,%s" % ("redshift",
region_name,
it["type"],
"",
"",
"",
self.none_as_string(it["price"])))
for r in ddb_data["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
writer.writerow(["dynamodb",
region_name,
it["type"],
"",
"",
"",
"",
self.none_as_string(it["price"])])
print("%s,%s,%s,%s,%s,%s,%s" % ("dynamodb",
region_name,
it["type"],
"",
"",
"",
self.none_as_string(it["price"])))
elif u=="reserved":
if name is None:
name="FULL_reserved_pricing.csv"
ec2_data=self.ec2.get_reserved_instances_prices()
elc_data=self.elc.get_reserved_instances_prices()
rds_data=self.rds.get_reserved_instances_prices()
rs_data=self.rs.get_reserved_instances_prices()
ddb_data=self.ddb.get_reserved_instances_prices()
writer = csv.writer(open(path+name, 'wb'))
print("service,region,type,multiaz,license,os/db,utilization,term,payment_type,price,upfront")
writer.writerow(["service",
"region",
"type",
"multiaz",
"license",
"os/db",
"utilization",
"term",
"payment_type",
"price",
"upfront"])
for r in ec2_data["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
for term in it["prices"]:
for po in it["prices"][term]:
print("%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s" % ("ec2",
region_name,
it["type"],
"",
"",
it["os"],
"heavy",
term,
po,
self.none_as_string(it["prices"][term][po]["hourly"]),
self.none_as_string(it["prices"][term][po]["upfront"])))
writer.writerow(["ec2",
region_name,
it["type"],
"",
"",
it["os"],
"heavy",
term,
po,
self.none_as_string(it["prices"][term][po]["hourly"]),
self.none_as_string(it["prices"][term][po]["upfront"])])
for r in elc_data["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
for term in it["prices"]:
print("%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s" % ("elasticache",
region_name,
it["type"],
"",
"",
"",
it["utilization"],
term,
"",
self.none_as_string(it["prices"][term]["hourly"]),
self.none_as_string(it["prices"][term]["upfront"])))
writer.writerow(["elasticache",
region_name,
it["type"],
"",
"",
"",
it["utilization"],
term,
"",
self.none_as_string(it["prices"][term]["hourly"]),
self.none_as_string(it["prices"][term]["upfront"])])
for r in rds_data["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
for term in it["prices"]:
if "noUpfront" in it["prices"][term] or "partialUpfront" in it["prices"][term] or "allUpfront" in it["prices"][term]:
for po in it["prices"][term]:
print("%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s" % ("rds",
region_name,
it["type"],
it["multiaz"],
it["license"],
it["db"],
it["utilization"],
term,
po,
self.none_as_string(it["prices"][term][po]["hourly"]),
self.none_as_string(it["prices"][term][po]["upfront"])))
writer.writerow(["rds",
region_name,
it["type"],
it["multiaz"],
it["license"],
it["db"],
it["utilization"],
term,
po,
self.none_as_string(it["prices"][term][po]["hourly"]),
self.none_as_string(it["prices"][term][po]["upfront"])])
else:
print("%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s" % ("rds",
region_name,
it["type"],
it["multiaz"],
it["license"],
it["db"],
it["utilization"],
term,
"",
self.none_as_string(it["prices"][term]["hourly"]),
self.none_as_string(it["prices"][term]["upfront"])))
writer.writerow(["rds",
region_name,
it["type"],
it["multiaz"],
it["license"],
it["db"],
it["utilization"],
term,
"",
self.none_as_string(it["prices"][term]["hourly"]),
self.none_as_string(it["prices"][term]["upfront"])])
for r in rs_data["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
for term in it["prices"]:
for po in it["prices"][term]:
print("%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s" % ("redshift",
region_name,
it["type"],
"",
"",
"",
"heavy",
term,
po,
self.none_as_string(it["prices"][term][po]["hourly"]),
self.none_as_string(it["prices"][term][po]["upfront"])))
writer.writerow(["redshift",
region_name,
it["type"],
"",
"",
"",
"heavy",
term,
po,
self.none_as_string(it["prices"][term][po]["hourly"]),
self.none_as_string(it["prices"][term][po]["upfront"])])
for r in ddb_data["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
for term in it["prices"]:
for po in it["prices"][term]:
print("%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s" % ("dynamodb",
region_name,
it["type"],
"",
"",
"",
"heavy",
term,
po,
self.none_as_string(it["prices"][term][po]["hourly"]),
self.none_as_string(it["prices"][term][po]["upfront"])))
writer.writerow(["dynamodb",
region_name,
it["type"],
"",
"",
"",
"heavy",
term,
po,
self.none_as_string(it["prices"][term][po]["hourly"]),
self.none_as_string(it["prices"][term][po]["upfront"])])
elif u=="all":
if name is None:
name="FULL_all_pricing.csv"
ec2_data_od=self.ec2.get_ondemand_instances_prices()
elc_data_od=self.elc.get_ondemand_instances_prices()
rds_data_od=self.rds.get_ondemand_instances_prices()
rs_data_od=self.rs.get_ondemand_instances_prices()
ddb_data_od=self.ddb.get_ondemand_instances_prices()
ec2_data_r=self.ec2.get_reserved_instances_prices()
elc_data_r=self.elc.get_reserved_instances_prices()
rds_data_r=self.rds.get_reserved_instances_prices()
rs_data_r=self.rs.get_reserved_instances_prices()
ddb_data_r=self.ddb.get_reserved_instances_prices()
writer = csv.writer(open(path+name, 'wb'))
print("reserved_od,service,region,type,multiaz,license,os/db,utilization,term,payment_type,price,upfront")
writer.writerow(["reserved_od",
"service",
"region",
"type",
"multiaz",
"license",
"os/db",
"utilization",
"term",
"payment_type",
"price",
"upfront"])
for r in ec2_data_od["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
writer.writerow(["ondemand",
"ec2",
region_name,
it["type"],
"",
"",
it["os"],
"",
"",
"",
self.none_as_string(it["price"]),
""])
print("%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s" % ("ondemand",
"ec2",
region_name,
it["type"],
"",
"",
it["os"],
"",
"",
"",
self.none_as_string(it["price"]),
""))
for r in elc_data_od["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
print("%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s" % ("ondemand",
"elasticache",
region_name,
it["type"],
"",
"",
"",
"",
"",
"",
self.none_as_string(it["price"]),
""))
writer.writerow(["ondemand",
"elasticache",
region_name,
it["type"],
"",
"",
"",
"",
"",
"",
self.none_as_string(it["price"]),
""])
for r in rds_data_od["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
print("%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s" % ("ondemand",
"rds",
region_name,
it["type"],
it["multiaz"],
it["license"],
it["db"],
"",
"",
"",
self.none_as_string(it["price"]),
""))
writer.writerow(["ondemand",
"rds",
region_name,
it["type"],
it["multiaz"],
it["license"],
it["db"],
"",
"",
"",
self.none_as_string(it["price"]),
""])
for r in rs_data_od["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
writer.writerow(["ondemand",
"redshift",
region_name,
it["type"],
"",
"",
"",
"",
"",
"",
self.none_as_string(it["price"]),
""])
print("%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s" % ("ondemand",
"redshift",
region_name,
it["type"],
"",
"",
"",
"",
"",
"",
self.none_as_string(it["price"]),
""))
for r in ddb_data_od["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
writer.writerow(["ondemand",
"dynamodb",
region_name,
it["type"],
"",
"",
"",
"",
"",
"",
self.none_as_string(it["price"]),
""])
print("%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s" % ("ondemand",
"dynamodb",
region_name,
it["type"],
"",
"",
"",
"",
"",
"",
self.none_as_string(it["price"]),
""))
for r in ec2_data_r["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
for term in it["prices"]:
for po in it["prices"][term]:
print("%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s" % ("reserved",
"ec2",
region_name,
it["type"],
"",
"",
it["os"],
"heavy",
term,
po,
self.none_as_string(it["prices"][term][po]["hourly"]),
self.none_as_string(it["prices"][term][po]["upfront"])))
writer.writerow(["reserved",
"ec2",
region_name,
it["type"],
"",
"",
it["os"],
"heavy",
term,
po,
self.none_as_string(it["prices"][term][po]["hourly"]),
self.none_as_string(it["prices"][term][po]["upfront"])])
for r in elc_data_r["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
for term in it["prices"]:
print("%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s" % ("reserved",
"elasticache",
region_name,
it["type"],
"",
"",
"",
it["utilization"],
term,
"",
self.none_as_string(it["prices"][term]["hourly"]),
self.none_as_string(it["prices"][term]["upfront"])))
writer.writerow(["reserved",
"elasticache",
region_name,
it["type"],
"",
"",
"",
it["utilization"],
term,
"",
self.none_as_string(it["prices"][term]["hourly"]),
self.none_as_string(it["prices"][term]["upfront"])])
for r in rds_data_r["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
for term in it["prices"]:
if "noUpfront" in it["prices"][term] or "partialUpfront" in it["prices"][term] or "allUpfront" in it["prices"][term]:
for po in it["prices"][term]:
print("%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s" % ("reserved",
"rds",
region_name,
it["type"],
it["multiaz"],
it["license"],
it["db"],
it["utilization"],
term,
po,
self.none_as_string(it["prices"][term][po]["hourly"]),
self.none_as_string(it["prices"][term][po]["upfront"])))
writer.writerow(["reserved",
"rds",
region_name,
it["type"],
it["multiaz"],
it["license"],
it["db"],
it["utilization"],
term,
po,
self.none_as_string(it["prices"][term][po]["hourly"]),
self.none_as_string(it["prices"][term][po]["upfront"])])
else:
print("%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s" % ("reserved",
"rds",
region_name,
it["type"],
it["multiaz"],
it["license"],
it["db"],
it["utilization"],
term,
"",
self.none_as_string(it["prices"][term]["hourly"]),
self.none_as_string(it["prices"][term]["upfront"])))
writer.writerow(["reserved",
"rds",
region_name,
it["type"],
it["multiaz"],
it["license"],
it["db"],
it["utilization"],
term,
"",
self.none_as_string(it["prices"][term]["hourly"]),
self.none_as_string(it["prices"][term]["upfront"])])
for r in rs_data_r["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
for term in it["prices"]:
for po in it["prices"][term]:
print("%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s" % ("reserved",
"redshift",
region_name,
it["type"],
"",
"",
"",
"heavy",
term,
po,
self.none_as_string(it["prices"][term][po]["hourly"]),
self.none_as_string(it["prices"][term][po]["upfront"])))
writer.writerow(["reserved",
"redshift",
region_name,
it["type"],
"",
"",
"",
"heavy",
term,
po,
self.none_as_string(it["prices"][term][po]["hourly"]),
self.none_as_string(it["prices"][term][po]["upfront"])])
for r in ddb_data_r["regions"]:
region_name = r["region"]
for it in r["instanceTypes"]:
for term in it["prices"]:
for po in it["prices"][term]:
print("%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s" % ("reserved",
"dynamodb",
region_name,
it["type"],
"",
"",
"",
"heavy",
term,
po,
self.none_as_string(it["prices"][term][po]["hourly"]),
self.none_as_string(it["prices"][term][po]["upfront"])))
writer.writerow(["reserved",
"dynamodb",
region_name,
it["type"],
"",
"",
"",
"heavy",
term,
po,
self.none_as_string(it["prices"][term][po]["hourly"]),
self.none_as_string(it["prices"][term][po]["upfront"])])
| 53.488646
| 145
| 0.376075
| 18,456
| 240,271
| 4.685739
| 0.028825
| 0.011494
| 0.015333
| 0.0179
| 0.92855
| 0.904938
| 0.85562
| 0.826353
| 0.781926
| 0.754625
| 0
| 0.006991
| 0.533277
| 240,271
| 4,491
| 146
| 53.500557
| 0.764188
| 0.124093
| 0
| 0.75512
| 0
| 0.016919
| 0.159688
| 0.045632
| 0
| 0
| 0
| 0
| 0
| 1
| 0.008608
| false
| 0
| 0.005046
| 0
| 0.061443
| 0.029979
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 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
| 8
|
fe560a060a303b0f57ef94c27c11a11bb5fa56e1
| 59
|
py
|
Python
|
lectures/code/tuples_packing.py
|
naskoch/python_course
|
84adfd3f8d48ca3ad5837f7acc59d2fa051e95d3
|
[
"MIT"
] | 4
|
2015-08-10T17:46:55.000Z
|
2020-04-18T21:09:03.000Z
|
lectures/code/tuples_packing.py
|
naskoch/python_course
|
84adfd3f8d48ca3ad5837f7acc59d2fa051e95d3
|
[
"MIT"
] | null | null | null |
lectures/code/tuples_packing.py
|
naskoch/python_course
|
84adfd3f8d48ca3ad5837f7acc59d2fa051e95d3
|
[
"MIT"
] | 2
|
2019-04-24T03:31:02.000Z
|
2019-05-13T07:36:06.000Z
|
t = 1, 2, 3
x, y, z = t
print t # (1, 2, 3)
print y # 2
| 9.833333
| 20
| 0.40678
| 16
| 59
| 1.5
| 0.5
| 0.166667
| 0.25
| 0.333333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.194444
| 0.389831
| 59
| 5
| 21
| 11.8
| 0.472222
| 0.186441
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 0.5
| 1
| 0
| 1
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 8
|
fe89e6bc8a5e3e39451201dbb0ab6a1746f9fb83
| 263
|
py
|
Python
|
pca/utils/tests/imports/test_get_dotted_path.py
|
pcah/python-clean-architecture
|
20630d0b3b4c00f6503a26cc98c45df12bc31b3b
|
[
"MIT"
] | 278
|
2019-01-10T07:57:29.000Z
|
2022-03-31T22:47:13.000Z
|
pca/utils/tests/imports/test_get_dotted_path.py
|
asuzukosi/python-clean-architecture
|
20630d0b3b4c00f6503a26cc98c45df12bc31b3b
|
[
"MIT"
] | 80
|
2018-11-17T23:44:39.000Z
|
2021-12-15T18:29:04.000Z
|
pca/utils/tests/imports/test_get_dotted_path.py
|
lhaze/dharma
|
20630d0b3b4c00f6503a26cc98c45df12bc31b3b
|
[
"MIT"
] | 29
|
2018-11-19T20:11:13.000Z
|
2022-03-02T06:27:34.000Z
|
from pca.utils.imports import get_dotted_path
def test_get_dotted_path():
assert get_dotted_path(get_dotted_path) == "pca.utils.imports.get_dotted_path"
assert get_dotted_path(str) == "builtins.str"
assert get_dotted_path(print) == "builtins.print"
| 32.875
| 82
| 0.771863
| 40
| 263
| 4.7
| 0.35
| 0.335106
| 0.484043
| 0.303191
| 0.340426
| 0.340426
| 0.340426
| 0
| 0
| 0
| 0
| 0
| 0.121673
| 263
| 7
| 83
| 37.571429
| 0.813853
| 0
| 0
| 0
| 0
| 0
| 0.224335
| 0.125475
| 0
| 0
| 0
| 0
| 0.6
| 1
| 0.2
| true
| 0
| 0.4
| 0
| 0.6
| 0.2
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| 0
| null | 1
| 1
| 1
| 0
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| 0
| 0
| 0
| 0
| 0
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| 1
| 0
| 0
| 0
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| 0
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| 0
| 0
| null | 0
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| 1
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 7
|
fe9125fa062118b857187d58b9d337af71e149e2
| 4,381
|
py
|
Python
|
tests/client/test_cookies.py
|
laggardkernel/httpx
|
e05a5372eb6172287458b37447c30f650047e1b8
|
[
"BSD-3-Clause"
] | 1
|
2021-04-13T15:55:56.000Z
|
2021-04-13T15:55:56.000Z
|
tests/client/test_cookies.py
|
laggardkernel/httpx
|
e05a5372eb6172287458b37447c30f650047e1b8
|
[
"BSD-3-Clause"
] | null | null | null |
tests/client/test_cookies.py
|
laggardkernel/httpx
|
e05a5372eb6172287458b37447c30f650047e1b8
|
[
"BSD-3-Clause"
] | 1
|
2020-06-30T17:38:47.000Z
|
2020-06-30T17:38:47.000Z
|
from http.cookiejar import Cookie, CookieJar
import httpx
def get_and_set_cookies(request: httpx.Request) -> httpx.Response:
if request.url.path == "/echo_cookies":
data = {"cookies": request.headers.get("cookie")}
return httpx.Response(200, json=data)
elif request.url.path == "/set_cookie":
return httpx.Response(200, headers={"set-cookie": "example-name=example-value"})
else:
raise NotImplementedError() # pragma: no cover
def test_set_cookie() -> None:
"""
Send a request including a cookie.
"""
url = "http://example.org/echo_cookies"
cookies = {"example-name": "example-value"}
client = httpx.Client(transport=httpx.MockTransport(get_and_set_cookies))
response = client.get(url, cookies=cookies)
assert response.status_code == 200
assert response.json() == {"cookies": "example-name=example-value"}
def test_set_cookie_with_cookiejar() -> None:
"""
Send a request including a cookie, using a `CookieJar` instance.
"""
url = "http://example.org/echo_cookies"
cookies = CookieJar()
cookie = Cookie(
version=0,
name="example-name",
value="example-value",
port=None,
port_specified=False,
domain="",
domain_specified=False,
domain_initial_dot=False,
path="/",
path_specified=True,
secure=False,
expires=None,
discard=True,
comment=None,
comment_url=None,
rest={"HttpOnly": ""},
rfc2109=False,
)
cookies.set_cookie(cookie)
client = httpx.Client(transport=httpx.MockTransport(get_and_set_cookies))
response = client.get(url, cookies=cookies)
assert response.status_code == 200
assert response.json() == {"cookies": "example-name=example-value"}
def test_setting_client_cookies_to_cookiejar() -> None:
"""
Send a request including a cookie, using a `CookieJar` instance.
"""
url = "http://example.org/echo_cookies"
cookies = CookieJar()
cookie = Cookie(
version=0,
name="example-name",
value="example-value",
port=None,
port_specified=False,
domain="",
domain_specified=False,
domain_initial_dot=False,
path="/",
path_specified=True,
secure=False,
expires=None,
discard=True,
comment=None,
comment_url=None,
rest={"HttpOnly": ""},
rfc2109=False,
)
cookies.set_cookie(cookie)
client = httpx.Client(transport=httpx.MockTransport(get_and_set_cookies))
client.cookies = cookies # type: ignore
response = client.get(url)
assert response.status_code == 200
assert response.json() == {"cookies": "example-name=example-value"}
def test_set_cookie_with_cookies_model() -> None:
"""
Send a request including a cookie, using a `Cookies` instance.
"""
url = "http://example.org/echo_cookies"
cookies = httpx.Cookies()
cookies["example-name"] = "example-value"
client = httpx.Client(transport=httpx.MockTransport(get_and_set_cookies))
response = client.get(url, cookies=cookies)
assert response.status_code == 200
assert response.json() == {"cookies": "example-name=example-value"}
def test_get_cookie() -> None:
url = "http://example.org/set_cookie"
client = httpx.Client(transport=httpx.MockTransport(get_and_set_cookies))
response = client.get(url)
assert response.status_code == 200
assert response.cookies["example-name"] == "example-value"
assert client.cookies["example-name"] == "example-value"
def test_cookie_persistence() -> None:
"""
Ensure that Client instances persist cookies between requests.
"""
client = httpx.Client(transport=httpx.MockTransport(get_and_set_cookies))
response = client.get("http://example.org/echo_cookies")
assert response.status_code == 200
assert response.json() == {"cookies": None}
response = client.get("http://example.org/set_cookie")
assert response.status_code == 200
assert response.cookies["example-name"] == "example-value"
assert client.cookies["example-name"] == "example-value"
response = client.get("http://example.org/echo_cookies")
assert response.status_code == 200
assert response.json() == {"cookies": "example-name=example-value"}
| 30.006849
| 88
| 0.654417
| 508
| 4,381
| 5.5
| 0.147638
| 0.080172
| 0.077309
| 0.098783
| 0.838583
| 0.809234
| 0.798139
| 0.776664
| 0.761274
| 0.747674
| 0
| 0.011581
| 0.211596
| 4,381
| 145
| 89
| 30.213793
| 0.797336
| 0.073271
| 0
| 0.747475
| 0
| 0
| 0.177504
| 0.039166
| 0
| 0
| 0
| 0
| 0.181818
| 1
| 0.070707
| false
| 0
| 0.020202
| 0
| 0.111111
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
fea970fe6a438bbe040226f1b6917c73da487b9e
| 84,519
|
py
|
Python
|
libs/python/qumranica/api/artefact_api.py
|
Scripta-Qumranica-Electronica/SQE_API_Connectors
|
aaa9b9eb8709d4257c32ea57321a179c6b1e041a
|
[
"MIT"
] | null | null | null |
libs/python/qumranica/api/artefact_api.py
|
Scripta-Qumranica-Electronica/SQE_API_Connectors
|
aaa9b9eb8709d4257c32ea57321a179c6b1e041a
|
[
"MIT"
] | null | null | null |
libs/python/qumranica/api/artefact_api.py
|
Scripta-Qumranica-Electronica/SQE_API_Connectors
|
aaa9b9eb8709d4257c32ea57321a179c6b1e041a
|
[
"MIT"
] | null | null | null |
# coding: utf-8
"""
SQE API
No description provided (generated by Openapi Generator https://github.com/openapitools/openapi-generator) # noqa: E501
The version of the OpenAPI document: v1
Generated by: https://openapi-generator.tech
"""
from __future__ import absolute_import
import re # noqa: F401
# python 2 and python 3 compatibility library
import six
from qumranica.api_client import ApiClient
from qumranica.exceptions import ( # noqa: F401
ApiTypeError,
ApiValueError
)
class ArtefactApi(object):
"""NOTE: This class is auto generated by OpenAPI Generator
Ref: https://openapi-generator.tech
Do not edit the class manually.
"""
def __init__(self, api_client=None):
if api_client is None:
api_client = ApiClient()
self.api_client = api_client
def v1_editions_edition_id_artefact_groups_artefact_group_id_delete(self, edition_id, artefact_group_id, **kwargs): # noqa: E501
"""Deletes the specified artefact group. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.v1_editions_edition_id_artefact_groups_artefact_group_id_delete(edition_id, artefact_group_id, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param int edition_id: Unique Id of the desired edition (required)
:param int artefact_group_id: Unique Id of the artefact group to be deleted (required)
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: DeleteDTO
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
return self.v1_editions_edition_id_artefact_groups_artefact_group_id_delete_with_http_info(edition_id, artefact_group_id, **kwargs) # noqa: E501
def v1_editions_edition_id_artefact_groups_artefact_group_id_delete_with_http_info(self, edition_id, artefact_group_id, **kwargs): # noqa: E501
"""Deletes the specified artefact group. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.v1_editions_edition_id_artefact_groups_artefact_group_id_delete_with_http_info(edition_id, artefact_group_id, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param int edition_id: Unique Id of the desired edition (required)
:param int artefact_group_id: Unique Id of the artefact group to be deleted (required)
:param _return_http_data_only: response data without head status code
and headers
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: tuple(DeleteDTO, status_code(int), headers(HTTPHeaderDict))
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [
'edition_id',
'artefact_group_id'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method v1_editions_edition_id_artefact_groups_artefact_group_id_delete" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'edition_id' is set
if self.api_client.client_side_validation and ('edition_id' not in local_var_params or # noqa: E501
local_var_params['edition_id'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `edition_id` when calling `v1_editions_edition_id_artefact_groups_artefact_group_id_delete`") # noqa: E501
# verify the required parameter 'artefact_group_id' is set
if self.api_client.client_side_validation and ('artefact_group_id' not in local_var_params or # noqa: E501
local_var_params['artefact_group_id'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `artefact_group_id` when calling `v1_editions_edition_id_artefact_groups_artefact_group_id_delete`") # noqa: E501
collection_formats = {}
path_params = {}
if 'edition_id' in local_var_params:
path_params['editionId'] = local_var_params['edition_id'] # noqa: E501
if 'artefact_group_id' in local_var_params:
path_params['artefactGroupId'] = local_var_params['artefact_group_id'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['text/plain', 'application/json', 'text/json']) # noqa: E501
# Authentication setting
auth_settings = ['Bearer'] # noqa: E501
return self.api_client.call_api(
'/v1/editions/{editionId}/artefact-groups/{artefactGroupId}', 'DELETE',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='DeleteDTO', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def v1_editions_edition_id_artefact_groups_artefact_group_id_get(self, edition_id, artefact_group_id, **kwargs): # noqa: E501
"""Gets the details of a specific artefact group in the edition # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.v1_editions_edition_id_artefact_groups_artefact_group_id_get(edition_id, artefact_group_id, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param int edition_id: Unique Id of the desired edition (required)
:param int artefact_group_id: Id of the desired artefact group (required)
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: ArtefactGroupDTO
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
return self.v1_editions_edition_id_artefact_groups_artefact_group_id_get_with_http_info(edition_id, artefact_group_id, **kwargs) # noqa: E501
def v1_editions_edition_id_artefact_groups_artefact_group_id_get_with_http_info(self, edition_id, artefact_group_id, **kwargs): # noqa: E501
"""Gets the details of a specific artefact group in the edition # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.v1_editions_edition_id_artefact_groups_artefact_group_id_get_with_http_info(edition_id, artefact_group_id, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param int edition_id: Unique Id of the desired edition (required)
:param int artefact_group_id: Id of the desired artefact group (required)
:param _return_http_data_only: response data without head status code
and headers
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: tuple(ArtefactGroupDTO, status_code(int), headers(HTTPHeaderDict))
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [
'edition_id',
'artefact_group_id'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method v1_editions_edition_id_artefact_groups_artefact_group_id_get" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'edition_id' is set
if self.api_client.client_side_validation and ('edition_id' not in local_var_params or # noqa: E501
local_var_params['edition_id'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `edition_id` when calling `v1_editions_edition_id_artefact_groups_artefact_group_id_get`") # noqa: E501
# verify the required parameter 'artefact_group_id' is set
if self.api_client.client_side_validation and ('artefact_group_id' not in local_var_params or # noqa: E501
local_var_params['artefact_group_id'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `artefact_group_id` when calling `v1_editions_edition_id_artefact_groups_artefact_group_id_get`") # noqa: E501
collection_formats = {}
path_params = {}
if 'edition_id' in local_var_params:
path_params['editionId'] = local_var_params['edition_id'] # noqa: E501
if 'artefact_group_id' in local_var_params:
path_params['artefactGroupId'] = local_var_params['artefact_group_id'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['text/plain', 'application/json', 'text/json']) # noqa: E501
# Authentication setting
auth_settings = ['Bearer'] # noqa: E501
return self.api_client.call_api(
'/v1/editions/{editionId}/artefact-groups/{artefactGroupId}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ArtefactGroupDTO', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def v1_editions_edition_id_artefact_groups_artefact_group_id_put(self, edition_id, artefact_group_id, **kwargs): # noqa: E501
"""Updates the details of an artefact group. The artefact group will now only contain the artefacts listed in the JSON payload. If the name is null, no change will be made, otherwise the name will also be updated. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.v1_editions_edition_id_artefact_groups_artefact_group_id_put(edition_id, artefact_group_id, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param int edition_id: Unique Id of the desired edition (required)
:param int artefact_group_id: Id of the artefact group to be updated (required)
:param UpdateArtefactGroupDTO update_artefact_group_dto: Parameters that the artefact group should be changed to
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: ArtefactGroupDTO
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
return self.v1_editions_edition_id_artefact_groups_artefact_group_id_put_with_http_info(edition_id, artefact_group_id, **kwargs) # noqa: E501
def v1_editions_edition_id_artefact_groups_artefact_group_id_put_with_http_info(self, edition_id, artefact_group_id, **kwargs): # noqa: E501
"""Updates the details of an artefact group. The artefact group will now only contain the artefacts listed in the JSON payload. If the name is null, no change will be made, otherwise the name will also be updated. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.v1_editions_edition_id_artefact_groups_artefact_group_id_put_with_http_info(edition_id, artefact_group_id, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param int edition_id: Unique Id of the desired edition (required)
:param int artefact_group_id: Id of the artefact group to be updated (required)
:param UpdateArtefactGroupDTO update_artefact_group_dto: Parameters that the artefact group should be changed to
:param _return_http_data_only: response data without head status code
and headers
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: tuple(ArtefactGroupDTO, status_code(int), headers(HTTPHeaderDict))
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [
'edition_id',
'artefact_group_id',
'update_artefact_group_dto'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method v1_editions_edition_id_artefact_groups_artefact_group_id_put" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'edition_id' is set
if self.api_client.client_side_validation and ('edition_id' not in local_var_params or # noqa: E501
local_var_params['edition_id'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `edition_id` when calling `v1_editions_edition_id_artefact_groups_artefact_group_id_put`") # noqa: E501
# verify the required parameter 'artefact_group_id' is set
if self.api_client.client_side_validation and ('artefact_group_id' not in local_var_params or # noqa: E501
local_var_params['artefact_group_id'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `artefact_group_id` when calling `v1_editions_edition_id_artefact_groups_artefact_group_id_put`") # noqa: E501
collection_formats = {}
path_params = {}
if 'edition_id' in local_var_params:
path_params['editionId'] = local_var_params['edition_id'] # noqa: E501
if 'artefact_group_id' in local_var_params:
path_params['artefactGroupId'] = local_var_params['artefact_group_id'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'update_artefact_group_dto' in local_var_params:
body_params = local_var_params['update_artefact_group_dto']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['text/plain', 'application/json', 'text/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json', 'text/json', 'application/*+json']) # noqa: E501
# Authentication setting
auth_settings = ['Bearer'] # noqa: E501
return self.api_client.call_api(
'/v1/editions/{editionId}/artefact-groups/{artefactGroupId}', 'PUT',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ArtefactGroupDTO', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def v1_editions_edition_id_artefact_groups_get(self, edition_id, **kwargs): # noqa: E501
"""Gets a listing of all artefact groups in the edition # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.v1_editions_edition_id_artefact_groups_get(edition_id, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param int edition_id: Unique Id of the desired edition (required)
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: ArtefactGroupListDTO
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
return self.v1_editions_edition_id_artefact_groups_get_with_http_info(edition_id, **kwargs) # noqa: E501
def v1_editions_edition_id_artefact_groups_get_with_http_info(self, edition_id, **kwargs): # noqa: E501
"""Gets a listing of all artefact groups in the edition # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.v1_editions_edition_id_artefact_groups_get_with_http_info(edition_id, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param int edition_id: Unique Id of the desired edition (required)
:param _return_http_data_only: response data without head status code
and headers
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: tuple(ArtefactGroupListDTO, status_code(int), headers(HTTPHeaderDict))
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [
'edition_id'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method v1_editions_edition_id_artefact_groups_get" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'edition_id' is set
if self.api_client.client_side_validation and ('edition_id' not in local_var_params or # noqa: E501
local_var_params['edition_id'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `edition_id` when calling `v1_editions_edition_id_artefact_groups_get`") # noqa: E501
collection_formats = {}
path_params = {}
if 'edition_id' in local_var_params:
path_params['editionId'] = local_var_params['edition_id'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['text/plain', 'application/json', 'text/json']) # noqa: E501
# Authentication setting
auth_settings = ['Bearer'] # noqa: E501
return self.api_client.call_api(
'/v1/editions/{editionId}/artefact-groups', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ArtefactGroupListDTO', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def v1_editions_edition_id_artefact_groups_post(self, edition_id, **kwargs): # noqa: E501
"""Creates a new artefact group with the submitted data. The new artefact must have a list of artefacts that belong to the group. It is not necessary to give the group a name. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.v1_editions_edition_id_artefact_groups_post(edition_id, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param int edition_id: Unique Id of the desired edition (required)
:param CreateArtefactGroupDTO create_artefact_group_dto: Parameters of the new artefact group
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: ArtefactGroupDTO
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
return self.v1_editions_edition_id_artefact_groups_post_with_http_info(edition_id, **kwargs) # noqa: E501
def v1_editions_edition_id_artefact_groups_post_with_http_info(self, edition_id, **kwargs): # noqa: E501
"""Creates a new artefact group with the submitted data. The new artefact must have a list of artefacts that belong to the group. It is not necessary to give the group a name. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.v1_editions_edition_id_artefact_groups_post_with_http_info(edition_id, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param int edition_id: Unique Id of the desired edition (required)
:param CreateArtefactGroupDTO create_artefact_group_dto: Parameters of the new artefact group
:param _return_http_data_only: response data without head status code
and headers
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: tuple(ArtefactGroupDTO, status_code(int), headers(HTTPHeaderDict))
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [
'edition_id',
'create_artefact_group_dto'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method v1_editions_edition_id_artefact_groups_post" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'edition_id' is set
if self.api_client.client_side_validation and ('edition_id' not in local_var_params or # noqa: E501
local_var_params['edition_id'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `edition_id` when calling `v1_editions_edition_id_artefact_groups_post`") # noqa: E501
collection_formats = {}
path_params = {}
if 'edition_id' in local_var_params:
path_params['editionId'] = local_var_params['edition_id'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'create_artefact_group_dto' in local_var_params:
body_params = local_var_params['create_artefact_group_dto']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['text/plain', 'application/json', 'text/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json', 'text/json', 'application/*+json']) # noqa: E501
# Authentication setting
auth_settings = ['Bearer'] # noqa: E501
return self.api_client.call_api(
'/v1/editions/{editionId}/artefact-groups', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ArtefactGroupDTO', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def v1_editions_edition_id_artefacts_artefact_id_delete(self, edition_id, artefact_id, **kwargs): # noqa: E501
"""Deletes the specified artefact # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.v1_editions_edition_id_artefacts_artefact_id_delete(edition_id, artefact_id, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param int edition_id: Unique Id of the desired edition (required)
:param int artefact_id: Unique Id of the desired artefact (required)
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: None
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
return self.v1_editions_edition_id_artefacts_artefact_id_delete_with_http_info(edition_id, artefact_id, **kwargs) # noqa: E501
def v1_editions_edition_id_artefacts_artefact_id_delete_with_http_info(self, edition_id, artefact_id, **kwargs): # noqa: E501
"""Deletes the specified artefact # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.v1_editions_edition_id_artefacts_artefact_id_delete_with_http_info(edition_id, artefact_id, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param int edition_id: Unique Id of the desired edition (required)
:param int artefact_id: Unique Id of the desired artefact (required)
:param _return_http_data_only: response data without head status code
and headers
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: None
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [
'edition_id',
'artefact_id'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method v1_editions_edition_id_artefacts_artefact_id_delete" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'edition_id' is set
if self.api_client.client_side_validation and ('edition_id' not in local_var_params or # noqa: E501
local_var_params['edition_id'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `edition_id` when calling `v1_editions_edition_id_artefacts_artefact_id_delete`") # noqa: E501
# verify the required parameter 'artefact_id' is set
if self.api_client.client_side_validation and ('artefact_id' not in local_var_params or # noqa: E501
local_var_params['artefact_id'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `artefact_id` when calling `v1_editions_edition_id_artefacts_artefact_id_delete`") # noqa: E501
collection_formats = {}
path_params = {}
if 'edition_id' in local_var_params:
path_params['editionId'] = local_var_params['edition_id'] # noqa: E501
if 'artefact_id' in local_var_params:
path_params['artefactId'] = local_var_params['artefact_id'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# Authentication setting
auth_settings = ['Bearer'] # noqa: E501
return self.api_client.call_api(
'/v1/editions/{editionId}/artefacts/{artefactId}', 'DELETE',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type=None, # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def v1_editions_edition_id_artefacts_artefact_id_get(self, edition_id, artefact_id, **kwargs): # noqa: E501
"""Provides a listing of all artefacts that are part of the specified edition # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.v1_editions_edition_id_artefacts_artefact_id_get(edition_id, artefact_id, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param int edition_id: Unique Id of the desired edition (required)
:param int artefact_id: Unique Id of the desired artefact (required)
:param list[str] optional: Add \"masks\" to include artefact polygons and \"images\" to include image data
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: ArtefactDTO
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
return self.v1_editions_edition_id_artefacts_artefact_id_get_with_http_info(edition_id, artefact_id, **kwargs) # noqa: E501
def v1_editions_edition_id_artefacts_artefact_id_get_with_http_info(self, edition_id, artefact_id, **kwargs): # noqa: E501
"""Provides a listing of all artefacts that are part of the specified edition # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.v1_editions_edition_id_artefacts_artefact_id_get_with_http_info(edition_id, artefact_id, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param int edition_id: Unique Id of the desired edition (required)
:param int artefact_id: Unique Id of the desired artefact (required)
:param list[str] optional: Add \"masks\" to include artefact polygons and \"images\" to include image data
:param _return_http_data_only: response data without head status code
and headers
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: tuple(ArtefactDTO, status_code(int), headers(HTTPHeaderDict))
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [
'edition_id',
'artefact_id',
'optional'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method v1_editions_edition_id_artefacts_artefact_id_get" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'edition_id' is set
if self.api_client.client_side_validation and ('edition_id' not in local_var_params or # noqa: E501
local_var_params['edition_id'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `edition_id` when calling `v1_editions_edition_id_artefacts_artefact_id_get`") # noqa: E501
# verify the required parameter 'artefact_id' is set
if self.api_client.client_side_validation and ('artefact_id' not in local_var_params or # noqa: E501
local_var_params['artefact_id'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `artefact_id` when calling `v1_editions_edition_id_artefacts_artefact_id_get`") # noqa: E501
collection_formats = {}
path_params = {}
if 'edition_id' in local_var_params:
path_params['editionId'] = local_var_params['edition_id'] # noqa: E501
if 'artefact_id' in local_var_params:
path_params['artefactId'] = local_var_params['artefact_id'] # noqa: E501
query_params = []
if 'optional' in local_var_params and local_var_params['optional'] is not None: # noqa: E501
query_params.append(('optional', local_var_params['optional'])) # noqa: E501
collection_formats['optional'] = 'multi' # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['text/plain', 'application/json', 'text/json']) # noqa: E501
# Authentication setting
auth_settings = ['Bearer'] # noqa: E501
return self.api_client.call_api(
'/v1/editions/{editionId}/artefacts/{artefactId}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ArtefactDTO', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def v1_editions_edition_id_artefacts_artefact_id_put(self, edition_id, artefact_id, **kwargs): # noqa: E501
"""Updates the specified artefact. There are many possible attributes that can be changed for an artefact. The caller should only input only those that should be changed. Attributes with a null value will be ignored. For instance, setting the mask to null or \"\" will result in no changes to the current mask, and no value for the mask will be returned (or broadcast). Likewise, the transformation, name, or status message may be set to null and no change will be made to those entities (though any unchanged values will be returned along with the changed values and also broadcast to co-editors). # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.v1_editions_edition_id_artefacts_artefact_id_put(edition_id, artefact_id, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param int edition_id: Unique Id of the desired edition (required)
:param int artefact_id: Unique Id of the desired artefact (required)
:param UpdateArtefactDTO update_artefact_dto: An UpdateArtefactDTO with the desired alterations to the artefact
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: ArtefactDTO
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
return self.v1_editions_edition_id_artefacts_artefact_id_put_with_http_info(edition_id, artefact_id, **kwargs) # noqa: E501
def v1_editions_edition_id_artefacts_artefact_id_put_with_http_info(self, edition_id, artefact_id, **kwargs): # noqa: E501
"""Updates the specified artefact. There are many possible attributes that can be changed for an artefact. The caller should only input only those that should be changed. Attributes with a null value will be ignored. For instance, setting the mask to null or \"\" will result in no changes to the current mask, and no value for the mask will be returned (or broadcast). Likewise, the transformation, name, or status message may be set to null and no change will be made to those entities (though any unchanged values will be returned along with the changed values and also broadcast to co-editors). # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.v1_editions_edition_id_artefacts_artefact_id_put_with_http_info(edition_id, artefact_id, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param int edition_id: Unique Id of the desired edition (required)
:param int artefact_id: Unique Id of the desired artefact (required)
:param UpdateArtefactDTO update_artefact_dto: An UpdateArtefactDTO with the desired alterations to the artefact
:param _return_http_data_only: response data without head status code
and headers
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: tuple(ArtefactDTO, status_code(int), headers(HTTPHeaderDict))
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [
'edition_id',
'artefact_id',
'update_artefact_dto'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method v1_editions_edition_id_artefacts_artefact_id_put" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'edition_id' is set
if self.api_client.client_side_validation and ('edition_id' not in local_var_params or # noqa: E501
local_var_params['edition_id'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `edition_id` when calling `v1_editions_edition_id_artefacts_artefact_id_put`") # noqa: E501
# verify the required parameter 'artefact_id' is set
if self.api_client.client_side_validation and ('artefact_id' not in local_var_params or # noqa: E501
local_var_params['artefact_id'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `artefact_id` when calling `v1_editions_edition_id_artefacts_artefact_id_put`") # noqa: E501
collection_formats = {}
path_params = {}
if 'edition_id' in local_var_params:
path_params['editionId'] = local_var_params['edition_id'] # noqa: E501
if 'artefact_id' in local_var_params:
path_params['artefactId'] = local_var_params['artefact_id'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'update_artefact_dto' in local_var_params:
body_params = local_var_params['update_artefact_dto']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['text/plain', 'application/json', 'text/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json', 'text/json', 'application/*+json']) # noqa: E501
# Authentication setting
auth_settings = ['Bearer'] # noqa: E501
return self.api_client.call_api(
'/v1/editions/{editionId}/artefacts/{artefactId}', 'PUT',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ArtefactDTO', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def v1_editions_edition_id_artefacts_artefact_id_rois_get(self, edition_id, artefact_id, **kwargs): # noqa: E501
"""Provides a listing of all rois belonging to an artefact in the specified edition # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.v1_editions_edition_id_artefacts_artefact_id_rois_get(edition_id, artefact_id, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param int edition_id: Unique Id of the desired edition (required)
:param int artefact_id: Unique Id of the desired artefact (required)
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: InterpretationRoiDTOList
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
return self.v1_editions_edition_id_artefacts_artefact_id_rois_get_with_http_info(edition_id, artefact_id, **kwargs) # noqa: E501
def v1_editions_edition_id_artefacts_artefact_id_rois_get_with_http_info(self, edition_id, artefact_id, **kwargs): # noqa: E501
"""Provides a listing of all rois belonging to an artefact in the specified edition # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.v1_editions_edition_id_artefacts_artefact_id_rois_get_with_http_info(edition_id, artefact_id, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param int edition_id: Unique Id of the desired edition (required)
:param int artefact_id: Unique Id of the desired artefact (required)
:param _return_http_data_only: response data without head status code
and headers
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: tuple(InterpretationRoiDTOList, status_code(int), headers(HTTPHeaderDict))
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [
'edition_id',
'artefact_id'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method v1_editions_edition_id_artefacts_artefact_id_rois_get" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'edition_id' is set
if self.api_client.client_side_validation and ('edition_id' not in local_var_params or # noqa: E501
local_var_params['edition_id'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `edition_id` when calling `v1_editions_edition_id_artefacts_artefact_id_rois_get`") # noqa: E501
# verify the required parameter 'artefact_id' is set
if self.api_client.client_side_validation and ('artefact_id' not in local_var_params or # noqa: E501
local_var_params['artefact_id'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `artefact_id` when calling `v1_editions_edition_id_artefacts_artefact_id_rois_get`") # noqa: E501
collection_formats = {}
path_params = {}
if 'edition_id' in local_var_params:
path_params['editionId'] = local_var_params['edition_id'] # noqa: E501
if 'artefact_id' in local_var_params:
path_params['artefactId'] = local_var_params['artefact_id'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['text/plain', 'application/json', 'text/json']) # noqa: E501
# Authentication setting
auth_settings = ['Bearer'] # noqa: E501
return self.api_client.call_api(
'/v1/editions/{editionId}/artefacts/{artefactId}/rois', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='InterpretationRoiDTOList', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def v1_editions_edition_id_artefacts_artefact_id_text_fragments_get(self, edition_id, artefact_id, **kwargs): # noqa: E501
"""Provides a listing of text fragments that have text in the specified artefact. With the optional query parameter \"suggested\", this endpoint will also return any text fragment that the system suggests might have text in the artefact. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.v1_editions_edition_id_artefacts_artefact_id_text_fragments_get(edition_id, artefact_id, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param int edition_id: Unique Id of the desired edition (required)
:param int artefact_id: Unique Id of the desired artefact (required)
:param list[str] optional: Add \"suggested\" to include possible matches suggested by the system
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: ArtefactTextFragmentMatchListDTO
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
return self.v1_editions_edition_id_artefacts_artefact_id_text_fragments_get_with_http_info(edition_id, artefact_id, **kwargs) # noqa: E501
def v1_editions_edition_id_artefacts_artefact_id_text_fragments_get_with_http_info(self, edition_id, artefact_id, **kwargs): # noqa: E501
"""Provides a listing of text fragments that have text in the specified artefact. With the optional query parameter \"suggested\", this endpoint will also return any text fragment that the system suggests might have text in the artefact. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.v1_editions_edition_id_artefacts_artefact_id_text_fragments_get_with_http_info(edition_id, artefact_id, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param int edition_id: Unique Id of the desired edition (required)
:param int artefact_id: Unique Id of the desired artefact (required)
:param list[str] optional: Add \"suggested\" to include possible matches suggested by the system
:param _return_http_data_only: response data without head status code
and headers
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: tuple(ArtefactTextFragmentMatchListDTO, status_code(int), headers(HTTPHeaderDict))
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [
'edition_id',
'artefact_id',
'optional'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method v1_editions_edition_id_artefacts_artefact_id_text_fragments_get" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'edition_id' is set
if self.api_client.client_side_validation and ('edition_id' not in local_var_params or # noqa: E501
local_var_params['edition_id'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `edition_id` when calling `v1_editions_edition_id_artefacts_artefact_id_text_fragments_get`") # noqa: E501
# verify the required parameter 'artefact_id' is set
if self.api_client.client_side_validation and ('artefact_id' not in local_var_params or # noqa: E501
local_var_params['artefact_id'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `artefact_id` when calling `v1_editions_edition_id_artefacts_artefact_id_text_fragments_get`") # noqa: E501
collection_formats = {}
path_params = {}
if 'edition_id' in local_var_params:
path_params['editionId'] = local_var_params['edition_id'] # noqa: E501
if 'artefact_id' in local_var_params:
path_params['artefactId'] = local_var_params['artefact_id'] # noqa: E501
query_params = []
if 'optional' in local_var_params and local_var_params['optional'] is not None: # noqa: E501
query_params.append(('optional', local_var_params['optional'])) # noqa: E501
collection_formats['optional'] = 'multi' # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['text/plain', 'application/json', 'text/json']) # noqa: E501
# Authentication setting
auth_settings = ['Bearer'] # noqa: E501
return self.api_client.call_api(
'/v1/editions/{editionId}/artefacts/{artefactId}/text-fragments', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ArtefactTextFragmentMatchListDTO', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def v1_editions_edition_id_artefacts_batch_transformation_post(self, edition_id, **kwargs): # noqa: E501
"""Updates the positional data for a batch of artefacts # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.v1_editions_edition_id_artefacts_batch_transformation_post(edition_id, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param int edition_id: Unique Id of the desired edition (required)
:param BatchUpdateArtefactPlacementDTO batch_update_artefact_placement_dto: A BatchUpdateArtefactTransformDTO with a list of the desired updates
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: BatchUpdatedArtefactTransformDTO
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
return self.v1_editions_edition_id_artefacts_batch_transformation_post_with_http_info(edition_id, **kwargs) # noqa: E501
def v1_editions_edition_id_artefacts_batch_transformation_post_with_http_info(self, edition_id, **kwargs): # noqa: E501
"""Updates the positional data for a batch of artefacts # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.v1_editions_edition_id_artefacts_batch_transformation_post_with_http_info(edition_id, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param int edition_id: Unique Id of the desired edition (required)
:param BatchUpdateArtefactPlacementDTO batch_update_artefact_placement_dto: A BatchUpdateArtefactTransformDTO with a list of the desired updates
:param _return_http_data_only: response data without head status code
and headers
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: tuple(BatchUpdatedArtefactTransformDTO, status_code(int), headers(HTTPHeaderDict))
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [
'edition_id',
'batch_update_artefact_placement_dto'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method v1_editions_edition_id_artefacts_batch_transformation_post" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'edition_id' is set
if self.api_client.client_side_validation and ('edition_id' not in local_var_params or # noqa: E501
local_var_params['edition_id'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `edition_id` when calling `v1_editions_edition_id_artefacts_batch_transformation_post`") # noqa: E501
collection_formats = {}
path_params = {}
if 'edition_id' in local_var_params:
path_params['editionId'] = local_var_params['edition_id'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'batch_update_artefact_placement_dto' in local_var_params:
body_params = local_var_params['batch_update_artefact_placement_dto']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['text/plain', 'application/json', 'text/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json', 'text/json', 'application/*+json']) # noqa: E501
# Authentication setting
auth_settings = ['Bearer'] # noqa: E501
return self.api_client.call_api(
'/v1/editions/{editionId}/artefacts/batch-transformation', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='BatchUpdatedArtefactTransformDTO', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def v1_editions_edition_id_artefacts_get(self, edition_id, **kwargs): # noqa: E501
"""Provides a listing of all artefacts that are part of the specified edition # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.v1_editions_edition_id_artefacts_get(edition_id, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param int edition_id: Unique Id of the desired edition (required)
:param list[str] optional: Add \"masks\" to include artefact polygons and \"images\" to include image data
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: ArtefactListDTO
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
return self.v1_editions_edition_id_artefacts_get_with_http_info(edition_id, **kwargs) # noqa: E501
def v1_editions_edition_id_artefacts_get_with_http_info(self, edition_id, **kwargs): # noqa: E501
"""Provides a listing of all artefacts that are part of the specified edition # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.v1_editions_edition_id_artefacts_get_with_http_info(edition_id, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param int edition_id: Unique Id of the desired edition (required)
:param list[str] optional: Add \"masks\" to include artefact polygons and \"images\" to include image data
:param _return_http_data_only: response data without head status code
and headers
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: tuple(ArtefactListDTO, status_code(int), headers(HTTPHeaderDict))
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [
'edition_id',
'optional'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method v1_editions_edition_id_artefacts_get" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'edition_id' is set
if self.api_client.client_side_validation and ('edition_id' not in local_var_params or # noqa: E501
local_var_params['edition_id'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `edition_id` when calling `v1_editions_edition_id_artefacts_get`") # noqa: E501
collection_formats = {}
path_params = {}
if 'edition_id' in local_var_params:
path_params['editionId'] = local_var_params['edition_id'] # noqa: E501
query_params = []
if 'optional' in local_var_params and local_var_params['optional'] is not None: # noqa: E501
query_params.append(('optional', local_var_params['optional'])) # noqa: E501
collection_formats['optional'] = 'multi' # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['text/plain', 'application/json', 'text/json']) # noqa: E501
# Authentication setting
auth_settings = ['Bearer'] # noqa: E501
return self.api_client.call_api(
'/v1/editions/{editionId}/artefacts', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ArtefactListDTO', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def v1_editions_edition_id_artefacts_post(self, edition_id, **kwargs): # noqa: E501
"""Creates a new artefact with the provided data. If no mask is provided, a placeholder mask will be created with the values: \"POLYGON((0 0,1 1,1 0,0 0))\" (the system requires a valid WKT polygon mask for every artefact). It is not recommended to leave the mask, name, or work status blank or null. It will often be advantageous to leave the transformation null when first creating a new artefact. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.v1_editions_edition_id_artefacts_post(edition_id, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param int edition_id: Unique Id of the desired edition (required)
:param CreateArtefactDTO create_artefact_dto: A CreateArtefactDTO with the data for the new artefact
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: ArtefactDTO
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
return self.v1_editions_edition_id_artefacts_post_with_http_info(edition_id, **kwargs) # noqa: E501
def v1_editions_edition_id_artefacts_post_with_http_info(self, edition_id, **kwargs): # noqa: E501
"""Creates a new artefact with the provided data. If no mask is provided, a placeholder mask will be created with the values: \"POLYGON((0 0,1 1,1 0,0 0))\" (the system requires a valid WKT polygon mask for every artefact). It is not recommended to leave the mask, name, or work status blank or null. It will often be advantageous to leave the transformation null when first creating a new artefact. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.v1_editions_edition_id_artefacts_post_with_http_info(edition_id, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param int edition_id: Unique Id of the desired edition (required)
:param CreateArtefactDTO create_artefact_dto: A CreateArtefactDTO with the data for the new artefact
:param _return_http_data_only: response data without head status code
and headers
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: tuple(ArtefactDTO, status_code(int), headers(HTTPHeaderDict))
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [
'edition_id',
'create_artefact_dto'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method v1_editions_edition_id_artefacts_post" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'edition_id' is set
if self.api_client.client_side_validation and ('edition_id' not in local_var_params or # noqa: E501
local_var_params['edition_id'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `edition_id` when calling `v1_editions_edition_id_artefacts_post`") # noqa: E501
collection_formats = {}
path_params = {}
if 'edition_id' in local_var_params:
path_params['editionId'] = local_var_params['edition_id'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'create_artefact_dto' in local_var_params:
body_params = local_var_params['create_artefact_dto']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['text/plain', 'application/json', 'text/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json', 'text/json', 'application/*+json']) # noqa: E501
# Authentication setting
auth_settings = ['Bearer'] # noqa: E501
return self.api_client.call_api(
'/v1/editions/{editionId}/artefacts', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ArtefactDTO', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
| 52.011692
| 630
| 0.626711
| 9,832
| 84,519
| 5.098251
| 0.030818
| 0.050453
| 0.057814
| 0.037525
| 0.979591
| 0.977916
| 0.977916
| 0.977856
| 0.977038
| 0.97632
| 0
| 0.014029
| 0.305044
| 84,519
| 1,624
| 631
| 52.043719
| 0.839369
| 0.453578
| 0
| 0.764411
| 1
| 0
| 0.225939
| 0.087398
| 0
| 0
| 0
| 0
| 0
| 1
| 0.033835
| false
| 0
| 0.006266
| 0
| 0.073935
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
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| 0
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| 1
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| 0
|
0
| 8
|
228021d0951c8b8e7085b3b756ecc0f4b546535b
| 22,065
|
py
|
Python
|
graphgym/contrib/layer/attconv.py
|
tigerneil/GraphGym
|
77f1e7acb4d08b6647b2cf1d147d86b736ac25e2
|
[
"MIT"
] | 7
|
2021-03-23T08:10:25.000Z
|
2022-01-10T05:51:38.000Z
|
graphgym/contrib/layer/attconv.py
|
batermj/GraphGym
|
05f749900ef07029a127dc36e74e43f4d0eb1a06
|
[
"MIT"
] | null | null | null |
graphgym/contrib/layer/attconv.py
|
batermj/GraphGym
|
05f749900ef07029a127dc36e74e43f4d0eb1a06
|
[
"MIT"
] | 2
|
2020-11-23T21:42:59.000Z
|
2021-03-10T11:43:27.000Z
|
import torch
import torch.nn as nn
from torch.nn import Parameter
import torch.nn.functional as F
from torch_scatter import scatter_add
from torch_geometric.nn.conv import MessagePassing
from torch_geometric.utils import add_remaining_self_loops, softmax
from torch_geometric.nn.inits import glorot, zeros
from graphgym.config import cfg
from graphgym.models.register import register_layer
class GeneralAddAttConvLayer(MessagePassing):
r"""General GNN layer, with add attention"""
def __init__(self, in_channels, out_channels,
improved=False, cached=False, bias=True, **kwargs):
super(GeneralAddAttConvLayer, self).__init__(aggr=cfg.gnn.agg, **kwargs)
self.heads = cfg.gnn.att_heads
self.in_channels = int(in_channels // self.heads * self.heads)
self.out_channels = int(out_channels // self.heads * self.heads)
self.improved = improved
self.cached = cached
self.normalize = cfg.gnn.normalize_adj
self.negative_slope = 0.2
self.head_channels = out_channels // self.heads
self.scaling = self.head_channels ** -0.5
self.linear_msg = nn.Linear(in_channels, out_channels, bias=False)
self.att = Parameter(
torch.Tensor(1, self.heads, 2 * self.head_channels))
if bias:
self.bias = Parameter(torch.Tensor(out_channels))
else:
self.register_parameter('bias', None)
self.reset_parameters()
def reset_parameters(self):
glorot(self.att)
zeros(self.bias)
self.cached_result = None
self.cached_num_edges = None
@staticmethod
def norm(edge_index, num_nodes, edge_weight=None, improved=False,
dtype=None):
if edge_weight is None:
edge_weight = torch.ones((edge_index.size(1),), dtype=dtype,
device=edge_index.device)
fill_value = 1 if not improved else 2
edge_index, edge_weight = add_remaining_self_loops(
edge_index, edge_weight, fill_value, num_nodes)
row, col = edge_index
deg = scatter_add(edge_weight, row, dim=0, dim_size=num_nodes)
deg_inv_sqrt = deg.pow(-0.5)
deg_inv_sqrt[deg_inv_sqrt == float('inf')] = 0
return edge_index, deg_inv_sqrt[row] * edge_weight * deg_inv_sqrt[col]
def forward(self, x, edge_index, edge_weight=None):
if self.cached and self.cached_result is not None:
if edge_index.size(1) != self.cached_num_edges:
raise RuntimeError(
'Cached {} number of edges, but found {}. Please '
'disable the caching behavior of this layer by removing '
'the `cached=True` argument in its constructor.'.format(
self.cached_num_edges, edge_index.size(1)))
if not self.cached or self.cached_result is None:
self.cached_num_edges = edge_index.size(1)
if self.normalize:
edge_index, norm = self.norm(edge_index, x.size(self.node_dim),
edge_weight, self.improved,
x.dtype)
else:
norm = edge_weight
self.cached_result = edge_index, norm
edge_index, norm = self.cached_result
x = self.linear_msg(x)
return self.propagate(edge_index, x=x, norm=norm)
def message(self, edge_index_i, x_i, x_j, norm, size_i):
x_i = x_i.view(-1, self.heads, self.head_channels)
x_j = x_j.view(-1, self.heads, self.head_channels)
alpha = (torch.cat([x_i, x_j], dim=-1) * self.att).sum(dim=-1)
alpha = F.leaky_relu(alpha, self.negative_slope)
alpha = softmax(alpha, edge_index_i, size_i)
alpha = alpha.view(-1, self.heads, 1)
return norm.view(-1,
1) * x_j * alpha if norm is not None else x_j * alpha
def update(self, aggr_out):
aggr_out = aggr_out.view(-1, self.out_channels)
if self.bias is not None:
aggr_out = aggr_out + self.bias
return aggr_out
def __repr__(self):
return '{}({}, {}, {})'.format(self.__class__.__name__,
self.in_channels,
self.out_channels, self.heads)
class GeneralMulAttConvLayer(MessagePassing):
r"""General GNN layer, with mul attention"""
def __init__(self, in_channels, out_channels,
improved=False, cached=False, bias=True, **kwargs):
super(GeneralMulAttConvLayer, self).__init__(aggr=cfg.gnn.agg, **kwargs)
self.heads = cfg.gnn.att_heads
self.in_channels = int(in_channels // self.heads * self.heads)
self.out_channels = int(out_channels // self.heads * self.heads)
self.improved = improved
self.cached = cached
self.normalize = cfg.gnn.normalize_adj
self.negative_slope = 0.2
self.head_channels = out_channels // self.heads
self.scaling = self.head_channels ** -0.5
self.linear_msg = nn.Linear(in_channels, out_channels, bias=False)
# todo: curently only for single head attention
# self.att = nn.Linear(out_channels, out_channels, bias=True)
self.bias_att = Parameter(torch.Tensor(out_channels))
self.scaler = torch.sqrt(torch.tensor(out_channels, dtype=torch.float))
if bias:
self.bias = Parameter(torch.Tensor(out_channels))
else:
self.register_parameter('bias', None)
self.reset_parameters()
def reset_parameters(self):
zeros(self.bias)
zeros(self.bias_att)
self.cached_result = None
self.cached_num_edges = None
@staticmethod
def norm(edge_index, num_nodes, edge_weight=None, improved=False,
dtype=None):
if edge_weight is None:
edge_weight = torch.ones((edge_index.size(1),), dtype=dtype,
device=edge_index.device)
fill_value = 1 if not improved else 2
edge_index, edge_weight = add_remaining_self_loops(
edge_index, edge_weight, fill_value, num_nodes)
row, col = edge_index
deg = scatter_add(edge_weight, row, dim=0, dim_size=num_nodes)
deg_inv_sqrt = deg.pow(-0.5)
deg_inv_sqrt[deg_inv_sqrt == float('inf')] = 0
return edge_index, deg_inv_sqrt[row] * edge_weight * deg_inv_sqrt[col]
def forward(self, x, edge_index, edge_weight=None):
if self.cached and self.cached_result is not None:
if edge_index.size(1) != self.cached_num_edges:
raise RuntimeError(
'Cached {} number of edges, but found {}. Please '
'disable the caching behavior of this layer by removing '
'the `cached=True` argument in its constructor.'.format(
self.cached_num_edges, edge_index.size(1)))
if not self.cached or self.cached_result is None:
self.cached_num_edges = edge_index.size(1)
if self.normalize:
edge_index, norm = self.norm(edge_index, x.size(self.node_dim),
edge_weight, self.improved,
x.dtype)
else:
norm = edge_weight
self.cached_result = edge_index, norm
edge_index, norm = self.cached_result
x = self.linear_msg(x)
return self.propagate(edge_index, x=x, norm=norm)
def message(self, edge_index_i, x_i, x_j, norm, size_i):
# todo: curently only for single head attention
x_i = x_i.view(-1, self.heads, self.head_channels)
x_j = x_j.view(-1, self.heads, self.head_channels)
alpha = (x_i * x_j + self.bias_att).sum(dim=-1) / self.scaler
# alpha = F.leaky_relu(alpha, self.negative_slope)
alpha = softmax(alpha, edge_index_i, size_i)
alpha = alpha.view(-1, self.heads, 1)
return norm.view(-1,
1) * x_j * alpha if norm is not None else x_j * alpha
def update(self, aggr_out):
aggr_out = aggr_out.view(-1, self.out_channels)
if self.bias is not None:
aggr_out = aggr_out + self.bias
return aggr_out
def __repr__(self):
return '{}({}, {}, {})'.format(self.__class__.__name__,
self.in_channels,
self.out_channels, self.heads)
class GeneralAddAttConv(nn.Module):
def __init__(self, dim_in, dim_out, bias=False, **kwargs):
super(GeneralAddAttConv, self).__init__()
self.model = GeneralAddAttConvLayer(dim_in, dim_out, bias=bias)
def forward(self, batch):
batch.node_feature = self.model(batch.node_feature, batch.edge_index)
return batch
class GeneralMulAttConv(nn.Module):
def __init__(self, dim_in, dim_out, bias=False, **kwargs):
super(GeneralMulAttConv, self).__init__()
self.model = GeneralMulAttConvLayer(dim_in, dim_out, bias=bias)
def forward(self, batch):
batch.node_feature = self.model(batch.node_feature, batch.edge_index)
return batch
register_layer('gaddconv', GeneralAddAttConv)
register_layer('gmulconv', GeneralMulAttConv)
class GeneralEdgeAttConvv1Layer(MessagePassing):
r"""Att conv with edge feature"""
def __init__(self, in_channels, out_channels, task_channels=None,
improved=False, cached=False, bias=True, **kwargs):
super(GeneralEdgeAttConvv1Layer, self).__init__(aggr=cfg.gnn.agg,
**kwargs)
self.heads = cfg.gnn.att_heads
self.in_channels = int(in_channels // self.heads * self.heads)
self.out_channels = int(out_channels // self.heads * self.heads)
self.task_channels = task_channels
self.improved = improved
self.cached = cached
self.normalize = cfg.gnn.normalize_adj
self.msg_direction = cfg.gnn.msg_direction
self.negative_slope = 0.2
self.head_channels = out_channels // self.heads
self.scaling = self.head_channels ** -0.5
if self.msg_direction == 'single':
self.linear_msg = nn.Linear(in_channels + cfg.dataset.edge_dim,
out_channels, bias=False)
else:
self.linear_msg = nn.Linear(in_channels * 2 + cfg.dataset.edge_dim,
out_channels, bias=False)
self.att_msg = Parameter(
torch.Tensor(1, self.heads, self.head_channels))
if self.task_channels is not None:
self.att_task = Parameter(
torch.Tensor(1, self.heads, self.task_channels))
if cfg.gnn.att_final_linear:
self.linear_final = nn.Linear(out_channels, out_channels,
bias=False)
if cfg.gnn.att_final_linear_bn:
self.linear_final_bn = nn.BatchNorm1d(out_channels, eps=cfg.bn.eps,
momentum=cfg.bn.mom)
if bias:
self.bias = Parameter(torch.Tensor(out_channels))
else:
self.register_parameter('bias', None)
self.reset_parameters()
def reset_parameters(self):
glorot(self.att_msg)
if self.task_channels is not None:
glorot(self.att_task)
zeros(self.bias)
self.cached_result = None
self.cached_num_edges = None
@staticmethod
def norm(edge_index, num_nodes, edge_weight=None, improved=False,
dtype=None):
if edge_weight is None:
edge_weight = torch.ones((edge_index.size(1),), dtype=dtype,
device=edge_index.device)
fill_value = 1 if not improved else 2
edge_index, edge_weight = add_remaining_self_loops(
edge_index, edge_weight, fill_value, num_nodes)
row, col = edge_index
deg = scatter_add(edge_weight, row, dim=0, dim_size=num_nodes)
deg_inv_sqrt = deg.pow(-0.5)
deg_inv_sqrt[deg_inv_sqrt == float('inf')] = 0
return edge_index, deg_inv_sqrt[row] * edge_weight * deg_inv_sqrt[col]
def forward(self, x, edge_index, edge_weight=None, edge_feature=None,
task_emb=None):
if self.cached and self.cached_result is not None:
if edge_index.size(1) != self.cached_num_edges:
raise RuntimeError(
'Cached {} number of edges, but found {}. Please '
'disable the caching behavior of this layer by removing '
'the `cached=True` argument in its constructor.'.format(
self.cached_num_edges, edge_index.size(1)))
if not self.cached or self.cached_result is None:
self.cached_num_edges = edge_index.size(1)
if self.normalize:
edge_index, norm = self.norm(edge_index, x.size(self.node_dim),
edge_weight, self.improved,
x.dtype)
else:
norm = edge_weight
self.cached_result = edge_index, norm
edge_index, norm = self.cached_result
return self.propagate(edge_index, x=x, norm=norm,
edge_feature=edge_feature, task_emb=task_emb)
def message(self, edge_index_i, x_i, x_j, norm, size_i, edge_feature,
task_emb):
if self.msg_direction == 'both':
x_j = torch.cat((x_i, x_j, edge_feature), dim=-1)
else:
x_j = torch.cat((x_j, edge_feature), dim=-1)
x_j = self.linear_msg(x_j)
x_j = x_j.view(-1, self.heads, self.head_channels)
if task_emb is not None:
task_emb = task_emb.view(1, 1, self.task_channels)
alpha = (x_j * self.att_msg).sum(-1) + (
task_emb * self.att_task).sum(-1)
else:
alpha = (x_j * self.att_msg).sum(-1)
alpha = F.leaky_relu(alpha, self.negative_slope)
alpha = softmax(alpha, edge_index_i, size_i)
alpha = alpha.view(-1, self.heads, 1)
return norm.view(-1,
1) * x_j * alpha if norm is not None else x_j * alpha
def update(self, aggr_out):
aggr_out = aggr_out.view(-1, self.out_channels)
if cfg.gnn.att_final_linear_bn:
aggr_out = self.linear_final_bn(aggr_out)
if cfg.gnn.att_final_linear:
aggr_out = self.linear_final(aggr_out)
if self.bias is not None:
aggr_out = aggr_out + self.bias
return aggr_out
def __repr__(self):
return '{}({}, {}, {})'.format(self.__class__.__name__,
self.in_channels,
self.out_channels, self.heads)
class GeneralEdgeAttConvv2Layer(MessagePassing):
r"""Att conv with edge feature v2"""
def __init__(self, in_channels, out_channels, task_channels=None,
improved=False, cached=False, bias=True, **kwargs):
super(GeneralEdgeAttConvv2Layer, self).__init__(aggr=cfg.gnn.agg,
**kwargs)
self.heads = cfg.gnn.att_heads
self.in_channels = int(in_channels // self.heads * self.heads)
self.out_channels = int(out_channels // self.heads * self.heads)
self.task_channels = task_channels
self.improved = improved
self.cached = cached
self.normalize = cfg.gnn.normalize_adj
self.msg_direction = cfg.gnn.msg_direction
self.negative_slope = 0.2
self.head_channels = out_channels // self.heads
self.scaling = self.head_channels ** -0.5
if self.msg_direction == 'single':
self.linear_value = nn.Linear(in_channels + cfg.dataset.edge_dim,
out_channels, bias=bias)
self.linear_key = nn.Linear(in_channels + cfg.dataset.edge_dim,
out_channels, bias=bias)
else:
self.linear_value = nn.Linear(
in_channels * 2 + cfg.dataset.edge_dim, out_channels, bias=bias)
self.linear_key = nn.Linear(in_channels * 2 + cfg.dataset.edge_dim,
out_channels, bias=bias)
self.att_msg = Parameter(
torch.Tensor(1, self.heads, self.head_channels))
if self.task_channels is not None:
self.att_task = Parameter(
torch.Tensor(1, self.heads, self.task_channels))
if cfg.gnn.att_final_linear:
self.linear_final = nn.Linear(out_channels, out_channels,
bias=False)
if cfg.gnn.att_final_linear_bn:
self.linear_final_bn = nn.BatchNorm1d(out_channels, eps=cfg.bn.eps,
momentum=cfg.bn.mom)
if bias:
self.bias = Parameter(torch.Tensor(out_channels))
else:
self.register_parameter('bias', None)
self.reset_parameters()
def reset_parameters(self):
glorot(self.att_msg)
if self.task_channels is not None:
glorot(self.att_task)
zeros(self.bias)
self.cached_result = None
self.cached_num_edges = None
@staticmethod
def norm(edge_index, num_nodes, edge_weight=None, improved=False,
dtype=None):
if edge_weight is None:
edge_weight = torch.ones((edge_index.size(1),), dtype=dtype,
device=edge_index.device)
fill_value = 1 if not improved else 2
edge_index, edge_weight = add_remaining_self_loops(
edge_index, edge_weight, fill_value, num_nodes)
row, col = edge_index
deg = scatter_add(edge_weight, row, dim=0, dim_size=num_nodes)
deg_inv_sqrt = deg.pow(-0.5)
deg_inv_sqrt[deg_inv_sqrt == float('inf')] = 0
return edge_index, deg_inv_sqrt[row] * edge_weight * deg_inv_sqrt[col]
def forward(self, x, edge_index, edge_weight=None, edge_feature=None,
task_emb=None):
if self.cached and self.cached_result is not None:
if edge_index.size(1) != self.cached_num_edges:
raise RuntimeError(
'Cached {} number of edges, but found {}. Please '
'disable the caching behavior of this layer by removing '
'the `cached=True` argument in its constructor.'.format(
self.cached_num_edges, edge_index.size(1)))
if not self.cached or self.cached_result is None:
self.cached_num_edges = edge_index.size(1)
if self.normalize:
edge_index, norm = self.norm(edge_index, x.size(self.node_dim),
edge_weight, self.improved,
x.dtype)
else:
norm = edge_weight
self.cached_result = edge_index, norm
edge_index, norm = self.cached_result
if self.msg_direction == 'both':
x = (x, x) # todo: check if expected
return self.propagate(edge_index, x=x, norm=norm,
edge_feature=edge_feature, task_emb=task_emb)
def message(self, edge_index_i, x_i, x_j, norm, size_i, edge_feature,
task_emb):
if self.msg_direction == 'both':
x_j = torch.cat((x_i, x_j, edge_feature), dim=-1)
else:
x_j = torch.cat((x_j, edge_feature), dim=-1)
x_j = self.linear_value(x_j)
x_j = x_j.view(-1, self.heads, self.head_channels)
if task_emb is not None:
task_emb = task_emb.view(1, 1, self.task_channels)
alpha = (x_j * self.att_msg).sum(-1) + (
task_emb * self.att_task).sum(-1)
else:
alpha = (x_j * self.att_msg).sum(-1)
alpha = F.leaky_relu(alpha, self.negative_slope)
alpha = softmax(alpha, edge_index_i, size_i)
alpha = alpha.view(-1, self.heads, 1)
return norm.view(-1,
1) * x_j * alpha if norm is not None else x_j * alpha
def update(self, aggr_out):
aggr_out = aggr_out.view(-1, self.out_channels)
if cfg.gnn.att_final_linear_bn:
aggr_out = self.linear_final_bn(aggr_out)
if cfg.gnn.att_final_linear:
aggr_out = self.linear_final(aggr_out)
if self.bias is not None:
aggr_out = aggr_out + self.bias
return aggr_out
def __repr__(self):
return '{}({}, {}, {})'.format(self.__class__.__name__,
self.in_channels,
self.out_channels, self.heads)
class GeneralEdgeAttConvv1(nn.Module):
def __init__(self, dim_in, dim_out, bias=False, **kwargs):
super(GeneralEdgeAttConvv1, self).__init__()
self.model = GeneralEdgeAttConvv1Layer(dim_in, dim_out, bias=bias)
def forward(self, batch):
batch.node_feature = self.model(batch.node_feature, batch.edge_index,
edge_feature=batch.edge_feature)
return batch
class GeneralEdgeAttConvv2(nn.Module):
def __init__(self, dim_in, dim_out, bias=False, **kwargs):
super(GeneralEdgeAttConvv2, self).__init__()
self.model = GeneralEdgeAttConvv2Layer(dim_in, dim_out, bias=bias)
def forward(self, batch):
batch.node_feature = self.model(batch.node_feature, batch.edge_index,
edge_feature=batch.edge_feature)
return batch
register_layer('generaledgeattconvv1', GeneralEdgeAttConvv1)
register_layer('generaledgeattconvv2', GeneralEdgeAttConvv2)
| 40.560662
| 80
| 0.591978
| 2,810
| 22,065
| 4.385053
| 0.059431
| 0.05551
| 0.031651
| 0.023373
| 0.898961
| 0.896445
| 0.885084
| 0.867229
| 0.866418
| 0.866418
| 0
| 0.008234
| 0.311987
| 22,065
| 543
| 81
| 40.635359
| 0.803439
| 0.016225
| 0
| 0.87067
| 0
| 0
| 0.035052
| 0
| 0
| 0
| 0
| 0.001842
| 0
| 1
| 0.083141
| false
| 0.011547
| 0.023095
| 0.009238
| 0.180139
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 1
| 0
| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
2296c77c1f6973b62f2ad39ff8e5401ea047bf3d
| 85
|
py
|
Python
|
cell_localization/flow/__init__.py
|
ver228/cell_localization
|
9739afc7a54f730056945c0a6380896747235099
|
[
"MIT"
] | 1
|
2021-08-10T08:24:23.000Z
|
2021-08-10T08:24:23.000Z
|
cell_localization/flow/__init__.py
|
ver228/cell_localization
|
9739afc7a54f730056945c0a6380896747235099
|
[
"MIT"
] | null | null | null |
cell_localization/flow/__init__.py
|
ver228/cell_localization
|
9739afc7a54f730056945c0a6380896747235099
|
[
"MIT"
] | null | null | null |
from .flow_coords import *
from .flow_masks import *
from .flow_segmentation import *
| 28.333333
| 32
| 0.8
| 12
| 85
| 5.416667
| 0.5
| 0.369231
| 0.430769
| 0
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| 0
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| 0
| 0
| 0
| 0.129412
| 85
| 3
| 32
| 28.333333
| 0.878378
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| true
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| null | 1
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| 0
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| 0
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| 0
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| 1
| 0
| 1
| 0
|
0
| 7
|
229c2bd1d4be22f31cc6d141740d6c62bbd32ef3
| 6,683
|
py
|
Python
|
loldib/getratings/models/NA/na_kindred/na_kindred_mid.py
|
koliupy/loldib
|
c9ab94deb07213cdc42b5a7c26467cdafaf81b7f
|
[
"Apache-2.0"
] | null | null | null |
loldib/getratings/models/NA/na_kindred/na_kindred_mid.py
|
koliupy/loldib
|
c9ab94deb07213cdc42b5a7c26467cdafaf81b7f
|
[
"Apache-2.0"
] | null | null | null |
loldib/getratings/models/NA/na_kindred/na_kindred_mid.py
|
koliupy/loldib
|
c9ab94deb07213cdc42b5a7c26467cdafaf81b7f
|
[
"Apache-2.0"
] | null | null | null |
from getratings.models.ratings import Ratings
class NA_Kindred_Mid_Aatrox(Ratings):
pass
class NA_Kindred_Mid_Ahri(Ratings):
pass
class NA_Kindred_Mid_Akali(Ratings):
pass
class NA_Kindred_Mid_Alistar(Ratings):
pass
class NA_Kindred_Mid_Amumu(Ratings):
pass
class NA_Kindred_Mid_Anivia(Ratings):
pass
class NA_Kindred_Mid_Annie(Ratings):
pass
class NA_Kindred_Mid_Ashe(Ratings):
pass
class NA_Kindred_Mid_AurelionSol(Ratings):
pass
class NA_Kindred_Mid_Azir(Ratings):
pass
class NA_Kindred_Mid_Bard(Ratings):
pass
class NA_Kindred_Mid_Blitzcrank(Ratings):
pass
class NA_Kindred_Mid_Brand(Ratings):
pass
class NA_Kindred_Mid_Braum(Ratings):
pass
class NA_Kindred_Mid_Caitlyn(Ratings):
pass
class NA_Kindred_Mid_Camille(Ratings):
pass
class NA_Kindred_Mid_Cassiopeia(Ratings):
pass
class NA_Kindred_Mid_Chogath(Ratings):
pass
class NA_Kindred_Mid_Corki(Ratings):
pass
class NA_Kindred_Mid_Darius(Ratings):
pass
class NA_Kindred_Mid_Diana(Ratings):
pass
class NA_Kindred_Mid_Draven(Ratings):
pass
class NA_Kindred_Mid_DrMundo(Ratings):
pass
class NA_Kindred_Mid_Ekko(Ratings):
pass
class NA_Kindred_Mid_Elise(Ratings):
pass
class NA_Kindred_Mid_Evelynn(Ratings):
pass
class NA_Kindred_Mid_Ezreal(Ratings):
pass
class NA_Kindred_Mid_Fiddlesticks(Ratings):
pass
class NA_Kindred_Mid_Fiora(Ratings):
pass
class NA_Kindred_Mid_Fizz(Ratings):
pass
class NA_Kindred_Mid_Galio(Ratings):
pass
class NA_Kindred_Mid_Gangplank(Ratings):
pass
class NA_Kindred_Mid_Garen(Ratings):
pass
class NA_Kindred_Mid_Gnar(Ratings):
pass
class NA_Kindred_Mid_Gragas(Ratings):
pass
class NA_Kindred_Mid_Graves(Ratings):
pass
class NA_Kindred_Mid_Hecarim(Ratings):
pass
class NA_Kindred_Mid_Heimerdinger(Ratings):
pass
class NA_Kindred_Mid_Illaoi(Ratings):
pass
class NA_Kindred_Mid_Irelia(Ratings):
pass
class NA_Kindred_Mid_Ivern(Ratings):
pass
class NA_Kindred_Mid_Janna(Ratings):
pass
class NA_Kindred_Mid_JarvanIV(Ratings):
pass
class NA_Kindred_Mid_Jax(Ratings):
pass
class NA_Kindred_Mid_Jayce(Ratings):
pass
class NA_Kindred_Mid_Jhin(Ratings):
pass
class NA_Kindred_Mid_Jinx(Ratings):
pass
class NA_Kindred_Mid_Kalista(Ratings):
pass
class NA_Kindred_Mid_Karma(Ratings):
pass
class NA_Kindred_Mid_Karthus(Ratings):
pass
class NA_Kindred_Mid_Kassadin(Ratings):
pass
class NA_Kindred_Mid_Katarina(Ratings):
pass
class NA_Kindred_Mid_Kayle(Ratings):
pass
class NA_Kindred_Mid_Kayn(Ratings):
pass
class NA_Kindred_Mid_Kennen(Ratings):
pass
class NA_Kindred_Mid_Khazix(Ratings):
pass
class NA_Kindred_Mid_Kindred(Ratings):
pass
class NA_Kindred_Mid_Kled(Ratings):
pass
class NA_Kindred_Mid_KogMaw(Ratings):
pass
class NA_Kindred_Mid_Leblanc(Ratings):
pass
class NA_Kindred_Mid_LeeSin(Ratings):
pass
class NA_Kindred_Mid_Leona(Ratings):
pass
class NA_Kindred_Mid_Lissandra(Ratings):
pass
class NA_Kindred_Mid_Lucian(Ratings):
pass
class NA_Kindred_Mid_Lulu(Ratings):
pass
class NA_Kindred_Mid_Lux(Ratings):
pass
class NA_Kindred_Mid_Malphite(Ratings):
pass
class NA_Kindred_Mid_Malzahar(Ratings):
pass
class NA_Kindred_Mid_Maokai(Ratings):
pass
class NA_Kindred_Mid_MasterYi(Ratings):
pass
class NA_Kindred_Mid_MissFortune(Ratings):
pass
class NA_Kindred_Mid_MonkeyKing(Ratings):
pass
class NA_Kindred_Mid_Mordekaiser(Ratings):
pass
class NA_Kindred_Mid_Morgana(Ratings):
pass
class NA_Kindred_Mid_Nami(Ratings):
pass
class NA_Kindred_Mid_Nasus(Ratings):
pass
class NA_Kindred_Mid_Nautilus(Ratings):
pass
class NA_Kindred_Mid_Nidalee(Ratings):
pass
class NA_Kindred_Mid_Nocturne(Ratings):
pass
class NA_Kindred_Mid_Nunu(Ratings):
pass
class NA_Kindred_Mid_Olaf(Ratings):
pass
class NA_Kindred_Mid_Orianna(Ratings):
pass
class NA_Kindred_Mid_Ornn(Ratings):
pass
class NA_Kindred_Mid_Pantheon(Ratings):
pass
class NA_Kindred_Mid_Poppy(Ratings):
pass
class NA_Kindred_Mid_Quinn(Ratings):
pass
class NA_Kindred_Mid_Rakan(Ratings):
pass
class NA_Kindred_Mid_Rammus(Ratings):
pass
class NA_Kindred_Mid_RekSai(Ratings):
pass
class NA_Kindred_Mid_Renekton(Ratings):
pass
class NA_Kindred_Mid_Rengar(Ratings):
pass
class NA_Kindred_Mid_Riven(Ratings):
pass
class NA_Kindred_Mid_Rumble(Ratings):
pass
class NA_Kindred_Mid_Ryze(Ratings):
pass
class NA_Kindred_Mid_Sejuani(Ratings):
pass
class NA_Kindred_Mid_Shaco(Ratings):
pass
class NA_Kindred_Mid_Shen(Ratings):
pass
class NA_Kindred_Mid_Shyvana(Ratings):
pass
class NA_Kindred_Mid_Singed(Ratings):
pass
class NA_Kindred_Mid_Sion(Ratings):
pass
class NA_Kindred_Mid_Sivir(Ratings):
pass
class NA_Kindred_Mid_Skarner(Ratings):
pass
class NA_Kindred_Mid_Sona(Ratings):
pass
class NA_Kindred_Mid_Soraka(Ratings):
pass
class NA_Kindred_Mid_Swain(Ratings):
pass
class NA_Kindred_Mid_Syndra(Ratings):
pass
class NA_Kindred_Mid_TahmKench(Ratings):
pass
class NA_Kindred_Mid_Taliyah(Ratings):
pass
class NA_Kindred_Mid_Talon(Ratings):
pass
class NA_Kindred_Mid_Taric(Ratings):
pass
class NA_Kindred_Mid_Teemo(Ratings):
pass
class NA_Kindred_Mid_Thresh(Ratings):
pass
class NA_Kindred_Mid_Tristana(Ratings):
pass
class NA_Kindred_Mid_Trundle(Ratings):
pass
class NA_Kindred_Mid_Tryndamere(Ratings):
pass
class NA_Kindred_Mid_TwistedFate(Ratings):
pass
class NA_Kindred_Mid_Twitch(Ratings):
pass
class NA_Kindred_Mid_Udyr(Ratings):
pass
class NA_Kindred_Mid_Urgot(Ratings):
pass
class NA_Kindred_Mid_Varus(Ratings):
pass
class NA_Kindred_Mid_Vayne(Ratings):
pass
class NA_Kindred_Mid_Veigar(Ratings):
pass
class NA_Kindred_Mid_Velkoz(Ratings):
pass
class NA_Kindred_Mid_Vi(Ratings):
pass
class NA_Kindred_Mid_Viktor(Ratings):
pass
class NA_Kindred_Mid_Vladimir(Ratings):
pass
class NA_Kindred_Mid_Volibear(Ratings):
pass
class NA_Kindred_Mid_Warwick(Ratings):
pass
class NA_Kindred_Mid_Xayah(Ratings):
pass
class NA_Kindred_Mid_Xerath(Ratings):
pass
class NA_Kindred_Mid_XinZhao(Ratings):
pass
class NA_Kindred_Mid_Yasuo(Ratings):
pass
class NA_Kindred_Mid_Yorick(Ratings):
pass
class NA_Kindred_Mid_Zac(Ratings):
pass
class NA_Kindred_Mid_Zed(Ratings):
pass
class NA_Kindred_Mid_Ziggs(Ratings):
pass
class NA_Kindred_Mid_Zilean(Ratings):
pass
class NA_Kindred_Mid_Zyra(Ratings):
pass
| 16.026379
| 46
| 0.77151
| 972
| 6,683
| 4.878601
| 0.151235
| 0.203712
| 0.407423
| 0.494728
| 0.808941
| 0.808941
| 0
| 0
| 0
| 0
| 0
| 0
| 0.166243
| 6,683
| 416
| 47
| 16.064904
| 0.851041
| 0
| 0
| 0.498195
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.498195
| 0.00361
| 0
| 0.501805
| 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
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 1
| 0
|
0
| 8
|
fe10f8e54ccbd193995588d898cdba8112cc7f09
| 15,366
|
py
|
Python
|
hailo_model_zoo/core/postprocessing/face_detection_postprocessing.py
|
markgrobman/hailo_model_zoo
|
2ea72272ed2debd7f6bee7c4a65bd41de57ec9cf
|
[
"MIT"
] | 2
|
2021-07-20T15:09:51.000Z
|
2021-11-17T11:05:02.000Z
|
hailo_model_zoo/core/postprocessing/face_detection_postprocessing.py
|
markgrobman/hailo_model_zoo
|
2ea72272ed2debd7f6bee7c4a65bd41de57ec9cf
|
[
"MIT"
] | null | null | null |
hailo_model_zoo/core/postprocessing/face_detection_postprocessing.py
|
markgrobman/hailo_model_zoo
|
2ea72272ed2debd7f6bee7c4a65bd41de57ec9cf
|
[
"MIT"
] | null | null | null |
from itertools import product
import numpy as np
import tensorflow as tf
from object_detection.core.post_processing import batch_multiclass_non_max_suppression
class FaceDetectionPostProc(object):
# The following params are corresponding to those used for training the model
LABEL_OFFSET = 1
NUM_CLASSES = 1
SCALE_FACTORS = (10., 5.)
