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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
5f7776b184cd279cad751b395354da54e4419f28
| 14,297
|
py
|
Python
|
tests/test_py_ev.py
|
JBielan/py_ev
|
0a2d48235b8ff2268254c151a179a2ece40cbd37
|
[
"MIT"
] | 5
|
2020-03-31T17:06:29.000Z
|
2020-07-01T22:43:42.000Z
|
tests/test_py_ev.py
|
JBielan/py_ev
|
0a2d48235b8ff2268254c151a179a2ece40cbd37
|
[
"MIT"
] | null | null | null |
tests/test_py_ev.py
|
JBielan/py_ev
|
0a2d48235b8ff2268254c151a179a2ece40cbd37
|
[
"MIT"
] | null | null | null |
from py_ev.py_ev import Evaluator
ev = Evaluator()
def test_reset():
ev.reset()
assert len(ev.deck) == 52
assert ev.board == []
def test_build_deck():
ev.reset()
assert len(ev.build_deck()) == 52
assert ev.new_deck == [(2, 1), (2, 2), (2, 3), (2, 4), (3, 1), (3, 2), (3, 3), (3, 4),
(4, 1), (4, 2), (4, 3), (4, 4), (5, 1), (5, 2), (5, 3), (5, 4),
(6, 1), (6, 2), (6, 3), (6, 4), (7, 1), (7, 2), (7, 3), (7, 4),
(8, 1), (8, 2), (8, 3), (8, 4), (9, 1), (9, 2), (9, 3), (9, 4),
(10, 1), (10, 2), (10, 3), (10, 4), (11, 1), (11, 2), (11, 3), (11, 4),
(12, 1), (12, 2), (12, 3), (12, 4), (13, 1), (13, 2), (13, 3), (13, 4),
(14, 1), (14, 2), (14, 3), (14, 4)]
def test_deal():
ev.reset()
assert len(ev.deal(5)) == 5
assert len(ev.deck) == 47
def test_set_cards():
ev.reset()
ev.board = ev.set_cards((14, 3), (2, 1), (12, 2))
assert ev.board == [(14, 3), (2, 1), (12, 2)]
assert len(ev.deck) == 49
def test_analyze_board():
ev.reset()
cards = [(6, 2), (6, 3), (3, 3), (2, 1)]
board = [(7, 4), (5, 2), (12, 1), (13, 4), (7, 2)]
total, pairness, suitness = ev.analyze_board(cards, board)
assert pairness == {14: 0, 13: 1, 12: 1, 11: 0, 10: 0, 9: 0, 8: 0, 7: 2, 6: 2, 5: 1, 4: 0, 3: 1, 2: 1}
assert suitness == {1: 2, 2: 3, 3: 2, 4: 2}
assert total == [(13, 4), (12, 1), (7, 4), (7, 2), (6, 2), (6, 3), (5, 2), (3, 3), (2, 1)]
def test_is_str8():
ev = Evaluator()
cards, pairness, suitness = ev.analyze_board([(11, 2), (10, 3), (9, 4), (14, 3)],
[(2, 2), (3, 3), (8, 1), (7, 2), (4, 3)])
result = ev.is_str8(cards, pairness, suitness)
assert result == (5, 11, 'Straight')
cards, pairness, suitness = ev.analyze_board([(11, 2), (10, 3), (12, 4), (14, 3)],
[(2, 2), (3, 3), (13, 1), (7, 2), (4, 3)])
result = ev.is_str8(cards, pairness, suitness)
assert result == (5, 14, 'Straight')
cards, pairness, suitness = ev.analyze_board([(11, 2), (10, 3), (5, 4), (14, 3)],
[(2, 2), (3, 3), (13, 1), (7, 2), (4, 3)])
result = ev.is_str8(cards, pairness, suitness)
assert result == (5, 5, 'Straight')
cards, pairness, suitness = ev.analyze_board([(11, 2), (10, 3), (5, 4), (14, 3)],
[(2, 2), (3, 3), (13, 1), (7, 2), (10, 3)])
result = ev.is_str8(cards, pairness, suitness)
assert result == (False, 0, None)
def test_is_flush():
ev = Evaluator()
cards, pairness, suitness = ev.analyze_board([(11, 2), (9, 2)],
[(2, 2), (3, 2), (8, 1), (7, 2), (4, 3)])
result = ev.is_flush(cards, pairness, suitness)
assert result == (6, 110000+9000+700+30+2, 'Flush')
cards, pairness, suitness = ev.analyze_board([(11, 2), (10, 2)],
[(2, 2), (9, 2), (14, 2), (7, 2), (4, 2)])
result = ev.is_flush(cards, pairness, suitness)
assert result == (6, 140000+11000+1000+90+7, 'Flush')
cards, pairness, suitness = ev.analyze_board([(11, 2), (9, 2)],
[(2, 2), (10, 2), (14, 2), (7, 3), (4, 3)])
result = ev.is_flush(cards, pairness, suitness)
assert result == (6, 140000+11000+1000+90+2, 'Flush')
cards, pairness, suitness = ev.analyze_board([(2, 2), (4, 2)],
[(5, 2), (7, 2), (3, 2), (7, 4), (14, 2)])
result = ev.is_flush(cards, pairness, suitness)
assert result == (6, 140000+7000+500+40+3, 'Flush')
cards, pairness, suitness = ev.analyze_board([(2, 2), (3, 2)],
[(5, 2), (7, 4), (8, 3), (7, 4), (4, 3)])
result = ev.is_flush(cards, pairness, suitness)
assert result == (False, 0, None)
def test_is_quad():
ev = Evaluator()
cards, pairness, suitness = ev.analyze_board([(11, 2), (10, 3), (9, 2), (14, 3)],
[(2, 2), (3, 2), (8, 1), (7, 2), (4, 3)])
result = ev.is_quad(cards, pairness, suitness)
assert result == (False, 0, None)
cards, pairness, suitness = ev.analyze_board([(11, 2), (11, 3), (9, 2), (14, 3)],
[(2, 2), (3, 2), (11, 1), (7, 2), (4, 3)])
result = ev.is_quad(cards, pairness, suitness)
assert result == (False, 0, None)
cards, pairness, suitness = ev.analyze_board([(11, 2), (11, 3), (9, 2), (14, 3)],
[(2, 2), (3, 2), (11, 1), (7, 2), (11, 3)])
result = ev.is_quad(cards, pairness, suitness)
assert result == (8, 110+14, 'Four of a Kind')
cards, pairness, suitness = ev.analyze_board([(11, 2), (11, 3), (14, 2), (14, 3)],
[(2, 2), (14, 2), (11, 1), (14, 2), (11, 3)])
result = ev.is_quad(cards, pairness, suitness)
assert result == (8, 140+11, 'Four of a Kind')
cards, pairness, suitness = ev.analyze_board([(11, 2), (11, 3)],
[(2, 2), (11, 4), (11, 1)])
result = ev.is_quad(cards, pairness, suitness)
assert result == (8, 110+2, 'Four of a Kind')
def test_is_fullhouse():
ev = Evaluator()
cards, pairness, suitness = ev.analyze_board([(11, 2), (11, 3)],
[(2, 2), (11, 1), (8, 1), (4, 2), (4, 3)])
result = ev.is_fullhouse(cards, pairness, suitness)
assert result == (7, 110+4, 'Full House')
cards, pairness, suitness = ev.analyze_board([(11, 2), (11, 3)],
[(12, 2), (11, 1), (12, 1), (12, 4), (4, 3)])
result = ev.is_fullhouse(cards, pairness, suitness)
assert result == (7, 120+11, 'Full House')
cards, pairness, suitness = ev.analyze_board([(11, 2), (11, 3)],
[(11, 4), (11, 1), (5, 1), (12, 4), (4, 3)])
result = ev.is_fullhouse(cards, pairness, suitness)
assert result == (False, 0, None)
cards, pairness, suitness = ev.analyze_board([(11, 2), (11, 3)],
[(11, 4), (10, 1), (5, 1), (12, 4), (4, 3)])
result = ev.is_fullhouse(cards, pairness, suitness)
assert result == (False, 0, None)
cards, pairness, suitness = ev.analyze_board([(11, 2), (11, 3)],
[(10, 4), (10, 1), (5, 1), (12, 4), (4, 3)])
result = ev.is_fullhouse(cards, pairness, suitness)
assert result == (False, 0, None)
def test_is_3_of_a_kind():
ev = Evaluator()
cards, pairness, suitness = ev.analyze_board([(11, 2), (11, 3)],
[(2, 2), (11, 1), (8, 1), (3, 2), (4, 3)])
result = ev.is_3_of_a_kind(cards, pairness, suitness)
assert result == (4, 1100+80+4, 'Three of a kind')
cards, pairness, suitness = ev.analyze_board([(11, 2), (11, 3)],
[(2, 2), (10, 1), (8, 1), (3, 2), (4, 3)])
result = ev.is_3_of_a_kind(cards, pairness, suitness)
assert result == (False, 0, None)
cards, pairness, suitness = ev.analyze_board([(11, 2), (12, 3)],
[(2, 2), (2, 1), (2, 3), (3, 2), (4, 3)])
result = ev.is_3_of_a_kind(cards, pairness, suitness)
assert result == (4, 200+120+11, 'Three of a kind')
cards, pairness, suitness = ev.analyze_board([(11, 2), (11, 3)],
[(2, 2), (2, 1), (5, 3), (3, 2), (4, 3)])
result = ev.is_3_of_a_kind(cards, pairness, suitness)
assert result == (False, 0, None)
cards, pairness, suitness = ev.analyze_board([(12, 4), (10, 1)],
[(10, 4), (10, 2), (4, 1), (3, 2), (2, 3)])
result = ev.is_3_of_a_kind(cards, pairness, suitness)
assert result == (4, 1000+120+4, 'Three of a kind')
def test_is_2_pairs():
ev = Evaluator()
cards, pairness, suitness = ev.analyze_board([(11, 2), (11, 3)],
[(12, 2), (12, 1), (3, 1), (3, 2), (4, 3)])
result = ev.is_2_pairs(cards, pairness, suitness)
assert result == (3, 1314, 'Two pair')
cards, pairness, suitness = ev.analyze_board([(11, 2), (11, 3)],
[(12, 2), (12, 1), (3, 1), (3, 2), (14, 3)])
result = ev.is_2_pairs(cards, pairness, suitness)
assert result == (3, 1324, 'Two pair')
cards, pairness, suitness = ev.analyze_board([(11, 2), (11, 3)],
[(10, 2), (12, 1), (3, 1), (3, 2), (14, 3)])
result = ev.is_2_pairs(cards, pairness, suitness)
assert result == (3, 1144, 'Two pair')
cards, pairness, suitness = ev.analyze_board([(11, 2), (11, 3)],
[(10, 2), (12, 1), (2, 1), (3, 2), (14, 3)])
result = ev.is_2_pairs(cards, pairness, suitness)
assert result == (False, 0, None)
cards, pairness, suitness = ev.analyze_board([(11, 2), (11, 3)],
[(10, 2), (11, 1), (2, 1), (3, 2), (14, 3)])
result = ev.is_2_pairs(cards, pairness, suitness)
assert result == (False, 0, None)
cards, pairness, suitness = ev.analyze_board([(11, 2), (11, 3)],
[(11, 2), (11, 1), (2, 1), (3, 2), (14, 3)])
result = ev.is_2_pairs(cards, pairness, suitness)
assert result == (False, 0, None)
def test_is_pair():
ev = Evaluator()
cards, pairness, suitness = ev.analyze_board([(11, 2), (11, 3)],
[(13, 2), (14, 1), (2, 1), (3, 2), (4, 3)])
result = ev.is_pair(cards, pairness, suitness)
assert result == (2, 11000+1400+130+4, 'One pair')
cards, pairness, suitness = ev.analyze_board([(10, 2), (11, 3)],
[(13, 2), (14, 1), (2, 1), (3, 2), (4, 3)])
result = ev.is_pair(cards, pairness, suitness)
assert result == (False, 0, None)
cards, pairness, suitness = ev.analyze_board([(11, 2), (14, 3)],
[(13, 2), (14, 1), (2, 1), (3, 2), (4, 3)])
result = ev.is_pair(cards, pairness, suitness)
assert result == (2, 14000+1300+110+4, 'One pair')
def test_is_air():
ev = Evaluator()
cards, pairness, suitness = ev.analyze_board([(11, 2), (11, 3)],
[(13, 2), (14, 1), (2, 1), (3, 2), (4, 3)])
result = ev.is_air(cards, pairness, suitness)
assert result == (False, 0, None)
cards, pairness, suitness = ev.analyze_board([(11, 2), (10, 3)],
[(13, 2), (14, 1), (2, 1), (3, 2), (4, 3)])
result = ev.is_air(cards, pairness, suitness)
assert result == (1, 140000+13000+1100+100+4, 'High card')
cards, pairness, suitness = ev.analyze_board([(11, 2), (10, 3)],
[(13, 2), (14, 1), (2, 1), (3, 2), (2, 3)])
result = ev.is_air(cards, pairness, suitness)
assert result == (False, 0, None)
cards, pairness, suitness = ev.analyze_board([(11, 2), (10, 3)],
[(13, 2), (14, 1), (2, 1), (3, 2), (8, 3)])
result = ev.is_air(cards, pairness, suitness)
assert result == (1, 140000+13000+1100+100+8, 'High card')
def test_is_str8_flush():
ev = Evaluator()
cards, pairness, suitness = ev.analyze_board([(11, 2), (12, 2)],
[(13, 2), (14, 2), (10, 2), (3, 2), (4, 3)])
result = ev.is_str8_flush(cards, pairness, suitness)
assert result == (9, 14, 'Straight Flush')
cards, pairness, suitness = ev.analyze_board([(12, 2), (12, 2)],
[(13, 2), (14, 2), (10, 2), (3, 2), (4, 3)])
result = ev.is_str8_flush(cards, pairness, suitness)
assert result == (False, 0, None)
cards, pairness, suitness = ev.analyze_board([(12, 2), (13, 2)],
[(13, 2), (8, 2), (10, 2), (3, 2), (4, 3)])
result = ev.is_str8_flush(cards, pairness, suitness)
assert result == (False, 0, None)
cards, pairness, suitness = ev.analyze_board([(14, 2), (5, 2)],
[(13, 2), (8, 2), (2, 2), (3, 2), (4, 2)])
result = ev.is_str8_flush(cards, pairness, suitness)
assert result == (9, 5, 'Straight Flush')
cards, pairness, suitness = ev.analyze_board([(14, 2), (5, 2)],
[(13, 2), (8, 2), (2, 2), (3, 3), (4, 2)])
result = ev.is_str8_flush(cards, pairness, suitness)
assert result == (False, 0, None)
def test_evaluate():
ev = Evaluator()
assert ev.evaluate([(3, 2), (3, 3)], [(3, 4), (10, 1), (12, 3), (14, 1), (6, 4)]) > \
ev.evaluate([(10, 2), (12, 4)], [(3, 4), (10, 1), (12, 3), (14, 1), (6, 4)])
assert ev.evaluate([(3, 2), (3, 3)], [(3, 4), (10, 1), (12, 3), (14, 1), (6, 4)]) > \
ev.evaluate([(14, 2), (13, 4)], [(12, 4), (11, 1), (9, 3), (8, 1), (7, 4)])
assert ev.evaluate([(2, 2), (2, 3)], [(2, 4), (3, 1), (3, 3), (14, 1), (6, 4)]) > \
ev.evaluate([(14, 2), (13, 2)], [(12, 2), (11, 2), (9, 3), (8, 2), (7, 4)])
assert ev.evaluate([(2, 2), (2, 3)], [(4, 4), (3, 1), (5, 3), (7, 1), (8, 4)]) > \
ev.evaluate([(14, 2), (13, 2)], [(12, 2), (11, 2), (9, 3), (8, 1), (7, 4)])
def test_equity():
ev = Evaluator()
h1, h2, draw = ev.equity(3000000, [(14, 4), (14, 2)], [(8, 3), (9, 3)])
assert 77.12 <= h1 <= 77.32
assert 22.37 <= h2 <= 22.57
# h1, h2, draw = ev.equity(3000000, [(10, 3), (11, 3)], [(8, 3), (9, 3)])
# assert 66.53 <= h1 <= 66.73
# assert 32.01 <= h2 <= 32.21
h1, h2, draw = ev.equity(3000000, [(2, 2), (3, 1)], [(4, 3), (5, 4)])
assert 29.7 <= h1 <= 29.9
assert 49.77 <= h2 <= 49.97
| 45.531847
| 106
| 0.446527
| 2,028
| 14,297
| 3.074458
| 0.052268
| 0.218124
| 0.282919
| 0.172414
| 0.852767
| 0.828869
| 0.8085
| 0.796472
| 0.784924
| 0.753007
| 0
| 0.15957
| 0.336364
| 14,297
| 314
| 107
| 45.531847
| 0.497576
| 0.008883
| 0
| 0.49789
| 0
| 0
| 0.016729
| 0
| 0
| 0
| 0
| 0
| 0.257384
| 1
| 0.067511
| false
| 0
| 0.004219
| 0
| 0.07173
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 10
|
5f9f4594ecd3477b65068c7ab52c613673540387
| 164
|
py
|
Python
|
server/apps/stream/tests/__init__.py
|
iotile/iotile_cloud
|
9dc65ac86d3a730bba42108ed7d9bbb963d22ba6
|
[
"MIT"
] | null | null | null |
server/apps/stream/tests/__init__.py
|
iotile/iotile_cloud
|
9dc65ac86d3a730bba42108ed7d9bbb963d22ba6
|
[
"MIT"
] | null | null | null |
server/apps/stream/tests/__init__.py
|
iotile/iotile_cloud
|
9dc65ac86d3a730bba42108ed7d9bbb963d22ba6
|
[
"MIT"
] | null | null | null |
from .test_helper import *
from .test_stream_id import *
from .test_stream_variable import *
from .test_system_variable import *
from .test_virtual_stream import *
| 27.333333
| 35
| 0.817073
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| 0.32
| 0.448
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| 164
| 5
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| 32.8
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| true
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0
| 8
|
5fad5878a3175ce61ecd211a99683fd9e0030602
| 36,680
|
py
|
Python
|
app/tests/unit/test_conversion_parser.py
|
willsower/latex2speech
|
36a69bb5ee74e1ca362968604b4a554034c5f408
|
[
"MIT"
] | 3
|
2021-03-17T22:13:23.000Z
|
2021-08-30T20:35:39.000Z
|
app/tests/unit/test_conversion_parser.py
|
willsower/latex2speech
|
36a69bb5ee74e1ca362968604b4a554034c5f408
|
[
"MIT"
] | 50
|
2021-03-15T23:03:43.000Z
|
2021-07-14T14:22:45.000Z
|
app/tests/unit/test_conversion_parser.py
|
willsower/latex2speech
|
36a69bb5ee74e1ca362968604b4a554034c5f408
|
[
"MIT"
] | 3
|
2021-03-30T18:18:40.000Z
|
2021-04-14T17:51:26.000Z
|
import unittest
from unittest.mock import patch, Mock
import xml.etree.ElementTree as ET
import TexSoup
from SSMLParsing.text_element import TextElement
from SSMLParsing.root_element import RootElement
from SSMLParsing.break_element import BreakElement
from SSMLParsing.arg_element import ArgElement
from SSMLParsing.content_element import ContentElement
from SSMLParsing.emphasis_element import EmphasisElement
from SSMLParsing.prosody_element import ProsodyElement
import conversion_db
from conversion_parser import ConversionParser
class testConversionParser(unittest.TestCase):
'''
Tests basic text replacement in commands and environments.
'''
@patch('conversion_db.ConversionDB')
def testTextElement(self, MockConversionDB):
# Set up mock database
db = conversion_db.ConversionDB()
def mockCmdConversion(cmd):
if cmd == 'a':
return [TextElement('text 1')]
else:
return None
def mockEnvConversion(env):
if env == 'b':
return [TextElement('text 2'), ContentElement()]
else:
return None
def mockEnvDefinition(env):
if env == 'b':
return {'a': [TextElement('text 3')], 'type': None}
else:
return None
db.getCmdConversion = Mock(side_effect=mockCmdConversion)
db.getEnvConversion = Mock(side_effect=mockEnvConversion)
db.getEnvDefinition = Mock(side_effect=mockEnvDefinition)
# Set up TexSoup parse tree to be parsed
doc = TexSoup.TexSoup(r'\a\begin{b}\a\end{b}')
# Parse on the given db and tree
parser = ConversionParser(db)
ssmlParseTree = parser.parse(doc, test=True)
print("TESTING SSSML PARSE " + str(ssmlParseTree))
# Check resulting tree structure
self.assertIsInstance(ssmlParseTree, RootElement)
self.assertEqual(len(ssmlParseTree.children), 0)
self.assertEqual(ssmlParseTree.getHeadText().strip().replace(" ", " "), 'text 1 text 2 text 3')
'''
Tests the BreakElement with various attributes in both commands and
environments.
'''
@patch('conversion_db.ConversionDB')
def testBreakElement(self, MockConversionDB):
# Set up mock database
db = conversion_db.ConversionDB()
def mockCmdConversion(cmd):
if cmd == 'a':
return [BreakElement(time='3ms')]
else:
return None
def mockEnvConversion(env):
if env == 'b':
return [BreakElement(strength='strong'), ContentElement(), BreakElement(strength='weak')]
else:
return None
def mockEnvDefinition(env):
if env == 'b':
return {'a': [BreakElement(time='5ms', strength='x-weak')], 'mathmode': False}
else:
return None
db.getCmdConversion = Mock(side_effect=mockCmdConversion)
db.getEnvConversion = Mock(side_effect=mockEnvConversion)
db.getEnvDefinition = Mock(side_effect=mockEnvDefinition)
# Set up TexSoup parse tree to be parsed
doc = TexSoup.TexSoup(r'\a\begin{b}\a\end{b}')
# Parse on the given db and tree
parser = ConversionParser(db)
ssmlParseTree = parser.parse(doc, test=True)
# Check resulting tree structure
self.assertIsInstance(ssmlParseTree, RootElement)
self.assertEqual(len(ssmlParseTree.children), 4)
self.assertIsInstance(ssmlParseTree.children[0], BreakElement)
self.assertEqual(ssmlParseTree.children[0].getTime(), '3ms')
self.assertEqual(ssmlParseTree.children[0].getStrength(), None)
self.assertIsInstance(ssmlParseTree.children[1], BreakElement)
self.assertEqual(ssmlParseTree.children[1].getTime(), None)
self.assertEqual(ssmlParseTree.children[1].getStrength(), 'strong')
self.assertIsInstance(ssmlParseTree.children[2], BreakElement)
self.assertEqual(ssmlParseTree.children[2].getTime(), '5ms')
self.assertEqual(ssmlParseTree.children[2].getStrength(), 'x-weak')
self.assertIsInstance(ssmlParseTree.children[3], BreakElement)
self.assertEqual(ssmlParseTree.children[3].getTime(), None)
self.assertEqual(ssmlParseTree.children[3].getStrength(), 'weak')
'''
Tests the EmphasisElement with various attributes in both commands and
environments. One important test is here is ensuring the ContentElement
and ArgElement work properly while being children of an EmphasisElement.
'''
@patch('conversion_db.ConversionDB')
def testEmphasisElement(self, MockConversionDB):
# Set up mock database
db = conversion_db.ConversionDB()
def mockCmdConversion(cmd):
if cmd == 'a':
a = [EmphasisElement(level='strong'), ArgElement(1)]
a[0].insertChild(0, EmphasisElement(level='reduced'))
a[0].children[0].insertChild(0, ArgElement(2))
return a
else:
return None
def mockEnvConversion(env):
if env == 'b':
b = [ContentElement(), EmphasisElement(level='moderate'), ArgElement(2), EmphasisElement(level='none')]
b[1].insertChild(0, ContentElement())
b[1].insertChild(0, ArgElement(1))
b[3].insertChild(0, EmphasisElement(level='strong'))
return b
else:
return None
def mockEnvDefinition(env):
return None
db.getCmdConversion = Mock(side_effect=mockCmdConversion)
db.getEnvConversion = Mock(side_effect=mockEnvConversion)
db.getEnvDefinition = Mock(side_effect=mockEnvDefinition)
# Set up TexSoup parse tree to be parsed
doc = TexSoup.TexSoup(r'\a{1}{2}\begin{b}{3}{4}\a{5}{6}\end{b}')
# Parse on the given db and tree
parser = ConversionParser(db)
ssmlParseTree = parser.parse(doc, test=True)
self.assertIsInstance(ssmlParseTree, RootElement)
self.assertEqual(len(ssmlParseTree.children), 4)
self.assertIsInstance(ssmlParseTree.children[0], EmphasisElement)
self.assertEqual(ssmlParseTree.children[0].getHeadText(), '')
self.assertEqual(ssmlParseTree.children[0].getTailText().strip(), '1')
self.assertEqual(ssmlParseTree.children[0].getLevel(), 'strong')
self.assertEqual(len(ssmlParseTree.children[0].children), 1)
self.assertIsInstance(ssmlParseTree.children[0].children[0], EmphasisElement)
self.assertEqual(ssmlParseTree.children[0].children[0].getHeadText().strip(), '2')
self.assertEqual(ssmlParseTree.children[0].children[0].getTailText(), '')
self.assertEqual(ssmlParseTree.children[0].children[0].getLevel(), 'reduced')
self.assertIsInstance(ssmlParseTree.children[1], EmphasisElement)
self.assertEqual(ssmlParseTree.children[1].getHeadText(), '')
self.assertEqual(ssmlParseTree.children[1].getTailText().strip(), '5')
self.assertEqual(ssmlParseTree.children[1].getLevel(), 'strong')
self.assertEqual(len(ssmlParseTree.children[1].children), 1)
self.assertIsInstance(ssmlParseTree.children[1].children[0], EmphasisElement)
self.assertEqual(ssmlParseTree.children[1].children[0].getHeadText().strip(), '6')
self.assertEqual(ssmlParseTree.children[1].children[0].getTailText(), '')
self.assertEqual(ssmlParseTree.children[1].children[0].getLevel(), 'reduced')
self.assertIsInstance(ssmlParseTree.children[2], EmphasisElement)
self.assertEqual(ssmlParseTree.children[2].getHeadText().strip(), '3')
self.assertEqual(ssmlParseTree.children[2].getTailText().strip(), '4')
self.assertEqual(ssmlParseTree.children[2].getLevel(), 'moderate')
self.assertEqual(len(ssmlParseTree.children[2].children), 1)
self.assertIsInstance(ssmlParseTree.children[2].children[0], EmphasisElement)
self.assertEqual(ssmlParseTree.children[2].children[0].getHeadText(), '')
self.assertEqual(ssmlParseTree.children[2].children[0].getTailText().strip(), '5')
self.assertEqual(ssmlParseTree.children[2].children[0].getLevel(), 'strong')
self.assertEqual(len(ssmlParseTree.children[2].children), 1)
self.assertIsInstance(ssmlParseTree.children[2].children[0].children[0], EmphasisElement)
self.assertEqual(ssmlParseTree.children[2].children[0].children[0].getHeadText().strip(), '6')
self.assertEqual(ssmlParseTree.children[2].children[0].children[0].getTailText(), '')
self.assertEqual(ssmlParseTree.children[2].children[0].children[0].getLevel(), 'reduced')
self.assertIsInstance(ssmlParseTree.children[3], EmphasisElement)
self.assertEqual(ssmlParseTree.children[3].getHeadText(), '')
self.assertEqual(ssmlParseTree.children[3].getTailText(), '')
self.assertEqual(ssmlParseTree.children[3].getLevel(), 'none')
self.assertEqual(len(ssmlParseTree.children[3].children), 1)
self.assertIsInstance(ssmlParseTree.children[3].children[0], EmphasisElement)
self.assertEqual(ssmlParseTree.children[3].children[0].getHeadText(), '')
self.assertEqual(ssmlParseTree.children[3].children[0].getTailText(), '')
self.assertEqual(ssmlParseTree.children[3].children[0].getLevel(), 'strong')
'''
Tests that arguments are properly expanded when the ArgElement object
is used in cmd/env definitions.
'''
@patch('conversion_db.ConversionDB')
def testArgElement(self, MockConversionDB):
# Set up mock database
db = conversion_db.ConversionDB()
def mockCmdConversion(cmd):
if cmd == 'a':
a = [ArgElement(2), ArgElement('1', argType='bracket')]
return a
elif cmd == 'd':
d = [BreakElement()]
return d
else:
return None
def mockEnvConversion(env):
if env == 'b':
b = [ArgElement(1, 'bracket'), ArgElement(4, argType='brace'), ContentElement()]
return b
else:
return None
def mockEnvDefinition(env):
if env == 'b':
return {'a': [ArgElement(1), ArgElement('2', argType='bracket')], \
'c': [ArgElement(3)], 'mathmode': False}
else:
return None
db.getCmdConversion = Mock(side_effect=mockCmdConversion)
db.getEnvConversion = Mock(side_effect=mockEnvConversion)
db.getEnvDefinition = Mock(side_effect=mockEnvDefinition)
# Set up TexSoup parse tree to be parsed
doc = TexSoup.TexSoup(r'\a{1}{\a{2}[3]{\d}}[\d]\begin{b}{4}{5}[6]{7}{\a[8]{9}{10}[\d]}\d\a[11]{12}[13]\d\c{14}{15}{\d 16}\d\end{b}')
# Should be <speak> <break/> 3 <break/> 6 9 <break/> <break/> 12 13 <break/> <break/> 16 <break/> <speak/>
# ^ Cmd ^ Env say ^ Env contents
parser = ConversionParser(db)
ssmlParseTree = parser.parse(doc, test=True)
self.assertIsInstance(ssmlParseTree, RootElement)
self.assertEqual(ssmlParseTree.getHeadText(), '')
self.assertEqual(len(ssmlParseTree.children), 7)
self.assertIsInstance(ssmlParseTree.children[0], BreakElement)
self.assertEqual(ssmlParseTree.children[0].getTailText().strip(), '3')
self.assertIsInstance(ssmlParseTree.children[1], BreakElement)
self.assertEqual(ssmlParseTree.children[1].getTailText().strip().replace(" ", " "), '6 9')
self.assertIsInstance(ssmlParseTree.children[2], BreakElement)
self.assertEqual(ssmlParseTree.children[2].getTailText(), '')
self.assertIsInstance(ssmlParseTree.children[3], BreakElement)
self.assertEqual(ssmlParseTree.children[3].getTailText().strip().replace(" ", " "), '12 13')
self.assertIsInstance(ssmlParseTree.children[4], BreakElement)
self.assertEqual(ssmlParseTree.children[4].getTailText(), '')
self.assertIsInstance(ssmlParseTree.children[5], BreakElement)
self.assertEqual(ssmlParseTree.children[5].getTailText(), ' 16')
self.assertIsInstance(ssmlParseTree.children[6], BreakElement)
self.assertEqual(ssmlParseTree.children[6].getTailText(), '')
'''
Testing environments and ensuring undefined environments still have
their contents read out, while defined environments without the content
tag are not.
'''
@patch('conversion_db.ConversionDB')
def testEnvironments(self, MockConversionDB):
# Set up mock database
db = conversion_db.ConversionDB()
def mockCmdConversion(cmd):
if cmd == 'a':
a = [TextElement('text1')]
return a
else:
return None
def mockEnvConversion(env):
if env == 'a':
a = [TextElement('text2')]
return a
if env == 'b':
b = [ContentElement()]
return b
else:
return None
def mockEnvDefinition(env):
if env == 'b':
return {'a': [TextElement('text3')], 'mathmode': False}
else:
return None
db.getCmdConversion = Mock(side_effect=mockCmdConversion)
db.getEnvConversion = Mock(side_effect=mockEnvConversion)
db.getEnvDefinition = Mock(side_effect=mockEnvDefinition)
# Set up TexSoup parse tree to be parsed
doc = TexSoup.TexSoup(r'\begin{a}\a\end{a}\begin{c}\begin{a}\a\end{a}\end{c}\begin{c}\begin{b}\a\end{b}\end{c}')
# Parse on the given db and tree
parser = ConversionParser(db)
ssmlParseTree = parser.parse(doc, test=True)
self.assertIsInstance(ssmlParseTree, RootElement)
self.assertEqual(len(ssmlParseTree.children), 0)
self.assertEqual(ssmlParseTree.getHeadText().strip().replace(" ", " "), 'text2 text2 text3')
'''
Prosody <prosody attribute = "value"></prosody>
<prosody volume = ""></prosody>
- default (regular)
- silent, x-soft, soft, medium, loud, x-loud. Sets volume
- +ndB, -ndB : Changes volume relative to the current
level. A value of +0dB means no change, +6dB means
approximately twice the current volume and -6dB means
approsimately half the current volume
'''
@patch('conversion_db.ConversionDB')
def testProsodyElementVolume(self, MockConversionDB):
# Set up mock database
db = conversion_db.ConversionDB()
def mockCmdConversion(cmd):
if cmd == 'a':
a = [ProsodyElement(volume='x-loud'), ArgElement(1)]
a[0].insertChild(0, ProsodyElement(volume='medium'))
a[0].children[0].insertChild(0, ArgElement(2))
return a
else:
return None
def mockEnvConversion(env):
if env == 'b':
b = [ContentElement(), ProsodyElement(volume='-3dB'), ArgElement(2), ProsodyElement(volume='none')]
b[1].insertChild(0, ContentElement())
b[1].insertChild(0, ArgElement(1))
b[3].insertChild(0, ProsodyElement(volume='loud'))
return b
else:
return None
def mockEnvDefinition(env):
return None
db.getCmdConversion = Mock(side_effect=mockCmdConversion)
db.getEnvConversion = Mock(side_effect=mockEnvConversion)
db.getEnvDefinition = Mock(side_effect=mockEnvDefinition)
# Set up TexSoup parse tree to be parsed
doc = TexSoup.TexSoup(r'\a{1}{2}\begin{b}{3}{4}\a{5}{6}\end{b}')
# Parse on the given db and tree
parser = ConversionParser(db)
ssmlParseTree = parser.parse(doc, test=True)
self.assertIsInstance(ssmlParseTree, RootElement)
self.assertEqual(len(ssmlParseTree.children), 4)
self.assertIsInstance(ssmlParseTree.children[0], ProsodyElement)
self.assertEqual(ssmlParseTree.children[0].getHeadText(), '')
self.assertEqual(ssmlParseTree.children[0].getTailText().strip(), '1')
self.assertEqual(ssmlParseTree.children[0].getVolume(), 'x-loud')
self.assertEqual(len(ssmlParseTree.children[0].children), 1)
self.assertIsInstance(ssmlParseTree.children[0].children[0], ProsodyElement)
self.assertEqual(ssmlParseTree.children[0].children[0].getHeadText().strip(), '2')
self.assertEqual(ssmlParseTree.children[0].children[0].getTailText(), '')
self.assertEqual(ssmlParseTree.children[0].children[0].getVolume(), 'medium')
self.assertIsInstance(ssmlParseTree.children[1], ProsodyElement)
self.assertEqual(ssmlParseTree.children[1].getHeadText(), '')
self.assertEqual(ssmlParseTree.children[1].getTailText().strip(), '5')
self.assertEqual(ssmlParseTree.children[1].getVolume(), 'x-loud')
self.assertEqual(len(ssmlParseTree.children[1].children), 1)
self.assertIsInstance(ssmlParseTree.children[1].children[0], ProsodyElement)
self.assertEqual(ssmlParseTree.children[1].children[0].getHeadText().strip(), '6')
self.assertEqual(ssmlParseTree.children[1].children[0].getTailText(), '')
self.assertEqual(ssmlParseTree.children[1].children[0].getVolume(), 'medium')
self.assertIsInstance(ssmlParseTree.children[2], ProsodyElement)
self.assertEqual(ssmlParseTree.children[2].getHeadText().strip(), '3')
self.assertEqual(ssmlParseTree.children[2].getTailText().strip(), '4')
self.assertEqual(ssmlParseTree.children[2].getVolume(), '-3dB')
self.assertEqual(len(ssmlParseTree.children[2].children), 1)
self.assertIsInstance(ssmlParseTree.children[2].children[0], ProsodyElement)
self.assertEqual(ssmlParseTree.children[2].children[0].getHeadText(), '')
self.assertEqual(ssmlParseTree.children[2].children[0].getTailText().strip(), '5')
self.assertEqual(ssmlParseTree.children[2].children[0].getVolume(), 'x-loud')
self.assertEqual(len(ssmlParseTree.children[2].children), 1)
self.assertIsInstance(ssmlParseTree.children[2].children[0].children[0], ProsodyElement)
self.assertEqual(ssmlParseTree.children[2].children[0].children[0].getHeadText().strip(), '6')
self.assertEqual(ssmlParseTree.children[2].children[0].children[0].getTailText(), '')
self.assertEqual(ssmlParseTree.children[2].children[0].children[0].getVolume(), 'medium')
self.assertIsInstance(ssmlParseTree.children[3], ProsodyElement)
self.assertEqual(ssmlParseTree.children[3].getHeadText(), '')
self.assertEqual(ssmlParseTree.children[3].getTailText(), '')
self.assertEqual(ssmlParseTree.children[3].getVolume(), 'medium')
self.assertEqual(len(ssmlParseTree.children[3].children), 1)
self.assertIsInstance(ssmlParseTree.children[3].children[0], ProsodyElement)
self.assertEqual(ssmlParseTree.children[3].children[0].getHeadText(), '')
self.assertEqual(ssmlParseTree.children[3].children[0].getTailText(), '')
self.assertEqual(ssmlParseTree.children[3].children[0].getVolume(), 'loud')
''' <prosody rate = ""></prosody>
- x-slow, slow, medium, fast, x-fast. Sets pitch
- n% a non negative percentage change in the speaking rate
For example, a value of 100% means no change in speaking
rate, a value of 200% means twice the default rate, value
of 50% means a speaking rate of half the default rate.
This value has a range of 20-200%'''
@patch('conversion_db.ConversionDB')
def testProsodyElementRate(self, MockConversionDB):
# Set up mock database
db = conversion_db.ConversionDB()
def mockCmdConversion(cmd):
if cmd == 'a':
a = [ProsodyElement(rate='slow'), ArgElement(1)]
a[0].insertChild(0, ProsodyElement(rate='x-fast'))
a[0].children[0].insertChild(0, ArgElement(2))
return a
else:
return None
def mockEnvConversion(env):
if env == 'b':
b = [ContentElement(), ProsodyElement(rate='40%'), ArgElement(2), ProsodyElement(rate='none')]
b[1].insertChild(0, ContentElement())
b[1].insertChild(0, ArgElement(1))
b[3].insertChild(0, ProsodyElement(rate='180%'))
return b
else:
return None
def mockEnvDefinition(env):
return None
db.getCmdConversion = Mock(side_effect=mockCmdConversion)
db.getEnvConversion = Mock(side_effect=mockEnvConversion)
db.getEnvDefinition = Mock(side_effect=mockEnvDefinition)
# Set up TexSoup parse tree to be parsed
doc = TexSoup.TexSoup(r'\a{1}{2}\begin{b}{3}{4}\a{5}{6}\end{b}')
# Parse on the given db and tree
parser = ConversionParser(db)
ssmlParseTree = parser.parse(doc, test=True)
self.assertIsInstance(ssmlParseTree, RootElement)
self.assertEqual(len(ssmlParseTree.children), 4)
self.assertIsInstance(ssmlParseTree.children[0], ProsodyElement)
self.assertEqual(ssmlParseTree.children[0].getHeadText(), '')
self.assertEqual(ssmlParseTree.children[0].getTailText().strip(), '1')
self.assertEqual(ssmlParseTree.children[0].getRate(), 'slow')
self.assertEqual(len(ssmlParseTree.children[0].children), 1)
self.assertIsInstance(ssmlParseTree.children[0].children[0], ProsodyElement)
self.assertEqual(ssmlParseTree.children[0].children[0].getHeadText().strip(), '2')
self.assertEqual(ssmlParseTree.children[0].children[0].getTailText(), '')
self.assertEqual(ssmlParseTree.children[0].children[0].getRate(), 'x-fast')
self.assertIsInstance(ssmlParseTree.children[1], ProsodyElement)
self.assertEqual(ssmlParseTree.children[1].getHeadText(), '')
self.assertEqual(ssmlParseTree.children[1].getTailText().strip(), '5')
self.assertEqual(ssmlParseTree.children[1].getRate(), 'slow')
self.assertEqual(len(ssmlParseTree.children[1].children), 1)
self.assertIsInstance(ssmlParseTree.children[1].children[0], ProsodyElement)
self.assertEqual(ssmlParseTree.children[1].children[0].getHeadText().strip(), '6')
self.assertEqual(ssmlParseTree.children[1].children[0].getTailText(), '')
self.assertEqual(ssmlParseTree.children[1].children[0].getRate(), 'x-fast')
self.assertIsInstance(ssmlParseTree.children[2], ProsodyElement)
self.assertEqual(ssmlParseTree.children[2].getHeadText().strip(), '3')
self.assertEqual(ssmlParseTree.children[2].getTailText().strip(), '4')
self.assertEqual(ssmlParseTree.children[2].getRate(), '40%')
self.assertEqual(len(ssmlParseTree.children[2].children), 1)
self.assertIsInstance(ssmlParseTree.children[2].children[0], ProsodyElement)
self.assertEqual(ssmlParseTree.children[2].children[0].getHeadText(), '')
self.assertEqual(ssmlParseTree.children[2].children[0].getTailText().strip(), '5')
self.assertEqual(ssmlParseTree.children[2].children[0].getRate(), 'slow')
self.assertEqual(len(ssmlParseTree.children[2].children), 1)
self.assertIsInstance(ssmlParseTree.children[2].children[0].children[0], ProsodyElement)
self.assertEqual(ssmlParseTree.children[2].children[0].children[0].getHeadText().strip(), '6')
self.assertEqual(ssmlParseTree.children[2].children[0].children[0].getTailText(), '')
self.assertEqual(ssmlParseTree.children[2].children[0].children[0].getRate(), 'x-fast')
self.assertIsInstance(ssmlParseTree.children[3], ProsodyElement)
self.assertEqual(ssmlParseTree.children[3].getHeadText(), '')
self.assertEqual(ssmlParseTree.children[3].getTailText(), '')
self.assertEqual(ssmlParseTree.children[3].getRate(), 'medium')
self.assertEqual(len(ssmlParseTree.children[3].children), 1)
self.assertIsInstance(ssmlParseTree.children[3].children[0], ProsodyElement)
self.assertEqual(ssmlParseTree.children[3].children[0].getHeadText(), '')
self.assertEqual(ssmlParseTree.children[3].children[0].getTailText(), '')
self.assertEqual(ssmlParseTree.children[3].children[0].getRate(), '180%')
'''<prosody pitch = ""></prosody>
- deafult (regular)
- x-low, low, medium, high, x-hgih. Sets pitch
- +n% or -n% adjusts pitch by a relative percentage. For
example, a value of +0% means no baseline pitch change, +5%
gives a little higher baseline pitch, and -5% results in a lower
baseline pitch'''
@patch('conversion_db.ConversionDB')
def testProsodyElementPitch(self, MockConversionDB):
# Set up mock database
db = conversion_db.ConversionDB()
def mockCmdConversion(cmd):
if cmd == 'a':
a = [ProsodyElement(pitch='x-low'), ArgElement(1)]
a[0].insertChild(0, ProsodyElement(pitch='high'))
a[0].children[0].insertChild(0, ArgElement(2))
return a
else:
return None
def mockEnvConversion(env):
if env == 'b':
b = [ContentElement(), ProsodyElement(pitch='-40%'), ArgElement(2), ProsodyElement(pitch='none')]
b[1].insertChild(0, ContentElement())
b[1].insertChild(0, ArgElement(1))
b[3].insertChild(0, ProsodyElement(pitch='90%'))
return b
else:
return None
def mockEnvDefinition(env):
return None
db.getCmdConversion = Mock(side_effect=mockCmdConversion)
db.getEnvConversion = Mock(side_effect=mockEnvConversion)
db.getEnvDefinition = Mock(side_effect=mockEnvDefinition)
# Set up TexSoup parse tree to be parsed
doc = TexSoup.TexSoup(r'\a{1}{2}\begin{b}{3}{4}\a{5}{6}\end{b}')
# Parse on the given db and tree
parser = ConversionParser(db)
ssmlParseTree = parser.parse(doc, test=True)
self.assertIsInstance(ssmlParseTree, RootElement)
self.assertEqual(len(ssmlParseTree.children), 4)
self.assertIsInstance(ssmlParseTree.children[0], ProsodyElement)
self.assertEqual(ssmlParseTree.children[0].getHeadText(), '')
self.assertEqual(ssmlParseTree.children[0].getTailText().strip(), '1')
self.assertEqual(ssmlParseTree.children[0].getPitch(), 'x-low')
self.assertEqual(len(ssmlParseTree.children[0].children), 1)
self.assertIsInstance(ssmlParseTree.children[0].children[0], ProsodyElement)
self.assertEqual(ssmlParseTree.children[0].children[0].getHeadText().strip(), '2')
self.assertEqual(ssmlParseTree.children[0].children[0].getTailText(), '')
self.assertEqual(ssmlParseTree.children[0].children[0].getPitch(), 'high')
self.assertIsInstance(ssmlParseTree.children[1], ProsodyElement)
self.assertEqual(ssmlParseTree.children[1].getHeadText(), '')
self.assertEqual(ssmlParseTree.children[1].getTailText().strip(), '5')
self.assertEqual(ssmlParseTree.children[1].getPitch(), 'x-low')
self.assertEqual(len(ssmlParseTree.children[1].children), 1)
self.assertIsInstance(ssmlParseTree.children[1].children[0], ProsodyElement)
self.assertEqual(ssmlParseTree.children[1].children[0].getHeadText().strip(), '6')
self.assertEqual(ssmlParseTree.children[1].children[0].getTailText(), '')
self.assertEqual(ssmlParseTree.children[1].children[0].getPitch(), 'high')
self.assertIsInstance(ssmlParseTree.children[2], ProsodyElement)
self.assertEqual(ssmlParseTree.children[2].getHeadText().strip(), '3')
self.assertEqual(ssmlParseTree.children[2].getTailText().strip(), '4')
self.assertEqual(ssmlParseTree.children[2].getPitch(), '-40%')
self.assertEqual(len(ssmlParseTree.children[2].children), 1)
self.assertIsInstance(ssmlParseTree.children[2].children[0], ProsodyElement)
self.assertEqual(ssmlParseTree.children[2].children[0].getHeadText(), '')
self.assertEqual(ssmlParseTree.children[2].children[0].getTailText().strip(), '5')
self.assertEqual(ssmlParseTree.children[2].children[0].getPitch(), 'x-low')
self.assertEqual(len(ssmlParseTree.children[2].children), 1)
self.assertIsInstance(ssmlParseTree.children[2].children[0].children[0], ProsodyElement)
self.assertEqual(ssmlParseTree.children[2].children[0].children[0].getHeadText().strip(), '6')
self.assertEqual(ssmlParseTree.children[2].children[0].children[0].getTailText(), '')
self.assertEqual(ssmlParseTree.children[2].children[0].children[0].getPitch(), 'high')
self.assertIsInstance(ssmlParseTree.children[3], ProsodyElement)
self.assertEqual(ssmlParseTree.children[3].getHeadText(), '')
self.assertEqual(ssmlParseTree.children[3].getTailText(), '')
self.assertEqual(ssmlParseTree.children[3].getPitch(), 'medium')
self.assertEqual(len(ssmlParseTree.children[3].children), 1)
self.assertIsInstance(ssmlParseTree.children[3].children[0], ProsodyElement)
self.assertEqual(ssmlParseTree.children[3].children[0].getHeadText(), '')
self.assertEqual(ssmlParseTree.children[3].children[0].getTailText(), '')
self.assertEqual(ssmlParseTree.children[3].children[0].getPitch(), '+90%')
'''<prosody amazon:max-duration = "2s"></prosody>
- "n"s maximum duration in seconds
- "n"ms maximum duration in milliseconds'''
@patch('conversion_db.ConversionDB')
def testProsodyElementMaxDura(self, MockConversionDB):
# Set up mock database
db = conversion_db.ConversionDB()
# def mockCmdConversion(cmd):
# if cmd == 'a':
# a = [ProsodyElement(duration='2000s'), ArgElement(1)]
# a[0].insertChild(0, ProsodyElement(duration='1000s'))
# a[0].children[0].insertChild(0, ArgElement(2))
# return a
# else:
# return None
# def mockEnvConversion(env):
# if env == 'b':
# b = [ContentElement(), ProsodyElement(duration='3000ms'), ArgElement(2), ProsodyElement(duration='5000ms')]
# b[1].insertChild(0, ContentElement())
# b[1].insertChild(0, ArgElement(1))
# b[3].insertChild(0, ProsodyElement(duration='9000s'))
# return b
# else:
# return None
# def mockEnvDefinition(env):
# return None
# db.getCmdConversion = Mock(side_effect=mockCmdConversion)
# db.getEnvConversion = Mock(side_effect=mockEnvConversion)
# db.getEnvDefinition = Mock(side_effect=mockEnvDefinition)
# # Set up TexSoup parse tree to be parsed
# doc = TexSoup.TexSoup(r'\a{1}{2}\begin{b}{3}{4}\a{5}{6}\end{b}')
# # Parse on the given db and tree
# parser = ConversionParser(db)
# ssmlParseTree = parser.parse(doc, test=True)
# self.assertIsInstance(ssmlParseTree, RootElement)
# self.assertEqual(len(ssmlParseTree.children), 4)
# self.assertIsInstance(ssmlParseTree.children[0], ProsodyElement)
# self.assertEqual(ssmlParseTree.children[0].getHeadText(), '')
# self.assertEqual(ssmlParseTree.children[0].getTailText(), '1')
# self.assertEqual(ssmlParseTree.children[0].getDuration(), '2000000ms')
# self.assertEqual(len(ssmlParseTree.children[0].children), 1)
# self.assertIsInstance(ssmlParseTree.children[0].children[0], ProsodyElement)
# self.assertEqual(ssmlParseTree.children[0].children[0].getHeadText(), '2')
# self.assertEqual(ssmlParseTree.children[0].children[0].getTailText(), '')
# self.assertEqual(ssmlParseTree.children[0].children[0].getDuration(), '1000000ms')
# self.assertIsInstance(ssmlParseTree.children[1], ProsodyElement)
# self.assertEqual(ssmlParseTree.children[1].getHeadText(), '')
# self.assertEqual(ssmlParseTree.children[1].getTailText(), '5')
# self.assertEqual(ssmlParseTree.children[1].getDuration(), '2000000ms')
# self.assertEqual(len(ssmlParseTree.children[1].children), 1)
# self.assertIsInstance(ssmlParseTree.children[1].children[0], ProsodyElement)
# self.assertEqual(ssmlParseTree.children[1].children[0].getHeadText(), '6')
# self.assertEqual(ssmlParseTree.children[1].children[0].getTailText(), '')
# self.assertEqual(ssmlParseTree.children[1].children[0].getDuration(), '1000000ms')
# self.assertIsInstance(ssmlParseTree.children[2], ProsodyElement)
# self.assertEqual(ssmlParseTree.children[2].getHeadText(), '3')
# self.assertEqual(ssmlParseTree.children[2].getTailText(), '4')
# self.assertEqual(ssmlParseTree.children[2].getDuration(), '3000ms')
# self.assertEqual(len(ssmlParseTree.children[2].children), 1)
# self.assertIsInstance(ssmlParseTree.children[2].children[0], ProsodyElement)
# self.assertEqual(ssmlParseTree.children[2].children[0].getHeadText(), '')
# self.assertEqual(ssmlParseTree.children[2].children[0].getTailText(), '5')
# self.assertEqual(ssmlParseTree.children[2].children[0].getDuration(), '2000000ms')
# self.assertEqual(len(ssmlParseTree.children[2].children), 1)
# self.assertIsInstance(ssmlParseTree.children[2].children[0].children[0], ProsodyElement)
# self.assertEqual(ssmlParseTree.children[2].children[0].children[0].getHeadText(), '6')
# self.assertEqual(ssmlParseTree.children[2].children[0].children[0].getTailText(), '')
# self.assertEqual(ssmlParseTree.children[2].children[0].children[0].getDuration(), '1000000ms')
# self.assertIsInstance(ssmlParseTree.children[3], ProsodyElement)
# self.assertEqual(ssmlParseTree.children[3].getHeadText(), '')
# self.assertEqual(ssmlParseTree.children[3].getTailText(), '')
# self.assertEqual(ssmlParseTree.children[3].getDuration(), '5000ms')
# self.assertEqual(len(ssmlParseTree.children[3].children), 1)
# self.assertIsInstance(ssmlParseTree.children[3].children[0], ProsodyElement)
# self.assertEqual(ssmlParseTree.children[3].children[0].getHeadText(), '')
# self.assertEqual(ssmlParseTree.children[3].children[0].getTailText(), '')
# self.assertEqual(ssmlParseTree.children[3].children[0].getDuration(), '9000000ms')
# Test cases for prosody -> A lot (Might need a different function for each attribute) Only weird if there is nested resolution (not sure if we will impelement it yet, whatJacob is doing for emphasis). -> Assume we will be doing it since the custoemr asked us to do it
# When the mocks are happenign you have to return mock objects
# Convert previous janky xml into the new format
# Update XML
# Design XML documentation
# For each node
# Looks at child but if has emphasis fine
# Look at next, possibly creates new node, reaches up to the parent, modifies the list of children, then leave, now it's the parents turn
| 49.972752
| 277
| 0.63615
| 3,688
| 36,680
| 6.312364
| 0.086768
| 0.216495
| 0.184021
| 0.231959
| 0.837715
| 0.81134
| 0.79884
| 0.785438
| 0.769588
| 0.763789
| 0
| 0.02523
| 0.231707
| 36,680
| 734
| 278
| 49.972752
| 0.800859
| 0.155643
| 0
| 0.715247
| 0
| 0.013453
| 0.042016
| 0.020152
| 0
| 0
| 0
| 0
| 0.46861
| 1
| 0.073991
| false
| 0
| 0.029148
| 0.008969
| 0.20852
| 0.002242
| 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 11
|
3971fafbd8fda3a1a800ec7a93e6df0af7d4f9f8
| 246
|
py
|
Python
|
superorm/orm.py
|
lyoshur/superorm
|
16bd9492fe9b224e6798e1d2895121adf3fefdfb
|
[
"MIT"
] | null | null | null |
superorm/orm.py
|
lyoshur/superorm
|
16bd9492fe9b224e6798e1d2895121adf3fefdfb
|
[
"MIT"
] | null | null | null |
superorm/orm.py
|
lyoshur/superorm
|
16bd9492fe9b224e6798e1d2895121adf3fefdfb
|
[
"MIT"
] | null | null | null |
# noinspection PyUnresolvedReferences
from superorm.factory import SQLSessionFactoryBuild as Builder
# noinspection PyUnresolvedReferences
from superorm.mapper import parse4file as parse_config_from_file, parse4string as parse_config_from_string
| 49.2
| 106
| 0.894309
| 27
| 246
| 7.925926
| 0.592593
| 0.317757
| 0.35514
| 0.429907
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.008889
| 0.085366
| 246
| 4
| 107
| 61.5
| 0.942222
| 0.288618
| 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
|
3982e3b831910ca16f6b4ba751cb93feada5f06e
| 166
|
py
|
Python
|
comet/validator/__init__.py
|
shinybrar/Comet
|
4229092fca74c130a7d4ecd4dbd22ae85f7e6308
|
[
"BSD-2-Clause"
] | 15
|
2015-11-29T18:53:58.000Z
|
2022-03-09T15:47:30.000Z
|
comet/validator/__init__.py
|
shinybrar/Comet
|
4229092fca74c130a7d4ecd4dbd22ae85f7e6308
|
[
"BSD-2-Clause"
] | 29
|
2016-01-21T18:10:45.000Z
|
2021-10-01T16:41:12.000Z
|
comet/validator/__init__.py
|
shinybrar/Comet
|
4229092fca74c130a7d4ecd4dbd22ae85f7e6308
|
[
"BSD-2-Clause"
] | 11
|
2016-01-22T14:05:51.000Z
|
2022-03-09T17:49:56.000Z
|
# Comet VOEvent Broker.
# VOEvent validation.
from comet.validator.ivoid import *
from comet.validator.previously_seen import *
from comet.validator.schema import *
| 23.714286
| 45
| 0.801205
| 21
| 166
| 6.285714
| 0.52381
| 0.204545
| 0.409091
| 0.363636
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.120482
| 166
| 6
| 46
| 27.666667
| 0.90411
| 0.246988
| 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
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 8
|
3986e1c98d102a9f66e500a4896ee32721ae0bb8
| 3,118
|
py
|
Python
|
pyaz/iot/dps/linked_hub/__init__.py
|
py-az-cli/py-az-cli
|
9a7dc44e360c096a5a2f15595353e9dad88a9792
|
[
"MIT"
] | null | null | null |
pyaz/iot/dps/linked_hub/__init__.py
|
py-az-cli/py-az-cli
|
9a7dc44e360c096a5a2f15595353e9dad88a9792
|
[
"MIT"
] | null | null | null |
pyaz/iot/dps/linked_hub/__init__.py
|
py-az-cli/py-az-cli
|
9a7dc44e360c096a5a2f15595353e9dad88a9792
|
[
"MIT"
] | 1
|
2022-02-03T09:12:01.000Z
|
2022-02-03T09:12:01.000Z
|
from .... pyaz_utils import _call_az
def list(dps_name, resource_group):
'''
List all linked IoT hubs in an Azure IoT Hub device provisioning service.
Required Parameters:
- dps_name -- IoT Provisioning Service name
- resource_group -- Name of resource group. You can configure the default group using `az configure --defaults group=<name>`
'''
return _call_az("az iot dps linked-hub list", locals())
def show(dps_name, linked_hub, resource_group):
'''
Show details of a linked IoT hub in an Azure IoT Hub device provisioning service.
Required Parameters:
- dps_name -- IoT Provisioning Service name
- linked_hub -- Host name of linked IoT Hub.
- resource_group -- Name of resource group. You can configure the default group using `az configure --defaults group=<name>`
'''
return _call_az("az iot dps linked-hub show", locals())
def create(connection_string, dps_name, location, resource_group, allocation_weight=None, apply_allocation_policy=None, no_wait=None):
'''
Create a linked IoT hub in an Azure IoT Hub device provisioning service.
Required Parameters:
- connection_string -- Connection string of the IoT hub.
- dps_name -- IoT Provisioning Service name
- location -- Location of the IoT hub.
- resource_group -- Name of resource group. You can configure the default group using `az configure --defaults group=<name>`
Optional Parameters:
- allocation_weight -- Allocation weight of the IoT hub.
- apply_allocation_policy -- A boolean indicating whether to apply allocation policy to the IoT hub.
- no_wait -- Do not wait for the long-running operation to finish.
'''
return _call_az("az iot dps linked-hub create", locals())
def update(dps_name, linked_hub, resource_group, allocation_weight=None, apply_allocation_policy=None, no_wait=None):
'''
Update a linked IoT hub in an Azure IoT Hub device provisioning service.
Required Parameters:
- dps_name -- IoT Provisioning Service name
- linked_hub -- Host name of linked IoT Hub.
- resource_group -- Name of resource group. You can configure the default group using `az configure --defaults group=<name>`
Optional Parameters:
- allocation_weight -- Allocation weight of the IoT hub.
- apply_allocation_policy -- A boolean indicating whether to apply allocation policy to the Iot hub.
- no_wait -- Do not wait for the long-running operation to finish.
'''
return _call_az("az iot dps linked-hub update", locals())
def delete(dps_name, linked_hub, resource_group, no_wait=None):
'''
Update a linked IoT hub in an Azure IoT Hub device provisioning service.
Required Parameters:
- dps_name -- IoT Provisioning Service name
- linked_hub -- Host name of linked IoT Hub.
- resource_group -- Name of resource group. You can configure the default group using `az configure --defaults group=<name>`
Optional Parameters:
- no_wait -- Do not wait for the long-running operation to finish.
'''
return _call_az("az iot dps linked-hub delete", locals())
| 41.573333
| 134
| 0.718409
| 441
| 3,118
| 4.945578
| 0.145125
| 0.049519
| 0.051353
| 0.02751
| 0.873453
| 0.873453
| 0.824392
| 0.824392
| 0.824392
| 0.824392
| 0
| 0
| 0.202373
| 3,118
| 74
| 135
| 42.135135
| 0.87696
| 0.6873
| 0
| 0
| 0
| 0
| 0.174807
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.454545
| false
| 0
| 0.090909
| 0
| 1
| 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
| 1
| 0
|
0
| 8
|
39923941601e1e7799f98273e93619aa0ccd91a4
| 1,718
|
py
|
Python
|
retrieval/hybrid/hybrid.py
|
park-sungmoo/odqa_baseline_code
|
45954be766e5f987bef18e5b8a2e47f1508742cd
|
[
"Apache-2.0"
] | 67
|
2021-05-12T15:54:28.000Z
|
2022-03-12T15:55:35.000Z
|
retrieval/hybrid/hybrid.py
|
park-sungmoo/odqa_baseline_code
|
45954be766e5f987bef18e5b8a2e47f1508742cd
|
[
"Apache-2.0"
] | 71
|
2021-05-01T06:07:37.000Z
|
2022-01-28T16:54:46.000Z
|
retrieval/hybrid/hybrid.py
|
park-sungmoo/odqa_baseline_code
|
45954be766e5f987bef18e5b8a2e47f1508742cd
|
[
"Apache-2.0"
] | 14
|
2021-05-24T10:57:27.000Z
|
2022-02-18T06:34:11.000Z
|
from retrieval.dense import DprBert
from retrieval.hybrid import HybridRetrieval, HybridLogisticRetrieval
from retrieval.sparse import TfidfRetrieval, ATIREBM25Retrieval
class TfidfDprBert(HybridRetrieval):
def __init__(self, args):
super().__init__(args)
temp = args.model.retriever_name
args.model.retriever_name = "TFIDF"
self.sparse_retriever = TfidfRetrieval(args)
args.model.retriever_name = "DPRBERT"
self.dense_retriever = DprBert(args)
args.model.retriever_name = temp
class AtireBm25DprBert(HybridRetrieval):
def __init__(self, args):
super().__init__(args)
temp = args.model.retriever_name
args.model.retriever_name = "ATIREBM25"
self.sparse_retriever = ATIREBM25Retrieval(args)
args.model.retriever_name = "DPRBERT"
self.dense_retriever = DprBert(args)
args.model.retriever_name = temp
class LogisticTfidfDprBert(HybridLogisticRetrieval):
def __init__(self, args):
super().__init__(args)
temp = args.model.retriever_name
args.model.retriever_name = "TFIDF"
self.sparse_retriever = ATIREBM25Retrieval(args)
args.model.retriever_name = "DPRBERT"
self.dense_retriever = DprBert(args)
args.model.retriever_name = temp
class LogisticAtireBm25DprBert(HybridLogisticRetrieval):
def __init__(self, args):
super().__init__(args)
temp = args.model.retriever_name
args.model.retriever_name = "ATIREBM25"
self.sparse_retriever = ATIREBM25Retrieval(args)
args.model.retriever_name = "DPRBERT"
self.dense_retriever = DprBert(args)
args.model.retriever_name = temp
| 30.678571
| 69
| 0.700233
| 177
| 1,718
| 6.480226
| 0.152542
| 0.125545
| 0.25109
| 0.306888
| 0.789015
| 0.789015
| 0.789015
| 0.789015
| 0.789015
| 0.789015
| 0
| 0.011834
| 0.213038
| 1,718
| 55
| 70
| 31.236364
| 0.836538
| 0
| 0
| 0.794872
| 0
| 0
| 0.032596
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.102564
| false
| 0
| 0.076923
| 0
| 0.282051
| 0
| 0
| 0
| 0
| null | 0
| 1
| 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
| 8
|
f2fc5649e7cfdac88de77d7b03a7838ce24467b1
| 9,177
|
py
|
Python
|
forecasting/short_term_forecasting.py
|
Matrixeigs/energy_management_system
|
b2af6a3cfa71173f33d798e943f605d802aed19f
|
[
"MIT"
] | 68
|
2017-11-21T02:49:11.000Z
|
2022-03-25T07:14:42.000Z
|
forecasting/short_term_forecasting.py
|
yifeili/energy_management_system
|
b2af6a3cfa71173f33d798e943f605d802aed19f
|
[
"MIT"
] | null | null | null |
forecasting/short_term_forecasting.py
|
yifeili/energy_management_system
|
b2af6a3cfa71173f33d798e943f605d802aed19f
|
[
"MIT"
] | 34
|
2017-11-21T02:52:15.000Z
|
2022-03-27T14:35:25.000Z
|
# Short_term forecasting for local energy management system
# Include the pv forecasting, wp forecasting,
# In this forecasting system, the tensor flow will be deployed and used.
# The training
from data_management.database_format import db_short_term_forecasting,one_minute_history_data
import random
from configuration.configuration_time_line import default_time
from configuration.configuration_database import local_history_database
from sqlalchemy import create_engine, and_ # Import database
from sqlalchemy.orm import sessionmaker
db_str = local_history_database["db_str"]
engine = create_engine(db_str, echo=False)
Session = sessionmaker(bind=engine)
session_source = Session()
def blank_forecasting_result(*args):
Target_time = args[0]
default_result = db_short_term_forecasting \
(TIME_STAMP=Target_time,
AC_PD=0,
AC_QD=0,
UAC_PD=0,
UAC_QD=0,
DC_PD=0,
UDC_PD=0,
PV_PG=0,
WP_PG=0,
PRICE=0, )
return default_result
def short_term_forecasting_pv(*args):
# Short term forecasting for photovoltaic
session = args[0]
Target_Time = args[1]
if session.query(db_short_term_forecasting).filter(
db_short_term_forecasting.TIME_STAMP == Target_Time).count() == 0:
blank_row = blank_forecasting_result(Target_Time)
session.add(blank_row)
session.commit()
PV_PG = random.random()
row = session.query(db_short_term_forecasting).filter_by(TIME_STAMP=Target_Time).first()
row.PV_PG = PV_PG
session.commit()
return PV_PG
def short_term_forecasting_wp(*args):
# Short term forecasting for wind power
session = args[0]
Target_Time = args[1]
if session.query(db_short_term_forecasting).filter(
db_short_term_forecasting.TIME_STAMP == Target_Time).count() == 0:
blank_row = blank_forecasting_result(Target_Time)
session.add(blank_row)
session.commit()
WP_PG = random.random()
row = session.query(db_short_term_forecasting).filter_by(TIME_STAMP=Target_Time).first()
row.WP_PG = WP_PG
session.commit()
return WP_PG
def short_term_forecasting_load_ac(*args):
# Short term forecasting for critical AC load
session = args[0]
Target_Time = args[1]
if session.query(db_short_term_forecasting).filter(
db_short_term_forecasting.TIME_STAMP == Target_Time).count() == 0:
blank_row = blank_forecasting_result(Target_Time)
session.add(blank_row)
session.commit()
AC_PD = random.random()
row = session.query(db_short_term_forecasting).filter_by(TIME_STAMP=Target_Time).first()
row.AC_PD = AC_PD
session.commit()
return AC_PD
def short_term_forecasting_load_uac(*args):
# Short term forecasting for non-critical AC load
session = args[0]
Target_Time = args[1]
if session.query(db_short_term_forecasting).filter(
db_short_term_forecasting.TIME_STAMP == Target_Time).count() == 0:
blank_row = blank_forecasting_result(Target_Time)
session.add(blank_row)
session.commit()
UAC_PD = random.random()
row = session.query(db_short_term_forecasting).filter_by(TIME_STAMP=Target_Time).first()
row.UAC_PD = UAC_PD
session.commit()
return UAC_PD
def short_term_forecasting_load_dc(*args):
# Short term forecasting for critical DC load
session = args[0]
Target_Time = args[1]
if session.query(db_short_term_forecasting).filter(
db_short_term_forecasting.TIME_STAMP == Target_Time).count() == 0:
blank_row = blank_forecasting_result(Target_Time)
session.add(blank_row)
session.commit()
DC_PD = random.random()
row = session.query(db_short_term_forecasting).filter_by(TIME_STAMP=Target_Time).first()
row.DC_PD = DC_PD
session.commit()
return DC_PD
def short_term_forecasting_load_udc(*args):
# Short term forecasting for non-critical DC load
session = args[0]
Target_Time = args[1]
if session.query(db_short_term_forecasting).filter(
db_short_term_forecasting.TIME_STAMP == Target_Time).count() == 0:
blank_row = blank_forecasting_result(Target_Time)
session.add(blank_row)
session.commit()
UDC_PD = random.random()
row = session.query(db_short_term_forecasting).filter_by(TIME_STAMP=Target_Time).first()
row.UDC_PD = UDC_PD
session.commit()
return UDC_PD
def short_term_forecasting_pv_history(*args):
# Short term forecasting for photovoltaic
session = args[0]
Target_Time = args[1]
if session.query(db_short_term_forecasting).filter(
db_short_term_forecasting.TIME_STAMP == Target_Time).count() == 0:
blank_row = blank_forecasting_result(Target_Time)
session.add(blank_row)
session.commit()
row_source = session_source.query(one_minute_history_data).filter_by(
TIME_STAMP=int((Target_Time - default_time["Base_time"]) / default_time["Time_step_opf"])).first()
PV_PG = row_source.PV_PG
row = session.query(db_short_term_forecasting).filter_by(TIME_STAMP=Target_Time).first()
row.PV_PG = PV_PG
session.commit()
return PV_PG
def short_term_forecasting_wp_history(*args):
# Short term forecasting for wind power
session = args[0]
Target_Time = args[1]
if session.query(db_short_term_forecasting).filter(
db_short_term_forecasting.TIME_STAMP == Target_Time).count() == 0:
blank_row = blank_forecasting_result(Target_Time)
session.add(blank_row)
session.commit()
row_source = session_source.query(one_minute_history_data).filter_by(
TIME_STAMP=int((Target_Time - default_time["Base_time"]) / default_time["Time_step_opf"])).first()
WP_PG = row_source.WP_PG
row = session.query(db_short_term_forecasting).filter_by(TIME_STAMP=Target_Time).first()
row.WP_PG = WP_PG
session.commit()
return WP_PG
def short_term_forecasting_load_ac_history(*args):
# Short term forecasting for critical AC load
session = args[0]
Target_Time = args[1]
if session.query(db_short_term_forecasting).filter(
db_short_term_forecasting.TIME_STAMP == Target_Time).count() == 0:
blank_row = blank_forecasting_result(Target_Time)
session.add(blank_row)
session.commit()
row_source = session_source.query(one_minute_history_data).filter_by(
TIME_STAMP=int((Target_Time - default_time["Base_time"]) / default_time["Time_step_opf"])).first()
AC_PD = row_source.AC_PD
row = session.query(db_short_term_forecasting).filter_by(TIME_STAMP=Target_Time).first()
row.AC_PD = AC_PD
session.commit()
return AC_PD
def short_term_forecasting_load_uac_history(*args):
# Short term forecasting for non-critical AC load
session = args[0]
Target_Time = args[1]
if session.query(db_short_term_forecasting).filter(
db_short_term_forecasting.TIME_STAMP == Target_Time).count() == 0:
blank_row = blank_forecasting_result(Target_Time)
session.add(blank_row)
session.commit()
row_source = session_source.query(one_minute_history_data).filter_by(
TIME_STAMP=int((Target_Time - default_time["Base_time"]) / default_time["Time_step_opf"])).first()
UAC_PD = row_source.NAC_PD
row = session.query(db_short_term_forecasting).filter_by(TIME_STAMP=Target_Time).first()
row.UAC_PD = UAC_PD
session.commit()
return UAC_PD
def short_term_forecasting_load_dc_history(*args):
# Short term forecasting for critical DC load
session = args[0]
Target_Time = args[1]
if session.query(db_short_term_forecasting).filter(
db_short_term_forecasting.TIME_STAMP == Target_Time).count() == 0:
blank_row = blank_forecasting_result(Target_Time)
session.add(blank_row)
session.commit()
row_source = session_source.query(one_minute_history_data).filter_by(
TIME_STAMP=int((Target_Time - default_time["Base_time"]) / default_time["Time_step_opf"])).first()
DC_PD= row_source.DC_PD
row = session.query(db_short_term_forecasting).filter_by(TIME_STAMP=Target_Time).first()
row.DC_PD = DC_PD
session.commit()
return DC_PD
def short_term_forecasting_load_udc_history(*args):
# Short term forecasting for non-critical DC load
session = args[0]
Target_Time = args[1]
if session.query(db_short_term_forecasting).filter(
db_short_term_forecasting.TIME_STAMP == Target_Time).count() == 0:
blank_row = blank_forecasting_result(Target_Time)
session.add(blank_row)
session.commit()
row_source = session_source.query(one_minute_history_data).filter_by(
TIME_STAMP=int((Target_Time - default_time["Base_time"]) / default_time["Time_step_opf"])).first()
UDC_PD = row_source.NDC_PD
row = session.query(db_short_term_forecasting).filter_by(TIME_STAMP=Target_Time).first()
row.UDC_PD = UDC_PD
session.commit()
return UDC_PD
| 33.25
| 106
| 0.708075
| 1,270
| 9,177
| 4.746457
| 0.066929
| 0.094061
| 0.209025
| 0.138686
| 0.868447
| 0.860153
| 0.85783
| 0.855508
| 0.846384
| 0.846384
| 0
| 0.006247
| 0.197668
| 9,177
| 276
| 107
| 33.25
| 0.812441
| 0.079002
| 0
| 0.75
| 0
| 0
| 0.01636
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.067708
| false
| 0
| 0.03125
| 0
| 0.166667
| 0
| 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
|
84024d95fc49552c36c741ab96fc8ccf3b7eb6d7
| 162
|
py
|
Python
|
test/test_cards_shuffle.py
|
erichaase/topcoder-python
|
de285d8092a94f2ec1b5c0c33eba55b5c27a5390
|
[
"MIT"
] | 1
|
2017-03-25T17:40:57.000Z
|
2017-03-25T17:40:57.000Z
|
test/test_cards_shuffle.py
|
erichaase/topcoder-python
|
de285d8092a94f2ec1b5c0c33eba55b5c27a5390
|
[
"MIT"
] | null | null | null |
test/test_cards_shuffle.py
|
erichaase/topcoder-python
|
de285d8092a94f2ec1b5c0c33eba55b5c27a5390
|
[
"MIT"
] | null | null | null |
from test.assert_json import assert_json
from topcoder.cards_shuffle import solution
def test_cards_shuffle ():
assert_json('cards_shuffle', solution)
| 27
| 46
| 0.790123
| 22
| 162
| 5.5
| 0.454545
| 0.247934
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.148148
| 162
| 5
| 47
| 32.4
| 0.876812
| 0
| 0
| 0
| 0
| 0
| 0.080247
| 0
| 0
| 0
| 0
| 0
| 0.5
| 1
| 0.25
| true
| 0
| 0.5
| 0
| 0.75
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 1
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 7
|
840a7efa9c03752d0b4d50a2471082cf4199581e
| 2,088
|
py
|
Python
|
tests/test_defect_species.py
|
j-m-dean/pyscses
|
6c2875cb87a8f91ae7aed382922c34b0e611ba85
|
[
"MIT"
] | null | null | null |
tests/test_defect_species.py
|
j-m-dean/pyscses
|
6c2875cb87a8f91ae7aed382922c34b0e611ba85
|
[
"MIT"
] | null | null | null |
tests/test_defect_species.py
|
j-m-dean/pyscses
|
6c2875cb87a8f91ae7aed382922c34b0e611ba85
|
[
"MIT"
] | null | null | null |
import unittest
from pyscses.defect_species import DefectSpecies
class TestDefectSpecies(unittest.TestCase):
def test_init(self):
defect_species = DefectSpecies(label='VO',
valence=+2.0,
mole_fraction=0.1,
mobility=0.1,
fixed=True)
self.assertEqual(defect_species.label, 'VO')
self.assertEqual(defect_species.valence, 2.0)
self.assertEqual(defect_species.mole_fraction, 0.1)
self.assertEqual(defect_species.mobility, 0.1)
self.assertEqual(defect_species.fixed, True)
def test_init_defaults(self):
defect_species = DefectSpecies(label='VO',
valence=+2.0,
mole_fraction=0.1)
self.assertEqual(defect_species.label, 'VO')
self.assertEqual(defect_species.valence, 2.0)
self.assertEqual(defect_species.mole_fraction, 0.1)
self.assertEqual(defect_species.mobility, 0.0)
self.assertEqual(defect_species.fixed, False)
def test_init_raises_TypeError_if_label_is_incorrect_type(self):
with self.assertRaises(TypeError):
defect_species = DefectSpecies(label=3.0,
valence=+2.0,
mole_fraction=0.1)
def test_init_raises_TypeError_if_valence_is_incorrect_type(self):
with self.assertRaises(TypeError):
defect_species = DefectSpecies(label='VO',
valence='foo',
mole_fraction=0.1)
def test_init_raises_TypeError_if_mole_fraction_is_incorrect_type(self):
with self.assertRaises(TypeError):
defect_species = DefectSpecies(label='VO',
valence=+2.0,
mole_fraction='foo')
if __name__ == '__main__':
unittest.main()
| 43.5
| 76
| 0.548851
| 204
| 2,088
| 5.328431
| 0.196078
| 0.191352
| 0.193192
| 0.25759
| 0.809568
| 0.773689
| 0.721251
| 0.712971
| 0.712971
| 0.712971
| 0
| 0.024316
| 0.369732
| 2,088
| 47
| 77
| 44.425532
| 0.801672
| 0
| 0
| 0.5
| 0
| 0
| 0.012452
| 0
| 0
| 0
| 0
| 0
| 0.325
| 1
| 0.125
| false
| 0
| 0.05
| 0
| 0.2
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
842f0ca14bf93481d51627035a2494301ee56627
| 145
|
py
|
Python
|
src/timer/model/__init__.py
|
jakob-bagterp/timer-for-python
|
a48b60c8782bbf6d368d6ca2be249054c3b66c21
|
[
"MIT"
] | 2
|
2022-03-22T11:14:37.000Z
|
2022-03-24T14:27:13.000Z
|
src/timer/model/__init__.py
|
jakob-bagterp/timer-for-python
|
a48b60c8782bbf6d368d6ca2be249054c3b66c21
|
[
"MIT"
] | null | null | null |
src/timer/model/__init__.py
|
jakob-bagterp/timer-for-python
|
a48b60c8782bbf6d368d6ca2be249054c3b66c21
|
[
"MIT"
] | null | null | null |
__all__ = ["elapsed_time_fractions", "thread_item", "timer", "timer_base"]
from . import elapsed_time_fractions, thread_item, timer, timer_base
| 36.25
| 74
| 0.77931
| 19
| 145
| 5.315789
| 0.526316
| 0.217822
| 0.39604
| 0.514851
| 0.871287
| 0.871287
| 0.871287
| 0.871287
| 0
| 0
| 0
| 0
| 0.096552
| 145
| 3
| 75
| 48.333333
| 0.770992
| 0
| 0
| 0
| 0
| 0
| 0.331034
| 0.151724
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.5
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 10
|
845213d1bdfb6080582887b30e7cba96f7201a93
| 19,931
|
py
|
Python
|
billforward/apis/metadata_api.py
|
billforward/bf-python
|
d2b812329ca3ed1fd94364d7f46f69ad74665596
|
[
"Apache-2.0"
] | 2
|
2016-11-23T17:32:37.000Z
|
2022-02-24T05:13:20.000Z
|
billforward/apis/metadata_api.py
|
billforward/bf-python
|
d2b812329ca3ed1fd94364d7f46f69ad74665596
|
[
"Apache-2.0"
] | null | null | null |
billforward/apis/metadata_api.py
|
billforward/bf-python
|
d2b812329ca3ed1fd94364d7f46f69ad74665596
|
[
"Apache-2.0"
] | 1
|
2016-12-30T20:02:48.000Z
|
2016-12-30T20:02:48.000Z
|
# coding: utf-8
"""
BillForward REST API
OpenAPI spec version: 1.0.0
Generated by: https://github.com/swagger-api/swagger-codegen.git
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
import re
# python 2 and python 3 compatibility library
from six import iteritems
from ..configuration import Configuration
from ..api_client import ApiClient
class MetadataApi(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 delete_metadata_key_values(self, **kwargs):
"""
Remove any associated metadata.
{\"nickname\":\"Clear metadata from organization\",\"request\" :\"deleteOrganizationMetadataRequest.html\",\"response\":\"deleteOrganizationMetadataResponse.html\"}
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_metadata_key_values(callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param list[str] organizations: A list of organization-IDs used to restrict the scope of API calls.
:return: DynamicMetadata
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('callback'):
return self.delete_metadata_key_values_with_http_info(**kwargs)
else:
(data) = self.delete_metadata_key_values_with_http_info(**kwargs)
return data
def delete_metadata_key_values_with_http_info(self, **kwargs):
"""
Remove any associated metadata.
{\"nickname\":\"Clear metadata from organization\",\"request\" :\"deleteOrganizationMetadataRequest.html\",\"response\":\"deleteOrganizationMetadataResponse.html\"}
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_metadata_key_values_with_http_info(callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param list[str] organizations: A list of organization-IDs used to restrict the scope of API calls.
:return: DynamicMetadata
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['organizations']
all_params.append('callback')
all_params.append('_return_http_data_only')
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_metadata_key_values" % key
)
params[key] = val
del params['kwargs']
resource_path = '/metadata'.replace('{format}', 'json')
path_params = {}
query_params = {}
if 'organizations' in params:
query_params['organizations'] = params['organizations']
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type(['text/plain'])
# Authentication setting
auth_settings = []
return 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='DynamicMetadata',
auth_settings=auth_settings,
callback=params.get('callback'),
_return_http_data_only=params.get('_return_http_data_only'))
def get_metadata_key_values(self, **kwargs):
"""
Retrieve any associated metadata.
{\"nickname\":\"Retrieve metadata on organization\",\"request\":\"getOrganizationMetadataRequest.html\",\"response\":\"getOrganizationMetadataResponse.html\"}
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_metadata_key_values(callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param list[str] organizations: A list of organization-IDs used to restrict the scope of API calls.
:return: DynamicMetadata
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('callback'):
return self.get_metadata_key_values_with_http_info(**kwargs)
else:
(data) = self.get_metadata_key_values_with_http_info(**kwargs)
return data
def get_metadata_key_values_with_http_info(self, **kwargs):
"""
Retrieve any associated metadata.
{\"nickname\":\"Retrieve metadata on organization\",\"request\":\"getOrganizationMetadataRequest.html\",\"response\":\"getOrganizationMetadataResponse.html\"}
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_metadata_key_values_with_http_info(callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param list[str] organizations: A list of organization-IDs used to restrict the scope of API calls.
:return: DynamicMetadata
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['organizations']
all_params.append('callback')
all_params.append('_return_http_data_only')
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_metadata_key_values" % key
)
params[key] = val
del params['kwargs']
resource_path = '/metadata'.replace('{format}', 'json')
path_params = {}
query_params = {}
if 'organizations' in params:
query_params['organizations'] = params['organizations']
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type(['text/plain'])
# Authentication setting
auth_settings = []
return 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='DynamicMetadata',
auth_settings=auth_settings,
callback=params.get('callback'),
_return_http_data_only=params.get('_return_http_data_only'))
def set_metadata_key_values(self, metadata, **kwargs):
"""
Remove any existing metadata keys and create the provided data.
{\"nickname\":\"Set metadata on organization\",\"request\":\"setOrganizationMetadataRequest.html\",\"response\":\"setOrganizationMetadataResponse.html\"}
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_metadata_key_values(metadata, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param DynamicMetadata metadata: (required)
:param list[str] organizations: A list of organization-IDs used to restrict the scope of API calls.
:return: DynamicMetadata
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('callback'):
return self.set_metadata_key_values_with_http_info(metadata, **kwargs)
else:
(data) = self.set_metadata_key_values_with_http_info(metadata, **kwargs)
return data
def set_metadata_key_values_with_http_info(self, metadata, **kwargs):
"""
Remove any existing metadata keys and create the provided data.
{\"nickname\":\"Set metadata on organization\",\"request\":\"setOrganizationMetadataRequest.html\",\"response\":\"setOrganizationMetadataResponse.html\"}
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_metadata_key_values_with_http_info(metadata, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param DynamicMetadata metadata: (required)
:param list[str] organizations: A list of organization-IDs used to restrict the scope of API calls.
:return: DynamicMetadata
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['metadata', 'organizations']
all_params.append('callback')
all_params.append('_return_http_data_only')
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_metadata_key_values" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'metadata' is set
if ('metadata' not in params) or (params['metadata'] is None):
raise ValueError("Missing the required parameter `metadata` when calling `set_metadata_key_values`")
resource_path = '/metadata'.replace('{format}', 'json')
path_params = {}
query_params = {}
if 'organizations' in params:
query_params['organizations'] = params['organizations']
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'metadata' in params:
body_params = params['metadata']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
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 = []
return 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='DynamicMetadata',
auth_settings=auth_settings,
callback=params.get('callback'),
_return_http_data_only=params.get('_return_http_data_only'))
def upsert_metadata_key_values(self, metadata, **kwargs):
"""
Update any existing metadata key-values and insert any new key-values, no keys will be removed.
{\"nickname\":\"Upsert metadata on organization\",\"request\":\"upsertOrganizationMetadataRequest.html\",\"response\":\"upsertOrganizationMetadataResponse.html\"}
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.upsert_metadata_key_values(metadata, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param DynamicMetadata metadata: (required)
:param list[str] organizations: A list of organization-IDs used to restrict the scope of API calls.
:return: DynamicMetadata
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('callback'):
return self.upsert_metadata_key_values_with_http_info(metadata, **kwargs)
else:
(data) = self.upsert_metadata_key_values_with_http_info(metadata, **kwargs)
return data
def upsert_metadata_key_values_with_http_info(self, metadata, **kwargs):
"""
Update any existing metadata key-values and insert any new key-values, no keys will be removed.
{\"nickname\":\"Upsert metadata on organization\",\"request\":\"upsertOrganizationMetadataRequest.html\",\"response\":\"upsertOrganizationMetadataResponse.html\"}
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.upsert_metadata_key_values_with_http_info(metadata, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param DynamicMetadata metadata: (required)
:param list[str] organizations: A list of organization-IDs used to restrict the scope of API calls.
:return: DynamicMetadata
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['metadata', 'organizations']
all_params.append('callback')
all_params.append('_return_http_data_only')
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 upsert_metadata_key_values" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'metadata' is set
if ('metadata' not in params) or (params['metadata'] is None):
raise ValueError("Missing the required parameter `metadata` when calling `upsert_metadata_key_values`")
resource_path = '/metadata'.replace('{format}', 'json')
path_params = {}
query_params = {}
if 'organizations' in params:
query_params['organizations'] = params['organizations']
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'metadata' in params:
body_params = params['metadata']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
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 = []
return 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='DynamicMetadata',
auth_settings=auth_settings,
callback=params.get('callback'),
_return_http_data_only=params.get('_return_http_data_only'))
| 42.406383
| 172
| 0.59144
| 2,004
| 19,931
| 5.685629
| 0.111776
| 0.05617
| 0.047744
| 0.025276
| 0.919782
| 0.910743
| 0.907758
| 0.899509
| 0.894418
| 0.887748
| 0
| 0.000742
| 0.323817
| 19,931
| 469
| 173
| 42.496802
| 0.844698
| 0.404194
| 0
| 0.787736
| 1
| 0
| 0.146939
| 0.039266
| 0
| 0
| 0
| 0
| 0
| 1
| 0.042453
| false
| 0
| 0.033019
| 0
| 0.136792
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
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| 0
| 0
|
0
| 8
|
082685a2d069cfadc36fb74db620cbb296fe71de
| 50,718
|
py
|
Python
|
nnunet/network_architecture/segnet.py
|
NabJa/nnUNet
|
f017003523f5619d5a4165575c8338bbb8733628
|
[
"Apache-2.0"
] | null | null | null |
nnunet/network_architecture/segnet.py
|
NabJa/nnUNet
|
f017003523f5619d5a4165575c8338bbb8733628
|
[
"Apache-2.0"
] | null | null | null |
nnunet/network_architecture/segnet.py
|
NabJa/nnUNet
|
f017003523f5619d5a4165575c8338bbb8733628
|
[
"Apache-2.0"
] | null | null | null |
from copy import deepcopy
from nnunet.utilities.nd_softmax import softmax_helper
from torch import nn
import torch
import numpy as np
from nnunet.network_architecture.initialization import InitWeights_He
from nnunet.network_architecture.neural_network import SegmentationNetwork
import torch.nn.functional
from nnunet.network_architecture.generic_UNet import (
ConvDropoutNonlinNorm,
StackedConvLayers,
Upsample,
)
class SegNet(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,
input_channels,
base_num_features,
num_classes,
num_pool,
num_conv_per_stage=2,
feat_map_mul_on_downscale=2,
conv_op=nn.Conv3d,
norm_op=nn.BatchNorm3d,
norm_op_kwargs=None,
dropout_op=nn.Dropout3d,
dropout_op_kwargs=None,
nonlin=nn.LeakyReLU,
nonlin_kwargs=None,
deep_supervision=True,
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,
basic_block=ConvDropoutNonlinNorm,
seg_output_use_bias=False,
):
"""
basically more flexible than v1, architecture is the same
Does this look complicated? Nah bro. Functionality > usability
This does everything you need, including world peace.
Questions? -> f.isensee@dkfz.de
"""
super(SegNet, self).__init__()
self.convolutional_upsampling = convolutional_upsampling
self.convolutional_pooling = convolutional_pooling
self.upscale_logits = upscale_logits
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
self.nonlin_kwargs = nonlin_kwargs
self.dropout_op_kwargs = dropout_op_kwargs
self.norm_op_kwargs = norm_op_kwargs
self.weightInitializer = weightInitializer
self.conv_op = conv_op
self.norm_op = norm_op
self.dropout_op = dropout_op
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:
upsample_mode = "bilinear"
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:
upsample_mode = "trilinear"
pool_op = nn.MaxPool3d
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
self.conv_blocks_context = []
self.conv_blocks_localization = []
self.transpose_down = []
self.down_idx = []
self.transpose_up = []
self.seg_outputs = []
output_features = base_num_features
input_features = input_channels
#############################################
# ENCODER #
#############################################
for npool in range(num_pool):
# determine the first stride
if npool != 0 and self.convolutional_pooling:
first_stride = pool_op_kernel_sizes[npool - 1]
else:
first_stride = None
self.conv_kwargs["kernel_size"] = self.conv_kernel_sizes[npool]
self.conv_kwargs["padding"] = self.conv_pad_sizes[npool]
# add convolutions
self.conv_blocks_context.append(
StackedConvLayers(
input_features,
output_features,
num_conv_per_stage,
self.conv_op,
self.conv_kwargs,
self.norm_op,
self.norm_op_kwargs,
self.dropout_op,
self.dropout_op_kwargs,
self.nonlin,
self.nonlin_kwargs,
first_stride,
basic_block=basic_block,
)
)
if not self.convolutional_pooling:
# NJ CHANGE 1: SET RETURN_INDICES TO TRUE POOL_OP (=nn.MaxPool3d)
tdown = pool_op(pool_op_kernel_sizes[npool], return_indices=True)
self.transpose_down.append(tdown)
input_features = output_features
output_features = int(np.round(output_features * feat_map_mul_on_downscale))
output_features = min(output_features, self.max_num_features)
#############################################
# BOTTLENECK #
#############################################
# determine the first stride
if self.convolutional_pooling:
first_stride = pool_op_kernel_sizes[-1]
else:
first_stride = None
# the output of the last conv must match the number of features from the skip connection if we are not using
# convolutional upsampling. If we use convolutional upsampling then the reduction in feature maps will be
# done by the transposed conv
if self.convolutional_upsampling:
final_num_features = output_features
else:
final_num_features = self.conv_blocks_context[-1].output_channels
self.conv_kwargs["kernel_size"] = self.conv_kernel_sizes[num_pool]
self.conv_kwargs["padding"] = self.conv_pad_sizes[num_pool]
self.conv_blocks_context.append(
nn.Sequential(
StackedConvLayers(
input_features,
output_features,
num_conv_per_stage - 1,
self.conv_op,
self.conv_kwargs,
self.norm_op,
self.norm_op_kwargs,
self.dropout_op,
self.dropout_op_kwargs,
self.nonlin,
self.nonlin_kwargs,
first_stride,
basic_block=basic_block,
),
StackedConvLayers(
output_features,
final_num_features,
1,
self.conv_op,
self.conv_kwargs,
self.norm_op,
self.norm_op_kwargs,
self.dropout_op,
self.dropout_op_kwargs,
self.nonlin,
self.nonlin_kwargs,
basic_block=basic_block,
),
)
)
# if we don't want to do dropout in the localization pathway then we set the dropout prob to zero here
if not dropout_in_localization:
old_dropout_p = self.dropout_op_kwargs["p"]
self.dropout_op_kwargs["p"] = 0.0
#############################################
# DECODER #
#############################################
for npool in range(num_pool):
nfeatures_from_down = final_num_features
nfeatures_from_skip = self.conv_blocks_context[
-(2 + npool)
].output_channels # self.conv_blocks_context[-1] is bottleneck, so start with -2
n_features_after_tu_and_concat = nfeatures_from_skip * 2
# the first conv reduces the number of features to match those of skip
# the following convs work on that number of features
# if not convolutional upsampling then the final conv reduces the num of features again
if npool != num_pool - 1 and not self.convolutional_upsampling:
final_num_features = self.conv_blocks_context[
-(3 + npool)
].output_channels
else:
final_num_features = nfeatures_from_skip
############
# NJ CHANGE 2: UPSAMPLING IS DONE VIA UNPOOLING!
############
# if not self.convolutional_upsampling:
# self.transpose_up.append(Upsample(scale_factor=pool_op_kernel_sizes[-(u + 1)], mode=upsample_mode))
# else:
# self.transpose_up.append(transpconv(nfeatures_from_down, nfeatures_from_skip, pool_op_kernel_sizes[-(u + 1)],
# pool_op_kernel_sizes[-(u + 1)], bias=False))
this_pool_op_kernel_size = pool_op_kernel_sizes[-(npool + 1)]
self.transpose_up.append(
nn.MaxUnpool3d(this_pool_op_kernel_size, this_pool_op_kernel_size)
)
############
# END CHANGE 2
############
self.conv_kwargs["kernel_size"] = self.conv_kernel_sizes[-(npool + 1)]
self.conv_kwargs["padding"] = self.conv_pad_sizes[-(npool + 1)]
self.conv_blocks_localization.append(
nn.Sequential(
StackedConvLayers(
n_features_after_tu_and_concat,
nfeatures_from_skip,
num_conv_per_stage - 1,
self.conv_op,
self.conv_kwargs,
self.norm_op,
self.norm_op_kwargs,
self.dropout_op,
self.dropout_op_kwargs,
self.nonlin,
self.nonlin_kwargs,
basic_block=basic_block,
),
StackedConvLayers(
nfeatures_from_skip,
final_num_features,
1,
self.conv_op,
self.conv_kwargs,
self.norm_op,
self.norm_op_kwargs,
self.dropout_op,
self.dropout_op_kwargs,
self.nonlin,
self.nonlin_kwargs,
basic_block=basic_block,
),
)
)
for ds in range(len(self.conv_blocks_localization)):
self.seg_outputs.append(
conv_op(
self.conv_blocks_localization[ds][-1].output_channels,
num_classes,
1,
1,
0,
1,
1,
seg_output_use_bias,
)
)
self.upscale_logits_ops = []
cum_upsample = np.cumprod(np.vstack(pool_op_kernel_sizes), axis=0)[::-1]
for usl in range(num_pool - 1):
if self.upscale_logits:
self.upscale_logits_ops.append(
Upsample(
scale_factor=tuple([int(i) for i in cum_upsample[usl + 1]]),
mode=upsample_mode,
)
)
else:
self.upscale_logits_ops.append(lambda x: x)
if not dropout_in_localization:
self.dropout_op_kwargs["p"] = old_dropout_p
# register all modules properly
self.conv_blocks_localization = nn.ModuleList(self.conv_blocks_localization)
self.conv_blocks_context = nn.ModuleList(self.conv_blocks_context)
self.transpose_down = nn.ModuleList(self.transpose_down)
self.transpose_up = nn.ModuleList(self.transpose_up)
self.seg_outputs = nn.ModuleList(self.seg_outputs)
if self.upscale_logits:
self.upscale_logits_ops = nn.ModuleList(
self.upscale_logits_ops
) # lambda x:x is not a Module so we need to distinguish here
if self.weightInitializer is not None:
self.apply(self.weightInitializer)
# self.apply(print_module_training_status)
def forward(self, x):
skips = []
indicis = [] # NJ Save indeces of nn.MaxPool3d(..., return_indicis=True)
seg_outputs = []
for d in range(len(self.conv_blocks_context) - 1):
x = self.conv_blocks_context[d](x)
skips.append(x)
if not self.convolutional_pooling:
x, index = self.transpose_down[d](x)
indicis.append(index) # NJ Save indeces for every pooling step
x = self.conv_blocks_context[-1](x)
for u in range(len(self.transpose_up)):
x = self.transpose_up[u](
x, indicis[-(u + 1)]
) # NJ Add index to nn.MaxUnpool3d()
x = torch.cat((x, skips[-(u + 1)]), dim=1)
x = self.conv_blocks_localization[u](x)
seg_outputs.append(self.final_nonlin(self.seg_outputs[u](x)))
if self._deep_supervision and self.do_ds:
return tuple(
[seg_outputs[-1]]
+ [
i(j)
for i, j in zip(
list(self.upscale_logits_ops)[::-1], seg_outputs[:-1][::-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 = (
(conv_per_stage * 2 + 1) if p < (npool - 1) else conv_per_stage
) # 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 SmallSegNet(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,
input_channels,
base_num_features,
num_classes,
num_pool,
num_conv_per_stage=2,
feat_map_mul_on_downscale=2,
conv_op=nn.Conv3d,
norm_op=nn.BatchNorm3d,
norm_op_kwargs=None,
dropout_op=nn.Dropout3d,
dropout_op_kwargs=None,
nonlin=nn.LeakyReLU,
nonlin_kwargs=None,
deep_supervision=True,
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,
basic_block=ConvDropoutNonlinNorm,
seg_output_use_bias=False,
):
"""
basically more flexible than v1, architecture is the same
Does this look complicated? Nah bro. Functionality > usability
This does everything you need, including world peace.