def __init__(self, image_dims=(300, 300), nms_iou_thresh=0.6, score_threshold=0.3, anchors=None):
self._image_dims = image_dims
self._nms_iou_thresh = nms_iou_thresh
self._score_threshold = score_threshold
self._num_branches = len(anchors['steps'])
if anchors is None:
raise ValueError('Missing detection anchors metadata')
self._anchors = self.extract_anchors(anchors['min_sizes'], anchors['steps'])
def collect_box_class_predictions(self, output_branches):
box_predictors_list = []
class_predictors_list = []
landmarks_predictors_list = []
sorted_output_branches = output_branches
num_branches = self._num_branches
assert len(sorted_output_branches) % num_branches == 0, "All branches must have the same number of output nodes"
num_output_nodes_per_branch = len(sorted_output_branches) // num_branches
for branch_index in range(0, len(sorted_output_branches), num_output_nodes_per_branch):
num_of_batches, _, _, _ = tf.unstack(tf.shape(sorted_output_branches[branch_index]))
box_predictors_list.append(tf.reshape(sorted_output_branches[branch_index], shape=[num_of_batches, -1, 4]))
class_predictors_list.append(tf.reshape(sorted_output_branches[branch_index + 1],
shape=[num_of_batches, -1, self.NUM_CLASSES + 1]))
if num_output_nodes_per_branch > 2:
# Assume output is landmarks
landmarks_predictors_list.append(tf.reshape(sorted_output_branches[branch_index + 2],
shape=[num_of_batches, -1, 10]))
box_predictors = tf.concat(box_predictors_list, axis=1)
class_predictors = tf.concat(class_predictors_list, axis=1)
landmarks_predictors = tf.concat(landmarks_predictors_list, axis=1) if landmarks_predictors_list else None
return box_predictors, class_predictors, landmarks_predictors
def extract_anchors(self, min_sizes, steps):
feature_maps = [[int(np.ceil(self._image_dims[0] / step)), int(np.ceil(self._image_dims[1] / step))] for step in
steps]
anchors = []
for feature_map_index, feature_map in enumerate(feature_maps):
current_min_sizes = min_sizes[feature_map_index]
for i, j in product(range(feature_map[0]), range(feature_map[1])):
for min_size in current_min_sizes:
s_kx = min_size / self._image_dims[1]
s_ky = min_size / self._image_dims[0]
cx = (j + 0.5) / feature_map[1]
cy = (i + 0.5) / feature_map[0]
anchor = np.clip(np.array([cx, cy, s_kx, s_ky], dtype=np.float32), 0.0, 1.0)
anchors.append(tf.convert_to_tensor(anchor))
anchors_tensor = tf.convert_to_tensor(anchors, name='Anchors')
return anchors_tensor
def _decode_landmarks(self, landmarks_detections, anchors):
return tf.concat((anchors[:, :2] + landmarks_detections[:, :2] / self.SCALE_FACTORS[0] * anchors[:, 2:],
anchors[:, :2] + landmarks_detections[:, 2:4] / self.SCALE_FACTORS[0] * anchors[:, 2:],
anchors[:, :2] + landmarks_detections[:, 4:6] / self.SCALE_FACTORS[0] * anchors[:, 2:],
anchors[:, :2] + landmarks_detections[:, 6:8] / self.SCALE_FACTORS[0] * anchors[:, 2:],
anchors[:, :2] + landmarks_detections[:, 8:10] / self.SCALE_FACTORS[0] * anchors[:, 2:],
), axis=1)
def _decode_boxes(self, box_detections, anchors):
boxes = tf.concat((
anchors[:, :2] + box_detections[:, :2] / self.SCALE_FACTORS[0] * anchors[:, 2:],
anchors[:, 2:] * tf.exp(box_detections[:, 2:] / self.SCALE_FACTORS[1])), 1)
boxes_low_dims = boxes[:, :2] - boxes[:, 2:] / 2
boxes_high_dims = boxes[:, 2:] + boxes_low_dims
new_boxes = tf.concat((boxes_low_dims, boxes_high_dims), axis=1)
return new_boxes
def tf_postproc(self, endnodes):
with tf.name_scope('Postprocessor'):
box_predictions, classes_predictions, landmarks_predictors = self.collect_box_class_predictions(endnodes)
additional_fields = {}
classes_predictions_softmax = tf.nn.softmax(classes_predictions, axis=2)
# Slicing Background class score
detection_scores = tf.slice(classes_predictions_softmax, [0, 0, 1], [-1, -1, -1])
batch_size, num_proposals = tf.unstack(tf.slice(tf.shape(box_predictions), [0], [2]))
tiled_anchor_boxes = tf.tile(tf.expand_dims(self._anchors, 0), [batch_size, 1, 1])
tiled_anchors_boxlist = tf.reshape(tiled_anchor_boxes, [-1, 4])
decoded_boxes = self._decode_boxes(tf.reshape(box_predictions, (-1, 4)), tiled_anchors_boxlist)
detection_boxes = tf.reshape(decoded_boxes, [batch_size, num_proposals, 4])
decoded_landmarks = None
if tf.is_tensor(landmarks_predictors):
decoded_landmarks = self._decode_landmarks(tf.reshape(landmarks_predictors, (-1, 10)),
tiled_anchors_boxlist)
decoded_landmarks = tf.reshape(decoded_landmarks, [batch_size, num_proposals, 10])
additional_fields['landmarks'] = decoded_landmarks
detection_boxes = tf.identity(tf.expand_dims(detection_boxes, axis=[2]), 'raw_box_locations')
(nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_masks, nmsed_additional_fields, num_detections) = \
batch_multiclass_non_max_suppression(boxes=detection_boxes, scores=detection_scores,
score_thresh=self._score_threshold,
iou_thresh=self._nms_iou_thresh,
additional_fields=additional_fields,
max_size_per_class=1000, max_total_size=1000)
# adding offset to the class prediction and cast to integer
nmsed_classes = tf.cast(tf.add(nmsed_classes, self.LABEL_OFFSET), tf.int16)
results = {'detection_boxes': nmsed_boxes,
'detection_scores': nmsed_scores,
'detection_classes': nmsed_classes,
'num_detections': num_detections, }
face_landmarks = nmsed_additional_fields.get('landmarks')
if tf.is_tensor(face_landmarks):
results['face_landmarks'] = face_landmarks
return results
class LibFaceDetectionPostProc(object):
# The following params are corresponding to those used for training the model
LABEL_OFFSET = 1
NUM_CLASSES = 1
SCALE_FACTORS = (10., 5.)
def __init__(self, image_dims=(300, 300), nms_iou_thresh=0.6, score_threshold=0.3, anchors=None):
self._image_dims = image_dims
self._nms_iou_thresh = nms_iou_thresh
self._score_threshold = score_threshold
self._num_branches = len(anchors['steps'])
self.num_anchors = [len(x) for x in anchors['min_sizes']]
if anchors is None:
raise ValueError('Missing detection anchors metadata')
self._anchors = self.extract_anchors(anchors['min_sizes'], anchors['steps'])
def collect_box_class_predictions(self, output_branches):
boxlandmark_predictors_list = []
class_predictors_list = []
iou_predictors_list = []
sorted_output_branches = output_branches
num_branches = self._num_branches
assert len(sorted_output_branches) % num_branches == 0, "All branches must have the same number of output nodes"
num_output_nodes_per_branch = len(sorted_output_branches) // num_branches
for branch_index in range(0, len(sorted_output_branches), num_output_nodes_per_branch):
num_of_batches, _, _, _ = tf.unstack(tf.shape(sorted_output_branches[branch_index]))
boxlandmarks_predictor = sorted_output_branches[branch_index]
boxlandmarks_predictor = tf.reshape(boxlandmarks_predictor, shape=[num_of_batches, -1, 14])
boxlandmark_predictors_list.append(boxlandmarks_predictor)
class_predictors_list.append(tf.reshape(sorted_output_branches[branch_index + 1],
shape=[num_of_batches, -1, self.NUM_CLASSES + 1]))
iou_predictors_list.append(tf.reshape(
sorted_output_branches[branch_index + 2], shape=[num_of_batches, -1, 1]))
boxlandmarks_predictors = tf.concat(boxlandmark_predictors_list, axis=1)
box_predictors = tf.slice(boxlandmarks_predictors, [0, 0, 0], [-1, -1, 4])
landmarks_predictors = tf.slice(boxlandmarks_predictors, [0, 0, 4], [-1, -1, -1])
class_predictors = tf.concat(class_predictors_list, axis=1)
iou_predictors = tf.concat(iou_predictors_list, axis=1)
return box_predictors, class_predictors, landmarks_predictors, iou_predictors
def extract_anchors(self, min_sizes, steps):
feature_maps = [[int(np.floor(self._image_dims[0] / step)),
int(np.floor(self._image_dims[1] / step))] for step in steps]
anchors = []
for feature_map_index, feature_map in enumerate(feature_maps):
current_min_sizes = min_sizes[feature_map_index]
for i, j in product(range(feature_map[0]), range(feature_map[1])):
for min_size in current_min_sizes:
s_kx = min_size / self._image_dims[1]
s_ky = min_size / self._image_dims[0]
cx = (j + 0.5) * steps[feature_map_index] / self._image_dims[1]
cy = (i + 0.5) * steps[feature_map_index] / self._image_dims[0]
anchor = np.clip(np.array([cx, cy, s_kx, s_ky], dtype=np.float32), 0.0, 1.0)
anchors.append(tf.convert_to_tensor(anchor))
anchors_tensor = tf.convert_to_tensor(anchors, name='Anchors')
return anchors_tensor
def _decode_landmarks(self, landmarks_detections, anchors):
return tf.concat((anchors[:, :2] + landmarks_detections[:, :2] / self.SCALE_FACTORS[0] * anchors[:, 2:],
anchors[:, :2] + landmarks_detections[:, 2:4] / self.SCALE_FACTORS[0] * anchors[:, 2:],
anchors[:, :2] + landmarks_detections[:, 4:6] / self.SCALE_FACTORS[0] * anchors[:, 2:],
anchors[:, :2] + landmarks_detections[:, 6:8] / self.SCALE_FACTORS[0] * anchors[:, 2:],
anchors[:, :2] + landmarks_detections[:, 8:10] / self.SCALE_FACTORS[0] * anchors[:, 2:],
), axis=1)
def _decode_boxes(self, box_detections, anchors):
boxes = tf.concat((
anchors[:, :2] + box_detections[:, :2] / self.SCALE_FACTORS[0] * anchors[:, 2:],
anchors[:, 2:] * tf.exp(box_detections[:, 2:] / self.SCALE_FACTORS[1])), 1)
boxes_low_dims = boxes[:, :2] - boxes[:, 2:] / 2
boxes_high_dims = boxes[:, 2:] + boxes_low_dims
new_boxes = tf.concat((boxes_low_dims, boxes_high_dims), axis=1)
return new_boxes
def tf_postproc(self, endnodes):
with tf.name_scope('Postprocessor'):
(box_predictions, classes_predictions,
landmarks_predictors, iou_predictors) = self.collect_box_class_predictions(endnodes)
additional_fields = {}
classes_predictions_softmax = tf.nn.softmax(classes_predictions, axis=2)
# Slicing Background class score
raw_detection_scores = tf.slice(classes_predictions_softmax, [0, 0, 1], [-1, -1, -1])
detection_scores = tf.sqrt(raw_detection_scores * tf.clip_by_value(iou_predictors, 0.0, 1.0))
batch_size, num_proposals = tf.unstack(tf.slice(tf.shape(box_predictions), [0], [2]))
tiled_anchor_boxes = tf.tile(tf.expand_dims(self._anchors, 0), [batch_size, 1, 1])
tiled_anchors_boxlist = tf.reshape(tiled_anchor_boxes, [-1, 4])
decoded_boxes = self._decode_boxes(tf.reshape(box_predictions, (-1, 4)), tiled_anchors_boxlist)
detection_boxes = tf.reshape(decoded_boxes, [batch_size, num_proposals, 4])
decoded_landmarks = None
if tf.is_tensor(landmarks_predictors):
decoded_landmarks = self._decode_landmarks(tf.reshape(landmarks_predictors, (-1, 10)),
tiled_anchors_boxlist)
decoded_landmarks = tf.reshape(decoded_landmarks, [batch_size, num_proposals, 10])
additional_fields['landmarks'] = decoded_landmarks
detection_boxes = tf.identity(tf.expand_dims(detection_boxes, axis=[2]), 'raw_box_locations')
(nmsed_boxes, nmsed_scores, nmsed_classes, nmsed_masks, nmsed_additional_fields, num_detections) = \
batch_multiclass_non_max_suppression(boxes=detection_boxes, scores=detection_scores,
score_thresh=self._score_threshold,
iou_thresh=self._nms_iou_thresh,
additional_fields=additional_fields,
max_size_per_class=1000, max_total_size=1000)
# adding offset to the class prediction and cast to integer
nmsed_classes = tf.cast(tf.add(nmsed_classes, self.LABEL_OFFSET), tf.int16)
results = {'raw_detection_scores': raw_detection_scores,
'iou_scores': iou_predictors,
'detection_boxes': nmsed_boxes,
'detection_scores': nmsed_scores,
'detection_classes': nmsed_classes,
'num_detections': num_detections, }
face_landmarks = nmsed_additional_fields.get('landmarks')
if tf.is_tensor(face_landmarks):
results['face_landmarks'] = face_landmarks
return results
META_ARCH_TO_CLASS = {
"libfacedetection": LibFaceDetectionPostProc,
"retinaface": FaceDetectionPostProc,
}
DEFAULT_CLASS = FaceDetectionPostProc
def face_detection_postprocessing(endnodes, device_pre_post_layers=None, **kwargs):
meta_arch = kwargs.get('meta_arch', None)
if meta_arch:
postproc_class = META_ARCH_TO_CLASS[meta_arch]
else:
postproc_class = DEFAULT_CLASS
postproc = postproc_class(image_dims=kwargs['img_dims'],
nms_iou_thresh=kwargs['nms_iou_thresh'],
score_threshold=kwargs['score_threshold'],
anchors=kwargs['anchors'])
return {'predictions': postproc.tf_postproc(endnodes)}
| 55.876364
| 120
| 0.626643
| 1,810
| 15,366
| 4.976243
| 0.105525
| 0.023093
| 0.035528
| 0.022649
| 0.864439
| 0.845898
| 0.843899
| 0.810925
| 0.810925
| 0.803375
| 0
| 0.022743
| 0.270337
| 15,366
| 274
| 121
| 56.080292
| 0.780592
| 0.023168
| 0
| 0.718182
| 0
| 0
| 0.040328
| 0
| 0
| 0
| 0
| 0
| 0.009091
| 1
| 0.059091
| false
| 0
| 0.018182
| 0.009091
| 0.163636
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
4aa73436eecf2e8f0772839f3f1f0b2b62316c9c
| 97
|
py
|
Python
|
6 kyu/Sum of many ints.py
|
mwk0408/codewars_solutions
|
9b4f502b5f159e68024d494e19a96a226acad5e5
|
[
"MIT"
] | 6
|
2020-09-03T09:32:25.000Z
|
2020-12-07T04:10:01.000Z
|
6 kyu/Sum of many ints.py
|
mwk0408/codewars_solutions
|
9b4f502b5f159e68024d494e19a96a226acad5e5
|
[
"MIT"
] | 1
|
2021-12-13T15:30:21.000Z
|
2021-12-13T15:30:21.000Z
|
6 kyu/Sum of many ints.py
|
mwk0408/codewars_solutions
|
9b4f502b5f159e68024d494e19a96a226acad5e5
|
[
"MIT"
] | null | null | null |
def f(n, m):
if n<m:
return n*(n+1)/2
return (n//m)*(m*(m-1)/2)+(n%m)*((n%m)+1)/2
| 24.25
| 47
| 0.391753
| 25
| 97
| 1.52
| 0.32
| 0.263158
| 0.157895
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.082192
| 0.247423
| 97
| 4
| 47
| 24.25
| 0.438356
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0
| 0
| 0.75
| 0
| 1
| 0
| 1
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 7
|
43b43d1f2a8921f0dad5ccf9c5dfb8d157d5c3b6
| 2,560
|
py
|
Python
|
tests/test_haystack_about.py
|
flzara/shaystack
|
6bf815f25f3a5d64494ec1c4a34a7b23ea0ad4ce
|
[
"BSD-2-Clause"
] | 9
|
2021-04-30T13:04:31.000Z
|
2022-01-11T14:11:53.000Z
|
tests/test_haystack_about.py
|
flzara/shaystack
|
6bf815f25f3a5d64494ec1c4a34a7b23ea0ad4ce
|
[
"BSD-2-Clause"
] | 7
|
2021-03-19T07:31:22.000Z
|
2021-03-26T12:31:45.000Z
|
tests/test_haystack_about.py
|
flzara/shaystack
|
6bf815f25f3a5d64494ec1c4a34a7b23ea0ad4ce
|
[
"BSD-2-Clause"
] | 5
|
2021-04-29T11:51:04.000Z
|
2022-02-22T21:10:19.000Z
|
from unittest.mock import patch
import shaystack
from shaystack import Grid
from shaystack.ops import HaystackHttpRequest
from shaystack.providers import ping
@patch('shaystack.providers.haystack_interface.no_cache')
@patch.object(ping.Provider, 'about')
def test_about_with_zinc(mock, no_cache) -> None:
# GIVEN
"""
Args:
mock:
no_cache:
"""
envs = {'HAYSTACK_PROVIDER': 'shaystack.providers.ping'}
no_cache.return_value = True
mock.return_value = ping._PingGrid
mime_type = shaystack.MODE_ZINC
request = HaystackHttpRequest()
request.headers["Content-Type"] = mime_type
request.headers["Accept"] = mime_type
# WHEN
response = shaystack.about(envs, request, "dev")
# THEN
mock.assert_called_once_with("https://localhost/dev")
assert response.status_code == 200
assert response.headers["Content-Type"].startswith(mime_type)
assert shaystack.parse(response.body, mime_type) is not None
@patch('shaystack.providers.haystack_interface.no_cache')
@patch.object(ping.Provider, 'about')
def test_about_without_headers(mock, no_cache) -> None:
# GIVEN
"""
Args:
mock:
no_cache:
"""
envs = {'HAYSTACK_PROVIDER': 'shaystack.providers.ping'}
no_cache.return_value = True
mock.return_value = Grid(columns=["a"])
mock.return_value.append({"a": 1})
mime_type = shaystack.MODE_CSV
request = HaystackHttpRequest()
# WHEN
response = shaystack.about(envs, request, "dev")
# THEN
mock.assert_called_once_with("https://localhost/dev")
assert response.status_code == 200
assert response.headers["Content-Type"].startswith(mime_type)
assert shaystack.parse(response.body, mime_type) is not None
@patch('shaystack.providers.haystack_interface.no_cache')
@patch.object(ping.Provider, 'about')
def test_about_with_multivalues_headers(mock, no_cache) -> None:
# GIVEN
"""
Args:
mock:
no_cache:
"""
envs = {'HAYSTACK_PROVIDER': 'shaystack.providers.ping'}
no_cache.return_value = True
mock.return_value = ping._PingGrid
mime_type = shaystack.MODE_ZINC
request = HaystackHttpRequest()
request.headers["Accept"] = "text/zinc, application/json"
# WHEN
response = shaystack.about(envs, request, "dev")
# THEN
mock.assert_called_once_with("https://localhost/dev")
assert response.status_code == 200
assert response.headers["Content-Type"].startswith(mime_type)
assert shaystack.parse(response.body, mime_type) is not None
| 29.767442
| 65
| 0.701563
| 311
| 2,560
| 5.572347
| 0.205788
| 0.048471
| 0.038084
| 0.053664
| 0.814772
| 0.814772
| 0.814772
| 0.814772
| 0.814772
| 0.814772
| 0
| 0.004757
| 0.178906
| 2,560
| 85
| 66
| 30.117647
| 0.819696
| 0.053906
| 0
| 0.708333
| 0
| 0
| 0.187713
| 0.09087
| 0
| 0
| 0
| 0
| 0.25
| 1
| 0.0625
| false
| 0
| 0.104167
| 0
| 0.166667
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
43d739258789bb5484797081bea11e7ebc8fb906
| 2,401
|
py
|
Python
|
fasttext_train.py
|
littletiger0712/nlp_adversarial_examples
|
aef4066a6a8d028ba4ae91a50700b8c937c08a14
|
[
"MIT"
] | 2
|
2020-04-20T04:14:43.000Z
|
2020-09-18T02:51:43.000Z
|
fasttext_train.py
|
littletiger0712/nlp_adversarial_examples
|
aef4066a6a8d028ba4ae91a50700b8c937c08a14
|
[
"MIT"
] | null | null | null |
fasttext_train.py
|
littletiger0712/nlp_adversarial_examples
|
aef4066a6a8d028ba4ae91a50700b8c937c08a14
|
[
"MIT"
] | null | null | null |
import fasttext
import pickle
import numpy as np
max_len = 250
MAX_VOCAB_SIZE = 50000
# from keras.preprocessing.sequence import pad_sequences
# with open(('aux_files/dataset_%d.pkl' %MAX_VOCAB_SIZE), 'rb') as f:
# dataset = pickle.load(f)
# train_x = pad_sequences(dataset.train_seqs2, maxlen=max_len, padding='post')
# train_y = np.array(dataset.train_y)
# print(len(train_x))
# print(train_x[0])
# print(len(train_y))
# print(train_y[0])
# model = fasttext.train_supervised(input="cail_0518/data_train_fasttext_num.txt")
# # model = fasttext.load_model("cail_0518/fasttext_model.bin")
# print(model.test('cail_0518/data_test_fasttext_num.txt'))
# print(model.predict(['16205 28415 59178 1403 24193 50131 3314 37462 6109 47819 58669 39413 18053 30370 46951 32242 9379 58162 57301 60090 13542 48641 39776 11752 30370 3398 54928 25104 40561 12299 46458 58162 33464 52480 38295 55139 1403 3314 25607 25608 26933 37462 55430 37553 58162 32159 17167 57459 23487 58162 19024 9023 13167 19024 23183 18053 43640 10924 1814 15023 1403 11005 25287 49733 1403 56170 55171 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0']))
# model.save_model("cail_0518/fasttext_model.bin")
model = fasttext.load_model("cail_0518/fasttext_model.bin")
print(model.test('cail_0518/data_test_fasttext_num.txt'))
print(model.predict(['16205 28415 59178 1403 24193 50131 3314 37462 6109 47819 58669 39413 18053 30370 46951 32242 9379 58162 57301 60090 13542 48641 39776 11752 30370 3398 54928 25104 40561 12299 46458 58162 33464 52480 38295 55139 1403 3314 25607 25608 26933 37462 55430 37553 58162 32159 17167 57459 23487 58162 19024 9023 13167 19024 23183 18053 43640 10924 1814 15023 1403 11005 25287 49733 1403 56170 55171 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0'],k=20))
| 92.346154
| 787
| 0.702207
| 640
| 2,401
| 2.571875
| 0.16875
| 0.442284
| 0.659781
| 0.874848
| 0.76367
| 0.76367
| 0.746051
| 0.746051
| 0.746051
| 0.746051
| 0
| 0.576206
| 0.240317
| 2,401
| 26
| 787
| 92.346154
| 0.326206
| 0.573095
| 0
| 0
| 0
| 0.125
| 0.811881
| 0.063366
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.375
| 0
| 0.375
| 0.25
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 12
|
43dd08798f71fc1e8a0d3c36bdeb6c94d03cd85b
| 12,328
|
py
|
Python
|
mars/dataframe/merge/tests/test_merge.py
|
hxri/mars
|
f7864f00911883b94800b63856f0e57648d3d9b4
|
[
"Apache-2.0"
] | 1
|
2021-09-03T18:52:06.000Z
|
2021-09-03T18:52:06.000Z
|
mars/dataframe/merge/tests/test_merge.py
|
hxri/mars
|
f7864f00911883b94800b63856f0e57648d3d9b4
|
[
"Apache-2.0"
] | null | null | null |
mars/dataframe/merge/tests/test_merge.py
|
hxri/mars
|
f7864f00911883b94800b63856f0e57648d3d9b4
|
[
"Apache-2.0"
] | null | null | null |
# Copyright 1999-2021 Alibaba Group Holding Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import pandas as pd
from mars.core import tile
from mars.core.operand import OperandStage
from mars.dataframe.core import IndexValue
from mars.dataframe.base.standardize_range_index import ChunkStandardizeRangeIndex
from mars.dataframe.datasource.dataframe import from_pandas
from mars.dataframe.merge import DataFrameMergeAlign, DataFrameShuffleMerge, concat
def test_merge():
df1 = pd.DataFrame(np.arange(20).reshape((4, 5)) + 1, columns=['a', 'b', 'c', 'd', 'e'])
df2 = pd.DataFrame(np.arange(20).reshape((5, 4)) + 1, columns=['a', 'b', 'x', 'y'])
mdf1 = from_pandas(df1, chunk_size=2)
mdf2 = from_pandas(df2, chunk_size=3)
parameters = [
{},
{'how': 'left', 'right_on': 'x', 'left_index': True},
{'how': 'right', 'left_on': 'a', 'right_index': True},
{'how': 'left', 'left_on': 'a', 'right_on': 'x'},
{'how': 'right', 'left_on': 'a', 'right_index': True},
{'how': 'right', 'on': 'a'},
{'how': 'inner', 'on': ['a', 'b']},
]
for kw in parameters:
df = mdf1.merge(mdf2, **kw)
df = tile(df)
assert df.chunk_shape == (2, 1)
for chunk in df.chunks:
assert isinstance(chunk.op, DataFrameShuffleMerge)
assert chunk.op.how == kw.get('how', 'inner')
left, right = chunk.op.inputs
assert isinstance(left.op, DataFrameMergeAlign)
assert left.op.stage == OperandStage.reduce
assert isinstance(right.op, DataFrameMergeAlign)
assert right.op.stage == OperandStage.reduce
assert len(left.inputs[0].inputs) == 2
assert len(right.inputs[0].inputs) == 2
for lchunk in left.inputs[0].inputs:
assert isinstance(lchunk.op, DataFrameMergeAlign)
assert lchunk.op.stage == OperandStage.map
assert lchunk.op.index_shuffle_size == 2
assert lchunk.op.shuffle_on == kw.get('on', None) or kw.get('left_on', None)
for rchunk in right.inputs[0].inputs:
assert isinstance(rchunk.op, DataFrameMergeAlign)
assert rchunk.op.stage == OperandStage.map
assert rchunk.op.index_shuffle_size == 2
assert rchunk.op.shuffle_on == kw.get('on', None) or kw.get('right_on', None)
pd.testing.assert_index_equal(chunk.columns_value.to_pandas(), df.columns_value.to_pandas())
def test_join():
df1 = pd.DataFrame([[1, 3, 3], [4, 2, 6], [7, 8, 9]], index=['a1', 'a2', 'a3'])
df2 = pd.DataFrame([[1, 2, 3], [1, 5, 6], [7, 8, 9]], index=['a1', 'b2', 'b3']) + 1
df2 = pd.concat([df2, df2 + 1])
mdf1 = from_pandas(df1, chunk_size=2)
mdf2 = from_pandas(df2, chunk_size=2)
parameters = [
{'lsuffix': 'l_', 'rsuffix': 'r_'},
{'lsuffix': 'l_', 'rsuffix': 'r_', 'how': 'left'},
{'lsuffix': 'l_', 'rsuffix': 'r_', 'how': 'right'},
{'lsuffix': 'l_', 'rsuffix': 'r_', 'how': 'inner'},
{'lsuffix': 'l_', 'rsuffix': 'r_', 'how': 'left'},
]
for kw in parameters:
df = mdf1.join(mdf2, **kw)
df = tile(df)
assert df.chunk_shape == (3, 1)
for chunk in df.chunks:
assert isinstance(chunk.op, DataFrameShuffleMerge)
assert chunk.op.how == kw.get('how', 'left')
left, right = chunk.op.inputs
assert isinstance(left.op, DataFrameMergeAlign)
assert left.op.stage == OperandStage.reduce
assert isinstance(right.op, DataFrameMergeAlign)
assert right.op.stage == OperandStage.reduce
assert len(left.inputs[0].inputs) == 2
assert len(right.inputs[0].inputs) == 3
for lchunk in left.inputs[0].inputs:
assert isinstance(lchunk.op, DataFrameMergeAlign)
assert lchunk.op.stage == OperandStage.map
assert lchunk.op.index_shuffle_size == 3
assert lchunk.op.shuffle_on == None
for rchunk in right.inputs[0].inputs:
assert isinstance(rchunk.op, DataFrameMergeAlign)
assert rchunk.op.stage == OperandStage.map
assert rchunk.op.index_shuffle_size == 3
assert rchunk.op.shuffle_on == None
pd.testing.assert_index_equal(chunk.columns_value.to_pandas(), df.columns_value.to_pandas())
def test_join_on():
df1 = pd.DataFrame([[1, 3, 3], [4, 2, 6], [7, 8, 9]], columns=['a1', 'a2', 'a3'])
df2 = pd.DataFrame([[1, 2, 3], [1, 5, 6], [7, 8, 9]], columns=['a1', 'b2', 'b3']) + 1
df2 = pd.concat([df2, df2 + 1])
mdf1 = from_pandas(df1, chunk_size=2)
mdf2 = from_pandas(df2, chunk_size=2)
parameters = [
{'lsuffix': 'l_', 'rsuffix': 'r_'},
{'lsuffix': 'l_', 'rsuffix': 'r_', 'how': 'left', 'on': 'a1'},
{'lsuffix': 'l_', 'rsuffix': 'r_', 'how': 'right', 'on': 'a2'},
{'lsuffix': 'l_', 'rsuffix': 'r_', 'how': 'inner', 'on': 'a2'},
{'lsuffix': 'l_', 'rsuffix': 'r_', 'how': 'outer', 'on': 'a2'},
]
for kw in parameters:
df = mdf1.join(mdf2, **kw)
df = tile(df)
assert df.chunk_shape == (3, 1)
for chunk in df.chunks:
assert isinstance(chunk.op, DataFrameShuffleMerge)
assert chunk.op.how == kw.get('how', 'left')
left, right = chunk.op.inputs
assert isinstance(left.op, DataFrameMergeAlign)
assert left.op.stage == OperandStage.reduce
assert isinstance(right.op, DataFrameMergeAlign)
assert right.op.stage == OperandStage.reduce
assert len(left.inputs[0].inputs) == 2
assert len(right.inputs[0].inputs) == 3
for lchunk in left.inputs[0].inputs:
assert isinstance(lchunk.op, DataFrameMergeAlign)
assert lchunk.op.stage == OperandStage.map
assert lchunk.op.index_shuffle_size == 3
assert lchunk.op.shuffle_on == kw.get('on', None)
for rchunk in right.inputs[0].inputs:
assert isinstance(rchunk.op, DataFrameMergeAlign)
assert rchunk.op.stage == OperandStage.map
assert rchunk.op.index_shuffle_size == 3
assert rchunk.op.shuffle_on == None
pd.testing.assert_index_equal(chunk.columns_value.to_pandas(), df.columns_value.to_pandas())
def test_merge_one_chunk():
df1 = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],
'value': [1, 2, 3, 5]})
df2 = pd.DataFrame({'rkey': ['foo', 'bar', 'baz', 'foo'],
'value': [5, 6, 7, 8]})
# all have one chunk
mdf1 = from_pandas(df1)
mdf2 = from_pandas(df2)
df = mdf1.merge(mdf2, left_on='lkey', right_on='rkey')
tiled, tiled1, tiled2 = tile(df, mdf1, mdf2)
assert tiled.chunk_shape == (1, 1)
assert tiled.chunks[0].inputs[0].key == tiled1.chunks[0].key
assert tiled.chunks[0].inputs[1].key == tiled2.chunks[0].key
# left has one chunk
mdf1 = from_pandas(df1)
mdf2 = from_pandas(df2, chunk_size=2)
df = mdf1.merge(mdf2, left_on='lkey', right_on='rkey')
tiled, tiled1, tiled2 = tile(df, mdf1, mdf2)
assert tiled.chunk_shape == (2, 1)
assert tiled.chunks[0].inputs[0].key == tiled1.chunks[0].key
assert tiled.chunks[0].inputs[1].key == tiled2.chunks[0].key
assert tiled.chunks[1].inputs[0].key == tiled1.chunks[0].key
assert tiled.chunks[1].inputs[1].key == tiled2.chunks[1].key
# right has one chunk
mdf1 = from_pandas(df1, chunk_size=2)
mdf2 = from_pandas(df2)
df = mdf1.merge(mdf2, left_on='lkey', right_on='rkey')
tiled, tiled1, tiled2 = tile(df, mdf1, mdf2)
assert tiled.chunk_shape == (2, 1)
assert tiled.chunks[0].inputs[0].key == tiled1.chunks[0].key
assert tiled.chunks[0].inputs[1].key == tiled2.chunks[0].key
assert tiled.chunks[1].inputs[0].key == tiled1.chunks[1].key
assert tiled.chunks[1].inputs[1].key == tiled2.chunks[0].key
def test_append():
df1 = pd.DataFrame(np.random.rand(10, 4), columns=list('ABCD'))
df2 = pd.DataFrame(np.random.rand(10, 4), columns=list('ABCD'))
mdf1 = from_pandas(df1, chunk_size=3)
mdf2 = from_pandas(df2, chunk_size=3)
adf = mdf1.append(mdf2)
assert adf.shape == (20, 4)
assert isinstance(adf.index_value.value, IndexValue.Int64Index)
tiled = tile(adf)
assert tiled.nsplits == ((3, 3, 3, 1, 3, 3, 3, 1), (3, 1))
assert tiled.chunk_shape == (8, 2)
for i, c in enumerate(tiled.chunks):
index = (i // 2, i % 2)
assert c.index == index
mdf1 = from_pandas(df1, chunk_size=3)
mdf2 = from_pandas(df2, chunk_size=3)
adf = mdf1.append(mdf2, ignore_index=True)
assert adf.shape == (20, 4)
assert isinstance(adf.index_value.value, IndexValue.RangeIndex)
pd.testing.assert_index_equal(adf.index_value.to_pandas(), pd.RangeIndex(20))
tiled = tile(adf)
assert tiled.nsplits == ((3, 3, 3, 1, 3, 3, 3, 1), (3, 1))
assert tiled.chunk_shape == (8, 2)
assert isinstance(tiled.chunks[0].op, ChunkStandardizeRangeIndex)
def test_concat():
df1 = pd.DataFrame(np.random.rand(10, 4), columns=list('ABCD'))
df2 = pd.DataFrame(np.random.rand(10, 4), columns=list('ABCD'))
mdf1 = from_pandas(df1, chunk_size=4)
mdf2 = from_pandas(df2, chunk_size=4)
r = concat([mdf1, mdf2], axis='index')
assert r.shape == (20, 4)
pd.testing.assert_series_equal(r.dtypes, df1.dtypes)
tiled = tile(r)
assert tiled.nsplits == ((4, 4, 2, 4, 4, 2), (4,))
for i, c in enumerate(tiled.chunks):
assert c.index == (i, 0)
df3 = pd.DataFrame(np.random.rand(10, 4), columns=list('ABCD'),
index=pd.RangeIndex(10, 20))
mdf3 = from_pandas(df3, chunk_size=4)
r = concat([mdf1, mdf3], axis='index')
assert r.shape == (20, 4)
pd.testing.assert_series_equal(r.dtypes, df1.dtypes)
pd.testing.assert_index_equal(r.index_value.to_pandas(), pd.RangeIndex(20))
df4 = pd.DataFrame(np.random.rand(10, 4), columns=list('ABCD'),
index=np.random.permutation(np.arange(10)))
mdf4 = from_pandas(df4, chunk_size=4)
r = concat([mdf1, mdf4], axis='index')
assert r.shape == (20, 4)
pd.testing.assert_series_equal(r.dtypes, df1.dtypes)
pd.testing.assert_index_equal(r.index_value.to_pandas(), pd.Index([], dtype=np.int64))
r = concat([mdf4, mdf1], axis='index')
assert r.shape == (20, 4)
pd.testing.assert_series_equal(r.dtypes, df1.dtypes)
pd.testing.assert_index_equal(r.index_value.to_pandas(), pd.Index([], dtype=np.int64))
r = concat([mdf4, mdf4], axis='index')
assert r.shape == (20, 4)
pd.testing.assert_series_equal(r.dtypes, df1.dtypes)
pd.testing.assert_index_equal(r.index_value.to_pandas(), pd.Index([], dtype=np.int64))
mdf1 = from_pandas(df1, chunk_size=3)
mdf2 = from_pandas(df2, chunk_size=4)
r = concat([mdf1, mdf2], axis='columns')
assert r.shape == (10, 8)
expected_dtypes = pd.concat([df1, df2], axis='columns').dtypes
pd.testing.assert_series_equal(r.dtypes, expected_dtypes)
tiled = tile(r)
assert tiled.nsplits == ((3, 3, 3, 1), (3, 1, 4))
for i, c in enumerate(tiled.chunks):
index = (i // 3, i % 3)
assert c.index == index
df1 = pd.DataFrame(np.random.rand(10, 4), columns=list('ABCD'))
df2 = pd.DataFrame(np.random.rand(10, 3), columns=list('ABC'))
mdf1 = from_pandas(df1, chunk_size=3)
mdf2 = from_pandas(df2, chunk_size=3)
r = concat([mdf1, mdf2], join='inner')
assert r.shape == (20, 3)
tiled = tile(r)
assert tiled.nsplits == ((3, 3, 3, 1, 3, 3, 3, 1), (3, ))
| 40.686469
| 104
| 0.605451
| 1,721
| 12,328
| 4.23649
| 0.110401
| 0.034289
| 0.028803
| 0.025648
| 0.804691
| 0.795776
| 0.760938
| 0.735427
| 0.73145
| 0.706899
| 0
| 0.044786
| 0.233858
| 12,328
| 302
| 105
| 40.821192
| 0.727157
| 0.05086
| 0
| 0.630435
| 0
| 0
| 0.051438
| 0
| 0
| 0
| 0
| 0
| 0.434783
| 1
| 0.026087
| false
| 0
| 0.034783
| 0
| 0.06087
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
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0
| 7
|
78f1907a4198aea9ab39a65e80d1c03972f4f316
| 334,570
|
py
|
Python
|
decision_tree_classifier.py
|
vdnew/Loan-Prediction
|
c5412551162a67ec1c37763ec5486ed7e2807883
|
[
"MIT"
] | null | null | null |
decision_tree_classifier.py
|
vdnew/Loan-Prediction
|
c5412551162a67ec1c37763ec5486ed7e2807883
|
[
"MIT"
] | null | null | null |
decision_tree_classifier.py
|
vdnew/Loan-Prediction
|
c5412551162a67ec1c37763ec5486ed7e2807883
|
[
"MIT"
] | null | null | null |
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Decision_Tree_Classifier.ipynb",
"provenance": [],
"authorship_tag": "ABX9TyOqPdVAAXzl4lXr4JfWfMvw",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/vdnew/Loan-Prediction/blob/main/decision_tree_classifier.py\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Na26TjViRPZX"
},
"source": [
"import numpy as np \r\n",
"import pandas as pd\r\n",
"import matplotlib.pyplot as plt\r\n",
"%matplotlib inline\r\n",
"import seaborn as sns\r\n",
"sns.set(style=\"white\", color_codes=True)"
],
"execution_count": 1,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "yzqolgTlRbVu"
},
"source": [
"path = '/content/train_loanprediction1.csv'\r\n",
"train = pd.read_csv(path)"
],
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 243
},
"id": "Yeg8HPIxRmot",
"outputId": "376ebc4d-c67c-4d18-dd6a-69bc6c849be4"
},
"source": [
"train.head()"
],
"execution_count": 3,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Loan_ID</th>\n",
" <th>Gender</th>\n",
" <th>Married</th>\n",
" <th>Dependents</th>\n",
" <th>Education</th>\n",
" <th>Self_Employed</th>\n",
" <th>ApplicantIncome</th>\n",
" <th>CoapplicantIncome</th>\n",
" <th>LoanAmount</th>\n",
" <th>Loan_Amount_Term</th>\n",
" <th>Credit_History</th>\n",
" <th>Property_Area</th>\n",
" <th>Loan_Status</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>LP001002</td>\n",
" <td>Male</td>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" <td>Graduate</td>\n",
" <td>No</td>\n",
" <td>5849</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Urban</td>\n",
" <td>Y</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>LP001003</td>\n",
" <td>Male</td>\n",
" <td>Yes</td>\n",
" <td>1</td>\n",
" <td>Graduate</td>\n",
" <td>No</td>\n",
" <td>4583</td>\n",
" <td>1508.0</td>\n",
" <td>128.0</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Rural</td>\n",
" <td>N</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>LP001005</td>\n",
" <td>Male</td>\n",
" <td>Yes</td>\n",
" <td>0</td>\n",
" <td>Graduate</td>\n",
" <td>Yes</td>\n",
" <td>3000</td>\n",
" <td>0.0</td>\n",
" <td>66.0</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Urban</td>\n",
" <td>Y</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>LP001006</td>\n",
" <td>Male</td>\n",
" <td>Yes</td>\n",
" <td>0</td>\n",
" <td>Not Graduate</td>\n",
" <td>No</td>\n",
" <td>2583</td>\n",
" <td>2358.0</td>\n",
" <td>120.0</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Urban</td>\n",
" <td>Y</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>LP001008</td>\n",
" <td>Male</td>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" <td>Graduate</td>\n",
" <td>No</td>\n",
" <td>6000</td>\n",
" <td>0.0</td>\n",
" <td>141.0</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Urban</td>\n",
" <td>Y</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Loan_ID Gender Married ... Credit_History Property_Area Loan_Status\n",
"0 LP001002 Male No ... 1.0 Urban Y\n",
"1 LP001003 Male Yes ... 1.0 Rural N\n",
"2 LP001005 Male Yes ... 1.0 Urban Y\n",
"3 LP001006 Male Yes ... 1.0 Urban Y\n",
"4 LP001008 Male No ... 1.0 Urban Y\n",
"\n",
"[5 rows x 13 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 3
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 300
},
"id": "86WeiKkvR06K",
"outputId": "9503f505-9757-4c5a-fad4-07f8ae91a061"
},
"source": [
"train.describe()"
],
"execution_count": 4,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ApplicantIncome</th>\n",
" <th>CoapplicantIncome</th>\n",
" <th>LoanAmount</th>\n",
" <th>Loan_Amount_Term</th>\n",
" <th>Credit_History</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>614.000000</td>\n",
" <td>614.000000</td>\n",
" <td>592.000000</td>\n",
" <td>600.00000</td>\n",
" <td>564.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>5403.459283</td>\n",
" <td>1621.245798</td>\n",
" <td>146.412162</td>\n",
" <td>342.00000</td>\n",
" <td>0.842199</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>6109.041673</td>\n",
" <td>2926.248369</td>\n",
" <td>85.587325</td>\n",
" <td>65.12041</td>\n",
" <td>0.364878</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>150.000000</td>\n",
" <td>0.000000</td>\n",
" <td>9.000000</td>\n",
" <td>12.00000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2877.500000</td>\n",
" <td>0.000000</td>\n",
" <td>100.000000</td>\n",
" <td>360.00000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>3812.500000</td>\n",
" <td>1188.500000</td>\n",
" <td>128.000000</td>\n",
" <td>360.00000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>5795.000000</td>\n",
" <td>2297.250000</td>\n",
" <td>168.000000</td>\n",
" <td>360.00000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>81000.000000</td>\n",
" <td>41667.000000</td>\n",
" <td>700.000000</td>\n",
" <td>480.00000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ApplicantIncome CoapplicantIncome ... Loan_Amount_Term Credit_History\n",
"count 614.000000 614.000000 ... 600.00000 564.000000\n",
"mean 5403.459283 1621.245798 ... 342.00000 0.842199\n",
"std 6109.041673 2926.248369 ... 65.12041 0.364878\n",
"min 150.000000 0.000000 ... 12.00000 0.000000\n",
"25% 2877.500000 0.000000 ... 360.00000 1.000000\n",
"50% 3812.500000 1188.500000 ... 360.00000 1.000000\n",
"75% 5795.000000 2297.250000 ... 360.00000 1.000000\n",
"max 81000.000000 41667.000000 ... 480.00000 1.000000\n",
"\n",
"[8 rows x 5 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 4
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "PVpK8eVjR4gq",
"outputId": "d7018436-2b34-4d50-e1d7-fff771e069cd"
},
"source": [
"train.info()"
],
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 614 entries, 0 to 613\n",
"Data columns (total 13 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Loan_ID 614 non-null object \n",
" 1 Gender 601 non-null object \n",
" 2 Married 611 non-null object \n",
" 3 Dependents 599 non-null object \n",
" 4 Education 614 non-null object \n",
" 5 Self_Employed 582 non-null object \n",
" 6 ApplicantIncome 614 non-null int64 \n",
" 7 CoapplicantIncome 614 non-null float64\n",
" 8 LoanAmount 592 non-null float64\n",
" 9 Loan_Amount_Term 600 non-null float64\n",
" 10 Credit_History 564 non-null float64\n",
" 11 Property_Area 614 non-null object \n",
" 12 Loan_Status 614 non-null object \n",
"dtypes: float64(4), int64(1), object(8)\n",
"memory usage: 62.5+ KB\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "rTsp2i_hR8fz",
"outputId": "d3feecae-ea2e-43fa-ab8b-7d34724890da"
},
"source": [
"train.shape"
],
"execution_count": 6,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(614, 13)"
]
},
"metadata": {
"tags": []
},
"execution_count": 6
}
]
},
{
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"colab": {
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},
"id": "aKSjOkN_R_5O",
"outputId": "fb9a3b9c-9dc1-4b41-d1a5-2cd9df921649"
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"source": [
"train.isnull().any()"
],
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"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Loan_ID False\n",
"Gender True\n",
"Married True\n",
"Dependents True\n",
"Education False\n",
"Self_Employed True\n",
"ApplicantIncome False\n",
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"Credit_History True\n",
"Property_Area False\n",
"Loan_Status False\n",
"dtype: bool"
]
},
"metadata": {
"tags": []
},
"execution_count": 7
}
]
},
{
"cell_type": "code",
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},
"id": "gqPDfHTzSCgx",
"outputId": "1acde3be-5d70-4f90-d3dc-0bf821e01e58"
},
"source": [
"train.isnull().sum()"
],
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Loan_ID 0\n",
"Gender 13\n",
"Married 3\n",
"Dependents 15\n",
"Education 0\n",
"Self_Employed 32\n",
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"Credit_History 50\n",
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"dtype: int64"
]
},
"metadata": {
"tags": []
},
"execution_count": 8
}
]
},
{
"cell_type": "code",
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},
"id": "mt1dlpYASG6M",
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},
"source": [
"train[['Gender']].info()"
],
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 614 entries, 0 to 613\n",
"Data columns (total 1 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Gender 601 non-null object\n",
"dtypes: object(1)\n",
"memory usage: 4.9+ KB\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 418
},
"id": "3dWwRVQCSLjj",
"outputId": "7e13066c-7982-4950-f454-10130e2b5b23"
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"source": [
"train.head(10)"
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"outputs": [
{
"output_type": "execute_result",
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" <td>4196.0</td>\n",
" <td>267.0</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
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],
"text/plain": [
" Loan_ID Gender Married ... Credit_History Property_Area Loan_Status\n",
"0 LP001002 Male No ... 1.0 Urban Y\n",
"1 LP001003 Male Yes ... 1.0 Rural N\n",
"2 LP001005 Male Yes ... 1.0 Urban Y\n",
"3 LP001006 Male Yes ... 1.0 Urban Y\n",
"4 LP001008 Male No ... 1.0 Urban Y\n",
"5 LP001011 Male Yes ... 1.0 Urban Y\n",
"6 LP001013 Male Yes ... 1.0 Urban Y\n",
"7 LP001014 Male Yes ... 0.0 Semiurban N\n",
"8 LP001018 Male Yes ... 1.0 Urban Y\n",
"9 LP001020 Male Yes ... 1.0 Semiurban N\n",
"\n",
"[10 rows x 13 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 10
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "9uP9wl0fSO14",
"outputId": "9ae83045-0632-48ba-f50d-3f12cd6377c7"
},
"source": [
"train['Property_Area'].unique()"
],
"execution_count": 11,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array(['Urban', 'Rural', 'Semiurban'], dtype=object)"
]
},
"metadata": {
"tags": []
},
"execution_count": 11
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "5Hh0ng8BSUKv",
"outputId": "9931a638-42f0-4def-c8de-1e3bf2a1fa41"
},
"source": [
"train['Property_Area'].value_counts()"
],
"execution_count": 12,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Semiurban 233\n",
"Urban 202\n",
"Rural 179\n",
"Name: Property_Area, dtype: int64"
]
},
"metadata": {
"tags": []
},
"execution_count": 12
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "BbpsOKtZSZUu",
"outputId": "a0c0451f-76f7-4c25-c623-34213e9010b6"
},
"source": [
"train_loan = train.dropna()\r\n",
"train_loan.info()"
],
"execution_count": 13,
"outputs": [
{
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 480 entries, 1 to 613\n",
"Data columns (total 13 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Loan_ID 480 non-null object \n",
" 1 Gender 480 non-null object \n",
" 2 Married 480 non-null object \n",
" 3 Dependents 480 non-null object \n",
" 4 Education 480 non-null object \n",
" 5 Self_Employed 480 non-null object \n",
" 6 ApplicantIncome 480 non-null int64 \n",
" 7 CoapplicantIncome 480 non-null float64\n",
" 8 LoanAmount 480 non-null float64\n",
" 9 Loan_Amount_Term 480 non-null float64\n",
" 10 Credit_History 480 non-null float64\n",
" 11 Property_Area 480 non-null object \n",
" 12 Loan_Status 480 non-null object \n",
"dtypes: float64(4), int64(1), object(8)\n",
"memory usage: 52.5+ KB\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "w_wFvFLHSgVe",
"outputId": "d9ce299e-9669-4139-9d63-8cefde4d96b2"
},
"source": [
"train.info()"
],
"execution_count": 14,
"outputs": [
{
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 614 entries, 0 to 613\n",
"Data columns (total 13 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Loan_ID 614 non-null object \n",
" 1 Gender 601 non-null object \n",
" 2 Married 611 non-null object \n",
" 3 Dependents 599 non-null object \n",
" 4 Education 614 non-null object \n",
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"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YM92dB2yS43D"
},
"source": [
"<h1> Data Preprocessing </h1>"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "6EoOOB7OS4Gy",
"outputId": "78599266-f5fd-4c39-a84b-70b58718297b"
},
"source": [
"train['Dependents'].fillna(1,inplace=True)\r\n",
"train.info()"
],
"execution_count": 15,
"outputs": [
{
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 614 entries, 0 to 613\n",
"Data columns (total 13 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Loan_ID 614 non-null object \n",
" 1 Gender 601 non-null object \n",
" 2 Married 611 non-null object \n",
" 3 Dependents 614 non-null object \n",
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" 9 Loan_Amount_Term 600 non-null float64\n",
" 10 Credit_History 564 non-null float64\n",
" 11 Property_Area 614 non-null object \n",
" 12 Loan_Status 614 non-null object \n",
"dtypes: float64(4), int64(1), object(8)\n",
"memory usage: 62.5+ KB\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ZMs9ccaWTEBS",
"outputId": "3f1e072e-78f5-4537-befc-41cc4eaf44d6"
},
"source": [
"train['LoanAmount'].fillna(train.LoanAmount.mean(),inplace=True)\r\n",
"train.info()"
],
"execution_count": 16,
"outputs": [
{
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 614 entries, 0 to 613\n",
"Data columns (total 13 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Loan_ID 614 non-null object \n",
" 1 Gender 601 non-null object \n",
" 2 Married 611 non-null object \n",
" 3 Dependents 614 non-null object \n",
" 4 Education 614 non-null object \n",
" 5 Self_Employed 582 non-null object \n",
" 6 ApplicantIncome 614 non-null int64 \n",
" 7 CoapplicantIncome 614 non-null float64\n",
" 8 LoanAmount 614 non-null float64\n",
" 9 Loan_Amount_Term 600 non-null float64\n",
" 10 Credit_History 564 non-null float64\n",
" 11 Property_Area 614 non-null object \n",
" 12 Loan_Status 614 non-null object \n",
"dtypes: float64(4), int64(1), object(8)\n",
"memory usage: 62.5+ KB\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 418
},
"id": "fbHNAyHlTL_N",
"outputId": "bf96b4bd-d6f0-4432-bcdc-832cb3f42eba"
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"source": [
"train.head(10)"
],
"execution_count": 17,
"outputs": [
{
"output_type": "execute_result",
"data": {
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" <td>Graduate</td>\n",
" <td>No</td>\n",
" <td>6000</td>\n",
" <td>0.0</td>\n",
" <td>141.000000</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Urban</td>\n",
" <td>Y</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>LP001011</td>\n",
" <td>Male</td>\n",
" <td>Yes</td>\n",
" <td>2</td>\n",
" <td>Graduate</td>\n",
" <td>Yes</td>\n",
" <td>5417</td>\n",
" <td>4196.0</td>\n",
" <td>267.000000</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Urban</td>\n",
" <td>Y</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>LP001013</td>\n",
" <td>Male</td>\n",
" <td>Yes</td>\n",
" <td>0</td>\n",
" <td>Not Graduate</td>\n",
" <td>No</td>\n",
" <td>2333</td>\n",
" <td>1516.0</td>\n",
" <td>95.000000</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Urban</td>\n",
" <td>Y</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>LP001014</td>\n",
" <td>Male</td>\n",
" <td>Yes</td>\n",
" <td>3+</td>\n",
" <td>Graduate</td>\n",
" <td>No</td>\n",
" <td>3036</td>\n",
" <td>2504.0</td>\n",
" <td>158.000000</td>\n",
" <td>360.0</td>\n",
" <td>0.0</td>\n",
" <td>Semiurban</td>\n",
" <td>N</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>LP001018</td>\n",
" <td>Male</td>\n",
" <td>Yes</td>\n",
" <td>2</td>\n",
" <td>Graduate</td>\n",
" <td>No</td>\n",
" <td>4006</td>\n",
" <td>1526.0</td>\n",
" <td>168.000000</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Urban</td>\n",
" <td>Y</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>LP001020</td>\n",
" <td>Male</td>\n",
" <td>Yes</td>\n",
" <td>1</td>\n",
" <td>Graduate</td>\n",
" <td>No</td>\n",
" <td>12841</td>\n",
" <td>10968.0</td>\n",
" <td>349.000000</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Semiurban</td>\n",
" <td>N</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Loan_ID Gender Married ... Credit_History Property_Area Loan_Status\n",
"0 LP001002 Male No ... 1.0 Urban Y\n",
"1 LP001003 Male Yes ... 1.0 Rural N\n",
"2 LP001005 Male Yes ... 1.0 Urban Y\n",
"3 LP001006 Male Yes ... 1.0 Urban Y\n",
"4 LP001008 Male No ... 1.0 Urban Y\n",
"5 LP001011 Male Yes ... 1.0 Urban Y\n",
"6 LP001013 Male Yes ... 1.0 Urban Y\n",
"7 LP001014 Male Yes ... 0.0 Semiurban N\n",
"8 LP001018 Male Yes ... 1.0 Urban Y\n",
"9 LP001020 Male Yes ... 1.0 Semiurban N\n",
"\n",
"[10 rows x 13 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 17
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 243
},
"id": "L0Nl-4q3TOWc",
"outputId": "b865c99b-14b4-485c-db66-947a9448842e"
},
"source": [
"ValueMapping = {'Yes': 1, 'No': 0}\r\n",
"train['Married_Section'] = train['Married'].map(ValueMapping)\r\n",
"train.head()"
],
"execution_count": 18,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
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" vertical-align: top;\n",
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Loan_ID</th>\n",
" <th>Gender</th>\n",
" <th>Married</th>\n",
" <th>Dependents</th>\n",
" <th>Education</th>\n",
" <th>Self_Employed</th>\n",
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" <th>LoanAmount</th>\n",
" <th>Loan_Amount_Term</th>\n",
" <th>Credit_History</th>\n",
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" <th>Married_Section</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>LP001002</td>\n",
" <td>Male</td>\n",
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" <td>No</td>\n",
" <td>5849</td>\n",
" <td>0.0</td>\n",
" <td>146.412162</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Urban</td>\n",
" <td>Y</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>LP001003</td>\n",
" <td>Male</td>\n",
" <td>Yes</td>\n",
" <td>1</td>\n",
" <td>Graduate</td>\n",
" <td>No</td>\n",
" <td>4583</td>\n",
" <td>1508.0</td>\n",
" <td>128.000000</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Rural</td>\n",
" <td>N</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>LP001005</td>\n",
" <td>Male</td>\n",
" <td>Yes</td>\n",
" <td>0</td>\n",
" <td>Graduate</td>\n",
" <td>Yes</td>\n",
" <td>3000</td>\n",
" <td>0.0</td>\n",
" <td>66.000000</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Urban</td>\n",
" <td>Y</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>LP001006</td>\n",
" <td>Male</td>\n",
" <td>Yes</td>\n",
" <td>0</td>\n",
" <td>Not Graduate</td>\n",
" <td>No</td>\n",
" <td>2583</td>\n",
" <td>2358.0</td>\n",
" <td>120.000000</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Urban</td>\n",
" <td>Y</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>LP001008</td>\n",
" <td>Male</td>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" <td>Graduate</td>\n",
" <td>No</td>\n",
" <td>6000</td>\n",
" <td>0.0</td>\n",
" <td>141.000000</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Urban</td>\n",
" <td>Y</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Loan_ID Gender Married ... Property_Area Loan_Status Married_Section\n",
"0 LP001002 Male No ... Urban Y 0.0\n",
"1 LP001003 Male Yes ... Rural N 1.0\n",
"2 LP001005 Male Yes ... Urban Y 1.0\n",
"3 LP001006 Male Yes ... Urban Y 1.0\n",
"4 LP001008 Male No ... Urban Y 0.0\n",
"\n",
"[5 rows x 14 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 18
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 243
},
"id": "yAa9cTtqTdFJ",
"outputId": "f925288d-8ab5-4c54-dc2f-23bbddd036ab"
},
"source": [
"ValueMapping1 = {'Male': 1, 'Female': 0}\r\n",
"train['Gender_Section'] = train['Gender'].map(ValueMapping1)\r\n",
"train.head()"
],
"execution_count": 47,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" <thead>\n",
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" <th></th>\n",
" <th>Loan_ID</th>\n",
" <th>Gender</th>\n",
" <th>Married</th>\n",
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" <th>Self_Employed</th>\n",
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" <th>Loan_Amount_Term</th>\n",
" <th>Credit_History</th>\n",
" <th>Property_Area</th>\n",
" <th>Loan_Status</th>\n",
" <th>Married_Section</th>\n",
" <th>Gender_Section</th>\n",
" <th>Edu_Section</th>\n",
" <th>Employed_Section</th>\n",
" <th>Property_Section</th>\n",
" <th>Loan_Section</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>LP001002</td>\n",
" <td>Male</td>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" <td>Graduate</td>\n",
" <td>No</td>\n",
" <td>5849</td>\n",
" <td>0.0</td>\n",
" <td>146.412162</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Urban</td>\n",
" <td>Y</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>LP001003</td>\n",
" <td>Male</td>\n",
" <td>Yes</td>\n",
" <td>1</td>\n",
" <td>Graduate</td>\n",
" <td>No</td>\n",
" <td>4583</td>\n",
" <td>1508.0</td>\n",
" <td>128.000000</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Rural</td>\n",
" <td>N</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>LP001005</td>\n",
" <td>Male</td>\n",
" <td>Yes</td>\n",
" <td>0</td>\n",
" <td>Graduate</td>\n",
" <td>Yes</td>\n",
" <td>3000</td>\n",
" <td>0.0</td>\n",
" <td>66.000000</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Urban</td>\n",
" <td>Y</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>1.0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>LP001006</td>\n",
" <td>Male</td>\n",
" <td>Yes</td>\n",
" <td>0</td>\n",
" <td>Not Graduate</td>\n",
" <td>No</td>\n",
" <td>2583</td>\n",
" <td>2358.0</td>\n",
" <td>120.000000</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Urban</td>\n",
" <td>Y</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>LP001008</td>\n",
" <td>Male</td>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" <td>Graduate</td>\n",
" <td>No</td>\n",
" <td>6000</td>\n",
" <td>0.0</td>\n",
" <td>141.000000</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Urban</td>\n",
" <td>Y</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Loan_ID Gender Married ... Employed_Section Property_Section Loan_Section\n",
"0 LP001002 Male No ... 0.0 2 1\n",
"1 LP001003 Male Yes ... 0.0 0 0\n",
"2 LP001005 Male Yes ... 1.0 2 1\n",
"3 LP001006 Male Yes ... 0.0 2 1\n",
"4 LP001008 Male No ... 0.0 2 1\n",
"\n",
"[5 rows x 19 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 47
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "yWMGAfQdTvfW",
"outputId": "6c073cb3-6ba3-49c5-e3ae-b0d083708db2"
},
"source": [
"train['Education'].unique()"
],
"execution_count": 20,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array(['Graduate', 'Not Graduate'], dtype=object)"
]
},
"metadata": {
"tags": []
},
"execution_count": 20
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 243
},
"id": "qV408vVGT-LG",
"outputId": "2313c9d6-fe5d-4b40-c9d6-ea77c5418f8e"
},
"source": [
"ValueMapping2 = {'Graduate': 1, 'Not Graduate': 0}\r\n",
"train['Edu_Section'] = train['Education'].map(ValueMapping2)\r\n",
"train.head()"
],
"execution_count": 21,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>LP001002</td>\n",
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" <td>No</td>\n",
" <td>5849</td>\n",
" <td>0.0</td>\n",
" <td>146.412162</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Urban</td>\n",
" <td>Y</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>LP001003</td>\n",
" <td>Male</td>\n",
" <td>Yes</td>\n",
" <td>1</td>\n",
" <td>Graduate</td>\n",
" <td>No</td>\n",
" <td>4583</td>\n",
" <td>1508.0</td>\n",
" <td>128.000000</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Rural</td>\n",
" <td>N</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>LP001005</td>\n",
" <td>Male</td>\n",
" <td>Yes</td>\n",
" <td>0</td>\n",
" <td>Graduate</td>\n",
" <td>Yes</td>\n",
" <td>3000</td>\n",
" <td>0.0</td>\n",
" <td>66.000000</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Urban</td>\n",
" <td>Y</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>LP001006</td>\n",
" <td>Male</td>\n",
" <td>Yes</td>\n",
" <td>0</td>\n",
" <td>Not Graduate</td>\n",
" <td>No</td>\n",
" <td>2583</td>\n",
" <td>2358.0</td>\n",
" <td>120.000000</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Urban</td>\n",
" <td>Y</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>LP001008</td>\n",
" <td>Male</td>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" <td>Graduate</td>\n",
" <td>No</td>\n",
" <td>6000</td>\n",
" <td>0.0</td>\n",
" <td>141.000000</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Urban</td>\n",
" <td>Y</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Loan_ID Gender Married ... Married_Section Gender_Section Edu_Section\n",
"0 LP001002 Male No ... 0.0 1.0 1\n",
"1 LP001003 Male Yes ... 1.0 1.0 1\n",
"2 LP001005 Male Yes ... 1.0 1.0 1\n",
"3 LP001006 Male Yes ... 1.0 1.0 0\n",
"4 LP001008 Male No ... 0.0 1.0 1\n",
"\n",
"[5 rows x 16 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 21
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "m4LRqYADUMqd",
"outputId": "11428578-ca8d-46ff-ed53-2cb7278501b7"
},
"source": [
"train.info()"
],
"execution_count": 22,
"outputs": [
{
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 614 entries, 0 to 613\n",
"Data columns (total 16 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Loan_ID 614 non-null object \n",
" 1 Gender 601 non-null object \n",
" 2 Married 611 non-null object \n",
" 3 Dependents 614 non-null object \n",
" 4 Education 614 non-null object \n",
" 5 Self_Employed 582 non-null object \n",
" 6 ApplicantIncome 614 non-null int64 \n",
" 7 CoapplicantIncome 614 non-null float64\n",
" 8 LoanAmount 614 non-null float64\n",
" 9 Loan_Amount_Term 600 non-null float64\n",
" 10 Credit_History 564 non-null float64\n",
" 11 Property_Area 614 non-null object \n",
" 12 Loan_Status 614 non-null object \n",
" 13 Married_Section 611 non-null float64\n",
" 14 Gender_Section 601 non-null float64\n",
" 15 Edu_Section 614 non-null int64 \n",
"dtypes: float64(6), int64(2), object(8)\n",
"memory usage: 76.9+ KB\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "G8twnWZ2UO2y",
"outputId": "3dc2efd8-851a-483f-a6cc-f705bb8a898f"
},
"source": [
"train['Married_Section'].fillna(train.Married_Section.mean(), inplace=True)\r\n",
"train['Gender_Section'].fillna(train.Gender_Section.mean(), inplace=True)\r\n",
"train['Loan_Amount_Term'].fillna(train.Loan_Amount_Term.mean(), inplace=True)\r\n",
"train['Credit_History'].fillna(train.Credit_History.mean(), inplace=True)\r\n",
"\r\n",
"train.info()"
],
"execution_count": 23,
"outputs": [
{
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 614 entries, 0 to 613\n",
"Data columns (total 16 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Loan_ID 614 non-null object \n",
" 1 Gender 601 non-null object \n",
" 2 Married 611 non-null object \n",
" 3 Dependents 614 non-null object \n",
" 4 Education 614 non-null object \n",
" 5 Self_Employed 582 non-null object \n",
" 6 ApplicantIncome 614 non-null int64 \n",
" 7 CoapplicantIncome 614 non-null float64\n",
" 8 LoanAmount 614 non-null float64\n",
" 9 Loan_Amount_Term 614 non-null float64\n",
" 10 Credit_History 614 non-null float64\n",
" 11 Property_Area 614 non-null object \n",
" 12 Loan_Status 614 non-null object \n",
" 13 Married_Section 614 non-null float64\n",
" 14 Gender_Section 614 non-null float64\n",
" 15 Edu_Section 614 non-null int64 \n",
"dtypes: float64(6), int64(2), object(8)\n",
"memory usage: 76.9+ KB\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 243
},
"id": "uqcnFnLnUzU5",
"outputId": "5da78b74-3adb-4725-a2d1-eedd345b3394"
},
"source": [
"ValueMapping3 = {'Yes': 1, 'No': 0}\r\n",
"train['Employed_Section'] = train['Self_Employed'].map(ValueMapping3)\r\n",
"\r\n",
"train.head()"
],
"execution_count": 24,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Loan_ID</th>\n",
" <th>Gender</th>\n",
" <th>Married</th>\n",
" <th>Dependents</th>\n",
" <th>Education</th>\n",
" <th>Self_Employed</th>\n",
" <th>ApplicantIncome</th>\n",
" <th>CoapplicantIncome</th>\n",
" <th>LoanAmount</th>\n",
" <th>Loan_Amount_Term</th>\n",
" <th>Credit_History</th>\n",
" <th>Property_Area</th>\n",
" <th>Loan_Status</th>\n",
" <th>Married_Section</th>\n",
" <th>Gender_Section</th>\n",
" <th>Edu_Section</th>\n",
" <th>Employed_Section</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>LP001002</td>\n",
" <td>Male</td>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" <td>Graduate</td>\n",
" <td>No</td>\n",
" <td>5849</td>\n",
" <td>0.0</td>\n",
" <td>146.412162</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Urban</td>\n",
" <td>Y</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>LP001003</td>\n",
" <td>Male</td>\n",
" <td>Yes</td>\n",
" <td>1</td>\n",
" <td>Graduate</td>\n",
" <td>No</td>\n",
" <td>4583</td>\n",
" <td>1508.0</td>\n",
" <td>128.000000</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Rural</td>\n",
" <td>N</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>LP001005</td>\n",
" <td>Male</td>\n",
" <td>Yes</td>\n",
" <td>0</td>\n",
" <td>Graduate</td>\n",
" <td>Yes</td>\n",
" <td>3000</td>\n",
" <td>0.0</td>\n",
" <td>66.000000</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Urban</td>\n",
" <td>Y</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>LP001006</td>\n",
" <td>Male</td>\n",
" <td>Yes</td>\n",
" <td>0</td>\n",
" <td>Not Graduate</td>\n",
" <td>No</td>\n",
" <td>2583</td>\n",
" <td>2358.0</td>\n",
" <td>120.000000</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Urban</td>\n",
" <td>Y</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>LP001008</td>\n",
" <td>Male</td>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" <td>Graduate</td>\n",
" <td>No</td>\n",
" <td>6000</td>\n",
" <td>0.0</td>\n",
" <td>141.000000</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Urban</td>\n",
" <td>Y</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Loan_ID Gender Married ... Gender_Section Edu_Section Employed_Section\n",
"0 LP001002 Male No ... 1.0 1 0.0\n",
"1 LP001003 Male Yes ... 1.0 1 0.0\n",
"2 LP001005 Male Yes ... 1.0 1 1.0\n",
"3 LP001006 Male Yes ... 1.0 0 0.0\n",
"4 LP001008 Male No ... 1.0 1 0.0\n",
"\n",
"[5 rows x 17 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 24
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "AjegjIOyVDI_",
"outputId": "33d98b9b-63f4-4438-c00d-f80151324f63"
},
"source": [
"train.info()"
],
"execution_count": 25,
"outputs": [
{
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 614 entries, 0 to 613\n",
"Data columns (total 17 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Loan_ID 614 non-null object \n",
" 1 Gender 601 non-null object \n",
" 2 Married 611 non-null object \n",
" 3 Dependents 614 non-null object \n",
" 4 Education 614 non-null object \n",
" 5 Self_Employed 582 non-null object \n",
" 6 ApplicantIncome 614 non-null int64 \n",
" 7 CoapplicantIncome 614 non-null float64\n",
" 8 LoanAmount 614 non-null float64\n",
" 9 Loan_Amount_Term 614 non-null float64\n",
" 10 Credit_History 614 non-null float64\n",
" 11 Property_Area 614 non-null object \n",
" 12 Loan_Status 614 non-null object \n",
" 13 Married_Section 614 non-null float64\n",
" 14 Gender_Section 614 non-null float64\n",
" 15 Edu_Section 614 non-null int64 \n",
" 16 Employed_Section 582 non-null float64\n",
"dtypes: float64(7), int64(2), object(8)\n",
"memory usage: 81.7+ KB\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 243
},
"id": "PGC-hA0QVHSw",
"outputId": "9850bc17-7d45-40cd-e848-b6442f7d2f98"
},
"source": [
"from sklearn.preprocessing import LabelEncoder\r\n",
"\r\n",
"lb = LabelEncoder()\r\n",
"\r\n",
"train['Property_Section'] = lb.fit_transform(train['Property_Area'])\r\n",
"\r\n",
"train.head()"
],
"execution_count": 26,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Loan_ID</th>\n",
" <th>Gender</th>\n",
" <th>Married</th>\n",
" <th>Dependents</th>\n",
" <th>Education</th>\n",
" <th>Self_Employed</th>\n",
" <th>ApplicantIncome</th>\n",
" <th>CoapplicantIncome</th>\n",
" <th>LoanAmount</th>\n",
" <th>Loan_Amount_Term</th>\n",
" <th>Credit_History</th>\n",
" <th>Property_Area</th>\n",
" <th>Loan_Status</th>\n",
" <th>Married_Section</th>\n",
" <th>Gender_Section</th>\n",
" <th>Edu_Section</th>\n",
" <th>Employed_Section</th>\n",
" <th>Property_Section</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>LP001002</td>\n",
" <td>Male</td>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" <td>Graduate</td>\n",
" <td>No</td>\n",
" <td>5849</td>\n",
" <td>0.0</td>\n",
" <td>146.412162</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Urban</td>\n",
" <td>Y</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>LP001003</td>\n",
" <td>Male</td>\n",
" <td>Yes</td>\n",
" <td>1</td>\n",
" <td>Graduate</td>\n",
" <td>No</td>\n",
" <td>4583</td>\n",
" <td>1508.0</td>\n",
" <td>128.000000</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Rural</td>\n",
" <td>N</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>LP001005</td>\n",
" <td>Male</td>\n",
" <td>Yes</td>\n",
" <td>0</td>\n",
" <td>Graduate</td>\n",
" <td>Yes</td>\n",
" <td>3000</td>\n",
" <td>0.0</td>\n",
" <td>66.000000</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Urban</td>\n",
" <td>Y</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>1.0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>LP001006</td>\n",
" <td>Male</td>\n",
" <td>Yes</td>\n",
" <td>0</td>\n",
" <td>Not Graduate</td>\n",
" <td>No</td>\n",
" <td>2583</td>\n",
" <td>2358.0</td>\n",
" <td>120.000000</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Urban</td>\n",
" <td>Y</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>LP001008</td>\n",
" <td>Male</td>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" <td>Graduate</td>\n",
" <td>No</td>\n",
" <td>6000</td>\n",
" <td>0.0</td>\n",
" <td>141.000000</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Urban</td>\n",
" <td>Y</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Loan_ID Gender Married ... Edu_Section Employed_Section Property_Section\n",
"0 LP001002 Male No ... 1 0.0 2\n",
"1 LP001003 Male Yes ... 1 0.0 0\n",
"2 LP001005 Male Yes ... 1 1.0 2\n",
"3 LP001006 Male Yes ... 0 0.0 2\n",
"4 LP001008 Male No ... 1 0.0 2\n",
"\n",
"[5 rows x 18 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 26
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 243
},
"id": "F72hYM5NVcZV",
"outputId": "377923e1-af55-4292-c97d-7ab65a29d5db"
},
"source": [
"ValueMapping4 = {'Y':1, 'N':0}\r\n",
"train['Loan_Section'] = train['Loan_Status'].map(ValueMapping4)\r\n",
"\r\n",
"train.head()"
],
"execution_count": 27,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Loan_ID</th>\n",
" <th>Gender</th>\n",
" <th>Married</th>\n",
" <th>Dependents</th>\n",
" <th>Education</th>\n",
" <th>Self_Employed</th>\n",
" <th>ApplicantIncome</th>\n",
" <th>CoapplicantIncome</th>\n",
" <th>LoanAmount</th>\n",
" <th>Loan_Amount_Term</th>\n",
" <th>Credit_History</th>\n",
" <th>Property_Area</th>\n",
" <th>Loan_Status</th>\n",
" <th>Married_Section</th>\n",
" <th>Gender_Section</th>\n",
" <th>Edu_Section</th>\n",
" <th>Employed_Section</th>\n",
" <th>Property_Section</th>\n",
" <th>Loan_Section</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>LP001002</td>\n",
" <td>Male</td>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" <td>Graduate</td>\n",
" <td>No</td>\n",
" <td>5849</td>\n",
" <td>0.0</td>\n",
" <td>146.412162</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Urban</td>\n",
" <td>Y</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>LP001003</td>\n",
" <td>Male</td>\n",
" <td>Yes</td>\n",
" <td>1</td>\n",
" <td>Graduate</td>\n",
" <td>No</td>\n",
" <td>4583</td>\n",
" <td>1508.0</td>\n",
" <td>128.000000</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Rural</td>\n",
" <td>N</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>LP001005</td>\n",
" <td>Male</td>\n",
" <td>Yes</td>\n",
" <td>0</td>\n",
" <td>Graduate</td>\n",
" <td>Yes</td>\n",
" <td>3000</td>\n",
" <td>0.0</td>\n",
" <td>66.000000</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Urban</td>\n",
" <td>Y</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>1.0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>LP001006</td>\n",
" <td>Male</td>\n",
" <td>Yes</td>\n",
" <td>0</td>\n",
" <td>Not Graduate</td>\n",
" <td>No</td>\n",
" <td>2583</td>\n",
" <td>2358.0</td>\n",
" <td>120.000000</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Urban</td>\n",
" <td>Y</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>LP001008</td>\n",
" <td>Male</td>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" <td>Graduate</td>\n",
" <td>No</td>\n",
" <td>6000</td>\n",
" <td>0.0</td>\n",
" <td>141.000000</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Urban</td>\n",
" <td>Y</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Loan_ID Gender Married ... Employed_Section Property_Section Loan_Section\n",
"0 LP001002 Male No ... 0.0 2 1\n",
"1 LP001003 Male Yes ... 0.0 0 0\n",
"2 LP001005 Male Yes ... 1.0 2 1\n",
"3 LP001006 Male Yes ... 0.0 2 1\n",
"4 LP001008 Male No ... 0.0 2 1\n",
"\n",
"[5 rows x 19 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 27
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 353
},
"id": "utb0IZRbWEYx",
"outputId": "e61f7d97-d9d0-4529-cbdc-9c595a99dede"
},
"source": [
"sns.FacetGrid(train,hue=\"Gender_Section\",size=4) \\\r\n",
".map(plt.scatter,\"Loan_Status\",\"LoanAmount\") \\\r\n",
".add_legend()\r\n",
"plt.show()"
],
"execution_count": 28,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.7/dist-packages/seaborn/axisgrid.py:316: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
" warnings.warn(msg, UserWarning)\n"
],
"name": "stderr"
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 449.6x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 353
},
"id": "GaYwTL3mWGpt",
"outputId": "853e26ad-2820-4a1d-e3af-0e7a083f9062"
},
"source": [
"sns.FacetGrid(train,hue=\"Property_Section\",size=4) \\\r\n",
".map(plt.scatter,\"ApplicantIncome\",\"CoapplicantIncome\") \\\r\n",
".add_legend()\r\n",
"plt.show()"
],
"execution_count": 29,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.7/dist-packages/seaborn/axisgrid.py:316: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
" warnings.warn(msg, UserWarning)\n"
],
"name": "stderr"
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 397.925x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 482
},
"id": "5vUCUVOkWOEd",
"outputId": "e83aa5c1-65e2-4f8e-8101-f727e42a0a00"
},
"source": [
"plt.figure(figsize = (10,7)) \r\n",
"x = train[\"LoanAmount\"] \r\n",
"plt.hist(x, bins = 30, color = \"pink\") \r\n",
"plt.title(\"Loan taken by Customers\") \r\n",
"plt.xlabel(\"Loan Figures\") \r\n",
"plt.ylabel(\"Count\")"
],
"execution_count": 30,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Text(0, 0.5, 'Count')"
]
},
"metadata": {
"tags": []
},
"execution_count": 30
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x504 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 303
},
"id": "oe80Az-4WRay",
"outputId": "52173865-e1fc-4478-95f8-4fa3a2c100e9"
},
"source": [
"sns.boxplot(x=\"Property_Area\", y=\"Gender_Section\", data=train)"
],
"execution_count": 31,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f70953e0890>"
]
},
"metadata": {
"tags": []
},
"execution_count": 31
},
{
"output_type": "display_data",
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 303
},
"id": "yBPax1jTWTsP",
"outputId": "7c1a35ab-0454-4823-ec90-bd4075bd0d96"
},
"source": [
"sns.boxplot(x=\"Married_Section\", y=\"ApplicantIncome\", data=train)"
],
"execution_count": 32,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f7095453890>"
]
},
"metadata": {
"tags": []
},
"execution_count": 32
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 286
},
"id": "ZpUlR9BGWXXf",
"outputId": "f9ccb317-a8c1-4a32-dd4c-5c375efbe614"
},
"source": [
"train_temp=train[train[\"Education\"]== \"Graduate\"]\r\n",
"train_temp[\"Self_Employed\"].hist()"
],
"execution_count": 33,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f7095499250>"
]
},
"metadata": {
"tags": []
},
"execution_count": 33
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 516
},
"id": "Z-hwvI70WgO-",
"outputId": "29c3e572-e605-4e64-8fec-14f2ca737857"
},
"source": [
"sns.FacetGrid(train, hue=\"Credit_History\", size=6).map(sns.kdeplot, \"CoapplicantIncome\").add_legend()"
],
"execution_count": 34,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.7/dist-packages/seaborn/axisgrid.py:316: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
" warnings.warn(msg, UserWarning)\n"
],
"name": "stderr"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x7f7095307bd0>"
]
},
"metadata": {
"tags": []
},
"execution_count": 34
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 593.35x432 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 535
},
"id": "kMG5WOGvWjRu",
"outputId": "c6d4e90d-a92b-4fc8-b160-e1903ca0b276"
},
"source": [
"cols = ['ApplicantIncome','CoapplicantIncome','LoanAmount','Loan_Amount_Term','Credit_History','Married_Section',\r\n",
" 'Gender_Section','Edu_Section','Employed_Section','Property_Section']\r\n",
"f, ax = plt.subplots(figsize=(10, 7))\r\n",
"cm = np.corrcoef(train[cols].values.T)\r\n",
"sns.set(font_scale=1.5)\r\n",
"hm = sns.heatmap(cm,\r\n",
" cbar=True,\r\n",
" annot=True,\r\n",
" square=True,\r\n",
" fmt='.2f',\r\n",
" annot_kws={'size': 15},\r\n",
" yticklabels=cols,\r\n",
" xticklabels=cols)\r\n",
"\r\n",
"plt.show()"
],
"execution_count": 35,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x504 with 2 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ls8lzl-QYSZs",
"outputId": "142c0918-aa7c-4bbb-9cd0-4d8044151de3"
},
"source": [
"train['Employed_Section'].unique()"
],
"execution_count": 49,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([ 0., 1., nan])"
]
},
"metadata": {
"tags": []
},
"execution_count": 49
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 243
},
"id": "dQto5rbEYqGA",
"outputId": "7bf01d47-f634-4349-cce5-2a60b7758ce9"
},
"source": [
"train['Employed_Section'].fillna(1,inplace=True)\r\n",
"train.head()"
],
"execution_count": 51,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Loan_ID</th>\n",
" <th>Gender</th>\n",
" <th>Married</th>\n",
" <th>Dependents</th>\n",
" <th>Education</th>\n",
" <th>Self_Employed</th>\n",
" <th>ApplicantIncome</th>\n",
" <th>CoapplicantIncome</th>\n",
" <th>LoanAmount</th>\n",
" <th>Loan_Amount_Term</th>\n",
" <th>Credit_History</th>\n",
" <th>Property_Area</th>\n",
" <th>Loan_Status</th>\n",
" <th>Married_Section</th>\n",
" <th>Gender_Section</th>\n",
" <th>Edu_Section</th>\n",
" <th>Employed_Section</th>\n",
" <th>Property_Section</th>\n",
" <th>Loan_Section</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>LP001002</td>\n",
" <td>Male</td>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" <td>Graduate</td>\n",
" <td>No</td>\n",
" <td>5849</td>\n",
" <td>0.0</td>\n",
" <td>146.412162</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Urban</td>\n",
" <td>Y</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>LP001003</td>\n",
" <td>Male</td>\n",
" <td>Yes</td>\n",
" <td>1</td>\n",
" <td>Graduate</td>\n",
" <td>No</td>\n",
" <td>4583</td>\n",
" <td>1508.0</td>\n",
" <td>128.000000</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Rural</td>\n",
" <td>N</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>LP001005</td>\n",
" <td>Male</td>\n",
" <td>Yes</td>\n",
" <td>0</td>\n",
" <td>Graduate</td>\n",
" <td>Yes</td>\n",
" <td>3000</td>\n",
" <td>0.0</td>\n",
" <td>66.000000</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Urban</td>\n",
" <td>Y</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>1.0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>LP001006</td>\n",
" <td>Male</td>\n",
" <td>Yes</td>\n",
" <td>0</td>\n",
" <td>Not Graduate</td>\n",
" <td>No</td>\n",
" <td>2583</td>\n",
" <td>2358.0</td>\n",
" <td>120.000000</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Urban</td>\n",
" <td>Y</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>LP001008</td>\n",
" <td>Male</td>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" <td>Graduate</td>\n",
" <td>No</td>\n",
" <td>6000</td>\n",
" <td>0.0</td>\n",
" <td>141.000000</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Urban</td>\n",
" <td>Y</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Loan_ID Gender Married ... Employed_Section Property_Section Loan_Section\n",
"0 LP001002 Male No ... 0.0 2 1\n",
"1 LP001003 Male Yes ... 0.0 0 0\n",
"2 LP001005 Male Yes ... 1.0 2 1\n",
"3 LP001006 Male Yes ... 0.0 2 1\n",
"4 LP001008 Male No ... 0.0 2 1\n",
"\n",
"[5 rows x 19 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 51
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "mcFFKInvZIZC",
"outputId": "53c1890f-cfe4-4bfd-d4a7-b7db535b2f5a"
},
"source": [
"train['Employed_Section'].unique()"
],
"execution_count": 52,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([0., 1.])"
]
},
"metadata": {
"tags": []
},
"execution_count": 52
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "5YQoRBatZUGH",
"outputId": "f2abfae1-3013-44b2-e40a-2dfe4bd38ce8"
},
"source": [
"train['Gender_Section'].unique()"
],
"execution_count": 57,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([ 1., 0., nan])"
]
},
"metadata": {
"tags": []
},
"execution_count": 57
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 243
},
"id": "BmfiLeA-Zecf",
"outputId": "4af1cf20-2333-4b64-b21c-c80f4bfdf7fd"
},
"source": [
"train['Gender_Section'].fillna(1,inplace=True)\r\n",
"train.head()"
],
"execution_count": 58,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Loan_ID</th>\n",
" <th>Gender</th>\n",
" <th>Married</th>\n",
" <th>Dependents</th>\n",
" <th>Education</th>\n",
" <th>Self_Employed</th>\n",
" <th>ApplicantIncome</th>\n",
" <th>CoapplicantIncome</th>\n",
" <th>LoanAmount</th>\n",
" <th>Loan_Amount_Term</th>\n",
" <th>Credit_History</th>\n",
" <th>Property_Area</th>\n",
" <th>Loan_Status</th>\n",
" <th>Married_Section</th>\n",
" <th>Gender_Section</th>\n",
" <th>Edu_Section</th>\n",
" <th>Employed_Section</th>\n",
" <th>Property_Section</th>\n",
" <th>Loan_Section</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>LP001002</td>\n",
" <td>Male</td>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" <td>Graduate</td>\n",
" <td>No</td>\n",
" <td>5849</td>\n",
" <td>0.0</td>\n",
" <td>146.412162</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Urban</td>\n",
" <td>Y</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>LP001003</td>\n",
" <td>Male</td>\n",
" <td>Yes</td>\n",
" <td>1</td>\n",
" <td>Graduate</td>\n",
" <td>No</td>\n",
" <td>4583</td>\n",
" <td>1508.0</td>\n",
" <td>128.000000</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Rural</td>\n",
" <td>N</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>LP001005</td>\n",
" <td>Male</td>\n",
" <td>Yes</td>\n",
" <td>0</td>\n",
" <td>Graduate</td>\n",
" <td>Yes</td>\n",
" <td>3000</td>\n",
" <td>0.0</td>\n",
" <td>66.000000</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Urban</td>\n",
" <td>Y</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>1.0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>LP001006</td>\n",
" <td>Male</td>\n",
" <td>Yes</td>\n",
" <td>0</td>\n",
" <td>Not Graduate</td>\n",
" <td>No</td>\n",
" <td>2583</td>\n",
" <td>2358.0</td>\n",
" <td>120.000000</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Urban</td>\n",
" <td>Y</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>LP001008</td>\n",
" <td>Male</td>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" <td>Graduate</td>\n",
" <td>No</td>\n",
" <td>6000</td>\n",
" <td>0.0</td>\n",
" <td>141.000000</td>\n",
" <td>360.0</td>\n",
" <td>1.0</td>\n",
" <td>Urban</td>\n",
" <td>Y</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Loan_ID Gender Married ... Employed_Section Property_Section Loan_Section\n",
"0 LP001002 Male No ... 0.0 2 1\n",
"1 LP001003 Male Yes ... 0.0 0 0\n",
"2 LP001005 Male Yes ... 1.0 2 1\n",
"3 LP001006 Male Yes ... 0.0 2 1\n",
"4 LP001008 Male No ... 0.0 2 1\n",
"\n",
"[5 rows x 19 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 58
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "IdQSXHADZi6K",
"outputId": "e872c52b-6f41-4117-b3d2-8c234ef71bb0"
},
"source": [
"train['Gender_Section'].unique()"
],
"execution_count": 59,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([1., 0.])"