Questions? -> f.isensee@dkfz.de
"""
super(SmallSegNet, self).__init__()
self.convolutional_upsampling = convolutional_upsampling
self.convolutional_pooling = convolutional_pooling
self.upscale_logits = upscale_logits
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
self.nonlin_kwargs = nonlin_kwargs
self.dropout_op_kwargs = dropout_op_kwargs
self.norm_op_kwargs = norm_op_kwargs
self.weightInitializer = weightInitializer
self.conv_op = conv_op
self.norm_op = norm_op
self.dropout_op = dropout_op
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:
upsample_mode = "bilinear"
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:
upsample_mode = "trilinear"
pool_op = nn.MaxPool3d
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
self.conv_blocks_context = []
self.conv_blocks_localization = []
self.transpose_down = []
self.down_idx = []
self.transpose_up = []
self.seg_outputs = []
output_features = base_num_features
input_features = input_channels
#############################################
# ENCODER #
#############################################
for npool in range(num_pool):
# determine the first stride
if npool != 0 and self.convolutional_pooling:
first_stride = pool_op_kernel_sizes[npool - 1]
else:
first_stride = None
self.conv_kwargs["kernel_size"] = self.conv_kernel_sizes[npool]
self.conv_kwargs["padding"] = self.conv_pad_sizes[npool]
# add convolutions
self.conv_blocks_context.append(
StackedConvLayers(
input_features,
output_features,
num_conv_per_stage,
self.conv_op,
self.conv_kwargs,
self.norm_op,
self.norm_op_kwargs,
self.dropout_op,
self.dropout_op_kwargs,
self.nonlin,
self.nonlin_kwargs,
first_stride,
basic_block=basic_block,
)
)
if not self.convolutional_pooling:
# NJ CHANGE 1: SET RETURN_INDICES TO TRUE POOL_OP (=nn.MaxPool3d)
tdown = pool_op(pool_op_kernel_sizes[npool], return_indices=True)
self.transpose_down.append(tdown)
input_features = output_features
output_features = int(np.round(output_features * feat_map_mul_on_downscale))
output_features = min(output_features, self.max_num_features)
#############################################
# BOTTLENECK #
#############################################
# determine the first stride
if self.convolutional_pooling:
first_stride = pool_op_kernel_sizes[-1]
else:
first_stride = None
# the output of the last conv must match the number of features from the skip connection if we are not using
# convolutional upsampling. If we use convolutional upsampling then the reduction in feature maps will be
# done by the transposed conv
if self.convolutional_upsampling:
final_num_features = output_features
else:
final_num_features = self.conv_blocks_context[-1].output_channels
self.conv_kwargs["kernel_size"] = self.conv_kernel_sizes[num_pool]
self.conv_kwargs["padding"] = self.conv_pad_sizes[num_pool]
self.conv_blocks_context.append(
nn.Sequential(
StackedConvLayers(
input_features,
output_features,
num_conv_per_stage - 1,
self.conv_op,
self.conv_kwargs,
self.norm_op,
self.norm_op_kwargs,
self.dropout_op,
self.dropout_op_kwargs,
self.nonlin,
self.nonlin_kwargs,
first_stride,
basic_block=basic_block,
),
StackedConvLayers(
output_features,
final_num_features,
1,
self.conv_op,
self.conv_kwargs,
self.norm_op,
self.norm_op_kwargs,
self.dropout_op,
self.dropout_op_kwargs,
self.nonlin,
self.nonlin_kwargs,
basic_block=basic_block,
),
)
)
# if we don't want to do dropout in the localization pathway then we set the dropout prob to zero here
if not dropout_in_localization:
old_dropout_p = self.dropout_op_kwargs["p"]
self.dropout_op_kwargs["p"] = 0.0
#############################################
# DECODER #
#############################################
for npool in range(num_pool):
nfeatures_from_down = final_num_features
nfeatures_from_skip = self.conv_blocks_context[
-(2 + npool)
].output_channels # self.conv_blocks_context[-1] is bottleneck, so start with -2
n_features_after_tu_and_concat = nfeatures_from_skip * 2
# the first conv reduces the number of features to match those of skip
# the following convs work on that number of features
# if not convolutional upsampling then the final conv reduces the num of features again
if npool != num_pool - 1 and not self.convolutional_upsampling:
final_num_features = self.conv_blocks_context[
-(3 + npool)
].output_channels
else:
final_num_features = nfeatures_from_skip
############
# NJ CHANGE 2: UPSAMPLING IS DONE VIA UNPOOLING!
############
# if not self.convolutional_upsampling:
# self.transpose_up.append(Upsample(scale_factor=pool_op_kernel_sizes[-(u + 1)], mode=upsample_mode))
# else:
# self.transpose_up.append(transpconv(nfeatures_from_down, nfeatures_from_skip, pool_op_kernel_sizes[-(u + 1)],
# pool_op_kernel_sizes[-(u + 1)], bias=False))
this_pool_op_kernel_size = pool_op_kernel_sizes[-(npool + 1)]
self.transpose_up.append(
nn.MaxUnpool3d(this_pool_op_kernel_size, this_pool_op_kernel_size)
)
############
# END CHANGE 2
############
self.conv_kwargs["kernel_size"] = self.conv_kernel_sizes[-(npool + 1)]
self.conv_kwargs["padding"] = self.conv_pad_sizes[-(npool + 1)]
######## NJ SmallResNet uses only nfeatures_from_skip
self.conv_blocks_localization.append(
StackedConvLayers(
nfeatures_from_skip,
final_num_features,
1,
self.conv_op,
self.conv_kwargs,
self.norm_op,
self.norm_op_kwargs,
self.dropout_op,
self.dropout_op_kwargs,
self.nonlin,
self.nonlin_kwargs,
basic_block=basic_block,
),
)
for ds in range(len(self.conv_blocks_localization)):
self.seg_outputs.append(
conv_op(
self.conv_blocks_localization[ds].output_channels,
num_classes,
1,
1,
0,
1,
1,
seg_output_use_bias,
)
)
self.upscale_logits_ops = []
cum_upsample = np.cumprod(np.vstack(pool_op_kernel_sizes), axis=0)[::-1]
for usl in range(num_pool - 1):
if self.upscale_logits:
self.upscale_logits_ops.append(
Upsample(
scale_factor=tuple([int(i) for i in cum_upsample[usl + 1]]),
mode=upsample_mode,
)
)
else:
self.upscale_logits_ops.append(lambda x: x)
if not dropout_in_localization:
self.dropout_op_kwargs["p"] = old_dropout_p
# register all modules properly
self.conv_blocks_localization = nn.ModuleList(self.conv_blocks_localization)
self.conv_blocks_context = nn.ModuleList(self.conv_blocks_context)
self.transpose_down = nn.ModuleList(self.transpose_down)
self.transpose_up = nn.ModuleList(self.transpose_up)
self.seg_outputs = nn.ModuleList(self.seg_outputs)
if self.upscale_logits:
self.upscale_logits_ops = nn.ModuleList(
self.upscale_logits_ops
) # lambda x:x is not a Module so we need to distinguish here
if self.weightInitializer is not None:
self.apply(self.weightInitializer)
# self.apply(print_module_training_status)
def forward(self, x):
skips = []
indicis = [] # NJ Save indeces of nn.MaxPool3d(..., return_indicis=True)
seg_outputs = []
for d in range(len(self.conv_blocks_context) - 1):
x = self.conv_blocks_context[d](x)
skips.append(x)
if not self.convolutional_pooling:
x, index = self.transpose_down[d](x)
indicis.append(index) # NJ Save indeces for every pooling step
x = self.conv_blocks_context[-1](x)
for u in range(len(self.transpose_up)):
x = self.transpose_up[u](
x, indicis[-(u + 1)]
) # NJ Add index to nn.MaxUnpool3d()
x = self.conv_blocks_localization[u](x)
seg_outputs.append(self.final_nonlin(self.seg_outputs[u](x)))
if self._deep_supervision and self.do_ds:
return tuple(
[seg_outputs[-1]]
+ [
i(j)
for i, j in zip(
list(self.upscale_logits_ops)[::-1], seg_outputs[:-1][::-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 = (
(conv_per_stage * 2 + 1) if p < (npool - 1) else conv_per_stage
) # 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 SegNetNPool(SegmentationNetwork):
"""
SegNet Variant with only a limited number of unpoolings.
TODO integrate in original SegNet!
"""
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,
input_channels,
base_num_features,
num_classes,
num_pool,
num_conv_per_stage=2,
feat_map_mul_on_downscale=2,
conv_op=nn.Conv3d,
norm_op=nn.BatchNorm3d,
norm_op_kwargs=None,
dropout_op=nn.Dropout3d,
dropout_op_kwargs=None,
nonlin=nn.LeakyReLU,
nonlin_kwargs=None,
deep_supervision=True,
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,
basic_block=ConvDropoutNonlinNorm,
seg_output_use_bias=False,
unpool_on_layers=None,
):
"""
basically more flexible than v1, architecture is the same
Does this look complicated? Nah bro. Functionality > usability
This does everything you need, including world peace.
Questions? -> f.isensee@dkfz.de
"""
super(SegNetNPool, self).__init__()
self.convolutional_upsampling = convolutional_upsampling
self.convolutional_pooling = convolutional_pooling
self.upscale_logits = upscale_logits
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
self.nonlin_kwargs = nonlin_kwargs
self.dropout_op_kwargs = dropout_op_kwargs
self.norm_op_kwargs = norm_op_kwargs
self.weightInitializer = weightInitializer
self.conv_op = conv_op
self.norm_op = norm_op
self.dropout_op = dropout_op
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:
upsample_mode = "bilinear"
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:
upsample_mode = "trilinear"
pool_op = nn.MaxPool3d
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
self.conv_blocks_context = []
self.conv_blocks_localization = []
self.transpose_down = []
self.down_idx = []
self.transpose_up = []
self.seg_outputs = []
output_features = base_num_features
input_features = input_channels
if unpool_on_layers is not None:
self.unpool_on_layers = unpool_on_layers
else:
self.unpool_on_layers = list(range(num_pool))
#############################################
# ENCODER #
#############################################
for npool in range(num_pool):
# determine the first stride
if npool != 0 and self.convolutional_pooling:
first_stride = pool_op_kernel_sizes[npool - 1]
else:
first_stride = None
self.conv_kwargs["kernel_size"] = self.conv_kernel_sizes[npool]
self.conv_kwargs["padding"] = self.conv_pad_sizes[npool]
# add convolutions
self.conv_blocks_context.append(
StackedConvLayers(
input_features,
output_features,
num_conv_per_stage,
self.conv_op,
self.conv_kwargs,
self.norm_op,
self.norm_op_kwargs,
self.dropout_op,
self.dropout_op_kwargs,
self.nonlin,
self.nonlin_kwargs,
first_stride,
basic_block=basic_block,
)
)
if not self.convolutional_pooling:
# NJ CHANGE 1: SET RETURN_INDICES TO TRUE POOL_OP (=nn.MaxPool3d)
tdown = pool_op(pool_op_kernel_sizes[npool], return_indices=True)
self.transpose_down.append(tdown)
input_features = output_features
output_features = int(np.round(output_features * feat_map_mul_on_downscale))
output_features = min(output_features, self.max_num_features)
#############################################
# BOTTLENECK #
#############################################
# determine the first stride
if self.convolutional_pooling:
first_stride = pool_op_kernel_sizes[-1]
else:
first_stride = None
# the output of the last conv must match the number of features from the skip connection if we are not using
# convolutional upsampling. If we use convolutional upsampling then the reduction in feature maps will be
# done by the transposed conv
if self.convolutional_upsampling:
final_num_features = output_features
else:
final_num_features = self.conv_blocks_context[-1].output_channels
self.conv_kwargs["kernel_size"] = self.conv_kernel_sizes[num_pool]
self.conv_kwargs["padding"] = self.conv_pad_sizes[num_pool]
self.conv_blocks_context.append(
nn.Sequential(
StackedConvLayers(
input_features,
output_features,
num_conv_per_stage - 1,
self.conv_op,
self.conv_kwargs,
self.norm_op,
self.norm_op_kwargs,
self.dropout_op,
self.dropout_op_kwargs,
self.nonlin,
self.nonlin_kwargs,
first_stride,
basic_block=basic_block,
),
StackedConvLayers(
output_features,
final_num_features,
1,
self.conv_op,
self.conv_kwargs,
self.norm_op,
self.norm_op_kwargs,
self.dropout_op,
self.dropout_op_kwargs,
self.nonlin,
self.nonlin_kwargs,
basic_block=basic_block,
),
)
)
# if we don't want to do dropout in the localization pathway then we set the dropout prob to zero here
if not dropout_in_localization:
old_dropout_p = self.dropout_op_kwargs["p"]
self.dropout_op_kwargs["p"] = 0.0
#############################################
# DECODER #
#############################################
for npool in range(num_pool):
nfeatures_from_down = final_num_features
nfeatures_from_skip = self.conv_blocks_context[
-(2 + npool)
].output_channels # self.conv_blocks_context[-1] is bottleneck, so start with -2
n_features_after_tu_and_concat = nfeatures_from_skip * 2
# the first conv reduces the number of features to match those of skip
# the following convs work on that number of features
# if not convolutional upsampling then the final conv reduces the num of features again
if npool != num_pool - 1 and not self.convolutional_upsampling:
final_num_features = self.conv_blocks_context[
-(3 + npool)
].output_channels
else:
final_num_features = nfeatures_from_skip
############
# NJ CHANGE 2: UPSAMPLING IS DONE VIA UNPOOLING!
############
# if not self.convolutional_upsampling:
# self.transpose_up.append(Upsample(scale_factor=pool_op_kernel_sizes[-(u + 1)], mode=upsample_mode))
# else:
# self.transpose_up.append(transpconv(nfeatures_from_down, nfeatures_from_skip, pool_op_kernel_sizes[-(u + 1)],
# pool_op_kernel_sizes[-(u + 1)], bias=False))
this_pool_op_kernel_size = pool_op_kernel_sizes[-(npool + 1)]
if npool in self.unpool_on_layers:
self.transpose_up.append(
nn.MaxUnpool3d(this_pool_op_kernel_size, this_pool_op_kernel_size)
)
else:
self.transpose_up.append(
Upsample(
scale_factor=pool_op_kernel_sizes[-(npool + 1)],
mode=upsample_mode,
)
)
############
# END CHANGE 2
############
self.conv_kwargs["kernel_size"] = self.conv_kernel_sizes[-(npool + 1)]
self.conv_kwargs["padding"] = self.conv_pad_sizes[-(npool + 1)]
self.conv_blocks_localization.append(
nn.Sequential(
StackedConvLayers(
n_features_after_tu_and_concat,
nfeatures_from_skip,
num_conv_per_stage - 1,
self.conv_op,
self.conv_kwargs,
self.norm_op,
self.norm_op_kwargs,
self.dropout_op,
self.dropout_op_kwargs,
self.nonlin,
self.nonlin_kwargs,
basic_block=basic_block,
),
StackedConvLayers(
nfeatures_from_skip,
final_num_features,
1,
self.conv_op,
self.conv_kwargs,
self.norm_op,
self.norm_op_kwargs,
self.dropout_op,
self.dropout_op_kwargs,
self.nonlin,
self.nonlin_kwargs,
basic_block=basic_block,
),
)
)
for ds in range(len(self.conv_blocks_localization)):
self.seg_outputs.append(
conv_op(
self.conv_blocks_localization[ds][-1].output_channels,
num_classes,
1,
1,
0,
1,
1,
seg_output_use_bias,
)
)
self.upscale_logits_ops = []
cum_upsample = np.cumprod(np.vstack(pool_op_kernel_sizes), axis=0)[::-1]
for usl in range(num_pool - 1):
if self.upscale_logits:
self.upscale_logits_ops.append(
Upsample(
scale_factor=tuple([int(i) for i in cum_upsample[usl + 1]]),
mode=upsample_mode,
)
)
else:
self.upscale_logits_ops.append(lambda x: x)
if not dropout_in_localization:
self.dropout_op_kwargs["p"] = old_dropout_p
# register all modules properly
self.conv_blocks_localization = nn.ModuleList(self.conv_blocks_localization)
self.conv_blocks_context = nn.ModuleList(self.conv_blocks_context)
self.transpose_down = nn.ModuleList(self.transpose_down)
self.transpose_up = nn.ModuleList(self.transpose_up)
self.seg_outputs = nn.ModuleList(self.seg_outputs)
if self.upscale_logits:
self.upscale_logits_ops = nn.ModuleList(
self.upscale_logits_ops
) # lambda x:x is not a Module so we need to distinguish here
if self.weightInitializer is not None:
self.apply(self.weightInitializer)
# self.apply(print_module_training_status)
def forward(self, x):
skips = []
indicis = [] # NJ Save indeces of nn.MaxPool3d(..., return_indicis=True)
seg_outputs = []
for d in range(len(self.conv_blocks_context) - 1):
x = self.conv_blocks_context[d](x)
skips.append(x)
if not self.convolutional_pooling:
x, index = self.transpose_down[d](x)
indicis.append(index) # NJ Save indeces for every pooling step
x = self.conv_blocks_context[-1](x)
for u in range(len(self.transpose_up)):
if u in self.unpool_on_layers:
x = self.transpose_up[u](x, indicis[-(u + 1)])
else:
x = self.transpose_up[u](x)
x = torch.cat((x, skips[-(u + 1)]), dim=1)
x = self.conv_blocks_localization[u](x)
seg_outputs.append(self.final_nonlin(self.seg_outputs[u](x)))
if self._deep_supervision and self.do_ds:
return tuple(
[seg_outputs[-1]]
+ [
i(j)
for i, j in zip(
list(self.upscale_logits_ops)[::-1], seg_outputs[:-1][::-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 = (
(conv_per_stage * 2 + 1) if p < (npool - 1) else conv_per_stage
) # 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
| 38.248869
| 127
| 0.548444
| 5,644
| 50,718
| 4.599575
| 0.057938
| 0.04376
| 0.032357
| 0.039946
| 0.97396
| 0.971572
| 0.969453
| 0.969453
| 0.969453
| 0.968413
| 0
| 0.01705
| 0.36283
| 50,718
| 1,325
| 128
| 38.277736
| 0.786267
| 0.137328
| 0
| 0.912315
| 0
| 0
| 0.013153
| 0
| 0
| 0
| 0
| 0.000755
| 0
| 1
| 0.008867
| false
| 0
| 0.008867
| 0
| 0.06798
| 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
|
08452be74610c423f3b1af37568b8140e1fb5c80
| 30,367
|
py
|
Python
|
guessing number/package/ui.py
|
mahdigoodarzi123/gussingnumbergui
|
5658674a3bea7a2432ecf48beb7080b9fc4fce26
|
[
"MIT"
] | null | null | null |
guessing number/package/ui.py
|
mahdigoodarzi123/gussingnumbergui
|
5658674a3bea7a2432ecf48beb7080b9fc4fce26
|
[
"MIT"
] | null | null | null |
guessing number/package/ui.py
|
mahdigoodarzi123/gussingnumbergui
|
5658674a3bea7a2432ecf48beb7080b9fc4fce26
|
[
"MIT"
] | null | null | null |
from abc import abstractclassmethod
from tkinter import *
from tkinter import font
from tkinter.messagebox import *
from .bl import *
def login_onclick(form , username_entry , password_entry):
username = username_entry.get()
password = password_entry.get()
res2 = get(username , password)
if res2[0]=="ERROR":
username_entry.delete(0 , END)
password_entry.delete(0 , END)
showerror("ERROR" , res2[1])
elif res2[1]:
# form.destroy()
main_form()
elif not res2[1]:
username_entry.delete(0,END)
password_entry.delete(0,END)
showerror("ERROR" , "USERNAME OR PASSWORD ERROR")
def register_onclick(form , username_entry , password_entry , confirm_password_entry):
username = username_entry.get()
password = password_entry.get()
confirm_password = confirm_password_entry.get()
res = add(username , password , confirm_password)
if res[0] == "ERROR":
username_entry.delete(0,END)
password_entry.delete(0,END)
confirm_password_entry.delete(0,END)
showerror("ERROR",res[1])
else:
showinfo("Success",res[1])
form.destroy()
login_form()
def load_page():
res = init()
if res[0] == "ERROR":
showerror("ERROR" ,res[1])
def login_to_register(form):
form.destroy()
register_form()
def register_to_login(form):
form.destroy()
login_form()
def login_form():
login = Tk()
login.title("login form")
login.geometry("350x350")
login.configure(bg="white")
body = Frame(
master=login,
bg="white"
)
body.pack(fill=BOTH , expand=True)
body.propagate(0)
# password label
Label(
master=body,
text="username:",
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
anchor=W
).pack(padx=(30,30) , pady=(70,0))
# username entry
username_entry = Entry(
master=body,
bg="white",
fg="gray",
font=("Tahoma",14,"normal")
)
username_entry.pack()
# password label
Label(
master=body,
text="password:",
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
anchor=W
).pack(padx=(30,30) , pady=(60,0))
# password entry
password_entry = Entry(
master=body,
bg="white",
fg="gray",
font=("Tahoma",14,"normal")
)
password_entry.pack(pady=(0,15))
# login button
Button(
master=body,
text="Login!!!",
bg="#28a745",
fg="white",
font= ("Tahoma",10,"bold"),
pady= 5,
command= lambda: login_onclick(login,username_entry,password_entry)
).pack(side=TOP,fill=X,pady=(0,3),padx=(30,30))
# register button
Button(
master=body,
text="register",
bg="red",
fg="white",
font=("Tahoma",10,"bold"),
pady=5,
command= lambda : login_to_register(login)
).pack(side=TOP,fill=X,pady=(0,3),padx=(30,30))
login.mainloop()
def register_form():
register = Tk()
register.title("register form")
register.geometry("450x450")
register.resizable(width=False , height=False)
register.configure(bg="white")
body = Frame(
master=register,
bg="white"
)
body.pack(fill=BOTH , expand=True)
body.propagate(0)
# username region
Label (
master=body,
text="username:",
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
anchor=W
).pack(side=TOP,fill=X,pady=(50,0),padx=(30,30))
#username entry
username_entry = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 14 , "normal")
)
username_entry.pack(side=TOP,fill=X,pady=(0,10),padx=(30,30))
#password region
Label (
master=body,
text="password:",
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
anchor=W
).pack(side=TOP,fill=X,pady=(20,0),padx=(30,30))
# password entry
password_entry = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 14 , "normal")
)
password_entry.pack(side=TOP,fill=X,pady=(0,10),padx=(30,30))
# confirm password region
Label (
master=body,
text="confirm password:",
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
anchor=W
).pack(side=TOP,fill=X,pady=(20,0),padx=(30,30))
# confirm password entry
confirm_password_entry = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 14 , "normal")
)
confirm_password_entry.pack(side=TOP,fill=X,pady=(0,10),padx=(30,30))
#region BUTTON
Button(
master=body,
text="register!!!",
bg="green",
fg="white",
font= ("Tahoma",10,"bold"),
pady= 5,
command= lambda : register_onclick(register , username_entry , password_entry , confirm_password_entry)
).pack(side=TOP,fill=X,pady=(0,3),padx=(30,30))
#back button
Button(
master=body,
text="BACK!!!",
bg="red",
fg="white",
font= ("Tahoma",10,"bold"),
pady= 5,
command= lambda : register_to_login(register)
).pack(side=TOP,fill=X,pady=(0,3),padx=(30,30))
#endregion
register.mainloop()
#forth one
def four():
main = Tk()
main.title("forth digit number")
main.resizable(0,0)
main.geometry("350x250")
main.configure(bg="white")
#this is a test
lev= 4
#this is a test
numb = number_split(lev)
# body
body = Frame(
master= main,
bg = "white"
)
body.pack(fill=BOTH , expand=True)
body.propagate(0)
#footer
footer = Frame(
master=main,
bg="white",
width=5
)
footer.pack(fill=BOTH,expand=1)
footer.propagate(0)
#first entry
e1 = Entry(
master=body,
bg="white",
fg="black",
justify='center'
)
e1.place(width=50 , height=50, x=15 , y=60)
e2 = Entry(
master=body,
bg="white",
fg="black",
justify='center'
)
e2.place(width=50 , height=50, x=100 , y=60)
e3 = Entry(
master=body,
bg="white",
fg="black",
justify='center'
)
e3.place(width=50 , height=50, x=185 , y=60)
e4 = Entry(
master=body,
bg="white",
fg="black",
justify='center'
)
e4.place(width=50 , height=50, x=270 , y=60)
Button(
master=footer,
text="check",
bg="green",
fg="black",
font=("Tahoma" , 10 , "bold"),
padx=45,
pady=20,
command= lambda: check(numb, e1 , e2 , e3 , e4, None , None , None , None, None, None, None , main)
).pack(side=TOP , pady=50)
main.mainloop()
################################################################
#fifth one
def five():
main = Tk()
main.title("forth digit number")
# main.resizable(0,0)
main.geometry("415x250")
main.configure(bg="white")
lev= 5
#this is a test
numb = number_split(lev)
# body
body = Frame(
master= main,
bg = "white"
)
body.pack(fill=BOTH , expand=True)
body.propagate(0)
#footer
footer = Frame(
master=main,
bg="white",
width=5
)
footer.pack(fill=BOTH,expand=1)
footer.propagate(0)
#first entry
e1 = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e1.place(width=50 , height=50, x=15 , y=60)
#second entry
e2 = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e2.place(width=50 , height=50, x=100 , y=60)
#third entry
e3 = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e3.place(width=50 , height=50, x=185 , y=60)
#forth entry
e4 = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e4.place(width=50 , height=50, x=270 , y=60)
#fifth entry
e5 = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e5.place(width=50 , height=50, x=355 , y=60)
#check button
Button(
master=footer,
text="check",
bg="green",
fg="black",
font=("Tahoma" , 10 , "bold"),
padx=45,
pady=20,
command= lambda: check(numb, e1 , e2 , e3 , e4, e5 , None , None , None, None, None, None , main)
).pack(side=TOP , pady=50)
# .place(width=20 , height=20 , x=170 , y=50)
main.mainloop()
#sixth one
def six():
main = Tk()
main.title("forth digit number")
main.resizable(0,0)
main.geometry("500x250")
main.configure(bg="white")
lev = 6
numb = number_split(lev)
# body
body = Frame(
master= main,
bg = "white"
)
body.pack(fill=BOTH , expand=True)
body.propagate(0)
#footer
footer = Frame(
master=main,
bg="white",
width=5
)
footer.pack(fill=BOTH,expand=1)
footer.propagate(0)
#first entry
e1= Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e1.place(width=50 , height=50, x=15 , y=60)
#second entry
e2 =Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e2.place(width=50 , height=50, x=100 , y=60)
#third entry
e3 = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e3.place(width=50 , height=50, x=185 , y=60)
#forth entry
e4 = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e4.place(width=50 , height=50, x=270 , y=60)
#fifth entry
e5 = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e5.place(width=50 , height=50, x=355 , y=60)
#sixth entry
e6 = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e6.place(width=50 , height=50, x=440 , y=60)
#check button
Button(
master=footer,
text="check",
bg="green",
fg="black",
font=("Tahoma" , 10 , "bold"),
padx=45,
pady=20,
command= lambda: check(numb, e1 , e2 , e3 , e4, e5 , e6 , None , None, None, None, None , main)
).pack(side=TOP , pady=50)
# .place(width=20 , height=20 , x=170 , y=50)
main.mainloop()
def seven():
main = Tk()
main.title("forth digit number")
main.resizable(0,0)
main.geometry("600x250")
main.configure(bg="white")
lev = 7
numb = number_split(lev)
# body
body = Frame(
master= main,
bg = "white"
)
body.pack(fill=BOTH , expand=True)
body.propagate(0)
#footer
footer = Frame(
master=main,
bg="white",
width=5
)
footer.pack(fill=BOTH,expand=1)
footer.propagate(0)
#first entry
e1= Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e1.place(width=50 , height=50, x=15 , y=60)
#second entry
e2 =Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e2.place(width=50 , height=50, x=100 , y=60)
#third entry
e3 = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e3.place(width=50 , height=50, x=185 , y=60)
#forth entry
e4 = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e4.place(width=50 , height=50, x=270 , y=60)
#fifth entry
e5 = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e5.place(width=50 , height=50, x=355 , y=60)
#sixth entry
e6 = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e6.place(width=50 , height=50, x=440 , y=60)
e7 = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e7.place(width=50 , height=50, x=525 , y=60)
#check button
Button(
master=footer,
text="check",
bg="green",
fg="black",
font=("Tahoma" , 10 , "bold"),
padx=45,
pady=20,
command= lambda: check(numb, e1 , e2 , e3 , e4, e5 , e6 , e7 , None, None, None, None , main)
).pack(side=TOP , pady=50)
# .place(width=20 , height=20 , x=170 , y=50)
main.mainloop()
def eight():
main = Tk()
main.title("forth digit number")
main.resizable(0,0)
main.geometry("700x250")
main.configure(bg="white")
lev = 8
numb = number_split(lev)
# body
body = Frame(
master= main,
bg = "white"
)
body.pack(fill=BOTH , expand=True)
body.propagate(0)
#footer
footer = Frame(
master=main,
bg="white",
width=5
)
footer.pack(fill=BOTH,expand=1)
footer.propagate(0)
#first entry
e1= Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e1.place(width=50 , height=50, x=15 , y=60)
#second entry
e2 =Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e2.place(width=50 , height=50, x=100 , y=60)
#third entry
e3 = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e3.place(width=50 , height=50, x=185 , y=60)
#forth entry
e4 = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e4.place(width=50 , height=50, x=270 , y=60)
#fifth entry
e5 = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e5.place(width=50 , height=50, x=355 , y=60)
#sixth entry
e6 = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e6.place(width=50 , height=50, x=440 , y=60)
e7 = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e7.place(width=50 , height=50, x=525 , y=60)
e8 = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e8.place(width=50 , height=50, x=610 , y=60)
#check button
Button(
master=footer,
text="check",
bg="green",
fg="black",
font=("Tahoma" , 10 , "bold"),
padx=45,
pady=20,
command= lambda: check(numb, e1 , e2 , e3 , e4, e5 , e6 , e7 , e8, None, None, None , main)
).pack(side=TOP , pady=50)
# .place(width=20 , height=20 , x=170 , y=50)
main.mainloop()
def ninth():
main = Tk()
main.title("forth digit number")
# main.resizable(0,0)
main.geometry("800x250")
main.configure(bg="white")
lev = 9
numb = number_split(lev)
# body
body = Frame(
master= main,
bg = "white"
)
body.pack(fill=BOTH , expand=True)
body.propagate(0)
#footer
footer = Frame(
master=main,
bg="white",
width=5
)
footer.pack(fill=BOTH,expand=1)
footer.propagate(0)
#first entry
e1= Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e1.place(width=50 , height=50, x=15 , y=60)
#second entry
e2 =Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e2.place(width=50 , height=50, x=100 , y=60)
#third entry
e3 = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e3.place(width=50 , height=50, x=185 , y=60)
#forth entry
e4 = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e4.place(width=50 , height=50, x=270 , y=60)
#fifth entry
e5 = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e5.place(width=50 , height=50, x=355 , y=60)
#sixth entry
e6 = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e6.place(width=50 , height=50, x=440 , y=60)
e7 = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e7.place(width=50 , height=50, x=525 , y=60)
e8 = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e8.place(width=50 , height=50, x=610 , y=60)
e9 = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e9.place(width=50 , height=50, x=695 , y=60)
#check button
Button(
master=footer,
text="check",
bg="green",
fg="black",
font=("Tahoma" , 10 , "bold"),
padx=45,
pady=20,
command= lambda: check(numb, e1 , e2 , e3 , e4, e5 , e6 , e7 , e8 , e9 , None , None , main)
).pack(side=TOP , pady=50)
# .place(width=20 , height=20 , x=170 , y=50)
main.mainloop()
def tenth():
main = Tk()
main.title("forth digit number")
main.resizable(0,0)
main.geometry("850x250")
main.configure(bg="white")
lev = 10
numb = number_split(lev)
# body
body = Frame(
master= main,
bg = "white"
)
body.pack(fill=BOTH , expand=True)
body.propagate(0)
#footer
footer = Frame(
master=main,
bg="white",
width=5
)
footer.pack(fill=BOTH,expand=1)
footer.propagate(0)
#first entry
e1= Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e1.place(width=50 , height=50, x=15 , y=60)
#second entry
e2 =Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e2.place(width=50 , height=50, x=100 , y=60)
#third entry
e3 = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e3.place(width=50 , height=50, x=185 , y=60)
#forth entry
e4 = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e4.place(width=50 , height=50, x=270 , y=60)
#fifth entry
e5 = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e5.place(width=50 , height=50, x=355 , y=60)
#sixth entry
e6 = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e6.place(width=50 , height=50, x=440 , y=60)
e7 = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e7.place(width=50 , height=50, x=525 , y=60)
e8 = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e8.place(width=50 , height=50, x=610 , y=60)
e9 = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e9.place(width=50 , height=50, x=695 , y=60)
e10 = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e10.place(width=50 , height=50, x=780 , y=60)
#check button
Button(
master=footer,
text="check",
bg="green",
fg="black",
font=("Tahoma" , 10 , "bold"),
padx=45,
pady=20,
command= lambda: check(numb, e1 , e2 , e3 , e4, e5 , e6 , e7 , e8 , e9 , e10 , None , main)
).pack(side=TOP , pady=50)
# .place(width=20 , height=20 , x=170 , y=50)
main.mainloop()
def eleventh():
main = Tk()
main.title("forth digit number")
main.resizable(0,0)
main.geometry("950x250")
main.configure(bg="white")
lev = 11
numb = number_split(lev)
# body
body = Frame(
master= main,
bg = "white"
)
body.pack(fill=BOTH , expand=True)
body.propagate(0)
#footer
footer = Frame(
master=main,
bg="white",
width=5
)
footer.pack(fill=BOTH,expand=1)
footer.propagate(0)
#first entry
e1= Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e1.place(width=50 , height=50, x=15 , y=60)
#second entry
e2 =Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e2.place(width=50 , height=50, x=100 , y=60)
#third entry
e3 = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e3.place(width=50 , height=50, x=185 , y=60)
#forth entry
e4 = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e4.place(width=50 , height=50, x=270 , y=60)
#fifth entry
e5 = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e5.place(width=50 , height=50, x=355 , y=60)
#sixth entry
e6 = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e6.place(width=50 , height=50, x=440 , y=60)
e7 = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e7.place(width=50 , height=50, x=525 , y=60)
e8 = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e8.place(width=50 , height=50, x=610 , y=60)
e9 = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e9.place(width=50 , height=50, x=695 , y=60)
e10 = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e10.place(width=50 , height=50, x=780 , y=60)
e11 = Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
)
e11.place(width=50 , height=50, x=865 , y=60)
#check button
Button(
master=footer,
text="check",
bg="green",
fg="black",
font=("Tahoma" , 10 , "bold"),
padx=45,
pady=20,
command= lambda: check(numb, e1 , e2 , e3 , e4, e5 , e6 , e7 , e8 , e9 , e10 , e11 , main)
).pack(side=TOP , pady=50)
# .place(width=20 , height=20 , x=170 , y=50)
main.mainloop()
# body
body = Frame(
master= main,
bg = "white"
)
body.pack(fill=BOTH , expand=True)
body.propagate(0)
#footer
footer = Frame(
master=main,
bg="white",
width=5
)
footer.pack(fill=BOTH,expand=1)
footer.propagate(0)
#first entry
Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
).place(width=50 , height=50, x=15 , y=60)
#second entry
Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
).place(width=50 , height=50, x=100 , y=60)
#third entry
Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
).place(width=50 , height=50, x=185 , y=60)
#forth entry
Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
).place(width=50 , height=50, x=270 , y=60)
#fifth entry
Entry(
master=body,
bg="white",
fg="black",
font=("Tahoma" , 10 , "bold"),
justify='center'
).place(width=50 , height=50, x=355 , y=60)
#check button
Button(
master=footer,
text="check",
bg="green",
fg="black",
font=("Tahoma" , 10 , "bold"),
padx=45,
pady=20
).pack(side=TOP , pady=50)
# .place(width=20 , height=20 , x=170 , y=50)
main.mainloop()
##############################################################
def main_form():
management = Tk()
management.title("main page")
management.resizable(0,0)
management.geometry("514x574")
management.configure(bg="white")
# body region
body = Frame(
master=management,
bg="white"
)
body.pack(fill=BOTH , expand=True)
body.propagate(0)
# label region for text
Label(
master=body,
text="levels : ",
bg="white",
fg="black",
font= ("Tahoma",10,"bold"),
anchor=W
).pack(side=TOP,fill=BOTH)
# button 4
Button(
master=body ,
text="4",
bg="gray",
fg="black",
padx=25,
pady=20,
command= lambda : four()
).pack(side=TOP , fill=BOTH,pady=(0,5))
# button 5
Button(
master=body ,
text="5",
bg="gray",
fg="black",
padx=25,
pady=20,
command= lambda : five()
).pack(side=TOP , fill=BOTH,pady=(0,5))
# button 6
Button(
master=body ,
text="6",
bg="gray",
fg="black",
padx=25,
pady=20,
command= lambda : six()
).pack(side=TOP , fill=BOTH,pady=(0,5))
# button 7
Button(
master=body ,
text="7",
bg="gray",
fg="black",
padx=25,
pady=20,
command= lambda : seven()
).pack(side=TOP , fill=BOTH,pady=(0,5))
# button 8
Button(
master=body ,
text="8",
bg="gray",
fg="black",
padx=25,
pady=20,
command= lambda : eight()
).pack(side=TOP , fill=BOTH,pady=(0,5))
# button 9
Button(
master=body ,
text="9",
bg="gray",
fg="black",
padx=25,
pady=20,
command= lambda : ninth()
).pack(side=TOP , fill=BOTH,pady=(0,5))
# button 10
Button(
master=body ,
text="10",
bg="gray",
fg="black",
padx=25,
pady=20,
command= lambda : tenth()
).pack(side=TOP , fill=BOTH,pady=(0,5))
# button 11
Button(
master=body ,
text="11",
bg="gray",
fg="black",
padx=25,
pady=20,
command= lambda : eleventh()
).pack(side=TOP , fill=BOTH,pady=(0,5))
management.mainloop()
load_page()
| 19.912787
| 112
| 0.470906
| 3,521
| 30,367
| 4.04175
| 0.047998
| 0.053123
| 0.067458
| 0.089944
| 0.896423
| 0.875132
| 0.861429
| 0.846813
| 0.844635
| 0.823273
| 0
| 0.071745
| 0.368887
| 30,367
| 1,525
| 113
| 19.912787
| 0.670806
| 0.050548
| 0
| 0.801131
| 0
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| 0.104925
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.01508
| false
| 0.021678
| 0.004713
| 0
| 0.019793
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
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| 1
| 0
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|
0
| 7
|
f2be656b86017d3734014b5017a58b9ce0b4076e
| 12,888
|
py
|
Python
|
simulate.py
|
apitsch/drl_mppo
|
5c76e1f96ec2854c4d446fbad0affaaad3364d51
|
[
"MIT"
] | 2
|
2019-01-11T05:01:45.000Z
|
2021-03-03T13:55:54.000Z
|
simulate.py
|
apitsch/drl_mppo
|
5c76e1f96ec2854c4d446fbad0affaaad3364d51
|
[
"MIT"
] | null | null | null |
simulate.py
|
apitsch/drl_mppo
|
5c76e1f96ec2854c4d446fbad0affaaad3364d51
|
[
"MIT"
] | 2
|
2019-05-24T06:38:38.000Z
|
2020-11-22T00:56:33.000Z
|
import numpy as np
import math
import copy
from helpers import compute_trade, argmax_index, compute_opt_weight
def portfolio_safe(eval_episodes, environment):
"""
Simulates a no-risk portfolio strategy in a given environment.
Parameters
----------
:param eval_episodes : int
Number of episodes to simulate.
:param environment : Env instance
Environment in which to simulate the portfolio strategy.
Returns
-------
:returns final_u : ndarray
Array containing utility of terminal wealth for each simulated
episode.
:returns alloc_to_risk : ndarray
Array containing the share of wealth invested into the risky
asset in each period for all simulated episodes.
:returns ret : ndarray
Array containing the simple gross returns realized in each
period for all simulated episodes.
"""
print("Simulating safe portfolio strategy.")
env = copy.deepcopy(environment)
final_u = []
alloc_to_risk = [[] for _ in range(eval_episodes)]
ret = []
np.random.seed(111)
for episode in range(eval_episodes):
env.reset()
while not env.done:
np.random.rand() # random context
action = 0.
trade = compute_trade(env.p, action, env.tcost)
env.trade(trade)
assert math.isclose(env.p[1] / np.sum(env.p), action)
alloc_to_risk[episode].append(env.p[1]/np.sum(env.p))
sgr = env.update()
ret.append(sgr)
final_u.append(env.get_utility())
final_u = np.array(final_u)
alloc_to_risk = np.array(alloc_to_risk)
ret = np.array(ret)
return final_u, alloc_to_risk, ret
def portfolio_risky(eval_episodes, environment):
"""
Simulates a full-risk portfolio strategy in a given environment.
Parameters
----------
:param eval_episodes : int
Number of episodes to simulate.
:param environment : Env instance
Environment in which to simulate the portfolio strategy.
Returns
-------
:returns final_u : ndarray
Array containing utility of terminal wealth for each simulated
episode.
:returns alloc_to_risk : ndarray
Array containing the share of wealth invested into the risky
asset in each period for all simulated episodes.
:returns ret : ndarray
Array containing the simple gross returns realized in each
period for all simulated episodes.
"""
print("Simulating risky portfolio strategy.")
env = copy.deepcopy(environment)
final_u = []
alloc_to_risk = [[] for _ in range(eval_episodes)]
ret = []
np.random.seed(111)
for episode in range(eval_episodes):
env.reset()
while not env.done:
np.random.rand() # random context
action = 1.
trade = compute_trade(env.p, action, env.tcost)
env.trade(trade)
assert math.isclose(env.p[1] / np.sum(env.p), action)
alloc_to_risk[episode].append(env.p[1]/np.sum(env.p))
sgr = env.update()
ret.append(sgr)
final_u.append(env.get_utility())
final_u = np.array(final_u)
alloc_to_risk = np.array(alloc_to_risk)
ret = np.array(ret)
return final_u, alloc_to_risk, ret
def portfolio_myopic(eval_episodes, environment):
"""
Simulates the optimal myopic portfolio strategy in a given environment.
Parameters
----------
:param eval_episodes : int
Number of episodes to simulate.
:param environment : Env instance
Environment in which to simulate the portfolio strategy.
Returns
-------
:returns final_u : ndarray
Array containing utility of terminal wealth for each simulated
episode.
:returns alloc_to_risk : ndarray
Array containing the share of wealth invested into the risky
asset in each period for all simulated episodes.
:returns ret : ndarray
Array containing the simple gross returns realized in each
period for all simulated episodes.
"""
print("Simulating myopic portfolio strategy.")
env = copy.deepcopy(environment)
final_u = []
alloc_to_risk = [[] for _ in range(eval_episodes)]
ret = []
np.random.seed(111)
for episode in range(eval_episodes):
env.reset()
while not env.done:
# compute optimal allocation to risky asset:
optimal_risky_weight = compute_opt_weight(env, env.time)
# no shorting constraint:
if optimal_risky_weight > 1:
optimal_risky_weight = 1.0
elif optimal_risky_weight < 0:
optimal_risky_weight = 0.0
np.random.rand() # random context
trade = compute_trade(env.p, optimal_risky_weight, env.tcost)
env.trade(trade)
assert math.isclose(env.p[1] / np.sum(env.p), optimal_risky_weight)
alloc_to_risk[episode].append(env.p[1]/np.sum(env.p))
sgr = env.update()
ret.append(sgr)
final_u.append(env.get_utility())
final_u = np.array(final_u)
alloc_to_risk = np.array(alloc_to_risk)
ret = np.array(ret)
return final_u, alloc_to_risk, ret
def portfolio_qtab(eval_episodes, environment, agent):
"""
Simulates a portfolio strategy suggested by a trained tabular Q-learning
agent in a given environment.
Parameters
----------
:param eval_episodes : int
Number of episodes to simulate.
:param environment : Env instance
Environment in which to simulate the portfolio strategy.
:param agent : AgentQtab instance
Tabular Q-learning agent (preferably trained in the same
environment as is simulated).
Returns
-------
:returns final_u : ndarray
Array containing utility of terminal wealth for each simulated
episode.
:returns alloc_to_risk : ndarray
Array containing the share of wealth invested into the risky
asset in each period for all simulated episodes.
:returns ret : ndarray
Array containing the simple gross returns realized in each
period for all simulated episodes.
"""
print("Simulating tabular Q-learning portfolio strategy.")
env = copy.deepcopy(environment)
final_u = []
alloc_to_risk = [[] for _ in range(eval_episodes)]
ret = []
np.random.seed(111)
for episode in range(eval_episodes):
env.reset()
while not env.done:
np.random.rand() # random context
action = agent.action_space[argmax_index(agent.q_tab)]
trade = compute_trade(env.p, action, env.tcost)
env.trade(trade)
assert math.isclose(env.p[1] / np.sum(env.p), action)
alloc_to_risk[episode].append(env.p[1]/np.sum(env.p))
sgr = env.update()
ret.append(sgr)
final_u.append(env.get_utility())
final_u = np.array(final_u)
alloc_to_risk = np.array(alloc_to_risk)
ret = np.array(ret)
return final_u, alloc_to_risk, ret
def portfolio_dqn(eval_episodes, environment, agent):
"""
Simulates a portfolio strategy suggested by a trained DQN
agent in a given environment.
Parameters
----------
:param eval_episodes : int
Number of episodes to simulate.
:param environment : Env instance
Environment in which to simulate the portfolio strategy.
:param agent : AgentDQN instance
DQN agent (preferably trained in the same environment as is
simulated).
Returns
-------
:returns final_u : ndarray
Array containing utility of terminal wealth for each simulated
episode.
:returns alloc_to_risk : ndarray
Array containing the share of wealth invested into the risky
asset in each period for all simulated episodes.
:returns ret : ndarray
Array containing the simple gross returns realized in each
period for all simulated episodes.
"""
print("Simulating DQN portfolio strategy.")
env = copy.deepcopy(environment)
final_u = []
alloc_to_risk = [[] for _ in range(eval_episodes)]
ret = []
np.random.seed(111)
for episode in range(eval_episodes):
env.reset()
while not env.done:
np.random.rand() # random context
state = env.get_state()
q_pred = np.squeeze(agent.qnn.predict(state))
action = agent.action_space[argmax_index(q_pred)]
trade = compute_trade(env.p, action, env.tcost)
env.trade(trade)
assert math.isclose(env.p[1]/np.sum(env.p), action)
alloc_to_risk[episode].append(env.p[1]/np.sum(env.p))
sgr = env.update()
ret.append(sgr)
final_u.append(env.get_utility())
final_u = np.array(final_u)
alloc_to_risk = np.array(alloc_to_risk)
ret = np.array(ret)
return final_u, alloc_to_risk, ret
def portfolio_reinforce(eval_episodes, environment, agent):
"""
Simulates a portfolio strategy suggested by a trained REINFORCE agent in a
given environment.
Parameters
----------
:param eval_episodes : int
Number of episodes to simulate.
:param environment : Env instance
Environment in which to simulate the portfolio strategy.
:param agent : AgentReinforce instance
REINFORCE agent (preferably trained in the same environment as is
simulated).
Returns
-------
:returns final_u : ndarray
Array containing utility of terminal wealth for each simulated
episode.
:returns alloc_to_risk : ndarray
Array containing the share of wealth invested into the risky
asset in each period for all simulated episodes.
:returns ret : ndarray
Array containing the simple gross returns realized in each
period for all simulated episodes.
"""
print("Simulating REINFORCE portfolio strategy.")
env = copy.deepcopy(environment)
final_u = []
alloc_to_risk = [[] for _ in range(eval_episodes)]
ret = []
np.random.seed(111)
for episode in range(eval_episodes):
env.reset()
while not env.done:
s = env.get_state()
a = agent.choose_action(s)
trade = compute_trade(env.p, a, env.tcost)
env.trade(trade)
assert math.isclose(env.p[1] / np.sum(env.p), a)
alloc_to_risk[episode].append(env.p[1] / np.sum(env.p))
sgr = env.update()
ret.append(sgr)
final_u.append(env.get_utility())
final_u = np.array(final_u)
alloc_to_risk = np.array(alloc_to_risk)
ret = np.array(ret)
return final_u, alloc_to_risk, ret
def portfolio_ac(eval_episodes, environment, agent):
"""
Simulates a portfolio strategy suggested by a trained actor-critic agent
in a given environment.
Parameters
----------
:param eval_episodes : int
Number of episodes to simulate.
:param environment : Env instance
Environment in which to simulate the portfolio strategy.
:param agent : AgentAC instance
Actor-critic agent (preferably trained in the same environment as is
simulated).
Returns
-------
:returns final_u : ndarray
Array containing utility of terminal wealth for each simulated
episode.
:returns alloc_to_risk : ndarray
Array containing the share of wealth invested into the risky
asset in each period for all simulated episodes.
:returns ret : ndarray
Array containing the simple gross returns realized in each
period for all simulated episodes.
"""
print("Simulating actor-critic portfolio strategy.")
env = copy.deepcopy(environment)
final_u = []
alloc_to_risk = [[] for _ in range(eval_episodes)]
ret = []
np.random.seed(111)
for episode in range(eval_episodes):
env.reset()
while not env.done:
s = env.get_state()
a = agent.choose_action(s)
trade = compute_trade(env.p, a, env.tcost)
env.trade(trade)
assert math.isclose(env.p[1] / np.sum(env.p), a)
alloc_to_risk[episode].append(env.p[1] / np.sum(env.p))
sgr = env.update()
ret.append(sgr)
final_u.append(env.get_utility())
final_u = np.array(final_u)
alloc_to_risk = np.array(alloc_to_risk)
ret = np.array(ret)
return final_u, alloc_to_risk, ret
| 30.468085
| 79
| 0.62306
| 1,625
| 12,888
| 4.809846
| 0.078154
| 0.032242
| 0.05911
| 0.034928
| 0.917861
| 0.898925
| 0.891249
| 0.891249
| 0.891249
| 0.891249
| 0
| 0.004713
| 0.292055
| 12,888
| 422
| 80
| 30.540284
| 0.851929
| 0.431021
| 0
| 0.818713
| 0
| 0
| 0.041012
| 0
| 0
| 0
| 0
| 0
| 0.040936
| 1
| 0.040936
| false
| 0
| 0.023392
| 0
| 0.105263
| 0.040936
| 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
|
29895ce6aad172ba546898f66790665e99e24916
| 252
|
py
|
Python
|
src/fsops/fso/__init__.py
|
nasgoncalves/fsops
|
01ecf2d5fe1553efb387eb9a67d9ecf34649bddf
|
[
"Apache-2.0"
] | null | null | null |
src/fsops/fso/__init__.py
|
nasgoncalves/fsops
|
01ecf2d5fe1553efb387eb9a67d9ecf34649bddf
|
[
"Apache-2.0"
] | 1
|
2019-10-20T22:50:41.000Z
|
2019-10-20T22:50:41.000Z
|
src/fsops/fso/__init__.py
|
nasgoncalves/fsops
|
01ecf2d5fe1553efb387eb9a67d9ecf34649bddf
|
[
"Apache-2.0"
] | null | null | null |
from .file_system_object import Object # NOQA
from .file_system_object import Type # NOQA
from .file_system_object import Time # NOQA
from .file_system_object import MetaType # NOQA
from .hash import Hash # NOQA
from .search import Search # NOQA
| 36
| 48
| 0.785714
| 38
| 252
| 5
| 0.289474
| 0.210526
| 0.294737
| 0.421053
| 0.610526
| 0.473684
| 0
| 0
| 0
| 0
| 0
| 0
| 0.166667
| 252
| 6
| 49
| 42
| 0.904762
| 0.115079
| 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
|
d9b21c8680df5cc3b675a53f65450572b7dbf519
| 87,381
|
py
|
Python
|
venv/Lib/site-packages/baidubce/services/blb/app_blb_client.py
|
dlfming/dd_catl_demo
|
6f6f3b502046f638150222c1eb3d68d2b65da04b
|
[
"MIT"
] | 22
|
2015-10-26T03:00:11.000Z
|
2021-09-08T09:30:51.000Z
|
venv/Lib/site-packages/baidubce/services/blb/app_blb_client.py
|
dlfming/dd_catl_demo
|
6f6f3b502046f638150222c1eb3d68d2b65da04b
|
[
"MIT"
] | 8
|
2018-07-18T02:47:09.000Z
|
2020-12-10T02:30:37.000Z
|
venv/Lib/site-packages/baidubce/services/blb/app_blb_client.py
|
dlfming/dd_catl_demo
|
6f6f3b502046f638150222c1eb3d68d2b65da04b
|
[
"MIT"
] | 14
|
2016-01-12T11:57:38.000Z
|
2021-03-10T03:35:12.000Z
|
# Copyright (c) 2019 Baidu.com, Inc. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions
# and limitations under the License.
"""
This module provides a client class for APP BLB.
"""
import copy
import json
import logging
import uuid
import sys
from baidubce import bce_base_client
from baidubce.auth import bce_v1_signer
from baidubce.http import bce_http_client
from baidubce.http import handler
from baidubce.http import http_methods
from baidubce import utils
from baidubce.utils import required
from baidubce import compat
if sys.version < '3':
sys.setdefaultencoding('utf-8')
_logger = logging.getLogger(__name__)
class AppBlbClient(bce_base_client.BceBaseClient):
"""
APP BLB base sdk client
"""
version = b'/v1'
def __init__(self, config=None):
bce_base_client.BceBaseClient.__init__(self, config)
def _merge_config(self, config=None):
"""
:param config:
:type config: baidubce.BceClientConfiguration
:return:
"""
if config is None:
return self.config
else:
new_config = copy.copy(self.config)
new_config.merge_non_none_values(config)
return new_config
def _send_request(self, http_method, path,
body=None, headers=None, params=None,
config=None, body_parser=None):
config = self._merge_config(config)
if body_parser is None:
body_parser = handler.parse_json
if headers is None:
headers = {b'Accept': b'*/*',
b'Content-Type': b'application/json;charset=utf-8'}
return bce_http_client.send_request(
config, bce_v1_signer.sign, [handler.parse_error, body_parser],
http_method, path, body, headers, params)
@required(vpc_id=(bytes, str),
subnet_id=(bytes, str))
def create_app_loadbalancer(self, vpc_id, subnet_id, name=None,
desc=None, client_token=None, config=None):
"""
Create a app LoadBalancer with the specified options.
:param name:
the name of LoadBalancer to create
:type name: string
:param desc:
The description of LoadBalancer
:type desc: string
:param vpc_id:
id of vpc which the LoadBalancer belong to
:type vpc_id: string
:param subnet_id:
id of subnet which the LoadBalancer belong to
:type subnet_id: string
:param client_token:
If the clientToken is not specified by the user, a random String
generated by default algorithm will be used.
:type client_token: string
:param config:
:type config: baidubce.BceClientConfiguration
:return:
:rtype baidubce.bce_response.BceResponse
"""
path = utils.append_uri(self.version, 'appblb')
params = {}
if client_token is None:
params[b'clientToken'] = generate_client_token()
else:
params[b'clientToken'] = client_token
body = {}
if name is not None:
body['name'] = compat.convert_to_string(name)
if desc is not None:
body['desc'] = compat.convert_to_string(desc)
body['vpcId'] = compat.convert_to_string(vpc_id)
body['subnetId'] = compat.convert_to_string(subnet_id)
return self._send_request(http_methods.POST, path,
body=json.dumps(body), params=params,
config=config)
@required(blb_id=(bytes, str))
def update_app_loadbalancer(self, blb_id, name=None, desc=None,
client_token=None, config=None):
"""
Modify the special attribute to new value of the LoadBalancer
owned by the user.
:param name:
name of LoadBalancer to describe
:type name: string
:param blb_id:
id of LoadBalancer to describe
:type blb_id: string
:param desc:
The description of LoadBalancer
:type desc: string
:param client_token:
If the clientToken is not specified by the user,
a random String generated by default algorithm
will be used.
:type client_token: string
:param config:
:type config: baidubce.BceClientConfiguration
:return:
:rtype baidubce.bce_response.BceResponse
"""
path = utils.append_uri(self.version, 'appblb', blb_id)
params = {}
if client_token is None:
params[b'clientToken'] = generate_client_token()
else:
params[b'clientToken'] = client_token
body = {}
if name is not None:
body['name'] = compat.convert_to_string(name)
if desc is not None:
body['desc'] = compat.convert_to_string(desc)
return self._send_request(http_methods.PUT, path, json.dumps(body),
params=params, config=config)
def describe_app_loadbalancers(self, address=None, name=None, blb_id=None,
bcc_id=None, marker=None, max_keys=None,
config=None):
"""
Return a list of LoadBalancers
:param address:
Intranet service address in dotted decimal notation
:type address: string
:param name:
name of LoadBalancer to describe
:type name: string
:param blb_id:
id of LoadBalancer to describe
:type blb_id: string
:param bcc_id:
bcc which bind the LoadBalancers
:type bcc_id: string
:param marker:
The optional parameter marker specified in the original
request to specify where in the results to begin listing.
Together with the marker, specifies the list result
which listing should begin.
If the marker is not specified, the list result will
listing from the first one.
:type marker: string
:param max_keys
The optional parameter to specifies the max number of list
result to return.
The default value is 1000.
:type max_keys: int
:param config:
:type config: baidubce.BceClientConfiguration
:return:
:rtype baidubce.bce_response.BceResponse
"""
path = utils.append_uri(self.version, 'appblb')
params = {}
if address is not None:
params[b'address'] = address
if name is not None:
params[b'name'] = name
if blb_id is not None:
params[b'blbId'] = blb_id
if bcc_id is not None:
params[b'bccId'] = bcc_id
if marker is not None:
params[b'marker'] = marker
if max_keys is not None:
params[b'maxKeys'] = max_keys
return self._send_request(http_methods.GET, path,
params=params, config=config)
@required(blb_id=(bytes, str))
def describe_app_loadbalancer_detail(self, blb_id, config=None):
"""
Return detail imformation of specific LoadBalancer
:param blb_id:
id of LoadBalancer to describe
:type blb_id: string
:param config:
:type config: baidubce.BceClientConfiguration
:return:
:rtype baidubce.bce_response.BceResponse
"""
path = utils.append_uri(self.version, 'appblb', blb_id)
return self._send_request(http_methods.GET, path,
config=config)
@required(blb_id=(bytes, str))
def delete_app_loadbalancer(self, blb_id, client_token=None, config=None):
"""
delete the LoadBalancer owned by the user.
:param blb_id:
id of LoadBalancer to describe
:type blb_id: string
:param client_token:
If the clientToken is not specified by the user,
a random String generated by default algorithm
will be used.
:type client_token: string
:param config:
:type config: baidubce.BceClientConfiguration
:return:
:rtype baidubce.bce_response.BceResponse
"""
path = utils.append_uri(self.version, 'appblb', blb_id)
params = {}
if client_token is None:
params[b'clientToken'] = generate_client_token()
else:
params[b'clientToken'] = client_token
return self._send_request(http_methods.DELETE, path,
params=params, config=config)
"""
Listener API
"""
@required(blb_id=(bytes, str),
listener_port=int,
scheduler=(bytes, str))
def create_app_tcp_listener(self, blb_id, listener_port,
scheduler, client_token=None,
config=None):
"""
Create a app tcp listener rule with the specified options.
:param blb_id:
the id of blb which the listener work on
:type blb_id: string
:param listener_port:
port to be linstened owned by listener
:value 1-65535
:type listener_port: int
:param scheduler
balancing algorithm
:value 'RoundRobin' or 'LeastConnection' or 'Hash'
:type scheduler: string
:param client_token:
If the clientToken is not specified by the user, a random String
generated by default algorithm will be used.
:type client_token: string
:param config:
:type config: baidubce.BceClientConfiguration
:return:
:rtype baidubce.bce_response.BceResponse
"""
path = utils.append_uri(self.version, 'appblb', blb_id, 'TCPlistener')
params = {}
if client_token is None:
params[b'clientToken'] = generate_client_token()
else:
params[b'clientToken'] = client_token
body = {
'listenerPort': listener_port,
'scheduler': compat.convert_to_string(scheduler)
}
return self._send_request(http_methods.POST, path,
body=json.dumps(body), params=params,
config=config)
@required(blb_id=(bytes, str),
listener_port=int,
scheduler=(bytes, str))
def create_app_udp_listener(self, blb_id, listener_port,
scheduler, client_token=None,
config=None):
"""
Create a app udp listener rule with the specified options.
:param blb_id:
the id of blb which the listener work on
:type blb_id: string
:param listener_port:
port to be linstened owned by listener
:value 1-65535
:type listener_port: int
:param scheduler
balancing algorithm
:value 'RoundRobin' or 'LeastConnection' or 'Hash'
:type scheduler: string
:param client_token:
If the clientToken is not specified by the user, a random String
generated by default algorithm will be used.
:type client_token: string
:param config:
:type config: baidubce.BceClientConfiguration
:return:
:rtype baidubce.bce_response.BceResponse
"""
path = utils.append_uri(self.version, 'appblb', blb_id, 'UDPlistener')
params = {}
if client_token is None:
params[b'clientToken'] = generate_client_token()
else:
params[b'clientToken'] = client_token
body = {
'listenerPort': listener_port,
'scheduler': compat.convert_to_string(scheduler)
}
return self._send_request(http_methods.POST, path,
body=json.dumps(body), params=params,
config=config)
@required(blb_id=(bytes, str), listener_port=int,
scheduler=(bytes, str))
def create_app_http_listener(self, blb_id, listener_port,
scheduler, keep_session=None,
keep_session_type=None,
keep_session_timeout=None,
keep_session_cookie_name=None,
x_forward_for=None,
server_timeout=None,
redirect_port=None,
client_token=None,
config=None):
"""
Create a app http listener rule with the specified options.
:param blb_id:
the id of blb which the listener work on
:type blb_id: string
:param listener_port:
port to be linstened owned by listener
:value 1-65535
:type listener_port: int
:param scheduler:
balancing algorithm
:value 'RoundRobin' or 'LeastConnection'
:type scheduler: string
:param keep_session:
Whether to enable the session hold function,
that is,the request sent by the same client will
reach the same backend server
:value true or false default:false
:type keep_session: bool
:param keep_session_type:
The cookie handling method maintained by the session,
valid only if the session is held open
:value 'insert' or 'rewrite' default:insert
:type keep_session_type: string
:param keep_session_timeout:
The time the cookie is kept in session (in seconds),
valid only if the session is held open
:value 1-15552000 default:3600
:type keep_session_timeout: int
:param keep_session_cookie_name:
The session keeps the name of the cookie that needs to be
overridden if and only if session persistence is enabled
and keep_session_type="rewrite"
:type keep_session_cookie_name: int
:param x_forward_for:
Whether to enable the real IP address of the client,
the backend server can obtain the real address of the client
through the X-Forwarded-For HTTP header.
:value true or false, default: False
:type x_forward_for: bool
:param server_timeout:
Backend server maximum timeout (unit: second)
:value 1-3600, default: 30
:type server_timeout:int
:param redirect_port:
Forward the request received by this listener to the
HTTPS listener, which is specified by the HTTPS listener.
:type redirect_port:int
:param client_token:
If the clientToken is not specified by the user,
a random String generated by default algorithm will be used.
:type client_token: string
:param config:
:type config: baidubce.BceClientConfiguration
:return:
:rtype baidubce.bce_response.BceResponse
"""
path = utils.append_uri(self.version, 'appblb', blb_id, 'HTTPlistener')
params = {}
if client_token is None:
params[b'clientToken'] = generate_client_token()
else:
params[b'clientToken'] = client_token
body = {
'listenerPort': listener_port,
'scheduler': compat.convert_to_string(scheduler)}
if keep_session is not None:
body['keepSession'] = keep_session
if keep_session_type is not None:
body['keepSessionType'] = \
compat.convert_to_string(keep_session_type)
if keep_session_timeout is not None:
body['keepSessionTimeout'] = keep_session_timeout
if keep_session_cookie_name is not None:
body['keepSessionCookieName'] = keep_session_cookie_name
if x_forward_for is not None:
body['xForwardFor'] = x_forward_for
if server_timeout is not None:
body['serverTimeout'] = server_timeout
if redirect_port is not None:
body['redirectPort'] = redirect_port
return self._send_request(http_methods.POST, path,
body=json.dumps(body), params=params,
config=config)
@required(blb_id=(bytes, str), listener_port=int,
scheduler=(bytes, str), cert_ids=list)
def create_app_https_listener(self, blb_id, listener_port,
scheduler, cert_ids,
keep_session=None,
keep_session_type=None,
keep_session_timeout=None,
keep_session_cookie_name=None,
x_forward_for=None, server_timeout=None,
ie6_compatible=None, encryption_type=None,
encryption_protocols=None,
dual_auth=None, client_certIds=None,
client_token=None, config=None):
"""
Create a app https listener rule with the specified options.
:param blb_id:
The id of blb which the listener work on
:type blb_id: string
:param listener_port:
port to be linstened owned by listener
:value 1-65535
:type listener_port: int
:param scheduler:
balancing algorithm
:value 'RoundRobin' or 'LeastConnection'
:type scheduler: string
:param cert_ids:
The certificate to be loaded by the listener.
:type cert_ids: List<String>
:param keep_session:
Whether to enable the session hold function,
that is, the request sent by the same client will reach the
same backend server
:value true or false, default: false
:type keep_session: bool
:param keep_session_type:
The cookie handling method maintained by the session,
valid only if the session is held open
:value 'insert' or 'rewrite', default:insert
:type keep_session_type: string
:param keep_session_timeout:
The time the cookie is kept in session (in seconds),
valid only if the session is held open
:value 1-15552000, default:3600
:type keep_session_timeout: int
:param keep_session_cookie_name:
The session keeps the name of the cookie that needs
to be overridden if and only if session persistence
is enabled and keep_session_type="rewrite"
:type keep_session_cookie_name: int
:param x_forward_for:
Whether to enable the real IP address of the client,
the backend server can obtain the real address of the client
through the X-Forwarded-For HTTP header.
:value true or false, default: flase
:type x_forward_for: bool
:param server_timeout:
Backend server maximum timeout (unit: second)
:value 1-3600, default: 30
:type server_timeout: int
:param ie6_compatible:
compatible with IE6 HTTPS request
(the protocol format is earlier SSL3.0, the security is poor)
:value true or false, default: true
:type ie6_compatible: bool
:param encryption_type:
Encryption options, support three types:
compatibleIE/incompatibleIE/userDefind,
corresponding to:
IE-compatible encryption/disabled unsecure encryption/custom encryption,
when encryptionType is valid and legitimate,
ie6Compatible field transfer value will not take effect
type: encryption_type:string
:param encryption_protocols:
When the encryptionType value is userDefind,
the list of protocol types is a string list composed of four protocols:
"sslv3", "tlsv10", "tlsv11", "tlsv12".
type: encryption_protocols:list
:param dual_auth:
Whether to Open Two-way Authentication,
default:false
:type dual_auth: boolean
:param client_certIds:
When dualAuth is true, the loaded client certificate chain
:type client_certIds: list
:param client_token:
If the clientToken is not specified by the user,
a random String generated by default algorithm will be used.
:type client_token: string
:param config:
:type config: baidubce.BceClientConfiguration
:return
:rtype baidubce.bce_response.BceResponse
"""
path = utils.append_uri(self.version, 'appblb', blb_id, 'HTTPSlistener')
params = {}
if client_token is None:
params[b'clientToken'] = generate_client_token()
else:
params[b'clientToken'] = client_token
body = {
'listenerPort': listener_port,
'scheduler': compat.convert_to_string(scheduler),
'certIds': cert_ids}
if keep_session is not None:
body['keepSession'] = keep_session
if keep_session_type is not None:
body['keepSessionType'] = \
compat.convert_to_string(keep_session_type)
if keep_session_timeout is not None:
body['keepSessionTimeout'] = keep_session_timeout
if keep_session_cookie_name is not None:
body['keepSessionCookieName'] = keep_session_cookie_name
if x_forward_for is not None:
body['xForwardFor'] = x_forward_for
if server_timeout is not None:
body['serverTimeout'] = server_timeout
if ie6_compatible is not None:
body['ie6Compatible'] = ie6_compatible
if encryption_type is not None:
body['encryptionType'] = \
compat.convert_to_string(encryption_type)
if encryption_protocols is not None:
body['encryptionProtocols'] = encryption_protocols
if dual_auth is not None:
body['dualAuth'] = dual_auth
if client_certIds is not None:
body['clientCertIds'] = client_certIds
return self._send_request(http_methods.POST, path,
body=json.dumps(body),
params=params, config=config)
@required(blb_id=(bytes, str), listener_port=int,
scheduler=(bytes, str), cert_ids=list)
def create_app_ssl_listener(self, blb_id, listener_port,
scheduler, cert_ids,
ie6_compatible=None,
encryption_type=None,
encryption_protocols=None,
dual_auth=None, client_certIds=None,
client_token=None, config=None):
"""
Create a app ssl listener rule with the specified options.
:param blb_id:
The id of blb which the listener work on
:type blb_id: string
:param listener_port:
port to be linstened owned by listener
:value 1-65535
:type listener_port: int
:param scheduler:
balancing algorithm
:value 'RoundRobin' or 'LeastConnection'
:type scheduler: string
:param cert_ids:
The SSL certificate to be loaded by the listener.
Currently HTTPS listeners can only bind one SSL certificate.
:type cert_ids: List<String>
:param ie6_compatible:
compatible with IE6 HTTPS request
(the protocol format is earlier SSL3.0, the security is poor)
:value true or false, default: true
:type ie6_compatible: bool
:param encryption_type:
Encryption options, support three types:
compatibleIE/incompatibleIE/userDefind,
corresponding to:
IE-compatible encryption/disabled unsecure encryption/custom encryption,
when encryptionType is valid and legitimate,
ie6Compatible field transfer value will not take effect
type: encryption_type:string
:param encryption_protocols:
When the encryptionType value is userDefind,
the list of protocol types is a string list composed of four protocols:
"sslv3", "tlsv10", "tlsv11", "tlsv12".
type: encryption_protocols:list
:param dual_auth:
Whether to Open Two-way Authentication,
default:false
:type dual_auth: boolean
:param client_certIds:
When dualAuth is true, the loaded client certificate chain
:type client_certIds: list
:param client_token:
If the clientToken is not specified by the user,
a random String generated by default algorithm will be used.
:type client_token: string
:param config:
:type config: baidubce.BceClientConfiguration
:return
:rtype baidubce.bce_response.BceResponse
"""
path = utils.append_uri(self.version, 'appblb', blb_id, 'SSLlistener')
params = {}
if client_token is None:
params[b'clientToken'] = generate_client_token()
else:
params[b'clientToken'] = client_token
body = {
'listenerPort': listener_port,
'scheduler': compat.convert_to_string(scheduler),
'certIds': cert_ids}
if ie6_compatible is not None:
body['ie6Compatible'] = ie6_compatible
if encryption_type is not None:
body['encryptionType'] = \
compat.convert_to_string(encryption_type)
if encryption_protocols is not None:
body['encryptionProtocols'] = encryption_protocols
if dual_auth is not None:
body['dualAuth'] = dual_auth
if client_certIds is not None:
body['clientCertIds'] = client_certIds
return self._send_request(http_methods.POST, path,
body=json.dumps(body),
params=params, config=config)
@required(blb_id=(bytes, str),
listener_port=int)
def update_app_tcp_listener(self, blb_id, listener_port,
scheduler=None,
client_token=None,
config=None):
"""
update a app tcp listener rule with the specified options.
:param blb_id:
the id of blb which the listener work on
:type blb_id:string
:param listener_port:
port to be linstened owned by listener
:value 1-65535
:type listener_port:int
:param scheduler
balancing algorithm
:value 'RoundRobin'or'LeastConnection'or'Hash'
:type scheduler:string
:param client_token:
If the clientToken is not specified by the user,
a random String generated by default algorithm will be used.
:type client_token: string
:param config:
:type config: baidubce.BceClientConfiguration
:return:
:rtype baidubce.bce_response.BceResponse
"""
path = utils.append_uri(self.version, 'appblb', blb_id, 'TCPlistener')
params = {}
if client_token is None:
params[b'clientToken'] = generate_client_token()
else:
params[b'clientToken'] = client_token
params[b'listenerPort'] = listener_port
body = {}
if scheduler is not None:
body['scheduler'] = compat.convert_to_string(scheduler)
return self._send_request(http_methods.PUT, path,
body=json.dumps(body), params=params,
config=config)
@required(blb_id=(bytes, str),
listener_port=int)
def update_app_udp_listener(self, blb_id, listener_port,
scheduler=None, client_token=None,
config=None):
"""
update a app udp listener rule with the specified options.
:param blb_id:
the id of blb which the listener work on
:type blb_id:string
:param listener_port:
port to be linstened owned by listener
:value 1-65535
:type listener_port:int
:param scheduler
balancing algorithm
:value 'RoundRobin'or'LeastConnection'or'Hash'
:type scheduler:string
:param client_token:
If the clientToken is not specified by the user,
a random String generated by default algorithm will be used.
:type client_token: string
:param config:
:type config: baidubce.BceClientConfiguration
:return:
:rtype baidubce.bce_response.BceResponse
"""
path = utils.append_uri(self.version, 'appblb', blb_id, 'UDPlistener')
params = {}
if client_token is None:
params[b'clientToken'] = generate_client_token()
else:
params[b'clientToken'] = client_token
params[b'listenerPort'] = listener_port
body = {
'scheduler': compat.convert_to_string(scheduler)
}
return self._send_request(http_methods.PUT, path,
body=json.dumps(body),
params=params, config=config)
@required(blb_id=(bytes, str),
listener_port=int)
def update_app_http_listener(self, blb_id, listener_port,
scheduler=None, keep_session=None,
keep_session_type=None,
keep_session_timeout=None,
keep_session_cookie_name=None,
x_forward_for=None,
server_timeout=None,
redirect_port=None,
client_token=None,
config=None):
"""
update a app http listener rule with the specified options.
:param blb_id:
The id of blb which the listener work on
:type blb_id: string
:param listener_port:
Port to be linstened owned by listener
:value 1-65535
:type listener_port: int
:param scheduler:
Balancing algorithm
:value 'RoundRobin' or 'LeastConnection' or 'Hash'
:type scheduler: string
:param keep_session:
Whether to enable the session hold function, that is,
the request sent by the same client will reach the
same backend server
:value true or false, default:false
:type keep_session: bool
:param keep_session_type:
The cookie handling method maintained by the session,
valid only if the session is held open
:value 'insert' or 'rewrite', default:insert
:type keep_session_type: string
:param keep_session_timeout:
The time the cookie is kept in session (in seconds),
valid only if the session is held open
:value 1-15552000, default:3600
:type keep_session_timeout: int
:param keep_session_cookie_name:
The session keeps the name of the cookie that needs
to be overridden,if and only if session persistence is
enabled and keep_session_type="rewrite"
:type keep_session_cookie_name: int
:param x_forward_for:
Whether to enable the real IP address of the client,
the backend server can obtain the real address of the
client through the X-Forwarded-For HTTP header.
:value true or false, default: flase
:type x_forward_for: bool
:param server_timeout:
Backend server maximum timeout (unit: second)
:value 1-3600, default: 30
:type server_timeout: int
:param redirect_port:
Forward the request received by this listener to the HTTPS
listener, which is specified by the HTTPS listener.
:type redirect_port: int
:param client_token:
If the clientToken is not specified by the user,
a random String generated by default algorithm will be used.
:type client_token: string
:param config:
:type config: baidubce.BceClientConfiguration
:return:
:rtype baidubce.bce_response.BceResponse
"""
path = utils.append_uri(self.version, 'appblb', blb_id, 'HTTPlistener')
params = {}
if client_token is None:
params[b'clientToken'] = generate_client_token()
else:
params[b'clientToken'] = client_token
params[b'listenerPort'] = listener_port
body = {}
if scheduler is not None:
body['scheduler'] = compat.convert_to_string(scheduler)
if keep_session is not None:
body['keepSession'] = keep_session
if keep_session_type is not None:
body['keepSessionType'] = \
compat.convert_to_string(keep_session_type)
if keep_session_timeout is not None:
body['keepSessionTimeout'] = keep_session_timeout
if keep_session_cookie_name is not None:
body['keepSessionCookieName'] = keep_session_cookie_name
if x_forward_for is not None:
body['xForwardFor'] = x_forward_for
if server_timeout is not None:
body['serverTimeout'] = server_timeout
if redirect_port is not None:
body['redirectPort'] = redirect_port
return self._send_request(http_methods.PUT, path,
body=json.dumps(body),
params=params, config=config)
@required(blb_id=(bytes, str), listener_port=int)
def update_app_https_listener(self, blb_id, listener_port,
scheduler=None,
keep_session=None,
keep_session_type=None,
keep_session_timeout=None,
keep_session_cookie_name=None,
x_forward_for=None,
server_timeout=None,
cert_ids=None,
ie6_compatible=None,
encryption_type=None,
encryption_protocols=None,
dual_auth=None,
client_certIds=None,
client_token=None,
config=None):
"""
update a app https listener rule with the specified options.
:param blb_id:
The id of blb which the listener work on
:type blb_id: string
:param listener_port:
Port to be linstened owned by listener
:value 1-65535
:type listener_port: int
:param scheduler:
Balancing algorithm
:value 'RoundRobin' or 'LeastConnection' or 'Hash'
:type scheduler: string
:param keep_session:
Whether to enable the session hold function, that is, the request
sent by the same client will reach the same backend server
:value true or false, default: false
:type keep_session: bool
:param keep_session_type:
The cookie handling method maintained by the session,
valid only if the session is held open
:value 'insert' or 'rewrite', default: insert
:type keep_session_type: string
:param keep_session_timeout:
The time the cookie is kept in session (in seconds),
valid only if the session is held open
:value 1-15552000, default:3600
:type keep_session_timeout: int
:param keep_session_cookie_name:
The session keeps the name of the cookie that needs to be
overridden,if and only if session persistence is enabled and
keep_session_type="rewrite"
:type keep_session_cookie_name: int
:param x_forward_for:
Whether to enable the real IP address of the client,
the backend server can obtain the real address of the client
through the X-Forwarded-For HTTP header.
:value true or false, default: False
:type x_forward_for: bool
:param server_timeout:
Backend server maximum timeout (unit: second)
:value 1-3600, default: 30
:type server_timeout: int
:param cert_ids:
The SSL certificate to be loaded by the listener.
Currently HTTPS listeners can only bind one SSL certificate.
:type cert_ids:List<String>
:param ie6_compatible:
Is it compatible with IE6 HTTPS request
(the protocol format is earlier SSL3.0, the security is poor)
:value true or false, default: true
:type ie6_compatible: bool
:param encryption_type:
Encryption options, support three types:
compatibleIE/incompatibleIE/userDefind,
corresponding to:
IE-compatible encryption/disabled unsecure encryption/custom encryption,
when encryptionType is valid and legitimate,
ie6Compatible field transfer value will not take effect
type: encryption_type:string
:param encryption_protocols:
When the encryptionType value is userDefind,
the list of protocol types is a string list composed of four protocols:
"sslv3", "tlsv10", "tlsv11", "tlsv12".
type: encryption_protocols:list
:param dual_auth:
Whether to Open Two-way Authentication,
default:false
:type dual_auth: boolean
:param client_certIds:
When dualAuth is true, the loaded client certificate chain
:type client_certIds: list
:param client_token:
If the clientToken is not specified by the user,
a random String generated by default algorithm will be used.
:type client_token: string
:param config:
:type config: baidubce.BceClientConfiguration
:return:
:rtype baidubce.bce_response.BceResponse
"""
path = utils.append_uri(self.version, 'appblb', blb_id, 'HTTPSlistener')
params = {}
if client_token is None:
params[b'clientToken'] = generate_client_token()
else:
params[b'clientToken'] = client_token
params[b'listenerPort'] = listener_port
body = {}
if scheduler is not None:
body['scheduler'] = compat.convert_to_string(scheduler)
if keep_session is not None:
body['keepSession'] = keep_session
if keep_session_type is not None:
body['keepSessionType'] = \
compat.convert_to_string(keep_session_type)
if keep_session_timeout is not None:
body['keepSessionTimeout'] = keep_session_timeout
if keep_session_cookie_name is not None:
body['keepSessionCookieName'] = keep_session_cookie_name
if x_forward_for is not None:
body['xForwardFor'] = x_forward_for
if server_timeout is not None:
body['serverTimeout'] = server_timeout
if cert_ids is not None:
body['certIds'] = cert_ids
if ie6_compatible is not None:
body['compatibleIE'] = ie6_compatible
if encryption_type is not None:
body['encryptionType'] = \
compat.convert_to_string(encryption_type)
if encryption_protocols is not None:
body['encryptionProtocols'] = encryption_protocols
if dual_auth is not None:
body['dualAuth'] = dual_auth
if client_certIds is not None:
body['clientCertIds'] = client_certIds
return self._send_request(http_methods.PUT, path,
body=json.dumps(body), params=params,
config=config)
@required(blb_id=(bytes, str), listener_port=int)
def update_app_ssl_listener(self, blb_id, listener_port,
scheduler=None,
cert_ids=None,
ie6_compatible=None,
encryption_type=None,
encryption_protocols=None,
dual_auth=None,
client_certIds=None,
client_token=None,
config=None):
"""
update a app ssl listener rule with the specified options.
:param blb_id:
The id of blb which the listener work on
:type blb_id: string
:param listener_port:
port to be linstened owned by listener
:value 1-65535
:type listener_port: int
:param scheduler:
balancing algorithm
:value 'RoundRobin' or 'LeastConnection'
:type scheduler: string
:param cert_ids:
The SSL certificate to be loaded by the listener.
Currently HTTPS listeners can only bind one SSL certificate.
:type cert_ids: List<String>
:param ie6_compatible:
compatible with IE6 HTTPS request
(the protocol format is earlier SSL3.0, the security is poor)
:value true or false, default: true
:type ie6_compatible: bool
:param encryption_type:
Encryption options, support three types:
compatibleIE/incompatibleIE/userDefind,
corresponding to:
IE-compatible encryption/disabled unsecure encryption/custom encryption,
when encryptionType is valid and legitimate,
ie6Compatible field transfer value will not take effect
type: encryption_type:string
:param encryption_protocols:
When the encryptionType value is userDefind,
the list of protocol types is a string list composed of four protocols:
"sslv3", "tlsv10", "tlsv11", "tlsv12".
type: encryption_protocols:list
:param dual_auth:
Whether to Open Two-way Authentication,
default:false
:type dual_auth: boolean
:param client_certIds:
When dualAuth is true, the loaded client certificate chain
:type client_certIds: list
:param client_token:
If the clientToken is not specified by the user,
a random String generated by default algorithm will be used.
:type client_token: string
:param config:
:type config: baidubce.BceClientConfiguration
:return
:rtype baidubce.bce_response.BceResponse
"""
path = utils.append_uri(self.version, 'appblb', blb_id, 'SSLlistener')
params = {}
if client_token is None:
params[b'clientToken'] = generate_client_token()
else:
params[b'clientToken'] = client_token
params[b'listenerPort'] = listener_port
body = {}
if scheduler is not None:
body['scheduler'] = compat.convert_to_string(scheduler)
if cert_ids is not None:
body['certIds'] = cert_ids
if ie6_compatible is not None:
body['compatibleIE'] = ie6_compatible
if encryption_type is not None:
body['encryptionType'] = \
compat.convert_to_string(encryption_type)
if encryption_protocols is not None:
body['encryptionProtocols'] = encryption_protocols
if dual_auth is not None:
body['dualAuth'] = dual_auth
if client_certIds is not None:
body['clientCertIds'] = client_certIds
return self._send_request(http_methods.PUT, path,
body=json.dumps(body),
params=params, config=config)
@required(blb_id=(bytes, str))
def describe_app_tcp_listener(self, blb_id, listener_port=None,
marker=None, max_keys=None,
config=None):
"""
get app tcp listeners identified by bibID
:param blb_id
the id of blb which the listener work on
:type blb_id:string
:param listener_port
The listener port to query
:type listener_port:int
:param marker
The optional parameter marker specified in the
original request to specify
where in the results to begin listing.
Together with the marker, specifies the list result
which listing should begin.
If the marker is not specified, the list result will
listing from the first one.
:type marker: string
:param max_keys
The optional parameter to specifies the max number of
list result to return.
The default value is 1000.
:type max_keys: int
:param config:
:type config: baidubce.BceClientConfiguration
:return:
:rtype baidubce.bce_response.BceResponse
"""
path = utils.append_uri(self.version, 'appblb', blb_id, 'TCPlistener')
params = {}
if listener_port is not None:
params[b'listenerPort'] = listener_port
if marker is not None:
params[b'marker'] = marker
if max_keys is not None:
params[b'maxKeys'] = max_keys
return self._send_request(http_methods.GET, path,
params=params, config=config)
@required(blb_id=(bytes, str))
def describe_app_udp_listener(self, blb_id, listener_port=None,
marker=None, max_keys=None,
config=None):
"""
get app udp listeners identified by bibID
:param blb_id
the id of blb which the listener work on
:type blb_id:string
:param listener_port
The listener port to query
:type listener_port:int
:param marker
The optional parameter marker specified in the original
request to specify where in the results to begin listing.
Together with the marker, specifies the list result which
listing should begin.
If the marker is not specified, the list result will
listing from the first one.
:type marker: string
:param max_keys
The optional parameter to specifies the max number of
list result to return.
The default value is 1000.
:type max_keys: int
:param config:
:type config: baidubce.BceClientConfiguration
:return:
:rtype baidubce.bce_response.BceResponse
"""
path = utils.append_uri(self.version, 'appblb', blb_id, 'UDPlistener')
params = {}
if listener_port is not None:
params[b'listenerPort'] = listener_port
if marker is not None:
params[b'marker'] = marker
if max_keys is not None:
params[b'maxKeys'] = max_keys
return self._send_request(http_methods.GET, path,
params=params, config=config)
@required(blb_id=(bytes, str))
def describe_app_http_listener(self, blb_id, listener_port=None,
marker=None, max_keys=None,
config=None):
"""
get app http listeners identified by bibID
:param blb_id
the id of blb which the listener work on
:type blb_id:string
:param listener_port
The listener port to query
:type listener_port:int
:param marker
The optional parameter marker specified in the original
request to specify where in the results to begin listing.
Together with the marker, specifies the list result which
listing should begin.
If the marker is not specified, the list result will listing
from the first one.
:type marker: string
:param max_keys
The optional parameter to specifies the max number of list
result to return.
The default value is 1000.
:type max_keys: int
:param config:
:type config: baidubce.BceClientConfiguration
:return:
:rtype baidubce.bce_response.BceResponse
"""
path = utils.append_uri(self.version, 'appblb', blb_id, 'HTTPlistener')
params = {}
if listener_port is not None:
params[b'listenerPort'] = listener_port
if marker is not None:
params[b'marker'] = marker
if max_keys is not None:
params[b'maxKeys'] = max_keys
return self._send_request(http_methods.GET, path,
params=params, config=config)
@required(blb_id=(bytes, str))
def describe_app_https_listener(self, blb_id, listener_port=None,
marker=None, max_keys=None,
config=None):
"""
get app https listeners identified by bibID
:param blb_id
the id of blb which the listener work on
:type blb_id:string
:param listener_port
The listener port to query
:type listener_port:int
:param marker
The optional parameter marker specified in the original
request to specify where in the results to begin listing.
Together with the marker, specifies the list result which
listing should begin.
If the marker is not specified, the list result will listing
from the first one.
:type marker: string
:param max_keys
The optional parameter to specifies the max number of list
result to return.
The default value is 1000.
:type max_keys: int
:param config:
:type config: baidubce.BceClientConfiguration
:return:
:rtype baidubce.bce_response.BceResponse
"""
path = utils.append_uri(self.version, 'appblb', blb_id, 'HTTPSlistener')
params = {}
if listener_port is not None:
params[b'listenerPort'] = listener_port
if marker is not None:
params[b'marker'] = marker
if max_keys is not None:
params[b'maxKeys'] = max_keys
return self._send_request(http_methods.GET, path,
params=params, config=config)
@required(blb_id=(bytes, str))
def describe_app_ssl_listener(self, blb_id, listener_port=None,
marker=None, max_keys=None, config=None):
"""
get app ssl listeners identified by bibID
:param blb_id
the id of blb which the listener work on
:type blb_id:string
:param listener_port
The listener port to query
:type listener_port:int
:param marker
The optional parameter marker specified in the original
request to specify where in the results to begin listing.
Together with the marker, specifies the list result which
listing should begin.
If the marker is not specified, the list result will listing
from the first one.
:type marker: string
:param max_keys
The optional parameter to specifies the max number of list
result to return.
The default value is 1000.
:type max_keys: int
:param config:
:type config: baidubce.BceClientConfiguration
:return:
:rtype baidubce.bce_response.BceResponse
"""
path = utils.append_uri(self.version, 'appblb', blb_id, 'SSLlistener')
params = {}
if listener_port is not None:
params[b'listenerPort'] = listener_port
if marker is not None:
params[b'marker'] = marker
if max_keys is not None:
params[b'maxKeys'] = max_keys
return self._send_request(http_methods.GET, path,
params=params, config=config)
@required(blb_id=(bytes, str),
portList=list)
def delete_app_listeners(self, blb_id, portList,
client_token=None,
config=None):
"""
Release app listener under the specified LoadBalancer,
the listener is specified by listening to the port.
:param blb_id:
id of LoadBalancer
:type blb_id:string
:param portList:
The ports of listeners to be released
:type portList:list<int>
:param client_token:
If the clientToken is not specified by the user, a random String
generated by default algorithm will be used.
:type client_token: string
:param config:
:type config: baidubce.BceClientConfiguration
:return:
:rtype baidubce.bce_response.BceResponse
"""
path = utils.append_uri(self.version, 'appblb', blb_id, 'listener')
params = {}
params[b'batchdelete'] = None
if client_token is None:
params[b'clientToken'] = generate_client_token()
else:
params[b'clientToken'] = client_token
body = {}
body['portList'] = portList
return self._send_request(http_methods.PUT, path,
body=json.dumps(body), params=params,
config=config)
@required(blb_id=(bytes, str),
listener_port=int,
app_policy_vos=list)
def create_policys(self, blb_id, listener_port,
app_policy_vos, client_token=None,
config=None):
"""
Create policys.
:param blb_id:
the id of blb which the listener work on
:type blb_id: string
:param listener_port:
port to be linstened owned by listener
:value 1-65535
:type listener_port: int
:param app_policy_vos
policy list the listener binds.
If the listener type is TCP,
there is only one policy
and only the full match is supported.
https://cloud.baidu.com/doc/BLB/API.html#AppPolicy
:type app_policy_vos: list<AppPolicy>
:param client_token:
If the clientToken is not specified by the user, a random String
generated by default algorithm will be used.
:type client_token: string
:param config:
:type config: baidubce.BceClientConfiguration
:return:
:rtype baidubce.bce_response.BceResponse
"""
path = utils.append_uri(self.version, 'appblb', blb_id, 'policys')
params = {}
if client_token is None:
params[b'clientToken'] = generate_client_token()
else:
params[b'clientToken'] = client_token
body = {
'listenerPort': listener_port,
'appPolicyVos': app_policy_vos
}
return self._send_request(http_methods.POST, path,
body=json.dumps(body), params=params,
config=config)
@required(blb_id=(bytes, str),
listener_port=int)
def describe_policys(self, blb_id, listener_port,
marker=None, max_keys=None,
config=None):
"""
get policys
:param blb_id
the id of blb which the listener work on
:type blb_id:string
:param listener_port
The listener port used by listener
:type listener_port:int
:param marker
The optional parameter marker specified in the original
request to specify where in the results to begin listing.
Together with the marker, specifies the list result which
listing should begin.
If the marker is not specified, the list result will listing
from the first one.
:type marker: string
:param max_keys
The optional parameter to specifies the max number of list
result to return.
The default value is 1000.
:type max_keys: int
:param config:
:type config: baidubce.BceClientConfiguration
:return:
:rtype baidubce.bce_response.BceResponse
"""
path = utils.append_uri(self.version, 'appblb', blb_id, 'policys')
params = {}
params[b'port'] = listener_port
if marker is not None:
params[b'marker'] = marker
if max_keys is not None:
params[b'maxKeys'] = max_keys
return self._send_request(http_methods.GET, path,
params=params, config=config)
@required(blb_id=(bytes, str),
listener_port=int,
policys_list=list)
def delete_policys(self, blb_id, listener_port,
policys_list,
client_token=None, config=None):
"""
Release the listener under the specified LoadBalancer,
the listener is specified by listening to the port.
:param blb_id:
id of LoadBalancer
:type blb_id:string
:param listener_port
The listener port used by listener
:type listener_port:int
:param policys_list
All policy identifiers to be released
:type policys_list:list<str>
:param client_token:
If the clientToken is not specified by the user, a random String
generated by default algorithm will be used.
:type client_token: string
:param config:
:type config: baidubce.BceClientConfiguration
:return:
:rtype baidubce.bce_response.BceResponse
"""
path = utils.append_uri(self.version, 'appblb', blb_id, 'policys')
params = {}
params[b'batchdelete'] = None
if client_token is None:
params[b'clientToken'] = generate_client_token()
else:
params[b'clientToken'] = client_token
body = {
'port': listener_port,
'policyIdList': policys_list
}
return self._send_request(http_methods.PUT, path,
body=json.dumps(body), params=params,
config=config)
"""
ServerGroup API
"""
@required(blb_id=(bytes, str))
def create_app_server_group(self, blb_id,
name=None,
desc=None,
backend_server_list=None,
client_token=None,
config=None):
"""
create server group for the specified LoadBalancer,
support batch add
:param blb_id:
id of LoadBalancer
:type blb_id:string
:param name:
name of server group
:type name:string
:param desc:
description of server group
:type desc:string
:param backend_server_list
List of backend servers to be added
https://cloud.baidu.com/doc/BLB/API.html#AppBackendServer
:type backend_server_list:List<AppBackendServer>
:param client_token:
If the clientToken is not specified by the user, a random String
generated by default algorithm will be used.
:type client_token: string
:param config:
:type config: baidubce.BceClientConfiguration
:return:
:rtype baidubce.bce_response.BceResponse
"""
path = utils.append_uri(self.version, 'appblb', blb_id, 'appservergroup')
params = {}
if client_token is None:
params[b'clientToken'] = generate_client_token()
else:
params[b'clientToken'] = client_token
body = {}
if name is not None:
body['name'] = compat.convert_to_string(name)
if desc is not None:
body['desc'] = compat.convert_to_string(desc)
if backend_server_list is not None:
body['backendServerList'] = backend_server_list
return self._send_request(http_methods.POST, path,
body=json.dumps(body), params=params,
config=config)
@required(blb_id=(bytes, str),
sg_id=(bytes, str))
def update_app_server_group(self, blb_id, sg_id,
name=None,
desc=None,
client_token=None,
config=None):
"""
update the information of the app server group
of the specified LoadBalancer
:param blb_id:
id of LoadBalancer
:type blb_id:string
:param sg_id:
id of the server group to be updated
:type sg_id:string
:param name:
name of server group
:type name:string
:param desc:
description of server group
:type desc:string
:param client_token:
If the clientToken is not specified by the user, a random String
generated by default algorithm will be used.
:type client_token: string
:param config:
:type config: baidubce.BceClientConfiguration
:return:
:rtype baidubce.bce_response.BceResponse
"""
path = utils.append_uri(self.version, 'appblb', blb_id, 'appservergroup')
params = {}
if client_token is None:
params[b'clientToken'] = generate_client_token()
else:
params[b'clientToken'] = client_token
body = {}
body['sgId'] = compat.convert_to_string(sg_id)
if name is not None:
body['name'] = compat.convert_to_string(name)
if desc is not None:
body['desc'] = compat.convert_to_string(desc)
return self._send_request(http_methods.PUT, path,
body=json.dumps(body), params=params,
config=config)
@required(blb_id=(bytes, str))
def describe_app_server_group(self, blb_id,
name=None,
exactly_match=None,
marker=None,
max_keys=None, config=None):
"""
Query the imformation of app server group
of the specified LoadBalancer
:param blb_id:
Id of LoadBalancer
:type blb_id:string
:param name:
name of server group
:type name:string
:param exactly_match:
Set whether the name matches globally
:type exactly_match:boolean
:param marker:
The optional parameter marker specified in the original
request to specify where in the results to begin listing.
Together with the marker, specifies the list result which
listing should begin. If the marker is not specified,
the list result will listing from the first one.
:type marker: string
:param max_keys:
The optional parameter to specifies the max number of
list result to return.
The default value is 1000.
:type max_keys: int
:param config:
:type config: baidubce.BceClientConfiguration
:return:
:rtype baidubce.bce_response.BceResponse
"""
path = utils.append_uri(self.version, 'appblb', blb_id, 'appservergroup')
params = {}
if name is not None:
params[b'name'] = name
if exactly_match is not None:
params[b'exactlyMatch'] = exactly_match
if marker is not None:
params[b'marker'] = marker
if max_keys is not None:
params[b'maxKeys'] = max_keys
return self._send_request(http_methods.GET, path, params=params,
config=config)
@required(blb_id=(bytes, str),
sg_id=(bytes, str))
def delete_app_server_group(self, blb_id, sg_id,
client_token=None,
config=None):
"""
delete the app server group of the specified LoadBalancer,
:param blb_id:
id of LoadBalancer
:type blb_id:string
:param sg_id:
id of the server group to be updated
:type sg_id:string
:param client_token:
If the clientToken is not specified by the user,
a random String generated by default algorithm will be used.
:type client_token: string
:param config:
:type config: baidubce.BceClientConfiguration
:return:
:rtype baidubce.bce_response.BceResponse
"""
path = utils.append_uri(self.version, 'appblb', blb_id, 'appservergroup')
params = {}
params[b'delete'] = None
if client_token is None:
params[b'clientToken'] = generate_client_token()
else:
params[b'clientToken'] = client_token
body = {}
body['sgId'] = compat.convert_to_string(sg_id)
return self._send_request(http_methods.PUT, path,
body=json.dumps(body), params=params,
config=config)
@required(blb_id=(bytes, str),
sg_id=(bytes, str), port=int,
protocol_type=(bytes, str))
def create_app_server_group_port(self, blb_id, sg_id,
port, protocol_type,
health_check=None,
health_check_port=None,
health_check_urlpath=None,
health_check_timeout_insecond=None,
health_check_interval_insecond=None,
health_check_down_retry=None,
health_check_up_retry=None,
health_check_normal_status=None,
client_token=None,
config=None):
"""
create server group for the specified LoadBalancer,
support batch add
:param blb_id:
id of LoadBalancer
:type blb_id:string
:param sg_id:
id of the server group
:type sg_id:string
:param port:
Port number, integer between 1 and 65535
:type port:string
:param protocol_type:
Protocol type of listening port, "TCP"/"UDP"/"HTTP"
:type protocol_type:string
:param health_check:
Health check protocol
:value 'HTTP' or 'TCP',default:'HTTP'
:type health_check: string
:param health_check_port:
Health check port, the default is the same as port
:type health_check_port: int
:param health_check_urlpath:
Health check URI, default '/'.
Effective when the health check protocol is "HTTP"
:type health_check_urlpath: string
:param health_check_timeout_insecond:
Health check timeout (unit: second)
:value 1-60, default: 3
:type health_check_timeout_insecond: int
:param health_check_interval_insecond:
Health check interval (unit: second)
:value 1-10, default: 3
:type health_check_interval_insecond: int
:param health_check_down_retry:
The unhealthy down retry, that is, how many consecutive health
check failures, shields the backend server.
:value 2-5, default: 3
:type health_check_down_retry: int
:param health_check_up_retry:
Health up retry, that is, how many consecutive health checks
are successful, then re-use the back-end server
:value:2-5, default: 3
:type health_check_up_retry: int
:param health_check_normal_status:
The HTTP status code when the health check is normal supports
a combination of five types of status codes,
such as "http_1xx|http_2xx", Effective when the health check
protocol is "HTTP"
:value default: http_2xx|http_3xx
:type health_check_normal_status: string
:param client_token:
If the clientToken is not specified by the user, a random String
generated by default algorithm will be used.
:type client_token: string
:param config:
:type config: baidubce.BceClientConfiguration
:return:
:rtype baidubce.bce_response.BceResponse
"""
path = utils.append_uri(self.version, 'appblb', blb_id, 'appservergroupport')
params = {}
if client_token is None:
params[b'clientToken'] = generate_client_token()
else:
params[b'clientToken'] = client_token
body = {
'sgId': compat.convert_to_string(sg_id),
'port': port,
'type': compat.convert_to_string(protocol_type)
}
if health_check is not None:
body['healthCheck'] = compat.convert_to_string(health_check)
if health_check_port is not None:
body['healthCheckPort'] = health_check_port
if health_check_urlpath is not None:
body['healthCheckUrlPath'] = \
compat.convert_to_string(health_check_urlpath)
if health_check_timeout_insecond is not None:
body['healthCheckTimeoutInSecond'] = health_check_timeout_insecond
if health_check_interval_insecond is not None:
body['healthCheckIntervalInSecond'] = health_check_interval_insecond
if health_check_down_retry is not None:
body['healthCheckDownRetry'] = health_check_down_retry
if health_check_up_retry is not None:
body['healthCheckUpRetry'] = health_check_up_retry
if health_check_normal_status is not None:
body['healthCheckNormalStatus'] = \
compat.convert_to_string(health_check_normal_status)
return self._send_request(http_methods.POST, path,
body=json.dumps(body), params=params,
config=config)
@required(blb_id=(bytes, str),
sg_id=(bytes, str),
port_id=(bytes, str))
def update_app_server_group_port(self, blb_id, sg_id, port_id,
health_check=None,
health_check_port=None,
health_check_urlpath=None,
health_check_timeout_insecond=None,
health_check_interval_insecond=None,
health_check_down_retry=None,
health_check_up_retry=None,
health_check_normal_status=None,
client_token=None,
config=None):
"""
update server group for the specified LoadBalancer,
support batch add
:param blb_id:
id of LoadBalancer
:type blb_id:string
:param sg_id:
id of the server group
:type sg_id:string
:param port_id:
The id of the server group port to be updated
:type port_id:string
:param health_check:
Health check protocol
:value 'HTTP' or 'TCP',default:'HTTP'
:type health_check: string
:param health_check_port:
Health check port, the default is the same as port
:type health_check_port: int
:param health_check_urlpath:
Health check URI, default '/'.