]
},
"metadata": {
"tags": []
},
"execution_count": 59
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "6T6ljSx9WxVC"
},
"source": [
"X=train[['ApplicantIncome','CoapplicantIncome','LoanAmount','Loan_Amount_Term','Credit_History','Married_Section',\r\n",
" 'Gender_Section','Edu_Section','Employed_Section','Property_Section']].values\r\n",
"y=train[[\"Loan_Section\"]].values"
],
"execution_count": 60,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "9wCMTHOLWytZ"
},
"source": [
"from sklearn.model_selection import train_test_split\r\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)"
],
"execution_count": 61,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "RtQFDcKHW1BQ",
"outputId": "9278ea8e-f1fc-47fc-9a9e-8faf598bddbf"
},
"source": [
"train.isna().any()"
],
"execution_count": 62,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Loan_ID False\n",
"Gender True\n",
"Married True\n",
"Dependents False\n",
"Education False\n",
"Self_Employed True\n",
"ApplicantIncome False\n",
"CoapplicantIncome False\n",
"LoanAmount False\n",
"Loan_Amount_Term False\n",
"Credit_History False\n",
"Property_Area False\n",
"Loan_Status False\n",
"Married_Section False\n",
"Gender_Section False\n",
"Edu_Section False\n",
"Employed_Section False\n",
"Property_Section False\n",
"Loan_Section False\n",
"dtype: bool"
]
},
"metadata": {
"tags": []
},
"execution_count": 62
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "F9iVG0FXcvpg"
},
"source": [
"from sklearn.tree import DecisionTreeClassifier\r\n",
"model = DecisionTreeClassifier()"
],
"execution_count": 90,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "wl1GhiLWc4U5",
"outputId": "019b7706-d946-4fe1-fbb3-1fd306f54ea6"
},
"source": [
"model.fit(X_train, y_train)"
],
"execution_count": 91,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
" max_depth=None, max_features=None, max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, presort='deprecated',\n",
" random_state=None, splitter='best')"
]
},
"metadata": {
"tags": []
},
"execution_count": 91
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "7EXSPhCfc-o_",
"outputId": "2f939e03-b939-4f3f-91df-027dd5bbc87a"
},
"source": [
"model.score(X_train, y_train)"
],
"execution_count": 92,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"1.0"
]
},
"metadata": {
"tags": []
},
"execution_count": 92
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "nRHAUlvQdDrU",
"outputId": "40c5c4c5-15c2-4e99-a3bf-495e0647f3ff"
},
"source": [
"model.score(X_test, y_test)"
],
"execution_count": 93,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.7513513513513513"
]
},
"metadata": {
"tags": []
},
"execution_count": 93
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "yfDBC11aZybT"
},
"source": [
"expected = y_test\r\n",
"predicted = model.predict(X_test)"
],
"execution_count": 94,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "4tGyJFouZ02c",
"outputId": "cf894a5d-e02b-4065-9612-2082d2e8dbb4"
},
"source": [
"from sklearn import metrics\r\n",
"print(metrics.classification_report(expected, predicted))"
],
"execution_count": 95,
"outputs": [
{
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.55 0.57 0.56 51\n",
" 1 0.83 0.82 0.83 134\n",
"\n",
" accuracy 0.75 185\n",
" macro avg 0.69 0.69 0.69 185\n",
"weighted avg 0.75 0.75 0.75 185\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "T9x3hpEmZ3kI",
"outputId": "ab39d9c5-5cb8-4d8f-f830-7b0e6b7efef6"
},
"source": [
"metrics.confusion_matrix(expected, predicted)"
],
"execution_count": 96,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[ 29, 22],\n",
" [ 24, 110]])"
]
},
"metadata": {
"tags": []
},
"execution_count": 96
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Fg3ARO_RZ5ib"
},
"source": [
""
],
"execution_count": 96,
"outputs": []
}
]
}
| 92.524889
| 108,434
| 0.720543
| 18,426
| 334,570
| 13.046945
| 0.372571
| 0.01354
| 0.021048
| 0.00896
| 0.130801
| 0.126783
| 0.123322
| 0.119192
| 0.115693
| 0.114362
| 0
| 0.140742
| 0.187333
| 334,570
| 3,616
| 108,435
| 92.524889
| 0.743437
| 0
| 0
| 0.737555
| 0
| 0.003319
| 0.826127
| 0.649079
| 0
| 1
| 0.000167
| 0
| 0
| 1
| 0
| true
| 0
| 0.002212
| 0
| 0.002212
| 0.000277
| 0
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 1
| null | 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
601d34b71af4ba5fd8a7f666c68b7b1604113d02
| 86
|
py
|
Python
|
d3flux-master/d3flux/__init__.py
|
man-shu/metquest-front
|
8955c450e0029506b13d1e3de092eea3f0371242
|
[
"MIT"
] | 22
|
2016-10-14T13:37:21.000Z
|
2022-01-08T23:19:42.000Z
|
d3flux/__init__.py
|
Melclic/d3flux
|
6b7bcbfc31e1529d7725b9512cc8b7defff09bd1
|
[
"MIT"
] | 12
|
2017-01-25T20:33:51.000Z
|
2021-09-23T07:48:04.000Z
|
d3flux/__init__.py
|
Melclic/d3flux
|
6b7bcbfc31e1529d7725b9512cc8b7defff09bd1
|
[
"MIT"
] | 10
|
2017-08-22T16:26:32.000Z
|
2022-01-08T23:44:35.000Z
|
from d3flux.core.flux_layouts import flux_map
from d3flux.core.display_tools import *
| 28.666667
| 45
| 0.848837
| 14
| 86
| 5
| 0.642857
| 0.285714
| 0.4
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.025641
| 0.093023
| 86
| 2
| 46
| 43
| 0.871795
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
60720dcc0dc1879db3754e76a9c1c6634454b33d
| 3,169
|
py
|
Python
|
msi_perkeyrgb/protocol_data/msi_keymaps.py
|
bwu62/msi-perkeyrgb
|
b1faac8a7fab6d0d8be7803214e0066ea9467516
|
[
"MIT"
] | null | null | null |
msi_perkeyrgb/protocol_data/msi_keymaps.py
|
bwu62/msi-perkeyrgb
|
b1faac8a7fab6d0d8be7803214e0066ea9467516
|
[
"MIT"
] | null | null | null |
msi_perkeyrgb/protocol_data/msi_keymaps.py
|
bwu62/msi-perkeyrgb
|
b1faac8a7fab6d0d8be7803214e0066ea9467516
|
[
"MIT"
] | null | null | null |
AVAILABLE_MSI_KEYMAPS = [
(['GE63', 'GE73', 'GE75', 'GS63', 'GS73', 'GS75', 'GX63', 'GT63', 'GL63', 'GL73'],
{9: 41,
10: 30,
11: 31,
12: 32,
13: 33,
14: 34,
15: 35,
16: 36,
17: 37,
18: 38,
19: 39,
20: 45,
21: 46,
22: 42,
23: 43,
24: 20,
25: 26,
26: 8,
27: 21,
28: 23,
29: 28,
30: 24,
31: 12,
32: 18,
33: 19,
34: 47,
35: 48,
36: 40,
37: 224,
38: 4,
39: 22,
40: 7,
41: 9,
42: 10,
43: 11,
44: 13,
45: 14,
46: 15,
47: 51,
48: 52,
49: 53,
50: 225,
51: 49,
52: 29,
53: 27,
54: 6,
55: 25,
56: 5,
57: 17,
58: 16,
59: 54,
60: 55,
61: 56,
62: 229,
63: 85,
64: 226,
65: 44,
66: 57,
67: 58,
68: 59,
69: 60,
70: 61,
71: 62,
72: 63,
73: 64,
74: 65,
75: 66,
76: 67,
77: 83,
78: 71,
79: 95,
80: 96,
81: 97,
82: 86,
83: 92,
84: 93,
85: 94,
86: 87,
87: 89,
88: 90,
89: 91,
90: 98,
91: 99,
94: 100,
95: 68,
96: 69,
104: 88,
105: 228,
106: 84,
107: 70,
108: 230,
111: 82,
112: 75,
113: 80,
114: 79,
116: 81,
117: 78,
118: 73,
119: 76,
127: 72,
133: 227,
'fn': 240}),
(['GS65'],
{9: 41,
10: 30,
11: 31,
12: 32,
13: 33,
14: 34,
15: 35,
16: 36,
17: 37,
18: 38,
19: 39,
20: 45,
21: 46,
22: 42,
23: 43,
24: 20,
25: 26,
26: 8,
27: 21,
28: 23,
29: 28,
30: 24,
31: 12,
32: 18,
33: 19,
34: 47,
35: 48,
36: 40,
37: 224,
38: 4,
39: 22,
40: 7,
41: 9,
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49: 53,
50: 225,
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52: 29,
53: 27,
54: 6,
55: 25,
56: 5,
57: 17,
58: 16,
59: 54,
60: 55,
61: 56,
62: 229,
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104: 88,
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127: 72,
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'fn': 240})
]
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0
| 10
|
6088c3221ea4df304a8ac85a93aa5852cb3cfd6e
| 70,961
|
py
|
Python
|
projects/src/main/python/CodeJam/Y13R5P1/Nin/generated_py_927c1322bbea4c60806f8b22e0f01acd.py
|
DynamicCodeSearch/CodeSeer
|
ee985ece7691691585952eb88565f0e08bdc9113
|
[
"MIT"
] | 5
|
2020-04-05T18:04:13.000Z
|
2021-04-13T20:34:19.000Z
|
projects/src/main/python/CodeJam/Y13R5P1/Nin/generated_py_927c1322bbea4c60806f8b22e0f01acd.py
|
DynamicCodeSearch/CodeSeer
|
ee985ece7691691585952eb88565f0e08bdc9113
|
[
"MIT"
] | 1
|
2020-04-29T21:42:26.000Z
|
2020-05-01T23:45:45.000Z
|
projects/src/main/python/CodeJam/Y13R5P1/Nin/generated_py_927c1322bbea4c60806f8b22e0f01acd.py
|
DynamicCodeSearch/CodeSeer
|
ee985ece7691691585952eb88565f0e08bdc9113
|
[
"MIT"
] | 3
|
2020-01-27T16:02:14.000Z
|
2021-02-08T13:25:15.000Z
|
import sys
sys.path.append('/home/george2/Raise/ProgramRepair/CodeSeer/projects/src/main/python')
from CodeJam.Y13R5P1.Nin.a import *
def func_0eee475358f4402ea618a3ddeb05bdb4(loss, my, X):
ans = -loss
outcome = min([(my[i] + X[i]) for i in range(37)])
return ans
def func_6c8a039a305e43e1a9c527ff14d53856(loss, my, X):
ans = -loss
outcome = min([(my[i] + X[i]) for i in range(37)])
return i
def func_5640c9fa03dc487e997a7e628e1b375c(loss, my, X):
ans = -loss
outcome = min([(my[i] + X[i]) for i in range(37)])
return outcome
def func_94459f9565fb4e3b9b426f6325e1149f(my, X):
outcome = min([(my[i] + X[i]) for i in range(37)])
pos = [i for i in range(37) if my[i] + X[i] == outcome]
return pos
def func_253ac93a5d244d5293f16139edb75e52(my, X):
outcome = min([(my[i] + X[i]) for i in range(37)])
pos = [i for i in range(37) if my[i] + X[i] == outcome]
return outcome
def func_da583f150ec94cfcb6d034d7bdba5674(my, X):
outcome = min([(my[i] + X[i]) for i in range(37)])
pos = [i for i in range(37) if my[i] + X[i] == outcome]
return i
def func_290c3ee750bc4c4ea6639caf438c33ed(my, outcome, X):
pos = [i for i in range(37) if my[i] + X[i] == outcome]
N = len(pos)
return N
def func_b0da776d2637428c99f88dd23d1fee98(my, outcome, X):
pos = [i for i in range(37) if my[i] + X[i] == outcome]
N = len(pos)
return pos
def func_15a66b62b4274612b85084ca19f09bfb(my, outcome, X):
pos = [i for i in range(37) if my[i] + X[i] == outcome]
N = len(pos)
return i
def func_c3ffb95c743c49a3988e55a328cab1a5(my, pos):
N = len(pos)
for p in pos:
ans += my[p] * 36 / N
return N
def func_633606a68d6c4fc381ece63307c839e1(my, pos):
N = len(pos)
for p in pos:
ans += my[p] * 36 / N
return ans
def func_01d3082caa3f44ffaacea218e87693de(my, pos):
N = len(pos)
for p in pos:
ans += my[p] * 36 / N
return p
def func_06d2997df59d477a946c16f477450c30(my, pos, N):
for p in pos:
ans += my[p] * 36 / N
return ans
def func_bc0ae5e393124c7cb3a599bab436f76f(loss, my, X):
ans = -loss
outcome = min([(my[i] + X[i]) for i in range(37)])
pos = [i for i in range(37) if my[i] + X[i] == outcome]
return i
def func_48ab7bcf42754dc4b666f35291f5f415(loss, my, X):
ans = -loss
outcome = min([(my[i] + X[i]) for i in range(37)])
pos = [i for i in range(37) if my[i] + X[i] == outcome]
return pos
def func_5b3608361ea24b8c8ef42d26cc25fb77(loss, my, X):
ans = -loss
outcome = min([(my[i] + X[i]) for i in range(37)])
pos = [i for i in range(37) if my[i] + X[i] == outcome]
return outcome
def func_c0af9cbb47014f6eb1148ce294b63755(loss, my, X):
ans = -loss
outcome = min([(my[i] + X[i]) for i in range(37)])
pos = [i for i in range(37) if my[i] + X[i] == outcome]
return ans
def func_2167ceab7ba94af88eb3416fe17406f3(my, X):
outcome = min([(my[i] + X[i]) for i in range(37)])
pos = [i for i in range(37) if my[i] + X[i] == outcome]
N = len(pos)
return pos
def func_5baeeb3f4486435fbd696a0ee2217b73(my, X):
outcome = min([(my[i] + X[i]) for i in range(37)])
pos = [i for i in range(37) if my[i] + X[i] == outcome]
N = len(pos)
return i
def func_bb6cd200e85144609deae806bcb5da2b(my, X):
outcome = min([(my[i] + X[i]) for i in range(37)])
pos = [i for i in range(37) if my[i] + X[i] == outcome]
N = len(pos)
return outcome
def func_3cd0d89e850849d0a8bf7205427fe776(my, X):
outcome = min([(my[i] + X[i]) for i in range(37)])
pos = [i for i in range(37) if my[i] + X[i] == outcome]
N = len(pos)
return N
def func_c24b7f999b8846b2b58a32b7b2b14e34(my, outcome, X):
pos = [i for i in range(37) if my[i] + X[i] == outcome]
N = len(pos)
for p in pos:
ans += my[p] * 36 / N
return p
def func_60eac66e15e441a88e1ee66e3b135622(my, outcome, X):
pos = [i for i in range(37) if my[i] + X[i] == outcome]
N = len(pos)
for p in pos:
ans += my[p] * 36 / N
return N
def func_f95733a3a65a41179f6123459b171f98(my, outcome, X):
pos = [i for i in range(37) if my[i] + X[i] == outcome]
N = len(pos)
for p in pos:
ans += my[p] * 36 / N
return i
def func_5602d37319534be6b582cef9a45f5f07(my, outcome, X):
pos = [i for i in range(37) if my[i] + X[i] == outcome]
N = len(pos)
for p in pos:
ans += my[p] * 36 / N
return ans
def func_ae5e55b2ccb740e9b85ed741dc2b78e9(my, outcome, X):
pos = [i for i in range(37) if my[i] + X[i] == outcome]
N = len(pos)
for p in pos:
ans += my[p] * 36 / N
return pos
def func_3ef2dc7e25bf412088ab9f5d4b769b4a(my, pos):
N = len(pos)
for p in pos:
ans += my[p] * 36 / N
return ans
def func_aea17d7f5f994c06a7c4d408923ae223(loss, my, X):
ans = -loss
outcome = min([(my[i] + X[i]) for i in range(37)])
pos = [i for i in range(37) if my[i] + X[i] == outcome]
N = len(pos)
return i
def func_290a724eb63d4a8d9558c29e1c45f3b6(loss, my, X):
ans = -loss
outcome = min([(my[i] + X[i]) for i in range(37)])
pos = [i for i in range(37) if my[i] + X[i] == outcome]
N = len(pos)
return outcome
def func_b7e14f5586ff469c9eac03d87150e8af(loss, my, X):
ans = -loss
outcome = min([(my[i] + X[i]) for i in range(37)])
pos = [i for i in range(37) if my[i] + X[i] == outcome]
N = len(pos)
return ans
def func_149b78ef03bf4900b91e3b57ee8f0424(loss, my, X):
ans = -loss
outcome = min([(my[i] + X[i]) for i in range(37)])
pos = [i for i in range(37) if my[i] + X[i] == outcome]
N = len(pos)
return pos
def func_08d9f854f2cf42b0a44a3c72b45160ba(loss, my, X):
ans = -loss
outcome = min([(my[i] + X[i]) for i in range(37)])
pos = [i for i in range(37) if my[i] + X[i] == outcome]
N = len(pos)
return N
def func_3bc825cf969a440597e2d7baa0a8a937(my, X):
outcome = min([(my[i] + X[i]) for i in range(37)])
pos = [i for i in range(37) if my[i] + X[i] == outcome]
N = len(pos)
for p in pos:
ans += my[p] * 36 / N
return pos
def func_d8fefe9c714f40ad9552c1672f001d8c(my, X):
outcome = min([(my[i] + X[i]) for i in range(37)])
pos = [i for i in range(37) if my[i] + X[i] == outcome]
N = len(pos)
for p in pos:
ans += my[p] * 36 / N
return ans
def func_08845a5ce21b4dacbf6eddcbf12d13f2(my, X):
outcome = min([(my[i] + X[i]) for i in range(37)])
pos = [i for i in range(37) if my[i] + X[i] == outcome]
N = len(pos)
for p in pos:
ans += my[p] * 36 / N
return N
def func_e59e5f79e03c4231ae6b37bbdbbdc3e0(my, X):
outcome = min([(my[i] + X[i]) for i in range(37)])
pos = [i for i in range(37) if my[i] + X[i] == outcome]
N = len(pos)
for p in pos:
ans += my[p] * 36 / N
return i
def func_21f137333c8e4ef386e2a7c5b0b6c8c1(my, X):
outcome = min([(my[i] + X[i]) for i in range(37)])
pos = [i for i in range(37) if my[i] + X[i] == outcome]
N = len(pos)
for p in pos:
ans += my[p] * 36 / N
return outcome
def func_a18d0207901c445b915bf93c75f41f91(my, X):
outcome = min([(my[i] + X[i]) for i in range(37)])
pos = [i for i in range(37) if my[i] + X[i] == outcome]
N = len(pos)
for p in pos:
ans += my[p] * 36 / N
return p
def func_fb63a3f69c9a495ea822d7e6a4043399(my, outcome, X):
pos = [i for i in range(37) if my[i] + X[i] == outcome]
N = len(pos)
for p in pos:
ans += my[p] * 36 / N
return ans
def func_2ada7995ae3f44d8b7446ca2f353d128(loss, my, X):
ans = -loss
outcome = min([(my[i] + X[i]) for i in range(37)])
pos = [i for i in range(37) if my[i] + X[i] == outcome]
N = len(pos)
for p in pos:
ans += my[p] * 36 / N
return pos
def func_c0472114dd4c4b19a45b2810b44477e2(loss, my, X):
ans = -loss
outcome = min([(my[i] + X[i]) for i in range(37)])
pos = [i for i in range(37) if my[i] + X[i] == outcome]
N = len(pos)
for p in pos:
ans += my[p] * 36 / N
return N
def func_28b70e6b7c5745549191563956ed1fe3(loss, my, X):
ans = -loss
outcome = min([(my[i] + X[i]) for i in range(37)])
pos = [i for i in range(37) if my[i] + X[i] == outcome]
N = len(pos)
for p in pos:
ans += my[p] * 36 / N
return outcome
def func_69728911becc4267a13051b3454bb446(loss, my, X):
ans = -loss
outcome = min([(my[i] + X[i]) for i in range(37)])
pos = [i for i in range(37) if my[i] + X[i] == outcome]
N = len(pos)
for p in pos:
ans += my[p] * 36 / N
return i
def func_b5ac914892fd4930a4e06482eea694e3(loss, my, X):
ans = -loss
outcome = min([(my[i] + X[i]) for i in range(37)])
pos = [i for i in range(37) if my[i] + X[i] == outcome]
N = len(pos)
for p in pos:
ans += my[p] * 36 / N
return p
def func_eaf0da47a0fe44eaaf31b9f134130a89(loss, my, X):
ans = -loss
outcome = min([(my[i] + X[i]) for i in range(37)])
pos = [i for i in range(37) if my[i] + X[i] == outcome]
N = len(pos)
for p in pos:
ans += my[p] * 36 / N
return ans
def func_6393c92dc6f54135b5ffd35832998a17(my, X):
outcome = min([(my[i] + X[i]) for i in range(37)])
pos = [i for i in range(37) if my[i] + X[i] == outcome]
N = len(pos)
for p in pos:
ans += my[p] * 36 / N
return ans
def func_f3622d3ab964432d9cab162facbcb9bb(loss, my, X):
ans = -loss
outcome = min([(my[i] + X[i]) for i in range(37)])
pos = [i for i in range(37) if my[i] + X[i] == outcome]
N = len(pos)
for p in pos:
ans += my[p] * 36 / N
return ans
def func_816a02c14f0d4e3a875a337006e01ccb(my, X):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
return j
def func_b55fdd0b6c1d437c97bdf5b462688324(my, X):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
return m
def func_0db9cc0e0d6e4818bac150a9d3ed92de(my, X):
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
return m
def func_320ad5a8925148afbf580272070e4702(my, X):
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
return j
def func_dd84f8172f2844d9879406185f73d239(my, i, m, X):
my[m] += 1
p = profit(my, X, i)
return p
def func_b616838f9227401296c89091cbe34e26(my, i, max_profit, X):
p = profit(my, X, i)
if p > max_profit:
max_profit = p
return p
def func_f4531c4c4cc84293b512bdd53c3015cd(my, X):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
return j
def func_96a5fb2227f04a93bb374e541b24f4a8(my, X):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
return m
def func_608e0bc491ce47e78adae4f31640d5a1(my, i, X):
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
return p
def func_3f5734d3ab9e45959d1728c133ed07dd(my, i, X):
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
return m
def func_7474bbd11a224c55bacab8562ef3cf79(my, i, X):
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
return j
def func_5371328d0b33424a9f30c3dc508ba170(my, i, m, max_profit, X):
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
return p
def func_e74befa8dfc344a38463c1d63b25d551(my, i, X):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
return p
def func_3ea3ce21eaa64e8ebbecf64702eddc2a(my, i, X):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
return m
def func_706ef53b34c4461ea65e9ea38361857c(my, i, X):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
return j
def func_b95c1fdc7b1c496696bcfb01f33cd931(my, i, max_profit, X):
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
return j
def func_a37e8dec9dad4e158fcb02467c333c33(my, i, max_profit, X):
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
return p
def func_efba2bc62a8f467dae638d78a6bc9b95(my, i, max_profit, X):
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
return m
def func_423fdf1d639448ccb78469aaee6917c6(my, i, max_profit, X):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
return m
def func_af2cafc5a66b43cda500316ab65e2b29(my, i, max_profit, X):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
return j
def func_a55b9bd873e24026a058e0fdaa03fa85(my, i, max_profit, X):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
return p
def func_a56ff3907a424f49bf943cffd76267bb(X):
X.sort()
my = [(0) for i in range(37)]
return my
def func_657960a2da44455eabcf687390ac94c6(X):
X.sort()
my = [(0) for i in range(37)]
return i
def func_004dc868e8444870bcac4a410f3b0160(X):
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
return my
def func_e38766fe2c6c48e0b921f235e0e6c6bc(X):
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
return i
def func_f1e3ad8c245643fbb712cd17b575e3c2(X):
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
return max_profit
def func_ccd7b0e512f641369c0dd773e989b50b(my, B, X):
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
return i
def func_13cafab14e5747b3a37b7712d6527ed6(my, B, X):
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
return m
def func_5ae55a2b93e145e2b7c6f53943f190e2(my, B, X):
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
return p
def func_968e36e1ad6f401b89a333308f4f25b9(my, B, X):
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
return j
def func_963b3574c82744c7a684230a7856c2f8(my, B, X):
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
return max_profit
def func_7e88b0faa4004bb48a36caf4c2fe23a8(my, B, X):
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
return m
def func_d9fffa3009d84576b209e1f5497a6e19(my, B, X):
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
return i
def func_b9d836edcb3f4f5b9e7af6a247425dc2(my, B, X):
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
return j
def func_b1724a333ed6436da43e344e96dcf3d9(my, j, X):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
if my[j] < my[m]:
m = j
return m
def func_e7e262361c634aa8bfe9e6542d76ec05(my, j, m, max_profit):
if my[j] < my[m]:
m = j
return max_profit
def func_b41eca057f68466da97e900bc24ac8f2(X):
while len(X) < 37:
X.append(0)
X.sort()
my = [(0) for i in range(37)]
return my
def func_6bca8a77a72549deb9f4bbe93678197c(X):
while len(X) < 37:
X.append(0)
X.sort()
my = [(0) for i in range(37)]
return i
def func_416091ce393a4f4183bac3407291906f(X):
X.sort()
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
return my
def func_0324a053a11c419aa01e73724ff8310b(X):
X.sort()
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
return max_profit
def func_9e10581735bc4873b212fa66aeb924c2(X):
X.sort()
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
return i
def func_5cd11951c2314d7bae68c2c69b075aaf(B, X):
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
return i
def func_9a724c8adbb549048a17ac61722caede(B, X):
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
return m
def func_6697862bd83944f88d37429ce58149f8(B, X):
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
return max_profit
def func_7be475dbca4c4babb927c074c475e03c(B, X):
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
return j
def func_2579c2ffe70842f3a54efd06008a4a18(B, X):
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
return my
def func_e669c27dab2048338af11aa6fd8309da(B, X):
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
return p
def func_3504e10921a94e38974dc249c3b2c512(my, B, X):
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
return i
def func_bd97dd4a3e42439fbba8c2c7047465cc(my, B, X):
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
return max_profit
def func_92a850419d5f4b4dbeb4d6eb85fa406e(my, B, X):
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
return m
def func_1b1abbf1ae5143099559724b8efdbb07(my, B, X):
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
return j
def func_3b84949397364b2489eea01af1976c77(my, B, X):
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
if my[j] < my[m]:
m = j
return j
def func_cd34e8c6637f496081feb88fee22ac7b(my, B, X):
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
if my[j] < my[m]:
m = j
return m
def func_70caf9d192674736b9d673dc1b5d0fad(my, B, X):
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
if my[j] < my[m]:
m = j
return i
def func_42db33eaf781443889554b21f0a199cf(my, j, max_profit, X):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
if my[j] < my[m]:
m = j
return max_profit
def func_ed9fb7fd16f14b87ba8cc15b47deea9a(X):
while len(X) < 37:
X.append(0)
X.sort()
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
return my
def func_8ceea7c190b043a1b32f1a00d080939f(X):
while len(X) < 37:
X.append(0)
X.sort()
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
return max_profit
def func_6bd4c64547db46a591bbc9c6626b2dcc(X):
while len(X) < 37:
X.append(0)
X.sort()
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
return i
def func_5690e1f0b9c445a8b00de4b5d6b554fb(B, X):
X.sort()
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
return m
def func_3344b154c237477cb85024ca04bb1a50(B, X):
X.sort()
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
return max_profit
def func_3514df801a7d459d9160d99f5cae557c(B, X):
X.sort()
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
return p
def func_3133b7f0fbce4d2fbf94f04aca969e5d(B, X):
X.sort()
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
return my
def func_729a520a8dbb4ceb9d2bdbcbc600343c(B, X):
X.sort()
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
return i
def func_27fe6089dd9945b3819ddf6ec2f5e41f(B, X):
X.sort()
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
return j
def func_a9c8c6731dce4ddab563d099642a78e0(B, X):
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
return max_profit
def func_9508c53752bc41b7b194222fda2f1f14(B, X):
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
return my
def func_33faf2ed5a7242a6b3a99166520d383d(B, X):
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
return j
def func_8ae3a3bb5ca24c02b125829aff9b14ae(B, X):
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
return i
def func_b2814ddbf869472cb008702241ae5603(B, X):
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
return m
def func_da84661264a9443f91cd823eef21c7a4(my, B, X):
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
if my[j] < my[m]:
m = j
return max_profit
def func_cc13476452d14baab30ebe1f69206123(my, B, X):
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
if my[j] < my[m]:
m = j
return i
def func_ce0cf583620f4d47a5dc4aeee6c20be0(my, B, X):
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
if my[j] < my[m]:
m = j
return j
def func_35ecd2dc423842ada76b7f83e5c48406(my, B, X):
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
if my[j] < my[m]:
m = j
return m
def func_ec08fefb8fb94aa58d64cef2b7cc2f87(my, B, max_profit, X):
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
if my[j] < my[m]:
m = j
return max_profit
def func_dfef908f0e5c4871ad74aa606f0d472e(B, X):
while len(X) < 37:
X.append(0)
X.sort()
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
return p
def func_8029f12250224294b018909363fcd546(B, X):
while len(X) < 37:
X.append(0)
X.sort()
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
return m
def func_b501820b55214fa7bfe2c1b2ee50a1d0(B, X):
while len(X) < 37:
X.append(0)
X.sort()
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
return max_profit
def func_fea835b71a774ced91a890b1598142fc(B, X):
while len(X) < 37:
X.append(0)
X.sort()
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
return i
def func_7262441b45024857a7eae8bd2e0e6596(B, X):
while len(X) < 37:
X.append(0)
X.sort()
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
return my
def func_3b449f4c84c1430b9528fb0d456ebbc5(B, X):
while len(X) < 37:
X.append(0)
X.sort()
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
return j
def func_a448dac9783c45f8aa76721e0e782e47(B, X):
X.sort()
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
return j
def func_12abe89756904a3886e6f1145575587a(B, X):
X.sort()
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
return max_profit
def func_73580bc621a64ff6ba2e23416bbce134(B, X):
X.sort()
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
return m
def func_683e5c6775f84d32bd986925c61755b6(B, X):
X.sort()
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
return my
def func_8810a1286ab440858745735c21288665(B, X):
X.sort()
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
return i
def func_48adbb4c2c334e059ec62c545817f330(B, X):
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
if my[j] < my[m]:
m = j
return j
def func_517d00ad8f02451d8656112daf09cd9c(B, X):
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
if my[j] < my[m]:
m = j
return my
def func_2e4f7717fb9b4a0fa9e65cfbdc82d9bf(B, X):
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
if my[j] < my[m]:
m = j
return max_profit
def func_e736a886d3b344e1be9cd54d2f12db09(B, X):
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
if my[j] < my[m]:
m = j
return m
def func_86afe01fb6da4f088771426694923a0b(B, X):
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
if my[j] < my[m]:
m = j
return i
def func_b9e7f97288c54855a7e58627b2087e97(my, B, X):
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
if my[j] < my[m]:
m = j
return max_profit
def func_29dd2aee750b42a6b6e9a12783ddc8af(B, X):
while len(X) < 37:
X.append(0)
X.sort()
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
return i
def func_3f9ee0347b074fb3b0323f45d2f07b0c(B, X):
while len(X) < 37:
X.append(0)
X.sort()
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
return max_profit
def func_dadfc8f0947d4c0ebbcf1a170817ed6d(B, X):
while len(X) < 37:
X.append(0)
X.sort()
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
return my
def func_876824302da04334aba50eca0d0b0c9d(B, X):
while len(X) < 37:
X.append(0)
X.sort()
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
return m
def func_cbb93ec237dc43abb2ca09eefa183ec8(B, X):
while len(X) < 37:
X.append(0)
X.sort()
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
return j
def func_d47f2ac9f2f94d99b9c4d13802634943(B, X):
X.sort()
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
if my[j] < my[m]:
m = j
return j
def func_7300dd27d85f4f5b99e042ca04a0fbd3(B, X):
X.sort()
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
if my[j] < my[m]:
m = j
return m
def func_7e54cf60fe5e4a3882a25b07098fa4a5(B, X):
X.sort()
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
if my[j] < my[m]:
m = j
return max_profit
def func_d5995a1c5e994747b431996bb2f8dc41(B, X):
X.sort()
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
if my[j] < my[m]:
m = j
return i
def func_6cc41058435a4dc092790c7b3f0a0e71(B, X):
X.sort()
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
if my[j] < my[m]:
m = j
return my
def func_95cb3bf9cff043b1a22f355ef61bccb4(B, X):
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
if my[j] < my[m]:
m = j
return max_profit
def func_bc3ee22e330943a89357abd1221fa3d1(B, X):
while len(X) < 37:
X.append(0)
X.sort()
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
if my[j] < my[m]:
m = j
return max_profit
def func_b4948befaf40437ba9f01edc7bcc26a8(B, X):
while len(X) < 37:
X.append(0)
X.sort()
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
if my[j] < my[m]:
m = j
return m
def func_9d07a7610c4647f3857a3a2c3769bcfa(B, X):
while len(X) < 37:
X.append(0)
X.sort()
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
if my[j] < my[m]:
m = j
return j
def func_21606599258a4e51918e1a1ad163b758(B, X):
while len(X) < 37:
X.append(0)
X.sort()
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
if my[j] < my[m]:
m = j
return my
def func_7d1595dba64742ad863db298f1a94b12(B, X):
while len(X) < 37:
X.append(0)
X.sort()
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
if my[j] < my[m]:
m = j
return i
def func_c4c501983fde488592326e5d39514160(B, X):
X.sort()
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
if my[j] < my[m]:
m = j
return max_profit
def func_c223218c85fd4f9998fbfedc6f1c4533(B, X):
while len(X) < 37:
X.append(0)
X.sort()
my = [(0) for i in range(37)]
max_profit = profit(my, X, 0)
for i in range(1, B + 1):
m = 0
for j in range(1, 37):
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
my[m] += 1
p = profit(my, X, i)
if p > max_profit:
max_profit = p
if my[j] + X[j] < my[m] + X[m]:
m = j
elif my[j] + X[j] == my[m] + X[m]:
if my[j] < my[m]:
m = j
if my[j] < my[m]:
m = j
return max_profit
def func_3bd729a7b2ce4ac8840535a05926c393(infile):
B, N = map(int, infile.readline().strip().split(' '))
X = list(map(int, infile.readline().strip().split(' ')))
return X
def func_8f0b4a046d3c426bab586c368b394d68(infile):
B, N = map(int, infile.readline().strip().split(' '))
X = list(map(int, infile.readline().strip().split(' ')))
return N
def func_0bdf3f705ed546949a556d7f57df4870(infile):
B, N = map(int, infile.readline().strip().split(' '))
X = list(map(int, infile.readline().strip().split(' ')))
return B
def func_b73df6e35ad0470fac127a64e8848a83():
infile = open('codejam/test_files/Y13R5P1/A.in')
T = int(infile.readline())
return infile
def func_375f227a84d5414383f8bbe5e256f8cf():
infile = open('codejam/test_files/Y13R5P1/A.in')
T = int(infile.readline())
return T
def func_67bf032a277c4531b6e00139081a417c(infile):
T = int(infile.readline())
for test_case in range(1, T + 1):
B, N = map(int, infile.readline().strip().split(' '))
X = list(map(int, infile.readline().strip().split(' ')))
print 'Case #{0}: {1}'.format(test_case, solve(B, N, X))
return N
def func_d2d6c7aa8b71409eacb5aa97c7f493f6(infile):
T = int(infile.readline())
for test_case in range(1, T + 1):
B, N = map(int, infile.readline().strip().split(' '))
X = list(map(int, infile.readline().strip().split(' ')))
print 'Case #{0}: {1}'.format(test_case, solve(B, N, X))
return X
def func_eba75c8c5fb04f759b814e786314e9eb(infile):
T = int(infile.readline())
for test_case in range(1, T + 1):
B, N = map(int, infile.readline().strip().split(' '))
X = list(map(int, infile.readline().strip().split(' ')))
print 'Case #{0}: {1}'.format(test_case, solve(B, N, X))
return T
def func_59022ca7e079456fb1607a7c25426ee4(infile):
T = int(infile.readline())
for test_case in range(1, T + 1):
B, N = map(int, infile.readline().strip().split(' '))
X = list(map(int, infile.readline().strip().split(' ')))
print 'Case #{0}: {1}'.format(test_case, solve(B, N, X))
return B
def func_da4677eb697746f0b180160cea9a16d4(infile):
T = int(infile.readline())
for test_case in range(1, T + 1):
B, N = map(int, infile.readline().strip().split(' '))
X = list(map(int, infile.readline().strip().split(' ')))
print 'Case #{0}: {1}'.format(test_case, solve(B, N, X))
return test_case
def func_4ed70763b3a24f44a4cc94b8c14de3db(T, infile):
for test_case in range(1, T + 1):
B, N = map(int, infile.readline().strip().split(' '))
X = list(map(int, infile.readline().strip().split(' ')))
print 'Case #{0}: {1}'.format(test_case, solve(B, N, X))
infile.close()
return B
def func_8c71108884ec445ab2dbcbd6cfda201a(T, infile):
for test_case in range(1, T + 1):
B, N = map(int, infile.readline().strip().split(' '))
X = list(map(int, infile.readline().strip().split(' ')))
print 'Case #{0}: {1}'.format(test_case, solve(B, N, X))
infile.close()
return N
def func_2cd717c6e6ae44de93f248e95f2481c4(T, infile):
for test_case in range(1, T + 1):
B, N = map(int, infile.readline().strip().split(' '))
X = list(map(int, infile.readline().strip().split(' ')))
print 'Case #{0}: {1}'.format(test_case, solve(B, N, X))
infile.close()
return test_case
def func_ebf0a08569dd48499623f02f19fcaa14(T, infile):
for test_case in range(1, T + 1):
B, N = map(int, infile.readline().strip().split(' '))
X = list(map(int, infile.readline().strip().split(' ')))
print 'Case #{0}: {1}'.format(test_case, solve(B, N, X))
infile.close()
return X
def func_235243a711fb4ef1a72945391745f1b1():
infile = open('codejam/test_files/Y13R5P1/A.in')
T = int(infile.readline())
for test_case in range(1, T + 1):
B, N = map(int, infile.readline().strip().split(' '))
X = list(map(int, infile.readline().strip().split(' ')))
print 'Case #{0}: {1}'.format(test_case, solve(B, N, X))
return infile
def func_b9d385c5db294d4fb6fcf2a1e1e976b5():
infile = open('codejam/test_files/Y13R5P1/A.in')
T = int(infile.readline())
for test_case in range(1, T + 1):
B, N = map(int, infile.readline().strip().split(' '))
X = list(map(int, infile.readline().strip().split(' ')))
print 'Case #{0}: {1}'.format(test_case, solve(B, N, X))
return B
def func_cb9fd90f2e39412fb0182ed976f05c77():
infile = open('codejam/test_files/Y13R5P1/A.in')
T = int(infile.readline())
for test_case in range(1, T + 1):
B, N = map(int, infile.readline().strip().split(' '))
X = list(map(int, infile.readline().strip().split(' ')))
print 'Case #{0}: {1}'.format(test_case, solve(B, N, X))
return N
def func_e7737f5804ff46c3a4ceecd2a8331fbf():
infile = open('codejam/test_files/Y13R5P1/A.in')
T = int(infile.readline())
for test_case in range(1, T + 1):
B, N = map(int, infile.readline().strip().split(' '))
X = list(map(int, infile.readline().strip().split(' ')))
print 'Case #{0}: {1}'.format(test_case, solve(B, N, X))
return T
def func_ff2200fd734f4d7ea037e991054e6d1b():
infile = open('codejam/test_files/Y13R5P1/A.in')
T = int(infile.readline())
for test_case in range(1, T + 1):
B, N = map(int, infile.readline().strip().split(' '))
X = list(map(int, infile.readline().strip().split(' ')))
print 'Case #{0}: {1}'.format(test_case, solve(B, N, X))
return X
def func_e2603893251a40e194db74d6cba2a153():
infile = open('codejam/test_files/Y13R5P1/A.in')
T = int(infile.readline())
for test_case in range(1, T + 1):
B, N = map(int, infile.readline().strip().split(' '))
X = list(map(int, infile.readline().strip().split(' ')))
print 'Case #{0}: {1}'.format(test_case, solve(B, N, X))
return test_case
def func_1caff92d1115439d9d129392e25322ba(infile):
T = int(infile.readline())
for test_case in range(1, T + 1):
B, N = map(int, infile.readline().strip().split(' '))
X = list(map(int, infile.readline().strip().split(' ')))
print 'Case #{0}: {1}'.format(test_case, solve(B, N, X))
infile.close()
return N
def func_44b6c047596f4749a5bfd4bf7c3f3910(infile):
T = int(infile.readline())
for test_case in range(1, T + 1):
B, N = map(int, infile.readline().strip().split(' '))
X = list(map(int, infile.readline().strip().split(' ')))
print 'Case #{0}: {1}'.format(test_case, solve(B, N, X))
infile.close()
return X
def func_e9443df68e164a4cb6c6972bc15789cf(infile):
T = int(infile.readline())
for test_case in range(1, T + 1):
B, N = map(int, infile.readline().strip().split(' '))
X = list(map(int, infile.readline().strip().split(' ')))
print 'Case #{0}: {1}'.format(test_case, solve(B, N, X))
infile.close()
return test_case
def func_2ea0fbf270b24cdd9b2b4eef3a647ec1(infile):
T = int(infile.readline())
for test_case in range(1, T + 1):
B, N = map(int, infile.readline().strip().split(' '))
X = list(map(int, infile.readline().strip().split(' ')))
print 'Case #{0}: {1}'.format(test_case, solve(B, N, X))
infile.close()
return T
def func_37d36f86dab343409e035bac4894777e(infile):
T = int(infile.readline())
for test_case in range(1, T + 1):
B, N = map(int, infile.readline().strip().split(' '))
X = list(map(int, infile.readline().strip().split(' ')))
print 'Case #{0}: {1}'.format(test_case, solve(B, N, X))
infile.close()
return B
def func_ac49a66c2a3445c8874ceead620c0146():
infile = open('codejam/test_files/Y13R5P1/A.in')
T = int(infile.readline())
for test_case in range(1, T + 1):
B, N = map(int, infile.readline().strip().split(' '))
X = list(map(int, infile.readline().strip().split(' ')))
print 'Case #{0}: {1}'.format(test_case, solve(B, N, X))
infile.close()
return X
def func_d40383a2e8864f9f82e415de092fcc02():
infile = open('codejam/test_files/Y13R5P1/A.in')
T = int(infile.readline())
for test_case in range(1, T + 1):
B, N = map(int, infile.readline().strip().split(' '))
X = list(map(int, infile.readline().strip().split(' ')))
print 'Case #{0}: {1}'.format(test_case, solve(B, N, X))
infile.close()
return N
def func_015d362ff6b544a6a136dcbb956e7f0d():
infile = open('codejam/test_files/Y13R5P1/A.in')
T = int(infile.readline())
for test_case in range(1, T + 1):
B, N = map(int, infile.readline().strip().split(' '))
X = list(map(int, infile.readline().strip().split(' ')))
print 'Case #{0}: {1}'.format(test_case, solve(B, N, X))
infile.close()
return T
def func_726e5edf1c5347d5b50acaf7ca2f9875():
infile = open('codejam/test_files/Y13R5P1/A.in')
T = int(infile.readline())
for test_case in range(1, T + 1):
B, N = map(int, infile.readline().strip().split(' '))
X = list(map(int, infile.readline().strip().split(' ')))
print 'Case #{0}: {1}'.format(test_case, solve(B, N, X))
infile.close()
return test_case
def func_85984027653444d49aa650b488069cbd():
infile = open('codejam/test_files/Y13R5P1/A.in')
T = int(infile.readline())
for test_case in range(1, T + 1):
B, N = map(int, infile.readline().strip().split(' '))
X = list(map(int, infile.readline().strip().split(' ')))
print 'Case #{0}: {1}'.format(test_case, solve(B, N, X))
infile.close()
return infile
def func_79c06e4eda8d49b6912547c160d7bed1():
infile = open('codejam/test_files/Y13R5P1/A.in')
T = int(infile.readline())
for test_case in range(1, T + 1):
B, N = map(int, infile.readline().strip().split(' '))
X = list(map(int, infile.readline().strip().split(' ')))
print 'Case #{0}: {1}'.format(test_case, solve(B, N, X))
infile.close()
return B
| 26.647015
| 86
| 0.450881
| 11,455
| 70,961
| 2.747272
| 0.021912
| 0.051764
| 0.068764
| 0.044805
| 0.80054
| 0.800127
| 0.800127
| 0.800127
| 0.800127
| 0.799809
| 0
| 0.115372
| 0.384507
| 70,961
| 2,662
| 87
| 26.657025
| 0.605161
| 0
| 0
| 0.916994
| 0
| 0
| 0.013007
| 0.00706
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0.000874
| null | null | 0.011359
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
7160dfa7c65527f16c82e9e1df56f257e3dc6515
| 2,370
|
py
|
Python
|
alembic/versions/8cdbfc9dc6d3_rename_tweet_removed_for_clarity.py
|
jonathanzong/dmca
|
70157cff983310e5951024aa80e99e7a5404d758
|
[
"MIT"
] | 2
|
2022-02-16T22:50:06.000Z
|
2022-02-21T19:38:02.000Z
|
alembic/versions/8cdbfc9dc6d3_rename_tweet_removed_for_clarity.py
|
jonathanzong/dmca
|
70157cff983310e5951024aa80e99e7a5404d758
|
[
"MIT"
] | 2
|
2022-02-01T05:48:07.000Z
|
2022-02-01T05:49:29.000Z
|
alembic/versions/8cdbfc9dc6d3_rename_tweet_removed_for_clarity.py
|
jonathanzong/bartleby
|
70157cff983310e5951024aa80e99e7a5404d758
|
[
"MIT"
] | null | null | null |
"""Rename tweet_removed for clarity
Revision ID: 8cdbfc9dc6d3
Revises: 29b5c90d0fc3
Create Date: 2017-12-17 18:26:28.774252
"""
# revision identifiers, used by Alembic.
revision = '8cdbfc9dc6d3'
down_revision = '29b5c90d0fc3'
branch_labels = None
depends_on = None
from alembic import op
import sqlalchemy as sa
from sqlalchemy.dialects import mysql
def upgrade(engine_name):
globals()["upgrade_%s" % engine_name]()
def downgrade(engine_name):
globals()["downgrade_%s" % engine_name]()
def upgrade_development():
# ### commands auto generated by Alembic - please adjust! ###
op.add_column('twitter_user_metadata', sa.Column('tweet_removed', sa.Boolean(), nullable=True))
op.drop_column('twitter_user_metadata', 'twitter_removed')
# ### end Alembic commands ###
def downgrade_development():
# ### commands auto generated by Alembic - please adjust! ###
op.add_column('twitter_user_metadata', sa.Column('twitter_removed', mysql.TINYINT(display_width=1), autoincrement=False, nullable=True))
op.drop_column('twitter_user_metadata', 'tweet_removed')
# ### end Alembic commands ###
def upgrade_test():
# ### commands auto generated by Alembic - please adjust! ###
op.add_column('twitter_user_metadata', sa.Column('tweet_removed', sa.Boolean(), nullable=True))
op.drop_column('twitter_user_metadata', 'twitter_removed')
# ### end Alembic commands ###
def downgrade_test():
# ### commands auto generated by Alembic - please adjust! ###
op.add_column('twitter_user_metadata', sa.Column('twitter_removed', mysql.TINYINT(display_width=1), autoincrement=False, nullable=True))
op.drop_column('twitter_user_metadata', 'tweet_removed')
# ### end Alembic commands ###
def upgrade_production():
# ### commands auto generated by Alembic - please adjust! ###
op.add_column('twitter_user_metadata', sa.Column('tweet_removed', sa.Boolean(), nullable=True))
op.drop_column('twitter_user_metadata', 'twitter_removed')
# ### end Alembic commands ###
def downgrade_production():
# ### commands auto generated by Alembic - please adjust! ###
op.add_column('twitter_user_metadata', sa.Column('twitter_removed', mysql.TINYINT(display_width=1), autoincrement=False, nullable=True))
op.drop_column('twitter_user_metadata', 'tweet_removed')
# ### end Alembic commands ###
| 33.380282
| 140
| 0.721941
| 291
| 2,370
| 5.649485
| 0.233677
| 0.118613
| 0.124088
| 0.182482
| 0.768856
| 0.768856
| 0.768856
| 0.768856
| 0.768856
| 0.768856
| 0
| 0.022102
| 0.140928
| 2,370
| 70
| 141
| 33.857143
| 0.785363
| 0.262447
| 0
| 0.413793
| 0
| 0
| 0.281741
| 0.152358
| 0
| 0
| 0
| 0
| 0
| 1
| 0.275862
| false
| 0
| 0.103448
| 0
| 0.37931
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
71a4387b2a71a63ef594a703cd43d915feb4c8dc
| 3,016
|
py
|
Python
|
Lib/pagebot/elements/textconditions.py
|
bghryct/PageBot
|
394150c0fd399f02faec28f4576046882f4d7d39
|
[
"MIT"
] | 68
|
2018-10-22T22:42:58.000Z
|
2022-03-19T11:07:31.000Z
|
Lib/pagebot/elements/textconditions.py
|
TypeNetwork/PageBot
|
394150c0fd399f02faec28f4576046882f4d7d39
|
[
"MIT"
] | 97
|
2017-07-10T23:49:30.000Z
|
2018-10-03T08:17:55.000Z
|
Lib/pagebot/elements/textconditions.py
|
TypeNetwork/PageBot
|
394150c0fd399f02faec28f4576046882f4d7d39
|
[
"MIT"
] | 9
|
2017-07-11T09:59:00.000Z
|
2018-09-12T11:59:30.000Z
|
#!/usr/bin/env python3
# -*- coding: UTF-8 -*-
# -----------------------------------------------------------------------------
#
# P A G E B O T
#
# Copyright (c) 2016+ Buro Petr van Blokland + Claudia Mens
# www.pagebot.io
# Licensed under MIT conditions
#
# Supporting DrawBot, www.drawbot.com
# Supporting Flat, xxyxyz.org/flat
# -----------------------------------------------------------------------------
#
# textconditions.py
#
class TextConditions:
def baseline2Top(self):
# Implemented for elements that support text boxes. Default is to do
# nothing for non-text elements.
pass
def baseline2Middle(self):
# Implemented for elements that support text boxes. Default is to do
# nothing for non-text elements.
pass
def baseline2Bottom(self):
# Implemented for elements that support text boxes. Default is to do
# nothing for non-text elements.
pass
def capHeight2Top(self):
# Implemented for elements that support text boxes. Default is to do
# nothing for non-text elements.
pass
def capHeight2Middle(self):
# Implemented for elements that support text boxes. Default is to do
# nothing for non-text elements.
pass
def capHeight2Bottom(self):
# Implemented for elements that support text boxes. Default is to do
# nothing for non-text elements.
pass
def xHeight2Top(self):
# Implemented for elements that support text boxes. Default is to do
# nothing for non-text elements.
pass
def xHeight2Middle(self):
# Implemented for elements that support text boxes. Default is to do
# nothing for non-text elements.
pass
def xHeight2Bottom(self):
# Implemented for elements that support text boxes. Default is to do
# nothing for non-text elements.
pass
def ascender2Top(self):
# Implemented for elements that support text boxes. Default is to do
# nothing for non-text elements.
pass
def ascender2Middle(self):
# Implemented for elements that support text boxes. Default is to do
# nothing for non-text elements.
pass
def ascender2Bottom(self):
# Implemented for elements that support text boxes. Default is to do
# nothing for non-text elements.
pass
def descender2Top(self):
# Implemented for elements that support text boxes. Default is to do
# nothing for non-text elements.
pass
def descender2Middle(self):
# Implemented for elements that support text boxes. Default is to do
# nothing for non-text elements.
pass
def descender2Bottom(self):
# Implemented for elements that support text boxes. Default is to do
# nothing for non-text elements.
pass
if __name__ == '__main__':
import doctest
import sys
sys.exit(doctest.testmod()[0])
| 30.464646
| 79
| 0.612401
| 356
| 3,016
| 5.16573
| 0.22191
| 0.122349
| 0.146819
| 0.212072
| 0.74062
| 0.74062
| 0.74062
| 0.74062
| 0.74062
| 0.74062
| 0
| 0.01011
| 0.278515
| 3,016
| 98
| 80
| 30.77551
| 0.835018
| 0.630305
| 0
| 0.428571
| 0
| 0
| 0.007484
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.428571
| false
| 0.428571
| 0.057143
| 0
| 0.514286
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 1
| 0
|
0
| 9
|
71b2e4d8754f667462ccedef68f6c3b289936207
| 132
|
py
|
Python
|
tests/test044.py
|
takipsizad/pyjs
|
54db0ba6747aca744f9f3c3e985a17e913dfb951
|
[
"ECL-2.0",
"Apache-2.0"
] | 739
|
2015-01-01T02:05:11.000Z
|
2022-03-30T15:26:16.000Z
|
tests/test044.py
|
takipsizad/pyjs
|
54db0ba6747aca744f9f3c3e985a17e913dfb951
|
[
"ECL-2.0",
"Apache-2.0"
] | 33
|
2015-03-25T23:17:04.000Z
|
2021-08-19T08:25:22.000Z
|
tests/test044.py
|
takipsizad/pyjs
|
54db0ba6747aca744f9f3c3e985a17e913dfb951
|
[
"ECL-2.0",
"Apache-2.0"
] | 167
|
2015-01-01T22:27:47.000Z
|
2022-03-17T13:29:19.000Z
|
def test():
x = -y
x = y + z
x = y - z
x = y * z
x = y / z
x = y % z
x = ~y
x = y & z
x = y | z
| 12
| 13
| 0.242424
| 27
| 132
| 1.185185
| 0.185185
| 0.5625
| 0.65625
| 0.75
| 0.78125
| 0.78125
| 0.78125
| 0.78125
| 0.53125
| 0.53125
| 0
| 0
| 0.598485
| 132
| 10
| 14
| 13.2
| 0.603774
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.1
| false
| 0
| 0
| 0
| 0.1
| 0
| 1
| 0
| 1
| null | 1
| 1
| 1
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 1
| 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
| 9
|
71d54927755967fcf9fa5027a3d24ba86fd445aa
| 184
|
py
|
Python
|
tests/transformer/operators/test_unary_operators.py
|
rahulbahal7/restricted-python
|
c39cffe71dfc30630e946977735303d3a65b0383
|
[
"ZPL-2.1"
] | 236
|
2015-01-03T17:14:53.000Z
|
2022-03-01T15:52:46.000Z
|
tests/transformer/operators/test_unary_operators.py
|
rahulbahal7/restricted-python
|
c39cffe71dfc30630e946977735303d3a65b0383
|
[
"ZPL-2.1"
] | 149
|
2016-10-24T06:56:44.000Z
|
2022-02-24T08:09:10.000Z
|
tests/transformer/operators/test_unary_operators.py
|
rahulbahal7/restricted-python
|
c39cffe71dfc30630e946977735303d3a65b0383
|
[
"ZPL-2.1"
] | 30
|
2015-04-03T05:38:13.000Z
|
2021-11-10T05:13:38.000Z
|
from tests.helper import restricted_eval
def test_UAdd():
assert restricted_eval('+a', {'a': 42}) == 42
def test_USub():
assert restricted_eval('-a', {'a': 2411}) == -2411
| 18.4
| 54
| 0.641304
| 26
| 184
| 4.346154
| 0.538462
| 0.371681
| 0.353982
| 0.371681
| 0.389381
| 0
| 0
| 0
| 0
| 0
| 0
| 0.078947
| 0.173913
| 184
| 9
| 55
| 20.444444
| 0.664474
| 0
| 0
| 0
| 0
| 0
| 0.032609
| 0
| 0
| 0
| 0
| 0
| 0.4
| 1
| 0.4
| true
| 0
| 0.2
| 0
| 0.6
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 1
| 0
|
0
| 8
|
e085bde55a57b3be7aa8b52b7eadc6176ff3bd14
| 195
|
py
|
Python
|
toss/accounts/admin.py
|
toss-app/toss-backend
|
66765bb350cb165340163ce34186dfc69d38dc02
|
[
"Apache-2.0"
] | null | null | null |
toss/accounts/admin.py
|
toss-app/toss-backend
|
66765bb350cb165340163ce34186dfc69d38dc02
|
[
"Apache-2.0"
] | 1
|
2016-10-22T21:32:35.000Z
|
2016-10-22T21:32:35.000Z
|
toss/accounts/admin.py
|
toss-app/toss-backend
|
66765bb350cb165340163ce34186dfc69d38dc02
|
[
"Apache-2.0"
] | null | null | null |
from .models import Account
from django.contrib.auth.admin import UserAdmin
from django.contrib import admin
class AccountAdmin(UserAdmin):
pass
admin.site.register(Account, AccountAdmin)
| 19.5
| 47
| 0.810256
| 25
| 195
| 6.32
| 0.56
| 0.126582
| 0.21519
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.123077
| 195
| 9
| 48
| 21.666667
| 0.923977
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.166667
| 0.5
| 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
| 1
| 1
| 0
| 1
| 0
|
0
| 7
|
e088a40acaac64d56b2845f13df625936e4211e9
| 16,426
|
py
|
Python
|
Python_OCR_JE/venv/Lib/site-packages/numpy/typing/tests/data/reveal/arithmetic.py
|
JE-Chen/je_old_repo
|
a8b2f1ac2eec25758bd15b71c64b59b27e0bcda5
|
[
"MIT"
] | null | null | null |
Python_OCR_JE/venv/Lib/site-packages/numpy/typing/tests/data/reveal/arithmetic.py
|
JE-Chen/je_old_repo
|
a8b2f1ac2eec25758bd15b71c64b59b27e0bcda5
|
[
"MIT"
] | null | null | null |
Python_OCR_JE/venv/Lib/site-packages/numpy/typing/tests/data/reveal/arithmetic.py
|
JE-Chen/je_old_repo
|
a8b2f1ac2eec25758bd15b71c64b59b27e0bcda5
|
[
"MIT"
] | 1
|
2021-04-26T22:41:56.000Z
|
2021-04-26T22:41:56.000Z
|
import numpy as np
c16 = np.complex128()
f8 = np.float64()
i8 = np.int64()
u8 = np.uint64()
c8 = np.complex64()
f4 = np.float32()
i4 = np.int32()
u4 = np.uint32()
dt = np.datetime64(0, "D")
td = np.timedelta64(0, "D")
b_ = np.bool_()
b = bool()
c = complex()
f = float()
i = int()
AR = np.array([0], dtype=np.float64)
AR.setflags(write=False)
# unary ops
reveal_type(-c16) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(-c8) # E: numpy.complexfloating[numpy.typing._32Bit, numpy.typing._32Bit]
reveal_type(-f8) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(-f4) # E: numpy.floating[numpy.typing._32Bit]
reveal_type(-i8) # E: numpy.signedinteger[numpy.typing._64Bit]
reveal_type(-i4) # E: numpy.signedinteger[numpy.typing._32Bit]
reveal_type(-u8) # E: numpy.unsignedinteger[numpy.typing._64Bit]
reveal_type(-u4) # E: numpy.unsignedinteger[numpy.typing._32Bit]
reveal_type(-td) # E: numpy.timedelta64
reveal_type(-AR) # E: Any
reveal_type(+c16) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(+c8) # E: numpy.complexfloating[numpy.typing._32Bit, numpy.typing._32Bit]
reveal_type(+f8) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(+f4) # E: numpy.floating[numpy.typing._32Bit]
reveal_type(+i8) # E: numpy.signedinteger[numpy.typing._64Bit]
reveal_type(+i4) # E: numpy.signedinteger[numpy.typing._32Bit]
reveal_type(+u8) # E: numpy.unsignedinteger[numpy.typing._64Bit]
reveal_type(+u4) # E: numpy.unsignedinteger[numpy.typing._32Bit]
reveal_type(+td) # E: numpy.timedelta64
reveal_type(+AR) # E: Any
reveal_type(abs(c16)) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(abs(c8)) # E: numpy.floating[numpy.typing._32Bit]
reveal_type(abs(f8)) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(abs(f4)) # E: numpy.floating[numpy.typing._32Bit]
reveal_type(abs(i8)) # E: numpy.signedinteger[numpy.typing._64Bit]
reveal_type(abs(i4)) # E: numpy.signedinteger[numpy.typing._32Bit]
reveal_type(abs(u8)) # E: numpy.unsignedinteger[numpy.typing._64Bit]
reveal_type(abs(u4)) # E: numpy.unsignedinteger[numpy.typing._32Bit]
reveal_type(abs(td)) # E: numpy.timedelta64
reveal_type(abs(b_)) # E: numpy.bool_
reveal_type(abs(AR)) # E: Any
# Time structures
reveal_type(dt + td) # E: numpy.datetime64
reveal_type(dt + i) # E: numpy.datetime64
reveal_type(dt + i4) # E: numpy.datetime64
reveal_type(dt + i8) # E: numpy.datetime64
reveal_type(dt - dt) # E: numpy.timedelta64
reveal_type(dt - i) # E: numpy.datetime64
reveal_type(dt - i4) # E: numpy.datetime64
reveal_type(dt - i8) # E: numpy.datetime64
reveal_type(td + td) # E: numpy.timedelta64
reveal_type(td + i) # E: numpy.timedelta64
reveal_type(td + i4) # E: numpy.timedelta64
reveal_type(td + i8) # E: numpy.timedelta64
reveal_type(td - td) # E: numpy.timedelta64
reveal_type(td - i) # E: numpy.timedelta64
reveal_type(td - i4) # E: numpy.timedelta64
reveal_type(td - i8) # E: numpy.timedelta64
reveal_type(td / f) # E: numpy.timedelta64
reveal_type(td / f4) # E: numpy.timedelta64
reveal_type(td / f8) # E: numpy.timedelta64
reveal_type(td / td) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(td // td) # E: numpy.signedinteger[numpy.typing._64Bit]
# boolean
reveal_type(b_ / b) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(b_ / b_) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(b_ / i) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(b_ / i8) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(b_ / i4) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(b_ / u8) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(b_ / u4) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(b_ / f) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(b_ / f8) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(b_ / f4) # E: numpy.floating[numpy.typing._32Bit]
reveal_type(b_ / c) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(b_ / c16) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(b_ / c8) # E: numpy.complexfloating[numpy.typing._32Bit, numpy.typing._32Bit]
reveal_type(b / b_) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(b_ / b_) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(i / b_) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(i8 / b_) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(i4 / b_) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(u8 / b_) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(u4 / b_) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(f / b_) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(f8 / b_) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(f4 / b_) # E: numpy.floating[numpy.typing._32Bit]
reveal_type(c / b_) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(c16 / b_) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(c8 / b_) # E: numpy.complexfloating[numpy.typing._32Bit, numpy.typing._32Bit]
# Complex
reveal_type(c16 + c16) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(c16 + f8) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(c16 + i8) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(c16 + c8) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(c16 + f4) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(c16 + i4) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(c16 + b_) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(c16 + b) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(c16 + c) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(c16 + f) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(c16 + i) # E: numpy.complexfloating[Any, Any]
reveal_type(c16 + AR) # E: Any
reveal_type(c16 + c16) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(f8 + c16) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(i8 + c16) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(c8 + c16) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(f4 + c16) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(i4 + c16) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(b_ + c16) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(b + c16) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(c + c16) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(f + c16) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(i + c16) # E: numpy.complexfloating[Any, Any]
reveal_type(AR + c16) # E: Any
reveal_type(c8 + c16) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(c8 + f8) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(c8 + i8) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(c8 + c8) # E: numpy.complexfloating[numpy.typing._32Bit, numpy.typing._32Bit]
reveal_type(c8 + f4) # E: numpy.complexfloating[numpy.typing._32Bit, numpy.typing._32Bit]
reveal_type(c8 + i4) # E: numpy.complexfloating[numpy.typing._32Bit, numpy.typing._32Bit]
reveal_type(c8 + b_) # E: numpy.complexfloating[numpy.typing._32Bit, numpy.typing._32Bit]
reveal_type(c8 + b) # E: numpy.complexfloating[numpy.typing._32Bit, numpy.typing._32Bit]
reveal_type(c8 + c) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(c8 + f) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(c8 + i) # E: numpy.complexfloating[Any, Any]
reveal_type(c8 + AR) # E: Any
reveal_type(c16 + c8) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(f8 + c8) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(i8 + c8) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(c8 + c8) # E: numpy.complexfloating[numpy.typing._32Bit, numpy.typing._32Bit]
reveal_type(f4 + c8) # E: numpy.complexfloating[numpy.typing._32Bit, numpy.typing._32Bit]
reveal_type(i4 + c8) # E: numpy.complexfloating[numpy.typing._32Bit, numpy.typing._32Bit]
reveal_type(b_ + c8) # E: numpy.complexfloating[numpy.typing._32Bit, numpy.typing._32Bit]
reveal_type(b + c8) # E: numpy.complexfloating[numpy.typing._32Bit, numpy.typing._32Bit]
reveal_type(c + c8) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(f + c8) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(i + c8) # E: numpy.complexfloating[Any, Any]
reveal_type(AR + c8) # E: Any
# Float
reveal_type(f8 + f8) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(f8 + i8) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(f8 + f4) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(f8 + i4) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(f8 + b_) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(f8 + b) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(f8 + c) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(f8 + f) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(f8 + i) # E: numpy.floating[Any]
reveal_type(f8 + AR) # E: Any
reveal_type(f8 + f8) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(i8 + f8) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(f4 + f8) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(i4 + f8) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(b_ + f8) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(b + f8) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(c + f8) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(f + f8) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(i + f8) # E: numpy.floating[Any]
reveal_type(AR + f8) # E: Any
reveal_type(f4 + f8) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(f4 + i8) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(f4 + f4) # E: numpy.floating[numpy.typing._32Bit]
reveal_type(f4 + i4) # E: numpy.floating[numpy.typing._32Bit]
reveal_type(f4 + b_) # E: numpy.floating[numpy.typing._32Bit]
reveal_type(f4 + b) # E: numpy.floating[numpy.typing._32Bit]
reveal_type(f4 + c) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(f4 + f) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(f4 + i) # E: numpy.floating[Any]
reveal_type(f4 + AR) # E: Any
reveal_type(f8 + f4) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(i8 + f4) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(f4 + f4) # E: umpy.floating[numpy.typing._32Bit]
reveal_type(i4 + f4) # E: umpy.floating[numpy.typing._32Bit]
reveal_type(b_ + f4) # E: umpy.floating[numpy.typing._32Bit]
reveal_type(b + f4) # E: umpy.floating[numpy.typing._32Bit]
reveal_type(c + f4) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(f + f4) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(i + f4) # E: numpy.floating[Any]
reveal_type(AR + f4) # E: Any
# Int
reveal_type(i8 + i8) # E: numpy.signedinteger[numpy.typing._64Bit]
reveal_type(i8 + u8) # E: Union[numpy.signedinteger[Any], numpy.floating[numpy.typing._64Bit]]
reveal_type(i8 + i4) # E: numpy.signedinteger[numpy.typing._64Bit]
reveal_type(i8 + u4) # E: Union[numpy.signedinteger[Any], numpy.floating[numpy.typing._64Bit]]
reveal_type(i8 + b_) # E: numpy.signedinteger[numpy.typing._64Bit]
reveal_type(i8 + b) # E: numpy.signedinteger[numpy.typing._64Bit]
reveal_type(i8 + c) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(i8 + f) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(i8 + i) # E: numpy.signedinteger[Any]
reveal_type(i8 + AR) # E: Any
reveal_type(u8 + u8) # E: numpy.unsignedinteger[numpy.typing._64Bit]
reveal_type(u8 + i4) # E: Union[numpy.signedinteger[Any], numpy.floating[numpy.typing._64Bit]]
reveal_type(u8 + u4) # E: numpy.unsignedinteger[numpy.typing._64Bit]
reveal_type(u8 + b_) # E: numpy.unsignedinteger[numpy.typing._64Bit]
reveal_type(u8 + b) # E: numpy.unsignedinteger[numpy.typing._64Bit]
reveal_type(u8 + c) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(u8 + f) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(u8 + i) # E: Union[numpy.signedinteger[Any], numpy.floating[numpy.typing._64Bit]]
reveal_type(u8 + AR) # E: Any
reveal_type(i8 + i8) # E: numpy.signedinteger[numpy.typing._64Bit]
reveal_type(u8 + i8) # E: Union[numpy.signedinteger[Any], numpy.floating[numpy.typing._64Bit]]
reveal_type(i4 + i8) # E: numpy.signedinteger[numpy.typing._64Bit]
reveal_type(u4 + i8) # E: Union[numpy.signedinteger[Any], numpy.floating[numpy.typing._64Bit]]
reveal_type(b_ + i8) # E: numpy.signedinteger[numpy.typing._64Bit]
reveal_type(b + i8) # E: numpy.signedinteger[numpy.typing._64Bit]
reveal_type(c + i8) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(f + i8) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(i + i8) # E: numpy.signedinteger[Any]
reveal_type(AR + i8) # E: Any
reveal_type(u8 + u8) # E: numpy.unsignedinteger[numpy.typing._64Bit]
reveal_type(i4 + u8) # E: Union[numpy.signedinteger[Any], numpy.floating[numpy.typing._64Bit]]
reveal_type(u4 + u8) # E: numpy.unsignedinteger[numpy.typing._64Bit]
reveal_type(b_ + u8) # E: numpy.unsignedinteger[numpy.typing._64Bit]
reveal_type(b + u8) # E: numpy.unsignedinteger[numpy.typing._64Bit]
reveal_type(c + u8) # E: numpy.complexfloating[numpy.typing._64Bit, numpy.typing._64Bit]
reveal_type(f + u8) # E: numpy.floating[numpy.typing._64Bit]
reveal_type(i + u8) # E: Union[numpy.signedinteger[Any], numpy.floating[numpy.typing._64Bit]]
reveal_type(AR + u8) # E: Any
reveal_type(i4 + i8) # E: numpy.signedinteger[numpy.typing._64Bit]
reveal_type(i4 + i4) # E: numpy.signedinteger[numpy.typing._32Bit]
reveal_type(i4 + i) # E: numpy.signedinteger[Any]
reveal_type(i4 + b_) # E: numpy.signedinteger[numpy.typing._32Bit]
reveal_type(i4 + b) # E: numpy.signedinteger[numpy.typing._32Bit]
reveal_type(i4 + AR) # E: Any
reveal_type(u4 + i8) # E: Union[numpy.signedinteger[Any], numpy.floating[numpy.typing._64Bit]]
reveal_type(u4 + i4) # E: Union[numpy.signedinteger[Any], numpy.floating[numpy.typing._64Bit]]
reveal_type(u4 + u8) # E: numpy.unsignedinteger[numpy.typing._64Bit]
reveal_type(u4 + u4) # E: numpy.unsignedinteger[numpy.typing._32Bit]
reveal_type(u4 + i) # E: Union[numpy.signedinteger[Any], numpy.floating[numpy.typing._64Bit]]
reveal_type(u4 + b_) # E: numpy.unsignedinteger[numpy.typing._32Bit]
reveal_type(u4 + b) # E: numpy.unsignedinteger[numpy.typing._32Bit]
reveal_type(u4 + AR) # E: Any
reveal_type(i8 + i4) # E: numpy.signedinteger[numpy.typing._64Bit]
reveal_type(i4 + i4) # E: numpy.signedinteger[numpy.typing._32Bit]
reveal_type(i + i4) # E: numpy.signedinteger[Any]
reveal_type(b_ + i4) # E: numpy.signedinteger[numpy.typing._32Bit]
reveal_type(b + i4) # E: numpy.signedinteger[numpy.typing._32Bit]
reveal_type(AR + i4) # E: Any
reveal_type(i8 + u4) # E: Union[numpy.signedinteger[Any], numpy.floating[numpy.typing._64Bit]]
reveal_type(i4 + u4) # E: Union[numpy.signedinteger[Any], numpy.floating[numpy.typing._64Bit]]
reveal_type(u8 + u4) # E: numpy.unsignedinteger[numpy.typing._64Bit]
reveal_type(u4 + u4) # E: numpy.unsignedinteger[numpy.typing._32Bit]
reveal_type(b_ + u4) # E: numpy.unsignedinteger[numpy.typing._32Bit]
reveal_type(b + u4) # E: numpy.unsignedinteger[numpy.typing._32Bit]
reveal_type(i + u4) # E: Union[numpy.signedinteger[Any], numpy.floating[numpy.typing._64Bit]]
reveal_type(AR + u4) # E: Any
| 56.253425
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| 4.690506
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| 0.252541
| 0.966001
| 0.96346
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| null | 1
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0
| 9
|
e0c7c7ede1f7ec2e3ef570565f3fc23c32063d8b
| 17,588
|
py
|
Python
|
qa327_test/Frontend/test_R6.py
|
lucidorangee/CISC-327-Course-Project
|
b86fe58e809f10a90134cbe33202c9e68a46d13b
|
[
"MIT"
] | null | null | null |
qa327_test/Frontend/test_R6.py
|
lucidorangee/CISC-327-Course-Project
|
b86fe58e809f10a90134cbe33202c9e68a46d13b
|
[
"MIT"
] | null | null | null |
qa327_test/Frontend/test_R6.py
|
lucidorangee/CISC-327-Course-Project
|
b86fe58e809f10a90134cbe33202c9e68a46d13b
|
[
"MIT"
] | null | null | null |
import pytest
from seleniumbase import BaseCase
from qa327_test.conftest import base_url
from unittest.mock import patch
from qa327.models import db, User, TicketInfo
from werkzeug.security import generate_password_hash, check_password_hash
"""
This file defines unit tests for the frontend homepage.
The tests will only test the frontend portion of the program, by patching the backend to return
specfic values. For example:
@patch('qa327.backend.get_user', return_value=test_user)
Will patch the backend get_user function (within the scope of the current test case)
so that it return 'test_user' instance below rather than reading
the user from the database.
Annotate @patch before unit tests can mock backend methods (for that testing function)
"""
# Mock a smple user (login)
test_user_login = User(
email='login@gmail.com',
name='LetsTestL',
password=generate_password_hash('Tester327!'),
balance=10000
)
# Moch some sample tickets
test_tickets = TicketInfo(
email='login@gmail.com',
name='t1',
quantity=100,
price=10,
date='20210408'
)
class TestR6(BaseCase):
# Test Case R6.0.1
@pytest.mark.timeout(60)
@patch('qa327.backend.get_user', return_value=test_user_login)
@patch('qa327.backend.get_ticket', return_value=test_tickets)
def test_positive_buy(self, *_):
"""
Checking for positive case for the fields of ticket's buy form with lower boundaries
"""
# open logout page to invalid any logged-in sessions that may exist, then open login page
self.open(base_url + '/logout')
self.open(base_url + '/')
# test that redirection to /login has occurred
# fill email and password
self.type("#email", test_user_login.email)
self.type("#password", "Tester327!")
# click enter button
self.click('input[type="submit"]')
# enter buy ticket form with low values
self.type("#name_buy", "t1")
self.type("#quantity_buy", "1")
# click buy button
self.click('input[value="Buy"]')
# assert no error text appears
self.assert_text_not_visible("Ticket name must be alphanumeric-only", "#message") # TODO update these asserts
self.assert_text_not_visible("Ticket name cannot begin with a space", "#message")
self.assert_text_not_visible("Ticket name cannot end with a space", "#message")
self.assert_text_not_visible("Ticket name cannot be longer than 60 characters", "#message")
self.assert_text_not_visible("At least 1 ticket must be sold", "#message")
self.assert_text_not_visible("At most 100 tickets can be sold", "#message")
self.assert_text_not_visible("Price of the ticket cannot be below 10", "#message")
self.assert_text_not_visible("Price of the ticket cannot be above 100", "#message")
self.assert_text_not_visible("Expiration date is in invalid format", "#message")
# open logout (for cleanup)
self.open(base_url + '/logout')
# Test Case R6.0.1
@pytest.mark.timeout(60)
@patch('qa327.backend.get_user', return_value=test_user_login)
@patch('qa327.backend.get_ticket', return_value=test_tickets)
def test_positive_buy2(self, *_):
"""
Checking for positive case for the fields of ticket's buy form with upper boundaries
"""
# open logout page to invalid any logged-in sessions that may exist, then open login page
self.open(base_url + '/logout')
self.open(base_url + '/')
# test that redirection to /login has occurred
# fill email and password
self.type("#email", test_user_login.email)
self.type("#password", "Tester327!")
# click enter button
self.click('input[type="submit"]')
# enter buy ticket form with high values
self.type("#name_buy", "t1")
self.type("#quantity_buy", "100") #TODO price exceeds user balance adjust ticket price
# click buy button
self.click('input[value="Buy"]')
# assert no error text appears
self.assert_text_not_visible("Ticket name must be alphanumeric-only", "#message")
self.assert_text_not_visible("Ticket name cannot begin with a space", "#message")
self.assert_text_not_visible("Ticket name cannot end with a space", "#message")
self.assert_text_not_visible("Ticket name cannot be longer than 60 characters",
"#message") # TODO update these asserts
self.assert_text_not_visible("At most 100 tickets can be sold", "#message")
self.assert_text_not_visible("Price of the ticket cannot be below 10", "#message")
self.assert_text_not_visible("Price of the ticket cannot be above 100", "#message")
self.assert_text_not_visible("Expiration date is in invalid format", "#message")
# open logout (for cleanup)
self.open(base_url + '/logout')
# Test Case R6.1.1
@pytest.mark.timeout(60)
@patch('qa327.backend.get_user', return_value=test_user_login)
@patch('qa327.backend.get_ticket', return_value=test_tickets)
def test_alphanumeric_only(self, *_):
"""
Check if name of the ticket is alphanumeric-only
"""
# open logout page to invalid any logged-in sessions that may exist, then open login page
self.open(base_url + '/logout')
self.open(base_url + '/')
# test that redirection to /login has occurred
# fill email and password
self.type("#email", test_user_login.email)
self.type("#password", "Tester327!")
# click enter button
self.click('input[type="submit"]')
# enter buy ticket form with low values
self.type("#name_buy", "Ht1&t2@!*\")(/.,<>[]-+")
self.type("#quantity_buy", "1")
# click buy button
self.click('input[value="Buy"]')
# assert proper error message
self.assert_text("The name of the ticket has to be alphanumeric-only, and space allowed only if it is not the "
"first or the last character.", "#buy_message")
# open logout (for cleanup)
self.open(base_url + '/logout')
# Test Case R6.1.2
@pytest.mark.timeout(60)
@patch('qa327.backend.get_user', return_value=test_user_login)
@patch('qa327.backend.get_ticket', return_value=test_tickets)
def test_spaces_only(self, *_):
"""
Check space is not allowed as first character
"""
# open logout page to invalid any logged-in sessions that may exist, then open login page
self.open(base_url + '/logout')
self.open(base_url + '/')
# test that redirection to /login has occurred
# fill email and password
self.type("#email", test_user_login.email)
self.type("#password", "Tester327!")
# click enter button
self.click('input[type="submit"]')
# enter buy ticket form with low values
self.type("#name_buy", " t1")
self.type("#quantity_buy", "1")
# click buy button
self.click('input[value="Buy"]')
# assert proper error message
self.assert_text("The name of the ticket has to be alphanumeric-only, and space allowed only if it is not the "
"first or the last character.", "#buy_message")
# open logout (for cleanup)
self.open(base_url + '/logout')
# Test Case R6.1.3
@pytest.mark.timeout(60)
@patch('qa327.backend.get_user', return_value=test_user_login)
@patch('qa327.backend.get_ticket', return_value=test_tickets)
def test_spaces_only2(self, *_):
"""
Check space is not allowed as last character
"""
# open logout page to invalid any logged-in sessions that may exist, then open login page
self.open(base_url + '/logout')
self.open(base_url + '/')
# test that redirection to /login has occurred
# fill email and password
self.type("#email", test_user_login.email)
self.type("#password", "Tester327!")
# click enter button
self.click('input[type="submit"]')
# enter buy ticket form with low values
self.type("#name_buy", "t1 ")
self.type("#quantity_buy", "1")
# click buy button
self.click('input[value="Buy"]')
# assert proper error message
self.assert_text("The name of the ticket has to be alphanumeric-only, and space allowed only if it is not the "
"first or the last character.", "#buy_message")
# open logout (for cleanup)
self.open(base_url + '/logout')
# Test Case R6.2.1
@pytest.mark.timeout(60)
@patch('qa327.backend.get_user', return_value=test_user_login)
@patch('qa327.backend.get_ticket', return_value=test_tickets)
def test_name_length(self, *_):
"""
The name of the ticket is no longer than 60 characters
"""
# open logout page to invalid any logged-in sessions that may exist, then open login page
self.open(base_url + '/logout')
self.open(base_url + '/')
# test that redirection to /login has occurred
# fill email and password
self.type("#email", test_user_login.email)
self.type("#password", "Tester327!")
# click enter button
self.click('input[type="submit"]')
# enter buy ticket form with low values
self.type("#name_buy", "abcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyzabcdefghi")
self.type("#quantity_buy", "1")
# click buy button
self.click('input[value="Buy"]')
# assert proper error message
self.assert_text("Ticket name cannot be longer than 60 characters", "#buy_message")
# open logout (for cleanup)
self.open(base_url + '/logout')
# Test Case R6.3.1
@pytest.mark.timeout(60)
@patch('qa327.backend.get_user', return_value=test_user_login)
@patch('qa327.backend.get_ticket', return_value=test_tickets)
def test_quantity_bound(self, *_):
"""
The quantity of the tickets has to be more than 0
"""
# open logout page to invalid any logged-in sessions that may exist, then open login page
self.open(base_url + '/logout')
self.open(base_url + '/')
# test that redirection to /login has occurred
# fill email and password
self.type("#email", test_user_login.email)
self.type("#password", "Tester327!")
# click enter button
self.click('input[type="submit"]')
# enter buy ticket form with low values
self.type("#name_buy", "t1")
self.type("#quantity_buy", "0")
# click buy button
self.click('input[value="Buy"]')
# assert proper error message
self.assert_text("The quantity of the tickets has to be more than 0, and less than or equal to 100.",
"#buy_message")
# open logout (for cleanup)
self.open(base_url + '/logout')
# Test Case R6.3.2
@pytest.mark.timeout(60)
@patch('qa327.backend.get_user', return_value=test_user_login)
@patch('qa327.backend.get_ticket', return_value=test_tickets)
def test_quantity_bound2(self, *_):
"""
The quantity of the tickets has to be less than or equal to 100
"""
# open logout page to invalid any logged-in sessions that may exist, then open login page
self.open(base_url + '/logout')
self.open(base_url + '/')
# test that redirection to /login has occurred
# fill email and password
self.type("#email", test_user_login.email)
self.type("#password", "Tester327!")
# click enter button
self.click('input[type="submit"]')
# enter buy ticket form with low values
self.type("#name_buy", "t1")
self.type("#quantity_buy", "101")
# click buy button
self.click('input[value="Buy"]')
# assert proper error message
self.assert_text("The quantity of the tickets has to be more than 0, and less than or equal to 100.",
"#buy_message")
# open logout (for cleanup)
self.open(base_url + '/logout')
# Test Case R6.4.1
@pytest.mark.timeout(60)
@patch('qa327.backend.get_user', return_value=test_user_login)
def test_ticket_exist(self, *_):
"""
The ticket of the given name must exist
"""
# open logout page to invalid any logged-in sessions that may exist, then open login page
self.open(base_url + '/logout')
self.open(base_url + '/')
# test that redirection to /login has occurred
# fill email and password
self.type("#email", test_user_login.email)
self.type("#password", "Tester327!")
# click enter button
self.click('input[type="submit"]')
# enter buy ticket form with low values
self.type("#name_buy", "thisDoesNotExist")
self.type("#quantity_buy", "1")
# click buy button
self.click('input[value="Buy"]')
# assert proper error message
self.assert_text("The ticket of the given name must exist", "#buy_message")
# open logout (for cleanup)
self.open(base_url + '/logout')
# Moch some sample tickets
test_tickets = TicketInfo(
email='login@gmail.com',
name='t1',
quantity=10,
price=10,
date='20210408'
)
# Test Case R6.4.2
@pytest.mark.timeout(60)
@patch('qa327.backend.get_user', return_value=test_user_login)
@patch('qa327.backend.get_ticket', return_value=test_tickets)
def test_quanity_more_bought(self, *_):
"""
The ticket quantity is more than the quantity bought - negative
"""
# open logout page to invalid any logged-in sessions that may exist, then open login page
self.open(base_url + '/logout')
self.open(base_url + '/')
# test that redirection to /login has occurred
# fill email and password
self.type("#email", test_user_login.email)
self.type("#password", "Tester327!")
# click enter button
self.click('input[type="submit"]')
# enter buy ticket form with low values
self.type("#name_buy", "t1")
self.type("#quantity_buy", "11")
# click buy button
self.click('input[value="Buy"]')
# assert proper error message
self.assert_text("ticket quantity cannot exceed more than what is listed", "#buy_message")
# open logout (for cleanup)
self.open(base_url + '/logout')
# Test Case R6.6.1
@pytest.mark.timeout(60)
@patch('qa327.backend.get_user', return_value=test_user_login)
@patch('qa327.backend.get_ticket', return_value=test_tickets)
def test_redirect_buy(self, *_):
"""
For any errors, redirect back to / and show an error message
"""
# open logout page to invalid any logged-in sessions that may exist, then open login page
self.open(base_url + '/logout')
self.open(base_url + '/')
# test that redirection to /login has occurred
# fill email and password
self.type("#email", test_user_login.email)
self.type("#password", "Tester327!")
# click enter button
self.click('input[type="submit"]')
# enter buy ticket form with low values
self.type("#name_buy", "%@#$%@^^#$%&^%$&")
self.type("#quantity_buy", "1")
# click buy button
self.click('input[value="Buy"]')
# assert proper header
self.assert_element("#welcome-header")
# open logout (for cleanup)
self.open(base_url + '/logout')
# Mock a smple user (login)
test_user_login = User(
email='login@gmail.com',
name='LetsTestL',
password=generate_password_hash('Tester327!'),
balance=800
)
# Moch some sample tickets
test_tickets = TicketInfo(
email='login@gmail.com',
name='t1',
quantity=15,
price=100,
date='20210408'
)
# Test Case R6.5.1
@pytest.mark.timeout(60)
@patch('qa327.backend.get_user', return_value=test_user_login)
@patch('qa327.backend.get_ticket', return_value=test_tickets)
def test_balance(self, *_):
"""
The user has less balance than the ticket price * quantity + service fee (35%) + tax (5%)
"""
# open logout page to invalid any logged-in sessions that may exist, then open login page
self.open(base_url + '/logout')
self.open(base_url + '/')
# test that redirection to /login has occurred
# fill email and password
self.type("#email", test_user_login.email)
self.type("#password", "Tester327!")
# click enter button
self.click('input[type="submit"]')
# enter buy ticket form with low values
self.type("#name_buy", "t1")
self.type("#quantity_buy", "12")
# click buy button
self.click('input[value="Buy"]')
# assert proper error message
self.assert_text("The user has less balance than the ticket price * quantity + service fee (35%) + tax (5%)", "#buy_message")
# open logout (for cleanup)
self.open(base_url + '/logout')
| 37.742489
| 133
| 0.623607
| 2,298
| 17,588
| 4.645344
| 0.09356
| 0.035972
| 0.040468
| 0.050585
| 0.890867
| 0.883279
| 0.881405
| 0.873724
| 0.863607
| 0.852178
| 0
| 0.023469
| 0.263532
| 17,588
| 465
| 134
| 37.823656
| 0.800664
| 0.260689
| 0
| 0.789474
| 0
| 0
| 0.301828
| 0.048706
| 0
| 0
| 0
| 0.004301
| 0.118421
| 1
| 0.052632
| false
| 0.065789
| 0.026316
| 0
| 0.096491
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 8
|
1ca4e37de2c9936340f6f120a25751989a58c08b
| 76
|
py
|
Python
|
ahgp/inference/__init__.py
|
Francois-Aubet/AHGP
|
3ecdd01d138f013ae8da196fbf3a71632aa2cd88
|
[
"MIT"
] | 19
|
2020-12-09T22:18:23.000Z
|
2021-12-18T10:06:18.000Z
|
ahgp/inference/__init__.py
|
Francois-Aubet/AHGP
|
3ecdd01d138f013ae8da196fbf3a71632aa2cd88
|
[
"MIT"
] | null | null | null |
ahgp/inference/__init__.py
|
Francois-Aubet/AHGP
|
3ecdd01d138f013ae8da196fbf3a71632aa2cd88
|
[
"MIT"
] | 3
|
2020-12-14T04:13:43.000Z
|
2021-05-17T05:38:28.000Z
|
from ahgp.inference.hyperparam import *
from ahgp.inference.predict import *
| 38
| 39
| 0.828947
| 10
| 76
| 6.3
| 0.6
| 0.253968
| 0.539683
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.092105
| 76
| 2
| 40
| 38
| 0.913043
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
1cf4a5fa90a378d9171f5ca4e41f568ecb0b5392
| 10,474
|
py
|
Python
|
src/larksuiteoapi/service/drive_permission/v1/api.py
|
keeperlibofan/oapi-sdk-python
|
5743dd34eb0bfc30693e9b6e6f5cf35ac82edb26
|
[
"Apache-2.0"
] | null | null | null |
src/larksuiteoapi/service/drive_permission/v1/api.py
|
keeperlibofan/oapi-sdk-python
|
5743dd34eb0bfc30693e9b6e6f5cf35ac82edb26
|
[
"Apache-2.0"
] | null | null | null |
src/larksuiteoapi/service/drive_permission/v1/api.py
|
keeperlibofan/oapi-sdk-python
|
5743dd34eb0bfc30693e9b6e6f5cf35ac82edb26
|
[
"Apache-2.0"
] | null | null | null |
# -*- coding: UTF-8 -*-
# Code generated by lark suite oapi sdk gen
from typing import *
from ....api import Request, Response, set_timeout, set_tenant_key, set_user_access_token, set_path_params, \
set_query_params, set_response_stream, set_is_response_stream, FormData, FormDataFile
from ....config import Config
from ....consts import ACCESS_TOKEN_TYPE_TENANT, ACCESS_TOKEN_TYPE_USER, ACCESS_TOKEN_TYPE_APP
from .model import *
class Service(object):
def __init__(self, conf):
# type: (Config) -> None
self.conf = conf
self.members = MemberService(self)
self.publics = PublicService(self)
class MemberService(object):
def __init__(self, service):
# type: (Service) -> None
self.service = service
def create(self, body, tenant_key=None, user_access_token=None, timeout=None):
# type: (MemberCreateReqBody, str, str, int) -> MemberCreateReqCall
request_opts = [] # type: List[Callable[[Any], Any]]
if timeout is not None:
request_opts += [set_timeout(timeout)]
if tenant_key is not None:
request_opts += [set_tenant_key(tenant_key)]
if user_access_token is not None:
request_opts += [set_user_access_token(user_access_token)]
return MemberCreateReqCall(self, body, request_opts=request_opts)
def delete(self, body, tenant_key=None, user_access_token=None, timeout=None):
# type: (MemberDeleteReqBody, str, str, int) -> MemberDeleteReqCall
request_opts = [] # type: List[Callable[[Any], Any]]
if timeout is not None:
request_opts += [set_timeout(timeout)]
if tenant_key is not None:
request_opts += [set_tenant_key(tenant_key)]
if user_access_token is not None:
request_opts += [set_user_access_token(user_access_token)]
return MemberDeleteReqCall(self, body, request_opts=request_opts)
def list(self, body, tenant_key=None, user_access_token=None, timeout=None):
# type: (MemberListReqBody, str, str, int) -> MemberListReqCall
request_opts = [] # type: List[Callable[[Any], Any]]
if timeout is not None:
request_opts += [set_timeout(timeout)]
if tenant_key is not None:
request_opts += [set_tenant_key(tenant_key)]
if user_access_token is not None:
request_opts += [set_user_access_token(user_access_token)]
return MemberListReqCall(self, body, request_opts=request_opts)
def permitted(self, body, tenant_key=None, user_access_token=None, timeout=None):
# type: (MemberPermittedReqBody, str, str, int) -> MemberPermittedReqCall
request_opts = [] # type: List[Callable[[Any], Any]]
if timeout is not None:
request_opts += [set_timeout(timeout)]
if tenant_key is not None:
request_opts += [set_tenant_key(tenant_key)]
if user_access_token is not None:
request_opts += [set_user_access_token(user_access_token)]
return MemberPermittedReqCall(self, body, request_opts=request_opts)
def transfer(self, body, tenant_key=None, user_access_token=None, timeout=None):
# type: (MemberTransferReqBody, str, str, int) -> MemberTransferReqCall
request_opts = [] # type: List[Callable[[Any], Any]]
if timeout is not None:
request_opts += [set_timeout(timeout)]
if tenant_key is not None:
request_opts += [set_tenant_key(tenant_key)]
if user_access_token is not None:
request_opts += [set_user_access_token(user_access_token)]
return MemberTransferReqCall(self, body, request_opts=request_opts)
def update(self, body, tenant_key=None, user_access_token=None, timeout=None):
# type: (MemberUpdateReqBody, str, str, int) -> MemberUpdateReqCall
request_opts = [] # type: List[Callable[[Any], Any]]
if timeout is not None:
request_opts += [set_timeout(timeout)]
if tenant_key is not None:
request_opts += [set_tenant_key(tenant_key)]
if user_access_token is not None:
request_opts += [set_user_access_token(user_access_token)]
return MemberUpdateReqCall(self, body, request_opts=request_opts)
class PublicService(object):
def __init__(self, service):
# type: (Service) -> None
self.service = service
def update(self, body, tenant_key=None, user_access_token=None, timeout=None):
# type: (PublicUpdateReqBody, str, str, int) -> PublicUpdateReqCall
request_opts = [] # type: List[Callable[[Any], Any]]
if timeout is not None:
request_opts += [set_timeout(timeout)]
if tenant_key is not None:
request_opts += [set_tenant_key(tenant_key)]
if user_access_token is not None:
request_opts += [set_user_access_token(user_access_token)]
return PublicUpdateReqCall(self, body, request_opts=request_opts)
class MemberCreateReqCall(object):
def __init__(self, service, body, request_opts=None):
# type: (MemberService, MemberCreateReqBody, List[Any]) -> None
self.service = service
self.body = body
if request_opts:
self.request_opts = request_opts
else:
self.request_opts = [] # type: List[Any]
def do(self):
# type: () -> Response[MemberCreateResult]
root_service = self.service.service
conf = root_service.conf
req = Request('drive/permission/member/create', 'POST', [ACCESS_TOKEN_TYPE_USER, ACCESS_TOKEN_TYPE_TENANT],
self.body, output_class=MemberCreateResult, request_opts=self.request_opts)
resp = req.do(conf)
return resp
class MemberDeleteReqCall(object):
def __init__(self, service, body, request_opts=None):
# type: (MemberService, MemberDeleteReqBody, List[Any]) -> None
self.service = service
self.body = body
if request_opts:
self.request_opts = request_opts
else:
self.request_opts = [] # type: List[Any]
def do(self):
# type: () -> Response[MemberDeleteResult]
root_service = self.service.service
conf = root_service.conf
req = Request('drive/permission/member/delete', 'POST', [ACCESS_TOKEN_TYPE_USER, ACCESS_TOKEN_TYPE_TENANT],
self.body, output_class=MemberDeleteResult, request_opts=self.request_opts)
resp = req.do(conf)
return resp
class MemberListReqCall(object):
def __init__(self, service, body, request_opts=None):
# type: (MemberService, MemberListReqBody, List[Any]) -> None
self.service = service
self.body = body
if request_opts:
self.request_opts = request_opts
else:
self.request_opts = [] # type: List[Any]
def do(self):
# type: () -> Response[MemberListResult]
root_service = self.service.service
conf = root_service.conf
req = Request('drive/permission/member/list', 'POST', [ACCESS_TOKEN_TYPE_USER, ACCESS_TOKEN_TYPE_TENANT],
self.body, output_class=MemberListResult, request_opts=self.request_opts)
resp = req.do(conf)
return resp
class MemberPermittedReqCall(object):
def __init__(self, service, body, request_opts=None):
# type: (MemberService, MemberPermittedReqBody, List[Any]) -> None
self.service = service
self.body = body
if request_opts:
self.request_opts = request_opts
else:
self.request_opts = [] # type: List[Any]
def do(self):
# type: () -> Response[MemberPermittedResult]
root_service = self.service.service
conf = root_service.conf
req = Request('drive/permission/member/permitted', 'POST', [ACCESS_TOKEN_TYPE_USER, ACCESS_TOKEN_TYPE_TENANT],
self.body, output_class=MemberPermittedResult, request_opts=self.request_opts)
resp = req.do(conf)
return resp
class MemberTransferReqCall(object):
def __init__(self, service, body, request_opts=None):
# type: (MemberService, MemberTransferReqBody, List[Any]) -> None
self.service = service
self.body = body
if request_opts:
self.request_opts = request_opts
else:
self.request_opts = [] # type: List[Any]
def do(self):
# type: () -> Response[MemberTransferResult]
root_service = self.service.service
conf = root_service.conf
req = Request('drive/permission/member/transfer', 'POST', [ACCESS_TOKEN_TYPE_USER, ACCESS_TOKEN_TYPE_TENANT],
self.body, output_class=MemberTransferResult, request_opts=self.request_opts)
resp = req.do(conf)
return resp
class MemberUpdateReqCall(object):
def __init__(self, service, body, request_opts=None):
# type: (MemberService, MemberUpdateReqBody, List[Any]) -> None
self.service = service
self.body = body
if request_opts:
self.request_opts = request_opts
else:
self.request_opts = [] # type: List[Any]
def do(self):
# type: () -> Response[MemberUpdateResult]
root_service = self.service.service
conf = root_service.conf
req = Request('drive/permission/member/update', 'POST', [ACCESS_TOKEN_TYPE_USER, ACCESS_TOKEN_TYPE_TENANT],
self.body, output_class=MemberUpdateResult, request_opts=self.request_opts)
resp = req.do(conf)
return resp
class PublicUpdateReqCall(object):
def __init__(self, service, body, request_opts=None):
# type: (PublicService, PublicUpdateReqBody, List[Any]) -> None
self.service = service
self.body = body
if request_opts:
self.request_opts = request_opts
else:
self.request_opts = [] # type: List[Any]
def do(self):
# type: () -> Response[PublicUpdateResult]
root_service = self.service.service
conf = root_service.conf
req = Request('drive/permission/public/update', 'POST', [ACCESS_TOKEN_TYPE_USER, ACCESS_TOKEN_TYPE_TENANT],
self.body, output_class=PublicUpdateResult, request_opts=self.request_opts)
resp = req.do(conf)
return resp
| 34.006494
| 118
| 0.64684
| 1,211
| 10,474
| 5.327828
| 0.078448
| 0.155146
| 0.08602
| 0.052077
| 0.764414
| 0.764414
| 0.764414
| 0.723187
| 0.723187
| 0.723187
| 0
| 0.000128
| 0.254249
| 10,474
| 307
| 119
| 34.117264
| 0.825887
| 0.159633
| 0
| 0.751351
| 1
| 0
| 0.027511
| 0.024315
| 0
| 0
| 0
| 0
| 0
| 1
| 0.12973
| false
| 0
| 0.027027
| 0
| 0.286486
| 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
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
1c0586a6a884f119e6c3cf985d623d0ffa8961c6
| 2,470
|
py
|
Python
|
Days/Day 8 - I Heard You Like Registers/Part 1.py
|
jamesjiang52/Advent-of-Code-2017
|
94c85696e1335d7b5b00717a0e5f31c3653ba394
|
[
"MIT"
] | null | null | null |
Days/Day 8 - I Heard You Like Registers/Part 1.py
|
jamesjiang52/Advent-of-Code-2017
|
94c85696e1335d7b5b00717a0e5f31c3653ba394
|
[
"MIT"
] | null | null | null |
Days/Day 8 - I Heard You Like Registers/Part 1.py
|
jamesjiang52/Advent-of-Code-2017
|
94c85696e1335d7b5b00717a0e5f31c3653ba394
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
"""
Created on Fri Dec 8 00:06:18 2017
@author: James Jiang
"""
all_lines = [line.rstrip('\n') for line in open('Data.txt')]
instructions = []
for line in all_lines:
strings = line.split()
instructions.append(strings)
values_dict = {}
for i in range(len(instructions)):
values_dict[instructions[i][0]] = 0
for i in range(len(instructions)):
if instructions[i][-2] == '>':
if values_dict[instructions[i][-3]] > int(instructions[i][-1]):
if instructions[i][1] == 'inc':
values_dict[instructions[i][0]] += int(instructions[i][2])
else:
values_dict[instructions[i][0]] -= int(instructions[i][2])
if instructions[i][-2] == '<':
if values_dict[instructions[i][-3]] < int(instructions[i][-1]):
if instructions[i][1] == 'inc':
values_dict[instructions[i][0]] += int(instructions[i][2])
else:
values_dict[instructions[i][0]] -= int(instructions[i][2])
if instructions[i][-2] == '>=':
if values_dict[instructions[i][-3]] >= int(instructions[i][-1]):
if instructions[i][1] == 'inc':
values_dict[instructions[i][0]] += int(instructions[i][2])
else:
values_dict[instructions[i][0]] -= int(instructions[i][2])
if instructions[i][-2] == '<=':
if values_dict[instructions[i][-3]] <= int(instructions[i][-1]):
if instructions[i][1] == 'inc':
values_dict[instructions[i][0]] += int(instructions[i][2])
else:
values_dict[instructions[i][0]] -= int(instructions[i][2])
if instructions[i][-2] == '==':
if values_dict[instructions[i][-3]] == int(instructions[i][-1]):
if instructions[i][1] == 'inc':
values_dict[instructions[i][0]] += int(instructions[i][2])
else:
values_dict[instructions[i][0]] -= int(instructions[i][2])
if instructions[i][-2] == '!=':
if values_dict[instructions[i][-3]] != int(instructions[i][-1]):
if instructions[i][1] == 'inc':
values_dict[instructions[i][0]] += int(instructions[i][2])
else:
values_dict[instructions[i][0]] -= int(instructions[i][2])
maximum = values_dict[instructions[0][0]]
for i in values_dict:
if values_dict[i] > maximum:
maximum = values_dict[i]
print(maximum)
| 38
| 74
| 0.548583
| 308
| 2,470
| 4.314935
| 0.13961
| 0.479308
| 0.331076
| 0.328819
| 0.804364
| 0.779533
| 0.740406
| 0.740406
| 0.740406
| 0.740406
| 0
| 0.034896
| 0.25749
| 2,470
| 64
| 75
| 38.59375
| 0.689749
| 0.032389
| 0
| 0.52
| 0
| 0
| 0.01596
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.02
| 0
| 0
| 0
| null | 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 9
|
1c43614701fd1d9f4f8204066b3f979895e61bb2
| 140
|
py
|
Python
|
mcc_f1/__init__.py
|
arthurcgusmao/py-mcc-f1
|
d1b7cb856fbf03faad6a9eeeaea08da049c603c0
|
[
"MIT"
] | 7
|
2020-10-26T21:33:40.000Z
|
2022-02-14T10:56:06.000Z
|
mcc_f1/__init__.py
|
arthurcgusmao/py-mcc-f1
|
d1b7cb856fbf03faad6a9eeeaea08da049c603c0
|
[
"MIT"
] | 1
|
2022-02-13T19:17:15.000Z
|
2022-02-13T19:17:15.000Z
|
mcc_f1/__init__.py
|
arthurcgusmao/py-mcc-f1
|
d1b7cb856fbf03faad6a9eeeaea08da049c603c0
|
[
"MIT"
] | 1
|
2022-02-14T10:56:08.000Z
|
2022-02-14T10:56:08.000Z
|
from .mcc_f1_curve import mcc_f1_curve
from ._plot.mcc_f1_curve import plot_mcc_f1_curve
from ._plot.mcc_f1_curve import MCCF1CurveDisplay
| 28
| 49
| 0.871429
| 25
| 140
| 4.36
| 0.28
| 0.229358
| 0.458716
| 0.440367
| 0.623853
| 0.623853
| 0.623853
| 0.623853
| 0.623853
| 0
| 0
| 0.047244
| 0.092857
| 140
| 4
| 50
| 35
| 0.811024
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 8
|
1c53b14905ef8ee394cdba3f0dcbd00a2771fa98
| 3,538
|
py
|
Python
|
tests/test_shift.py
|
ryutok/mpl_axes_aligner
|
bc956ffbdaad0dc6fefc1aaa98f5bc6b55e8ea8a
|
[
"MIT"
] | 6
|
2018-11-18T21:39:56.000Z
|
2022-01-28T12:58:18.000Z
|
tests/test_shift.py
|
ryutok/mpl_axes_aligner
|
bc956ffbdaad0dc6fefc1aaa98f5bc6b55e8ea8a
|
[
"MIT"
] | 2
|
2018-11-07T05:08:12.000Z
|
2021-09-10T19:40:12.000Z
|
tests/test_shift.py
|
ryutok/mpl_axes_aligner
|
bc956ffbdaad0dc6fefc1aaa98f5bc6b55e8ea8a
|
[
"MIT"
] | 2
|
2018-11-19T11:18:51.000Z
|
2019-12-23T16:13:41.000Z
|
import pytest
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from mpl_axes_aligner import shift
fig = plt.figure()
def test_expand_range_simple():
org = 0.0
ival = 0.0
fval = 2.0
pos = 0.5
ival, fval = shift._expand_range(org, pos, ival, fval)
assert round(ival, 15) == -2.0
assert round(fval, 15) == 2.0
def test_expand_range_inverted():
org = 0.0
ival = 2.0
fval = 0.0
pos = 0.5
ival, fval = shift._expand_range(org, pos, ival, fval)
assert round(ival, 15) == 2.0
assert round(fval, 15) == -2.0
def test_expand_range_outrange_n():
org = -1.0
ival = 0.0
fval = 2.0
pos = 0.5
ival, fval = shift._expand_range(org, pos, ival, fval)
assert round(ival, 15) == -4.0
assert round(fval, 15) == 2.0
def test_expand_range_outrange_p():
org = 3.0
ival = 0.0
fval = 2.0
pos = 0.5
ival, fval = shift._expand_range(org, pos, ival, fval)
assert round(ival, 15) == 0.0
assert round(fval, 15) == 6.0
def test_shift_range_simple():
org = 0.5
ival = 0.0
fval = 2.0
pos = 0.5
ival, fval = shift._shift_range(org, pos, ival, fval)
assert round(ival, 15) == -0.5
assert round(fval, 15) == 1.5
def test_shift_range_inverted():
org = 0.5
ival = 2.0
fval = 0.0
pos = 0.5
ival, fval = shift._shift_range(org, pos, ival, fval)
assert round(ival, 15) == 1.5
assert round(fval, 15) == -0.5
def test_shift_range_outrange_n():
org = -1.0
ival = 0.0
fval = 2.0
pos = 0.5
ival, fval = shift._shift_range(org, pos, ival, fval)
assert round(ival, 15) == -2.0
assert round(fval, 15) == 0.0
def test_shift_range_outrange_p():
org = 3.0
ival = 0.0
fval = 2.0
pos = 0.5
ival, fval = shift._shift_range(org, pos, ival, fval)
assert round(ival, 15) == 2.0
assert round(fval, 15) == 4.0
@pytest.mark.parametrize('pos', [-2, 2])
def test_yaxis_shift_ValueError(pos):
fig.clear()
ax = fig.add_subplot(111)
org = 0.5
with pytest.raises(ValueError):
shift.yaxis(ax, org, pos)
@pytest.mark.parametrize('pos', [-2, 2])
def test_xaxis_shift_ValueError(pos):
fig.clear()
ax = fig.add_subplot(111)
org = 0.5
with pytest.raises(ValueError):
shift.xaxis(ax, org, pos)
@pytest.mark.parametrize('pos', [0, 1])
def test_yaxis_shift_NoError(pos):
fig.clear()
ax = fig.add_subplot(111)
org = 0.5
shift.yaxis(ax, org, pos)
@pytest.mark.parametrize('pos', [0, 1])
def test_xaxis_shift_NoError(pos):
fig.clear()
ax = fig.add_subplot(111)
org = 0.5
shift.xaxis(ax, org, pos)
@pytest.mark.parametrize('pos', [-2, 0, 1, 2])
def test_yaxis_expand_ValueError(pos):
fig.clear()
ax = fig.add_subplot(111)
org = 0.5
with pytest.raises(ValueError):
shift.yaxis(ax, org, pos, True)
@pytest.mark.parametrize('pos', [-2, 0, 1, 2])
def test_xaxis_expand_ValueError(pos):
fig.clear()
ax = fig.add_subplot(111)
org = 0.5
with pytest.raises(ValueError):
shift.xaxis(ax, org, pos, True)
def test_yaxes_expand_simple():
fig.clear()
ax = fig.add_subplot(111)
ax.set_xlim(0.0, 1.0)
org = 0.0
pos = 0.5
shift.yaxis(ax, org, pos, True)
assert ax.get_ylim() == (-1.0, 1.0)
def test_xaxes_expand_simple():
fig.clear()
ax = fig.add_subplot(111)
ax.set_xlim(0.0, 1.0)
org = 0.0
pos = 0.5
shift.xaxis(ax, org, pos, True)
assert ax.get_xlim() == (-1.0, 1.0)
| 21.975155
| 58
| 0.607123
| 591
| 3,538
| 3.49577
| 0.098139
| 0.019361
| 0.024201
| 0.029042
| 0.877541
| 0.820426
| 0.816554
| 0.789448
| 0.769119
| 0.74879
| 0
| 0.076063
| 0.241945
| 3,538
| 160
| 59
| 22.1125
| 0.694258
| 0
| 0
| 0.730159
| 0
| 0
| 0.005936
| 0
| 0
| 0
| 0
| 0
| 0.142857
| 1
| 0.126984
| false
| 0
| 0.031746
| 0
| 0.15873
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
c744c1393eb92226591a5c68575f595f355a1221
| 4,171
|
py
|
Python
|
setup.py
|
RiskAmerica/api-client-python
|
468c554a0440bef5086828631e25d99d41e28571
|
[
"MIT"
] | null | null | null |
setup.py
|
RiskAmerica/api-client-python
|
468c554a0440bef5086828631e25d99d41e28571
|
[
"MIT"
] | null | null | null |
setup.py
|
RiskAmerica/api-client-python
|
468c554a0440bef5086828631e25d99d41e28571
|
[
"MIT"
] | 1
|
2021-04-14T15:52:03.000Z
|
2021-04-14T15:52:03.000Z
|
# coding: utf-8
"""
APIs RISKAMERICA
A continuación les presentamos la documentación las **APIs** **de** **RiskAmerica**, el cual es un servicio pagado ofrecido por RiskAmerica que se contrata por separado a nuestras otras ofertas de software. Algunas consideraciones que debe tener al momento de usar las APIs: - El APIKEY o Token lo puede conseguir solicitándolo al equipo comercial de RiskAmerica - El request necesita ser enviado con el header **Accept:** **application/json** para que responda en formato **JSON** (de no ser enviado con esto se responderá en formato **XML**) - Todos los Servicios son **REST** y sus parametros pueden ser enviados tanto en **POST** como **GET** - El uso de las APIs puede llevar un cobro asociado según se pacte en el acuerdo comercial, por lo que le recomendamos ser cuidadosos en el uso de éstas para evitar sobre-cargos innecesarios. - RiskAmerica funciona con un mecanismo de **WhiteList** **de** **IPs** para las consultas de las API. Para habilitar o modificar la lista de IPs permitidas debe contactarse al mail **contacto@riskamerica.com**. - En caso de usar **Python** como lenguaje de programación puede visitar nuestro SKD disponible en [https://github.com/RiskAmerica/api-client-python](https://github.com/RiskAmerica/api-client-python) . - En caso de usar otros lenguajes de programación puede usar el proyecto [https://github.com/swagger-api/swagger-codegen/tree/3.0.0](https://github.com/swagger-api/swagger-codegen/tree/3.0.0) para generar su propio SDK a partir del archivo [openapi.json](https://ra-public-files.s3-sa-east-1.amazonaws.com/wide-public/riam-api/openapi.json) . - Todas las APIs funcionan exclusivamente bajo el protocolo HTTPS usando TLS 1.2 o 1.3 # noqa: E501
OpenAPI spec version: 1.0.0
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from setuptools import setup, find_packages # noqa: H301
NAME = "riam-api-client"
VERSION = "1.0.0"
# To install the library, run the following
#
# python setup.py install
#
# prerequisite: setuptools
# http://pypi.python.org/pypi/setuptools
REQUIRES = ["urllib3 >= 1.15", "six >= 1.10", "certifi", "python-dateutil"]
setup(
name=NAME,
version=VERSION,
description="APIs RISKAMERICA",
author_email="",
url="",
keywords=["Swagger", "APIs RISKAMERICA"],
install_requires=REQUIRES,
packages=find_packages(),
include_package_data=True,
long_description="""\
A continuación les presentamos la documentación las **APIs** **de** **RiskAmerica**, el cual es un servicio pagado ofrecido por RiskAmerica que se contrata por separado a nuestras otras ofertas de software. Algunas consideraciones que debe tener al momento de usar las APIs: - El APIKEY o Token lo puede conseguir solicitándolo al equipo comercial de RiskAmerica - El request necesita ser enviado con el header **Accept:** **application/json** para que responda en formato **JSON** (de no ser enviado con esto se responderá en formato **XML**) - Todos los Servicios son **REST** y sus parametros pueden ser enviados tanto en **POST** como **GET** - El uso de las APIs puede llevar un cobro asociado según se pacte en el acuerdo comercial, por lo que le recomendamos ser cuidadosos en el uso de éstas para evitar sobre-cargos innecesarios. - RiskAmerica funciona con un mecanismo de **WhiteList** **de** **IPs** para las consultas de las API. Para habilitar o modificar la lista de IPs permitidas debe contactarse al mail **contacto@riskamerica.com**. - En caso de usar **Python** como lenguaje de programación puede visitar nuestro SKD disponible en [https://github.com/RiskAmerica/api-client-python](https://github.com/RiskAmerica/api-client-python) . - En caso de usar otros lenguajes de programación puede usar el proyecto [https://github.com/swagger-api/swagger-codegen/tree/3.0.0](https://github.com/swagger-api/swagger-codegen/tree/3.0.0) para generar su propio SDK a partir del archivo [openapi.json](https://ra-public-files.s3-sa-east-1.amazonaws.com/wide-public/riam-api/openapi.json) . - Todas las APIs funcionan exclusivamente bajo el protocolo HTTPS usando TLS 1.2 o 1.3 # noqa: E501
"""
)
| 104.275
| 1,703
| 0.750899
| 635
| 4,171
| 4.92126
| 0.303937
| 0.03168
| 0.04032
| 0.0336
| 0.85248
| 0.85248
| 0.85248
| 0.84032
| 0.84032
| 0.84032
| 0
| 0.013191
| 0.145768
| 4,171
| 39
| 1,704
| 106.948718
| 0.863879
| 0.471829
| 0
| 0
| 0
| 0.055556
| 0.8381
| 0.013376
| 0
| 0
| 0
| 0.025641
| 0
| 1
| 0
| false
| 0
| 0.055556
| 0
| 0.055556
| 0
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| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 10
|
c7816df33897d86c2fa1f85629c607bc19730052
| 2,290
|
py
|
Python
|
ELIT-main/logo.py
|
Zusyaku/Termux-And-Lali-Linux-V2
|
b1a1b0841d22d4bf2cc7932b72716d55f070871e
|
[
"Apache-2.0"
] | 2
|
2021-11-17T03:35:03.000Z
|
2021-12-08T06:00:31.000Z
|
ELIT-main/logo.py
|
Zusyaku/Termux-And-Lali-Linux-V2
|
b1a1b0841d22d4bf2cc7932b72716d55f070871e
|
[
"Apache-2.0"
] | null | null | null |
ELIT-main/logo.py
|
Zusyaku/Termux-And-Lali-Linux-V2
|
b1a1b0841d22d4bf2cc7932b72716d55f070871e
|
[
"Apache-2.0"
] | 2
|
2021-11-05T18:07:48.000Z
|
2022-02-24T21:25:07.000Z
|
zmbflogo="""
____ __ ____ ______\n / __/ / / / _//_ __/\n / _/ / /__ _/ / / / \n /___/ /____//___/ /_/ \n
"""
loginlogo = """
_ _ \n | | (_) \n | | ___ __ _ _ _ __ \n | | / _ \ / _` | | '_ \ \n | |___| (_) | (_| | | | | |\n |______\___/ \__, |_|_| |_|\n __/ | \n |___/
"""
cookieslogo="""
___ _ _ \n / __\___ ___ | | _(_) ___ ___ \n / / / _ \ / _ \| |/ / |/ _ \/ __| \n / /__| (_) | (_) | <| | __/\__ \ \n \____/\___/ \___/|_|\_\_|\___||___/
"""
tokenlogo="""
_______ _ \n |__ __| | | \n | | ___ | | _____ _ __ \n | |/ _ \| |/ / _ \ '_ \ \n | | (_) | < __/ | | |\n |_|\___/|_|\_\___|_| |_|
"""
idPasslogo="""
_____ _ _____ \n |_ _| | | ___ | __ \ \n | | __| |( _ ) | |__) |_ _ ___ ___ \n | | / _` |/ _ \/\ ___/ _` / __/ __|\n _| || (_| | (_> < | | (_| \__ \__ \ \n |_____\__,_|\___/\/_| \__,_|___/___/
"""
cracklogo="""
______ _____ _ \n | ____| / ____| | | \n | |__ | | _ __ __ _ ___ | | __\n | __| | | | '__| / _` | / __| | |/ /\n | |____ | |____ | | | (_| | | (__ | < \n |______| \_____| |_| \__,_| \___| |_|\_\
"""
crackinglogo="""
___, __, __, _,_ _ _ _, __, _, __,\n ` / |_ |_ |_/ | | / \ |_) | | \ \n / | | | \ |/\| \ / | \ | , |_/\n ~~~ ~~~ ~~~ ~ ~ ~ ~ ~ ~ ~ ~~~ ~
"""
checkerlogo="""
_____ _ _ \n / ____| | | | \n | | | |__ ___ ___| | _____ _ __ \n | | | '_ \ / _ \/ __| |/ / _ \ '__| \n | |____| | | | __/ (__| < __/ | \n \_____|_| |_|\___|\___|_|\_\___|_|
"""
passwordlogo="""
___ __ \n / _ \ ___ _ ___ ___ _ __ ___ ____ ___/ / ___\n / ___// _ `/ (_-< (_-<| |/|/ // _ \ / __// _ / (_-<\n/_/ \_,_/ /___//___/|__,__/ \___//_/ \_,_/ /___/
"""
| 61.891892
| 298
| 0.258952
| 50
| 2,290
| 2.64
| 0.2
| 0.484848
| 0.522727
| 0.424242
| 0.204545
| 0.05303
| 0
| 0
| 0
| 0
| 0
| 0
| 0.50393
| 2,290
| 36
| 299
| 63.611111
| 0.116197
| 0
| 0
| 0.333333
| 0
| 0.333333
| 0.91441
| 0.029694
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0.074074
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 8
|
c78d9e6dc15b8babcc381f95c1ecdf345d9ef3f5
| 20,880
|
py
|
Python
|
rescript/tests/test_derep.py
|
thermokarst-forks/RESCRIPt
|
39f608e6f817688a7201e42ae7bbe9eb247ab8aa
|
[
"BSD-3-Clause"
] | null | null | null |
rescript/tests/test_derep.py
|
thermokarst-forks/RESCRIPt
|
39f608e6f817688a7201e42ae7bbe9eb247ab8aa
|
[
"BSD-3-Clause"
] | null | null | null |
rescript/tests/test_derep.py
|
thermokarst-forks/RESCRIPt
|
39f608e6f817688a7201e42ae7bbe9eb247ab8aa
|
[
"BSD-3-Clause"
] | null | null | null |
# ----------------------------------------------------------------------------
# Copyright (c) 2020, QIIME 2 development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE, distributed with this software.
# ----------------------------------------------------------------------------
from qiime2.plugin.testing import TestPluginBase
from qiime2.plugins import rescript
import qiime2
import pandas as pd
import pandas.util.testing as pdt
from rescript.dereplicate import _backfill_taxonomy
import_data = qiime2.Artifact.import_data
class TestDerep(TestPluginBase):
package = 'rescript.tests'
def setUp(self):
super().setUp()
self.dereplicate = rescript.actions.dereplicate
self.seqs = import_data(
'FeatureData[Sequence]', self.get_data_path('derep-test.fasta'))
self.taxa = import_data(
'FeatureData[Taxonomy]', self.get_data_path('derep-taxa.tsv'))
self.seqsnumericids = import_data(
'FeatureData[Sequence]', self.get_data_path(
'derep-test-numericIDs.fasta'))
self.taxanumericids = import_data(
'FeatureData[Taxonomy]', self.get_data_path(
'derep-taxa-numericIDs.tsv'))
def test_dereplicate_uniq(self):
seqs, taxa, = self.dereplicate(
self.seqs, self.taxa, mode='uniq', rank_handles='disable')
exp_taxa = pd.DataFrame({'Taxon': {
'A1': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Bacillales; '
'f__Paenibacillaceae; g__Paenibacillus; s__chondroitinus',
'B1': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales;'
' f__Lactobacillaceae; g__Lactobacillus; s__brevis',
'C1': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales;'
' f__Lactobacillaceae; g__Pediococcus; s__damnosus',
'B1a': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales'
'; f__Lactobacillaceae; g__Lactobacillus; s__vaginalis',
'B1b': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales'
'; f__Lactobacillaceae; g__Lactobacillus; s__pseudocasei',
'C1a': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales'
'; f__Lactobacillaceae; g__Pediococcus; s__acidilacti',
'A3': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Bacillales; '
'f__Paenibacillaceae; g__Paenibacillus; s__alvei',
'B2': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales;'
' f__Lactobacillaceae; g__Lactobacillus; s__casei',
'C1b': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales'
'; f__Lactobacillaceae'}})
pdt.assert_frame_equal(taxa.view(pd.DataFrame).sort_index(),
exp_taxa.sort_index(), check_names=False)
pdt.assert_index_equal(seqs.view(pd.Series).sort_index().index,
exp_taxa.sort_index().index, check_names=False)
# use derep_prefix=True; should still obtain same result if the prefix
# seqs bear unique taxonomic labels, as seen in this test case
seqs, taxa, = self.dereplicate(self.seqs, self.taxa, mode='uniq',
derep_prefix=True,
rank_handles='disable')
pdt.assert_frame_equal(taxa.view(pd.DataFrame).sort_index(),
exp_taxa.sort_index(), check_names=False)
pdt.assert_index_equal(seqs.view(pd.Series).sort_index().index,
exp_taxa.sort_index().index, check_names=False)
def test_dereplicate_uniq_99_perc(self):
seqs, taxa, = self.dereplicate(self.seqs, self.taxa, mode='uniq',
perc_identity=0.99,
rank_handles='disable')
exp_taxa = pd.DataFrame({'Taxon': {
'A1': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Bacillales; '
'f__Paenibacillaceae; g__Paenibacillus; s__chondroitinus',
'B1': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales;'
' f__Lactobacillaceae; g__Lactobacillus; s__brevis',
'C1': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales;'
' f__Lactobacillaceae; g__Pediococcus; s__damnosus',
'B1a': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales'
'; f__Lactobacillaceae; g__Lactobacillus; s__vaginalis',
'B1b': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales'
'; f__Lactobacillaceae; g__Lactobacillus; s__pseudocasei',
'C1a': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales'
'; f__Lactobacillaceae; g__Pediococcus; s__acidilacti',
'A3': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Bacillales; '
'f__Paenibacillaceae; g__Paenibacillus; s__alvei',
'B2': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales;'
' f__Lactobacillaceae; g__Lactobacillus; s__casei',
'C1b': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales'
'; f__Lactobacillaceae'}})
pdt.assert_frame_equal(taxa.view(pd.DataFrame).sort_index(),
exp_taxa.sort_index(), check_names=False)
pdt.assert_index_equal(seqs.view(pd.Series).sort_index().index,
exp_taxa.sort_index().index, check_names=False)
# use derep_prefix=True; should still obtain same result if the prefix
# seqs bear unique taxonomic labels, as seen in this test case
seqs, taxa, = self.dereplicate(self.seqs, self.taxa, mode='uniq',
perc_identity=0.99, derep_prefix=True,
rank_handles='disable')
pdt.assert_frame_equal(taxa.view(pd.DataFrame).sort_index(),
exp_taxa.sort_index(), check_names=False)
pdt.assert_index_equal(seqs.view(pd.Series).sort_index().index,
exp_taxa.sort_index().index, check_names=False)
def test_dereplicate_lca(self):
seqs, taxa, = self.dereplicate(
self.seqs, self.taxa, mode='lca', rank_handles='disable')
exp_taxa = pd.DataFrame({'Taxon': {
'A1': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Bacillales; '
'f__Paenibacillaceae; g__Paenibacillus',
'B1': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales;'
' f__Lactobacillaceae; g__Lactobacillus',
'C1': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales;'
' f__Lactobacillaceae; g__Pediococcus; s__damnosus',
'B1a': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales'
'; f__Lactobacillaceae; g__Lactobacillus; s__vaginalis',
'B1b': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales'
'; f__Lactobacillaceae; g__Lactobacillus; s__pseudocasei',
'C1a': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales'
'; f__Lactobacillaceae'}})
pdt.assert_frame_equal(taxa.view(pd.DataFrame).sort_index(),
exp_taxa.sort_index(), check_names=False)
pdt.assert_index_equal(seqs.view(pd.Series).sort_index().index,
exp_taxa.sort_index().index, check_names=False)
def test_dereplicate_super_lca_majority(self):
seqs, taxa, = self.dereplicate(
self.seqs, self.taxa, mode='super', rank_handles='disable')
exp_taxa = pd.DataFrame({'Taxon': {
'A1': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Bacillales; '
'f__Paenibacillaceae; g__Paenibacillus; s__alvei',
'B1': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales;'
' f__Lactobacillaceae; g__Lactobacillus; s__casei',
'C1': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales;'
' f__Lactobacillaceae; g__Pediococcus; s__damnosus',
'B1a': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales'
'; f__Lactobacillaceae; g__Lactobacillus; s__vaginalis',
'B1b': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales'
'; f__Lactobacillaceae; g__Lactobacillus; s__pseudocasei',
'C1a': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales'
'; f__Lactobacillaceae; g__Pediococcus; s__acidilacti'}})
pdt.assert_frame_equal(taxa.view(pd.DataFrame).sort_index(),
exp_taxa.sort_index(), check_names=False)
pdt.assert_index_equal(seqs.view(pd.Series).sort_index().index,
exp_taxa.sort_index().index, check_names=False)
def test_dereplicate_super_lca_majority_perc99(self):
seqs, taxa, = self.dereplicate(self.seqs, self.taxa, mode='super',
perc_identity=0.99,
rank_handles='disable')
exp_taxa = pd.DataFrame({'Taxon': {
'A1': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Bacillales; '
'f__Paenibacillaceae; g__Paenibacillus; s__alvei',
'B1': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales;'
' f__Lactobacillaceae; g__Lactobacillus; s__casei',
'C1': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales;'
' f__Lactobacillaceae; g__Pediococcus; s__acidilacti',
'B1b': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales'
'; f__Lactobacillaceae; g__Lactobacillus; s__pseudocasei'}})
pdt.assert_frame_equal(taxa.view(pd.DataFrame).sort_index(),
exp_taxa.sort_index(), check_names=False)
pdt.assert_index_equal(seqs.view(pd.Series).sort_index().index,
exp_taxa.sort_index().index, check_names=False)
# test that LCA taxonomy assignment works when derep_prefix=True
# here derep_prefix + LCA leads to collapsed C-group seqs + LCA taxonomy
def test_dereplicate_prefix_lca(self):
seqs, taxa, = self.dereplicate(self.seqs, self.taxa, mode='lca',
derep_prefix=True,
rank_handles='disable')
exp_taxa = pd.DataFrame({'Taxon': {
'A1': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Bacillales; '
'f__Paenibacillaceae; g__Paenibacillus',
'B1': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales;'
' f__Lactobacillaceae; g__Lactobacillus',
'C1': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales;'
' f__Lactobacillaceae',
'B1a': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales'
'; f__Lactobacillaceae; g__Lactobacillus; s__vaginalis',
'B1b': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales'
'; f__Lactobacillaceae; g__Lactobacillus; s__pseudocasei'}})
pdt.assert_frame_equal(taxa.view(pd.DataFrame).sort_index(),
exp_taxa.sort_index(), check_names=False)
pdt.assert_index_equal(seqs.view(pd.Series).sort_index().index,
exp_taxa.sort_index().index, check_names=False)
def test_dereplicate_lca_99_perc(self):
seqs, taxa, = self.dereplicate(self.seqs, self.taxa, mode='lca',
perc_identity=0.99,
rank_handles='disable')
exp_taxa = pd.DataFrame({'Taxon': {
'A1': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Bacillales; '
'f__Paenibacillaceae; g__Paenibacillus',
'B1': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales;'
' f__Lactobacillaceae; g__Lactobacillus',
'C1': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales;'
' f__Lactobacillaceae',
'B1b': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales'
'; f__Lactobacillaceae; g__Lactobacillus; s__pseudocasei'}})
pdt.assert_frame_equal(taxa.view(pd.DataFrame).sort_index(),
exp_taxa.sort_index(), check_names=False)
pdt.assert_index_equal(seqs.view(pd.Series).sort_index().index,
exp_taxa.sort_index().index, check_names=False)
def test_dereplicate_majority(self):
seqs, taxa, = self.dereplicate(
self.seqs, self.taxa, mode='majority', rank_handles='disable')
exp_taxa = pd.DataFrame({'Taxon': {
'A1': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Bacillales; '
'f__Paenibacillaceae; g__Paenibacillus; s__alvei',
'B1': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales;'
' f__Lactobacillaceae; g__Lactobacillus; s__casei',
'C1': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales;'
' f__Lactobacillaceae; g__Pediococcus; s__damnosus',
'B1a': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales'
'; f__Lactobacillaceae; g__Lactobacillus; s__vaginalis',
'B1b': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales'
'; f__Lactobacillaceae; g__Lactobacillus; s__pseudocasei',
'C1a': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales'
'; f__Lactobacillaceae; g__Pediococcus; s__acidilacti'}})
pdt.assert_frame_equal(taxa.view(pd.DataFrame).sort_index(),
exp_taxa.sort_index(), check_names=False)
pdt.assert_index_equal(seqs.view(pd.Series).sort_index().index,
exp_taxa.sort_index().index, check_names=False)
# test that majority taxonomy assignment works when derep_prefix=True
# all C-group seqs should be merged, and P. acidilacti is the majority
def test_dereplicate_prefix_majority(self):
seqs, taxa, = self.dereplicate(self.seqs, self.taxa, mode='majority',
derep_prefix=True,
rank_handles='disable')
exp_taxa = pd.DataFrame({'Taxon': {
'A1': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Bacillales; '
'f__Paenibacillaceae; g__Paenibacillus; s__alvei',
'B1': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales;'
' f__Lactobacillaceae; g__Lactobacillus; s__casei',
'C1': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales;'
' f__Lactobacillaceae; g__Pediococcus; s__acidilacti',
'B1a': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales'
'; f__Lactobacillaceae; g__Lactobacillus; s__vaginalis',
'B1b': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales'
'; f__Lactobacillaceae; g__Lactobacillus; s__pseudocasei'}})
pdt.assert_frame_equal(taxa.view(pd.DataFrame).sort_index(),
exp_taxa.sort_index(), check_names=False)
pdt.assert_index_equal(seqs.view(pd.Series).sort_index().index,
exp_taxa.sort_index().index, check_names=False)
def test_dereplicate_majority_perc99(self):
seqs, taxa, = self.dereplicate(self.seqs, self.taxa, mode='majority',
perc_identity=0.99,
rank_handles='disable')
exp_taxa = pd.DataFrame({'Taxon': {
'A1': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Bacillales; '
'f__Paenibacillaceae; g__Paenibacillus; s__alvei',
'B1': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales;'
' f__Lactobacillaceae; g__Lactobacillus; s__casei',
'C1': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales;'
' f__Lactobacillaceae; g__Pediococcus; s__acidilacti',
'B1b': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales'
'; f__Lactobacillaceae; g__Lactobacillus; s__pseudocasei'}})
pdt.assert_frame_equal(taxa.view(pd.DataFrame).sort_index(),
exp_taxa.sort_index(), check_names=False)
pdt.assert_index_equal(seqs.view(pd.Series).sort_index().index,
exp_taxa.sort_index().index, check_names=False)
# the above tests check actual derep functionality; this test just makes
# sure that the same tests/modes above operate on numeric seq IDs, using
# the same test data above (with numeric IDs).
# See https://github.com/bokulich-lab/RESCRIPt/issues/49
def test_dereplicate_numericIDs(self):
self.dereplicate(self.seqsnumericids, self.taxanumericids, mode='uniq')
self.assertTrue(True)
self.dereplicate(self.seqsnumericids, self.taxanumericids, mode='lca')
self.assertTrue(True)
self.dereplicate(self.seqsnumericids, self.taxanumericids,
mode='majority')
self.assertTrue(True)
# Now test with backfilling. These parameters were chosen to set up a
# variety of backfill levels.
def test_dereplicate_lca_99_perc_backfill(self):
# note backfills SILVA-style rank handles by default, so we use default
seqs, taxa, = self.dereplicate(self.seqs, self.taxa, mode='lca',
perc_identity=0.99)
exp_taxa = pd.DataFrame({'Taxon': {
'A1': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Bacillales; '
'f__Paenibacillaceae; g__Paenibacillus; s__',
'B1': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales;'
' f__Lactobacillaceae; g__Lactobacillus; s__',
'C1': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales;'
' f__Lactobacillaceae; g__; s__',
'B1b': 'k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales'
'; f__Lactobacillaceae; g__Lactobacillus; s__pseudocasei'}})
pdt.assert_frame_equal(taxa.view(pd.DataFrame).sort_index(),
exp_taxa.sort_index(), check_names=False)
pdt.assert_index_equal(seqs.view(pd.Series).sort_index().index,
exp_taxa.sort_index().index, check_names=False)
def test_backfill_taxonomy(self):
default_rank_handle = "d__; p__; c__; o__; f__; g__; s__"
def _backfill_series(series, rank_handles=default_rank_handle):
rank_handles = rank_handles.split(';')
return series.apply(_backfill_taxonomy, args=([rank_handles]))
taxa = self.taxa.view(pd.Series).sort_index()
exp_taxa = taxa.copy()
# note: taxonomy is unchanged if rank handles are shorter than taxon
backfilled_taxa = _backfill_series(taxa, "my;taxonomy;is;too;short")
pdt.assert_series_equal(backfilled_taxa, exp_taxa, check_names=False)
# manually backfill to match expected
exp_taxa.loc['C1b'] += '; g__; s__'
# backfill with defaults
backfilled_taxa = _backfill_series(taxa)
pdt.assert_series_equal(backfilled_taxa, exp_taxa, check_names=False)
# trim back arbitrarily to backfill again
trimmed_taxa = backfilled_taxa.apply(
lambda x: ';'.join(x.split(';')[:3]))
# manually backfill
exp_taxa = trimmed_taxa.apply(lambda x: x + '; o__; f__; g__; s__')
backfilled_taxa = _backfill_series(trimmed_taxa)
pdt.assert_series_equal(backfilled_taxa, exp_taxa, check_names=False)
# backfill to root
# note: taxon labels can never be empty, so this test covers cases
# where there is no classification beyond root/domain/kingdom
backfilled_taxa = _backfill_series(trimmed_taxa.apply(lambda x: 'd__'))
exp_taxa = trimmed_taxa.apply(lambda x: default_rank_handle)
pdt.assert_series_equal(backfilled_taxa, exp_taxa, check_names=False)
# backfill custom labels
custom_rank_handles = "p;e;a;n;u;t;s"
exp_taxa = trimmed_taxa.apply(lambda x: x + ';n;u;t;s')
backfilled_taxa = _backfill_series(trimmed_taxa, custom_rank_handles)
pdt.assert_series_equal(backfilled_taxa, exp_taxa, check_names=False)
| 60.874636
| 79
| 0.62318
| 2,279
| 20,880
| 5.188679
| 0.103554
| 0.047188
| 0.052431
| 0.104863
| 0.850233
| 0.842368
| 0.836786
| 0.816068
| 0.810825
| 0.810825
| 0
| 0.006763
| 0.270594
| 20,880
| 342
| 80
| 61.052632
| 0.769665
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0
| 7
|
c7a99786775a96e6007fdd55aaa7b91eda9a2ff7
| 14,815
|
py
|
Python
|
tests/const.py
|
pejotes/Home-Assistant-Mail-And-Packages
|
73b4ae364c5f5126e8a5b7faf73c341d0dd209e2
|
[
"MIT"
] | 301
|
2019-05-23T20:36:39.000Z
|
2022-03-28T15:51:25.000Z
|
tests/const.py
|
pejotes/Home-Assistant-Mail-And-Packages
|
73b4ae364c5f5126e8a5b7faf73c341d0dd209e2
|
[
"MIT"
] | 388
|
2019-06-02T04:39:06.000Z
|
2022-03-31T02:44:46.000Z
|
tests/const.py
|
pejotes/Home-Assistant-Mail-And-Packages
|
73b4ae364c5f5126e8a5b7faf73c341d0dd209e2
|
[
"MIT"
] | 52
|
2019-06-24T21:01:36.000Z
|
2022-03-25T19:02:04.000Z
|
""" Constants for tests. """
FAKE_CONFIG_DATA_BAD = {
"folder": '"INBOX"',
"generate_mp4": "false",
"gif_duration": 5,
"host": None,
"image_name": "mail_today.gif",
"image_path": "/config/www/mail_and_packages/",
"image_security": False,
"imap_timeout": 30,
"password": "suchfakemuchpassword",
"port": 993,
"resources": [
"amazon_packages",
"fedex_delivered",
"fedex_delivering",
"fedex_packages",
"mail_updated",
"ups_delivered",
"ups_delivering",
"ups_packages",
"usps_delivered",
"usps_delivering",
"usps_mail",
"usps_packages",
"zpackages_delivered",
"zpackages_transit",
"dhl_delivered",
"dhl_delivering",
"dhl_packages",
"amazon_delivered",
"auspost_delivered",
"auspost_packages",
"auspost_delivering",
],
"scan_interval": 20,
"username": "user@fake.email",
}
FAKE_CONFIG_DATA = {
"amazon_fwds": "fakeuser@fake.email, fakeuser2@fake.email",
"custom_img": False,
"folder": '"INBOX"',
"generate_mp4": False,
"gif_duration": 5,
"host": "imap.test.email",
"image_name": "mail_today.gif",
"image_path": "/config/www/mail_and_packages/",
"image_security": True,
"imap_timeout": 30,
"password": "suchfakemuchpassword",
"port": 993,
"resources": [
"zpackages_delivered",
"zpackages_transit",
"amazon_delivered",
"amazon_exception",
"amazon_hub",
"amazon_packages",
"capost_delivered",
"capost_delivering",
"capost_packages",
"dhl_delivered",
"dhl_delivering",
"dhl_packages",
"fedex_delivered",
"fedex_delivering",
"fedex_packages",
"hermes_delivered",
"hermes_delivering",
"mail_updated",
"royal_delivered",
"royal_delivering",
"ups_delivered",
"ups_delivering",
"ups_packages",
"usps_delivered",
"usps_delivering",
"usps_mail",
"usps_packages",
"auspost_delivered",
"auspost_delivering",
"auspost_packages",
],
"scan_interval": 20,
"username": "user@fake.email",
}
FAKE_CONFIG_DATA_EXTERNAL = {
"allow_external": True,
"amazon_fwds": "fakeuser@fake.email, fakeuser2@fake.email",
"custom_img": False,
"folder": '"INBOX"',
"generate_mp4": False,
"gif_duration": 5,
"host": "imap.test.email",
"image_name": "mail_today.gif",
"image_path": "custom_components/mail_and_packages/images/",
"image_security": True,
"imap_timeout": 30,
"password": "suchfakemuchpassword",
"port": 993,
"resources": [
"zpackages_delivered",
"zpackages_transit",
"amazon_delivered",
"amazon_hub",
"amazon_packages",
"capost_delivered",
"capost_delivering",
"capost_packages",
"dhl_delivered",
"dhl_delivering",
"dhl_packages",
"fedex_delivered",
"fedex_delivering",
"fedex_packages",
"hermes_delivered",
"hermes_delivering",
"mail_updated",
"royal_delivered",
"royal_delivering",
"ups_delivered",
"ups_delivering",
"ups_packages",
"usps_delivered",
"usps_delivering",
"usps_mail",
"usps_packages",
"auspost_delivered",
"auspost_delivering",
"auspost_packages",
],
"scan_interval": 20,
"username": "user@fake.email",
}
FAKE_CONFIG_DATA_CORRECTED_EXTERNAL = {
"allow_external": True,
"amazon_fwds": ["fakeuser@fake.email", "fakeuser2@fake.email"],
"custom_img": False,
"folder": '"INBOX"',
"generate_mp4": False,
"gif_duration": 5,
"host": "imap.test.email",
"image_name": "mail_today.gif",
"image_path": "custom_components/mail_and_packages/images/",
"image_security": True,
"imap_timeout": 30,
"password": "suchfakemuchpassword",
"port": 993,
"resources": [
"amazon_delivered",
"amazon_hub",
"amazon_packages",
"auspost_delivered",
"auspost_delivering",
"auspost_packages",
"capost_delivered",
"capost_delivering",
"capost_packages",
"dhl_delivered",
"dhl_delivering",
"dhl_packages",
"fedex_delivered",
"fedex_delivering",
"fedex_packages",
"hermes_delivered",
"hermes_delivering",
"mail_updated",
"royal_delivered",
"royal_delivering",
"ups_delivered",
"ups_delivering",
"ups_packages",
"usps_delivered",
"usps_delivering",
"usps_mail",
"usps_packages",
"zpackages_delivered",
"zpackages_transit",
],
"scan_interval": 20,
"username": "user@fake.email",
}
FAKE_CONFIG_DATA_CORRECTED = {
"allow_external": False,
"amazon_fwds": ["fakeuser@fake.email", "fakeuser2@fake.email"],
"custom_img": False,
"folder": '"INBOX"',
"generate_mp4": False,
"gif_duration": 5,
"host": "imap.test.email",
"image_name": "mail_today.gif",
"image_path": "custom_components/mail_and_packages/images/",
"image_security": True,
"imap_timeout": 30,
"password": "suchfakemuchpassword",
"port": 993,
"resources": [
"amazon_delivered",
"amazon_exception",
"amazon_hub",
"amazon_packages",
"auspost_delivered",
"auspost_delivering",
"auspost_packages",
"capost_delivered",
"capost_delivering",
"capost_packages",
"dhl_delivered",
"dhl_delivering",
"dhl_packages",
"fedex_delivered",
"fedex_delivering",
"fedex_packages",
"hermes_delivered",
"hermes_delivering",
"mail_updated",
"royal_delivered",
"royal_delivering",
"ups_delivered",
"ups_delivering",
"ups_packages",
"usps_delivered",
"usps_delivering",
"usps_mail",
"usps_packages",
"zpackages_delivered",
"zpackages_transit",
],
"scan_interval": 20,
"username": "user@fake.email",
}
FAKE_CONFIG_DATA_NO_PATH = {
"amazon_fwds": ["fakeuser@fake.email", "fakeuser2@fake.email"],
"custom_img": False,
"folder": '"INBOX"',
"generate_mp4": False,
"gif_duration": 5,
"host": "imap.test.email",
"image_name": "mail_today.gif",
"imap_timeout": 30,
"password": "suchfakemuchpassword",
"port": 993,
"resources": [
"amazon_delivered",
"amazon_hub",
"amazon_packages",
"auspost_delivered",
"auspost_delivering",
"auspost_packages",
"capost_delivered",
"capost_delivering",
"capost_packages",
"dhl_delivered",
"dhl_delivering",
"dhl_packages",
"fedex_delivered",
"fedex_delivering",
"fedex_packages",
"hermes_delivered",
"hermes_delivering",
"mail_updated",
"royal_delivered",
"royal_delivering",
"ups_delivered",
"ups_delivering",
"ups_packages",
"usps_delivered",
"usps_delivering",
"usps_mail",
"usps_packages",
"zpackages_delivered",
"zpackages_transit",
],
"scan_interval": 20,
"username": "user@fake.email",
}
FAKE_CONFIG_DATA_NO_RND = {
"amazon_fwds": ["fakeuser@fake.email"],
"custom_img": False,
"folder": '"INBOX"',
"generate_mp4": True,
"gif_duration": 5,
"host": "imap.test.email",
"image_name": "mail_today.gif",
"image_path": "custom_components/mail_and_packages/images/",
"image_security": False,
"imap_timeout": 30,
"password": "suchfakemuchpassword",
"port": 993,
"resources": [
"amazon_delivered",
"amazon_hub",
"amazon_packages",
"auspost_delivered",
"auspost_delivering",
"auspost_packages",
"capost_delivered",
"capost_delivering",
"capost_packages",
"dhl_delivered",
"dhl_delivering",
"dhl_packages",
"fedex_delivered",
"fedex_delivering",
"fedex_packages",
"mail_updated",
"ups_delivered",
"ups_delivering",
"ups_packages",
"usps_delivered",
"usps_delivering",
"usps_mail",
"usps_packages",
"zpackages_delivered",
"zpackages_transit",
],
"scan_interval": 20,
"username": "user@fake.email",
}
FAKE_CONFIG_DATA_MP4 = {
"amazon_fwds": ["fakeuser@fake.email"],
"custom_img": False,
"folder": '"INBOX"',
"generate_mp4": True,
"gif_duration": 5,
"host": "imap.test.email",
"image_name": "mail_today.gif",
"image_path": "/config/custom_components/mail_and_packages/images/",
"image_security": True,
"imap_timeout": 30,
"password": "suchfakemuchpassword",
"port": 993,
"resources": [
"amazon_delivered",
"amazon_packages",
"auspost_delivered",
"auspost_delivering",
"auspost_packages",
"capost_delivered",
"capost_delivering",
"capost_packages",
"dhl_delivered",
"dhl_delivering",
"dhl_packages",
"fedex_delivered",
"fedex_delivering",
"fedex_packages",
"mail_updated",
"ups_delivered",
"ups_delivering",
"ups_packages",
"usps_delivered",
"usps_delivering",
"usps_mail",
"usps_packages",
"zpackages_delivered",
"zpackages_transit",
],
"scan_interval": 20,
"username": "user@fake.email",
}
FAKE_UPDATE_DATA = {
"image_name": "mail_today.gif",
"mail_updated": "Sep-18-2020 06:29 PM",
"usps_mail": 6,
"usps_delivered": 3,
"usps_delivering": 3,
"usps_packages": 3,
"usps_tracking": ["92123456789012345"],
"ups_delivered": 1,
"ups_delivering": 1,
"ups_packages": 1,
"ups_tracking": ["1Z123456789"],
"fedex_delivered": 0,
"fedex_delivering": 2,
"fedex_packages": 2,
"fedex_tracking": ["1234567890"],
"amazon_packages": 7,
"amazon_delivered": 2,
"amazon_order": ["#123-4567890"],
"amazon_hub": 2,
"amazon_hub_code": 123456,
"capost_delivered": 1,
"capost_delivering": 1,
"capost_packages": 2,
"dhl_delivered": 0,
"dhl_delivering": 1,
"dhl_packages": 2,
"dhl_tracking": ["1234567890"],
"zpackages_delivered": 7,
"zpackages_transit": 8,
"auspost_delivered": 2,
"auspost_delivering":1,
"auspost_packages":3,
}
FAKE_CONFIG_DATA_MISSING_TIMEOUT = {
"amazon_fwds": "fakeuser@fake.email, fakeuser2@fake.email",
"custom_img": False,
"folder": '"INBOX"',
"generate_mp4": False,
"gif_duration": 5,
"host": "imap.test.email",
"image_name": "mail_today.gif",
"image_path": "/config/custom_components/mail_and_packages/images/",
"image_security": True,
"password": "suchfakemuchpassword",
"port": 993,
"resources": [
"zpackages_delivered",
"zpackages_transit",
"amazon_delivered",
"amazon_hub",
"amazon_packages",
"capost_delivered",
"capost_delivering",
"capost_packages",
"dhl_delivered",
"dhl_delivering",
"dhl_packages",
"fedex_delivered",
"fedex_delivering",
"fedex_packages",
"hermes_delivered",
"hermes_delivering",
"mail_updated",
"royal_delivered",
"royal_delivering",
"ups_delivered",
"ups_delivering",
"ups_packages",
"usps_delivered",
"usps_delivering",
"usps_mail",
"usps_packages",
"auspost_delivered",
"auspost_delivering",
"auspost_packages",
],
"scan_interval": 20,
"username": "user@fake.email",
}
FAKE_CONFIG_DATA_AMAZON_FWD_STRING = {
"allow_external": True,
"amazon_fwds": "fakeuser@fake.email",
"custom_img": False,
"folder": '"INBOX"',
"generate_mp4": False,
"gif_duration": 5,
"host": "imap.test.email",
"image_name": "mail_today.gif",
"image_path": "custom_components/mail_and_packages/images/",
"image_security": True,
"imap_timeout": 30,
"password": "suchfakemuchpassword",
"port": 993,
"resources": [
"zpackages_delivered",
"zpackages_transit",
"amazon_delivered",
"amazon_hub",
"amazon_packages",
"capost_delivered",
"capost_delivering",
"capost_packages",
"dhl_delivered",
"dhl_delivering",
"dhl_packages",
"fedex_delivered",
"fedex_delivering",
"fedex_packages",
"hermes_delivered",
"hermes_delivering",
"mail_updated",
"royal_delivered",
"royal_delivering",
"ups_delivered",
"ups_delivering",
"ups_packages",
"usps_delivered",
"usps_delivering",
"usps_mail",
"usps_packages",
"auspost_delivered",
"auspost_delivering",
"auspost_packages",
],
"scan_interval": 20,
"username": "user@fake.email",
}
FAKE_CONFIG_DATA_CUSTOM_IMG = {
"allow_external": False,
"amazon_fwds": ["fakeuser@fake.email", "fakeuser2@fake.email"],
"custom_img": True,
"custom_img_file": "images/test.gif",
"folder": '"INBOX"',
"generate_mp4": False,
"gif_duration": 5,
"host": "imap.test.email",
"image_name": "mail_today.gif",
"image_path": "custom_components/mail_and_packages/images/",
"image_security": True,
"imap_timeout": 30,
"password": "suchfakemuchpassword",
"port": 993,
"resources": [
"amazon_delivered",
"amazon_exception",
"amazon_hub",
"amazon_packages",
"capost_delivered",
"capost_delivering",
"capost_packages",
"dhl_delivered",
"dhl_delivering",
"dhl_packages",
"fedex_delivered",
"fedex_delivering",
"fedex_packages",
"hermes_delivered",
"hermes_delivering",
"mail_updated",
"royal_delivered",
"royal_delivering",
"ups_delivered",
"ups_delivering",
"ups_packages",
"usps_delivered",
"usps_delivering",
"usps_mail",
"usps_packages",
"zpackages_delivered",
"zpackages_transit",
"auspost_delivered",
"auspost_delivering",
"auspost_packages",
],
"scan_interval": 20,
"username": "user@fake.email",
}
| 26.887477
| 72
| 0.580223
| 1,350
| 14,815
| 5.982222
| 0.071852
| 0.031204
| 0.019316
| 0.026746
| 0.912333
| 0.909733
| 0.905275
| 0.905275
| 0.90317
| 0.897845
| 0
| 0.018836
| 0.276139
| 14,815
| 550
| 73
| 26.936364
| 0.734241
| 0.00135
| 0
| 0.888476
| 1
| 0
| 0.551904
| 0.028403
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0.020446
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
c7bed19d4c8ab922edc907dcee5d3306793f212c
| 100
|
py
|
Python
|
tabula/__init__.py
|
kirkholloway/tabula-py
|
afe4554c8ec818f3f5f70037d0aabab7522af7ab
|
[
"MIT"
] | null | null | null |
tabula/__init__.py
|
kirkholloway/tabula-py
|
afe4554c8ec818f3f5f70037d0aabab7522af7ab
|
[
"MIT"
] | null | null | null |
tabula/__init__.py
|
kirkholloway/tabula-py
|
afe4554c8ec818f3f5f70037d0aabab7522af7ab
|
[
"MIT"
] | null | null | null |
from .wrapper import read_pdf_table
from .wrapper import read_pdf
from .wrapper import convert_into
| 25
| 35
| 0.85
| 16
| 100
| 5.0625
| 0.5
| 0.407407
| 0.62963
| 0.518519
| 0.592593
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.12
| 100
| 3
| 36
| 33.333333
| 0.920455
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
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| 0
| 0
| 0
| 0
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| 0
| 0
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| 0
| 1
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| 0
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| null | 0
| 0
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| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 7
|
1be91d445414f90c10121e411b7d915049e0960c
| 9,782
|
py
|
Python
|
facebook_api.py
|
sjtichenor/midway-ford
|
43bf8770f2edd483d7c27dede8b9ac1fb8f10152
|
[
"MIT"
] | null | null | null |
facebook_api.py
|
sjtichenor/midway-ford
|
43bf8770f2edd483d7c27dede8b9ac1fb8f10152
|
[
"MIT"
] | null | null | null |
facebook_api.py
|
sjtichenor/midway-ford
|
43bf8770f2edd483d7c27dede8b9ac1fb8f10152
|
[
"MIT"
] | null | null | null |
from pprint import pprint
import locale
import requests
import hashlib
import sqlite3
from datetime import datetime
import ds_scrape
import db_master
locale.setlocale(locale.LC_ALL, 'en_US')
# Convert date_string in format of '2018/01/15' to seconds from epoch string
def date_to_timestamp(date_string):
date_object = datetime.strptime(date_string, '%Y/%m/%d')
date_string = int((date_object - datetime(1970, 1, 1)).days * (24 * 60 * 60))
return date_string
def get_service_data():
# Connect to db
conn = sqlite3.connect('data/midway.db')
c = conn.cursor()
query = ("""
SELECT Customer.id, Customer.first_name, Customer.last_name, Customer.city, Customer.state, Customer.zip, Customer.home_phone, Customer.mobile_phone, Customer.work_phone, Customer.email1, Customer.email2, Customer.email3, Customer.birthdate, Service.id, Service.gross, Service.service_date
FROM ((Customer
INNER JOIN Vehicle ON Vehicle.customer_id = Customer.id)
INNER JOIN Service ON Service.vehicle_id = Vehicle.id)
WHERE Service.service_date >= '2018/07/31' AND Customer.first_name != ''
ORDER BY Service.service_date DESC;
""")
c.execute(query)
results = c.fetchall()
conn.close()
return results
def get_sales_data():
# Connect to db
conn = sqlite3.connect('data/midway.db')
c = conn.cursor()
query = ("""
SELECT Customer.id, Customer.first_name, Customer.last_name, Customer.city, Customer.state, Customer.zip, Customer.home_phone, Customer.mobile_phone, Customer.work_phone, Customer.email1, Customer.email2, Customer.email3, Customer.birthdate, Sale.id, Sale.gross_profit, Sale.purchase_date
FROM ((Customer
INNER JOIN Vehicle ON Vehicle.customer_id = Customer.id)
INNER JOIN Sale ON Sale.vehicle_id = Vehicle.id)
WHERE Sale.purchase_date >= '2018/07/31'
ORDER BY Sale.purchase_date DESC;
""")
c.execute(query)
results = c.fetchall()
conn.close()
return results
def upload_service_data(event_data):
version = 'v3.0'
FACEBOOK_API_TOKEN = 'EAACEWe6RzUwBALuyYhSTKBBjbjoSfR6tAhFMAcwV6QcrNIkrjTRf2bU1V6Xbmk8DZBB469fL3tJUrvetYwv6DKR94c9EwYycEY1OZCw537cEbvRps0mAWgd3ZAn9NzqdvLdPJ4WFmf0lL5roWpU2dcm5w7hhZAQTWhiJ1UNQaJTZCF7cDgHzu'
BUSINESS_MANAGER_ID = '488585047978938'
AD_ACCOUNT_ID = '49968439'
base_url = 'https://graph.facebook.com'
service_purchase_event_id = '1187134724751909'
data = []
for z, event in enumerate(event_data):
# Reformat city
city = event[3].replace(' ', '')
# Reformat birthdate
if event[12]:
doby, dobm, dobd, = event[12].split('/')
else:
doby, dobm, dobd, = ['', '', '']
# Reformat event_time
event_time = date_to_timestamp(event[15])
# Build phone list
phone_list = [event[6], event[7], event[8]]
phone_list = [x for x in phone_list if x is not '']
print(phone_list)
# Build email list
email_list = [event[9], event[10], event[11]]
email_list = [x for x in email_list if x is not '']
print(email_list)
match_keys = {
'fn': event[1],
'ln': event[2],
'ct': city,
'st': event[4],
'zip': event[5],
'phone': phone_list,
'email': email_list,
'doby': doby,
'dobm': dobm,
'dobd': dobd,
}
pprint(match_keys)
# Hash the data
for k in match_keys:
if type(match_keys[k]) is str:
match_keys[k] = hashlib.sha256(match_keys[k].encode('utf-8')).hexdigest()
elif type(match_keys[k]) is list:
for i in range(len(match_keys[k])):
match_keys[k][i] = hashlib.sha256(match_keys[k][i].encode('utf-8')).hexdigest()
event_dict = {
'match_keys': match_keys,
'currency': 'USD',
'value': event[14],
'event_name': 'Purchase',
'event_time': event_time,
'custom_data': {
'event_source': 'dealersocket'
},
}
data.append(event_dict)
# if z == 50:
# break
with requests.Session() as s:
for d in data:
d = [d]
payload = {
'access_token': FACEBOOK_API_TOKEN,
'session': s,
'upload_tag': 'store_data',
'data': str(d),
}
url = 'https://graph.facebook.com/{}/{}/events'.format(version, service_purchase_event_id)
r = requests.post(url, params=payload)
print('r:', r)
a = r.json()
print('a:', a)
def upload_sales_data(event_data):
version = 'v3.0'
FACEBOOK_API_TOKEN = 'EAACEWe6RzUwBALuyYhSTKBBjbjoSfR6tAhFMAcwV6QcrNIkrjTRf2bU1V6Xbmk8DZBB469fL3tJUrvetYwv6DKR94c9EwYycEY1OZCw537cEbvRps0mAWgd3ZAn9NzqdvLdPJ4WFmf0lL5roWpU2dcm5w7hhZAQTWhiJ1UNQaJTZCF7cDgHzu'
BUSINESS_MANAGER_ID = '488585047978938'
AD_ACCOUNT_ID = '49968439'
base_url = 'https://graph.facebook.com'
vehicle_purchase_event_id = '473150923086219'
data = []
for z, event in enumerate(event_data):
# Reformat city
city = event[3].replace(' ', '')
# Reformat birthdate
if event[12]:
doby, dobm, dobd, = event[12].split('/')
else:
doby, dobm, dobd, = ['', '', '']
# Reformat event_time
event_time = date_to_timestamp(event[15])
# Build phone list
phone_list = [event[6], event[7], event[8]]
phone_list = [x for x in phone_list if x is not '']
# Build email list
email_list = [event[9], event[10], event[11]]
email_list = [x for x in email_list if x is not '']
match_keys = {
'fn': event[1],
'ln': event[2],
'ct': city,
'st': event[4],
'zip': event[5],
'phone': phone_list,
'email': email_list,
'doby': doby,
'dobm': dobm,
'dobd': dobd,
}
pprint(match_keys)
# Hash the data
for k in match_keys:
if type(match_keys[k]) is str:
match_keys[k] = hashlib.sha256(match_keys[k].encode('utf-8')).hexdigest()
elif type(match_keys[k]) is list:
for i in range(len(match_keys[k])):
match_keys[k][i] = hashlib.sha256(match_keys[k][i].encode('utf-8')).hexdigest()
event_dict = {
'match_keys': match_keys,
'currency': 'USD',
'value': event[14],
'event_name': 'Purchase',
'event_time': event_time,
'custom_data': {
'event_source': 'dealersocket'
},
}
data.append(event_dict)
with requests.Session() as s:
for d in data:
d = [d]
payload = {
'access_token': FACEBOOK_API_TOKEN,
'session': s,
'upload_tag': 'store_data',
'data': str(d),
}
url = 'https://graph.facebook.com/{}/{}/events'.format(version, vehicle_purchase_event_id)
r = requests.post(url, params=payload)
print('r:', r)
a = r.json()
print('a:', a)
def uploadSalesDataOld():
version = 'v2.11'
FACEBOOK_API_TOKEN = 'EAACEWe6RzUwBALuyYhSTKBBjbjoSfR6tAhFMAcwV6QcrNIkrjTRf2bU1V6Xbmk8DZBB469fL3tJUrvetYwv6DKR94c9EwYycEY1OZCw537cEbvRps0mAWgd3ZAn9NzqdvLdPJ4WFmf0lL5roWpU2dcm5w7hhZAQTWhiJ1UNQaJTZCF7cDgHzu'
BUSINESS_MANAGER_ID = '488585047978938'
AD_ACCOUNT_ID = '49968439'
base_url = 'https://graph.facebook.com'
vehicle_purchase_event_id = '473150923086219'
service_purchase_event_id = '1187134724751909'
match_keys = {
'fn': 'Claudia',
'ln': 'Skinner',
'phone': ['16513984222'],
'email': ['claudiajskinner@gmail.com'],
}
for k in match_keys:
if type(match_keys[k]) is str:
match_keys[k] = hashlib.sha256(match_keys[k].encode('utf-8')).hexdigest()
elif type(match_keys[k]) is list:
for i in range(len(match_keys[k])):
match_keys[k][i] = hashlib.sha256(match_keys[k][i].encode('utf-8')).hexdigest()
print(match_keys)
with requests.Session() as s:
data = [
{
'match_keys': match_keys,
'currency': 'USD',
'value': 1,
'event_name': 'Purchase',
'event_time': '1515852065',
# 'custom_data': {
# 'event_source': "in_store"
# },
},
]
payload = {
'access_token': FACEBOOK_API_TOKEN,
'session': s,
'upload_tag': 'store_data',
'data': str(data),
}
url = 'https://graph.facebook.com/{}/{}/events'.format(version, vehicle_purchase_event_id)
#url = 'https://graph.facebook.com/{}/{}/offline_conversion_data_sets'.format(version, BUSINESS_MANAGER_ID)
r = requests.post(url, params=payload)
print(r)
a = r.json()
pprint(a)
def main():
#ds_scrape.main()
db_master.main()
service_data = get_service_data()
upload_service_data(service_data)
sales_data = get_sales_data()
pprint(sales_data)
print(len(sales_data))
upload_sales_data(sales_data)
if __name__ == '__main__':
main()
| 30.858044
| 301
| 0.5688
| 1,075
| 9,782
| 4.976744
| 0.183256
| 0.060561
| 0.039252
| 0.027477
| 0.806916
| 0.770654
| 0.768785
| 0.76243
| 0.76243
| 0.755514
| 0
| 0.053738
| 0.309446
| 9,782
| 316
| 302
| 30.955696
| 0.738268
| 0.051012
| 0
| 0.713004
| 0
| 0.008969
| 0.286825
| 0.066415
| 0
| 0
| 0
| 0
| 0
| 1
| 0.03139
| false
| 0
| 0.035874
| 0
| 0.080717
| 0.06278
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
40012bf765580fa072940dad7de8491c16faeeec
| 3,760
|
py
|
Python
|
tools/TWM_ROUTES_ANALYZER/analize.py
|
uahservtel/uah-gist-mutraff-bastra
|
b5a4eab4763e1cf9d914c4af8a77426391e71e31
|
[
"Xnet",
"Linux-OpenIB",
"X11"
] | 3
|
2019-11-20T15:22:27.000Z
|
2021-06-13T07:52:14.000Z
|
tools/TWM_ROUTES_ANALYZER_GRID/analize.py
|
uahservtel/uah-gist-mutraff-bastra
|
b5a4eab4763e1cf9d914c4af8a77426391e71e31
|
[
"Xnet",
"Linux-OpenIB",
"X11"
] | null | null | null |
tools/TWM_ROUTES_ANALYZER_GRID/analize.py
|
uahservtel/uah-gist-mutraff-bastra
|
b5a4eab4763e1cf9d914c4af8a77426391e71e31
|
[
"Xnet",
"Linux-OpenIB",
"X11"
] | null | null | null |
old_routes={}
old_routes[63]="22726045 180049719 180049722 125233795#0 125233795#1 22725997#4 23957429#0 23957429#1 23957428#2 23957428#3 23957428#4 23957447 23904701#1 23904701#2 23904701#3 23904701#4 77806028#0 77806028#1 77806028#2 77806028#3 77806026#3 77806026#4 77806016#0 77806016#1 77806016#2 77806016#3 77806016#4 19916359#7 19916359#0 19916359#1 151292464#0 151292464#1 151292464#2 151292464#3 -304149611#6 -304149611#5 -304149611#4 -304149611#3 -304149611#2 -304149611#1 -304149611#0 304149612#0 304149612#1 304149612#2 304149612#3 409579349 174188604#0 174188604#1 174188604#2 174188604#3 32900470#5 395906341#0 395906341#1 -227354784#6 -227354784#5 -227354784#4 -227354784#3 -227354784#2 -227354784#1 -158349118 -158349113#1 -158349113#0 357265153#2 357265153#3 357265153#4 32900481#0 32900481#1 32900481#2 32900481#3 75117911#0 75117911#1 75117911#2 75117911#3 75117911#4 75117911#5 75117911#6 75117911#7 75117911#8 75117911#9 75117911#10 75117911#11 75117911#12 23907635#1 23907635#2 23907635#3 151292453#0 151292453#1 151292453#2 151292453#3 151292453#4 151292453#5 151292453#6 151292453#7 151292453#8 151292453#9 151292453#10 151292453#11 23907628#3 23907628#4 -75689947#6 -75689947#5 -75689947#4 -75689947#3 -75689947#2 -75689947#1 -75689947#0 -75482905 75482908 123483825#0 123483825#1 123483825#2 221092677#1 29289637 29289692#0 29289692#1 29289692#2 29289692#3 29289692#4 29289692#5 29289692#6 29289692#7 29289692#8 29289692#9 29289692#10 29289692#11 29289692#12 29289692#13 29289692#14 29289692#15 29289692#16 29289692#17".split(' ')
old_routes[519]="22726045 180049719 180049722 125233795#0 125233795#1 22725997#4 23957429#0 23957429#1 23957428#2 23957428#3 23957428#4 23957447 23904701#1 23904701#2 23904701#3 23904701#4 77806028#0 77806028#1 77806028#2 77806028#3 77806026#3 77806026#4 77806016#0 77806016#1 77806016#2 77806016#3 77806016#4 19916359#7 19916359#0 19916359#1 151292464#0 151292464#1 151292464#2 151292464#3 -304149611#6 -304149611#5 -304149611#4 -304149611#3 -304149611#2 -304149611#1 -304149611#0 304149612#0 304149612#1 304149612#2 304149612#3 409579349 174188604#0 174188604#1 174188604#2 174188604#3 32900470#5 395906341#0 395906341#1 -227354784#6 -227354784#5 -227354784#4 -227354784#3 -227354784#2 -227354784#1 -158349118 -158349113#1 -158349113#0 357265153#2 357265153#3 357265153#4 32900481#0 32900481#1 32900481#2 32900481#3 75117911#0 75117911#1 75117911#2 75117911#3 75117911#4 75117911#5 75117911#6 75117911#7 75117911#8 75117911#9 75117911#10 75117911#11 75117911#12 23907635#1 23907635#2 23907635#3 151292453#0 151292453#1 151292453#2 151292453#3 151292453#4 151292453#5 151292453#6 151292453#7 151292453#8 151292453#9 151292453#10 151292453#11 23907628#3 23907628#4 -75689947#6 -75689947#5 -75689947#4 -75689947#3 -75689947#2 -75689947#1 -75689947#0 -382134172#2 -382134172#1 -382134172#0 382134171#0 382134171#1 382134171#2 180619324#2 180619324#3 183334630 405649771 -183372461 -313495200 313495203 179175827#1 179175827#2 179175827#3 35968161 313495209 179175847 35968162#0 35968162#1 83134395 45896979#5 45896970#0 45896970#1 45896970#2 45896970#3 50170637".split(' ')
import new_routes as new
# print(old_routes)
# print(new.new_routes)
for veh in [63,519]:
print("ANALIZING ROUTES OBTAINED FOR VEHICLE {}".format(veh))
print("--> old_route has {} edges\n--> new route has {} edges".format(len(old_routes[veh]), len(new.new_routes[veh])))
diff = [item for item in old_routes[veh] if item not in new.new_routes[veh]]
print("--> Difference old <-> new : "+str(diff))
diff = [item for item in new.new_routes[veh] if item not in old_routes[veh]]
print("--> Difference new <-> old : "+str(diff))
# print(list(set(old_routes[veh]).symmetric_difference(set(new.new_routes[veh]))))
print()
| 208.888889
| 1,571
| 0.792021
| 618
| 3,760
| 4.79288
| 0.16343
| 0.024308
| 0.020257
| 0.020257
| 0.717758
| 0.690749
| 0.677245
| 0.677245
| 0.677245
| 0.677245
| 0
| 0.737368
| 0.094681
| 3,760
| 17
| 1,572
| 221.176471
| 0.132785
| 0.031915
| 0
| 0
| 0
| 0.166667
| 0.882563
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.083333
| 0
| 0.083333
| 0.416667
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 7
|
4003b8bdd6380416559723e1f6e65f6919082cde
| 5,201
|
py
|
Python
|
e2cnn/group/groups/factory.py
|
steven-lang/e2cnn
|
48f49760766ec958b52d0dd7b02483886dfa2096
|
[
"BSD-3-Clause"
] | 356
|
2019-11-22T10:37:22.000Z
|
2022-03-25T14:42:45.000Z
|
e2cnn/group/groups/factory.py
|
steven-lang/e2cnn
|
48f49760766ec958b52d0dd7b02483886dfa2096
|
[
"BSD-3-Clause"
] | 52
|
2020-01-20T16:51:36.000Z
|
2022-03-31T21:40:19.000Z
|
e2cnn/group/groups/factory.py
|
steven-lang/e2cnn
|
48f49760766ec958b52d0dd7b02483886dfa2096
|
[
"BSD-3-Clause"
] | 48
|
2019-12-11T09:29:30.000Z
|
2022-03-18T17:51:55.000Z
|
from .cyclicgroup import CyclicGroup
from .dihedralgroup import DihedralGroup
from .so2group import SO2
from .o2group import O2
__all__ = [
"cyclic_group",
"dihedral_group",
"so2_group",
"o2_group",
"trivial_group",
]
def trivial_group():
r"""
Builds the trivial group :math:`C_1` which contains only the identity element :math:`e`.
You should use this factory function to build an instance of the trivial group.
Only one instance is built and, in case of multiple calls to this function, the same instance is returned.
In case of multiple calls of this function with different parameters or in case new representations are built,
this unique instance is updated with the new representations and, therefore, all its references will see the new
representations.
Returns:
the trivial group
"""
return CyclicGroup._generator(1)
def cyclic_group(N: int):
r"""
Builds a cyclic group :math:`C_N`of order ``N``, i.e. the group of ``N`` discrete planar rotations.
You should use this factory function to build an instance of :class:`e2cnn.group.CyclicGroup`.
Only one instance is built and, in case of multiple calls to this function, the same instance is returned.
In case of multiple calls of this function with different parameters or in case new representations are built
(eg. through the method :meth:`~e2cnn.group.Group.quotient_representation`), this unique instance is updated with
the new representations and, therefore, all its references will see the new representations.
Args:
N (int): number of discrete rotations in the group
Returns:
the cyclic group of order ``N``
"""
return CyclicGroup._generator(N)
def dihedral_group(N: int):
r"""
Builds a dihedral group :math:`D_{2N}`of order ``2N``, i.e. the group of ``N`` discrete planar rotations
and reflections.
You should use this factory function to build an instance of :class:`e2cnn.group.DihedralGroup`.
Only one instance is built and, in case of multiple calls to this function, the same instance is returned.
In case of multiple calls of this function with different parameters or in case new representations are built
(eg. through the method :meth:`~e2cnn.group.Group.quotient_representation`), this unique instance is updated with
the new representations and, therefore, all its references will see the new representations.
Args:
N (int): number of discrete rotations in the group
Returns:
the dihedral group of order ``2N``
"""
return DihedralGroup._generator(N)
def so2_group(maximum_frequency: int = 10):
r"""
Builds the group :math:`SO(2)`, i.e. the group of continuous planar rotations.
Since the group has infinitely many irreducible representations, it is not possible to build all of them.
Each irrep is associated to one unique frequency and the parameter ``maximum_frequency`` specifies
the maximum frequency of the irreps to build.
New irreps (associated to higher frequencies) can be manually created by calling the method
:meth:`e2cnn.group.SO2.irrep` (see the method's documentation).
You should use this factory function to build an instance of :class:`e2cnn.group.SO2`.
Only one instance is built and, in case of multiple calls to this function, the same instance is returned.
In case of multiple calls of this function with different parameters or in case new representations are built
(eg. through the method :meth:`e2cnn.group.SO2.irrep`), this unique instance is updated with the new representations
and, therefore, all its references will see the new representations.
Args:
maximum_frequency (int): maximum frequency of the irreps
Returns:
the group :math:`SO(2)`
"""
return SO2._generator(maximum_frequency)
def o2_group(maximum_frequency: int = 10):
r"""
Builds the group :math:`O(2)`, i.e. the group of continuous planar rotations and reflections.
Since the group has infinitely many irreducible representations, it is not possible to build all of them.
Each irrep is associated to one unique frequency and the parameter ``maximum_frequency`` specifies
the maximum frequency of the irreps to build.
New irreps (associated to higher frequencies) can be manually created by calling the method
:meth:`e2cnn.group.O2.irrep` (see the method's documentation).
You should use this factory function to build an instance of :class:`e2cnn.group.O2`.
Only one instance is built and, in case of multiple calls to this function, the same instance is returned.
In case of multiple calls of this function with different parameters or in case new representations are built
(eg. through the method :meth:`e2cnn.group.O2.irrep`), this unique instance is updated with the new representations
and, therefore, all its references will see the new representations.