Effective when the health check protocol is "HTTP"
:type health_check_urlpath: string
:param health_check_timeout_insecond:
Health check timeout (unit: second)
:value 1-60, default: 3
:type health_check_timeout_insecond: int
:param health_check_interval_insecond:
Health check interval (unit: second)
:value 1-10, default: 3
:type health_check_interval_insecond: int
:param health_check_down_retry:
The unhealthy down retry, that is, how many consecutive health
check failures, shields the backend server.
:value 2-5, default: 3
:type health_check_down_retry: int
:param health_check_up_retry:
Health up retry, that is, how many consecutive health checks
are successful, then re-use the back-end server
:value:2-5, default: 3
:type health_check_up_retry: int
:param health_check_normal_status:
The HTTP status code when the health check is normal supports
a combination of five types of status codes,
such as "http_1xx|http_2xx", Effective when the health check
protocol is "HTTP"
:value default: http_2xx|http_3xx
:type health_check_normal_status: string
:param client_token:
If the clientToken is not specified by the user, a random String
generated by default algorithm will be used.
:type client_token: string
:param config:
:type config: baidubce.BceClientConfiguration
:return:
:rtype baidubce.bce_response.BceResponse
"""
path = utils.append_uri(self.version, 'appblb', blb_id, 'appservergroupport')
params = {}
if client_token is None:
params[b'clientToken'] = generate_client_token()
else:
params[b'clientToken'] = client_token
body = {
'sgId': compat.convert_to_string(sg_id),
'portId': compat.convert_to_string(port_id)
}
if health_check is not None:
body['healthCheck'] = compat.convert_to_string(health_check)
if health_check_port is not None:
body['healthCheckPort'] = health_check_port
if health_check_urlpath is not None:
body['healthCheckUrlPath'] = \
compat.convert_to_string(health_check_urlpath)
if health_check_timeout_insecond is not None:
body['healthCheckTimeoutInSecond'] = health_check_timeout_insecond
if health_check_interval_insecond is not None:
body['healthCheckIntervalInSecond'] = health_check_interval_insecond
if health_check_down_retry is not None:
body['healthCheckDownRetry'] = health_check_down_retry
if health_check_up_retry is not None:
body['healthCheckUpRetry'] = health_check_up_retry
if health_check_normal_status is not None:
body['healthCheckNormalStatus'] = \
compat.convert_to_string(health_check_normal_status)
return self._send_request(http_methods.PUT, path,
body=json.dumps(body), params=params,
config=config)
@required(blb_id=(bytes, str),
sg_id=(bytes, str),
port_list=list)
def delete_app_server_group_port(self, blb_id, sg_id,
port_list,
client_token=None, config=None):
"""
delete server group of the specified LoadBalancer,
:param blb_id:
id of LoadBalancer
:type blb_id:string
:param sg_id:
id of the server group
:type sg_id:string
:param port_list:
The ports of listeners to be released
:type port_list:list<string>
:param client_token:
If the clientToken is not specified by the user, a random String
generated by default algorithm will be used.
:type client_token: string
:param config:
:type config: baidubce.BceClientConfiguration
:return:
:rtype baidubce.bce_response.BceResponse
"""
path = utils.append_uri(self.version, 'appblb', blb_id, 'appservergroupport')
params = {}
params[b'batchdelete'] = None
if client_token is None:
params[b'clientToken'] = generate_client_token()
else:
params[b'clientToken'] = client_token
body = {
'sgId': compat.convert_to_string(sg_id),
'portIdList': port_list
}
return self._send_request(http_methods.PUT, path,
body=json.dumps(body), params=params,
config=config)
@required(blb_id=(bytes, str),
sg_id=(bytes, str),
backend_server_list=list)
def create_app_blb_rs(self, blb_id, sg_id,
backend_server_list,
client_token=None,
config=None):
"""
Add backend server for the specified LoadBalancer and server group,
support batch add
:param blb_id:
id of LoadBalancer
:type blb_id:string
:param sg_id:
id of the server group
:type sg_id:string
:param backend_server_list
List of backend servers to be added
https://cloud.baidu.com/doc/BLB/API.html#AppBackendServer
:type backend_server_list:List<AppBackendServer>
:param client_token:
If the clientToken is not specified by the user, a random String
generated by default algorithm will be used.
:type client_token: string
:param config:
:type config: baidubce.BceClientConfiguration
:return:
:rtype baidubce.bce_response.BceResponse
"""
path = utils.append_uri(self.version, 'appblb', blb_id, 'blbrs')
params = {}
if client_token is None:
params[b'clientToken'] = generate_client_token()
else:
params[b'clientToken'] = client_token
body = {
'sgId': compat.convert_to_string(sg_id),
'backendServerList': backend_server_list
}
return self._send_request(http_methods.POST, path,
body=json.dumps(body), params=params,
config=config)
@required(blb_id=(bytes, str),
sg_id=(bytes, str),
backend_server_list=list)
def update_app_blb_rs(self, blb_id, sg_id,
backend_server_list,
client_token=None,
config=None):
"""
update backend server for the specified LoadBalancer and server group,
support batch update
:param blb_id:
id of LoadBalancer
:type blb_id:string
:param sg_id:
id of the server group
:type sg_id:string
:param backend_server_list
List of backend servers to be added
https://cloud.baidu.com/doc/BLB/API.html#AppBackendServer
:type backend_server_list:List<AppBackendServer>
:param client_token:
If the clientToken is not specified by the user, a random String
generated by default algorithm will be used.
:type client_token: string
:param config:
:type config: baidubce.BceClientConfiguration
:return:
:rtype baidubce.bce_response.BceResponse
"""
path = utils.append_uri(self.version, 'appblb', blb_id, 'blbrs')
params = {}
if client_token is None:
params[b'clientToken'] = generate_client_token()
else:
params[b'clientToken'] = client_token
body = {
'sgId': compat.convert_to_string(sg_id),
'backendServerList': backend_server_list
}
return self._send_request(http_methods.PUT, path,
body=json.dumps(body), params=params,
config=config)
@required(blb_id=(bytes, str),
sg_id=(bytes, str))
def describe_app_blb_rs(self, blb_id, sg_id,
marker=None, max_keys=None,
config=None):
"""
Query the list of backend servers under the specified LoadBalancer
and server group
:param blb_id:
Id of LoadBalancer
:type blb_id:string
:param sg_id:
id of the server group
:type sg_id:string
:param marker:
The optional parameter marker specified in the original
request to specify where in the results to begin listing.
Together with the marker, specifies the list result which
listing should begin. If the marker is not specified,
the list result will listing from the first one.
:type marker: string
:param max_keys:
The optional parameter to specifies the max number of
list result to return.
The default value is 1000.
:type max_keys: int
:param config:
:type config: baidubce.BceClientConfiguration
:return:
:rtype baidubce.bce_response.BceResponse
"""
path = utils.append_uri(self.version, 'appblb', blb_id, 'blbrs')
params = {}
params[b'sgId'] = compat.convert_to_string(sg_id)
if marker is not None:
params[b'marker'] = marker
if max_keys is not None:
params[b'maxKeys'] = max_keys
return self._send_request(http_methods.GET, path, params=params,
config=config)
@required(blb_id=(bytes, str),
sg_id=(bytes, str),
backend_server_list=list)
def delete_app_blb_rs(self, blb_id, sg_id,
backend_server_list,
client_token=None,
config=None):
"""
delete backend server for the specified LoadBalancer and server group,
support batch delete
:param blb_id:
id of LoadBalancer
:type blb_id:string
:param sg_id:
id of the server group
:type sg_id:string
:param backend_server_list
List of backend servers to be deleted
:type backend_server_list:List<string>
:param client_token:
If the clientToken is not specified by the user, a random String
generated by default algorithm will be used.
:type client_token: string
:param config:
:type config: baidubce.BceClientConfiguration
:return:
:rtype baidubce.bce_response.BceResponse
"""
path = utils.append_uri(self.version, 'appblb', blb_id, 'blbrs')
params = {}
params[b'batchdelete'] = None
if client_token is None:
params[b'clientToken'] = generate_client_token()
else:
params[b'clientToken'] = client_token
body = {
'sgId': compat.convert_to_string(sg_id),
'backendServerIdList': backend_server_list
}
return self._send_request(http_methods.PUT, path,
body=json.dumps(body), params=params,
config=config)
@required(blb_id=(bytes, str),
sg_id=(bytes, str))
def describe_rs_mount(self, blb_id, sg_id, config=None):
"""
describe servers of specific server group
:param blb_id:
id of LoadBalancer
:type blb_id:string
:param sg_id:
id of the server group
:type sg_id:string
:param config:
:type config: baidubce.BceClientConfiguration
:return:
:rtype baidubce.bce_response.BceResponse
"""
path = utils.append_uri(self.version, 'appblb', blb_id, 'blbrsmount')
params = {
'sgId': compat.convert_to_string(sg_id)
}
return self._send_request(http_methods.GET, path, params=params,
config=config)
@required(blb_id=(bytes, str),
sg_id=(bytes, str))
def describe_rs_unmount(self, blb_id, sg_id, config=None):
"""
describe servers of specific server group
:param blb_id:
id of LoadBalancer
:type blb_id:string
:param sg_id:
id of the server group
:type sg_id:string
:param config:
:type config: baidubce.BceClientConfiguration
:return:
:rtype baidubce.bce_response.BceResponse
"""
path = utils.append_uri(self.version, 'appblb', blb_id, 'blbrsunmount')
params = {
'sgId': compat.convert_to_string(sg_id)
}
return self._send_request(http_methods.GET, path, params=params,
config=config)
def generate_client_token_by_uuid():
"""
The default method to generate the random string for client_token
if the optional parameter client_token is not specified by the user.
:return:
:rtype string
"""
return str(uuid.uuid4())
generate_client_token = generate_client_token_by_uuid
| 36.48476
| 85
| 0.580481
| 9,775
| 87,381
| 5.021074
| 0.045422
| 0.018337
| 0.019437
| 0.020395
| 0.933477
| 0.928445
| 0.919256
| 0.913164
| 0.903405
| 0.896457
| 0
| 0.005411
| 0.354986
| 87,381
| 2,394
| 86
| 36.5
| 0.865406
| 0.424989
| 0
| 0.831409
| 0
| 0
| 0.070373
| 0.006486
| 0
| 0
| 0
| 0
| 0
| 1
| 0.047344
| false
| 0
| 0.015012
| 0
| 0.112009
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
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| 0
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| null | 0
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|
0
| 7
|
d9b57c49485840cca689cb6060c1e7555950bf51
| 104,290
|
bzl
|
Python
|
rust/known_shas.bzl
|
meteorcloudy/rules_rust
|
215a8decfb06525a3f13b23fac5b3124eedabd27
|
[
"Apache-2.0"
] | null | null | null |
rust/known_shas.bzl
|
meteorcloudy/rules_rust
|
215a8decfb06525a3f13b23fac5b3124eedabd27
|
[
"Apache-2.0"
] | null | null | null |
rust/known_shas.bzl
|
meteorcloudy/rules_rust
|
215a8decfb06525a3f13b23fac5b3124eedabd27
|
[
"Apache-2.0"
] | 1
|
2021-06-21T20:35:33.000Z
|
2021-06-21T20:35:33.000Z
|
"""A module containing a mapping of Rust tools to checksums
This is a generated file -- see //util:fetch_shas
"""
FILE_KEY_TO_SHA = {
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"2018-10-30/llvm-tools-beta-x86_64-pc-windows-msvc": "cf74e15df51033370d4225fd9141f4cfc5c37145070d2296915fbecab9275b03",
"2018-10-30/llvm-tools-beta-x86_64-unknown-freebsd": "38ba85ec56c374b606a586221185ac351d04871892055f8936f28958e5e7a5cb",
"2018-10-30/llvm-tools-beta-x86_64-unknown-linux-gnu": "8d96f1475fc27f21d80bda80496783aa3181a4b33a138b7a89edcef2ddb6cf58",
"2018-10-30/rust-beta-aarch64-unknown-linux-gnu": "e9bc9d4a89299595ef42cde7303b50a7921e20a5ee4d131c4e1770a391611303",
"2018-10-30/rust-beta-x86_64-apple-darwin": "51acc0077d6abae5beed0e7b99c39ae9b8a0ff0f66daec227caef653046144e6",
"2018-10-30/rust-beta-x86_64-pc-windows-msvc": "07f3e42ba299b3b5341b410c4317161eba4b40bbc9fc449a9c187193fe49250c",
"2018-10-30/rust-beta-x86_64-unknown-freebsd": "49f1efe5bf10319446a298096dea73e478a466daca20a5b93d5263925e4ba9be",
"2018-10-30/rust-beta-x86_64-unknown-linux-gnu": "07963c2d6d56d077856f17e786414cf965832e7942f4ec72dec2eb51452e74b7",
"2018-10-30/rust-std-beta-aarch64-unknown-linux-gnu": "bf0895ccf65a86c1f51dbbebd0980bd07dcdc3919407fc84f5838cbbcb29e309",
"2018-10-30/rust-std-beta-wasm32-unknown-unknown": "5453e05993aaa90c8ac361086dd888a29a48f1b90b2c2184202c4b44b2e5569d",
"2018-10-30/rust-std-beta-x86_64-apple-darwin": "96fc1daea8868e176a14706333590b13a41193bd8ed5a711f23a15eceb5c6ce0",
"2018-10-30/rust-std-beta-x86_64-pc-windows-msvc": "6af758524d77288cb5c548d26467680d57aeb778063787662b1e00ef3887866a",
"2018-10-30/rust-std-beta-x86_64-unknown-freebsd": "5aef62464a5580ab6b38c9e54203db12f90ad42de538b7a5eefc7778b55b6497",
"2018-10-30/rust-std-beta-x86_64-unknown-linux-gnu": "34996a688d6a4c3587f873b0a8c86fe1d2fee2a269b6e669b1cb8c6908fb77b8",
"2018-10-30/rustc-beta-aarch64-unknown-linux-gnu": "e6bb89261baa494ef98239bde9821b66671de5cd78352a9c100abce3a18ca250",
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"2018-10-30/rustc-beta-x86_64-unknown-freebsd": "e207562cd5e3a17497e029bcb1cab56d9fa474d788906f13e389a3cb804ea4d6",
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"2018-10-30/rustfmt-beta-x86_64-apple-darwin": "2c213f43902104ebefe9eee6fa49aa36e16af972cb7aa1c63d772e5d05f74b59",
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| 115.110375
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| 0
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| null | 0
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0
| 7
|
d9ea74428301d248b01bad9ff0096a5740710b96
| 29,237
|
py
|
Python
|
tests/unit_tests/test_michelson/test_micheline.py
|
konchunas/pytezos
|
65576d18bdf1956fae8ea21241b6c43a38921b83
|
[
"MIT"
] | 98
|
2019-02-07T16:33:38.000Z
|
2022-03-31T15:53:41.000Z
|
tests/unit_tests/test_michelson/test_micheline.py
|
konchunas/pytezos
|
65576d18bdf1956fae8ea21241b6c43a38921b83
|
[
"MIT"
] | 152
|
2019-05-20T16:38:56.000Z
|
2022-03-30T14:24:38.000Z
|
tests/unit_tests/test_michelson/test_micheline.py
|
konchunas/pytezos
|
65576d18bdf1956fae8ea21241b6c43a38921b83
|
[
"MIT"
] | 34
|
2019-07-25T12:03:51.000Z
|
2021-11-11T22:23:38.000Z
|
from unittest import TestCase
from parameterized import parameterized
from pytezos.michelson.micheline import blind_unpack
from pytezos.michelson.types.base import MichelsonType
from pytezos.michelson.forge import forge_script_expr, forge_micheline, unforge_micheline
from pytezos.operation.forge import forge_operation_group
unknown_data = [
'0501000000056f776e6572',
'050a000000160000e8b36c80efb51ec85a14562426049aa182a3ce38',
'050100000006706175736564',
'050303',
'05010000000866616c6c6261636b',
'0502000000270316031607430368010000001655706172616d4e6f53756368456e747279506f696e7403420327',
'0501000000086e65774f776e6572',
'050306',
'0501000000096f70657261746f7273',
'050200000000',
'050100000009746f6b656e436f6465',
'050100000005545a425443',
'050100000009746f6b656e4e616d65',
'050100000005545a425443',
'05010000000b746f74616c4275726e6564',
'050000',
'05010000000b746f74616c4d696e746564',
'050000',
'05010000000b746f74616c537570706c79',
'050000',
'05010000000d72656465656d41646472657373',
'050a000000160000e8b36c80efb51ec85a14562426049aa182a3ce38',
'0507070100000004636f6465010000000863616c6c4275726e',
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'0507070100000004636f6465010000000863616c6c4d696e74',
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'0507070100000004636f6465010000000963616c6c5061757365',
'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',
'0507070100000004636f6465010000000b63616c6c417070726f7665',
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'0507070100000004636f6465010000000b63616c6c556e7061757365',
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'0507070100000004636f6465010000000c63616c6c4765744f776e6572',
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'0507070100000004636f6465010000001563616c6c5472616e736665724f776e657273686970',
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class TestPacking(TestCase):
@parameterized.expand([
({"bytes": "000018896fcfc6690baefa9aedc6d759f9bf05727e8c"},
{"prim": "address"},
"expru2YV8AanTTUSV4K21P7X4DzbuWQFVk7NewDuP1A5uamffiiFA3"),
({"string": "tz1MsmYzmqxHs9trE1qQugZxxcLPqAXdQaX9"},
{"prim": "address"},
"expru2YV8AanTTUSV4K21P7X4DzbuWQFVk7NewDuP1A5uamffiiFA3"),
({"string": "Game one!"},
{"prim": "string"},
"exprtiRSZkLKYRess9GZ3ryb4cVQD36WLo2oysZBFxKTZ2jXqcHWGj"),
({"int": "505506"},
{"prim": "int"},
"exprufzwVGdAX7zG91UpiAkR2yVxEDE75tHD5YgSBmYMUx22teZTCM"),
([{"int": "1"}, {"int": "1"}, {"int": "1"}, {"int": "1"}],
{"prim": "pair", "args": [{"prim": "int"}, {"prim": "int"}, {"prim": "int"}, {"prim": "int"}]},
"expruN32WETsB2Dx1AynDmMufVr1As9qdnjRxKQ82rk2qZ4uxuKVMK")
])
def test_get_key_hash(self, val_expr, type_expr, expected):
ty = MichelsonType.match(type_expr)
key = ty.from_micheline_value(val_expr).pack(legacy=True)
self.assertEqual(expected, forge_script_expr(key))
@parameterized.expand([(x,) for x in unknown_data])
def test_blind_unpack(self, data):
data = bytes.fromhex(data)
res = blind_unpack(data)
self.assertNotEqual(data, res)
def test_regr_local_remote_diff(self):
opg = {'branch': 'BKpLvH3E3bUa5Z2nb3RkH2p6EKLfymvxUAEgtRJnu4m9UX1TWUb',
'contents': [{'amount': '0',
'counter': '446245',
'destination': 'KT1VYUxhLoSvouozCaDGL1XcswnagNfwr3yi',
'fee': '104274',
'gas_limit': '1040000',
'kind': 'transaction',
'parameters': {'entrypoint': 'default',
'value': {'prim': 'Unit'}},
'source': 'tz1grSQDByRpnVs7sPtaprNZRp531ZKz6Jmm',
'storage_limit': '60000'}],
'protocol': 'PsCARTHAGazKbHtnKfLzQg3kms52kSRpgnDY982a9oYsSXRLQEb',
'signature': None}
local = forge_operation_group(opg).hex()
remote = "0dc397b7865779d87bd47d406e8b4eee84498f22ab01dff124433c7f057af5ae6c00e8b36c80efb51ec85a1456" \
"2426049aa182a3ce38d2ae06a59e1b80bd3fe0d4030001e5ebf2dcc7dcc9d13c2c45cd76823dd604740c7f0000"
self.assertEqual(remote, local)
def test_forge_combs(self):
expr = {'prim': 'Pair', 'args': [{'int': '1'}, {'int': '2'}, {'int': '3'}, {'int': '4'}]}
self.assertEqual(expr, unforge_micheline(forge_micheline(expr)))
| 230.212598
| 4,535
| 0.942949
| 288
| 29,237
| 95.604167
| 0.559028
| 0.001271
| 0.001017
| 0.000872
| 0.001598
| 0.001598
| 0
| 0
| 0
| 0
| 0
| 0.848631
| 0.036734
| 29,237
| 126
| 4,536
| 232.039683
| 0.129035
| 0
| 0
| 0.094017
| 0
| 0
| 0.914663
| 0.902145
| 0
| 1
| 0
| 0
| 0.034188
| 1
| 0.034188
| false
| 0
| 0.051282
| 0
| 0.094017
| 0
| 0
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
d9f34b9314fd8e3a689ab67f5b71e7ba87a6d47f
| 102
|
py
|
Python
|
tests/test_version.py
|
cariad/stackwhy
|
5d5f0764ab86740fbbc3ae25170d149b388b949c
|
[
"MIT"
] | null | null | null |
tests/test_version.py
|
cariad/stackwhy
|
5d5f0764ab86740fbbc3ae25170d149b388b949c
|
[
"MIT"
] | 4
|
2021-10-31T15:52:31.000Z
|
2021-11-01T13:06:27.000Z
|
tests/test_version.py
|
cariad/stackwhy
|
5d5f0764ab86740fbbc3ae25170d149b388b949c
|
[
"MIT"
] | null | null | null |
from stackwhy.version import get_version
def test() -> None:
assert get_version() == "-1.-1.-1"
| 17
| 40
| 0.656863
| 15
| 102
| 4.333333
| 0.666667
| 0.307692
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.035714
| 0.176471
| 102
| 5
| 41
| 20.4
| 0.738095
| 0
| 0
| 0
| 0
| 0
| 0.078431
| 0
| 0
| 0
| 0
| 0
| 0.333333
| 1
| 0.333333
| true
| 0
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
8a1b8e369c389b5a34630895043adf43e528b743
| 45,947
|
py
|
Python
|
plugin.video.rebirth/resources/lib/modules/cleangenre.py
|
TheWardoctor/wardoctors-repo
|
893f646d9e27251ffc00ca5f918e4eb859a5c8f0
|
[
"Apache-2.0"
] | 1
|
2019-03-05T09:38:10.000Z
|
2019-03-05T09:38:10.000Z
|
plugin.video.rebirth/resources/lib/modules/cleangenre.py
|
TheWardoctor/wardoctors-repo
|
893f646d9e27251ffc00ca5f918e4eb859a5c8f0
|
[
"Apache-2.0"
] | null | null | null |
plugin.video.rebirth/resources/lib/modules/cleangenre.py
|
TheWardoctor/wardoctors-repo
|
893f646d9e27251ffc00ca5f918e4eb859a5c8f0
|
[
"Apache-2.0"
] | 1
|
2021-11-05T20:48:09.000Z
|
2021-11-05T20:48:09.000Z
|
# -*- coding: utf-8 -*-
################################################################################
# | #
# | ______________________________________________________________ #
# | :~8a.`~888a:::::::::::::::88......88:::::::::::::::;a8~".a88::| #
# | ::::~8a.`~888a::::::::::::88......88::::::::::::;a8~".a888~:::| #
# | :::::::~8a.`~888a:::::::::88......88:::::::::;a8~".a888~::::::| #
# | ::::::::::~8a.`~888a::::::88......88::::::;a8~".a888~:::::::::| #
# | :::::::::::::~8a.`~888a:::88......88:::;a8~".a888~::::::::::::| #
# | :::::::::::: :~8a.`~888a:88 .....88;a8~".a888~:::::::::::::::| #
# | :::::::::::::::::::~8a.`~888......88~".a888~::::::::::::::::::| #
# | 8888888888888888888888888888......8888888888888888888888888888| #
# | ..............................................................| #
# | ..............................................................| #
# | 8888888888888888888888888888......8888888888888888888888888888| #
# | ::::::::::::::::::a888~".a88......888a."~8;:::::::::::::::::::| #
# | :::::::::::::::a888~".a8~:88......88~888a."~8;::::::::::::::::| #
# | ::::::::::::a888~".a8~::::88......88:::~888a."~8;:::::::::::::| #
# | :::::::::a888~".a8~:::::::88......88::::::~888a."~8;::::::::::| #
# | ::::::a888~".a8~::::::::::88......88:::::::::~888a."~8;:::::::| #
# | :::a888~".a8~:::::::::::::88......88::::::::::::~888a."~8;::::| #
# | a888~".a8~::::::::::::::::88......88:::::::::::::::~888a."~8;:| #
# | #
# | Rebirth Addon #
# | Copyright (C) 2017 Cypher #
# | #
# | This program is free software: you can redistribute it and/or modify #
# | it under the terms of the GNU General Public License as published by #
# | the Free Software Foundation, either version 3 of the License, or #
# | (at your option) any later version. #
# | #
# | This program is distributed in the hope that it will be useful, #
# | but WITHOUT ANY WARRANTY; without even the implied warranty of #
# | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
# | GNU General Public License for more details. #
# | #
################################################################################
def lang(i, lang):
if lang == 'bg':
i = i.replace('Action', u'\u0415\u043a\u0448\u044a\u043d')
i = i.replace('Adventure', u'\u041f\u0440\u0438\u043a\u043b\u044e\u0447\u0435\u043d\u0438\u0435')
i = i.replace('Animation', u'\u0410\u043d\u0438\u043c\u0430\u0446\u0438\u044f')
i = i.replace('Anime', u'Anime')
i = i.replace('Biography', u'Biography')
i = i.replace('Comedy', u'\u041a\u043e\u043c\u0435\u0434\u0438\u044f')
i = i.replace('Crime', u'\u041a\u0440\u0438\u043c\u0438\u043d\u0430\u043b\u0435\u043d')
i = i.replace('Documentary', u'\u0414\u043e\u043a\u0443\u043c\u0435\u043d\u0442\u0430\u043b\u0435\u043d')
i = i.replace('Drama', u'\u0414\u0440\u0430\u043c\u0430')
i = i.replace('Family', u'\u0421\u0435\u043c\u0435\u0435\u043d')
i = i.replace('Fantasy', u'\u0424\u0435\u043d\u0442\u044a\u0437\u0438')
i = i.replace('Game-Show', u'Game-Show')
i = i.replace('History', u'\u0418\u0441\u0442\u043e\u0440\u0438\u0447\u0435\u0441\u043a\u0438')
i = i.replace('Horror', u'\u0423\u0436\u0430\u0441')
i = i.replace('Music ', u'\u041c\u0443\u0437\u0438\u043a\u0430')
i = i.replace('Musical', u'Musical')
i = i.replace('Mystery', u'\u041c\u0438\u0441\u0442\u0435\u0440\u0438\u044f')
i = i.replace('News', u'News')
i = i.replace('Reality-TV', u'Reality-TV')
i = i.replace('Romance', u'\u0420\u043e\u043c\u0430\u043d\u0441')
i = i.replace('Science Fiction', u'\u041d\u0430\u0443\u0447\u043d\u0430\u002d\u0444\u0430\u043d\u0442\u0430\u0441\u0442\u0438\u043a\u0430')
i = i.replace('Sci-Fi', u'\u041d\u0430\u0443\u0447\u043d\u0430\u002d\u0444\u0430\u043d\u0442\u0430\u0441\u0442\u0438\u043a\u0430')
i = i.replace('Sport', u'Sport')
i = i.replace('Talk-Show', u'Talk-Show')
i = i.replace('Thriller', u'\u0422\u0440\u0438\u043b\u044a\u0440')
i = i.replace('War', u'\u0412\u043e\u0435\u043d\u0435\u043d')
i = i.replace('Western', u'\u0423\u0435\u0441\u0442\u044a\u0440\u043d')
elif lang == 'cs':
i = i.replace('Action', u'\u0041\u006b\u010d\u006e\u00ed')
i = i.replace('Adventure', u'\u0044\u006f\u0062\u0072\u006f\u0064\u0072\u0075\u017e\u006e\u00fd')
i = i.replace('Animation', u'\u0041\u006e\u0069\u006d\u006f\u0076\u0061\u006e\u00fd')
i = i.replace('Anime', u'Anime')
i = i.replace('Biography', u'Biography')
i = i.replace('Comedy', u'\u004b\u006f\u006d\u0065\u0064\u0069\u0065')
i = i.replace('Crime', u'\u004b\u0072\u0069\u006d\u0069')
i = i.replace('Documentary', u'\u0044\u006f\u006b\u0075\u006d\u0065\u006e\u0074\u00e1\u0072\u006e\u00ed')
i = i.replace('Drama', u'\u0044\u0072\u0061\u006d\u0061')
i = i.replace('Family', u'\u0052\u006f\u0064\u0069\u006e\u006e\u00fd')
i = i.replace('Fantasy', u'\u0046\u0061\u006e\u0074\u0061\u0073\u0079')
i = i.replace('Game-Show', u'Game-Show')
i = i.replace('History', u'\u0048\u0069\u0073\u0074\u006f\u0072\u0069\u0063\u006b\u00fd')
i = i.replace('Horror', u'\u0048\u006f\u0072\u006f\u0072')
i = i.replace('Music ', u'\u0048\u0075\u0064\u0065\u0062\u006e\u00ed')
i = i.replace('Musical', u'Musical')
i = i.replace('Mystery', u'\u004d\u0079\u0073\u0074\u0065\u0072\u0069\u00f3\u007a\u006e\u00ed')
i = i.replace('News', u'News')
i = i.replace('Reality-TV', u'Reality-TV')
i = i.replace('Romance', u'\u0052\u006f\u006d\u0061\u006e\u0074\u0069\u0063\u006b\u00fd')
i = i.replace('Science Fiction', u'\u0056\u011b\u0064\u0065\u0063\u006b\u006f\u0066\u0061\u006e\u0074\u0061\u0073\u0074\u0069\u0063\u006b\u00fd')
i = i.replace('Sci-Fi', u'\u0056\u011b\u0064\u0065\u0063\u006b\u006f\u0066\u0061\u006e\u0074\u0061\u0073\u0074\u0069\u0063\u006b\u00fd')
i = i.replace('Sport', u'Sport')
i = i.replace('Talk-Show', u'Talk-Show')
i = i.replace('Thriller', u'\u0054\u0068\u0072\u0069\u006c\u006c\u0065\u0072')
i = i.replace('War', u'\u0056\u00e1\u006c\u0065\u010d\u006e\u00fd')
i = i.replace('Western', u'\u0057\u0065\u0073\u0074\u0065\u0072\u006e')
elif lang == 'da':
i = i.replace('Action', u'\u0041\u0063\u0074\u0069\u006f\u006e')
i = i.replace('Adventure', u'\u0045\u0076\u0065\u006e\u0074\u0079\u0072')
i = i.replace('Animation', u'\u0041\u006e\u0069\u006d\u0061\u0074\u0069\u006f\u006e')
i = i.replace('Anime', u'Anime')
i = i.replace('Biography', u'Biography')
i = i.replace('Comedy', u'\u004b\u006f\u006d\u0065\u0064\u0069\u0065')
i = i.replace('Crime', u'\u004b\u0072\u0069\u006d\u0069\u006e\u0061\u006c\u0069\u0074\u0065\u0074')
i = i.replace('Documentary', u'\u0044\u006f\u0063\u0075\u006d\u0065\u006e\u0074\u0061\u0072\u0079')
i = i.replace('Drama', u'\u0044\u0072\u0061\u006d\u0061')
i = i.replace('Family', u'\u0046\u0061\u006d\u0069\u006c\u0069\u0065')
i = i.replace('Fantasy', u'\u0046\u0061\u006e\u0074\u0061\u0073\u0079')
i = i.replace('Game-Show', u'Game-Show')
i = i.replace('History', u'\u0048\u0069\u0073\u0074\u006f\u0072\u0069\u0065 ')
i = i.replace('Horror', u'\u0047\u0079\u0073\u0065\u0072')
i = i.replace('Music ', u'\u004d\u0075\u0073\u0069\u006b')
i = i.replace('Musical', u'Musical')
i = i.replace('Mystery', u'\u004d\u0079\u0073\u0074\u0065\u0072\u0069\u0075\u006d')
i = i.replace('News', u'News')
i = i.replace('Reality-TV', u'Reality-TV')
i = i.replace('Romance', u'\u0052\u006f\u006d\u0061\u006e\u0074\u0069\u006b')
i = i.replace('Science Fiction', u'\u0053\u0063\u0069\u002d\u0066\u0069')
i = i.replace('Sci-Fi', u'\u0053\u0063\u0069\u002d\u0066\u0069')
i = i.replace('Sport', u'Sport')
i = i.replace('Talk-Show', u'Talk-Show')
i = i.replace('Thriller', u'\u0054\u0068\u0072\u0069\u006c\u006c\u0065\u0072')
i = i.replace('War', u'\u004b\u0072\u0069\u0067')
i = i.replace('Western', u'\u0057\u0065\u0073\u0074\u0065\u0072\u006e')
elif lang == 'de':
i = i.replace('Action', u'\u0041\u0063\u0074\u0069\u006f\u006e')
i = i.replace('Adventure', u'\u0041\u0062\u0065\u006e\u0074\u0065\u0075\u0065\u0072')
i = i.replace('Animation', u'\u0041\u006e\u0069\u006d\u0061\u0074\u0069\u006f\u006e')
i = i.replace('Anime', u'Anime')
i = i.replace('Biography', u'Biography')
i = i.replace('Comedy', u'\u004b\u006f\u006d\u00f6\u0064\u0069\u0065')
i = i.replace('Crime', u'\u004b\u0072\u0069\u006d\u0069')
i = i.replace('Documentary', u'\u0044\u006f\u006b\u0075\u006d\u0065\u006e\u0074\u0061\u0072\u0066\u0069\u006c\u006d')
i = i.replace('Drama', u'\u0044\u0072\u0061\u006d\u0061')
i = i.replace('Family', u'\u0046\u0061\u006d\u0069\u006c\u0069\u0065')
i = i.replace('Fantasy', u'\u0046\u0061\u006e\u0074\u0061\u0073\u0079')
i = i.replace('Game-Show', u'Game-Show')
i = i.replace('History', u'\u0048\u0069\u0073\u0074\u006f\u0072\u0069\u0065')
i = i.replace('Horror', u'\u0048\u006f\u0072\u0072\u006f\u0072')
i = i.replace('Music ', u'\u004d\u0075\u0073\u0069\u006b')
i = i.replace('Musical', u'Musical')
i = i.replace('Mystery', u'\u004d\u0079\u0073\u0074\u0065\u0072\u0079')
i = i.replace('News', u'News')
i = i.replace('Reality-TV', u'Reality-TV')
i = i.replace('Romance', u'\u004c\u006f\u0076\u0065\u0073\u0074\u006f\u0072\u0079')
i = i.replace('Science Fiction', u'\u0053\u0063\u0069\u0065\u006e\u0063\u0065 \u0046\u0069\u0063\u0074\u0069\u006f\u006e')
i = i.replace('Sci-Fi', u'\u0053\u0063\u0069\u0065\u006e\u0063\u0065 \u0046\u0069\u0063\u0074\u0069\u006f\u006e')
i = i.replace('Sport', u'Sport')
i = i.replace('Talk-Show', u'Talk-Show')
i = i.replace('Thriller', u'\u0054\u0068\u0072\u0069\u006c\u006c\u0065\u0072')
i = i.replace('War', u'\u004b\u0072\u0069\u0065\u0067\u0073\u0066\u0069\u006c\u006d')
i = i.replace('Western', u'\u0057\u0065\u0073\u0074\u0065\u0072\u006e')
elif lang == 'el':
i = i.replace('Action', u'\u0394\u03c1\u03ac\u03c3\u03b7')
i = i.replace('Adventure', u'\u03a0\u03b5\u03c1\u03b9\u03c0\u03ad\u03c4\u03b5\u03b9\u03b1')
i = i.replace('Animation', u'\u039a\u03b9\u03bd\u03bf\u03cd\u03bc\u03b5\u03bd\u03b1 \u03a3\u03c7\u03ad\u03b4\u03b9\u03b1')
i = i.replace('Anime', u'Anime')
i = i.replace('Biography', u'\u0392\u03b9\u03bf\u03b3\u03c1\u03b1\u03c6\u03b9\u03ba\u03ae')
i = i.replace('Comedy', u'\u039a\u03c9\u03bc\u03c9\u03b4\u03af\u03b1')
i = i.replace('Crime', u'\u0391\u03c3\u03c4\u03c5\u03bd\u03bf\u03bc\u03b9\u03ba\u03ae')
i = i.replace('Documentary', u'\u039d\u03c4\u03bf\u03ba\u03c5\u03bc\u03b1\u03bd\u03c4\u03ad\u03c1')
i = i.replace('Drama', u'\u0394\u03c1\u03ac\u03bc\u03b1')
i = i.replace('Family', u'\u039f\u03b9\u03ba\u03bf\u03b3\u03b5\u03bd\u03b5\u03b9\u03b1\u03ba\u03ae')
i = i.replace('Fantasy', u'\u03a6\u03b1\u03bd\u03c4\u03b1\u03c3\u03af\u03b1\u03c2')
i = i.replace('Game-Show', u'\u03a4\u03b7\u03bb\u03b5\u03c0\u03b1\u03b9\u03c7\u03bd\u03af\u03b4\u03b9')
i = i.replace('History', u'\u0399\u03c3\u03c4\u03bf\u03c1\u03b9\u03ba\u03ae')
i = i.replace('Horror', u'\u03a4\u03c1\u03cc\u03bc\u03bf\u03c5')
i = i.replace('Music ', u'\u039c\u03bf\u03c5\u03c3\u03b9\u03ba\u03ae')
i = i.replace('Musical', u'Musical')
i = i.replace('Mystery', u'\u039c\u03c5\u03c3\u03c4\u03b7\u03c1\u03af\u03bf\u03c5')
i = i.replace('News', u'\u0395\u03b9\u03b4\u03ae\u03c3\u03b5\u03b9\u03c2')
i = i.replace('Reality-TV', u'\u03a1\u03b9\u03ac\u03bb\u03b9\u03c4\u03c5')
i = i.replace('Romance', u'\u03a1\u03bf\u03bc\u03b1\u03bd\u03c4\u03b9\u03ba\u03ae')
i = i.replace('Science Fiction', u'\u0395\u03c0\u002e \u03a6\u03b1\u03bd\u03c4\u03b1\u03c3\u03af\u03b1\u03c2')
i = i.replace('Sci-Fi', u'\u0395\u03c0\u002e \u03a6\u03b1\u03bd\u03c4\u03b1\u03c3\u03af\u03b1\u03c2')
i = i.replace('Sport', u'\u0391\u03b8\u03bb\u03b7\u03c4\u03b9\u03ba\u03ae')
i = i.replace('Talk-Show', u'Talk-Show')
i = i.replace('Thriller', u'\u0398\u03c1\u03af\u03bb\u03b5\u03c1')
i = i.replace('War', u'\u03a0\u03bf\u03bb\u03b5\u03bc\u03b9\u03ba\u03ae')
i = i.replace('Western', u'\u0393\u03bf\u03c5\u03ad\u03c3\u03c4\u03b5\u03c1\u03bd')
elif lang == 'es':
i = i.replace('Action', u'\u0041\u0063\u0063\u0069\u00f3\u006e')
i = i.replace('Adventure', u'\u0041\u0076\u0065\u006e\u0074\u0075\u0072\u0061')
i = i.replace('Animation', u'\u0041\u006e\u0069\u006d\u0061\u0063\u0069\u00f3\u006e')
i = i.replace('Anime', u'Anime')
i = i.replace('Biography', u'Biography')
i = i.replace('Comedy', u'\u0043\u006f\u006d\u0065\u0064\u0069\u0061')
i = i.replace('Crime', u'\u0043\u0072\u0069\u006d\u0065\u006e')
i = i.replace('Documentary', u'\u0044\u006f\u0063\u0075\u006d\u0065\u006e\u0074\u0061\u006c')
i = i.replace('Drama', u'\u0044\u0072\u0061\u006d\u0061')
i = i.replace('Family', u'\u0046\u0061\u006d\u0069\u006c\u0069\u0061')
i = i.replace('Fantasy', u'\u0046\u0061\u006e\u0074\u0061\u0073\u00ed\u0061')
i = i.replace('Game-Show', u'Game-Show')
i = i.replace('History', u'\u0048\u0069\u0073\u0074\u006f\u0072\u0069\u0061')
i = i.replace('Horror', u'\u0054\u0065\u0072\u0072\u006f\u0072')
i = i.replace('Music ', u'\u004d\u00fa\u0073\u0069\u0063\u0061')
i = i.replace('Musical', u'Musical')
i = i.replace('Mystery', u'\u004d\u0069\u0073\u0074\u0065\u0072\u0069\u006f')
i = i.replace('News', u'News')
i = i.replace('Reality-TV', u'Reality-TV')
i = i.replace('Romance', u'\u0052\u006f\u006d\u0061\u006e\u0063\u0065')
i = i.replace('Science Fiction', u'\u0043\u0069\u0065\u006e\u0063\u0069\u0061 \u0066\u0069\u0063\u0063\u0069\u00f3\u006e')
i = i.replace('Sci-Fi', u'\u0043\u0069\u0065\u006e\u0063\u0069\u0061 \u0066\u0069\u0063\u0063\u0069\u00f3\u006e')
i = i.replace('Sport', u'Sport')
i = i.replace('Talk-Show', u'Talk-Show')
i = i.replace('Thriller', u'\u0053\u0075\u0073\u0070\u0065\u006e\u0073\u0065')
i = i.replace('War', u'\u0047\u0075\u0065\u0072\u0072\u0061')
i = i.replace('Western', u'\u0057\u0065\u0073\u0074\u0065\u0072\u006e')
elif lang == 'fr':
i = i.replace('Action', u'\u0041\u0063\u0074\u0069\u006f\u006e')
i = i.replace('Adventure', u'\u0041\u0076\u0065\u006e\u0074\u0075\u0072\u0065')
i = i.replace('Animation', u'\u0041\u006e\u0069\u006d\u0061\u0074\u0069\u006f\u006e')
i = i.replace('Anime', u'Anime')
i = i.replace('Biography', u'Biography')
i = i.replace('Comedy', u'\u0043\u006f\u006d\u00e9\u0064\u0069\u0065')
i = i.replace('Crime', u'\u0043\u0072\u0069\u006d\u0065')
i = i.replace('Documentary', u'\u0044\u006f\u0063\u0075\u006d\u0065\u006e\u0074\u0061\u0069\u0072\u0065')
i = i.replace('Drama', u'\u0044\u0072\u0061\u006d\u0065')
i = i.replace('Family', u'\u0046\u0061\u006d\u0069\u006c\u0069\u0061\u006c')
i = i.replace('Fantasy', u'\u0046\u0061\u006e\u0074\u0061\u0073\u0074\u0069\u0071\u0075\u0065')
i = i.replace('Game-Show', u'Game-Show')
i = i.replace('History', u'\u0048\u0069\u0073\u0074\u006f\u0069\u0072\u0065')
i = i.replace('Horror', u'\u0048\u006f\u0072\u0072\u0065\u0075\u0072')
i = i.replace('Music ', u'\u004d\u0075\u0073\u0069\u0071\u0075\u0065')
i = i.replace('Musical', u'Musical')
i = i.replace('Mystery', u'\u004d\u0079\u0073\u0074\u00e8\u0072\u0065')
i = i.replace('News', u'News')
i = i.replace('Reality-TV', u'Reality-TV')
i = i.replace('Romance', u'\u0052\u006f\u006d\u0061\u006e\u0063\u0065')
i = i.replace('Science Fiction', u'\u0053\u0063\u0069\u0065\u006e\u0063\u0065\u002d\u0046\u0069\u0063\u0074\u0069\u006f\u006e')
i = i.replace('Sci-Fi', u'\u0053\u0063\u0069\u0065\u006e\u0063\u0065\u002d\u0046\u0069\u0063\u0074\u0069\u006f\u006e')
i = i.replace('Sport', u'Sport')
i = i.replace('Talk-Show', u'Talk-Show')
i = i.replace('Thriller', u'\u0054\u0068\u0072\u0069\u006c\u006c\u0065\u0072')
i = i.replace('War', u'\u0047\u0075\u0065\u0072\u0072\u0065')
i = i.replace('Western', u'\u0057\u0065\u0073\u0074\u0065\u0072\u006e')
elif lang == 'he':
i = i.replace('Action', u'\u05d0\u05e7\u05e9\u05df')
i = i.replace('Adventure', u'\u05d4\u05e8\u05e4\u05ea\u05e7\u05d0\u05d5\u05ea')
i = i.replace('Animation', u'\u05d0\u05e0\u05d9\u05de\u05e6\u05d9\u05d4')
i = i.replace('Anime', u'Anime')
i = i.replace('Biography', u'Biography')
i = i.replace('Comedy', u'\u05e7\u05d5\u05de\u05d3\u05d9\u05d4')
i = i.replace('Crime', u'\u05e4\u05e9\u05e2')
i = i.replace('Documentary', u'\u05d3\u05d5\u05e7\u05d5\u05de\u05e0\u05d8\u05e8\u05d9')
i = i.replace('Drama', u'\u05d3\u05e8\u05de\u05d4')
i = i.replace('Family', u'\u05de\u05e9\u05e4\u05d7\u05d4')
i = i.replace('Fantasy', u'\u05e4\u05e0\u05d8\u05d6\u05d9\u05d4')
i = i.replace('Game-Show', u'Game-Show')
i = i.replace('History', u'\u05d4\u05e1\u05d8\u05d5\u05e8\u05d9\u05d4')
i = i.replace('Horror', u'\u05d0\u05d9\u05de\u05d4')
i = i.replace('Music ', u'\u05de\u05d5\u05e1\u05d9\u05e7\u05d4')
i = i.replace('Musical', u'Musical')
i = i.replace('Mystery', u'\u05de\u05e1\u05ea\u05d5\u05e8\u05d9\u05df')
i = i.replace('News', u'News')
i = i.replace('Reality-TV', u'Reality-TV')
i = i.replace('Romance', u'\u05e8\u05d5\u05de\u05e0\u05d8\u05d9')
i = i.replace('Science Fiction', u'\u05de\u05d3\u05e2 \u05d1\u05d3\u05d9\u05d5\u05e0\u05d9')
i = i.replace('Sci-Fi', u'\u05de\u05d3\u05e2 \u05d1\u05d3\u05d9\u05d5\u05e0\u05d9')
i = i.replace('Sport', u'Sport')
i = i.replace('Talk-Show', u'Talk-Show')
i = i.replace('Thriller', u'\u05de\u05d5\u05ea\u05d7\u05df')
i = i.replace('War', u'\u05de\u05dc\u05d7\u05de\u05d4')
i = i.replace('Western', u'\u05de\u05e2\u05e8\u05d1\u05d5\u05df')
elif lang == 'hu':
i = i.replace('Action', u'\u0041\u006b\u0063\u0069\u00f3')
i = i.replace('Adventure', u'\u004b\u0061\u006c\u0061\u006e\u0064')
i = i.replace('Animation', u'\u0041\u006e\u0069\u006d\u00e1\u0063\u0069\u00f3\u0073')
i = i.replace('Anime', u'Anime')
i = i.replace('Biography', u'Biography')
i = i.replace('Comedy', u'\u0056\u00ed\u0067\u006a\u00e1\u0074\u00e9\u006b')
i = i.replace('Crime', u'\u0042\u0171\u006e\u00fc\u0067\u0079\u0069')
i = i.replace('Documentary', u'\u0044\u006f\u006b\u0075\u006d\u0065\u006e\u0074\u0075\u006d')
i = i.replace('Drama', u'\u0044\u0072\u00e1\u006d\u0061')
i = i.replace('Family', u'\u0043\u0073\u0061\u006c\u00e1\u0064\u0069')
i = i.replace('Fantasy', u'\u0046\u0061\u006e\u0074\u0061\u0073\u0079')
i = i.replace('Game-Show', u'Game-Show')
i = i.replace('History', u'\u0054\u00f6\u0072\u0074\u00e9\u006e\u0065\u006c\u006d\u0069')
i = i.replace('Horror', u'\u0048\u006f\u0072\u0072\u006f\u0072')
i = i.replace('Music ', u'\u005a\u0065\u006e\u0065\u0069')
i = i.replace('Musical', u'Musical')
i = i.replace('Mystery', u'\u0052\u0065\u006a\u0074\u00e9\u006c\u0079')
i = i.replace('News', u'News')
i = i.replace('Reality-TV', u'Reality-TV')
i = i.replace('Romance', u'\u0052\u006f\u006d\u0061\u006e\u0074\u0069\u006b\u0075\u0073')
i = i.replace('Science Fiction', u'\u0053\u0063\u0069\u002d\u0046\u0069')
i = i.replace('Sci-Fi', u'\u0053\u0063\u0069\u002d\u0046\u0069')
i = i.replace('Sport', u'Sport')
i = i.replace('Talk-Show', u'Talk-Show')
i = i.replace('Thriller', u'\u0054\u0068\u0072\u0069\u006c\u006c\u0065\u0072')
i = i.replace('War', u'\u0048\u00e1\u0062\u006f\u0072\u00fa\u0073')
i = i.replace('Western', u'\u0057\u0065\u0073\u0074\u0065\u0072\u006e')
elif lang == 'it':
i = i.replace('Action', u'\u0041\u007a\u0069\u006f\u006e\u0065')
i = i.replace('Adventure', u'\u0041\u0076\u0076\u0065\u006e\u0074\u0075\u0072\u0061')
i = i.replace('Animation', u'\u0041\u006e\u0069\u006d\u0061\u007a\u0069\u006f\u006e\u0065')
i = i.replace('Anime', u'Anime')
i = i.replace('Biography', u'Biography')
i = i.replace('Comedy', u'\u0043\u006f\u006d\u006d\u0065\u0064\u0069\u0061')
i = i.replace('Crime', u'\u0043\u0072\u0069\u006d\u0065')
i = i.replace('Documentary', u'\u0044\u006f\u0063\u0075\u006d\u0065\u006e\u0074\u0061\u0072\u0069\u006f')
i = i.replace('Drama', u'\u0044\u0072\u0061\u006d\u006d\u0061')
i = i.replace('Family', u'\u0046\u0061\u006d\u0069\u0067\u006c\u0069\u0061')
i = i.replace('Fantasy', u'\u0046\u0061\u006e\u0074\u0061\u0073\u0079')
i = i.replace('Game-Show', u'Game-Show')
i = i.replace('History', u'\u0053\u0074\u006f\u0072\u0069\u0061')
i = i.replace('Horror', u'\u0048\u006f\u0072\u0072\u006f\u0072')
i = i.replace('Music ', u'\u004d\u0075\u0073\u0069\u0063\u0061')
i = i.replace('Musical', u'Musical')
i = i.replace('Mystery', u'\u004d\u0069\u0073\u0074\u0065\u0072\u006f')
i = i.replace('News', u'News')
i = i.replace('Reality-TV', u'Reality-TV')
i = i.replace('Romance', u'\u0052\u006f\u006d\u0061\u006e\u0063\u0065')
i = i.replace('Science Fiction', u'\u0046\u0061\u006e\u0074\u0061\u0073\u0063\u0069\u0065\u006e\u007a\u0061')
i = i.replace('Sci-Fi', u'\u0046\u0061\u006e\u0074\u0061\u0073\u0063\u0069\u0065\u006e\u007a\u0061')
i = i.replace('Sport', u'Sport')
i = i.replace('Talk-Show', u'Talk-Show')
i = i.replace('Thriller', u'\u0054\u0068\u0072\u0069\u006c\u006c\u0065\u0072')
i = i.replace('War', u'\u0047\u0075\u0065\u0072\u0072\u0061')
i = i.replace('Western', u'\u0057\u0065\u0073\u0074\u0065\u0072\u006e')
elif lang == 'ja':
i = i.replace('Action', u'\u30a2\u30af\u30b7\u30e7\u30f3')
i = i.replace('Adventure', u'\u30a2\u30c9\u30d9\u30f3\u30c1\u30e3\u30fc')
i = i.replace('Animation', u'\u30a2\u30cb\u30e1\u30fc\u30b7\u30e7\u30f3')
i = i.replace('Anime', u'\u30a2\u30cb\u30e1')
i = i.replace('Biography', u'Biography')
i = i.replace('Comedy', u'\u30b3\u30e1\u30c7\u30a3')
i = i.replace('Crime', u'\u72af\u7f6a')
i = i.replace('Documentary', u'\u30c9\u30ad\u30e5\u30e1\u30f3\u30bf\u30ea\u30fc')
i = i.replace('Drama', u'\u30c9\u30e9\u30de')
i = i.replace('Family', u'\u30d5\u30a1\u30df\u30ea\u30fc')
i = i.replace('Fantasy', u'\u30d5\u30a1\u30f3\u30bf\u30b8\u30fc')
i = i.replace('Game-Show', u'Game-Show')
i = i.replace('History', u'\u5c65\u6b74')
i = i.replace('Horror', u'\u30db\u30e9\u30fc')
i = i.replace('Music ', u'\u97f3\u697d')
i = i.replace('Musical', u'Musical')
i = i.replace('Mystery', u'\u8b0e')
i = i.replace('News', u'News')
i = i.replace('Reality-TV', u'Reality-TV')
i = i.replace('Romance', u'\u30ed\u30de\u30f3\u30b9')
i = i.replace('Science Fiction', u'\u30b5\u30a4\u30a8\u30f3\u30b9\u30d5\u30a3\u30af\u30b7\u30e7\u30f3')
i = i.replace('Sci-Fi', u'\u30b5\u30a4\u30a8\u30f3\u30b9\u30d5\u30a3\u30af\u30b7\u30e7\u30f3')
i = i.replace('Sport', u'Sport')
i = i.replace('Talk-Show', u'Talk-Show')
i = i.replace('Thriller', u'\u30b9\u30ea\u30e9\u30fc')
i = i.replace('War', u'\u6226\u4e89')
i = i.replace('Western', u'\u897f\u6d0b')
elif lang == 'ko':
i = i.replace('Action', u'\uc561\uc158')
i = i.replace('Adventure', u'\ubaa8\ud5d8')
i = i.replace('Animation', u'\uc560\ub2c8\uba54\uc774\uc158')
i = i.replace('Anime', u'Anime')
i = i.replace('Biography', u'Biography')
i = i.replace('Comedy', u'\ucf54\ubbf8\ub514')
i = i.replace('Crime', u'\ubc94\uc8c4')
i = i.replace('Documentary', u'\ub2e4\ud050\uba58\ud130\ub9ac')
i = i.replace('Drama', u'\ub4dc\ub77c\ub9c8')
i = i.replace('Family', u'\uac00\uc871')
i = i.replace('Fantasy', u'\ud310\ud0c0\uc9c0')
i = i.replace('Game-Show', u'Game-Show')
i = i.replace('History', u'\uc5ed\uc0ac')
i = i.replace('Horror', u'\uacf5\ud3ec')
i = i.replace('Music ', u'\uc74c\uc545')
i = i.replace('Musical', u'Musical')
i = i.replace('Mystery', u'\ubbf8\uc2a4\ud130\ub9ac')
i = i.replace('News', u'News')
i = i.replace('Reality-TV', u'Reality-TV')
i = i.replace('Romance', u'\ub85c\ub9e8\uc2a4')
i = i.replace('Science Fiction', u'\u0053\u0046')
i = i.replace('Sci-Fi', u'\u0053\u0046')
i = i.replace('Sport', u'Sport')
i = i.replace('Talk-Show', u'Talk-Show')
i = i.replace('Thriller', u'\uc2a4\ub9b4\ub7ec')
i = i.replace('War', u'\uc804\uc7c1')
i = i.replace('Western', u'\uc11c\ubd80')
elif lang == 'nl':
i = i.replace('Action', u'\u0041\u0063\u0074\u0069\u0065')
i = i.replace('Adventure', u'\u0041\u0076\u006f\u006e\u0074\u0075\u0075\u0072')
i = i.replace('Animation', u'\u0041\u006e\u0069\u006d\u0061\u0074\u0069\u0065')
i = i.replace('Anime', u'Anime')
i = i.replace('Biography', u'Biography')
i = i.replace('Comedy', u'\u004b\u006f\u006d\u0065\u0064\u0069\u0065')
i = i.replace('Crime', u'\u004d\u0069\u0073\u0064\u0061\u0061\u0064')
i = i.replace('Documentary', u'\u0044\u006f\u0063\u0075\u006d\u0065\u006e\u0074\u0061\u0069\u0072\u0065')
i = i.replace('Drama', u'\u0044\u0072\u0061\u006d\u0061')
i = i.replace('Family', u'\u0046\u0061\u006d\u0069\u006c\u0069\u0065')
i = i.replace('Fantasy', u'\u0046\u0061\u006e\u0074\u0061\u0073\u0069\u0065')
i = i.replace('Game-Show', u'Game-Show')
i = i.replace('History', u'\u0048\u0069\u0073\u0074\u006f\u0072\u0069\u0073\u0063\u0068')
i = i.replace('Horror', u'\u0048\u006f\u0072\u0072\u006f\u0072')
i = i.replace('Music ', u'\u004d\u0075\u007a\u0069\u0065\u006b')
i = i.replace('Musical', u'Musical')
i = i.replace('Mystery', u'\u004d\u0079\u0073\u0074\u0065\u0072\u0069\u0065')
i = i.replace('News', u'News')
i = i.replace('Reality-TV', u'Reality-TV')
i = i.replace('Romance', u'\u0052\u006f\u006d\u0061\u006e\u0074\u0069\u0065\u006b')
i = i.replace('Science Fiction', u'\u0053\u0063\u0069\u0065\u006e\u0063\u0065\u0066\u0069\u0063\u0074\u0069\u006f\u006e')
i = i.replace('Sci-Fi', u'\u0053\u0063\u0069\u0065\u006e\u0063\u0065\u0066\u0069\u0063\u0074\u0069\u006f\u006e')
i = i.replace('Sport', u'Sport')
i = i.replace('Talk-Show', u'Talk-Show')
i = i.replace('Thriller', u'\u0054\u0068\u0072\u0069\u006c\u006c\u0065\u0072')
i = i.replace('War', u'\u004f\u006f\u0072\u006c\u006f\u0067')
i = i.replace('Western', u'\u0057\u0065\u0073\u0074\u0065\u0072\u006e')
elif lang == 'pl':
i = i.replace('Action', u'\u0041\u006b\u0063\u006a\u0061')
i = i.replace('Adventure', u'\u0050\u0072\u007a\u0079\u0067\u006f\u0064\u006f\u0077\u0079')
i = i.replace('Animation', u'\u0041\u006e\u0069\u006d\u0061\u0063\u006a\u0061')
i = i.replace('Anime', u'Anime')
i = i.replace('Biography', u'Biography')
i = i.replace('Comedy', u'\u004b\u006f\u006d\u0065\u0064\u0069\u0061')
i = i.replace('Crime', u'\u004b\u0072\u0079\u006d\u0069\u006e\u0061\u0142')
i = i.replace('Documentary', u'\u0044\u006f\u006b\u0075\u006d\u0065\u006e\u0074\u0061\u006c\u006e\u0079')
i = i.replace('Drama', u'\u0044\u0072\u0061\u006d\u0061\u0074')
i = i.replace('Family', u'\u0046\u0061\u006d\u0069\u006c\u0069\u006a\u006e\u0079')
i = i.replace('Fantasy', u'\u0046\u0061\u006e\u0074\u0061\u0073\u0079')
i = i.replace('Game-Show', u'Game-Show')
i = i.replace('History', u'\u0048\u0069\u0073\u0074\u006f\u0072\u0079\u0063\u007a\u006e\u0079')
i = i.replace('Horror', u'\u0048\u006f\u0072\u0072\u006f\u0072')
i = i.replace('Music ', u'\u004d\u0075\u007a\u0079\u0063\u007a\u006e\u0079')
i = i.replace('Musical', u'Musical')
i = i.replace('Mystery', u'\u0054\u0061\u006a\u0065\u006d\u006e\u0069\u0063\u0061')
i = i.replace('News', u'News')
i = i.replace('Reality-TV', u'Reality-TV')
i = i.replace('Romance', u'\u0052\u006f\u006d\u0061\u006e\u0073')
i = i.replace('Science Fiction', u'\u0053\u0063\u0069\u002d\u0046\u0069')
i = i.replace('Sci-Fi', u'\u0053\u0063\u0069\u002d\u0046\u0069')
i = i.replace('Sport', u'Sport')
i = i.replace('Talk-Show', u'Talk-Show')
i = i.replace('Thriller', u'\u0054\u0068\u0072\u0069\u006c\u006c\u0065\u0072')
i = i.replace('War', u'\u0057\u006f\u006a\u0065\u006e\u006e\u0079')
i = i.replace('Western', u'\u0057\u0065\u0073\u0074\u0065\u0072\u006e')
elif lang == 'pt':
i = i.replace('Action', u'\u0041\u00e7\u00e3\u006f')
i = i.replace('Adventure', u'\u0041\u0076\u0065\u006e\u0074\u0075\u0072\u0061')
i = i.replace('Animation', u'\u0041\u006e\u0069\u006d\u0061\u00e7\u00e3\u006f')
i = i.replace('Anime', u'Anime')
i = i.replace('Biography', u'Biography')
i = i.replace('Comedy', u'\u0043\u006f\u006d\u00e9\u0064\u0069\u0061')
i = i.replace('Crime', u'\u0043\u0072\u0069\u006d\u0065')
i = i.replace('Documentary', u'\u0044\u006f\u0063\u0075\u006d\u0065\u006e\u0074\u00e1\u0072\u0069\u006f')
i = i.replace('Drama', u'\u0044\u0072\u0061\u006d\u0061')
i = i.replace('Family', u'\u0046\u0061\u006d\u00ed\u006c\u0069\u0061')
i = i.replace('Fantasy', u'\u0046\u0061\u006e\u0074\u0061\u0073\u0069\u0061')
i = i.replace('Game-Show', u'Game-Show')
i = i.replace('History', u'\u0048\u0069\u0073\u0074\u00f3\u0072\u0069\u0061')
i = i.replace('Horror', u'\u0054\u0065\u0072\u0072\u006f\u0072')
i = i.replace('Music ', u'\u004d\u00fa\u0073\u0069\u0063\u0061')
i = i.replace('Musical', u'Musical')
i = i.replace('Mystery', u'\u004d\u0069\u0073\u0074\u00e9\u0072\u0069\u006f')
i = i.replace('News', u'News')
i = i.replace('Reality-TV', u'Reality-TV')
i = i.replace('Romance', u'\u0052\u006f\u006d\u0061\u006e\u0063\u0065')
i = i.replace('Science Fiction', u'\u0046\u0069\u0063\u00e7\u00e3\u006f \u0063\u0069\u0065\u006e\u0074\u00ed\u0066\u0069\u0063\u0061')
i = i.replace('Sci-Fi', u'\u0046\u0069\u0063\u00e7\u00e3\u006f \u0063\u0069\u0065\u006e\u0074\u00ed\u0066\u0069\u0063\u0061')
i = i.replace('Sport', u'Sport')
i = i.replace('Talk-Show', u'Talk-Show')
i = i.replace('Thriller', u'\u0054\u0068\u0072\u0069\u006c\u006c\u0065\u0072')
i = i.replace('War', u'\u0047\u0075\u0065\u0072\u0072\u0061')
i = i.replace('Western', u'\u0046\u0061\u0072\u006f\u0065\u0073\u0074\u0065')
elif lang == 'ro':
i = i.replace('Action', u'\u0041\u0063\u021b\u0069\u0075\u006e\u0065')
i = i.replace('Adventure', u'\u0041\u0076\u0065\u006e\u0074\u0075\u0072\u0069')
i = i.replace('Animation', u'\u0041\u006e\u0069\u006d\u0061\u0163\u0069\u0065')
i = i.replace('Anime', u'Anime')
i = i.replace('Biography', u'Biography')
i = i.replace('Comedy', u'\u0043\u006f\u006d\u0065\u0064\u0069\u0065')
i = i.replace('Crime', u'\u0043\u0072\u0069\u006d\u0103')
i = i.replace('Documentary', u'\u0044\u006f\u0063\u0075\u006d\u0065\u006e\u0074\u0061\u0072')
i = i.replace('Drama', u'\u0044\u0072\u0061\u006d\u0103')
i = i.replace('Family', u'\u0046\u0061\u006d\u0069\u006c\u0069\u0065')
i = i.replace('Fantasy', u'\u0046\u0061\u006e\u0074\u0061\u0073\u0079')
i = i.replace('Game-Show', u'Game-Show')
i = i.replace('History', u'\u0049\u0073\u0074\u006f\u0072\u0069\u0063')
i = i.replace('Horror', u'\u0048\u006f\u0072\u0072\u006f\u0072')
i = i.replace('Music ', u'\u004d\u0075\u007a\u0069\u0063\u0103')
i = i.replace('Musical', u'Musical')
i = i.replace('Mystery', u'\u004d\u0069\u0073\u0074\u0065\u0072')
i = i.replace('News', u'News')
i = i.replace('Reality-TV', u'Reality-TV')
i = i.replace('Romance', u'\u0052\u006f\u006d\u0061\u006e\u0074\u0069\u0063')
i = i.replace('Science Fiction', u'\u0053\u0046')
i = i.replace('Sci-Fi', u'\u0053\u0046')
i = i.replace('Sport', u'Sport')
i = i.replace('Talk-Show', u'Talk-Show')
i = i.replace('Thriller', u'\u0054\u0068\u0072\u0069\u006c\u006c\u0065\u0072')
i = i.replace('War', u'\u0052\u0103\u007a\u0062\u006f\u0069')
i = i.replace('Western', u'\u0057\u0065\u0073\u0074\u0065\u0072\u006e')
elif lang == 'ru':
i = i.replace('Action', u'\u0431\u043e\u0435\u0432\u0438\u043a')
i = i.replace('Adventure', u'\u043f\u0440\u0438\u043a\u043b\u044e\u0447\u0435\u043d\u0438\u044f')
i = i.replace('Animation', u'\u043c\u0443\u043b\u044c\u0442\u0444\u0438\u043b\u044c\u043c')
i = i.replace('Anime', u'Anime')
i = i.replace('Biography', u'Biography')
i = i.replace('Comedy', u'\u043a\u043e\u043c\u0435\u0434\u0438\u044f')
i = i.replace('Crime', u'\u043a\u0440\u0438\u043c\u0438\u043d\u0430\u043b')
i = i.replace('Documentary', u'\u0434\u043e\u043a\u0443\u043c\u0435\u043d\u0442\u0430\u043b\u044c\u043d\u044b\u0439')
i = i.replace('Drama', u'\u0434\u0440\u0430\u043c\u0430')
i = i.replace('Family', u'\u0441\u0435\u043c\u0435\u0439\u043d\u044b\u0439')
i = i.replace('Fantasy', u'\u0444\u044d\u043d\u0442\u0435\u0437\u0438')
i = i.replace('Game-Show', u'Game-Show')
i = i.replace('History', u'\u0438\u0441\u0442\u043e\u0440\u0438\u044f')
i = i.replace('Horror', u'\u0443\u0436\u0430\u0441\u044b')
i = i.replace('Music ', u'\u043c\u0443\u0437\u044b\u043a\u0430')
i = i.replace('Musical', u'Musical')
i = i.replace('Mystery', u'\u0434\u0435\u0442\u0435\u043a\u0442\u0438\u0432')
i = i.replace('News', u'News')
i = i.replace('Reality-TV', u'Reality-TV')
i = i.replace('Romance', u'\u043c\u0435\u043b\u043e\u0434\u0440\u0430\u043c\u0430')
i = i.replace('Science Fiction', u'\u0444\u0430\u043d\u0442\u0430\u0441\u0442\u0438\u043a\u0430')
i = i.replace('Sci-Fi', u'\u0444\u0430\u043d\u0442\u0430\u0441\u0442\u0438\u043a\u0430')
i = i.replace('Sport', u'Sport')
i = i.replace('Talk-Show', u'Talk-Show')
i = i.replace('Thriller', u'\u0442\u0440\u0438\u043b\u043b\u0435\u0440')
i = i.replace('War', u'\u0432\u043e\u0435\u043d\u043d\u044b\u0439')
i = i.replace('Western', u'\u0432\u0435\u0441\u0442\u0435\u0440\u043d')
elif lang == 'sl':
i = i.replace('Action', u'\u0041\u006b\u0063\u0069\u006a\u0061')
i = i.replace('Adventure', u'\u0041\u0076\u0061\u006e\u0074\u0075\u0072\u0061')
i = i.replace('Animation', u'\u0041\u006e\u0069\u006d\u0061\u0063\u0069\u006a\u0061')
i = i.replace('Anime', u'Anime')
i = i.replace('Biography', u'Biography')
i = i.replace('Comedy', u'\u041a\u043e\u043c\u0435\u0064\u0069\u006a\u0061')
i = i.replace('Crime', u'\u041a\u0072\u0069\u006d\u0069\u006e\u0061\u006c\u006e\u0069')
i = i.replace('Documentary', u'\u0044\u006f\u006b\u0075\u006d\u0065\u006e\u0074\u0061\u0072\u006e\u0069')
i = i.replace('Drama', u'\u0044\u0072\u0430\u043c\u0430')
i = i.replace('Family', u'\u0044\u0072\u0075\u017e\u0069\u006e\u0073\u006b\u0069')
i = i.replace('Fantasy', u'\u0046\u0061\u006e\u0074\u0061\u0073\u0074\u0069\u006b\u0061')
i = i.replace('Game-Show', u'Game-Show')
i = i.replace('History', u'\u005a\u0067\u006f\u0064\u006f\u0076\u0069\u006e\u0073\u006b\u0069')
i = i.replace('Horror', u'\u0047\u0072\u006f\u007a\u006c\u006a\u0069\u0076\u006b\u0061')
i = i.replace('Music ', u'\u0047\u006c\u0061\u007a\u0062\u0065\u006e\u0069')
i = i.replace('Musical', u'Musical')
i = i.replace('Mystery', u'\u004d\u0069\u0073\u0074\u0065\u0072\u0069\u006a\u0061')
i = i.replace('News', u'News')
i = i.replace('Reality-TV', u'Reality-TV')
i = i.replace('Romance', u'\u0052\u006f\u006d\u0061\u006e\u0074\u0069\u006b\u0061')
i = i.replace('Science Fiction', u'\u005a\u006e\u0061\u006e\u0073\u0074\u0076\u0065\u006e\u0061 \u0066\u0061\u006e\u0074\u0061\u0073\u0074\u0069\u006b\u0061')
i = i.replace('Sci-Fi', u'\u005a\u006e\u0061\u006e\u0073\u0074\u0076\u0065\u006e\u0061 \u0066\u0061\u006e\u0074\u0061\u0073\u0074\u0069\u006b\u0061')
i = i.replace('Sport', u'Sport')
i = i.replace('Talk-Show', u'Talk-Show')
i = i.replace('Thriller', u'\u0422\u0072\u0069\u006c\u0065\u0072')
i = i.replace('War', u'\u0056\u006f\u006a\u006e\u006f\u002d\u0070\u006f\u006c\u0069\u0074\u0069\u010d\u006e\u0069')
i = i.replace('Western', u'\u0057\u0065\u0073\u0074\u0065\u0072\u006e')
elif lang == 'sr':
i = i.replace('Action', u'\u0410\u043a\u0446\u0438\u043e\u043d\u0438')
i = i.replace('Adventure', u'\u0410\u0432\u0430\u043d\u0442\u0443\u0440\u0438\u0441\u0442\u0438\u0447\u043a\u0438')
i = i.replace('Animation', u'\u0426\u0440\u0442\u0430\u043d\u0438')
i = i.replace('Anime', u'Anime')
i = i.replace('Biography', u'Biography')
i = i.replace('Comedy', u'\u041a\u043e\u043c\u0435\u0434\u0438\u0458\u0430')
i = i.replace('Crime', u'\u041a\u0440\u0438\u043c\u0438')
i = i.replace('Documentary', u'\u0414\u043e\u043a\u0443\u043c\u0435\u043d\u0442\u0430\u0440\u043d\u0438')
i = i.replace('Drama', u'\u0414\u0440\u0430\u043c\u0430')
i = i.replace('Family', u'\u041f\u043e\u0440\u043e\u0434\u0438\u0447\u043d\u0438')
i = i.replace('Fantasy', u'\u0424\u0430\u043d\u0442\u0430\u0441\u0442\u0438\u043a\u0430')
i = i.replace('Game-Show', u'Game-Show')
i = i.replace('History', u'\u0418\u0441\u0442\u043e\u0440\u0438\u0458\u0441\u043a\u0438')
i = i.replace('Horror', u'\u0425\u043e\u0440\u043e\u0440')
i = i.replace('Music ', u'\u041c\u0443\u0437\u0438\u0447\u043a\u0438')
i = i.replace('Musical', u'Musical')
i = i.replace('Mystery', u'\u041c\u0438\u0441\u0442\u0435\u0440\u0438\u0458\u0430')
i = i.replace('News', u'News')
i = i.replace('Reality-TV', u'Reality-TV')
i = i.replace('Romance', u'\u0409\u0443\u0431\u0430\u0432\u043d\u0438')
i = i.replace('Science Fiction', u'\u041d\u0430\u0443\u0447\u043d\u0430 \u0444\u0430\u043d\u0442\u0430\u0441\u0442\u0438\u043a\u0430')
i = i.replace('Sci-Fi', u'\u041d\u0430\u0443\u0447\u043d\u0430 \u0444\u0430\u043d\u0442\u0430\u0441\u0442\u0438\u043a\u0430')
i = i.replace('Sport', u'Sport')
i = i.replace('Talk-Show', u'Talk-Show')
i = i.replace('Thriller', u'\u0422\u0440\u0438\u043b\u0435\u0440')
i = i.replace('War', u'\u0420\u0430\u0442\u043d\u0438')
i = i.replace('Western', u'\u0412\u0435\u0441\u0442\u0435\u0440\u043d')
elif lang == 'sv':
i = i.replace('Action', u'\u0041\u0063\u0074\u0069\u006f\u006e')
i = i.replace('Adventure', u'\u00c4\u0076\u0065\u006e\u0074\u0079\u0072')
i = i.replace('Animation', u'\u0041\u006e\u0069\u006d\u0065\u0072\u0061\u0074')
i = i.replace('Anime', u'Anime')
i = i.replace('Biography', u'Biography')
i = i.replace('Comedy', u'\u004b\u006f\u006d\u0065\u0064\u0069')
i = i.replace('Crime', u'\u004b\u0072\u0069\u006d\u0069\u006e\u0061\u006c')
i = i.replace('Documentary', u'\u0044\u006f\u006b\u0075\u006d\u0065\u006e\u0074\u00e4\u0072')
i = i.replace('Drama', u'\u0044\u0072\u0061\u006d\u0061')
i = i.replace('Family', u'\u0046\u0061\u006d\u0069\u006c\u006a')
i = i.replace('Fantasy', u'\u0046\u0061\u006e\u0074\u0061\u0073\u0079')
i = i.replace('Game-Show', u'Game-Show')
i = i.replace('History', u'\u0048\u0069\u0073\u0074\u006f\u0072\u0069\u0073\u006b')
i = i.replace('Horror', u'\u0053\u006b\u0072\u00e4\u0063\u006b')
i = i.replace('Music ', u'\u004d\u0075\u0073\u0069\u0063')
i = i.replace('Musical', u'Musical')
i = i.replace('Mystery', u'\u004d\u0079\u0073\u0074\u0069\u006b')
i = i.replace('News', u'News')
i = i.replace('Reality-TV', u'Reality-TV')
i = i.replace('Romance', u'\u0052\u006f\u006d\u0061\u006e\u0074\u0069\u006b')
i = i.replace('Science Fiction', u'\u0053\u0063\u0069\u0065\u006e\u0063\u0065 \u0046\u0069\u0063\u0074\u0069\u006f\u006e')
i = i.replace('Sci-Fi', u'\u0053\u0063\u0069\u0065\u006e\u0063\u0065 \u0046\u0069\u0063\u0074\u0069\u006f\u006e')
i = i.replace('Sport', u'Sport')
i = i.replace('Talk-Show', u'Talk-Show')
i = i.replace('Thriller', u'\u0054\u0068\u0072\u0069\u006c\u006c\u0065\u0072')
i = i.replace('War', u'\u004b\u0072\u0069\u0067')
i = i.replace('Western', u'\u0056\u00e4\u0073\u0074\u0065\u0072\u006e')
elif lang == 'tr':
i = i.replace('Action', u'\u0041\u006b\u0073\u0069\u0079\u006f\u006e')
i = i.replace('Adventure', u'\u004d\u0061\u0063\u0065\u0072\u0061')
i = i.replace('Animation', u'\u0041\u006e\u0069\u006d\u0061\u0073\u0079\u006f\u006e')
i = i.replace('Anime', u'Anime')
i = i.replace('Biography', u'Biography')
i = i.replace('Comedy', u'\u004b\u006f\u006d\u0065\u0064\u0069')
i = i.replace('Crime', u'\u0053\u0075\u00e7')
i = i.replace('Documentary', u'\u0042\u0065\u006c\u0067\u0065\u0073\u0065\u006c')
i = i.replace('Drama', u'\u0044\u0072\u0061\u006d')
i = i.replace('Family', u'\u0041\u0069\u006c\u0065')
i = i.replace('Fantasy', u'\u0046\u0061\u006e\u0074\u0061\u0073\u0074\u0069\u006b')
i = i.replace('Game-Show', u'Game-Show')
i = i.replace('History', u'\u0054\u0061\u0072\u0069\u0068')
i = i.replace('Horror', u'\u004b\u006f\u0072\u006b\u0075')
i = i.replace('Music ', u'\u004d\u00fc\u007a\u0069\u006b')
i = i.replace('Musical', u'Musical')
i = i.replace('Mystery', u'\u0047\u0069\u007a\u0065\u006d')
i = i.replace('News', u'News')
i = i.replace('Reality-TV', u'Reality-TV')
i = i.replace('Romance', u'\u0052\u006f\u006d\u0061\u006e\u0074\u0069\u006b')
i = i.replace('Science Fiction', u'\u0042\u0069\u006c\u0069\u006d\u002d\u004b\u0075\u0072\u0067\u0075')
i = i.replace('Sci-Fi', u'\u0042\u0069\u006c\u0069\u006d\u002d\u004b\u0075\u0072\u0067\u0075')
i = i.replace('Sport', u'Sport')
i = i.replace('Talk-Show', u'Talk-Show')
i = i.replace('Thriller', u'\u0047\u0065\u0072\u0069\u006c\u0069\u006d')
i = i.replace('War', u'\u0053\u0061\u0076\u0061\u015f')
i = i.replace('Western', u'\u0056\u0061\u0068\u015f\u0069 \u0042\u0061\u0074\u0131')
elif lang == 'zh':
i = i.replace('Action', u'\u52a8\u4f5c')
i = i.replace('Adventure', u'\u5192\u9669')
i = i.replace('Animation', u'\u52a8\u753b')
i = i.replace('Anime', u'Anime')
i = i.replace('Biography', u'Biography')
i = i.replace('Comedy', u'\u559c\u5267')
i = i.replace('Crime', u'\u72af\u7f6a')
i = i.replace('Documentary', u'\u7eaa\u5f55')
i = i.replace('Drama', u'\u5267\u60c5')
i = i.replace('Family', u'\u5bb6\u5ead')
i = i.replace('Fantasy', u'\u5947\u5e7b')
i = i.replace('Game-Show', u'Game-Show')
i = i.replace('History', u'\u5386\u53f2')
i = i.replace('Horror', u'\u6050\u6016')
i = i.replace('Music ', u'\u97f3\u4e50')
i = i.replace('Musical', u'Musical')
i = i.replace('Mystery', u'\u60ac\u7591')
i = i.replace('News', u'News')
i = i.replace('Reality-TV', u'Reality-TV')
i = i.replace('Romance', u'\u7231\u60c5')
i = i.replace('Science Fiction', u'\u79d1\u5e7b')
i = i.replace('Sci-Fi', u'\u79d1\u5e7b')
i = i.replace('Sport', u'Sport')
i = i.replace('Talk-Show', u'Talk-Show')
i = i.replace('Thriller', u'\u60ca\u609a')
i = i.replace('War', u'\u6218\u4e89')
i = i.replace('Western', u'\u897f\u90e8')
return i
| 65.17305
| 166
| 0.60276
| 6,640
| 45,947
| 4.161596
| 0.063404
| 0.042992
| 0.193464
| 0.024319
| 0.832845
| 0.764629
| 0.735642
| 0.70803
| 0.686209
| 0.667427
| 0
| 0.296775
| 0.184212
| 45,947
| 704
| 167
| 65.265625
| 0.44044
| 0.056543
| 0
| 0.443366
| 0
| 0.088997
| 0.558793
| 0.413187
| 0
| 0
| 0
| 0
| 0
| 1
| 0.001618
| false
| 0
| 0
| 0
| 0.003236
| 0
| 0
| 0
| 0
| null | 0
| 1
| 0
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 9
|
8a378ec84e9121971164512e16ad4dcda31f608a
| 11,933
|
py
|
Python
|
tests/core/test_task_operators.py
|
jamestwebber/prefect
|
410c4ac37d2595ab61007742883687f5e284821d
|
[
"Apache-2.0"
] | null | null | null |
tests/core/test_task_operators.py
|
jamestwebber/prefect
|
410c4ac37d2595ab61007742883687f5e284821d
|
[
"Apache-2.0"
] | null | null | null |
tests/core/test_task_operators.py
|
jamestwebber/prefect
|
410c4ac37d2595ab61007742883687f5e284821d
|
[
"Apache-2.0"
] | null | null | null |
from prefect.core import Edge, Flow, Parameter, Task
class TestMagicInteractionMethods:
# -----------------------------------------
# getitem
def test_getitem_list(self):
with Flow(name="test") as f:
z = Parameter("x")[Parameter("y")]
state = f.run(parameters=dict(x=[1, 2, 3], y=1))
assert state.result[z].result == 2
def test_getitem_dict(self):
with Flow(name="test") as f:
z = Parameter("x")[Parameter("y")]
state = f.run(parameters=dict(x=dict(a=1, b=2, c=3), y="b"))
assert state.result[z].result == 2
def test_getitem_constant(self):
with Flow(name="test") as f:
z = Parameter("x")["b"]
state = f.run(parameters=dict(x=dict(a=1, b=2, c=3)))
assert state.result[z].result == 2
# -----------------------------------------
# or / pipe / |
def test_or(self):
with Flow(name="test") as f:
t1 = Task()
t2 = Task()
t1 | t2
assert Edge(t1, t2) in f.edges
def test_or_with_constant(self):
with Flow(name="test") as f:
t1 = Task()
t1 | 1
assert len(f.tasks) == 2
assert len(f.edges) == 1
def test_ror_with_constant(self):
with Flow(name="test") as f:
t1 = Task()
1 | t1
assert len(f.tasks) == 2
assert len(f.edges) == 1
# -----------------------------------------
# Chain
def test_chained_operators(self):
with Flow(name="test") as f:
t1 = Task("t1")
t2 = Task("t2")
t3 = Task("t3")
t4 = Task("t4")
t5 = Task("t5")
t6 = Task("t6")
(t1 | t2 | t3 | t4)
assert all([e in f.edges for e in [Edge(t1, t2), Edge(t2, t3), Edge(t3, t4)]])
class TestMagicOperatorMethods:
# -----------------------------------------
# addition
def test_addition(self):
with Flow(name="test") as f:
z = Parameter("x") + Parameter("y")
state = f.run(parameters=dict(x=1, y=2))
assert state.result[z].result == 3
def test_addition_with_constant(self):
with Flow(name="test") as f:
z = Parameter("x") + 10
state = f.run(parameters=dict(x=1))
assert state.result[z].result == 11
def test_right_addition(self):
with Flow(name="test") as f:
z = 10 + Parameter("x")
state = f.run(parameters=dict(x=1))
assert state.result[z].result == 11
# -----------------------------------------
# subtraction
def test_subtraction(self):
with Flow(name="test") as f:
z = Parameter("x") - Parameter("y")
state = f.run(parameters=dict(x=1, y=2))
assert state.result[z].result == -1
def test_subtraction_with_constant(self):
with Flow(name="test") as f:
z = Parameter("x") - 10
state = f.run(parameters=dict(x=1))
assert state.result[z].result == -9
def test_right_subtraction(self):
with Flow(name="test") as f:
z = 10 - Parameter("x")
state = f.run(parameters=dict(x=1))
assert state.result[z].result == 9
# -----------------------------------------
# multiplication
def test_multiplication(self):
with Flow(name="test") as f:
z = Parameter("x") * Parameter("y")
state = f.run(parameters=dict(x=2, y=3))
assert state.result[z].result == 6
def test_multiplication_with_constant(self):
with Flow(name="test") as f:
z = Parameter("x") * 10
state = f.run(parameters=dict(x=2))
assert state.result[z].result == 20
def test_right_multiplication(self):
with Flow(name="test") as f:
z = 10 * Parameter("x")
state = f.run(parameters=dict(x=2))
assert state.result[z].result == 20
# -----------------------------------------
# division
def test_division(self):
with Flow(name="test") as f:
z = Parameter("x") / Parameter("y")
state = f.run(parameters=dict(x=5, y=2))
assert state.result[z].result == 2.5
def test_division_with_constant(self):
with Flow(name="test") as f:
z = Parameter("x") / 10
state = f.run(parameters=dict(x=35))
assert state.result[z].result == 3.5
def test_right_division(self):
with Flow(name="test") as f:
z = 10 / Parameter("x")
state = f.run(parameters=dict(x=4))
assert state.result[z].result == 2.5
# -----------------------------------------
# floor division
def test_floor_division(self):
with Flow(name="test") as f:
z = Parameter("x") // Parameter("y")
state = f.run(parameters=dict(x=5, y=2))
assert state.result[z].result == 2
def test_floor_division_with_constant(self):
with Flow(name="test") as f:
z = Parameter("x") // 10
state = f.run(parameters=dict(x=38))
assert state.result[z].result == 3
def test_right_floor_division(self):
with Flow(name="test") as f:
z = 10 // Parameter("x")
state = f.run(parameters=dict(x=4))
assert state.result[z].result == 2
# -----------------------------------------
# mod
def test_mod(self):
with Flow(name="test") as f:
z = Parameter("x") % Parameter("y")
state = f.run(parameters=dict(x=5, y=2))
assert state.result[z].result == 1
def test_mod_with_constant(self):
with Flow(name="test") as f:
z = Parameter("x") % 10
state = f.run(parameters=dict(x=12))
assert state.result[z].result == 2
def test_right_mod(self):
with Flow(name="test") as f:
z = 10 % Parameter("x")
state = f.run(parameters=dict(x=14))
assert state.result[z].result == 10
# -----------------------------------------
# pow
def test_pow(self):
with Flow(name="test") as f:
z = Parameter("x") ** Parameter("y")
state = f.run(parameters=dict(x=5, y=2))
assert state.result[z].result == 25
def test_pow_with_constant(self):
with Flow(name="test") as f:
z = Parameter("x") ** 3
state = f.run(parameters=dict(x=2))
assert state.result[z].result == 8
def test_right_pow(self):
with Flow(name="test") as f:
z = 10 ** Parameter("x")
state = f.run(parameters=dict(x=2))
assert state.result[z].result == 100
# -----------------------------------------
# gt
def test_gt(self):
with Flow(name="test") as f:
z = Parameter("x") > Parameter("y")
state = f.run(parameters=dict(x=5, y=2))
assert state.result[z].result is True
def test_gt_with_constant(self):
with Flow(name="test") as f:
z = Parameter("x") > 3
state = f.run(parameters=dict(x=2))
assert state.result[z].result is False
def test_right_gt(self):
with Flow(name="test") as f:
z = 10 > Parameter("x")
state = f.run(parameters=dict(x=10))
assert state.result[z].result is False
# -----------------------------------------
# gte
def test_gte(self):
with Flow(name="test") as f:
z = Parameter("x") >= Parameter("y")
state = f.run(parameters=dict(x=5, y=2))
assert state.result[z].result is True
def test_gte_with_constant(self):
with Flow(name="test") as f:
z = Parameter("x") >= 3
state = f.run(parameters=dict(x=2))
assert state.result[z].result is False
def test_right_gte(self):
with Flow(name="test") as f:
z = 10 >= Parameter("x")
state = f.run(parameters=dict(x=10))
assert state.result[z].result is True
# -----------------------------------------
# lt
def test_lt(self):
with Flow(name="test") as f:
z = Parameter("x") < Parameter("y")
state = f.run(parameters=dict(x=5, y=2))
assert state.result[z].result is False
def test_lt_with_constant(self):
with Flow(name="test") as f:
z = Parameter("x") < 3
state = f.run(parameters=dict(x=2))
assert state.result[z].result is True
def test_right_lt(self):
with Flow(name="test") as f:
z = 10 < Parameter("x")
state = f.run(parameters=dict(x=10))
assert state.result[z].result is False
# -----------------------------------------
# lte
def test_lte(self):
with Flow(name="test") as f:
z = Parameter("x") <= Parameter("y")
state = f.run(parameters=dict(x=5, y=2))
assert state.result[z].result is False
def test_lte_with_constant(self):
with Flow(name="test") as f:
z = Parameter("x") <= 3
state = f.run(parameters=dict(x=2))
assert state.result[z].result is True
def test_right_lte(self):
with Flow(name="test") as f:
z = 10 <= Parameter("x")
state = f.run(parameters=dict(x=10))
assert state.result[z].result is True
# -----------------------------------------
# and
def test_and(self):
with Flow(name="test") as f:
z = Parameter("x") & Parameter("y")
state = f.run(parameters=dict(x=True, y=False))
assert state.result[z].result is False
state = f.run(parameters=dict(x=True, y=True))
assert state.result[z].result is True
state = f.run(parameters=dict(x=False, y=True))
assert state.result[z].result is False
state = f.run(parameters=dict(x=False, y=False))
assert state.result[z].result is False
def test_and_with_constant(self):
with Flow(name="test") as f:
z = Parameter("x") & True
state = f.run(parameters=dict(x=True))
assert state.result[z].result is True
state = f.run(parameters=dict(x=False))
assert state.result[z].result is False
with Flow(name="test") as f:
z = Parameter("x") & False
state = f.run(parameters=dict(x=True))
assert state.result[z].result is False
state = f.run(parameters=dict(x=False))
assert state.result[z].result is False
def test_right_and(self):
with Flow(name="test") as f:
z = True & Parameter("x")
state = f.run(parameters=dict(x=True))
assert state.result[z].result is True
state = f.run(parameters=dict(x=False))
assert state.result[z].result is False
with Flow(name="test") as f:
z = False & Parameter("x")
state = f.run(parameters=dict(x=True))
assert state.result[z].result is False
state = f.run(parameters=dict(x=False))
assert state.result[z].result is False
class TestNonMagicOperatorMethods:
def test_equals(self):
with Flow(name="test") as f:
z = Parameter("x").is_equal(Parameter("y"))
state = f.run(parameters=dict(x=5, y=2))
assert state.result[z].result is False
state = f.run(parameters=dict(x=5, y=5))
assert state.result[z].result is True
def test_not_equals(self):
with Flow(name="test") as f:
z = Parameter("x").is_not_equal(Parameter("y"))
state = f.run(parameters=dict(x=5, y=2))
assert state.result[z].result is True
state = f.run(parameters=dict(x=5, y=5))
assert state.result[z].result is False
def test_not(self):
with Flow(name="test") as f:
z = Parameter("x").not_()
state = f.run(parameters=dict(x=True))
assert state.result[z].result is False
state = f.run(parameters=dict(x=False))
assert state.result[z].result is True
| 32.16442
| 86
| 0.521663
| 1,620
| 11,933
| 3.782099
| 0.054938
| 0.052881
| 0.079321
| 0.167456
| 0.88314
| 0.879223
| 0.866819
| 0.865024
| 0.849682
| 0.804798
| 0
| 0.021382
| 0.286684
| 11,933
| 370
| 87
| 32.251351
| 0.698426
| 0.062264
| 0
| 0.52963
| 0
| 0
| 0.023837
| 0
| 0
| 0
| 0
| 0
| 0.222222
| 1
| 0.17037
| false
| 0
| 0.003704
| 0
| 0.185185
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
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| 0
| 0
| 0
| 0
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| 0
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| null | 0
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| 0
| 0
| 0
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| 0
| 0
| 0
| 0
|
0
| 7
|
8a4a087571b0887ecdc7dc345c82e277c9e1a3e0
| 95
|
py
|
Python
|
src/phonebot/vis/viewer/_pyqtgraph/__init__.py
|
vi-robotics/pyphonebot-extra
|
5db65d95fe1fafff2cbac7ca8dba66a71d363d6b
|
[
"MIT"
] | null | null | null |
src/phonebot/vis/viewer/_pyqtgraph/__init__.py
|
vi-robotics/pyphonebot-extra
|
5db65d95fe1fafff2cbac7ca8dba66a71d363d6b
|
[
"MIT"
] | null | null | null |
src/phonebot/vis/viewer/_pyqtgraph/__init__.py
|
vi-robotics/pyphonebot-extra
|
5db65d95fe1fafff2cbac7ca8dba66a71d363d6b
|
[
"MIT"
] | null | null | null |
from .pyqtgraph_3d import *
from .pyqtgraph_backend import *
from .pyqtgraph_handlers import *
| 23.75
| 33
| 0.810526
| 12
| 95
| 6.166667
| 0.5
| 0.527027
| 0.513514
| 0
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| 0
| 0
| 0
| 0.012048
| 0.126316
| 95
| 3
| 34
| 31.666667
| 0.879518
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| 0
| 1
| 0
|
0
| 7
|
8a525552cc8ae2223489d522cfb0fb805182c2ec
| 9,859
|
py
|
Python
|
userbot/modules/file_summary.py
|
Danzo18/Man-Userbot
|
b8fdcae0951357406f670f67c9af4510b348f08b
|
[
"Naumen",
"Condor-1.1",
"MS-PL"
] | null | null | null |
userbot/modules/file_summary.py
|
Danzo18/Man-Userbot
|
b8fdcae0951357406f670f67c9af4510b348f08b
|
[
"Naumen",
"Condor-1.1",
"MS-PL"
] | null | null | null |
userbot/modules/file_summary.py
|
Danzo18/Man-Userbot
|
b8fdcae0951357406f670f67c9af4510b348f08b
|
[
"Naumen",
"Condor-1.1",
"MS-PL"
] | 1
|
2022-01-26T13:02:09.000Z
|
2022-01-26T13:02:09.000Z
|
# Copyright (C) 2021 Catuserbot <https://github.com/sandy1709/catuserbot>
# Ported by @mrismanaziz
# FROM Man-Userbot <https://github.com/mrismanaziz/Man-Userbot>
# t.me/SharingUserbot & t.me/Lunatic0de
import time
from prettytable import PrettyTable
from userbot import CMD_HELP
from userbot.events import register
from userbot.utils import _format, edit_delete, edit_or_reply, humanbytes, media_type
TYPES = [
"Photo",
"Audio",
"Video",
"Document",
"Sticker",
"Gif",
"Voice",
"Round Video",
]
def weird_division(n, d):
return n / d if d else 0
@register(outgoing=True, pattern=r"^\.chatfs(?: |$)(.*)")
async def _(event): # sourcery no-metrics
"Shows you the complete media/file summary of the that group"
entity = event.chat_id
input_str = event.pattern_match.group(1)
if input_str:
try:
entity = int(input_str)
except ValueError:
entity = input_str
starttime = int(time.monotonic())
x = PrettyTable()
totalcount = totalsize = msg_count = 0
x.title = "File Summary"
x.field_names = ["Media", "Count", "File size"]
largest = " <b>Largest Size</b>\n"
try:
chatdata = await event.client.get_entity(entity)
except Exception as e:
return await edit_delete(
event,
f"<b>Error : </b><code>{e}</code>",
time=5,
parse_mode="HTML",
)
if type(chatdata).__name__ == "Channel":
if chatdata.username:
link = f"<a href='t.me/{chatdata.username}'>{chatdata.title}</a>"
else:
link = chatdata.title
else:
link = f"<a href='tg://user?id={chatdata.id}'>{chatdata.first_name}</a>"
event = await edit_or_reply(
event,
f"<b>Menghitung ukuran File dari group </b><code>{link}</code>\n<b>Harap Tunggu Ini mungkin memakan waktu yang lama tergantung pada jumlah pesan grup</b>",
parse_mode="HTML",
)
media_dict = {
m: {"file_size": 0, "count": 0, "max_size": 0, "max_file_link": ""}
for m in TYPES
}
async for message in event.client.iter_messages(entity=entity, limit=None):
msg_count += 1
media = media_type(message)
if media is not None:
media_dict[media]["file_size"] += message.file.size
media_dict[media]["count"] += 1
if message.file.size > media_dict[media]["max_size"]:
media_dict[media]["max_size"] = message.file.size
if type(chatdata).__name__ == "Channel":
media_dict[media][
"max_file_link"
] = f"https://t.me/c/{chatdata.id}/{message.id}" # pylint: disable=line-too-long
else:
media_dict[media][
"max_file_link"
] = f"tg://openmessage?user_id={chatdata.id}&message_id={message.id}" # pylint: disable=line-too-long
totalsize += message.file.size
totalcount += 1
for mediax in TYPES:
x.add_row(
[
mediax,
media_dict[mediax]["count"],
humanbytes(media_dict[mediax]["file_size"]),
]
)
if media_dict[mediax]["count"] != 0:
largest += f" • <b><a href='{media_dict[mediax]['max_file_link']}'>{mediax}</a> : </b><code>{humanbytes(media_dict[mediax]['max_size'])}</code>\n"
endtime = int(time.monotonic())
if endtime - starttime >= 120:
runtime = str(round(((endtime - starttime) / 60), 2)) + " minutes"
else:
runtime = str(endtime - starttime) + " seconds"
avghubytes = humanbytes(weird_division(totalsize, totalcount))
avgruntime = (
str(round((weird_division((endtime - starttime), totalcount)) * 1000, 2))
+ " ms"
)
totalstring = f"<b>Total Files :</b> <code>{totalcount}</code>\n<b>Total File Size :</b> <code>{humanbytes(totalsize)}</code>\n<b>Avg. File Size :</b> <code>{avghubytes}</code>\n"
runtimestring = f"<b>Runtime :</b> <code>{runtime}</code>\
\n<b>Runtime per file :</b> <code>{avgruntime}</code>\
\n"
line = "<b>━━━━━━━━━━━━━━━━━━━━</b>\n"
result = f"<b>Group : {link}</b>\n\n"
result += f"<b>Total Messages:</b><code> {msg_count}</code>\n"
result += "<b>File Summary : </b>\n"
result += f"<code>{x}</code>\n"
result += f"{largest}"
result += line + totalstring + line + runtimestring + line
await event.edit(result, parse_mode="HTML", link_preview=False)
@register(outgoing=True, pattern=r"^\.userfs(?: |$)(.*)")
async def _(event): # sourcery no-metrics
"Shows you the complete media/file summary of the that user in that group."