Args:
maximum_frequency (int): maximum frequency of the irreps
Returns:
the group :math:`O(2)`
"""
return O2._generator(maximum_frequency)
| 40.317829
| 120
| 0.722938
| 752
| 5,201
| 4.957447
| 0.155585
| 0.040236
| 0.021459
| 0.042918
| 0.835032
| 0.832886
| 0.823766
| 0.821084
| 0.821084
| 0.781384
| 0
| 0.009517
| 0.212075
| 5,201
| 128
| 121
| 40.632813
| 0.900195
| 0.810037
| 0
| 0
| 0
| 0
| 0.087092
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.192308
| false
| 0
| 0.153846
| 0
| 0.538462
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 8
|
40319551da4d87caf3bfc408a38edf2e70423cc9
| 316
|
py
|
Python
|
aetherling/helpers/nameCleanup.py
|
David-Durst/aetherling
|
91bcf0579608ccbf7d42a7bddf90ccd4257d6571
|
[
"MIT"
] | 10
|
2018-04-03T01:51:16.000Z
|
2022-02-07T04:27:26.000Z
|
aetherling/helpers/nameCleanup.py
|
David-Durst/aetherling
|
91bcf0579608ccbf7d42a7bddf90ccd4257d6571
|
[
"MIT"
] | 19
|
2018-05-20T00:43:31.000Z
|
2021-03-18T20:36:52.000Z
|
aetherling/helpers/nameCleanup.py
|
David-Durst/aetherling
|
91bcf0579608ccbf7d42a7bddf90ccd4257d6571
|
[
"MIT"
] | 1
|
2018-07-11T23:36:43.000Z
|
2018-07-11T23:36:43.000Z
|
import re
def cleanName(name: str):
return name.replace("(", "_").replace(")", "_").replace(",", "_").replace(" ", "").replace(":", "_").replace("-","_")\
.replace("[", "_").replace("]","_").replace("=","_").replace(">", "_").replace("<", "_")
def undup_(name: str):
return re.sub('__+', '_', name)
| 39.5
| 122
| 0.5
| 27
| 316
| 5.333333
| 0.37037
| 0.972222
| 1.3125
| 1.555556
| 0.534722
| 0.534722
| 0.534722
| 0.534722
| 0.534722
| 0.534722
| 0
| 0
| 0.129747
| 316
| 7
| 123
| 45.142857
| 0.523636
| 0
| 0
| 0
| 0
| 0
| 0.079114
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0.166667
| 0.333333
| 0.833333
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 8
|
405dc0074ed90751b61d449303e044305af28d7a
| 131,530
|
py
|
Python
|
oblib/tests/test_json_clips.py
|
SunSpecOrangeButton/pyoblib
|
5d91e4f94ac7939cd9a8df753313de07b420e722
|
[
"Apache-2.0"
] | 9
|
2019-04-02T20:55:37.000Z
|
2022-03-11T05:32:38.000Z
|
oblib/tests/test_json_clips.py
|
SunSpecOrangeButton/pyoblib
|
5d91e4f94ac7939cd9a8df753313de07b420e722
|
[
"Apache-2.0"
] | 139
|
2018-11-08T22:23:20.000Z
|
2021-01-07T22:45:02.000Z
|
oblib/tests/test_json_clips.py
|
SunSpecOrangeButton/pyoblib
|
5d91e4f94ac7939cd9a8df753313de07b420e722
|
[
"Apache-2.0"
] | 18
|
2018-11-20T21:42:03.000Z
|
2020-11-30T20:17:00.000Z
|
# Copyright 2019 SunSpec Alliance
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re
from inspect import currentframe
import unittest
import pytest
from oblib import parser, taxonomy, ob
taxonomy = taxonomy.Taxonomy()
parser = parser.Parser(taxonomy)
def _ln():
# Returns line number of caller.
cf = currentframe()
return cf.f_back.f_lineno
# class TestJsonClips(unittest.TestCase):
# # Note: this module is tested differently than others. Erroneous JSON clips are run through
# # the parser validator method and should cause various error methods to occur. The resulting
# # exception string is expected to match a regular expression which should prove that enough
# # information is returned to correctly diagnose the error (although a perfect match is not
# # necessarily required unless noted via the expression). A line number in the JSON also is
# # present and in an ideal world the line number should also be decipherable fromt he parser.
#
# def test_clips(self):
# failure_list = []
# for clip in CLIPS:
# try:
# # print(JSON_HEADER + clip[4] + JSON_FOOTER)
# # return
# parser.from_JSON_string(JSON_HEADER + clip[4] + JSON_FOOTER, entrypoint_name=clip[1])
# if clip[2] is not None:
# failure_list.append("Case {} did not cause a failure condition as expected".format(clip[0]))
# except Exception as e:
# if clip[2] is None:
# if isinstance(e, ob.OBValidationErrors):
# for e2 in e.get_errors():
# s = str(e2)
# failure_list.append("Case {} should have succeeded, raised {}".format(clip[0], s))
# else:
# failure_list.append("Case {} should have succeeded, raised an unexpected exception ''".format(clip[0], str(e)))
# else:
# if isinstance(e, ob.OBValidationErrors):
# for e2 in e.get_errors():
# s = str(e2)
# if re.search(clip[2], s, re.IGNORECASE) is None:
# failure_list.append("Case {} exception text '{}' did not meet expected value '{}'".format(clip[0], s, clip[2]))
# else:
# failure_list.append("Case {} raised an unexpected exception '{}'".format(clip[0], str(e)))
#
# # if not isinstance(e, ob.OBValidationErrors):
# # failure_list.append("Case {} raised an unexpected exception '{}'".format(clip[0], str(e)))
#
# if len(failure_list) > 0:
# msg = "\n"
# for f in failure_list:
# msg = msg + f + "\n"
# self.fail(msg)
# # NOTE: For debugging purposes it may be helpful to temporarily remove the line above this one
# # and uncomment the two lines that are listed below.
# # print(msg)
# # print("{} issues found out of {} test cases".format(len(failure_list), len(CLIPS)))
CLIPS = [
# TODO: validate identifier not only strin but UUID also?
# [_ln(), "MonthlyOperatingReport", "Identifier is not a uuid", 1, """
# "illegal-identifier": {
# "value": "93.26",
# "aspects": {
# "concept": "solar:MeasuredEnergyAvailabilityPercent",
# "entity": "JUPITER",
# "period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
# }
# }
# """
# ],
[_ln(), "MonthlyOperatingReport", "is the wrong datatype for solar:MeasuredEnergyAvailabilityPercent", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Bad Data",
"aspects": {
"concept": "solar:MeasuredEnergyAvailabilityPercent",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), "MonthlyOperatingReport", "is not a writeable concept", 4, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Bad Data",
"aspects": {
"concept": 2,
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), "MonthlyOperatingReport", "is the wrong datatype for solar:MeasuredEnergyAvailabilityPercent", 5, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Bad Data",
"aspects": {
"concept": "solar:MeasuredEnergyAvailabilityPercent",
"entity": 3,
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), "MonthlyOperatingReport", "period start component is in an incorrect format", 6, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "93.26",
"aspects": {
"concept": "solar:MeasuredEnergyAvailabilityPercent",
"entity": "JUPITER",
"period": "2017-13-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), "MonthlyOperatingReport", "period end component is in an incorrect format", 7, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "93.26",
"aspects": {
"concept": "solar:MeasuredEnergyAvailabilityPercent",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-13-30T00:00:00"
}
}
"""
],
# TODO: validate identifier not only strin but UUID also?
# [_ln(), "MonthlyOperatingReport", "Identifier is not a uuid", 1, """
# "illegal-identifier": {
# "value": "93.26",
# "aspects": {
# "concept": "solar:MeasuredEnergyAvailabilityPercent",
# "entity": "JUPITER",
# "period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
# }
# }
# """
# ],
[_ln(), "MonthlyOperatingReport", "fact tag is missing value tag", 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"aspects": {
"concept": "solar:MeasuredEnergyAvailabilityPercent",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), "MonthlyOperatingReport", "fact tag is missing aspects tag", 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "93.26"
}
"""
],
[_ln(), "MonthlyOperatingReport", "aspects tag is missing concept tag", 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "93.26",
"aspects": {
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), "MonthlyOperatingReport", "aspects tag is missing entity tag", 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "93.26",
"aspects": {
"concept": "solar:MeasuredEnergyAvailabilityPercent",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
# TODO: Correctly check null as an input value.
# [_ln(), "MasterPurchaseAgreement", "Non-nillable value is set to null", 3, """
# "d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
# "value": null,
# "aspects": {
# "concept": "solar:PreparerOfMasterPurchaseAgreement",
# "entity": "JUPITER",
# "period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
# }
# }
# """
# ],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": true,
"aspects": {
"concept": "solar:MonthlyOperatingReportAvailabilityOfDocument",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:MonthlyOperatingReportAvailabilityOfDocument",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:MonthlyOperatingReportAvailabilityOfDocument", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "non-boolean",
"aspects": {
"concept": "solar:MonthlyOperatingReportAvailabilityOfDocument",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, None, 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "true",
"aspects": {
"concept": "solar:MonthlyOperatingReportAvailabilityOfDocument",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, None, 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "false",
"aspects": {
"concept": "solar:MonthlyOperatingReportAvailabilityOfDocument",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
# TODO: verify that 0 or 1 is a valid value for boolean type
[_ln(), None, None, 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "1",
"aspects": {
"concept": "solar:MonthlyOperatingReportAvailabilityOfDocument",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
# TODO: verify that 0 or 1 is a valid value for boolean type
[_ln(), None, None, 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "0",
"aspects": {
"concept": "solar:MonthlyOperatingReportAvailabilityOfDocument",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, " is the wrong datatype for solar:MonthlyOperatingReportAvailabilityOfDocument", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "1.0",
"aspects": {
"concept": "solar:MonthlyOperatingReportAvailabilityOfDocument",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:MonthlyOperatingReportAvailabilityOfDocument", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "0.0",
"aspects": {
"concept": "solar:MonthlyOperatingReportAvailabilityOfDocument",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "2018-01-01",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "2018-01-31",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "2018-02-01",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "2017-02-28",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "2018-02-28",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "2019-02-28",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "2020-02-29",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "2018-03-01",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "2018-03-31",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "2018-04-01",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "2018-04-30",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "2018-05-01",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "2018-05-31",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "2018-06-01",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "2018-06-30",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "2018-07-01",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "2018-07-31",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "2018-08-01",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "2018-08-31",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "2018-01-01",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "2018-09-30",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "2018-10-01",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "2018-10-31",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "2018-11-01",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "2018-11-30",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "2018-12-01",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "2018-12-31",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:MonthlyOperatingReportEndDate", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "2018-13-02",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:MonthlyOperatingReportEndDate", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "2018-01-32",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:MonthlyOperatingReportEndDate", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "2016-02-30",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
# TODO: validate date against period information
# [_ln(), None, "value is not legal for type xbrli:dateItemType", 2, """
# "d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
# "value": "2017-02-28",
# "aspects": {
# "concept": "solar:MonthlyOperatingReportEndDate",
# "entity": "JUPITER",
# "period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
# }
# }
# """
# ],
[_ln(), None, "is the wrong datatype for solar:MonthlyOperatingReportEndDate", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "2019-02-29",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:MonthlyOperatingReportEndDate", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "2020-02-30",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:MonthlyOperatingReportEndDate", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "2018-03-32",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
# TODO: validate date against period information
# [_ln(), None, "value is not legal for type xbrli:dateItemType", 2, """
# "d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
# "value": "2018-04-30",
# "aspects": {
# "concept": "solar:MonthlyOperatingReportEndDate",
# "entity": "JUPITER",
# "period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
# }
# }
# """
# ],
[_ln(), None, "is the wrong datatype for solar:MonthlyOperatingReportEndDate", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "2018-05-32",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:MonthlyOperatingReportEndDate", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "2018-06-31",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:MonthlyOperatingReportEndDate", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "2018-08-32",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:MonthlyOperatingReportEndDate", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "2018-09-31",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:MonthlyOperatingReportEndDate", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "2018-10-32",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:MonthlyOperatingReportEndDate", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "2018-11-31",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:MonthlyOperatingReportEndDate", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "2018-12-32",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
# TODO: validate date against period information
# [_ln(), None, "value is not legal for type xbrli:dateItemType", 2, """
# "d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
# "value": "2018-1-01",
# "aspects": {
# "concept": "solar:MonthlyOperatingReportEndDate",
# "entity": "JUPITER",
# "period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
# }
# }
# """
# ],
# [_ln(), None, "value is not legal for type xbrli:dateItemType", 2, """
# "d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
# "value": "2018-01-1",
# "aspects": {
# "concept": "solar:MonthlyOperatingReportEndDate",
# "entity": "JUPITER",
# "period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
# }
# }
# """
# ],
[_ln(), None, "is the wrong datatype for solar:MonthlyOperatingReportEndDate", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "2018_01_01",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:MonthlyOperatingReportEndDate", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "01-01-2018",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:MonthlyOperatingReportEndDate", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "01/01/2018",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:MonthlyOperatingReportEndDate", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:MonthlyOperatingReportEndDate", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:MonthlyOperatingReportEndDate", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "99",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:MonthlyOperatingReportEndDate", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "99.99",
"aspects": {
"concept": "solar:MonthlyOperatingReportEndDate",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), "System", None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "99.99",
"aspects": {
"concept": "solar:MonitoringSolutionSoftwareVersion",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:ProductIdentifierAxis": "1",
"solar:PVSystemIdentifierAxis": "1",
"solar:TestConditionAxis": "solar:StandardTestConditionMember"
}
}
"""
],
[_ln(), "System", None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "99.0",
"aspects": {
"concept": "solar:MonitoringSolutionSoftwareVersion",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:ProductIdentifierAxis": "1",
"solar:PVSystemIdentifierAxis": "1",
"solar:TestConditionAxis": "solar:StandardTestConditionMember"
}
}
"""
],
[_ln(), "System", None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "-99.99",
"aspects": {
"concept": "solar:MonitoringSolutionSoftwareVersion",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:ProductIdentifierAxis": "1",
"solar:PVSystemIdentifierAxis": "1",
"solar:TestConditionAxis": "solar:StandardTestConditionMember"
}
}
"""
],
[_ln(), "System", None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "99",
"aspects": {
"concept": "solar:MonitoringSolutionSoftwareVersion",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:ProductIdentifierAxis": "1",
"solar:PVSystemIdentifierAxis": "1",
"solar:TestConditionAxis": "solar:StandardTestConditionMember"
}
}
"""
],
[_ln(), "System", "is the wrong datatype for solar:MonitoringSolutionSoftwareVersion", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:MonitoringSolutionSoftwareVersion",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:ProductIdentifierAxis": "1",
"solar:PVSystemIdentifierAxis": "1",
"solar:TestConditionAxis": "solar:StandardTestConditionMember"
}
}
"""
],
[_ln(), "System", None, 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "99.99",
"aspects": {
"concept": "solar:MonitoringSolutionSoftwareVersion",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:ProductIdentifierAxis": "1",
"solar:PVSystemIdentifierAxis": "1",
"solar:TestConditionAxis": "solar:StandardTestConditionMember"
}
}
"""
],
[_ln(), "System", "is the wrong datatype for solar:MonitoringSolutionSoftwareVersion", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:MonitoringSolutionSoftwareVersion",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:ProductIdentifierAxis": "1",
"solar:PVSystemIdentifierAxis": "1",
"solar:TestConditionAxis": "solar:StandardTestConditionMember"
}
}
"""
],
# TODO: Correcctly validate period
# [_ln(), None, None, 0, """
# "d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
# "value": "P1Y",
# "aspects": {
# "concept": "solar:EstimationPeriodForCurtailment",
# "entity": "JUPITER",
# "period": "2017-11-30T00:00:00",
# "unit": "H",
# "solar:PVSystemIdentifierAxis": "1",
# "solar:EstimationPeriodStartDateAxis": "1"
# }
# }
# """
# ],
# [_ln(), None, None, 0, """
# "d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
# "value": "PT1004199059S",
# "aspects": {
# "concept": "solar:EstimationPeriodForCurtailment",
# "entity": "JUPITER",
# "period": "2017-11-30T00:00:00",
# "unit": "H",
# "solar:PVSystemIdentifierAxis": "1",
# "solar:EstimationPeriodStartDateAxis": "1"
# }
# }
# """
# ],
# [_ln(), None, None, 0, """
# "d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
# "value": "PT130S",
# "aspects": {
# "concept": "solar:EstimationPeriodForCurtailment",
# "entity": "JUPITER",
# "period": "2017-11-30T00:00:00",
# "unit": "H",
# "solar:PVSystemIdentifierAxis": "1",
# "solar:EstimationPeriodStartDateAxis": "1"
# }
# }
# """
# ],
# [_ln(), None, None, 0, """
# "d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
# "value": "PT2M10S",
# "aspects": {
# "concept": "solar:EstimationPeriodForCurtailment",
# "entity": "JUPITER",
# "period": "2017-11-30T00:00:00",
# "unit": "H",
# "solar:PVSystemIdentifierAxis": "1",
# "solar:EstimationPeriodStartDateAxis": "1"
# }
# }
# """
# ],
# [_ln(), None, None, 0, """
# "d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
# "value": "P1DT2S",
# "aspects": {
# "concept": "solar:EstimationPeriodForCurtailment",
# "entity": "JUPITER",
# "period": "2017-11-30T00:00:00",
# "unit": "H",
# "solar:PVSystemIdentifierAxis": "1",
# "solar:EstimationPeriodStartDateAxis": "1"
# }
# }
# """
# ],
# [_ln(), None, None, 0, """
# "d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
# "value": "-P1Y",
# "aspects": {
# "concept": "solar:EstimationPeriodForCurtailment",
# "entity": "JUPITER",
# "period": "2017-11-30T00:00:00",
# "unit": "H",
# "solar:PVSystemIdentifierAxis": "1",
# "solar:EstimationPeriodStartDateAxis": "1"
# }
# }
# """
# ],
# [_ln(), None, None, 0, """
# "d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
# "value": "P1Y2M3DT5H20M30.123S",
# "aspects": {
# "concept": "solar:EstimationPeriodForCurtailment",
# "entity": "JUPITER",
# "period": "2017-11-30T00:00:00",
# "unit": "H",
# "solar:PVSystemIdentifierAxis": "1",
# "solar:EstimationPeriodStartDateAxis": "1"
# }
# }
# """
# ],
[_ln(), None, "is the wrong datatype for solar:EstimationPeriodForCurtailment", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid",
"aspects": {
"concept": "solar:EstimationPeriodForCurtailment",
"entity": "JUPITER",
"period": "2017-11-30T00:00:00",
"unit": "H",
"solar:PVSystemIdentifierAxis": "1",
"solar:EstimationPeriodStartDateAxis": "1"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:EstimationPeriodForCurtailment", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "1Y",
"aspects": {
"concept": "solar:EstimationPeriodForCurtailment",
"entity": "JUPITER",
"period": "2017-11-30T00:00:00",
"unit": "H",
"solar:PVSystemIdentifierAxis": "1",
"solar:EstimationPeriodStartDateAxis": "1"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:EstimationPeriodForCurtailment", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "P1S",
"aspects": {
"concept": "solar:EstimationPeriodForCurtailment",
"entity": "JUPITER",
"period": "2017-11-30T00:00:00",
"unit": "H",
"solar:PVSystemIdentifierAxis": "1",
"solar:EstimationPeriodStartDateAxis": "1"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:EstimationPeriodForCurtailment", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "P1-Y",
"aspects": {
"concept": "solar:EstimationPeriodForCurtailment",
"entity": "JUPITER",
"period": "2017-11-30T00:00:00",
"unit": "H",
"solar:PVSystemIdentifierAxis": "1",
"solar:EstimationPeriodStartDateAxis": "1"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:EstimationPeriodForCurtailment", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "P1M2Y",
"aspects": {
"concept": "solar:EstimationPeriodForCurtailment",
"entity": "JUPITER",
"period": "2017-11-30T00:00:00",
"unit": "H",
"solar:PVSystemIdentifierAxis": "1",
"solar:EstimationPeriodStartDateAxis": "1"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:EstimationPeriodForCurtailment", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "P1Y-1M",
"aspects": {
"concept": "solar:EstimationPeriodForCurtailment",
"entity": "JUPITER",
"period": "2017-11-30T00:00:00",
"unit": "H",
"solar:PVSystemIdentifierAxis": "1",
"solar:EstimationPeriodStartDateAxis": "1"
}
}
"""
],
# TODO: These should fail
# [_ln(), None, "value is not legal for type xbrli:durationItemType", 2, """
# "d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
# "value": false,
# "aspects": {
# "concept": "solar:EstimationPeriodForCurtailment",
# "entity": "JUPITER",
# "period": "2017-11-30T00:00:00",
# "unit": "H",
# "solar:PVSystemIdentifierAxis": "1",
# "solar:EstimationPeriodStartDateAxis": "1"
# }
# }
# """
# ],
# [_ln(), None, "value is not legal for type xbrli:durationItemType", 2, """
# "d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
# "value": "99",
# "aspects": {
# "concept": "solar:EstimationPeriodForCurtailment",
# "entity": "JUPITER",
# "period": "2017-11-30T00:00:00",
# "unit": "H",
# "solar:PVSystemIdentifierAxis": "1",
# "solar:EstimationPeriodStartDateAxis": "1"
# }
# }
# """
# ],
[_ln(), None, "is the wrong datatype for solar:EstimationPeriodForCurtailment", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "99.99",
"aspects": {
"concept": "solar:EstimationPeriodForCurtailment",
"entity": "JUPITER",
"period": "2017-11-30T00:00:00",
"unit": "H",
"solar:PVSystemIdentifierAxis": "1",
"solar:EstimationPeriodStartDateAxis": "1"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:EstimationPeriodForCurtailment", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid",
"aspects": {
"concept": "solar:EstimationPeriodForCurtailment",
"entity": "JUPITER",
"period": "2017-11-30T00:00:00",
"unit": "H",
"solar:PVSystemIdentifierAxis": "1",
"solar:EstimationPeriodStartDateAxis": "1"
}
}
"""
],
[_ln(), "WashingAndWasteAgreement", None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "99",
"aspects": {
"concept": "solar:WashingAndWasteFrequencyOfWashing",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:SiteIdentifierAxis": "1"
}
}
"""
],
[_ln(), "WashingAndWasteAgreement", None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "-99",
"aspects": {
"concept": "solar:WashingAndWasteFrequencyOfWashing",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:SiteIdentifierAxis": "1"
}
}
"""
],
[_ln(), "WashingAndWasteAgreement", None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "0",
"aspects": {
"concept": "solar:WashingAndWasteFrequencyOfWashing",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:SiteIdentifierAxis": "1"
}
}
"""
],
# TODO: converting false to int is possible because it is resulting
# [_ln(), "WashingAndWasteAgreement", "value is not legal for type xbrli:integerItemType", 2, """
# "d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
# "value": false,
# "aspects": {
# "concept": "solar:WashingAndWasteFrequencyOfWashing",
# "entity": "JUPITER",
# "period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
# "solar:SiteIdentifierAxis": "1"
# }
# }
# """
# ],
[_ln(), "WashingAndWasteAgreement", "is the wrong datatype for solar:WashingAndWasteFrequencyOfWashing", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "99.99",
"aspects": {
"concept": "solar:WashingAndWasteFrequencyOfWashing",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:SiteIdentifierAxis": "1"
}
}
"""
],
#TODO: not sure why 99 can not be converted to integer?
[_ln(), "WashingAndWasteAgreement", None, 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "99",
"aspects": {
"concept": "solar:WashingAndWasteFrequencyOfWashing",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:SiteIdentifierAxis": "1"
}
}
"""
],
[_ln(), "WashingAndWasteAgreement", "is the wrong datatype for solar:WashingAndWasteFrequencyOfWashing", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid",
"aspects": {
"concept": "solar:WashingAndWasteFrequencyOfWashing",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:SiteIdentifierAxis": "1"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "9999.99",
"aspects": {
"concept": "us-gaap:PrepaidExpenseCurrentAndNoncurrent",
"entity": "JUPITER",
"period": "2017-11-30T00:00:00",
"unit": "USD"
}
}
"""
],
[_ln(), None, "is the wrong datatype for us-gaap:PrepaidExpenseCurrentAndNoncurren", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "us-gaap:PrepaidExpenseCurrentAndNoncurrent",
"entity": "JUPITER",
"period": "2017-11-30T00:00:00",
"unit":"USD"
}
}
"""
],
# TODO: Implement test cases for money types.
# [_ln(), None, "value is not legal for type xbrli:monetaryItemType", 2, """
# "d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
# "value": "9999",
# "aspects": {
# "concept": "us-gaap:PrepaidExpenseCurrentAndNoncurrent",
# "entity": "JUPITER",
# "period": "2017-11-30T00:00:00",
# "unit":"USD"
# }
# }
# """
# ],
# [_ln(), None, "value is not legal for type xbrli:monetaryItemType", 2, """
# "d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
# "value": "9999.9",
# "aspects": {
# "concept": "us-gaap:PrepaidExpenseCurrentAndNoncurrent",
# "entity": "JUPITER",
# "period": "2017-11-30T00:00:00",
# "unit":"USD"
# }
# }
# """
# ],
# [_ln(), None, "value is not legal for type xbrli:monetaryItemType", 2, """
# "d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
# "value": "9999.999",
# "aspects": {
# "concept": "us-gaap:PrepaidExpenseCurrentAndNoncurrent",
# "entity": "JUPITER",
# "period": "2017-11-30T00:00:00",
# "unit":"USD"
# }
# }
# """
# ],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "9999.99",
"aspects": {
"concept": "us-gaap:PrepaidExpenseCurrentAndNoncurrent",
"entity": "JUPITER",
"period": "2017-11-30T00:00:00",
"unit":"USD"
}
}
"""
],
[_ln(), None, "datatype for us-gaap:PrepaidExpenseCurrentAndNoncurrent", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid",
"aspects": {
"concept": "us-gaap:PrepaidExpenseCurrentAndNoncurrent",
"entity": "JUPITER",
"period": "2017-11-30T00:00:00",
"unit":"USD"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Sample String",
"aspects": {
"concept": "solar:MonthlyOperatingReportExceptionDescription",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
# TODO: false can perfectly be converted to string value
[_ln(), None, None, 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:MonthlyOperatingReportExceptionDescription",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
# TODO: integer can perfectly be converted to string value
[_ln(), None, None, 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "99",
"aspects": {
"concept": "solar:MonthlyOperatingReportExceptionDescription",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
# TODO: decimal/float can perfectly be converted to string
[_ln(), None, None, 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "99.99",
"aspects": {
"concept": "solar:MonthlyOperatingReportExceptionDescription",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), "IECRECertificate", None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "99.99",
"aspects": {
"concept": "solar:AerosolModelFactorTMMPercent",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), "IECRECertificate", None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "0.0",
"aspects": {
"concept": "solar:AerosolModelFactorTMMPercent",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), "IECRECertificate", None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "99",
"aspects": {
"concept": "solar:AerosolModelFactorTMMPercent",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
#TODO: negative percentage check implementation
# [_ln(), "IECRECertificate", "value is not legal for type num:percentItemType", 2, """
# "d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
# "value": "-0.01",
# "aspects": {
# "concept": "solar:AerosolModelFactorTMMPercent",
# "entity": "JUPITER",
# "period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
# }
# }
# """
# ],
# TODO: why percentage can not be over 100? in some cases this can be perfectly possible
# [_ln(), "IECRECertificate", "value is not legal for type num:percentItemType", 2, """
# "d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
# "value": "100.01",
# "aspects": {
# "concept": "solar:AerosolModelFactorTMMPercent",
# "entity": "JUPITER",
# "period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
# }
# }
# """
# ],
[_ln(), "IECRECertificate", "is the wrong datatype for solar:AerosolModelFactorTMMPercent", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:AerosolModelFactorTMMPercent",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), "IECRECertificate", "is the wrong datatype for solar:AerosolModelFactorTMMPercent", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:AerosolModelFactorTMMPercent",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "http://www.google.com",
"aspects": {
"concept": "solar:CutSheetDocumentLink",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:TestConditionAxis": "solar:CustomTestConditionMember",
"solar:ProductIdentifierAxis": "1"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "https://www.google.com",
"aspects": {
"concept": "solar:CutSheetDocumentLink",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:TestConditionAxis": "solar:CustomTestConditionMember",
"solar:ProductIdentifierAxis": "1"
}
}
"""
],
# TODO: Supply better error message
# NOTE: incomplete message because it changes between Python 3.4 and 3.5for
[_ln(), "", "expected string or", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:CutSheetDocumentLink",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:TestConditionAxis": "solar:CustomTestConditionMember",
"solar:ProductIdentifierAxis": "1"
}
}
"""
],
[_ln(), "", "is the wrong datatype for solar:CutSheetDocumentLink", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "99.99",
"aspects": {
"concept": "solar:CutSheetDocumentLink",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:TestConditionAxis": "solar:CustomTestConditionMember",
"solar:ProductIdentifierAxis": "1"
}
}
"""
],
[_ln(), "", "is the wrong datatype for solar:CutSheetDocumentLink", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "99",
"aspects": {
"concept": "solar:CutSheetDocumentLink",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:TestConditionAxis": "solar:CustomTestConditionMember",
"solar:ProductIdentifierAxis": "1"
}
}
"""
],
# TODO: Supply Correct Unit
# [_ln(), "Participant", None, 0, """
# "d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
# "value": "5493006MHB84DD0ZWV18",
# "aspects": {
# "concept": "dei:LegalEntityIdentifier",
# "entity": "JUPITER",
# "period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
# "solar:ParticipantAxis": "1"
# }
# }
# """
# ],
# [_ln(), "Participant", "value is not legal for type dei:legalEntityIdentifierItemType", 2, """
# "d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
# "value": false,
# "aspects": {
# "concept": "dei:LegalEntityIdentifier",
# "entity": "JUPITER",
# "period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
# "solar:ParticipantAxis": "1"
# }
# }
# """
# ],
[_ln(), "CutSheet", None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "99.99",
"aspects": {
"concept": "solar:ModuleShortCircuitCurrent",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"unit": "A",
"solar:TestConditionAxis": "solar:CustomTestConditionMember",
"solar:ProductIdentifierAxis": "1"
}
}
"""
],
[_ln(), "CutSheet", "is the wrong datatype for solar:ModuleShortCircuitCurrent", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:ModuleShortCircuitCurrent",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"unit": "A",
"solar:TestConditionAxis": "solar:CustomTestConditionMember",
"solar:ProductIdentifierAxis": "1"
}
}
"""
],
[_ln(), "CutSheet", None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "99.99",
"aspects": {
"concept": "solar:InverterOutputRatedFrequency",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"unit": "Hz",
"solar:TestConditionAxis": "solar:CustomTestConditionMember",
"solar:ProductIdentifierAxis": "1"
}
}
"""
],
[_ln(), "CutSheet", "is the wrong datatype for solar:InverterOutputRatedFrequency", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:InverterOutputRatedFrequency",
"entity": "JUPITER",
"unit": "Hz",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:TestConditionAxis": "solar:CustomTestConditionMember",
"solar:ProductIdentifierAxis": "1"
}
}
"""
],
# TODO: Supply Correct Unit
# [_ln(), "MonthlyOperatingReport", None, 0, """
# "d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
# "value": "99.99",
# "aspects": {
# "concept": "solar:ExpectedInsolationAtP50",
# "entity": "JUPITER",
# "period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
# }
# }
# """
# ],
# TODO: Supply correct unit
# [_ln(), "MonthlyOperatingReport", "value is not legal for type num-us:insolationItemType", 2, """
# "d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
# "value": false,
# "aspects": {
# "concept": "solar:ExpectedInsolationAtP50",
# "entity": "JUPITER",
# "period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
# }
# }
# """
# ],
# [_ln(), "MonthlyOperatingReport", "value is out of range for type num-us:insolationItemType", 2, """
# "d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
# "value": "101.01",
# "aspects": {
# "concept": "solar:ExpectedInsolationAtP50",
# "entity": "JUPITER",
# "period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
# }
# }
# """
# ],
# TODO: Supply correct unit
# [_ln(), None, None, 0, """
# "d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
# "value": "99.99",
# "aspects": {
# "concept": "solar:SystemMinimumIrradianceThreshold",
# "entity": "JUPITER",
# "period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
# "solar:PVSystemIdentifierAxis": "1"
# }
# }
# """
# ],
# TODO: Supply correct unit
# [_ln(), None, "value is not legal for type num-us:irradianceItemType", 2, """
# "d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
# "value": false,
# "aspects": {
# "concept": "solar:SystemMinimumIrradianceThreshold",
# "entity": "JUPITER",
# "period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
# "solar:PVSystemIdentifierAxis": "1"
# }
# }
# """
# ],
[_ln(), "SystemDeviceListing", None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "33.33",
"aspects": {
"concept": "solar:TrackerAzimuth",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00",
"unit": "Degree",
"solar:DeviceIdentifierAxis": "1"
}
}
"""
],
# TODO: Failure expected
# [_ln(), "SystemDeviceListing", "value is out of range for type num-us:planeAngleItemType", 0, """
# "d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
# "value": "361.1",
# "aspects": {
# "concept": "solar:TrackerAzimuth",
# "entity": "JUPITER",
# "unit": "Degree",
# "period": "2017-11-01T00:00:00",
# "solar:DeviceIdentifierAxis": "1"
# }
# }
# """
# ],
[_ln(), "SystemDeviceListing", "is the wrong datatype for solar:TrackerAzimuth", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:TrackerAzimuth",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00",
"unit": "Degree",
"solar:DeviceIdentifierAxis": "1"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "99.99",
"aspects": {
"concept": "solar:SiteBarometricPressure",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00",
"unit": "Pa",
"solar:SiteIdentifierAxis": "1"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:SiteBarometricPressure", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:SiteBarometricPressure",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00",
"unit": "Pa",
"solar:SiteIdentifierAxis": "1"
}
}
"""
],
# TODO: Supply Correct Unit
# [_ln(), "CutSheet", None, 0, """
# "d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
# "value": "19.19",
# "aspects": {
# "concept": "solar:TrackerStowWindSpeed",
# "entity": "JUPITER",
# "period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
# "solar:TestConditionAxis": "solar:CustomTestConditionMember",
# "solar:ProductIdentifierAxis": "1"
# }
# }
# """
# ],
# [_ln(), "CutSheet", "value is not legal for type num-us:speedItemType", 2, """
# "d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
# "value": false,
# "aspects": {
# "concept": "solar:TrackerStowWindSpeed",
# "entity": "JUPITER",
# "period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
# "solar:TestConditionAxis": "solar:CustomTestConditionMember",
# "solar:ProductIdentifierAxis": "1"
# }
# }
# """
# ],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "74.00",
"aspects": {
"concept": "solar:ModelAmbientTemperature",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"unit": "F",
"solar:PVSystemIdentifierAxis": "1"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:ModelAmbientTemperature", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:ModelAmbientTemperature",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"unit": "F",
"solar:PVSystemIdentifierAxis": "1"
}
}
"""
],
[_ln(), "CutSheet", None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "99.99",
"aspects": {
"concept": "solar:InverterInputMaximumVoltageDC",
"entity": "JUPITER",
"unit": "V",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:TestConditionAxis": "solar:CustomTestConditionMember",
"solar:ProductIdentifierAxis": "1"
}
}
"""
],
[_ln(), "CutSheet", "is the wrong datatype for solar:InverterInputMaximumVoltageDC", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:InverterInputMaximumVoltageDC",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"unit": "V",
"solar:TestConditionAxis": "solar:CustomTestConditionMember",
"solar:ProductIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Site", None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "99.99",
"aspects": {
"concept": "solar:SiteAcreage",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"unit": "acre",
"solar:SiteIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Site", "is the wrong datatype for solar:SiteAcreage", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:SiteAcreage",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"unit": "acre",
"solar:SiteIdentifierAxis": "1"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "99.99",
"aspects": {
"concept": "solar:ExpectedEnergyAtP50",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00",
"unit": "J",
"solar:PVSystemIdentifierAxis": "1",
"solar:PeriodAxis": "solar:PeriodMonthMember"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:ExpectedEnergyAtP50", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:ExpectedEnergyAtP50",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00",
"unit": "J",
"solar:PVSystemIdentifierAxis": "1",
"solar:PeriodAxis": "solar:PeriodMonthMember"
}
}
"""
],
[_ln(), "CutSheet", None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "425.00",
"aspects": {
"concept": "solar:ModuleLength",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"unit": "cm",
"solar:TestConditionAxis": "solar:CustomTestConditionMember",
"solar:ProductIdentifierAxis": "1"
}
}
"""
],
[_ln(), "CutSheet", "is the wrong datatype for solar:ModuleLength", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:ModuleLength",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"unit": "cm",
"solar:TestConditionAxis": "solar:CustomTestConditionMember",
"solar:ProductIdentifierAxis": "1"
}
}
"""
],
[_ln(), "CutSheet", None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "99.99",
"aspects": {
"concept": "solar:InverterWeight",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"unit": "kg",
"solar:TestConditionAxis": "solar:CustomTestConditionMember",
"solar:ProductIdentifierAxis": "1"
}
}
"""
],
[_ln(), "CutSheet", "is the wrong datatype for solar:InverterWeight", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:InverterWeight",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"unit": "kg",
"solar:TestConditionAxis": "solar:CustomTestConditionMember",
"solar:ProductIdentifierAxis": "1"
}
}
"""
],
[_ln(), "IECRECertificate", None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "99.99",
"aspects": {
"concept": "solar:BatteryInverterACPowerRating",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"unit": "W"
}
}
"""
],
[_ln(), "IECRECertificate", "is the wrong datatype for solar:BatteryInverterACPowerRating", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:BatteryInverterACPowerRating",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"unit": "W"
}
}
"""
],
[_ln(), "WashingAndWasteAgreement", None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "99.99",
"aspects": {
"concept": "solar:WashingAndWasteQuantityOfWater",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"unit": "gal",
"solar:SiteIdentifierAxis": "1"
}
}
"""
],
[_ln(), "WashingAndWasteAgreement", "is the wrong datatype for solar:WashingAndWasteQuantityOfWater", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:WashingAndWasteQuantityOfWater",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"unit": "gal",
"solar:SiteIdentifierAxis": "1"
}
}
"""
],
[_ln(), "System", None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Storage",
"aspects": {
"concept": "solar:SystemDERType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:PVSystemIdentifierAxis": "1"
}
}
"""
],
[_ln(), "System", "is the wrong datatype for solar:SystemDERType", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:SystemDERType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:PVSystemIdentifierAxis": "1"
}
}
"""
],
[_ln(), "System", "is the wrong datatype for solar:SystemDERType", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:SystemDERType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:PVSystemIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Site", None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Preliminary",
"aspects": {
"concept": "solar:AmericanLandTitleAssociationSurveyStatus",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:SiteIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Site", "is the wrong datatype for solar:AmericanLandTitleAssociationSurveyStatus", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:AmericanLandTitleAssociationSurveyStatus",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:SiteIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Site", "is the wrong datatype for solar:AmericanLandTitleAssociationSurveyStatus", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:AmericanLandTitleAssociationSurveyStatus",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:SiteIdentifierAxis": "1"
}
}
"""
],
[_ln(), "IECRECertificate", None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "NiCad",
"aspects": {
"concept": "solar:BatteryStyle",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), "IECRECertificate", "is the wrong datatype for solar:BatteryStyle", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:BatteryStyle",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), "IECRECertificate", "is the wrong datatype for solar:BatteryStyle", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:BatteryStyle",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), "System", None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "DC-Coupled",
"aspects": {
"concept": "solar:SystemBatteryConnection",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:PVSystemIdentifierAxis": "1",
"solar:InstallationTypeAxis": "solar:GroundMember",
"solar:SolarSubArrayIdentifierAxis": "1"
}
}
"""
],
[_ln(), "System", "is the wrong datatype for solar:SystemBatteryConnection", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:SystemBatteryConnection",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:PVSystemIdentifierAxis": "1",
"solar:InstallationTypeAxis": "solar:GroundMember",
"solar:SolarSubArrayIdentifierAxis": "1"
}
}
"""
],
[_ln(), "System", "is the wrong datatype for solar:SystemBatteryConnection", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:SystemBatteryConnection",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:PVSystemIdentifierAxis": "1",
"solar:InstallationTypeAxis": "solar:GroundMember",
"solar:SolarSubArrayIdentifierAxis": "1"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "2.4.1 Hot summer continental climates",
"aspects": {
"concept": "solar:SiteClimateClassificationKoppen",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:SiteIdentifierAxis": "1"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:SiteClimateClassificationKoppen", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:SiteClimateClassificationKoppen",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:SiteIdentifierAxis": "1"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:SiteClimateClassificationKoppen", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:SiteClimateClassificationKoppen",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:SiteIdentifierAxis": "1"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Mixed - Marine",
"aspects": {
"concept": "solar:SiteClimateZoneTypeANSI",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:SiteIdentifierAxis": "1"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:SiteClimateZoneTypeANSI", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:SiteClimateZoneTypeANSI",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:SiteIdentifierAxis": "1"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:SiteClimateZoneTypeANSI", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:SiteClimateZoneTypeANSI",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:SiteIdentifierAxis": "1"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Modbus",
"aspects": {
"concept": "solar:DataAcquisitionSystemCommunicationProtocol",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:PVSystemIdentifierAxis": "1"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:DataAcquisitionSystemCommunicationProtocol", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:DataAcquisitionSystemCommunicationProtocol",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:PVSystemIdentifierAxis": "1"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:DataAcquisitionSystemCommunicationProtocol", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:DataAcquisitionSystemCommunicationProtocol",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:PVSystemIdentifierAxis": "1"
}
}
"""
],
[_ln(), "CutSheet", None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "BatteryManagementSystemMember",
"aspects": {
"concept": "solar:TypeOfDevice",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:TestConditionAxis": "solar:CustomTestConditionMember",
"solar:ProductIdentifierAxis": "1"
}
}
"""
],
[_ln(), "CutSheet", "is the wrong datatype for solar:TypeOfDevice", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:TypeOfDevice",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:TestConditionAxis": "solar:CustomTestConditionMember",
"solar:ProductIdentifierAxis": "1"
}
}
"""
],
[_ln(), "CutSheet", "is the wrong datatype for solar:TypeOfDevice", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:TypeOfDevice",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:TestConditionAxis": "solar:CustomTestConditionMember",
"solar:ProductIdentifierAxis": "1"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Distributed Generation",
"aspects": {
"concept": "solar:ProjectDistributedGenerationPortfolioOrUtilityScale",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:IndependentEngineeringServicesChecklistAxis": "solar:IndependentEngineeringServicesChecklistPostFundingActivityMember"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:ProjectDistributedGenerationPortfolioOrUtilityScale", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:ProjectDistributedGenerationPortfolioOrUtilityScale",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:IndependentEngineeringServicesChecklistAxis": "solar:IndependentEngineeringServicesChecklistPostFundingActivityMember"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:ProjectDistributedGenerationPortfolioOrUtilityScale", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:ProjectDistributedGenerationPortfolioOrUtilityScale",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:IndependentEngineeringServicesChecklistAxis": "solar:IndependentEngineeringServicesChecklistPostFundingActivityMember"
}
}
"""
],
[_ln(), "Site", None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Final Approval",
"aspects": {
"concept": "solar:DivisionOfStateArchitectApprovalStatus",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:SiteIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Site", "is the wrong datatype for solar:DivisionOfStateArchitectApprovalStatus", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:DivisionOfStateArchitectApprovalStatus",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:SiteIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Site", "is the wrong datatype for solar:DivisionOfStateArchitectApprovalStatus", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:DivisionOfStateArchitectApprovalStatus",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:SiteIdentifierAxis": "1"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Moderate",
"aspects": {
"concept": "solar:ProjectRecentEventSeverityOfEvent",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:ProjectIdentifierAxis": "1"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:ProjectRecentEventSeverityOfEvent", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:ProjectRecentEventSeverityOfEvent",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:ProjectIdentifierAxis": "1"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:ProjectRecentEventSeverityOfEvent", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:ProjectRecentEventSeverityOfEvent",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:ProjectIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Site", "is the wrong datatype for solar:ZoningPermitUpfrontFeeStatus", 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:ZoningPermitUpfrontFeeStatus",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:ZoningPermitIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Site", "is the wrong datatype for solar:ZoningPermitUpfrontFeeStatus", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:ZoningPermitUpfrontFeeStatus",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:ZoningPermitIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Site", "is the wrong datatype for solar:ZoningPermitUpfrontFeeStatus", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invald Value",
"aspects": {
"concept": "solar:ZoningPermitUpfrontFeeStatus",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:ZoningPermitIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Fund", "is the wrong datatype for solar:FundStatus", 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:FundStatus",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:FundIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Fund", "is the wrong datatype for solar:FundStatus", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:FundStatus",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:FundIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Fund", "is the wrong datatype for solar:FundStatus", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:FundStatus",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:FundIdentifierAxis": "1"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "GEOJson",
"aspects": {
"concept": "solar:SiteGeospatialBoundaryGISFileFormat",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:SiteIdentifierAxis": "1"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:SiteGeospatialBoundaryGISFileFormat", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:SiteGeospatialBoundaryGISFileFormat",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:SiteIdentifierAxis": "1"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:SiteGeospatialBoundaryGISFileFormat", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:SiteGeospatialBoundaryGISFileFormat",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:SiteIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Project", None, 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Revenue Put",
"aspects": {
"concept": "solar:ProjectHedgeAgreementType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:ProjectIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Project", "is the wrong datatype for solar:ProjectHedgeAgreementType", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:ProjectHedgeAgreementType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:ProjectIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Project", "is the wrong datatype for solar:ProjectHedgeAgreementType", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:ProjectHedgeAgreementType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:ProjectIdentifierAxis": "1"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Surety Solar Module Supply Bond",
"aspects": {
"concept": "solar:InsuranceType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:EntityAxis": "1",
"solar:PVSystemIdentifierAxis": "1",
"solar:InsuranceAxis": "1"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:InsuranceType", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:InsuranceType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:EntityAxis": "1",
"solar:PVSystemIdentifierAxis": "1",
"solar:InsuranceAxis": "1"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:InsuranceType", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:InsuranceType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:EntityAxis": "1",
"solar:PVSystemIdentifierAxis": "1",
"solar:InsuranceAxis": "1"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:NetworkType", 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:NetworkType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:ProductIdentifierAxis": "1",
"solar:PVSystemIdentifierAxis": "1",
"solar:TestConditionAxis": "solar:StandardTestConditionMember"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:NetworkType", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:NetworkType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:ProductIdentifierAxis": "1",
"solar:PVSystemIdentifierAxis": "1",
"solar:TestConditionAxis": "solar:StandardTestConditionMember"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:NetworkType", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:NetworkType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:ProductIdentifierAxis": "1",
"solar:PVSystemIdentifierAxis": "1",
"solar:TestConditionAxis": "solar:StandardTestConditionMember"
}
}
"""
],
[_ln(), "IECRECertificate", None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "MicroInverter",
"aspects": {
"concept": "solar:InverterStyle",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), "IECRECertificate", "is the wrong datatype for solar:InverterStyle", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:InverterStyle",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), "IECRECertificate", "is the wrong datatype for solar:InverterStyle", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:InverterStyle",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), "CutSheet", None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Three Phase WYE",
"aspects": {
"concept": "solar:InverterOutputPhaseType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:TestConditionAxis": "solar:CustomTestConditionMember",
"solar:ProductIdentifierAxis": "1"
}
}
"""
],
[_ln(), "CutSheet", "is the wrong datatype for solar:InverterOutputPhaseType", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:InverterOutputPhaseType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:TestConditionAxis": "solar:CustomTestConditionMember",
"solar:ProductIdentifierAxis": "1"
}
}
"""
],
[_ln(), "CutSheet", "is the wrong datatype for solar:InverterOutputPhaseType", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:InverterOutputPhaseType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:TestConditionAxis": "solar:CustomTestConditionMember",
"solar:ProductIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Project", None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Partial Funding",
"aspects": {
"concept": "solar:ProjectInvestmentStatus",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:ProjectIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Project", "is the wrong datatype for solar:ProjectInvestmentStatus", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:ProjectInvestmentStatus",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:ProjectIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Project", "is the wrong datatype for solar:ProjectInvestmentStatus", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:ProjectInvestmentStatus",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:ProjectIdentifierAxis": "1"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Fund Level",
"aspects": {
"concept": "solar:MonthlyOperatingReportLevel",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:MonthlyOperatingReportLevel", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:MonthlyOperatingReportLevel",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:MonthlyOperatingReportLevel", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:MonthlyOperatingReportLevel",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00"
}
}
"""
],
[_ln(), "CutSheet", None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "BiFacial",
"aspects": {
"concept": "solar:ModuleStyle",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:TestConditionAxis": "solar:CustomTestConditionMember",
"solar:ProductIdentifierAxis": "1"
}
}
"""
],
[_ln(), "CutSheet", "is the wrong datatype for solar:ModuleStyle", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:ModuleStyle",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:TestConditionAxis": "solar:CustomTestConditionMember",
"solar:ProductIdentifierAxis": "1"
}
}
"""
],
[_ln(), "CutSheet", "is the wrong datatype for solar:ModuleStyle", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:ModuleStyle",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:TestConditionAxis": "solar:CustomTestConditionMember",
"solar:ProductIdentifierAxis": "1"
}
}
"""
],
[_ln(), "CutSheet", None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Portrait",
"aspects": {
"concept": "solar:ModuleOrientation",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:TestConditionAxis": "solar:CustomTestConditionMember",
"solar:ProductIdentifierAxis": "1"
}
}
"""
],
[_ln(), "CutSheet", "is the wrong datatype for solar:ModuleOrientation", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:ModuleOrientation",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:TestConditionAxis": "solar:CustomTestConditionMember",
"solar:ProductIdentifierAxis": "1"
}
}
"""
],
[_ln(), "CutSheet", "is the wrong datatype for solar:ModuleOrientation", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:ModuleOrientation",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:TestConditionAxis": "solar:CustomTestConditionMember",
"solar:ProductIdentifierAxis": "1"
}
}
"""
],
[_ln(), "CutSheet", None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Multi-C-Si",
"aspects": {
"concept": "solar:ModuleTechnology",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:TestConditionAxis": "solar:CustomTestConditionMember",
"solar:ProductIdentifierAxis": "1"
}
}
"""
],
[_ln(), "CutSheet", "is the wrong datatype for solar:ModuleTechnology", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:ModuleTechnology",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:TestConditionAxis": "solar:CustomTestConditionMember",
"solar:ProductIdentifierAxis": "1"
}
}
"""
],
[_ln(), "CutSheet", "is the wrong datatype for solar:ModuleTechnology", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:ModuleTechnology",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:TestConditionAxis": "solar:CustomTestConditionMember",
"solar:ProductIdentifierAxis": "1"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Ballasted",
"aspects": {
"concept": "solar:MountingType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:PVSystemIdentifierAxis": "1",
"solar:InstallationTypeAxis": "solar:GroundMember",
"solar:SolarSubArrayIdentifierAxis": "1"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:MountingType", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:MountingType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:PVSystemIdentifierAxis": "1",
"solar:InstallationTypeAxis": "solar:GroundMember",
"solar:SolarSubArrayIdentifierAxis": "1"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:MountingType", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:MountingType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:PVSystemIdentifierAxis": "1",
"solar:InstallationTypeAxis": "solar:GroundMember",
"solar:SolarSubArrayIdentifierAxis": "1"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Owner Occupied",
"aspects": {
"concept": "solar:SitePropertyOccupancyType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:SiteIdentifierAxis": "1"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:SitePropertyOccupancyType", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:SitePropertyOccupancyType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:SiteIdentifierAxis": "1"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:SitePropertyOccupancyType", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:SitePropertyOccupancyType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:SiteIdentifierAxis": "1"
}
}
"""
],
[_ln(), "CutSheet", None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Attached",
"aspects": {
"concept": "solar:OptimizerType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:TestConditionAxis": "solar:CustomTestConditionMember",
"solar:ProductIdentifierAxis": "1"
}
}
"""
],
[_ln(), "CutSheet", "is the wrong datatype for solar:OptimizerType", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:OptimizerType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:TestConditionAxis": "solar:CustomTestConditionMember",
"solar:ProductIdentifierAxis": "1"
}
}
"""
],
[_ln(), "CutSheet", "is the wrong datatype for solar:OptimizerType", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:OptimizerType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:TestConditionAxis": "solar:CustomTestConditionMember",
"solar:ProductIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Entity", None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Workers Compensation Insurer",
"aspects": {
"concept": "solar:EntityRole",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:EntityAxis": "1"
}
}
"""
],
[_ln(), "Entity", "is the wrong datatype for solar:EntityRole", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:EntityRole",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:EntityAxis": "1"
}
}
"""
],
[_ln(), "Entity", "is the wrong datatype for solar:EntityRole", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:EntityRole",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:EntityAxis": "1"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Incomplete",
"aspects": {
"concept": "solar:SystemPreventiveMaintenanceTasksStatus",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:PVSystemIdentifierAxis": "1"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:SystemPreventiveMaintenanceTasksStatus", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:SystemPreventiveMaintenanceTasksStatus",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:PVSystemIdentifierAxis": "1"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:SystemPreventiveMaintenanceTasksStatus", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:SystemPreventiveMaintenanceTasksStatus",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:PVSystemIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Project", None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Solar Plus Storage",
"aspects": {
"concept": "solar:ProjectAssetType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:ProjectIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Project", "is the wrong datatype for solar:ProjectAssetType", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:ProjectAssetType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:ProjectIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Project", "is the wrong datatype for solar:ProjectAssetType", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:ProjectAssetType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:ProjectIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Project", None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Community Solar",
"aspects": {
"concept": "solar:ProjectClassType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:ProjectIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Project", "is the wrong datatype for solar:ProjectClassType", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:ProjectClassType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:ProjectIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Project", "is the wrong datatype for solar:ProjectClassType", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:ProjectClassType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:ProjectIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Project", None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Virtual Net Meter",
"aspects": {
"concept": "solar:ProjectInterconnectionType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:ProjectIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Project", "is the wrong datatype for solar:ProjectInterconnectionType", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:ProjectInterconnectionType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:ProjectIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Project", "is the wrong datatype for solar:ProjectInterconnectionType", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:ProjectInterconnectionType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:ProjectIdentifierAxis": "1"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Early Construction",
"aspects": {
"concept": "solar:PhaseOfProjectNeeded",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:IndependentEngineeringServicesChecklistAxis": "solar:IndependentEngineeringServicesChecklistPostFundingActivityMember"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:PhaseOfProjectNeeded", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:PhaseOfProjectNeeded",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:IndependentEngineeringServicesChecklistAxis": "solar:IndependentEngineeringServicesChecklistPostFundingActivityMember"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:PhaseOfProjectNeeded", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:PhaseOfProjectNeeded",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:IndependentEngineeringServicesChecklistAxis": "solar:IndependentEngineeringServicesChecklistPostFundingActivityMember"
}
}
"""
],
[_ln(), "Project", None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "In Operation",
"aspects": {
"concept": "solar:ProjectStage",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:ProjectIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Project", "is the wrong datatype for solar:ProjectStage", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:ProjectStage",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:ProjectIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Project", "is the wrong datatype for solar:ProjectStage", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:ProjectStage",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:ProjectIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Project", None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Not Submitted",
"aspects": {
"concept": "solar:RegulatoryApprovalStatus",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:ProjectIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Project", "is the wrong datatype for solar:RegulatoryApprovalStatus", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:RegulatoryApprovalStatus",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:ProjectIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Project", "is the wrong datatype for solar:RegulatoryApprovalStatus", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:RegulatoryApprovalStatus",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:ProjectIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Project", None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "EWG",
"aspects": {
"concept": "solar:RegulatoryFacilityType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:ProjectIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Project", "is the wrong datatype for solar:RegulatoryFacilityType", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:RegulatoryFacilityType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:ProjectIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Project", "is the wrong datatype for solar:RegulatoryFacilityType", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:RegulatoryFacilityType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:ProjectIdentifierAxis": "1"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Letter of Credit",
"aspects": {
"concept": "solar:ReserveCollateralType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:FundIdentifierAxis": "1",
"solar:ReserveTypeAxis": "solar:FundReserveMember"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:ReserveCollateralType", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:ReserveCollateralType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:FundIdentifierAxis": "1",
"solar:ReserveTypeAxis": "solar:FundReserveMember"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:ReserveCollateralType", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:ReserveCollateralType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:FundIdentifierAxis": "1",
"solar:ReserveTypeAxis": "solar:FundReserveMember"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Maintenance",
"aspects": {
"concept": "solar:ReserveUse",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:FundIdentifierAxis": "1",
"solar:ReserveTypeAxis": "solar:FundReserveMember"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:ReserveUse", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:ReserveUse",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:FundIdentifierAxis": "1",
"solar:ReserveTypeAxis": "solar:FundReserveMember"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:ReserveUse", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:ReserveUse",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:FundIdentifierAxis": "1",
"solar:ReserveTypeAxis": "solar:FundReserveMember"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:RoofType", 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:RoofType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:PVSystemIdentifierAxis": "1",
"solar:InstallationTypeAxis": "solar:RooftopMember",
"solar:SolarSubArrayIdentifierAxis": "1"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:RoofType", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:RoofType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:PVSystemIdentifierAxis": "1",
"solar:InstallationTypeAxis": "solar:RooftopMember",
"solar:SolarSubArrayIdentifierAxis": "1"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:RoofType", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:RoofType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:PVSystemIdentifierAxis": "1",
"solar:InstallationTypeAxis": "solar:RooftopMember",
"solar:SolarSubArrayIdentifierAxis": "1"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:RoofSlopeType", 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:RoofSlopeType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:PVSystemIdentifierAxis": "1",
"solar:InstallationTypeAxis": "solar:RooftopMember",
"solar:SolarSubArrayIdentifierAxis": "1"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:RoofSlopeType", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:RoofSlopeType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:PVSystemIdentifierAxis": "1",
"solar:InstallationTypeAxis": "solar:RooftopMember",
"solar:SolarSubArrayIdentifierAxis": "1"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:RoofSlopeType", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:RoofSlopeType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:PVSystemIdentifierAxis": "1",
"solar:InstallationTypeAxis": "solar:RooftopMember",
"solar:SolarSubArrayIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Site", None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Lease",
"aspects": {
"concept": "solar:SiteControlType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:SiteIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Site", "is the wrong datatype for solar:SiteControlType", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:SiteControlType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:SiteIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Site", "is the wrong datatype for solar:SiteControlType", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:SiteControlType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:SiteIdentifierAxis": "1"
}
}
"""
],
[_ln(), "System", None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Agricultural",
"aspects": {
"concept": "solar:SystemType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:PVSystemIdentifierAxis": "1"
}
}
"""
],
[_ln(), "System", "is the wrong datatype for solar:SystemType", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:SystemType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:PVSystemIdentifierAxis": "1"
}
}
"""
],
[_ln(), "System", "is the wrong datatype for solar:SystemType", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:SystemType",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:PVSystemIdentifierAxis": "1"
}
}
"""
],
[_ln(), None, None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Insufficient",
"aspects": {
"concept": "solar:SystemSparePartsStatusLevel",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:PVSystemIdentifierAxis": "1"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:SystemSparePartsStatusLevel", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:SystemSparePartsStatusLevel",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:PVSystemIdentifierAxis": "1"
}
}
"""
],
[_ln(), None, "is the wrong datatype for solar:SystemSparePartsStatusLevel", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:SystemSparePartsStatusLevel",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:PVSystemIdentifierAxis": "1"
}
}
"""
],
[_ln(), "System", None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Islanded",
"aspects": {
"concept": "solar:SystemAvailabilityMode",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:PVSystemIdentifierAxis": "1"
}
}
"""
],
[_ln(), "System", "is the wrong datatype for solar:SystemAvailabilityMode", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:SystemAvailabilityMode",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:PVSystemIdentifierAxis": "1"
}
}
"""
],
[_ln(), "System", "is the wrong datatype for solar:SystemAvailabilityMode", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:SystemAvailabilityMode",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:PVSystemIdentifierAxis": "1"
}
}
"""
],
[_ln(), "System", None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Communication Failure",
"aspects": {
"concept": "solar:SystemOperationStatus",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:PVSystemIdentifierAxis": "1"
}
}
"""
],
[_ln(), "System", "is the wrong datatype for solar:SystemOperationStatus", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:SystemOperationStatus",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:PVSystemIdentifierAxis": "1"
}
}
"""
],
[_ln(), "System", "is the wrong datatype for solar:SystemOperationStatus", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:SystemOperationStatus",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:PVSystemIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Site", None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Pro Forma",
"aspects": {
"concept": "solar:TitlePolicyInsuranceStatus",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:SiteIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Site", "is the wrong datatype for solar:TitlePolicyInsuranceStatus", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:TitlePolicyInsuranceStatus",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:SiteIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Site", "is the wrong datatype for solar:TitlePolicyInsuranceStatus", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:TitlePolicyInsuranceStatus",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:SiteIdentifierAxis": "1"
}
}
"""
],
[_ln(), "System", None, 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Azimuth Axis Tracking",
"aspects": {
"concept": "solar:TrackerStyle",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:PVSystemIdentifierAxis": "1",
"solar:EquipmentTypeAxis": "solar:ModuleMember",
"solar:SolarSubArrayIdentifierAxis": "1"
}
}
"""
],
[_ln(), "System", "is the wrong datatype for solar:TrackerStyle", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:TrackerStyle",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:PVSystemIdentifierAxis": "1",
"solar:EquipmentTypeAxis": "solar:ModuleMember",
"solar:SolarSubArrayIdentifierAxis": "1"
}
}
"""
],
[_ln(), "System", "is the wrong datatype for solar:TrackerStyle", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:TrackerStyle",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:PVSystemIdentifierAxis": "1",
"solar:EquipmentTypeAxis": "solar:ModuleMember",
"solar:SolarSubArrayIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Site", "is the wrong datatype for solar:ZoningPermitProperty", 0, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:ZoningPermitProperty",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:ZoningPermitIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Site", "is the wrong datatype for solar:ZoningPermitProperty", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": false,
"aspects": {
"concept": "solar:ZoningPermitProperty",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:ZoningPermitIdentifierAxis": "1"
}
}
"""
],
[_ln(), "Site", "is the wrong datatype for solar:ZoningPermitProperty", 2, """
"d5ead87b-58c6-4aab-9795-e7e92ca0bcf2": {
"value": "Invalid Value",
"aspects": {
"concept": "solar:ZoningPermitProperty",
"entity": "JUPITER",
"period": "2017-11-01T00:00:00/2017-11-30T00:00:00",
"solar:ZoningPermitIdentifierAxis": "1"
}
}
"""
]
]
JSON_HEADER = """
{
"documentType": "http://www.xbrl.org/WGWD/YYYY-MM-DD/xbrl-json",
"prefixes": {
"xbrl": "http://www.xbrl.org/WGWD/YYYY-MM-DD/oim",
"solar": "http://xbrl.us/Solar/v1.1/2018-02-09/solar",
"us-gaap": "http://fasb.org/us-gaap/2017-01-31",
"iso4217": "http://www.xbrl.org/2003/iso4217",
"SI": "http://www.xbrl.org/2009/utr"
},
"dtsReferences": [
{
"type": "schema",
"href": "https://raw.githubusercontent.com/xbrlus/solar/v1.2/core/solar_all_2018-03-31_r01.xsd"
}
],
"facts": {
"""
JSON_FOOTER = """
}
}
"""
| 34.306208
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| 11,559
| 131,530
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| 0.941262
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| 0.154905
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| 0.887047
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| 0.000299
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| 0.001496
| 0
| 0.002095
| 0
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| null | 0
| 0
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| 1
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| 0
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|
0
| 8
|
dc02b73383776bfe8d8f1b3ae2413aea2eaec02e
| 13,796
|
py
|
Python
|
plotly_study/graph_objs/layout/scene/camera/__init__.py
|
lucasiscovici/plotly_py
|
42ab769febb45fbbe0a3c677dc4306a4f59cea36
|
[
"MIT"
] | null | null | null |
plotly_study/graph_objs/layout/scene/camera/__init__.py
|
lucasiscovici/plotly_py
|
42ab769febb45fbbe0a3c677dc4306a4f59cea36
|
[
"MIT"
] | null | null | null |
plotly_study/graph_objs/layout/scene/camera/__init__.py
|
lucasiscovici/plotly_py
|
42ab769febb45fbbe0a3c677dc4306a4f59cea36
|
[
"MIT"
] | null | null | null |
from plotly_study.basedatatypes import BaseLayoutHierarchyType as _BaseLayoutHierarchyType
import copy as _copy
class Up(_BaseLayoutHierarchyType):
# x
# -
@property
def x(self):
"""
The 'x' property is a number and may be specified as:
- An int or float
Returns
-------
int|float
"""
return self["x"]
@x.setter
def x(self, val):
self["x"] = val
# y
# -
@property
def y(self):
"""
The 'y' property is a number and may be specified as:
- An int or float
Returns
-------
int|float
"""
return self["y"]
@y.setter
def y(self, val):
self["y"] = val
# z
# -
@property
def z(self):
"""
The 'z' property is a number and may be specified as:
- An int or float
Returns
-------
int|float
"""
return self["z"]
@z.setter
def z(self, val):
self["z"] = val
# property parent name
# --------------------
@property
def _parent_path_str(self):
return "layout.scene.camera"
# Self properties description
# ---------------------------
@property
def _prop_descriptions(self):
return """\
x
y
z
"""
def __init__(self, arg=None, x=None, y=None, z=None, **kwargs):
"""
Construct a new Up object
Sets the (x,y,z) components of the 'up' camera vector. This
vector determines the up direction of this scene with respect
to the page. The default is *{x: 0, y: 0, z: 1}* which means
that the z axis points up.
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of plotly_study.graph_objs.layout.scene.camera.Up
x
y
z
Returns
-------
Up
"""
super(Up, self).__init__("up")
# Validate arg
# ------------
if arg is None:
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError(
"""\
The first argument to the plotly_study.graph_objs.layout.scene.camera.Up
constructor must be a dict or
an instance of plotly_study.graph_objs.layout.scene.camera.Up"""
)
# Handle skip_invalid
# -------------------
self._skip_invalid = kwargs.pop("skip_invalid", False)
# Import validators
# -----------------
from plotly_study.validators.layout.scene.camera import up as v_up
# Initialize validators
# ---------------------
self._validators["x"] = v_up.XValidator()
self._validators["y"] = v_up.YValidator()
self._validators["z"] = v_up.ZValidator()
# Populate data dict with properties
# ----------------------------------
_v = arg.pop("x", None)
self["x"] = x if x is not None else _v
_v = arg.pop("y", None)
self["y"] = y if y is not None else _v
_v = arg.pop("z", None)
self["z"] = z if z is not None else _v
# Process unknown kwargs
# ----------------------
self._process_kwargs(**dict(arg, **kwargs))
# Reset skip_invalid
# ------------------
self._skip_invalid = False
from plotly_study.basedatatypes import BaseLayoutHierarchyType as _BaseLayoutHierarchyType
import copy as _copy
class Projection(_BaseLayoutHierarchyType):
# type
# ----
@property
def type(self):
"""
Sets the projection type. The projection type could be either
"perspective" or "orthographic". The default is "perspective".
The 'type' property is an enumeration that may be specified as:
- One of the following enumeration values:
['perspective', 'orthographic']
Returns
-------
Any
"""
return self["type"]
@type.setter
def type(self, val):
self["type"] = val
# property parent name
# --------------------
@property
def _parent_path_str(self):
return "layout.scene.camera"
# Self properties description
# ---------------------------
@property
def _prop_descriptions(self):
return """\
type
Sets the projection type. The projection type could be
either "perspective" or "orthographic". The default is
"perspective".
"""
def __init__(self, arg=None, type=None, **kwargs):
"""
Construct a new Projection object
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of
plotly_study.graph_objs.layout.scene.camera.Projection
type
Sets the projection type. The projection type could be
either "perspective" or "orthographic". The default is
"perspective".
Returns
-------
Projection
"""
super(Projection, self).__init__("projection")
# Validate arg
# ------------
if arg is None:
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError(
"""\
The first argument to the plotly_study.graph_objs.layout.scene.camera.Projection
constructor must be a dict or
an instance of plotly_study.graph_objs.layout.scene.camera.Projection"""
)
# Handle skip_invalid
# -------------------
self._skip_invalid = kwargs.pop("skip_invalid", False)
# Import validators
# -----------------
from plotly_study.validators.layout.scene.camera import projection as v_projection
# Initialize validators
# ---------------------
self._validators["type"] = v_projection.TypeValidator()
# Populate data dict with properties
# ----------------------------------
_v = arg.pop("type", None)
self["type"] = type if type is not None else _v
# Process unknown kwargs
# ----------------------
self._process_kwargs(**dict(arg, **kwargs))
# Reset skip_invalid
# ------------------
self._skip_invalid = False
from plotly_study.basedatatypes import BaseLayoutHierarchyType as _BaseLayoutHierarchyType
import copy as _copy
class Eye(_BaseLayoutHierarchyType):
# x
# -
@property
def x(self):
"""
The 'x' property is a number and may be specified as:
- An int or float
Returns
-------
int|float
"""
return self["x"]
@x.setter
def x(self, val):
self["x"] = val
# y
# -
@property
def y(self):
"""
The 'y' property is a number and may be specified as:
- An int or float
Returns
-------
int|float
"""
return self["y"]
@y.setter
def y(self, val):
self["y"] = val
# z
# -
@property
def z(self):
"""
The 'z' property is a number and may be specified as:
- An int or float
Returns
-------
int|float
"""
return self["z"]
@z.setter
def z(self, val):
self["z"] = val
# property parent name
# --------------------
@property
def _parent_path_str(self):
return "layout.scene.camera"
# Self properties description
# ---------------------------
@property
def _prop_descriptions(self):
return """\
x
y
z
"""
def __init__(self, arg=None, x=None, y=None, z=None, **kwargs):
"""
Construct a new Eye object
Sets the (x,y,z) components of the 'eye' camera vector. This
vector determines the view point about the origin of this
scene.
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of
plotly_study.graph_objs.layout.scene.camera.Eye
x
y
z
Returns
-------
Eye
"""
super(Eye, self).__init__("eye")
# Validate arg
# ------------
if arg is None:
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError(
"""\
The first argument to the plotly_study.graph_objs.layout.scene.camera.Eye
constructor must be a dict or
an instance of plotly_study.graph_objs.layout.scene.camera.Eye"""
)
# Handle skip_invalid
# -------------------
self._skip_invalid = kwargs.pop("skip_invalid", False)
# Import validators
# -----------------
from plotly_study.validators.layout.scene.camera import eye as v_eye
# Initialize validators
# ---------------------
self._validators["x"] = v_eye.XValidator()
self._validators["y"] = v_eye.YValidator()
self._validators["z"] = v_eye.ZValidator()
# Populate data dict with properties
# ----------------------------------
_v = arg.pop("x", None)
self["x"] = x if x is not None else _v
_v = arg.pop("y", None)
self["y"] = y if y is not None else _v
_v = arg.pop("z", None)
self["z"] = z if z is not None else _v
# Process unknown kwargs
# ----------------------
self._process_kwargs(**dict(arg, **kwargs))
# Reset skip_invalid
# ------------------
self._skip_invalid = False
from plotly_study.basedatatypes import BaseLayoutHierarchyType as _BaseLayoutHierarchyType
import copy as _copy
class Center(_BaseLayoutHierarchyType):
# x
# -
@property
def x(self):
"""
The 'x' property is a number and may be specified as:
- An int or float
Returns
-------
int|float
"""
return self["x"]
@x.setter
def x(self, val):
self["x"] = val
# y
# -
@property
def y(self):
"""
The 'y' property is a number and may be specified as:
- An int or float
Returns
-------
int|float
"""
return self["y"]
@y.setter
def y(self, val):
self["y"] = val
# z
# -
@property
def z(self):
"""
The 'z' property is a number and may be specified as:
- An int or float
Returns
-------
int|float
"""
return self["z"]
@z.setter
def z(self, val):
self["z"] = val
# property parent name
# --------------------
@property
def _parent_path_str(self):
return "layout.scene.camera"
# Self properties description
# ---------------------------
@property
def _prop_descriptions(self):
return """\
x
y
z
"""
def __init__(self, arg=None, x=None, y=None, z=None, **kwargs):
"""
Construct a new Center object
Sets the (x,y,z) components of the 'center' camera vector This
vector determines the translation (x,y,z) space about the
center of this scene. By default, there is no such translation.
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of
plotly_study.graph_objs.layout.scene.camera.Center
x
y
z
Returns
-------
Center
"""
super(Center, self).__init__("center")
# Validate arg
# ------------
if arg is None:
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError(
"""\
The first argument to the plotly_study.graph_objs.layout.scene.camera.Center
constructor must be a dict or
an instance of plotly_study.graph_objs.layout.scene.camera.Center"""
)
# Handle skip_invalid
# -------------------
self._skip_invalid = kwargs.pop("skip_invalid", False)
# Import validators
# -----------------
from plotly_study.validators.layout.scene.camera import center as v_center
# Initialize validators
# ---------------------
self._validators["x"] = v_center.XValidator()
self._validators["y"] = v_center.YValidator()
self._validators["z"] = v_center.ZValidator()
# Populate data dict with properties
# ----------------------------------
_v = arg.pop("x", None)
self["x"] = x if x is not None else _v
_v = arg.pop("y", None)
self["y"] = y if y is not None else _v
_v = arg.pop("z", None)
self["z"] = z if z is not None else _v
# Process unknown kwargs
# ----------------------
self._process_kwargs(**dict(arg, **kwargs))
# Reset skip_invalid
# ------------------
self._skip_invalid = False
__all__ = ["Center", "Eye", "Projection", "Up"]
| 24.118881
| 90
| 0.502392
| 1,485
| 13,796
| 4.527273
| 0.087542
| 0.032723
| 0.050573
| 0.035698
| 0.877436
| 0.848133
| 0.816451
| 0.816451
| 0.807824
| 0.793991
| 0
| 0.000333
| 0.34684
| 13,796
| 571
| 91
| 24.161121
| 0.745755
| 0.317411
| 0
| 0.764706
| 0
| 0
| 0.073148
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.156863
| false
| 0
| 0.058824
| 0.039216
| 0.323529
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| null | 0
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|
0
| 7
|
dc0e14d9feb631fb6b93a1941b7b7ae9a9518371
| 12,937
|
py
|
Python
|
Passing/coordinates_generator.py
|
FootBrawlers/Passing_Algo
|
5341168dc12f7c4cb254a0a4901de7c3766cc823
|
[
"MIT"
] | 1
|
2020-01-16T13:19:19.000Z
|
2020-01-16T13:19:19.000Z
|
Passing/coordinates_generator.py
|
FootBrawlers/Passing_Algo
|
5341168dc12f7c4cb254a0a4901de7c3766cc823
|
[
"MIT"
] | null | null | null |
Passing/coordinates_generator.py
|
FootBrawlers/Passing_Algo
|
5341168dc12f7c4cb254a0a4901de7c3766cc823
|
[
"MIT"
] | 1
|
2020-01-09T21:04:30.000Z
|
2020-01-09T21:04:30.000Z
|
# this script generates a file "input_coords_all.txt which contains the input coordinates."
import pygame
from pygame.locals import *
from sys import exit
from random import *
pygame.init()
# screen = pygame.display.set_mode((1280, 960), 0, 32)
# DIMENSIONS
screen = pygame.display.set_mode((0, 0), pygame.RESIZABLE)
factor_horizontal = 120
factor_vertical = 10
field_x = 0 + factor_horizontal
field_y = 0 + factor_vertical
field_length = 900
field_width = 600
center_x = 570
center_y = 310
circle_radius = 80
goal_post_length = (2 * circle_radius) - 20
width_small_d = 50
width_big_d = 150
# STYLING
screen_color = (0, 0, 0)
field_color = (4, 255, 100)
field_border_color = (255, 255, 255)
host_bot_color = (255, 255, 0)
opp_bot_color = (255, 0, 0)
goal_post_color = (101, 67, 33)
host_cord = {}
opp_cord = {}
temp_switch_logic = 0
count = 0
count1=0
upd=0
bot = 0
def generate():
bot = 0
while True:
for event in pygame.event.get():
if event.type == QUIT:
print(host_cord)
print(temp_switch_logic)
print(opp_cord)
exit()
if event.type == pygame.KEYDOWN:
if event.key == pygame.K_RETURN:
host_cord_list = [0 for i in range(6)]
for i in host_cord.keys():
if host_cord[i] <= 5:
host_cord_list[host_cord[i]] = i
print(host_cord_list)
file1 = open("input.txt", "w")
text_to_write = ""
for i in range(len(host_cord_list)):
text_to_write += str(i + 1) + " " + " ".join(list(map(str, host_cord_list[i]))) + "\n"
opp_cord_list = [0 for i in range(6)]
for i in opp_cord.keys():
if opp_cord[i] <= 5:
opp_cord_list[opp_cord[i]] = i
print(opp_cord_list)
for i in range(len(opp_cord_list)):
text_to_write += str(i + 11) + " " + " ".join(list(map(str, opp_cord_list[i]))) + "\n"
file1.write(text_to_write)
file1.close()
rect_save = pygame.Rect(field_x, field_y, field_length, field_width)
sub = screen.subsurface(rect_save)
pygame.image.save(sub, "screenshot.jpg")
return 0
# FIELD STYLING
screen.lock()
rectangle_main_border = (field_x - 10, field_y - 10)
rectangle_main_border_size = (field_length + 20, field_width + 20)
rectangle_main = (field_x, field_y)
rectangle_main_size = (field_length, field_width)
screen.fill(screen_color)
pygame.draw.rect(screen, field_border_color, Rect(rectangle_main_border, rectangle_main_border_size))
pygame.draw.rect(screen, field_color, Rect(rectangle_main, rectangle_main_size))
# center line vertical
pygame.draw.line(screen, field_border_color, (center_x, field_y), (center_x, field_y + field_width), 1)
# center circle
pygame.draw.circle(screen, field_border_color, (center_x, 310), circle_radius, 1)
# center line horizontal
# pygame.draw.line(screen, field_border_color, (field_x,center_y), (field_x + field_length, center_y),1)
# LEFT Side
# goalpost
pygame.draw.line(screen, goal_post_color, (field_x - 20, center_y - (goal_post_length // 2)),
(field_x - 20, center_y + (goal_post_length // 2)), 19)
# smallD
pygame.draw.rect(screen, field_border_color,
Rect((field_x - 1, center_y - (circle_radius)), (width_small_d, 2 * circle_radius)), 1)
# bigD
pygame.draw.rect(screen, field_border_color, Rect((field_x - 1, center_y - ((5 / 8) * (field_width // 2))),
(width_big_d, (10 / 8) * (field_width // 2))), 1)
# RIGHT side
# goalpost
pygame.draw.line(screen, goal_post_color, (field_x + field_length + 19, center_y - (goal_post_length // 2)),
(field_x + field_length + 19, center_y + (goal_post_length // 2)), 19)
# smallD
pygame.draw.rect(screen, field_border_color,
Rect((field_x + field_length - (width_small_d - 1), center_y - (circle_radius)),
(width_small_d, 2 * circle_radius)), 1)
# bigD
pygame.draw.rect(screen, field_border_color,
Rect((field_x + field_length - (width_big_d - 1), center_y - ((5 / 8) * (field_width // 2))),
(width_big_d, (10 / 8) * (field_width // 2))), 1)
screen.unlock()
# SETUP FOR TEXT
font = pygame.font.Font('freesansbold.ttf', 10)
if event.type == pygame.MOUSEBUTTONDOWN and len(host_cord) < 6 and event.button == 1:
temp = pygame.mouse.get_pos()
if temp not in host_cord.keys():
host_cord[pygame.mouse.get_pos()] = len(host_cord)
if event.type == pygame.MOUSEBUTTONDOWN and len(opp_cord) < 6 and event.button == 3:
temp = pygame.mouse.get_pos()
if temp not in opp_cord.keys():
opp_cord[pygame.mouse.get_pos()] = len(opp_cord)
if len(list(host_cord.keys())[:-1]) <= 6:
for cord in host_cord.keys():
temp = tuple(cord)
pygame.draw.circle(screen, host_bot_color, temp, 10)
text = font.render(str(host_cord[temp] + 1) + str(temp), True, (0, 0, 0))
textRect = text.get_rect()
textRect.center = temp
screen.blit(text, textRect)
if len(list(opp_cord.keys())) <= 6:
for cord in opp_cord.keys():
temp = tuple(cord)
pygame.draw.circle(screen, opp_bot_color, temp, 10)
text = font.render(str(opp_cord[temp] + 1) + str(temp), True, (0, 0, 0))
textRect = text.get_rect()
textRect.center = temp
screen.blit(text, textRect)
pygame.display.update()
def show():
host_cords1 = []
opp_cords1 = []
input_file = open("input.txt", "r")
op = input_file.readlines()
for i in range(len(op)):
if i < 6:
host_cords1.append(list(map(int, op[i].strip().split()[1:])))
else:
opp_cords1.append(list(map(int, op[i].strip().split()[1:])))
input_file.close()
for i in range(0, 6):
temp1 = tuple(host_cords1[i])
temp2 = tuple(opp_cords1[i])
host_cord[temp1] = i
opp_cord[temp2] = i
temp_switch_logic = 0
count = 0
count1 = 0
upd = 0
bot = 0
while True:
for event in pygame.event.get():
if event.type == QUIT:
exit()
if event.type == pygame.KEYDOWN:
if event.key == pygame.K_RETURN:
return 0
# FIELD STYLING
screen.lock()
rectangle_main_border = (field_x - 10, field_y - 10)
rectangle_main_border_size = (field_length + 20, field_width + 20)
rectangle_main = (field_x, field_y)
rectangle_main_size = (field_length, field_width)
screen.fill(screen_color)
pygame.draw.rect(screen, field_border_color, Rect(rectangle_main_border, rectangle_main_border_size))
pygame.draw.rect(screen, field_color, Rect(rectangle_main, rectangle_main_size))
# center line vertical
pygame.draw.line(screen, field_border_color, (center_x, field_y), (center_x, field_y + field_width), 1)
# center circle
pygame.draw.circle(screen, field_border_color, (center_x, 310), circle_radius, 1)
# center line horizontal
# pygame.draw.line(screen, field_border_color, (field_x,center_y), (field_x + field_length, center_y),1)
# LEFT Side
# goalpost
pygame.draw.line(screen, goal_post_color, (field_x - 20, center_y - (goal_post_length // 2)),
(field_x - 20, center_y + (goal_post_length // 2)), 19)
# smallD
pygame.draw.rect(screen, field_border_color,
Rect((field_x - 1, center_y - (circle_radius)), (width_small_d, 2 * circle_radius)), 1)
# bigD
pygame.draw.rect(screen, field_border_color, Rect((field_x - 1, center_y - ((5 / 8) * (field_width // 2))),
(width_big_d, (10 / 8) * (field_width // 2))), 1)
# RIGHT side
# goalpost
pygame.draw.line(screen, goal_post_color, (field_x + field_length + 19, center_y - (goal_post_length // 2)),
(field_x + field_length + 19, center_y + (goal_post_length // 2)), 19)
# smallD
pygame.draw.rect(screen, field_border_color,
Rect((field_x + field_length - (width_small_d - 1), center_y - (circle_radius)),
(width_small_d, 2 * circle_radius)), 1)
# bigD
pygame.draw.rect(screen, field_border_color,
Rect((field_x + field_length - (width_big_d - 1), center_y - ((5 / 8) * (field_width // 2))),
(width_big_d, (10 / 8) * (field_width // 2))), 1)
screen.unlock()
# SETUP FOR TEXT
font = pygame.font.Font('freesansbold.ttf', 10)
for event in pygame.event.get():
if event.type == pygame.KEYDOWN:
if event.key == pygame.K_0:
bot = 0
if event.key == pygame.K_1:
bot = 1
if event.key == pygame.K_2:
bot = 2
if event.key == pygame.K_3:
bot = 3
if event.key == pygame.K_4:
bot = 4
if event.key == pygame.K_5:
bot = 5
if event.key == pygame.K_6:
bot = 6
if bot and event.type == pygame.MOUSEBUTTONDOWN and event.button == 1:
temp = pygame.mouse.get_pos()
if temp not in host_cord.keys():
for cord in host_cord.keys():
if host_cord[cord] == bot - 1:
temp2 = cord
host_cord[pygame.mouse.get_pos()] = bot - 1
del host_cord[temp2]
upd = count
if bot and event.type == pygame.MOUSEBUTTONDOWN and event.button == 3:
temp = pygame.mouse.get_pos()
if temp not in opp_cord.keys():
for cord in opp_cord.keys():
if opp_cord[cord] == bot - 1:
temp2 = cord
opp_cord[pygame.mouse.get_pos()] = bot - 1
del opp_cord[temp2]
upd = count
if len(list(host_cord.keys())[:-1]) <= 6:
for cord in host_cord.keys():
temp = tuple(cord)
pygame.draw.circle(screen, host_bot_color, temp, 10)
text = font.render(str(host_cord[temp] + 1) + str(temp), True, (0, 0, 0))
textRect = text.get_rect()
textRect.center = temp
screen.blit(text, textRect)
if len(list(opp_cord.keys())) <= 6:
for cord in opp_cord.keys():
temp = tuple(cord)
pygame.draw.circle(screen, opp_bot_color, temp, 10)
text = font.render(str(opp_cord[temp] + 1) + str(temp), True, (0, 0, 0))
textRect = text.get_rect()
textRect.center = temp
screen.blit(text, textRect)
if len(host_cord) == 6 and len(opp_cord) == 6:
if upd == count or count1 < 1:
host_cord_list = [0 for i in range(6)]
for i in host_cord.keys():
if host_cord[i] <= 5:
host_cord_list[host_cord[i]] = i
file1 = open("input.txt", "w")
text_to_write = ""
for i in range(len(host_cord_list)):
text_to_write += str(i + 1) + " " + " ".join(list(map(str, host_cord_list[i]))) + "\n"
opp_cord_list = [0 for i in range(6)]
for i in opp_cord.keys():
if opp_cord[i] <= 5:
opp_cord_list[opp_cord[i]] = i
for i in range(len(opp_cord_list)):
text_to_write += str(i + 11) + " " + " ".join(list(map(str, opp_cord_list[i]))) + "\n"
file1.write(text_to_write)
file1.close()
rect_save = pygame.Rect(field_x, field_y, field_length, field_width)
sub = screen.subsurface(rect_save)
pygame.image.save(sub, "screenshot.jpg")
count1 += 1
count += 1
pygame.display.update()
| 38.84985
| 118
| 0.535982
| 1,669
| 12,937
| 3.913721
| 0.099461
| 0.041641
| 0.041641
| 0.053889
| 0.860686
| 0.823637
| 0.806797
| 0.78812
| 0.779547
| 0.775873
| 0
| 0.036669
| 0.346525
| 12,937
| 333
| 119
| 38.84985
| 0.735983
| 0.051558
| 0
| 0.683128
| 1
| 0
| 0.008662
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.00823
| false
| 0
| 0.016461
| 0
| 0.032922
| 0.020576
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
dc1161f48961a7d0c44172cc0cf7e14b4268303b
| 2,217
|
py
|
Python
|
data/migrations/0006_auto_20211222_0844.py
|
neversay4ever/herb
|
2309129607b3b09428d3930af5f3f5a76c4689e1
|
[
"MIT"
] | null | null | null |
data/migrations/0006_auto_20211222_0844.py
|
neversay4ever/herb
|
2309129607b3b09428d3930af5f3f5a76c4689e1
|
[
"MIT"
] | null | null | null |
data/migrations/0006_auto_20211222_0844.py
|
neversay4ever/herb
|
2309129607b3b09428d3930af5f3f5a76c4689e1
|
[
"MIT"
] | null | null | null |
# Generated by Django 3.2.10 on 2021-12-22 00:44
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('data', '0005_auto_20211221_2155'),
]
operations = [
migrations.AddField(
model_name='cog',
name='herb_id',
field=models.CharField(default=200, max_length=250, verbose_name='药物编码'),
preserve_default=False,
),
migrations.AddField(
model_name='cpc',
name='herb_id',
field=models.CharField(default=200, max_length=250, verbose_name='药物编码'),
preserve_default=False,
),
migrations.AddField(
model_name='go',
name='herb_id',
field=models.CharField(default=200, max_length=250, verbose_name='药物编码'),
preserve_default=False,
),
migrations.AddField(
model_name='kegg',
name='herb_id',
field=models.CharField(default=200, max_length=250, verbose_name='药物编码'),
preserve_default=False,
),
migrations.AddField(
model_name='kog',
name='herb_id',
field=models.CharField(default=200, max_length=250, verbose_name='药物编码'),
preserve_default=False,
),
migrations.AddField(
model_name='nr',
name='herb_id',
field=models.CharField(default=200, max_length=250, verbose_name='药物编码'),
preserve_default=False,
),
migrations.AddField(
model_name='pfam',
name='herb_id',
field=models.CharField(default=200, max_length=250, verbose_name='药物编码'),
preserve_default=False,
),
migrations.AddField(
model_name='ssr',
name='herb_id',
field=models.CharField(default=200, max_length=250, verbose_name='药物编码'),
preserve_default=False,
),
migrations.AddField(
model_name='swissprot',
name='herb_id',
field=models.CharField(default=200, max_length=250, verbose_name='药物编码'),
preserve_default=False,
),
]
| 32.602941
| 85
| 0.56608
| 227
| 2,217
| 5.317181
| 0.22467
| 0.134217
| 0.1715
| 0.201326
| 0.827672
| 0.827672
| 0.827672
| 0.827672
| 0.827672
| 0.827672
| 0
| 0.056766
| 0.316644
| 2,217
| 67
| 86
| 33.089552
| 0.739934
| 0.020749
| 0
| 0.737705
| 1
| 0
| 0.073306
| 0.010604
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.016393
| 0
| 0.065574
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 9
|
905e4900116698dae2a7724185f6a8d31a4fe679
| 30
|
py
|
Python
|
src/face_lib/__init__.py
|
a-akram-98/face_lib
|
1ffffe8130690428ec0ec7144aac41cc9df49dda
|
[
"MIT"
] | 28
|
2021-10-15T22:01:52.000Z
|
2022-03-25T07:57:21.000Z
|
src/face_lib/__init__.py
|
a-akram-98/face_lib
|
1ffffe8130690428ec0ec7144aac41cc9df49dda
|
[
"MIT"
] | 4
|
2021-10-18T07:25:34.000Z
|
2021-10-30T09:19:00.000Z
|
src/face_lib/__init__.py
|
a-akram-98/face_lib
|
1ffffe8130690428ec0ec7144aac41cc9df49dda
|
[
"MIT"
] | 3
|
2021-10-16T03:06:30.000Z
|
2021-12-11T09:21:14.000Z
|
from .face_lib import face_lib
| 30
| 30
| 0.866667
| 6
| 30
| 4
| 0.666667
| 0.583333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.1
| 30
| 1
| 30
| 30
| 0.888889
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 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
| 7
|
90626cac5d2165ed2467f23cbf4fb2b2fe8b4bf2
| 1,443
|
py
|
Python
|
Platform Software/desktop/apps/EoT_Python_Desktop/Examples/EoT_Test_Image_Algebra.py
|
EyesOfThings/Software
|
8932c2bf78c729c285853e51c8875a863496b561
|
[
"MIT"
] | 38
|
2016-06-08T19:47:43.000Z
|
2021-07-02T15:14:13.000Z
|
Platform Software/desktop/apps/EoT_Python_Desktop/Examples/EoT_Test_Image_Algebra.py
|
MAVProxyUser/Software
|
8932c2bf78c729c285853e51c8875a863496b561
|
[
"MIT"
] | 3
|
2017-07-24T03:41:53.000Z
|
2021-02-23T16:48:05.000Z
|
Platform Software/desktop/apps/EoT_Python_Desktop/Examples/EoT_Test_Image_Algebra.py
|
MAVProxyUser/Software
|
8932c2bf78c729c285853e51c8875a863496b561
|
[
"MIT"
] | 18
|
2016-02-18T08:34:17.000Z
|
2021-07-11T17:57:28.000Z
|
from eot import Image
image1 = Image(2,2, Image.CCV_8U | Image.CCV_C1)
image2 = Image(2,2, Image.CCV_8U | Image.CCV_C1)
image1.set_pixel(0,0,1)
image1.set_pixel(0,1,2)
image1.set_pixel(1,0,3)
image1.set_pixel(1,1,4)
image2.set_pixel(0,0,1)
image2.set_pixel(0,1,2)
image2.set_pixel(1,0,3)
image2.set_pixel(1,1,4)
print ("Suma")
imageRes = image1.add(image2, 0)
imageRes.get_pixel(0,0)
imageRes.get_pixel(0,1)
imageRes.get_pixel(1,0)
imageRes.get_pixel(1,1)
print ("Resta")
imageRes = image1.subtract(image2, 0)
imageRes.get_pixel(0,0)
imageRes.get_pixel(0,1)
imageRes.get_pixel(1,0)
imageRes.get_pixel(1,1)
print ("Multiply")
imageRes = image1.multiply(image2, 0)
imageRes.get_pixel(0,0)
imageRes.get_pixel(0,1)
imageRes.get_pixel(1,0)
imageRes.get_pixel(1,1)
print ("Sum")
image1.sum(Image.CCV_UNSIGNED)
print ("Variance")
image1.variance()
print ("Scale")
imageRes = image1.scale(0, 2)
imageRes.get_pixel(0,0)
imageRes.get_pixel(0,1)
imageRes.get_pixel(1,0)
imageRes.get_pixel(1,1)
print ("Gemm")
image1 = Image(2,2, Image.CCV_64F | Image.CCV_C1)
image2 = Image(2,2, Image.CCV_64F | Image.CCV_C1)
image1.set_pixel(0,0,1)
image1.set_pixel(0,1,2)
image1.set_pixel(1,0,3)
image1.set_pixel(1,1,4)
image2.set_pixel(0,0,1)
image2.set_pixel(0,1,2)
image2.set_pixel(1,0,3)
image2.set_pixel(1,1,4)
imageRes = image1.gemm(image2, 1, 0, 0, 0, 0)
imageRes.get_pixel(0,0)
imageRes.get_pixel(0,1)
imageRes.get_pixel(1,0)
imageRes.get_pixel(1,1)
| 21.537313
| 49
| 0.742897
| 290
| 1,443
| 3.541379
| 0.103448
| 0.214216
| 0.311587
| 0.231743
| 0.787731
| 0.787731
| 0.775073
| 0.775073
| 0.775073
| 0.675755
| 0
| 0.110693
| 0.079695
| 1,443
| 66
| 50
| 21.863636
| 0.662651
| 0
| 0
| 0.654545
| 0
| 0
| 0.025659
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.018182
| 0
| 0.018182
| 0.127273
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
907d1ba4790dbbfccfbc20e2e95e8ea3d1b80887
| 8,958
|
py
|
Python
|
Bot/sqlite_view.py
|
MaexMaex/python_discord_bot
|
8bbccbcfb6b6ae3d1ad670df03ff3c41feb65a59
|
[
"MIT"
] | null | null | null |
Bot/sqlite_view.py
|
MaexMaex/python_discord_bot
|
8bbccbcfb6b6ae3d1ad670df03ff3c41feb65a59
|
[
"MIT"
] | 10
|
2018-04-05T06:16:15.000Z
|
2020-02-09T08:33:13.000Z
|
Bot/sqlite_view.py
|
MaexMaex/python_discord_bot
|
8bbccbcfb6b6ae3d1ad670df03ff3c41feb65a59
|
[
"MIT"
] | null | null | null |
import sqlite3
from sqlite_models import User, Bttn, TelegramBttn, TelegramUser
class DBView:
def __init__(self, dbname="bttn.sqlite"):
self.dbname = dbname
self.conn = sqlite3.connect(dbname, check_same_thread=False)
self.c = self.conn.cursor()
def is_not_drinking(self, discord_id):
with self.conn:
self.c.execute("SELECT status FROM users WHERE discord_id = :discord_id", {
'discord_id': discord_id})
if self.c.fetchone() is 0:
return True
def get_discord_id(self, discord_id):
with self.conn:
self.c.execute("SELECT discord_id FROM users WHERE discord_id = :discord_id", {
'discord_id': discord_id})
return self.c.fetchone()
# add a user to the database
def add_user(self, user):
with self.conn:
self.c.execute("INSERT INTO users VALUES (:discord_id, :name, :score, :status)", {
'discord_id': user.discord_id, 'name': user.name, 'score': user.score, 'status': user.status})
# updatea nick in the database
def update_nickname(self, user):
with self.conn:
self.c.execute("UPDATE users SET name = :name WHERE discord_id = :discord_id", {
'discord_id': user.discord_id, 'name': user.name})
# fetch user.id
def get_user(self, discord_id):
with self.conn:
self.c.execute(
"SELECT * FROM users WHERE discord_id = :discord_id", {'discord_id': discord_id})
return self.c.fetchone()
# fetch user.id
def get_users(self):
with self.conn:
self.c.execute("SELECT name, discord_id FROM users")
return self.c.fetchall()
# adds a bttn for a user.id
def add_score(self, user):
with self.conn:
self.c.execute("""UPDATE users SET score = score + 1
WHERE discord_id = :discord_id""",
{'discord_id': user.discord_id})
# removes a bttn for a user
def remove_score(self, user):
with self.conn:
self.c.execute("""UPDATE users SET score = score - 1
WHERE discord_id = :discord_id""",
{'discord_id': user.discord_id})
# sets a stat for a user, used for manual db edits
def edit_score(self, user, score):
with self.conn:
self.c.execute("""UPDATE users SET score = :score
WHERE discord_id = :discord_id""",
{'discord_id': user.discord_id, 'score': score})
# get status for user.id
def get_status(self, user):
with self.conn:
self.c.execute("SELECT status FROM users WHERE discord_id = :discord_id", {
'discord_id': user.discord_id})
return self.c.fetchone()
# change the status of a user.id (/mystatus)
def edit_status(self, user, status):
with self.conn:
self.c.execute("""UPDATE users SET status = :status
WHERE discord_id = :discord_id""",
{'discord_id': user.discord_id, 'status': status})
# returns the statistics of the user.id (/myscore)
def get_score(self, user):
with self.conn:
self.c.execute("SELECT score FROM users WHERE discord_id = :discord_id", {
'discord_id': user.discord_id})
return self.c.fetchone()
# returns a the status for all users (/status)
def get_all_status(self):
with self.conn:
self.c.execute("SELECT name, status FROM users")
return self.c.fetchall()
# returns the statistics for all users in the database (/score)
def get_all_score(self):
with self.conn:
self.c.execute("SELECT name, score FROM users ORDER BY score DESC")
return self.c.fetchall()
# add event to the bttn table
def add_bttn(self, user, bttn):
with self.conn:
self.c.execute("INSERT INTO bttns VALUES (:discord_id, :party_name, :timestamp)", {
'discord_id': user.discord_id, 'party_name': bttn.party_name, 'timestamp': bttn.timestamp})
def get_signup(self):
with self.conn:
self.c.execute(
"SELECT users.name, users.discord_id from users LEFT JOIN telegram_users ON users.discord_id = telegram_users.discord_id WHERE telegram_users.discord_id IS NULL")
return self.c.fetchall()
#
# CTRLV CTRLC DB VIEWS FOR TELEGRAM STUFF BELOW
#
def tel_clone_discord_user(self, user):
with self.conn:
self.c.execute("INSERT INTO telegram_users VALUES (:telegram_id, :discord_id, :name, :score, :status)", {
'telegram_id': user.telegram_id, 'discord_id': user.discord_id, 'name': user.name, 'score': user.score, 'status': user.status})
def tel_is_not_drinking(self, telegram_id):
with self.conn:
self.c.execute("SELECT status FROM telegram_users WHERE telegram_id = :telegram_id", {
'telegram_id': telegram_id})
if self.c.fetchone() is 0:
return True
def tel_get_telegram_id(self, telegram_id):
with self.conn:
self.c.execute("SELECT telegram_id FROM telegram_users WHERE telegram_id = :telegram_id", {
'telegram_id': telegram_id})
return self.c.fetchone()
# updatea nick in the database
def tel_update_nickname(self, user):
with self.conn:
self.c.execute("UPDATE telegram_users SET name = :name WHERE telegram_id = :telegram_id", {
'telegram_id': user.telegram_id, 'name': user.name})
# fetch user.id
def tel_get_user(self, telegram_id):
with self.conn:
self.c.execute(
"SELECT * FROM telegram_users WHERE telegram_id = :telegram_id", {'telegram_id': telegram_id})
return self.c.fetchone()
# fetch user.id
def tel_get_users(self):
with self.conn:
self.c.execute(
"SELECT name, telegram_id, discord_id FROM telegram_users")
return self.c.fetchall()
# adds a bttn for a user.id
def tel_add_score(self, user):
with self.conn:
self.c.execute("""UPDATE telegram_users SET score = score + 1
WHERE telegram_id = :telegram_id""",
{'telegram_id': user.telegram_id})
# removes a bttn for a user
def tel_remove_score(self, user):
with self.conn:
self.c.execute("""UPDATE telegram_users SET score = score - 1
WHERE telegram_id = :telegram_id""",
{'telegram_id': user.telegram_id})
# sets a stat for a user, used for manual db edits
def tel_edit_score(self, user, score):
with self.conn:
self.c.execute("""UPDATE telegram_users SET score = :score
WHERE telegram_id = :telegram_id""",
{'telegram_id': user.telegram_id, 'score': score})
# get status for user.id
def tel_get_status(self, user):
with self.conn:
self.c.execute("SELECT status FROM telegram_users WHERE telegram_id = :telegram_id", {
'telegram_id': user.telegram_id})
return self.c.fetchone()
# change the status of a user.id (/mystatus)
def tel_edit_status(self, user, status):
with self.conn:
self.c.execute("""UPDATE telegram_users SET status = :status
WHERE telegram_id = :telegram_id""",
{'telegram_id': user.telegram_id, 'status': status})
# returns the statistics of the user.id (/myscore)
def tel_get_score(self, user):
with self.conn:
self.c.execute("SELECT score FROM telegram_users WHERE telegram_id = :telegram_id", {
'telegram_id': user.telegram_id})
return self.c.fetchone()
# returns a the status for all users (/status)
def tel_get_all_status(self):
with self.conn:
self.c.execute("SELECT name, status FROM telegram_users")
return self.c.fetchall()
# returns the statistics for all users in the database (/score)
def tel_get_all_score(self):
with self.conn:
self.c.execute(
"SELECT name, score FROM telegram_users ORDER BY score DESC")
return self.c.fetchall()
# add event to the bttn table
def tel_add_bttn(self, user, bttn):
with self.conn:
self.c.execute("INSERT INTO telegram_bttns VALUES (:telegram_id, :party_name, :timestamp)", {
'telegram_id': user.telegram_id, 'party_name': bttn.party_name, 'timestamp': bttn.timestamp})
| 40.718182
| 179
| 0.586068
| 1,138
| 8,958
| 4.441125
| 0.086116
| 0.106846
| 0.073605
| 0.09814
| 0.86387
| 0.832608
| 0.819351
| 0.815394
| 0.79165
| 0.74812
| 0
| 0.001297
| 0.311342
| 8,958
| 219
| 180
| 40.90411
| 0.817961
| 0.099129
| 0
| 0.512987
| 0
| 0.006494
| 0.314513
| 0.006218
| 0
| 0
| 0
| 0
| 0
| 1
| 0.207792
| false
| 0
| 0.012987
| 0
| 0.337662
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
29079eff5e554fbe39c85b2186adcfebbd15349b
| 255
|
py
|
Python
|
utils/__init__.py
|
jfc43/robust-ood-detection
|
fbeb63017f44b16b2911e61a1f7b7982a2621ee5
|
[
"Apache-2.0"
] | 55
|
2020-03-24T00:57:25.000Z
|
2022-03-26T16:06:59.000Z
|
utils/__init__.py
|
jfc43/robust-ood-detection
|
fbeb63017f44b16b2911e61a1f7b7982a2621ee5
|
[
"Apache-2.0"
] | 4
|
2020-04-24T23:36:17.000Z
|
2021-10-15T00:39:51.000Z
|
utils/__init__.py
|
jfc43/robust-ood-detection
|
fbeb63017f44b16b2911e61a1f7b7982a2621ee5
|
[
"Apache-2.0"
] | 5
|
2020-04-27T07:42:19.000Z
|
2022-03-23T16:14:28.000Z
|
from __future__ import absolute_import
from .pgd_attack import *
from .tinyimages_80mn_loader import *
from .lib import *
from .confidence_pgd_attack import *
from .mahalanobis_pgd_attack import *
from .mahalanobis_lib import *
from .cal_metric import *
| 25.5
| 38
| 0.815686
| 35
| 255
| 5.542857
| 0.4
| 0.360825
| 0.231959
| 0.293814
| 0.309278
| 0
| 0
| 0
| 0
| 0
| 0
| 0.009009
| 0.129412
| 255
| 9
| 39
| 28.333333
| 0.864865
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
29705814c9d232ac9c76897bd0191c283aa76d9e
| 3,426
|
py
|
Python
|
tests/test_update.py
|
luerhard/pymemdb
|
1cccd75c6918daf31babdef9cc8f94c3cee8bea6
|
[
"MIT"
] | 1
|
2020-09-01T20:07:05.000Z
|
2020-09-01T20:07:05.000Z
|
tests/test_update.py
|
luerhard/pymemdb
|
1cccd75c6918daf31babdef9cc8f94c3cee8bea6
|
[
"MIT"
] | 6
|
2020-03-02T07:32:16.000Z
|
2020-06-24T12:01:29.000Z
|
tests/test_update.py
|
luerhard/pymemdb
|
1cccd75c6918daf31babdef9cc8f94c3cee8bea6
|
[
"MIT"
] | null | null | null |
from pymemdb import Table
def test_update_single():
t = Table()
row1 = dict(vorname="rainer", nachname="greiff")
row2 = dict(vorname="rainer", nachname="greiff")
row3 = dict(vorname="rainer", nachname="erhard")
for row in [row1, row2, row3]:
t.insert(row)
row_vals_before = set(t["vorname"].values["rainer"])
t.update(dict(vorname="rainer", nachname="erhard"), n_values=45)
row_vals_after = set(t["vorname"].values["rainer"])
result = list(t.find(n_values=45))
assert len(result) == 1
assert row_vals_before == row_vals_after
def test_update_replace():
t = Table()
row1 = dict(vorname="rainer", nachname="greiff")
row2 = dict(vorname="rainer", nachname="smith")
row3 = dict(vorname="rainer", nachname="erhard")
for row in [row1, row2, row3]:
t.insert(row)
t.update_replace(where={"nachname": "smith"}, nachname="greiff")
assert len(t) == 2
assert len(list(t.find(nachname="greiff"))) == 1
def test_update_replace_with_context():
t = Table()
row1 = dict(vorname="rainer", nachname="greiff", address="123street")
row2 = dict(vorname="rainer", nachname="smith", address="123street")
row3 = dict(vorname="rainer", nachname="smith", address="456street")
row4 = dict(vorname="luke", nachname="doe", address="23Street")
for row in [row1, row2, row3, row4]:
t.insert(row)
t.update_replace(where={"nachname": "smith"}, nachname="greiff")
assert len(t) == 3
assert len(list(t.find(nachname="greiff"))) == 2
def test_update_replace_no_update():
t = Table()
row1 = dict(vorname="rainer", nachname="greiff", address="123street")
row2 = dict(vorname="rainer", nachname="smith", address="123street")
row3 = dict(vorname="rainer", nachname="smith", address="456street")
row4 = dict(vorname="luke", nachname="doe", address="23Street")
for row in [row1, row2, row3, row4]:
t.insert(row)
t.update_replace(where={"nachname": "pete"}, nachname="greiff")
assert len(t) == 4
assert len(list(t.find(nachname="greiff"))) == 1
def test_update_replace_no_replace():
t = Table()
row1 = dict(vorname="rainer", nachname="greiff", address="123street")
row2 = dict(vorname="rainer", nachname="smith", address="123street")
row3 = dict(vorname="rainer", nachname="smith", address="456street")
row4 = dict(vorname="luke", nachname="doe", address="23Street")
for row in [row1, row2, row3, row4]:
t.insert(row)
t.update_replace(where={"nachname": "doe"}, nachname="greiff")
assert len(t) == 4
assert len(list(t.find(nachname="greiff"))) == 2
def test_update_replace_multiple_replace():
t = Table()
rows = [
dict(vorname="rainer", nachname="greiff", address="123street"),
dict(vorname="rainer", nachname="greiff", address="123street"),
dict(vorname="rainer", nachname="smith", address="123street"),
dict(vorname="rainer", nachname="smith", address="123street"),
dict(vorname="rainer", nachname="smith", address="123street"),
dict(vorname="rainer", nachname="smith", address="456street"),
dict(vorname="luke", nachname="doe", address="23Street"),
]
for row in rows:
t.insert(row)
t.update_replace(where={"nachname": "smith"}, nachname="greiff")
assert len(t) == 3
assert len(list(t.find(nachname="greiff"))) == 2
| 31.145455
| 73
| 0.640689
| 429
| 3,426
| 5.034965
| 0.125874
| 0.132407
| 0.173148
| 0.25463
| 0.898148
| 0.85787
| 0.855556
| 0.851389
| 0.851389
| 0.844907
| 0
| 0.036971
| 0.178926
| 3,426
| 109
| 74
| 31.431193
| 0.730892
| 0
| 0
| 0.684932
| 0
| 0
| 0.173964
| 0
| 0
| 0
| 0
| 0
| 0.164384
| 1
| 0.082192
| false
| 0
| 0.013699
| 0
| 0.09589
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
3139aad39ce6f720a93b58df614fae23713da302
| 29,176
|
py
|
Python
|
test/unit/mysql_class/slaverep_updslvstatus.py
|
mjpernot/mysql-lib
|
aabc0c3b3120c0ec5344dc460092d830e796d43c
|
[
"MIT"
] | null | null | null |
test/unit/mysql_class/slaverep_updslvstatus.py
|
mjpernot/mysql-lib
|
aabc0c3b3120c0ec5344dc460092d830e796d43c
|
[
"MIT"
] | null | null | null |
test/unit/mysql_class/slaverep_updslvstatus.py
|
mjpernot/mysql-lib
|
aabc0c3b3120c0ec5344dc460092d830e796d43c
|
[
"MIT"
] | null | null | null |
#!/usr/bin/python
# Classification (U)
"""Program: slaverep_updslvstatus.py
Description: Unit testing of SlaveRep.upd_slv_status in mysql_class.py.
Usage:
test/unit/mysql_class/slaverep_updslvstatus.py
Arguments:
"""
# Libraries and Global Variables
# Standard
import sys
import os
if sys.version_info < (2, 7):
import unittest2 as unittest
else:
import unittest
# Third-party
import mock
# Local
sys.path.append(os.getcwd())
import mysql_class
import lib.machine as machine
import version
__version__ = version.__version__
class UnitTest(unittest.TestCase):
"""Class: UnitTest
Description: Class which is a representation of a unit testing.
Methods:
setUp
test_post_8026
test_pre_8026
test_post_8022
test_pre_8022
test_run
test_run_pre
test_none_secsbehind
test_int_secsbehind
test_string_secsbehind
test_except_secsbehind -> Test raising exception: Seconds_Behind_Master
test_int_skipcounter
test_string_skipcounter
test_except_skipcounter
test_int_masterserverid
test_string_masterserverid
test_except_masterserverid
test_int_lastsqlerror
test_string_lastsqlerror
test_except_lastsqlerror
test_int_lastioerror
test_string_lastioerror
test_except_lastioerror
test_value
"""
def setUp(self):
"""Function: setUp
Description: Initialization for unit testing.
Arguments:
"""
self.name = "Mysql_Server"
self.server_id = 10
self.sql_user = "mysql_user"
self.sql_pass = "my_japd"
self.machine = getattr(machine, "Linux")()
self.host = "host_server"
self.port = 3307
self.defaults_file = "def_cfg_file"
self.extra_def_file = "extra_cfg_file"
self.version = (5, 7, 33)
self.version2 = (8, 0, 0)
self.version3 = (8, 0, 21)
self.version4 = (8, 0, 23)
self.version5 = (8, 0, 28)
self.fetch_vars = [
{"Slave_running": "ON"}, {"Slave_retried_transactions": 0},
{"Slave_open_temp_tables": "1"}]
self.fetch_vars2 = [{"Slave_open_temp_tables": "1"}]
self.fetch_vars3 = [{"Replica_open_temp_tables": "1"}]
self.query = [
[{"SERVICE_STATE": "ON"}], [{"COUNT_TRANSACTIONS_RETRIES": 0}]]
self.read_only = {"read_only": "ON"}
self.show_stat = [
{"Slave_IO_State": "up",
"Master_Host": "masterhost",
"Master_Port": "masterport",
"Connect_Retry": "conn_retry",
"Master_Log_File": "masterlog",
"Read_Master_Log_Pos": "masterpos",
"Relay_Log_File": "relaylog",
"Relay_Log_Pos": "relaypos",
"Relay_Master_Log_File": "relaymasterlog",
"Slave_IO_Running": "running",
"Slave_SQL_Running": "sqlcode",
"Replicate_Do_DB": "dodb",
"Replicate_Ignore_DB": "ignoredb",
"Replicate_Do_Table": "dotable",
"Replicate_Ignore_Table": "ignoretable",
"Replicate_Wild_Do_Table": "wilddo",
"Replicate_Wild_Ignore_Table": "wildignore",
"Last_Errno": "lastnumber",
"Last_Error": "lasterror",
"Skip_Counter": "skipcnt",
"Exec_Master_Log_Pos": "execmasterpos",
"Relay_Log_Space": "logspave",
"Until_Condition": "untilcond",
"Until_Log_File": "untilog",
"Until_Log_Pos": "untilpos",
"Master_SSL_Allowed": "sslallow",
"Master_SSL_CA_File": "sslcafile",
"Master_SSL_CA_Path": "sslcapath",
"Master_SSL_Cert": "sslcert",
"Master_SSL_Cipher": "cipher",
"Master_SSL_Key": "sllkey",
"Seconds_Behind_Master": "secsbehind",
"Master_SSL_Verify_Server_Cert": "sslverify",
"Last_IO_Errno": "lastionumber",
"Last_IO_Error": "lastioerror",
"Last_SQL_Errno": "lastsqlnumber",
"Last_SQL_Error": "lastsqlerror",
"Replicate_Ignore_Server_Ids": "ignoreids",
"Master_Server_Id": "serverid",
"Master_UUID": "uuid",
"Master_Info_File": "infofile",
"SQL_Delay": "delay",
"SQL_Remaining_Delay": "remaindelay",
"Slave_SQL_Running_State": "sqlstate",
"Master_Retry_Count": "retrycnt",
"Master_Bind": "bind",
"Last_IO_Error_Timestamp": "iotime",
"Last_SQL_Error_Timestamp": "sqltime",
"Master_SSL_Crl": "sslcrl",
"Master_SSL_Crlpath": "sslpath",
"Retrieved_Gtid_Set": "retgtid",
"Executed_Gtid_Set": "exegtid",
"Auto_Position": "autopos"}]
self.show_stat2 = [
{"Replica_IO_State": "up",
"Source_Host": "masterhost",
"Source_Port": "masterport",
"Connect_Retry": "conn_retry",
"Source_Log_File": "masterlog",
"Read_Source_Log_Pos": "masterpos",
"Relay_Log_File": "relaylog",
"Relay_Log_Pos": "relaypos",
"Relay_Source_Log_File": "relaymasterlog",
"Replica_IO_Running": "running",
"Replica_SQL_Running": "sqlcode",
"Replicate_Do_DB": "dodb",
"Replicate_Ignore_DB": "ignoredb",
"Replicate_Do_Table": "dotable",
"Replicate_Ignore_Table": "ignoretable",
"Replicate_Wild_Do_Table": "wilddo",
"Replicate_Wild_Ignore_Table": "wildignore",
"Last_Errno": "lastnumber",
"Last_Error": "lasterror",
"Skip_Counter": "skipcnt",
"Exec_Source_Log_Pos": "execmasterpos",
"Relay_Log_Space": "logspave",
"Until_Condition": "untilcond",
"Until_Log_File": "untilog",
"Until_Log_Pos": "untilpos",
"Source_SSL_Allowed": "sslallow",
"Source_SSL_CA_File": "sslcafile",
"Source_SSL_CA_Path": "sslcapath",
"Source_SSL_Cert": "sslcert",
"Source_SSL_Cipher": "cipher",
"Source_SSL_Key": "sllkey",
"Seconds_Behind_Source": "secsbehind",
"Source_SSL_Verify_Server_Cert": "sslverify",
"Last_IO_Errno": "lastionumber",
"Last_IO_Error": "lastioerror",
"Last_SQL_Errno": "lastsqlnumber",
"Last_SQL_Error": "lastsqlerror",
"Replicate_Ignore_Server_Ids": "ignoreids",
"Source_Server_Id": "serverid",
"Source_UUID": "uuid",
"Source_Info_File": "infofile",
"SQL_Delay": "delay",
"SQL_Remaining_Delay": "remaindelay",
"Replica_SQL_Running_State": "sqlstate",
"Source_Retry_Count": "retrycnt",
"Source_Bind": "bind",
"Last_IO_Error_Timestamp": "iotime",
"Last_SQL_Error_Timestamp": "sqltime",
"Source_SSL_Crl": "sslcrl",
"Source_SSL_Crlpath": "sslpath",
"Retrieved_Gtid_Set": "retgtid",
"Executed_Gtid_Set": "exegtid",
"Auto_Position": "autopos"}]
@mock.patch(
"mysql_class.SlaveRep.upd_gtid_pos", mock.Mock(return_value=True))
@mock.patch("mysql_class.Server.col_sql")
@mock.patch("mysql_class.fetch_sys_var")
@mock.patch("mysql_class.fetch_global_var")
@mock.patch("mysql_class.show_slave_stat")
def test_post_8026(self, mock_stat, mock_global, mock_var, mock_qry):
"""Function: test_post_8026
Description: Test with post-MySQL 8.0.26.
Arguments:
"""
mock_var.return_value = self.read_only
mock_global.side_effect = self.fetch_vars3
mock_stat.return_value = self.show_stat2
mock_qry.side_effect = self.query
mysqlrep = mysql_class.SlaveRep(
self.name, self.server_id, self.sql_user, self.sql_pass,
self.machine, defaults_file=self.defaults_file)
mysqlrep.version = self.version5
mysqlrep.upd_slv_status()
self.assertEqual(
(mysqlrep.io_state, mysqlrep.tmp_tbl), ("up", "1"))
@mock.patch(
"mysql_class.SlaveRep.upd_gtid_pos", mock.Mock(return_value=True))
@mock.patch("mysql_class.Server.col_sql")
@mock.patch("mysql_class.fetch_sys_var")
@mock.patch("mysql_class.fetch_global_var")
@mock.patch("mysql_class.show_slave_stat")
def test_pre_8026(self, mock_stat, mock_global, mock_var, mock_qry):
"""Function: test_pre_8026
Description: Test with pre-MySQL 8.0.26.
Arguments:
"""
mock_var.return_value = self.read_only
mock_global.side_effect = self.fetch_vars2
mock_stat.return_value = self.show_stat2
mock_qry.side_effect = self.query
mysqlrep = mysql_class.SlaveRep(
self.name, self.server_id, self.sql_user, self.sql_pass,
self.machine, defaults_file=self.defaults_file)
mysqlrep.version = self.version4
mysqlrep.upd_slv_status()
self.assertEqual(
(mysqlrep.io_state, mysqlrep.tmp_tbl), ("up", "1"))
@mock.patch(
"mysql_class.SlaveRep.upd_gtid_pos", mock.Mock(return_value=True))
@mock.patch("mysql_class.Server.col_sql")
@mock.patch("mysql_class.fetch_sys_var")
@mock.patch("mysql_class.fetch_global_var")
@mock.patch("mysql_class.show_slave_stat")
def test_post_8022(self, mock_stat, mock_global, mock_var, mock_qry):
"""Function: test_post_8022
Description: Test with post-MySQL 8.0.22, but pre-MySQL 8.0.26.
Arguments:
"""
mock_var.return_value = self.read_only
mock_global.side_effect = self.fetch_vars2
mock_stat.return_value = self.show_stat2
mock_qry.side_effect = self.query
mysqlrep = mysql_class.SlaveRep(
self.name, self.server_id, self.sql_user, self.sql_pass,
self.machine, defaults_file=self.defaults_file)
mysqlrep.version = self.version4
mysqlrep.upd_slv_status()
self.assertEqual(
(mysqlrep.io_state, mysqlrep.mst_host), ("up", "masterhost"))
@mock.patch(
"mysql_class.SlaveRep.upd_gtid_pos", mock.Mock(return_value=True))
@mock.patch("mysql_class.Server.col_sql")
@mock.patch("mysql_class.fetch_sys_var")
@mock.patch("mysql_class.fetch_global_var")
@mock.patch("mysql_class.show_slave_stat")
def test_pre_8022(self, mock_stat, mock_global, mock_var, mock_qry):
"""Function: test_pre_8022
Description: Test with pre-MySQL 8.0.22, but post-MySQL 8.0.0.
Arguments:
"""
mock_var.return_value = self.read_only
mock_global.side_effect = self.fetch_vars2
mock_stat.return_value = self.show_stat
mock_qry.side_effect = self.query
mysqlrep = mysql_class.SlaveRep(
self.name, self.server_id, self.sql_user, self.sql_pass,
self.machine, defaults_file=self.defaults_file)
mysqlrep.version = self.version3
mysqlrep.upd_slv_status()
self.assertEqual(
(mysqlrep.io_state, mysqlrep.mst_host), ("up", "masterhost"))
@mock.patch(
"mysql_class.SlaveRep.upd_gtid_pos", mock.Mock(return_value=True))
@mock.patch("mysql_class.Server.col_sql")
@mock.patch("mysql_class.fetch_sys_var")
@mock.patch("mysql_class.fetch_global_var")
@mock.patch("mysql_class.show_slave_stat")
def test_run(self, mock_stat, mock_global, mock_var, mock_qry):
"""Function: test_run
Description: Test with run attribute in MySQL 8.0.0.
Arguments:
"""
mock_var.return_value = self.read_only
mock_global.side_effect = self.fetch_vars2
mock_stat.return_value = self.show_stat
mock_qry.side_effect = self.query
mysqlrep = mysql_class.SlaveRep(
self.name, self.server_id, self.sql_user, self.sql_pass,
self.machine, defaults_file=self.defaults_file)
mysqlrep.version = self.version2
mysqlrep.upd_slv_status()
self.assertEqual((mysqlrep.run, mysqlrep.tran_retry), ("ON", 0))
@mock.patch(
"mysql_class.SlaveRep.upd_gtid_pos", mock.Mock(return_value=True))
@mock.patch("mysql_class.fetch_sys_var")
@mock.patch("mysql_class.fetch_global_var")
@mock.patch("mysql_class.show_slave_stat")
def test_run_pre(self, mock_stat, mock_global, mock_var):
"""Function: test_run_pre
Description: Test with run attribute in pre-MySQL 8.0.
Arguments:
"""
mock_var.return_value = self.read_only
mock_global.side_effect = self.fetch_vars
mock_stat.return_value = self.show_stat
mysqlrep = mysql_class.SlaveRep(
self.name, self.server_id, self.sql_user, self.sql_pass,
self.machine, defaults_file=self.defaults_file)
mysqlrep.version = self.version
mysqlrep.upd_slv_status()
self.assertEqual((mysqlrep.run, mysqlrep.tran_retry), ("ON", 0))
@mock.patch(
"mysql_class.SlaveRep.upd_gtid_pos", mock.Mock(return_value=True))
@mock.patch("mysql_class.fetch_sys_var")
@mock.patch("mysql_class.fetch_global_var")
@mock.patch("mysql_class.show_slave_stat")
def test_none_secsbehind(self, mock_stat, mock_global, mock_var):
"""Function: test_none_secsbehind
Description: Test None for Seconds_Behind_Master.
Arguments:
"""
self.show_stat[0]["Seconds_Behind_Master"] = None
mock_var.return_value = self.read_only
mock_global.side_effect = self.fetch_vars
mock_stat.return_value = self.show_stat
mysqlrep = mysql_class.SlaveRep(
self.name, self.server_id, self.sql_user, self.sql_pass,
self.machine, defaults_file=self.defaults_file)
mysqlrep.version = self.version
mysqlrep.upd_slv_status()
self.assertEqual(mysqlrep.secs_behind, None)
@mock.patch(
"mysql_class.SlaveRep.upd_gtid_pos", mock.Mock(return_value=True))
@mock.patch("mysql_class.fetch_sys_var")
@mock.patch("mysql_class.fetch_global_var")
@mock.patch("mysql_class.show_slave_stat")
def test_int_secsbehind(self, mock_stat, mock_global, mock_var):
"""Function: test_int_secsbehind
Description: Test integer for Seconds_Behind_Master.
Arguments:
"""
self.show_stat[0]["Seconds_Behind_Master"] = 1
mock_var.return_value = self.read_only
mock_global.side_effect = self.fetch_vars
mock_stat.return_value = self.show_stat
mysqlrep = mysql_class.SlaveRep(
self.name, self.server_id, self.sql_user, self.sql_pass,
self.machine, defaults_file=self.defaults_file)
mysqlrep.version = self.version
mysqlrep.upd_slv_status()
self.assertEqual(mysqlrep.secs_behind, 1)
@mock.patch(
"mysql_class.SlaveRep.upd_gtid_pos", mock.Mock(return_value=True))
@mock.patch("mysql_class.fetch_sys_var")
@mock.patch("mysql_class.fetch_global_var")
@mock.patch("mysql_class.show_slave_stat")
def test_string_secsbehind(self, mock_stat, mock_global, mock_var):
"""Function: test_string_secsbehind
Description: Test string for Seconds_Behind_Master.
Arguments:
"""
self.show_stat[0]["Seconds_Behind_Master"] = "1"
mock_var.return_value = self.read_only
mock_global.side_effect = self.fetch_vars
mock_stat.return_value = self.show_stat
mysqlrep = mysql_class.SlaveRep(
self.name, self.server_id, self.sql_user, self.sql_pass,
self.machine, defaults_file=self.defaults_file)
mysqlrep.version = self.version
mysqlrep.upd_slv_status()
self.assertEqual(mysqlrep.secs_behind, 1)
@mock.patch(
"mysql_class.SlaveRep.upd_gtid_pos", mock.Mock(return_value=True))
@mock.patch("mysql_class.fetch_sys_var")
@mock.patch("mysql_class.fetch_global_var")
@mock.patch("mysql_class.show_slave_stat")
def test_except_secsbehind(self, mock_stat, mock_global, mock_var):
"""Function: test_except_secsbehind
Description: Test raising exception for Seconds_Behind_Master.
Arguments:
"""
mock_var.return_value = self.read_only
mock_global.side_effect = self.fetch_vars
mock_stat.return_value = self.show_stat
mysqlrep = mysql_class.SlaveRep(
self.name, self.server_id, self.sql_user, self.sql_pass,
self.machine, defaults_file=self.defaults_file)
mysqlrep.version = self.version
mysqlrep.upd_slv_status()
self.assertEqual(mysqlrep.secs_behind, "secsbehind")
@mock.patch(
"mysql_class.SlaveRep.upd_gtid_pos", mock.Mock(return_value=True))
@mock.patch("mysql_class.fetch_sys_var")
@mock.patch("mysql_class.fetch_global_var")
@mock.patch("mysql_class.show_slave_stat")
def test_int_skipcounter(self, mock_stat, mock_global, mock_var):
"""Function: test_int_skipcounter
Description: Test integer for Skip_Counter.
Arguments:
"""
self.show_stat[0]["Skip_Counter"] = 1
mock_var.return_value = self.read_only
mock_global.side_effect = self.fetch_vars
mock_stat.return_value = self.show_stat
mysqlrep = mysql_class.SlaveRep(
self.name, self.server_id, self.sql_user, self.sql_pass,
self.machine, defaults_file=self.defaults_file)
mysqlrep.version = self.version
mysqlrep.upd_slv_status()
self.assertEqual(mysqlrep.skip_ctr, 1)
@mock.patch(
"mysql_class.SlaveRep.upd_gtid_pos", mock.Mock(return_value=True))
@mock.patch("mysql_class.fetch_sys_var")
@mock.patch("mysql_class.fetch_global_var")
@mock.patch("mysql_class.show_slave_stat")
def test_string_skipcounter(self, mock_stat, mock_global, mock_var):
"""Function: test_string_skipcounter
Description: Test string for Skip_Counter.
Arguments:
"""
self.show_stat[0]["Skip_Counter"] = "1"
mock_var.return_value = self.read_only
mock_global.side_effect = self.fetch_vars
mock_stat.return_value = self.show_stat
mysqlrep = mysql_class.SlaveRep(
self.name, self.server_id, self.sql_user, self.sql_pass,
self.machine, defaults_file=self.defaults_file)
mysqlrep.version = self.version
mysqlrep.upd_slv_status()
self.assertEqual(mysqlrep.skip_ctr, 1)
@mock.patch(
"mysql_class.SlaveRep.upd_gtid_pos", mock.Mock(return_value=True))
@mock.patch("mysql_class.fetch_sys_var")
@mock.patch("mysql_class.fetch_global_var")
@mock.patch("mysql_class.show_slave_stat")
def test_except_skipcounter(self, mock_stat, mock_global, mock_var):
"""Function: test_except_skipcounter
Description: Test raising exception for Skip_Counter.
Arguments:
"""
mock_var.return_value = self.read_only
mock_global.side_effect = self.fetch_vars
mock_stat.return_value = self.show_stat
mysqlrep = mysql_class.SlaveRep(
self.name, self.server_id, self.sql_user, self.sql_pass,
self.machine, defaults_file=self.defaults_file)
mysqlrep.version = self.version
mysqlrep.upd_slv_status()
self.assertEqual(mysqlrep.skip_ctr, "skipcnt")
@mock.patch(
"mysql_class.SlaveRep.upd_gtid_pos", mock.Mock(return_value=True))
@mock.patch("mysql_class.fetch_sys_var")
@mock.patch("mysql_class.fetch_global_var")
@mock.patch("mysql_class.show_slave_stat")
def test_int_masterserverid(self, mock_stat, mock_global, mock_var):
"""Function: test_int_masterserverid
Description: Test integer for Master_Server_Id.
Arguments:
"""
self.show_stat[0]["Master_Server_Id"] = 11
mock_var.return_value = self.read_only
mock_global.side_effect = self.fetch_vars
mock_stat.return_value = self.show_stat
mysqlrep = mysql_class.SlaveRep(
self.name, self.server_id, self.sql_user, self.sql_pass,
self.machine, defaults_file=self.defaults_file)
mysqlrep.version = self.version
mysqlrep.upd_slv_status()
self.assertEqual(mysqlrep.mst_id, 11)
@mock.patch(
"mysql_class.SlaveRep.upd_gtid_pos", mock.Mock(return_value=True))
@mock.patch("mysql_class.fetch_sys_var")
@mock.patch("mysql_class.fetch_global_var")
@mock.patch("mysql_class.show_slave_stat")
def test_string_masterserverid(self, mock_stat, mock_global, mock_var):
"""Function: test_string_masterserverid
Description: Test string for Master_Server_Id.
Arguments:
"""
self.show_stat[0]["Master_Server_Id"] = "11"
mock_var.return_value = self.read_only
mock_global.side_effect = self.fetch_vars
mock_stat.return_value = self.show_stat
mysqlrep = mysql_class.SlaveRep(
self.name, self.server_id, self.sql_user, self.sql_pass,
self.machine, defaults_file=self.defaults_file)
mysqlrep.version = self.version
mysqlrep.upd_slv_status()
self.assertEqual(mysqlrep.mst_id, 11)
@mock.patch(
"mysql_class.SlaveRep.upd_gtid_pos", mock.Mock(return_value=True))
@mock.patch("mysql_class.fetch_sys_var")
@mock.patch("mysql_class.fetch_global_var")
@mock.patch("mysql_class.show_slave_stat")
def test_except_masterserverid(self, mock_stat, mock_global, mock_var):
"""Function: test_except_masterserverid
Description: Test raising exception for Master_Server_Id.
Arguments:
"""
mock_var.return_value = self.read_only
mock_global.side_effect = self.fetch_vars
mock_stat.return_value = self.show_stat
mysqlrep = mysql_class.SlaveRep(
self.name, self.server_id, self.sql_user, self.sql_pass,
self.machine, defaults_file=self.defaults_file)
mysqlrep.version = self.version
mysqlrep.upd_slv_status()
self.assertEqual(mysqlrep.mst_id, "serverid")
@mock.patch(
"mysql_class.SlaveRep.upd_gtid_pos", mock.Mock(return_value=True))
@mock.patch("mysql_class.fetch_sys_var")
@mock.patch("mysql_class.fetch_global_var")
@mock.patch("mysql_class.show_slave_stat")
def test_int_lastsqlerror(self, mock_stat, mock_global, mock_var):
"""Function: test_int_lastsqlerror
Description: Test integer for Last_SQL_Errno.
Arguments:
"""
self.show_stat[0]["Last_SQL_Errno"] = 1
mock_var.return_value = self.read_only
mock_global.side_effect = self.fetch_vars
mock_stat.return_value = self.show_stat
mysqlrep = mysql_class.SlaveRep(
self.name, self.server_id, self.sql_user, self.sql_pass,
self.machine, defaults_file=self.defaults_file)
mysqlrep.version = self.version
mysqlrep.upd_slv_status()
self.assertEqual(mysqlrep.sql_err, 1)
@mock.patch(
"mysql_class.SlaveRep.upd_gtid_pos", mock.Mock(return_value=True))
@mock.patch("mysql_class.fetch_sys_var")
@mock.patch("mysql_class.fetch_global_var")
@mock.patch("mysql_class.show_slave_stat")
def test_string_lastsqlerror(self, mock_stat, mock_global, mock_var):
"""Function: test_string_lastsqlerror
Description: Test string for Last_SQL_Errno.
Arguments:
"""
self.show_stat[0]["Last_SQL_Errno"] = "1"
mock_var.return_value = self.read_only
mock_global.side_effect = self.fetch_vars
mock_stat.return_value = self.show_stat
mysqlrep = mysql_class.SlaveRep(
self.name, self.server_id, self.sql_user, self.sql_pass,
self.machine, defaults_file=self.defaults_file)
mysqlrep.version = self.version
mysqlrep.upd_slv_status()
self.assertEqual(mysqlrep.sql_err, 1)
@mock.patch(
"mysql_class.SlaveRep.upd_gtid_pos", mock.Mock(return_value=True))
@mock.patch("mysql_class.fetch_sys_var")
@mock.patch("mysql_class.fetch_global_var")
@mock.patch("mysql_class.show_slave_stat")
def test_except_lastsqlerror(self, mock_stat, mock_global, mock_var):
"""Function: test_except_lastsqlerror
Description: Test raising exception for Last_SQL_Errno.
Arguments:
"""
mock_var.return_value = self.read_only
mock_global.side_effect = self.fetch_vars
mock_stat.return_value = self.show_stat
mysqlrep = mysql_class.SlaveRep(
self.name, self.server_id, self.sql_user, self.sql_pass,
self.machine, defaults_file=self.defaults_file)
mysqlrep.version = self.version
mysqlrep.upd_slv_status()
self.assertEqual(mysqlrep.sql_err, "lastsqlnumber")
@mock.patch(
"mysql_class.SlaveRep.upd_gtid_pos", mock.Mock(return_value=True))
@mock.patch("mysql_class.fetch_sys_var")
@mock.patch("mysql_class.fetch_global_var")
@mock.patch("mysql_class.show_slave_stat")
def test_int_lastioerror(self, mock_stat, mock_global, mock_var):
"""Function: test_int_lastioerror
Description: Test integer for Last_IO_Errno.
Arguments:
"""
self.show_stat[0]["Last_IO_Errno"] = 1
mock_var.return_value = self.read_only
mock_global.side_effect = self.fetch_vars
mock_stat.return_value = self.show_stat
mysqlrep = mysql_class.SlaveRep(
self.name, self.server_id, self.sql_user, self.sql_pass,
self.machine, defaults_file=self.defaults_file)
mysqlrep.version = self.version
mysqlrep.upd_slv_status()
self.assertEqual(mysqlrep.io_err, 1)
@mock.patch(
"mysql_class.SlaveRep.upd_gtid_pos", mock.Mock(return_value=True))
@mock.patch("mysql_class.fetch_sys_var")
@mock.patch("mysql_class.fetch_global_var")
@mock.patch("mysql_class.show_slave_stat")
def test_string_lastioerror(self, mock_stat, mock_global, mock_var):
"""Function: test_string_lastioerror
Description: Test string for Last_IO_Errno.
Arguments:
"""
self.show_stat[0]["Last_IO_Errno"] = "1"
mock_var.return_value = self.read_only
mock_global.side_effect = self.fetch_vars
mock_stat.return_value = self.show_stat
mysqlrep = mysql_class.SlaveRep(
self.name, self.server_id, self.sql_user, self.sql_pass,
self.machine, defaults_file=self.defaults_file)
mysqlrep.version = self.version
mysqlrep.upd_slv_status()
self.assertEqual(mysqlrep.io_err, 1)
@mock.patch(
"mysql_class.SlaveRep.upd_gtid_pos", mock.Mock(return_value=True))
@mock.patch("mysql_class.fetch_sys_var")
@mock.patch("mysql_class.fetch_global_var")
@mock.patch("mysql_class.show_slave_stat")
def test_except_lastioerror(self, mock_stat, mock_global, mock_var):
"""Function: test_except_lastioerror
Description: Test raising exception for Last_IO_Errno.
Arguments:
"""
mock_var.return_value = self.read_only
mock_global.side_effect = self.fetch_vars
mock_stat.return_value = self.show_stat
mysqlrep = mysql_class.SlaveRep(
self.name, self.server_id, self.sql_user, self.sql_pass,
self.machine, defaults_file=self.defaults_file)
mysqlrep.version = self.version
mysqlrep.upd_slv_status()
self.assertEqual(mysqlrep.io_err, "lastionumber")
@mock.patch(
"mysql_class.SlaveRep.upd_gtid_pos", mock.Mock(return_value=True))
@mock.patch("mysql_class.fetch_sys_var")
@mock.patch("mysql_class.fetch_global_var")
@mock.patch("mysql_class.show_slave_stat")
def test_value(self, mock_stat, mock_global, mock_var):
"""Function: test_value
Description: Test with values returned.
Arguments:
"""
mock_var.return_value = self.read_only
mock_global.side_effect = self.fetch_vars
mock_stat.return_value = self.show_stat
mysqlrep = mysql_class.SlaveRep(
self.name, self.server_id, self.sql_user, self.sql_pass,
self.machine, defaults_file=self.defaults_file)
mysqlrep.version = self.version
mysqlrep.upd_slv_status()
self.assertEqual((mysqlrep.io_state, mysqlrep.slv_io,
mysqlrep.slv_sql, mysqlrep.auto_pos),
("up", "running", "sqlcode", "autopos"))
if __name__ == "__main__":
unittest.main()
| 33.305936
| 79
| 0.648615
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0
| 7
|
318490e9dc6cc602d194459f3c67175270c7f895
| 155
|
py
|
Python
|
src/compas_rcf/utils/__init__.py
|
FatimAmiri/compas_rcf
|
6922036dbb033a83b9ff1cc8c016f865f6ee8e34
|
[
"MIT"
] | null | null | null |
src/compas_rcf/utils/__init__.py
|
FatimAmiri/compas_rcf
|
6922036dbb033a83b9ff1cc8c016f865f6ee8e34
|
[
"MIT"
] | null | null | null |
src/compas_rcf/utils/__init__.py
|
FatimAmiri/compas_rcf
|
6922036dbb033a83b9ff1cc8c016f865f6ee8e34
|
[
"MIT"
] | null | null | null |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from .util_funcs import * # noqa: F401,F403
| 25.833333
| 44
| 0.832258
| 21
| 155
| 5.428571
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| 0.263158
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| 5
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0
| 7
|
31d52c900ed077c155e0189e753a807512df1aca
| 17,151
|
py
|
Python
|
devilry/devilry_group/tests/test_download/test_feedbackfeed_bulkfiledownload.py
|
devilry/devilry-django
|
9ae28e462dfa4cfee966ebacbca04ade9627e715
|
[
"BSD-3-Clause"
] | 29
|
2015-01-18T22:56:23.000Z
|
2020-11-10T21:28:27.000Z
|
devilry/devilry_group/tests/test_download/test_feedbackfeed_bulkfiledownload.py
|
devilry/devilry-django
|
9ae28e462dfa4cfee966ebacbca04ade9627e715
|
[
"BSD-3-Clause"
] | 786
|
2015-01-06T16:10:18.000Z
|
2022-03-16T11:10:50.000Z
|
devilry/devilry_group/tests/test_download/test_feedbackfeed_bulkfiledownload.py
|
devilry/devilry-django
|
9ae28e462dfa4cfee966ebacbca04ade9627e715
|
[
"BSD-3-Clause"
] | 15
|
2015-04-06T06:18:43.000Z
|
2021-02-24T12:28:30.000Z
|
import datetime
import shutil
from io import BytesIO
from zipfile import ZipFile
from django import test
from django.core.files.base import ContentFile
from django.utils import timezone
from model_bakery import baker
from devilry.devilry_comment.models import Comment
from devilry.devilry_dbcache.customsql import AssignmentGroupDbCacheCustomSql
from devilry.devilry_group import models as groupmodels
from devilry.devilry_group.views.download_files import feedbackfeed_bulkfiledownload
class BulkDownloadTestClass(feedbackfeed_bulkfiledownload.BulkFileDownloadBaseView):
def get_queryset(self, request):
return groupmodels.FeedbackSet.objects.all()
def get_zipfilename(self, request):
return 'testfile.zip'
class AbstractTestCase(test.TestCase):
def tearDown(self):
# Ignores errors if the path is not created.
shutil.rmtree('devilry_testfiles/filestore/', ignore_errors=True)
class TestBulkFileDownloadBase(AbstractTestCase):
def setUp(self):
AssignmentGroupDbCacheCustomSql().initialize()
def test_get_zipfile(self):
assignmentgroup1 = baker.make('core.AssignmentGroup',
parentnode__parentnode__parentnode__short_name="test2100",
parentnode__parentnode__short_name="spring2015",
parentnode__short_name="oblig1")
baker.make('core.Candidate',
assignment_group=assignmentgroup1,
relatedstudent__user__shortname="testuser1")
tomorrow = timezone.now() + datetime.timedelta(days=1)
feedbackset1 = baker.make('devilry_group.FeedbackSet',
group=assignmentgroup1,
feedbackset_type=groupmodels.FeedbackSet.FEEDBACKSET_TYPE_FIRST_ATTEMPT,
deadline_datetime=tomorrow)
comment_fbs1_1 = baker.make('devilry_group.GroupComment',
feedback_set=feedbackset1,
user_role=Comment.USER_ROLE_STUDENT)
commentfile_fbs1_1 = baker.make('devilry_comment.CommentFile',
comment=comment_fbs1_1, filename='testfile1.txt')
commentfile_fbs1_1.file.save('testfile1.txt', ContentFile('test'))
testclass = BulkDownloadTestClass()
response = testclass.get(None)
zipfileobject = ZipFile(BytesIO(response.content))
filecontents = zipfileobject.read('test2100.spring2015.oblig1.testuser1/attempt1/testfile1.txt')
self.assertEqual(filecontents, b"test")
def test_multiple_students_in_assignmentgroup(self):
assignmentgroup1 = baker.make('core.AssignmentGroup',
parentnode__parentnode__parentnode__short_name="test2100",
parentnode__parentnode__short_name="spring2015",
parentnode__short_name="oblig1")
baker.make('core.Candidate',
assignment_group=assignmentgroup1,
relatedstudent__user__shortname="testuser1")
baker.make('core.Candidate',
assignment_group=assignmentgroup1,
relatedstudent__user__shortname="testuser2")
tomorrow = timezone.now() + datetime.timedelta(days=1)
feedbackset1 = baker.make('devilry_group.FeedbackSet',
group=assignmentgroup1,
feedbackset_type=groupmodels.FeedbackSet.FEEDBACKSET_TYPE_FIRST_ATTEMPT,
deadline_datetime=tomorrow)
comment_fbs1_1 = baker.make('devilry_group.GroupComment',
feedback_set=feedbackset1,
user_role=Comment.USER_ROLE_STUDENT)
commentfile_fbs1_1 = baker.make('devilry_comment.CommentFile',
comment=comment_fbs1_1, filename='testfile1.txt')
commentfile_fbs1_1.file.save('testfile1.txt', ContentFile('test'))
testclass = BulkDownloadTestClass()
response = testclass.get(None)
zipfileobject = ZipFile(BytesIO(response.content))
filecontents = zipfileobject.read('test2100.spring2015.oblig1.testuser1.testuser2/attempt1/testfile1.txt')
self.assertEqual(filecontents, b"test")
def test_multiple_attempts(self):
assignmentgroup1 = baker.make('core.AssignmentGroup',
parentnode__parentnode__parentnode__short_name="test2100",
parentnode__parentnode__short_name="spring2015",
parentnode__short_name="oblig1")
baker.make('core.Candidate',
assignment_group=assignmentgroup1,
relatedstudent__user__shortname="testuser1")
tomorrow = timezone.now() + datetime.timedelta(days=1)
feedbackset1 = baker.make('devilry_group.FeedbackSet',
group=assignmentgroup1,
feedbackset_type=groupmodels.FeedbackSet.FEEDBACKSET_TYPE_FIRST_ATTEMPT,
deadline_datetime=tomorrow)
comment_fbs1_1 = baker.make('devilry_group.GroupComment',
feedback_set=feedbackset1,
user_role=Comment.USER_ROLE_STUDENT)
commentfile_fbs1_1 = baker.make('devilry_comment.CommentFile',
comment=comment_fbs1_1, filename='testfile1.txt')
commentfile_fbs1_1.file.save('testfile1.txt', ContentFile('test'))
feedbackset2 = baker.make('devilry_group.FeedbackSet',
group=assignmentgroup1,
feedbackset_type=groupmodels.FeedbackSet.FEEDBACKSET_TYPE_NEW_ATTEMPT,
deadline_datetime=tomorrow)
comment_fbs2_1 = baker.make('devilry_group.GroupComment',
feedback_set=feedbackset2,
user_role=Comment.USER_ROLE_STUDENT)
commentfile_fbs2_1 = baker.make('devilry_comment.CommentFile',
comment=comment_fbs2_1, filename='testfile2.txt')
commentfile_fbs2_1.file.save('testfile2.txt', ContentFile('test2'))
testclass = BulkDownloadTestClass()
response = testclass.get(None)
zipfileobject = ZipFile(BytesIO(response.content))
filecontents = zipfileobject.read('test2100.spring2015.oblig1.testuser1/attempt1/testfile1.txt')
self.assertEqual(filecontents, b"test")
filecontents = zipfileobject.read('test2100.spring2015.oblig1.testuser1/attempt2/testfile2.txt')
self.assertEqual(filecontents, b"test2")
def test_multiple_files_same_attempt_same_name(self):
assignmentgroup1 = baker.make('core.AssignmentGroup',
parentnode__parentnode__parentnode__short_name="test2100",
parentnode__parentnode__short_name="spring2015",
parentnode__short_name="oblig1")
baker.make('core.Candidate',
assignment_group=assignmentgroup1,
relatedstudent__user__shortname="testuser1")
tomorrow = timezone.now() + datetime.timedelta(days=1)
feedbackset1 = baker.make('devilry_group.FeedbackSet',
group=assignmentgroup1,
feedbackset_type=groupmodels.FeedbackSet.FEEDBACKSET_TYPE_FIRST_ATTEMPT,
deadline_datetime=tomorrow)
comment_fbs1_1 = baker.make('devilry_group.GroupComment',
feedback_set=feedbackset1,
user_role=Comment.USER_ROLE_STUDENT)
commentfile_fbs1_1 = baker.make('devilry_comment.CommentFile',
comment=comment_fbs1_1, filename='testfile1.txt')
commentfile_fbs1_1.file.save('testfile1.txt', ContentFile('test'))
comment_fbs1_2 = baker.make('devilry_group.GroupComment',
feedback_set=feedbackset1,
user_role=Comment.USER_ROLE_STUDENT)
commentfile_fbs1_2 = baker.make('devilry_comment.CommentFile',
comment=comment_fbs1_2, filename='testfile1.txt')
commentfile_fbs1_2.file.save('testfile1.txt', ContentFile('test2'))
testclass = BulkDownloadTestClass()
response = testclass.get(None)
zipfileobject = ZipFile(BytesIO(response.content))
filecontents = zipfileobject.read('test2100.spring2015.oblig1.testuser1/attempt1/testfile1.txt')
self.assertEqual(filecontents, b"test")
filecontents = zipfileobject.read('test2100.spring2015.oblig1.testuser1/attempt1/testfile1-1.txt')
self.assertEqual(filecontents, b"test2")
def test_multiple_files_same_comment_same_name(self):
assignmentgroup1 = baker.make('core.AssignmentGroup',
parentnode__parentnode__parentnode__short_name="test2100",
parentnode__parentnode__short_name="spring2015",
parentnode__short_name="oblig1")
baker.make('core.Candidate',
assignment_group=assignmentgroup1,
relatedstudent__user__shortname="testuser1")
tomorrow = timezone.now() + datetime.timedelta(days=1)
feedbackset1 = baker.make('devilry_group.FeedbackSet',
group=assignmentgroup1,
feedbackset_type=groupmodels.FeedbackSet.FEEDBACKSET_TYPE_FIRST_ATTEMPT,
deadline_datetime=tomorrow)
comment_fbs1_2 = baker.make('devilry_group.GroupComment',
feedback_set=feedbackset1,
user_role=Comment.USER_ROLE_STUDENT)
commentfile_fbs1_2 = baker.make('devilry_comment.CommentFile',
comment=comment_fbs1_2, filename='testfile1.txt')
commentfile_fbs1_2.file.save('testfile1.txt', ContentFile('test2'))
commentfile_fbs1_2_2 = baker.make('devilry_comment.CommentFile',
comment=comment_fbs1_2, filename='testfile1.txt')
commentfile_fbs1_2_2.file.save('testfile1.txt', ContentFile('test3'))
testclass = BulkDownloadTestClass()
response = testclass.get(None)
zipfileobject = ZipFile(BytesIO(response.content))
filecontents = zipfileobject.read('test2100.spring2015.oblig1.testuser1/attempt1/testfile1.txt')
self.assertEqual(filecontents, b"test2")
filecontents = zipfileobject.read('test2100.spring2015.oblig1.testuser1/attempt1/testfile1-1.txt')
self.assertEqual(filecontents, b"test3")
def test_file_from_examiner(self):
assignmentgroup1 = baker.make('core.AssignmentGroup',
parentnode__parentnode__parentnode__short_name="test2100",
parentnode__parentnode__short_name="spring2015",
parentnode__short_name="oblig1")
baker.make('core.Candidate',
assignment_group=assignmentgroup1,
relatedstudent__user__shortname="testuser1")
tomorrow = timezone.now() + datetime.timedelta(days=1)
feedbackset1 = baker.make('devilry_group.FeedbackSet',
group=assignmentgroup1,
feedbackset_type=groupmodels.FeedbackSet.FEEDBACKSET_TYPE_FIRST_ATTEMPT,
deadline_datetime=tomorrow)
comment_fbs1_1 = baker.make('devilry_group.GroupComment',
feedback_set=feedbackset1,
user_role=Comment.USER_ROLE_EXAMINER)
commentfile_fbs1_1 = baker.make('devilry_comment.CommentFile',
comment=comment_fbs1_1, filename='testfile1.txt')
commentfile_fbs1_1.file.save('testfile1.txt', ContentFile('test'))
testclass = BulkDownloadTestClass()
response = testclass.get(None)
zipfileobject = ZipFile(BytesIO(response.content))
filecontents = zipfileobject.read('test2100.spring2015.oblig1.testuser1/attempt1/from_examiner/testfile1.txt')
self.assertEqual(filecontents, b"test")
def test_file_after_deadline(self):
assignmentgroup1 = baker.make('core.AssignmentGroup',
parentnode__parentnode__parentnode__short_name="test2100",
parentnode__parentnode__short_name="spring2015",
parentnode__short_name="oblig1")
baker.make('core.Candidate',
assignment_group=assignmentgroup1,
relatedstudent__user__shortname="testuser1")
yesterday = timezone.now() - datetime.timedelta(days=1)
feedbackset1 = baker.make('devilry_group.FeedbackSet',
group=assignmentgroup1,
feedbackset_type=groupmodels.FeedbackSet.FEEDBACKSET_TYPE_FIRST_ATTEMPT,
deadline_datetime=yesterday)
comment_fbs1_1 = baker.make('devilry_group.GroupComment',
feedback_set=feedbackset1,
user_role=Comment.USER_ROLE_STUDENT)
commentfile_fbs1_1 = baker.make('devilry_comment.CommentFile',
comment=comment_fbs1_1, filename='testfile1.txt')
commentfile_fbs1_1.file.save('testfile1.txt', ContentFile('test'))
testclass = BulkDownloadTestClass()
response = testclass.get(None)
zipfileobject = ZipFile(BytesIO(response.content))
filecontents = zipfileobject.read(
'test2100.spring2015.oblig1.testuser1/attempt1/not_part_of_delivery/testfile1.txt')
self.assertEqual(filecontents, b"test")
def test_multiple_assignmentgroups(self):
assignmentgroup1 = baker.make('core.AssignmentGroup',
parentnode__parentnode__parentnode__short_name="test2100",
parentnode__parentnode__short_name="spring2015",
parentnode__short_name="oblig1")
baker.make('core.Candidate',
assignment_group=assignmentgroup1,
relatedstudent__user__shortname="testuser1")
tomorrow = timezone.now() + datetime.timedelta(days=1)
feedbackset1 = baker.make('devilry_group.FeedbackSet',
group=assignmentgroup1,
feedbackset_type=groupmodels.FeedbackSet.FEEDBACKSET_TYPE_FIRST_ATTEMPT,
deadline_datetime=tomorrow)
comment_fbs1 = baker.make('devilry_group.GroupComment',
feedback_set=feedbackset1,
user_role=Comment.USER_ROLE_STUDENT)
commentfile_fbs1 = baker.make('devilry_comment.CommentFile',
comment=comment_fbs1, filename='testfile1.txt')
commentfile_fbs1.file.save('testfile1.txt', ContentFile('test'))
assignmentgroup2 = baker.make('core.AssignmentGroup',
parentnode=assignmentgroup1.parentnode)
baker.make('core.Candidate',
assignment_group=assignmentgroup2,
relatedstudent__user__shortname="testuser2")
feedbackset2 = baker.make('devilry_group.FeedbackSet',
group=assignmentgroup2,
feedbackset_type=groupmodels.FeedbackSet.FEEDBACKSET_TYPE_FIRST_ATTEMPT,
deadline_datetime=tomorrow)
comment_fbs2 = baker.make('devilry_group.GroupComment',
feedback_set=feedbackset2,
user_role=Comment.USER_ROLE_STUDENT)
commentfile_fbs2 = baker.make('devilry_comment.CommentFile',
comment=comment_fbs2, filename='testfile2.txt')
commentfile_fbs2.file.save('testfile1.txt', ContentFile('test2'))
testclass = BulkDownloadTestClass()
response = testclass.get(None)
zipfileobject = ZipFile(BytesIO(response.content))
filecontents = zipfileobject.read('test2100.spring2015.oblig1.testuser1/attempt1/testfile1.txt')
self.assertEqual(filecontents, b"test")
filecontents = zipfileobject.read('test2100.spring2015.oblig1.testuser2/attempt1/testfile2.txt')
self.assertEqual(filecontents, b"test2")
| 58.938144
| 118
| 0.614833
| 1,441
| 17,151
| 7.020125
| 0.092991
| 0.046263
| 0.052195
| 0.043594
| 0.879696
| 0.86556
| 0.850237
| 0.838869
| 0.82325
| 0.818209
| 0
| 0.034445
| 0.305988
| 17,151
| 290
| 119
| 59.141379
| 0.815425
| 0.002449
| 0
| 0.770115
| 0
| 0
| 0.156486
| 0.096159
| 0
| 0
| 0
| 0
| 0.045977
| 1
| 0.045977
| false
| 0
| 0.045977
| 0.007663
| 0.111111
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
734941ade580c0807ec85a4258dd9d7bf87df6e5
| 141
|
py
|
Python
|
rswarp/utilities/conductor_plot.py
|
tanxicccc/rswarp
|
3cead0d4e96c73caaae15e4376ac4637dc34c5dc
|
[
"Apache-2.0"
] | 4
|
2016-07-08T04:39:49.000Z
|
2017-11-03T18:00:38.000Z
|
rswarp/utilities/conductor_plot.py
|
tanxicccc/rswarp
|
3cead0d4e96c73caaae15e4376ac4637dc34c5dc
|
[
"Apache-2.0"
] | 18
|
2016-10-31T20:13:29.000Z
|
2020-12-21T16:29:23.000Z
|
rswarp/utilities/conductor_plot.py
|
tanxicccc/rswarp
|
3cead0d4e96c73caaae15e4376ac4637dc34c5dc
|
[
"Apache-2.0"
] | 8
|
2017-02-09T22:23:52.000Z
|
2021-12-10T15:50:32.000Z
|
# Maintain backwards compatibility after move to rswarp.diagnostics.ConductorPlot
from rswarp.diagnostics.ConductorPlot import PlotConductors
| 70.5
| 81
| 0.886525
| 15
| 141
| 8.333333
| 0.8
| 0.272
| 0.48
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.078014
| 141
| 2
| 82
| 70.5
| 0.961538
| 0.560284
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
b404124e20300527775033aedb15760dd661cc3c
| 88
|
py
|
Python
|
gcpds/filters/spatial/Ear_Reference.py
|
UN-GCPDS/python-gcpds.filters
|
3e3776a8ac22aa2b53e0ec24895829a437c44a20
|
[
"BSD-2-Clause"
] | 1
|
2021-03-09T23:52:15.000Z
|
2021-03-09T23:52:15.000Z
|
gcpds/filters/spatial/Ear_Reference.py
|
UN-GCPDS/python-gcpds.filters
|
3e3776a8ac22aa2b53e0ec24895829a437c44a20
|
[
"BSD-2-Clause"
] | null | null | null |
gcpds/filters/spatial/Ear_Reference.py
|
UN-GCPDS/python-gcpds.filters
|
3e3776a8ac22aa2b53e0ec24895829a437c44a20
|
[
"BSD-2-Clause"
] | 1
|
2021-03-09T23:51:39.000Z
|
2021-03-09T23:51:39.000Z
|
# Standard ear-reference
import numpy as np
def ear_reference(data):
return data
| 11
| 24
| 0.738636
| 13
| 88
| 4.923077
| 0.769231
| 0.375
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.204545
| 88
| 7
| 25
| 12.571429
| 0.914286
| 0.25
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0.333333
| 0.333333
| 1
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 1
| 1
| 0
|
0
| 7
|
b438c6779040f1d3c35656cb2666aca209dd847a
| 674,162
|
py
|
Python
|
test.py
|
Jeremip11/precog
|
b3558da74c9f8057b5313968b451614363cdb273
|
[
"0BSD"
] | null | null | null |
test.py
|
Jeremip11/precog
|
b3558da74c9f8057b5313968b451614363cdb273
|
[
"0BSD"
] | null | null | null |
test.py
|
Jeremip11/precog
|
b3558da74c9f8057b5313968b451614363cdb273
|
[
"0BSD"
] | 1
|
2018-07-17T18:29:38.000Z
|
2018-07-17T18:29:38.000Z
|
# coding: utf-8
import doctest
import unittest
import tempfile
import importlib
from os.path import basename
from urllib import urlencode
from urlparse import urlparse, parse_qsl
from httmock import HTTMock, response
from mock import patch, Mock
from shutil import rmtree
from time import sleep
import hmac, hashlib
import json
import util
import href
import git
app = importlib.import_module('make-it-so').app
def signed(data, key):
''' Get a signature header for a string of data.
'''
hash = hmac.new(key, data.encode('utf8'), hashlib.sha1)
signature = 'sha1={}'.format(hash.hexdigest())
return {'X-Hub-Signature': signature}
class TestGit (unittest.TestCase):
def setUp(self):
self.GET = git.Getter(tuple(), dict()).get
self.old_tempdir = tempfile.tempdir
tempfile.tempdir = tempfile.mkdtemp(prefix='TestGit-')
def tearDown(self):
rmtree(tempfile.tempdir)
tempfile.tempdir = self.old_tempdir
def response_content(self, url, request):
'''
'''
MHP = request.method, url.hostname, url.path
MHPQ = request.method, url.hostname, url.path, url.query
GH, CC = 'api.github.com', 'circleci.com'
response_headers = {'Content-Type': 'application/json; charset=utf-8'}
if MHP == ('GET', 'api.github.com', '/user'):
if request.headers.get('Authorization') == 'Basic dmFsaWQ6eC1vYXV0aC1iYXNpYw==':
data = u'''{\r "login": "migurski",\r "id": 58730,\r "avatar_url": "https://avatars.githubusercontent.com/u/58730?v=3",\r "gravatar_id": "",\r "url": "https://api.github.com/users/migurski",\r "html_url": "https://github.com/migurski",\r "followers_url": "https://api.github.com/users/migurski/followers",\r "following_url": "https://api.github.com/users/migurski/following{/other_user}",\r "gists_url": "https://api.github.com/users/migurski/gists{/gist_id}",\r "starred_url": "https://api.github.com/users/migurski/starred{/owner}{/repo}",\r "subscriptions_url": "https://api.github.com/users/migurski/subscriptions",\r "organizations_url": "https://api.github.com/users/migurski/orgs",\r "repos_url": "https://api.github.com/users/migurski/repos",\r "events_url": "https://api.github.com/users/migurski/events{/privacy}",\r "received_events_url": "https://api.github.com/users/migurski/received_events",\r "type": "User",\r "site_admin": false,\r "name": null,\r "company": null,\r "blog": null,\r "location": null,\r "email": "mike-github@teczno.com",\r "hireable": null,\r "bio": null,\r "public_repos": 91,\r "public_gists": 45,\r "followers": 439,\r "following": 94,\r "created_at": "2009-02-27T23:44:32Z",\r "updated_at": "2015-12-26T20:09:55Z",\r "private_gists": 23,\r "total_private_repos": 1,\r "owned_private_repos": 0,\r "disk_usage": 249156,\r "collaborators": 0,\r "plan": {\r "name": "free",\r "space": 976562499,\r "collaborators": 0,\r "private_repos": 0\r }\r}'''
return response(200, data.encode('utf8'), headers=response_headers)
else:
data = u'''{\r "message": "Bad credentials",\r "documentation_url": "https://developer.github.com/v3"\r}'''
return response(401, data.encode('utf8'), headers=response_headers)
if MHP == ('GET', 'api.github.com', '/repos/migurski/circlejek/statuses/master'):
data = u'''[\r {\r "url": "https://api.github.com/repos/migurski/circlejek/statuses/6f82dac4d909926b2d099ef9ef2db7bd3e97e1a7",\r "id": 403320253,\r "state": "success",\r "description": "Your tests passed on CircleCI!",\r "target_url": "https://circleci.com/gh/migurski/circlejek/13",\r "context": "ci/circleci",\r "created_at": "2015-12-30T22:49:42Z",\r "updated_at": "2015-12-30T22:49:42Z",\r "creator": {\r "login": "migurski",\r "id": 58730,\r "avatar_url": "https://avatars.githubusercontent.com/u/58730?v=3",\r "gravatar_id": "",\r "url": "https://api.github.com/users/migurski",\r "html_url": "https://github.com/migurski",\r "followers_url": "https://api.github.com/users/migurski/followers",\r "following_url": "https://api.github.com/users/migurski/following{/other_user}",\r "gists_url": "https://api.github.com/users/migurski/gists{/gist_id}",\r "starred_url": "https://api.github.com/users/migurski/starred{/owner}{/repo}",\r "subscriptions_url": "https://api.github.com/users/migurski/subscriptions",\r "organizations_url": "https://api.github.com/users/migurski/orgs",\r "repos_url": "https://api.github.com/users/migurski/repos",\r "events_url": "https://api.github.com/users/migurski/events{/privacy}",\r "received_events_url": "https://api.github.com/users/migurski/received_events",\r "type": "User",\r "site_admin": false\r }\r },\r {\r "url": "https://api.github.com/repos/migurski/circlejek/statuses/6f82dac4d909926b2d099ef9ef2db7bd3e97e1a7",\r "id": 403319747,\r "state": "pending",\r "description": "CircleCI is running your tests",\r "target_url": "https://circleci.com/gh/migurski/circlejek/13",\r "context": "ci/circleci",\r "created_at": "2015-12-30T22:48:48Z",\r "updated_at": "2015-12-30T22:48:48Z",\r "creator": {\r "login": "migurski",\r "id": 58730,\r "avatar_url": "https://avatars.githubusercontent.com/u/58730?v=3",\r "gravatar_id": "",\r "url": "https://api.github.com/users/migurski",\r "html_url": "https://github.com/migurski",\r "followers_url": "https://api.github.com/users/migurski/followers",\r "following_url": "https://api.github.com/users/migurski/following{/other_user}",\r "gists_url": "https://api.github.com/users/migurski/gists{/gist_id}",\r "starred_url": "https://api.github.com/users/migurski/starred{/owner}{/repo}",\r "subscriptions_url": "https://api.github.com/users/migurski/subscriptions",\r "organizations_url": "https://api.github.com/users/migurski/orgs",\r "repos_url": "https://api.github.com/users/migurski/repos",\r "events_url": "https://api.github.com/users/migurski/events{/privacy}",\r "received_events_url": "https://api.github.com/users/migurski/received_events",\r "type": "User",\r "site_admin": false\r }\r },\r {\r "url": "https://api.github.com/repos/migurski/circlejek/statuses/6f82dac4d909926b2d099ef9ef2db7bd3e97e1a7",\r "id": 403319729,\r "state": "pending",\r "description": "Your tests are queued behind your running builds",\r "target_url": "https://circleci.com/gh/migurski/circlejek/13",\r "context": "ci/circleci",\r "created_at": "2015-12-30T22:48:47Z",\r "updated_at": "2015-12-30T22:48:47Z",\r "creator": {\r "login": "migurski",\r "id": 58730,\r "avatar_url": "https://avatars.githubusercontent.com/u/58730?v=3",\r "gravatar_id": "",\r "url": "https://api.github.com/users/migurski",\r "html_url": "https://github.com/migurski",\r "followers_url": "https://api.github.com/users/migurski/followers",\r "following_url": "https://api.github.com/users/migurski/following{/other_user}",\r "gists_url": "https://api.github.com/users/migurski/gists{/gist_id}",\r "starred_url": "https://api.github.com/users/migurski/starred{/owner}{/repo}",\r "subscriptions_url": "https://api.github.com/users/migurski/subscriptions",\r "organizations_url": "https://api.github.com/users/migurski/orgs",\r "repos_url": "https://api.github.com/users/migurski/repos",\r "events_url": "https://api.github.com/users/migurski/events{/privacy}",\r "received_events_url": "https://api.github.com/users/migurski/received_events",\r "type": "User",\r "site_admin": false\r }\r },\r {\r "url": "https://api.github.com/repos/migurski/circlejek/statuses/6f82dac4d909926b2d099ef9ef2db7bd3e97e1a7",\r "id": 403274989,\r "state": "success",\r "description": "Your tests passed on CircleCI!",\r "target_url": "https://circleci.com/gh/migurski/circlejek/12",\r "context": "ci/circleci",\r "created_at": "2015-12-30T21:38:53Z",\r "updated_at": "2015-12-30T21:38:53Z",\r "creator": {\r "login": "migurski",\r "id": 58730,\r "avatar_url": "https://avatars.githubusercontent.com/u/58730?v=3",\r "gravatar_id": "",\r "url": "https://api.github.com/users/migurski",\r "html_url": "https://github.com/migurski",\r "followers_url": "https://api.github.com/users/migurski/followers",\r "following_url": "https://api.github.com/users/migurski/following{/other_user}",\r "gists_url": "https://api.github.com/users/migurski/gists{/gist_id}",\r "starred_url": "https://api.github.com/users/migurski/starred{/owner}{/repo}",\r "subscriptions_url": "https://api.github.com/users/migurski/subscriptions",\r "organizations_url": "https://api.github.com/users/migurski/orgs",\r "repos_url": "https://api.github.com/users/migurski/repos",\r "events_url": "https://api.github.com/users/migurski/events{/privacy}",\r "received_events_url": "https://api.github.com/users/migurski/received_events",\r "type": "User",\r "site_admin": false\r }\r },\r {\r "url": "https://api.github.com/repos/migurski/circlejek/statuses/6f82dac4d909926b2d099ef9ef2db7bd3e97e1a7",\r "id": 403274443,\r "state": "pending",\r "description": "CircleCI is running your tests",\r "target_url": "https://circleci.com/gh/migurski/circlejek/12",\r "context": "ci/circleci",\r "created_at": "2015-12-30T21:38:00Z",\r "updated_at": "2015-12-30T21:38:00Z",\r "creator": {\r "login": "migurski",\r "id": 58730,\r "avatar_url": "https://avatars.githubusercontent.com/u/58730?v=3",\r "gravatar_id": "",\r "url": "https://api.github.com/users/migurski",\r "html_url": "https://github.com/migurski",\r "followers_url": "https://api.github.com/users/migurski/followers",\r "following_url": "https://api.github.com/users/migurski/following{/other_user}",\r "gists_url": "https://api.github.com/users/migurski/gists{/gist_id}",\r "starred_url": "https://api.github.com/users/migurski/starred{/owner}{/repo}",\r "subscriptions_url": "https://api.github.com/users/migurski/subscriptions",\r "organizations_url": "https://api.github.com/users/migurski/orgs",\r "repos_url": "https://api.github.com/users/migurski/repos",\r "events_url": "https://api.github.com/users/migurski/events{/privacy}",\r "received_events_url": "https://api.github.com/users/migurski/received_events",\r "type": "User",\r "site_admin": false\r }\r },\r {\r "url": "https://api.github.com/repos/migurski/circlejek/statuses/6f82dac4d909926b2d099ef9ef2db7bd3e97e1a7",\r "id": 403274434,\r "state": "pending",\r "description": "Your tests are queued behind your running builds",\r "target_url": "https://circleci.com/gh/migurski/circlejek/12",\r "context": "ci/circleci",\r "created_at": "2015-12-30T21:37:59Z",\r "updated_at": "2015-12-30T21:37:59Z",\r "creator": {\r "login": "migurski",\r "id": 58730,\r "avatar_url": "https://avatars.githubusercontent.com/u/58730?v=3",\r "gravatar_id": "",\r "url": "https://api.github.com/users/migurski",\r "html_url": "https://github.com/migurski",\r "followers_url": "https://api.github.com/users/migurski/followers",\r "following_url": "https://api.github.com/users/migurski/following{/other_user}",\r "gists_url": "https://api.github.com/users/migurski/gists{/gist_id}",\r "starred_url": "https://api.github.com/users/migurski/starred{/owner}{/repo}",\r "subscriptions_url": "https://api.github.com/users/migurski/subscriptions",\r "organizations_url": "https://api.github.com/users/migurski/orgs",\r "repos_url": "https://api.github.com/users/migurski/repos",\r "events_url": "https://api.github.com/users/migurski/events{/privacy}",\r "received_events_url": "https://api.github.com/users/migurski/received_events",\r "type": "User",\r "site_admin": false\r }\r }\r]'''
return response(200, data.encode('utf8'), headers=response_headers)
if MHP == ('GET', 'api.github.com', '/repos/migurski/circlejek/statuses/tarballize'):
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[Transportation Camp](http://transportationcamp.org/events/dc-2016/) is an unconference that welcomes enthusiasts of all stripes, while the [Transportation Research Board's annual meeting](http://www.trb.org/AnnualMeeting/AnnualMeeting.aspx) is a more staid if also more massive meeting of professionals.\\n+\\n+Mapzen will be at the TRB meeting to discuss the [Transitland open transit data service](https://transit.land) and the [Mapzen Turn-by-Turn routing engine](https://mapzen.com/projects/valhalla). We'll be presenting at a TRB workshop called ***[Transformative Trends in Transit Data: General Transit Feed Specifications Bonanza](https://annualmeeting.mytrb.org/Workshop/Details/2446)*** on Sunday, January 10. Bring a laptop and learn how to create transit data from our colleagues at [The World Bank](http://www.worldbank.org/en/topic/transport), [Trillium Solutions](http://trilliumtransit.com/), [SUNY Albany](http://www.albany.edu/avail/), [MIT](http://www.civicdatadesignlab.org/), and [Azavea](http://www.azavea.com/). (TRB registration is required to attend.)\\n+\\n+And for a second year in a row, Mapzen is joining up with our colleagues at [Conveyal](http://conveyal.com/) and [TransitScreen](http://transitscreen.com/) to host a happy hour for mapping and \"transpo\" types of all sorts. All are welcome on the evening of Tuesday, January 12—TRB registration is not required to attend—but ***[please RSVP if you'll be joining us at the happy hour](https://trbparty.splashthat.com/)***. \\n+\\n+Can't make it to D.C. next week? Transportation Camp is popping up in at least [four more cities around the U.S. later in 2016](http://transportationcamp.org/). And in the meantime, you're invited to try out [Transitland](https://transit.land) and [Mapzen Turn-by-Turn](https://mapzen.com/projects/valhalla) here on the Internet.\\n+\\n+*[photo by Jeremy Segrott](https://www.flickr.com/photos/126337928@N05/18246320916/), CC BY 2.0*"\r }\r ]\r}'''
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if MHPQ == ('GET', 'circle-artifacts.com', '/gh/migurski/circlejek/28/artifacts/0/tmp/circle-artifacts.RyBi4pI/precog-content.tar.gz', 'circle-token=a17131792f4c4bcb97f2f66d9c58258a0ee0e621'):
return response(302, '', headers={'Location': 'https://circle-artifacts.com/gh/migurski/circlejek/28/artifacts/0/tmp/circle-artifacts.RyBi4pI/precog-content.tar.gz'})
if MHP == ('GET', 'circle-artifacts.com', '/gh/migurski/circlejek/28/artifacts/0/tmp/circle-artifacts.RyBi4pI/precog-content.tar.gz'):
data = b'\x1f\x8b\x08\x00J\xaa\xe0V\x00\x03\xed\xd5Kn\xc20\x14\x05P\x8fY\x85\x95\x05\xd8~\x0e\xb6\'\xc1S\xba\x8d\xa0\x84\x8f\x14\x08\n\x89\x80YW\xd3\x85u%mZ!\x12$`\x82\xf9\x88{&\x1e8\x92\x9ft}c!Yp\xea\x97s\xa6]\xc9\x19\xd5]\x0f\x18i\xeb\x14iCDL\x91\xb2\xb1c\xdc\x84\x1f\x8d\xb1fS\xa7\x15\xe7\xac\x994\xab\xba\xb9\xf0\xdd\x95\xfd\x17%\xe4\xac,\xb3\xc9>\x17\xf3zY\x849\xa3\r\xd8\xda\xe1\xf9\xfc\xedi\xfe1\x19b\\\x85\x19\xa7\xef\xcd\xf3O\xd6>I\xf9\xbc\xca\xa7\xa3H.VY\xbe\xfb\xbb\x08\x91\x1f\xff_\x0b\xbe-\xab"Kd\xeaE"\xd7~\xf0\xe8y\xe1\xb6D\'\xf4Pg\\\xef\xbf>\xe9\xbf\xb6\xb1F\xff\xef\xa1\xd7\xff\xeeS\x10\xf9\x8f\xbc(\xcac\xff\xbf?\xbf\xf0\x07\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00xJ?\xe9\xe1\x06\xd0\x00(\x00\x00'
return response(200, data, headers={'Content-Type': 'application/x-gzip'})
if MHP == ('GET', 'api.github.com', '/repos/migurski/circlejek/git/refs/heads'):
data = u'''[\r {\r "ref": "refs/heads/make-it-pop",\r "url": "https://api.github.com/repos/migurski/circlejek/git/refs/heads/make-it-pop",\r "object": {\r "sha": "992071bebb72e99ef8293dc77b74c03ab07ffa1b",\r "type": "commit",\r "url": "https://api.github.com/repos/migurski/circlejek/git/commits/992071bebb72e99ef8293dc77b74c03ab07ffa1b"\r }\r },\r {\r "ref": "refs/heads/many-little-files",\r "url": "https://api.github.com/repos/migurski/circlejek/git/refs/heads/many-little-files",\r "object": {\r "sha": "b7b85c936205ca72db80f05d11d2f4962facb9e5",\r "type": "(something other than) commit",\r "url": "https://api.github.com/repos/migurski/circlejek/git/commits/b7b85c936205ca72db80f05d11d2f4962facb9e5"\r }\r },\r {\r "ref": "refs/heads/master",\r "url": "https://api.github.com/repos/migurski/circlejek/git/refs/heads/master",\r "object": {\r "sha": "4872caf3203972ebbe13e3863e4c47c407ee4bbf",\r "type": "commit",\r "url": "https://api.github.com/repos/migurski/circlejek/git/commits/4872caf3203972ebbe13e3863e4c47c407ee4bbf"\r }\r },\r {\r "ref": "refs/heads/tinker-with-config",\r "url": "https://api.github.com/repos/migurski/circlejek/git/refs/heads/tinker-with-config",\r "object": {\r "sha": "3c6431c3c1fa730b792bc039877623ef60435a77",\r "type": "(something other than) commit",\r "url": "https://api.github.com/repos/migurski/circlejek/git/commits/3c6431c3c1fa730b792bc039877623ef60435a77"\r }\r }\r]'''
return response(200, data.encode('utf8'), headers=response_headers)
if MHP == ('GET', 'api.github.com', '/repos/migurski/circlejek/git/commits/992071bebb72e99ef8293dc77b74c03ab07ffa1b'):
data = u'''{\r "sha": "992071bebb72e99ef8293dc77b74c03ab07ffa1b",\r "url": "https://api.github.com/repos/migurski/circlejek/git/commits/992071bebb72e99ef8293dc77b74c03ab07ffa1b",\r "html_url": "https://github.com/migurski/circlejek/commit/992071bebb72e99ef8293dc77b74c03ab07ffa1b",\r "author": {\r "name": "Michal Migurski",\r "email": "mike@teczno.com",\r "date": "2015-12-30T22:50:18Z"\r },\r "committer": {\r "name": "Michal Migurski",\r "email": "mike@teczno.com",\r "date": "2015-12-30T22:50:18Z"\r },\r "tree": {\r "sha": "3019fa8e88512b6e325b2116997b527834ff5f71",\r "url": "https://api.github.com/repos/migurski/circlejek/git/trees/3019fa8e88512b6e325b2116997b527834ff5f71"\r },\r "message": "Added exclamation points",\r "parents": [\r {\r "sha": "6f82dac4d909926b2d099ef9ef2db7bd3e97e1a7",\r "url": "https://api.github.com/repos/migurski/circlejek/git/commits/6f82dac4d909926b2d099ef9ef2db7bd3e97e1a7",\r "html_url": "https://github.com/migurski/circlejek/commit/6f82dac4d909926b2d099ef9ef2db7bd3e97e1a7"\r }\r ]\r}'''
return response(200, data.encode('utf8'), headers=response_headers)
if MHP == ('GET', 'api.github.com', '/repos/migurski/circlejek/git/commits/4872caf3203972ebbe13e3863e4c47c407ee4bbf'):
data = u'''{\r "sha": "4872caf3203972ebbe13e3863e4c47c407ee4bbf",\r "url": "https://api.github.com/repos/migurski/circlejek/git/commits/4872caf3203972ebbe13e3863e4c47c407ee4bbf",\r "html_url": "https://github.com/migurski/circlejek/commit/4872caf3203972ebbe13e3863e4c47c407ee4bbf",\r "author": {\r "name": "migurski",\r "email": "mike-github@teczno.com",\r "date": "2016-01-06T05:36:42Z"\r },\r "committer": {\r "name": "migurski",\r "email": "mike-github@teczno.com",\r "date": "2016-01-06T05:36:42Z"\r },\r "tree": {\r "sha": "f890f98bd0d50cf5ddba3d59e9dec2e282075d9b",\r "url": "https://api.github.com/repos/migurski/circlejek/git/trees/f890f98bd0d50cf5ddba3d59e9dec2e282075d9b"\r },\r "message": "Update index.md",\r "parents": [\r {\r "sha": "6f82dac4d909926b2d099ef9ef2db7bd3e97e1a7",\r "url": "https://api.github.com/repos/migurski/circlejek/git/commits/6f82dac4d909926b2d099ef9ef2db7bd3e97e1a7",\r "html_url": "https://github.com/migurski/circlejek/commit/6f82dac4d909926b2d099ef9ef2db7bd3e97e1a7"\r }\r ]\r}'''
return response(200, data.encode('utf8'), headers=response_headers)
if MHP == ('GET', 'api.github.com', '/repos/mapzen/metro-extracts/git/trees/1cc0a0db8'):
data = u'''{\r "sha": "1cc0a0db873499b79e1cf81d75c50b203665ce96",\r "url": "https://api.github.com/repos/mapzen/metro-extracts/git/trees/1cc0a0db873499b79e1cf81d75c50b203665ce96",\r "tree": [\r {\r "path": ".gitignore",\r "mode": "100644",\r "type": "blob",\r "sha": "2f13b0ce6c0180fac624e0d92c6900f70120ea70",\r "size": 46,\r "url": "https://api.github.com/repos/mapzen/metro-extracts/git/blobs/2f13b0ce6c0180fac624e0d92c6900f70120ea70"\r },\r {\r "path": "circle.yml",\r "mode": "100644",\r "type": "blob",\r "sha": "62291dddd1a41bc4f6d1b73a33ce64162d2dcf73",\r "size": 158,\r "url": "https://api.github.com/repos/mapzen/metro-extracts/git/blobs/62291dddd1a41bc4f6d1b73a33ce64162d2dcf73"\r },\r {\r "path": "dist",\r "mode": "040000",\r "type": "tree",\r "sha": "d564d0bc3dd917926892c55e3706cc116d5b165e",\r "url": "https://api.github.com/repos/mapzen/metro-extracts/git/trees/d564d0bc3dd917926892c55e3706cc116d5b165e"\r },\r {\r "path": "lib",\r "mode": "040000",\r "type": "tree",\r "sha": "1955c19dcdda8ce54966482e76188a4338a9205f",\r "url": "https://api.github.com/repos/mapzen/metro-extracts/git/trees/1955c19dcdda8ce54966482e76188a4338a9205f"\r },\r {\r "path": "package.json",\r "mode": "100644",\r "type": "blob",\r "sha": "07ba74ca0f5f50992336f793e7eed612c0b243cc",\r "size": 724,\r "url": "https://api.github.com/repos/mapzen/metro-extracts/git/blobs/07ba74ca0f5f50992336f793e7eed612c0b243cc"\r },\r {\r "path": "src",\r "mode": "040000",\r "type": "tree",\r "sha": "b63402bd4262fbf61262ea14506bf4df803641aa",\r "url": "https://api.github.com/repos/mapzen/metro-extracts/git/trees/b63402bd4262fbf61262ea14506bf4df803641aa"\r }\r ],\r "truncated": false\r}'''
return response(200, data.encode('utf8'), headers=response_headers)
if MHP == ('GET', 'api.github.com', '/repos/mapzen/metro-extracts/git/blobs/62291dddd1a41bc4f6d1b73a33ce64162d2dcf73'):
data = u'''{\r "sha": "62291dddd1a41bc4f6d1b73a33ce64162d2dcf73",\r "size": 158,\r "url": "https://api.github.com/repos/mapzen/metro-extracts/git/blobs/62291dddd1a41bc4f6d1b73a33ce64162d2dcf73",\r "content": "bWFjaGluZToKICBub2RlOgogICAgdmVyc2lvbjogNC4yCgpkZXBlbmRlbmNp\\nZXM6CiAgcG9zdDoKICAgIC0gbnBtIHJ1biBidWlsZAoKdGVzdDoKICBvdmVy\\ncmlkZToKICAgIC0gZWNobyAnTm8gdGVzdHMg8J+OqScKCmdlbmVyYWw6CiAg\\nYXJ0aWZhY3RzOgogICAgLSAiZGlzdCI=\\n",\r "encoding": "base64"\r}'''
return response(200, data.encode('utf8'), headers=response_headers)
raise Exception(MHPQ)
def test_getter_timeout(self):
with patch('requests.get') as get:
get.side_effect = lambda url, headers, auth, timeout: [url]
got1 = self.GET('http://example.com/', .2)
sleep(.1)
got2 = self.GET('http://example.com/', .2)
self.assertIs(got1, got2)
sleep(.3)
got3 = self.GET('http://example.com/')
self.assertIsNot(got3, got2)
def test_authenticated_user(self):
with HTTMock(self.response_content):
self.assertFalse(git.is_authenticated(git.Getter(('invalid', 'x-oauth-basic')).get))
self.assertTrue(git.is_authenticated(git.Getter(('valid', 'x-oauth-basic')).get))
def test_existing_repo(self):
with HTTMock(self.response_content):
self.assertFalse(git.repo_exists('migurski', 'no-repo', self.GET))
self.assertTrue(git.repo_exists('migurski', 'circlejek', self.GET))
def test_split_branch_path(self):
with HTTMock(self.response_content):
self.assertEqual(git.split_branch_path('mapzen', 'blog', 'drew/dc-transit-events-2016/blog/mapzen-in-dc', self.GET), ('drew/dc-transit-events-2016', 'blog/mapzen-in-dc'))
self.assertEqual(git.split_branch_path('mapzen', 'blog', 'drew/dc-transit-events-2016/blog/', self.GET), ('drew/dc-transit-events-2016', 'blog/'))
self.assertEqual(git.split_branch_path('mapzen', 'blog', 'drew/dc-transit-events-2016/', self.GET), ('drew/dc-transit-events-2016', ''))
self.assertEqual(git.split_branch_path('mapzen', 'blog', 'drew/dc-transit-events-2016', self.GET), ('drew/dc-transit-events-2016', ''))
self.assertEqual(git.split_branch_path('mapzen', 'blog', 'drew', self.GET), (None, 'drew'))
self.assertEqual(git.split_branch_path('mapzen', 'blog', '8d0c610/etc.', self.GET), ('8d0c610', 'etc.'))