reply = await event.get_reply_message()
input_str = event.pattern_match.group(1)
if reply and input_str:
try:
entity = int(input_str)
except ValueError:
entity = input_str
userentity = reply.sender_id
elif reply:
entity = event.chat_id
userentity = reply.sender_id
elif input_str:
entity = event.chat_id
try:
userentity = int(input_str)
except ValueError:
userentity = input_str
else:
entity = event.chat_id
userentity = event.sender_id
starttime = int(time.monotonic())
x = PrettyTable()
totalcount = totalsize = msg_count = 0
x.title = "File Summary"
x.field_names = ["Media", "Count", "File size"]
largest = " <b>Largest Size</b>\n"
try:
chatdata = await event.client.get_entity(entity)
except Exception as e:
return await edit_delete(
event, f"<b>Error : </b><code>{e}</code>", 5, parse_mode="HTML"
)
try:
userdata = await event.client.get_entity(userentity)
except Exception as e:
return await edit_delete(
event,
f"<b>Error : </b><code>{e}</code>",
time=5,
parse_mode="HTML",
)
if type(chatdata).__name__ == "Channel":
if chatdata.username:
link = f"<a href='t.me/{chatdata.username}'>{chatdata.title}</a>"
else:
link = chatdata.title
else:
link = f"<a href='tg://user?id={chatdata.id}'>{chatdata.first_name}</a>"
event = await edit_or_reply(
event,
f"<b>Menghitung ukuran File yang dikirim </b>{_format.htmlmentionuser(userdata.first_name,userdata.id)}<b> di Grup </b><code>{link}</code>\n<b>Harap Tunggu Ini mungkin memakan waktu yang lama tergantung pada jumlah pesan grup</b>",
parse_mode="HTML",
)
media_dict = {
m: {"file_size": 0, "count": 0, "max_size": 0, "max_file_link": ""}
for m in TYPES
}
async for message in event.client.iter_messages(
entity=entity, limit=None, from_user=userentity
):
msg_count += 1
media = media_type(message)
if media is not None:
media_dict[media]["file_size"] += message.file.size
media_dict[media]["count"] += 1
if message.file.size > media_dict[media]["max_size"]:
media_dict[media]["max_size"] = message.file.size
if type(chatdata).__name__ == "Channel":
media_dict[media][
"max_file_link"
] = f"https://t.me/c/{chatdata.id}/{message.id}"
else:
media_dict[media][
"max_file_link"
] = f"tg://openmessage?user_id={chatdata.id}&message_id={message.id}"
totalsize += message.file.size
totalcount += 1
for mediax in TYPES:
x.add_row(
[
mediax,
media_dict[mediax]["count"],
humanbytes(media_dict[mediax]["file_size"]),
]
)
if media_dict[mediax]["count"] != 0:
largest += f" • <b><a href='{media_dict[mediax]['max_file_link']}'>{mediax}</a> : </b><code>{humanbytes(media_dict[mediax]['max_size'])}</code>\n"
endtime = int(time.monotonic())
if endtime - starttime >= 120:
runtime = str(round(((endtime - starttime) / 60), 2)) + " minutes"
else:
runtime = str(endtime - starttime) + " seconds"
avghubytes = humanbytes(weird_division(totalsize, totalcount))
avgruntime = (
str(round((weird_division((endtime - starttime), totalcount)) * 1000, 2))
+ " ms"
)
totalstring = f"<b>Total Files :</b> <code>{totalcount}</code>\n<b>Total File Size :</b> <code>{humanbytes(totalsize)}</code>\n<b>Avg. File Size :</b> <code>{avghubytes}\\\x1f \n</code>"
runtimestring = f"<b>Runtime :</b> <code>{runtime}</code>\
\n<b>Runtime Per File :</b> <code>{avgruntime}</code>\
\n"
line = "<b>━━━━━━━━━━━━━━━━━━━━</b>\n"
result = f"<b>Group : {link}\nUser : {_format.htmlmentionuser(userdata.first_name,userdata.id)}</b>\n\n"
result += f"<b>Total Messages:</b> <code>{msg_count}</code>\n"
result += "<b>File Summary : </b>\n"
result += f"<code>{x}</code>\n"
result += f"{largest}"
result += line + totalstring + line + runtimestring + line
await event.edit(result, parse_mode="HTML", link_preview=False)
CMD_HELP.update(
{
"file-summary": "**Plugin : **`file-summery`\
\n\n • **Syntax :** `.chatfs` <username/id>\
\n • **Function : **Untuk Menampilkan ringkasan media/file lengkap dari grup itu\
\n\n • **Syntax :** `.userfs` <reply/username/id>\
\n • **Function : **Untuk Menampilkan ringkasan media/file lengkap dari anggota group tersebut.\
\n\n • **NOTE :** Untuk sekarang terbatas pada 10.000 terakhir di grup yang Anda gunakan\
"
}
)
| 38.814961
| 239
| 0.567197
| 1,214
| 9,859
| 4.521417
| 0.171334
| 0.039351
| 0.030607
| 0.024777
| 0.847149
| 0.809619
| 0.809619
| 0.809619
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| 0.2766
| 9,859
| 253
| 240
| 38.968379
| 0.754767
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| 0.112809
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| 0.021645
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|
0
| 7
|
8a76a764e4aa6e39061c52c6432b72fa4dce0929
| 6,814
|
py
|
Python
|
regress/PORT_ME_TESTS/tests-qos.py
|
fp7-ofelia/VeRTIGO
|
11f39f819196c8352611852435dea17bc6a2292f
|
[
"BSD-3-Clause"
] | 2
|
2016-10-12T08:20:00.000Z
|
2017-05-09T13:13:18.000Z
|
regress/PORT_ME_TESTS/tests-qos.py
|
fp7-ofelia/VeRTIGO
|
11f39f819196c8352611852435dea17bc6a2292f
|
[
"BSD-3-Clause"
] | null | null | null |
regress/PORT_ME_TESTS/tests-qos.py
|
fp7-ofelia/VeRTIGO
|
11f39f819196c8352611852435dea17bc6a2292f
|
[
"BSD-3-Clause"
] | 1
|
2020-10-01T07:57:34.000Z
|
2020-10-01T07:57:34.000Z
|
#!/usr/bin/python
from fvregress import *
import string # really? you have to do this?
if len(sys.argv) > 1 :
wantPause = True
timeout=9999999
valgrindArgs= []
else:
wantPause = False
timeout=5
valgrindArgs= None
# start up a flowvisor with 1 switch (default) and two guests
# out of the flowvisor-conf.d-mobility config dir
#h= HyperTest(guests=[('localhost',54321),('localhost',54322), ('localhost',54323)],
# hyperargs=['-v0',"-a", "flowvisor-conf.d-qos","ptcp:%d" % HyperTest.OFPORT ],valgrind=valgrindArgs)
h = FvRegress.parseConfig(configDir='flowvisor-conf.d-qos', valgrind=valgrindArgs)
if wantPause:
doPause("start tests")
#################################### Start Tests
try:
feature_request = FvRegress.OFVERSION + '05 0008 2d47 c5eb'
feature_request_after = FvRegress.OFVERSION + '05 0008 0001 0000'
h.runTest(name="feature_request",timeout=timeout, events= [
TestEvent( "send","guest","alice", feature_request),
TestEvent( "recv","switch","switch1", feature_request_after),
])
########################################
udp = FvRegress.OFVERSION + '''0d 0058 0000 abcd ffff ffff
ffff 0008 0000 0008 0001 0080 0123 2000
0001 0000 0000 0000 0800 4500 0032 0000
4000 4011 2868 c0a8 c800 c0a8 c901 0001
0000 001e d7c3 cdc0 251b e6dc ea0c 726d
973f 2b71 c2e4 1b6f bc11 8250'''
udp_and_vlan = FvRegress.OFVERSION + '''0d 00 60 01 01 00 00 ff ff ff ff ff ff 00 10
00 01 00 08 00 0f 00 00 00 00 00 08 00 01 00 80
01 23 20 ff 00 01 00 00 00 00 00 00 08 00 45 00
00 32 00 00 40 00 40 11 28 68 c0 a8 c8 00 c0 a8
c9 01 00 01 00 00 00 1e d7 c3 cd c0 25 1b e6 dc
ea 0c 72 6d 97 3f 2b 71 c2 e4 1b 6f bc 11 82 50'''
udp_and_pcp = FvRegress.OFVERSION + '''0d 00 60 02 01 00 00 ff ff ff ff ff ff 00 10
00 02 00 08 03 00 00 00 00 00 00 08 00 01 00 80
01 23 20 ff 00 02 00 00 00 00 00 00 08 00 45 00
00 32 00 00 40 00 40 11 28 68 c0 a8 c8 00 c0 a8
c9 01 00 01 00 00 00 1e d7 c3 cd c0 25 1b e6 dc
ea 0c 72 6d 97 3f 2b 71 c2 e4 1b 6f bc 11 82 50'''
udp_and_both = FvRegress.OFVERSION + '''0d 00 68 03 01 00 00 ff ff ff ff ff ff 00 18
00 01 00 08 0f a0 00 00 00 02 00 08 05 00 00 00
00 00 00 08 00 01 00 80 01 23 20 ff 00 03 00 00
00 00 00 00 08 00 45 00 00 32 00 00 40 00 40 11
28 68 c0 a8 c8 00 c0 a8 c9 01 00 01 00 00 00 1e
d7 c3 cd c0 25 1b e6 dc ea 0c 72 6d 97 3f 2b 71
c2 e4 1b 6f bc 11 82 50'''
udp_and_slicing = FvRegress.OFVERSION + '''0d 00 60 09 01 00 00 ff ff ff ff ff ff 00 10
00 0b 00 10 00 01 00 00 00 00 00 00 00 00 00 06
01 23 20 ff 00 04 00 00 00 00 00 00 08 00 45 00
00 32 00 00 40 00 40 11 28 68 c0 a8 c8 00 c0 a8
c9 01 00 01 00 00 00 1e d7 c3 cd c0 25 1b e6 dc
ea 0c 72 6d 97 3f 2b 71 c2 e4 1b 6f bc 11 82 50
'''
udp_and_both_and_slicing = FvRegress.OFVERSION + '''0d 00 70 0a 01 00 00 ff ff ff ff ff ff 00 20
00 01 00 08 0f a0 00 00 00 02 00 08 05 00 00 00
00 0b 00 10 00 01 00 00 00 00 00 00 00 00 00 07
01 23 20 ff 00 05 00 00 00 00 00 00 08 00 45 00
00 32 00 00 40 00 40 11 28 68 c0 a8 c8 00 c0 a8
c9 01 00 01 00 00 00 1e d7 c3 cd c0 25 1b e6 dc
ea 0c 72 6d 97 3f 2b 71 c2 e4 1b 6f bc 11 82 50
'''
h.runTest(name="packet_out qos re-write",timeout=timeout, events= [
TestEvent( "send","guest","alice", udp),
TestEvent( "recv","switch","switch1", udp_and_vlan),
TestEvent( "send","guest","bob", udp),
TestEvent( "recv","switch","switch1", udp_and_pcp),
TestEvent( "send","guest","cathy", udp),
TestEvent( "recv","switch","switch1", udp_and_both),
TestEvent( "send","guest","doug", udp),
TestEvent( "recv","switch","switch1", udp_and_slicing),
TestEvent( "send","guest","erik", udp),
TestEvent( "recv","switch","switch1", udp_and_both_and_slicing),
])
######################################
flow_mod = FvRegress.OFVERSION + '''0e 00 50 01 01 00 00 00 00 00 00 00 02 00 10
18 07 67 87 00 0d b9 15 c0 44 ff ff 08 00 11 00
c0 a8 02 fe c0 a8 02 02 00 43 00 44 00 00 00 05
00 00 00 00 00 00 00 00 00 00 80 00 00 17 70 97 40 6f 98 02 00 00 00 00
00 00 00 08 00 01 00 00'''
flow_mod_and_vlan = FvRegress.OFVERSION + '''0e 00 58 01 01 00 00 00 00 00 00 00 02 00 10
18 07 67 87 00 0d b9 15 c0 44 ff ff 08 00 11 00
c0 a8 02 fe c0 a8 02 02 00 43 00 44 00 00 00 05
00 00 00 00 00 00 00 00 00 00 80 00 00 17 70 97 40 6f 98 02 00 00 00 00
00 01 00 08 00 0f 00 00 00 00 00 08 00 01 00 00'''
flow_mod_and_pcp = FvRegress.OFVERSION + '''0e 00 58 05 01 00 00 00 00 00 00 00 02 00 10
18 07 67 87 00 0d b9 15 c0 44 ff ff 08 00 11 00
c0 a8 02 fe c0 a8 02 02 00 43 00 44 00 00 00 05
00 00 00 00 00 00 00 00 00 00 80 00 00 17 70 97
40 6f 98 02 00 00 00 00 00 02 00 08 03 00 00 00
00 00 00 08 00 01 00 00
'''
flow_mod_and_both= FvRegress.OFVERSION + '''0e 00 60 06 01 00 00 00 00 00 00 00 02 00 10
18 07 67 87 00 0d b9 15 c0 44 ff ff 08 00 11 00
c0 a8 02 fe c0 a8 02 02 00 43 00 44 00 00 00 05
00 00 00 00 00 00 00 00 00 00 80 00 00 17 70 97
40 6f 98 02 00 00 00 00 00 01 00 08 0f a0 00 00
00 02 00 08 05 00 00 00 00 00 00 08 00 01 00 00
'''
flow_mod_and_slicing = FvRegress.OFVERSION + '''0e 00 58 09 01 00 00 00 00 00 00 00 02 00 10
18 07 67 87 00 0d b9 15 c0 44 ff ff 08 00 11 00
c0 a8 02 fe c0 a8 02 02 00 43 00 44 00 00 00 05
00 00 00 00 00 00 00 00 00 00 80 00 00 17 70 97
40 6f 98 02 00 00 00 00 00 0b 00 10 00 01 00 00
00 00 00 00 00 00 00 06
'''
flow_mod_and_both_and_slicing = FvRegress.OFVERSION + '''0e 00 68 0a 01 00 00 00 00 00 00 00 02 00 10
18 07 67 87 00 0d b9 15 c0 44 ff ff 08 00 11 00
c0 a8 02 fe c0 a8 02 02 00 43 00 44 00 00 00 05
00 00 00 00 00 00 00 00 00 00 80 00 00 17 70 97
40 6f 98 02 00 00 00 00 00 01 00 08 0f a0 00 00
00 02 00 08 05 00 00 00 00 0b 00 10 00 01 00 00
00 00 00 00 00 00 00 07
'''
h.runTest(name="flow_mod qos re-write",timeout=timeout, events= [
TestEvent( "send","guest","alice", flow_mod),
TestEvent( "recv","switch","switch1", flow_mod_and_vlan),
TestEvent( "send","guest","bob", flow_mod),
TestEvent( "recv","switch","switch1", flow_mod_and_pcp),
TestEvent( "send","guest","cathy", flow_mod),
TestEvent( "recv","switch","switch1", flow_mod_and_both),
TestEvent( "send","guest","doug", flow_mod),
TestEvent( "recv","switch","switch1", flow_mod_and_slicing),
TestEvent( "send","guest","erik", flow_mod),
TestEvent( "recv","switch","switch1", flow_mod_and_both_and_slicing),
])
#########################################
# more tests for this setup HERE
#################################### End Tests
finally:
if wantPause:
doPause("start cleanup")
h.cleanup()
| 45.125828
| 102
| 0.623569
| 1,448
| 6,814
| 2.883978
| 0.134669
| 0.250958
| 0.268678
| 0.262452
| 0.735393
| 0.704741
| 0.620929
| 0.58501
| 0.565374
| 0.533525
| 0
| 0.391024
| 0.270766
| 6,814
| 150
| 103
| 45.426667
| 0.449386
| 0.057382
| 0
| 0.354331
| 0
| 0.015748
| 0.671433
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | null | 0
| 0.015748
| null | null | 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
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| 0
| null | 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 9
|
8abe31de9e332aa83a5826923a5a0a710e39b6d7
| 68,389
|
py
|
Python
|
test/python/test_operation.py
|
Wentong-DST/incubator-singa
|
0d1eaaac549e574d75a496eee3037ba91fc8f6b9
|
[
"Apache-2.0"
] | 1
|
2019-11-15T12:46:10.000Z
|
2019-11-15T12:46:10.000Z
|
test/python/test_operation.py
|
laojizi/singa
|
58e346eb1188faf78497ae2c8e129c99de3d743d
|
[
"Apache-2.0"
] | null | null | null |
test/python/test_operation.py
|
laojizi/singa
|
58e346eb1188faf78497ae2c8e129c99de3d743d
|
[
"Apache-2.0"
] | null | null | null |
# 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.
# =============================================================================
import unittest
from builtins import str
from singa import tensor
from singa import singa_wrap as singa
from singa import autograd
from singa import singa_wrap
from cuda_helper import gpu_dev, cpu_dev
import numpy as np
autograd.training = True
CTensor = singa.Tensor
dy = CTensor([2, 1, 2, 2])
singa.Gaussian(0.0, 1.0, dy)
def _tuple_to_string(t):
lt = [str(x) for x in t]
return '(' + ', '.join(lt) + ')'
def prepare_inputs_targets_for_rnn_test():
x_0 = np.random.random((2, 3)).astype(np.float32)
x_1 = np.random.random((2, 3)).astype(np.float32)
x_2 = np.random.random((2, 3)).astype(np.float32)
h_0 = np.zeros((2, 2)).astype(
np.float32)
t_0 = np.random.random((2, 2)).astype(np.float32)
t_1 = np.random.random((2, 2)).astype(np.float32)
t_2 = np.random.random((2, 2)).astype(np.float32)
x0 = tensor.Tensor(device=gpu_dev, data=x_0)
x1 = tensor.Tensor(device=gpu_dev, data=x_1)
x2 = tensor.Tensor(device=gpu_dev, data=x_2)
h0 = tensor.Tensor(device=gpu_dev, data=h_0)
t0 = tensor.Tensor(device=gpu_dev, data=t_0)
t1 = tensor.Tensor(device=gpu_dev, data=t_1)
t2 = tensor.Tensor(device=gpu_dev, data=t_2)
inputs = [x0, x1, x2]
targets = [t0, t1, t2]
return inputs, targets, h0
class TestPythonOperation(unittest.TestCase):
def check_shape(self, actual, expect):
self.assertEqual(actual, expect, 'shape mismatch, actual shape is %s'
' exepcted is %s' % (_tuple_to_string(actual),
_tuple_to_string(expect))
)
def test_Greater_cpu(self):
x0 = np.array([-0.9, -0.3, -0.1, 0.1, 0.5, 0.9]).reshape(3, 2).astype(np.float32)
x1 = np.array([0, -0.3, 0, 0.1, 0, 0.9]).reshape(3, 2).astype(np.float32)
y = np.greater(x0,x1)
x0 = tensor.from_numpy(x0)
x1 = tensor.from_numpy(x1)
x0.to_device(cpu_dev)
x1.to_device(cpu_dev)
result = autograd.greater(x0,x1)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), y, decimal=5)
def test_Greater_gpu(self):
x0 = np.array([-0.9, -0.3, -0.1, 0.1, 0.5, 0.9]).reshape(3, 2).astype(np.float32)
x1 = np.array([0, -0.3, 0, 0.1, 0, 0.9]).reshape(3, 2).astype(np.float32)
y = np.greater(x0,x1)
x0 = tensor.from_numpy(x0)
x1 = tensor.from_numpy(x1)
x0.to_device(gpu_dev)
x1.to_device(gpu_dev)
result = autograd.greater(x0,x1)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), y, decimal=5)
def test_conv2d_cpu(self):
# (in_channels, out_channels, kernel_size)
conv_0 = autograd.Conv2d(3, 1, 2)
conv_without_bias_0 = autograd.Conv2d(3, 1, 2, bias=False)
cpu_input_tensor = tensor.Tensor(shape=(2, 3, 3, 3), device=cpu_dev)
cpu_input_tensor.gaussian(0.0, 1.0)
dy = tensor.Tensor(shape=(2, 1, 2, 2), device=cpu_dev)
dy.gaussian(0.0, 1.0)
y = conv_0(cpu_input_tensor) # PyTensor
dx, dW, db = y.creator.backward(dy.data) # CTensor
self.check_shape(y.shape, (2, 1, 2, 2))
self.check_shape(dx.shape(), (2, 3, 3, 3))
self.check_shape(dW.shape(), (1, 3, 2, 2))
self.check_shape(db.shape(), (1,))
# forward without bias
y_without_bias = conv_without_bias_0(cpu_input_tensor)
self.check_shape(y_without_bias.shape, (2, 1, 2, 2))
def test_conv2d_gpu(self):
# (in_channels, out_channels, kernel_size)
conv_0 = autograd.Conv2d(3, 1, 2)
conv_without_bias_0 = autograd.Conv2d(3, 1, 2, bias=False)
gpu_input_tensor = tensor.Tensor(shape=(2, 3, 3, 3), device=gpu_dev)
gpu_input_tensor.gaussian(0.0, 1.0)
dy = tensor.Tensor(shape=(2, 1, 2, 2), device=gpu_dev)
dy.gaussian(0.0, 1.0)
y = conv_0(gpu_input_tensor) # PyTensor
dx, dW, db = y.creator.backward(dy.data) # CTensor
self.check_shape(y.shape, (2, 1, 2, 2))
self.check_shape(dx.shape(), (2, 3, 3, 3))
self.check_shape(dW.shape(), (1, 3, 2, 2))
self.check_shape(db.shape(), (1,))
# forward without bias
y_without_bias = conv_without_bias_0(gpu_input_tensor)
self.check_shape(y_without_bias.shape, (2, 1, 2, 2))
def test_sum_cpu(self):
x = np.array([0.1,-1.0,0.4,4.0,-0.9,9.0]).reshape(3,2).astype(np.float32)
x1 = np.array([0.1,1.0,0.4,4.0,0.9,9.0]).reshape(3,2).astype(np.float32)
y = x+x1
dy = np.ones((3, 2), dtype = np.float32)
grad0=dy
grad1=dy
x = tensor.from_numpy(x)
x1 = tensor.from_numpy(x1)
dy = tensor.from_numpy(dy)
x.to_device(cpu_dev)
x1.to_device(cpu_dev)
dy.to_device(cpu_dev)
result = autograd.sum(x,x1)
dx0,dx1 = result.creator.backward(dy.data)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), y, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx0)), grad0, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx1)), grad1, decimal=5)
def test_sum_gpu(self):
x = np.array([0.1,-1.0,0.4,4.0,-0.9,9.0]).reshape(3,2).astype(np.float32)
x1 = np.array([0.1,1.0,0.4,4.0,0.9,9.0]).reshape(3,2).astype(np.float32)
y = x+x1
dy = np.ones((3, 2), dtype = np.float32)
grad0=dy
grad1=dy
x = tensor.from_numpy(x)
x1 = tensor.from_numpy(x1)
dy = tensor.from_numpy(dy)
x.to_device(gpu_dev)
x1.to_device(gpu_dev)
dy.to_device(gpu_dev)
result = autograd.sum(x,x1)
dx0,dx1 = result.creator.backward(dy.data)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), y, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx0)), grad0, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx1)), grad1, decimal=5)
def test_conv2d_cpu(self):
# (in_channels, out_channels, kernel_size)
conv_1 = autograd.Conv2d(3, 1, 2)
conv_without_bias_1 = autograd.Conv2d(3, 1, 2, bias=False)
cpu_input_tensor = tensor.Tensor(shape=(2, 3, 3, 3), device=cpu_dev)
cpu_input_tensor.gaussian(0.0, 1.0)
y = conv_1(cpu_input_tensor) # PyTensor
dx, dW, db = y.creator.backward(dy) # CTensor
self.check_shape(y.shape, (2, 1, 2, 2))
self.check_shape(dx.shape(), (2, 3, 3, 3))
self.check_shape(dW.shape(), (1, 3, 2, 2))
self.check_shape(db.shape(), (1,))
# forward without bias
y_without_bias = conv_without_bias_1(cpu_input_tensor)
self.check_shape(y_without_bias.shape, (2, 1, 2, 2))
def test_SeparableConv2d_gpu(self):
# SeparableConv2d(in_channels, out_channels, kernel_size)
separ_conv=autograd.SeparableConv2d(8, 16, 3, padding=1)
x=np.random.random((10,8,28,28)).astype(np.float32)
x=tensor.Tensor(device=gpu_dev, data=x)
y1 = separ_conv.depthwise_conv(x)
y2 = separ_conv.point_conv(y1)
dy1, dW_depth = y2.creator.backward(y2.data)
dx, dW_spacial = y1.creator.backward(dy1)
self.check_shape(y2.shape, (10, 16, 28, 28))
self.check_shape(dy1.shape(), (10, 8, 28, 28))
self.check_shape(dW_depth.shape(), (16, 8, 1, 1))
self.check_shape(dx.shape(), (10, 8, 28, 28))
self.check_shape(dW_spacial.shape(), (8, 1, 3, 3))
y = separ_conv(x)
self.check_shape(y.shape, (10, 16, 28, 28))
def test_batchnorm2d_cpu(self):
batchnorm_0 = autograd.BatchNorm2d(3)
cpu_input_tensor = tensor.Tensor(shape=(2, 3, 3, 3), device=cpu_dev)
cpu_input_tensor.gaussian(0.0, 1.0)
dy = cpu_input_tensor.clone().data
y = batchnorm_0(cpu_input_tensor)
dx, ds, db = y.creator.backward(dy)
self.check_shape(y.shape, (2, 3, 3, 3))
self.check_shape(dx.shape(), (2, 3, 3, 3))
self.check_shape(ds.shape(), (3,))
self.check_shape(db.shape(), (3,))
def test_batchnorm2d_gpu(self):
batchnorm_0 = autograd.BatchNorm2d(3)
gpu_input_tensor = tensor.Tensor(shape=(2, 3, 3, 3), device=gpu_dev)
gpu_input_tensor.gaussian(0.0, 1.0)
dy = gpu_input_tensor.clone().data
y = batchnorm_0(gpu_input_tensor)
dx, ds, db = y.creator.backward(dy)
self.check_shape(y.shape, (2, 3, 3, 3))
self.check_shape(dx.shape(), (2, 3, 3, 3))
self.check_shape(ds.shape(), (3,))
self.check_shape(db.shape(), (3,))
def test_vanillaRNN_gpu_tiny_ops_shape_check(self):
# gradients shape check.
inputs, target, h0 = prepare_inputs_targets_for_rnn_test()
rnn = autograd.RNN(3, 2)
hs, _ = rnn(inputs, h0)
loss = autograd.softmax_cross_entropy(hs[0], target[0])
for i in range(1, len(hs)):
l = autograd.softmax_cross_entropy(hs[i], target[i])
loss = autograd.add(loss, l)
# d=autograd.infer_dependency(loss.creator)
# print(d)
for t, dt in autograd.backward(loss):
self.check_shape(t.shape, dt.shape)
def test_LSTM_gpu_tiny_ops_shape_check(self):
# gradients shape check.
inputs, target, h0 = prepare_inputs_targets_for_rnn_test()
c_0 = np.random.random((2, 1)).astype(np.float32)
c0 = tensor.Tensor(device=gpu_dev, data=c_0)
rnn = autograd.LSTM(3, 2)
hs, _, _ = rnn(inputs, (h0, c0))
loss = autograd.softmax_cross_entropy(hs[0], target[0])
for i in range(1, len(hs)):
l = autograd.softmax_cross_entropy(hs[i], target[i])
loss = autograd.add(loss, l)
# d=autograd.infer_dependency(loss.creator)
# print(d)
for t, dt in autograd.backward(loss):
self.check_shape(t.shape, dt.shape)
def gradients_check(self, func, param, autograds, h=0.0005, df=1):
# param: PyTensor
# autograds: numpy_tensor
p = tensor.to_numpy(param)
it = np.nditer(p, flags=['multi_index'], op_flags=['readwrite'])
while not it.finished:
idx = it.multi_index
diff = np.zeros_like(p)
diff[idx] += h
diff = tensor.from_numpy(diff)
diff.to_device(gpu_dev)
param += diff
pos = func()
pos = tensor.to_numpy(pos)
param -= diff
param -= diff
neg = func()
neg = tensor.to_numpy(neg)
numerical_grad = np.sum((pos - neg) * df) / (2 * h)
#print((autograds[idx] - numerical_grad)/numerical_grad)
# threshold set as -5% to +5%
#self.assertAlmostEqual((autograds[idx] - numerical_grad)/(numerical_grad+0.0000001), 0., places=1)
self.assertAlmostEqual(
autograds[idx] - numerical_grad, 0., places=2)
it.iternext()
def test_numerical_gradients_check_for_vallina_rnn(self):
inputs, target, h0 = prepare_inputs_targets_for_rnn_test()
rnn = autograd.RNN(3, 2)
def valinna_rnn_forward():
hs, _ = rnn(inputs, h0)
loss = autograd.softmax_cross_entropy(hs[0], target[0])
for i in range(1, len(hs)):
l = autograd.softmax_cross_entropy(hs[i], target[i])
loss = autograd.add(loss, l)
#grads = autograd.gradients(loss)
return loss
loss1 = valinna_rnn_forward()
auto_grads = autograd.gradients(loss1)
for param in rnn.params:
auto_grad = tensor.to_numpy(auto_grads[param])
self.gradients_check(valinna_rnn_forward, param, auto_grad)
def test_numerical_gradients_check_for_lstm(self):
inputs, target, h0 = prepare_inputs_targets_for_rnn_test()
c_0 = np.zeros((2, 2)).astype(np.float32)
c0 = tensor.Tensor(device=gpu_dev, data=c_0)
rnn = autograd.LSTM(3, 2)
def lstm_forward():
hs, _, _ = rnn(inputs, (h0, c0))
loss = autograd.softmax_cross_entropy(hs[0], target[0])
for i in range(1, len(hs)):
l = autograd.softmax_cross_entropy(hs[i], target[i])
loss = autograd.add(loss, l)
return loss
loss1 = lstm_forward()
auto_grads = autograd.gradients(loss1)
for param in rnn.params:
auto_grad = tensor.to_numpy(auto_grads[param])
self.gradients_check(lstm_forward, param, auto_grad)
def test_MeanSquareError(self):
X=np.array([4.3,5.4,3.3,3.6,5.7,6.0]).reshape(3,2).astype(np.float32)
T=np.array([4.4,5.3,3.2,3.7,5.4,6.3]).reshape(3,2).astype(np.float32)
x=tensor.from_numpy(X)
t=tensor.from_numpy(T)
x.to_device(gpu_dev)
t.to_device(gpu_dev)
loss= autograd.mse_loss(x,t)
dx=loss.creator.backward()[0]
loss_np=tensor.to_numpy(loss)[0]
self.assertAlmostEqual(loss_np, 0.0366666, places=4)
self.check_shape(dx.shape(), (3, 2))
def test_Abs(self):
X=np.array([0.8,-1.2,3.3,-3.6,-0.5,0.5]).reshape(3,2).astype(np.float32)
XT=np.array([0.8,1.2,3.3,3.6,0.5,0.5]).reshape(3,2).astype(np.float32)
x=tensor.from_numpy(X)
x.to_device(gpu_dev)
result=autograd.abs(x)
dx=result.creator.backward(x.data)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT)
self.check_shape(dx.shape(), (3, 2))
def test_Mean_gpu(self):
x0 = np.array([-0.9, -0.3, -0.1, 0.1, 0.5, 0.9]).reshape(3, 2).astype(np.float32)
x1 = np.array([0, -0.3, 0, 0.1, 0, 0.9]).reshape(3, 2).astype(np.float32)
y = (x0+x1)/2
grad=np.ones(x0.shape)/2
x0 = tensor.from_numpy(x0)
x1 = tensor.from_numpy(x1)
x0.to_device(gpu_dev)
x1.to_device(gpu_dev)
result = autograd.mean(x0,x1)
dy = tensor.from_numpy(np.ones((3,2)).astype(np.float32))
dy.to_device(gpu_dev)
dx0,dx1 = result.creator.backward(dy.data)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), y, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx0)), grad, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx1)), grad, decimal=5)
def test_Mean_cpu(self):
x0 = np.array([-0.9, -0.3, -0.1, 0.1, 0.5, 0.9]).reshape(3, 2).astype(np.float32)
x1 = np.array([0, -0.3, 0, 0.1, 0, 0.9]).reshape(3, 2).astype(np.float32)
y = (x0+x1)/2
grad=np.ones(x0.shape)/2
x0 = tensor.from_numpy(x0)
x1 = tensor.from_numpy(x1)
x0.to_device(cpu_dev)
x1.to_device(cpu_dev)
result = autograd.mean(x0,x1)
dy = tensor.from_numpy(np.ones((3,2)).astype(np.float32))
dy.to_device(cpu_dev)
dx0,dx1 = result.creator.backward(dy.data)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), y, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx0)), grad, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx1)), grad, decimal=5)
def test_Exp(self):
X=np.array([0.8,-1.2,3.3,-3.6,-0.5,0.5]).reshape(3,2).astype(np.float32)
XT=np.exp(X)
x=tensor.from_numpy(X)
x.to_device(gpu_dev)
result=autograd.exp(x)
dx=result.creator.backward(x.data)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
self.check_shape(dx.shape(), (3, 2))
def test_Identity_cpu(self):
x = np.array([-0.9, -0.3, -0.1, 0.1, 0.5, 0.9]).reshape(3, 2).astype(np.float32)
y = x.copy()
grad=np.ones(x.shape)
x = tensor.from_numpy(x)
x.to_device(cpu_dev)
result = autograd.identity(x)
dy = tensor.from_numpy(np.ones((3,2)).astype(np.float32))
dy.to_device(cpu_dev)
dx = result.creator.backward(dy.data)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), y, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), grad, decimal=5)
self.check_shape(dx.shape(), (3, 2))
def test_Identity_gpu(self):
x = np.array([-0.9, -0.3, -0.1, 0.1, 0.5, 0.9]).reshape(3, 2).astype(np.float32)
y = x.copy()
grad=np.ones(x.shape)
x = tensor.from_numpy(x)
x.to_device(gpu_dev)
result = autograd.identity(x)
dy = tensor.from_numpy(np.ones((3,2)).astype(np.float32))
dy.to_device(gpu_dev)
dx = result.creator.backward(dy.data)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), y, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), grad, decimal=5)
self.check_shape(dx.shape(), (3, 2))
def test_LeakyRelu(self):
X=np.array([0.8,-1.2,3.3,-3.6,-0.5,0.5]).reshape(3,2).astype(np.float32)
XT=np.array([0.8,-0.012,3.3,-0.036,-0.005,0.5]).reshape(3,2).astype(np.float32)
x=tensor.from_numpy(X)
x.to_device(gpu_dev)
result=autograd.leakyrelu(x)
dx=result.creator.backward(x.data)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT)
self.check_shape(dx.shape(), (3, 2))
def test_Cos_cpu(self):
X = np.array([0.8, -1.2, 3.3, -3.6, -0.5, 0.5]).reshape(3, 2).astype(np.float32)
XT = np.cos(X)
DY = np.ones((3, 2), dtype = np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(cpu_dev)
dy.to_device(cpu_dev)
result = autograd.cos(x)
dx = result.creator.backward(dy.data)
G = - np.sin(X)
DX = np.multiply(G, DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), DX, decimal=5)
def test_Cos_gpu(self):
X = np.array([0.8, -1.2, 3.3, -3.6, -0.5, 0.5]).reshape(3, 2).astype(np.float32)
XT = np.cos(X)
DY = np.ones((3, 2), dtype = np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(gpu_dev)
dy.to_device(gpu_dev)
result = autograd.cos(x)
dx = result.creator.backward(dy.data)
G = - np.sin(X)
DX = np.multiply(G, DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), DX, decimal=5)
def test_Cosh_cpu(self):
X = np.array([0.8, -1.2, 3.3, -3.6, -0.5, 0.5]).reshape(3, 2).astype(np.float32)
XT = np.cosh(X)
DY = np.ones((3, 2), dtype = np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(cpu_dev)
dy.to_device(cpu_dev)
result = autograd.cosh(x)
dx = result.creator.backward(dy.data)
G = np.sinh(X)
DX = np.multiply(G, DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), DX, decimal=5)
def test_Cosh_gpu(self):
X = np.array([0.8, -1.2, 3.3, -3.6, -0.5, 0.5]).reshape(3, 2).astype(np.float32)
XT = np.cosh(X)
DY = np.ones((3, 2), dtype = np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(gpu_dev)
dy.to_device(gpu_dev)
result = autograd.cosh(x)
dx = result.creator.backward(dy.data)
G = np.sinh(X)
DX = np.multiply(G, DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), DX, decimal=5)
def test_Acos_cpu(self):
X = np.array([-0.9, -0.3, -0.1, 0.1, 0.5, 0.9]).reshape(3, 2).astype(np.float32)
XT = np.arccos(X)
DY = np.ones((3, 2), dtype = np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(cpu_dev)
dy.to_device(cpu_dev)
result = autograd.acos(x)
dx = result.creator.backward(dy.data)
G = - 1.0 / np.sqrt( 1.0 - np.square(X) )
DX = np.multiply(G, DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), DX, decimal=5)
def test_Acos_gpu(self):
X = np.array([-0.9, -0.3, -0.1, 0.1, 0.5, 0.9]).reshape(3, 2).astype(np.float32)
XT = np.arccos(X)
DY = np.ones((3, 2), dtype = np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(gpu_dev)
dy.to_device(gpu_dev)
result = autograd.acos(x)
dx = result.creator.backward(dy.data)
G = - 1.0 / np.sqrt( 1.0 - np.square(X) )
DX = np.multiply(G, DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), DX, decimal=5)
def test_Acosh_cpu(self):
X = np.array([1.1, 1.5, 1.9, 2.2, 2.5, 2.8]).reshape(3, 2).astype(np.float32)
XT = np.arccosh(X)
DY = np.ones((3, 2), dtype = np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(cpu_dev)
dy.to_device(cpu_dev)
result = autograd.acosh(x)
dx = result.creator.backward(dy.data)
G = 1.0 / np.multiply( np.sqrt( X - 1.0 ) , np.sqrt( X + 1.0 ) )
DX = np.multiply(G, DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), DX, decimal=5)
def test_Acosh_gpu(self):
X = np.array([1.1, 1.5, 1.9, 2.2, 2.5, 2.8]).reshape(3, 2).astype(np.float32)
XT = np.arccosh(X)
DY = np.ones((3, 2), dtype = np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(gpu_dev)
dy.to_device(gpu_dev)
result = autograd.acosh(x)
dx = result.creator.backward(dy.data)
G = 1.0 / np.multiply( np.sqrt( X - 1.0 ) , np.sqrt( X + 1.0 ) )
DX = np.multiply(G, DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), DX, decimal=5)
def test_Sin_cpu(self):
X = np.array([0.8, -1.2, 3.3, -3.6, -0.5, 0.5]).reshape(3, 2).astype(np.float32)
XT = np.sin(X)
DY = np.ones((3, 2), dtype = np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(cpu_dev)
dy.to_device(cpu_dev)
result = autograd.sin(x)
dx = result.creator.backward(dy.data)
G = np.cos(X)
DX = np.multiply(G, DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), DX, decimal=5)
def test_Sin_gpu(self):
X = np.array([0.8, -1.2, 3.3, -3.6, -0.5, 0.5]).reshape(3, 2).astype(np.float32)
XT = np.sin(X)
DY = np.ones((3, 2), dtype = np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(gpu_dev)
dy.to_device(gpu_dev)
result = autograd.sin(x)
dx = result.creator.backward(dy.data)
G = np.cos(X)
DX = np.multiply(G, DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), DX, decimal=5)
def test_Sinh_cpu(self):
X = np.array([0.8, -1.2, 3.3, -3.6, -0.5, 0.5]).reshape(3, 2).astype(np.float32)
XT = np.sinh(X)
DY = np.ones((3, 2), dtype = np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(cpu_dev)
dy.to_device(cpu_dev)
result = autograd.sinh(x)
dx = result.creator.backward(dy.data)
G = np.cosh(X)
DX = np.multiply(G, DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), DX, decimal=5)
def test_Sinh_gpu(self):
X = np.array([0.8, -1.2, 3.3, -3.6, -0.5, 0.5]).reshape(3, 2).astype(np.float32)
XT = np.sinh(X)
DY = np.ones((3, 2), dtype = np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(gpu_dev)
dy.to_device(gpu_dev)
result = autograd.sinh(x)
dx = result.creator.backward(dy.data)
G = np.cosh(X)
DX = np.multiply(G, DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), DX, decimal=5)
def test_Asin_cpu(self):
X = np.array([-0.9, -0.3, -0.1, 0.1, 0.5, 0.9]).reshape(3, 2).astype(np.float32)
XT = np.arcsin(X)
DY = np.ones((3, 2), dtype = np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(cpu_dev)
dy.to_device(cpu_dev)
result = autograd.asin(x)
dx = result.creator.backward(dy.data)
G = 1.0 / np.sqrt( 1.0 - np.square(X) )
DX = np.multiply(G, DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), DX, decimal=5)
def test_Asin_gpu(self):
X = np.array([-0.9, -0.3, -0.1, 0.1, 0.5, 0.9]).reshape(3, 2).astype(np.float32)
XT = np.arcsin(X)
DY = np.ones((3, 2), dtype = np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(gpu_dev)
dy.to_device(gpu_dev)
result = autograd.asin(x)
dx = result.creator.backward(dy.data)
G = 1.0 / np.sqrt( 1.0 - np.square(X) )
DX = np.multiply(G, DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), DX, decimal=5)
def test_Asinh_cpu(self):
X = np.array([-0.9, -0.3, -0.1, 0.1, 0.5, 0.9]).reshape(3, 2).astype(np.float32)
XT = np.arcsinh(X)
DY = np.ones((3, 2), dtype = np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(cpu_dev)
dy.to_device(cpu_dev)
result = autograd.asinh(x)
dx = result.creator.backward(dy.data)
G = 1.0 / np.sqrt( np.square(X) + 1.0 )
DX = np.multiply(G, DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), DX, decimal=5)
def test_Less_gpu(self):
x0 = np.array([-0.9, -0.3, -0.1, 0.1, 0.5, 0.9]).reshape(3, 2).astype(np.float32)
x1 = np.array([0, -0.3, 0, 0.1, 0, 0.9]).reshape(3, 2).astype(np.float32)
y = np.less(x0,x1)
x0 = tensor.from_numpy(x0)
x1 = tensor.from_numpy(x1)
x0.to_device(gpu_dev)
x1.to_device(gpu_dev)
result = autograd.less(x0,x1)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), y, decimal=5)
def test_Less_cpu(self):
x0 = np.array([-0.9, -0.3, -0.1, 0.1, 0.5, 0.9]).reshape(3, 2).astype(np.float32)
x1 = np.array([0, -0.3, 0, 0.1, 0, 0.9]).reshape(3, 2).astype(np.float32)
y = np.less(x0,x1)
x0 = tensor.from_numpy(x0)
x1 = tensor.from_numpy(x1)
x0.to_device(cpu_dev)
x1.to_device(cpu_dev)
result = autograd.less(x0,x1)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), y, decimal=5)
def test_Asinh_gpu(self):
X = np.array([-0.9, -0.3, -0.1, 0.1, 0.5, 0.9]).reshape(3, 2).astype(np.float32)
XT = np.arcsinh(X)
DY = np.ones((3, 2), dtype = np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(gpu_dev)
dy.to_device(gpu_dev)
result = autograd.asinh(x)
dx = result.creator.backward(dy.data)
G = 1.0 / np.sqrt( np.square(X) + 1.0 )
DX = np.multiply(G, DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), DX, decimal=5)
def test_Tan_cpu(self):
X = np.array([0.8, -1.2, 3.3, -3.6, -0.5, 0.5]).reshape(3, 2).astype(np.float32)
XT = np.tan(X)
DY = np.ones((3, 2), dtype = np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(cpu_dev)
dy.to_device(cpu_dev)
result = autograd.tan(x)
dx = result.creator.backward(dy.data)
G = 1.0 / np.square( np.cos(X) )
DX = np.multiply(G, DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), DX, decimal=5)
def test_Tan_gpu(self):
X = np.array([0.8, -1.2, 3.3, -3.6, -0.5, 0.5]).reshape(3, 2).astype(np.float32)
XT = np.tan(X)
DY = np.ones((3, 2), dtype = np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(gpu_dev)
dy.to_device(gpu_dev)
result = autograd.tan(x)
dx = result.creator.backward(dy.data)
G = 1.0 / np.square( np.cos(X) )
DX = np.multiply(G, DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), DX, decimal=5)
def test_Tanh_cpu(self):
X = np.array([0.8, -1.2, 3.3, -3.6, -0.5, 0.5]).reshape(3, 2).astype(np.float32)
XT = np.tanh(X)
DY = np.ones((3, 2), dtype = np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(cpu_dev)
dy.to_device(cpu_dev)
result = autograd.tanh(x)
dx = result.creator.backward(dy.data)
G = 1.0 / np.square( np.cosh(X) )
DX = np.multiply(G, DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), DX, decimal=5)
def test_Tanh_gpu(self):
X = np.array([0.8, -1.2, 3.3, -3.6, -0.5, 0.5]).reshape(3, 2).astype(np.float32)
XT = np.tanh(X)
DY = np.ones((3, 2), dtype = np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(gpu_dev)
dy.to_device(gpu_dev)
result = autograd.tanh(x)
dx = result.creator.backward(dy.data)
G = 1.0 / np.square( np.cosh(X) )
DX = np.multiply(G, DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), DX, decimal=5)
def test_Atan_cpu(self):
X = np.array([-0.9, -0.3, -0.1, 0.1, 0.5, 0.9]).reshape(3, 2).astype(np.float32)
XT = np.arctan(X)
DY = np.ones((3, 2), dtype = np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(cpu_dev)
dy.to_device(cpu_dev)
result = autograd.atan(x)
dx = result.creator.backward(dy.data)
G = 1.0 / ( 1.0 + np.square(X) )
DX = np.multiply(G, DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), DX, decimal=5)
def test_Atan_gpu(self):
X = np.array([-0.9, -0.3, -0.1, 0.1, 0.5, 0.9]).reshape(3, 2).astype(np.float32)
XT = np.arctan(X)
DY = np.ones((3, 2), dtype = np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(gpu_dev)
dy.to_device(gpu_dev)
result = autograd.atan(x)
dx = result.creator.backward(dy.data)
G = 1.0 / ( 1.0 + np.square(X) )
DX = np.multiply(G, DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), DX, decimal=5)
def test_Atanh_cpu(self):
X = np.array([-0.9, -0.3, -0.1, 0.1, 0.5, 0.9]).reshape(3, 2).astype(np.float32)
XT = np.arctanh(X)
DY = np.ones((3, 2), dtype = np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(cpu_dev)
dy.to_device(cpu_dev)
result = autograd.atanh(x)
dx = result.creator.backward(dy.data)
G = 1.0 / ( 1.0 - np.square(X) )
DX = np.multiply(G, DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), DX, decimal=5)
def test_Atanh_gpu(self):
X = np.array([-0.9, -0.3, -0.1, 0.1, 0.5, 0.9]).reshape(3, 2).astype(np.float32)
XT = np.arctanh(X)
DY = np.ones((3, 2), dtype = np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(gpu_dev)
dy.to_device(gpu_dev)
result = autograd.atanh(x)
dx = result.creator.backward(dy.data)
G = 1.0 / ( 1.0 - np.square(X) )
DX = np.multiply(G, DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), DX, decimal=5)
def test_Sub_cpu(self):
X0 = np.array([7, -5, 0.2, -0.1, 0.3, 4]).reshape(3, 2).astype(np.float32)
X1 = np.array([0.6, -1.3, 0.1, -0.1, 0.4, 0.3]).reshape(3, 2).astype(np.float32)
XT = np.subtract(X0, X1)
DY = np.ones((3, 2), dtype = np.float32)
x0 = tensor.from_numpy(X0)
x1 = tensor.from_numpy(X1)
dy = tensor.from_numpy(DY)
x0.to_device(cpu_dev)
x1.to_device(cpu_dev)
dy.to_device(cpu_dev)
result = autograd.sub(x0, x1)
dx0, dx1 = result.creator.backward(dy.data)
DX0 = np.multiply(DY, 1.0)
DX1 = np.multiply(DY, -1.0)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx0)), DX0, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx1)), DX1, decimal=5)
def test_Sub_gpu(self):
X0 = np.array([7, -5, 0.2, -0.1, 0.3, 4]).reshape(3, 2).astype(np.float32)
X1 = np.array([0.6, -1.3, 0.1, -0.1, 0.4, 0.3]).reshape(3, 2).astype(np.float32)
XT = np.subtract(X0, X1)
DY = np.ones((3, 2), dtype = np.float32)
x0 = tensor.from_numpy(X0)
x1 = tensor.from_numpy(X1)
dy = tensor.from_numpy(DY)
x0.to_device(gpu_dev)
x1.to_device(gpu_dev)
dy.to_device(gpu_dev)
result = autograd.sub(x0, x1)
dx0, dx1 = result.creator.backward(dy.data)
DX0 = np.multiply(DY, 1.0)
DX1 = np.multiply(DY, -1.0)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx0)), DX0, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx1)), DX1, decimal=5)
def test_Pow_cpu(self):
X0 = np.array([7, 5, 0.2, 0.1, 0.3, 4]).reshape(3, 2).astype(np.float32)
X1 = np.array([-1.0, 2.0, -1.0, -2.1, 1.0, -2.0]).reshape(3, 2).astype(np.float32)
XT = np.power(X0, X1)
DY = np.ones((3, 2), dtype = np.float32)
x0 = tensor.from_numpy(X0)
x1 = tensor.from_numpy(X1)
dy = tensor.from_numpy(DY)
x0.to_device(cpu_dev)
x1.to_device(cpu_dev)
dy.to_device(cpu_dev)
result = autograd.pow(x0, x1)
dx0, dx1 = result.creator.backward(dy.data)
G0 = np.multiply(X1, np.power(X0, (X1 - 1.0)) )
DX0 = np.multiply(G0, DY)
G1 = np.multiply(np.power(X0, X1), np.log(X0) )
DX1 = np.multiply(G1, DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx0)), DX0, decimal=4)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx1)), DX1, decimal=4)
def test_Pow_gpu(self):
X0 = np.array([7, 5, 0.2, 0.1, 0.3, 4]).reshape(3, 2).astype(np.float32)
X1 = np.array([-1.0, 2.0, -1.0, -2.1, 1.0, -2.0]).reshape(3, 2).astype(np.float32)
XT = np.power(X0, X1)
DY = np.ones((3, 2), dtype = np.float32)
x0 = tensor.from_numpy(X0)
x1 = tensor.from_numpy(X1)
dy = tensor.from_numpy(DY)
x0.to_device(gpu_dev)
x1.to_device(gpu_dev)
dy.to_device(gpu_dev)
result = autograd.pow(x0, x1)
dx0, dx1 = result.creator.backward(dy.data)
G0 = np.multiply(X1, np.power(X0, (X1 - 1.0)) )
DX0 = np.multiply(G0, DY)
G1 = np.multiply(np.power(X0, X1), np.log(X0) )
DX1 = np.multiply(G1, DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx0)), DX0, decimal=4)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx1)), DX1, decimal=4)
def test_SoftSign_cpu(self):
# y = x / (1 + np.abs(x))
X = np.array([0.8, -1.2, 3.3, -3.6, -0.5, 0.5]).reshape(3, 2).astype(np.float32)
XT = X/(1 + np.absolute(X))
DY = np.ones((3, 2), dtype = np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(cpu_dev)
dy.to_device(cpu_dev)
result = autograd.softsign(x)
dx = result.creator.backward(dy.data)
G = 1.0/np.square(np.absolute(X)+1.0)
DX = np.multiply(G, DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), DX, decimal=5)
def test_SoftSign_gpu(self):
# y = x / (1 + np.abs(x))
X = np.array([0.8, -1.2, 3.3, -3.6, -0.5, 0.5]).reshape(3, 2).astype(np.float32)
XT = X/(1 + np.absolute(X))
DY = np.ones((3, 2), dtype = np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(gpu_dev)
dy.to_device(gpu_dev)
result = autograd.softsign(x)
dx = result.creator.backward(dy.data)
G = 1.0/np.square(np.absolute(X)+1.0)
DX = np.multiply(G, DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), DX, decimal=5)
def test_SoftPlus_cpu(self):
#y = np.log(np.exp(x) + 1)
X = np.array([0.8, -1.2, 3.3, -3.6, -0.5, 0.5]).reshape(3, 2).astype(np.float32)
XT = np.log(np.exp(X) + 1)
DY = np.ones((3, 2), dtype = np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(cpu_dev)
dy.to_device(cpu_dev)
result = autograd.softplus(x)
dx = result.creator.backward(dy.data)
G = 1.0 / (1.0 + np.exp(-X))
DX = np.multiply(G, DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), DX, decimal=5)
def test_SoftPlus_gpu(self):
#y = np.log(np.exp(x) + 1)
X = np.array([0.8, -1.2, 3.3, -3.6, -0.5, 0.5]).reshape(3, 2).astype(np.float32)
XT = np.log(np.exp(X) + 1)
DY = np.ones((3, 2), dtype = np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(gpu_dev)
dy.to_device(gpu_dev)
result = autograd.softplus(x)
dx = result.creator.backward(dy.data)
G = 1.0 / (1.0 + np.exp(-X))
DX = np.multiply(G, DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), DX, decimal=5)
def test_unsqueeze_cpu(self):
x = np.array([0.1,-1.0,0.4,4.0,-0.9,9.0]).reshape(1,2,3).astype(np.float32)
y = x.reshape(1, 1, 2, 3, 1)
dy = np.ones((1, 1, 2, 3, 1), dtype = np.float32)
grad = dy.reshape(1,2,3)
x = tensor.from_numpy(x)
dy = tensor.from_numpy(dy)
x.to_device(cpu_dev)
dy.to_device(cpu_dev)
result = autograd.unsqueeze(x,[0, 4])
dx = result.creator.backward(dy.data)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), y, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), grad, decimal=5)
def test_unsqueeze_gpu(self):
x = np.array([0.1,-1.0,0.4,4.0,-0.9,9.0]).reshape(1,2,3).astype(np.float32)
y = x.reshape(1, 1, 2, 3, 1)
dy = np.ones((1, 1, 2, 3, 1), dtype = np.float32)
grad = dy.reshape(1,2,3)
x = tensor.from_numpy(x)
dy = tensor.from_numpy(dy)
x.to_device(cpu_dev)
dy.to_device(cpu_dev)
result = autograd.unsqueeze(x,[0, 4])
dx = result.creator.backward(dy.data)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), y, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), grad, decimal=5)
def test_Sqrt_cpu(self):
X = np.array([0.1,1.0,0.4,4.0,0.9,9.0]).reshape(3,2).astype(np.float32)
XT = np.sqrt(X)
DY = np.ones((3, 2), dtype = np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(cpu_dev)
dy.to_device(cpu_dev)
result = autograd.sqrt(x)
dx = result.creator.backward(dy.data)
G = 0.5 * np.power(X, -0.5)
DX = np.multiply(G, DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), DX, decimal=5)
def test_Sqrt_gpu(self):
X = np.array([0.1,1.0,0.4,4.0,0.9,9.0]).reshape(3,2).astype(np.float32)
XT = np.sqrt(X)
DY = np.ones((3, 2), dtype = np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(gpu_dev)
dy.to_device(gpu_dev)
result = autograd.sqrt(x)
dx = result.creator.backward(dy.data)
G = 0.5 * np.power(X, -0.5)
DX = np.multiply(G, DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), DX, decimal=5)
def test_transpose_cpu(self):
x = np.random.randn(3,2,1)
y = x.transpose(1,2,0)
dy = np.random.randn(*(y.shape))
grad = dy.transpose((2,0,1))
x = tensor.from_numpy(x)
dy = tensor.from_numpy(dy)
x.to_device(cpu_dev)
dy.to_device(cpu_dev)
result = autograd.transpose(x,(1,2,0))
dx = result.creator.backward(dy.data)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), y, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), grad, decimal=5)
def test_transpose_gpu(self):
x = np.random.randn(3,2,1)
y = x.transpose(1,2,0)
dy = np.random.randn(*(y.shape))
grad = dy.transpose((2,0,1))
x = tensor.from_numpy(x)
dy = tensor.from_numpy(dy)
x.to_device(gpu_dev)
dy.to_device(gpu_dev)
result = autograd.transpose(x,(1,2,0))
dx = result.creator.backward(dy.data)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), y, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), grad, decimal=5)
def test_Sign_cpu(self):
X = np.array([0.8, -1.2, 3.3, -3.6, -0.5, 0.5]).reshape(3, 2).astype(np.float32)
XT = np.sign(X)
DY = np.ones((3, 2), dtype = np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(gpu_dev)
dy.to_device(gpu_dev)
result = autograd.sign(x)
dx = result.creator.backward(dy.data)
DX = np.multiply(DY,0)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), DX, decimal=5)
def test_Sign_gpu(self):
X = np.array([0.8, -1.2, 3.3, -3.6, -0.5, 0.5]).reshape(3, 2).astype(np.float32)
XT = np.sign(X)
DY = np.ones((3, 2), dtype = np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(gpu_dev)
dy.to_device(gpu_dev)
result = autograd.sign(x)
dx = result.creator.backward(dy.data)
DX = np.multiply(DY,0)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), DX, decimal=5)
def test_Log_cpu(self):
X = np.array([0.1,1.0,0.4,1.4,0.9,2.0]).reshape(3,2).astype(np.float32)
XT = np.log(X)
DY = np.ones((3, 2), dtype = np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(gpu_dev)
dy.to_device(gpu_dev)
result = autograd.log(x)
dx = result.creator.backward(dy.data)
#dx = 1/x
G = 1.0 / X
DX = np.multiply(G, DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), DX, decimal=5)
def test_Log_gpu(self):
X = np.array([0.1,1.0,0.4,1.4,0.9,2.0]).reshape(3,2).astype(np.float32)
XT = np.log(X)
DY = np.ones((3, 2), dtype = np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(gpu_dev)
dy.to_device(gpu_dev)
result = autograd.log(x)
dx = result.creator.backward(dy.data)
#dx = 1/x
G = 1.0 / X
DX = np.multiply(G, DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), DX, decimal=5)
def test_mul_cpu(self):
x = np.array([0.1,-1.0,0.4,4.0,-0.9,9.0]).reshape(3,2).astype(np.float32)
x1 = np.array([0.1,1.0,0.4,4.0,0.9,9.0]).reshape(3,2).astype(np.float32)
y = x*x1
dy = np.array([0.1,1.0,0.4,4.0,0.9,9.0]).reshape(3,2).astype(np.float32)
grad0=x1*dy
grad1=x*dy
x = tensor.from_numpy(x)
slope = tensor.from_numpy(x1)
dy = tensor.from_numpy(dy)
x.to_device(cpu_dev)
slope.to_device(cpu_dev)
dy.to_device(cpu_dev)
result = autograd.mul(x,slope)
dx0,dx1 = result.creator.backward(dy.data)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), y, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx0)), grad0, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx1)), grad1, decimal=5)
def test_mul_gpu(self):
x = np.array([0.1,-1.0,0.4,4.0,-0.9,9.0]).reshape(3,2).astype(np.float32)
x1 = np.array([0.1,1.0,0.4,4.0,0.9,9.0]).reshape(3,2).astype(np.float32)
y = x*x1
dy = np.array([0.1,1.0,0.4,4.0,0.9,9.0]).reshape(3,2).astype(np.float32)
grad0=x1*dy
grad1=x*dy
x = tensor.from_numpy(x)
slope = tensor.from_numpy(x1)
dy = tensor.from_numpy(dy)
x.to_device(gpu_dev)
slope.to_device(gpu_dev)
dy.to_device(gpu_dev)
result = autograd.mul(x,slope)
dx0,dx1 = result.creator.backward(dy.data)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), y, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx0)), grad0, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx1)), grad1, decimal=5)
def test_reshape_cpu(self):
x = np.array([0.1,-1.0,0.4,4.0,-0.9,9.0]).reshape(3,2).astype(np.float32)
y = x.reshape(2,3)
dy = np.ones((3, 2), dtype = np.float32)
grad = dy.reshape(3,2)
x = tensor.from_numpy(x)
dy = tensor.from_numpy(dy)
x.to_device(cpu_dev)
dy.to_device(cpu_dev)
result = autograd.reshape(x,(2,3))
dx = result.creator.backward(dy.data)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), y, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), grad, decimal=5)
def test_reshape_gpu(self):
x = np.array([0.1,-1.0,0.4,4.0,-0.9,9.0]).reshape(3,2).astype(np.float32)
y = x.reshape(2,3)
dy = np.ones((3, 2), dtype = np.float32)
grad = dy.reshape(3,2)
x = tensor.from_numpy(x)
dy = tensor.from_numpy(dy)
x.to_device(gpu_dev)
dy.to_device(gpu_dev)
result = autograd.reshape(x,(2,3))
dx = result.creator.backward(dy.data)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), y, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), grad, decimal=5)
def test_max_cpu(self):
X0 = np.array([0.1, 0.2, 2.0, 0.0, 0.1, 0.2]).reshape(3, 2).astype(np.float32)
X1 = np.array([1.0, 2.0, 1.0, 2.1, 0.0, 2.0]).reshape(3, 2).astype(np.float32)
XT=np.maximum(X0,X1)
DY = np.ones((3, 2), dtype = np.float32)
x0 = tensor.from_numpy(X0)
x1 = tensor.from_numpy(X1)
dy = tensor.from_numpy(DY)
x0.to_device(cpu_dev)
x1.to_device(cpu_dev)
dy.to_device(cpu_dev)
result = autograd.max(x0,x1)
dx0,dx1 = result.creator.backward(dy.data)
G = np.subtract(X0,X1)
DX0 = np.where(G>0 , 1, G*0)
DX1 = np.where(G<0 , 1, G*0)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx0)), DX0, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx1)), DX1, decimal=5)
def test_max_gpu(self):
X0 = np.array([0.1, 0.2, 2.0, 0.0, 0.1, 0.2]).reshape(3, 2).astype(np.float32)
X1 = np.array([1.0, 2.0, 1.0, 2.1, 0.0, 2.0]).reshape(3, 2).astype(np.float32)
XT=np.maximum(X0,X1)
DY = np.ones((3, 2), dtype = np.float32)
x0 = tensor.from_numpy(X0)
x1 = tensor.from_numpy(X1)
dy = tensor.from_numpy(DY)
x0.to_device(gpu_dev)
x1.to_device(gpu_dev)
dy.to_device(gpu_dev)
result = autograd.max(x0,x1)
dx0,dx1 = result.creator.backward(dy.data)
G = np.subtract(X0,X1)
DX0 = np.where(G>0 , 1, G*0)
DX1 = np.where(G<0 , 1, G*0)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx0)), DX0, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx1)), DX1, decimal=5)
def test_Div_cpu(self):
X0 = np.array([7, -5, 0.2, -0.1, 0.3, 4]).reshape(3, 2).astype(np.float32)
X1 = np.array([0.6, -1.3, 0.1, -0.1, 0.4, 0.3]).reshape(3, 2).astype(np.float32)
XT = np.divide(X0, X1)
DY = np.ones((3, 2), dtype = np.float32)
x0 = tensor.from_numpy(X0)
x1 = tensor.from_numpy(X1)
dy = tensor.from_numpy(DY)
x0.to_device(cpu_dev)
x1.to_device(cpu_dev)
dy.to_device(cpu_dev)
result = autograd.div(x0, x1)
dx0, dx1 = result.creator.backward(dy.data)
G0 = 1.0 / X1
DX0 = np.multiply(G0, DY)
G1 = np.divide(-X0, np.square(X1))
DX1 = np.multiply(G1, DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx0)), DX0, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx1)), DX1, decimal=5)
def test_Div_gpu(self):
X0 = np.array([7, -5, 0.2, -0.1, 0.3, 4]).reshape(3, 2).astype(np.float32)
X1 = np.array([0.6, -1.3, 0.1, -0.1, 0.4, 0.3]).reshape(3, 2).astype(np.float32)
XT = np.divide(X0, X1)
DY = np.ones((3, 2), dtype = np.float32)
x0 = tensor.from_numpy(X0)
x1 = tensor.from_numpy(X1)
dy = tensor.from_numpy(DY)
x0.to_device(gpu_dev)
x1.to_device(gpu_dev)
dy.to_device(gpu_dev)
result = autograd.div(x0, x1)
dx0, dx1 = result.creator.backward(dy.data)
G0 = 1.0 / X1
DX0 = np.multiply(G0, DY)
G1 = np.divide(-X0, np.square(X1))
DX1 = np.multiply(G1, DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx0)), DX0, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx1)), DX1, decimal=5)
def test_squeeze(self):
def squeeze_helper(gpu=False):
x = np.random.randn(3,1,2,1,1)
y = x.reshape(3, 2)
dy = np.random.randn(3, 2)
grad = dy.reshape(3,1,2,1,1)
x = tensor.from_numpy(x)
dy = tensor.from_numpy(dy)
if(gpu):
x.to_device(gpu_dev)
dy.to_device(gpu_dev)
result = autograd.squeeze(x,[1,3,4])
dx = result.creator.backward(dy.data)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), y, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), grad, decimal=5)
squeeze_helper(False)
squeeze_helper(True)
def test_shape_cpu(self):
x = np.array([0.1,-1.0,0.4,4.0,-0.9,9.0]).reshape(3,2).astype(np.float32)
y = list(x.shape)
dy = np.ones((3, 2), dtype = np.float32)
grad = list(dy.shape)
x = tensor.from_numpy(x)
dy = tensor.from_numpy(dy)
x.to_device(cpu_dev)
dy.to_device(cpu_dev)
result=autograd.shape(x)
dx = result.creator.backward(dy.data)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), y, decimal=5)
np.testing.assert_array_almost_equal(dx, grad, decimal=5)
def test_shape_gpu(self):
x = np.array([0.1,-1.0,0.4,4.0,-0.9,9.0]).reshape(3,2).astype(np.float32)
y = list(x.shape)
dy = np.ones((3, 2), dtype = np.float32)
grad = list(dy.shape)
x = tensor.from_numpy(x)
dy = tensor.from_numpy(dy)
x.to_device(gpu_dev)
dy.to_device(gpu_dev)
result=autograd.shape(x)
dx = result.creator.backward(dy.data)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), y, decimal=5)
np.testing.assert_array_almost_equal(dx, grad, decimal=5)
def test_min_cpu(self):
X0 = np.array([0.1, 0.2, 2.0, 0.0, 0.1, 0.2]).reshape(3, 2).astype(np.float32)
X1 = np.array([1.0, 2.0, 1.0, 2.1, 0.0, 2.0]).reshape(3, 2).astype(np.float32)
XT=np.minimum(X0,X1)
DY = np.ones((3, 2), dtype = np.float32)
x0 = tensor.from_numpy(X0)
x1 = tensor.from_numpy(X1)
dy = tensor.from_numpy(DY)
x0.to_device(cpu_dev)
x1.to_device(cpu_dev)
dy.to_device(cpu_dev)
result = autograd.min(x0,x1)
dx0,dx1 = result.creator.backward(dy.data)
G = np.subtract(X0,X1)
DX0 = np.where(G<0 , 1, G*0)
DX1 = np.where(G>0 , 1, G*0)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx0)), DX0, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx1)), DX1, decimal=5)
def test_min_gpu(self):
X0 = np.array([0.1, 0.2, 2.0, 0.0, 0.1, 0.2]).reshape(3, 2).astype(np.float32)
X1 = np.array([1.0, 2.0, 1.0, 2.1, 0.0, 2.0]).reshape(3, 2).astype(np.float32)
XT=np.minimum(X0,X1)
DY = np.ones((3, 2), dtype = np.float32)
x0 = tensor.from_numpy(X0)
x1 = tensor.from_numpy(X1)
dy = tensor.from_numpy(DY)
x0.to_device(gpu_dev)
x1.to_device(gpu_dev)
dy.to_device(gpu_dev)
result = autograd.min(x0,x1)
dx0,dx1 = result.creator.backward(dy.data)
G = np.subtract(X0,X1)
DX0 = np.where(G<0 , 1, G*0)
DX1 = np.where(G>0 , 1, G*0)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx0)), DX0, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx1)), DX1, decimal=5)
def test_HardSigmoid(self):
def test_helper(gpu=False):
x = np.random.randn(3, 2)
#y = max(0, min(1, alpha * x + gamma))
a=0.2
g=0.5
y = np.clip(x * 0.2 + 0.5, 0, 1)
dy=np.random.randn(3,2)
grad=(0<(np.clip(x * 0.2 + 0.5, 0, 1)) * (np.clip(x * 0.2 + 0.5, 0, 1)<1))*0.2 * dy
x = tensor.from_numpy(x)
dy = tensor.from_numpy(dy)
if(gpu):
x.to_device(gpu_dev)
dy.to_device(gpu_dev)
result = autograd.hardsigmoid(x,a,g)
dx = result.creator.backward(dy.data)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), y, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), grad, decimal=5)
test_helper(False)
test_helper(True)
def test_prelu(self):
def test_helper(gpu):
x = np.random.randn(3, 2)
slope = np.random.randn(3, 2)
y = np.clip(x, 0, np.inf) + np.clip(x, -np.inf, 0) * slope
dy = np.random.randn(3, 2)
x0=x.copy()
x0[x0>0]=1
x0[x0<1]=0
grad0=(x0+(1-x0)*slope)*dy
grad1 = (1-x0)*x*dy
x = tensor.from_numpy(x)
slope = tensor.from_numpy(slope)
dy = tensor.from_numpy(dy)
if(gpu):
x.to_device(gpu_dev)
slope.to_device(gpu_dev)
dy.to_device(gpu_dev)
result = autograd.prelu(x,slope)
dx0,dx1 = result.creator.backward(dy.data)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), y, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx0)), grad0, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx1)), grad1, decimal=5)
test_helper(False)
if(singa_wrap.USE_CUDA):
test_helper(True)
def test_SeLU(self):
def test_helper(gpu):
x = np.random.randn(3, 2)
a=0.2
g=0.3
y = np.clip(x, 0, np.inf) * g + (np.exp(np.clip(x, -np.inf, 0)) - 1) * a * g
dy=np.random.randn(3, 2)
grad = (np.exp(np.clip(x, -np.inf, 0))) * g
grad[x<=0]=grad[x<=0]*a
grad*=dy
x = tensor.from_numpy(x)
def test_and_cpu(self):
x0 = np.array([0, -0.3, -0.1, 0.1, 0.5, 0.9]).reshape(3, 2).astype(np.float32)
x1 = np.array([0, -0.3, 0, 0.1, 0.5, 0.9]).reshape(3, 2).astype(np.float32)
y = np.logical_and(x0,x1)
x0 = tensor.from_numpy(x0)
x1 = tensor.from_numpy(x1)
x0.to_device(cpu_dev)
x1.to_device(cpu_dev)
result = autograd._and(x0,x1)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), y, decimal=5)
def test_and_gpu(self):
x0 = np.array([0, -0.3, -0.1, 0.1, 0.5, 0.9]).reshape(3, 2).astype(np.float32)
x1 = np.array([0, -0.3, 0, 0.1, 0.5, 0.9]).reshape(3, 2).astype(np.float32)
y = np.logical_and(x0,x1)
x0 = tensor.from_numpy(x0)
x1 = tensor.from_numpy(x1)
x0.to_device(gpu_dev)
x1.to_device(gpu_dev)
result = autograd._and(x0,x1)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), y, decimal=5)
def test_or_cpu(self):
x0 = np.array([1.0, 1.0, 2.0, -3.0, 0, -7.0]).reshape(3, 2).astype(np.float32)
x1 = np.array([-1.0, 0, 2.0, 4.0, 0, -7.0]).reshape(3, 2).astype(np.float32)
y = np.logical_or(x0,x1)
x0 = tensor.from_numpy(x0)
x1 = tensor.from_numpy(x1)
x0.to_device(cpu_dev)
x1.to_device(cpu_dev)
result = autograd._or(x0,x1)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), y, decimal=5)
def test_or_gpu(self):
x0 = np.array([1.0, 1.0, 2.0, -3.0, 0, -7.0]).reshape(3, 2).astype(np.float32)
x1 = np.array([-1.0, 0, 2.0, 4.0, 0, -7.0]).reshape(3, 2).astype(np.float32)
y = np.logical_or(x0,x1)
x0 = tensor.from_numpy(x0)
x1 = tensor.from_numpy(x1)
x0.to_device(gpu_dev)
x1.to_device(gpu_dev)
result = autograd._or(x0,x1)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), y, decimal=5)
def test_not_cpu(self):
x = np.array([1.0, -1.0, 0, -0.1, 0, -7.0]).reshape(3, 2).astype(np.float32)
y = np.logical_not(x)
x = tensor.from_numpy(x)
x.to_device(cpu_dev)
result = autograd._not(x)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), y, decimal=5)
def test_not_gpu(self):
x = np.array([1.0, -1.0, 0, -0.1, 0, -7.0]).reshape(3, 2).astype(np.float32)
y = np.logical_not(x)
x = tensor.from_numpy(x)
x.to_device(gpu_dev)
result = autograd._not(x)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), y, decimal=5)
def test_xor_cpu(self):
x0 = np.array([0, -0.3, -0.1, 0.1, 0.5, 9.0]).reshape(3, 2).astype(np.float32)
x1 = np.array([0, -0.3, 0, 0.1, 0, 0.9]).reshape(3, 2).astype(np.float32)
y = np.logical_xor(x0,x1)
x0 = tensor.from_numpy(x0)
x1 = tensor.from_numpy(x1)
x0.to_device(cpu_dev)
x1.to_device(cpu_dev)
result = autograd._xor(x0,x1)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), y, decimal=5)
def test_xor_gpu(self):
x0 = np.array([0, -0.3, -0.1, 0.1, 0.5, 9.0]).reshape(3, 2).astype(np.float32)
x1 = np.array([0, -0.3, 0, 0.1, 0, 0.9]).reshape(3, 2).astype(np.float32)
y = np.logical_xor(x0,x1)
x0 = tensor.from_numpy(x0)
x1 = tensor.from_numpy(x1)
x0.to_device(gpu_dev)
x1.to_device(gpu_dev)
result = autograd._xor(x0,x1)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), y, decimal=5)
def test_negative_cpu(self):
X = np.array([0.1,0,0.4,1.-4,0.9,-2.0]).reshape(3,2).astype(np.float32)
XT = np.negative(X)
DY = np.ones((3, 2), dtype = np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(cpu_dev)
dy.to_device(cpu_dev)
result = autograd.negative(x)
dx = result.creator.backward(dy.data)
DX = np.negative(DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), DX, decimal=5)
def test_negative_gpu(self):
X = np.array([0.1,0,0.4,1.-4,0.9,-2.0]).reshape(3,2).astype(np.float32)
XT = np.negative(X)
DY = np.ones((3, 2), dtype = np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(gpu_dev)
dy.to_device(gpu_dev)
result = autograd.negative(x)
dx = result.creator.backward(dy.data)
DX = np.negative(DY)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), DX, decimal=5)
def test_reciprocal_cpu(self):
X = np.array([0.1,0,0.4,1.-4,0.9,-2.0]).reshape(3,2).astype(np.float32)
DY = np.ones((3, 2), dtype = np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(cpu_dev)
dy.to_device(cpu_dev)
result = autograd.reciprocal(x)
dx = result.creator.backward(dy.data)
#dy/dx = -1/x**2
with np.errstate(divide='ignore'):
XT = np.reciprocal(X)
DX = -1/np.square(X)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), DX, decimal=5)
def test_reciprocal_gpu(self):
X = np.array([0.1,0,0.4,1.-4,0.9,-2.0]).reshape(3,2).astype(np.float32)
DY = np.ones((3, 2), dtype = np.float32)
x = tensor.from_numpy(X)
dy = tensor.from_numpy(DY)
x.to_device(gpu_dev)
dy.to_device(gpu_dev)
result = autograd.reciprocal(x)
dx = result.creator.backward(dy.data)
#dy/dx = -1/x**2
with np.errstate(divide='ignore'):
XT = np.reciprocal(X)
DX = -1/np.square(X)
np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)), DX, decimal=5)
if __name__ == '__main__':
unittest.main()
| 36.591225
| 112
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0
| 7
|
76e2340689e3f62ee6bee0f3bd19ecc30cdcf5e0
| 61,303
|
py
|
Python
|
tb_rest_client/api/api_pe/entity_view_controller_api.py
|
samson0v/python_tb_rest_client
|
08ff7898740f7cec2170e85d5c3c89e222e967f7
|
[
"Apache-2.0"
] | 30
|
2020-06-19T06:42:50.000Z
|
2021-08-23T21:16:36.000Z
|
tb_rest_client/api/api_pe/entity_view_controller_api.py
|
samson0v/python_tb_rest_client
|
08ff7898740f7cec2170e85d5c3c89e222e967f7
|
[
"Apache-2.0"
] | 25
|
2021-08-30T01:17:27.000Z
|
2022-03-16T14:10:14.000Z
|
tb_rest_client/api/api_pe/entity_view_controller_api.py
|
samson0v/python_tb_rest_client
|
08ff7898740f7cec2170e85d5c3c89e222e967f7
|
[
"Apache-2.0"
] | 23
|
2020-07-06T13:41:54.000Z
|
2021-08-23T21:04:50.000Z
|
# coding: utf-8
"""
ThingsBoard REST API
ThingsBoard Professional Edition IoT platform REST API documentation. # noqa: E501
OpenAPI spec version: 3.3.3PAAS-RC1
Contact: info@thingsboard.io
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
import re # noqa: F401
# python 2 and python 3 compatibility library
import six
from tb_rest_client.api_client import ApiClient
class EntityViewControllerApi(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 delete_entity_view_using_delete(self, entity_view_id, **kwargs): # noqa: E501
"""Delete entity view (deleteEntityView) # noqa: E501
Delete the EntityView object based on the provided entity view id. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_entity_view_using_delete(entity_view_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str entity_view_id: A string value representing the entity view id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required)
:return: None
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.delete_entity_view_using_delete_with_http_info(entity_view_id, **kwargs) # noqa: E501
else:
(data) = self.delete_entity_view_using_delete_with_http_info(entity_view_id, **kwargs) # noqa: E501
return data
def delete_entity_view_using_delete_with_http_info(self, entity_view_id, **kwargs): # noqa: E501
"""Delete entity view (deleteEntityView) # noqa: E501
Delete the EntityView object based on the provided entity view id. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_entity_view_using_delete_with_http_info(entity_view_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str entity_view_id: A string value representing the entity view id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required)
:return: None
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['entity_view_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 delete_entity_view_using_delete" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'entity_view_id' is set
if ('entity_view_id' not in params or
params['entity_view_id'] is None):
raise ValueError("Missing the required parameter `entity_view_id` when calling `delete_entity_view_using_delete`") # noqa: E501
collection_formats = {}
path_params = {}
if 'entity_view_id' in params:
path_params['entityViewId'] = params['entity_view_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
# Authentication setting
auth_settings = ['X-Authorization'] # noqa: E501
return self.api_client.call_api(
'/api/entityView/{entityViewId}', 'DELETE',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type=None, # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def find_by_query_using_post4(self, **kwargs): # noqa: E501
"""Find related entity views (findByQuery) # noqa: E501
Returns all entity views that are related to the specific entity. The entity id, relation type, entity view types, depth of the search, and other query parameters defined using complex 'EntityViewSearchQuery' object. See 'Model' tab of the Parameters for more info. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.find_by_query_using_post4(async_req=True)
>>> result = thread.get()
:param async_req bool
:param EntityViewSearchQuery body:
:return: list[EntityView]
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.find_by_query_using_post4_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.find_by_query_using_post4_with_http_info(**kwargs) # noqa: E501
return data
def find_by_query_using_post4_with_http_info(self, **kwargs): # noqa: E501
"""Find related entity views (findByQuery) # noqa: E501
Returns all entity views that are related to the specific entity. The entity id, relation type, entity view types, depth of the search, and other query parameters defined using complex 'EntityViewSearchQuery' object. See 'Model' tab of the Parameters for more info. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.find_by_query_using_post4_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:param EntityViewSearchQuery body:
:return: list[EntityView]
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['body'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method find_by_query_using_post4" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'body' in params:
body_params = params['body']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['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 = ['X-Authorization'] # noqa: E501
return self.api_client.call_api(
'/api/entityViews', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='list[EntityView]', # 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_customer_entity_views_using_get(self, customer_id, page_size, page, **kwargs): # noqa: E501
"""Get Customer Entity Views (getCustomerEntityViews) # noqa: E501
Returns a page of Entity View objects assigned to customer. You can specify parameters to filter the results. The result is wrapped with PageData object that allows you to iterate over result set using pagination. See the 'Model' tab of the Response Class for more details. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. # 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_customer_entity_views_using_get(customer_id, page_size, page, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str customer_id: A string value representing the customer id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required)
:param int page_size: Maximum amount of entities in a one page (required)
:param int page: Sequence number of page starting from 0 (required)
:param str type: ## Entity View Filter Allows to filter entity views based on their type and the **'starts with'** expression over their name. For example, this entity filter selects all 'Concrete Mixer' entity views which name starts with 'CAT': ```json { \"type\": \"entityViewType\", \"entityViewType\": \"Concrete Mixer\", \"entityViewNameFilter\": \"CAT\" } ```
:param str text_search: The case insensitive 'startsWith' filter based on the entity view name.
:param str sort_property: Property of entity to sort by
:param str sort_order: Sort order. ASC (ASCENDING) or DESC (DESCENDING)
:return: PageDataEntityView
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_customer_entity_views_using_get_with_http_info(customer_id, page_size, page, **kwargs) # noqa: E501
else:
(data) = self.get_customer_entity_views_using_get_with_http_info(customer_id, page_size, page, **kwargs) # noqa: E501
return data
def get_customer_entity_views_using_get_with_http_info(self, customer_id, page_size, page, **kwargs): # noqa: E501
"""Get Customer Entity Views (getCustomerEntityViews) # noqa: E501
Returns a page of Entity View objects assigned to customer. You can specify parameters to filter the results. The result is wrapped with PageData object that allows you to iterate over result set using pagination. See the 'Model' tab of the Response Class for more details. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. # 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_customer_entity_views_using_get_with_http_info(customer_id, page_size, page, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str customer_id: A string value representing the customer id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required)
:param int page_size: Maximum amount of entities in a one page (required)
:param int page: Sequence number of page starting from 0 (required)
:param str type: ## Entity View Filter Allows to filter entity views based on their type and the **'starts with'** expression over their name. For example, this entity filter selects all 'Concrete Mixer' entity views which name starts with 'CAT': ```json { \"type\": \"entityViewType\", \"entityViewType\": \"Concrete Mixer\", \"entityViewNameFilter\": \"CAT\" } ```
:param str text_search: The case insensitive 'startsWith' filter based on the entity view name.
:param str sort_property: Property of entity to sort by
:param str sort_order: Sort order. ASC (ASCENDING) or DESC (DESCENDING)
:return: PageDataEntityView
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['customer_id', 'page_size', 'page', 'type', 'text_search', 'sort_property', 'sort_order'] # 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_customer_entity_views_using_get" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'customer_id' is set
if ('customer_id' not in params or
params['customer_id'] is None):
raise ValueError("Missing the required parameter `customer_id` when calling `get_customer_entity_views_using_get`") # noqa: E501
# verify the required parameter 'page_size' is set
if ('page_size' not in params or
params['page_size'] is None):
raise ValueError("Missing the required parameter `page_size` when calling `get_customer_entity_views_using_get`") # noqa: E501
# verify the required parameter 'page' is set
if ('page' not in params or
params['page'] is None):
raise ValueError("Missing the required parameter `page` when calling `get_customer_entity_views_using_get`") # noqa: E501
collection_formats = {}
path_params = {}
if 'customer_id' in params:
path_params['customerId'] = params['customer_id'] # noqa: E501
query_params = []
if 'page_size' in params:
query_params.append(('pageSize', params['page_size'])) # noqa: E501
if 'page' in params:
query_params.append(('page', params['page'])) # noqa: E501
if 'type' in params:
query_params.append(('type', params['type'])) # noqa: E501
if 'text_search' in params:
query_params.append(('textSearch', params['text_search'])) # noqa: E501
if 'sort_property' in params:
query_params.append(('sortProperty', params['sort_property'])) # noqa: E501
if 'sort_order' in params:
query_params.append(('sortOrder', params['sort_order'])) # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['X-Authorization'] # noqa: E501
return self.api_client.call_api(
'/api/customer/{customerId}/entityViews{?page,pageSize,sortOrder,sortProperty,textSearch,type}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='PageDataEntityView', # 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_entity_view_by_id_using_get(self, entity_view_id, **kwargs): # noqa: E501
"""Get entity view (getEntityViewById) # noqa: E501
Fetch the EntityView object based on the provided entity view id. Entity Views limit the degree of exposure of the Device or Asset telemetry and attributes to the Customers. Every Entity View references exactly one entity (device or asset) and defines telemetry and attribute keys that will be visible to the assigned Customer. As a Tenant Administrator you are able to create multiple EVs per Device or Asset and assign them to different Customers. See the 'Model' tab for more details. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. # 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_entity_view_by_id_using_get(entity_view_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str entity_view_id: A string value representing the entity view id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required)
:return: EntityView
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_entity_view_by_id_using_get_with_http_info(entity_view_id, **kwargs) # noqa: E501
else:
(data) = self.get_entity_view_by_id_using_get_with_http_info(entity_view_id, **kwargs) # noqa: E501
return data
def get_entity_view_by_id_using_get_with_http_info(self, entity_view_id, **kwargs): # noqa: E501
"""Get entity view (getEntityViewById) # noqa: E501
Fetch the EntityView object based on the provided entity view id. Entity Views limit the degree of exposure of the Device or Asset telemetry and attributes to the Customers. Every Entity View references exactly one entity (device or asset) and defines telemetry and attribute keys that will be visible to the assigned Customer. As a Tenant Administrator you are able to create multiple EVs per Device or Asset and assign them to different Customers. See the 'Model' tab for more details. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. # 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_entity_view_by_id_using_get_with_http_info(entity_view_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str entity_view_id: A string value representing the entity view id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required)
:return: EntityView
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['entity_view_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_entity_view_by_id_using_get" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'entity_view_id' is set
if ('entity_view_id' not in params or
params['entity_view_id'] is None):
raise ValueError("Missing the required parameter `entity_view_id` when calling `get_entity_view_by_id_using_get`") # noqa: E501
collection_formats = {}
path_params = {}
if 'entity_view_id' in params:
path_params['entityViewId'] = params['entity_view_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
# Authentication setting
auth_settings = ['X-Authorization'] # noqa: E501
return self.api_client.call_api(
'/api/entityView/{entityViewId}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='EntityView', # 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_entity_view_types_using_get(self, **kwargs): # noqa: E501
"""Get Entity View Types (getEntityViewTypes) # noqa: E501
Returns a set of unique entity view types based on entity views that are either owned by the tenant or assigned to the customer which user is performing the request. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. # 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_entity_view_types_using_get(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: list[EntitySubtype]
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_entity_view_types_using_get_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.get_entity_view_types_using_get_with_http_info(**kwargs) # noqa: E501
return data
def get_entity_view_types_using_get_with_http_info(self, **kwargs): # noqa: E501
"""Get Entity View Types (getEntityViewTypes) # noqa: E501
Returns a set of unique entity view types based on entity views that are either owned by the tenant or assigned to the customer which user is performing the request. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. # 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_entity_view_types_using_get_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: list[EntitySubtype]
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_entity_view_types_using_get" % 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
# Authentication setting
auth_settings = ['X-Authorization'] # noqa: E501
return self.api_client.call_api(
'/api/entityView/types', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='list[EntitySubtype]', # 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_entity_views_by_entity_group_id_using_get(self, entity_group_id, page_size, page, **kwargs): # noqa: E501
"""Get entity views by Entity Group Id (getEntityViewsByEntityGroupId) # noqa: E501
Returns a page of Entity View objects that belongs to specified Entity View Id. You can specify parameters to filter the results. The result is wrapped with PageData object that allows you to iterate over result set using pagination. See the 'Model' tab of the Response Class for more details. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'READ' permission for specified 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.get_entity_views_by_entity_group_id_using_get(entity_group_id, page_size, page, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str entity_group_id: A string value representing the Entity Group Id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required)
:param int page_size: Maximum amount of entities in a one page (required)
:param int page: Sequence number of page starting from 0 (required)
:param str text_search: The case insensitive 'startsWith' filter based on the entity view name.
:param str sort_property: Property of entity to sort by
:param str sort_order: Sort order. ASC (ASCENDING) or DESC (DESCENDING)
:return: PageDataEntityView
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_entity_views_by_entity_group_id_using_get_with_http_info(entity_group_id, page_size, page, **kwargs) # noqa: E501
else:
(data) = self.get_entity_views_by_entity_group_id_using_get_with_http_info(entity_group_id, page_size, page, **kwargs) # noqa: E501
return data
def get_entity_views_by_entity_group_id_using_get_with_http_info(self, entity_group_id, page_size, page, **kwargs): # noqa: E501
"""Get entity views by Entity Group Id (getEntityViewsByEntityGroupId) # noqa: E501
Returns a page of Entity View objects that belongs to specified Entity View Id. You can specify parameters to filter the results. The result is wrapped with PageData object that allows you to iterate over result set using pagination. See the 'Model' tab of the Response Class for more details. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'READ' permission for specified 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.get_entity_views_by_entity_group_id_using_get_with_http_info(entity_group_id, page_size, page, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str entity_group_id: A string value representing the Entity Group Id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required)
:param int page_size: Maximum amount of entities in a one page (required)
:param int page: Sequence number of page starting from 0 (required)
:param str text_search: The case insensitive 'startsWith' filter based on the entity view name.
:param str sort_property: Property of entity to sort by
:param str sort_order: Sort order. ASC (ASCENDING) or DESC (DESCENDING)
:return: PageDataEntityView
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['entity_group_id', 'page_size', 'page', 'text_search', 'sort_property', 'sort_order'] # 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_entity_views_by_entity_group_id_using_get" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'entity_group_id' is set
if ('entity_group_id' not in params or
params['entity_group_id'] is None):
raise ValueError("Missing the required parameter `entity_group_id` when calling `get_entity_views_by_entity_group_id_using_get`") # noqa: E501
# verify the required parameter 'page_size' is set
if ('page_size' not in params or
params['page_size'] is None):
raise ValueError("Missing the required parameter `page_size` when calling `get_entity_views_by_entity_group_id_using_get`") # noqa: E501
# verify the required parameter 'page' is set
if ('page' not in params or
params['page'] is None):
raise ValueError("Missing the required parameter `page` when calling `get_entity_views_by_entity_group_id_using_get`") # noqa: E501
collection_formats = {}
path_params = {}
if 'entity_group_id' in params:
path_params['entityGroupId'] = params['entity_group_id'] # noqa: E501
query_params = []
if 'page_size' in params:
query_params.append(('pageSize', params['page_size'])) # noqa: E501
if 'page' in params:
query_params.append(('page', params['page'])) # noqa: E501
if 'text_search' in params:
query_params.append(('textSearch', params['text_search'])) # noqa: E501
if 'sort_property' in params:
query_params.append(('sortProperty', params['sort_property'])) # noqa: E501
if 'sort_order' in params:
query_params.append(('sortOrder', params['sort_order'])) # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['X-Authorization'] # noqa: E501
return self.api_client.call_api(
'/api/entityGroup/{entityGroupId}/entityViews{?page,pageSize,sortOrder,sortProperty,textSearch}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='PageDataEntityView', # 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_entity_views_by_ids_using_get(self, entity_view_ids, **kwargs): # noqa: E501
"""Get Entity Views By Ids (getEntityViewsByIds) # noqa: E501
Requested entity views must be owned by tenant or assigned to customer which user is performing the request. Security check is performed to verify that the user has 'READ' permission for the entity (entities). # 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_entity_views_by_ids_using_get(entity_view_ids, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str entity_view_ids: A list of entity view ids, separated by comma ',' (required)
:return: list[EntityView]
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_entity_views_by_ids_using_get_with_http_info(entity_view_ids, **kwargs) # noqa: E501
else:
(data) = self.get_entity_views_by_ids_using_get_with_http_info(entity_view_ids, **kwargs) # noqa: E501
return data
def get_entity_views_by_ids_using_get_with_http_info(self, entity_view_ids, **kwargs): # noqa: E501
"""Get Entity Views By Ids (getEntityViewsByIds) # noqa: E501
Requested entity views must be owned by tenant or assigned to customer which user is performing the request. Security check is performed to verify that the user has 'READ' permission for the entity (entities). # 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_entity_views_by_ids_using_get_with_http_info(entity_view_ids, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str entity_view_ids: A list of entity view ids, separated by comma ',' (required)
:return: list[EntityView]
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['entity_view_ids'] # 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_entity_views_by_ids_using_get" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'entity_view_ids' is set
if ('entity_view_ids' not in params or
params['entity_view_ids'] is None):
raise ValueError("Missing the required parameter `entity_view_ids` when calling `get_entity_views_by_ids_using_get`") # noqa: E501
collection_formats = {}
path_params = {}
query_params = []
if 'entity_view_ids' in params:
query_params.append(('entityViewIds', params['entity_view_ids'])) # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['X-Authorization'] # noqa: E501
return self.api_client.call_api(
'/api/entityViews{?entityViewIds}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='list[EntityView]', # 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_tenant_entity_view_using_get(self, entity_view_name, **kwargs): # noqa: E501
"""Get Entity View by name (getTenantEntityView) # noqa: E501
Fetch the Entity View object based on the tenant id and entity view name. Available for users with 'TENANT_ADMIN' authority. # 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_tenant_entity_view_using_get(entity_view_name, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str entity_view_name: Entity View name (required)
:return: EntityView
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_tenant_entity_view_using_get_with_http_info(entity_view_name, **kwargs) # noqa: E501
else:
(data) = self.get_tenant_entity_view_using_get_with_http_info(entity_view_name, **kwargs) # noqa: E501
return data
def get_tenant_entity_view_using_get_with_http_info(self, entity_view_name, **kwargs): # noqa: E501
"""Get Entity View by name (getTenantEntityView) # noqa: E501
Fetch the Entity View object based on the tenant id and entity view name. Available for users with 'TENANT_ADMIN' authority. # 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_tenant_entity_view_using_get_with_http_info(entity_view_name, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str entity_view_name: Entity View name (required)
:return: EntityView
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['entity_view_name'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
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_tenant_entity_view_using_get" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'entity_view_name' is set
if ('entity_view_name' not in params or
params['entity_view_name'] is None):
raise ValueError("Missing the required parameter `entity_view_name` when calling `get_tenant_entity_view_using_get`") # noqa: E501
collection_formats = {}
path_params = {}
query_params = []
if 'entity_view_name' in params:
query_params.append(('entityViewName', params['entity_view_name'])) # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['X-Authorization'] # noqa: E501
return self.api_client.call_api(
'/api/tenant/entityViews{?entityViewName}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='EntityView', # 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_tenant_entity_views_using_get(self, page_size, page, **kwargs): # noqa: E501
"""Get Tenant Entity Views (getTenantEntityViews) # noqa: E501
Returns a page of entity views owned by tenant. Entity Views limit the degree of exposure of the Device or Asset telemetry and attributes to the Customers. Every Entity View references exactly one entity (device or asset) and defines telemetry and attribute keys that will be visible to the assigned Customer. As a Tenant Administrator you are able to create multiple EVs per Device or Asset and assign them to different Customers. You can specify parameters to filter the results. The result is wrapped with PageData object that allows you to iterate over result set using pagination. See the 'Model' tab of the Response Class for more details. Available for users with 'TENANT_ADMIN' authority. # 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_tenant_entity_views_using_get(page_size, page, async_req=True)
>>> result = thread.get()
:param async_req bool
:param int page_size: Maximum amount of entities in a one page (required)
:param int page: Sequence number of page starting from 0 (required)
:param str type: ## Entity View Filter Allows to filter entity views based on their type and the **'starts with'** expression over their name. For example, this entity filter selects all 'Concrete Mixer' entity views which name starts with 'CAT': ```json { \"type\": \"entityViewType\", \"entityViewType\": \"Concrete Mixer\", \"entityViewNameFilter\": \"CAT\" } ```
:param str text_search: The case insensitive 'startsWith' filter based on the entity view name.
:param str sort_property: Property of entity to sort by
:param str sort_order: Sort order. ASC (ASCENDING) or DESC (DESCENDING)
:return: PageDataEntityView
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_tenant_entity_views_using_get_with_http_info(page_size, page, **kwargs) # noqa: E501
else:
(data) = self.get_tenant_entity_views_using_get_with_http_info(page_size, page, **kwargs) # noqa: E501
return data
def get_tenant_entity_views_using_get_with_http_info(self, page_size, page, **kwargs): # noqa: E501
"""Get Tenant Entity Views (getTenantEntityViews) # noqa: E501
Returns a page of entity views owned by tenant. Entity Views limit the degree of exposure of the Device or Asset telemetry and attributes to the Customers. Every Entity View references exactly one entity (device or asset) and defines telemetry and attribute keys that will be visible to the assigned Customer. As a Tenant Administrator you are able to create multiple EVs per Device or Asset and assign them to different Customers. You can specify parameters to filter the results. The result is wrapped with PageData object that allows you to iterate over result set using pagination. See the 'Model' tab of the Response Class for more details. Available for users with 'TENANT_ADMIN' authority. # 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_tenant_entity_views_using_get_with_http_info(page_size, page, async_req=True)
>>> result = thread.get()
:param async_req bool
:param int page_size: Maximum amount of entities in a one page (required)
:param int page: Sequence number of page starting from 0 (required)
:param str type: ## Entity View Filter Allows to filter entity views based on their type and the **'starts with'** expression over their name. For example, this entity filter selects all 'Concrete Mixer' entity views which name starts with 'CAT': ```json { \"type\": \"entityViewType\", \"entityViewType\": \"Concrete Mixer\", \"entityViewNameFilter\": \"CAT\" } ```
:param str text_search: The case insensitive 'startsWith' filter based on the entity view name.
:param str sort_property: Property of entity to sort by
:param str sort_order: Sort order. ASC (ASCENDING) or DESC (DESCENDING)
:return: PageDataEntityView
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['page_size', 'page', 'type', 'text_search', 'sort_property', 'sort_order'] # 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_tenant_entity_views_using_get" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'page_size' is set
if ('page_size' not in params or
params['page_size'] is None):
raise ValueError("Missing the required parameter `page_size` when calling `get_tenant_entity_views_using_get`") # noqa: E501
# verify the required parameter 'page' is set
if ('page' not in params or
params['page'] is None):
raise ValueError("Missing the required parameter `page` when calling `get_tenant_entity_views_using_get`") # noqa: E501
collection_formats = {}
path_params = {}
query_params = []
if 'page_size' in params:
query_params.append(('pageSize', params['page_size'])) # noqa: E501
if 'page' in params:
query_params.append(('page', params['page'])) # noqa: E501
if 'type' in params:
query_params.append(('type', params['type'])) # noqa: E501
if 'text_search' in params:
query_params.append(('textSearch', params['text_search'])) # noqa: E501
if 'sort_property' in params:
query_params.append(('sortProperty', params['sort_property'])) # noqa: E501
if 'sort_order' in params:
query_params.append(('sortOrder', params['sort_order'])) # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['X-Authorization'] # noqa: E501
return self.api_client.call_api(
'/api/tenant/entityViews{?page,pageSize,sortOrder,sortProperty,textSearch,type}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='PageDataEntityView', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def get_user_entity_views_using_get(self, page_size, page, **kwargs): # noqa: E501
"""Get Entity Views (getUserEntityViews) # noqa: E501
Returns a page of entity views that are available for the current user. You can specify parameters to filter the results. The result is wrapped with PageData object that allows you to iterate over result set using pagination. See the 'Model' tab of the Response Class for more details. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'READ' permission for the entity (entities). # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_user_entity_views_using_get(page_size, page, async_req=True)
>>> result = thread.get()
:param async_req bool
:param int page_size: Maximum amount of entities in a one page (required)
:param int page: Sequence number of page starting from 0 (required)
:param str type: ## Entity View Filter Allows to filter entity views based on their type and the **'starts with'** expression over their name. For example, this entity filter selects all 'Concrete Mixer' entity views which name starts with 'CAT': ```json { \"type\": \"entityViewType\", \"entityViewType\": \"Concrete Mixer\", \"entityViewNameFilter\": \"CAT\" } ```
:param str text_search: The case insensitive 'startsWith' filter based on the entity view name.
:param str sort_property: Property of entity to sort by
:param str sort_order: Sort order. ASC (ASCENDING) or DESC (DESCENDING)
:return: PageDataEntityView
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_user_entity_views_using_get_with_http_info(page_size, page, **kwargs) # noqa: E501
else:
(data) = self.get_user_entity_views_using_get_with_http_info(page_size, page, **kwargs) # noqa: E501
return data
def get_user_entity_views_using_get_with_http_info(self, page_size, page, **kwargs): # noqa: E501
"""Get Entity Views (getUserEntityViews) # noqa: E501
Returns a page of entity views that are available for the current user. You can specify parameters to filter the results. The result is wrapped with PageData object that allows you to iterate over result set using pagination. See the 'Model' tab of the Response Class for more details. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'READ' permission for the entity (entities). # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_user_entity_views_using_get_with_http_info(page_size, page, async_req=True)
>>> result = thread.get()
:param async_req bool
:param int page_size: Maximum amount of entities in a one page (required)
:param int page: Sequence number of page starting from 0 (required)
:param str type: ## Entity View Filter Allows to filter entity views based on their type and the **'starts with'** expression over their name. For example, this entity filter selects all 'Concrete Mixer' entity views which name starts with 'CAT': ```json { \"type\": \"entityViewType\", \"entityViewType\": \"Concrete Mixer\", \"entityViewNameFilter\": \"CAT\" } ```
:param str text_search: The case insensitive 'startsWith' filter based on the entity view name.
:param str sort_property: Property of entity to sort by
:param str sort_order: Sort order. ASC (ASCENDING) or DESC (DESCENDING)
:return: PageDataEntityView
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['page_size', 'page', 'type', 'text_search', 'sort_property', 'sort_order'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_user_entity_views_using_get" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'page_size' is set
if ('page_size' not in params or
params['page_size'] is None):
raise ValueError("Missing the required parameter `page_size` when calling `get_user_entity_views_using_get`") # noqa: E501
# verify the required parameter 'page' is set
if ('page' not in params or
params['page'] is None):
raise ValueError("Missing the required parameter `page` when calling `get_user_entity_views_using_get`") # noqa: E501
collection_formats = {}
path_params = {}
query_params = []
if 'page_size' in params:
query_params.append(('pageSize', params['page_size'])) # noqa: E501
if 'page' in params:
query_params.append(('page', params['page'])) # noqa: E501
if 'type' in params:
query_params.append(('type', params['type'])) # noqa: E501
if 'text_search' in params:
query_params.append(('textSearch', params['text_search'])) # noqa: E501
if 'sort_property' in params:
query_params.append(('sortProperty', params['sort_property'])) # noqa: E501
if 'sort_order' in params:
query_params.append(('sortOrder', params['sort_order'])) # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['X-Authorization'] # noqa: E501
return self.api_client.call_api(
'/api/user/entityViews{?page,pageSize,sortOrder,sortProperty,textSearch,type}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='PageDataEntityView', # 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 save_entity_view_using_post(self, **kwargs): # noqa: E501
"""Save or update entity view (saveEntityView) # noqa: E501
Entity Views limit the degree of exposure of the Device or Asset telemetry and attributes to the Customers. Every Entity View references exactly one entity (device or asset) and defines telemetry and attribute keys that will be visible to the assigned Customer. As a Tenant Administrator you are able to create multiple EVs per Device or Asset and assign them to different Customers. See the 'Model' tab for more details. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.save_entity_view_using_post(async_req=True)
>>> result = thread.get()
:param async_req bool
:param EntityView body:
:param str entity_group_id: entityGroupId
:return: EntityView
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.save_entity_view_using_post_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.save_entity_view_using_post_with_http_info(**kwargs) # noqa: E501
return data
def save_entity_view_using_post_with_http_info(self, **kwargs): # noqa: E501
"""Save or update entity view (saveEntityView) # noqa: E501
Entity Views limit the degree of exposure of the Device or Asset telemetry and attributes to the Customers. Every Entity View references exactly one entity (device or asset) and defines telemetry and attribute keys that will be visible to the assigned Customer. As a Tenant Administrator you are able to create multiple EVs per Device or Asset and assign them to different Customers. See the 'Model' tab for more details. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.save_entity_view_using_post_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:param EntityView body:
:param str entity_group_id: entityGroupId
:return: EntityView
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['body', 'entity_group_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 save_entity_view_using_post" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
if 'entity_group_id' in params:
query_params.append(('entityGroupId', params['entity_group_id'])) # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'body' in params:
body_params = params['body']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['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 = ['X-Authorization'] # noqa: E501
return self.api_client.call_api(
'/api/entityView{?entityGroupId}', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='EntityView', # 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)
| 51.819949
| 720
| 0.655906
| 7,696
| 61,303
| 4.992204
| 0.04119
| 0.037689
| 0.016033
| 0.020614
| 0.98113
| 0.973477
| 0.965851
| 0.956793
| 0.953514
| 0.9462
| 0
| 0.016253
| 0.261309
| 61,303
| 1,182
| 721
| 51.86379
| 0.83217
| 0.45143
| 0
| 0.803459
| 0
| 0.001572
| 0.218066
| 0.068798
| 0
| 0
| 0
| 0
| 0
| 1
| 0.036164
| false
| 0
| 0.006289
| 0
| 0.095912
| 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
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| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
|
0
| 7
|
6a5ad84559bccf6fa04a383a8bc1a51161c5ae64
| 9,293
|
py
|
Python
|
auth-api/tests/unit/models/views/test_authorization.py
|
bsnopek-freshworks/sbc-auth
|
871800922461239c7a09225a3d708c79173410f9
|
[
"Apache-2.0"
] | null | null | null |
auth-api/tests/unit/models/views/test_authorization.py
|
bsnopek-freshworks/sbc-auth
|
871800922461239c7a09225a3d708c79173410f9
|
[
"Apache-2.0"
] | null | null | null |
auth-api/tests/unit/models/views/test_authorization.py
|
bsnopek-freshworks/sbc-auth
|
871800922461239c7a09225a3d708c79173410f9
|
[
"Apache-2.0"
] | null | null | null |
# Copyright © 2019 Province of British Columbia
#
# 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.
"""Tests for the Authorizations view.
Test suite to ensure that the Authorizations view routines are working as expected.
"""
import uuid
from auth_api.models.views.authorization import Authorization
from tests.utilities.factory_scenarios import TestUserInfo
from tests.utilities.factory_utils import (
factory_affiliation_model, factory_entity_model, factory_membership_model, factory_org_model, factory_user_model)
def test_find_user_authorization_by_business_number(session): # pylint:disable=unused-argument
"""Assert that authorization view is returning result."""
user = factory_user_model()
org = factory_org_model()
membership = factory_membership_model(user.id, org.id)
entity = factory_entity_model()
factory_affiliation_model(entity.id, org.id)
authorization = Authorization.find_user_authorization_by_business_number(str(user.keycloak_guid),
entity.business_identifier)
assert authorization is not None
assert authorization.org_membership == membership.membership_type_code
def test_find_user_authorization_by_org_id(session): # pylint:disable=unused-argument
"""Assert that authorization view is returning result."""
user = factory_user_model()
org = factory_org_model()
membership = factory_membership_model(user.id, org.id)
entity = factory_entity_model()
factory_affiliation_model(entity.id, org.id)
authorization = Authorization.find_user_authorization_by_org_id(str(user.keycloak_guid),
org.id)
assert authorization is not None
assert authorization.org_membership == membership.membership_type_code
def test_find_user_authorization_by_org_id_and_corp_type(session): # pylint:disable=unused-argument
"""Assert that authorization view returns result when fetched using Corp type instead of jwt.
Service accounts passes corp type instead of jwt.
"""
user = factory_user_model()
org = factory_org_model()
membership = factory_membership_model(user.id, org.id)
entity = factory_entity_model()
factory_affiliation_model(entity.id, org.id)
authorization = Authorization.find_user_authorization_by_org_id_and_corp_type(org.id, 'CP')
assert authorization is not None
assert authorization.org_membership == membership.membership_type_code
def test_find_user_authorization_by_org_id_and_corp_type_multiple_membership(session): # pylint:disable=unused-argument
"""Assert that authorization view returns result when fetched using Corp type instead of jwt.
When multiple membership is present , return the one with Owner access.
"""
user1 = factory_user_model()
user2 = factory_user_model(user_info=TestUserInfo.user2)
org = factory_org_model()
factory_membership_model(user1.id, org.id, member_type='ADMIN')
membership_owner = factory_membership_model(user2.id, org.id)
entity = factory_entity_model()
factory_affiliation_model(entity.id, org.id)
authorization = Authorization.find_user_authorization_by_org_id_and_corp_type(org.id, 'CP')
assert authorization is not None
assert authorization.org_membership == membership_owner.membership_type_code
def test_find_user_authorization_by_business_number_and_corp_type_multiple_membership(
session): # pylint:disable=unused-argument
"""Assert that authorization view returns result when fetched using Corp type instead of jwt.
When multiple membership is present , return the one with Owner access
"""
user1 = factory_user_model()
user2 = factory_user_model(user_info=TestUserInfo.user2)
org = factory_org_model()
factory_membership_model(user1.id, org.id, member_type='ADMIN')
membership_owner = factory_membership_model(user2.id, org.id)
entity = factory_entity_model()
factory_affiliation_model(entity.id, org.id)
authorization = Authorization.find_user_authorization_by_business_number_and_corp_type(entity.business_identifier,
'CP')
assert authorization is not None
assert authorization.org_membership == membership_owner.membership_type_code
def test_find_user_authorization_by_org_id_and_invalid_corp_type(session): # pylint:disable=unused-argument
"""Assert that authorization view is not returning result when invalid corp type is passed."""
user = factory_user_model()
org = factory_org_model()
factory_membership_model(user.id, org.id)
entity = factory_entity_model()
factory_affiliation_model(entity.id, org.id)
authorization = Authorization.find_user_authorization_by_org_id_and_corp_type(org.id, 'invalid_corp_type')
assert authorization is None
def test_find_user_authorization_by_org_id_and_invalid_corp_type_no_affliation(
session): # pylint:disable=unused-argument # noqa: E501
"""Assert that authorization view is returning correct result for an unclaimed/unaffiliated organization."""
user = factory_user_model()
org = factory_org_model()
factory_membership_model(user.id, org.id)
authorization = Authorization.find_user_authorization_by_org_id_and_corp_type(org.id, 'invalid_corp_type')
assert authorization is not None
def test_find_user_authorization_by_business_number_and_corp_type(session): # pylint:disable=unused-argument
"""Assert that authorization view returns result when fetched using Corp type instead of jwt.
Service accounts passes corp type instead of jwt.
"""
user = factory_user_model()
org = factory_org_model()
membership = factory_membership_model(user.id, org.id)
entity = factory_entity_model()
factory_affiliation_model(entity.id, org.id)
authorization = Authorization.find_user_authorization_by_business_number_and_corp_type(entity.business_identifier,
'CP')
assert authorization is not None
assert authorization.org_membership == membership.membership_type_code
def test_find_user_authorization_by_business_number_and_invalid_corp_type(session): # pylint:disable=unused-argument
"""Assert that authorization view is not returning result when invalid corp type is passed."""
user = factory_user_model()
org = factory_org_model()
factory_membership_model(user.id, org.id)
entity = factory_entity_model()
factory_affiliation_model(entity.id, org.id)
authorization = Authorization.find_user_authorization_by_business_number_and_corp_type(entity.business_identifier,
'invalid_corp_type')
assert authorization is None
def test_find_invalid_user_authorization_by_business_number(session): # pylint:disable=unused-argument
"""Test with invalid user id and assert that auth is None."""
user = factory_user_model()
org = factory_org_model()
factory_membership_model(user.id, org.id)
entity = factory_entity_model()
factory_affiliation_model(entity.id, org.id)
authorization = Authorization.find_user_authorization_by_business_number(str(uuid.uuid4()),
entity.business_identifier)
assert authorization is None
# Test with invalid business identifier
authorization = Authorization.find_user_authorization_by_business_number(str(uuid.uuid4()), '')
assert authorization is None
def test_find_all_user_authorizations(session): # pylint:disable=unused-argument
"""Test find all user authoirzations."""
user = factory_user_model()
org = factory_org_model()
membership = factory_membership_model(user.id, org.id)
entity = factory_entity_model()
factory_affiliation_model(entity.id, org.id)
authorizations = Authorization.find_all_authorizations_for_user(str(user.keycloak_guid))
assert authorizations is not None
assert authorizations[0].org_membership == membership.membership_type_code
assert authorizations[0].business_identifier == entity.business_identifier
def test_find_all_user_authorizations_for_empty(session): # pylint:disable=unused-argument
"""Test with invalid user id and assert that auth is None."""
user = factory_user_model()
org = factory_org_model()
factory_membership_model(user.id, org.id)
authorizations = Authorization.find_all_authorizations_for_user(str(user.keycloak_guid))
assert authorizations is not None
assert authorizations[0].business_identifier is None
| 46.934343
| 120
| 0.745723
| 1,170
| 9,293
| 5.611966
| 0.128205
| 0.029698
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| 0.830643
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| 0.786476
| 0.786476
| 0
| 0.003438
| 0.186162
| 9,293
| 197
| 121
| 47.172589
| 0.864604
| 0.242441
| 0
| 0.783333
| 0
| 0
| 0.009984
| 0
| 0
| 0
| 0
| 0
| 0.183333
| 1
| 0.1
| false
| 0
| 0.033333
| 0
| 0.133333
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
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| 0
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| null | 0
| 0
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| 0
| 0
| 0
| 0
|
0
| 7
|
6a7af97af6e8180b0f42b729f208f58cc06e4962
| 3,814
|
py
|
Python
|
tesserakti/models.py
|
sainioan/extractiontool
|
9908b7ff1915b00a5721405a48b13d941442e1dd
|
[
"MIT"
] | 2
|
2021-05-18T17:25:06.000Z
|
2021-05-28T04:24:16.000Z
|
tesserakti/models.py
|
sainioan/extractiontool
|
9908b7ff1915b00a5721405a48b13d941442e1dd
|
[
"MIT"
] | 38
|
2021-01-20T09:38:37.000Z
|
2021-05-15T13:10:05.000Z
|
tesserakti/models.py
|
sainioan/extractiontool
|
9908b7ff1915b00a5721405a48b13d941442e1dd
|
[
"MIT"
] | 3
|
2021-01-20T13:18:31.000Z
|
2021-02-25T13:34:49.000Z
|
# This is an auto-generated Django model module.
# You'll have to do the following manually to clean this up:
# * Rearrange models' order
# * Make sure each model has one field with primary_key=True
# * Make sure each ForeignKey and OneToOneField has `on_delete` set to the desired behavior
# * Remove `managed = True` lines if you wish to allow Django to create, modify, and delete the table
# Feel free to rename the models, but don't rename db_table values or field names.
from django.db import models
from django.utils import timezone
import pytz
class Page(models.Model):
page_id = models.IntegerField(db_index=True)
document_id = models.IntegerField(db_index=True)
vasen = models.IntegerField(db_index=True)
top = models.IntegerField(db_index=True)
width = models.IntegerField(db_index=True)
height = models.IntegerField(db_index=True)
created = models.DateTimeField(default=timezone.now)
class Meta:
managed = True
db_table = 'tes_page'
unique_together = (('page_id', 'document_id'),)
class Block(models.Model):
block_id = models.IntegerField(db_index=True)
page_id = models.IntegerField(db_index=True)
document_id = models.IntegerField(db_index=True)
vasen = models.IntegerField(db_index=True)
top = models.IntegerField(db_index=True)
width = models.IntegerField(db_index=True)
height = models.IntegerField(db_index=True)
created = models.DateTimeField(default=timezone.now)
class Meta:
managed = True
db_table = 'tes_block'
unique_together = (('block_id', 'page_id', 'document_id'),)
class Paragraph(models.Model):
paragraph_id = models.IntegerField(db_index=True)
block_id = models.IntegerField(db_index=True)
page_id = models.IntegerField(db_index=True)
document_id = models.IntegerField(db_index=True)
vasen = models.IntegerField(db_index=True)
top = models.IntegerField(db_index=True)
width = models.IntegerField(db_index=True)
height = models.IntegerField(db_index=True)
created = models.DateTimeField(default=timezone.now)
class Meta:
managed = True
db_table = 'tes_paragraph'
unique_together = (('paragraph_id', 'block_id', 'page_id', 'document_id'),)
class Line(models.Model):
line_id = models.IntegerField(db_index=True)
paragraph_id = models.IntegerField(db_index=True)
block_id = models.IntegerField(db_index=True)
page_id = models.IntegerField(db_index=True)
document_id = models.IntegerField(db_index=True)
vasen = models.IntegerField(db_index=True)
top = models.IntegerField(db_index=True)
width = models.IntegerField(db_index=True)
height = models.IntegerField(db_index=True)
created = models.DateTimeField(default=timezone.now)
class Meta:
managed = True
db_table = 'tes_line'
unique_together = (('line_id', 'paragraph_id', 'block_id', 'page_id', 'document_id'),)
class Word(models.Model):
word_id = models.IntegerField(db_index=True)
line_id = models.IntegerField(db_index=True)
paragraph_id = models.IntegerField(db_index=True)
block_id = models.IntegerField(db_index=True)
page_id = models.IntegerField(db_index=True)
document_id = models.IntegerField(db_index=True)
vasen = models.IntegerField(db_index=True)
top = models.IntegerField(db_index=True)
width = models.IntegerField(db_index=True)
height = models.IntegerField(db_index=True)
conf = models.IntegerField(db_index=True)
text = models.CharField(max_length=200, db_index=True)
created = models.DateTimeField(default=timezone.now)
class Meta:
managed = True
db_table = 'tes_word'
unique_together = (('word_id', 'line_id', 'paragraph_id', 'block_id', 'page_id', 'document_id'),)
| 38.918367
| 105
| 0.719455
| 508
| 3,814
| 5.202756
| 0.179134
| 0.111237
| 0.174801
| 0.387817
| 0.754067
| 0.735149
| 0.72342
| 0.712826
| 0.712826
| 0.696179
| 0
| 0.000954
| 0.175406
| 3,814
| 97
| 106
| 39.319588
| 0.839428
| 0.122968
| 0
| 0.72
| 1
| 0
| 0.067426
| 0
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| 0
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| 1
| 0
| false
| 0
| 0.04
| 0
| 0.8
| 0
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| null | 0
| 0
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| 1
| 1
| 1
| 1
| 1
| 0
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| 1
| 1
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| null | 0
| 0
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| 0
| 0
| 0
| 1
| 0
|
0
| 9
|
6a9134622536fb13668a176be9026fcf7777a42a
| 20,550
|
py
|
Python
|
tests/milvus_python_test/test_get_vector_ids.py
|
youny626/milvus
|
9e55802c5d515ceecc4cadab9f2fd1cb477d75d5
|
[
"Apache-2.0"
] | null | null | null |
tests/milvus_python_test/test_get_vector_ids.py
|
youny626/milvus
|
9e55802c5d515ceecc4cadab9f2fd1cb477d75d5
|
[
"Apache-2.0"
] | null | null | null |
tests/milvus_python_test/test_get_vector_ids.py
|
youny626/milvus
|
9e55802c5d515ceecc4cadab9f2fd1cb477d75d5
|
[
"Apache-2.0"
] | 1
|
2021-07-08T07:22:59.000Z
|
2021-07-08T07:22:59.000Z
|
import time
import random
import pdb
import threading
import logging
from multiprocessing import Pool, Process
import pytest
from milvus import IndexType, MetricType
from utils import *
dim = 128
index_file_size = 10
GET_TIMEOUT = 30
nprobe = 1
top_k = 1
epsilon = 0.001
tag = "1970-01-01"
nb = 6000
class TestGetVectorIdsBase:
def get_valid_segment_name(self, connect, table):
vectors = gen_vector(nb, dim)
status, ids = connect.add_vectors(table, vectors)
assert status.OK()
status = connect.flush([table])
assert status.OK()
status, info = connect.table_info(table)
assert status.OK()
logging.getLogger().info(info.partitions_stat[0].segments_stat[0].segment_name)
return info.partitions_stat[0].segments_stat[0].segment_name
"""
******************************************************************
The following cases are used to test `get_vector_ids` function
******************************************************************
"""
@pytest.mark.timeout(GET_TIMEOUT)
def test_get_vector_ids_table_name_None(self, connect, table):
'''
target: get vector ids where table name is None
method: call get_vector_ids with the table_name: None
expected: exception raised
'''
table_name = None
segment_name = self.get_valid_segment_name(connect, table)
with pytest.raises(Exception) as e:
status, vector_ids = connect.get_vector_ids(table_name, segment_name)
@pytest.mark.timeout(GET_TIMEOUT)
def test_get_vector_ids_table_name_not_existed(self, connect, table):
'''
target: get vector ids where table name does not exist
method: call get_vector_ids with a random table_name, which is not in db
expected: status not ok
'''
table_name = gen_unique_str("not_existed_table")
segment_name = self.get_valid_segment_name(connect, table)
status, vector_ids = connect.get_vector_ids(table_name, segment_name)
assert not status.OK()
@pytest.fixture(
scope="function",
params=gen_invalid_table_names()
)
def get_table_name(self, request):
yield request.param
@pytest.mark.timeout(GET_TIMEOUT)
def test_get_vector_ids_table_name_invalid(self, connect, table, get_table_name):
'''
target: get vector ids where table name is invalid
method: call get_vector_ids with invalid table_name
expected: status not ok
'''
table_name = get_table_name
segment_name = self.get_valid_segment_name(connect, table)
status, vector_ids = connect.get_vector_ids(table_name, segment_name)
assert not status.OK()
@pytest.mark.timeout(GET_TIMEOUT)
def test_get_vector_ids_segment_name_None(self, connect, table):
'''
target: get vector ids where segment name is None
method: call get_vector_ids with the segment_name: None
expected: exception raised
'''
valid_segment_name = self.get_valid_segment_name(connect, table)
segment = None
with pytest.raises(Exception) as e:
status, vector_ids = connect.get_vector_ids(table, segment)
@pytest.mark.timeout(GET_TIMEOUT)
def test_get_vector_ids_segment_name_not_existed(self, connect, table):
'''
target: get vector ids where segment name does not exist
method: call get_vector_ids with a random segment name
expected: status not ok
'''
valid_segment_name = self.get_valid_segment_name(connect, table)
segment = gen_unique_str("not_existed_segment")
status, vector_ids = connect.get_vector_ids(table, segment)
logging.getLogger().info(vector_ids)
assert not status.OK()
@pytest.mark.timeout(GET_TIMEOUT)
def test_get_vector_ids_without_index_A(self, connect, table):
'''
target: get vector ids when there is no index
method: call get_vector_ids and check if the segment contains vectors
expected: status ok
'''
vectors = gen_vector(10, dim)
status, ids = connect.add_vectors(table, vectors)
assert status.OK()
status = connect.flush([table])
assert status.OK()
status, info = connect.table_info(table)
assert status.OK()
status, vector_ids = connect.get_vector_ids(table, info.partitions_stat[0].segments_stat[0].segment_name)
# vector_ids should match ids
assert len(vector_ids) == 10
for i in range(10):
assert vector_ids[i] == ids[i]
@pytest.mark.timeout(GET_TIMEOUT)
def test_get_vector_ids_without_index_B(self, connect, table):
'''
target: get vector ids when there is no index but with partition
method: create partition, add vectors to it and call get_vector_ids, check if the segment contains vectors
expected: status ok
'''
status = connect.create_partition(table, tag)
assert status.OK()
vectors = gen_vector(10, dim)
status, ids = connect.add_vectors(table, vectors, partition_tag=tag)
assert status.OK()
status = connect.flush([table])
assert status.OK()
status, info = connect.table_info(table)
assert status.OK()
assert info.partitions_stat[1].tag == tag
status, vector_ids = connect.get_vector_ids(table, info.partitions_stat[1].segments_stat[0].segment_name)
# vector_ids should match ids
assert len(vector_ids) == 10
for i in range(10):
assert vector_ids[i] == ids[i]
@pytest.fixture(
scope="function",
params=gen_simple_index_params()
)
def get_simple_index_params(self, request, connect):
if str(connect._cmd("mode")[1]) == "CPU":
if request.param["index_type"] not in [IndexType.IVF_SQ8, IndexType.IVFLAT, IndexType.FLAT]:
pytest.skip("Only support index_type: flat/ivf_flat/ivf_sq8")
else:
pytest.skip("Only support CPU mode")
return request.param
@pytest.mark.timeout(GET_TIMEOUT)
def test_get_vector_ids_with_index_A(self, connect, table, get_simple_index_params):
'''
target: get vector ids when there is index
method: call get_vector_ids and check if the segment contains vectors
expected: status ok
'''
index_params = get_simple_index_params
status = connect.create_index(table, index_params)
assert status.OK()
vectors = gen_vector(10, dim)
status, ids = connect.add_vectors(table, vectors)
assert status.OK()
status = connect.flush([table])
assert status.OK()
status, info = connect.table_info(table)
assert status.OK()
status, vector_ids = connect.get_vector_ids(table, info.partitions_stat[0].segments_stat[0].segment_name)
# vector_ids should match ids
assert len(vector_ids) == 10
for i in range(10):
assert vector_ids[i] == ids[i]
@pytest.mark.timeout(GET_TIMEOUT)
def test_get_vector_ids_with_index_B(self, connect, table, get_simple_index_params):
'''
target: get vector ids when there is index and with partition
method: create partition, add vectors to it and call get_vector_ids, check if the segment contains vectors
expected: status ok
'''
status = connect.create_partition(table, tag)
assert status.OK()
index_params = get_simple_index_params
status = connect.create_index(table, index_params)
assert status.OK()
vectors = gen_vector(10, dim)
status, ids = connect.add_vectors(table, vectors, partition_tag=tag)
assert status.OK()
status = connect.flush([table])
assert status.OK()
status, info = connect.table_info(table)
assert status.OK()
assert info.partitions_stat[1].tag == tag
status, vector_ids = connect.get_vector_ids(table, info.partitions_stat[1].segments_stat[0].segment_name)
# vector_ids should match ids
assert len(vector_ids) == 10
for i in range(10):
assert vector_ids[i] == ids[i]
@pytest.mark.timeout(GET_TIMEOUT)
def test_get_vector_ids_after_delete_vectors(self, connect, table):
'''
target: get vector ids after vectors are deleted
method: add vectors and delete a few, call get_vector_ids
expected: status ok, vector_ids decreased after vectors deleted
'''
vectors = gen_vector(2, dim)
status, ids = connect.add_vectors(table, vectors)
assert status.OK()
delete_ids = [ids[0]]
status = connect.delete_by_id(table, delete_ids)
status = connect.flush([table])
assert status.OK()
status, info = connect.table_info(table)
assert status.OK()
status, vector_ids = connect.get_vector_ids(table, info.partitions_stat[0].segments_stat[0].segment_name)
assert len(vector_ids) == 1
assert vector_ids[0] == ids[1]
class TestGetVectorIdsIP:
"""
******************************************************************
The following cases are used to test `get_vector_ids` function
******************************************************************
"""
@pytest.mark.timeout(GET_TIMEOUT)
def test_get_vector_ids_without_index_A(self, connect, ip_table):
'''
target: get vector ids when there is no index
method: call get_vector_ids and check if the segment contains vectors
expected: status ok
'''
vectors = gen_vector(10, dim)
status, ids = connect.add_vectors(ip_table, vectors)
assert status.OK()
status = connect.flush([ip_table])
assert status.OK()
status, info = connect.table_info(ip_table)
assert status.OK()
status, vector_ids = connect.get_vector_ids(ip_table, info.partitions_stat[0].segments_stat[0].segment_name)
# vector_ids should match ids
assert len(vector_ids) == 10
for i in range(10):
assert vector_ids[i] == ids[i]
@pytest.mark.timeout(GET_TIMEOUT)
def test_get_vector_ids_without_index_B(self, connect, ip_table):
'''
target: get vector ids when there is no index but with partition
method: create partition, add vectors to it and call get_vector_ids, check if the segment contains vectors
expected: status ok
'''
status = connect.create_partition(ip_table, tag)
assert status.OK()
vectors = gen_vector(10, dim)
status, ids = connect.add_vectors(ip_table, vectors, partition_tag=tag)
assert status.OK()
status = connect.flush([ip_table])
assert status.OK()
status, info = connect.table_info(ip_table)
assert status.OK()
assert info.partitions_stat[1].tag == tag
status, vector_ids = connect.get_vector_ids(ip_table, info.partitions_stat[1].segments_stat[0].segment_name)
# vector_ids should match ids
assert len(vector_ids) == 10
for i in range(10):
assert vector_ids[i] == ids[i]
@pytest.fixture(
scope="function",
params=gen_simple_index_params()
)
def get_simple_index_params(self, request, connect):
if str(connect._cmd("mode")[1]) == "CPU":
if request.param["index_type"] not in [IndexType.IVF_SQ8, IndexType.IVFLAT, IndexType.FLAT]:
pytest.skip("Only support index_type: flat/ivf_flat/ivf_sq8")
else:
pytest.skip("Only support CPU mode")
return request.param
@pytest.mark.timeout(GET_TIMEOUT)
def test_get_vector_ids_with_index_A(self, connect, ip_table, get_simple_index_params):
'''
target: get vector ids when there is index
method: call get_vector_ids and check if the segment contains vectors
expected: status ok
'''
index_params = get_simple_index_params
status = connect.create_index(ip_table, index_params)
assert status.OK()
vectors = gen_vector(10, dim)
status, ids = connect.add_vectors(ip_table, vectors)
assert status.OK()
status = connect.flush([ip_table])
assert status.OK()
status, info = connect.table_info(ip_table)
assert status.OK()
status, vector_ids = connect.get_vector_ids(ip_table, info.partitions_stat[0].segments_stat[0].segment_name)
# vector_ids should match ids
assert len(vector_ids) == 10
for i in range(10):
assert vector_ids[i] == ids[i]
@pytest.mark.timeout(GET_TIMEOUT)
def test_get_vector_ids_with_index_B(self, connect, ip_table, get_simple_index_params):
'''
target: get vector ids when there is index and with partition
method: create partition, add vectors to it and call get_vector_ids, check if the segment contains vectors
expected: status ok
'''
status = connect.create_partition(ip_table, tag)
assert status.OK()
index_params = get_simple_index_params
status = connect.create_index(ip_table, index_params)
assert status.OK()
vectors = gen_vector(10, dim)
status, ids = connect.add_vectors(ip_table, vectors, partition_tag=tag)
assert status.OK()
status = connect.flush([ip_table])
assert status.OK()
status, info = connect.table_info(ip_table)
assert status.OK()
assert info.partitions_stat[1].tag == tag
status, vector_ids = connect.get_vector_ids(ip_table, info.partitions_stat[1].segments_stat[0].segment_name)
# vector_ids should match ids
assert len(vector_ids) == 10
for i in range(10):
assert vector_ids[i] == ids[i]
@pytest.mark.timeout(GET_TIMEOUT)
def test_get_vector_ids_after_delete_vectors(self, connect, ip_table):
'''
target: get vector ids after vectors are deleted
method: add vectors and delete a few, call get_vector_ids
expected: status ok, vector_ids decreased after vectors deleted
'''
vectors = gen_vector(2, dim)
status, ids = connect.add_vectors(ip_table, vectors)
assert status.OK()
delete_ids = [ids[0]]
status = connect.delete_by_id(ip_table, delete_ids)
status = connect.flush([ip_table])
assert status.OK()
status, info = connect.table_info(ip_table)
assert status.OK()
status, vector_ids = connect.get_vector_ids(ip_table, info.partitions_stat[0].segments_stat[0].segment_name)
assert len(vector_ids) == 1
assert vector_ids[0] == ids[1]
class TestGetVectorIdsJAC:
"""
******************************************************************
The following cases are used to test `get_vector_ids` function
******************************************************************
"""
@pytest.mark.timeout(GET_TIMEOUT)
def test_get_vector_ids_without_index_A(self, connect, jac_table):
'''
target: get vector ids when there is no index
method: call get_vector_ids and check if the segment contains vectors
expected: status ok
'''
tmp, vectors = gen_binary_vectors(10, dim)
status, ids = connect.add_vectors(jac_table, vectors)
assert status.OK()
status = connect.flush([jac_table])
assert status.OK()
status, info = connect.table_info(jac_table)
assert status.OK()
status, vector_ids = connect.get_vector_ids(jac_table, info.partitions_stat[0].segments_stat[0].segment_name)
# vector_ids should match ids
assert len(vector_ids) == 10
for i in range(10):
assert vector_ids[i] == ids[i]
@pytest.mark.timeout(GET_TIMEOUT)
def test_get_vector_ids_without_index_B(self, connect, jac_table):
'''
target: get vector ids when there is no index but with partition
method: create partition, add vectors to it and call get_vector_ids, check if the segment contains vectors
expected: status ok
'''
status = connect.create_partition(jac_table, tag)
assert status.OK()
tmp, vectors = gen_binary_vectors(10, dim)
status, ids = connect.add_vectors(jac_table, vectors, partition_tag=tag)
assert status.OK()
status = connect.flush([jac_table])
assert status.OK()
status, info = connect.table_info(jac_table)
assert status.OK()
assert info.partitions_stat[1].tag == tag
status, vector_ids = connect.get_vector_ids(jac_table, info.partitions_stat[1].segments_stat[0].segment_name)
# vector_ids should match ids
assert len(vector_ids) == 10
for i in range(10):
assert vector_ids[i] == ids[i]
@pytest.fixture(
scope="function",
params=gen_simple_index_params()
)
def get_jaccard_index_params(self, request, connect):
logging.getLogger().info(request.param)
if request.param["index_type"] == IndexType.IVFLAT or request.param["index_type"] == IndexType.FLAT:
return request.param
else:
pytest.skip("Skip index Temporary")
@pytest.mark.timeout(GET_TIMEOUT)
def test_get_vector_ids_with_index_A(self, connect, jac_table, get_jaccard_index_params):
'''
target: get vector ids when there is index
method: call get_vector_ids and check if the segment contains vectors
expected: status ok
'''
index_params = get_jaccard_index_params
status = connect.create_index(jac_table, index_params)
assert status.OK()
tmp, vectors = gen_binary_vectors(10, dim)
status, ids = connect.add_vectors(jac_table, vectors)
assert status.OK()
status = connect.flush([jac_table])
assert status.OK()
status, info = connect.table_info(jac_table)
assert status.OK()
status, vector_ids = connect.get_vector_ids(jac_table, info.partitions_stat[0].segments_stat[0].segment_name)
# vector_ids should match ids
assert len(vector_ids) == 10
for i in range(10):
assert vector_ids[i] == ids[i]
@pytest.mark.timeout(GET_TIMEOUT)
def test_get_vector_ids_with_index_B(self, connect, jac_table, get_jaccard_index_params):
'''
target: get vector ids when there is index and with partition
method: create partition, add vectors to it and call get_vector_ids, check if the segment contains vectors
expected: status ok
'''
status = connect.create_partition(jac_table, tag)
assert status.OK()
index_params = get_jaccard_index_params
status = connect.create_index(jac_table, index_params)
assert status.OK()
tmp, vectors = gen_binary_vectors(10, dim)
status, ids = connect.add_vectors(jac_table, vectors, partition_tag=tag)
assert status.OK()
status = connect.flush([jac_table])
assert status.OK()
status, info = connect.table_info(jac_table)
assert status.OK()
assert info.partitions_stat[1].tag == tag
status, vector_ids = connect.get_vector_ids(jac_table, info.partitions_stat[1].segments_stat[0].segment_name)
# vector_ids should match ids
assert len(vector_ids) == 10
for i in range(10):
assert vector_ids[i] == ids[i]
@pytest.mark.timeout(GET_TIMEOUT)
def test_get_vector_ids_after_delete_vectors(self, connect, jac_table):
'''
target: get vector ids after vectors are deleted
method: add vectors and delete a few, call get_vector_ids
expected: status ok, vector_ids decreased after vectors deleted
'''
tmp, vectors = gen_binary_vectors(2, dim)
status, ids = connect.add_vectors(jac_table, vectors)
assert status.OK()
delete_ids = [ids[0]]
status = connect.delete_by_id(jac_table, delete_ids)
status = connect.flush([jac_table])
assert status.OK()
status, info = connect.table_info(jac_table)
assert status.OK()
status, vector_ids = connect.get_vector_ids(jac_table, info.partitions_stat[0].segments_stat[0].segment_name)
assert len(vector_ids) == 1
assert vector_ids[0] == ids[1]
| 41.768293
| 117
| 0.644234
| 2,690
| 20,550
| 4.686245
| 0.05539
| 0.106378
| 0.07901
| 0.060289
| 0.944947
| 0.925115
| 0.912978
| 0.912899
| 0.909805
| 0.895129
| 0
| 0.010234
| 0.24871
| 20,550
| 492
| 118
| 41.768293
| 0.80627
| 0.188516
| 0
| 0.804805
| 0
| 0
| 0.018313
| 0.002689
| 0
| 0
| 0
| 0
| 0.297297
| 1
| 0.075075
| false
| 0
| 0.027027
| 0
| 0.123123
| 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
|
0a74725d49b5d1cf081d9a4679cf1f13feebb585
| 6,581
|
py
|
Python
|
src/genie/libs/parser/iosxr/tests/ShowIgmpGroupsDetails/cli/equal/golden_output_2_expected.py
|
balmasea/genieparser
|
d1e71a96dfb081e0a8591707b9d4872decd5d9d3
|
[
"Apache-2.0"
] | 204
|
2018-06-27T00:55:27.000Z
|
2022-03-06T21:12:18.000Z
|
src/genie/libs/parser/iosxr/tests/ShowIgmpGroupsDetails/cli/equal/golden_output_2_expected.py
|
balmasea/genieparser
|
d1e71a96dfb081e0a8591707b9d4872decd5d9d3
|
[
"Apache-2.0"
] | 468
|
2018-06-19T00:33:18.000Z
|
2022-03-31T23:23:35.000Z
|
src/genie/libs/parser/iosxr/tests/ShowIgmpGroupsDetails/cli/equal/golden_output_2_expected.py
|
balmasea/genieparser
|
d1e71a96dfb081e0a8591707b9d4872decd5d9d3
|
[
"Apache-2.0"
] | 309
|
2019-01-16T20:21:07.000Z
|
2022-03-30T12:56:41.000Z
|
expected_output = {
"vrf": {
"VRF1": {
"interfaces": {
"Loopback300": {
"group": {
"224.0.0.2": {
"host_mode": "exclude",
"last_reporter": "10.16.2.2",
"router_mode": "EXCLUDE",
"router_mode_expires": "never",
"suppress": 0,
"up_time": "02:43:30"
},
"224.0.0.9": {
"host_mode": "exclude",
"last_reporter": "10.16.2.2",
"router_mode": "EXCLUDE",
"router_mode_expires": "never",
"suppress": 0,
"up_time": "09:48:07"
},
"224.0.0.13": {
"host_mode": "exclude",
"last_reporter": "10.16.2.2",
"router_mode": "EXCLUDE",
"router_mode_expires": "never",
"suppress": 0,
"up_time": "02:43:30"
},
"224.0.0.22": {
"host_mode": "exclude",
"last_reporter": "10.16.2.2",
"router_mode": "EXCLUDE",
"router_mode_expires": "never",
"suppress": 0,
"up_time": "02:43:30"
}
}
},
"GigabitEthernet0/0/0/0.390": {
"group": {
"224.0.0.10": {
"host_mode": "exclude",
"last_reporter": "0.0.0.0",
"router_mode": "INCLUDE",
"router_mode_expires": "None",
"suppress": 0,
"up_time": "01:54:16"
}
}
},
"GigabitEthernet0/0/0/0.410": {
"group": {
"224.0.0.2": {
"host_mode": "exclude",
"last_reporter": "10.12.110.2",
"router_mode": "EXCLUDE",
"router_mode_expires": "never",
"suppress": 0,
"up_time": "02:43:30"
},
"224.0.0.5": {
"host_mode": "exclude",
"last_reporter": "10.12.110.2",
"router_mode": "EXCLUDE",
"router_mode_expires": "never",
"suppress": 0,
"up_time": "10:37:41"
},
"224.0.0.6": {
"host_mode": "exclude",
"last_reporter": "10.12.110.2",
"router_mode": "EXCLUDE",
"router_mode_expires": "never",
"suppress": 0,
"up_time": "10:37:41"
},
"224.0.0.13": {
"host_mode": "exclude",
"last_reporter": "10.12.110.2",
"router_mode": "EXCLUDE",
"router_mode_expires": "never",
"suppress": 0,
"up_time": "02:43:30"
},
"224.0.0.22": {
"host_mode": "exclude",
"last_reporter": "10.12.110.2",
"router_mode": "EXCLUDE",
"router_mode_expires": "never",
"suppress": 0,
"up_time": "02:43:30"
},
"224.0.1.39": {
"host_mode": "include",
"last_reporter": "10.12.110.1",
"router_mode": "EXCLUDE",
"router_mode_expires": "00:01:21",
"suppress": 0,
"up_time": "02:30:06"
},
"224.0.1.40": {
"host_mode": "exclude",
"last_reporter": "10.12.110.2",
"router_mode": "EXCLUDE",
"router_mode_expires": "never",
"suppress": 0,
"up_time": "02:43:30"
}
}
},
"GigabitEthernet0/0/0/0.420": {
"group": {
"224.0.0.9": {
"host_mode": "exclude",
"last_reporter": "0.0.0.0",
"router_mode": "INCLUDE",
"router_mode_expires": "None",
"suppress": 0,
"up_time": "09:48:07"
}
}
},
"GigabitEthernet0/0/0/1.390": {
"group": {
"224.0.0.10": {
"host_mode": "exclude",
"last_reporter": "0.0.0.0",
"router_mode": "INCLUDE",
"router_mode_expires": "None",
"suppress": 0,
"up_time": "01:54:16"
}
}
},
"GigabitEthernet0/0/0/1.420": {
"group": {
"224.0.0.9": {
"host_mode": "exclude",
"last_reporter": "0.0.0.0",
"router_mode": "INCLUDE",
"router_mode_expires": "None",
"suppress": 0,
"up_time": "09:48:07"
}
}
}
}
}
}
}
| 42.185897
| 62
| 0.268804
| 444
| 6,581
| 3.779279
| 0.117117
| 0.039333
| 0.151967
| 0.134088
| 0.938021
| 0.916567
| 0.896305
| 0.892729
| 0.892729
| 0.892133
| 0
| 0.133696
| 0.609026
| 6,581
| 155
| 63
| 42.458065
| 0.518461
| 0
| 0
| 0.677632
| 0
| 0
| 0.285953
| 0.019763
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 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
| 7
|
0a882154a2908e4e95afec0f02c237ae262fd917
| 5,724
|
py
|
Python
|
dojo/finding/queries.py
|
axelpavageau/django-DefectDojo
|
00b425742b783ada0f432241c2812ac1257feb73
|
[
"BSD-3-Clause"
] | 1,772
|
2018-01-22T23:32:15.000Z
|
2022-03-31T14:49:33.000Z
|
dojo/finding/queries.py
|
axelpavageau/django-DefectDojo
|
00b425742b783ada0f432241c2812ac1257feb73
|
[
"BSD-3-Clause"
] | 3,461
|
2018-01-20T19:12:28.000Z
|
2022-03-31T17:14:39.000Z
|
dojo/finding/queries.py
|
axelpavageau/django-DefectDojo
|
00b425742b783ada0f432241c2812ac1257feb73
|
[
"BSD-3-Clause"
] | 1,173
|
2018-01-23T07:10:23.000Z
|
2022-03-31T14:40:43.000Z
|
from crum import get_current_user
from django.conf import settings
from django.db.models import Exists, OuterRef, Q
from dojo.models import Finding, Product_Member, Product_Type_Member, Stub_Finding, \
Product_Group, Product_Type_Group
from dojo.authorization.authorization import get_roles_for_permission, role_has_permission, \
get_groups
def get_authorized_findings(permission, queryset=None, user=None):
if user is None:
user = get_current_user()
if user is None:
return Finding.objects.none()
if queryset is None:
findings = Finding.objects.all()
else:
findings = queryset
if user.is_superuser:
return findings
if settings.FEATURE_AUTHORIZATION_V2:
if user.is_staff and settings.AUTHORIZATION_STAFF_OVERRIDE:
return findings
if hasattr(user, 'global_role') and user.global_role.role is not None and role_has_permission(user.global_role.role.id, permission):
return findings
for group in get_groups(user):
if hasattr(group, 'global_role') and group.global_role.role is not None and role_has_permission(group.global_role.role.id, permission):
return findings
roles = get_roles_for_permission(permission)
authorized_product_type_roles = Product_Type_Member.objects.filter(
product_type=OuterRef('test__engagement__product__prod_type_id'),
user=user,
role__in=roles)
authorized_product_roles = Product_Member.objects.filter(
product=OuterRef('test__engagement__product_id'),
user=user,
role__in=roles)
authorized_product_type_groups = Product_Type_Group.objects.filter(
product_type=OuterRef('test__engagement__product__prod_type_id'),
group__users=user,
role__in=roles)
authorized_product_groups = Product_Group.objects.filter(
product=OuterRef('test__engagement__product_id'),
group__users=user,
role__in=roles)
findings = findings.annotate(
test__engagement__product__prod_type__member=Exists(authorized_product_type_roles),
test__engagement__product__member=Exists(authorized_product_roles),
test__engagement__product__prod_type__authorized_group=Exists(authorized_product_type_groups),
test__engagement__product__authorized_group=Exists(authorized_product_groups))
findings = findings.filter(
Q(test__engagement__product__prod_type__member=True) |
Q(test__engagement__product__member=True) |
Q(test__engagement__product__prod_type__authorized_group=True) |
Q(test__engagement__product__authorized_group=True))
else:
if not user.is_staff:
findings = findings.filter(
Q(test__engagement__product__authorized_users__in=[user]) |
Q(test__engagement__product__prod_type__authorized_users__in=[user]))
return findings
def get_authorized_stub_findings(permission):
user = get_current_user()
if user is None:
return Stub_Finding.objects.none()
if user.is_superuser:
return Stub_Finding.objects.all()
if settings.FEATURE_AUTHORIZATION_V2:
if user.is_staff and settings.AUTHORIZATION_STAFF_OVERRIDE:
return Stub_Finding.objects.all()
if hasattr(user, 'global_role') and user.global_role.role is not None and role_has_permission(user.global_role.role.id, permission):
return Stub_Finding.objects.all()
for group in get_groups(user):
if hasattr(group, 'global_role') and group.global_role.role is not None and role_has_permission(group.global_role.role.id, permission):
return Stub_Finding.objects.all()
roles = get_roles_for_permission(permission)
authorized_product_type_roles = Product_Type_Member.objects.filter(
product_type=OuterRef('test__engagement__product__prod_type_id'),
user=user,
role__in=roles)
authorized_product_roles = Product_Member.objects.filter(
product=OuterRef('test__engagement__product_id'),
user=user,
role__in=roles)
authorized_product_type_groups = Product_Type_Group.objects.filter(
product_type=OuterRef('test__engagement__product__prod_type_id'),
group__users=user,
role__in=roles)
authorized_product_groups = Product_Group.objects.filter(
product=OuterRef('test__engagement__product_id'),
group__users=user,
role__in=roles)
findings = Stub_Finding.objects.annotate(
test__engagement__product__prod_type__member=Exists(authorized_product_type_roles),
test__engagement__product__member=Exists(authorized_product_roles),
test__engagement__product__prod_type__authorized_group=Exists(authorized_product_type_groups),
test__engagement__product__authorized_group=Exists(authorized_product_groups))
findings = findings.filter(
Q(test__engagement__product__prod_type__member=True) |
Q(test__engagement__product__member=True) |
Q(test__engagement__product__prod_type__authorized_group=True) |
Q(test__engagement__product__authorized_group=True))
else:
if user.is_staff:
findings = Stub_Finding.objects.all()
else:
findings = Stub_Finding.objects.filter(
Q(test__engagement__product__authorized_users__in=[user]) |
Q(test__engagement__product__prod_type__authorized_users__in=[user]))
return findings
| 45.070866
| 147
| 0.710517
| 672
| 5,724
| 5.49256
| 0.084821
| 0.106204
| 0.159306
| 0.094825
| 0.850718
| 0.824167
| 0.811162
| 0.802493
| 0.802493
| 0.782986
| 0
| 0.00045
| 0.222746
| 5,724
| 126
| 148
| 45.428571
| 0.829175
| 0
| 0
| 0.814815
| 0
| 0
| 0.054507
| 0.04682
| 0
| 0
| 0
| 0
| 0
| 1
| 0.018519
| false
| 0
| 0.046296
| 0
| 0.175926
| 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
|
0a8dd63f67d9920ffc57c5128e72020819dc6deb
| 421
|
py
|
Python
|
type.py
|
jeffb4real/scripts
|
349bc3d3d819684261281a05db7a5b9389d664f1
|
[
"MIT"
] | null | null | null |
type.py
|
jeffb4real/scripts
|
349bc3d3d819684261281a05db7a5b9389d664f1
|
[
"MIT"
] | null | null | null |
type.py
|
jeffb4real/scripts
|
349bc3d3d819684261281a05db7a5b9389d664f1
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python
a = 5
if type(a) is int:
print 'a = {} and it is an int'.format(a)
else:
print 'a = {} and it is not an int'.format(a)
a = '5'
if type(a) is int:
print 'a = {} and it is an int'.format(a)
else:
print 'a = {} and it is not an int'.format(a)
a = int(a) + 2
if type(a) is int:
print 'a = {} and it is an int'.format(a)
else:
print 'a = {} and it is not an int'.format(a)
| 17.541667
| 49
| 0.551069
| 87
| 421
| 2.666667
| 0.206897
| 0.155172
| 0.232759
| 0.284483
| 0.913793
| 0.913793
| 0.913793
| 0.913793
| 0.913793
| 0.913793
| 0
| 0.009836
| 0.275534
| 421
| 23
| 50
| 18.304348
| 0.75082
| 0.047506
| 0
| 0.8
| 0
| 0
| 0.381313
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 0.4
| 0
| 0
| 0
| null | 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 10
|
0aacbe3ad25f7090d9596083c8df5263dd88b943
| 71,821
|
py
|
Python
|
tst/schedulers/bayesopt/test_iss_model.py
|
hfurkanbozkurt/syne-tune
|
05ee2668f0155b40c3ee3b61e4b3d58f3f9f3c4f
|
[
"ECL-2.0",
"Apache-2.0"
] | null | null | null |
tst/schedulers/bayesopt/test_iss_model.py
|
hfurkanbozkurt/syne-tune
|
05ee2668f0155b40c3ee3b61e4b3d58f3f9f3c4f
|
[
"ECL-2.0",
"Apache-2.0"
] | 1
|
2022-02-25T15:56:36.000Z
|
2022-02-25T17:53:10.000Z
|
tst/schedulers/bayesopt/test_iss_model.py
|
hfurkanbozkurt/syne-tune
|
05ee2668f0155b40c3ee3b61e4b3d58f3f9f3c4f
|
[
"ECL-2.0",
"Apache-2.0"
] | null | null | null |
# Copyright 2021 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license" file accompanying this file. This file 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 typing import Dict
import json
import numpy as np
import pytest
from syne_tune.optimizer.schedulers.searchers.gp_searcher_factory import \
gp_multifidelity_searcher_defaults, gp_multifidelity_searcher_factory
from syne_tune.optimizer.schedulers.searchers.utils.default_arguments \
import check_and_merge_defaults
from syne_tune.optimizer.schedulers.searchers.gp_searcher_utils import \
decode_state_from_old_encoding
from syne_tune.config_space import randint, uniform, loguniform
def _common_kwargs(config_space: Dict) -> Dict:
return {
'config_space': config_space,
'max_epochs': config_space['epochs'],
'metric': 'accuracy',
'resource_attr': 'epoch',
'scheduler': 'hyperband_stopping',
'scheduler_mode': 'max',
'debug_log': False,
'normalize_targets': True,
}
def build_gpiss_model_factory(
config_space: Dict, model_params: Dict, **kwargs):
kwargs = dict(
_common_kwargs(config_space),
model='gp_issm',
issm_gamma_one=False,
**kwargs)
_kwargs = check_and_merge_defaults(
kwargs, *gp_multifidelity_searcher_defaults(),
dict_name='search_options')
kwargs_int = gp_multifidelity_searcher_factory(**_kwargs)
# Need to convert `model_params`
kwargs_int['model_factory'].set_params(model_params)
return kwargs_int
# We ran launch_sample_searcher_states.py to sample the searcher states
# used here, which runs MOBSTER (hyperband_stopping, bayesopt) with the
# mlp_fashionmnist_benchmark
_model_params = []
_state = []
_model_params.append('{"noise_variance": 0.008381548138906916, "kernel_inv_bw0": 0.004177002691678498, "kernel_inv_bw1": 0.000402494802013946, "kernel_inv_bw2": 0.00036005844016162423, "kernel_inv_bw3": 4.278552430496177, "kernel_inv_bw4": 0.38190450370225937, "kernel_inv_bw5": 0.0001674608736118065, "kernel_inv_bw6": 0.5371572608999335, "kernel_covariance_scale": 1.0487725555603677, "mean_mean_value": -0.37162308332346305, "issm_gamma": 0.0010000000000000002, "issm_alpha": -0.18364130320022903, "issm_beta": 1.1069304811899965}')
_state.append('{"candidate_evaluations": [{"candidate": {"n_units_1": 38, "n_units_2": 187, "batch_size": 53, "dropout_1": 0.36209963448394383, "dropout_2": 0.09749003575393035, "learning_rate": 1.180123718822517e-05, "wd": 0.00011948182727147607}, "metrics": {"cost_metric": {"1": 12.25258493423462, "2": 24.305160999298096, "3": 44.05741477012634, "4": 62.029183864593506, "5": 81.38737893104553, "6": 99.16185593605042, "7": 118.72888779640198, "8": 133.45333671569824, "9": 148.23734402656555, "10": 166.52369689941406, "11": 194.99460196495056, "12": 215.73117184638977, "13": 235.3977439403534, "14": 253.71279788017273, "15": 267.6743288040161, "16": 281.8612160682678, "17": 296.0602250099182, "18": 310.0040330886841, "19": 324.75612902641296, "20": 344.674284696579, "21": 360.0983910560608, "22": 375.9487638473511, "23": 395.81145191192627, "24": 411.6494069099426, "25": 426.79202795028687, "26": 448.74489879608154, "27": 464.90988278388977, "28": 480.28413486480713, "29": 494.5631868839264, "30": 510.31515073776245, "31": 527.6290948390961, "32": 542.7905468940735, "33": 558.1524910926819, "34": 572.6776859760284, "35": 588.3533399105072}, "active_metric": {"1": 0.4978924126856684, "2": 0.3896025692492975, "3": 0.3546768366118025, "4": 0.33289843436370936, "5": 0.3259735046166198, "6": 0.30971497390606184, "7": 0.29626655961461257, "8": 0.2863307908470494, "9": 0.2753914090726616, "10": 0.26455238859895625, "11": 0.25491770373344036, "12": 0.2485949417904456, "13": 0.24678843837816133, "14": 0.23996386993175434, "15": 0.2332396627860297, "16": 0.23143315937374553, "17": 0.22390606182256123, "18": 0.22350461661983134, "19": 0.2195905258932156, "20": 0.217482938578884, "21": 0.21176234443998398, "22": 0.2106583701324769, "23": 0.20764753111200318, "24": 0.2052388598956243, "25": 0.20102368526696102, "26": 0.1963067041348856, "27": 0.1949016459253312, "28": 0.19409875551987155, "29": 0.1904857486953031, "30": 0.18747490967482938, "31": 0.18516659975913285, "32": 0.18446407065435566, "33": 0.18115214773183463, "34": 0.18004817342432755, "35": 0.17844239261340822}}}, {"candidate": {"n_units_1": 514, "n_units_2": 514, "batch_size": 68, "dropout_1": 0.495, "dropout_2": 0.495, "learning_rate": 0.0010000000000000002, "wd": 9.999999999999991e-05}, "metrics": {"cost_metric": {"1": 18.50484609603882, "2": 45.980664014816284, "3": 96.58328175544739, "4": 148.91678476333618, "5": 229.4046459197998, "6": 300.6477417945862, "7": 383.9124698638916, "8": 473.07399010658264, "9": 555.2527649402618}, "active_metric": {"1": 0.15496198479391754, "2": 0.1519607843137255, "3": 0.13645458183273307, "4": 0.14435774309723892, "5": 0.13225290116046418, "6": 0.1253501400560224, "7": 0.12735094037615047, "8": 0.13265306122448983, "9": 0.12104841936774713}}}, {"candidate": {"n_units_1": 347, "n_units_2": 566, "batch_size": 48, "dropout_1": 0.40991313560097764, "dropout_2": 0.1486640484580416, "learning_rate": 0.0001521657976426163, "wd": 2.46706548222209e-07}, "metrics": {"cost_metric": {"1": 18.432047128677368}, "active_metric": {"1": 0.16786858974358976}}}, {"candidate": {"n_units_1": 91, "n_units_2": 459, "batch_size": 105, "dropout_1": 0.48639033141890325, "dropout_2": 0.21324913218446714, "learning_rate": 0.00013769715715418189, "wd": 0.02017249366944585}, "metrics": {"cost_metric": {"1": 17.439072132110596}, "active_metric": {"1": 0.3006516290726817}}}, {"candidate": {"n_units_1": 774, "n_units_2": 917, "batch_size": 29, "dropout_1": 0.7778923725289609, "dropout_2": 0.7413003050986398, "learning_rate": 6.472832341968678e-05, "wd": 0.0007744951242384949}, "metrics": {"cost_metric": {"1": 44.07283306121826}, "active_metric": {"1": 0.23085404971932644}}}, {"candidate": {"n_units_1": 673, "n_units_2": 262, "batch_size": 78, "dropout_1": 0.9510740133913004, "dropout_2": 0.3263851441475057, "learning_rate": 0.009715536539110267, "wd": 0.0002984576239921338}, "metrics": {"cost_metric": {"1": 29.966220140457153}, "active_metric": {"1": 0.6366185897435898}}}, {"candidate": {"n_units_1": 672, "n_units_2": 820, "batch_size": 108, "dropout_1": 0.6443283647430158, "dropout_2": 0.8194904484310889, "learning_rate": 9.196365243521935e-05, "wd": 0.002536625472111785}, "metrics": {"cost_metric": {"1": 32.458306074142456}, "active_metric": {"1": 0.24486714975845414}}}, {"candidate": {"n_units_1": 688, "n_units_2": 597, "batch_size": 123, "dropout_1": 0.7829512576762913, "dropout_2": 0.2834197685256876, "learning_rate": 0.1784738929251937, "wd": 4.489784182359429e-08}, "metrics": {"cost_metric": {"1": 30.61675500869751}, "active_metric": {"1": 0.8976211984342066}}}, {"candidate": {"n_units_1": 501, "n_units_2": 601, "batch_size": 34, "dropout_1": 0.7410256603874262, "dropout_2": 0.046625361151571336, "learning_rate": 0.07937041160202492, "wd": 8.340962845965557e-07}, "metrics": {"cost_metric": {"1": 38.63721990585327}, "active_metric": {"1": 0.904561824729892}}}, {"candidate": {"n_units_1": 1024, "n_units_2": 1002, "batch_size": 116, "dropout_1": 0.0366350257842321, "dropout_2": 0.6883751950302733, "learning_rate": 0.0003133897834907133, "wd": 1.1611672813117278e-08}, "metrics": {"cost_metric": {"1": 44.914865016937256, "2": 95.70968389511108, "3": 134.2296760082245, "4": 167.57774996757507, "5": 205.92636585235596, "6": 242.58663892745972, "7": 283.2623338699341, "8": 326.5033459663391, "9": 360.76255893707275, "10": 398.4191679954529, "11": 434.4982190132141}, "active_metric": {"1": 0.15477145148356053, "2": 0.13502405773857262, "3": 0.1245990376904571, "4": 0.12840817963111473, "5": 0.12219326383319973, "6": 0.11778267842822776, "7": 0.1133720930232558, "8": 0.11166800320769843, "9": 0.10976343223736973, "10": 0.1067562149157979, "11": 0.10555332798716921}}}, {"candidate": {"n_units_1": 1024, "n_units_2": 707, "batch_size": 89, "dropout_1": 0.19654676887125966, "dropout_2": 0.8682666451901773, "learning_rate": 0.00031134631996358774, "wd": 1e-08}, "metrics": {"cost_metric": {"1": 50.98606991767883, "2": 92.16159892082214, "3": 123.69115900993347, "4": 154.21311402320862, "5": 192.06259202957153, "6": 227.72296023368835, "7": 263.6407790184021, "8": 304.7051441669464, "9": 336.6670799255371, "10": 372.1759181022644, "11": 405.45882201194763}, "active_metric": {"1": 0.1700441412520064, "2": 0.1407504012841091, "3": 0.1359349919743178, "4": 0.131922150882825, "5": 0.1220906902086677, "6": 0.1213884430176565, "7": 0.1182784911717496, "8": 0.120284911717496, "9": 0.1221910112359551, "10": 0.1134630818619583, "11": 0.1151685393258427}}}], "failed_candidates": [], "pending_candidates": [{"n_units_1": 514, "n_units_2": 514, "batch_size": 68, "dropout_1": 0.495, "dropout_2": 0.495, "learning_rate": 0.0010000000000000002, "wd": 9.999999999999991e-05, "RESOURCE_ATTR_epoch": 10}, {"n_units_1": 1024, "n_units_2": 707, "batch_size": 89, "dropout_1": 0.19654676887125966, "dropout_2": 0.8682666451901773, "learning_rate": 0.00031134631996358774, "wd": 1e-08, "RESOURCE_ATTR_epoch": 12}, {"n_units_1": 1024, "n_units_2": 1002, "batch_size": 116, "dropout_1": 0.0366350257842321, "dropout_2": 0.6883751950302733, "learning_rate": 0.0003133897834907133, "wd": 1.1611672813117278e-08, "RESOURCE_ATTR_epoch": 12}, {"n_units_1": 38, "n_units_2": 187, "batch_size": 53, "dropout_1": 0.36209963448394383, "dropout_2": 0.09749003575393035, "learning_rate": 1.180123718822517e-05, "wd": 0.00011948182727147607, "RESOURCE_ATTR_epoch": 36}]}')
# elapsed_time = 595.700856924057
# num_observations = 73
# num_configs = 11
_model_params.append('{"noise_variance": 0.008381548138906916, "kernel_inv_bw0": 0.004177002691678498, "kernel_inv_bw1": 0.000402494802013946, "kernel_inv_bw2": 0.00036005844016162423, "kernel_inv_bw3": 4.278552430496177, "kernel_inv_bw4": 0.38190450370225937, "kernel_inv_bw5": 0.0001674608736118065, "kernel_inv_bw6": 0.5371572608999335, "kernel_covariance_scale": 1.0487725555603677, "mean_mean_value": -0.37162308332346305, "issm_gamma": 0.0010000000000000002, "issm_alpha": -0.18364130320022903, "issm_beta": 1.1069304811899965}')
_state.append('{"candidate_evaluations": [{"candidate": {"n_units_1": 38, "n_units_2": 187, "batch_size": 53, "dropout_1": 0.36209963448394383, "dropout_2": 0.09749003575393035, "learning_rate": 1.180123718822517e-05, "wd": 0.00011948182727147607}, "metrics": {"cost_metric": {"1": 12.25258493423462, "2": 24.305160999298096, "3": 44.05741477012634, "4": 62.029183864593506, "5": 81.38737893104553, "6": 99.16185593605042, "7": 118.72888779640198, "8": 133.45333671569824, "9": 148.23734402656555, "10": 166.52369689941406, "11": 194.99460196495056, "12": 215.73117184638977, "13": 235.3977439403534, "14": 253.71279788017273, "15": 267.6743288040161, "16": 281.8612160682678, "17": 296.0602250099182, "18": 310.0040330886841, "19": 324.75612902641296, "20": 344.674284696579, "21": 360.0983910560608, "22": 375.9487638473511, "23": 395.81145191192627, "24": 411.6494069099426, "25": 426.79202795028687, "26": 448.74489879608154, "27": 464.90988278388977, "28": 480.28413486480713, "29": 494.5631868839264, "30": 510.31515073776245, "31": 527.6290948390961, "32": 542.7905468940735, "33": 558.1524910926819, "34": 572.6776859760284, "35": 588.3533399105072}, "active_metric": {"1": 0.4978924126856684, "2": 0.3896025692492975, "3": 0.3546768366118025, "4": 0.33289843436370936, "5": 0.3259735046166198, "6": 0.30971497390606184, "7": 0.29626655961461257, "8": 0.2863307908470494, "9": 0.2753914090726616, "10": 0.26455238859895625, "11": 0.25491770373344036, "12": 0.2485949417904456, "13": 0.24678843837816133, "14": 0.23996386993175434, "15": 0.2332396627860297, "16": 0.23143315937374553, "17": 0.22390606182256123, "18": 0.22350461661983134, "19": 0.2195905258932156, "20": 0.217482938578884, "21": 0.21176234443998398, "22": 0.2106583701324769, "23": 0.20764753111200318, "24": 0.2052388598956243, "25": 0.20102368526696102, "26": 0.1963067041348856, "27": 0.1949016459253312, "28": 0.19409875551987155, "29": 0.1904857486953031, "30": 0.18747490967482938, "31": 0.18516659975913285, "32": 0.18446407065435566, "33": 0.18115214773183463, "34": 0.18004817342432755, "35": 0.17844239261340822}}}, {"candidate": {"n_units_1": 514, "n_units_2": 514, "batch_size": 68, "dropout_1": 0.495, "dropout_2": 0.495, "learning_rate": 0.0010000000000000002, "wd": 9.999999999999991e-05}, "metrics": {"cost_metric": {"1": 18.50484609603882, "2": 45.980664014816284, "3": 96.58328175544739, "4": 148.91678476333618, "5": 229.4046459197998, "6": 300.6477417945862, "7": 383.9124698638916, "8": 473.07399010658264, "9": 555.2527649402618}, "active_metric": {"1": 0.15496198479391754, "2": 0.1519607843137255, "3": 0.13645458183273307, "4": 0.14435774309723892, "5": 0.13225290116046418, "6": 0.1253501400560224, "7": 0.12735094037615047, "8": 0.13265306122448983, "9": 0.12104841936774713}}}, {"candidate": {"n_units_1": 347, "n_units_2": 566, "batch_size": 48, "dropout_1": 0.40991313560097764, "dropout_2": 0.1486640484580416, "learning_rate": 0.0001521657976426163, "wd": 2.46706548222209e-07}, "metrics": {"cost_metric": {"1": 18.432047128677368}, "active_metric": {"1": 0.16786858974358976}}}, {"candidate": {"n_units_1": 91, "n_units_2": 459, "batch_size": 105, "dropout_1": 0.48639033141890325, "dropout_2": 0.21324913218446714, "learning_rate": 0.00013769715715418189, "wd": 0.02017249366944585}, "metrics": {"cost_metric": {"1": 17.439072132110596}, "active_metric": {"1": 0.3006516290726817}}}, {"candidate": {"n_units_1": 774, "n_units_2": 917, "batch_size": 29, "dropout_1": 0.7778923725289609, "dropout_2": 0.7413003050986398, "learning_rate": 6.472832341968678e-05, "wd": 0.0007744951242384949}, "metrics": {"cost_metric": {"1": 44.07283306121826}, "active_metric": {"1": 0.23085404971932644}}}, {"candidate": {"n_units_1": 673, "n_units_2": 262, "batch_size": 78, "dropout_1": 0.9510740133913004, "dropout_2": 0.3263851441475057, "learning_rate": 0.009715536539110267, "wd": 0.0002984576239921338}, "metrics": {"cost_metric": {"1": 29.966220140457153}, "active_metric": {"1": 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116, "dropout_1": 0.0366350257842321, "dropout_2": 0.6883751950302733, "learning_rate": 0.0003133897834907133, "wd": 1.1611672813117278e-08}, "metrics": {"cost_metric": {"1": 44.914865016937256, "2": 95.70968389511108, "3": 134.2296760082245, "4": 167.57774996757507, "5": 205.92636585235596, "6": 242.58663892745972, "7": 283.2623338699341, "8": 326.5033459663391, "9": 360.76255893707275, "10": 398.4191679954529, "11": 434.4982190132141}, "active_metric": {"1": 0.15477145148356053, "2": 0.13502405773857262, "3": 0.1245990376904571, "4": 0.12840817963111473, "5": 0.12219326383319973, "6": 0.11778267842822776, "7": 0.1133720930232558, "8": 0.11166800320769843, "9": 0.10976343223736973, "10": 0.1067562149157979, "11": 0.10555332798716921}}}, {"candidate": {"n_units_1": 1024, "n_units_2": 707, "batch_size": 89, "dropout_1": 0.19654676887125966, "dropout_2": 0.8682666451901773, "learning_rate": 0.00031134631996358774, "wd": 1e-08}, "metrics": {"cost_metric": {"1": 50.98606991767883, "2": 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1024, "n_units_2": 1002, "batch_size": 116, "dropout_1": 0.0366350257842321, "dropout_2": 0.6883751950302733, "learning_rate": 0.0003133897834907133, "wd": 1.1611672813117278e-08, "RESOURCE_ATTR_epoch": 10}, {"n_units_1": 38, "n_units_2": 187, "batch_size": 53, "dropout_1": 0.36209963448394383, "dropout_2": 0.09749003575393035, "learning_rate": 1.180123718822517e-05, "wd": 0.00011948182727147607, "RESOURCE_ATTR_epoch": 31}]}')
# elapsed_time = 520.2518529891968
# num_observations = 63
# num_configs = 11
_model_params.append('{"noise_variance": 0.008381548138906916, "kernel_inv_bw0": 0.004177002691678498, "kernel_inv_bw1": 0.000402494802013946, "kernel_inv_bw2": 0.00036005844016162423, "kernel_inv_bw3": 4.278552430496177, "kernel_inv_bw4": 0.38190450370225937, "kernel_inv_bw5": 0.0001674608736118065, "kernel_inv_bw6": 0.5371572608999335, "kernel_covariance_scale": 1.0487725555603677, "mean_mean_value": -0.37162308332346305, "issm_gamma": 0.0010000000000000002, "issm_alpha": -0.18364130320022903, "issm_beta": 1.1069304811899965}')
_state.append('{"candidate_evaluations": [{"candidate": {"n_units_1": 38, "n_units_2": 187, "batch_size": 53, "dropout_1": 0.36209963448394383, "dropout_2": 0.09749003575393035, "learning_rate": 1.180123718822517e-05, "wd": 0.00011948182727147607}, "metrics": {"cost_metric": {"1": 12.25258493423462, "2": 24.305160999298096, "3": 44.05741477012634, "4": 62.029183864593506, "5": 81.38737893104553, "6": 99.16185593605042, "7": 118.72888779640198, "8": 133.45333671569824, "9": 148.23734402656555, "10": 166.52369689941406, "11": 194.99460196495056, "12": 215.73117184638977, "13": 235.3977439403534, "14": 253.71279788017273, "15": 267.6743288040161, "16": 281.8612160682678, "17": 296.0602250099182, "18": 310.0040330886841, "19": 324.75612902641296, "20": 344.674284696579, "21": 360.0983910560608, "22": 375.9487638473511, "23": 395.81145191192627, "24": 411.6494069099426, "25": 426.79202795028687, "26": 448.74489879608154, "27": 464.90988278388977, "28": 480.28413486480713, "29": 494.5631868839264, "30": 510.31515073776245, "31": 527.6290948390961, "32": 542.7905468940735, "33": 558.1524910926819, "34": 572.6776859760284, "35": 588.3533399105072}, "active_metric": {"1": 0.4978924126856684, "2": 0.3896025692492975, "3": 0.3546768366118025, "4": 0.33289843436370936, "5": 0.3259735046166198, "6": 0.30971497390606184, "7": 0.29626655961461257, "8": 0.2863307908470494, "9": 0.2753914090726616, "10": 0.26455238859895625, "11": 0.25491770373344036, "12": 0.2485949417904456, "13": 0.24678843837816133, "14": 0.23996386993175434, "15": 0.2332396627860297, "16": 0.23143315937374553, "17": 0.22390606182256123, "18": 0.22350461661983134, "19": 0.2195905258932156, "20": 0.217482938578884, "21": 0.21176234443998398, "22": 0.2106583701324769, "23": 0.20764753111200318, "24": 0.2052388598956243, "25": 0.20102368526696102, "26": 0.1963067041348856, "27": 0.1949016459253312, "28": 0.19409875551987155, "29": 0.1904857486953031, "30": 0.18747490967482938, "31": 0.18516659975913285, "32": 0.18446407065435566, "33": 0.18115214773183463, "34": 0.18004817342432755, "35": 0.17844239261340822}}}, {"candidate": {"n_units_1": 514, "n_units_2": 514, "batch_size": 68, "dropout_1": 0.495, "dropout_2": 0.495, "learning_rate": 0.0010000000000000002, "wd": 9.999999999999991e-05}, "metrics": {"cost_metric": {"1": 18.50484609603882, "2": 45.980664014816284, "3": 96.58328175544739, "4": 148.91678476333618, "5": 229.4046459197998, "6": 300.6477417945862, "7": 383.9124698638916, "8": 473.07399010658264, "9": 555.2527649402618}, "active_metric": {"1": 0.15496198479391754, "2": 0.1519607843137255, "3": 0.13645458183273307, "4": 0.14435774309723892, "5": 0.13225290116046418, "6": 0.1253501400560224, "7": 0.12735094037615047, "8": 0.13265306122448983, "9": 0.12104841936774713}}}, {"candidate": {"n_units_1": 347, "n_units_2": 566, "batch_size": 48, "dropout_1": 0.40991313560097764, "dropout_2": 0.1486640484580416, "learning_rate": 0.0001521657976426163, "wd": 2.46706548222209e-07}, "metrics": {"cost_metric": {"1": 18.432047128677368}, "active_metric": {"1": 0.16786858974358976}}}, {"candidate": {"n_units_1": 91, "n_units_2": 459, "batch_size": 105, "dropout_1": 0.48639033141890325, "dropout_2": 0.21324913218446714, "learning_rate": 0.00013769715715418189, "wd": 0.02017249366944585}, "metrics": {"cost_metric": {"1": 17.439072132110596}, "active_metric": {"1": 0.3006516290726817}}}, {"candidate": {"n_units_1": 774, "n_units_2": 917, "batch_size": 29, "dropout_1": 0.7778923725289609, "dropout_2": 0.7413003050986398, "learning_rate": 6.472832341968678e-05, "wd": 0.0007744951242384949}, "metrics": {"cost_metric": {"1": 44.07283306121826}, "active_metric": {"1": 0.23085404971932644}}}, {"candidate": {"n_units_1": 673, "n_units_2": 262, "batch_size": 78, "dropout_1": 0.9510740133913004, "dropout_2": 0.3263851441475057, "learning_rate": 0.009715536539110267, "wd": 0.0002984576239921338}, "metrics": {"cost_metric": {"1": 29.966220140457153}, "active_metric": {"1": 0.6366185897435898}}}, {"candidate": {"n_units_1": 672, "n_units_2": 820, "batch_size": 108, "dropout_1": 0.6443283647430158, "dropout_2": 0.8194904484310889, "learning_rate": 9.196365243521935e-05, "wd": 0.002536625472111785}, "metrics": {"cost_metric": {"1": 32.458306074142456}, "active_metric": {"1": 0.24486714975845414}}}, {"candidate": {"n_units_1": 688, "n_units_2": 597, "batch_size": 123, "dropout_1": 0.7829512576762913, "dropout_2": 0.2834197685256876, "learning_rate": 0.1784738929251937, "wd": 4.489784182359429e-08}, "metrics": {"cost_metric": {"1": 30.61675500869751}, "active_metric": {"1": 0.8976211984342066}}}, {"candidate": {"n_units_1": 501, "n_units_2": 601, "batch_size": 34, "dropout_1": 0.7410256603874262, "dropout_2": 0.046625361151571336, "learning_rate": 0.07937041160202492, "wd": 8.340962845965557e-07}, "metrics": {"cost_metric": {"1": 38.63721990585327}, "active_metric": {"1": 0.904561824729892}}}, {"candidate": {"n_units_1": 1024, "n_units_2": 1002, "batch_size": 116, "dropout_1": 0.0366350257842321, "dropout_2": 0.6883751950302733, "learning_rate": 0.0003133897834907133, "wd": 1.1611672813117278e-08}, "metrics": {"cost_metric": {"1": 44.914865016937256, "2": 95.70968389511108, "3": 134.2296760082245, "4": 167.57774996757507, "5": 205.92636585235596, "6": 242.58663892745972, "7": 283.2623338699341, "8": 326.5033459663391, "9": 360.76255893707275, "10": 398.4191679954529, "11": 434.4982190132141}, "active_metric": {"1": 0.15477145148356053, "2": 0.13502405773857262, "3": 0.1245990376904571, "4": 0.12840817963111473, "5": 0.12219326383319973, "6": 0.11778267842822776, "7": 0.1133720930232558, "8": 0.11166800320769843, "9": 0.10976343223736973, "10": 0.1067562149157979, "11": 0.10555332798716921}}}, {"candidate": {"n_units_1": 1024, "n_units_2": 707, "batch_size": 89, "dropout_1": 0.19654676887125966, "dropout_2": 0.8682666451901773, "learning_rate": 0.00031134631996358774, "wd": 1e-08}, "metrics": {"cost_metric": {"1": 50.98606991767883, "2": 92.16159892082214, "3": 123.69115900993347, "4": 154.21311402320862, "5": 192.06259202957153, "6": 227.72296023368835, "7": 263.6407790184021, "8": 304.7051441669464, "9": 336.6670799255371, "10": 372.1759181022644, "11": 405.45882201194763}, "active_metric": {"1": 0.1700441412520064, "2": 0.1407504012841091, "3": 0.1359349919743178, "4": 0.131922150882825, "5": 0.1220906902086677, "6": 0.1213884430176565, "7": 0.1182784911717496, "8": 0.120284911717496, "9": 0.1221910112359551, "10": 0.1134630818619583, "11": 0.1151685393258427}}}], "failed_candidates": [], "pending_candidates": [{"n_units_1": 514, "n_units_2": 514, "batch_size": 68, "dropout_1": 0.495, "dropout_2": 0.495, "learning_rate": 0.0010000000000000002, "wd": 9.999999999999991e-05, "RESOURCE_ATTR_epoch": 8}, {"n_units_1": 38, "n_units_2": 187, "batch_size": 53, "dropout_1": 0.36209963448394383, "dropout_2": 0.09749003575393035, "learning_rate": 1.180123718822517e-05, "wd": 0.00011948182727147607, "RESOURCE_ATTR_epoch": 27}, {"n_units_1": 1024, "n_units_2": 1002, "batch_size": 116, "dropout_1": 0.0366350257842321, "dropout_2": 0.6883751950302733, "learning_rate": 0.0003133897834907133, "wd": 1.1611672813117278e-08, "RESOURCE_ATTR_epoch": 9}, {"n_units_1": 1024, "n_units_2": 707, "batch_size": 89, "dropout_1": 0.19654676887125966, "dropout_2": 0.8682666451901773, "learning_rate": 0.00031134631996358774, "wd": 1e-08, "RESOURCE_ATTR_epoch": 9}]}')
# elapsed_time = 469.9041178226471
# num_observations = 56
# num_configs = 11
_model_params.append('{"noise_variance": 0.008381548138906916, "kernel_inv_bw0": 0.004177002691678498, "kernel_inv_bw1": 0.000402494802013946, "kernel_inv_bw2": 0.00036005844016162423, "kernel_inv_bw3": 4.278552430496177, "kernel_inv_bw4": 0.38190450370225937, "kernel_inv_bw5": 0.0001674608736118065, "kernel_inv_bw6": 0.5371572608999335, "kernel_covariance_scale": 1.0487725555603677, "mean_mean_value": -0.37162308332346305, "issm_gamma": 0.0010000000000000002, "issm_alpha": -0.18364130320022903, "issm_beta": 1.1069304811899965}')
_state.append('{"candidate_evaluations": [{"candidate": {"n_units_1": 38, "n_units_2": 187, "batch_size": 53, "dropout_1": 0.36209963448394383, "dropout_2": 0.09749003575393035, "learning_rate": 1.180123718822517e-05, "wd": 0.00011948182727147607}, "metrics": {"cost_metric": {"1": 12.25258493423462, "2": 24.305160999298096, "3": 44.05741477012634, "4": 62.029183864593506, "5": 81.38737893104553, "6": 99.16185593605042, "7": 118.72888779640198, "8": 133.45333671569824, "9": 148.23734402656555, "10": 166.52369689941406, "11": 194.99460196495056, "12": 215.73117184638977, "13": 235.3977439403534, "14": 253.71279788017273, "15": 267.6743288040161, "16": 281.8612160682678, "17": 296.0602250099182, "18": 310.0040330886841, "19": 324.75612902641296, "20": 344.674284696579, "21": 360.0983910560608, "22": 375.9487638473511, "23": 395.81145191192627, "24": 411.6494069099426, "25": 426.79202795028687, "26": 448.74489879608154, "27": 464.90988278388977, "28": 480.28413486480713, "29": 494.5631868839264, "30": 510.31515073776245, "31": 527.6290948390961, "32": 542.7905468940735, "33": 558.1524910926819, "34": 572.6776859760284, "35": 588.3533399105072}, "active_metric": {"1": 0.4978924126856684, "2": 0.3896025692492975, "3": 0.3546768366118025, "4": 0.33289843436370936, "5": 0.3259735046166198, "6": 0.30971497390606184, "7": 0.29626655961461257, "8": 0.2863307908470494, "9": 0.2753914090726616, "10": 0.26455238859895625, "11": 0.25491770373344036, "12": 0.2485949417904456, "13": 0.24678843837816133, "14": 0.23996386993175434, "15": 0.2332396627860297, "16": 0.23143315937374553, "17": 0.22390606182256123, "18": 0.22350461661983134, "19": 0.2195905258932156, "20": 0.217482938578884, "21": 0.21176234443998398, "22": 0.2106583701324769, "23": 0.20764753111200318, "24": 0.2052388598956243, "25": 0.20102368526696102, "26": 0.1963067041348856, "27": 0.1949016459253312, "28": 0.19409875551987155, "29": 0.1904857486953031, "30": 0.18747490967482938, "31": 0.18516659975913285, "32": 0.18446407065435566, "33": 0.18115214773183463, "34": 0.18004817342432755, "35": 0.17844239261340822}}}, {"candidate": {"n_units_1": 514, "n_units_2": 514, "batch_size": 68, "dropout_1": 0.495, "dropout_2": 0.495, "learning_rate": 0.0010000000000000002, "wd": 9.999999999999991e-05}, "metrics": {"cost_metric": {"1": 18.50484609603882, "2": 45.980664014816284, "3": 96.58328175544739, "4": 148.91678476333618, "5": 229.4046459197998, "6": 300.6477417945862, "7": 383.9124698638916, "8": 473.07399010658264, "9": 555.2527649402618}, "active_metric": {"1": 0.15496198479391754, "2": 0.1519607843137255, "3": 0.13645458183273307, "4": 0.14435774309723892, "5": 0.13225290116046418, "6": 0.1253501400560224, "7": 0.12735094037615047, "8": 0.13265306122448983, "9": 0.12104841936774713}}}, {"candidate": {"n_units_1": 347, "n_units_2": 566, "batch_size": 48, "dropout_1": 0.40991313560097764, "dropout_2": 0.1486640484580416, "learning_rate": 0.0001521657976426163, "wd": 2.46706548222209e-07}, "metrics": {"cost_metric": {"1": 18.432047128677368}, "active_metric": {"1": 0.16786858974358976}}}, {"candidate": {"n_units_1": 91, "n_units_2": 459, "batch_size": 105, "dropout_1": 0.48639033141890325, "dropout_2": 0.21324913218446714, "learning_rate": 0.00013769715715418189, "wd": 0.02017249366944585}, "metrics": {"cost_metric": {"1": 17.439072132110596}, "active_metric": {"1": 0.3006516290726817}}}, {"candidate": {"n_units_1": 774, "n_units_2": 917, "batch_size": 29, "dropout_1": 0.7778923725289609, "dropout_2": 0.7413003050986398, "learning_rate": 6.472832341968678e-05, "wd": 0.0007744951242384949}, "metrics": {"cost_metric": {"1": 44.07283306121826}, "active_metric": {"1": 0.23085404971932644}}}, {"candidate": {"n_units_1": 673, "n_units_2": 262, "batch_size": 78, "dropout_1": 0.9510740133913004, "dropout_2": 0.3263851441475057, "learning_rate": 0.009715536539110267, "wd": 0.0002984576239921338}, "metrics": {"cost_metric": {"1": 29.966220140457153}, "active_metric": {"1": 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116, "dropout_1": 0.0366350257842321, "dropout_2": 0.6883751950302733, "learning_rate": 0.0003133897834907133, "wd": 1.1611672813117278e-08}, "metrics": {"cost_metric": {"1": 44.914865016937256, "2": 95.70968389511108, "3": 134.2296760082245, "4": 167.57774996757507, "5": 205.92636585235596, "6": 242.58663892745972, "7": 283.2623338699341, "8": 326.5033459663391, "9": 360.76255893707275, "10": 398.4191679954529, "11": 434.4982190132141}, "active_metric": {"1": 0.15477145148356053, "2": 0.13502405773857262, "3": 0.1245990376904571, "4": 0.12840817963111473, "5": 0.12219326383319973, "6": 0.11778267842822776, "7": 0.1133720930232558, "8": 0.11166800320769843, "9": 0.10976343223736973, "10": 0.1067562149157979, "11": 0.10555332798716921}}}, {"candidate": {"n_units_1": 1024, "n_units_2": 707, "batch_size": 89, "dropout_1": 0.19654676887125966, "dropout_2": 0.8682666451901773, "learning_rate": 0.00031134631996358774, "wd": 1e-08}, "metrics": {"cost_metric": {"1": 50.98606991767883, "2": 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"n_units_2": 187, "batch_size": 53, "dropout_1": 0.36209963448394383, "dropout_2": 0.09749003575393035, "learning_rate": 1.180123718822517e-05, "wd": 0.00011948182727147607, "RESOURCE_ATTR_epoch": 20}, {"n_units_1": 1024, "n_units_2": 1002, "batch_size": 116, "dropout_1": 0.0366350257842321, "dropout_2": 0.6883751950302733, "learning_rate": 0.0003133897834907133, "wd": 1.1611672813117278e-08, "RESOURCE_ATTR_epoch": 6}]}')
# elapsed_time = 349.2686309814453
# num_observations = 41
# num_configs = 11
_model_params.append('{"noise_variance": 0.008381548138906916, "kernel_inv_bw0": 0.004177002691678498, "kernel_inv_bw1": 0.000402494802013946, "kernel_inv_bw2": 0.00036005844016162423, "kernel_inv_bw3": 4.278552430496177, "kernel_inv_bw4": 0.38190450370225937, "kernel_inv_bw5": 0.0001674608736118065, "kernel_inv_bw6": 0.5371572608999335, "kernel_covariance_scale": 1.0487725555603677, "mean_mean_value": -0.37162308332346305, "issm_gamma": 0.0010000000000000002, "issm_alpha": -0.18364130320022903, "issm_beta": 1.1069304811899965}')
_state.append('{"candidate_evaluations": [{"candidate": {"n_units_1": 38, "n_units_2": 187, "batch_size": 53, "dropout_1": 0.36209963448394383, "dropout_2": 0.09749003575393035, "learning_rate": 1.180123718822517e-05, "wd": 0.00011948182727147607}, "metrics": {"cost_metric": {"1": 12.25258493423462, "2": 24.305160999298096, "3": 44.05741477012634, "4": 62.029183864593506, "5": 81.38737893104553, "6": 99.16185593605042, "7": 118.72888779640198, "8": 133.45333671569824, "9": 148.23734402656555, "10": 166.52369689941406, "11": 194.99460196495056, "12": 215.73117184638977, "13": 235.3977439403534, "14": 253.71279788017273, "15": 267.6743288040161, "16": 281.8612160682678, "17": 296.0602250099182, "18": 310.0040330886841, "19": 324.75612902641296, "20": 344.674284696579, "21": 360.0983910560608, "22": 375.9487638473511, "23": 395.81145191192627, "24": 411.6494069099426, "25": 426.79202795028687, "26": 448.74489879608154, "27": 464.90988278388977, "28": 480.28413486480713, "29": 494.5631868839264, "30": 510.31515073776245, "31": 527.6290948390961, "32": 542.7905468940735, "33": 558.1524910926819, "34": 572.6776859760284, "35": 588.3533399105072}, "active_metric": {"1": 0.4978924126856684, "2": 0.3896025692492975, "3": 0.3546768366118025, "4": 0.33289843436370936, "5": 0.3259735046166198, "6": 0.30971497390606184, "7": 0.29626655961461257, "8": 0.2863307908470494, "9": 0.2753914090726616, "10": 0.26455238859895625, "11": 0.25491770373344036, "12": 0.2485949417904456, "13": 0.24678843837816133, "14": 0.23996386993175434, "15": 0.2332396627860297, "16": 0.23143315937374553, "17": 0.22390606182256123, "18": 0.22350461661983134, "19": 0.2195905258932156, "20": 0.217482938578884, "21": 0.21176234443998398, "22": 0.2106583701324769, "23": 0.20764753111200318, "24": 0.2052388598956243, "25": 0.20102368526696102, "26": 0.1963067041348856, "27": 0.1949016459253312, "28": 0.19409875551987155, "29": 0.1904857486953031, "30": 0.18747490967482938, "31": 0.18516659975913285, "32": 0.18446407065435566, "33": 0.18115214773183463, "34": 0.18004817342432755, "35": 0.17844239261340822}}}, {"candidate": {"n_units_1": 514, "n_units_2": 514, "batch_size": 68, "dropout_1": 0.495, "dropout_2": 0.495, "learning_rate": 0.0010000000000000002, "wd": 9.999999999999991e-05}, "metrics": {"cost_metric": {"1": 18.50484609603882, "2": 45.980664014816284, "3": 96.58328175544739, "4": 148.91678476333618, "5": 229.4046459197998, "6": 300.6477417945862, "7": 383.9124698638916, "8": 473.07399010658264, "9": 555.2527649402618}, "active_metric": {"1": 0.15496198479391754, "2": 0.1519607843137255, "3": 0.13645458183273307, "4": 0.14435774309723892, "5": 0.13225290116046418, "6": 0.1253501400560224, "7": 0.12735094037615047, "8": 0.13265306122448983, "9": 0.12104841936774713}}}, {"candidate": {"n_units_1": 347, "n_units_2": 566, "batch_size": 48, "dropout_1": 0.40991313560097764, "dropout_2": 0.1486640484580416, "learning_rate": 0.0001521657976426163, "wd": 2.46706548222209e-07}, "metrics": {"cost_metric": {"1": 18.432047128677368}, "active_metric": {"1": 0.16786858974358976}}}, {"candidate": {"n_units_1": 91, "n_units_2": 459, "batch_size": 105, "dropout_1": 0.48639033141890325, "dropout_2": 0.21324913218446714, "learning_rate": 0.00013769715715418189, "wd": 0.02017249366944585}, "metrics": {"cost_metric": {"1": 17.439072132110596}, "active_metric": {"1": 0.3006516290726817}}}, {"candidate": {"n_units_1": 774, "n_units_2": 917, "batch_size": 29, "dropout_1": 0.7778923725289609, "dropout_2": 0.7413003050986398, "learning_rate": 6.472832341968678e-05, "wd": 0.0007744951242384949}, "metrics": {"cost_metric": {"1": 44.07283306121826}, "active_metric": {"1": 0.23085404971932644}}}, {"candidate": {"n_units_1": 673, "n_units_2": 262, "batch_size": 78, "dropout_1": 0.9510740133913004, "dropout_2": 0.3263851441475057, "learning_rate": 0.009715536539110267, "wd": 0.0002984576239921338}, "metrics": {"cost_metric": {"1": 29.966220140457153}, "active_metric": {"1": 0.6366185897435898}}}, {"candidate": {"n_units_1": 672, "n_units_2": 820, "batch_size": 108, "dropout_1": 0.6443283647430158, "dropout_2": 0.8194904484310889, "learning_rate": 9.196365243521935e-05, "wd": 0.002536625472111785}, "metrics": {"cost_metric": {"1": 32.458306074142456}, "active_metric": {"1": 0.24486714975845414}}}, {"candidate": {"n_units_1": 688, "n_units_2": 597, "batch_size": 123, "dropout_1": 0.7829512576762913, "dropout_2": 0.2834197685256876, "learning_rate": 0.1784738929251937, "wd": 4.489784182359429e-08}, "metrics": {"cost_metric": {"1": 30.61675500869751}, "active_metric": {"1": 0.8976211984342066}}}, {"candidate": {"n_units_1": 501, "n_units_2": 601, "batch_size": 34, "dropout_1": 0.7410256603874262, "dropout_2": 0.046625361151571336, "learning_rate": 0.07937041160202492, "wd": 8.340962845965557e-07}, "metrics": {"cost_metric": {"1": 38.63721990585327}, "active_metric": {"1": 0.904561824729892}}}, {"candidate": {"n_units_1": 1024, "n_units_2": 1002, "batch_size": 116, "dropout_1": 0.0366350257842321, "dropout_2": 0.6883751950302733, "learning_rate": 0.0003133897834907133, "wd": 1.1611672813117278e-08}, "metrics": {"cost_metric": {"1": 44.914865016937256, "2": 95.70968389511108, "3": 134.2296760082245, "4": 167.57774996757507, "5": 205.92636585235596, "6": 242.58663892745972, "7": 283.2623338699341, "8": 326.5033459663391, "9": 360.76255893707275, "10": 398.4191679954529, "11": 434.4982190132141}, "active_metric": {"1": 0.15477145148356053, "2": 0.13502405773857262, "3": 0.1245990376904571, "4": 0.12840817963111473, "5": 0.12219326383319973, "6": 0.11778267842822776, "7": 0.1133720930232558, "8": 0.11166800320769843, "9": 0.10976343223736973, "10": 0.1067562149157979, "11": 0.10555332798716921}}}], "failed_candidates": [], "pending_candidates": [{"batch_size": 89, "dropout_1": 0.19654676887125966, "dropout_2": 0.8682666451901773, "learning_rate": 0.00031134631996358774, "n_units_1": 1024, "n_units_2": 707, "wd": 1e-08, "RESOURCE_ATTR_epoch": 1}, {"n_units_1": 514, "n_units_2": 514, "batch_size": 68, "dropout_1": 0.495, "dropout_2": 0.495, "learning_rate": 0.0010000000000000002, "wd": 9.999999999999991e-05, "RESOURCE_ATTR_epoch": 5}, {"n_units_1": 1024, "n_units_2": 1002, "batch_size": 116, "dropout_1": 0.0366350257842321, "dropout_2": 0.6883751950302733, "learning_rate": 0.0003133897834907133, "wd": 1.1611672813117278e-08, "RESOURCE_ATTR_epoch": 2}, {"n_units_1": 38, "n_units_2": 187, "batch_size": 53, "dropout_1": 0.36209963448394383, "dropout_2": 0.09749003575393035, "learning_rate": 1.180123718822517e-05, "wd": 0.00011948182727147607, "RESOURCE_ATTR_epoch": 12}]}')