def test_find_base_path(self):
with HTTMock(self.response_content):
self.assertEqual(git.find_base_path('migurski', 'circlejek', 'master', self.GET), '$CIRCLE_ARTIFACTS')
self.assertEqual(git.find_base_path('migurski', 'circlejek', 'tinker-with-config', self.GET), '/home/ubuntu/circlejek/_site')
with self.assertRaises(RuntimeError) as r:
git.find_base_path('mapzen', 'metro-extracts', '1cc0a0db8', self.GET), '/home/ubuntu/metro-extracts/dist'
self.assertIn('Problem reading configuration from circle.yml', r.exception.message)
def test_existing_master(self):
with HTTMock(self.response_content):
artifacts = git.get_circle_artifacts('migurski', 'circlejek', 'master', self.GET)
self.assertIn('index.html', artifacts)
def test_existing_tarball(self):
with HTTMock(self.response_content):
artifacts = git.get_circle_artifacts('migurski', 'circlejek', 'tarballize', self.GET)
self.assertIn('index.html', artifacts)
def test_untested_branch(self):
with HTTMock(self.response_content):
with self.assertRaises(RuntimeError) as r:
git.get_circle_artifacts('migurski', 'circlejek', 'untested', self.GET)
self.assertEqual(r.exception.args[0], git.ERR_NO_REF_STATUS)
def test_nonexistent_repository(self):
with HTTMock(self.response_content):
with self.assertRaises(RuntimeError) as r:
git.get_circle_artifacts('migurski', 'no-repo', 'master', self.GET)
self.assertEqual(r.exception.args[0], git.ERR_NO_REPOSITORY)
def test_unfinished_test(self):
with HTTMock(self.response_content):
with self.assertRaises(RuntimeError) as r:
git.get_circle_artifacts('migurski', 'circlejek', '4872caf32', self.GET)
self.assertEqual(r.exception.args[0], git.ERR_TESTS_PENDING)
def test_failed_test(self):
with HTTMock(self.response_content):
with self.assertRaises(RuntimeError) as r:
git.get_circle_artifacts('migurski', 'circlejek', 'd6f1c445e', self.GET)
self.assertEqual(r.exception.args[0], git.ERR_TESTS_FAILED)
def test_select_path(self):
self.assertEqual(git.select_path(tuple(), ''), 'index.html')
self.assertEqual(git.select_path(tuple(), 'foo'), 'foo/index.html')
self.assertEqual(git.select_path(('foo', ), 'foo'), 'foo')
def test_branch_link(self):
link1 = git.get_branch_link('migurski', 'circlejek', 'master')
self.assertIsNone(link1)
link2 = git.get_branch_link('migurski', 'circlejek', 'migurski/blog')
self.assertIsNone(link2)
link3 = git.get_branch_link('mapzen', 'styleguide', 'migurski/blog')
self.assertIsNone(link3)
link4 = git.get_branch_link('mapzen', 'blog', 'migurski/blog')
self.assertEqual(link4, 'blog')
link5 = git.get_branch_link('mapzen', 'blog', 'migurski/blog-hello')
self.assertEqual(link5, 'blog')
link6 = git.get_branch_link('mapzen', 'blog', 'migurski/blog/hello')
self.assertEqual(link6, 'blog')
link7 = git.get_branch_link('mapzen', 'blog', 'migurski/slog-hello')
self.assertIsNone(link7)
link8 = git.get_branch_link('mapzen', 'blog', 'migurski/blog_hello')
self.assertIsNone(link8)
def test_get_branch_info(self):
with HTTMock(self.response_content):
branch_info = git.get_branch_info('migurski', 'circlejek', self.GET)
self.assertIn('make-it-pop', [branch.name for branch in branch_info])
self.assertEqual(len(branch_info), 2)
class TestApp (unittest.TestCase):
def setUp(self):
self.okhand = urlencode({'go': u'\U0001f44c'.encode('utf8')}) # go=%F0%9F%91%8C or go=👌
self.client = app.test_client()
git._defaultcache.clear()
webhook_configs = 'blah:blah:blah', 'openaddresses/hooked-on-sources:hos-secret:abracadabra'
app.config['HOOK_SECRETS_TOKENS'] = util.parse_webhook_config(*webhook_configs)
@staticmethod
def scrub_query(url):
'''
'''
if url.hostname != 'api.github.com':
return url.query
raw_query = dict(parse_qsl(url.query))
args = {k: v for (k, v) in raw_query.items()
if k not in ('client_id', 'client_secret')}
return urlencode(args)
def response_content(self, url, request):
'''
'''
clean_query = TestApp.scrub_query(url)
MHP = request.method, url.hostname, url.path
MHPQ = request.method, url.hostname, url.path, clean_query
GH, CC = 'api.github.com', 'circleci.com'
response_headers = {'Content-Type': 'application/json; charset=utf-8'}
if MHP == ('GET', 'api.github.com', '/user'):
data = u'''{\r "login": "migurski",\r "id": 58730,\r "avatar_url": "https://avatars.githubusercontent.com/u/58730?v=3",\r "gravatar_id": "",\r "url": "https://api.github.com/users/migurski",\r "html_url": "https://github.com/migurski",\r "followers_url": "https://api.github.com/users/migurski/followers",\r "following_url": "https://api.github.com/users/migurski/following{/other_user}",\r "gists_url": "https://api.github.com/users/migurski/gists{/gist_id}",\r "starred_url": "https://api.github.com/users/migurski/starred{/owner}{/repo}",\r "subscriptions_url": "https://api.github.com/users/migurski/subscriptions",\r "organizations_url": "https://api.github.com/users/migurski/orgs",\r "repos_url": "https://api.github.com/users/migurski/repos",\r "events_url": "https://api.github.com/users/migurski/events{/privacy}",\r "received_events_url": "https://api.github.com/users/migurski/received_events",\r "type": "User",\r "site_admin": false,\r "name": null,\r "company": null,\r "blog": null,\r "location": null,\r "email": "mike-github@teczno.com",\r "hireable": null,\r "bio": null,\r "public_repos": 91,\r "public_gists": 45,\r "followers": 439,\r "following": 94,\r "created_at": "2009-02-27T23:44:32Z",\r "updated_at": "2015-12-26T20:09:55Z",\r "private_gists": 23,\r "total_private_repos": 1,\r "owned_private_repos": 0,\r "disk_usage": 249156,\r "collaborators": 0,\r "plan": {\r "name": "free",\r "space": 976562499,\r "collaborators": 0,\r "private_repos": 0\r }\r}'''
return response(200, data.encode('utf8'), headers=response_headers)
if MHP == ('GET', 'api.github.com', '/repos/mapzen/blog/commits/master'):
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With your enthusiasm for design, code, and maps you’ll be collaborating with engineering teams across the company to build and showcase awesome products using our Mapzen APIs and services. You’ll work with a [diverse team of veterans](https://mapzen.com/about/#team) from world-class mapping, design, and technology organizations in our San Francisco or New York offices. During a typical week, you might build a new interface for our groundbreaking WebGL toolkit, advance internal development processes for team members, demonstrate new capabilities of our search and routing services, and brainstorm with our data team on new ways to publish open geographic information on the web.\\n+\\n+At Mapzen we take a different approach to the core components of mapping—we do it open. Open source, open data, open access. We make modular tools for building better maps because we believe that healthy mapping ecosystems are ones that are diverse, sustainable, and accessible to all. Come join us to make our work in data, search, navigation, and design easy to understand and easy to use.\\n+\\n+Why Join Mapzen?\\n+---\\n+\\n+* [Commitment to exclusively open tools and data](https://mapzen.com/blog/our-magna-carto) for real change\\n+* Work alongside and learn from top experts and leaders in digital mapping\\n+* Everything you do will be public and open source:\\n+ * Open source navigation engine: [github.com/valhalla](https://github.com/valhalla)\\n+ * Distributed full-text geographic search engine: [github.com/pelias](https://github.com/pelias)\\n+ * Gazetteer and location data: [github.com/whosonfirst](https://github.com/whosonfirst)\\n+ * WebGL rendering library: [github.com/tangrams](https://github.com/tangrams)\\n+ * …and loads more: [github.com/mapzen](https://github.com/mapzen)\\n+* Healthy grownup working hours\\n+* World-class benefits\\n+\\n+Responsibilities\\n+---\\n+\\n+* Design and develop interfaces and demos for Mapzen services.\\n+* Mentor front-end developers on code and maintenance process.\\n+* Collaborate with engineering teams working on new features and services.\\n+* Design concepts for new demos and products building on Mapzen’s work.\\n+\\n+Requirements\\n+---\\n+\\n+* 3-5 years fluency in front-end desktop and mobile Javascript development.\\n+* Portfolio of creative, effective browser-based interactive design and the ability to clearly communicate your process.\\n+* Experience assessing project scope and timelines, balancing internal needs with external communications.\\n+* Ability to understand interface goals from a user, technical, and business perspective.\\n+\\n+Bonus Points\\n+---\\n+\\n+* You like maps!\\n+* Experience in an agency environment, with clients and deadlines.\\n+* Experience with agile, test-driven development and deployment.\\n+* Curiosity and a commitment to learning and teaching.\\n+* Contributions to open source projects.\\n+* Contributions to open source geospatial projects.\\n+\\n+Join Us!\\n+---\\n+\\n+If any of this sounds interesting to you, write to [jobs@mapzen.com](mailto:jobs@mapzen.com?subject=Senior%20UI%20Engineer). Please include “Senior UI Engineer” in the subject line and three items:\\n+\\n+1. Something résumé-like, e.g. your website, LinkedIn, or an attachment.\\n+2. Other links you’d like us to see, such as a blog, Github, or a sample project.\\n+3. A short answer to just one of these:\\n+ * What was the last good book, article, or presentation you saw on design or development? What made it stand out for you?\\n+ * What’s a favorite open-source library? What do you like about it?\\n+ * What’s the tool you learned most recently but haven’t yet had a chance to use in production? What have you learned from it so far?\\n+ * What methods did you most recently mentor a colleague on? 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return response(200, data.encode('utf8'), headers=response_headers)
if MHP == ('GET', 'api.github.com', '/repos/mapzen/blog/git/trees/john/test'):
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return response(200, data.encode('utf8'), headers=response_headers)
if MHP == ('GET', 'api.github.com', '/repos/mapzen/blog'):
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data = u'''{\r "sha": "fake-sha-fake-sha-fake-sha-fake-sha-fake-sha",\r "url": "https://api.github.com/repos/mapzen/blog/git/commits/fake-sha-fake-sha-fake-sha-fake-sha-fake-sha",\r "html_url": "https://github.com/mapzen/blog/commit/fake-sha-fake-sha-fake-sha-fake-sha-fake-sha",\r "author": {\r "name": "Riordan",\r "email": "dr@daveriordan.com",\r "date": "2015-12-16T21:57:56Z"\r },\r "committer": {\r "name": "Riordan",\r "email": "dr@daveriordan.com",\r "date": "2015-12-16T21:57:56Z"\r },\r "tree": {\r "sha": "fake-sha-fake-sha-fake-sha-fake-sha-fake-sha",\r "url": "https://api.github.com/repos/mapzen/blog/git/trees/fake-sha-fake-sha-fake-sha-fake-sha-fake-sha"\r },\r "message": "fixes queens address example in footnote, thx @amandabee",\r "parents": [\r {\r "sha": "fake-sha-fake-sha-fake-sha-fake-sha-fake-sha",\r "url": "https://api.github.com/repos/mapzen/blog/git/commits/fake-sha-fake-sha-fake-sha-fake-sha-fake-sha",\r "html_url": "https://github.com/mapzen/blog/commit/fake-sha-fake-sha-fake-sha-fake-sha-fake-sha"\r }\r ]\r}'''
return response(200, data.encode('utf8'), headers=response_headers)
if MHPQ == ('GET', 'circleci.com', '/api/v1/project/mapzen/blog/1947/artifacts', 'circle-token=a17131792f4c4bcb97f2f66d9c58258a0ee0e621'):
data = u'''[ {\r "path" : "/home/ubuntu/blog/_site/index.html",\r "pretty_path" : "/home/ubuntu/blog/_site/index.html",\r "node_index" : 0,\r "url" : "https://circle-artifacts.com/gh/mapzen/blog/1947/artifacts/0/home/ubuntu/blog/_site/index.html"\r}, {\r "path" : "/home/ubuntu/blog/_site/atom.xml",\r "pretty_path" : "/home/ubuntu/blog/_site/atom.xml",\r "node_index" : 0,\r "url" : "https://circle-artifacts.com/gh/mapzen/blog/1947/artifacts/0/home/ubuntu/blog/_site/atom.xml"\r}, {\r "path" : "/home/ubuntu/blog/_site/feed.xml",\r "pretty_path" : "/home/ubuntu/blog/_site/feed.xml",\r "node_index" : 0,\r "url" : "https://circle-artifacts.com/gh/mapzen/blog/1947/artifacts/0/home/ubuntu/blog/_site/feed.xml"\r}, {\r "path" : "/home/ubuntu/blog/_site/422.html",\r "pretty_path" : "/home/ubuntu/blog/_site/422.html",\r "node_index" : 0,\r "url" : "https://circle-artifacts.com/gh/mapzen/blog/1947/artifacts/0/home/ubuntu/blog/_site/422.html"\r}, {\r "path" : "/home/ubuntu/blog/_site/500.html",\r 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if MHPQ == ('GET', 'circle-artifacts.com', '/gh/mapzen/blog/1947/artifacts/0/home/ubuntu/blog/_site/index.html', 'circle-token=a17131792f4c4bcb97f2f66d9c58258a0ee0e621'):
data = u'''<!DOCTYPE html>\r<html lang='en-us'>\r<head>\r <meta charset='utf-8'>\r<meta http-equiv='X-UA-Compatible' content='IE=edge'>\r<meta name='viewport' content='width=device-width, initial-scale=1'>\r\r<title>Mapzen · an open-source mapping lab.</title>\r\r<meta name='description' content='Start where you are'>\r\r\r\r<link rel='shortcut icon' href='/resources/favicon.ico'>\r\r<link rel='stylesheet' href='/stylesheets/pygments.css' type='text/css'>\r<link rel='stylesheet' href='https://mapzen.com/common/styleguide/styles/blog.css' type='text/css'>\r<link rel='canonical' href='https://mapzen.com/'>\r\r\r <link rel='alternate' type='application/atom+xml' title='Mapzen (Atom)' href='/atom.xml'>\r <link rel='alternate' type='application/rss+xml' title='Mapzen (RSS)' href='/rss.xml'>\r\r\r</head>\r<body class='default'>\r <nav class='navbar navbar-default navbar-fixed-top' role='navigation'>\r <div class='container'>\r <div class='navbar-header'>\r <button type='button' class='navbar-toggle' data-toggle='collapse' data-target='#navbar-menu'>\r <span class='sr-only'>Toggle navigation</span>\r <span class='icon-bar'></span>\r <span class='icon-bar'></span>\r <span class='icon-bar'></span>\r </button>\r <a class='navbar-brand' href='/'>\r <div class='mapzen-logo'></div>\r <h1 class='sr-only'>Mapzen</h1>\r </a>\r </div>\r <div class='collapse navbar-collapse' id='navbar-menu'>\r <ul class='nav navbar-nav navbar-right'>\r <li class='inactive'><a href='/projects/'>Projects</a></li>\r <li class='inactive'><a href='/data/'>Data</a></li>\r <li class='inactive'><a href='/documentation/'>Documentation</a></li>\r <li class='inactive'><a href='/blog/'>Blog</a></li>\r <li class='inactive'><a href='/developers/'><div class='animated-key'></div>Developers</a></li>\r </ul>\r </div>\r </div>\r</nav>\r\r\r <div class='gallery full' id='gallery'>\r <img class='gallery-image' id='gallery-image' alt='' sizes='100vw'>\r <script>\r (function () {\r var el = document.getElementById('gallery-image')\r var l = 12 // Number of images\r var d = new Date()\r var t = Math.floor(d.getMinutes() / 5) % l // every five minutes\r el.src = '/gallery/g{i}-sm.jpg'.replace(/\{i\}/, t)\r el.srcset = '/gallery/g{i}-sm.jpg 600w, /gallery/g{i}-md.jpg 1300w, /gallery/g{i}-lg.jpg'.replace(/\{i\}/g, t)\r }())\r </script>\r</div>\r\r <div class='container' id='content'>\r \r<div class='row headroom-large'>\r <div class='col-xs-12 text-center'>\r <h1 class='red-text'>\r an open-source mapping lab\r </h1>\r \r <h3 class='gray-text headroom'>\r Mapzen builds open-source mapping tools and collaborates on open geodata initiatives.\r </h3>\r \r </div>\r</div>\r\r<div class='row'>\r <div class='col-xs-12 text-center headroom-extra-large footroom-extra-large'>\r <img class='red-compass' src='/resources/compass-red.png'>\r </div>\r</div>\r\r\r <div class="row">\r <div class="col-md-6 no-padding">\r <div class="background-image-lg" style='background-image: url("/resources/tangram.jpg");'></div>\r </div>\r <div class="col-md-6 featured-item hor-pad-left">\r <span class="red-text">Tangram</span> is a flexible mapping engine, designed for real-time rendering of 2D and 3D maps.\r <span class="row">\r <a class="btn btn-mapzen headroom-large horz-marg-right" href="/projects/tangram">LEARN MORE</a>\r <a class="btn btn-transparent headroom-large" href="https://github.com/tangram-map/tangram" target="_blank">VIEW ON GITHUB</a>\r </span>\r </div>\r</div>\r\r<div class='row'>\r <div class='col-xs-12 text-center headroom-large footroom-large'>\r <img src='/resources/compass-blue.png'>\r </div>\r</div>\r\r<div class="row">\r <div class="col-md-6 col-md-push-6 no-padding">\r <div class="background-image-lg" style='background-image: url("/resources/valhalla.png");'></div>\r </div>\r <div class="col-md-6 col-md-pull-6 featured-item hor-pad-right">\r <span class="red-text">Mapzen Turn-by-Turn</span> is an open-source routing service for client-side routing applications.\r <span class="row">\r <a class="btn btn-mapzen headroom-large horz-marg-right" href="/projects/valhalla">LEARN MORE</a>\r <a class="btn btn-transparent headroom-large" href="https://github.com/valhalla" target="_blank">VIEW ON GITHUB</a>\r </span>\r </div>\r</div>\r\r<div class='row'>\r <div class='col-xs-12 text-center headroom-large footroom-large'>\r <img src='/resources/compass-blue.png'>\r </div>\r</div>\r\r<div class="row">\r <div class="col-md-6 no-padding">\r <div class="background-image-lg" style='background-image: url("/resources/search-bakerst-960px.png");'></div>\r </div>\r <div class="col-md-6 featured-item hor-pad-left">\r <span class="red-text">Mapzen Search</span> is a geographic search engine for places based entirely on open-source tools and powered by entirely open data.\r <span class="row">\r <a class="btn btn-mapzen headroom-large" href="/projects/search">Learn more</a>\r </span>\r </span>\r </div>\r</div>\r\r<div class="text-center headroom-extra-large">\r <img src="/resources/community-lg-blue.png"/>\r <h5 class="dark-gray-text headroom">Blog</h5>\r</div>\r\r<div class='row headroom'>\r <div class='col-md-4 sm-no-padding'>\r \r <div class='headroom-med'><a href='/blog/mapzen-search-data-pipeline'>\r \r <div class='background-image' style='background-image: url(https://s3.amazonaws.com/assets-staging.mapzen.com/images/mapzen-search-data-pipeline/pelias_build_pipeline_small.png);'>\r </div>\r \r <div class='blog-excerpt'>\r <div class='blog-title-sm footroom-sm vert-pad'>\r <a href='/blog/mapzen-search-data-pipeline'>\r Mapzen Search Data Pipeline\r </a>\r </div>\r <div class='footroom text-16 gray-text'>\r Building a data pipeline to keep Mapzen Search data fresh and available.\r </div>\r <div class='text-center'>\r <a class='btn btn-default btn-transparent headroom footroom-large' href='/blog/mapzen-search-data-pipeline'>Read More</a>\r </div>\r </div>\r </div>\r \r \r </div><div class='col-md-4 sm-no-padding'>\r \r \r <div class='headroom-med'><a href='/blog/engineering-series'>\r \r <div class='background-image' style='background-image: url(https://s3.amazonaws.com/assets-staging.mapzen.com/images/engineering-series/matrix.gif);'>\r </div>\r \r <div class='blog-excerpt'>\r <div class='blog-title-sm footroom-sm vert-pad'>\r <a href='/blog/engineering-series'>\r What We Talk About When We Talk About Engineering\r </a>\r </div>\r <div class='footroom text-16 gray-text'>\r “Pain is inevitable. Suffering is optional.” <i>What I Talk About When I Talk About Running, Haruki Murakami</i>\r </div>\r <div class='text-center'>\r <a class='btn btn-default btn-transparent headroom footroom-large' href='/blog/engineering-series'>Read More</a>\r </div>\r </div>\r </div>\r \r \r </div><div class='col-md-4 sm-no-padding'>\r \r \r <div class='headroom-med'><a href='/blog/mapzen-in-dc'>\r \r <div class='background-image' style='background-image: url(https://s3.amazonaws.com/assets-staging.mapzen.com/images/mapzen-in-dc/dcmetro.jpg);'>\r </div>\r \r <div class='blog-excerpt'>\r <div class='blog-title-sm footroom-sm vert-pad'>\r <a href='/blog/mapzen-in-dc'>\r Mapzen "transpo" in DC\r </a>\r </div>\r <div class='footroom text-16 gray-text'>\r Mapzen's joining thousands of enthusiasts and professionals in Washington, D.C. to start off a new year of transportation research, planning, and advocacy.\r </div>\r <div class='text-center'>\r <a class='btn btn-default btn-transparent headroom footroom-large' href='/blog/mapzen-in-dc'>Read More</a>\r </div>\r </div>\r </div>\r \r \r </div><div class='col-md-4 sm-no-padding'>\r \r \r </div>\r</div>\r\r<div class="text-center headroom-extra-large footroom-large">\r <a class="btn btn-mapzen" href="/blog">MORE POSTS</a>\r</div>\r\r </div>\r\r <footer>\r <div class='container hidden-print'>\r <div class='row headroom-extra-large'>\r <div class='col-xs-12 text-center'>\r <img src='/resources/compass-lg-red.png'>\r <h5 class="headroom">Get involved</h5>\r </div>\r </div>\r <div class='row headroom footroom-large'>\r <div class='col-xs-12 col-sm-10 col-sm-offset-1 col-md-8 col-md-offset-2 text-center text-18 gray-text'>\r <p>\r Got an interesting problem? Want to know more about things we’re doing?\r Just want to say hi?\r <a href='https://twitter.com/mapzen/' target='_blank'>Send us <3</a>\r </p>\r </div>\r </div>\r </div>\r\r <div class='headroom-large footer-background'>\r <div class='container'>\r <div class='row headroom'>\r <div class='col-xs-12 text-center'>\r <img height='81px' src='/resources/mapzen_logo@2x.png'>\r <div class='text-18 gray-text headroom'>\r <span class='slogan'>start where you are</span>\r </div>\r </div>\r </div>\r <div class='row headroom-large footer-menu hidden-print'>\r <div class='col-xs-5 col-xs-offset-1 col-sm-6 col-sm-offset-0 col-md-6 footer-column'>\r <div class='row'>\r <div class='col-xs-12 col-sm-6 col-md-6 footer-section'>\r <h6><a href='/projects/'>Projects</a></h6>\r <ul>\r <li><a href='/projects/tangram/'>Tangram</a></li>\r <li><a href='/projects/search'>Mapzen Search</a></li>\r <li><a href='/projects/valhalla'>Mapzen Turn-by-Turn</a></li>\r <li><a href='/projects/vector-tiles/'>Vector Tile Service</a></li>\r </ul>\r </div>\r <div class='col-xs-12 col-sm-6 col-md-6 footer-section'>\r <h6><a href='/data/'>Data</a></h6>\r <ul>\r <li><a href='/data/metro-extracts/'>Metro Extracts</a></li>\r <li><a href='/data/borders/'>Borders</a></li>\r <li><a href='https://transit.land/'>Transitland</a></li>\r <li><a href='https://whosonfirst.mapzen.com/'>Who’s On First</a></li>\r </ul>\r\r <h6>Developers</h6>\r <ul>\r <li><a href='/developers/'>Developer Platform</a></li>\r <li><a href='/documentation/'>Documentation</a></li>\r </ul>\r </div>\r </div>\r </div>\r <div class='col-xs-5 col-sm-6 col-md-6 footer-column'>\r <div class='row'>\r <div class='col-xs-12 col-sm-6 col-md-6 footer-section'>\r <h6>About</h6>\r <ul>\r <li><a href='/about/'>Who we are</a></li>\r <li><a href='/about/#team'>Team</a></li>\r <li><a href='/jobs/'>Jobs</a></li>\r <li><a href='/terms/'>Terms</a></li>\r <li><a href='/privacy/'>Privacy</a></li>\r <li><a href='/media-resources/'>Media resources</a></li>\r </ul>\r </div>\r <div class='col-xs-12 col-sm-6 col-md-6 footer-section'>\r <h6>Hello</h6>\r <ul>\r <li><a href='/blog/'><i class='fa fa-fw fa-rss'></i>Blog</a></li>\r <li><a href='https://github.com/mapzen/'><i class='fa fa-fw fa-github'></i>GitHub</a></li>\r <li><a href='https://twitter.com/mapzen/'><i class='fa fa-fw fa-twitter'></i>Twitter</a></li>\r <li><a href='https://www.pinterest.com/mapzen/'><i class='fa fa-fw fa-pinterest'></i>Pinterest</a></li>\r <li><a href='mailto:hello@mapzen.com'><i class='fa fa-fw fa-envelope'></i>E-mail</a></li>\r </ul>\r </div>\r </div>\r </div>\r </div>\r <div class='row headroom-large footroom-large'>\r <div class='col-xs-12 text-center'>\r <small class='copyright'>© 2016 Mapzen</small>\r </div>\r </div>\r </div>\r </div>\r</footer>\r\r\r <script src='/resources/application.js'></script>\r <script src='/resources/social.js'></script>\r <script src='/resources/analytics.js'></script>\r</body>\r</html>\r'''
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class='navbar-toggle' data-toggle='collapse' data-target='#navbar-menu'>\r <span class='sr-only'>Toggle navigation</span>\r <span class='icon-bar'></span>\r <span class='icon-bar'></span>\r <span class='icon-bar'></span>\r </button>\r <a class='navbar-brand' href='/'>\r <div class='mapzen-logo'></div>\r <h1 class='sr-only'>Mapzen</h1>\r </a>\r </div>\r <div class='collapse navbar-collapse' id='navbar-menu'>\r <ul class='nav navbar-nav navbar-right'>\r <li class='inactive'><a href='/projects/'>Projects</a></li>\r <li class='inactive'><a href='/data/'>Data</a></li>\r <li class='inactive'><a href='/documentation/'>Documentation</a></li>\r <li class='inactive'><a href='/blog/'>Blog</a></li>\r <li class='inactive'><a href='/developers/'><div class='animated-key'></div>Developers</a></li>\r </ul>\r </div>\r </div>\r</nav>\r\r\r <div class='gallery full' id='gallery'>\r <img class='gallery-image' id='gallery-image' alt='' sizes='100vw'>\r <script>\r (function () {\r var el = document.getElementById('gallery-image')\r var l = 12 // Number of images\r var d = new Date()\r var t = Math.floor(d.getMinutes() / 5) % l // every five minutes\r el.src = '/gallery/g{i}-sm.jpg'.replace(/\{i\}/, t)\r el.srcset = '/gallery/g{i}-sm.jpg 600w, /gallery/g{i}-md.jpg 1300w, /gallery/g{i}-lg.jpg'.replace(/\{i\}/g, t)\r }())\r </script>\r</div>\r\r <div class='container' id='content'>\r \r<div class='row headroom-large'>\r <div class='col-xs-12 text-center'>\r <h1 class='red-text'>\r an open-source mapping lab\r </h1>\r \r <h3 class='gray-text headroom'>\r Mapzen builds open-source mapping tools and collaborates on open geodata initiatives.\r </h3>\r \r </div>\r</div>\r\r<div class='row'>\r <div class='col-xs-12 text-center headroom-extra-large footroom-extra-large'>\r <img class='red-compass' src='/resources/compass-red.png'>\r </div>\r</div>\r\r\r <div class="row">\r <div class="col-md-6 no-padding">\r <div class="background-image-lg" style='background-image: url("/resources/tangram.jpg");'></div>\r </div>\r <div class="col-md-6 featured-item hor-pad-left">\r <span class="red-text">Tangram</span> is a flexible mapping engine, designed for real-time rendering of 2D and 3D maps.\r <span class="row">\r <a class="btn btn-mapzen headroom-large horz-marg-right" href="/projects/tangram">LEARN MORE</a>\r <a class="btn btn-transparent headroom-large" href="https://github.com/tangram-map/tangram" target="_blank">VIEW ON GITHUB</a>\r </span>\r </div>\r</div>\r\r<div class='row'>\r <div class='col-xs-12 text-center headroom-large footroom-large'>\r <img src='/resources/compass-blue.png'>\r </div>\r</div>\r\r<div class="row">\r <div class="col-md-6 col-md-push-6 no-padding">\r <div class="background-image-lg" style='background-image: url("/resources/valhalla.png");'></div>\r </div>\r <div class="col-md-6 col-md-pull-6 featured-item hor-pad-right">\r <span class="red-text">Mapzen Turn-by-Turn</span> is an open-source routing service for client-side routing applications.\r <span class="row">\r <a class="btn btn-mapzen headroom-large horz-marg-right" href="/projects/valhalla">LEARN MORE</a>\r <a class="btn btn-transparent headroom-large" href="https://github.com/valhalla" target="_blank">VIEW ON GITHUB</a>\r </span>\r </div>\r</div>\r\r<div class='row'>\r <div class='col-xs-12 text-center headroom-large footroom-large'>\r <img src='/resources/compass-blue.png'>\r </div>\r</div>\r\r<div class="row">\r <div class="col-md-6 no-padding">\r <div class="background-image-lg" style='background-image: url("/resources/search-bakerst-960px.png");'></div>\r </div>\r <div class="col-md-6 featured-item hor-pad-left">\r <span class="red-text">Mapzen Search</span> is a geographic search engine for places based entirely on open-source tools and powered by entirely open data.\r <span class="row">\r <a class="btn btn-mapzen headroom-large" href="/projects/search">Learn more</a>\r </span>\r </span>\r </div>\r</div>\r\r<div class="text-center headroom-extra-large">\r <img src="/resources/community-lg-blue.png"/>\r <h5 class="dark-gray-text headroom">Blog</h5>\r</div>\r\r<div class='row headroom'>\r <div class='col-md-4 sm-no-padding'>\r \r <div class='headroom-med'><a href='/blog/mapzen-search-data-pipeline'>\r \r <div class='background-image' style='background-image: url(https://s3.amazonaws.com/assets-staging.mapzen.com/images/mapzen-search-data-pipeline/pelias_build_pipeline_small.png);'>\r </div>\r \r <div class='blog-excerpt'>\r <div class='blog-title-sm footroom-sm vert-pad'>\r <a href='/blog/mapzen-search-data-pipeline'>\r Mapzen Search Data Pipeline\r </a>\r </div>\r <div class='footroom text-16 gray-text'>\r Building a data pipeline to keep Mapzen Search data fresh and available.\r </div>\r <div class='text-center'>\r <a class='btn btn-default btn-transparent headroom footroom-large' href='/blog/mapzen-search-data-pipeline'>Read More</a>\r </div>\r </div>\r </div>\r \r \r </div><div class='col-md-4 sm-no-padding'>\r \r \r <div class='headroom-med'><a href='/blog/engineering-series'>\r \r <div class='background-image' style='background-image: url(https://s3.amazonaws.com/assets-staging.mapzen.com/images/engineering-series/matrix.gif);'>\r </div>\r \r <div class='blog-excerpt'>\r <div class='blog-title-sm footroom-sm vert-pad'>\r <a href='/blog/engineering-series'>\r What We Talk About When We Talk About Engineering\r </a>\r </div>\r <div class='footroom text-16 gray-text'>\r “Pain is inevitable. Suffering is optional.” <i>What I Talk About When I Talk About Running, Haruki Murakami</i>\r </div>\r <div class='text-center'>\r <a class='btn btn-default btn-transparent headroom footroom-large' href='/blog/engineering-series'>Read More</a>\r </div>\r </div>\r </div>\r \r \r </div><div class='col-md-4 sm-no-padding'>\r \r \r <div class='headroom-med'><a href='/blog/mapzen-in-dc'>\r \r <div class='background-image' style='background-image: url(https://s3.amazonaws.com/assets-staging.mapzen.com/images/mapzen-in-dc/dcmetro.jpg);'>\r </div>\r \r <div class='blog-excerpt'>\r <div class='blog-title-sm footroom-sm vert-pad'>\r <a href='/blog/mapzen-in-dc'>\r Mapzen "transpo" in DC\r </a>\r </div>\r <div class='footroom text-16 gray-text'>\r Mapzen's joining thousands of enthusiasts and professionals in Washington, D.C. to start off a new year of transportation research, planning, and advocacy.\r </div>\r <div class='text-center'>\r <a class='btn btn-default btn-transparent headroom footroom-large' href='/blog/mapzen-in-dc'>Read More</a>\r </div>\r </div>\r </div>\r \r \r </div><div class='col-md-4 sm-no-padding'>\r \r \r </div>\r</div>\r\r<div class="text-center headroom-extra-large footroom-large">\r <a class="btn btn-mapzen" href="/blog">MORE POSTS</a>\r</div>\r\r </div>\r\r <footer>\r <div class='container hidden-print'>\r <div class='row headroom-extra-large'>\r <div class='col-xs-12 text-center'>\r <img src='/resources/compass-lg-red.png'>\r <h5 class="headroom">Get involved</h5>\r </div>\r </div>\r <div class='row headroom footroom-large'>\r <div class='col-xs-12 col-sm-10 col-sm-offset-1 col-md-8 col-md-offset-2 text-center text-18 gray-text'>\r <p>\r Got an interesting problem? Want to know more about things we’re doing?\r Just want to say hi?\r <a href='https://twitter.com/mapzen/' target='_blank'>Send us <3</a>\r </p>\r </div>\r </div>\r </div>\r\r <div class='headroom-large footer-background'>\r <div class='container'>\r <div class='row headroom'>\r <div class='col-xs-12 text-center'>\r <img height='81px' src='/resources/mapzen_logo@2x.png'>\r <div class='text-18 gray-text headroom'>\r <span class='slogan'>start where you are</span>\r </div>\r </div>\r </div>\r <div class='row headroom-large footer-menu hidden-print'>\r <div class='col-xs-5 col-xs-offset-1 col-sm-6 col-sm-offset-0 col-md-6 footer-column'>\r <div class='row'>\r <div class='col-xs-12 col-sm-6 col-md-6 footer-section'>\r <h6><a href='/projects/'>Projects</a></h6>\r <ul>\r <li><a href='/projects/tangram/'>Tangram</a></li>\r <li><a href='/projects/search'>Mapzen Search</a></li>\r <li><a href='/projects/valhalla'>Mapzen Turn-by-Turn</a></li>\r <li><a href='/projects/vector-tiles/'>Vector Tile Service</a></li>\r </ul>\r </div>\r <div class='col-xs-12 col-sm-6 col-md-6 footer-section'>\r <h6><a href='/data/'>Data</a></h6>\r <ul>\r <li><a href='/data/metro-extracts/'>Metro Extracts</a></li>\r <li><a href='/data/borders/'>Borders</a></li>\r <li><a href='https://transit.land/'>Transitland</a></li>\r <li><a href='https://whosonfirst.mapzen.com/'>Who’s On First</a></li>\r </ul>\r\r <h6>Developers</h6>\r <ul>\r <li><a href='/developers/'>Developer Platform</a></li>\r <li><a href='/documentation/'>Documentation</a></li>\r </ul>\r </div>\r </div>\r </div>\r <div class='col-xs-5 col-sm-6 col-md-6 footer-column'>\r <div class='row'>\r <div class='col-xs-12 col-sm-6 col-md-6 footer-section'>\r <h6>About</h6>\r <ul>\r <li><a href='/about/'>Who we are</a></li>\r <li><a href='/about/#team'>Team</a></li>\r <li><a href='/jobs/'>Jobs</a></li>\r <li><a href='/terms/'>Terms</a></li>\r <li><a href='/privacy/'>Privacy</a></li>\r <li><a href='/media-resources/'>Media resources</a></li>\r </ul>\r </div>\r <div class='col-xs-12 col-sm-6 col-md-6 footer-section'>\r <h6>Hello</h6>\r <ul>\r <li><a href='/blog/'><i class='fa fa-fw fa-rss'></i>Blog</a></li>\r <li><a href='https://github.com/mapzen/'><i class='fa fa-fw fa-github'></i>GitHub</a></li>\r <li><a href='https://twitter.com/mapzen/'><i class='fa fa-fw fa-twitter'></i>Twitter</a></li>\r <li><a href='https://www.pinterest.com/mapzen/'><i class='fa fa-fw fa-pinterest'></i>Pinterest</a></li>\r <li><a href='mailto:hello@mapzen.com'><i class='fa fa-fw fa-envelope'></i>E-mail</a></li>\r </ul>\r </div>\r </div>\r </div>\r </div>\r <div class='row headroom-large footroom-large'>\r <div class='col-xs-12 text-center'>\r <small class='copyright'>© 2016 Mapzen</small>\r </div>\r </div>\r </div>\r </div>\r</footer>\r\r\r <script src='/resources/application.js'></script>\r <script src='/resources/social.js'></script>\r <script src='/resources/analytics.js'></script>\r</body>\r</html>\r'''
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data = u'''[\r {\r "ref": "refs/heads/heffergm/pelias-build",\r "url": "https://api.github.com/repos/mapzen/blog/git/refs/heads/heffergm/pelias-build",\r "object": {\r "sha": "59123caa7ba6d6494868528d4247bd4bfd37f608",\r "type": "commit",\r "url": "https://api.github.com/repos/mapzen/blog/git/commits/59123caa7ba6d6494868528d4247bd4bfd37f608"\r }\r },\r {\r "ref": "refs/heads/ian/schedule-api-finalize",\r "url": "https://api.github.com/repos/mapzen/blog/git/refs/heads/ian/schedule-api-finalize",\r "object": {\r "sha": "97742c684e943806f938209819f876a59a470de8",\r "type": "commit",\r "url": "https://api.github.com/repos/mapzen/blog/git/commits/97742c684e943806f938209819f876a59a470de8"\r }\r },\r {\r "ref": "refs/heads/indy/Name-That-Building",\r "url": "https://api.github.com/repos/mapzen/blog/git/refs/heads/indy/Name-That-Building",\r "object": {\r "sha": "49ecd93ec6f70c597e1e6c0ca1d4e462fee2bc5d",\r "type": "commit",\r "url": "https://api.github.com/repos/mapzen/blog/git/commits/49ecd93ec6f70c597e1e6c0ca1d4e462fee2bc5d"\r }\r },\r {\r "ref": "refs/heads/indy/airport",\r "url": "https://api.github.com/repos/mapzen/blog/git/refs/heads/indy/airport",\r "object": {\r "sha": "64bf6adc62bd28f0539a6c528957b2317dba6d8f",\r "type": "commit",\r "url": "https://api.github.com/repos/mapzen/blog/git/commits/64bf6adc62bd28f0539a6c528957b2317dba6d8f"\r }\r },\r {\r "ref": "refs/heads/indy/updated-targeted-editing-hiatus",\r "url": "https://api.github.com/repos/mapzen/blog/git/refs/heads/indy/updated-targeted-editing-hiatus",\r "object": {\r "sha": "53bf63fda6bd6f493b46f9d54fa459d3adbeac1e",\r "type": "commit",\r "url": "https://api.github.com/repos/mapzen/blog/git/commits/53bf63fda6bd6f493b46f9d54fa459d3adbeac1e"\r }\r },\r {\r "ref": "refs/heads/indyhurt/targeted-editing-holiday-hiatus",\r "url": "https://api.github.com/repos/mapzen/blog/git/refs/heads/indyhurt/targeted-editing-holiday-hiatus",\r "object": {\r "sha": "998265cf08eef84f2b007b64c87a26e6427791e2",\r "type": "commit",\r "url": "https://api.github.com/repos/mapzen/blog/git/commits/998265cf08eef84f2b007b64c87a26e6427791e2"\r }\r },\r {\r "ref": "refs/heads/john/test",\r "url": "https://api.github.com/repos/mapzen/blog/git/refs/heads/john/test",\r "object": {\r "sha": "159d528d17b234349141309094b5c8807173682c",\r "type": "commit",\r "url": "https://api.github.com/repos/mapzen/blog/git/commits/159d528d17b234349141309094b5c8807173682c"\r }\r },\r {\r "ref": "refs/heads/lou/fonts",\r "url": "https://api.github.com/repos/mapzen/blog/git/refs/heads/lou/fonts",\r "object": {\r "sha": "803b9e2fe230916f249278bd5f8c8f2a256a427a",\r "type": "commit",\r "url": "https://api.github.com/repos/mapzen/blog/git/commits/803b9e2fe230916f249278bd5f8c8f2a256a427a"\r }\r },\r {\r "ref": "refs/heads/lou/future-posts",\r "url": "https://api.github.com/repos/mapzen/blog/git/refs/heads/lou/future-posts",\r "object": {\r "sha": "9b0727becbf74887d22a730fa1c51a0ac2f8b8d0",\r "type": "commit",\r "url": "https://api.github.com/repos/mapzen/blog/git/commits/9b0727becbf74887d22a730fa1c51a0ac2f8b8d0"\r }\r },\r {\r "ref": "refs/heads/lou/project-nav-mobile",\r "url": "https://api.github.com/repos/mapzen/blog/git/refs/heads/lou/project-nav-mobile",\r "object": {\r "sha": "aed68f2d32496c5ae8908d531d6ba04953b53f88",\r "type": "commit",\r "url": "https://api.github.com/repos/mapzen/blog/git/commits/aed68f2d32496c5ae8908d531d6ba04953b53f88"\r }\r },\r {\r "ref": "refs/heads/master",\r "url": "https://api.github.com/repos/mapzen/blog/git/refs/heads/master",\r "object": {\r "sha": "159d528d17b234349141309094b5c8807173682c",\r "type": "commit",\r "url": "https://api.github.com/repos/mapzen/blog/git/commits/159d528d17b234349141309094b5c8807173682c"\r }\r },\r {\r "ref": "refs/heads/migurski/update-ui-engineer-job",\r "url": "https://api.github.com/repos/mapzen/blog/git/refs/heads/migurski/update-ui-engineer-job",\r "object": {\r "sha": "e464c47fbbac6e16306700898071c1e5dc09e3e3",\r "type": "commit",\r "url": "https://api.github.com/repos/mapzen/blog/git/commits/e464c47fbbac6e16306700898071c1e5dc09e3e3"\r }\r },\r {\r "ref": "refs/heads/nvkelso/fix-intro-map-styles-post",\r "url": "https://api.github.com/repos/mapzen/blog/git/refs/heads/nvkelso/fix-intro-map-styles-post",\r "object": {\r "sha": "486d01e41103e66f44b4875263a6392428192c31",\r "type": "commit",\r "url": "https://api.github.com/repos/mapzen/blog/git/commits/486d01e41103e66f44b4875263a6392428192c31"\r }\r },\r {\r "ref": "refs/heads/nvkelso/traditional-style",\r "url": "https://api.github.com/repos/mapzen/blog/git/refs/heads/nvkelso/traditional-style",\r "object": {\r "sha": "cbc135319feccbe01b39a05e3888f106d01d4eaf",\r "type": "commit",\r "url": "https://api.github.com/repos/mapzen/blog/git/commits/cbc135319feccbe01b39a05e3888f106d01d4eaf"\r }\r },\r {\r "ref": "refs/heads/orangejulius-patch-1",\r "url": "https://api.github.com/repos/mapzen/blog/git/refs/heads/orangejulius-patch-1",\r "object": {\r "sha": "287b866fc48efe39cc1c4b42d7983b8ed098e92f",\r "type": "commit",\r "url": "https://api.github.com/repos/mapzen/blog/git/commits/287b866fc48efe39cc1c4b42d7983b8ed098e92f"\r }\r },\r {\r "ref": "refs/heads/peter/kotlin",\r "url": "https://api.github.com/repos/mapzen/blog/git/refs/heads/peter/kotlin",\r "object": {\r "sha": "e5fdb0247fde743bd9294afc820f13c345b842f0",\r "type": "commit",\r "url": "https://api.github.com/repos/mapzen/blog/git/commits/e5fdb0247fde743bd9294afc820f13c345b842f0"\r }\r },\r {\r "ref": "refs/heads/production",\r "url": "https://api.github.com/repos/mapzen/blog/git/refs/heads/production",\r "object": {\r "sha": "4f208d9d3ab640e8e29ccbba8a27ada6584a5c1c",\r "type": "commit",\r "url": "https://api.github.com/repos/mapzen/blog/git/commits/4f208d9d3ab640e8e29ccbba8a27ada6584a5c1c"\r }\r }\r]'''
response_headers.update(Link='<https://api.github.com/repositories/34413671/git/refs?page=1>; rel="first", <https://api.github.com/repositories/34413671/git/refs?page=1>; rel="prev"')
return response(200, data.encode('utf8'), headers=response_headers)
if MHP == ('POST', GH, '/repos/openaddresses/hooked-on-sources/statuses/e91fbc420f08890960f50f863626e1062f922522') \
or MHP == ('POST', GH, '/repos/openaddresses/hooked-on-sources/statuses/6460668909a85d9db8df871d91e9b25bc5192add') \
or MHP == ('POST', GH, '/repos/openaddresses/hooked-on-sources/statuses/8dd262c2f30a70b27e371869c54315b1abc32247') \
or MHP == ('POST', GH, '/repos/openaddresses/hooked-on-sources/statuses/aed74b0784f696c3cf10e8e260865ae18ffd3aa8'):
input = json.loads(request.body)
self.last_status_state = input['state']
self.last_status_message = input['description']
self.assertEqual(input['context'], 'mapzen/precog')
self.assertEqual(request.headers['Authorization'], 'Basic YWJyYWNhZGFicmE6eC1vYXV0aC1iYXNpYw==')
target_path = urlparse(input['target_url']).path
self.assertIn(basename(url.path), target_path)
data = '''{{\r "context": "openaddresses/hooked", \r "created_at": "2015-04-26T23:45:39Z", \r "creator": {{\r "avatar_url": "https://avatars.githubusercontent.com/u/58730?v=3", \r "events_url": "https://api.github.com/users/migurski/events{{/privacy}}", \r "followers_url": "https://api.github.com/users/migurski/followers", \r "following_url": "https://api.github.com/users/migurski/following{{/other_user}}", \r "gists_url": "https://api.github.com/users/migurski/gists{{/gist_id}}", \r "gravatar_id": "", \r "html_url": "https://github.com/migurski", \r "id": 58730, \r "login": "migurski", \r "organizations_url": "https://api.github.com/users/migurski/orgs", \r "received_events_url": "https://api.github.com/users/migurski/received_events", \r "repos_url": "https://api.github.com/users/migurski/repos", \r "site_admin": false, \r "starred_url": "https://api.github.com/users/migurski/starred{{/owner}}{{/repo}}", \r "subscriptions_url": "https://api.github.com/users/migurski/subscriptions", \r "type": "User", \r "url": "https://api.github.com/users/migurski"\r }}, \r "description": "Checking ", \r "id": 999999999, \r "state": "{state}", \r "target_url": null, \r "updated_at": "2015-04-26T23:45:39Z", \r "url": "https://api.github.com/repos/openaddresses/hooked-on-sources/statuses/xxxxxxxxx"\r }}'''
return response(201, data.format(**input).encode('utf8'), headers=response_headers)
raise Exception(url)
def test_index(self):
'''
'''
with HTTMock(self.response_content):
index = self.client.get('/')
self.assertEqual(index.status_code, 200)
self.assertIn('Precog', index.data)
def test_login(self):
'''
'''
def response_content1(url, request):
'''
'''
MHP = request.method, url.hostname, url.path
response_headers = {'Content-Type': 'application/json; charset=utf-8'}
if MHP == ('GET', 'api.github.com', '/repos/mapzen/blog/commits/master') \
or MHP == ('GET', 'api.github.com', '/repos/mapzen/blog/git/refs/heads/master') \
or MHP == ('GET', 'api.github.com', '/repos/mapzen/blog/git/refs/heads/master/') \
or MHP == ('GET', 'api.github.com', '/repos/mapzen/blog'):
data = u'''{\r "message": "Not Found",\r "documentation_url": "https://developer.github.com/v3"\r}'''
return response(404, data.encode('utf8'), headers=response_headers)
if MHP == ('GET', 'api.github.com', '/user'):
data = u'''{\r "message": "Requires authentication",\r "documentation_url": "https://developer.github.com/v3"\r}'''
return response(401, data.encode('utf8'), headers=response_headers)
raise Exception(request.method, url, request.headers, request.body)
with HTTMock(response_content1):
blog1 = self.client.get('/mapzen/blog/master/')
redirect1 = urlparse(blog1.headers.get('X-Redirect', ''))
self.assertEqual(blog1.status_code, 401)
self.assertEqual(redirect1.hostname, 'github.com')
self.assertEqual(redirect1.path, '/login/oauth/authorize')
query = dict(parse_qsl(redirect1.query))
def response_content2(url, request):
'''
'''
MHP = request.method, url.hostname, url.path
response_headers = {'Content-Type': 'application/json; charset=utf-8'}
if MHP == ('GET', 'api.github.com', '/repos/mapzen/blog/commits/master') \
or MHP == ('GET', 'api.github.com', '/repos/mapzen/blog/git/refs/heads/master') \
or MHP == ('GET', 'api.github.com', '/repos/mapzen/blog/git/refs/heads/master/') \
or MHP == ('GET', 'api.github.com', '/repos/mapzen/blog/git/refs/heads') \
or MHP == ('GET', 'api.github.com', '/repos/mapzen/blog'):
data = u'''{\r "message": "Not Found",\r "documentation_url": "https://developer.github.com/v3"\r}'''
return response(404, data.encode('utf8'), headers=response_headers)
if MHP == ('POST', 'github.com', '/login/oauth/access_token'):
form = dict(parse_qsl(request.body))
if form['code'] == 'let-me-in':
data = u'''{"access_token":"working-access-token", "scope":"user,repo", "token_type":"bearer"}'''
return response(200, data.encode('utf8'), headers=response_headers)
if MHP == ('GET', 'api.github.com', '/user'):
if request.headers['Authorization'] == 'Bearer working-access-token':
data = u'''{\r "login": "migurski",\r "id": 58730,\r "avatar_url": "https://avatars.githubusercontent.com/u/58730?v=3",\r "gravatar_id": "",\r "url": "https://api.github.com/users/migurski",\r "html_url": "https://github.com/migurski",\r "followers_url": "https://api.github.com/users/migurski/followers",\r "following_url": "https://api.github.com/users/migurski/following{/other_user}",\r "gists_url": "https://api.github.com/users/migurski/gists{/gist_id}",\r "starred_url": "https://api.github.com/users/migurski/starred{/owner}{/repo}",\r "subscriptions_url": "https://api.github.com/users/migurski/subscriptions",\r "organizations_url": "https://api.github.com/users/migurski/orgs",\r "repos_url": "https://api.github.com/users/migurski/repos",\r "events_url": "https://api.github.com/users/migurski/events{/privacy}",\r "received_events_url": "https://api.github.com/users/migurski/received_events",\r "type": "User",\r "site_admin": false,\r "name": null,\r "company": null,\r "blog": null,\r "location": null,\r "email": "mike-github@teczno.com",\r "hireable": null,\r "bio": null,\r "public_repos": 91,\r "public_gists": 45,\r "followers": 439,\r "following": 94,\r "created_at": "2009-02-27T23:44:32Z",\r "updated_at": "2015-12-26T20:09:55Z",\r "private_gists": 23,\r "total_private_repos": 1,\r "owned_private_repos": 0,\r "disk_usage": 249156,\r "collaborators": 0,\r "plan": {\r "name": "free",\r "space": 976562499,\r "collaborators": 0,\r "private_repos": 0\r }\r}'''
return response(200, data.encode('utf8'), headers=response_headers)
raise Exception(request.method, url, request.headers, request.body)
with HTTMock(response_content2):
auth2 = self.client.get('/oauth/callback?code=let-me-in&state={state}'.format(**query))
redirect2 = urlparse(auth2.headers.get('Location'))
self.assertEqual(auth2.status_code, 302)
self.assertEqual(redirect2.hostname, 'localhost')
self.assertEqual(redirect2.path, '/mapzen/blog/master/')
logout3 = self.client.post('/logout')
for path in ['/mapzen/blog/master/', '/mapzen/blog/master', '/mapzen/blog/', '/mapzen/blog']:
blog4 = self.client.get(path)
redirect4 = urlparse(blog4.headers.get('X-Redirect', ''))
self.assertEqual(blog4.status_code, blog1.status_code, 'Status {} instead of {} for path {}'.format(blog4.status_code, blog1.status_code, path))
self.assertEqual(redirect4.hostname, redirect1.hostname, 'Hostname {} instead of {} for path {}'.format(redirect4.hostname, redirect1.hostname, path))
self.assertEqual(redirect4.path, redirect1.path, 'Path {} instead of {} for path {}'.format(redirect4.path, redirect1.path, path))
def test_branch_listing(self):
'''
'''
with HTTMock(self.response_content):
blog = self.client.get('/mapzen/blog')
self.assertIn(blog.status_code, (301, 302))
redirect = urlparse(blog.headers['Location'])
self.assertEqual(redirect.path, '/mapzen/blog/')
branches = self.client.get(redirect.path)
self.assertEqual(branches.status_code, 200)
self.assertIn('"migurski/update-ui-engineer-job"', branches.data)
self.assertIn('days ago', branches.data)
def test_redirect_addslash(self):
'''
'''
with HTTMock(self.response_content):
root1 = self.client.get('/mapzen/blog/master')
self.assertIn(root1.status_code, (301, 302))
redirect1 = urlparse(root1.headers['Location'])
self.assertEqual(redirect1.path, '/mapzen/blog/master/')
root2 = self.client.get('/mapzen/blog/john/test')
self.assertIn(root2.status_code, (301, 302))
redirect2 = urlparse(root2.headers['Location'])
self.assertEqual(redirect2.path, '/mapzen/blog/john/test/')
root3 = self.client.get('/mapzen/blog/master?{}'.format(self.okhand))
self.assertEqual(root3.status_code, 200)
# # This should actually work, but handle_authentication() changed.
# root4 = self.client.get('/mapzen/blog/john/test?{}'.format(self.okhand))
# self.assertEqual(root4.status_code, 200)
def test_redirect_site_root(self):
'''
'''
with HTTMock(self.response_content):
root1 = self.client.get('/', headers={'Referer': 'http://localhost/mapzen/blog/master/page'})
self.assertIn(root1.status_code, (301, 302))
redirect1 = urlparse(root1.headers['Location'])
self.assertEqual(redirect1.path, '/mapzen/blog/master/')
root2 = self.client.get('/', headers={'Referer': 'http://localhost/mapzen/blog/john/test/page'})
self.assertIn(root2.status_code, (301, 302))
redirect2 = urlparse(root2.headers['Location'])
self.assertEqual(redirect2.path, '/mapzen/blog/john/test/')
root3 = self.client.get('/?{}'.format(self.okhand), headers={'Referer': 'http://localhost/mapzen/blog/master/page'})
self.assertEqual(root3.status_code, 200)
root4 = self.client.get('/?{}'.format(self.okhand), headers={'Referer': 'http://localhost/mapzen/blog/john/test/page'})
self.assertEqual(root4.status_code, 200)
root5 = self.client.get('/', headers={'X-Forwarded-Proto': 'https', 'Referer': 'https://localhost/mapzen/blog/master/page'})
self.assertIn(root5.status_code, (301, 302))
redirect5 = urlparse(root5.headers['Location'])
self.assertEqual(redirect5.scheme, 'https')
self.assertEqual(redirect5.path, '/mapzen/blog/master/')
def test_redirect_site_page(self):
'''
'''
with HTTMock(self.response_content):
root1 = self.client.get('/projects?q=Hi', headers={'Referer': 'http://localhost/mapzen/blog/master/page'})
self.assertIn(root1.status_code, (301, 302))
redirect1 = urlparse(root1.headers['Location'])
self.assertEqual(redirect1.path, '/mapzen/blog/master/projects')
self.assertEqual(redirect1.query, 'q=Hi')
root2 = self.client.get('/projects?q=Hi', headers={'Referer': 'http://localhost/mapzen/blog/john/test/page'})
self.assertIn(root2.status_code, (301, 302))
redirect2 = urlparse(root2.headers['Location'])
self.assertEqual(redirect2.path, '/mapzen/blog/john/test/projects')
self.assertEqual(redirect2.query, 'q=Hi')
root3 = self.client.get('/projects?{}'.format(self.okhand), headers={'Referer': 'http://localhost/mapzen/blog/master/page'})
self.assertEqual(root3.status_code, 200)
root4 = self.client.get('/projects?{}'.format(self.okhand), headers={'Referer': 'http://localhost/mapzen/blog/john/test/page'})
self.assertEqual(root4.status_code, 200)
root5 = self.client.get('/projects/tangram?q=Hi', headers={'Referer': 'http://localhost/mapzen/blog/master/page'})
self.assertIn(root5.status_code, (301, 302))
redirect5 = urlparse(root5.headers['Location'])
self.assertEqual(redirect5.path, '/mapzen/blog/master/projects/tangram')
self.assertEqual(redirect5.query, 'q=Hi')
root6 = self.client.get('/projects/tangram/tron?q=Hi', headers={'Referer': 'http://localhost/mapzen/blog/master/page'})
self.assertIn(root6.status_code, (301, 302))
redirect6 = urlparse(root6.headers['Location'])
self.assertEqual(redirect6.path, '/mapzen/blog/master/projects/tangram/tron')
self.assertEqual(redirect6.query, 'q=Hi')
root7 = self.client.get('/projects/tangram/tron/etc?q=Hi', headers={'Referer': 'http://localhost/mapzen/blog/master/page'})
self.assertIn(root7.status_code, (301, 302))
redirect7 = urlparse(root7.headers['Location'])
self.assertEqual(redirect7.path, '/mapzen/blog/master/projects/tangram/tron/etc')
self.assertEqual(redirect7.query, 'q=Hi')
def test_site_index(self):
'''
'''
with HTTMock(self.response_content):
index = self.client.get('/mapzen/blog/master/')
self.assertEqual(index.status_code, 200)
self.assertIn('Mapzen', index.data)
def test_circle_pending(self):
'''
'''
with HTTMock(self.response_content):
index = self.client.get('/mapzen/blog/dde72b5/')
self.assertEqual(index.status_code, 200)
self.assertIn('Hold Your Horses', index.data)
self.assertIn('https://circleci.com/gh/mapzen/blog/1987', index.data)
self.assertIn('<meta http-equiv="refresh" content="15; url=/mapzen/blog/dde72b5/">', index.data)
page = self.client.get('/mapzen/blog/dde72b5/page')
self.assertEqual(page.status_code, 200)
self.assertIn('<meta http-equiv="refresh" content="15; url=/mapzen/blog/dde72b5/page">', page.data)
def test_circle_failed(self):
'''
'''
with HTTMock(self.response_content):
index = self.client.get('/mapzen/styleguide/91e4950/')
self.assertEqual(index.status_code, 200)
self.assertIn('Preview Failed', index.data)
self.assertIn('https://circleci.com/gh/mapzen/styleguide/86', index.data)
def test_circle_yaml_reader_error(self):
'''
'''
with HTTMock(self.response_content):
index = self.client.get('/mapzen/metro-extracts/1cc0a0db8/')
self.assertEqual(index.status_code, 400)
self.assertIn('Precog Error', index.data)
self.assertIn('Problem reading configuration from circle.yml', index.data)
def test_webhook_bad_signature(self):
''' Send a request to /hook with an invalid signature.
'''
data = '''{ }'''
with HTTMock(self.response_content):
posted = self.client.post('/hook', data=data, headers=signed(data, 'junk'))
self.assertEqual(posted.status_code, 401)
def test_webhook_missing_signature(self):
''' Send a request to /hook with no signature.
'''
data = '''{ }'''
with HTTMock(self.response_content):
posted = self.client.post('/hook', data=data)
self.assertEqual(posted.status_code, 401)
def test_webhook_commit(self):
'''
'''
data = '''{\r "after": "e91fbc420f08890960f50f863626e1062f922522", \r "base_ref": null, \r "before": "c52204fd40f17f9da243df09e6d1107d48768afd", \r "commits": [\r {\r "added": [\r "sources/us-ca-alameda_county.json"\r ], \r "author": {\r "email": "mike@teczno.com", \r "name": "Michal Migurski", \r "username": "migurski"\r }, \r "committer": {\r "email": "mike@teczno.com", \r "name": "Michal Migurski", \r "username": "migurski"\r }, \r "distinct": true, \r "id": "e91fbc420f08890960f50f863626e1062f922522", \r "message": "Added first source", \r "modified": [], \r "removed": [], \r "timestamp": "2015-04-25T17:16:12-07:00", \r "url": "https://github.com/openaddresses/hooked-on-sources/commit/e91fbc420f08890960f50f863626e1062f922522"\r }\r ], \r "compare": "https://github.com/openaddresses/hooked-on-sources/compare/c52204fd40f1...e91fbc420f08", \r "created": false, \r "deleted": false, \r "forced": false, \r "head_commit": {\r "added": [\r "sources/us-ca-alameda_county.json"\r ], \r "author": {\r "email": "mike@teczno.com", \r "name": "Michal Migurski", \r "username": "migurski"\r }, \r "committer": {\r "email": "mike@teczno.com", \r "name": "Michal Migurski", \r "username": "migurski"\r }, \r "distinct": true, \r "id": "e91fbc420f08890960f50f863626e1062f922522", \r "message": "Added first source", \r "modified": [], \r "removed": [], \r "timestamp": "2015-04-25T17:16:12-07:00", \r "url": "https://github.com/openaddresses/hooked-on-sources/commit/e91fbc420f08890960f50f863626e1062f922522"\r }, \r "organization": {\r "avatar_url": "https://avatars.githubusercontent.com/u/6895392?v=3", \r "description": "The free and open global address collection ", \r "events_url": "https://api.github.com/orgs/openaddresses/events", \r "id": 6895392, \r "login": "openaddresses", \r "members_url": "https://api.github.com/orgs/openaddresses/members{/member}", \r "public_members_url": "https://api.github.com/orgs/openaddresses/public_members{/member}", \r "repos_url": "https://api.github.com/orgs/openaddresses/repos", \r "url": "https://api.github.com/orgs/openaddresses"\r }, \r "pusher": {\r "email": "mike-github@teczno.com", \r "name": "migurski"\r }, \r "ref": "refs/heads/master", \r "repository": {\r "archive_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/{archive_format}{/ref}", \r "assignees_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/assignees{/user}", \r "blobs_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/git/blobs{/sha}", \r "branches_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/branches{/branch}", \r "clone_url": "https://github.com/openaddresses/hooked-on-sources.git", \r "collaborators_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/collaborators{/collaborator}", \r "comments_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/comments{/number}", \r "commits_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/commits{/sha}", \r "compare_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/compare/{base}...{head}", \r "contents_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/contents/{+path}", \r "contributors_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/contributors", \r "created_at": 1430006167, \r "default_branch": "master", \r "description": "Temporary repository for testing Github webhook features", \r "downloads_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/downloads", \r "events_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/events", \r "fork": false, \r "forks": 0, \r "forks_count": 0, \r "forks_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/forks", \r "full_name": "openaddresses/hooked-on-sources", \r "git_commits_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/git/commits{/sha}", \r "git_refs_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/git/refs{/sha}", \r "git_tags_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/git/tags{/sha}", \r "git_url": "git://github.com/openaddresses/hooked-on-sources.git", \r "has_downloads": true, \r "has_issues": true, \r "has_pages": false, \r "has_wiki": true, \r "homepage": null, \r "hooks_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/hooks", \r "html_url": "https://github.com/openaddresses/hooked-on-sources", \r "id": 34590951, \r "issue_comment_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/issues/comments{/number}", \r "issue_events_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/issues/events{/number}", \r "issues_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/issues{/number}", \r "keys_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/keys{/key_id}", \r "labels_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/labels{/name}", \r "language": null, \r "languages_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/languages", \r "master_branch": "master", \r "merges_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/merges", \r "milestones_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/milestones{/number}", \r "mirror_url": null, \r "name": "hooked-on-sources", \r "notifications_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/notifications{?since,all,participating}", \r "open_issues": 0, \r "open_issues_count": 0, \r "organization": "openaddresses", \r "owner": {\r "email": "openaddresses@gmail.com", \r "name": "openaddresses"\r }, \r "private": false, \r "pulls_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/pulls{/number}", \r "pushed_at": 1430007676, \r "releases_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/releases{/id}", \r "size": 0, \r "ssh_url": "git@github.com:openaddresses/hooked-on-sources.git", \r "stargazers": 0, \r "stargazers_count": 0, \r "stargazers_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/stargazers", \r "statuses_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/statuses/{sha}", \r "subscribers_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/subscribers", \r "subscription_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/subscription", \r "svn_url": "https://github.com/openaddresses/hooked-on-sources", \r "tags_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/tags", \r "teams_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/teams", \r "trees_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/git/trees{/sha}", \r "updated_at": "2015-04-25T23:56:07Z", \r "url": "https://github.com/openaddresses/hooked-on-sources", \r "watchers": 0, \r "watchers_count": 0\r }, \r "sender": {\r "avatar_url": "https://avatars.githubusercontent.com/u/58730?v=3", \r "events_url": "https://api.github.com/users/migurski/events{/privacy}", \r "followers_url": "https://api.github.com/users/migurski/followers", \r "following_url": "https://api.github.com/users/migurski/following{/other_user}", \r "gists_url": "https://api.github.com/users/migurski/gists{/gist_id}", \r "gravatar_id": "", \r "html_url": "https://github.com/migurski", \r "id": 58730, \r "login": "migurski", \r "organizations_url": "https://api.github.com/users/migurski/orgs", \r "received_events_url": "https://api.github.com/users/migurski/received_events", \r "repos_url": "https://api.github.com/users/migurski/repos", \r "site_admin": false, \r "starred_url": "https://api.github.com/users/migurski/starred{/owner}{/repo}", \r "subscriptions_url": "https://api.github.com/users/migurski/subscriptions", \r "type": "User", \r "url": "https://api.github.com/users/migurski"\r }\r }'''
with HTTMock(self.response_content):
posted = self.client.post('/hook', data=data, headers=signed(data, 'hos-secret'))
self.assertEqual(posted.status_code, 200)
self.assertEqual(self.last_status_state, 'success')
def test_webhook_delete_branch(self):
''' Delete a branch.
'''
data = '''{\r "ref": "refs/heads/whitespace",\r "before": "237a09603386664658fba6bcc843044a2ede79fb",\r "after": "0000000000000000000000000000000000000000",\r "created": false,\r "deleted": true,\r "forced": true,\r "base_ref": null,\r "compare": "https://github.com/openaddresses/openaddresses/compare/237a09603386...000000000000",\r "commits": [\r\r ],\r "head_commit": null,\r "repository": {\r "id": 16594532,\r "name": "openaddresses",\r "full_name": "openaddresses/openaddresses",\r "owner": {\r "name": "openaddresses",\r "email": "openaddresses@gmail.com"\r },\r "private": false,\r "html_url": "https://github.com/openaddresses/openaddresses",\r "description": "A repository of address data.",\r "fork": false,\r "url": "https://github.com/openaddresses/openaddresses",\r "forks_url": "https://api.github.com/repos/openaddresses/openaddresses/forks",\r "keys_url": "https://api.github.com/repos/openaddresses/openaddresses/keys{/key_id}",\r "collaborators_url": "https://api.github.com/repos/openaddresses/openaddresses/collaborators{/collaborator}",\r "teams_url": "https://api.github.com/repos/openaddresses/openaddresses/teams",\r "hooks_url": "https://api.github.com/repos/openaddresses/openaddresses/hooks",\r "issue_events_url": "https://api.github.com/repos/openaddresses/openaddresses/issues/events{/number}",\r "events_url": "https://api.github.com/repos/openaddresses/openaddresses/events",\r "assignees_url": "https://api.github.com/repos/openaddresses/openaddresses/assignees{/user}",\r "branches_url": "https://api.github.com/repos/openaddresses/openaddresses/branches{/branch}",\r "tags_url": "https://api.github.com/repos/openaddresses/openaddresses/tags",\r "blobs_url": "https://api.github.com/repos/openaddresses/openaddresses/git/blobs{/sha}",\r "git_tags_url": "https://api.github.com/repos/openaddresses/openaddresses/git/tags{/sha}",\r "git_refs_url": "https://api.github.com/repos/openaddresses/openaddresses/git/refs{/sha}",\r "trees_url": "https://api.github.com/repos/openaddresses/openaddresses/git/trees{/sha}",\r "statuses_url": "https://api.github.com/repos/openaddresses/openaddresses/statuses/{sha}",\r "languages_url": "https://api.github.com/repos/openaddresses/openaddresses/languages",\r "stargazers_url": "https://api.github.com/repos/openaddresses/openaddresses/stargazers",\r "contributors_url": "https://api.github.com/repos/openaddresses/openaddresses/contributors",\r "subscribers_url": "https://api.github.com/repos/openaddresses/openaddresses/subscribers",\r "subscription_url": "https://api.github.com/repos/openaddresses/openaddresses/subscription",\r "commits_url": "https://api.github.com/repos/openaddresses/openaddresses/commits{/sha}",\r "git_commits_url": "https://api.github.com/repos/openaddresses/openaddresses/git/commits{/sha}",\r "comments_url": "https://api.github.com/repos/openaddresses/openaddresses/comments{/number}",\r "issue_comment_url": "https://api.github.com/repos/openaddresses/openaddresses/issues/comments{/number}",\r "contents_url": "https://api.github.com/repos/openaddresses/openaddresses/contents/{+path}",\r "compare_url": "https://api.github.com/repos/openaddresses/openaddresses/compare/{base}...{head}",\r "merges_url": "https://api.github.com/repos/openaddresses/openaddresses/merges",\r "archive_url": "https://api.github.com/repos/openaddresses/openaddresses/{archive_format}{/ref}",\r "downloads_url": "https://api.github.com/repos/openaddresses/openaddresses/downloads",\r "issues_url": "https://api.github.com/repos/openaddresses/openaddresses/issues{/number}",\r "pulls_url": "https://api.github.com/repos/openaddresses/openaddresses/pulls{/number}",\r "milestones_url": "https://api.github.com/repos/openaddresses/openaddresses/milestones{/number}",\r "notifications_url": "https://api.github.com/repos/openaddresses/openaddresses/notifications{?since,all,participating}",\r "labels_url": "https://api.github.com/repos/openaddresses/openaddresses/labels{/name}",\r "releases_url": "https://api.github.com/repos/openaddresses/openaddresses/releases{/id}",\r "created_at": 1391722160,\r "updated_at": "2015-06-11T11:42:47Z",\r "pushed_at": 1434506471,\r "git_url": "git://github.com/openaddresses/openaddresses.git",\r "ssh_url": "git@github.com:openaddresses/openaddresses.git",\r "clone_url": "https://github.com/openaddresses/openaddresses.git",\r "svn_url": "https://github.com/openaddresses/openaddresses",\r "homepage": "http://openaddresses.io/",\r "size": 9790,\r "stargazers_count": 275,\r "watchers_count": 275,\r "language": "JavaScript",\r "has_issues": true,\r "has_downloads": true,\r "has_wiki": true,\r "has_pages": false,\r "forks_count": 313,\r "mirror_url": null,\r "open_issues_count": 214,\r "forks": 313,\r "open_issues": 214,\r "watchers": 275,\r "default_branch": "master",\r "stargazers": 275,\r "master_branch": "master",\r "organization": "openaddresses"\r },\r "pusher": {\r "name": "ingalls",\r "email": "nick@mapbox.com"\r },\r "organization": {\r "login": "openaddresses",\r "id": 6895392,\r "url": "https://api.github.com/orgs/openaddresses",\r "repos_url": "https://api.github.com/orgs/openaddresses/repos",\r "events_url": "https://api.github.com/orgs/openaddresses/events",\r "members_url": "https://api.github.com/orgs/openaddresses/members{/member}",\r "public_members_url": "https://api.github.com/orgs/openaddresses/public_members{/member}",\r "avatar_url": "https://avatars.githubusercontent.com/u/6895392?v=3",\r "description": "The free and open global address collection "\r },\r "sender": {\r "login": "ingalls",\r "id": 1297009,\r "avatar_url": "https://avatars.githubusercontent.com/u/1297009?v=3",\r "gravatar_id": "",\r "url": "https://api.github.com/users/ingalls",\r "html_url": "https://github.com/ingalls",\r "followers_url": "https://api.github.com/users/ingalls/followers",\r "following_url": "https://api.github.com/users/ingalls/following{/other_user}",\r "gists_url": "https://api.github.com/users/ingalls/gists{/gist_id}",\r "starred_url": "https://api.github.com/users/ingalls/starred{/owner}{/repo}",\r "subscriptions_url": "https://api.github.com/users/ingalls/subscriptions",\r "organizations_url": "https://api.github.com/users/ingalls/orgs",\r "repos_url": "https://api.github.com/users/ingalls/repos",\r "events_url": "https://api.github.com/users/ingalls/events{/privacy}",\r "received_events_url": "https://api.github.com/users/ingalls/received_events",\r "type": "User",\r "site_admin": false\r }\r}'''
with HTTMock(self.response_content):
posted = self.client.post('/hook', data=data, headers=signed(data, 'hos-secret'))
self.assertEqual(posted.status_code, 401)
def test_webhook_pull_request(self):
''' Pull request from an outside contributor.