# elapsed_time = 203.53759908676147
# num_observations = 23
# num_configs = 10
_model_params.append('{"noise_variance": 0.012624704488939506, "kernel_inv_bw0": 0.0026714958295617746, "kernel_inv_bw1": 0.002294225496133934, "kernel_inv_bw2": 0.0005810444910329019, "kernel_inv_bw3": 4.756569311119674, "kernel_inv_bw4": 0.41912704911412996, "kernel_inv_bw5": 0.007082508117597436, "kernel_inv_bw6": 0.6008226671164758, "kernel_covariance_scale": 1.2790537663629489, "mean_mean_value": -0.29754767463440174, "issm_gamma": 0.0010000000000000002, "issm_alpha": -0.20709875141786813, "issm_beta": 1.1564145320327957}')
_state.append('{"candidate_evaluations": [{"candidate": {"n_units_1": 38, "n_units_2": 187, "batch_size": 53, "dropout_1": 0.36209963448394383, "dropout_2": 0.09749003575393035, "learning_rate": 1.180123718822517e-05, "wd": 0.00011948182727147607}, "metrics": {"cost_metric": {"1": 12.25258493423462, "2": 24.305160999298096, "3": 44.05741477012634, "4": 62.029183864593506, "5": 81.38737893104553, "6": 99.16185593605042, "7": 118.72888779640198, "8": 133.45333671569824, "9": 148.23734402656555, "10": 166.52369689941406, "11": 194.99460196495056, "12": 215.73117184638977, "13": 235.3977439403534, "14": 253.71279788017273, "15": 267.6743288040161, "16": 281.8612160682678, "17": 296.0602250099182, "18": 310.0040330886841, "19": 324.75612902641296, "20": 344.674284696579, "21": 360.0983910560608, "22": 375.9487638473511, "23": 395.81145191192627, "24": 411.6494069099426, "25": 426.79202795028687, "26": 448.74489879608154, "27": 464.90988278388977, "28": 480.28413486480713, "29": 494.5631868839264, "30": 510.31515073776245, "31": 527.6290948390961, "32": 542.7905468940735, "33": 558.1524910926819, "34": 572.6776859760284, "35": 588.3533399105072}, "active_metric": {"1": 0.4978924126856684, "2": 0.3896025692492975, "3": 0.3546768366118025, "4": 0.33289843436370936, "5": 0.3259735046166198, "6": 0.30971497390606184, "7": 0.29626655961461257, "8": 0.2863307908470494, "9": 0.2753914090726616, "10": 0.26455238859895625, "11": 0.25491770373344036, "12": 0.2485949417904456, "13": 0.24678843837816133, "14": 0.23996386993175434, "15": 0.2332396627860297, "16": 0.23143315937374553, "17": 0.22390606182256123, "18": 0.22350461661983134, "19": 0.2195905258932156, "20": 0.217482938578884, "21": 0.21176234443998398, "22": 0.2106583701324769, "23": 0.20764753111200318, "24": 0.2052388598956243, "25": 0.20102368526696102, "26": 0.1963067041348856, "27": 0.1949016459253312, "28": 0.19409875551987155, "29": 0.1904857486953031, "30": 0.18747490967482938, "31": 0.18516659975913285, "32": 0.18446407065435566, "33": 0.18115214773183463, "34": 0.18004817342432755, "35": 0.17844239261340822}}}, {"candidate": {"n_units_1": 514, "n_units_2": 514, "batch_size": 68, "dropout_1": 0.495, "dropout_2": 0.495, "learning_rate": 0.0010000000000000002, "wd": 9.999999999999991e-05}, "metrics": {"cost_metric": {"1": 18.50484609603882, "2": 45.980664014816284, "3": 96.58328175544739, "4": 148.91678476333618, "5": 229.4046459197998, "6": 300.6477417945862, "7": 383.9124698638916, "8": 473.07399010658264, "9": 555.2527649402618}, "active_metric": {"1": 0.15496198479391754, "2": 0.1519607843137255, "3": 0.13645458183273307, "4": 0.14435774309723892, "5": 0.13225290116046418, "6": 0.1253501400560224, "7": 0.12735094037615047, "8": 0.13265306122448983, "9": 0.12104841936774713}}}, {"candidate": {"n_units_1": 347, "n_units_2": 566, "batch_size": 48, "dropout_1": 0.40991313560097764, "dropout_2": 0.1486640484580416, "learning_rate": 0.0001521657976426163, "wd": 2.46706548222209e-07}, "metrics": {"cost_metric": {"1": 18.432047128677368}, "active_metric": {"1": 0.16786858974358976}}}, {"candidate": {"n_units_1": 91, "n_units_2": 459, "batch_size": 105, "dropout_1": 0.48639033141890325, "dropout_2": 0.21324913218446714, "learning_rate": 0.00013769715715418189, "wd": 0.02017249366944585}, "metrics": {"cost_metric": {"1": 17.439072132110596}, "active_metric": {"1": 0.3006516290726817}}}, {"candidate": {"n_units_1": 774, "n_units_2": 917, "batch_size": 29, "dropout_1": 0.7778923725289609, "dropout_2": 0.7413003050986398, "learning_rate": 6.472832341968678e-05, "wd": 0.0007744951242384949}, "metrics": {"cost_metric": {"1": 44.07283306121826}, "active_metric": {"1": 0.23085404971932644}}}, {"candidate": {"n_units_1": 673, "n_units_2": 262, "batch_size": 78, "dropout_1": 0.9510740133913004, "dropout_2": 0.3263851441475057, "learning_rate": 0.009715536539110267, "wd": 0.0002984576239921338}, "metrics": {"cost_metric": {"1": 29.966220140457153}, "active_metric": {"1": 0.6366185897435898}}}, {"candidate": {"n_units_1": 672, "n_units_2": 820, "batch_size": 108, "dropout_1": 0.6443283647430158, "dropout_2": 0.8194904484310889, "learning_rate": 9.196365243521935e-05, "wd": 0.002536625472111785}, "metrics": {"cost_metric": {"1": 32.458306074142456}, "active_metric": {"1": 0.24486714975845414}}}, {"candidate": {"n_units_1": 688, "n_units_2": 597, "batch_size": 123, "dropout_1": 0.7829512576762913, "dropout_2": 0.2834197685256876, "learning_rate": 0.1784738929251937, "wd": 4.489784182359429e-08}, "metrics": {"cost_metric": {"1": 30.61675500869751}, "active_metric": {"1": 0.8976211984342066}}}], "failed_candidates": [], "pending_candidates": [{"batch_size": 34, "dropout_1": 0.7410256603874262, "dropout_2": 0.046625361151571336, "learning_rate": 0.07937041160202492, "n_units_1": 501, "n_units_2": 601, "wd": 8.340962845965557e-07, "RESOURCE_ATTR_epoch": 1}, {"n_units_1": 514, "n_units_2": 514, "batch_size": 68, "dropout_1": 0.495, "dropout_2": 0.495, "learning_rate": 0.0010000000000000002, "wd": 9.999999999999991e-05, "RESOURCE_ATTR_epoch": 4}, {"batch_size": 116, "dropout_1": 0.0366350257842321, "dropout_2": 0.6883751950302733, "learning_rate": 0.0003133897834907133, "n_units_1": 1024, "n_units_2": 1002, "wd": 1.1611672813117278e-08, "RESOURCE_ATTR_epoch": 1}, {"n_units_1": 38, "n_units_2": 187, "batch_size": 53, "dropout_1": 0.36209963448394383, "dropout_2": 0.09749003575393035, "learning_rate": 1.180123718822517e-05, "wd": 0.00011948182727147607, "RESOURCE_ATTR_epoch": 9}]}')
# elapsed_time = 141.0116560459137
# num_observations = 17
# num_configs = 8
_model_params.append('{"noise_variance": 0.02443305886195063, "kernel_inv_bw0": 0.01410539584512635, "kernel_inv_bw1": 1.4106734173901074, "kernel_inv_bw2": 0.002912772873874073, "kernel_inv_bw3": 0.00010000000000000009, "kernel_inv_bw4": 0.001289783525647755, "kernel_inv_bw5": 6.274402643366595, "kernel_inv_bw6": 0.014263119266637505, "kernel_covariance_scale": 1.0004606474604771, "mean_mean_value": -1.0965610760358047, "issm_gamma": 0.0010000000000000002, "issm_alpha": -0.6394260638653898, "issm_beta": 0.8896093870386187}')
_state.append('{"candidate_evaluations": [{"candidate": {"n_units_1": 38, "n_units_2": 187, "batch_size": 53, "dropout_1": 0.36209963448394383, "dropout_2": 0.09749003575393035, "learning_rate": 1.180123718822517e-05, "wd": 0.00011948182727147607}, "metrics": {"cost_metric": {"1": 12.25258493423462, "2": 24.305160999298096, "3": 44.05741477012634, "4": 62.029183864593506, "5": 81.38737893104553, "6": 99.16185593605042, "7": 118.72888779640198, "8": 133.45333671569824, "9": 148.23734402656555, "10": 166.52369689941406, "11": 194.99460196495056, "12": 215.73117184638977, "13": 235.3977439403534, "14": 253.71279788017273, "15": 267.6743288040161, "16": 281.8612160682678, "17": 296.0602250099182, "18": 310.0040330886841, "19": 324.75612902641296, "20": 344.674284696579, "21": 360.0983910560608, "22": 375.9487638473511, "23": 395.81145191192627, "24": 411.6494069099426, "25": 426.79202795028687, "26": 448.74489879608154, "27": 464.90988278388977, "28": 480.28413486480713, "29": 494.5631868839264, "30": 510.31515073776245, "31": 527.6290948390961, "32": 542.7905468940735, "33": 558.1524910926819, "34": 572.6776859760284, "35": 588.3533399105072}, "active_metric": {"1": 0.4978924126856684, "2": 0.3896025692492975, "3": 0.3546768366118025, "4": 0.33289843436370936, "5": 0.3259735046166198, "6": 0.30971497390606184, "7": 0.29626655961461257, "8": 0.2863307908470494, "9": 0.2753914090726616, "10": 0.26455238859895625, "11": 0.25491770373344036, "12": 0.2485949417904456, "13": 0.24678843837816133, "14": 0.23996386993175434, "15": 0.2332396627860297, "16": 0.23143315937374553, "17": 0.22390606182256123, "18": 0.22350461661983134, "19": 0.2195905258932156, "20": 0.217482938578884, "21": 0.21176234443998398, "22": 0.2106583701324769, "23": 0.20764753111200318, "24": 0.2052388598956243, "25": 0.20102368526696102, "26": 0.1963067041348856, "27": 0.1949016459253312, "28": 0.19409875551987155, "29": 0.1904857486953031, "30": 0.18747490967482938, "31": 0.18516659975913285, "32": 0.18446407065435566, "33": 0.18115214773183463, "34": 0.18004817342432755, "35": 0.17844239261340822}}}, {"candidate": {"n_units_1": 514, "n_units_2": 514, "batch_size": 68, "dropout_1": 0.495, "dropout_2": 0.495, "learning_rate": 0.0010000000000000002, "wd": 9.999999999999991e-05}, "metrics": {"cost_metric": {"1": 18.50484609603882, "2": 45.980664014816284, "3": 96.58328175544739, "4": 148.91678476333618, "5": 229.4046459197998, "6": 300.6477417945862, "7": 383.9124698638916, "8": 473.07399010658264, "9": 555.2527649402618}, "active_metric": {"1": 0.15496198479391754, "2": 0.1519607843137255, "3": 0.13645458183273307, "4": 0.14435774309723892, "5": 0.13225290116046418, "6": 0.1253501400560224, "7": 0.12735094037615047, "8": 0.13265306122448983, "9": 0.12104841936774713}}}, {"candidate": {"n_units_1": 347, "n_units_2": 566, "batch_size": 48, "dropout_1": 0.40991313560097764, "dropout_2": 0.1486640484580416, "learning_rate": 0.0001521657976426163, "wd": 2.46706548222209e-07}, "metrics": {"cost_metric": {"1": 18.432047128677368}, "active_metric": {"1": 0.16786858974358976}}}, {"candidate": {"n_units_1": 91, "n_units_2": 459, "batch_size": 105, "dropout_1": 0.48639033141890325, "dropout_2": 0.21324913218446714, "learning_rate": 0.00013769715715418189, "wd": 0.02017249366944585}, "metrics": {"cost_metric": {"1": 17.439072132110596}, "active_metric": {"1": 0.3006516290726817}}}, {"candidate": {"n_units_1": 774, "n_units_2": 917, "batch_size": 29, "dropout_1": 0.7778923725289609, "dropout_2": 0.7413003050986398, "learning_rate": 6.472832341968678e-05, "wd": 0.0007744951242384949}, "metrics": {"cost_metric": {"1": 44.07283306121826}, "active_metric": {"1": 0.23085404971932644}}}, {"candidate": {"n_units_1": 673, "n_units_2": 262, "batch_size": 78, "dropout_1": 0.9510740133913004, "dropout_2": 0.3263851441475057, "learning_rate": 0.009715536539110267, "wd": 0.0002984576239921338}, "metrics": {"cost_metric": {"1": 29.966220140457153}, "active_metric": {"1": 0.6366185897435898}}}, {"candidate": {"n_units_1": 672, "n_units_2": 820, "batch_size": 108, "dropout_1": 0.6443283647430158, "dropout_2": 0.8194904484310889, "learning_rate": 9.196365243521935e-05, "wd": 0.002536625472111785}, "metrics": {"cost_metric": {"1": 32.458306074142456}, "active_metric": {"1": 0.24486714975845414}}}], "failed_candidates": [], "pending_candidates": [{"batch_size": 123, "dropout_1": 0.7829512576762913, "dropout_2": 0.2834197685256876, "learning_rate": 0.1784738929251937, "n_units_1": 688, "n_units_2": 597, "wd": 4.489784182359429e-08, "RESOURCE_ATTR_epoch": 1}, {"batch_size": 34, "dropout_1": 0.7410256603874262, "dropout_2": 0.046625361151571336, "learning_rate": 0.07937041160202492, "n_units_1": 501, "n_units_2": 601, "wd": 8.340962845965557e-07, "RESOURCE_ATTR_epoch": 1}, {"n_units_1": 514, "n_units_2": 514, "batch_size": 68, "dropout_1": 0.495, "dropout_2": 0.495, "learning_rate": 0.0010000000000000002, "wd": 9.999999999999991e-05, "RESOURCE_ATTR_epoch": 4}, {"n_units_1": 38, "n_units_2": 187, "batch_size": 53, "dropout_1": 0.36209963448394383, "dropout_2": 0.09749003575393035, "learning_rate": 1.180123718822517e-05, "wd": 0.00011948182727147607, "RESOURCE_ATTR_epoch": 7}]}')
# elapsed_time = 108.29521012306213
# num_observations = 14
# num_configs = 7
_model_params.append('{"noise_variance": 0.02433360380308369, "kernel_inv_bw0": 0.033230128902756034, "kernel_inv_bw1": 1.3832161502574647, "kernel_inv_bw2": 0.0010926642716173997, "kernel_inv_bw3": 0.0009913284444091315, "kernel_inv_bw4": 0.00037318250862594773, "kernel_inv_bw5": 7.150355629993121, "kernel_inv_bw6": 0.005367219098449991, "kernel_covariance_scale": 0.9234243919759128, "mean_mean_value": -1.0950448515295788, "issm_gamma": 0.0010000000000000002, "issm_alpha": -0.6000883418698378, "issm_beta": 0.814092090699343}')
_state.append('{"candidate_evaluations": [{"candidate": {"n_units_1": 38, "n_units_2": 187, "batch_size": 53, "dropout_1": 0.36209963448394383, "dropout_2": 0.09749003575393035, "learning_rate": 1.180123718822517e-05, "wd": 0.00011948182727147607}, "metrics": {"cost_metric": {"1": 12.25258493423462, "2": 24.305160999298096, "3": 44.05741477012634, "4": 62.029183864593506, "5": 81.38737893104553, "6": 99.16185593605042, "7": 118.72888779640198, "8": 133.45333671569824, "9": 148.23734402656555, "10": 166.52369689941406, "11": 194.99460196495056, "12": 215.73117184638977, "13": 235.3977439403534, "14": 253.71279788017273, "15": 267.6743288040161, "16": 281.8612160682678, "17": 296.0602250099182, "18": 310.0040330886841, "19": 324.75612902641296, "20": 344.674284696579, "21": 360.0983910560608, "22": 375.9487638473511, "23": 395.81145191192627, "24": 411.6494069099426, "25": 426.79202795028687, "26": 448.74489879608154, "27": 464.90988278388977, "28": 480.28413486480713, "29": 494.5631868839264, "30": 510.31515073776245, "31": 527.6290948390961, "32": 542.7905468940735, "33": 558.1524910926819, "34": 572.6776859760284, "35": 588.3533399105072}, "active_metric": {"1": 0.4978924126856684, "2": 0.3896025692492975, "3": 0.3546768366118025, "4": 0.33289843436370936, "5": 0.3259735046166198, "6": 0.30971497390606184, "7": 0.29626655961461257, "8": 0.2863307908470494, "9": 0.2753914090726616, "10": 0.26455238859895625, "11": 0.25491770373344036, "12": 0.2485949417904456, "13": 0.24678843837816133, "14": 0.23996386993175434, "15": 0.2332396627860297, "16": 0.23143315937374553, "17": 0.22390606182256123, "18": 0.22350461661983134, "19": 0.2195905258932156, "20": 0.217482938578884, "21": 0.21176234443998398, "22": 0.2106583701324769, "23": 0.20764753111200318, "24": 0.2052388598956243, "25": 0.20102368526696102, "26": 0.1963067041348856, "27": 0.1949016459253312, "28": 0.19409875551987155, "29": 0.1904857486953031, "30": 0.18747490967482938, "31": 0.18516659975913285, "32": 0.18446407065435566, "33": 0.18115214773183463, "34": 0.18004817342432755, "35": 0.17844239261340822}}}, {"candidate": {"n_units_1": 514, "n_units_2": 514, "batch_size": 68, "dropout_1": 0.495, "dropout_2": 0.495, "learning_rate": 0.0010000000000000002, "wd": 9.999999999999991e-05}, "metrics": {"cost_metric": {"1": 18.50484609603882, "2": 45.980664014816284, "3": 96.58328175544739, "4": 148.91678476333618, "5": 229.4046459197998, "6": 300.6477417945862, "7": 383.9124698638916, "8": 473.07399010658264, "9": 555.2527649402618}, "active_metric": {"1": 0.15496198479391754, "2": 0.1519607843137255, "3": 0.13645458183273307, "4": 0.14435774309723892, "5": 0.13225290116046418, "6": 0.1253501400560224, "7": 0.12735094037615047, "8": 0.13265306122448983, "9": 0.12104841936774713}}}, {"candidate": {"n_units_1": 347, "n_units_2": 566, "batch_size": 48, "dropout_1": 0.40991313560097764, "dropout_2": 0.1486640484580416, "learning_rate": 0.0001521657976426163, "wd": 2.46706548222209e-07}, "metrics": {"cost_metric": {"1": 18.432047128677368}, "active_metric": {"1": 0.16786858974358976}}}, {"candidate": {"n_units_1": 91, "n_units_2": 459, "batch_size": 105, "dropout_1": 0.48639033141890325, "dropout_2": 0.21324913218446714, "learning_rate": 0.00013769715715418189, "wd": 0.02017249366944585}, "metrics": {"cost_metric": {"1": 17.439072132110596}, "active_metric": {"1": 0.3006516290726817}}}, {"candidate": {"n_units_1": 774, "n_units_2": 917, "batch_size": 29, "dropout_1": 0.7778923725289609, "dropout_2": 0.7413003050986398, "learning_rate": 6.472832341968678e-05, "wd": 0.0007744951242384949}, "metrics": {"cost_metric": {"1": 44.07283306121826}, "active_metric": {"1": 0.23085404971932644}}}, {"candidate": {"n_units_1": 673, "n_units_2": 262, "batch_size": 78, "dropout_1": 0.9510740133913004, "dropout_2": 0.3263851441475057, "learning_rate": 0.009715536539110267, "wd": 0.0002984576239921338}, "metrics": {"cost_metric": {"1": 29.966220140457153}, "active_metric": {"1": 0.6366185897435898}}}], "failed_candidates": [], "pending_candidates": [{"batch_size": 108, "dropout_1": 0.6443283647430158, "dropout_2": 0.8194904484310889, "learning_rate": 9.196365243521935e-05, "n_units_1": 672, "n_units_2": 820, "wd": 0.002536625472111785, "RESOURCE_ATTR_epoch": 1}, {"n_units_1": 514, "n_units_2": 514, "batch_size": 68, "dropout_1": 0.495, "dropout_2": 0.495, "learning_rate": 0.0010000000000000002, "wd": 9.999999999999991e-05, "RESOURCE_ATTR_epoch": 3}, {"batch_size": 123, "dropout_1": 0.7829512576762913, "dropout_2": 0.2834197685256876, "learning_rate": 0.1784738929251937, "n_units_1": 688, "n_units_2": 597, "wd": 4.489784182359429e-08, "RESOURCE_ATTR_epoch": 1}, {"n_units_1": 38, "n_units_2": 187, "batch_size": 53, "dropout_1": 0.36209963448394383, "dropout_2": 0.09749003575393035, "learning_rate": 1.180123718822517e-05, "wd": 0.00011948182727147607, "RESOURCE_ATTR_epoch": 6}]}')
# elapsed_time = 91.06228709220886
# num_observations = 11
# num_configs = 6
_model_params.append('{"noise_variance": 4.023868196955162e-08, "kernel_inv_bw0": 0.0499484523829736, "kernel_inv_bw1": 0.5041477744353572, "kernel_inv_bw2": 0.045440426051123285, "kernel_inv_bw3": 3.509634264819305, "kernel_inv_bw4": 1.8117976798318889, "kernel_inv_bw5": 16.29050792588867, "kernel_inv_bw6": 0.011845890028904541, "kernel_covariance_scale": 2.72277711886595, "mean_mean_value": -1.412204593314323, "issm_gamma": 0.0010000000000000002, "issm_alpha": -1.7778733115941194, "issm_beta": 1.226405864173305}')
_state.append('{"candidate_evaluations": [{"candidate": {"n_units_1": 38, "n_units_2": 187, "batch_size": 53, "dropout_1": 0.36209963448394383, "dropout_2": 0.09749003575393035, "learning_rate": 1.180123718822517e-05, "wd": 0.00011948182727147607}, "metrics": {"cost_metric": {"1": 12.25258493423462, "2": 24.305160999298096, "3": 44.05741477012634, "4": 62.029183864593506, "5": 81.38737893104553, "6": 99.16185593605042, "7": 118.72888779640198, "8": 133.45333671569824, "9": 148.23734402656555, "10": 166.52369689941406, "11": 194.99460196495056, "12": 215.73117184638977, "13": 235.3977439403534, "14": 253.71279788017273, "15": 267.6743288040161, "16": 281.8612160682678, "17": 296.0602250099182, "18": 310.0040330886841, "19": 324.75612902641296, "20": 344.674284696579, "21": 360.0983910560608, "22": 375.9487638473511, "23": 395.81145191192627, "24": 411.6494069099426, "25": 426.79202795028687, "26": 448.74489879608154, "27": 464.90988278388977, "28": 480.28413486480713, "29": 494.5631868839264, "30": 510.31515073776245, "31": 527.6290948390961, "32": 542.7905468940735, "33": 558.1524910926819, "34": 572.6776859760284, "35": 588.3533399105072}, "active_metric": {"1": 0.4978924126856684, "2": 0.3896025692492975, "3": 0.3546768366118025, "4": 0.33289843436370936, "5": 0.3259735046166198, "6": 0.30971497390606184, "7": 0.29626655961461257, "8": 0.2863307908470494, "9": 0.2753914090726616, "10": 0.26455238859895625, "11": 0.25491770373344036, "12": 0.2485949417904456, "13": 0.24678843837816133, "14": 0.23996386993175434, "15": 0.2332396627860297, "16": 0.23143315937374553, "17": 0.22390606182256123, "18": 0.22350461661983134, "19": 0.2195905258932156, "20": 0.217482938578884, "21": 0.21176234443998398, "22": 0.2106583701324769, "23": 0.20764753111200318, "24": 0.2052388598956243, "25": 0.20102368526696102, "26": 0.1963067041348856, "27": 0.1949016459253312, "28": 0.19409875551987155, "29": 0.1904857486953031, "30": 0.18747490967482938, "31": 0.18516659975913285, "32": 0.18446407065435566, "33": 0.18115214773183463, "34": 0.18004817342432755, "35": 0.17844239261340822}}}, {"candidate": {"n_units_1": 514, "n_units_2": 514, "batch_size": 68, "dropout_1": 0.495, "dropout_2": 0.495, "learning_rate": 0.0010000000000000002, "wd": 9.999999999999991e-05}, "metrics": {"cost_metric": {"1": 18.50484609603882, "2": 45.980664014816284, "3": 96.58328175544739, "4": 148.91678476333618, "5": 229.4046459197998, "6": 300.6477417945862, "7": 383.9124698638916, "8": 473.07399010658264, "9": 555.2527649402618}, "active_metric": {"1": 0.15496198479391754, "2": 0.1519607843137255, "3": 0.13645458183273307, "4": 0.14435774309723892, "5": 0.13225290116046418, "6": 0.1253501400560224, "7": 0.12735094037615047, "8": 0.13265306122448983, "9": 0.12104841936774713}}}, {"candidate": {"n_units_1": 347, "n_units_2": 566, "batch_size": 48, "dropout_1": 0.40991313560097764, "dropout_2": 0.1486640484580416, "learning_rate": 0.0001521657976426163, "wd": 2.46706548222209e-07}, "metrics": {"cost_metric": {"1": 18.432047128677368}, "active_metric": {"1": 0.16786858974358976}}}, {"candidate": {"n_units_1": 91, "n_units_2": 459, "batch_size": 105, "dropout_1": 0.48639033141890325, "dropout_2": 0.21324913218446714, "learning_rate": 0.00013769715715418189, "wd": 0.02017249366944585}, "metrics": {"cost_metric": {"1": 17.439072132110596}, "active_metric": {"1": 0.3006516290726817}}}, {"candidate": {"n_units_1": 774, "n_units_2": 917, "batch_size": 29, "dropout_1": 0.7778923725289609, "dropout_2": 0.7413003050986398, "learning_rate": 6.472832341968678e-05, "wd": 0.0007744951242384949}, "metrics": {"cost_metric": {"1": 44.07283306121826}, "active_metric": {"1": 0.23085404971932644}}}], "failed_candidates": [], "pending_candidates": [{"n_units_1": 673, "n_units_2": 262, "batch_size": 78, "dropout_1": 0.9510740133913004, "dropout_2": 0.3263851441475057, "learning_rate": 0.009715536539110267, "wd": 0.0002984576239921338, "RESOURCE_ATTR_epoch": 1}, {"batch_size": 108, "dropout_1": 0.6443283647430158, "dropout_2": 0.8194904484310889, "learning_rate": 9.196365243521935e-05, "n_units_1": 672, "n_units_2": 820, "wd": 0.002536625472111785, "RESOURCE_ATTR_epoch": 1}, {"n_units_1": 514, "n_units_2": 514, "batch_size": 68, "dropout_1": 0.495, "dropout_2": 0.495, "learning_rate": 0.0010000000000000002, "wd": 9.999999999999991e-05, "RESOURCE_ATTR_epoch": 3}, {"n_units_1": 38, "n_units_2": 187, "batch_size": 53, "dropout_1": 0.36209963448394383, "dropout_2": 0.09749003575393035, "learning_rate": 1.180123718822517e-05, "wd": 0.00011948182727147607, "RESOURCE_ATTR_epoch": 4}]}')
# elapsed_time = 55.957242012023926
# num_observations = 8
# num_configs = 5
_model_params.append('{"noise_variance": 0.0010000000000000002, "kernel_inv_bw0": 1.0, "kernel_inv_bw1": 1.0, "kernel_inv_bw2": 1.0, "kernel_inv_bw3": 1.0, "kernel_inv_bw4": 1.0, "kernel_inv_bw5": 1.0, "kernel_inv_bw6": 1.0, "kernel_covariance_scale": 1.0, "mean_mean_value": 0.0, "issm_gamma": 1.0, "issm_alpha": -0.5, "issm_beta": 0.0}')
_state.append('{"candidate_evaluations": [{"candidate": {"n_units_1": 38, "n_units_2": 187, "batch_size": 53, "dropout_1": 0.36209963448394383, "dropout_2": 0.09749003575393035, "learning_rate": 1.180123718822517e-05, "wd": 0.00011948182727147607}, "metrics": {"cost_metric": {"1": 12.25258493423462, "2": 24.305160999298096, "3": 44.05741477012634, "4": 62.029183864593506, "5": 81.38737893104553, "6": 99.16185593605042, "7": 118.72888779640198, "8": 133.45333671569824, "9": 148.23734402656555, "10": 166.52369689941406, "11": 194.99460196495056, "12": 215.73117184638977, "13": 235.3977439403534, "14": 253.71279788017273, "15": 267.6743288040161, "16": 281.8612160682678, "17": 296.0602250099182, "18": 310.0040330886841, "19": 324.75612902641296, "20": 344.674284696579, "21": 360.0983910560608, "22": 375.9487638473511, "23": 395.81145191192627, "24": 411.6494069099426, "25": 426.79202795028687, "26": 448.74489879608154, "27": 464.90988278388977, "28": 480.28413486480713, "29": 494.5631868839264, "30": 510.31515073776245, "31": 527.6290948390961, "32": 542.7905468940735, "33": 558.1524910926819, "34": 572.6776859760284, "35": 588.3533399105072}, "active_metric": {"1": 0.4978924126856684, "2": 0.3896025692492975, "3": 0.3546768366118025, "4": 0.33289843436370936, "5": 0.3259735046166198, "6": 0.30971497390606184, "7": 0.29626655961461257, "8": 0.2863307908470494, "9": 0.2753914090726616, "10": 0.26455238859895625, "11": 0.25491770373344036, "12": 0.2485949417904456, "13": 0.24678843837816133, "14": 0.23996386993175434, "15": 0.2332396627860297, "16": 0.23143315937374553, "17": 0.22390606182256123, "18": 0.22350461661983134, "19": 0.2195905258932156, "20": 0.217482938578884, "21": 0.21176234443998398, "22": 0.2106583701324769, "23": 0.20764753111200318, "24": 0.2052388598956243, "25": 0.20102368526696102, "26": 0.1963067041348856, "27": 0.1949016459253312, "28": 0.19409875551987155, "29": 0.1904857486953031, "30": 0.18747490967482938, "31": 0.18516659975913285, "32": 0.18446407065435566, "33": 0.18115214773183463, "34": 0.18004817342432755, "35": 0.17844239261340822}}}, {"candidate": {"n_units_1": 514, "n_units_2": 514, "batch_size": 68, "dropout_1": 0.495, "dropout_2": 0.495, "learning_rate": 0.0010000000000000002, "wd": 9.999999999999991e-05}, "metrics": {"cost_metric": {"1": 18.50484609603882, "2": 45.980664014816284, "3": 96.58328175544739, "4": 148.91678476333618, "5": 229.4046459197998, "6": 300.6477417945862, "7": 383.9124698638916, "8": 473.07399010658264, "9": 555.2527649402618}, "active_metric": {"1": 0.15496198479391754, "2": 0.1519607843137255, "3": 0.13645458183273307, "4": 0.14435774309723892, "5": 0.13225290116046418, "6": 0.1253501400560224, "7": 0.12735094037615047, "8": 0.13265306122448983, "9": 0.12104841936774713}}}, {"candidate": {"n_units_1": 347, "n_units_2": 566, "batch_size": 48, "dropout_1": 0.40991313560097764, "dropout_2": 0.1486640484580416, "learning_rate": 0.0001521657976426163, "wd": 2.46706548222209e-07}, "metrics": {"cost_metric": {"1": 18.432047128677368}, "active_metric": {"1": 0.16786858974358976}}}, {"candidate": {"n_units_1": 91, "n_units_2": 459, "batch_size": 105, "dropout_1": 0.48639033141890325, "dropout_2": 0.21324913218446714, "learning_rate": 0.00013769715715418189, "wd": 0.02017249366944585}, "metrics": {"cost_metric": {"1": 17.439072132110596}, "active_metric": {"1": 0.3006516290726817}}}], "failed_candidates": [], "pending_candidates": [{"n_units_1": 774, "n_units_2": 917, "batch_size": 29, "dropout_1": 0.7778923725289609, "dropout_2": 0.7413003050986398, "learning_rate": 6.472832341968678e-05, "wd": 0.0007744951242384949, "RESOURCE_ATTR_epoch": 1}, {"n_units_1": 514, "n_units_2": 514, "batch_size": 68, "dropout_1": 0.495, "dropout_2": 0.495, "learning_rate": 0.0010000000000000002, "wd": 9.999999999999991e-05, "RESOURCE_ATTR_epoch": 2}, {"n_units_1": 38, "n_units_2": 187, "batch_size": 53, "dropout_1": 0.36209963448394383, "dropout_2": 0.09749003575393035, "learning_rate": 1.180123718822517e-05, "wd": 0.00011948182727147607, "RESOURCE_ATTR_epoch": 3}]}')
# elapsed_time = 50.29185605049133
# num_observations = 5
# num_configs = 4
@pytest.mark.parametrize(
"_model_params, _state", zip(_model_params, _state))
def test_compare_gpiss_likelihood_oldnew(_model_params, _state):
config_space = {
'n_units_1': randint(4, 1024),
'n_units_2': randint(4, 1024),
'batch_size': randint(8, 128),
'dropout_1': uniform(0, 0.99),
'dropout_2': uniform(0, 0.99),
'learning_rate': loguniform(1e-6, 1),
'wd': loguniform(1e-8, 1),
'epochs': 81,
}
gpiss_model_factory = [] # new, old
model_params = json.loads(_model_params)
kwargs = dict(no_fantasizing=True)
gpiss_objs = build_gpiss_model_factory(
config_space, model_params, **kwargs)
config_space_ext = gpiss_objs['config_space_ext']
gpiss_model_factory.append(gpiss_objs['model_factory'])
gpiss_model_factory.append(build_gpiss_model_factory(
config_space, model_params, use_new_code=False,
**kwargs)['model_factory'])
state = decode_state_from_old_encoding(
enc_state=json.loads(_state), hp_ranges=config_space_ext.hp_ranges_ext)
# Compare likelihoods
likelihood = [
factory.model(
state,
fit_params=False).posterior_states[0].poster_state['likelihood']
for factory in gpiss_model_factory]
for name, value in likelihood[0].items():
if name != 'num_data':
np.testing.assert_almost_equal(value, likelihood[1][name])
@pytest.mark.parametrize(
"_model_params, _state", zip(_model_params, _state))
def test_compare_gpiss_likelihood_fantasizing_oldnew(_model_params, _state):
config_space = {
'n_units_1': randint(4, 1024),
'n_units_2': randint(4, 1024),
'batch_size': randint(8, 128),
'dropout_1': uniform(0, 0.99),
'dropout_2': uniform(0, 0.99),
'learning_rate': loguniform(1e-6, 1),
'wd': loguniform(1e-8, 1),
'epochs': 81,
}
num_fantasy_samples = 10
gpiss_model_factory = [] # new, old
model_params = json.loads(_model_params)
kwargs = dict(
num_fantasy_samples=num_fantasy_samples,
no_fantasizing=False)
gpiss_objs = build_gpiss_model_factory(
config_space, model_params, **kwargs)
config_space_ext = gpiss_objs['config_space_ext']
gpiss_model_factory.append(gpiss_objs['model_factory'])
gpiss_model_factory.append(build_gpiss_model_factory(
config_space, model_params, use_new_code=False,
**kwargs)['model_factory'])
state = decode_state_from_old_encoding(
enc_state=json.loads(_state), hp_ranges=config_space_ext.hp_ranges_ext)
# Compare likelihoods
# We need to force them to use the same fantasy samples
gpiss_model1 = gpiss_model_factory[0].model(state, fit_params=False)
likelihood = [gpiss_model1.posterior_states[0].poster_state['likelihood']]
gpiss_model2 = gpiss_model_factory[1].model_for_fantasy_samples(
state, fantasy_samples=gpiss_model1.fantasy_samples)
likelihood.append(
gpiss_model2.posterior_states[0].poster_state['likelihood'])
for name, value in likelihood[0].items():
if name != 'num_data':
np.testing.assert_almost_equal(value, likelihood[1][name])
| 352.063725
| 7,405
| 0.727127
| 9,042
| 71,821
| 5.578744
| 0.068016
| 0.029737
| 0.017346
| 0.026644
| 0.926571
| 0.924509
| 0.915112
| 0.909522
| 0.906528
| 0.906528
| 0
| 0.521058
| 0.083235
| 71,821
| 203
| 7,406
| 353.79803
| 0.245053
| 0.02193
| 0
| 0.448819
| 0
| 0.15748
| 0.935735
| 0.142078
| 0
| 0
| 0
| 0
| 0.015748
| 1
| 0.031496
| false
| 0
| 0.062992
| 0.007874
| 0.110236
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 1
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| 1
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|
0
| 11
|
0aba33fb0920a7ce97c01be6b53768918e904683
| 27,669
|
py
|
Python
|
src/xr_events/migrations/0004_add_localgroup_references_and_adjust_event_fields.py
|
JulianAkkaya95/xr-web
|
f86bb8f00173c73350f7283fa22dcbdbf9660bd3
|
[
"MIT"
] | 4
|
2019-03-28T20:49:59.000Z
|
2019-08-11T19:31:35.000Z
|
src/xr_events/migrations/0004_add_localgroup_references_and_adjust_event_fields.py
|
JulianAkkaya95/xr-web
|
f86bb8f00173c73350f7283fa22dcbdbf9660bd3
|
[
"MIT"
] | 4
|
2019-05-08T18:07:45.000Z
|
2021-05-08T17:29:46.000Z
|
src/xr_events/migrations/0004_add_localgroup_references_and_adjust_event_fields.py
|
JulianAkkaya95/xr-web
|
f86bb8f00173c73350f7283fa22dcbdbf9660bd3
|
[
"MIT"
] | 5
|
2019-03-28T20:50:15.000Z
|
2020-01-17T21:16:57.000Z
|
# Generated by Django 2.1.7 on 2019-03-24 18:08
from django.db import migrations, models
import django.db.models.deletion
import wagtail.core.blocks
import wagtail.core.fields
import wagtail.embeds.blocks
import wagtail.images.blocks
class Migration(migrations.Migration):
dependencies = [
("xr_pages", "0024_create_model_localgroup"),
("xr_events", "0003_page_content_fields_allow_blank"),
]
operations = [
migrations.AddField(
model_name="eventgrouppage",
name="group",
field=models.OneToOneField(
null=True,
on_delete=django.db.models.deletion.PROTECT,
to="xr_pages.LocalGroup",
),
),
migrations.AddField(
model_name="eventpage",
name="content",
field=wagtail.core.fields.StreamField(
[
(
"text",
wagtail.core.blocks.StructBlock(
[
(
"text",
wagtail.core.blocks.RichTextBlock(
features=[
"h2",
"h3",
"h4",
"bold",
"italic",
"ul",
"ol",
"hr",
"link",
"document-link",
]
),
)
]
),
),
(
"image",
wagtail.core.blocks.StructBlock(
[
("image", wagtail.images.blocks.ImageChooserBlock()),
(
"alternative_title",
wagtail.core.blocks.CharBlock(required=False),
),
(
"caption",
wagtail.core.blocks.CharBlock(required=False),
),
(
"attribution",
wagtail.core.blocks.CharBlock(required=False),
),
(
"link",
wagtail.core.blocks.StructBlock(
[
(
"internal_link",
wagtail.core.blocks.PageChooserBlock(
required=False
),
),
(
"external_link",
wagtail.core.blocks.URLBlock(
required=False
),
),
],
required=False,
),
),
(
"align",
wagtail.core.blocks.ChoiceBlock(
choices=[
("full_content", "Full content"),
("left", "Left"),
("right", "Right"),
("full_page", "Full page"),
]
),
),
]
),
),
(
"video",
wagtail.core.blocks.StructBlock(
[
("video", wagtail.embeds.blocks.EmbedBlock()),
(
"caption",
wagtail.core.blocks.CharBlock(required=False),
),
(
"align",
wagtail.core.blocks.ChoiceBlock(
choices=[
("full_content", "Full content"),
("left", "Left"),
("right", "Right"),
("full_page", "Full page"),
]
),
),
]
),
),
(
"message",
wagtail.core.blocks.StructBlock(
[
(
"message",
wagtail.core.blocks.StructBlock(
[
(
"text",
wagtail.core.blocks.RichTextBlock(
features=[
"h2",
"h3",
"h4",
"bold",
"italic",
"ul",
"ol",
"hr",
"link",
"document-link",
]
),
)
]
),
),
(
"font_size_factor",
wagtail.core.blocks.FloatBlock(default=1),
),
(
"font_color",
wagtail.core.blocks.ChoiceBlock(
choices=[
("xr-green", "XR green"),
("xr-yellow", "XR yellow"),
("xr-light-blue", "XR light blue"),
("xr-dark-blue", "XR dark blue"),
("xr-white", "XR white"),
("xr-black", "XR black"),
]
),
),
(
"background_color",
wagtail.core.blocks.ChoiceBlock(
choices=[
("xr-green", "XR green"),
("xr-yellow", "XR yellow"),
("xr-light-blue", "XR light blue"),
("xr-dark-blue", "XR dark blue"),
("xr-white", "XR white"),
("xr-black", "XR black"),
]
),
),
(
"background_image",
wagtail.images.blocks.ImageChooserBlock(
required=False
),
),
(
"link",
wagtail.core.blocks.StructBlock(
[
(
"internal_link",
wagtail.core.blocks.PageChooserBlock(
required=False
),
),
(
"external_link",
wagtail.core.blocks.URLBlock(
required=False
),
),
]
),
),
(
"align",
wagtail.core.blocks.ChoiceBlock(
choices=[
("full_content", "Full content"),
("left", "Left"),
("right", "Right"),
("full_page", "Full page"),
]
),
),
]
),
),
(
"carousel",
wagtail.core.blocks.StructBlock(
[
(
"items",
wagtail.core.blocks.StreamBlock(
[
(
"image",
wagtail.core.blocks.StructBlock(
[
(
"image",
wagtail.images.blocks.ImageChooserBlock(),
),
(
"alternative_title",
wagtail.core.blocks.CharBlock(
required=False
),
),
(
"caption",
wagtail.core.blocks.CharBlock(
required=False
),
),
(
"attribution",
wagtail.core.blocks.CharBlock(
required=False
),
),
(
"link",
wagtail.core.blocks.StructBlock(
[
(
"internal_link",
wagtail.core.blocks.PageChooserBlock(
required=False
),
),
(
"external_link",
wagtail.core.blocks.URLBlock(
required=False
),
),
],
required=False,
),
),
]
),
),
(
"video",
wagtail.core.blocks.StructBlock(
[
(
"video",
wagtail.embeds.blocks.EmbedBlock(),
),
(
"caption",
wagtail.core.blocks.CharBlock(
required=False
),
),
]
),
),
(
"message",
wagtail.core.blocks.StructBlock(
[
(
"message",
wagtail.core.blocks.StructBlock(
[
(
"text",
wagtail.core.blocks.RichTextBlock(
features=[
"h2",
"h3",
"h4",
"bold",
"italic",
"ul",
"ol",
"hr",
"link",
"document-link",
]
),
)
]
),
),
(
"font_size_factor",
wagtail.core.blocks.FloatBlock(
default=1
),
),
(
"font_color",
wagtail.core.blocks.ChoiceBlock(
choices=[
(
"xr-green",
"XR green",
),
(
"xr-yellow",
"XR yellow",
),
(
"xr-light-blue",
"XR light blue",
),
(
"xr-dark-blue",
"XR dark blue",
),
(
"xr-white",
"XR white",
),
(
"xr-black",
"XR black",
),
]
),
),
(
"background_color",
wagtail.core.blocks.ChoiceBlock(
choices=[
(
"xr-green",
"XR green",
),
(
"xr-yellow",
"XR yellow",
),
(
"xr-light-blue",
"XR light blue",
),
(
"xr-dark-blue",
"XR dark blue",
),
(
"xr-white",
"XR white",
),
(
"xr-black",
"XR black",
),
]
),
),
(
"background_image",
wagtail.images.blocks.ImageChooserBlock(
required=False
),
),
(
"link",
wagtail.core.blocks.StructBlock(
[
(
"internal_link",
wagtail.core.blocks.PageChooserBlock(
required=False
),
),
(
"external_link",
wagtail.core.blocks.URLBlock(
required=False
),
),
]
),
),
]
),
),
]
),
),
(
"align",
wagtail.core.blocks.ChoiceBlock(
choices=[
("full_content", "Full content"),
("left", "Left"),
("right", "Right"),
("full_page", "Full page"),
]
),
),
]
),
),
],
blank=True,
help_text="The content is only visible on the detail page.",
),
),
migrations.AddField(
model_name="eventpage",
name="group",
field=models.ForeignKey(
editable=False,
null=True,
on_delete=django.db.models.deletion.PROTECT,
related_name="events",
to="xr_pages.LocalGroup",
),
),
migrations.AlterField(
model_name="eventpage",
name="description",
field=models.CharField(
blank=True,
default="",
help_text="A description not only for the detail view, but also for lists, teasers or social media.",
max_length=254,
),
),
migrations.AlterField(
model_name="eventpage",
name="image",
field=models.ForeignKey(
blank=True,
help_text="An image that can be used not only for the detail view, but also for lists, teasers or social media.",
null=True,
on_delete=django.db.models.deletion.SET_NULL,
related_name="+",
to="wagtailimages.Image",
),
),
migrations.AlterField(
model_name="eventpage",
name="location",
field=models.CharField(
blank=True,
help_text='Some city or address, like you would enter in GMaps or OpenStreetMap, e.g. "Berlin", "Somestreet 84, 12345 Samplecity".',
max_length=255,
),
),
]
| 56.352342
| 148
| 0.157505
| 772
| 27,669
| 5.569948
| 0.204663
| 0.120233
| 0.177907
| 0.091163
| 0.793953
| 0.771628
| 0.723721
| 0.723721
| 0.714884
| 0.693953
| 0
| 0.008172
| 0.79215
| 27,669
| 490
| 149
| 56.467347
| 0.739524
| 0.001626
| 0
| 0.700413
| 1
| 0.004132
| 0.067229
| 0.002317
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.012397
| 0
| 0.018595
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 1
| 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
|
e407511524e727ed91c2dc332076a28af6d183f8
| 16,694
|
py
|
Python
|
python/test/types_tests/int32_type.py
|
Bhaskers-Blu-Org2/omi-script-provider
|
f2efe4434617d02887f1b99f7467e9d9203364a1
|
[
"MIT"
] | 4
|
2019-06-16T02:29:23.000Z
|
2021-04-20T16:09:19.000Z
|
python/test/types_tests/int32_type.py
|
microsoft/omi-script-provider
|
f2efe4434617d02887f1b99f7467e9d9203364a1
|
[
"MIT"
] | null | null | null |
python/test/types_tests/int32_type.py
|
microsoft/omi-script-provider
|
f2efe4434617d02887f1b99f7467e9d9203364a1
|
[
"MIT"
] | 4
|
2019-11-03T11:52:56.000Z
|
2020-08-05T14:54:06.000Z
|
from omi import *
try:
from utils import *
except ImportError:
import sys
sys.path.insert(0, '..')
from utils import *
def uint32_test():
be = BookEnd('uint32_test')
rval = True
# init (empty)
v0 = MI_Uint32()
if v0.getType() != MI_UINT32:
BookEndPrint('----- getType failed')
rval = False
if v0.value is not None:
BookEndPrint('----- empty init failed')
rval = False
# init to None
v1 = MI_Uint32(None)
if v1.value is not None:
BookEndPrint('----- NULL init failed')
rval = False
# init to value
r2 = random.randint(0, 0xFFFFFFFF)
v2 = MI_Uint32(r2)
if v2.value != r2:
BookEndPrint('----- value init failed')
rval = False
# init to MI_Uint32 (None)
t3 = MI_Uint32()
if COPY_CTOR:
v3 = MI_Uint32(t3)
if v3.value != t3.value:
BookEndPrint('----- MI_Uint32 (None) init failed')
rval = False
else:
try:
v3 = MI_Uint32(t3)
except ValueError:
pass
else:
BookEndPrint('----- init using copy ctor failed')
rval = False
# init to MI_Uint32
t4 = MI_Uint32(random.randint(0, 0xFFFFFFFF))
if COPY_CTOR:
v4 = MI_Uint32(t4)
if v4.value != t4.value:
BookEndPrint('----- MI_Uint32 init failed')
rval = False
else:
try:
v4 = MI_Uint32(t4)
except ValueError:
pass
else:
BookEndPrint('----- init using copy ctor failed')
rval = False
# init to a different MI type (None) **error**
t5 = MI_Boolean()
try:
v5 = MI_Uint32(t5)
except ValueError:
pass
else:
BookEndPrint('----- init to a different MI type (None) failed')
rval = False
# init to a different MI type **error**
t6 = MI_Boolean(True)
try:
v6 = MI_Uint32(t6)
except ValueError:
pass
else:
BookEndPrint('----- init to a different MI type failed')
rval = False
# init to invalid literal value **error**
try:
v7 = MI_Uint32('seven')
except ValueError:
pass
else:
BookEndPrint('----- init to invalid literal failed')
rval = False
# init to under-range value **error**
try:
v8 = MI_Uint32(-1)
except:
pass
else:
BookEndPrint('----- init to under-range value failed')
rval = False
# init to over-range value **error**
try:
v9 = MI_Uint32(0x100000000)
except:
pass
else:
BookEndPrint('----- init to over-range value failed')
rval = False
# assign None to None
v10 = MI_Uint32()
v10.value = None
if v10.value is not None:
BookEndPrint('----- None assignment to None failed')
rval = False
# assign a value to None
v11 = MI_Uint32()
r11 = random.randint(0, 0xFFFFFFFF)
v11.value = r11
if v11.value != r11:
BookEndPrint('----- literal value assignment to None failed')
rval = False
# assign MI_Uint32 (None) to None
v12 = MI_Uint32()
t12 = MI_Uint32()
if ASSIGN_OP:
v12.value = t12
if v12.value != t12.value:
BookEndPrint('----- MI_Uint32 (None) assignment to None failed')
rval = False
else:
try:
v12.value = t12
except ValueError:
pass
else:
BookEndPrint('----- assignment operator failed')
rval = False
# assign MI_Uint32 to None
v13 = MI_Uint32()
t13 = MI_Uint32(random.randint(0, 0xFFFFFFFF))
if ASSIGN_OP:
v13.value = t13
if v13.value != t13.value:
BookEndPrint('----- MI_Uint32 assignment to None failed')
rval = False
else:
try:
v13.value = t13
except ValueError:
pass
else:
BookEndPrint('----- assignment operator failed')
rval = False
# assign a different MI type (None) to None **error**
v14 = MI_Uint32()
t14 = MI_Boolean()
try:
v14.value = t14
except ValueError:
pass
else:
BookEndPrint('----- assign a different MI type (None) failed')
rval = False
# assign a different MI type to None **error**
v15 = MI_Uint32()
t15 = MI_Boolean(False)
try:
v15.value = t15
except ValueError:
pass
else:
BookEndPrint('----- assign a different MI type failed')
rval = False
# assign invalid literal to None **error**
v16 = MI_Uint32()
try:
v16.value = 'sixteen'
except:
pass
else:
BookEndPrint('----- MI_Boolean assign invalid literal failed')
rval = False
# assign under-range value to None **error**
v17 = MI_Uint32()
try:
v17.value = -1
except:
pass
else:
BookEndPrint('----- assign under-range value to None failed')
rval = False
# assign over-range value to None **error**
v18 = MI_Uint32()
try:
v18.value = 0x100000000
except:
pass
else:
BookEndPrint('----- assign over-range value to None failed')
rval = False
# assign None
v19 = MI_Uint32(random.randint(0, 0xFFFFFFFF))
v19.value = None
if v19.value is not None:
BookEndPrint('----- None assignment failed')
rval = False
# assign a literal value
r20a = random.randint(0, 0xFFFFFFFF)
r20b = random.randint(0, 0xFFFFFFFF)
while r20a == r20b:
r20b = random.randint(0, 0xFFFFFFFF)
v20 = MI_Uint32(r20a)
v20.value = r20b
if v20.value != r20b:
BookEndPrint('----- value assignment failed')
rval = False
# assign MI_Uint32 (None)
v21 = MI_Uint32(random.randint(0, 0xFFFFFFFF))
t21 = MI_Uint32()
if ASSIGN_OP:
v21.value = t21
if v21.value != t21.value:
BookEndPrint('----- MI_Uint32 (None) assignment failed')
rval = False
else:
try:
v21.value = t21
except ValueError:
pass
else:
BookEndPrint('----- assignment operator failed')
rval = False
# assign MI_Uint32
r22a = random.randint(0, 0xFFFFFFFF)
r22b = random.randint(0, 0xFFFFFFFF)
while r22a == r22b:
r22b = random.randint(0, 0xFFFFFFFF)
v22 = MI_Uint32(r22a)
t22 = MI_Uint32(r22b)
if ASSIGN_OP:
v22.value = t22
if v22.value != t22.value:
BookEndPrint('----- MI_Uint32 assignment failed')
rval = False
else:
try:
v22.value = t22
except ValueError:
pass
else:
BookEndPrint('----- assignment operator failed')
rval = False
# assign a different MI type (None) **error**
v23 = MI_Uint32(random.randint(0, 0xFFFFFFFF))
t23 = MI_Boolean()
try:
v23.value = t23
except ValueError:
pass
else:
BookEndPrint('----- assign a different MI type (None) failed')
rval = False
# assign a different MI type **error**
v24 = MI_Uint32(random.randint(0, 0xFFFFFFFF))
t24 = MI_Boolean(False)
try:
v24.value = t24
except ValueError:
pass
else:
BookEndPrint('----- assign a different MI type failed')
rval = False
# assign invalid literal **error**
v25 = MI_Uint32(random.randint(0, 0xFFFFFFFF))
try:
v25.value = 'twenty-five'
except ValueError:
pass
else:
BookEndPrint('----- assign invalid literal failed')
rval = False
# assign under-range value **error**
v26 = MI_Uint32(random.randint(0, 0xFFFFFFFF))
try:
v26.value = -1
except:
pass
else:
BookEndPrint('----- assign under-range value failed')
rval = False
# assign over-range value **error**
v27 = MI_Uint32(random.randint(0, 0xFFFFFFFF))
try:
v27.value = 0x100000000
except:
pass
else:
BookEndPrint('----- assign over-range value failed')
rval = False
if not rval:
BookEndPrint('!!!!! Tests have failed! (MI_Uint32)')
return rval
def sint32_test():
be = BookEnd('sint32_test')
rval = True
# init (empty)
v0 = MI_Sint32()
if v0.getType() != MI_SINT32:
BookEndPrint('----- getType failed')
rval = False
if v0.value is not None:
BookEndPrint('----- empty init failed')
rval = False
# init to None
v1 = MI_Sint32(None)
if v1.value is not None:
BookEndPrint('----- NULL init failed')
rval = False
# init to value
r2 = random.randint(-0x80000000, 0x7FFFFFFF)
v2 = MI_Sint32(r2)
if v2.value != r2:
BookEndPrint('----- value init failed')
rval = False
# init to MI_Sint32 (None)
t3 = MI_Sint32()
if COPY_CTOR:
v3 = MI_Sint32(t3)
if v3.value != t3.value:
BookEndPrint('----- MI_Sint32 (None) init failed')
rval = False
else:
try:
v3 = MI_Sint32(t3)
except ValueError:
pass
else:
BookEndPrint('----- init using copy ctor failed')
rval = False
# init to MI_Sint32
t4 = MI_Sint32(random.randint(-0x80000000, 0x7FFFFFFF))
if COPY_CTOR:
v4 = MI_Sint32(t4)
if v4.value != t4.value:
BookEndPrint('----- MI_Sint32 init failed')
rval = False
else:
try:
v4 = MI_Sint32(t4)
except ValueError:
pass
else:
BookEndPrint('----- init using copy ctor failed')
rval = False
# init to a different MI type (None) **error**
t5 = MI_Boolean()
try:
v5 = MI_Sint32(t5)
except ValueError:
pass
else:
BookEndPrint('----- init to a different MI type (None) failed')
rval = False
# init to a different MI type **error**
t6 = MI_Boolean(True)
try:
v6 = MI_Sint32(t6)
except ValueError:
pass
else:
BookEndPrint('----- init to a different MI type failed')
rval = False
# init to invalid literal value **error**
try:
v7 = MI_Sint32('seven')
except ValueError:
pass
else:
BookEndPrint('----- init to invalid literal failed')
rval = False
# init to under-range value **error**
try:
v8 = MI_Sint32(-0x80000001)
except:
pass
else:
BookEndPrint('----- init to under-range value failed')
rval = False
# init to over-range value **error**
try:
v9 = MI_Sint32(0x80000000)
except:
pass
else:
BookEndPrint('----- init to over-range value failed')
rval = False
# assign None to None
v10 = MI_Sint32()
v10.value = None
if v10.value is not None:
BookEndPrint('----- None assignment to None failed')
rval = False
# assign a value to None
v11 = MI_Sint32()
r11 = random.randint(-0x80000000, 0x7FFFFFFF)
v11.value = r11
if v11.value != r11:
BookEndPrint('----- literal value assignment to None failed')
rval = False
# assign MI_Sint32 (None) to None
v12 = MI_Sint32()
t12 = MI_Sint32()
if ASSIGN_OP:
v12.value = t12
if v12.value != t12.value:
BookEndPrint('----- MI_Sint32 (None) assignment to None failed')
rval = False
else:
try:
v12.value = t12
except ValueError:
pass
else:
BookEndPrint('----- assignment operator failed')
rval = False
# assign MI_Sint32 to None
v13 = MI_Sint32()
t13 = MI_Sint32(random.randint(-0x80000000, 0x7FFFFFFF))
if ASSIGN_OP:
v13.value = t13
if v13.value != t13.value:
BookEndPrint('----- MI_Sint32 assignment to None failed')
rval = False
else:
try:
v13.value = t13
except ValueError:
pass
else:
BookEndPrint('----- assignment operator failed')
rval = False
# assign a different MI type (None) to None **error**
v14 = MI_Sint32()
t14 = MI_Boolean()
try:
v14.value = t14
except ValueError:
pass
else:
BookEndPrint('----- assign a different MI type (None) failed')
rval = False
# assign a different MI type to None **error**
v15 = MI_Sint32()
t15 = MI_Boolean(False)
try:
v15.value = t15
except ValueError:
pass
else:
BookEndPrint('----- assign a different MI type failed')
rval = False
# assign invalid literal to None **error**
v16 = MI_Sint32()
try:
v16.value = 'sixteen'
except:
pass
else:
BookEndPrint('----- MI_Boolean assign invalid literal failed')
rval = False
# assign under-range value to None **error**
v17 = MI_Sint32()
try:
v17.value = -0x80000001
except:
pass
else:
BookEndPrint('----- assign under-range value to None failed')
rval = False
# assign over-range value to None **error**
v18 = MI_Sint32()
try:
v18.value = 0x80000000
except:
pass
else:
BookEndPrint('----- assign over-range value to None failed')
rval = False
# assign None
v19 = MI_Sint32(random.randint(-0x80000000, 0x7FFFFFFF))
v19.value = None
if v19.value is not None:
BookEndPrint('----- None assignment failed')
rval = False
# assign a literal value
r20a = random.randint(-0x80000000, 0x7FFFFFFF)
r20b = random.randint(-0x80000000, 0x7FFFFFFF)
while r20a == r20b:
r20b = random.randint(-0x80000000, 0x7FFFFFFF)
v20 = MI_Sint32(r20a)
v20.value = r20b
if v20.value != r20b:
BookEndPrint('----- value assignment failed')
rval = False
# assign MI_Sint32 (None)
v21 = MI_Sint32(random.randint(-0x80000000, 0x7FFFFFFF))
t21 = MI_Sint32()
if ASSIGN_OP:
v21.value = t21
if v21.value != t21.value:
BookEndPrint('----- MI_Sint32 (None) assignment failed')
rval = False
else:
try:
v21.value = t21
except ValueError:
pass
else:
BookEndPrint('----- assignment operator failed')
rval = False
# assign MI_Sint32
r22a = random.randint(-0x80000000, 0x7FFFFFFF)
r22b = random.randint(-0x80000000, 0x7FFFFFFF)
while r22a == r22b:
r22b = random.randint(-0x80000000, 0x7FFFFFFF)
v22 = MI_Sint32(r22a)
t22 = MI_Sint32(r22b)
if ASSIGN_OP:
v22.value = t22
if v22.value != t22.value:
BookEndPrint('----- MI_Sint32 assignment failed')
rval = False
else:
try:
v22.value = t22
except ValueError:
pass
else:
BookEndPrint('----- assignment operator failed')
rval = False
# assign a different MI type (None) **error**
v23 = MI_Sint32(random.randint(-0x80000000, 0x7FFFFFFF))
t23 = MI_Boolean()
try:
v23.value = t23
except ValueError:
pass
else:
BookEndPrint('----- assign a different MI type (None) failed')
rval = False
# assign a different MI type **error**
v24 = MI_Sint32(random.randint(-0x80000000, 0x7FFFFFFF))
t24 = MI_Boolean(False)
try:
v24.value = t24
except ValueError:
pass
else:
BookEndPrint('----- assign a different MI type failed')
rval = False
# assign invalid literal **error**
v25 = MI_Sint32(random.randint(-0x80000000, 0x7FFFFFFF))
try:
v25.value = 'twenty-five'
except ValueError:
pass
else:
BookEndPrint('----- assign invalid literal failed')
rval = False
# assign under-range value **error**
v26 = MI_Sint32(random.randint(-0x80000000, 0x7FFFFFFF))
try:
v26.value = -0x80000001
except:
pass
else:
BookEndPrint('----- assign under-range value failed')
rval = False
# assign over-range value **error**
v27 = MI_Sint32(random.randint(-0x80000000, 0x7FFFFFFF))
try:
v27.value = 0x80000000
except:
pass
else:
BookEndPrint('----- assign over-range value failed')
rval = False
if not rval:
BookEndPrint('!!!!! Tests have failed! (MI_Sint32)')
return rval
| 25.643625
| 76
| 0.553193
| 1,878
| 16,694
| 4.848243
| 0.061235
| 0.076881
| 0.115321
| 0.083031
| 0.905327
| 0.865129
| 0.818561
| 0.765513
| 0.744865
| 0.737397
| 0
| 0.085355
| 0.338205
| 16,694
| 650
| 77
| 25.683077
| 0.738776
| 0.102971
| 0
| 0.802657
| 0
| 0
| 0.177772
| 0
| 0
| 0
| 0.040421
| 0
| 0
| 1
| 0.003795
| false
| 0.079696
| 0.009488
| 0
| 0.017078
| 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
|
7c2cae6f7aa3e0309ea74cd1b2de9d0ff83a17da
| 140,771
|
py
|
Python
|
applications/multisite/intersite_test.py
|
richardstrnad/acitoolkit
|
7e559dbb0b2d22ecf980733d6f8b7c894ecfdbde
|
[
"Apache-2.0"
] | 351
|
2015-01-15T14:44:36.000Z
|
2022-03-27T17:06:52.000Z
|
applications/multisite/intersite_test.py
|
richardstrnad/acitoolkit
|
7e559dbb0b2d22ecf980733d6f8b7c894ecfdbde
|
[
"Apache-2.0"
] | 215
|
2015-01-11T07:05:12.000Z
|
2021-12-12T15:18:10.000Z
|
applications/multisite/intersite_test.py
|
richardstrnad/acitoolkit
|
7e559dbb0b2d22ecf980733d6f8b7c894ecfdbde
|
[
"Apache-2.0"
] | 324
|
2015-01-07T10:03:18.000Z
|
2022-02-23T21:48:13.000Z
|
"""
Test suite for Intersite application
"""
import unittest
from acitoolkit import (AppProfile, EPG, Endpoint, Interface, L2Interface, Context, BridgeDomain, Session, Tenant,
IPEndpoint, OutsideL3, OutsideEPG, OutsideNetwork, Contract)
from intersite import execute_tool, IntersiteTag, CommandLine, get_arg_parser
import argparse
import logging
from StringIO import StringIO
import mock
import sys
if sys.version_info.major == 2:
import __builtin__ as builtins
else:
import builtins
import json
import time
import logging
try:
from multisite_test_credentials import (SITE1_IPADDR, SITE1_LOGIN, SITE1_PASSWORD, SITE1_URL,
SITE2_IPADDR, SITE2_LOGIN, SITE2_PASSWORD, SITE2_URL,
SITE3_IPADDR, SITE3_LOGIN, SITE3_PASSWORD, SITE3_URL,
SITE4_IPADDR, SITE4_LOGIN, SITE4_PASSWORD, SITE4_URL)
except ImportError:
print('''
Please create a file called multisite_test_credentials.py with the following:
SITE1_IPADDR = ''
SITE1_LOGIN = ''
SITE1_PASSWORD = ''
SITE1_URL = 'http://' + SITE1_IPADDR # change http to https for SSL
SITE2_IPADDR = ''
SITE2_LOGIN = ''
SITE2_PASSWORD = ''
SITE2_URL = 'http://' + SITE2_IPADDR
SITE3_IPADDR = ''
SITE3_LOGIN = ''
SITE3_PASSWORD = ''
SITE3_URL = 'http://' + SITE3_IPADDR
SITE4_IPADDR = ''
SITE4_LOGIN = ''
SITE4_PASSWORD = ''
SITE4_URL = 'http://' + SITE3_IPADDR
''')
sys.exit(0)
class FakeStdio(object):
"""
FakeStdio : Class to fake writing to stdio and store it so that it can be verified
"""
def __init__(self):
self.output = []
def write(self, *args, **kwargs):
"""
Mock the write routine
:param args: Args passed to stdio write
:param kwargs: Kwargs passed to stdio write
:return: None
"""
for arg in args:
self.output.append(arg)
def verify_output(self, output):
"""
Verify that the output is the same as generated previously
:param output: Output to test for
:return: True if the same as the stored output. False otherwise
"""
return output == self.output
class TestToolOptions(unittest.TestCase):
"""
Test cases for testing the command line arguments
"""
@staticmethod
def get_logging_level():
"""
Return the current logger level
:return: Logger level
"""
return logging.getLevelName(logging.getLogger().getEffectiveLevel())
def test_no_options(self):
"""
Test no configuration file given. Verify that it generates an error message
"""
args = mock.Mock()
args.debug = None
args.generateconfig = None
args.config = None
with mock.patch('sys.stdout', new=StringIO()) as fake_out:
execute_tool(args)
self.assertEqual(fake_out.getvalue(), '%% No configuration file given.\n')
def test_generateconfig(self):
"""
Test generate sample configuration file. Verify that it generates the correct text message
"""
args = mock.Mock()
args.debug = None
args.generateconfig = True
args.config = None
expected_text = ('Sample configuration file written to sample_config.json\n'
"Replicate the site JSON for each site.\n"
" Valid values for use_https and local are 'True' and 'False'\n"
" One site must have local set to 'True'\n"
'Replicate the export JSON for each exported contract.\n')
with mock.patch('sys.stdout', new=StringIO()) as fake_out:
execute_tool(args)
self.assertEqual(fake_out.getvalue(), expected_text)
def test_set_debug_to_verbose(self):
"""
Test setting the debug level to verbose
"""
args = mock.Mock()
args.debug = 'verbose'
args.config = None
execute_tool(args)
def test_set_debug_to_warnings(self):
"""
Test setting the debug level to warnings
"""
args = mock.Mock()
args.debug = 'warnings'
args.config = None
execute_tool(args)
def test_set_debug_to_critical(self):
"""
Test setting the debug level to critical
"""
args = mock.Mock()
args.debug = 'critical'
args.config = None
execute_tool(args)
def test_config_bad_filename(self):
"""
Test no configuration file given. Verify that it generates an error message
"""
args = mock.Mock()
args.debug = None
args.generateconfig = None
args.config = 'jkdhfdskjfhdsfkjhdsfdskjhf.jdkhfkfjh'
expected_text = '%% Unable to open configuration file jkdhfdskjfhdsfkjhdsfdskjhf.jdkhfkfjh\n'
with mock.patch('sys.stdout', new=StringIO()) as fake_out:
execute_tool(args)
self.assertEqual(fake_out.getvalue(), expected_text)
def test_get_arg_parser(self):
self.assertIsInstance(get_arg_parser(), argparse.ArgumentParser)
class TestBadConfiguration(unittest.TestCase):
"""
Test various invalid configuration files
"""
@staticmethod
def create_empty_config_file():
"""
Generate an empty configuration file with only a single empty Site policy
:return: dictionary containing the configuration
"""
config = {
"config": [
{
"site": {
"username": SITE1_LOGIN,
"name": "site1",
"ip_address": SITE1_IPADDR,
"password": SITE1_PASSWORD,
"local": "True",
"use_https": "True"
}
}
]
}
return config
@staticmethod
def get_args():
"""
Generate an empty command line arguments
:return: Instance of Mock to represent the command line arguments
"""
args = mock.Mock()
args.debug = None
args.generateconfig = None
args.config = 'doesntmatter'
return args
@staticmethod
def create_config_file(args, config, with_bad_json=False):
config_filename = 'testsuite_cfg.json'
args.config = config_filename
config_file = open(config_filename, 'w')
config_file.write(str(json.dumps(config)))
if with_bad_json:
config_file.write(']]]')
config_file.close()
def test_no_config_keyword(self):
"""
Test no "config" present in the JSON. Verify that the correct error message is generated.
:return: None
"""
args = self.get_args()
config = {
"site": {
"username": "",
"name": "",
"ip_address": "",
"password": "",
"local": "",
"use_https": ""
}
}
temp = sys.stdout
fake_out = FakeStdio()
sys.stdout = fake_out
self.create_config_file(args, config)
execute_tool(args, test_mode=True)
sys.stdout = temp
self.assertTrue(fake_out.verify_output(['%% Invalid configuration file', '\n']))
def test_bad_json_file(self):
"""
Test bad JSON in the file. Verify that the correct error message is generated.
:return: None
"""
args = self.get_args()
config = {
"site": {
"username": "",
"name": "",
"ip_address": "",
"password": "",
"local": "",
"use_https": ""
}
}
temp = sys.stdout
fake_out = FakeStdio()
sys.stdout = fake_out
self.create_config_file(args, config, with_bad_json=True)
execute_tool(args, test_mode=True)
sys.stdout = temp
self.assertTrue(fake_out.verify_output(['%% File could not be decoded as JSON.', '\n']))
def test_site_with_bad_ipaddress(self):
"""
Test invalid IP address value in the JSON. Verify that the correct exception is generated.
:return: None
"""
args = self.get_args()
config = self.create_empty_config_file()
config['config'][0]['site']['ip_address'] = 'bogu$'
self.create_config_file(args, config)
self.assertRaises(ValueError, execute_tool, args, test_mode=True)
def test_site_with_bad_ipaddress_as_number(self):
"""
Test invalid IP address value in the JSON. Verify that the correct exception is generated.
:return: None
"""
args = self.get_args()
config = self.create_empty_config_file()
config['config'][0]['site']['ip_address'] = 100
self.create_config_file(args, config)
self.assertRaises(TypeError, execute_tool, args, test_mode=True)
def test_site_with_good_ipaddress_and_bad_userid(self):
"""
Test good IP address value but invalid username in the JSON. Verify that the correct exception is generated.
:return: None
"""
args = self.get_args()
config = self.create_empty_config_file()
config['config'][0]['site']['username'] = ''
config['config'][0]['site']['ip_address'] = SITE1_IPADDR
config['config'][0]['site']['local'] = 'True'
config['config'][0]['site']['use_https'] = 'True'
self.create_config_file(args, config)
self.assertRaises(ValueError, execute_tool, args, test_mode=True)
def test_site_with_bad_local_setting(self):
"""
Test with bad local setting in the site JSON. Verify that the correct exception is generated.
:return: None
"""
args = self.get_args()
config = self.create_empty_config_file()
config['config'][0]['site']['username'] = 'admin'
config['config'][0]['site']['ip_address'] = SITE1_IPADDR
config['config'][0]['site']['local'] = 'BAD'
config['config'][0]['site']['use_https'] = 'True'
self.create_config_file(args, config)
self.assertRaises(ValueError, execute_tool, args, test_mode=True)
def test_site_with_bad_use_https(self):
"""
Test with bad use_https setting in the site JSON. Verify that the correct exception is generated.
:return: None
"""
args = self.get_args()
config = self.create_empty_config_file()
config['config'][0]['site']['username'] = 'admin'
config['config'][0]['site']['ip_address'] = SITE1_IPADDR
config['config'][0]['site']['local'] = 'True'
config['config'][0]['site']['use_https'] = 'BAD'
self.create_config_file(args, config)
self.assertRaises(ValueError, execute_tool, args, test_mode=True)
def test_reload_bad_config_filename(self):
"""
Test reload_config with a non-existent filename
:return: None
"""
# Create a valid configuration
args = self.get_args()
config = self.create_empty_config_file()
self.create_config_file(args, config)
collector = execute_tool(args, test_mode=True)
# Check that a bad config filename reload behaves as expected
collector.config_filename = 'nonexistent.json'
self.assertFalse(collector.reload_config())
def test_reload_bad_json_in_file(self):
"""
Test reload_config with a badly formatted JSON file
:return: None
"""
# Create a valid configuration
args = self.get_args()
config = self.create_empty_config_file()
self.create_config_file(args, config)
collector = execute_tool(args, test_mode=True)
# Create a badly formatted config file
self.create_config_file(args, config, with_bad_json=True)
self.assertFalse(collector.reload_config())
def test_reload_with_no_config_keyword(self):
"""
Test reload_config with no 'config' keyword in the JSON
:return: None
"""
# Create a valid configuration
args = self.get_args()
config = self.create_empty_config_file()
self.create_config_file(args, config)
collector = execute_tool(args, test_mode=True)
# Create a configuration file with no 'config' keyword
config = {
"site": {
"username": "",
"name": "",
"ip_address": "",
"password": "",
"local": "",
"use_https": ""
}
}
self.create_config_file(args, config)
self.assertFalse(collector.reload_config())
def test_reload_no_local_site_in_reloaded_config(self):
"""
Test reload_config with no local site specified in the JSON
:return: None
"""
# Create a valid configuration
args = self.get_args()
config = self.create_empty_config_file()
self.create_config_file(args, config)
collector = execute_tool(args, test_mode=True)
# Create a configuration with no local site
config = self.create_empty_config_file()
config['config'][0]['site']['local'] = 'False'
self.create_config_file(args, config)
# Reload
self.assertFalse(collector.reload_config())
def test_oversized_intersite_tag(self):
"""
Test oversized string lengths for the entities that make up a Intersite tag
"""
# Create a configuration with long names
args = self.get_args()
config = self.create_empty_config_file()
export_policy = {
"export":
{
"tenant": "a" * 64,
"app": "b" * 64,
"epg": "c" * 64,
"remote_epg": "intersite-testsuite-app-epg",
"remote_sites":
[
{
"site":
{
"name": "d" * 64,
}
}
]
}
}
config['config'].append(export_policy)
self.create_config_file(args, config)
self.assertRaises(ValueError, execute_tool, args, test_mode=True)
def test_duplicate_export_policy(self):
"""
Test oversized string lengths for the entities that make up a Intersite tag
"""
# Create a configuration with long names
args = self.get_args()
config = self.create_empty_config_file()
export_policy = {
"export":
{
"tenant": "mytenant",
"app": "myapp",
"epg": "myepg",
"remote_epg": "intersite-testsuite-app-epg",
"remote_sites":
[
{
"site":
{
"name": "mysite",
}
}
]
}
}
config['config'].append(export_policy)
config['config'].append(export_policy)
self.create_config_file(args, config)
self.assertRaises(ValueError, execute_tool, args, test_mode=True)
def test_bad_intersite_tag(self):
"""
Test bad intersite tag creation
"""
with self.assertRaises(AssertionError):
IntersiteTag.fromstring('badstring')
class BaseTestCase(unittest.TestCase):
"""
BaseTestCase: Base class to be used for creating other TestCases. Not to be instantiated directly.
"""
def setup_remote_site(self):
"""
Set up the remote site. Meant to be overridden by inheriting classes
"""
raise NotImplementedError
def setup_local_site(self):
"""
Set up the local site. Meant to be overridden by inheriting classes
"""
raise NotImplementedError
def setUp(self):
"""
Set up the test case. Setup the remote and local site.
:return: None
"""
self.setup_remote_site()
self.setup_local_site()
def tearDown(self):
"""
Tear down the test case. Tear down the remote and local site.
:return: None
"""
self.teardown_local_site()
self.teardown_remote_site()
time.sleep(2)
@staticmethod
def create_site_config():
"""
Generate a basic configuration containing the local and remote site policies.
Actual site credentials are set in global variables imported from multisite_test_credentials
:return: dictionary containing the configuration
"""
config = {
"config": [
{
"site": {
"username": "%s" % SITE1_LOGIN,
"name": "Site1",
"ip_address": "%s" % SITE1_IPADDR,
"password": "%s" % SITE1_PASSWORD,
"local": "True",
"use_https": "False"
}
},
{
"site": {
"username": "%s" % SITE2_LOGIN,
"name": "Site2",
"ip_address": "%s" % SITE2_IPADDR,
"password": "%s" % SITE2_PASSWORD,
"local": "False",
"use_https": "False"
}
}
]
}
return config
@staticmethod
def write_config_file(config, args):
"""
Write the configuration as a temporary file and set the command line arguments to read the file
:param config: dictionary containing the configuration
:param args: Mock of the command line arguments
:return: None
"""
config_filename = 'testsuite_cfg.json'
args.config = config_filename
config_file = open(config_filename, 'w')
config_file.write(str(json.dumps(config)))
config_file.close()
def verify_remote_site_has_entry(self, mac, ip, tenant_name, l3out_name, remote_epg_name):
"""
Verify that the remote site has the entry
:param mac: String containing the MAC address of the endpoint to find on the remote site
:param ip: String containing the IP address of the endpoint to find on the remote site
:param tenant_name: String containing the remote tenant name holding the endpoint
:param l3out_name: String containing the remote OutsideL3 name holding the endpoint
:param remote_epg_name: String containing the remote OutsideEPG on the remote OutsideL3 holding the endpoint
:return: True if the remote site has the endpoint. False otherwise
"""
site2 = Session(SITE2_URL, SITE2_LOGIN, SITE2_PASSWORD)
resp = site2.login()
self.assertTrue(resp.ok)
query = ('/api/mo/uni/tn-%s/out-%s/instP-%s.json?query-target=children' % (tenant_name,
l3out_name,
remote_epg_name))
resp = site2.get(query)
self.assertTrue(resp.ok)
found = False
for item in resp.json()['imdata']:
if 'l3extSubnet' in item:
if item['l3extSubnet']['attributes']['ip'] == ip + '/32':
found = True
break
if not found:
return False
return True
def verify_remote_site_has_entry_with_contract(self, mac, ip, tenant_name, l3out_name, remote_epg_name,
contract_name, contract_type):
"""
Verify that the remote site has the entry and provides the specfied contract
:param mac: String containing the MAC address of the endpoint to find on the remote site
:param ip: String containing the IP address of the endpoint to find on the remote site
:param tenant_name: String containing the remote tenant name holding the endpoint
:param l3out_name: String containing the remote OutsideL3 name holding the endpoint
:param remote_epg_name: String containing the remote OutsideEPG on the remote OutsideL3 holding the endpoint
:param contract_name: String containing the contract name that the remote OutsideEPG should be providing
:param contract_type: String containing the contract usage.
Valid values are 'provides', 'consumes', 'consumes_interface', and 'protected_by'
:return: True if the remote site has the endpoint. False otherwise
"""
site2 = Session(SITE2_URL, SITE2_LOGIN, SITE2_PASSWORD)
resp = site2.login()
self.assertTrue(resp.ok)
assert contract_type in ['provides', 'consumes', 'consumes_interface', 'protected_by']
query = '/api/mo/uni/tn-%s/out-%s.json?query-target=subtree' % (tenant_name, l3out_name)
resp = site2.get(query)
self.assertTrue(resp.ok)
# Look for l3extInstP
found = False
for item in resp.json()['imdata']:
if 'l3extInstP' in item:
if item['l3extInstP']['attributes']['name'] == remote_epg_name:
found = True
break
if not found:
return False
# Verify that the l3extInstP is providing the contract
found = False
contract_types = {'provides': ['fvRsProv', 'tnVzBrCPName'],
'consumes': ['fvRsCons', 'tnVzBrCPName'],
'consumes_interface': ['fvRsConsIf', 'tnVzCPIfName'],
'protected_by': ['fvRsProtBy', 'tnVzTabooName']
}
(aci_class, aci_class_ref) = contract_types[contract_type]
for item in resp.json()['imdata']:
if aci_class in item:
if item[aci_class]['attributes'][aci_class_ref] == contract_name:
found = True
break
if not found:
return False
return self.verify_remote_site_has_entry(mac, ip, tenant_name, l3out_name, remote_epg_name)
def verify_remote_site_has_policy(self, tenant_name, l3out_name, instp_name):
"""
Verify that the remote site has the policy
:param tenant_name: String containing the remote tenant name holding the policy
:param l3out_name: String containing the remote OutsideL3 name holding the policy
:param instp_name: String containing the remote OutsideEPG holding the policy
:return: True if the remote site has the policy. False otherwise
"""
site2 = Session(SITE2_URL, SITE2_LOGIN, SITE2_PASSWORD)
resp = site2.login()
self.assertTrue(resp.ok)
query = ('/api/mo/uni/tn-%s/out-%s/instP-%s.json' % (tenant_name, l3out_name, instp_name))
resp = site2.get(query)
self.assertTrue(resp.ok)
found = False
for item in resp.json()['imdata']:
if 'l3extInstP' in item:
found = True
break
if not found:
return False
return True
def teardown_local_site(self):
"""
Teardown the local site configuration
"""
site1 = Session(SITE1_URL, SITE1_LOGIN, SITE1_PASSWORD)
resp = site1.login()
if not resp.ok:
print(str(resp.status_code) + ' ' + resp.text)
self.assertTrue(resp.ok)
tenant = Tenant('intersite-testsuite')
tenant.mark_as_deleted()
resp = tenant.push_to_apic(site1)
self.assertTrue(resp.ok)
def teardown_remote_site(self):
"""
Teardown the remote site configuration
"""
site2 = Session(SITE2_URL, SITE2_LOGIN, SITE2_PASSWORD)
resp = site2.login()
self.assertTrue(resp.ok)
tenant = Tenant('intersite-testsuite')
tenant.mark_as_deleted()
resp = tenant.push_to_apic(site2)
self.assertTrue(resp.ok)
time.sleep(2)
@staticmethod
def get_args():
"""
Get a mock of the command line arguments
:return: Mock instance representing the command line arguments
"""
args = mock.Mock()
args.debug = None
args.generateconfig = None
args.config = 'doesntmatter'
return args
def remove_endpoint(self, mac, ip, tenant_name, app_name, epg_name):
"""
Remove the endpoint
:param mac: String containing the MAC address of the endpoint
:param ip: String containing the IP address of the endpoint
:param tenant_name: String containing the tenant name of the endpoint
:param app_name: String containing the AppProfile name holding the endpoint
:param epg_name: String containing the EPG name holding the endpoint
:return: None
"""
self.add_endpoint(mac, ip, tenant_name, app_name, epg_name, mark_as_deleted=True)
def add_endpoint(self, mac, ip, tenant_name, app_name, epg_name, mark_as_deleted=False):
"""
Add the endpoint
:param mac: String containing the MAC address of the endpoint
:param ip: String containing the IP address of the endpoint
:param tenant_name: String containing the tenant name of the endpoint
:param app_name: String containing the AppProfile name holding the endpoint
:param epg_name: String containing the EPG name holding the endpoint
:param mark_as_deleted: True or False. True if the endpoint is to be marked as deleted. Default is False
:return: None
"""
# create Tenant, App, EPG on site 1
site1 = Session(SITE1_URL, SITE1_LOGIN, SITE1_PASSWORD)
resp = site1.login()
self.assertTrue(resp.ok)
tenant = Tenant(tenant_name)
app = AppProfile(app_name, tenant)
epg = EPG(epg_name, app)
ep = Endpoint(mac, epg)
ep.mac = mac
ep.ip = ip
if mark_as_deleted:
ep.mark_as_deleted()
l3ep = IPEndpoint(ip, ep)
# Create the physical interface object
intf = Interface('eth', '1', '101', '1', '38')
vlan_intf = L2Interface('vlan-5', 'vlan', '5')
vlan_intf.attach(intf)
# Attach the EPG to the VLAN interface
epg.attach(vlan_intf)
# Assign it to the L2Interface
ep.attach(vlan_intf)
urls = intf.get_url()
jsons = intf.get_json()
# Set the the phys domain, infra, and fabric
for k in range(0, len(urls)):
if jsons[k] is not None:
resp = site1.push_to_apic(urls[k], jsons[k])
self.assertTrue(resp.ok)
# Push the endpoint
resp = tenant.push_to_apic(site1)
self.assertTrue(resp.ok)
time.sleep(1)
class BaseEndpointTestCase(BaseTestCase):
"""
Base class for the endpoint test cases
"""
def setup_local_site(self):
"""
Set up the local site
"""
# create Tenant, App, EPG on site 1
site1 = Session(SITE1_URL, SITE1_LOGIN, SITE1_PASSWORD)
resp = site1.login()
self.assertTrue(resp.ok)
tenant = Tenant('intersite-testsuite')
app = AppProfile('app', tenant)
epg = EPG('epg', app)
resp = tenant.push_to_apic(site1)
self.assertTrue(resp.ok)
def setup_remote_site(self):
"""
Set up the remote site
"""
# Create tenant, L3out with contract on site 2
site2 = Session(SITE2_URL, SITE2_LOGIN, SITE2_PASSWORD)
resp = site2.login()
self.assertTrue(resp.ok)
tenant = Tenant('intersite-testsuite')
l3out = OutsideL3('l3out', tenant)
resp = tenant.push_to_apic(site2)
self.assertTrue(resp.ok)
def create_config_file(self):
"""
Create the configuration
:return: Dictionary containing the configuration
"""
config = self.create_site_config()
export_policy = {
"export":
{
"tenant": "intersite-testsuite",
"app": "app",
"epg": "epg",
"remote_epg": "intersite-testsuite-app-epg",
"remote_sites":
[
{
"site":
{
"name": "Site2",
"interfaces":
[
{
"l3out":
{
"name": "l3out",
"tenant": "intersite-testsuite"
}
}
]
}
}
]
}
}
config['config'].append(export_policy)
return config
def setup_with_endpoint(self, mac='00:11:22:33:33:33'):
"""
Set up the configuration with an endpoint
:return: 2 strings containing the MAC and IP address of the endpoint
"""
args = self.get_args()
self.write_config_file(self.create_config_file(), args)
execute_tool(args, test_mode=True)
ip = '3.4.3.4'
self.assertFalse(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite', 'l3out', 'intersite-testsuite-app-epg'))
time.sleep(2)
self.add_endpoint(mac, ip, 'intersite-testsuite', 'app', 'epg')
return mac, ip
class TestBasicEndpoints(BaseEndpointTestCase):
"""
Basic tests for endpoints
"""
def test_basic_add_endpoint(self):
"""
Test add endpoint
"""
mac, ip = self.setup_with_endpoint()
time.sleep(2)
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite',
'l3out', 'intersite-testsuite-app-epg'))
def test_basic_add_multiple_endpoint(self):
"""
Test add multiple endpoints
"""
mac1, ip1 = self.setup_with_endpoint()
mac2 = '00:11:22:33:33:35'
ip2 = '3.4.3.6'
self.add_endpoint(mac2, ip2, 'intersite-testsuite', 'app', 'epg')
time.sleep(2)
self.assertTrue(self.verify_remote_site_has_entry(mac1, ip1, 'intersite-testsuite',
'l3out', 'intersite-testsuite-app-epg'))
self.assertTrue(self.verify_remote_site_has_entry(mac2, ip2, 'intersite-testsuite',
'l3out', 'intersite-testsuite-app-epg'))
def test_basic_remove_endpoint(self):
"""
Test remove endpoint
"""
mac, ip = self.setup_with_endpoint()
time.sleep(2)
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite',
'l3out', 'intersite-testsuite-app-epg'))
self.remove_endpoint(mac, ip, 'intersite-testsuite', 'app', 'epg')
self.assertFalse(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite',
'l3out', 'intersite-testsuite-app-epg'))
def test_basic_remove_one_of_multiple_endpoint(self):
"""
Test remove one of multiple endpoints
"""
mac1, ip1 = self.setup_with_endpoint()
mac2 = '00:11:22:33:33:35'
ip2 = '3.4.3.6'
self.add_endpoint(mac2, ip2, 'intersite-testsuite', 'app', 'epg')
time.sleep(2)
self.assertTrue(self.verify_remote_site_has_entry(mac1, ip1, 'intersite-testsuite',
'l3out', 'intersite-testsuite-app-epg'))
self.assertTrue(self.verify_remote_site_has_entry(mac2, ip2, 'intersite-testsuite',
'l3out', 'intersite-testsuite-app-epg'))
self.remove_endpoint(mac1, ip1, 'intersite-testsuite', 'app', 'epg')
self.assertFalse(self.verify_remote_site_has_entry(mac1, ip1, 'intersite-testsuite',
'l3out', 'intersite-testsuite-app-epg'))
self.assertTrue(self.verify_remote_site_has_entry(mac2, ip2, 'intersite-testsuite',
'l3out', 'intersite-testsuite-app-epg'))
class TestBasicEndpointsWithMultipleRemoteSites(BaseEndpointTestCase):
"""
Basic tests for endpoints with multiple remote sites
"""
def setup_remote_site(self):
"""
Set up the remote site
"""
# Set up site 2
super(TestBasicEndpointsWithMultipleRemoteSites, self).setup_remote_site()
# Create tenant, L3out with contract on site 3
site3 = Session(SITE3_URL, SITE3_LOGIN, SITE3_PASSWORD)
resp = site3.login()
self.assertTrue(resp.ok)
tenant = Tenant('intersite-testsuite-site3')
vrf = Context('myvrf', tenant)
l3out = OutsideL3('l3out', tenant)
resp = tenant.push_to_apic(site3)
self.assertTrue(resp.ok)
def teardown_remote_site(self):
"""
Teardown the remote site configuration
"""
time.sleep(2)
super(TestBasicEndpointsWithMultipleRemoteSites, self).teardown_remote_site()
site3 = Session(SITE3_URL, SITE3_LOGIN, SITE3_PASSWORD)
resp = site3.login()
self.assertTrue(resp.ok)
tenant = Tenant('intersite-testsuite-site3')
tenant.mark_as_deleted()
resp = tenant.push_to_apic(site3)
self.assertTrue(resp.ok)
time.sleep(2)
def create_additional_site_config(self, login, ip_address, password):
"""
Add the additional site to the configuration
:return: Dictionary containing the configuration
"""
config = super(TestBasicEndpointsWithMultipleRemoteSites, self).create_config_file()
site3_config = {
"site": {
"username": "%s" % login,
"name": "Site3",
"ip_address": "%s" % ip_address,
"password": "%s" % password,
"local": "False",
"use_https": "False"
}
}
config['config'].append(site3_config)
return config
def create_config_file(self):
"""
Create the configuration
:return: Dictionary containing the configuration
"""
config = self.create_additional_site_config(SITE3_LOGIN, SITE3_IPADDR, SITE3_PASSWORD)
site3_export_config = {
"site":
{
"name": "Site3",
"interfaces":
[
{
"l3out":
{
"name": "l3out",
"tenant": "intersite-testsuite-site3"
}
}
]
}
}
for item in config['config']:
if 'export' in item:
item['export']['remote_sites'].append(site3_export_config)
return config
def setup_with_endpoint(self, mac='00:11:22:33:33:33', ip='3.4.3.4'):
"""
Set up the configuration with an endpoint
:return: 2 strings containing the MAC and IP address of the endpoint
"""
args = self.get_args()
config = self.create_config_file()
self.write_config_file(config, args)
execute_tool(args, test_mode=True)
self.assertFalse(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite',
'l3out', 'intersite-testsuite-app-epg'))
self.assertFalse(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite-site3',
'l3out', 'intersite-testsuite-app-epg'))
time.sleep(2)
self.add_endpoint(mac, ip, 'intersite-testsuite', 'app', 'epg')
return mac, ip
def test_basic_add_endpoint(self):
"""
Test add endpoint
"""
mac, ip = self.setup_with_endpoint()
time.sleep(2)
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite',
'l3out', 'intersite-testsuite-app-epg'))
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite-site3',
'l3out', 'intersite-testsuite-app-epg'))
def test_basic_remove_endpoint(self):
"""
Test remove endpoint
"""
mac, ip = self.setup_with_endpoint()
time.sleep(2)
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite',
'l3out', 'intersite-testsuite-app-epg'))
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite-site3',
'l3out', 'intersite-testsuite-app-epg'))
self.remove_endpoint(mac, ip, 'intersite-testsuite', 'app', 'epg')
time.sleep(2)
self.assertFalse(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite',
'l3out', 'intersite-testsuite-app-epg'))
self.assertFalse(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite-site3',
'l3out', 'intersite-testsuite-app-epg'))
class TestBasicEndpointsWithMultipleRemoteSitesButOnlyExportToOne(TestBasicEndpointsWithMultipleRemoteSites):
def create_config_file(self):
"""
Create the configuration
:return: Dictionary containing the configuration
"""
config = self.create_additional_site_config(SITE3_LOGIN, SITE3_IPADDR, SITE3_PASSWORD)
return config
def test_basic_add_endpoint(self):
"""
Test add endpoint
"""
mac, ip = self.setup_with_endpoint()
time.sleep(2)
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite',
'l3out', 'intersite-testsuite-app-epg'))
self.assertFalse(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite-site3',
'l3out', 'intersite-testsuite-app-epg'))
def test_basic_remove_endpoint(self):
"""
Test remove endpoint
"""
mac, ip = self.setup_with_endpoint()
time.sleep(2)
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite',
'l3out', 'intersite-testsuite-app-epg'))
self.assertFalse(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite-site3',
'l3out', 'intersite-testsuite-app-epg'))
self.remove_endpoint(mac, ip, 'intersite-testsuite', 'app', 'epg')
time.sleep(2)
self.assertFalse(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite',
'l3out', 'intersite-testsuite-app-epg'))
self.assertFalse(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite-site3',
'l3out', 'intersite-testsuite-app-epg'))
class TestBasicEndpointsWithThreeRemoteSites(TestBasicEndpointsWithMultipleRemoteSites):
def create_config_file(self):
"""
Create the configuration
:return: Dictionary containing the configuration
"""
config = super(TestBasicEndpointsWithThreeRemoteSites, self).create_config_file()
site4_config = {
"site": {
"username": "%s" % SITE4_LOGIN,
"name": "Site4",
"ip_address": "%s" % SITE4_IPADDR,
"password": "%s" % SITE4_PASSWORD,
"local": "False",
"use_https": "False"
}
}
config['config'].append(site4_config)
site4_export_config = {
"site":
{
"name": "Site4",
"interfaces":
[
{
"l3out":
{
"name": "l3out",
"tenant": "intersite-testsuite-site4"
}
}
]
}
}
for item in config['config']:
if 'export' in item:
item['export']['remote_sites'].append(site4_export_config)
return config
def test_basic_add_endpoint(self):
"""
Test add endpoint
"""
mac, ip = self.setup_with_endpoint()
time.sleep(2)
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite',
'l3out', 'intersite-testsuite-app-epg'))
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite-site3',
'l3out', 'intersite-testsuite-app-epg'))
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite-site4',
'l3out', 'intersite-testsuite-app-epg'))
def test_basic_remove_endpoint(self):
"""
Test remove endpoint
"""
mac, ip = self.setup_with_endpoint()
time.sleep(2)
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite',
'l3out', 'intersite-testsuite-app-epg'))
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite-site3',
'l3out', 'intersite-testsuite-app-epg'))
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite-site4',
'l3out', 'intersite-testsuite-app-epg'))
self.remove_endpoint(mac, ip, 'intersite-testsuite', 'app', 'epg')
time.sleep(2)
self.assertFalse(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite',
'l3out', 'intersite-testsuite-app-epg'))
self.assertFalse(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite-site3',
'l3out', 'intersite-testsuite-app-epg'))
self.assertFalse(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite-site4',
'l3out', 'intersite-testsuite-app-epg'))
class TestBasicMacMove(BaseEndpointTestCase):
"""
Basic test for MAC move.
i.e. the same IP address appears with a different MAC address. This case can appear in failovers such as redundant
loadbalancers
"""
def test_basic_mac_move(self):
"""
Test basic MAC move
"""
args = self.get_args()
self.write_config_file(self.create_config_file(), args)
execute_tool(args, test_mode=True)
ip = '3.4.3.4'
mac = '00:11:22:33:33:33'
self.assertFalse(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite', 'l3out',
'intersite-testsuite-app-epg'))
time.sleep(2)
self.add_endpoint(mac, ip, 'intersite-testsuite', 'app', 'epg')
time.sleep(2)
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite',
'l3out', 'intersite-testsuite-app-epg'))
mac = '00:11:22:33:44:44'
self.add_endpoint(mac, ip, 'intersite-testsuite', 'app', 'epg')
self.remove_endpoint('00:11:22:33:33:33', ip, 'intersite-testsuite', 'app', 'epg')
time.sleep(2)
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite',
'l3out', 'intersite-testsuite-app-epg'))
class TestMultipleEPG(BaseTestCase):
"""
Test multiple EPGs
"""
def setup_local_site(self):
"""
Set up the local site
"""
# create Tenant, App, EPG on site 1
site1 = Session(SITE1_URL, SITE1_LOGIN, SITE1_PASSWORD)
resp = site1.login()
self.assertTrue(resp.ok)
tenant = Tenant('intersite-testsuite')
app1 = AppProfile('app1', tenant)
epg1 = EPG('epg1', app1)
app2 = AppProfile('app2', tenant)
epg2 = EPG('epg2', app2)
resp = tenant.push_to_apic(site1)
self.assertTrue(resp.ok)
def setup_remote_site(self):
"""
Set up the remote site
"""
# Create tenant, L3out with contract on site 2
site2 = Session(SITE2_URL, SITE2_LOGIN, SITE2_PASSWORD)
resp = site2.login()
self.assertTrue(resp.ok)
tenant = Tenant('intersite-testsuite')
l3out = OutsideL3('l3out', tenant)
resp = tenant.push_to_apic(site2)
self.assertTrue(resp.ok)
def create_config_file(self):
"""
Create the configuration
:return: Dictionary containing the configuration
"""
config = self.create_site_config()
export_policy = {
"export": {
"tenant": "intersite-testsuite",
"app": "app1",
"epg": "epg1",
"remote_epg": "intersite-testsuite-app1-epg1",
"remote_sites": [
{
"site": {
"name": "Site2",
"interfaces": [
{
"l3out": {
"name": "l3out",
"tenant": "intersite-testsuite"
}
}
]
}
}
]
}
}
config['config'].append(export_policy)
export_policy = {
"export": {
"tenant": "intersite-testsuite",
"app": "app2",
"epg": "epg2",
"remote_epg": "intersite-testsuite-app2-epg2",
"remote_sites": [
{
"site": {
"name": "Site2",
"interfaces": [
{
"l3out": {
"name": "l3out",
"tenant": "intersite-testsuite"
}
}
]
}
}
]
}
}
config['config'].append(export_policy)
return config
def test_basic_add_endpoint(self):
"""
Test add endpoint
"""
args = self.get_args()
config = self.create_config_file()
config_filename = 'testsuite_cfg.json'
args.config = config_filename
config_file = open(config_filename, 'w')
config_file.write(str(json.dumps(config)))
config_file.close()
execute_tool(args, test_mode=True)
mac = '00:11:22:33:33:33'
ip = '3.4.3.4'
self.assertFalse(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite',
'l3out', 'intersite-testsuite-app1-epg1'))
time.sleep(2)
self.add_endpoint(mac, ip, 'intersite-testsuite', 'app1', 'epg1')
time.sleep(2)
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite',
'l3out', 'intersite-testsuite-app1-epg1'))
def test_basic_add_multiple_endpoint(self):
"""
Test adding multiple endpoints
"""
args = self.get_args()
config = self.create_config_file()
config_filename = 'testsuite_cfg.json'
args.config = config_filename
config_file = open(config_filename, 'w')
config_file.write(str(json.dumps(config)))
config_file.close()
execute_tool(args, test_mode=True)
time.sleep(2)
mac1 = '00:11:22:33:33:34'
ip1 = '3.4.3.5'
self.add_endpoint(mac1, ip1, 'intersite-testsuite', 'app1', 'epg1')
mac2 = '00:11:22:33:33:35'
ip2 = '3.4.3.6'
self.add_endpoint(mac2, ip2, 'intersite-testsuite', 'app2', 'epg2')
mac3 = '00:11:22:33:33:36'
ip3 = '3.4.3.7'
self.add_endpoint(mac3, ip3, 'intersite-testsuite', 'app2', 'epg2')
time.sleep(2)
self.assertTrue(self.verify_remote_site_has_entry(mac1, ip1, 'intersite-testsuite',
'l3out', 'intersite-testsuite-app1-epg1'))
self.assertTrue(self.verify_remote_site_has_entry(mac2, ip2, 'intersite-testsuite',
'l3out', 'intersite-testsuite-app2-epg2'))
self.assertTrue(self.verify_remote_site_has_entry(mac3, ip3, 'intersite-testsuite',
'l3out', 'intersite-testsuite-app2-epg2'))
def test_basic_remove_endpoint(self):
"""
Test remove the endpoint
"""
args = self.get_args()
config = self.create_config_file()
self.write_config_file(config, args)
execute_tool(args, test_mode=True)
time.sleep(2)
mac = '00:11:22:33:33:33'
ip = '3.4.3.4'
self.add_endpoint(mac, ip, 'intersite-testsuite', 'app1', 'epg1')
time.sleep(2)
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite',
'l3out', 'intersite-testsuite-app1-epg1'))
self.remove_endpoint(mac, ip, 'intersite-testsuite', 'app1', 'epg1')
time.sleep(2)
self.assertFalse(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite',
'l3out', 'intersite-testsuite-app1-epg1'))
def test_basic_remove_one_of_multiple_endpoint(self):
"""
Test remove one of multiple endpoints
"""
args = self.get_args()
config = self.create_config_file()
self.write_config_file(config, args)
execute_tool(args, test_mode=True)
time.sleep(2)
mac1 = '00:11:22:33:33:34'
ip1 = '3.4.3.5'
self.add_endpoint(mac1, ip1, 'intersite-testsuite', 'app1', 'epg1')
mac2 = '00:11:22:33:33:35'
ip2 = '3.4.3.6'
self.add_endpoint(mac2, ip2, 'intersite-testsuite', 'app2', 'epg2')
time.sleep(2)
self.assertTrue(self.verify_remote_site_has_entry(mac1, ip1, 'intersite-testsuite', 'l3out',
'intersite-testsuite-app1-epg1'))
self.assertTrue(self.verify_remote_site_has_entry(mac2, ip2, 'intersite-testsuite', 'l3out',
'intersite-testsuite-app2-epg2'))
self.remove_endpoint(mac1, ip1, 'intersite-testsuite', 'app1', 'epg1')
self.assertFalse(self.verify_remote_site_has_entry(mac1, ip1, 'intersite-testsuite', 'l3out',
'intersite-testsuite-app1-epg1'))
self.assertTrue(self.verify_remote_site_has_entry(mac2, ip2, 'intersite-testsuite', 'l3out',
'intersite-testsuite-app2-epg2'))
class BaseExistingEndpointsTestCase(BaseTestCase):
"""
Base class for tests where endpoints already exist
"""
def setup_local_site(self):
"""
Set up the local site
"""
# create Tenant, App, EPG on site 1
site1 = Session(SITE1_URL, SITE1_LOGIN, SITE1_PASSWORD)
resp = site1.login()
self.assertTrue(resp.ok)
tenant = Tenant('intersite-testsuite')
app = AppProfile('app', tenant)
epg = EPG('epg', app)
mac = '00:11:22:33:33:33'
ip = '3.4.3.4'
self.assertFalse(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite',
'l3out', 'intersite-testsuite-app-epg'))
self.add_endpoint(mac, ip, 'intersite-testsuite', 'app', 'epg')
resp = tenant.push_to_apic(site1)
self.assertTrue(resp.ok)
def setup_remote_site(self):
"""
Set up the remote site
"""
# Create tenant, L3out with contract on site 2
site2 = Session(SITE2_URL, SITE2_LOGIN, SITE2_PASSWORD)
resp = site2.login()
self.assertTrue(resp.ok)
tenant = Tenant('intersite-testsuite')
l3out = OutsideL3('l3out', tenant)
resp = tenant.push_to_apic(site2)
self.assertTrue(resp.ok)
def create_config_file(self):
"""
Create the configuration
:return: Dictionary containing the configuration
"""
config = self.create_site_config()
export_policy = {
"export": {
"tenant": "intersite-testsuite",
"app": "app",
"epg": "epg",
"remote_epg": "intersite-testsuite-app-epg",
"remote_sites": [
{
"site": {
"name": "Site2",
"interfaces": [
{
"l3out": {
"name": "l3out",
"tenant": "intersite-testsuite"
}
}
]
}
}
]
}
}
config['config'].append(export_policy)
return config
class TestBasicExistingEndpoints(BaseExistingEndpointsTestCase):
def test_basic_add_endpoint(self):
"""
Test add the endpoint
"""
args = self.get_args()
config = self.create_config_file()
self.write_config_file(config, args)
execute_tool(args, test_mode=True)
time.sleep(2)
mac = '00:11:22:33:33:33'
ip = '3.4.3.4'
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite',
'l3out', 'intersite-testsuite-app-epg'))
def test_basic_remove_endpoint(self):
"""
Test remove the endpoint
"""
args = self.get_args()
config = self.create_config_file()
self.write_config_file(config, args)
execute_tool(args, test_mode=True)
time.sleep(2)
mac = '00:11:22:33:33:33'
ip = '3.4.3.4'
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite',
'l3out', 'intersite-testsuite-app-epg'))
self.remove_endpoint(mac, ip, 'intersite-testsuite', 'app', 'epg')
time.sleep(2)
self.assertFalse(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite',
'l3out', 'intersite-testsuite-app-epg'))
class BaseExistingEndpointsWith3RemoteSites(BaseExistingEndpointsTestCase):
def setup_remote_tenant(self, url, login, password, tenant_name):
"""
Set up the remote site
"""
# Create tenant, L3out with contract on site 2
site = Session(url, login, password)
resp = site.login()
self.assertTrue(resp.ok)
tenant = Tenant(tenant_name)
l3out = OutsideL3('l3out', tenant)
resp = tenant.push_to_apic(site)
self.assertTrue(resp.ok)
def setup_remote_site(self):
"""
Set up the remote sites
"""
self.setup_remote_tenant(SITE2_URL, SITE2_LOGIN, SITE2_PASSWORD, 'intersite-testsuite-site-2')
self.setup_remote_tenant(SITE3_URL, SITE3_LOGIN, SITE3_PASSWORD, 'intersite-testsuite-site-3')
self.setup_remote_tenant(SITE4_URL, SITE4_LOGIN, SITE4_PASSWORD, 'intersite-testsuite-site-4')
def teardown_remote_tenant(self, url, login, password, tenant_name):
"""
Teardown the remote site configuration
"""
site2 = Session(url, login, password)
resp = site2.login()
self.assertTrue(resp.ok)
tenant = Tenant(tenant_name)
tenant.mark_as_deleted()
resp = tenant.push_to_apic(site2)
self.assertTrue(resp.ok)
def teardown_remote_site(self):
"""
Teardown the remote sites
"""
self.teardown_remote_tenant(SITE2_URL, SITE2_LOGIN, SITE2_PASSWORD, 'intersite-testsuite-site-2')
self.teardown_remote_tenant(SITE2_URL, SITE2_LOGIN, SITE2_PASSWORD, 'intersite-testsuite-site-3')
self.teardown_remote_tenant(SITE2_URL, SITE2_LOGIN, SITE2_PASSWORD, 'intersite-testsuite-site-4')
time.sleep(2)
def add_remote_site_to_config_file(self, config, site_name, ip_address, login, password, tenant_name):
site_config = {
"site": {
"username": "%s" % login,
"name": "%s" % site_name,
"ip_address": "%s" % ip_address,
"password": "%s" % password,
"local": "False",
"use_https": "False"
}
}
site_export_config = {
"site": {
"name": site_name,
"interfaces": [
{
"l3out": {
"name": "l3out",
"tenant": tenant_name
}
}
]
}
}
for item in config['config']:
if 'export' in item:
item['export']['remote_sites'].append(site_export_config)
config['config'].append(site_config)
return config
def create_config_file(self):
"""
Create the configuration
:return: Dictionary containing the configuration
"""
config = self.create_site_config()
export_policy = {
"export": {
"tenant": "intersite-testsuite",
"app": "app",
"epg": "epg",
"remote_epg": "intersite-testsuite-app-epg",
"remote_sites": [
]
}
}
config['config'].append(export_policy)
config = self.add_remote_site_to_config_file(config,
'Site2',
SITE2_IPADDR, SITE2_LOGIN, SITE2_PASSWORD,
'intersite-testsuite-site2')
config = self.add_remote_site_to_config_file(config,
'Site3',
SITE3_IPADDR, SITE3_LOGIN, SITE3_PASSWORD,
'intersite-testsuite-site3')
config = self.add_remote_site_to_config_file(config,
'Site4',
SITE4_IPADDR, SITE4_LOGIN, SITE4_PASSWORD,
'intersite-testsuite-site4')
return config
class TestBasicExistingEndpointsWith3RemoteSites(BaseExistingEndpointsWith3RemoteSites):
def test_basic_add_endpoint(self):
"""
Test add the endpoint
"""
args = self.get_args()
config = self.create_config_file()
self.write_config_file(config, args)
execute_tool(args, test_mode=True)
time.sleep(2)
mac = '00:11:22:33:33:33'
ip = '3.4.3.4'
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite-site2',
'l3out', 'intersite-testsuite-app-epg'))
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite-site3',
'l3out', 'intersite-testsuite-app-epg'))
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite-site4',
'l3out', 'intersite-testsuite-app-epg'))
def test_basic_remove_endpoint(self):
"""
Test remove the endpoint
"""
args = self.get_args()
config = self.create_config_file()
self.write_config_file(config, args)
execute_tool(args, test_mode=True)
time.sleep(2)
mac = '00:11:22:33:33:33'
ip = '3.4.3.4'
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite-site2',
'l3out', 'intersite-testsuite-app-epg'))
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite-site3',
'l3out', 'intersite-testsuite-app-epg'))
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite-site4',
'l3out', 'intersite-testsuite-app-epg'))
self.remove_endpoint(mac, ip, 'intersite-testsuite', 'app', 'epg')
time.sleep(2)
self.assertFalse(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite-site2',
'l3out', 'intersite-testsuite-app-epg'))
self.assertFalse(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite-site3',
'l3out', 'intersite-testsuite-app-epg'))
self.assertFalse(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite-site4',
'l3out', 'intersite-testsuite-app-epg'))
class TestLargeScaleExistingEndpointsWith3RemoteSites(BaseExistingEndpointsWith3RemoteSites):
def setup_local_site(self):
"""
Set up the local site
"""
for i in range(0, 3):
# create Tenant, App, EPG on site 1
site1 = Session(SITE1_URL, SITE1_LOGIN, SITE1_PASSWORD)
resp = site1.login()
self.assertTrue(resp.ok)
tenant = Tenant('intersite-testsuite')
app = AppProfile('app', tenant)
epg = EPG('epg', app)
# Create the physical interface object
intf = Interface('eth', '1', '101', '1', '38')
vlan_intf = L2Interface('vlan-5', 'vlan', '5')
vlan_intf.attach(intf)
# Attach the EPG to the VLAN interface
epg.attach(vlan_intf)
for j in range(0, 254):
mac = '00:11:22:33:%s:%s' % (hex(i)[2:].zfill(2), hex(j)[2:].zfill(2))
ip = '3.4.%s.%s' % (i, j)
ep = Endpoint(mac, epg)
ep.mac = mac
ep.ip = ip
l3ep = IPEndpoint(ip, ep)
# Assign it to the L2Interface
ep.attach(vlan_intf)
urls = intf.get_url()
jsons = intf.get_json()
# Set the the phys domain, infra, and fabric
for k in range(0, len(urls)):
if jsons[k] is not None:
resp = site1.push_to_apic(urls[k], jsons[k])
self.assertTrue(resp.ok)
# Push the endpoint
resp = tenant.push_to_apic(site1)
self.assertTrue(resp.ok)
time.sleep(1)
def verify_remote_site_has_entries(self, tenant_name, l3out_name, remote_epg_name):
"""
Verify that the remote site has the entry
:param mac: String containing the MAC address of the endpoint to find on the remote site
:param ip: String containing the IP address of the endpoint to find on the remote site
:param tenant_name: String containing the remote tenant name holding the endpoint
:param l3out_name: String containing the remote OutsideL3 name holding the endpoint
:param remote_epg_name: String containing the remote OutsideEPG on the remote OutsideL3 holding the endpoint
:return: True if the remote site has the endpoint. False otherwise
"""
site2 = Session(SITE2_URL, SITE2_LOGIN, SITE2_PASSWORD)
resp = site2.login()
self.assertTrue(resp.ok)
query = ('/api/mo/uni/tn-%s/out-%s/instP-%s.json?query-target=children' % (tenant_name,
l3out_name,
remote_epg_name))
resp = site2.get(query)
self.assertTrue(resp.ok)
subnets = set()
for item in resp.json()['imdata']:
if 'l3extSubnet' in item:
subnets.add(item['l3extSubnet']['attributes']['ip'])
for i in range(0, 3):
for j in range(0, 254):
ip = '3.4.%s.%s/32' % (i, j)
if ip not in subnets:
return False
return True
def test_add_large_scale_endpoints(self):
"""
Test add the endpoint
"""
args = self.get_args()
config = self.create_config_file()
self.write_config_file(config, args)
execute_tool(args, test_mode=True)
time.sleep(20)
self.assertTrue(self.verify_remote_site_has_entries('intersite-testsuite-site2',
'l3out',
'intersite-testsuite-app-epg'))
self.assertTrue(self.verify_remote_site_has_entries('intersite-testsuite-site3',
'l3out',
'intersite-testsuite-app-epg'))
self.assertTrue(self.verify_remote_site_has_entries('intersite-testsuite-site4',
'l3out',
'intersite-testsuite-app-epg'))
class TestBasicExistingEndpointsAddPolicyLater(BaseTestCase):
"""
Tests for previously existing endpoints and policy is added later
"""
def setup_local_site(self):
"""
Set up the local site
"""
# create Tenant, App, EPG on site 1
site1 = Session(SITE1_URL, SITE1_LOGIN, SITE1_PASSWORD)
resp = site1.login()
self.assertTrue(resp.ok)
tenant = Tenant('intersite-testsuite')
app = AppProfile('app', tenant)
epg = EPG('epg', app)
mac = '00:11:22:33:33:33'
ip = '3.4.3.4'
self.assertFalse(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite',
'l3out', 'intersite-testsuite-app-epg'))
self.add_endpoint(mac, ip, 'intersite-testsuite', 'app', 'epg')
resp = tenant.push_to_apic(site1)
self.assertTrue(resp.ok)
def setup_remote_site(self):
"""
Set up the remote site
"""
# Create tenant, L3out with contract on site 2
site2 = Session(SITE2_URL, SITE2_LOGIN, SITE2_PASSWORD)
resp = site2.login()
self.assertTrue(resp.ok)
tenant = Tenant('intersite-testsuite')
l3out = OutsideL3('l3out', tenant)
resp = tenant.push_to_apic(site2)
self.assertTrue(resp.ok)
def create_config_file(self):
"""
Create the configuration
:return: Dictionary containing the configuration
"""
return self.create_site_config()
@staticmethod
def create_export_policy():
"""
Create the export policy
:return: Dictionary containing the configuration
"""
config = {
"export": {
"tenant": "intersite-testsuite",
"app": "app",
"epg": "epg",
"remote_epg": "intersite-testsuite-app-epg",
"remote_sites": [
{
"site": {
"name": "Site2",
"interfaces": [
{
"l3out": {
"name": "l3out",
"tenant": "intersite-testsuite"
}
}
]
}
}
]
}
}
return config
def test_basic_add_endpoint(self):
"""
Test adding the endpoint
"""
args = self.get_args()
config = self.create_config_file()
self.write_config_file(config, args)
collector = execute_tool(args, test_mode=True)
time.sleep(2)
config['config'].append(self.create_export_policy())
self.write_config_file(config, args)
collector.reload_config()
time.sleep(2)
mac = '00:11:22:33:33:33'
ip = '3.4.3.4'
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite', 'l3out',
'intersite-testsuite-app-epg'))
def test_basic_remove_endpoint(self):
"""
Test removing the endpoint
"""
args = self.get_args()
config = self.create_config_file()
config['config'].append(self.create_export_policy())
self.write_config_file(config, args)
collector = execute_tool(args, test_mode=True)
time.sleep(2)
mac = '00:11:22:33:33:33'
ip = '3.4.3.4'
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite', 'l3out',
'intersite-testsuite-app-epg'))
config = self.create_config_file()
self.write_config_file(config, args)
collector.reload_config()
time.sleep(2)
self.assertFalse(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite', 'l3out',
'intersite-testsuite-app-epg'))
class TestExportPolicyRemoval(BaseTestCase):
"""
Tests for export policy removal
"""
def setup_local_site(self):
"""
Set up the local site
"""
# create Tenant, App, EPG on site 1
site1 = Session(SITE1_URL, SITE1_LOGIN, SITE1_PASSWORD)
resp = site1.login()
self.assertTrue(resp.ok)
site2 = Session(SITE2_URL, SITE2_LOGIN, SITE2_PASSWORD)
resp = site2.login()
self.assertTrue(resp.ok)
tenant = Tenant('intersite-testsuite')
tenant.mark_as_deleted()
resp = tenant.push_to_apic(site1)
self.assertTrue(resp.ok)
resp = tenant.push_to_apic(site2)
self.assertTrue(resp.ok)
time.sleep(2)
tenant = Tenant('intersite-testsuite')
app = AppProfile('app', tenant)
epg1 = EPG('epg', app)
epg2 = EPG('epg2', app)
mac = '00:11:22:33:33:33'
ip = '3.4.3.4'
self.assertFalse(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite', 'l3out',
'intersite-testsuite-app-epg'))
self.add_endpoint(mac, ip, 'intersite-testsuite', 'app', 'epg')
resp = tenant.push_to_apic(site1)
self.assertTrue(resp.ok)
time.sleep(2)
def setup_remote_site(self):
"""
Set up the remote site
"""
# Create tenant, L3out with contract on site 2
site2 = Session(SITE2_URL, SITE2_LOGIN, SITE2_PASSWORD)
resp = site2.login()
self.assertTrue(resp.ok)
tenant = Tenant('intersite-testsuite')
l3out = OutsideL3('l3out', tenant)
l3out2 = OutsideL3('l3out2', tenant)
resp = tenant.push_to_apic(site2)
self.assertTrue(resp.ok)
def create_diff_epg_config_file(self):
"""
Create a configuration with different EPGs
:return: Dictionary containing the configuration
"""
config = self.create_site_config()
export_policy = {
"export": {
"tenant": "intersite-testsuite",
"app": "app",
"epg": "epg",
"remote_epg": "intersite-testsuite-app-epg2",
"remote_sites": [
{
"site": {
"name": "Site2",
"interfaces": [
{
"l3out": {
"name": "l3out",
"tenant": "intersite-testsuite"
}
}
]
}
}
]
}
}
config['config'].append(export_policy)
return config
def create_config_file(self):
"""
Create the configuration
:return: Dictionary containing the configuration
"""
config = self.create_site_config()
export_policy = {
"export": {
"tenant": "intersite-testsuite",
"app": "app",
"epg": "epg",
"remote_epg": "intersite-testsuite-app-epg",
"remote_sites": [
{
"site": {
"name": "Site2",
"interfaces": [
{
"l3out": {
"name": "l3out",
"tenant": "intersite-testsuite"
}
}
]
}
}
]
}
}
config['config'].append(export_policy)
export_policy = {
"export": {
"tenant": "intersite-testsuite",
"app": "app",
"epg": "epg2",
"remote_epg": "intersite-testsuite-app-epg2",
"remote_sites": [
{
"site": {
"name": "Site2",
"interfaces": [
{
"l3out": {
"name": "l3out2",
"tenant": "intersite-testsuite"
}
}
]
}
}
]
}
}
config['config'].append(export_policy)
return config
def test_basic_remove_policy(self):
"""
Test removing the policy
"""
args = self.get_args()
config = self.create_config_file()
self.write_config_file(config, args)
collector = execute_tool(args, test_mode=True)
time.sleep(4)
mac = '00:11:22:33:33:33'
ip = '3.4.3.4'
self.assertTrue(self.verify_remote_site_has_policy('intersite-testsuite',
'l3out', 'intersite-testsuite-app-epg'))
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite',
'l3out', 'intersite-testsuite-app-epg'))
self.assertTrue(self.verify_remote_site_has_policy('intersite-testsuite',
'l3out2', 'intersite-testsuite-app-epg2'))
config = self.create_site_config()
self.write_config_file(config, args)
collector.reload_config()
time.sleep(4)
self.assertFalse(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite',
'l3out', 'intersite-testsuite-app-epg'))
self.assertFalse(self.verify_remote_site_has_policy('intersite-testsuite',
'l3out', 'intersite-testsuite-app-epg'))
self.assertFalse(self.verify_remote_site_has_policy('intersite-testsuite',
'l3out2', 'intersite-testsuite-app-epg2'))
def test_basic_change_policy_name(self):
"""
Test changing the policy name
"""
args = self.get_args()
config = self.create_config_file()
mac = '00:11:22:33:33:33'
ip = '3.4.3.4'
self.write_config_file(config, args)
collector = execute_tool(args, test_mode=True)
time.sleep(4)
self.assertTrue(self.verify_remote_site_has_policy('intersite-testsuite',
'l3out', 'intersite-testsuite-app-epg'))
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite',
'l3out', 'intersite-testsuite-app-epg'))
config = self.create_diff_epg_config_file()
self.write_config_file(config, args)
collector.reload_config()
time.sleep(4)
self.assertFalse(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite',
'l3out', 'intersite-testsuite-app-epg'))
self.assertFalse(self.verify_remote_site_has_policy('intersite-testsuite',
'l3out', 'intersite-testsuite-app-epg'))
self.assertTrue(self.verify_remote_site_has_policy('intersite-testsuite',
'l3out', 'intersite-testsuite-app-epg2'))
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite',
'l3out', 'intersite-testsuite-app-epg2'))
class BaseTestCaseEndpointsWithContract(BaseTestCase):
"""
Base class for Tests for endpoints with a contract
"""
def setup_local_site(self):
"""
Set up the local site
"""
# create Tenant, App, EPG on site 1
site1 = Session(SITE1_URL, SITE1_LOGIN, SITE1_PASSWORD)
resp = site1.login()
self.assertTrue(resp.ok)
tenant = Tenant('intersite-testsuite')
app = AppProfile('app', tenant)
epg = EPG('epg', app)
resp = tenant.push_to_apic(site1)
self.assertTrue(resp.ok)
def setup_remote_site(self):
"""
Set up the remote site
"""
# Create tenant, L3out with contract on site 2
site2 = Session(SITE2_URL, SITE2_LOGIN, SITE2_PASSWORD)
resp = site2.login()
self.assertTrue(resp.ok)
tenant = Tenant('intersite-testsuite')
l3out = OutsideL3('l3out', tenant)
contract = Contract('contract-1', tenant)
resp = tenant.push_to_apic(site2)
self.assertTrue(resp.ok)
def create_config_file(self, contract_type):
"""
Create the configuration
:return: Dictionary containing the configuration
"""
config = self.create_site_config()
if contract_type == 'protected_by':
contract_name = 'taboo_name'
elif contract_type == 'consumes_interface':
contract_name = 'cif_name'
else:
contract_name = 'contract_name'
export_policy = {
"export": {
"tenant": "intersite-testsuite",
"app": "app",
"epg": "epg",
"remote_epg": "intersite-testsuite-app-epg",
"remote_sites": [
{
"site": {
"name": "Site2",
"interfaces": [
{
"l3out": {
"name": "l3out",
"tenant": "intersite-testsuite",
contract_type: [
{
contract_name: "contract-1"
}
]
}
}
]
}
}
]
}
}
config['config'].append(export_policy)
return config
def common_test_basic_add_endpoint(self, contract_type):
"""
Test adding endpoint
"""
args = self.get_args()
config = self.create_config_file(contract_type)
self.write_config_file(config, args)
execute_tool(args, test_mode=True)
mac = '00:11:22:33:33:33'
ip = '3.4.3.4'
self.assertFalse(self.verify_remote_site_has_entry_with_contract(mac, ip, 'intersite-testsuite', 'l3out',
'intersite-testsuite-app-epg', 'contract-1',
contract_type))
time.sleep(2)
self.add_endpoint(mac, ip, 'intersite-testsuite', 'app', 'epg')
time.sleep(2)
self.assertTrue(self.verify_remote_site_has_entry_with_contract(mac, ip, 'intersite-testsuite', 'l3out',
'intersite-testsuite-app-epg', 'contract-1',
contract_type))
def common_test_basic_add_multiple_endpoint(self, contract_type):
"""
Test adding multiple endpoints
"""
args = self.get_args()
config = self.create_config_file(contract_type)
self.write_config_file(config, args)
execute_tool(args, test_mode=True)
time.sleep(2)
mac1 = '00:11:22:33:33:34'
ip1 = '3.4.3.5'
self.add_endpoint(mac1, ip1, 'intersite-testsuite', 'app', 'epg')
mac2 = '00:11:22:33:33:35'
ip2 = '3.4.3.6'
self.add_endpoint(mac2, ip2, 'intersite-testsuite', 'app', 'epg')
time.sleep(2)
self.assertTrue(self.verify_remote_site_has_entry_with_contract(mac1, ip1, 'intersite-testsuite', 'l3out',
'intersite-testsuite-app-epg', 'contract-1',
contract_type))
self.assertTrue(self.verify_remote_site_has_entry_with_contract(mac2, ip2, 'intersite-testsuite', 'l3out',
'intersite-testsuite-app-epg', 'contract-1',
contract_type))
def common_test_basic_remove_endpoint(self, contract_type):
"""
Test removing endpoint
"""
args = self.get_args()
config = self.create_config_file(contract_type)
self.write_config_file(config, args)
execute_tool(args, test_mode=True)
time.sleep(2)
mac = '00:11:22:33:33:33'
ip = '3.4.3.4'
self.add_endpoint(mac, ip, 'intersite-testsuite', 'app', 'epg')
time.sleep(2)
self.assertTrue(self.verify_remote_site_has_entry_with_contract(mac, ip, 'intersite-testsuite', 'l3out',
'intersite-testsuite-app-epg', 'contract-1',
contract_type))
self.remove_endpoint(mac, ip, 'intersite-testsuite', 'app', 'epg')
self.assertFalse(self.verify_remote_site_has_entry_with_contract(mac, ip, 'intersite-testsuite', 'l3out',
'intersite-testsuite-app-epg', 'contract-1',
contract_type))
def common_test_basic_remove_one_of_multiple_endpoint(self, contract_type):
"""
Test removing one of multiple endpoints
"""
args = self.get_args()
config = self.create_config_file(contract_type)
self.write_config_file(config, args)
execute_tool(args, test_mode=True)
time.sleep(2)
mac1 = '00:11:22:33:33:34'
ip1 = '3.4.3.5'
self.add_endpoint(mac1, ip1, 'intersite-testsuite', 'app', 'epg')
mac2 = '00:11:22:33:33:35'
ip2 = '3.4.3.6'
self.add_endpoint(mac2, ip2, 'intersite-testsuite', 'app', 'epg')
time.sleep(2)
self.assertTrue(self.verify_remote_site_has_entry_with_contract(mac1, ip1, 'intersite-testsuite', 'l3out',
'intersite-testsuite-app-epg', 'contract-1',
contract_type))
self.assertTrue(self.verify_remote_site_has_entry_with_contract(mac2, ip2, 'intersite-testsuite', 'l3out',
'intersite-testsuite-app-epg', 'contract-1',
contract_type))
self.remove_endpoint(mac1, ip1, 'intersite-testsuite', 'app', 'epg')
self.assertFalse(self.verify_remote_site_has_entry_with_contract(mac1, ip1, 'intersite-testsuite', 'l3out',
'intersite-testsuite-app-epg', 'contract-1',
contract_type))
self.assertTrue(self.verify_remote_site_has_entry_with_contract(mac2, ip2, 'intersite-testsuite', 'l3out',
'intersite-testsuite-app-epg', 'contract-1',
contract_type))
class TestBasicEndpointsWithProvidedContract(BaseTestCaseEndpointsWithContract):
"""
Basic Tests for endpoints with a provided contract
"""
def test_basic_add_endpoint(self):
"""
Test adding endpoint
"""
self.common_test_basic_add_endpoint(contract_type='provides')
def test_basic_add_multiple_endpoint(self):
"""
Test adding multiple endpoints
"""
self.common_test_basic_add_multiple_endpoint(contract_type='provides')
def test_basic_remove_endpoint(self):
"""
Test removing endpoint
"""
self.common_test_basic_remove_endpoint(contract_type='provides')
def test_basic_remove_one_of_multiple_endpoint(self):
"""
Test removing one of multiple endpoints
"""
self.common_test_basic_remove_one_of_multiple_endpoint(contract_type='provides')
class TestBasicEndpointsWithConsumedContract(BaseTestCaseEndpointsWithContract):
"""
Basic Tests for endpoints with a consumed contract
"""
def test_basic_add_endpoint(self):
"""
Test adding endpoint
"""
self.common_test_basic_add_endpoint(contract_type='consumes')
def test_basic_add_multiple_endpoint(self):
"""
Test adding multiple endpoints
"""
self.common_test_basic_add_multiple_endpoint(contract_type='consumes')
def test_basic_remove_endpoint(self):
"""
Test removing endpoint
"""
self.common_test_basic_remove_endpoint(contract_type='consumes')
def test_basic_remove_one_of_multiple_endpoint(self):
"""
Test removing one of multiple endpoints
"""
self.common_test_basic_remove_one_of_multiple_endpoint(contract_type='consumes')
class TestBasicEndpointsWithConsumedContractInterface(BaseTestCaseEndpointsWithContract):
"""
Basic Tests for endpoints with a consumed contract interface
"""
def test_basic_add_endpoint(self):
"""
Test adding endpoint
"""
self.common_test_basic_add_endpoint(contract_type='consumes_interface')
def test_basic_add_multiple_endpoint(self):
"""
Test adding multiple endpoints
"""
self.common_test_basic_add_multiple_endpoint(contract_type='consumes_interface')
def test_basic_remove_endpoint(self):
"""
Test removing endpoint
"""
self.common_test_basic_remove_endpoint(contract_type='consumes_interface')
def test_basic_remove_one_of_multiple_endpoint(self):
"""
Test removing one of multiple endpoints
"""
self.common_test_basic_remove_one_of_multiple_endpoint(contract_type='consumes_interface')
class TestBasicEndpointsWithTaboo(BaseTestCaseEndpointsWithContract):
"""
Basic Tests for endpoints with a Taboo
"""
def test_basic_add_endpoint(self):
"""
Test adding endpoint
"""
self.common_test_basic_add_endpoint(contract_type='protected_by')
def test_basic_add_multiple_endpoint(self):
"""
Test adding multiple endpoints
"""
self.common_test_basic_add_multiple_endpoint(contract_type='protected_by')
def test_basic_remove_endpoint(self):
"""
Test removing endpoint
"""
self.common_test_basic_remove_endpoint(contract_type='protected_by')
def test_basic_remove_one_of_multiple_endpoint(self):
"""
Test removing one of multiple endpoints
"""
self.common_test_basic_remove_one_of_multiple_endpoint(contract_type='protected_by')
class TestBasicEndpointMove(BaseTestCase):
"""
Tests for an endpoint that moves
"""
def setup_local_site(self):
"""
Set up the local site
"""
# create Tenant, App, EPG on site 1
site1 = Session(SITE1_URL, SITE1_LOGIN, SITE1_PASSWORD)
resp = site1.login()
self.assertTrue(resp.ok)
tenant = Tenant('intersite-testsuite')
context = Context('vrf', tenant)
bd = BridgeDomain('bd', tenant)
app = AppProfile('app', tenant)
epg = EPG('epg1', app)
epg2 = EPG('epg2', app)
bd.add_context(context)
epg.add_bd(bd)
epg2.add_bd(bd)
resp = tenant.push_to_apic(site1)
self.assertTrue(resp.ok)
def setup_remote_site(self):
"""
Set up the remote site
"""
# Create tenant, L3out with contract on site 2
site2 = Session(SITE2_URL, SITE2_LOGIN, SITE2_PASSWORD)
resp = site2.login()
self.assertTrue(resp.ok)
tenant = Tenant('intersite-testsuite')
l3out = OutsideL3('l3out', tenant)
resp = tenant.push_to_apic(site2)
self.assertTrue(resp.ok)
def create_config_file(self):
"""
Create the configuration
:return: Dictionary containing the configuration
"""
config = self.create_site_config()
export_policy = {
"export": {
"tenant": "intersite-testsuite",
"app": "app",
"epg": "epg1",
"remote_epg": "intersite-testsuite-app-epg1",
"remote_sites": [
{
"site": {
"name": "Site2",
"interfaces": [
{
"l3out": {
"name": "l3out",
"tenant": "intersite-testsuite"
}
}
]
}
}
]
}
}
config['config'].append(export_policy)
export_policy = {
"export": {
"tenant": "intersite-testsuite",
"app": "app",
"epg": "epg2",
"remote_epg": "intersite-testsuite-app-epg2",
"remote_sites": [
{
"site": {
"name": "Site2",
"interfaces": [
{
"l3out": {
"name": "l3out",
"tenant": "intersite-testsuite"
}
}
]
}
}
]
}
}
config['config'].append(export_policy)
return config
def setup_with_endpoint(self):
"""
Set up the local site with the endpoint
:return: 2 strings containing the MAC and IP address of the endpoint
"""
args = self.get_args()
config = self.create_config_file()
self.write_config_file(config, args)
execute_tool(args, test_mode=True)
mac = '00:11:22:33:33:33'
ip = '3.4.3.4'
self.assertFalse(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite',
'l3out', 'intersite-testsuite-app-epg1'))
time.sleep(2)
self.add_endpoint(mac, ip, 'intersite-testsuite', 'app', 'epg1')
return mac, ip
def test_basic_add_endpoint(self):
"""
Test add endpoint
"""
mac, ip = self.setup_with_endpoint()
time.sleep(2)
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite',
'l3out', 'intersite-testsuite-app-epg1'))
def test_basic_add_multiple_endpoint(self):
"""
Test add multiple endpoints
"""
mac1, ip1 = self.setup_with_endpoint()
mac2 = '00:11:22:33:33:35'
ip2 = '3.4.3.6'
self.add_endpoint(mac2, ip2, 'intersite-testsuite', 'app', 'epg2')
time.sleep(2)
self.assertTrue(self.verify_remote_site_has_entry(mac1, ip1, 'intersite-testsuite', 'l3out',
'intersite-testsuite-app-epg1'))
self.assertTrue(self.verify_remote_site_has_entry(mac2, ip2, 'intersite-testsuite', 'l3out',
'intersite-testsuite-app-epg2'))
def test_basic_remove_endpoint(self):
"""
Test removing the endpoint
"""
mac, ip = self.setup_with_endpoint()
time.sleep(2)
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite', 'l3out',
'intersite-testsuite-app-epg1'))
self.remove_endpoint(mac, ip, 'intersite-testsuite', 'app', 'epg1')
self.assertFalse(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite', 'l3out',
'intersite-testsuite-app-epg1'))
def test_basic_remove_one_of_multiple_endpoint(self):
"""
Test removing one of multiple endpoints
"""
mac1, ip1 = self.setup_with_endpoint()
mac2 = '00:11:22:33:33:35'
ip2 = '3.4.3.6'
self.add_endpoint(mac2, ip2, 'intersite-testsuite', 'app', 'epg1')
time.sleep(2)
self.assertTrue(self.verify_remote_site_has_entry(mac1, ip1, 'intersite-testsuite', 'l3out',
'intersite-testsuite-app-epg1'))
self.assertTrue(self.verify_remote_site_has_entry(mac2, ip2, 'intersite-testsuite', 'l3out',
'intersite-testsuite-app-epg1'))
self.remove_endpoint(mac1, ip1, 'intersite-testsuite', 'app', 'epg1')
self.assertFalse(self.verify_remote_site_has_entry(mac1, ip1, 'intersite-testsuite', 'l3out',
'intersite-testsuite-app-epg1'))
self.assertTrue(self.verify_remote_site_has_entry(mac2, ip2, 'intersite-testsuite', 'l3out',
'intersite-testsuite-app-epg1'))
class TestPolicyChangeProvidedContract(BaseTestCase):
"""
Tests to cover changing the provided contract within the policy
"""
def setup_local_site(self):
"""
Set up the local site
"""
# create Tenant, App, EPG on site 1
site1 = Session(SITE1_URL, SITE1_LOGIN, SITE1_PASSWORD)
resp = site1.login()
self.assertTrue(resp.ok)
tenant = Tenant('intersite-testsuite')
app = AppProfile('app', tenant)
epg = EPG('epg', app)
resp = tenant.push_to_apic(site1)
self.assertTrue(resp.ok)
def setup_remote_site(self):
"""
Set up the remote site
"""
# Create tenant, L3out with contract on site 2
site2 = Session(SITE2_URL, SITE2_LOGIN, SITE2_PASSWORD)
resp = site2.login()
self.assertTrue(resp.ok)
tenant = Tenant('intersite-testsuite')
l3out = OutsideL3('l3out', tenant)
contract = Contract('contract-1', tenant)
contract = Contract('contract-2', tenant)
resp = tenant.push_to_apic(site2)
self.assertTrue(resp.ok)
def create_config_file_before(self):
"""
Create the configuration before changing the provided contract
:return: Dictionary containing the configuration
"""
config = self.create_site_config()
export_policy = {
"export": {
"tenant": "intersite-testsuite",
"app": "app",
"epg": "epg",
"remote_epg": "intersite-testsuite-app-epg",
"remote_sites": [
{
"site": {
"name": "Site2",
"interfaces": [
{
"l3out": {
"name": "l3out",
"tenant": "intersite-testsuite",
"provides": [
{
"contract_name": "contract-1",
},
{
"contract_name": "contract-2",
}
]
}
}
]
}
}
]
}
}
config['config'].append(export_policy)
return config
def create_config_file_after(self):
"""
Create the configuration after changing the provided contract
:return: Dictionary containing the configuration
"""
config = self.create_site_config()
export_policy = {
"export": {
"tenant": "intersite-testsuite",
"app": "app",
"epg": "epg",
"remote_epg": "intersite-testsuite-app-epg",
"remote_sites": [
{
"site": {
"name": "Site2",
"interfaces": [
{
"l3out": {
"name": "l3out",
"tenant": "intersite-testsuite",
"provides": [
{
"contract_name": "contract-1"
}
]
}
}
]
}
}
]
}
}
config['config'].append(export_policy)
return config
def verify_remote_site_has_entry_before(self, mac, ip):
"""
Verify that the remote site has the entry before changing the policy
:param mac: String containing the endpoint MAC address
:param ip: String containing the endpoint IP address
:return: True or False. True if the remote site has the entry
"""
site2 = Session(SITE2_URL, SITE2_LOGIN, SITE2_PASSWORD)
resp = site2.login()
self.assertTrue(resp.ok)
query = ('/api/mo/uni/tn-intersite-testsuite/out-l3out.json?query-target=subtree')
resp = site2.get(query)
self.assertTrue(resp.ok)
# Look for l3extInstP
found = False
for item in resp.json()['imdata']:
if 'l3extInstP' in item:
if item['l3extInstP']['attributes']['name'] == 'intersite-testsuite-app-epg':
found = True
break
if not found:
return False
# Verify that the l3extInstP is providing the contracts
found_contract1 = False
found_contract2 = False
for item in resp.json()['imdata']:
if 'fvRsProv' in item:
if item['fvRsProv']['attributes']['tnVzBrCPName'] == 'contract-1':
found_contract1 = True
if item['fvRsProv']['attributes']['tnVzBrCPName'] == 'contract-2':
found_contract2 = True
if not found_contract1 or not found_contract2:
return False
# Look for l3extSubnet
query = ('/api/mo/uni/tn-intersite-testsuite/out-l3out'
'/instP-intersite-testsuite-app-epg.json?query-target=subtree')
resp = site2.get(query)
self.assertTrue(resp.ok)
# Look for l3extSubnet
found = False
for item in resp.json()['imdata']:
if 'l3extSubnet' in item:
if item['l3extSubnet']['attributes']['name'] == ip:
found = True
break
if not found:
return False
return True
def verify_remote_site_has_entry_after(self, mac, ip):
"""
Verify that the remote site has the entry after changing the policy
:param mac: String containing the endpoint MAC address
:param ip: String containing the endpoint IP address
:return: True or False. True if the remote site has the entry
"""
site2 = Session(SITE2_URL, SITE2_LOGIN, SITE2_PASSWORD)
resp = site2.login()
self.assertTrue(resp.ok)
query = ('/api/mo/uni/tn-intersite-testsuite/out-l3out.json?query-target=subtree')
resp = site2.get(query)
self.assertTrue(resp.ok)
# Look for l3extInstP
found = False
for item in resp.json()['imdata']:
if 'l3extInstP' in item:
if item['l3extInstP']['attributes']['name'] == 'intersite-testsuite-app-epg':
found = True
break
if not found:
return False
# Verify that the l3extInstP is providing the contract
found_contract1 = False
found_contract2 = False
for item in resp.json()['imdata']:
if 'fvRsProv' in item:
if item['fvRsProv']['attributes']['tnVzBrCPName'] == 'contract-1':
found_contract1 = True
if item['fvRsProv']['attributes']['tnVzBrCPName'] == 'contract-2':
found_contract2 = True
if not found_contract1 or found_contract2:
return False
# Look for l3extSubnet
query = ('/api/mo/uni/tn-intersite-testsuite/out-l3out'
'/instP-intersite-testsuite-app-epg.json?query-target=subtree')
resp = site2.get(query)
self.assertTrue(resp.ok)
# Look for l3extSubnet
found = False
for item in resp.json()['imdata']:
if 'l3extSubnet' in item:
if item['l3extSubnet']['attributes']['ip'] == ip + '/32':
found = True
break
if not found:
return False
return True
def test_basic_add_endpoint(self):
"""
Test add endpoint
"""
args = self.get_args()
config = self.create_config_file_before()
self.write_config_file(config, args)
collector = execute_tool(args, test_mode=True)
mac = '00:11:22:33:33:33'
ip = '3.4.3.4'
time.sleep(2)
self.assertFalse(self.verify_remote_site_has_entry_before(mac, ip))
time.sleep(2)
self.add_endpoint(mac, ip, 'intersite-testsuite', 'app', 'epg')
time.sleep(2)
self.assertTrue(self.verify_remote_site_has_entry_before(mac, ip))
config = self.create_config_file_after()
self.write_config_file(config, args)
collector.reload_config()
time.sleep(4)
self.assertTrue(self.verify_remote_site_has_entry_after(mac, ip))
def test_basic_add_multiple_endpoint(self):
"""
Test adding multiple endpoints
"""
args = self.get_args()
config = self.create_config_file_before()
self.write_config_file(config, args)
collector = execute_tool(args, test_mode=True)
time.sleep(2)
mac1 = '00:11:22:33:33:34'
ip1 = '3.4.3.5'
self.add_endpoint(mac1, ip1, 'intersite-testsuite', 'app', 'epg')
mac2 = '00:11:22:33:33:35'
ip2 = '3.4.3.6'
self.add_endpoint(mac2, ip2, 'intersite-testsuite', 'app', 'epg')
time.sleep(2)
self.assertTrue(self.verify_remote_site_has_entry_before(mac1, ip1))
self.assertTrue(self.verify_remote_site_has_entry_before(mac2, ip2))
config = self.create_config_file_after()
self.write_config_file(config, args)
collector.reload_config()
time.sleep(2)
self.assertTrue(self.verify_remote_site_has_entry_after(mac1, ip1))
self.assertTrue(self.verify_remote_site_has_entry_after(mac2, ip2))
class TestChangeL3Out(BaseTestCase):
"""
Tests for changing OutsideL3 interfaces
"""
def setup_local_site(self):
"""
Set up the local site
"""
# create Tenant, App, EPG on site 1
site1 = Session(SITE1_URL, SITE1_LOGIN, SITE1_PASSWORD)
resp = site1.login()
self.assertTrue(resp.ok)
tenant = Tenant('intersite-testsuite')
app = AppProfile('app', tenant)
epg = EPG('epg', app)
resp = tenant.push_to_apic(site1)
self.assertTrue(resp.ok)
def setup_remote_site(self):
"""
Set up the remote site
"""
# Create tenant, L3out with contract on site 2
site2 = Session(SITE2_URL, SITE2_LOGIN, SITE2_PASSWORD)
resp = site2.login()
self.assertTrue(resp.ok)
tenant = Tenant('intersite-testsuite')
l3out1 = OutsideL3('l3out1', tenant)
l3out2 = OutsideL3('l3out2', tenant)
resp = tenant.push_to_apic(site2)
self.assertTrue(resp.ok)
@staticmethod
def create_export_policy(l3out_name):
"""
Create the export policy
:param l3out_name: String containing the OutsideL3 name
:return: Dictionary containing the export policy
"""
export_policy = {
"export": {
"tenant": "intersite-testsuite",
"app": "app",
"epg": "epg",
"remote_epg": "intersite-testsuite-app-epg",
"remote_sites": [
{
"site": {
"name": "Site2",
"interfaces": [
{
"l3out": {
"name": l3out_name,
"tenant": "intersite-testsuite"
}
}
]
}
}
]
}
}
return export_policy
def create_config_file(self, l3out_name):
"""
Create the configuration
:param l3out_name: String containing the OutsideL3 name
:return: Dictionary containing the configuration
"""
config = self.create_site_config()
export_policy = self.create_export_policy(l3out_name)
config['config'].append(export_policy)
return config
def test_basic_add_endpoint(self):
"""
Basic test for adding endpoint
"""
args = self.get_args()
config = self.create_config_file('l3out1')
self.write_config_file(config, args)
collector = execute_tool(args, test_mode=True)
mac = '00:11:22:33:33:33'
ip = '3.4.3.4'
time.sleep(2)
self.assertFalse(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite',
'l3out1', 'intersite-testsuite-app-epg'))
time.sleep(2)
self.add_endpoint(mac, ip, 'intersite-testsuite', 'app', 'epg')
time.sleep(2)
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite',
'l3out1', 'intersite-testsuite-app-epg'))
self.assertTrue(self.verify_remote_site_has_policy('intersite-testsuite',
'l3out1', 'intersite-testsuite-app-epg'))
config = self.create_config_file('l3out2')
self.write_config_file(config, args)
collector.reload_config()
time.sleep(4)
self.assertFalse(self.verify_remote_site_has_policy('intersite-testsuite',
'l3out1', 'intersite-testsuite-app-epg'))
self.assertTrue(self.verify_remote_site_has_policy('intersite-testsuite',
'l3out2', 'intersite-testsuite-app-epg'))
self.assertFalse(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite',
'l3out1', 'intersite-testsuite-app-epg'))
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite',
'l3out2', 'intersite-testsuite-app-epg'))
def test_basic_add_endpoint_multiple_l3out(self):
"""
Test adding endpoint with multiple OutsideL3 interfaces
"""
args = self.get_args()
config = self.create_config_file('l3out1')
for policy in config['config']:
if 'export' in policy:
for site_policy in policy['export']['remote_sites']:
interface_policy = {"l3out": {"name": "l3out2",
"tenant": "intersite-testsuite"}}
site_policy['site']['interfaces'].append(interface_policy)
policy['export']['remote_sites'].append(site_policy)
self.write_config_file(config, args)
collector = execute_tool(args, test_mode=True)
mac = '00:11:22:33:33:33'
ip = '3.4.3.4'
time.sleep(2)
self.assertFalse(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite',
'l3out1', 'intersite-testsuite-app-epg'))
self.assertFalse(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite',
'l3out2', 'intersite-testsuite-app-epg'))
time.sleep(2)
self.add_endpoint(mac, ip, 'intersite-testsuite', 'app', 'epg')
time.sleep(2)
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite',
'l3out1', 'intersite-testsuite-app-epg'))
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite',
'l3out2', 'intersite-testsuite-app-epg'))
self.assertTrue(self.verify_remote_site_has_policy('intersite-testsuite', 'l3out1',
'intersite-testsuite-app-epg'))
self.assertTrue(self.verify_remote_site_has_policy('intersite-testsuite', 'l3out2',
'intersite-testsuite-app-epg'))
config = self.create_config_file('l3out2')
self.write_config_file(config, args)
collector.reload_config()
time.sleep(4)
self.assertFalse(self.verify_remote_site_has_policy('intersite-testsuite',
'l3out1', 'intersite-testsuite-app-epg'))
self.assertTrue(self.verify_remote_site_has_policy('intersite-testsuite',
'l3out2', 'intersite-testsuite-app-epg'))
self.assertFalse(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite',
'l3out1', 'intersite-testsuite-app-epg'))
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite',
'l3out2', 'intersite-testsuite-app-epg'))
def test_basic_add_multiple_endpoint(self):
"""
Test adding multiple endopoints
"""
args = self.get_args()
config = self.create_config_file('l3out1')
self.write_config_file(config, args)
collector = execute_tool(args, test_mode=True)
time.sleep(2)
mac1 = '00:11:22:33:33:34'
ip1 = '3.4.3.5'
self.add_endpoint(mac1, ip1, 'intersite-testsuite', 'app', 'epg')
mac2 = '00:11:22:33:33:35'
ip2 = '3.4.3.6'
self.add_endpoint(mac2, ip2, 'intersite-testsuite', 'app', 'epg')
time.sleep(2)
self.assertTrue(self.verify_remote_site_has_entry(mac1, ip1, 'intersite-testsuite',
'l3out1', 'intersite-testsuite-app-epg'))
self.assertTrue(self.verify_remote_site_has_entry(mac2, ip2, 'intersite-testsuite',
'l3out1', 'intersite-testsuite-app-epg'))
config = self.create_config_file('l3out2')
self.write_config_file(config, args)
collector.reload_config()
time.sleep(2)
self.assertTrue(self.verify_remote_site_has_entry(mac1, ip1, 'intersite-testsuite',
'l3out2', 'intersite-testsuite-app-epg'))
self.assertTrue(self.verify_remote_site_has_entry(mac2, ip2, 'intersite-testsuite',
'l3out2', 'intersite-testsuite-app-epg'))
# test basic install of a single EPG and 1 endpoint being pushed to other site
# test remove EPG from policy and that
class TestDuplicates(BaseTestCase):
"""
Test duplicate existing entry on the remote site
"""
def create_config_file(self):
"""
Create the configuration file
:return: dictionary containing the configuration
"""
config = self.create_site_config()
export_policy = {
"export": {
"tenant": "intersite-testsuite-local",
"app": "app",
"epg": "epg",
"remote_epg": "intersite-testsuite-app-epg",
"remote_sites": [
{
"site": {
"name": "Site2",
"interfaces": [
{
"l3out": {
"name": "l3out",
"tenant": "intersite-testsuite-remote"
}
}
]
}
}
]
}
}
config['config'].append(export_policy)
return config
def setup_local_site(self):
"""
Set up the local site
"""
site1 = Session(SITE1_URL, SITE1_LOGIN, SITE1_PASSWORD)
resp = site1.login()
self.assertTrue(resp.ok)
tenant = Tenant('intersite-testsuite-local')
app = AppProfile('app', tenant)
epg = EPG('epg', app)
resp = tenant.push_to_apic(site1)
self.assertTrue(resp.ok)
def setup_remote_site(self):
"""
Set up the remote site
"""
# Create tenant, L3out with contract on site 2
site2 = Session(SITE2_URL, SITE2_LOGIN, SITE2_PASSWORD)
resp = site2.login()
self.assertTrue(resp.ok)
tenant = Tenant('intersite-testsuite-remote')
l3out = OutsideL3('l3out', tenant)
epg = OutsideEPG('intersite-testsuite-app-epg', l3out)
other_epg = OutsideEPG('other', l3out)
resp = tenant.push_to_apic(site2)
self.assertTrue(resp.ok)
def teardown_local_site(self):
"""
Tear down the local site
"""
site1 = Session(SITE1_URL, SITE1_LOGIN, SITE1_PASSWORD)
resp = site1.login()
self.assertTrue(resp.ok)
tenant = Tenant('intersite-testsuite-local')
tenant.mark_as_deleted()
resp = tenant.push_to_apic(site1)
self.assertTrue(resp.ok)
def teardown_remote_site(self):
"""
Tear down the remote site
"""
site2 = Session(SITE2_URL, SITE2_LOGIN, SITE2_PASSWORD)
resp = site2.login()
self.assertTrue(resp.ok)
tenant = Tenant('intersite-testsuite-remote')
tenant.mark_as_deleted()
resp = tenant.push_to_apic(site2)
self.assertTrue(resp.ok)
def add_remote_duplicate_entry(self, ip):
"""
Add a remote entry
:param ip: String containing the IP address
:return: None
"""
site2 = Session(SITE2_URL, SITE2_LOGIN, SITE2_PASSWORD)
resp = site2.login()
self.assertTrue(resp.ok)
tenant = Tenant('intersite-testsuite-remote')
l3out = OutsideL3('l3out', tenant)
other_epg = OutsideEPG('other', l3out)
subnet = OutsideNetwork(ip, other_epg)
subnet.ip = ip + '/32'
resp = tenant.push_to_apic(site2)
self.assertTrue(resp.ok)
def test_basic_duplicate(self):
"""
Test a basic duplicate entry scenario. An existing entry exists on the remote site but on
a different OutsideEPG on the same OutsideL3.
"""
args = self.get_args()
config = self.create_config_file()
self.write_config_file(config, args)
execute_tool(args, test_mode=True)
mac = '00:11:22:33:33:33'
ip = '3.4.3.4'
self.assertFalse(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite-remote', 'l3out', 'intersite-testsuite-app-epg'))
self.add_remote_duplicate_entry(ip)
time.sleep(2)
self.add_endpoint(mac, ip, 'intersite-testsuite-local', 'app', 'epg')
time.sleep(2)
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite-remote', 'l3out', 'intersite-testsuite-app-epg'))
def test_basic_multiple_duplicate(self):
"""
Test a basic multiple duplicate entry scenario.
"""
args = self.get_args()
config = self.create_config_file()
self.write_config_file(config, args)
execute_tool(args, test_mode=True)
for i in range(0, 5):
mac = '00:11:22:33:33:3' + str(i)
ip = '3.4.3.' + str(i)
self.assertFalse(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite-remote',
'l3out', 'intersite-testsuite-app-epg'))
self.add_remote_duplicate_entry(ip)
time.sleep(2)
for i in range(0, 5):
mac = '00:11:22:33:33:3' + str(i)
ip = '3.4.3.' + str(i)
self.add_endpoint(mac, ip, 'intersite-testsuite-local', 'app', 'epg')
time.sleep(2)
for i in range(0, 5):
mac = '00:11:22:33:33:3' + str(i)
ip = '3.4.3.' + str(i)
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite-remote',
'l3out', 'intersite-testsuite-app-epg'))
def test_basic_partial_duplicate(self):
"""
Test a basic multiple duplicate entry scenario where some of the entries in the set being added are duplicate.
"""
args = self.get_args()
config = self.create_config_file()
self.write_config_file(config, args)
execute_tool(args, test_mode=True)
for i in range(0, 7):
mac = '00:11:22:33:33:3' + str(i)
ip = '3.4.3.' + str(i)
self.assertFalse(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite-remote',
'l3out', 'intersite-testsuite-app-epg'))
self.add_remote_duplicate_entry(ip)
time.sleep(2)
for i in range(4, 9):
mac = '00:11:22:33:33:3' + str(i)
ip = '3.4.3.' + str(i)
self.add_endpoint(mac, ip, 'intersite-testsuite-local', 'app', 'epg')
time.sleep(2)
for i in range(4, 9):
mac = '00:11:22:33:33:3' + str(i)
ip = '3.4.3.' + str(i)
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite-remote',
'l3out', 'intersite-testsuite-app-epg'))
class SetupDuplicateTests(BaseTestCase):
"""
Base class to setup the duplicate tests
"""
def create_config_file(self):
"""
Create the configuration file
:return: dictionary containing the configuration
"""
config = self.create_site_config()
export_policy = {
"export": {
"tenant": "intersite-testsuite-local",
"app": "app",
"epg": "epg",
"remote_epg": "intersite-testsuite-app-epg",
"remote_sites": [
{
"site": {
"name": "Site2",
"interfaces": [
{
"l3out": {
"name": "l3out1",
"tenant": "intersite-testsuite-remote"
}
},
{
"l3out": {
"name": "l3out2",
"tenant": "intersite-testsuite-remote"
}
}
]
}
}
]
}
}
config['config'].append(export_policy)
return config
def setup_local_site(self):
"""
Set up the local site
"""
site1 = Session(SITE1_URL, SITE1_LOGIN, SITE1_PASSWORD)
resp = site1.login()
self.assertTrue(resp.ok)
tenant = Tenant('intersite-testsuite-local')
app = AppProfile('app', tenant)
epg = EPG('epg', app)
resp = tenant.push_to_apic(site1)
self.assertTrue(resp.ok)
def setup_remote_site(self):
"""
Set up the remote site
"""
# Create tenant, L3out with contract on site 2
site2 = Session(SITE2_URL, SITE2_LOGIN, SITE2_PASSWORD)
resp = site2.login()
self.assertTrue(resp.ok)
tenant = Tenant('intersite-testsuite-remote')
l3out1 = OutsideL3('l3out1', tenant)
l3out2 = OutsideL3('l3out2', tenant)
epg1 = OutsideEPG('intersite-testsuite-app-epg', l3out1)
other_epg = OutsideEPG('other', l3out1)
epg2 = OutsideEPG('intersite-testsuite-app-epg', l3out2)
resp = tenant.push_to_apic(site2)
self.assertTrue(resp.ok)
def teardown_local_site(self):
"""
Tear down the local site
"""
site1 = Session(SITE1_URL, SITE1_LOGIN, SITE1_PASSWORD)
resp = site1.login()
self.assertTrue(resp.ok)
tenant = Tenant('intersite-testsuite-local')
tenant.mark_as_deleted()
resp = tenant.push_to_apic(site1)
self.assertTrue(resp.ok)
def teardown_remote_site(self):
"""
Tear down the remote site
"""
site2 = Session(SITE2_URL, SITE2_LOGIN, SITE2_PASSWORD)
resp = site2.login()
self.assertTrue(resp.ok)
tenant = Tenant('intersite-testsuite-remote')
tenant.mark_as_deleted()
resp = tenant.push_to_apic(site2)
self.assertTrue(resp.ok)
class TestDuplicatesTwoL3Outs(SetupDuplicateTests):
"""
Test duplicate entries with 2 OutsideL3 interfaces on the remote site
"""
def add_remote_duplicate_entry(self, ip):
"""
Add a remote entry
:param ip: String containing the IP address
:return: None
"""
site2 = Session(SITE2_URL, SITE2_LOGIN, SITE2_PASSWORD)
resp = site2.login()
self.assertTrue(resp.ok)
tenant = Tenant('intersite-testsuite-remote')
l3out = OutsideL3('l3out1', tenant)
other_epg = OutsideEPG('other', l3out)
subnet = OutsideNetwork(ip, other_epg)
subnet.ip = ip + '/32'
resp = tenant.push_to_apic(site2)
self.assertTrue(resp.ok)
def test_basic_duplicate(self):
"""
Test a basic duplicate entry scenario. An existing entry exists on the remote site but on
a different OutsideEPG on the same OutsideL3.
"""
args = self.get_args()
config = self.create_config_file()
self.write_config_file(config, args)
execute_tool(args, test_mode=True)
mac = '00:11:22:33:33:33'
ip = '3.4.3.4'
self.assertFalse(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite-remote',
'l3out1', 'intersite-testsuite-app-epg'))
self.add_remote_duplicate_entry(ip)
time.sleep(2)
self.add_endpoint(mac, ip, 'intersite-testsuite-local', 'app', 'epg')
mac2 = '00:11:22:33:33:44'
ip2 = '3.4.3.44'
self.add_endpoint(mac2, ip2, 'intersite-testsuite-local', 'app', 'epg')
time.sleep(2)
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite-remote',
'l3out1', 'intersite-testsuite-app-epg'))
self.assertTrue(self.verify_remote_site_has_entry(mac2, ip2, 'intersite-testsuite-remote',
'l3out1', 'intersite-testsuite-app-epg'))
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite-remote',
'l3out2', 'intersite-testsuite-app-epg'))
self.assertTrue(self.verify_remote_site_has_entry(mac2, ip2, 'intersite-testsuite-remote',
'l3out2', 'intersite-testsuite-app-epg'))
def test_basic_multiple_duplicate(self):
"""
Test a basic multiple duplicate entry scenario.
"""
args = self.get_args()
config = self.create_config_file()
self.write_config_file(config, args)
execute_tool(args, test_mode=True)
for i in range(0, 5):
mac = '00:11:22:33:33:3' + str(i)
ip = '3.4.3.' + str(i)
self.assertFalse(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite-remote',
'l3out1', 'intersite-testsuite-app-epg'))
self.assertFalse(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite-remote',
'l3out2', 'intersite-testsuite-app-epg'))
self.add_remote_duplicate_entry(ip)
time.sleep(2)
for i in range(0, 5):
mac = '00:11:22:33:33:3' + str(i)
ip = '3.4.3.' + str(i)
self.add_endpoint(mac, ip, 'intersite-testsuite-local', 'app', 'epg')
time.sleep(2)
for i in range(0, 5):
mac = '00:11:22:33:33:3' + str(i)
ip = '3.4.3.' + str(i)
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite-remote',
'l3out1', 'intersite-testsuite-app-epg'))
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite-remote',
'l3out2', 'intersite-testsuite-app-epg'))
def test_basic_partial_duplicate(self):
"""
Test a basic multiple duplicate entry scenario where some of the entries in the set being added are duplicate.
"""
args = self.get_args()
config = self.create_config_file()
self.write_config_file(config, args)
execute_tool(args, test_mode=True)
for i in range(0, 7):
mac = '00:11:22:33:33:3' + str(i)
ip = '3.4.3.' + str(i)
self.assertFalse(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite-remote',
'l3out1', 'intersite-testsuite-app-epg'))
self.assertFalse(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite-remote',
'l3out2', 'intersite-testsuite-app-epg'))
self.add_remote_duplicate_entry(ip)
time.sleep(2)
for i in range(4, 9):
mac = '00:11:22:33:33:3' + str(i)
ip = '3.4.3.' + str(i)
self.add_endpoint(mac, ip, 'intersite-testsuite-local', 'app', 'epg')
time.sleep(2)
for i in range(4, 9):
mac = '00:11:22:33:33:3' + str(i)
ip = '3.4.3.' + str(i)
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite-remote',
'l3out1', 'intersite-testsuite-app-epg'))
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite-remote',
'l3out2', 'intersite-testsuite-app-epg'))
class TestDeletions(BaseEndpointTestCase):
"""
Tests for deletion of stale entries
"""
def test_basic_deletion(self):
"""
Test basic deletion of a stale entry on tool startup
:return:
"""
args = self.get_args()
config_filename = 'testsuite_cfg.json'
args.config = config_filename
config = self.create_config_file()
config_file = open(config_filename, 'w')
config_file.write(str(json.dumps(config)))
config_file.close()
# Create the "stale" entry on the remote site
mac = '00:11:22:33:33:33'
ip = '3.4.3.4'
site2 = Session(SITE2_URL, SITE2_LOGIN, SITE2_PASSWORD)
resp = site2.login()
self.assertTrue(resp.ok)
tag = IntersiteTag('intersite-testsuite', 'app', 'epg', 'Site1')
remote_tenant = Tenant('intersite-testsuite')
remote_l3out = OutsideL3('l3out', remote_tenant)
remote_epg = OutsideEPG('intersite-testsuite-app-epg', remote_l3out)
remote_ep = OutsideNetwork(ip, remote_epg)
remote_ep.ip = ip + '/32'
remote_tenant.push_to_apic(site2)
time.sleep(2)
self.assertTrue(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite',
'l3out', 'intersite-testsuite-app-epg'))
execute_tool(args, test_mode=True)
time.sleep(2)
self.assertFalse(self.verify_remote_site_has_entry(mac, ip, 'intersite-testsuite',
'l3out', 'intersite-testsuite-app-epg'))
class TestCli(BaseTestCase):
"""
Tests for the CLI
"""
def setup_remote_site(self):
"""
Set up the remote site.
"""
pass
def setup_local_site(self):
"""
Set up the local site.
"""
pass
def teardown_local_site(self):
"""
Teardown the local site configuration
"""
pass
def teardown_remote_site(self):
"""
Teardown the remote site configuration
"""
pass
def _create_commandline(self):
"""
Internal function to create a CommandLine instance
"""
args = self.get_args()
self.write_config_file(self.create_site_config(), args)
cmdline = CommandLine(execute_tool(args, test_mode=True))
self.assertTrue(isinstance(cmdline, CommandLine))
return cmdline
def _test_show_cmd(self, cmd, output):
"""
Internal common function for checking show commands
:param cmd: String containing show command keyword
:param output: List of strings to compare with the command output
"""
cmdline = self._create_commandline()
temp = sys.stdout
fake_out = FakeStdio()
sys.stdout = fake_out
cmdline.do_show(cmd)
sys.stdout = temp
self.assertTrue(fake_out.verify_output(output))
def test_show_debug(self):
"""
Test show debug command
"""
self._test_show_cmd('debug', ['Debug level currently set to: CRITICAL', '\n'])
def test_show_configfile(self):
"""
Test show configfile command
"""
self._test_show_cmd('configfile', ['Configuration file is set to: testsuite_cfg.json', '\n'])
def test_show_config(self):
"""
Test show config command
"""
self._test_show_cmd('config', [json.dumps(self.create_site_config(), indent=4, separators=(',', ':')), '\n'])
def test_show_sites(self):
"""
Test show sites command
"""
self._test_show_cmd('sites', [u'Site1', ' ', ':', ' ', 'Connected', '\n',
u'Site2', ' ', ':', ' ', 'Connected', '\n'])
def test_show_stats(self):
"""
Test show stats command
"""
self._test_show_cmd('stats', ['Endpoint addition events: 0', '\n',
'Endpoint deletion events: 0', '\n'])
def main_test():
"""
Main execution routine. Create the test suites and run.
"""
full = unittest.TestSuite()
full.addTest(unittest.makeSuite(TestToolOptions))
full.addTest(unittest.makeSuite(TestBadConfiguration))
full.addTest(unittest.makeSuite(TestBasicEndpoints))
full.addTest(unittest.makeSuite(TestMultipleEPG))
full.addTest(unittest.makeSuite(TestBasicExistingEndpoints))
full.addTest(unittest.makeSuite(TestBasicExistingEndpointsAddPolicyLater))
full.addTest(unittest.makeSuite(TestExportPolicyRemoval))
full.addTest(unittest.makeSuite(TestBasicEndpointsWithProvidedContract))
full.addTest(unittest.makeSuite(TestBasicEndpointsWithConsumedContract))
full.addTest(unittest.makeSuite(TestBasicEndpointsWithConsumedContractInterface))
full.addTest(unittest.makeSuite(TestBasicEndpointsWithTaboo))
full.addTest(unittest.makeSuite(TestBasicEndpointMove))
full.addTest(unittest.makeSuite(TestPolicyChangeProvidedContract))
full.addTest(unittest.makeSuite(TestChangeL3Out))
full.addTest(unittest.makeSuite(TestDuplicates))
full.addTest(unittest.makeSuite(TestDuplicatesTwoL3Outs))
full.addTest(unittest.makeSuite(TestDeletions))
full.addTest(unittest.makeSuite(TestCli))
full.addTest(unittest.makeSuite(TestBasicEndpointsWithMultipleRemoteSites))
full.addTest(unittest.makeSuite(TestBasicEndpointsWithMultipleRemoteSitesButOnlyExportToOne))
full.addTest(unittest.makeSuite(TestBasicEndpointsWithThreeRemoteSites))
unittest.main()
if __name__ == '__main__':
try:
main_test()
except KeyboardInterrupt:
pass
| 38.430521
| 138
| 0.52422
| 14,003
| 140,771
| 5.085767
| 0.036992
| 0.113738
| 0.063988
| 0.057628
| 0.84825
| 0.831063
| 0.810281
| 0.798514
| 0.78106
| 0.763606
| 0
| 0.02532
| 0.373514
| 140,771
| 3,662
| 139
| 38.441016
| 0.7822
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| 0.728501
| 0
| 0.002048
| 0.161722
| 0.060317
| 0
| 0
| 0
| 0
| 0.107289
| 1
| 0.072072
| false
| 0.03317
| 0.006143
| 0
| 0.110565
| 0.000819
| 0
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| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
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| 0
| 0
| 0
|
0
| 7
|
7c37bdf6f75ec9ca42d9532bcd9052dbb3b16b40
| 803
|
py
|
Python
|
tests/test_ping.py
|
libero/search
|
f13c7fe2aa5f3cd1e2f62234995788bed7147b91
|
[
"MIT"
] | null | null | null |
tests/test_ping.py
|
libero/search
|
f13c7fe2aa5f3cd1e2f62234995788bed7147b91
|
[
"MIT"
] | 14
|
2019-01-31T08:34:30.000Z
|
2019-11-21T10:06:13.000Z
|
tests/test_ping.py
|
giorgiosironi/search
|
4a117c88c59627041c2058c9a41b69b01e2f3fcc
|
[
"MIT"
] | 3
|
2019-01-30T10:49:01.000Z
|
2019-06-11T14:42:03.000Z
|
def test_http_1_0_ping_response(client) -> None:
response = client.get('/ping', environ_overrides={'SERVER_PROTOCOL': 'HTTP/1.0'})
assert response.status_code == 200
assert response.content_type == 'text/plain; charset=utf-8'
assert response.data == b'pong'
assert response.headers['Cache-Control'] == 'no-store, must-revalidate'
assert response.headers['Expires'] == '0'
def test_http_1_1_ping_response(client) -> None:
response = client.get('/ping', environ_overrides={'SERVER_PROTOCOL': 'HTTP/1.1'})
assert response.status_code == 200
assert response.content_type == 'text/plain; charset=utf-8'
assert response.data == b'pong'
assert response.headers['Cache-Control'] == 'no-store, must-revalidate'
assert response.headers.get('Expires') is None
| 38.238095
| 85
| 0.704857
| 108
| 803
| 5.074074
| 0.342593
| 0.255474
| 0.153285
| 0.043796
| 0.905109
| 0.905109
| 0.905109
| 0.905109
| 0.905109
| 0.905109
| 0
| 0.024745
| 0.144458
| 803
| 20
| 86
| 40.15
| 0.772926
| 0
| 0
| 0.571429
| 0
| 0
| 0.25593
| 0
| 0
| 0
| 0
| 0
| 0.714286
| 1
| 0.142857
| false
| 0
| 0
| 0
| 0.142857
| 0
| 0
| 0
| 0
| null | 1
| 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
7c415a354fe892db7aeed45962a7bfcf91437986
| 31,191
|
py
|
Python
|
closed/Lenovo/configs/3d-unet/Offline/__init__.py
|
ctuning/inference_results_v1.1
|
d9176eca28fcf6d7a05ccb97994362a76a1eb5ab
|
[
"Apache-2.0"
] | 12
|
2021-09-23T08:05:57.000Z
|
2022-03-21T03:52:11.000Z
|
closed/Lenovo/configs/3d-unet/Offline/__init__.py
|
ctuning/inference_results_v1.1
|
d9176eca28fcf6d7a05ccb97994362a76a1eb5ab
|
[
"Apache-2.0"
] | 11
|
2021-09-23T20:34:06.000Z
|
2022-01-22T07:58:02.000Z
|
closed/Lenovo/configs/3d-unet/Offline/__init__.py
|
ctuning/inference_results_v1.1
|
d9176eca28fcf6d7a05ccb97994362a76a1eb5ab
|
[
"Apache-2.0"
] | 16
|
2021-09-23T20:26:38.000Z
|
2022-03-09T12:59:56.000Z
|
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import sys
sys.path.insert(0, os.getcwd())
from importlib import import_module
from code.common.constants import Benchmark, Scenario
from code.common.system_list import System, Architecture, MIGConfiguration, MIGSlice
from configs.configuration import *
ParentConfig = import_module("configs.3d-unet")
GPUBaseConfig = ParentConfig.GPUBaseConfig
CPUBaseConfig = ParentConfig.CPUBaseConfig
class OfflineGPUBaseConfig(GPUBaseConfig):
scenario = Scenario.Offline
gpu_inference_streams = 1
gpu_copy_streams = 2
class OfflineCPUBaseConfig(CPUBaseConfig):
scenario = Scenario.Offline
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99, PowerSetting.MaxP)
class A100_PCIe_80GBx1(OfflineGPUBaseConfig):
system = System("A100-PCIe-80GB", Architecture.Ampere, 1)
gpu_batch_size = 2
offline_expected_qps = 53
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class A100_PCIe_80GBx1_HighAccuracy(A100_PCIe_80GBx1):
pass
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99, PowerSetting.MaxP)
class A100_PCIe_80GBx1_Triton(A100_PCIe_80GBx1):
use_triton = True
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class A100_PCIe_80GBx1_HighAccuracy_Triton(A100_PCIe_80GBx1_Triton):
pass
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99, PowerSetting.MaxP)
class A100_PCIe_80GBx8(A100_PCIe_80GBx1):
system = System("A100-PCIe-80GB", Architecture.Ampere, 8)
gpu_batch_size = 2
offline_expected_qps = 412
numa_config = "3:0-15&2:16-31&1:32-47&0:48-63&7:64-79&6:80-95&5:96-111&4:112-127"
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class A100_PCIe_80GBx8_HighAccuracy(A100_PCIe_80GBx8):
pass
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99, PowerSetting.MaxP)
class A100_PCIe_80GBx8_Triton(A100_PCIe_80GBx8):
input_dtype = "fp16"
input_format = "dhwc8"
tensor_path = "${PREPROCESSED_DATA_DIR}/brats/brats_npy/fp16_dhwc8"
use_triton = True
output_pinned_memory = False
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class A100_PCIe_80GBx8_HighAccuracy_Triton(A100_PCIe_80GBx8_Triton):
pass
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99, PowerSetting.MaxQ)
class A100_PCIe_80GBx8_MaxQ(A100_PCIe_80GBx8):
gpu_batch_size = 2
offline_expected_qps = 370
power_limit = 175
numa_config = "3:0-7&2:8-15&1:16-23&0:24-31&7:32-39&6:40-47&5:48-55&4:56-63"
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99_9, PowerSetting.MaxQ)
class A100_PCIe_80GBx8_HighAccuracy_MaxQ(A100_PCIe_80GBx8_MaxQ):
pass
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99, PowerSetting.MaxQ)
class A100_PCIe_80GBx8_Triton_MaxQ(A100_PCIe_80GBx8_MaxQ):
use_triton = True
offline_expected_qps = 412
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99_9, PowerSetting.MaxQ)
class A100_PCIe_80GBx8_HighAccuracy_Triton_MaxQ(A100_PCIe_80GBx8_Triton_MaxQ):
pass
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99, PowerSetting.MaxP)
class A100_PCIe_80GB_aarch64x1(OfflineGPUBaseConfig):
system = System("A100-PCIe-80GB", Architecture.Ampere, 1, cpu_arch=CPUArch.aarch64)
gpu_batch_size = 2
offline_expected_qps = 53
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class A100_PCIe_80GB_aarch64x1_HighAccuracy(A100_PCIe_80GB_aarch64x1):
pass
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99, PowerSetting.MaxP)
class A100_PCIe_80GB_aarch64x2(OfflineGPUBaseConfig):
system = System("A100-PCIe-80GB", Architecture.Ampere, 2, cpu_arch=CPUArch.aarch64)
gpu_batch_size = 2
offline_expected_qps = 106
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class A100_PCIe_80GB_aarch64x2_HighAccuracy(A100_PCIe_80GB_aarch64x2):
pass
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99, PowerSetting.MaxP)
class A100_PCIe_80GB_aarch64x4(OfflineGPUBaseConfig):
system = System("A100-PCIe-80GB", Architecture.Ampere, 4, cpu_arch=CPUArch.aarch64)
gpu_batch_size = 2
offline_expected_qps = 210
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class A100_PCIe_80GB_aarch64x4_HighAccuracy(A100_PCIe_80GB_aarch64x4):
pass
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99, PowerSetting.MaxQ)
class A100_PCIe_80GB_aarch64x4_MaxQ(OfflineGPUBaseConfig):
system = System("A100-PCIe-80GB", Architecture.Ampere, 4, cpu_arch=CPUArch.aarch64)
gpu_batch_size = 2
# TODO: Set power_limit properly
power_limit = 200
offline_expected_qps = 185
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99_9, PowerSetting.MaxQ)
class A100_PCIe_80GB_aarch64x4_HighAccuracy_MaxQ(A100_PCIe_80GB_aarch64x4_MaxQ):
pass
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99, PowerSetting.MaxP)
class A100_PCIe_MIG_1x1g5gb(OfflineGPUBaseConfig):
_mig_configuration = MIGConfiguration({0: {MIGSlice(1, 5): 1}})
system = System("A100-PCIe", Architecture.Ampere, 1, mig_conf=_mig_configuration)
input_dtype = "fp16"
input_format = "linear"
tensor_path = "${PREPROCESSED_DATA_DIR}/brats/brats_npy/fp16_linear"
workspace_size = 1073741824
gpu_batch_size = 1
gpu_copy_streams = 1
offline_expected_qps = 7
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class A100_PCIe_MIG_1x1g5gb_HighAccuracy(A100_PCIe_MIG_1x1g5gb):
pass
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99, PowerSetting.MaxP)
class A100_PCIe_MIG_1x1g5gb_Triton(A100_PCIe_MIG_1x1g5gb):
use_triton = True
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class A100_PCIe_MIG_1x1g5gb_HighAccuracy_Triton(A100_PCIe_MIG_1x1g5gb_Triton):
pass
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99, PowerSetting.MaxP)
class A100_PCIex1(OfflineGPUBaseConfig):
system = System("A100-PCIe", Architecture.Ampere, 1)
gpu_batch_size = 2
offline_expected_qps = 53
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class A100_PCIex1_HighAccuracy(A100_PCIex1):
pass
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99, PowerSetting.MaxP)
class A100_PCIex1_Triton(A100_PCIex1):
use_triton = True
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class A100_PCIex1_HighAccuracy_Triton(A100_PCIex1_Triton):
pass
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99, PowerSetting.MaxP)
class A100_PCIex8(A100_PCIex1):
system = System("A100-PCIe", Architecture.Ampere, 8)
gpu_batch_size = 2
offline_expected_qps = 412
numa_config = "3:0-15&2:16-31&1:32-47&0:48-63&7:64-79&6:80-95&5:96-111&4:112-127"
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class A100_PCIex8_HighAccuracy(A100_PCIex8):
pass
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99, PowerSetting.MaxP)
class A100_PCIex8_Triton(A100_PCIex8):
input_dtype = "fp16"
input_format = "dhwc8"
tensor_path = "${PREPROCESSED_DATA_DIR}/brats/brats_npy/fp16_dhwc8"
use_triton = True
output_pinned_memory = False
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class A100_PCIex8_HighAccuracy_Triton(A100_PCIex8_Triton):
pass
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99, PowerSetting.MaxQ)
class A100_PCIex8_MaxQ(A100_PCIex8):
gpu_batch_size = 2
offline_expected_qps = 370
power_limit = 175
numa_config = "3:0-7&2:8-15&1:16-23&0:24-31&7:32-39&6:40-47&5:48-55&4:56-63"
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99_9, PowerSetting.MaxQ)
class A100_PCIex8_HighAccuracy_MaxQ(A100_PCIex8_MaxQ):
pass
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99, PowerSetting.MaxQ)
class A100_PCIex8_Triton_MaxQ(A100_PCIex8_MaxQ):
gpu_batch_size = 2
offline_expected_qps = 412
use_triton = True
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99_9, PowerSetting.MaxQ)
class A100_PCIex8_HighAccuracy_Triton_MaxQ(A100_PCIex8_Triton_MaxQ):
pass
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99, PowerSetting.MaxP)
class A100_SXM_80GB_MIG_1x1g10gb(OfflineGPUBaseConfig):
_mig_configuration = MIGConfiguration({0: {MIGSlice(1, 10): 1}})
system = System("A100-SXM-80GB", Architecture.Ampere, 1, mig_conf=_mig_configuration)
input_dtype = "fp16"
input_format = "linear"
tensor_path = "${PREPROCESSED_DATA_DIR}/brats/brats_npy/fp16_linear"
workspace_size = 1073741824
gpu_batch_size = 1
gpu_copy_streams = 1
offline_expected_qps = 7
start_from_device = True
@ConfigRegistry.register(HarnessType.HeteroMIG, AccuracyTarget.k_99, PowerSetting.MaxP)
class A100_SXM_80GB_MIG_1x1g10gb_Hetero(A100_SXM_80GB_MIG_1x1g10gb):
pass
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class A100_SXM_80GB_MIG_1x1g10gb_HighAccuracy(A100_SXM_80GB_MIG_1x1g10gb):
pass
@ConfigRegistry.register(HarnessType.HeteroMIG, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class A100_SXM_80GB_MIG_1x1g10gb_Hetero_HighAccuracy(A100_SXM_80GB_MIG_1x1g10gb_HighAccuracy):
pass
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99, PowerSetting.MaxP)
class A100_SXM_80GB_MIG_1x1g10gb_Triton(A100_SXM_80GB_MIG_1x1g10gb):
use_triton = True
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class A100_SXM_80GB_MIG_1x1g10gb_HighAccuracy_Triton(A100_SXM_80GB_MIG_1x1g10gb_Triton):
pass
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99, PowerSetting.MaxP)
class A100_SXM_80GB_MIG_56x1g10gb(A100_SXM_80GB_MIG_1x1g10gb):
_mig_configuration = MIGConfiguration({
0: {MIGSlice(1, 10): 7},
1: {MIGSlice(1, 10): 7},
2: {MIGSlice(1, 10): 7},
3: {MIGSlice(1, 10): 7},
4: {MIGSlice(1, 10): 7},
5: {MIGSlice(1, 10): 7},
6: {MIGSlice(1, 10): 7},
7: {MIGSlice(1, 10): 7},
})
system = System("A100-SXM-80GB", Architecture.Ampere, 8, mig_conf=_mig_configuration)
gpu_batch_size = 1
gpu_copy_streams = 1
offline_expected_qps = 392
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class A100_SXM_80GB_MIG_56x1g10gb_HighAccuracy(A100_SXM_80GB_MIG_56x1g10gb):
pass
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99, PowerSetting.MaxP)
class A100_SXM_80GB_MIG_56x1g10gb_Triton(A100_SXM_80GB_MIG_56x1g10gb):
use_triton = True
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class A100_SXM_80GB_MIG_56x1g10gb_HighAccuracy_Triton(A100_SXM_80GB_MIG_56x1g10gb_Triton):
pass
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99, PowerSetting.MaxP)
class A100_SXM_80GBx1(OfflineGPUBaseConfig):
system = System("A100-SXM-80GB", Architecture.Ampere, 1)
gpu_batch_size = 2
offline_expected_qps = 60
start_from_device = True
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class A100_SXM_80GBx1_HighAccuracy(A100_SXM_80GBx1):
pass
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99, PowerSetting.MaxP)
class A100_SXM_80GBx1_Triton(A100_SXM_80GBx1):
instance_group_count = 1
use_triton = True
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class A100_SXM_80GBx1_HighAccuracy_Triton(A100_SXM_80GBx1_Triton):
pass
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99, PowerSetting.MaxP)
class A100_SXM_80GBx4(OfflineGPUBaseConfig):
_system_alias = "DGX Station A100 - Red October"
_notes = "This should not inherit from A100_SXM_80GB (DGX-A100), and cannot use start_from_device"
system = System("A100-SXM-80GB", Architecture.Ampere, 4)
gpu_batch_size = 2
offline_expected_qps = 220
numa_config = "3:0-15,64-79&2:16-31,80-95&1:32-47,96-111&0:48-63,112-127"
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class A100_SXM_80GBx4_HighAccuracy(A100_SXM_80GBx4):
pass
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99, PowerSetting.MaxP)
class A100_SXM_80GBx4_Triton(A100_SXM_80GBx4):
instance_group_count = 1
use_triton = True
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class A100_SXM_80GBx4_HighAccuracy_Triton(A100_SXM_80GBx4_Triton):
pass
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99, PowerSetting.MaxQ)
class A100_SXM_80GBx4_MaxQ(A100_SXM_80GBx4):
gpu_batch_size = 2
offline_expected_qps = 220
power_limit = 225
numa_config = "3:0-7,32-39&2:8-15,40-47&1:16-23,48-55&0:24-31,56-63"
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99_9, PowerSetting.MaxQ)
class A100_SXM_80GBx4_HighAccuracy_MaxQ(A100_SXM_80GBx4_MaxQ):
pass
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99, PowerSetting.MaxQ)
class A100_SXM_80GBx4_Triton_MaxQ(A100_SXM_80GBx4_MaxQ):
numa_config = "" # TODO: Artifact from old configs. Should Red October Triton use numa_config?