'''
with HTTMock(self.response_content):
self.last_status_state = None
data1 = u'''{\r "action": "opened",\r "number": 4,\r "pull_request": {\r "url": "https://api.github.com/repos/openaddresses/hooked-on-sources/pulls/4",\r "id": 38716878,\r "html_url": "https://github.com/openaddresses/hooked-on-sources/pull/4",\r "diff_url": "https://github.com/openaddresses/hooked-on-sources/pull/4.diff",\r "patch_url": "https://github.com/openaddresses/hooked-on-sources/pull/4.patch",\r "issue_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/issues/4",\r "number": 4,\r "state": "open",\r "locked": false,\r "title": "Added La Réunion",\r "user": {\r "login": "migurski",\r "id": 58730,\r "avatar_url": "https://avatars.githubusercontent.com/u/58730?v=3",\r "gravatar_id": "",\r "url": "https://api.github.com/users/migurski",\r "html_url": "https://github.com/migurski",\r "followers_url": "https://api.github.com/users/migurski/followers",\r "following_url": "https://api.github.com/users/migurski/following{/other_user}",\r "gists_url": "https://api.github.com/users/migurski/gists{/gist_id}",\r "starred_url": "https://api.github.com/users/migurski/starred{/owner}{/repo}",\r "subscriptions_url": "https://api.github.com/users/migurski/subscriptions",\r "organizations_url": "https://api.github.com/users/migurski/orgs",\r "repos_url": "https://api.github.com/users/migurski/repos",\r "events_url": "https://api.github.com/users/migurski/events{/privacy}",\r "received_events_url": "https://api.github.com/users/migurski/received_events",\r "type": "User",\r "site_admin": false\r },\r "body": "",\r "created_at": "2015-06-27T22:51:14Z",\r "updated_at": "2015-06-27T22:51:14Z",\r "closed_at": null,\r "merged_at": null,\r "merge_commit_sha": null,\r "assignee": null,\r "milestone": null,\r "commits_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/pulls/4/commits",\r "review_comments_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/pulls/4/comments",\r "review_comment_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/pulls/comments{/number}",\r "comments_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/issues/4/comments",\r "statuses_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/statuses/aed74b0784f696c3cf10e8e260865ae18ffd3aa8",\r "head": {\r "label": "migurski:master",\r "ref": "master",\r "sha": "aed74b0784f696c3cf10e8e260865ae18ffd3aa8",\r "user": {\r "login": "migurski",\r "id": 58730,\r "avatar_url": "https://avatars.githubusercontent.com/u/58730?v=3",\r "gravatar_id": "",\r "url": "https://api.github.com/users/migurski",\r "html_url": "https://github.com/migurski",\r "followers_url": "https://api.github.com/users/migurski/followers",\r "following_url": "https://api.github.com/users/migurski/following{/other_user}",\r "gists_url": "https://api.github.com/users/migurski/gists{/gist_id}",\r "starred_url": "https://api.github.com/users/migurski/starred{/owner}{/repo}",\r "subscriptions_url": "https://api.github.com/users/migurski/subscriptions",\r "organizations_url": "https://api.github.com/users/migurski/orgs",\r "repos_url": "https://api.github.com/users/migurski/repos",\r "events_url": "https://api.github.com/users/migurski/events{/privacy}",\r "received_events_url": "https://api.github.com/users/migurski/received_events",\r "type": "User",\r "site_admin": false\r },\r "repo": {\r "id": 38178103,\r "name": "hooked-on-sources",\r "full_name": "migurski/hooked-on-sources",\r "owner": {\r "login": "migurski",\r "id": 58730,\r "avatar_url": "https://avatars.githubusercontent.com/u/58730?v=3",\r "gravatar_id": "",\r "url": "https://api.github.com/users/migurski",\r "html_url": "https://github.com/migurski",\r "followers_url": "https://api.github.com/users/migurski/followers",\r "following_url": "https://api.github.com/users/migurski/following{/other_user}",\r "gists_url": "https://api.github.com/users/migurski/gists{/gist_id}",\r "starred_url": "https://api.github.com/users/migurski/starred{/owner}{/repo}",\r "subscriptions_url": "https://api.github.com/users/migurski/subscriptions",\r "organizations_url": "https://api.github.com/users/migurski/orgs",\r "repos_url": "https://api.github.com/users/migurski/repos",\r "events_url": "https://api.github.com/users/migurski/events{/privacy}",\r "received_events_url": "https://api.github.com/users/migurski/received_events",\r "type": "User",\r "site_admin": false\r },\r "private": false,\r "html_url": "https://github.com/migurski/hooked-on-sources",\r "description": "Temporary repository for testing Github webhook features",\r "fork": true,\r "url": "https://api.github.com/repos/migurski/hooked-on-sources",\r "forks_url": "https://api.github.com/repos/migurski/hooked-on-sources/forks",\r "keys_url": "https://api.github.com/repos/migurski/hooked-on-sources/keys{/key_id}",\r "collaborators_url": "https://api.github.com/repos/migurski/hooked-on-sources/collaborators{/collaborator}",\r "teams_url": "https://api.github.com/repos/migurski/hooked-on-sources/teams",\r "hooks_url": "https://api.github.com/repos/migurski/hooked-on-sources/hooks",\r "issue_events_url": "https://api.github.com/repos/migurski/hooked-on-sources/issues/events{/number}",\r "events_url": "https://api.github.com/repos/migurski/hooked-on-sources/events",\r "assignees_url": "https://api.github.com/repos/migurski/hooked-on-sources/assignees{/user}",\r "branches_url": "https://api.github.com/repos/migurski/hooked-on-sources/branches{/branch}",\r "tags_url": "https://api.github.com/repos/migurski/hooked-on-sources/tags",\r "blobs_url": "https://api.github.com/repos/migurski/hooked-on-sources/git/blobs{/sha}",\r "git_tags_url": "https://api.github.com/repos/migurski/hooked-on-sources/git/tags{/sha}",\r "git_refs_url": "https://api.github.com/repos/migurski/hooked-on-sources/git/refs{/sha}",\r "trees_url": "https://api.github.com/repos/migurski/hooked-on-sources/git/trees{/sha}",\r "statuses_url": "https://api.github.com/repos/migurski/hooked-on-sources/statuses/{sha}",\r "languages_url": "https://api.github.com/repos/migurski/hooked-on-sources/languages",\r "stargazers_url": "https://api.github.com/repos/migurski/hooked-on-sources/stargazers",\r "contributors_url": "https://api.github.com/repos/migurski/hooked-on-sources/contributors",\r "subscribers_url": "https://api.github.com/repos/migurski/hooked-on-sources/subscribers",\r "subscription_url": "https://api.github.com/repos/migurski/hooked-on-sources/subscription",\r "commits_url": "https://api.github.com/repos/migurski/hooked-on-sources/commits{/sha}",\r "git_commits_url": "https://api.github.com/repos/migurski/hooked-on-sources/git/commits{/sha}",\r "comments_url": "https://api.github.com/repos/migurski/hooked-on-sources/comments{/number}",\r "issue_comment_url": "https://api.github.com/repos/migurski/hooked-on-sources/issues/comments{/number}",\r "contents_url": "https://api.github.com/repos/migurski/hooked-on-sources/contents/{+path}",\r "compare_url": "https://api.github.com/repos/migurski/hooked-on-sources/compare/{base}...{head}",\r "merges_url": "https://api.github.com/repos/migurski/hooked-on-sources/merges",\r "archive_url": "https://api.github.com/repos/migurski/hooked-on-sources/{archive_format}{/ref}",\r "downloads_url": "https://api.github.com/repos/migurski/hooked-on-sources/downloads",\r "issues_url": "https://api.github.com/repos/migurski/hooked-on-sources/issues{/number}",\r "pulls_url": "https://api.github.com/repos/migurski/hooked-on-sources/pulls{/number}",\r "milestones_url": "https://api.github.com/repos/migurski/hooked-on-sources/milestones{/number}",\r "notifications_url": "https://api.github.com/repos/migurski/hooked-on-sources/notifications{?since,all,participating}",\r "labels_url": "https://api.github.com/repos/migurski/hooked-on-sources/labels{/name}",\r "releases_url": "https://api.github.com/repos/migurski/hooked-on-sources/releases{/id}",\r "created_at": "2015-06-27T22:47:37Z",\r "updated_at": "2015-04-25T23:56:07Z",\r "pushed_at": "2015-06-27T22:48:55Z",\r "git_url": "git://github.com/migurski/hooked-on-sources.git",\r "ssh_url": "git@github.com:migurski/hooked-on-sources.git",\r "clone_url": "https://github.com/migurski/hooked-on-sources.git",\r "svn_url": "https://github.com/migurski/hooked-on-sources",\r "homepage": null,\r "size": 192,\r "stargazers_count": 0,\r "watchers_count": 0,\r "language": null,\r "has_issues": false,\r "has_downloads": true,\r "has_wiki": true,\r "has_pages": false,\r "forks_count": 0,\r "mirror_url": null,\r "open_issues_count": 0,\r "forks": 0,\r "open_issues": 0,\r "watchers": 0,\r "default_branch": "master"\r }\r },\r "base": {\r "label": "openaddresses:master",\r "ref": "master",\r "sha": "c3c7de37f96d38534dc6297a2483c218994241b6",\r "user": {\r "login": "openaddresses",\r "id": 6895392,\r "avatar_url": "https://avatars.githubusercontent.com/u/6895392?v=3",\r "gravatar_id": "",\r "url": "https://api.github.com/users/openaddresses",\r "html_url": "https://github.com/openaddresses",\r "followers_url": "https://api.github.com/users/openaddresses/followers",\r "following_url": "https://api.github.com/users/openaddresses/following{/other_user}",\r "gists_url": "https://api.github.com/users/openaddresses/gists{/gist_id}",\r "starred_url": "https://api.github.com/users/openaddresses/starred{/owner}{/repo}",\r "subscriptions_url": "https://api.github.com/users/openaddresses/subscriptions",\r "organizations_url": "https://api.github.com/users/openaddresses/orgs",\r "repos_url": "https://api.github.com/users/openaddresses/repos",\r "events_url": "https://api.github.com/users/openaddresses/events{/privacy}",\r "received_events_url": "https://api.github.com/users/openaddresses/received_events",\r "type": "Organization",\r "site_admin": false\r },\r "repo": {\r "id": 34590951,\r "name": "hooked-on-sources",\r "full_name": "openaddresses/hooked-on-sources",\r "owner": {\r "login": "openaddresses",\r "id": 6895392,\r "avatar_url": "https://avatars.githubusercontent.com/u/6895392?v=3",\r "gravatar_id": "",\r "url": "https://api.github.com/users/openaddresses",\r "html_url": "https://github.com/openaddresses",\r "followers_url": "https://api.github.com/users/openaddresses/followers",\r "following_url": "https://api.github.com/users/openaddresses/following{/other_user}",\r "gists_url": "https://api.github.com/users/openaddresses/gists{/gist_id}",\r "starred_url": "https://api.github.com/users/openaddresses/starred{/owner}{/repo}",\r "subscriptions_url": "https://api.github.com/users/openaddresses/subscriptions",\r "organizations_url": "https://api.github.com/users/openaddresses/orgs",\r "repos_url": "https://api.github.com/users/openaddresses/repos",\r "events_url": "https://api.github.com/users/openaddresses/events{/privacy}",\r "received_events_url": "https://api.github.com/users/openaddresses/received_events",\r "type": "Organization",\r "site_admin": false\r },\r "private": false,\r "html_url": "https://github.com/openaddresses/hooked-on-sources",\r "description": "Temporary repository for testing Github webhook features",\r "fork": false,\r "url": "https://api.github.com/repos/openaddresses/hooked-on-sources",\r "forks_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/forks",\r "keys_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/keys{/key_id}",\r "collaborators_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/collaborators{/collaborator}",\r "teams_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/teams",\r "hooks_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/hooks",\r "issue_events_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/issues/events{/number}",\r "events_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/events",\r "assignees_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/assignees{/user}",\r "branches_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/branches{/branch}",\r "tags_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/tags",\r "blobs_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/git/blobs{/sha}",\r "git_tags_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/git/tags{/sha}",\r "git_refs_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/git/refs{/sha}",\r "trees_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/git/trees{/sha}",\r "statuses_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/statuses/{sha}",\r "languages_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/languages",\r "stargazers_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/stargazers",\r "contributors_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/contributors",\r "subscribers_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/subscribers",\r "subscription_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/subscription",\r "commits_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/commits{/sha}",\r "git_commits_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/git/commits{/sha}",\r "comments_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/comments{/number}",\r "issue_comment_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/issues/comments{/number}",\r "contents_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/contents/{+path}",\r "compare_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/compare/{base}...{head}",\r "merges_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/merges",\r "archive_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/{archive_format}{/ref}",\r "downloads_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/downloads",\r "issues_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/issues{/number}",\r "pulls_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/pulls{/number}",\r "milestones_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/milestones{/number}",\r "notifications_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/notifications{?since,all,participating}",\r "labels_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/labels{/name}",\r "releases_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/releases{/id}",\r "created_at": "2015-04-25T23:56:07Z",\r "updated_at": "2015-04-25T23:56:07Z",\r "pushed_at": "2015-06-27T22:49:30Z",\r "git_url": "git://github.com/openaddresses/hooked-on-sources.git",\r "ssh_url": "git@github.com:openaddresses/hooked-on-sources.git",\r "clone_url": "https://github.com/openaddresses/hooked-on-sources.git",\r "svn_url": "https://github.com/openaddresses/hooked-on-sources",\r "homepage": null,\r "size": 192,\r "stargazers_count": 0,\r "watchers_count": 0,\r "language": null,\r "has_issues": true,\r "has_downloads": true,\r "has_wiki": true,\r "has_pages": false,\r "forks_count": 1,\r "mirror_url": null,\r "open_issues_count": 3,\r "forks": 1,\r "open_issues": 3,\r "watchers": 0,\r "default_branch": "master"\r }\r },\r "_links": {\r "self": {\r "href": "https://api.github.com/repos/openaddresses/hooked-on-sources/pulls/4"\r },\r "html": {\r "href": "https://github.com/openaddresses/hooked-on-sources/pull/4"\r },\r "issue": {\r "href": "https://api.github.com/repos/openaddresses/hooked-on-sources/issues/4"\r },\r "comments": {\r "href": "https://api.github.com/repos/openaddresses/hooked-on-sources/issues/4/comments"\r },\r "review_comments": {\r "href": "https://api.github.com/repos/openaddresses/hooked-on-sources/pulls/4/comments"\r },\r "review_comment": {\r "href": "https://api.github.com/repos/openaddresses/hooked-on-sources/pulls/comments{/number}"\r },\r "commits": {\r "href": "https://api.github.com/repos/openaddresses/hooked-on-sources/pulls/4/commits"\r },\r "statuses": {\r "href": "https://api.github.com/repos/openaddresses/hooked-on-sources/statuses/aed74b0784f696c3cf10e8e260865ae18ffd3aa8"\r }\r },\r "merged": false,\r "mergeable": null,\r "mergeable_state": "unknown",\r "merged_by": null,\r "comments": 0,\r "review_comments": 0,\r "commits": 1,\r "additions": 22,\r "deletions": 0,\r "changed_files": 1\r },\r "repository": {\r "id": 34590951,\r "name": "hooked-on-sources",\r "full_name": "openaddresses/hooked-on-sources",\r "owner": {\r "login": "openaddresses",\r "id": 6895392,\r "avatar_url": "https://avatars.githubusercontent.com/u/6895392?v=3",\r "gravatar_id": "",\r "url": "https://api.github.com/users/openaddresses",\r "html_url": "https://github.com/openaddresses",\r "followers_url": "https://api.github.com/users/openaddresses/followers",\r "following_url": "https://api.github.com/users/openaddresses/following{/other_user}",\r "gists_url": "https://api.github.com/users/openaddresses/gists{/gist_id}",\r "starred_url": "https://api.github.com/users/openaddresses/starred{/owner}{/repo}",\r "subscriptions_url": "https://api.github.com/users/openaddresses/subscriptions",\r "organizations_url": "https://api.github.com/users/openaddresses/orgs",\r "repos_url": "https://api.github.com/users/openaddresses/repos",\r "events_url": "https://api.github.com/users/openaddresses/events{/privacy}",\r "received_events_url": "https://api.github.com/users/openaddresses/received_events",\r "type": "Organization",\r "site_admin": false\r },\r "private": false,\r "html_url": "https://github.com/openaddresses/hooked-on-sources",\r "description": "Temporary repository for testing Github webhook features",\r "fork": false,\r "url": "https://api.github.com/repos/openaddresses/hooked-on-sources",\r "forks_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/forks",\r "keys_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/keys{/key_id}",\r "collaborators_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/collaborators{/collaborator}",\r "teams_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/teams",\r "hooks_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/hooks",\r "issue_events_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/issues/events{/number}",\r "events_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/events",\r "assignees_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/assignees{/user}",\r "branches_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/branches{/branch}",\r "tags_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/tags",\r "blobs_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/git/blobs{/sha}",\r "git_tags_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/git/tags{/sha}",\r "git_refs_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/git/refs{/sha}",\r "trees_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/git/trees{/sha}",\r "statuses_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/statuses/{sha}",\r "languages_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/languages",\r "stargazers_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/stargazers",\r "contributors_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/contributors",\r "subscribers_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/subscribers",\r "subscription_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/subscription",\r "commits_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/commits{/sha}",\r "git_commits_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/git/commits{/sha}",\r "comments_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/comments{/number}",\r "issue_comment_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/issues/comments{/number}",\r "contents_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/contents/{+path}",\r "compare_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/compare/{base}...{head}",\r "merges_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/merges",\r "archive_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/{archive_format}{/ref}",\r "downloads_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/downloads",\r "issues_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/issues{/number}",\r "pulls_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/pulls{/number}",\r "milestones_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/milestones{/number}",\r "notifications_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/notifications{?since,all,participating}",\r "labels_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/labels{/name}",\r "releases_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/releases{/id}",\r "created_at": "2015-04-25T23:56:07Z",\r "updated_at": "2015-04-25T23:56:07Z",\r "pushed_at": "2015-06-27T22:49:30Z",\r "git_url": "git://github.com/openaddresses/hooked-on-sources.git",\r "ssh_url": "git@github.com:openaddresses/hooked-on-sources.git",\r "clone_url": "https://github.com/openaddresses/hooked-on-sources.git",\r "svn_url": "https://github.com/openaddresses/hooked-on-sources",\r "homepage": null,\r "size": 192,\r "stargazers_count": 0,\r "watchers_count": 0,\r "language": null,\r "has_issues": true,\r "has_downloads": true,\r "has_wiki": true,\r "has_pages": false,\r "forks_count": 1,\r "mirror_url": null,\r "open_issues_count": 3,\r "forks": 1,\r "open_issues": 3,\r "watchers": 0,\r "default_branch": "master"\r },\r "organization": {\r "login": "openaddresses",\r "id": 6895392,\r "url": "https://api.github.com/orgs/openaddresses",\r "repos_url": "https://api.github.com/orgs/openaddresses/repos",\r "events_url": "https://api.github.com/orgs/openaddresses/events",\r "members_url": "https://api.github.com/orgs/openaddresses/members{/member}",\r "public_members_url": "https://api.github.com/orgs/openaddresses/public_members{/member}",\r "avatar_url": "https://avatars.githubusercontent.com/u/6895392?v=3",\r "description": "The free and open global address collection "\r },\r "sender": {\r "login": "migurski",\r "id": 58730,\r "avatar_url": "https://avatars.githubusercontent.com/u/58730?v=3",\r "gravatar_id": "",\r "url": "https://api.github.com/users/migurski",\r "html_url": "https://github.com/migurski",\r "followers_url": "https://api.github.com/users/migurski/followers",\r "following_url": "https://api.github.com/users/migurski/following{/other_user}",\r "gists_url": "https://api.github.com/users/migurski/gists{/gist_id}",\r "starred_url": "https://api.github.com/users/migurski/starred{/owner}{/repo}",\r "subscriptions_url": "https://api.github.com/users/migurski/subscriptions",\r "organizations_url": "https://api.github.com/users/migurski/orgs",\r "repos_url": "https://api.github.com/users/migurski/repos",\r "events_url": "https://api.github.com/users/migurski/events{/privacy}",\r "received_events_url": "https://api.github.com/users/migurski/received_events",\r "type": "User",\r "site_admin": false\r }\r}'''
posted1 = self.client.post('/hook', data=data1, headers=signed(data1, 'hos-secret'))
self.assertEqual(self.last_status_state, 'success', 'Status should be success even for a newly-opened pull request')
self.last_status_state = None
data2 = u'''{\r "action": "synchronize",\r "number": 4,\r "pull_request": {\r "url": "https://api.github.com/repos/openaddresses/hooked-on-sources/pulls/4",\r "id": 38716878,\r "html_url": "https://github.com/openaddresses/hooked-on-sources/pull/4",\r "diff_url": "https://github.com/openaddresses/hooked-on-sources/pull/4.diff",\r "patch_url": "https://github.com/openaddresses/hooked-on-sources/pull/4.patch",\r "issue_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/issues/4",\r "number": 4,\r "state": "open",\r "locked": false,\r "title": "Added La Réunion",\r "user": {\r "login": "migurski",\r "id": 58730,\r "avatar_url": "https://avatars.githubusercontent.com/u/58730?v=3",\r "gravatar_id": "",\r "url": "https://api.github.com/users/migurski",\r "html_url": "https://github.com/migurski",\r "followers_url": "https://api.github.com/users/migurski/followers",\r "following_url": "https://api.github.com/users/migurski/following{/other_user}",\r "gists_url": 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"https://api.github.com/repos/openaddresses/hooked-on-sources/branches{/branch}",\r "tags_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/tags",\r "blobs_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/git/blobs{/sha}",\r "git_tags_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/git/tags{/sha}",\r "git_refs_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/git/refs{/sha}",\r "trees_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/git/trees{/sha}",\r "statuses_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/statuses/{sha}",\r "languages_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/languages",\r "stargazers_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/stargazers",\r "contributors_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/contributors",\r "subscribers_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/subscribers",\r "subscription_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/subscription",\r "commits_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/commits{/sha}",\r "git_commits_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/git/commits{/sha}",\r "comments_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/comments{/number}",\r "issue_comment_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/issues/comments{/number}",\r "contents_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/contents/{+path}",\r "compare_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/compare/{base}...{head}",\r "merges_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/merges",\r "archive_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/{archive_format}{/ref}",\r "downloads_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/downloads",\r "issues_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/issues{/number}",\r "pulls_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/pulls{/number}",\r "milestones_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/milestones{/number}",\r "notifications_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/notifications{?since,all,participating}",\r "labels_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/labels{/name}",\r "releases_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/releases{/id}",\r "created_at": "2015-04-25T23:56:07Z",\r "updated_at": "2015-04-25T23:56:07Z",\r "pushed_at": "2015-06-27T22:51:15Z",\r "git_url": "git://github.com/openaddresses/hooked-on-sources.git",\r "ssh_url": "git@github.com:openaddresses/hooked-on-sources.git",\r "clone_url": "https://github.com/openaddresses/hooked-on-sources.git",\r "svn_url": "https://github.com/openaddresses/hooked-on-sources",\r "homepage": null,\r "size": 192,\r "stargazers_count": 0,\r "watchers_count": 0,\r "language": null,\r "has_issues": true,\r "has_downloads": true,\r "has_wiki": true,\r "has_pages": false,\r "forks_count": 1,\r "mirror_url": null,\r "open_issues_count": 3,\r "forks": 1,\r "open_issues": 3,\r "watchers": 0,\r "default_branch": "master"\r },\r "organization": {\r "login": "openaddresses",\r "id": 6895392,\r "url": "https://api.github.com/orgs/openaddresses",\r "repos_url": "https://api.github.com/orgs/openaddresses/repos",\r "events_url": "https://api.github.com/orgs/openaddresses/events",\r "members_url": "https://api.github.com/orgs/openaddresses/members{/member}",\r "public_members_url": "https://api.github.com/orgs/openaddresses/public_members{/member}",\r "avatar_url": "https://avatars.githubusercontent.com/u/6895392?v=3",\r "description": "The free and open global address collection "\r },\r "sender": {\r "login": "migurski",\r "id": 58730,\r "avatar_url": "https://avatars.githubusercontent.com/u/58730?v=3",\r "gravatar_id": "",\r "url": "https://api.github.com/users/migurski",\r "html_url": "https://github.com/migurski",\r "followers_url": "https://api.github.com/users/migurski/followers",\r "following_url": "https://api.github.com/users/migurski/following{/other_user}",\r "gists_url": "https://api.github.com/users/migurski/gists{/gist_id}",\r "starred_url": "https://api.github.com/users/migurski/starred{/owner}{/repo}",\r "subscriptions_url": "https://api.github.com/users/migurski/subscriptions",\r "organizations_url": "https://api.github.com/users/migurski/orgs",\r "repos_url": "https://api.github.com/users/migurski/repos",\r "events_url": "https://api.github.com/users/migurski/events{/privacy}",\r "received_events_url": "https://api.github.com/users/migurski/received_events",\r "type": "User",\r "site_admin": false\r }\r}'''
posted2 = self.client.post('/hook', data=data2, headers=signed(data2, 'hos-secret'))
self.assertEqual(self.last_status_state, 'success', 'Status should be success even for a pull request synch')
self.last_status_state = None
data3 = u'''{\r "ref": "refs/heads/master",\r "before": "c3c7de37f96d38534dc6297a2483c218994241b6",\r "after": "8dd262c2f30a70b27e371869c54315b1abc32247",\r "created": false,\r "deleted": false,\r "forced": false,\r "base_ref": null,\r "compare": "https://github.com/openaddresses/hooked-on-sources/compare/c3c7de37f96d...8dd262c2f30a",\r "commits": [\r {\r "id": "aed74b0784f696c3cf10e8e260865ae18ffd3aa8",\r "distinct": true,\r "message": "Added La Réunion",\r "timestamp": "2015-06-27T15:48:33-07:00",\r "url": "https://github.com/openaddresses/hooked-on-sources/commit/aed74b0784f696c3cf10e8e260865ae18ffd3aa8",\r "author": {\r "name": "Michal Migurski",\r "email": "mike@teczno.com",\r "username": "migurski"\r },\r "committer": {\r "name": "Michal Migurski",\r "email": "mike@teczno.com",\r "username": "migurski"\r },\r "added": [\r "sources/fr/la-réunion.json"\r ],\r "removed": [\r\r ],\r "modified": [\r\r ]\r },\r {\r "id": "6460668909a85d9db8df871d91e9b25bc5192add",\r "distinct": true,\r "message": "Added Latin-1 encoding",\r "timestamp": "2015-06-27T15:53:19-07:00",\r "url": "https://github.com/openaddresses/hooked-on-sources/commit/6460668909a85d9db8df871d91e9b25bc5192add",\r "author": {\r "name": "Michal Migurski",\r "email": "mike@teczno.com",\r "username": "migurski"\r },\r "committer": {\r "name": "Michal Migurski",\r "email": "mike@teczno.com",\r "username": "migurski"\r },\r "added": [\r\r ],\r "removed": [\r\r ],\r "modified": [\r "sources/fr/la-réunion.json"\r ]\r },\r {\r "id": "8dd262c2f30a70b27e371869c54315b1abc32247",\r "distinct": true,\r "message": "Merge pull request #4 from migurski/master\\n\\nAdded La Réunion",\r "timestamp": "2015-06-27T15:55:38-07:00",\r "url": "https://github.com/openaddresses/hooked-on-sources/commit/8dd262c2f30a70b27e371869c54315b1abc32247",\r "author": {\r "name": "migurski",\r "email": "mike-github@teczno.com",\r "username": "migurski"\r },\r "committer": {\r "name": "migurski",\r "email": "mike-github@teczno.com",\r "username": "migurski"\r },\r "added": [\r "sources/fr/la-réunion.json"\r ],\r "removed": [\r\r ],\r "modified": [\r\r ]\r }\r ],\r "head_commit": {\r "id": "8dd262c2f30a70b27e371869c54315b1abc32247",\r "distinct": true,\r "message": "Merge pull request #4 from migurski/master\\n\\nAdded La Réunion",\r "timestamp": "2015-06-27T15:55:38-07:00",\r "url": "https://github.com/openaddresses/hooked-on-sources/commit/8dd262c2f30a70b27e371869c54315b1abc32247",\r "author": {\r "name": "migurski",\r "email": "mike-github@teczno.com",\r "username": "migurski"\r },\r "committer": {\r "name": "migurski",\r "email": "mike-github@teczno.com",\r "username": "migurski"\r },\r "added": [\r "sources/fr/la-réunion.json"\r ],\r "removed": [\r\r ],\r "modified": [\r\r ]\r },\r "repository": {\r "id": 34590951,\r "name": "hooked-on-sources",\r "full_name": "openaddresses/hooked-on-sources",\r "owner": {\r "name": "openaddresses",\r "email": "openaddresses@gmail.com"\r },\r "private": false,\r "html_url": "https://github.com/openaddresses/hooked-on-sources",\r "description": "Temporary repository for testing Github webhook features",\r "fork": false,\r "url": "https://github.com/openaddresses/hooked-on-sources",\r "forks_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/forks",\r "keys_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/keys{/key_id}",\r "collaborators_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/collaborators{/collaborator}",\r "teams_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/teams",\r "hooks_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/hooks",\r "issue_events_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/issues/events{/number}",\r "events_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/events",\r "assignees_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/assignees{/user}",\r "branches_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/branches{/branch}",\r "tags_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/tags",\r "blobs_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/git/blobs{/sha}",\r "git_tags_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/git/tags{/sha}",\r "git_refs_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/git/refs{/sha}",\r "trees_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/git/trees{/sha}",\r "statuses_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/statuses/{sha}",\r "languages_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/languages",\r "stargazers_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/stargazers",\r "contributors_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/contributors",\r "subscribers_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/subscribers",\r "subscription_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/subscription",\r "commits_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/commits{/sha}",\r "git_commits_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/git/commits{/sha}",\r "comments_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/comments{/number}",\r "issue_comment_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/issues/comments{/number}",\r "contents_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/contents/{+path}",\r "compare_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/compare/{base}...{head}",\r "merges_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/merges",\r "archive_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/{archive_format}{/ref}",\r "downloads_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/downloads",\r "issues_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/issues{/number}",\r "pulls_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/pulls{/number}",\r "milestones_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/milestones{/number}",\r "notifications_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/notifications{?since,all,participating}",\r "labels_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/labels{/name}",\r "releases_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/releases{/id}",\r "created_at": 1430006167,\r "updated_at": "2015-04-25T23:56:07Z",\r "pushed_at": 1435445738,\r "git_url": "git://github.com/openaddresses/hooked-on-sources.git",\r "ssh_url": "git@github.com:openaddresses/hooked-on-sources.git",\r "clone_url": "https://github.com/openaddresses/hooked-on-sources.git",\r "svn_url": "https://github.com/openaddresses/hooked-on-sources",\r "homepage": null,\r "size": 268,\r "stargazers_count": 0,\r "watchers_count": 0,\r "language": null,\r "has_issues": true,\r "has_downloads": true,\r "has_wiki": true,\r "has_pages": false,\r "forks_count": 1,\r "mirror_url": null,\r "open_issues_count": 2,\r "forks": 1,\r "open_issues": 2,\r "watchers": 0,\r "default_branch": "master",\r "stargazers": 0,\r "master_branch": "master",\r "organization": "openaddresses"\r },\r "pusher": {\r "name": "migurski",\r "email": "mike-github@teczno.com"\r },\r "organization": {\r "login": "openaddresses",\r "id": 6895392,\r "url": "https://api.github.com/orgs/openaddresses",\r "repos_url": "https://api.github.com/orgs/openaddresses/repos",\r "events_url": "https://api.github.com/orgs/openaddresses/events",\r "members_url": "https://api.github.com/orgs/openaddresses/members{/member}",\r "public_members_url": "https://api.github.com/orgs/openaddresses/public_members{/member}",\r "avatar_url": "https://avatars.githubusercontent.com/u/6895392?v=3",\r "description": "The free and open global address collection "\r },\r "sender": {\r "login": "migurski",\r "id": 58730,\r "avatar_url": "https://avatars.githubusercontent.com/u/58730?v=3",\r "gravatar_id": "",\r "url": "https://api.github.com/users/migurski",\r "html_url": "https://github.com/migurski",\r "followers_url": "https://api.github.com/users/migurski/followers",\r "following_url": "https://api.github.com/users/migurski/following{/other_user}",\r "gists_url": "https://api.github.com/users/migurski/gists{/gist_id}",\r "starred_url": "https://api.github.com/users/migurski/starred{/owner}{/repo}",\r "subscriptions_url": "https://api.github.com/users/migurski/subscriptions",\r "organizations_url": "https://api.github.com/users/migurski/orgs",\r "repos_url": "https://api.github.com/users/migurski/repos",\r "events_url": "https://api.github.com/users/migurski/events{/privacy}",\r "received_events_url": "https://api.github.com/users/migurski/received_events",\r "type": "User",\r "site_admin": false\r }\r}'''
posted3 = self.client.post('/hook', data=data3, headers=signed(data3, 'hos-secret'))
self.assertEqual(self.last_status_state, 'success', 'Status should be success even for a new commit to master')
self.last_status_state = None
data4 = u'''{\r "action": "closed",\r "number": 4,\r "pull_request": {\r "url": "https://api.github.com/repos/openaddresses/hooked-on-sources/pulls/4",\r "id": 38716878,\r "html_url": "https://github.com/openaddresses/hooked-on-sources/pull/4",\r "diff_url": "https://github.com/openaddresses/hooked-on-sources/pull/4.diff",\r "patch_url": "https://github.com/openaddresses/hooked-on-sources/pull/4.patch",\r "issue_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/issues/4",\r "number": 4,\r "state": "closed",\r "locked": false,\r "title": "Added La Réunion",\r "user": {\r "login": "migurski",\r "id": 58730,\r "avatar_url": "https://avatars.githubusercontent.com/u/58730?v=3",\r "gravatar_id": "",\r "url": "https://api.github.com/users/migurski",\r "html_url": "https://github.com/migurski",\r "followers_url": "https://api.github.com/users/migurski/followers",\r "following_url": "https://api.github.com/users/migurski/following{/other_user}",\r "gists_url": "https://api.github.com/users/migurski/gists{/gist_id}",\r "starred_url": "https://api.github.com/users/migurski/starred{/owner}{/repo}",\r "subscriptions_url": "https://api.github.com/users/migurski/subscriptions",\r "organizations_url": "https://api.github.com/users/migurski/orgs",\r "repos_url": "https://api.github.com/users/migurski/repos",\r "events_url": "https://api.github.com/users/migurski/events{/privacy}",\r "received_events_url": "https://api.github.com/users/migurski/received_events",\r "type": "User",\r "site_admin": false\r },\r "body": "",\r "created_at": "2015-06-27T22:51:14Z",\r "updated_at": "2015-06-27T22:55:38Z",\r "closed_at": "2015-06-27T22:55:38Z",\r "merged_at": "2015-06-27T22:55:38Z",\r "merge_commit_sha": "a24d66f118649a3ff5bf5731f5c54933b94cac8d",\r "assignee": null,\r "milestone": null,\r "commits_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/pulls/4/commits",\r "review_comments_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/pulls/4/comments",\r "review_comment_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/pulls/comments{/number}",\r "comments_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/issues/4/comments",\r "statuses_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/statuses/6460668909a85d9db8df871d91e9b25bc5192add",\r "head": {\r "label": "migurski:master",\r "ref": "master",\r "sha": "6460668909a85d9db8df871d91e9b25bc5192add",\r "user": {\r "login": "migurski",\r "id": 58730,\r "avatar_url": "https://avatars.githubusercontent.com/u/58730?v=3",\r "gravatar_id": "",\r "url": "https://api.github.com/users/migurski",\r "html_url": "https://github.com/migurski",\r "followers_url": "https://api.github.com/users/migurski/followers",\r "following_url": "https://api.github.com/users/migurski/following{/other_user}",\r "gists_url": "https://api.github.com/users/migurski/gists{/gist_id}",\r "starred_url": "https://api.github.com/users/migurski/starred{/owner}{/repo}",\r "subscriptions_url": "https://api.github.com/users/migurski/subscriptions",\r "organizations_url": "https://api.github.com/users/migurski/orgs",\r "repos_url": "https://api.github.com/users/migurski/repos",\r "events_url": "https://api.github.com/users/migurski/events{/privacy}",\r "received_events_url": "https://api.github.com/users/migurski/received_events",\r "type": "User",\r "site_admin": false\r },\r "repo": {\r "id": 38178103,\r "name": "hooked-on-sources",\r "full_name": "migurski/hooked-on-sources",\r "owner": {\r "login": "migurski",\r "id": 58730,\r "avatar_url": "https://avatars.githubusercontent.com/u/58730?v=3",\r "gravatar_id": "",\r "url": "https://api.github.com/users/migurski",\r "html_url": "https://github.com/migurski",\r "followers_url": "https://api.github.com/users/migurski/followers",\r "following_url": "https://api.github.com/users/migurski/following{/other_user}",\r "gists_url": "https://api.github.com/users/migurski/gists{/gist_id}",\r "starred_url": "https://api.github.com/users/migurski/starred{/owner}{/repo}",\r "subscriptions_url": "https://api.github.com/users/migurski/subscriptions",\r "organizations_url": "https://api.github.com/users/migurski/orgs",\r "repos_url": "https://api.github.com/users/migurski/repos",\r "events_url": "https://api.github.com/users/migurski/events{/privacy}",\r "received_events_url": "https://api.github.com/users/migurski/received_events",\r "type": "User",\r "site_admin": false\r },\r "private": false,\r "html_url": "https://github.com/migurski/hooked-on-sources",\r "description": "Temporary repository for testing Github webhook features",\r "fork": true,\r "url": "https://api.github.com/repos/migurski/hooked-on-sources",\r "forks_url": "https://api.github.com/repos/migurski/hooked-on-sources/forks",\r "keys_url": "https://api.github.com/repos/migurski/hooked-on-sources/keys{/key_id}",\r "collaborators_url": "https://api.github.com/repos/migurski/hooked-on-sources/collaborators{/collaborator}",\r "teams_url": "https://api.github.com/repos/migurski/hooked-on-sources/teams",\r "hooks_url": "https://api.github.com/repos/migurski/hooked-on-sources/hooks",\r "issue_events_url": "https://api.github.com/repos/migurski/hooked-on-sources/issues/events{/number}",\r "events_url": "https://api.github.com/repos/migurski/hooked-on-sources/events",\r "assignees_url": "https://api.github.com/repos/migurski/hooked-on-sources/assignees{/user}",\r "branches_url": "https://api.github.com/repos/migurski/hooked-on-sources/branches{/branch}",\r "tags_url": "https://api.github.com/repos/migurski/hooked-on-sources/tags",\r "blobs_url": "https://api.github.com/repos/migurski/hooked-on-sources/git/blobs{/sha}",\r "git_tags_url": "https://api.github.com/repos/migurski/hooked-on-sources/git/tags{/sha}",\r "git_refs_url": "https://api.github.com/repos/migurski/hooked-on-sources/git/refs{/sha}",\r "trees_url": "https://api.github.com/repos/migurski/hooked-on-sources/git/trees{/sha}",\r "statuses_url": "https://api.github.com/repos/migurski/hooked-on-sources/statuses/{sha}",\r "languages_url": "https://api.github.com/repos/migurski/hooked-on-sources/languages",\r "stargazers_url": "https://api.github.com/repos/migurski/hooked-on-sources/stargazers",\r "contributors_url": "https://api.github.com/repos/migurski/hooked-on-sources/contributors",\r "subscribers_url": "https://api.github.com/repos/migurski/hooked-on-sources/subscribers",\r "subscription_url": "https://api.github.com/repos/migurski/hooked-on-sources/subscription",\r "commits_url": "https://api.github.com/repos/migurski/hooked-on-sources/commits{/sha}",\r "git_commits_url": "https://api.github.com/repos/migurski/hooked-on-sources/git/commits{/sha}",\r "comments_url": "https://api.github.com/repos/migurski/hooked-on-sources/comments{/number}",\r "issue_comment_url": "https://api.github.com/repos/migurski/hooked-on-sources/issues/comments{/number}",\r "contents_url": "https://api.github.com/repos/migurski/hooked-on-sources/contents/{+path}",\r "compare_url": "https://api.github.com/repos/migurski/hooked-on-sources/compare/{base}...{head}",\r "merges_url": "https://api.github.com/repos/migurski/hooked-on-sources/merges",\r "archive_url": "https://api.github.com/repos/migurski/hooked-on-sources/{archive_format}{/ref}",\r "downloads_url": "https://api.github.com/repos/migurski/hooked-on-sources/downloads",\r "issues_url": "https://api.github.com/repos/migurski/hooked-on-sources/issues{/number}",\r "pulls_url": "https://api.github.com/repos/migurski/hooked-on-sources/pulls{/number}",\r "milestones_url": "https://api.github.com/repos/migurski/hooked-on-sources/milestones{/number}",\r "notifications_url": "https://api.github.com/repos/migurski/hooked-on-sources/notifications{?since,all,participating}",\r "labels_url": "https://api.github.com/repos/migurski/hooked-on-sources/labels{/name}",\r "releases_url": "https://api.github.com/repos/migurski/hooked-on-sources/releases{/id}",\r "created_at": "2015-06-27T22:47:37Z",\r "updated_at": "2015-04-25T23:56:07Z",\r "pushed_at": "2015-06-27T22:53:18Z",\r "git_url": "git://github.com/migurski/hooked-on-sources.git",\r "ssh_url": "git@github.com:migurski/hooked-on-sources.git",\r "clone_url": "https://github.com/migurski/hooked-on-sources.git",\r "svn_url": "https://github.com/migurski/hooked-on-sources",\r "homepage": null,\r "size": 88,\r "stargazers_count": 0,\r "watchers_count": 0,\r "language": null,\r "has_issues": false,\r "has_downloads": true,\r "has_wiki": true,\r "has_pages": false,\r "forks_count": 0,\r "mirror_url": null,\r "open_issues_count": 0,\r "forks": 0,\r "open_issues": 0,\r "watchers": 0,\r "default_branch": "master"\r }\r },\r "base": {\r "label": "openaddresses:master",\r "ref": "master",\r "sha": "c3c7de37f96d38534dc6297a2483c218994241b6",\r "user": {\r "login": "openaddresses",\r "id": 6895392,\r "avatar_url": "https://avatars.githubusercontent.com/u/6895392?v=3",\r "gravatar_id": "",\r "url": "https://api.github.com/users/openaddresses",\r "html_url": "https://github.com/openaddresses",\r "followers_url": "https://api.github.com/users/openaddresses/followers",\r "following_url": "https://api.github.com/users/openaddresses/following{/other_user}",\r "gists_url": "https://api.github.com/users/openaddresses/gists{/gist_id}",\r "starred_url": "https://api.github.com/users/openaddresses/starred{/owner}{/repo}",\r "subscriptions_url": "https://api.github.com/users/openaddresses/subscriptions",\r "organizations_url": "https://api.github.com/users/openaddresses/orgs",\r "repos_url": "https://api.github.com/users/openaddresses/repos",\r "events_url": "https://api.github.com/users/openaddresses/events{/privacy}",\r "received_events_url": "https://api.github.com/users/openaddresses/received_events",\r "type": "Organization",\r "site_admin": false\r },\r "repo": {\r "id": 34590951,\r "name": "hooked-on-sources",\r "full_name": "openaddresses/hooked-on-sources",\r "owner": {\r "login": "openaddresses",\r "id": 6895392,\r "avatar_url": "https://avatars.githubusercontent.com/u/6895392?v=3",\r "gravatar_id": "",\r "url": "https://api.github.com/users/openaddresses",\r "html_url": "https://github.com/openaddresses",\r "followers_url": "https://api.github.com/users/openaddresses/followers",\r "following_url": "https://api.github.com/users/openaddresses/following{/other_user}",\r "gists_url": "https://api.github.com/users/openaddresses/gists{/gist_id}",\r "starred_url": "https://api.github.com/users/openaddresses/starred{/owner}{/repo}",\r "subscriptions_url": "https://api.github.com/users/openaddresses/subscriptions",\r "organizations_url": "https://api.github.com/users/openaddresses/orgs",\r "repos_url": "https://api.github.com/users/openaddresses/repos",\r "events_url": "https://api.github.com/users/openaddresses/events{/privacy}",\r "received_events_url": "https://api.github.com/users/openaddresses/received_events",\r "type": "Organization",\r "site_admin": false\r },\r "private": false,\r "html_url": "https://github.com/openaddresses/hooked-on-sources",\r "description": "Temporary repository for testing Github webhook features",\r "fork": false,\r "url": "https://api.github.com/repos/openaddresses/hooked-on-sources",\r "forks_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/forks",\r "keys_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/keys{/key_id}",\r "collaborators_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/collaborators{/collaborator}",\r "teams_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/teams",\r "hooks_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/hooks",\r "issue_events_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/issues/events{/number}",\r "events_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/events",\r "assignees_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/assignees{/user}",\r "branches_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/branches{/branch}",\r "tags_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/tags",\r "blobs_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/git/blobs{/sha}",\r "git_tags_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/git/tags{/sha}",\r "git_refs_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/git/refs{/sha}",\r "trees_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/git/trees{/sha}",\r "statuses_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/statuses/{sha}",\r "languages_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/languages",\r "stargazers_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/stargazers",\r "contributors_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/contributors",\r "subscribers_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/subscribers",\r "subscription_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/subscription",\r "commits_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/commits{/sha}",\r "git_commits_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/git/commits{/sha}",\r "comments_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/comments{/number}",\r "issue_comment_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/issues/comments{/number}",\r "contents_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/contents/{+path}",\r "compare_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/compare/{base}...{head}",\r "merges_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/merges",\r "archive_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/{archive_format}{/ref}",\r "downloads_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/downloads",\r "issues_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/issues{/number}",\r "pulls_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/pulls{/number}",\r "milestones_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/milestones{/number}",\r "notifications_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/notifications{?since,all,participating}",\r "labels_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/labels{/name}",\r "releases_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/releases{/id}",\r "created_at": "2015-04-25T23:56:07Z",\r "updated_at": "2015-04-25T23:56:07Z",\r "pushed_at": "2015-06-27T22:55:38Z",\r "git_url": "git://github.com/openaddresses/hooked-on-sources.git",\r "ssh_url": "git@github.com:openaddresses/hooked-on-sources.git",\r "clone_url": "https://github.com/openaddresses/hooked-on-sources.git",\r "svn_url": "https://github.com/openaddresses/hooked-on-sources",\r "homepage": null,\r "size": 268,\r "stargazers_count": 0,\r "watchers_count": 0,\r "language": null,\r "has_issues": true,\r "has_downloads": true,\r "has_wiki": true,\r "has_pages": false,\r "forks_count": 1,\r "mirror_url": null,\r "open_issues_count": 2,\r "forks": 1,\r "open_issues": 2,\r "watchers": 0,\r "default_branch": "master"\r }\r },\r "_links": {\r "self": {\r "href": "https://api.github.com/repos/openaddresses/hooked-on-sources/pulls/4"\r },\r "html": {\r "href": "https://github.com/openaddresses/hooked-on-sources/pull/4"\r },\r "issue": {\r "href": "https://api.github.com/repos/openaddresses/hooked-on-sources/issues/4"\r },\r "comments": {\r "href": "https://api.github.com/repos/openaddresses/hooked-on-sources/issues/4/comments"\r },\r "review_comments": {\r "href": "https://api.github.com/repos/openaddresses/hooked-on-sources/pulls/4/comments"\r },\r "review_comment": {\r "href": "https://api.github.com/repos/openaddresses/hooked-on-sources/pulls/comments{/number}"\r },\r "commits": {\r "href": "https://api.github.com/repos/openaddresses/hooked-on-sources/pulls/4/commits"\r },\r "statuses": {\r "href": "https://api.github.com/repos/openaddresses/hooked-on-sources/statuses/6460668909a85d9db8df871d91e9b25bc5192add"\r }\r },\r "merged": true,\r "mergeable": null,\r "mergeable_state": "unknown",\r "merged_by": {\r "login": "migurski",\r "id": 58730,\r "avatar_url": "https://avatars.githubusercontent.com/u/58730?v=3",\r "gravatar_id": "",\r "url": "https://api.github.com/users/migurski",\r "html_url": "https://github.com/migurski",\r "followers_url": "https://api.github.com/users/migurski/followers",\r "following_url": "https://api.github.com/users/migurski/following{/other_user}",\r "gists_url": "https://api.github.com/users/migurski/gists{/gist_id}",\r "starred_url": "https://api.github.com/users/migurski/starred{/owner}{/repo}",\r "subscriptions_url": "https://api.github.com/users/migurski/subscriptions",\r "organizations_url": "https://api.github.com/users/migurski/orgs",\r "repos_url": "https://api.github.com/users/migurski/repos",\r "events_url": "https://api.github.com/users/migurski/events{/privacy}",\r "received_events_url": "https://api.github.com/users/migurski/received_events",\r "type": "User",\r "site_admin": false\r },\r "comments": 0,\r "review_comments": 0,\r "commits": 2,\r "additions": 23,\r "deletions": 0,\r "changed_files": 1\r },\r "repository": {\r "id": 34590951,\r "name": "hooked-on-sources",\r "full_name": "openaddresses/hooked-on-sources",\r "owner": {\r "login": "openaddresses",\r "id": 6895392,\r "avatar_url": "https://avatars.githubusercontent.com/u/6895392?v=3",\r "gravatar_id": "",\r "url": "https://api.github.com/users/openaddresses",\r "html_url": "https://github.com/openaddresses",\r "followers_url": "https://api.github.com/users/openaddresses/followers",\r "following_url": "https://api.github.com/users/openaddresses/following{/other_user}",\r "gists_url": "https://api.github.com/users/openaddresses/gists{/gist_id}",\r "starred_url": "https://api.github.com/users/openaddresses/starred{/owner}{/repo}",\r "subscriptions_url": "https://api.github.com/users/openaddresses/subscriptions",\r "organizations_url": "https://api.github.com/users/openaddresses/orgs",\r "repos_url": "https://api.github.com/users/openaddresses/repos",\r "events_url": "https://api.github.com/users/openaddresses/events{/privacy}",\r "received_events_url": "https://api.github.com/users/openaddresses/received_events",\r "type": "Organization",\r "site_admin": false\r },\r "private": false,\r "html_url": "https://github.com/openaddresses/hooked-on-sources",\r "description": "Temporary repository for testing Github webhook features",\r "fork": false,\r "url": "https://api.github.com/repos/openaddresses/hooked-on-sources",\r "forks_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/forks",\r "keys_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/keys{/key_id}",\r "collaborators_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/collaborators{/collaborator}",\r "teams_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/teams",\r "hooks_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/hooks",\r "issue_events_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/issues/events{/number}",\r "events_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/events",\r "assignees_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/assignees{/user}",\r "branches_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/branches{/branch}",\r "tags_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/tags",\r "blobs_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/git/blobs{/sha}",\r "git_tags_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/git/tags{/sha}",\r "git_refs_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/git/refs{/sha}",\r "trees_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/git/trees{/sha}",\r "statuses_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/statuses/{sha}",\r "languages_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/languages",\r "stargazers_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/stargazers",\r "contributors_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/contributors",\r "subscribers_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/subscribers",\r "subscription_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/subscription",\r "commits_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/commits{/sha}",\r "git_commits_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/git/commits{/sha}",\r "comments_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/comments{/number}",\r "issue_comment_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/issues/comments{/number}",\r "contents_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/contents/{+path}",\r "compare_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/compare/{base}...{head}",\r "merges_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/merges",\r "archive_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/{archive_format}{/ref}",\r "downloads_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/downloads",\r "issues_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/issues{/number}",\r "pulls_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/pulls{/number}",\r "milestones_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/milestones{/number}",\r "notifications_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/notifications{?since,all,participating}",\r "labels_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/labels{/name}",\r "releases_url": "https://api.github.com/repos/openaddresses/hooked-on-sources/releases{/id}",\r "created_at": "2015-04-25T23:56:07Z",\r "updated_at": "2015-04-25T23:56:07Z",\r "pushed_at": "2015-06-27T22:55:38Z",\r "git_url": "git://github.com/openaddresses/hooked-on-sources.git",\r "ssh_url": "git@github.com:openaddresses/hooked-on-sources.git",\r "clone_url": "https://github.com/openaddresses/hooked-on-sources.git",\r "svn_url": "https://github.com/openaddresses/hooked-on-sources",\r "homepage": null,\r "size": 268,\r "stargazers_count": 0,\r "watchers_count": 0,\r "language": null,\r "has_issues": true,\r "has_downloads": true,\r "has_wiki": true,\r "has_pages": false,\r "forks_count": 1,\r "mirror_url": null,\r "open_issues_count": 2,\r "forks": 1,\r "open_issues": 2,\r "watchers": 0,\r "default_branch": "master"\r },\r "organization": {\r "login": "openaddresses",\r "id": 6895392,\r "url": "https://api.github.com/orgs/openaddresses",\r "repos_url": "https://api.github.com/orgs/openaddresses/repos",\r "events_url": "https://api.github.com/orgs/openaddresses/events",\r "members_url": "https://api.github.com/orgs/openaddresses/members{/member}",\r "public_members_url": "https://api.github.com/orgs/openaddresses/public_members{/member}",\r "avatar_url": "https://avatars.githubusercontent.com/u/6895392?v=3",\r "description": "The free and open global address collection "\r },\r "sender": {\r "login": "migurski",\r "id": 58730,\r "avatar_url": "https://avatars.githubusercontent.com/u/58730?v=3",\r "gravatar_id": "",\r "url": "https://api.github.com/users/migurski",\r "html_url": "https://github.com/migurski",\r "followers_url": "https://api.github.com/users/migurski/followers",\r "following_url": "https://api.github.com/users/migurski/following{/other_user}",\r "gists_url": "https://api.github.com/users/migurski/gists{/gist_id}",\r "starred_url": "https://api.github.com/users/migurski/starred{/owner}{/repo}",\r "subscriptions_url": "https://api.github.com/users/migurski/subscriptions",\r "organizations_url": "https://api.github.com/users/migurski/orgs",\r "repos_url": "https://api.github.com/users/migurski/repos",\r "events_url": "https://api.github.com/users/migurski/events{/privacy}",\r "received_events_url": "https://api.github.com/users/migurski/received_events",\r "type": "User",\r "site_admin": false\r }\r}'''
posted4 = self.client.post('/hook', data=data4, headers=signed(data4, 'hos-secret'))
self.assertEqual(self.last_status_state, None, 'Status should be blank for a closed pull request')
self.assertEqual(posted1.status_code, 200)
self.assertEqual(posted2.status_code, 200)
self.assertEqual(posted3.status_code, 200)
self.assertEqual(posted4.status_code, 200)
class TestFunctions (unittest.TestCase):
def test_absolute_url(self):
req1 = Mock()
req1.scheme, req1.host, req1.path = 'http', 'example.com', '/foo/'
req1.headers = dict()
self.assertEqual(href.absolute_url(req1, '/bar'), '/bar')
self.assertEqual(href.absolute_url(req1, 'http://example.org/bar'), 'http://example.org/bar')
req2 = Mock()
req2.scheme, req2.host, req2.path = 'http', 'example.com', '/foo/'
req2.headers = {'X-Forwarded-Proto': 'https'}
self.assertEqual(href.absolute_url(req2, '/bar'), 'https://example.com/bar')
self.assertEqual(href.absolute_url(req2, 'http://example.org/bar'), 'http://example.org/bar')
self.assertEqual(href.absolute_url(req2, 'bar'), 'https://example.com/foo/bar')
def test_util_doctest(self):
doctest.testmod(util, raise_on_error=True)
def test_href_doctest(self):
doctest.testmod(href, raise_on_error=True)
if __name__ == '__main__':
unittest.main()
| 824.158924
| 152,746
| 0.685154
| 99,221
| 674,162
| 4.568065
| 0.020459
| 0.057964
| 0.096804
| 0.124462
| 0.958116
| 0.95365
| 0.948935
| 0.943964
| 0.938336
| 0.929008
| 0
| 0.051014
| 0.102926
| 674,162
| 817
| 152,747
| 825.167687
| 0.698419
| 0.000823
| 0
| 0.328283
| 0
| 0.143098
| 0.957406
| 0.234897
| 0
| 0
| 0
| 0
| 0.203704
| 1
| 0.069024
| false
| 0.016835
| 0.030303
| 0
| 0.218855
| 0.006734
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 9
|
b459ead29a9adf37efec64008828ec8b7819586a
| 50
|
py
|
Python
|
instance/config.py
|
cnyakundi/nyaksnewsapp
|
dbbcac632c06c256d9bbd25e8b12b8b43be09caf
|
[
"MIT"
] | null | null | null |
instance/config.py
|
cnyakundi/nyaksnewsapp
|
dbbcac632c06c256d9bbd25e8b12b8b43be09caf
|
[
"MIT"
] | null | null | null |
instance/config.py
|
cnyakundi/nyaksnewsapp
|
dbbcac632c06c256d9bbd25e8b12b8b43be09caf
|
[
"MIT"
] | null | null | null |
NEWS_API_KEY = '4494fa2524ad4803aecf28930d1448a3'
| 25
| 49
| 0.88
| 4
| 50
| 10.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.468085
| 0.06
| 50
| 1
| 50
| 50
| 0.425532
| 0
| 0
| 0
| 0
| 0
| 0.64
| 0.64
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
b47d6ab5843ca38e8bafbe1ee6a6ff6622c0c114
| 170
|
py
|
Python
|
tests/basics/int_bytes_notimpl.py
|
kaffir/circuitpython
|
0930f7a7972dc006c079102e292babb1ae02aa1a
|
[
"MIT"
] | 5
|
2017-07-17T23:28:09.000Z
|
2020-06-16T17:28:47.000Z
|
tests/basics/int_bytes_notimpl.py
|
kaffir/circuitpython
|
0930f7a7972dc006c079102e292babb1ae02aa1a
|
[
"MIT"
] | null | null | null |
tests/basics/int_bytes_notimpl.py
|
kaffir/circuitpython
|
0930f7a7972dc006c079102e292babb1ae02aa1a
|
[
"MIT"
] | null | null | null |
try:
print((10).to_bytes(1, "big"))
except Exception as e:
print(type(e))
try:
print(int.from_bytes(b"\0", "big"))
except Exception as e:
print(type(e))
| 17
| 39
| 0.611765
| 29
| 170
| 3.517241
| 0.551724
| 0.156863
| 0.352941
| 0.392157
| 0.607843
| 0.607843
| 0.607843
| 0.607843
| 0
| 0
| 0
| 0.029197
| 0.194118
| 170
| 9
| 40
| 18.888889
| 0.715328
| 0
| 0
| 0.75
| 0
| 0
| 0.047059
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 0.5
| 1
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 7
|
b4873a2a58588434ecb96b1dd08b59755916eb20
| 132
|
py
|
Python
|
rio_tiler_pds/cbers/__init__.py
|
cogeotiff/rio-tiler-pds
|
52482c80baf7fd26cf06cd2af2961cca396b20e0
|
[
"BSD-3-Clause"
] | 30
|
2020-07-21T23:32:14.000Z
|
2022-02-21T23:35:35.000Z
|
rio_tiler_pds/cbers/__init__.py
|
cogeotiff/rio-tiler-pds
|
52482c80baf7fd26cf06cd2af2961cca396b20e0
|
[
"BSD-3-Clause"
] | 36
|
2020-07-21T20:48:51.000Z
|
2021-10-06T08:15:00.000Z
|
rio_tiler_pds/cbers/__init__.py
|
cogeotiff/rio-tiler-pds
|
52482c80baf7fd26cf06cd2af2961cca396b20e0
|
[
"BSD-3-Clause"
] | 4
|
2020-07-23T06:19:30.000Z
|
2021-11-18T03:27:04.000Z
|
"""rio-tiler-pds.cbers"""
from rio_tiler_pds.cbers import aws # noqa
from rio_tiler_pds.cbers.utils import sceneid_parser # noqa
| 26.4
| 60
| 0.772727
| 22
| 132
| 4.409091
| 0.5
| 0.247423
| 0.340206
| 0.494845
| 0.412371
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.121212
| 132
| 4
| 61
| 33
| 0.836207
| 0.227273
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 8
|
b48bc69b34e262b14de7090bd7fc27cd1afaffe3
| 205
|
py
|
Python
|
__init__.py
|
hraoyama/dataprocessor
|
86fab2fac692c314b18bb3fcd5fb335117425fc4
|
[
"MIT"
] | null | null | null |
__init__.py
|
hraoyama/dataprocessor
|
86fab2fac692c314b18bb3fcd5fb335117425fc4
|
[
"MIT"
] | null | null | null |
__init__.py
|
hraoyama/dataprocessor
|
86fab2fac692c314b18bb3fcd5fb335117425fc4
|
[
"MIT"
] | null | null | null |
from dataprocessor.data_processor import DataProcessor
from dataprocessor.feed_filter import TimeFreqFilter
from dataprocessor.feed_filter import TimeIndexing
from dataprocessor.constants import TimePeriod
| 51.25
| 54
| 0.907317
| 23
| 205
| 7.956522
| 0.478261
| 0.371585
| 0.229508
| 0.295082
| 0.360656
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.073171
| 205
| 4
| 55
| 51.25
| 0.963158
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 7
|
c30a59f79e1fa0514946fcc47eb877a46f323af9
| 611
|
py
|
Python
|
gabriel10/examples/ruler/__main__.py
|
turbobert/gabriel10
|
2abf3e927e76f800fad4ca6738c6874cc7ff9f66
|
[
"MIT"
] | null | null | null |
gabriel10/examples/ruler/__main__.py
|
turbobert/gabriel10
|
2abf3e927e76f800fad4ca6738c6874cc7ff9f66
|
[
"MIT"
] | null | null | null |
gabriel10/examples/ruler/__main__.py
|
turbobert/gabriel10
|
2abf3e927e76f800fad4ca6738c6874cc7ff9f66
|
[
"MIT"
] | null | null | null |
from gabriel10 import Document7396
doc = Document7293()
for r in range(1, 73):
if r % 10 == 0:
doc.page(1).text(r, 1, "(73x96) |10-------|20-------|30-------|40-------|50-------|60-------|70-------|80-------| %02d" % r)
else:
if r % 2 == 0:
doc.page(1).text(r, 1, "%02d -5-7-9|1-3-5-7-9|1-3-5-7-9|1-3-5-7-9|1-3-5-7-9|1-3-5-7-9|1-3-5-7-9|1-3-5-7-9|1-3-5-7-9| %02d" % (r, r))
else:
doc.page(1).textb(r, 1, "%02d -5-7-9|1-3-5-7-9|1-3-5-7-9|1-3-5-7-9|1-3-5-7-9|1-3-5-7-9|1-3-5-7-9|1-3-5-7-9|1-3-5-7-9| %02d" % (r, r))
doc.build("gabriel10_ruler7293.pdf")
| 40.733333
| 145
| 0.456628
| 150
| 611
| 1.853333
| 0.24
| 0.129496
| 0.194245
| 0.230216
| 0.482014
| 0.482014
| 0.482014
| 0.381295
| 0.381295
| 0.381295
| 0
| 0.291417
| 0.180033
| 611
| 14
| 146
| 43.642857
| 0.263473
| 0
| 0
| 0.181818
| 0
| 0.272727
| 0.510638
| 0.454992
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.090909
| 0
| 0.090909
| 0
| 0
| 0
| 1
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
c32b1dd7aaf3a82edbdcb8e2281904b923f07748
| 15,676
|
py
|
Python
|
defaults.py
|
val-iisc/OAAT
|
d947708e549305645fcb9977c89426908f99e2ca
|
[
"MIT"
] | null | null | null |
defaults.py
|
val-iisc/OAAT
|
d947708e549305645fcb9977c89426908f99e2ca
|
[
"MIT"
] | null | null | null |
defaults.py
|
val-iisc/OAAT
|
d947708e549305645fcb9977c89426908f99e2ca
|
[
"MIT"
] | 2
|
2021-11-08T08:48:39.000Z
|
2022-02-15T05:24:28.000Z
|
''' Default hyperparamters for CIFAR10_RN18, CIFAR100_PRN18, CIFAR10_WRN, CIFAR100_WRN and SVHN_PRN18 '''
import argparse
def use_default(default_arg):
parser = argparse.ArgumentParser(description='PyTorch OAAT Adversarial Training')
if default_arg == "CIFAR10_RN18":
parser.add_argument('--arch', type=str, default='ResNet18', choices=['ResNet18', 'PreActResNet18','WideResNet34'])
parser.add_argument('--epochs', type=int, default=110, metavar='N',
help='number of epochs to train')
parser.add_argument('--data', type=str, default='CIFAR10', choices=['CIFAR10', 'CIFAR100','SVHN'])
parser.add_argument('--weight-decay', '--wd', default=5e-4,
type=float, metavar='W')
parser.add_argument('--lr', type=float, default=0.2, metavar='LR',
help='learning rate')
parser.add_argument('--epsilon', default=16/255, type=float,
help='perturbation')
parser.add_argument('--beta', default=1.5, type=float,
help='regularization, i.e., 1/lambda in TRADES inital value of beta')
parser.add_argument('--model-dir', default='./model-cifar-ResNet',
help='directory of model for saving checkpoint')
parser.add_argument('--beta_final', default=3, type=float,
help='the final value of beta at the end of training ')
parser.add_argument('--mixup_alpha', default=0.2, type=float,
help='the value of mixup coeeficient in KL loss ')
parser.add_argument('--mixup_epsilon', default=24/255, type=float,
help='the epsilon value used to generate mixup attack ')
parser.add_argument('--lpips_weight', default=1, type=float,
help='the value of weight of lpips term in inner maximization')
parser.add_argument('--use_CE', default=1, type=int,
help='uses CE loss for inner maximization when set to 1 else uses KL loss')
parser.add_argument('--auto', default=0, type=float,
help='0 for no autoaugment 0.5 for autoaugment with probabilty 0.5 and 1 for autoaugment with probability 1')
parser.add_argument('--tau_swa_list', type=float, nargs='*', default=[0.995,0.9996,0.9998], help='The tau values for SWA')
parser.add_argument('--label_smoothing', default=0, type=int,
help='put it as 1 it want to use label smoothing for the clean loss in outer minimization')
parser.add_argument('--OAAT_warmup', default=0, type=int,
help='put it as 1 if want to use linear warmup of 10 epochs')
parser.add_argument('--alternate_iter_eps', default=12/255, type=float,
help='the epsilon value after which alternate iters start ')
elif default_arg == "CIFAR10_WRN":
parser.add_argument('--arch', type=str, default='WideResNet34', choices=['ResNet18', 'PreActResNet18','WideResNet34'])
parser.add_argument('--epochs', type=int, default=200, metavar='N',
help='number of epochs to train')
parser.add_argument('--data', type=str, default='CIFAR10', choices=['CIFAR10', 'CIFAR100','SVHN'])
parser.add_argument('--weight-decay', '--wd', default=3e-4,
type=float, metavar='W')
parser.add_argument('--lr', type=float, default=0.2, metavar='LR',
help='learning rate')
parser.add_argument('--epsilon', default=16/255, type=float,
help='perturbation')
parser.add_argument('--beta', default=2, type=float,
help='regularization, i.e., 1/lambda in TRADES inital value of beta')
parser.add_argument('--model-dir', default='./model-cifar-WideResNet',
help='directory of model for saving checkpoint')
parser.add_argument('--beta_final', default=3, type=float,
help='the final value of beta at the end of training ')
parser.add_argument('--mixup_alpha', default=0.45, type=float,
help='the value of mixup coeeficient in KL loss ')
parser.add_argument('--mixup_epsilon', default=16/255, type=float,
help='the epsilon value used to generate mixup attack ')
parser.add_argument('--lpips_weight', default=1, type=float,
help='the value of weight of lpips term in inner maximization')
parser.add_argument('--use_CE', default=1, type=int,
help='uses CE loss for inner maximization when set to 1 else uses KL loss')
parser.add_argument('--auto', default=1, type=float,
help='0 for no autoaugment 0.5 for autoaugment with probabilty 0.5 and 1 for autoaugment with probability 1')
parser.add_argument('--tau_swa_list', type=float, nargs='*', default=[0.995,0.9996,0.9998], help='The tau values for SWA')
parser.add_argument('--label_smoothing', default=0, type=int,
help='put it as 1 it want to use label smoothing for the clean loss in outer minimization')
parser.add_argument('--OAAT_warmup', default=0, type=int,
help='put it as 1 if want to use linear warmup of 10 epochs')
parser.add_argument('--alternate_iter_eps', default=12/255, type=float,
help='the epsilon value after which alternate iters start ')
elif default_arg == "CIFAR100_WRN":
parser.add_argument('--arch', type=str, default='WideResNet34', choices=['ResNet18', 'PreActResNet18','WideResNet34'])
parser.add_argument('--epochs', type=int, default=110, metavar='N',
help='number of epochs to train')
parser.add_argument('--data', type=str, default='CIFAR100', choices=['CIFAR10', 'CIFAR100','SVHN'])
parser.add_argument('--weight-decay', '--wd', default=3e-4,
type=float, metavar='W')
parser.add_argument('--lr', type=float, default=0.2, metavar='LR',
help='learning rate')
parser.add_argument('--epsilon', default=16/255, type=float,
help='perturbation')
parser.add_argument('--beta', default=2, type=float,
help='regularization, i.e., 1/lambda in TRADES inital value of beta')
parser.add_argument('--model-dir', default='./model-cifar-WideResNet',
help='directory of model for saving checkpoint')
parser.add_argument('--beta_final', default=4, type=float,
help='the final value of beta at the end of training ')
parser.add_argument('--mixup_alpha', default=0.2, type=float,
help='the value of mixup coeeficient in KL loss ')
parser.add_argument('--mixup_epsilon', default=24/255, type=float,
help='the epsilon value used to generate mixup attack ')
parser.add_argument('--lpips_weight', default=2, type=float,
help='the value of weight of lpips term in inner maximization')
parser.add_argument('--use_CE', default=1, type=int,
help='uses CE loss for inner maximization when set to 1 else uses KL loss')
parser.add_argument('--auto', default=0.5, type=float,
help='0 for no autoaugment 0.5 for autoaugment with probabilty 0.5 and 1 for autoaugment with probability 1')
parser.add_argument('--tau_swa_list', type=float, nargs='*', default=[0.995,0.9996,0.9998], help='The tau values for SWA')
parser.add_argument('--label_smoothing', default=1, type=int,
help='put it as 1 it want to use label smoothing for the clean loss in outer minimization')
parser.add_argument('--OAAT_warmup', default=1, type=int,
help='put it as 1 if want to use linear warmup of 10 epochs')
parser.add_argument('--alternate_iter_eps', default=12/255, type=float,
help='the epsilon value after which alternate iters start ')
elif default_arg == "CIFAR100_PRN18":
parser.add_argument('--arch', type=str, default='PreActResNet18', choices=['ResNet18', 'PreActResNet18','WideResNet34'])
parser.add_argument('--epochs', type=int, default=200, metavar='N',
help='number of epochs to train')
parser.add_argument('--data', type=str, default='CIFAR100', choices=['CIFAR10', 'CIFAR100','SVHN'])
parser.add_argument('--weight-decay', '--wd', default=3e-4,
type=float, metavar='W')
parser.add_argument('--lr', type=float, default=0.2, metavar='LR',
help='learning rate')
parser.add_argument('--epsilon', default=16/255, type=float,
help='perturbation')
parser.add_argument('--beta', default=2, type=float,
help='regularization, i.e., 1/lambda in TRADES inital value of beta')
parser.add_argument('--model-dir', default='./model-cifar-PreactResNet',
help='directory of model for saving checkpoint')
parser.add_argument('--beta_final', default=3, type=float,
help='the final value of beta at the end of training ')
parser.add_argument('--mixup_alpha', default=0.25, type=float,
help='the value of mixup coeeficient in KL loss ')
parser.add_argument('--mixup_epsilon', default=24/255, type=float,
help='the epsilon value used to generate mixup attack ')
parser.add_argument('--lpips_weight', default=3, type=float,
help='the value of weight of lpips term in inner maximization')
parser.add_argument('--use_CE', default=1, type=int,
help='uses CE loss for inner maximization when set to 1 else uses KL loss')
parser.add_argument('--auto', default=0.5, type=float,
help='0 for no autoaugment 0.5 for autoaugment with probabilty 0.5 and 1 for autoaugment with probability 1')
parser.add_argument('--tau_swa_list', type=float, nargs='*', default=[0.995,0.9996,0.9998], help='The tau values for SWA')
parser.add_argument('--label_smoothing', default=1, type=int,
help='put it as 1 it want to use label smoothing for the clean loss in outer minimization')
parser.add_argument('--OAAT_warmup', default=1, type=int,
help='put it as 1 if want to use linear warmup of 10 epochs')
parser.add_argument('--alternate_iter_eps', default=12/255, type=float,
help='the epsilon value after which alternate iters start ')
elif default_arg == 'SVHN_PRN18':
parser.add_argument('--arch', type=str, default='PreActResNet18', choices=['ResNet18', 'PreActResNet18','WideResNet34'])
parser.add_argument('--epochs', type=int, default=110, metavar='N',
help='number of epochs to train')
parser.add_argument('--data', type=str, default='SVHN', choices=['CIFAR10', 'CIFAR100','SVHN'])
parser.add_argument('--weight-decay', '--wd', default=5e-4,
type=float, metavar='W')
parser.add_argument('--lr', type=float, default=0.05, metavar='LR',
help='learning rate')
parser.add_argument('--epsilon', default=12/255, type=float,
help='perturbation')
parser.add_argument('--beta', default=4, type=float,
help='regularization, i.e., 1/lambda in TRADES inital value of beta')
parser.add_argument('--model-dir', default='./model-svhn-PreactResNet',
help='directory of model for saving checkpoint')
parser.add_argument('--beta_final', default=4, type=float,
help='the final value of beta at the end of training ')
parser.add_argument('--mixup_alpha', default=0.25, type=float,
help='the value of mixup coeeficient in KL loss ')
parser.add_argument('--mixup_epsilon', default=12/255, type=float,
help='the epsilon value used to generate mixup attack ')
parser.add_argument('--lpips_weight', default=3, type=float,
help='the value of weight of lpips term in inner maximization')
parser.add_argument('--use_CE', default=0, type=int,
help='uses CE loss for inner maximization when set to 1 else uses KL loss')
parser.add_argument('--auto', default=1, type=float,
help='0 for no autoaugment 0.5 for autoaugment with probabilty 0.5 and 1 for autoaugment with probability 1')
parser.add_argument('--tau_swa_list', type=float, nargs='*', default=[0.995,0.9996,0.9998], help='The tau values for SWA')
parser.add_argument('--label_smoothing', default=0, type=int,
help='put it as 1 it want to use label smoothing for the clean loss in outer minimization')
parser.add_argument('--OAAT_warmup', default=0, type=int,
help='put it as 1 if want to use linear warmup of 10 epochs')
parser.add_argument('--alternate_iter_eps', default=9/255, type=float,
help='the epsilon value after which alternate iters start ')
else:
print("Use_default not Found")
exit
parser.add_argument('--batch-size', type=int, default=128, metavar='N',help='input batch size for training (default: 128)')
parser.add_argument('--test-batch-size', type=int, default=128, metavar='N',help='input batch size for testing (default: 128)')
parser.add_argument('--start_epoch', type=int, default=1, metavar='N',help='resume training from which epoch')
parser.add_argument('--data-path', type=str, default='../data',help='where is the dataset')
parser.add_argument('--momentum', type=float, default=0.9, metavar='M',help='SGD momentum')
parser.add_argument('--no-cuda', action='store_true', default=False,help='disables CUDA training')
parser.add_argument('--norm', default='l_inf', type=str, choices=['l_inf'],help='The threat model')
parser.add_argument('--resume-model', default='', type=str,help='path of model to resume training')
parser.add_argument('--resume-optim', default='', type=str,help='path of optimizer to resume training')
parser.add_argument('--save-freq', '-s', default=1, type=int, metavar='N',help='save frequency')
parser.add_argument('--awp-gamma', default=0.005, type=float,help='whether or not to add parametric noise')
parser.add_argument('--awp-warmup', default=10, type=int,help='We could apply AWP after some epochs for accelerating.')
parser.add_argument('--swa_save_epoch', default=1, type=int,help='Start saving SWA models after this epoch')
parser.add_argument('--lr_schedule', default='cosine',choices=['cosine', 'step'],help='schedule used for training')
parser.add_argument('--exp_name', default='OAAT',help='name of the method used for training')
parser.add_argument('--seed', type=int, default=1, metavar='S',help='random seed (default: 1)')
parser.add_argument('--use_defaults', type=str, default='CIFAR10_RN18' ,choices=['NONE','CIFAR10_RN18', 'CIFAR10_WRN','CIFAR100_WRN', 'CIFAR100_PRN18','SVHN_PRN18'],help='Use None is want to use the hyperparamters passed in the python training command else use the desired set of default hyperparameters')
parser.add_argument('--wandb-run', default="OAAT")
parser.add_argument('--wandb-notes', default="OAAT")
parser.add_argument('--wandb-project', default="OAAT")
parser.add_argument('--wandb-dir', default="./wandb_log")
args = parser.parse_args()
return args
| 73.252336
| 309
| 0.635813
| 2,083
| 15,676
| 4.68843
| 0.103697
| 0.102294
| 0.193221
| 0.040958
| 0.8547
| 0.842515
| 0.814151
| 0.810055
| 0.810055
| 0.809851
| 0
| 0.037028
| 0.228183
| 15,676
| 213
| 310
| 73.596244
| 0.770146
| 0.006188
| 0
| 0.748744
| 0
| 0.030151
| 0.401002
| 0.006358
| 0
| 0
| 0
| 0
| 0
| 1
| 0.005025
| false
| 0.005025
| 0.005025
| 0
| 0.015075
| 0.005025
| 0
| 0
| 0
| null | 0
| 1
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
c371e6617a9041f96377af9eeb02b83b94cd8daa
| 5,471
|
py
|
Python
|
seg/lib/models/nets/fcnet.py
|
Frank-Abagnal/HRFormer
|
d7d362770de8648f8e0a379a71cee25f42954503
|
[
"MIT"
] | 254
|
2021-08-13T10:05:22.000Z
|
2022-03-25T09:21:45.000Z
|
seg/lib/models/nets/fcnet.py
|
Sense-X/HRFormer
|
1245b88b5824fbd8cdb358b5ee909a4e537a2ef5
|
[
"MIT"
] | 17
|
2021-09-08T01:40:49.000Z
|
2022-03-23T10:53:47.000Z
|
seg/lib/models/nets/fcnet.py
|
Sense-X/HRFormer
|
1245b88b5824fbd8cdb358b5ee909a4e537a2ef5
|
[
"MIT"
] | 48
|
2021-08-13T14:06:58.000Z
|
2022-03-30T02:41:26.000Z
|
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: RainbowSecret
## Microsoft Research
## yuyua@microsoft.com
## Copyright (c) 2018
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the root directory of this source tree
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
import pdb
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from lib.models.backbones.backbone_selector import BackboneSelector
from lib.models.tools.module_helper import ModuleHelper
class FcnNet(nn.Module):
def __init__(self, configer):
self.inplanes = 128
super(FcnNet, self).__init__()
self.configer = configer
self.num_classes = self.configer.get("data", "num_classes")
self.backbone = BackboneSelector(configer).get_backbone()
# extra added layers
if "wide_resnet38" in self.configer.get("network", "backbone"):
in_channels = [2048, 4096]
elif "mobilenetv2" in self.configer.get("network", "backbone"):
in_channels = [160, 320]
else:
in_channels = [1024, 2048]
self.cls_head = nn.Sequential(
nn.Conv2d(in_channels[1], 512, kernel_size=3, stride=1, padding=1),
ModuleHelper.BNReLU(512, bn_type=self.configer.get("network", "bn_type")),
nn.Dropout2d(0.10),
nn.Conv2d(
512, self.num_classes, kernel_size=1, stride=1, padding=0, bias=False
),
)
self.dsn_head = nn.Sequential(
nn.Conv2d(in_channels[0], 512, kernel_size=3, stride=1, padding=1),
ModuleHelper.BNReLU(512, bn_type=self.configer.get("network", "bn_type")),
nn.Dropout2d(0.10),
nn.Conv2d(
512, self.num_classes, kernel_size=1, stride=1, padding=0, bias=False
),
)
if "mobilenetv2" in self.configer.get("network", "backbone"):
self.cls_head = nn.Sequential(
nn.Conv2d(in_channels[1], 256, kernel_size=3, stride=1, padding=1),
ModuleHelper.BNReLU(
256, bn_type=self.configer.get("network", "bn_type")
),
nn.Dropout2d(0.10),
nn.Conv2d(
256,
self.num_classes,
kernel_size=1,
stride=1,
padding=0,
bias=False,
),
)
self.dsn_head = nn.Sequential(
nn.Conv2d(in_channels[0], 128, kernel_size=3, stride=1, padding=1),
ModuleHelper.BNReLU(
128, bn_type=self.configer.get("network", "bn_type")
),
nn.Dropout2d(0.10),
nn.Conv2d(
128,
self.num_classes,
kernel_size=1,
stride=1,
padding=0,
bias=False,
),
)
def forward(self, x_):
x = self.backbone(x_)
aux_x = self.dsn_head(x[-2])
x = self.cls_head(x[-1])
aux_x = F.interpolate(
aux_x, size=(x_.size(2), x_.size(3)), mode="bilinear", align_corners=True
)
x = F.interpolate(
x, size=(x_.size(2), x_.size(3)), mode="bilinear", align_corners=True
)
return aux_x, x
class FcnNet_wo_dsn(nn.Module):
def __init__(self, configer):
self.inplanes = 128
super(FcnNet_wo_dsn, self).__init__()
self.configer = configer
self.num_classes = self.configer.get("data", "num_classes")
self.backbone = BackboneSelector(configer).get_backbone()
# extra added layers
if "wide_resnet38" in self.configer.get("network", "backbone"):
in_channels = [2048, 4096]
elif "mobilenetv2" in self.configer.get("network", "backbone"):
in_channels = [160, 320]
else:
in_channels = [1024, 2048]
self.cls_head = nn.Sequential(
nn.Conv2d(in_channels[1], 512, kernel_size=3, stride=1, padding=1),
ModuleHelper.BNReLU(512, bn_type=self.configer.get("network", "bn_type")),
nn.Dropout2d(0.10),
nn.Conv2d(
512, self.num_classes, kernel_size=1, stride=1, padding=0, bias=True
),
)
if "mobilenetv2" in self.configer.get("network", "backbone"):
self.cls_head = nn.Sequential(
nn.Conv2d(in_channels[1], 256, kernel_size=3, stride=1, padding=1),
ModuleHelper.BNReLU(
256, bn_type=self.configer.get("network", "bn_type")
),
nn.Dropout2d(0.10),
nn.Conv2d(
256,
self.num_classes,
kernel_size=1,
stride=1,
padding=0,
bias=False,
),
)
def forward(self, x_):
x = self.backbone(x_)
x = self.cls_head(x[-1])
x = F.interpolate(
x, size=(x_.size(2), x_.size(3)), mode="bilinear", align_corners=True
)
return x
| 37.472603
| 87
| 0.50265
| 600
| 5,471
| 4.42
| 0.181667
| 0.081448
| 0.079186
| 0.099548
| 0.832202
| 0.832202
| 0.821644
| 0.821644
| 0.821644
| 0.805053
| 0
| 0.05772
| 0.357156
| 5,471
| 145
| 88
| 37.731034
| 0.696332
| 0.071833
| 0
| 0.754098
| 0
| 0
| 0.060655
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.032787
| false
| 0
| 0.057377
| 0
| 0.122951
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
6f1992d4f5eff7a9a5d4e72d3827bc8ff4c6a3ad
| 200
|
py
|
Python
|
cougar/graphs/__init__.py
|
Swall0w/cougar
|
9161b2b1d0c256f4bb952ec190351684f28ec1b7
|
[
"MIT"
] | 1
|
2019-11-23T12:20:50.000Z
|
2019-11-23T12:20:50.000Z
|
cougar/graphs/__init__.py
|
Swall0w/cougar
|
9161b2b1d0c256f4bb952ec190351684f28ec1b7
|
[
"MIT"
] | 1
|
2022-01-13T01:41:34.000Z
|
2022-01-13T01:41:34.000Z
|
cougar/graphs/__init__.py
|
Swall0w/cougar
|
9161b2b1d0c256f4bb952ec190351684f28ec1b7
|
[
"MIT"
] | null | null | null |
from cougar.graphs import loss
from cougar.graphs import modules
from cougar.graphs import models
from cougar.graphs import backbone
__all__ = ['loss', 'modules', 'backbone', 'models',
]
| 22.222222
| 51
| 0.725
| 25
| 200
| 5.64
| 0.36
| 0.283688
| 0.453901
| 0.624113
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.18
| 200
| 8
| 52
| 25
| 0.859756
| 0
| 0
| 0
| 0
| 0
| 0.125
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.666667
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
6f1f19de7037b2f90f2155eee5eb1232eb9659d5
| 5,854
|
py
|
Python
|
appi2c/ext/device/device_forms.py
|
andrequeiroz2/appi2c
|
82e0a6e48ae69f301173c6d8c5990ee083c95da3
|
[
"MIT"
] | 9
|
2020-12-28T14:37:11.000Z
|
2021-02-01T10:16:19.000Z
|
appi2c/ext/device/device_forms.py
|
andrequeiroz2/appi2c
|
82e0a6e48ae69f301173c6d8c5990ee083c95da3
|
[
"MIT"
] | 2
|
2021-01-04T11:57:10.000Z
|
2021-01-28T22:06:38.000Z
|
appi2c/ext/device/device_forms.py
|
andrequeiroz2/appi2c
|
82e0a6e48ae69f301173c6d8c5990ee083c95da3
|
[
"MIT"
] | 1
|
2021-01-28T21:37:19.000Z
|
2021-01-28T21:37:19.000Z
|
from appi2c.ext.device.device_models import Device
from flask_wtf import FlaskForm
from wtforms import (StringField,
SubmitField,
SelectField,
IntegerField)
from wtforms.validators import InputRequired, Length, ValidationError
from wtforms_sqlalchemy.fields import QuerySelectField
from appi2c.ext.group.group_controller import choice_query
from flask_login import current_user
from wtforms.widgets import HiddenInput
def validator_data_select(form, field):
if field.data is None:
raise ValidationError('Select an option')
def validator_topic(form, field):
if len(field.data) > 120:
raise ValidationError('Max 120 digits')
if '/' not in field.data:
raise ValidationError('Invalid topic')
def validator_topic_not_imput(form, field):
if len(field.data) > 0:
if len(field.data) > 120:
raise ValidationError('Max 120 digits')
if '/' not in field.data:
raise ValidationError('Invalid topic')
else:
pass
class DeviceSwitchForm(FlaskForm):
groups = QuerySelectField('Group', validators=[InputRequired(), validator_data_select], query_factory=choice_query, get_label='name')
name = StringField('Name', validators=[InputRequired(), Length(min=5, max=60, message=('Max 60 digits'))])
topic_pub = StringField('Topic Publish', validators=[InputRequired(), validator_topic])
last_will_topic = StringField('Topic Last Will', validators=[validator_topic_not_imput])
command_on = StringField('On Command', validators=[InputRequired(), Length(max=60, message=('Max 60 digits'))])
command_off = StringField('Off Command', validators=[InputRequired(), Length(max=60, message=('Max 60 digits'))])
qos = SelectField("Qos", choices=[(0, 0), (1, 1), (2, 2)], validators=[InputRequired()])
retained = SelectField('Retained', choices=[(False, False), (True, True)], default=True)
icon_id = IntegerField('icon_id', default=1)
submit = SubmitField('Insert')
def validate_name(self, name):
device = Device.query.filter_by(name=name.data).filter_by(user_id=current_user.id).first()
if device is not None:
raise ValidationError('Please use a different device name.')
class DeviceSensorForm(FlaskForm):
groups = QuerySelectField('Group', validators=[InputRequired(), validator_data_select], query_factory=choice_query, get_label='name')
name = StringField('Name', validators=[InputRequired(), Length(min=1, max=60, message=('Max 60 digits'))])
topic_sub = StringField('Topic Subscrib', validators=[InputRequired(), validator_topic])
measure = StringField('Measure', validators=[Length(min=3, max=60, message=('Min 3 and Max 60 digits'))])
postfix = StringField('Postfix', validators=[Length(max=3, message=('Max 3 digits'))])
last_will_topic = StringField('Topic Last Will', validators=[validator_topic_not_imput])
qos = SelectField("Qos", choices=[(0, 0), (1, 1), (2, 2)], validators=[InputRequired()])
icon_id = IntegerField('icon_id')
submit = SubmitField('Insert')
def validate_name(self, name):
device = Device.query.filter_by(name=name.data).filter_by(user_id=current_user.id).first()
if device is not None:
raise ValidationError('Please use a different device name.')
class EditSwitchForm(FlaskForm):
id = IntegerField(widget=HiddenInput())
groups = QuerySelectField('Group', validators=[InputRequired(), validator_data_select], query_factory=choice_query, get_label='name')
name = StringField('Name', validators=[InputRequired(), Length(max=60, message=('Max 60 digits'))])
topic_pub = StringField('Topic Publish', validators=[InputRequired(), validator_topic])
last_will_topic = StringField('Topic Last Will', validators=[validator_topic_not_imput])
command_on = StringField('On Command', validators=[InputRequired(), Length(max=60, message=('Max 60 digits'))])
command_off = StringField('Off Command', validators=[InputRequired(), Length(max=60, message=('Max 60 digits'))])
qos = SelectField("Qos", choices=[(0, 0), (1, 1), (2, 2)], validators=[InputRequired()], default=0)
retained = SelectField('Retained', validators=[InputRequired(), validator_data_select], choices=[("False", False), ("True", True)])
icon_id = IntegerField('icon_id')
submit = SubmitField('Confirm')
def validate_name(self, name):
device = Device.query.filter_by(name=name.data).filter_by(user_id=current_user.id).first()
if device is not None:
if self.id.data != device.id:
raise ValidationError('Please use a different device name.')
class EditSensorForm(FlaskForm):
id = IntegerField(widget=HiddenInput())
groups = QuerySelectField('Group', validators=[InputRequired(), validator_data_select], query_factory=choice_query, get_label='name')
name = StringField('Name', validators=[InputRequired(), Length(max=60, message=('Max 60 digits'))])
topic_sub = StringField('Topic Subscrib', validators=[InputRequired(), validator_topic])
measure = StringField('Measure', validators=[Length(min=3, max=60, message=('Min 3 and Max 60 digits'))])
postfix = StringField('Postfix', validators=[Length(max=10, message=('Max 10 digits'))])
last_will_topic = StringField('Topic Last Will', validators=[validator_topic_not_imput])
qos = SelectField("Qos", choices=[(0, 0), (1, 1), (2, 2)], validators=[InputRequired()])
icon_id = IntegerField('icon_id')
submit = SubmitField('Confirm')
def validate_name(self, name):
device = Device.query.filter_by(name=name.data).filter_by(user_id=current_user.id).first()
if device is not None:
if self.id.data != device.id:
raise ValidationError('Please use a different device name.')
| 53.218182
| 137
| 0.700205
| 707
| 5,854
| 5.663366
| 0.148515
| 0.120629
| 0.02997
| 0.02997
| 0.834915
| 0.812937
| 0.796953
| 0.796953
| 0.796953
| 0.795704
| 0
| 0.018949
| 0.161599
| 5,854
| 109
| 138
| 53.706422
| 0.796862
| 0
| 0
| 0.651685
| 0
| 0
| 0.121968
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.078652
| false
| 0.011236
| 0.089888
| 0
| 0.662921
| 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
| 1
| 0
|
0
| 7
|
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