instance_group_count = 1
use_triton = True
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99_9, PowerSetting.MaxQ)
class A100_SXM_80GBx4_HighAccuracy_Triton_MaxQ(A100_SXM_80GBx4_Triton_MaxQ):
pass
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99, PowerSetting.MaxP)
class A100_SXM_80GBx8(A100_SXM_80GBx1):
system = System("A100-SXM-80GB", Architecture.Ampere, 8)
gpu_batch_size = 2
offline_expected_qps = 480
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class A100_SXM_80GBx8_HighAccuracy(A100_SXM_80GBx8):
pass
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99, PowerSetting.MaxP)
class A100_SXM_80GBx8_Triton(A100_SXM_80GBx8):
use_graphs = True
instance_group_count = 4
use_triton = True
output_pinned_memory = False
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class A100_SXM_80GBx8_HighAccuracy_Triton(A100_SXM_80GBx8_Triton):
pass
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99, PowerSetting.MaxQ)
class A100_SXM_80GBx8_MaxQ(A100_SXM_80GBx8):
gpu_batch_size = 2
offline_expected_qps = 480
power_limit = 225
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99_9, PowerSetting.MaxQ)
class A100_SXM_80GBx8_HighAccuracy_MaxQ(A100_SXM_80GBx8_MaxQ):
pass
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99, PowerSetting.MaxQ)
class A100_SXM_80GBx8_Triton_MaxQ(A100_SXM_80GBx8_MaxQ):
instance_group_count = 2
use_triton = True
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99_9, PowerSetting.MaxQ)
class A100_SXM_80GBx8_HighAccuracy_Triton_MaxQ(A100_SXM_80GBx8_Triton_MaxQ):
pass
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99, PowerSetting.MaxP)
class A100_SXM4_40GB_MIG_1x1g5gb(OfflineGPUBaseConfig):
_mig_configuration = MIGConfiguration({0: {MIGSlice(1, 5): 1}})
system = System("A100-SXM4-40GB", Architecture.Ampere, 1, mig_conf=_mig_configuration)
input_dtype = "fp16"
input_format = "linear"
tensor_path = "${PREPROCESSED_DATA_DIR}/brats/brats_npy/fp16_linear"
workspace_size = 1073741824
gpu_batch_size = 1
gpu_copy_streams = 1
offline_expected_qps = 7
start_from_device = True
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class A100_SXM4_40GB_MIG_1x1g5gb_HighAccuracy(A100_SXM4_40GB_MIG_1x1g5gb):
pass
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99, PowerSetting.MaxP)
class A100_SXM4_40GB_MIG_1x1g5gb_Triton(A100_SXM4_40GB_MIG_1x1g5gb):
use_triton = True
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class A100_SXM4_40GB_MIG_1x1g5gb_HighAccuracy_Triton(A100_SXM4_40GB_MIG_1x1g5gb_Triton):
pass
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99, PowerSetting.MaxP)
class A100_SXM4_40GBx1(OfflineGPUBaseConfig):
system = System("A100-SXM4-40GB", Architecture.Ampere, 1)
gpu_batch_size = 2
offline_expected_qps = 60
start_from_device = True
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class A100_SXM4_40GBx1_HighAccuracy(A100_SXM4_40GBx1):
pass
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99, PowerSetting.MaxP)
class A100_SXM4_40GBx1_Triton(A100_SXM4_40GBx1):
instance_group_count = 1
use_triton = True
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class A100_SXM4_40GBx1_HighAccuracy_Triton(A100_SXM4_40GBx1_Triton):
pass
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99, PowerSetting.MaxP)
class A100_SXM4_40GBx8(A100_SXM4_40GBx1):
system = System("A100-SXM4-40GB", Architecture.Ampere, 8)
offline_expected_qps = 480
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class A100_SXM4_40GBx8_HighAccuracy(A100_SXM4_40GBx8):
pass
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99, PowerSetting.MaxP)
class A100_SXM4_40GBx8_Triton(A100_SXM4_40GBx8):
instance_group_count = 4
use_triton = True
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class A100_SXM4_40GBx8_HighAccuracy_Triton(A100_SXM4_40GBx8_Triton):
pass
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99, PowerSetting.MaxQ)
class A100_SXM4_40GBx8_MaxQ(A100_SXM4_40GBx8):
power_limit = 225
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99_9, PowerSetting.MaxQ)
class A100_SXM4_40GBx8_HighAccuracy_MaxQ(A100_SXM4_40GBx8_MaxQ):
pass
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99, PowerSetting.MaxQ)
class A100_SXM4_40GBx8_Triton_MaxQ(A100_SXM4_40GBx8_MaxQ):
instance_group_count = 2
use_triton = True
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99_9, PowerSetting.MaxQ)
class A100_SXM4_40GBx8_HighAccuracy_Triton_MaxQ(A100_SXM4_40GBx8_Triton_MaxQ):
pass
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99, PowerSetting.MaxP)
class A10x1(OfflineGPUBaseConfig):
system = System("A10", Architecture.Ampere, 1)
gpu_batch_size = 2
offline_expected_qps = 22
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class A10x1_HighAccuracy(A10x1):
pass
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99, PowerSetting.MaxP)
class A10x1_Triton(A10x1):
gpu_batch_size = 2
offline_expected_qps = 20
use_triton = True
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class A10x1_HighAccuracy_Triton(A10x1_Triton):
offline_expected_qps = 22
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99, PowerSetting.MaxP)
class A10x8(A10x1):
system = System("A10", Architecture.Ampere, 8)
offline_expected_qps = 170
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class A10x8_HighAccuracy(A10x8):
pass
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99, PowerSetting.MaxP)
class A10x8_Triton(A10x8):
gpu_batch_size = 2
offline_expected_qps = 160.0
use_triton = True
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class A10x8_HighAccuracy_Triton(A10x8_Triton):
offline_expected_qps = 170
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99, PowerSetting.MaxP)
class A30_MIG_1x1g6gb(OfflineGPUBaseConfig):
_mig_configuration = MIGConfiguration({0: {MIGSlice(1, 6): 1}})
system = System("A30", Architecture.Ampere, 1, mig_conf=_mig_configuration)
input_dtype = "fp16"
input_format = "linear"
tensor_path = "${PREPROCESSED_DATA_DIR}/brats/brats_npy/fp16_linear"
workspace_size = 805306368
gpu_batch_size = 1
gpu_copy_streams = 1
offline_expected_qps = 7.55
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class A30_MIG_1x1g6gb_HighAccuracy(A30_MIG_1x1g6gb):
pass
@ConfigRegistry.register(HarnessType.HeteroMIG, AccuracyTarget.k_99, PowerSetting.MaxP)
class A30_MIG_1x1g6gb_Hetero(A30_MIG_1x1g6gb):
offline_expected_qps = 6.847
@ConfigRegistry.register(HarnessType.HeteroMIG, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class A30_MIG_1x1g6gb_Hetero_HighAccuracy(A30_MIG_1x1g6gb_Hetero):
pass
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99, PowerSetting.MaxP)
class A30_MIG_1x1g6gb_Triton(A30_MIG_1x1g6gb):
use_triton = True
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class A30_MIG_1x1g6gb_HighAccuracy_Triton(A30_MIG_1x1g6gb_Triton):
pass
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99, PowerSetting.MaxP)
class A30_MIG_32x1g6gb(OfflineGPUBaseConfig):
_mig_configuration = MIGConfiguration({
0: {MIGSlice(1, 6): 4},
1: {MIGSlice(1, 6): 4},
2: {MIGSlice(1, 6): 4},
3: {MIGSlice(1, 6): 4},
4: {MIGSlice(1, 6): 4},
5: {MIGSlice(1, 6): 4},
6: {MIGSlice(1, 6): 4},
7: {MIGSlice(1, 6): 4},
})
system = System("A30", Architecture.Ampere, 8, mig_conf=_mig_configuration)
input_dtype = "fp16"
input_format = "linear"
tensor_path = "${PREPROCESSED_DATA_DIR}/brats/brats_npy/fp16_linear"
workspace_size = 805306368
gpu_batch_size = 1
gpu_copy_streams = 1
offline_expected_qps = 224
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class A30_MIG_32x1g6gb_HighAccuracy(A30_MIG_32x1g6gb):
pass
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99, PowerSetting.MaxP)
class A30_MIG_32x1g6gb_Triton(A30_MIG_32x1g6gb):
use_triton = True
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class A30_MIG_32x1g6gb_HighAccuracy_Triton(A30_MIG_32x1g6gb_Triton):
pass
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99, PowerSetting.MaxP)
class A30x1(OfflineGPUBaseConfig):
system = System("A30", Architecture.Ampere, 1)
gpu_batch_size = 2
offline_expected_qps = 30.74
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class A30x1_HighAccuracy(A30x1):
pass
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99, PowerSetting.MaxP)
class A30x1_Triton(A30x1):
use_triton = True
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class A30x1_HighAccuracy_Triton(A30x1_Triton):
pass
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99, PowerSetting.MaxP)
class A30x8(A30x1):
system = System("A30", Architecture.Ampere, 8)
offline_expected_qps = 230
numa_config = "3:0-15&2:16-31&1:32-47&0:48-63&7:64-79&6:80-95&5:96-111&4:112-127"
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class A30x8_HighAccuracy(A30x8):
pass
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99, PowerSetting.MaxP)
class A30x8_Triton(A30x8):
input_dtype = "fp16"
input_format = "dhwc8"
tensor_path = "${PREPROCESSED_DATA_DIR}/brats/brats_npy/fp16_dhwc8"
gpu_batch_size = 2
offline_expected_qps = 230
use_triton = True
output_pinned_memory = False
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class A30x8_HighAccuracy_Triton(A30x8_Triton):
pass
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99, PowerSetting.MaxP)
class AGX_Xavier(OfflineGPUBaseConfig):
system = System("AGX_Xavier", Architecture.Xavier, 1, cpu_arch=CPUArch.aarch64)
gpu_batch_size = 1
offline_expected_qps = 3
use_direct_host_access = True
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class AGX_Xavier_HighAccuracy(AGX_Xavier):
pass
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99, PowerSetting.MaxP)
class AGX_Xavier_Triton(AGX_Xavier):
offline_expected_qps = 3
use_triton = True
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class AGX_Xavier_HighAccuracy_Triton(AGX_Xavier_Triton):
pass
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99, PowerSetting.MaxQ)
class AGX_Xavier_MaxQ(AGX_Xavier):
offline_expected_qps = 2.1
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99_9, PowerSetting.MaxQ)
class AGX_Xavier_HighAccuracy_MaxQ(AGX_Xavier_MaxQ):
pass
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99, PowerSetting.MaxP)
class Xavier_NX(OfflineGPUBaseConfig):
system = System("Xavier_NX", Architecture.Xavier, 1, cpu_arch=CPUArch.aarch64)
input_dtype = "fp16"
input_format = "dhwc8"
tensor_path = "${PREPROCESSED_DATA_DIR}/brats/brats_npy/fp16_dhwc8"
workspace_size = 1073741824
gpu_batch_size = 1
gpu_copy_streams = 1
offline_expected_qps = 1.5
use_direct_host_access = True
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class Xavier_NX_HighAccuracy(Xavier_NX):
pass
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99, PowerSetting.MaxP)
class Xavier_NX_Triton(Xavier_NX):
use_triton = True
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class Xavier_NX_HighAccuracy_Triton(Xavier_NX_Triton):
pass
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99, PowerSetting.MaxQ)
class Xavier_NX_MaxQ(Xavier_NX):
pass
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99_9, PowerSetting.MaxQ)
class Xavier_NX_HighAccuracy_MaxQ(Xavier_NX_MaxQ):
pass
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99, PowerSetting.MaxP)
class T4x1(OfflineGPUBaseConfig):
system = System("T4", Architecture.Turing, 1)
gpu_batch_size = 2
offline_expected_qps = 8
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class T4x1_HighAccuracy(T4x1):
pass
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99, PowerSetting.MaxP)
class T4x1_Triton(T4x1):
use_triton = True
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class T4x1_HighAccuracy_Triton(T4x1_Triton):
pass
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99, PowerSetting.MaxP)
class T4x20(T4x1):
system = System("T4", Architecture.Turing, 20)
offline_expected_qps = 160
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class T4x20_HighAccuracy(T4x20):
pass
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99, PowerSetting.MaxP)
class T4x20_Triton(T4x20):
use_triton = True
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class T4x20_HighAccuracy_Triton(T4x20_Triton):
pass
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99, PowerSetting.MaxP)
class T4x8(T4x1):
system = System("T4", Architecture.Turing, 8)
offline_expected_qps = 64
@ConfigRegistry.register(HarnessType.LWIS, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class T4x8_HighAccuracy(T4x8):
pass
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99, PowerSetting.MaxP)
class T4x8_Triton(T4x8):
use_triton = True
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class T4x8_HighAccuracy_Triton(T4x8_Triton):
pass
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99, PowerSetting.MaxP)
class Triton_CPU_2S_8360Yx1(OfflineCPUBaseConfig):
system = System("Triton_CPU_2S_8360Y", Architecture.Intel_CPU_x86_64, 1)
precision = "fp32"
offline_expected_qps = 6
batch_size = 0
input_dtype = "fp32"
max_queue_delay_usec = 100
model_name = "3dunet_int8_openvino"
num_instances = 16
ov_parameters = {
'CPU_THREADS_NUM': '72',
'CPU_THROUGHPUT_STREAMS': '8',
'ENABLE_BATCH_PADDING': 'NO',
'SKIP_OV_DYNAMIC_BATCHSIZE': 'YES'
}
tensor_path = "${PREPROCESSED_DATA_DIR}/brats/brats_npy/fp32"
use_triton = True
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class Triton_CPU_2S_8360Yx1_HighAccuracy(Triton_CPU_2S_8360Yx1):
pass
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99, PowerSetting.MaxP)
class Triton_CPU_2S_6258Rx1(OfflineCPUBaseConfig):
system = System("Triton_CPU_2S_6258R", Architecture.Intel_CPU_x86_64, 1)
precision = "fp32"
offline_expected_qps = 4
batch_size = 0
input_dtype = "fp32"
max_queue_delay_usec = 100
model_name = "3dunet_int8_openvino"
num_instances = 16
ov_parameters = {
'CPU_THREADS_NUM': '56',
'CPU_THROUGHPUT_STREAMS': '8',
'ENABLE_BATCH_PADDING': 'NO',
'SKIP_OV_DYNAMIC_BATCHSIZE': 'YES'
}
tensor_path = "${PREPROCESSED_DATA_DIR}/brats/brats_npy/fp32"
use_triton = True
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class Triton_CPU_2S_6258Rx1_HighAccuracy(Triton_CPU_2S_6258Rx1):
pass
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99, PowerSetting.MaxP)
class Triton_CPU_4S_8380Hx1(OfflineCPUBaseConfig):
system = System("Triton_CPU_4S_8380H", Architecture.Intel_CPU_x86_64, 1)
precision = "fp32"
offline_expected_qps = 10
batch_size = 0
input_dtype = "fp32"
max_queue_delay_usec = 100
model_name = "3dunet_int8_openvino"
num_instances = 32
ov_parameters = {
'CPU_THREADS_NUM': '112',
'CPU_THROUGHPUT_STREAMS': '16',
'ENABLE_BATCH_PADDING': 'NO',
'SKIP_OV_DYNAMIC_BATCHSIZE': 'YES'
}
tensor_path = "${PREPROCESSED_DATA_DIR}/brats/brats_npy/fp32"
use_triton = True
@ConfigRegistry.register(HarnessType.Triton, AccuracyTarget.k_99_9, PowerSetting.MaxP)
class Triton_CPU_4S_8380Hx1_HighAccuracy(Triton_CPU_4S_8380Hx1):
pass
| 33.288154
| 102
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7c5d7308007377173b306ce693d2732486aec3e8
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|
py
|
Python
|
python/testData/copyPaste/SelectionReverse3.after.py
|
jnthn/intellij-community
|
8fa7c8a3ace62400c838e0d5926a7be106aa8557
|
[
"Apache-2.0"
] | 2
|
2018-12-29T09:53:39.000Z
|
2018-12-29T09:53:42.000Z
|
python/testData/copyPaste/SelectionReverse3.after.py
|
Cyril-lamirand/intellij-community
|
60ab6c61b82fc761dd68363eca7d9d69663cfa39
|
[
"Apache-2.0"
] | 173
|
2018-07-05T13:59:39.000Z
|
2018-08-09T01:12:03.000Z
|
python/testData/copyPaste/SelectionReverse3.after.py
|
Cyril-lamirand/intellij-community
|
60ab6c61b82fc761dd68363eca7d9d69663cfa39
|
[
"Apache-2.0"
] | 2
|
2020-03-15T08:57:37.000Z
|
2020-04-07T04:48:14.000Z
|
if True:
a = 1
b = 2
def f():
if True:
a = 1
b = 2
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7ca61dbbfbdc935dbff3557cc81726494f278882
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py
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Python
|
benchmarks/SimResults/_bigLittle_hrrs_spec_tugberk_heteroFair/cmp_cactusADM/power.py
|
TugberkArkose/MLScheduler
|
e493b6cbf7b9d29a2c9300d7dd6f0c2f102e4061
|
[
"Unlicense"
] | null | null | null |
benchmarks/SimResults/_bigLittle_hrrs_spec_tugberk_heteroFair/cmp_cactusADM/power.py
|
TugberkArkose/MLScheduler
|
e493b6cbf7b9d29a2c9300d7dd6f0c2f102e4061
|
[
"Unlicense"
] | null | null | null |
benchmarks/SimResults/_bigLittle_hrrs_spec_tugberk_heteroFair/cmp_cactusADM/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': 0.191274,
'Execution Unit/Complex ALUs/Runtime Dynamic': 0.352924,
'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': 1.21162,
'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': 0.427463,
'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': 0.740212,
'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': 0.424532,
'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': 1.59221,
'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': 0.236773,
'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': 7.40368,
'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': 0.2289,
'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.0154959,
'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': 0.1764,
'Execution Unit/Register Files/Integer RF/Runtime Dynamic': 0.114602,
'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': 0.4053,
'Execution Unit/Register Files/Runtime Dynamic': 0.130097,
'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': 0.478657,
'Execution Unit/Results Broadcast Bus/Runtime Dynamic': 1.24384,
'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': 3.72444,
'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': 4.21395e-05,
'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': 4.21395e-05,
'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,
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'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage': 0.00181347,
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'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838,
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'Instruction Fetch Unit/Branch Predictor/RAS/Gate Leakage': 4.63858e-05,
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'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.00164626,
'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': 0.00176698,
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'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': 0.000413568,
'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,
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'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827,
'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.110169,
'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,
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'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.271146,
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'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,
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'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage with power gating': 0.166104,
'Instruction Fetch Unit/Peak Dynamic': 8.96874,
'Instruction Fetch Unit/Runtime Dynamic': 0.757681,
'Instruction Fetch Unit/Subthreshold Leakage': 0.932587,
'Instruction Fetch Unit/Subthreshold Leakage with power gating': 0.408542,
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'L2/Gate Leakage': 0.015464,
'L2/Peak Dynamic': 0.0784567,
'L2/Runtime Dynamic': 0.0216862,
'L2/Subthreshold Leakage': 0.834142,
'L2/Subthreshold Leakage with power gating': 0.401066,
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'Load Store Unit/Data Cache/Area': 6.84535,
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'Load Store Unit/Gate Leakage': 0.0351387,
'Load Store Unit/LoadQ/Area': 0.0836782,
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'Load Store Unit/LoadQ/Runtime Dynamic': 0.150398,
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'Load Store Unit/Peak Dynamic': 6.59898,
'Load Store Unit/Runtime Dynamic': 3.15108,
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'Load Store Unit/Subthreshold Leakage': 0.591622,
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'Memory Management Unit/Dtlb/Area': 0.0879726,
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'Memory Management Unit/Gate Leakage': 0.00813591,
'Memory Management Unit/Itlb/Area': 0.301552,
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'Renaming Unit/Area': 0.369768,
'Renaming Unit/FP Front End RAT/Area': 0.168486,
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'Renaming Unit/Free List/Area': 0.0414755,
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'Renaming Unit/Int Front End RAT/Subthreshold Leakage with power gating': 0.00348781,
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'Renaming Unit/Subthreshold Leakage with power gating': 0.0362779,
'Runtime Dynamic': 8.86995,
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'Execution Unit/Area': 7.68434,
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'Execution Unit/Gate Leakage': 0.120359,
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'Execution Unit/Instruction Scheduler/Gate Leakage': 0.00568913,
'Execution Unit/Instruction Scheduler/Instruction Window/Area': 0.805223,
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'Execution Unit/Instruction Scheduler/Peak Dynamic': 3.82262,
'Execution Unit/Instruction Scheduler/ROB/Area': 0.584388,
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'Execution Unit/Peak Dynamic': 4.894,
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'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': 0.0982255,
'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.00665286,
'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,
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'Execution Unit/Register Files/Integer RF/Peak Dynamic': 0.0757283,
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'Execution Unit/Register Files/Integer RF/Subthreshold Leakage with power gating': 0.00246675,
'Execution Unit/Register Files/Peak Dynamic': 0.173954,
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'Execution Unit/Results Broadcast Bus/Area Overhead': 0.0390912,
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'Execution Unit/Runtime Dynamic': 1.74287,
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'Instruction Fetch Unit/Branch Predictor/Chooser/Gate Leakage': 0.000278362,
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'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,
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'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Peak Dynamic': 0.00527447,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Runtime Dynamic': 5.79984e-06,
'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,
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'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.00070679,
'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': 0.000756176,
'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': 0.000164941,
'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': 0.0472991,
'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': 3.00863,
'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.116385,
'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': 0.160649,
'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': 5.37316,
'Instruction Fetch Unit/Runtime Dynamic': 0.325254,
'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': 0.0351162,
'L2/Runtime Dynamic': 0.00990718,
'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': 3.23492,
'Load Store Unit/Data Cache/Runtime Dynamic': 0.97174,
'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': 0.0646337,
'Load Store Unit/LoadQ/Runtime Dynamic': 0.0646336,
'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961,
'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918,
'Load Store Unit/Peak Dynamic': 3.54014,
'Load Store Unit/Runtime Dynamic': 1.35512,
'Load Store Unit/StoreQ/Area': 0.322079,
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'Load Store Unit/StoreQ/Peak Dynamic': 0.159376,
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'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/Gate Leakage': 0.00808595,
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'Renaming Unit/FP Front End RAT/Area': 0.131045,
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'Renaming Unit/Free List/Area': 0.0340654,
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'Renaming Unit/Gate Leakage': 0.00708398,
'Renaming Unit/Int Front End RAT/Area': 0.0941223,
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'Execution Unit/Floating Point Units/Area': 4.6585,
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'Execution Unit/Instruction Scheduler/FP Instruction Window/Peak Dynamic': 1.04181,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Runtime Dynamic': 0.157709,
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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,
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'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': 0.128402,
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'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage with power gating': 0.00364446,
'Execution Unit/Instruction Scheduler/Runtime Dynamic': 0.540489,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage': 0.0859892,
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'Execution Unit/Integer ALUs/Peak Dynamic': 0.101133,
'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': 4.88771,
'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': 0.0976433,
'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.00661502,
'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': 0.0752872,
'Execution Unit/Register Files/Integer RF/Runtime Dynamic': 0.0489221,
'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': 0.17293,
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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,
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'Execution Unit/Results Broadcast Bus/Subthreshold Leakage with power gating': 0.0305543,
'Execution Unit/Runtime Dynamic': 1.73622,
'Execution Unit/Subthreshold Leakage': 1.79543,
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'Gate Leakage': 0.368936,
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'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': 1.89701e-05,
'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/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Runtime Dynamic': 1.89701e-05,
'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': 1.65671e-05,
'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': 6.4376e-06,
'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': 0.00070277,
'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': 0.000757278,
'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': 0.000180304,
'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': 0.0470301,
'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': 2.99152,
'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.115756,
'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': 0.159735,
'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': 5.35522,
'Instruction Fetch Unit/Runtime Dynamic': 0.323459,
'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': 0.035218,
'L2/Runtime Dynamic': 0.0100071,
'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': 3.22239,
'Load Store Unit/Data Cache/Runtime Dynamic': 0.965885,
'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': 0.0642282,
'Load Store Unit/LoadQ/Runtime Dynamic': 0.0642282,
'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961,
'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918,
'Load Store Unit/Peak Dynamic': 3.52569,
'Load Store Unit/Runtime Dynamic': 1.34686,
'Load Store Unit/StoreQ/Area': 0.322079,
'Load Store Unit/StoreQ/Gate Leakage': 0.00329971,
'Load Store Unit/StoreQ/Peak Dynamic': 0.158376,
'Load Store Unit/StoreQ/Runtime Dynamic': 0.316752,
'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': 0.0562081,
'Memory Management Unit/Dtlb/Runtime Dynamic': 0.056724,
'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': 0.186002,
'Memory Management Unit/Itlb/Runtime Dynamic': 0.0190149,
'Memory Management Unit/Itlb/Subthreshold Leakage': 0.0413758,
'Memory Management Unit/Itlb/Subthreshold Leakage with power gating': 0.0235842,
'Memory Management Unit/Peak Dynamic': 0.438666,
'Memory Management Unit/Runtime Dynamic': 0.0757389,
'Memory Management Unit/Subthreshold Leakage': 0.0766103,
'Memory Management Unit/Subthreshold Leakage with power gating': 0.0398333,
'Peak Dynamic': 17.832,
'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': 0.256855,
'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': 0.0102413,
'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': 0.0755441,
'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': 0.34264,
'Renaming Unit/Subthreshold Leakage': 0.0552466,
'Renaming Unit/Subthreshold Leakage with power gating': 0.0276461,
'Runtime Dynamic': 3.83493,
'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': 0.0836494,
'Execution Unit/Complex ALUs/Runtime Dynamic': 0.26839,
'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': 0.529876,
'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': 0.16157,
'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': 0.260607,
'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': 0.131546,
'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': 0.553723,
'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': 0.103553,
'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': 4.91429,
'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': 0.100105,
'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.00677699,
'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': 0.0771461,
'Execution Unit/Register Files/Integer RF/Runtime Dynamic': 0.05012,
'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': 0.177251,
'Execution Unit/Register Files/Runtime Dynamic': 0.056897,
'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': 0.183442,
'Execution Unit/Results Broadcast Bus/Runtime Dynamic': 0.479492,
'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': 1.76388,
'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': 1.92389e-05,
'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': 1.92389e-05,
'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': 1.68005e-05,
'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': 6.52749e-06,
'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': 0.000719978,
'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': 0.000775256,
'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': 0.00018291,
'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': 0.0481816,
'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': 3.06477,
'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.118588,
'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': 0.163646,
'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': 5.43202,
'Instruction Fetch Unit/Runtime Dynamic': 0.331374,
'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': 0.0351213,
'L2/Runtime Dynamic': 0.00974348,
'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': 3.27002,
'Load Store Unit/Data Cache/Runtime Dynamic': 0.988267,
'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': 0.0657692,
'Load Store Unit/LoadQ/Runtime Dynamic': 0.0657692,
'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961,
'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918,
'Load Store Unit/Peak Dynamic': 3.5806,
'Load Store Unit/Runtime Dynamic': 1.37839,
'Load Store Unit/StoreQ/Area': 0.322079,
'Load Store Unit/StoreQ/Gate Leakage': 0.00329971,
'Load Store Unit/StoreQ/Peak Dynamic': 0.162176,
'Load Store Unit/StoreQ/Runtime Dynamic': 0.324351,
'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': 0.0575566,
'Memory Management Unit/Dtlb/Runtime Dynamic': 0.0580698,
'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': 0.190556,
'Memory Management Unit/Itlb/Runtime Dynamic': 0.0194831,
'Memory Management Unit/Itlb/Subthreshold Leakage': 0.0413758,
'Memory Management Unit/Itlb/Subthreshold Leakage with power gating': 0.0235842,
'Memory Management Unit/Peak Dynamic': 0.445537,
'Memory Management Unit/Runtime Dynamic': 0.077553,
'Memory Management Unit/Subthreshold Leakage': 0.0766103,
'Memory Management Unit/Subthreshold Leakage with power gating': 0.0398333,
'Peak Dynamic': 17.997,
'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': 0.26333,
'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': 0.0104943,
'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': 0.0773874,
'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': 0.351211,
'Renaming Unit/Subthreshold Leakage': 0.0552466,
'Renaming Unit/Subthreshold Leakage with power gating': 0.0276461,
'Runtime Dynamic': 3.91215,
'Subthreshold Leakage': 6.16288,
'Subthreshold Leakage with power gating': 2.55328}],
'DRAM': {'Area': 0,
'Gate Leakage': 0,
'Peak Dynamic': 6.968709236359436,
'Runtime Dynamic': 6.968709236359436,
'Subthreshold Leakage': 4.252,
'Subthreshold Leakage with power gating': 4.252},
'L3': [{'Area': 61.9075,
'Gate Leakage': 0.0484137,
'Peak Dynamic': 0.272973,
'Runtime Dynamic': 0.109611,
'Subthreshold Leakage': 6.80085,
'Subthreshold Leakage with power gating': 3.32364}],
'Processor': {'Area': 191.908,
'Gate Leakage': 1.53485,
'Peak Dynamic': 82.3718,
'Peak Power': 115.484,
'Runtime Dynamic': 20.5807,
'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': 82.0988,
'Total Cores/Runtime Dynamic': 20.471,
'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': 0.272973,
'Total L3s/Runtime Dynamic': 0.109611,
'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}}
| 75.061269
| 124
| 0.681835
| 8,098
| 68,606
| 5.770561
| 0.067424
| 0.123604
| 0.11299
| 0.093473
| 0.939675
| 0.932356
| 0.91896
| 0.886882
| 0.863642
| 0.844404
| 0
| 0.131298
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| 68,606
| 914
| 125
| 75.061269
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0
| 7
|
7cb130c41763f05f26c638fca70585f0091b8c83
| 15,969
|
py
|
Python
|
env3.10/lib/python3.10/site-packages/pyramid/tests/test_scripts/test_pshell.py
|
slmaankhaan/todo_app
|
4e5a81a789e02be84525682f3ec5d0bfc3d91e8d
|
[
"MIT"
] | null | null | null |
env3.10/lib/python3.10/site-packages/pyramid/tests/test_scripts/test_pshell.py
|
slmaankhaan/todo_app
|
4e5a81a789e02be84525682f3ec5d0bfc3d91e8d
|
[
"MIT"
] | null | null | null |
env3.10/lib/python3.10/site-packages/pyramid/tests/test_scripts/test_pshell.py
|
slmaankhaan/todo_app
|
4e5a81a789e02be84525682f3ec5d0bfc3d91e8d
|
[
"MIT"
] | null | null | null |
import unittest
from pyramid.tests.test_scripts import dummy
class TestPShellCommand(unittest.TestCase):
def _getTargetClass(self):
from pyramid.scripts.pshell import PShellCommand
return PShellCommand
def _makeOne(self, patch_bootstrap=True, patch_config=True,
patch_args=True, patch_options=True):
cmd = self._getTargetClass()([])
if patch_bootstrap:
self.bootstrap = dummy.DummyBootstrap()
cmd.bootstrap = (self.bootstrap,)
if patch_config:
self.config_factory = dummy.DummyConfigParserFactory()
cmd.ConfigParser = self.config_factory
if patch_args:
self.args = ('/foo/bar/myapp.ini#myapp',)
cmd.args = self.args
if patch_options:
class Options(object): pass
self.options = Options()
self.options.python_shell = ''
self.options.setup = None
cmd.options = self.options
return cmd
def test_make_default_shell(self):
command = self._makeOne()
interact = dummy.DummyInteractor()
shell = command.make_default_shell(interact)
shell({'foo': 'bar'}, 'a help message')
self.assertEqual(interact.local, {'foo': 'bar'})
self.assertTrue('a help message' in interact.banner)
def test_make_bpython_shell(self):
command = self._makeOne()
bpython = dummy.DummyBPythonShell()
shell = command.make_bpython_shell(bpython)
shell({'foo': 'bar'}, 'a help message')
self.assertEqual(bpython.locals_, {'foo': 'bar'})
self.assertTrue('a help message' in bpython.banner)
def test_make_ipython_v1_1_shell(self):
command = self._makeOne()
ipshell_factory = dummy.DummyIPShellFactory()
shell = command.make_ipython_v1_1_shell(ipshell_factory)
shell({'foo': 'bar'}, 'a help message')
self.assertEqual(ipshell_factory.kw['user_ns'], {'foo': 'bar'})
self.assertTrue('a help message' in ipshell_factory.kw['banner2'])
self.assertTrue(ipshell_factory.shell.called)
def test_make_ipython_v0_11_shell(self):
command = self._makeOne()
ipshell_factory = dummy.DummyIPShellFactory()
shell = command.make_ipython_v0_11_shell(ipshell_factory)
shell({'foo': 'bar'}, 'a help message')
self.assertEqual(ipshell_factory.kw['user_ns'], {'foo': 'bar'})
self.assertTrue('a help message' in ipshell_factory.kw['banner2'])
self.assertTrue(ipshell_factory.shell.called)
def test_make_ipython_v0_10_shell(self):
command = self._makeOne()
ipshell_factory = dummy.DummyIPShellFactory()
shell = command.make_ipython_v0_10_shell(ipshell_factory)
shell({'foo': 'bar'}, 'a help message')
self.assertEqual(ipshell_factory.kw['argv'], [])
self.assertEqual(ipshell_factory.kw['user_ns'], {'foo': 'bar'})
self.assertTrue('a help message' in ipshell_factory.shell.banner)
self.assertTrue(ipshell_factory.shell.called)
def test_command_loads_default_shell(self):
command = self._makeOne()
shell = dummy.DummyShell()
command.make_ipython_shell = lambda: None
command.make_bpython_shell = lambda: None
command.make_default_shell = lambda: shell
command.run()
self.assertTrue(self.config_factory.parser)
self.assertEqual(self.config_factory.parser.filename,
'/foo/bar/myapp.ini')
self.assertEqual(self.bootstrap.a[0], '/foo/bar/myapp.ini#myapp')
self.assertEqual(shell.env, {
'app':self.bootstrap.app, 'root':self.bootstrap.root,
'registry':self.bootstrap.registry,
'request':self.bootstrap.request,
'root_factory':self.bootstrap.root_factory,
})
self.assertTrue(self.bootstrap.closer.called)
self.assertTrue(shell.help)
def test_command_loads_default_shell_with_unknown_shell(self):
command = self._makeOne()
shell = dummy.DummyShell()
bad_shell = dummy.DummyShell()
command.make_ipython_shell = lambda: bad_shell
command.make_bpython_shell = lambda: bad_shell
command.make_default_shell = lambda: shell
command.options.python_shell = 'unknow_python_shell'
command.run()
self.assertTrue(self.config_factory.parser)
self.assertEqual(self.config_factory.parser.filename,
'/foo/bar/myapp.ini')
self.assertEqual(self.bootstrap.a[0], '/foo/bar/myapp.ini#myapp')
self.assertEqual(shell.env, {
'app':self.bootstrap.app, 'root':self.bootstrap.root,
'registry':self.bootstrap.registry,
'request':self.bootstrap.request,
'root_factory':self.bootstrap.root_factory,
})
self.assertEqual(bad_shell.env, {})
self.assertTrue(self.bootstrap.closer.called)
self.assertTrue(shell.help)
def test_command_loads_ipython_v1_1(self):
command = self._makeOne()
shell = dummy.DummyShell()
command.make_ipython_v1_1_shell = lambda: shell
command.make_ipython_v0_11_shell = lambda: None
command.make_ipython_v0_10_shell = lambda: None
command.make_bpython_shell = lambda: None
command.make_default_shell = lambda: None
command.options.python_shell = 'ipython'
command.run()
self.assertTrue(self.config_factory.parser)
self.assertEqual(self.config_factory.parser.filename,
'/foo/bar/myapp.ini')
self.assertEqual(self.bootstrap.a[0], '/foo/bar/myapp.ini#myapp')
self.assertEqual(shell.env, {
'app':self.bootstrap.app, 'root':self.bootstrap.root,
'registry':self.bootstrap.registry,
'request':self.bootstrap.request,
'root_factory':self.bootstrap.root_factory,
})
self.assertTrue(self.bootstrap.closer.called)
self.assertTrue(shell.help)
def test_command_loads_ipython_v0_11(self):
command = self._makeOne()
shell = dummy.DummyShell()
command.make_ipython_v1_1_shell = lambda: None
command.make_ipython_v0_11_shell = lambda: shell
command.make_ipython_v0_10_shell = lambda: None
command.make_bpython_shell = lambda: None
command.make_default_shell = lambda: None
command.options.python_shell = 'ipython'
command.run()
self.assertTrue(self.config_factory.parser)
self.assertEqual(self.config_factory.parser.filename,
'/foo/bar/myapp.ini')
self.assertEqual(self.bootstrap.a[0], '/foo/bar/myapp.ini#myapp')
self.assertEqual(shell.env, {
'app':self.bootstrap.app, 'root':self.bootstrap.root,
'registry':self.bootstrap.registry,
'request':self.bootstrap.request,
'root_factory':self.bootstrap.root_factory,
})
self.assertTrue(self.bootstrap.closer.called)
self.assertTrue(shell.help)
def test_command_loads_ipython_v0_10(self):
command = self._makeOne()
shell = dummy.DummyShell()
command.make_ipython_v1_1_shell = lambda: None
command.make_ipython_v0_11_shell = lambda: None
command.make_ipython_v0_10_shell = lambda: shell
command.make_bpython_shell = lambda: None
command.make_default_shell = lambda: None
command.options.python_shell = 'ipython'
command.run()
self.assertTrue(self.config_factory.parser)
self.assertEqual(self.config_factory.parser.filename,
'/foo/bar/myapp.ini')
self.assertEqual(self.bootstrap.a[0], '/foo/bar/myapp.ini#myapp')
self.assertEqual(shell.env, {
'app':self.bootstrap.app, 'root':self.bootstrap.root,
'registry':self.bootstrap.registry,
'request':self.bootstrap.request,
'root_factory':self.bootstrap.root_factory,
})
self.assertTrue(self.bootstrap.closer.called)
self.assertTrue(shell.help)
def test_command_loads_bpython_shell(self):
command = self._makeOne()
shell = dummy.DummyBPythonShell()
command.make_ipython_shell = lambda: None
command.make_bpython_shell = lambda: shell
command.options.python_shell = 'bpython'
command.run()
self.assertTrue(self.config_factory.parser)
self.assertEqual(self.config_factory.parser.filename,
'/foo/bar/myapp.ini')
self.assertEqual(self.bootstrap.a[0], '/foo/bar/myapp.ini#myapp')
self.assertEqual(shell.locals_, {
'app':self.bootstrap.app, 'root':self.bootstrap.root,
'registry':self.bootstrap.registry,
'request':self.bootstrap.request,
'root_factory':self.bootstrap.root_factory,
})
self.assertTrue(self.bootstrap.closer.called)
self.assertTrue(shell.banner)
def test_shell_ipython_ordering(self):
command = self._makeOne()
shell1_1 = dummy.DummyShell()
shell0_11 = dummy.DummyShell()
shell0_10 = dummy.DummyShell()
command.make_ipython_v1_1_shell = lambda: shell1_1
shell = command.make_shell()
self.assertEqual(shell, shell1_1)
command.make_ipython_v1_1_shell = lambda: None
command.make_ipython_v0_11_shell = lambda: shell0_11
shell = command.make_shell()
self.assertEqual(shell, shell0_11)
command.make_ipython_v0_11_shell = lambda: None
command.make_ipython_v0_10_shell = lambda: shell0_10
shell = command.make_shell()
self.assertEqual(shell, shell0_10)
command.options.python_shell = 'ipython'
command.make_ipython_v1_1_shell = lambda: shell1_1
shell = command.make_shell()
self.assertEqual(shell, shell1_1)
def test_shell_ordering(self):
command = self._makeOne()
ipshell = dummy.DummyShell()
bpshell = dummy.DummyShell()
dshell = dummy.DummyShell()
command.make_ipython_shell = lambda: None
command.make_bpython_shell = lambda: None
command.make_default_shell = lambda: dshell
shell = command.make_shell()
self.assertEqual(shell, dshell)
command.options.python_shell = 'ipython'
shell = command.make_shell()
self.assertEqual(shell, dshell)
command.options.python_shell = 'bpython'
shell = command.make_shell()
self.assertEqual(shell, dshell)
command.make_ipython_shell = lambda: ipshell
command.make_bpython_shell = lambda: bpshell
command.options.python_shell = 'ipython'
shell = command.make_shell()
self.assertEqual(shell, ipshell)
command.options.python_shell = 'bpython'
shell = command.make_shell()
self.assertEqual(shell, bpshell)
command.options.python_shell = 'python'
shell = command.make_shell()
self.assertEqual(shell, dshell)
def test_command_loads_custom_items(self):
command = self._makeOne()
model = dummy.Dummy()
self.config_factory.items = [('m', model)]
shell = dummy.DummyShell()
command.run(shell)
self.assertTrue(self.config_factory.parser)
self.assertEqual(self.config_factory.parser.filename,
'/foo/bar/myapp.ini')
self.assertEqual(self.bootstrap.a[0], '/foo/bar/myapp.ini#myapp')
self.assertEqual(shell.env, {
'app':self.bootstrap.app, 'root':self.bootstrap.root,
'registry':self.bootstrap.registry,
'request':self.bootstrap.request,
'root_factory':self.bootstrap.root_factory,
'm':model,
})
self.assertTrue(self.bootstrap.closer.called)
self.assertTrue(shell.help)
def test_command_setup(self):
command = self._makeOne()
def setup(env):
env['a'] = 1
env['root'] = 'root override'
self.config_factory.items = [('setup', setup)]
shell = dummy.DummyShell()
command.run(shell)
self.assertTrue(self.config_factory.parser)
self.assertEqual(self.config_factory.parser.filename,
'/foo/bar/myapp.ini')
self.assertEqual(self.bootstrap.a[0], '/foo/bar/myapp.ini#myapp')
self.assertEqual(shell.env, {
'app':self.bootstrap.app, 'root':'root override',
'registry':self.bootstrap.registry,
'request':self.bootstrap.request,
'root_factory':self.bootstrap.root_factory,
'a':1,
})
self.assertTrue(self.bootstrap.closer.called)
self.assertTrue(shell.help)
def test_command_loads_check_variable_override_order(self):
command = self._makeOne()
model = dummy.Dummy()
def setup(env):
env['a'] = 1
env['m'] = 'model override'
env['root'] = 'root override'
self.config_factory.items = [('setup', setup), ('m', model)]
shell = dummy.DummyShell()
command.run(shell)
self.assertTrue(self.config_factory.parser)
self.assertEqual(self.config_factory.parser.filename,
'/foo/bar/myapp.ini')
self.assertEqual(self.bootstrap.a[0], '/foo/bar/myapp.ini#myapp')
self.assertEqual(shell.env, {
'app':self.bootstrap.app, 'root':'root override',
'registry':self.bootstrap.registry,
'request':self.bootstrap.request,
'root_factory':self.bootstrap.root_factory,
'a':1, 'm':model,
})
self.assertTrue(self.bootstrap.closer.called)
self.assertTrue(shell.help)
def test_command_loads_setup_from_options(self):
command = self._makeOne()
def setup(env):
env['a'] = 1
env['root'] = 'root override'
model = dummy.Dummy()
self.config_factory.items = [('setup', 'abc'),
('m', model)]
command.options.setup = setup
shell = dummy.DummyShell()
command.run(shell)
self.assertTrue(self.config_factory.parser)
self.assertEqual(self.config_factory.parser.filename,
'/foo/bar/myapp.ini')
self.assertEqual(self.bootstrap.a[0], '/foo/bar/myapp.ini#myapp')
self.assertEqual(shell.env, {
'app':self.bootstrap.app, 'root':'root override',
'registry':self.bootstrap.registry,
'request':self.bootstrap.request,
'root_factory':self.bootstrap.root_factory,
'a':1, 'm':model,
})
self.assertTrue(self.bootstrap.closer.called)
self.assertTrue(shell.help)
def test_command_custom_section_override(self):
command = self._makeOne()
dummy_ = dummy.Dummy()
self.config_factory.items = [('app', dummy_), ('root', dummy_),
('registry', dummy_), ('request', dummy_)]
shell = dummy.DummyShell()
command.run(shell)
self.assertTrue(self.config_factory.parser)
self.assertEqual(self.config_factory.parser.filename,
'/foo/bar/myapp.ini')
self.assertEqual(self.bootstrap.a[0], '/foo/bar/myapp.ini#myapp')
self.assertEqual(shell.env, {
'app':dummy_, 'root':dummy_, 'registry':dummy_, 'request':dummy_,
'root_factory':self.bootstrap.root_factory,
})
self.assertTrue(self.bootstrap.closer.called)
self.assertTrue(shell.help)
class Test_main(unittest.TestCase):
def _callFUT(self, argv):
from pyramid.scripts.pshell import main
return main(argv, quiet=True)
def test_it(self):
result = self._callFUT(['pshell'])
self.assertEqual(result, 2)
| 41.803665
| 79
| 0.630785
| 1,783
| 15,969
| 5.455973
| 0.060572
| 0.096217
| 0.050678
| 0.0331
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| 0.83491
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| 41.913386
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| 1
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| false
| 0.002874
| 0.011494
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|
0
| 7
|
7cbeec913b636e424574e9fa76ffadcb7faec830
| 5,964
|
py
|
Python
|
main.py
|
YUND4/WebScarpingPUC
|
08de5683cf86a6d073935662e1934c56491d56c9
|
[
"MIT"
] | null | null | null |
main.py
|
YUND4/WebScarpingPUC
|
08de5683cf86a6d073935662e1934c56491d56c9
|
[
"MIT"
] | null | null | null |
main.py
|
YUND4/WebScarpingPUC
|
08de5683cf86a6d073935662e1934c56491d56c9
|
[
"MIT"
] | null | null | null |
#Esta aplicacion nos permite consumir paginas web
#con web scarping
#Implementacion por SwankySniperGG
#MIT
#github.com/
from bs4 import BeautifulSoup
import json
from scrapscript import simple_get
class loadPUC:
result = {
'cuentas': [],
'total': 0
}
def merge(self, lis):
result = ''
for l in lis:
result = result + l
return result
def mergeNames(self, lis):
result = ''
for l in lis:
result = result + ' ' + l
return result
def __init__(self):
dictionary = self.result
i = 1
while i < 10:
url = 'https://puc.com.co/'+str(i)
print(url, end = '')
if (i == 1):
tipo = 'Debito'
if (i == 2):
tipo = 'Credito'
if (i == 3):
tipo = 'Credito'
if (i == 4):
tipo = 'Credito'
if (i == 5):
tipo = 'Debito'
if (i == 6):
tipo = 'Debito'
if (i == 7):
tipo = 'Debito'
if (i == 8):
tipo = 'Debito'
if (i == 9):
tipo = 'Credito'
raw_html = simple_get(url)
html = BeautifulSoup(raw_html, 'html.parser')
if raw_html is not None:
print(' Status - OK')
x = html.h1.string
x = x.split(' ')
try:
y = html.find('div', class_='col-md-7').p.string
except:
y = None
dictionary['total'] = dictionary['total'] + 1
dictionary['cuentas'].append({
'nombre': self.mergeNames(x[1:]),
'codigo': x[0],
'descripcion':y,
'tipo':tipo,
'hijos': []
})
lis = html.find_all('span', class_='code')
for l in lis[1:]:
url = 'https://puc.com.co/'+l.string
print(url, end = '')
raw_html = simple_get(url)
html = BeautifulSoup(raw_html, 'html.parser')
if raw_html is not None:
print(' Status - OK')
x = html.h1.string
x = x.split(' ')
try:
y = html.find('div', class_='col-md-7').p.string
except:
y = None
dictionary['total'] = dictionary['total'] + 1
dictionary['cuentas'][-1]['hijos'].append({
'nombre': self.mergeNames(x[1:]),
'codigo': self.merge(x[0][-2:]),
'descripcion':y,
'tipo':tipo,
'hijos': []
})
lis = html.find_all('span', class_='code')
for l in lis[2:]:
url = 'https://puc.com.co/'+l.string
print(url, end = '')
raw_html = simple_get(url)
html = BeautifulSoup(raw_html, 'html.parser')
if raw_html is not None:
print(' Status - OK')
x = html.h1.string
x = x.split(' ')
try:
y = html.find('div', class_='col-md-7').p.string
except:
y = None
dictionary['total'] = dictionary['total'] + 1
dictionary['cuentas'][-1]['hijos'][-1]['hijos'].append({
'nombre': self.mergeNames(x[1:]),
'codigo': self.merge(x[0][-2:]),
'descripcion':y,
'tipo':tipo,
'hijos': []
})
lis = html.find_all('span', class_='code')
for l in lis[3:]:
url = 'https://puc.com.co/'+l.string
print(url, end = '')
raw_html = simple_get(url)
html = BeautifulSoup(raw_html, 'html.parser')
if raw_html is not None:
print(' Status - OK')
x = html.h1.string
x = x.split(' ')
dictionary['total'] = dictionary['total'] + 1
dictionary['cuentas'][-1]['hijos'][-1]['hijos'][-1]['hijos'].append({
'nombre': self.mergeNames(x[1:]),
'codigo': self.merge(x[0][-2:]),
'descripcion': None,
'tipo':tipo,
'hijos': []
})
else:
print(' Status - Not found')
else:
print(' Status - Not found')
else:
print(' Status - Not found')
else:
print(' Status - Not found')
i = i + 1
with open('result.json', 'w') as fp:
json.dump(dictionary, fp)
if __name__ == '__main__':
loadPUC()
| 40.571429
| 109
| 0.330651
| 486
| 5,964
| 3.979424
| 0.207819
| 0.043433
| 0.015512
| 0.023268
| 0.750776
| 0.742503
| 0.742503
| 0.724922
| 0.724922
| 0.724922
| 0
| 0.017306
| 0.554326
| 5,964
| 147
| 110
| 40.571429
| 0.710309
| 0.018612
| 0
| 0.703704
| 0
| 0
| 0.106343
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.022222
| false
| 0
| 0.022222
| 0
| 0.074074
| 0.088889
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 1
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| 0
|
0
| 7
|
7cc8e277f74b0866af0c18e9d2beb0b098ed68e0
| 14,455
|
py
|
Python
|
tests/hdx/utilities/test_path.py
|
OCHA-DAP/hdx-python-utilities
|
3ff2720bddf7b4ee107adbe3a2222c9e8fbd487f
|
[
"MIT"
] | 4
|
2019-01-04T10:44:18.000Z
|
2020-01-23T14:06:38.000Z
|
tests/hdx/utilities/test_path.py
|
OCHA-DAP/hdx-python-utilities
|
3ff2720bddf7b4ee107adbe3a2222c9e8fbd487f
|
[
"MIT"
] | 3
|
2017-11-01T08:57:02.000Z
|
2021-10-17T20:51:15.000Z
|
tests/hdx/utilities/test_path.py
|
OCHA-DAP/hdx-python-utilities
|
3ff2720bddf7b4ee107adbe3a2222c9e8fbd487f
|
[
"MIT"
] | 1
|
2018-09-12T18:02:22.000Z
|
2018-09-12T18:02:22.000Z
|
"""Path Utility Tests"""
import copy
from os.path import exists, join
from shutil import rmtree
from tempfile import gettempdir
import pytest
from hdx.utilities.loader import load_file_to_str
from hdx.utilities.path import (
get_filename_extension_from_url,
get_filename_from_url,
get_temp_dir,
multiple_progress_storing_tempdir,
progress_storing_tempdir,
temp_dir,
)
class TestPath:
@pytest.fixture(scope="class")
def mytestdir(self):
return join("haha", "lala")
@pytest.fixture(scope="class")
def fixtureurl(self):
return "https://raw.githubusercontent.com/OCHA-DAP/hdx-python-utilities/master/tests/fixtures/test_data.csv"
def test_get_temp_dir(self, monkeypatch, mytestdir):
assert get_temp_dir() == gettempdir()
assert get_temp_dir("TEST") == join(gettempdir(), "TEST")
monkeypatch.setenv("TEMP_DIR", mytestdir)
assert get_temp_dir() == mytestdir
monkeypatch.delenv("TEMP_DIR")
def test_temp_dir(self, monkeypatch, mytestdir):
monkeypatch.setenv("TEMP_DIR", mytestdir)
with temp_dir() as tempdir:
assert tempdir == mytestdir
monkeypatch.delenv("TEMP_DIR")
tempfolder = "papa"
expected_dir = join(gettempdir(), tempfolder)
with temp_dir(tempfolder) as tempdir:
assert tempdir == expected_dir
assert exists(tempdir) is False
try:
with temp_dir(tempfolder) as tempdir:
assert tempdir == expected_dir
raise ValueError("Fail!")
except ValueError:
pass
assert exists(tempdir) is False
with temp_dir(
tempfolder, delete_on_success=True, delete_on_failure=True
) as tempdir:
assert tempdir == expected_dir
assert exists(tempdir) is False
try:
with temp_dir(
tempfolder, delete_on_success=True, delete_on_failure=True
) as tempdir:
assert tempdir == expected_dir
raise ValueError("Fail!")
except ValueError:
pass
assert exists(tempdir) is False
with temp_dir(
tempfolder, delete_on_success=False, delete_on_failure=False
) as tempdir:
assert tempdir == expected_dir
assert exists(tempdir) is True
rmtree(tempdir)
try:
with temp_dir(
tempfolder, delete_on_success=False, delete_on_failure=False
) as tempdir:
assert tempdir == expected_dir
raise ValueError("Fail!")
except ValueError:
pass
assert exists(tempdir) is True
with temp_dir(
tempfolder, delete_on_success=True, delete_on_failure=False
) as tempdir:
assert tempdir == expected_dir
assert exists(tempdir) is False
try:
with temp_dir(
tempfolder, delete_on_success=True, delete_on_failure=False
) as tempdir:
assert tempdir == expected_dir
raise ValueError("Fail!")
except ValueError:
pass
assert exists(tempdir) is True
rmtree(tempdir)
with temp_dir(
tempfolder, delete_on_success=False, delete_on_failure=True
) as tempdir:
assert tempdir == expected_dir
assert exists(tempdir) is True
rmtree(tempdir)
try:
with temp_dir(
tempfolder, delete_on_success=False, delete_on_failure=True
) as tempdir:
assert tempdir == expected_dir
raise ValueError("Fail!")
except ValueError:
pass
assert exists(tempdir) is False
def test_progress_storing_tempdir(self, monkeypatch):
tempfolder = "papa"
expected_dir = join(gettempdir(), tempfolder)
rmtree(expected_dir, ignore_errors=True)
iterator = [
{"iso3": "AFG", "name": "Afghanistan"},
{"iso3": "SDN", "name": "Sudan"},
{"iso3": "YEM", "name": "Yemen"},
{"iso3": "ZAM", "name": "Zambia"},
]
expected_batch_file = join(expected_dir, "batch.txt")
result = list()
for info, nextdict in progress_storing_tempdir(
tempfolder, iterator, "iso3"
):
assert info["folder"] == expected_dir
expected_batch = load_file_to_str(expected_batch_file, strip=True)
result.append(nextdict)
assert result == iterator
assert expected_batch == info["batch"]
assert exists(expected_dir) is False
monkeypatch.setenv("WHERETOSTART", "iso3=SDN")
result = list()
for info, nextdict in progress_storing_tempdir(
tempfolder, iterator, "iso3"
):
assert exists(info["folder"]) is True
assert info["folder"] == expected_dir
expected_batch = load_file_to_str(expected_batch_file, strip=True)
result.append(nextdict)
assert result == iterator[1:]
assert expected_batch == info["batch"]
assert exists(expected_dir) is False
monkeypatch.delenv("WHERETOSTART")
try:
for info, nextdict in progress_storing_tempdir(
tempfolder, iterator, "iso3"
):
if nextdict["iso3"] == "YEM":
start_batch = info["batch"]
raise ValueError("Problem!")
except ValueError:
pass
assert exists(expected_dir) is True
result = list()
for info, nextdict in progress_storing_tempdir(
tempfolder, iterator, "iso3"
):
assert exists(info["folder"]) is True
assert info["folder"] == expected_dir
assert info["batch"] == start_batch
result.append(nextdict)
assert result == iterator[2:]
assert exists(expected_dir) is False
try:
for info, nextdict in progress_storing_tempdir(
tempfolder, iterator, "iso3"
):
if nextdict["iso3"] == "YEM":
start_batch = info["batch"]
raise ValueError("Problem!")
except ValueError:
pass
assert exists(expected_dir) is True
monkeypatch.setenv("WHERETOSTART", "RESET")
result = list()
for info, nextdict in progress_storing_tempdir(
tempfolder, iterator, "iso3"
):
assert exists(info["folder"]) is True
assert info["folder"] == expected_dir
assert info["batch"] != start_batch
result.append(nextdict)
assert result == iterator
assert exists(expected_dir) is False
monkeypatch.delenv("WHERETOSTART")
try:
for info, nextdict in progress_storing_tempdir(
tempfolder, iterator, "iso3"
):
if nextdict["iso3"] == "YEM":
start_batch = info["batch"]
raise ValueError("Problem!")
except ValueError:
pass
assert exists(expected_dir) is True
monkeypatch.setenv("WHERETOSTART", "iso3=SDN")
result = list()
for info, nextdict in progress_storing_tempdir(
tempfolder, iterator, "iso3"
):
assert exists(info["folder"]) is True
assert info["folder"] == expected_dir
assert info["batch"] == start_batch
result.append(nextdict)
assert result == iterator[1:]
assert exists(expected_dir) is False
monkeypatch.delenv("WHERETOSTART")
try:
for info, nextdict in progress_storing_tempdir(
tempfolder, iterator, "iso3"
):
if nextdict["iso3"] == "YEM":
start_batch = info["batch"]
raise ValueError("Problem!")
except ValueError:
pass
monkeypatch.setenv("WHERETOSTART", "iso3=NOTFOUND")
found = False
for _ in progress_storing_tempdir(tempfolder, iterator, "iso3"):
found = True
assert found is False
assert exists(expected_dir) is True
batch = load_file_to_str(expected_batch_file, strip=True)
assert batch == start_batch
monkeypatch.delenv("WHERETOSTART")
monkeypatch.setenv("WHERETOSTART", "NOTFOUND=SDN")
found = False
for _ in progress_storing_tempdir(tempfolder, iterator, "iso3"):
found = True
assert found is False
assert exists(expected_dir) is True
batch = load_file_to_str(expected_batch_file, strip=True)
assert batch == start_batch
monkeypatch.delenv("WHERETOSTART")
rmtree(expected_dir, ignore_errors=True)
def test_multiple_progress_storing_tempdir(self, monkeypatch):
tempfolder = "gaga"
expected_dir = join(gettempdir(), tempfolder)
rmtree(expected_dir, ignore_errors=True)
iterator1 = [{"emergency_id": "911"}]
iterator2 = [
{"iso3": "AFG", "name": "Afghanistan"},
{"iso3": "SDN", "name": "Sudan"},
{"iso3": "YEM", "name": "Yemen"},
{"iso3": "ZAM", "name": "Zambia"},
]
iterators = [iterator1, iterator2]
keys = ["emergency_id", "iso3"]
results = list()
for result in multiple_progress_storing_tempdir(
tempfolder, iterators, keys, "1234"
):
results.append(copy.deepcopy(result))
expected_results = [
(
0,
{
"folder": "/tmp/gaga/0",
"batch": "1234",
"progress": "emergency_id=911",
},
{"emergency_id": "911"},
),
(
1,
{
"folder": "/tmp/gaga/1",
"batch": "1234",
"progress": "iso3=AFG",
},
{"iso3": "AFG", "name": "Afghanistan"},
),
(
1,
{
"folder": "/tmp/gaga/1",
"batch": "1234",
"progress": "iso3=SDN",
},
{"iso3": "SDN", "name": "Sudan"},
),
(
1,
{
"folder": "/tmp/gaga/1",
"batch": "1234",
"progress": "iso3=YEM",
},
{"iso3": "YEM", "name": "Yemen"},
),
(
1,
{
"folder": "/tmp/gaga/1",
"batch": "1234",
"progress": "iso3=ZAM",
},
{"iso3": "ZAM", "name": "Zambia"},
),
]
assert results == expected_results
assert exists(expected_dir) is False
results = list()
try:
for result in multiple_progress_storing_tempdir(
tempfolder, iterators, keys
):
results.append(copy.deepcopy(result))
i, info, nextdict = result
if "iso3" in nextdict and nextdict["iso3"] == "YEM":
start_batch = info["batch"]
raise ValueError("Problem!")
except ValueError:
pass
for result in expected_results:
result[1]["batch"] = start_batch
assert results == expected_results[:4]
assert exists(expected_dir) is True
result = list()
for _, info, nextdict in multiple_progress_storing_tempdir(
tempfolder, iterators, keys
):
assert exists(info["folder"]) is True
assert info["folder"] == join(expected_dir, "1")
assert info["batch"] == start_batch
result.append(nextdict)
assert result == iterator2[2:]
assert exists(expected_dir) is False
try:
for _, info, nextdict in multiple_progress_storing_tempdir(
tempfolder, iterators, keys
):
if "iso3" in nextdict and nextdict["iso3"] == "YEM":
start_batch = info["batch"]
raise ValueError("Problem!")
except ValueError:
pass
for result in expected_results:
result[1]["batch"] = start_batch
assert exists(expected_dir) is True
monkeypatch.setenv("WHERETOSTART", "RESET")
results = list()
for result in multiple_progress_storing_tempdir(
tempfolder, iterators, keys, "1234"
):
results.append(copy.deepcopy(result))
for result in expected_results:
result[1]["batch"] = "1234"
assert results == expected_results
assert exists(expected_dir) is False
monkeypatch.delenv("WHERETOSTART")
try:
for _, info, nextdict in multiple_progress_storing_tempdir(
tempfolder, iterators, keys
):
if "iso3" in nextdict and nextdict["iso3"] == "YEM":
start_batch = info["batch"]
raise ValueError("Problem!")
except ValueError:
pass
for result in expected_results:
result[1]["batch"] = start_batch
assert exists(expected_dir) is True
monkeypatch.setenv("WHERETOSTART", "iso3=SDN")
result = list()
for _, info, nextdict in multiple_progress_storing_tempdir(
tempfolder, iterators, keys
):
assert exists(info["folder"]) is True
assert info["folder"] == join(expected_dir, "1")
assert info["batch"] == start_batch
result.append(nextdict)
assert result == iterator2[1:]
assert exists(expected_dir) is False
monkeypatch.delenv("WHERETOSTART")
rmtree(expected_dir, ignore_errors=True)
def test_get_filename_extension_from_url(self, fixtureurl):
filename = get_filename_from_url(fixtureurl)
assert filename == "test_data.csv"
filename, extension = get_filename_extension_from_url(fixtureurl)
assert filename == "test_data"
assert extension == ".csv"
| 35.868486
| 116
| 0.547008
| 1,395
| 14,455
| 5.490323
| 0.089606
| 0.060321
| 0.063194
| 0.075206
| 0.865779
| 0.815772
| 0.804805
| 0.784698
| 0.779606
| 0.760804
| 0
| 0.011897
| 0.354549
| 14,455
| 402
| 117
| 35.957711
| 0.809003
| 0.001245
| 0
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| 0
| 0.00266
| 0.088421
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| 0
| 0
| 0
| 0.207447
| 1
| 0.018617
| false
| 0.031915
| 0.018617
| 0.005319
| 0.045213
| 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
|
6b11e74e28410d1cd32e2afba7a83bd84cd5e120
| 50,879
|
py
|
Python
|
tests/src/OneLogin/saml2_tests/idp_metadata_parser_test.py
|
tuvshuud/python-saml
|
3bbc0a99659a7d71b70784a479c2aed3d14001f5
|
[
"MIT"
] | 2
|
2018-12-05T12:45:59.000Z
|
2019-06-27T12:01:47.000Z
|
tests/src/OneLogin/saml2_tests/idp_metadata_parser_test.py
|
sighttviewliu/python-saml
|
3814b0fe98d6ab78cf92b39c15e1785b1cab22bb
|
[
"MIT"
] | null | null | null |
tests/src/OneLogin/saml2_tests/idp_metadata_parser_test.py
|
sighttviewliu/python-saml
|
3814b0fe98d6ab78cf92b39c15e1785b1cab22bb
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
# Copyright (c) 2010-2018 OneLogin, Inc.
# MIT License
from copy import deepcopy
import json
from os.path import dirname, join, exists
from lxml.etree import XMLSyntaxError
import unittest
from urllib2 import URLError
from onelogin.saml2.idp_metadata_parser import OneLogin_Saml2_IdPMetadataParser
from onelogin.saml2.constants import OneLogin_Saml2_Constants
class OneLogin_Saml2_IdPMetadataParser_Test(unittest.TestCase):
# Instruct unittest to not hide diffs upon test failure, even for complex
# dictionaries. This prevents the message "Diff is 907 characters long.
# Set self.maxDiff to None to see it." from showing up.
maxDiff = None
data_path = join(dirname(dirname(dirname(dirname(__file__)))), 'data')
settings_path = join(dirname(dirname(dirname(dirname(__file__)))), 'settings')
def loadSettingsJSON(self, filename='settings1.json'):
filename = join(self.settings_path, filename)
if exists(filename):
stream = open(filename, 'r')
settings = json.load(stream)
stream.close()
return settings
else:
raise Exception('Settings json file does not exist')
def file_contents(self, filename):
f = open(filename, 'r')
content = f.read()
f.close()
return content
def testGetMetadata(self):
"""
Tests the get_metadata method of the OneLogin_Saml2_IdPMetadataParser
"""
with self.assertRaises(Exception):
data = OneLogin_Saml2_IdPMetadataParser.get_metadata('http://google.es')
try:
data = OneLogin_Saml2_IdPMetadataParser.get_metadata('https://www.testshib.org/metadata/testshib-providers.xml')
except URLError:
data = self.file_contents(join(self.data_path, 'metadata', 'testshib-providers.xml'))
self.assertTrue(data is not None and data is not {})
def testParseRemote(self):
"""
Tests the parse_remote method of the OneLogin_Saml2_IdPMetadataParser
"""
with self.assertRaises(Exception):
data = OneLogin_Saml2_IdPMetadataParser.parse_remote('http://google.es')
try:
data = OneLogin_Saml2_IdPMetadataParser.parse_remote('https://www.testshib.org/metadata/testshib-providers.xml')
except URLError:
xml = self.file_contents(join(self.data_path, 'metadata', 'testshib-providers.xml'))
data = OneLogin_Saml2_IdPMetadataParser.parse(xml)
self.assertTrue(data is not None and data is not {})
expected_settings_json = """
{
"sp": {
"NameIDFormat": "urn:mace:shibboleth:1.0:nameIdentifier"
},
"idp": {
"x509cert": "MIIDAzCCAeugAwIBAgIVAPX0G6LuoXnKS0Muei006mVSBXbvMA0GCSqGSIb3DQEBCwUAMBsxGTAXBgNVBAMMEGlkcC50ZXN0c2hpYi5vcmcwHhcNMTYwODIzMjEyMDU0WhcNMzYwODIzMjEyMDU0WjAbMRkwFwYDVQQDDBBpZHAudGVzdHNoaWIub3JnMIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEAg9C4J2DiRTEhJAWzPt1S3ryhm3M2P3hPpwJwvt2q948vdTUxhhvNMuc3M3S4WNh6JYBs53R+YmjqJAII4ShMGNEmlGnSVfHorex7IxikpuDPKV3SNf28mCAZbQrX+hWA+ann/uifVzqXktOjs6DdzdBnxoVhniXgC8WCJwKcx6JO/hHsH1rG/0DSDeZFpTTcZHj4S9MlLNUtt5JxRzV/MmmB3ObaX0CMqsSWUOQeE4nylSlp5RWHCnx70cs9kwz5WrflnbnzCeHU2sdbNotBEeTHot6a2cj/pXlRJIgPsrL/4VSicPZcGYMJMPoLTJ8mdy6mpR6nbCmP7dVbCIm/DQIDAQABoz4wPDAdBgNVHQ4EFgQUUfaDa2mPi24x09yWp1OFXmZ2GPswGwYDVR0RBBQwEoIQaWRwLnRlc3RzaGliLm9yZzANBgkqhkiG9w0BAQsFAAOCAQEASKKgqTxhqBzROZ1eVy++si+eTTUQZU4+8UywSKLia2RattaAPMAcXUjO+3cYOQXLVASdlJtt+8QPdRkfp8SiJemHPXC8BES83pogJPYEGJsKo19l4XFJHPnPy+Dsn3mlJyOfAa8RyWBS80u5lrvAcr2TJXt9fXgkYs7BOCigxtZoR8flceGRlAZ4p5FPPxQR6NDYb645jtOTMVr3zgfjP6Wh2dt+2p04LG7ENJn8/gEwtXVuXCsPoSCDx9Y0QmyXTJNdV1aB0AhORkWPlFYwp+zOyOIR+3m1+pqWFpn0eT/HrxpdKa74FA3R2kq4R7dXe4G0kUgXTdqXMLRKhDgdmA==",
"entityId": "https://idp.testshib.org/idp/shibboleth",
"singleSignOnService": {
"url": "https://idp.testshib.org/idp/profile/SAML2/Redirect/SSO",
"binding": "urn:oasis:names:tc:SAML:2.0:bindings:HTTP-Redirect"
}
}
}
"""
expected_settings = json.loads(expected_settings_json)
self.assertEqual(expected_settings, data)
def testParse(self):
"""
Tests the parse method of the OneLogin_Saml2_IdPMetadataParser
"""
with self.assertRaises(XMLSyntaxError):
data = OneLogin_Saml2_IdPMetadataParser.parse('')
xml_sp_metadata = self.file_contents(join(self.data_path, 'metadata', 'metadata_settings1.xml'))
data = OneLogin_Saml2_IdPMetadataParser.parse(xml_sp_metadata)
self.assertEqual({}, data)
xml_idp_metadata = self.file_contents(join(self.data_path, 'metadata', 'idp_metadata.xml'))
data = OneLogin_Saml2_IdPMetadataParser.parse(xml_idp_metadata)
# W/o further specification, expect to get the redirect binding SSO
# URL extracted.
expected_settings_json = """
{
"idp": {
"singleSignOnService": {
"url": "https://app.onelogin.com/trust/saml2/http-post/sso/383123",
"binding": "urn:oasis:names:tc:SAML:2.0:bindings:HTTP-Redirect"
},
"x509cert": "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",
"entityId": "https://app.onelogin.com/saml/metadata/383123"
},
"sp": {
"NameIDFormat": "urn:oasis:names:tc:SAML:1.1:nameid-format:emailAddress"
}
}
"""
expected_settings = json.loads(expected_settings_json)
self.assertEqual(expected_settings, data)
def test_parse_testshib_required_binding_sso_redirect(self):
"""
Test with testshib metadata.
Especially test extracting SSO with REDIRECT binding.
Note that the testshib metadata does not contain an SLO specification
in the first <IDPSSODescriptor> tag.
"""
expected_settings_json = """
{
"sp": {
"NameIDFormat": "urn:mace:shibboleth:1.0:nameIdentifier"
},
"idp": {
"entityId": "https://idp.testshib.org/idp/shibboleth",
"singleSignOnService": {
"url": "https://idp.testshib.org/idp/profile/SAML2/Redirect/SSO",
"binding": "urn:oasis:names:tc:SAML:2.0:bindings:HTTP-Redirect"
},
"x509cert": "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"
}
}
"""
try:
xmldoc = OneLogin_Saml2_IdPMetadataParser.get_metadata(
'https://www.testshib.org/metadata/testshib-providers.xml')
except Exception:
xmldoc = self.file_contents(join(self.data_path, 'metadata', 'testshib-providers.xml'))
# Parse, require SSO REDIRECT binding, implicitly.
settings1 = OneLogin_Saml2_IdPMetadataParser.parse(xmldoc)
# Parse, require SSO REDIRECT binding, explicitly.
settings2 = OneLogin_Saml2_IdPMetadataParser.parse(
xmldoc,
required_sso_binding=OneLogin_Saml2_Constants.BINDING_HTTP_REDIRECT
)
expected_settings = json.loads(expected_settings_json)
self.assertEqual(expected_settings, settings1)
self.assertEqual(expected_settings, settings2)
def test_parse_testshib_required_binding_sso_post(self):
"""
Test with testshib metadata.
Especially test extracting SSO with POST binding.
"""
expected_settings_json = """
{
"sp": {
"NameIDFormat": "urn:mace:shibboleth:1.0:nameIdentifier"
},
"idp": {
"x509cert": "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",
"entityId": "https://idp.testshib.org/idp/shibboleth",
"singleSignOnService": {
"url": "https://idp.testshib.org/idp/profile/SAML2/POST/SSO",
"binding": "urn:oasis:names:tc:SAML:2.0:bindings:HTTP-POST"
}
}
}
"""
try:
xmldoc = OneLogin_Saml2_IdPMetadataParser.get_metadata(
'https://www.testshib.org/metadata/testshib-providers.xml')
except URLError:
xmldoc = self.file_contents(join(self.data_path, 'metadata', 'testshib-providers.xml'))
# Parse, require POST binding.
settings = OneLogin_Saml2_IdPMetadataParser.parse(
xmldoc,
required_sso_binding=OneLogin_Saml2_Constants.BINDING_HTTP_POST
)
expected_settings = json.loads(expected_settings_json)
self.assertEqual(expected_settings, settings)
def test_parse_required_binding_all(self):
"""
Test all combinations of the `require_slo_binding` and
`require_sso_binding` parameters.
Note: IdP metadata contains a single logout (SLO)
service and does not specify any endpoint for the POST binding.
"""
expected_settings_json = """
{
"sp": {
"NameIDFormat": "urn:oasis:names:tc:SAML:1.1:nameid-format:emailAddress"
},
"idp": {
"entityId": "urn:example:idp",
"x509cert": "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",
"singleSignOnService": {
"url": "http://idp.example.com",
"binding": "urn:oasis:names:tc:SAML:2.0:bindings:HTTP-Redirect"
},
"singleLogoutService": {
"url": "http://idp.example.com/logout",
"binding": "urn:oasis:names:tc:SAML:2.0:bindings:HTTP-Redirect"
}
}
}
"""
xmldoc = self.file_contents(join(self.data_path, 'metadata', 'idp_metadata2.xml'))
expected_settings = json.loads(expected_settings_json)
# Parse, require SLO and SSO REDIRECT binding, implicitly.
settings1 = OneLogin_Saml2_IdPMetadataParser.parse(xmldoc)
# Parse, require SLO and SSO REDIRECT binding, explicitly.
settings2 = OneLogin_Saml2_IdPMetadataParser.parse(
xmldoc,
required_sso_binding=OneLogin_Saml2_Constants.BINDING_HTTP_REDIRECT,
required_slo_binding=OneLogin_Saml2_Constants.BINDING_HTTP_REDIRECT
)
expected_settings1_2 = deepcopy(expected_settings)
self.assertEqual(expected_settings1_2, settings1)
self.assertEqual(expected_settings1_2, settings2)
settings3 = OneLogin_Saml2_IdPMetadataParser.parse(
xmldoc,
required_sso_binding=OneLogin_Saml2_Constants.BINDING_HTTP_POST,
required_slo_binding=OneLogin_Saml2_Constants.BINDING_HTTP_POST
)
expected_settings3 = deepcopy(expected_settings)
del expected_settings3['idp']['singleLogoutService']
del expected_settings3['idp']['singleSignOnService']
self.assertEqual(expected_settings3, settings3)
settings4 = OneLogin_Saml2_IdPMetadataParser.parse(
xmldoc,
required_sso_binding=OneLogin_Saml2_Constants.BINDING_HTTP_POST,
required_slo_binding=OneLogin_Saml2_Constants.BINDING_HTTP_REDIRECT
)
settings5 = OneLogin_Saml2_IdPMetadataParser.parse(
xmldoc,
required_sso_binding=OneLogin_Saml2_Constants.BINDING_HTTP_POST
)
expected_settings4_5 = deepcopy(expected_settings)
del expected_settings4_5['idp']['singleSignOnService']
self.assertEqual(expected_settings4_5, settings4)
self.assertEqual(expected_settings4_5, settings5)
settings6 = OneLogin_Saml2_IdPMetadataParser.parse(
xmldoc,
required_sso_binding=OneLogin_Saml2_Constants.BINDING_HTTP_REDIRECT,
required_slo_binding=OneLogin_Saml2_Constants.BINDING_HTTP_POST
)
settings7 = OneLogin_Saml2_IdPMetadataParser.parse(
xmldoc,
required_slo_binding=OneLogin_Saml2_Constants.BINDING_HTTP_POST
)
expected_settings6_7 = deepcopy(expected_settings)
del expected_settings6_7['idp']['singleLogoutService']
self.assertEqual(expected_settings6_7, settings6)
self.assertEqual(expected_settings6_7, settings7)
def test_parse_with_entity_id(self):
"""
Tests the parse method of the OneLogin_Saml2_IdPMetadataParser
Case: Provide entity_id to identify the desired IdPDescriptor from
EntitiesDescriptor
"""
xml_idp_metadata = self.file_contents(join(self.data_path, 'metadata', 'idp_multiple_descriptors.xml'))
# should find first descriptor
data = OneLogin_Saml2_IdPMetadataParser.parse(xml_idp_metadata)
self.assertEqual("https://foo.example.com/access/saml/idp.xml", data["idp"]["entityId"])
# should find desired descriptor
data2 = OneLogin_Saml2_IdPMetadataParser.parse(xml_idp_metadata, entity_id="https://bar.example.com/access/saml/idp.xml")
self.assertEqual("https://bar.example.com/access/saml/idp.xml", data2["idp"]["entityId"])
expected_settings_json = """
{
"sp": {
"NameIDFormat": "urn:oasis:names:tc:SAML:1.1:nameid-format:unspecified"
},
"idp": {
"singleLogoutService": {
"url": "https://hello.example.com/access/saml/logout",
"binding": "urn:oasis:names:tc:SAML:2.0:bindings:HTTP-Redirect"
},
"entityId": "https://bar.example.com/access/saml/idp.xml",
"x509cert": "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",
"singleSignOnService": {
"url": "https://hello.example.com/access/saml/login",
"binding": "urn:oasis:names:tc:SAML:2.0:bindings:HTTP-Redirect"
}
}
}
"""
expected_settings = json.loads(expected_settings_json)
self.assertEqual(expected_settings, data2)
def test_parse_multi_certs(self):
"""
Tests the parse method of the OneLogin_Saml2_IdPMetadataParser
Case: IdP metadata contains multiple certs
"""
xml_idp_metadata = self.file_contents(join(self.data_path, 'metadata', 'idp_metadata_multi_certs.xml'))
data = OneLogin_Saml2_IdPMetadataParser.parse(xml_idp_metadata)
expected_settings_json = """
{
"sp": {
"NameIDFormat": "urn:oasis:names:tc:SAML:2.0:nameid-format:transient"
},
"idp": {
"singleLogoutService": {
"url": "https://idp.examle.com/saml/slo",
"binding": "urn:oasis:names:tc:SAML:2.0:bindings:HTTP-Redirect"
},
"x509certMulti": {
"encryption": [
"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"
],
"signing": [
"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",
"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"
]
},
"entityId": "https://idp.examle.com/saml/metadata",
"singleSignOnService": {
"url": "https://idp.examle.com/saml/sso",
"binding": "urn:oasis:names:tc:SAML:2.0:bindings:HTTP-Redirect"
}
}
}
"""
expected_settings = json.loads(expected_settings_json)
self.assertEqual(expected_settings, data)
def test_parse_multi_singing_certs(self):
"""
Tests the parse method of the OneLogin_Saml2_IdPMetadataParser
Case: IdP metadata contains multiple signing certs and no encryption certs
"""
xml_idp_metadata = self.file_contents(join(self.data_path, 'metadata', 'idp_metadata_multi_signing_certs.xml'))
data = OneLogin_Saml2_IdPMetadataParser.parse(xml_idp_metadata)
expected_settings_json = """
{
"sp": {
"NameIDFormat": "urn:oasis:names:tc:SAML:2.0:nameid-format:transient"
},
"idp": {
"singleLogoutService": {
"url": "https://idp.examle.com/saml/slo",
"binding": "urn:oasis:names:tc:SAML:2.0:bindings:HTTP-Redirect"
},
"x509certMulti": {
"signing": [
"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",
"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",
"MIIEZTCCA02gAwIBAgIUPyy/A3bZAZ4m28PzEUUoT7RJhxIwDQYJKoZIhvcNAQEFBQAwcjELMAkGA1UEBhMCVVMxKzApBgNVBAoMIk9uZUxvZ2luIFRlc3QgKHNnYXJjaWEtdXMtcHJlcHJvZCkxFTATBgNVBAsMDE9uZUxvZ2luIElkUDEfMB0GA1UEAwwWT25lTG9naW4gQWNjb3VudCA4OTE0NjAeFw0xNjA4MDQyMjI5MzdaFw0yMTA4MDUyMjI5MzdaMHIxCzAJBgNVBAYTAlVTMSswKQYDVQQKDCJPbmVMb2dpbiBUZXN0IChzZ2FyY2lhLXVzLXByZXByb2QpMRUwEwYDVQQLDAxPbmVMb2dpbiBJZFAxHzAdBgNVBAMMFk9uZUxvZ2luIEFjY291bnQgODkxNDYwggEiMA0GCSqGSIb3DQEBAQUAA4IBDwAwggEKAoIBAQDN6iqQGcLOCglNO42I2rkzE05UXSiMXT6c8ALThMMiaDw6qqzo3sd/tKK+NcNKWLIIC8TozWVyh5ykUiVZps+08xil7VsTU7E+wKu3kvmOsvw2wlRwtnoKZJwYhnr+RkBa+h1r3ZYUgXm1ZPeHMKj1g18KaWz9+MxYL6BhKqrOzfW/P2xxVRcFH7/pq+ZsDdgNzD2GD+apzY4MZyZj/N6BpBWJ0GlFsmtBegpbX3LBitJuFkk5L4/U/jjF1AJa3boBdCUVfATqO5G03H4XS1GySjBIRQXmlUF52rLjg6xCgWJ30/+t1X+IHLJeixiQ0vxyh6C4/usCEt94cgD1r8ADAgMBAAGjgfIwge8wDAYDVR0TAQH/BAIwADAdBgNVHQ4EFgQUPW0DcH0G3IwynWgi74co4wZ6n7gwga8GA1UdIwSBpzCBpIAUPW0DcH0G3IwynWgi74co4wZ6n7ihdqR0MHIxCzAJBgNVBAYTAlVTMSswKQYDVQQKDCJPbmVMb2dpbiBUZXN0IChzZ2FyY2lhLXVzLXByZXByb2QpMRUwEwYDVQQLDAxPbmVMb2dpbiBJZFAxHzAdBgNVBAMMFk9uZUxvZ2luIEFjY291bnQgODkxNDaCFD8svwN22QGeJtvD8xFFKE+0SYcSMA4GA1UdDwEB/wQEAwIHgDANBgkqhkiG9w0BAQUFAAOCAQEAQhB4q9jrycwbHrDSoYR1X4LFFzvJ9Us75wQquRHXpdyS9D6HUBXMGI6ahPicXCQrfLgN8vzMIiqZqfySXXv/8/dxe/X4UsWLYKYJHDJmxXD5EmWTa65chjkeP1oJAc8f3CKCpcP2lOBTthbnk2fEVAeLHR4xNdQO0VvGXWO9BliYPpkYqUIBvlm+Fg9mF7AM/Uagq2503XXIE1Lq//HON68P10vNMwLSKOtYLsoTiCnuIKGJqG37MsZVjQ1ZPRcO+LSLkq0i91gFxrOrVCrgztX4JQi5XkvEsYZGIXXjwHqxTVyt3adZWQO0LPxPqRiUqUzyhDhLo/xXNrHCu4VbMw=="
]
},
"entityId": "https://idp.examle.com/saml/metadata",
"singleSignOnService": {
"url": "https://idp.examle.com/saml/sso",
"binding": "urn:oasis:names:tc:SAML:2.0:bindings:HTTP-Redirect"
}
}
}
"""
expected_settings = json.loads(expected_settings_json)
self.assertEqual(expected_settings, data)
def test_parse_multi_same_signing_and_encrypt_cert(self):
"""
Tests the parse method of the OneLogin_Saml2_IdPMetadataParser
Case: IdP metadata contains multiple signature cert and encrypt cert
that is the same
"""
xml_idp_metadata = self.file_contents(join(self.data_path, 'metadata', 'idp_metadata_same_sign_and_encrypt_cert.xml'))
data = OneLogin_Saml2_IdPMetadataParser.parse(xml_idp_metadata)
expected_settings_json = """
{
"sp": {
"NameIDFormat": "urn:oasis:names:tc:SAML:1.1:nameid-format:emailAddress"
},
"idp": {
"x509cert": "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",
"entityId": "https://app.onelogin.com/saml/metadata/383123",
"singleSignOnService": {
"url": "https://app.onelogin.com/trust/saml2/http-post/sso/383123",
"binding": "urn:oasis:names:tc:SAML:2.0:bindings:HTTP-Redirect"
}
}
}
"""
expected_settings = json.loads(expected_settings_json)
self.assertEqual(expected_settings, data)
xml_idp_metadata_2 = self.file_contents(join(self.data_path, 'metadata', 'idp_metadata_different_sign_and_encrypt_cert.xml'))
data_2 = OneLogin_Saml2_IdPMetadataParser.parse(xml_idp_metadata_2)
expected_settings_json_2 = """
{
"sp": {
"NameIDFormat": "urn:oasis:names:tc:SAML:1.1:nameid-format:emailAddress"
},
"idp": {
"x509certMulti": {
"encryption": [
"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"
],
"signing": [
"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"
]
},
"entityId": "https://app.onelogin.com/saml/metadata/383123",
"singleSignOnService": {
"url": "https://app.onelogin.com/trust/saml2/http-post/sso/383123",
"binding": "urn:oasis:names:tc:SAML:2.0:bindings:HTTP-Redirect"
}
}
}
"""
expected_settings_2 = json.loads(expected_settings_json_2)
self.assertEqual(expected_settings_2, data_2)
def test_merge_settings(self):
"""
Tests the merge_settings method of the OneLogin_Saml2_IdPMetadataParser
"""
with self.assertRaises(TypeError):
settings_result = OneLogin_Saml2_IdPMetadataParser.merge_settings(None, {})
with self.assertRaises(TypeError):
settings_result = OneLogin_Saml2_IdPMetadataParser.merge_settings({}, None)
xml_idp_metadata = self.file_contents(join(self.data_path, 'metadata', 'idp_metadata.xml'))
# Parse XML metadata.
data = OneLogin_Saml2_IdPMetadataParser.parse(xml_idp_metadata)
# Read base settings.
settings = self.loadSettingsJSON()
# Merge settings from XML metadata into base settings,
# let XML metadata have priority if there are conflicting
# attributes.
settings_result = OneLogin_Saml2_IdPMetadataParser.merge_settings(settings, data)
# Generate readable JSON representation:
# print("%s" % json.dumps(settings_result, indent=2).replace(r'\n', r'\\n'))
expected_settings_json = """
{
"custom_base_path": "../../../tests/data/customPath/",
"contactPerson": {
"support": {
"emailAddress": "support@example.com",
"givenName": "support_name"
},
"technical": {
"emailAddress": "technical@example.com",
"givenName": "technical_name"
}
},
"idp": {
"singleSignOnService": {
"url": "https://app.onelogin.com/trust/saml2/http-post/sso/383123",
"binding": "urn:oasis:names:tc:SAML:2.0:bindings:HTTP-Redirect"
},
"entityId": "https://app.onelogin.com/saml/metadata/383123",
"singleLogoutService": {
"url": "http://idp.example.com/SingleLogoutService.php"
},
"x509cert": "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"
},
"sp": {
"NameIDFormat": "urn:oasis:names:tc:SAML:1.1:nameid-format:emailAddress",
"entityId": "http://stuff.com/endpoints/metadata.php",
"assertionConsumerService": {
"url": "http://stuff.com/endpoints/endpoints/acs.php"
},
"singleLogoutService": {
"url": "http://stuff.com/endpoints/endpoints/sls.php"
}
},
"security": {
"wantAssertionsSigned": false,
"authnRequestsSigned": false,
"signMetadata": false
},
"debug": false,
"organization": {
"en-US": {
"displayname": "SP test",
"url": "http://sp.example.com",
"name": "sp_test"
}
},
"strict": false
}
"""
expected_settings = json.loads(expected_settings_json)
self.assertEqual(expected_settings, settings_result)
# Commute merge operation. As the order determines which settings
# dictionary has priority, here we expect a different result.
settings_result2 = OneLogin_Saml2_IdPMetadataParser.merge_settings(data, settings)
expected_settings2_json = """
{
"debug": false,
"idp": {
"singleLogoutService": {
"url": "http://idp.example.com/SingleLogoutService.php"
},
"singleSignOnService": {
"url": "http://idp.example.com/SSOService.php",
"binding": "urn:oasis:names:tc:SAML:2.0:bindings:HTTP-Redirect"
},
"entityId": "http://idp.example.com/",
"x509cert": "MIICgTCCAeoCCQCbOlrWDdX7FTANBgkqhkiG9w0BAQUFADCBhDELMAkGA1UEBhMCTk8xGDAWBgNVBAgTD0FuZHJlYXMgU29sYmVyZzEMMAoGA1UEBxMDRm9vMRAwDgYDVQQKEwdVTklORVRUMRgwFgYDVQQDEw9mZWlkZS5lcmxhbmcubm8xITAfBgkqhkiG9w0BCQEWEmFuZHJlYXNAdW5pbmV0dC5ubzAeFw0wNzA2MTUxMjAxMzVaFw0wNzA4MTQxMjAxMzVaMIGEMQswCQYDVQQGEwJOTzEYMBYGA1UECBMPQW5kcmVhcyBTb2xiZXJnMQwwCgYDVQQHEwNGb28xEDAOBgNVBAoTB1VOSU5FVFQxGDAWBgNVBAMTD2ZlaWRlLmVybGFuZy5ubzEhMB8GCSqGSIb3DQEJARYSYW5kcmVhc0B1bmluZXR0Lm5vMIGfMA0GCSqGSIb3DQEBAQUAA4GNADCBiQKBgQDivbhR7P516x/S3BqKxupQe0LONoliupiBOesCO3SHbDrl3+q9IbfnfmE04rNuMcPsIxB161TdDpIesLCn7c8aPHISKOtPlAeTZSnb8QAu7aRjZq3+PbrP5uW3TcfCGPtKTytHOge/OlJbo078dVhXQ14d1EDwXJW1rRXuUt4C8QIDAQABMA0GCSqGSIb3DQEBBQUAA4GBACDVfp86HObqY+e8BUoWQ9+VMQx1ASDohBjwOsg2WykUqRXF+dLfcUH9dWR63CtZIKFDbStNomPnQz7nbK+onygwBspVEbnHuUihZq3ZUdmumQqCw4Uvs/1Uvq3orOo/WJVhTyvLgFVK2QarQ4/67OZfHd7R+POBXhophSMv1ZOo"
},
"security": {
"authnRequestsSigned": false,
"wantAssertionsSigned": false,
"signMetadata": false
},
"contactPerson": {
"technical": {
"emailAddress": "technical@example.com",
"givenName": "technical_name"
},
"support": {
"emailAddress": "support@example.com",
"givenName": "support_name"
}
},
"strict": false,
"sp": {
"singleLogoutService": {
"url": "http://stuff.com/endpoints/endpoints/sls.php"
},
"assertionConsumerService": {
"url": "http://stuff.com/endpoints/endpoints/acs.php"
},
"entityId": "http://stuff.com/endpoints/metadata.php",
"NameIDFormat": "urn:oasis:names:tc:SAML:1.1:nameid-format:unspecified"
},
"custom_base_path": "../../../tests/data/customPath/",
"organization": {
"en-US": {
"displayname": "SP test",
"url": "http://sp.example.com",
"name": "sp_test"
}
}
}
"""
expected_settings2 = json.loads(expected_settings2_json)
self.assertEqual(expected_settings2, settings_result2)
# Test merging multiple certs
xml_idp_metadata = self.file_contents(join(self.data_path, 'metadata', 'idp_metadata_multi_certs.xml'))
data3 = OneLogin_Saml2_IdPMetadataParser.parse(xml_idp_metadata)
settings_result3 = OneLogin_Saml2_IdPMetadataParser.merge_settings(settings, data3)
expected_settings3_json = """
{
"debug": false,
"strict": false,
"custom_base_path": "../../../tests/data/customPath/",
"sp": {
"singleLogoutService": {
"url": "http://stuff.com/endpoints/endpoints/sls.php"
},
"assertionConsumerService": {
"url": "http://stuff.com/endpoints/endpoints/acs.php"
},
"entityId": "http://stuff.com/endpoints/metadata.php",
"NameIDFormat": "urn:oasis:names:tc:SAML:2.0:nameid-format:transient"
},
"idp": {
"singleLogoutService": {
"url": "https://idp.examle.com/saml/slo",
"binding": "urn:oasis:names:tc:SAML:2.0:bindings:HTTP-Redirect"
},
"x509certMulti": {
"encryption": [
"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"
],
"signing": [
"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",
"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"
]
},
"entityId": "https://idp.examle.com/saml/metadata",
"singleSignOnService": {
"url": "https://idp.examle.com/saml/sso",
"binding": "urn:oasis:names:tc:SAML:2.0:bindings:HTTP-Redirect"
}
},
"security": {
"authnRequestsSigned": false,
"wantAssertionsSigned": false,
"signMetadata": false
},
"contactPerson": {
"technical": {
"emailAddress": "technical@example.com",
"givenName": "technical_name"
},
"support": {
"emailAddress": "support@example.com",
"givenName": "support_name"
}
},
"organization": {
"en-US": {
"displayname": "SP test",
"url": "http://sp.example.com",
"name": "sp_test"
}
}
}
"""
expected_settings3 = json.loads(expected_settings3_json)
self.assertEqual(expected_settings3, settings_result3)
if __name__ == '__main__':
runner = unittest.TextTestRunner()
unittest.main(testRunner=runner)
| 78.516975
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0
| 11
|
6b32df9f64c8c19fb4c9cf60f29c0e1a18804d26
| 6,341
|
py
|
Python
|
functions/getter.py
|
mramirid/Whatsapp-Bot-Covid19
|
29472c9d9918f819296a07d1436b0988e5bbe73a
|
[
"MIT"
] | null | null | null |
functions/getter.py
|
mramirid/Whatsapp-Bot-Covid19
|
29472c9d9918f819296a07d1436b0988e5bbe73a
|
[
"MIT"
] | null | null | null |
functions/getter.py
|
mramirid/Whatsapp-Bot-Covid19
|
29472c9d9918f819296a07d1436b0988e5bbe73a
|
[
"MIT"
] | null | null | null |
global mysql
def init_connection(new_mysql):
global mysql
mysql = new_mysql
################### Nasional ###################
def get_nasional():
today = get_today_nasional()
yesterday = get_yesterday_nasional()
# Memgambil index array agar saat pemanggilan variabel mudah, tidak today[0] dst
if len(today) > 0:
positif = 1
sembuh = 2
meninggal = 3
perawatan = 4
datetime = 6
# Penambahan masing2 kasus positif, sembuh & meninggal dari kemarin
selisih_positif = today[positif] - yesterday[positif]
selisih_sembuh = today[sembuh] - yesterday[sembuh]
selisih_meninggal = today[meninggal] - yesterday[meninggal]
selisih_perawatan = today[perawatan] - yesterday[perawatan]
# Selisih total kasus dari kemarin
total_yesterday = yesterday[positif] + \
yesterday[sembuh] + yesterday[meninggal]
total_today = today[positif] + today[sembuh] + today[meninggal]
selisih_total = total_today - total_yesterday
tempTime = str(today[datetime])
readableTime = tempTime[11:16]
message = ''
if selisih_total > 0:
message += 'Statistik kasus di Indonesia\n\n'
message += '- Positif: {} (+{})\n'.format(
today[positif], abs(selisih_positif))
message += '- Sembuh: {} (+{})\n'.format(
today[sembuh], abs(selisih_sembuh))
message += '- Meninggal: {} (+{})\n'.format(
today[meninggal], abs(selisih_meninggal))
message += '- Dalam perawatan: {} (+{})\n\n'.format(
today[perawatan], abs(selisih_perawatan))
else:
message += 'Statistik kasus di Indonesia\n\n'
message += '- Positif: {}\n'.format(today[positif])
message += '- Sembuh: {}\n'.format(today[sembuh])
message += '- Meninggal: {}\n'.format(today[meninggal])
message += '- Dalam perawatan: {}\n\n'.format(today[perawatan])
message += 'Tetap jaga kesehatan dan apabila memungkinkan #DirumahAja\n\n'
message += 'Pembaruan terakhir pada {}'.format(readableTime)
else:
return False
return message
def get_today_nasional():
cur = mysql.connection.cursor()
cur.execute("SELECT * FROM nasional WHERE DATE(created_at) = CURDATE()")
data = cur.fetchone()
cur.close()
return data
def get_yesterday_nasional():
cur = mysql.connection.cursor()
cur.execute("SELECT * FROM nasional WHERE DATE(created_at) = CURDATE()-1")
data = cur.fetchone()
cur.close()
return data
################### End of Nasional ###################
################### Provinsi ###################
def get_prov_byname(name):
today = get_today_prov_byname(name)
yesterday = get_yesterday_prov_byname(name)
if len(today) > 0:
# Index, mempermudah saja
datetime = 0
nama_provinsi = 1
positif = 2
sembuh = 3
perawatan = 4
meninggal = 5
# Penambahan masing2 kasus positif, sembuh & meninggal dari kemarin
selisih_positif = today[positif] - yesterday[positif]
selisih_sembuh = today[sembuh] - yesterday[sembuh]
selisih_meninggal = today[meninggal] - yesterday[meninggal]
selisih_perawatan = today[perawatan] - yesterday[perawatan]
# Selisih total kasus dari kemarin
total_yesterday = yesterday[positif] + \
yesterday[sembuh] + yesterday[meninggal]
total_today = today[positif] + today[sembuh] + today[meninggal]
selisih_total = total_today - total_yesterday
tempTime = str(today[datetime])
readableTime = tempTime[11:16]
message = ''
if selisih_total > 0:
message += 'Statistik kasus di {}\n\n'.format(today[nama_provinsi])
message += '- Positif: {} (+{})\n'.format(
today[positif], abs(selisih_positif))
message += '- Sembuh: {} (+{})\n'.format(
today[sembuh], abs(selisih_sembuh))
message += '- Meninggal: {} (+{})\n'.format(
today[meninggal], abs(selisih_meninggal))
message += '- Dalam perawatan: {} (+{})\n\n'.format(
today[perawatan], abs(selisih_perawatan))
else:
message += 'Statistik kasus di {}\n\n'.format(today[nama_provinsi])
message += '- Positif: {}\n'.format(today[positif])
message += '- Sembuh: {}\n'.format(today[sembuh])
message += '- Meninggal: {}\n'.format(today[meninggal])
message += '- Dalam perawatan: {}\n\n'.format(today[perawatan])
message += 'Tetap jaga kesehatan dan apabila memungkinkan #DirumahAja\n\n'
message += 'Pembaruan terakhir pada {}'.format(readableTime)
else:
return False
return message
def get_today_prov_byname(name):
cur = mysql.connection.cursor()
cur.execute('''SELECT
pengambilan_provinsi.updated_at,
nama_provinsi,
positif,
sembuh,
dalam_perawatan,
meninggal
FROM pengambilan_provinsi
LEFT JOIN detail_pengambilan_provinsi
ON pengambilan_provinsi.id = detail_pengambilan_provinsi.id_pengambilan_provinsi
WHERE DATE(pengambilan_provinsi.created_at) = CURDATE() AND nama_provinsi LIKE '%{}%\''''.format(name))
data = cur.fetchone()
cur.close()
return data
def get_yesterday_prov_byname(name):
cur = mysql.connection.cursor()
cur.execute('''SELECT
pengambilan_provinsi.updated_at,
nama_provinsi,
positif,
sembuh,
dalam_perawatan,
meninggal
FROM pengambilan_provinsi
LEFT JOIN detail_pengambilan_provinsi
ON pengambilan_provinsi.id = detail_pengambilan_provinsi.id_pengambilan_provinsi
WHERE DATE(pengambilan_provinsi.created_at) = CURDATE()-1 AND nama_provinsi LIKE '%{}%\''''.format(name))
data = cur.fetchone()
cur.close()
return data
################### End of Provinsi ###################
| 35.824859
| 121
| 0.577985
| 620
| 6,341
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| 0.158065
| 0.035235
| 0.060403
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| 0.87472
| 0.862696
| 0
| 0.006211
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| 6,341
| 176
| 122
| 36.028409
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| 0.053435
| false
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0
| 7
|
860f893d646dbd52965b39832b915dbb5940b46c
| 8,513
|
py
|
Python
|
options/meta_options.py
|
Wanggcong/SolutionSimilarityLearning
|
26279b61686b3c34745c369b2cc4175c71c55403
|
[
"MIT"
] | 7
|
2019-12-23T02:37:27.000Z
|
2020-09-05T08:08:22.000Z
|
options/meta_options.py
|
Wanggcong/SolutionSimilarityLearning
|
26279b61686b3c34745c369b2cc4175c71c55403
|
[
"MIT"
] | null | null | null |
options/meta_options.py
|
Wanggcong/SolutionSimilarityLearning
|
26279b61686b3c34745c369b2cc4175c71c55403
|
[
"MIT"
] | null | null | null |
import argparse
import os
class MetaOptions():
def __init__(self,parser,dataset_name):
"""Reset the class; indicates the class hasn't been initailized"""
self.parser = parser
self.dataset_name = dataset_name
def initialize(self):
self.parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed (default: 1)')
def initialize_datasets(self):
"""Define the common options that are used in both training and test."""
# basic parameters
self.initialize()
if self.dataset_name == 'mnist':
self.parser.add_argument('--root-path', type=str,
default='/media/data2/anonymous/projects/LearnableParameterSimilarity/weights/mnist', metavar='RP',
help='root path for weights')
self.parser.add_argument('--batch-size', type=int, default=1, metavar='N',
help='input batch size for training (default: 64)')
self.parser.add_argument('--test-batch-size', type=int, default=1, metavar='N',
help='input batch size for testing (default: 1000)')
self.parser.add_argument('--epochs', type=int, default=100, metavar='N',
help='number of epochs to train (default: 10)')
self.parser.add_argument('--lr', type=float, default=0.001, metavar='LR',
help='learning rate (default: 0.01)')
self.parser.add_argument('--momentum', type=float, default=0.9, metavar='M',
help='SGD momentum (default: 0.5)')
self.parser.add_argument('--log-interval', type=int, default=10, metavar='N',
help='how many batches to wait before logging training status')
self.parser.add_argument('--not-save-model', action='store_true', default=True,
help='For Saving the current Model')
self.parser.add_argument('--weight-decay', '--wd', default=1e-4, type=float, metavar='W',
help='weight decay (default: 1e-4)')
self.parser.add_argument('--meta-model', type=str, default='cifar_mlp', metavar='M',
help='meta model type')
self.parser.add_argument('--step1', default=30, type=int, metavar='N',
help='step1 lr')
self.parser.add_argument('--log-file', type=str, default='', metavar='M',
help='log file')
self.parser.add_argument('--selected-layers', type=str, default='0', metavar='M',
help='selected layers')
self.parser.add_argument('--cls-or-retr', action='store_true',
help='True for classification, False for retrieval.')
elif self.dataset_name == 'cifar100' or self.dataset_name == 'TinyImageNet':
self.parser.add_argument('--root-path', type=str,
default='/media/data2/anonymous/projects/LearnableParameterSimilarity/weights/cifar100_100', metavar='RP',
help='root path for weights')
self.parser.add_argument('--batch-size', type=int, default=1, metavar='N',
help='input batch size for training (default: 64)')
self.parser.add_argument('--test-batch-size', type=int, default=1, metavar='N',
help='input batch size for testing (default: 1000)')
self.parser.add_argument('--epochs', type=int, default=100, metavar='N',
help='number of epochs to train (default: 10)')
self.parser.add_argument('--lr', type=float, default=0.001, metavar='LR',
help='learning rate (default: 0.01)')
self.parser.add_argument('--momentum', type=float, default=0.9, metavar='M',
help='SGD momentum (default: 0.5)')
self.parser.add_argument('--log-interval', type=int, default=10, metavar='N',
help='how many batches to wait before logging training status')
self.parser.add_argument('--not-save-model', action='store_true', default=True,
help='For Saving the current Model')
self.parser.add_argument('--weight-decay', '--wd', default=1e-4, type=float, metavar='W',
help='weight decay (default: 1e-4)')
self.parser.add_argument('--meta-model', type=str, default='cifar_mlp', metavar='M',
help='meta model type')
self.parser.add_argument('--target-model', type=str, default='cifar_mlp', metavar='M',
help='target model type')
self.parser.add_argument('--log-file', type=str, default='', metavar='M',
help='log file')
self.parser.add_argument('--step1', default=30, type=int, metavar='N',
help='step1 lr')
self.parser.add_argument('--model-path', type=str, default='v1', metavar='M',
help='model path')
self.parser.add_argument('--selected-layers', type=str, default='0', metavar='M',
help='selected layers')
self.parser.add_argument('--cls-or-retr', action='store_true',
help='True for classification, False for retrieval.')
else:
self.parser.add_argument('--root-path', type=str,
default='/media/data2/anonymous/projects/LearnableParameterSimilarity/weights/cifar100_rnn_v1', metavar='RP',
help='root path for weights')
self.parser.add_argument('--batch-size', type=int, default=1, metavar='N',
help='input batch size for training (default: 64)')
self.parser.add_argument('--test-batch-size', type=int, default=1, metavar='N',
help='input batch size for testing (default: 1000)')
self.parser.add_argument('--epochs', type=int, default=100, metavar='N',
help='number of epochs to train (default: 10)')
self.parser.add_argument('--lr', type=float, default=0.001, metavar='LR',
help='learning rate (default: 0.01)')
self.parser.add_argument('--momentum', type=float, default=0.9, metavar='M',
help='SGD momentum (default: 0.5)')
self.parser.add_argument('--log-interval', type=int, default=10, metavar='N',
help='how many batches to wait before logging training status')
self.parser.add_argument('--not-save-model', action='store_true', default=True,
help='For Saving the current Model')
self.parser.add_argument('--weight-decay', '--wd', default=1e-4, type=float, metavar='W',
help='weight decay (default: 1e-4)')
self.parser.add_argument('--meta-model', type=str, default='cifar_mlp', metavar='M',
help='meta model type')
self.parser.add_argument('--log-file', type=str, default='', metavar='M',
help='log file')
self.parser.add_argument('--step1', default=30, type=int, metavar='N',
help='step1 lr')
self.parser.add_argument('--model-path', type=str, default='v1', metavar='M',
help='model path')
self.parser.add_argument('--selected-layers', type=str, default='0', metavar='M',
help='selected layers')
self.parser.add_argument('--cls-or-retr', action='store_true',
help='True for classification, False for retrieval.')
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| 163
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| 114
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0
| 8
|
862e76885428e343b1cd6f92a768d1a129846329
| 6,038
|
py
|
Python
|
tests/utils/file_utils/test_safe_file_write.py
|
Purg/SMQTK
|
705a2b2979935ed129aac7db578571c4ae1343e7
|
[
"BSD-3-Clause"
] | 1
|
2021-04-25T16:53:50.000Z
|
2021-04-25T16:53:50.000Z
|
tests/utils/file_utils/test_safe_file_write.py
|
Purg/SMQTK
|
705a2b2979935ed129aac7db578571c4ae1343e7
|
[
"BSD-3-Clause"
] | 3
|
2021-09-08T02:17:49.000Z
|
2022-03-12T00:40:33.000Z
|
tests/utils/file_utils/test_safe_file_write.py
|
Purg/SMQTK
|
705a2b2979935ed129aac7db578571c4ae1343e7
|
[
"BSD-3-Clause"
] | null | null | null |
import mock
import unittest
from smqtk.utils.file import safe_file_write
class TestSafeFileWrite (unittest.TestCase):
"""
Tests for the ``smqtk.utils.file.safe_file_write`` function.
Mocking out underlying function that would have filesystem side effects.
"""
@mock.patch('smqtk.utils.file.safe_create_dir')
@mock.patch('smqtk.utils.file.os.rename')
@mock.patch('smqtk.utils.file.os.remove')
@mock.patch('smqtk.utils.file.tempfile.NamedTemporaryFile')
def test_safe_file_write_relative_simple(
self, m_NTF, m_remove, m_rename, m_scd):
# Experimental filepath and content.
fp = 'bar.txt'
expected_bytes = 'hello world'
# Mock return for temp file creation so we can check os.* calls.
m_file = m_NTF.return_value
test_tmp_fp = 'temp fp'
m_file.name = test_tmp_fp
safe_file_write(fp, expected_bytes)
m_scd.assert_called_once_with('')
m_NTF.assert_called_once_with(suffix='.txt', prefix='bar.', dir='',
delete=False)
m_file.write.assert_called_once_with(expected_bytes)
m_file.__exit__.assert_called_once_with(None, None, None)
self.assertEqual(m_remove.call_count, 0)
m_rename.assert_called_once_with(test_tmp_fp, fp)
@mock.patch('smqtk.utils.file.safe_create_dir')
@mock.patch('smqtk.utils.file.os.rename')
@mock.patch('smqtk.utils.file.os.remove')
@mock.patch('smqtk.utils.file.tempfile.NamedTemporaryFile')
def test_safe_file_write_relative_subdir(
self, m_NTF, m_remove, m_rename, m_scd):
# Experimental filepath and content.
fp = 'foo/other/bar.txt'
expected_bytes = 'hello world'
# Mock return for temp file creation so we can check os.* calls.
m_file = m_NTF.return_value
test_tmp_fp = 'temp fp'
m_file.name = test_tmp_fp
safe_file_write(fp, expected_bytes)
m_scd.assert_called_once_with('foo/other')
m_NTF.assert_called_once_with(suffix='.txt', prefix='bar.',
dir='foo/other', delete=False)
m_file.write.assert_called_once_with(expected_bytes)
m_file.__exit__.assert_called_once_with(None, None, None)
self.assertEqual(m_remove.call_count, 0)
m_rename.assert_called_once_with(test_tmp_fp, fp)
@mock.patch('smqtk.utils.file.safe_create_dir')
@mock.patch('smqtk.utils.file.os.rename')
@mock.patch('smqtk.utils.file.os.remove')
@mock.patch('smqtk.utils.file.tempfile.NamedTemporaryFile')
def test_safe_file_write_custom_tmp_dir(
self, m_NTF, m_remove, m_rename, m_scd):
# Experimental filepath and content.
fp = 'foo/other/bar.txt'
expected_bytes = 'hello world'
custom_tmp_dir = '/some/other/directory'
# Mock return for temp file creation so we can check os.* calls.
m_file = m_NTF.return_value
test_tmp_fp = 'temp fp'
m_file.name = test_tmp_fp
safe_file_write(fp, expected_bytes, custom_tmp_dir)
m_scd.assert_called_once_with('foo/other')
m_NTF.assert_called_once_with(suffix='.txt', prefix='bar.',
dir=custom_tmp_dir, delete=False)
m_file.write.assert_called_once_with(expected_bytes)
m_file.__exit__.assert_called_once_with(None, None, None)
self.assertEqual(m_remove.call_count, 0)
m_rename.assert_called_once_with(test_tmp_fp, fp)
@mock.patch('smqtk.utils.file.safe_create_dir')
@mock.patch('smqtk.utils.file.os.rename')
@mock.patch('smqtk.utils.file.os.remove')
@mock.patch('smqtk.utils.file.tempfile.NamedTemporaryFile')
def test_safe_file_write_absolute(
self, m_NTF, m_remove, m_rename, m_scd):
# Experimental filepath and content.
fp = '/some/absolute/dir/bar.txt'
expected_bytes = 'hello world'
# Mock return for temp file creation so we can check os.* calls.
m_file = m_NTF.return_value
test_tmp_fp = 'temp fp'
m_file.name = test_tmp_fp
safe_file_write(fp, expected_bytes)
m_scd.assert_called_once_with('/some/absolute/dir')
m_NTF.assert_called_once_with(suffix='.txt', prefix='bar.',
dir='/some/absolute/dir', delete=False)
m_file.write.assert_called_once_with(expected_bytes)
m_file.__exit__.assert_called_once_with(None, None, None)
self.assertEqual(m_remove.call_count, 0)
m_rename.assert_called_once_with(test_tmp_fp, fp)
@mock.patch('smqtk.utils.file.safe_create_dir')
@mock.patch('smqtk.utils.file.os.rename')
@mock.patch('smqtk.utils.file.os.remove')
@mock.patch('smqtk.utils.file.tempfile.NamedTemporaryFile')
def test_safe_file_write_raising_write(
self, m_NTF, m_remove, m_rename, m_scd):
# Test for what happens when file.write raises an exception.
# Experimental filepath and content.
fp = 'bar.txt'
expected_bytes = 'hello world'
# Mock return for temp file creation so we can check os.* calls.
m_file = m_NTF.return_value
test_tmp_fp = 'temp fp'
m_file.name = test_tmp_fp
# Mock return from write simulating not all bytes being written.
m_file.write.side_effect = OSError
self.assertRaises(
OSError,
safe_file_write, fp, expected_bytes
)
m_scd.assert_called_once_with('')
m_NTF.assert_called_once_with(suffix='.txt', prefix='bar.', dir='',
delete=False)
m_file.write.assert_called_once_with(expected_bytes)
# Remove should now be called on temp file path
self.assertEqual(m_remove.call_count, 1)
m_remove.assert_called_once_with(test_tmp_fp)
self.assertEqual(m_file.__exit__.call_count, 1)
# Rename should no longer be called.
self.assertEqual(m_rename.call_count, 0)
| 40.52349
| 77
| 0.662637
| 845
| 6,038
| 4.413018
| 0.126627
| 0.077233
| 0.102977
| 0.128721
| 0.828104
| 0.828104
| 0.819791
| 0.812014
| 0.812014
| 0.805042
| 0
| 0.001516
| 0.235343
| 6,038
| 148
| 78
| 40.797297
| 0.806151
| 0.137132
| 0
| 0.761905
| 0
| 0
| 0.179254
| 0.132702
| 0
| 0
| 0
| 0
| 0.304762
| 1
| 0.047619
| false
| 0
| 0.028571
| 0
| 0.085714
| 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
|
86643ebec6d6dd6c7a0c2a878a60211ba8097fa4
| 3,820
|
py
|
Python
|
Product Manager/style.py
|
Vatsalgarg2000/Product_Manager
|
c129461233d1a394a2cf3365186994ca414bd74e
|
[
"Apache-2.0"
] | null | null | null |
Product Manager/style.py
|
Vatsalgarg2000/Product_Manager
|
c129461233d1a394a2cf3365186994ca414bd74e
|
[
"Apache-2.0"
] | null | null | null |
Product Manager/style.py
|
Vatsalgarg2000/Product_Manager
|
c129461233d1a394a2cf3365186994ca414bd74e
|
[
"Apache-2.0"
] | null | null | null |
def searchBoxStyle():
return """
QGroupBox{
background-color:#9bc9ff;
font:15pt Times Bold;
color:white;
border:2px solid gray;
border-radius:15px;
}
"""
def listBoxStyle():
return """
QGroupBox{
background-color:#fcc324;
font:15pt Arial Bold;
color:white;
border:2px solid gray;
border-radius:15px;
}
"""
def searchButtonStyle():
return """
QPushButton{
background-color:#fcc324;
border-style:outset;
border-width:2px;
border-radius:10px;
border-color:beige;
font:12px;
padding:6px;
min-width:6em;
}
"""
def listButtonStyle():
return """
QPushButton{
background-color:#9bc9ff;
border-style:outset;
border-width:2px;
border-radius:10px;
border-color:beige;
font:12px;
padding:6px;
min-width:6em;
}
"""
def productBottomFrame():
return """
QFrame{
background-color:#fcc324;
font:15pt Times Bold;
}
"""
def productTopFrame():
return """
QFrame{
background-color:white;
font:20pt Times Bold;
}
"""
def memberTopFrame():
return """
QFrame{
background-color:white;
font:20pt Times Bold;
}
"""
def memberBottomFrame():
return """
QFrame{
background-color:#fcc324;
font:15pt Times Bold;
}
"""
def sellProductTopFrame():
return """
QFrame{
background-color:white;
font:20pt Times Bold;
}
"""
def sellProductBottomFrame():
return """
QFrame{
background-color:#fcc324;
font:15pt Times Bold;
}
"""
def confirmProductTopFrame():
return """
QFrame{
background-color:white;
font:20pt Times Bold;
}
"""
def confirmProductBottomFrame():
return """
QFrame{
background-color:#fcc324;
font:15pt Times Bold;
}
"""
def addMemberTopFrame():
return """
QFrame{
background-color:white;
font:20pt Times Bold;
}
"""
def addMemberBottomFrame():
return """
QFrame{
background-color:#fcc324;
font:15pt Times Bold;
}
"""
def addProductTopFrame():
return """
QFrame{
background-color:white;
font:20pt Times Bold;
}
"""
def addProductBottomFrame():
return """
QFrame{
background-color:#fcc324;
font:15pt Times Bold;
}
"""
def memberSearchBoxStyle():
return """
QGroupBox{
background-color:#9bc9ff;
font:15pt Times Bold;
color:white;
border:2px solid gray;
border-radius:15px;
}
"""
| 22.209302
| 46
| 0.393194
| 244
| 3,820
| 6.155738
| 0.20082
| 0.169774
| 0.175766
| 0.215712
| 0.73036
| 0.711052
| 0.711052
| 0.711052
| 0.711052
| 0.711052
| 0
| 0.045109
| 0.518325
| 3,820
| 172
| 47
| 22.209302
| 0.771196
| 0
| 0
| 0.75
| 0
| 0
| 0.792055
| 0.113151
| 0
| 0
| 0
| 0
| 0
| 1
| 0.121429
| true
| 0
| 0
| 0.121429
| 0.242857
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
|
0
| 11
|
86d4c5029690fffd504ce9f5d345ba75609792f1
| 11,792
|
py
|
Python
|
tests/pyformance_test.py
|
Starz-Github/signalfx-python
|
2d07b0f0ffb91ccba7071eafab306673c3d71cb7
|
[
"Apache-2.0"
] | 41
|
2015-06-17T16:44:25.000Z
|
2021-08-16T15:12:44.000Z
|
tests/pyformance_test.py
|
Starz-Github/signalfx-python
|
2d07b0f0ffb91ccba7071eafab306673c3d71cb7
|
[
"Apache-2.0"
] | 74
|
2015-05-07T19:36:34.000Z
|
2021-12-29T15:29:33.000Z
|
tests/pyformance_test.py
|
Starz-Github/signalfx-python
|
2d07b0f0ffb91ccba7071eafab306673c3d71cb7
|
[
"Apache-2.0"
] | 46
|
2015-05-07T23:23:07.000Z
|
2022-02-28T20:55:14.000Z
|
#!/usr/bin/env python
# Copyright (C) 2018 SignalFx, Inc. All rights reserved.
from pyformance.registry import get_qualname
import os
import sys
import unittest
sys.path.insert(0, os.path.join(
os.path.dirname(os.path.abspath(__file__)), '..'))
# import the signalfx pyformance library
import signalfx.pyformance as pyf # noqa
class TestPyformance(unittest.TestCase):
def tearDown(self):
pyf.clear()
def test_gauge(self):
reg = pyf.MetricsRegistry()
reg.gauge('test_gauge').set_value(1)
reg.gauge('test_gauge_with_dim', default=3,
gauge_dim='hello_gauge').set_value(2)
self.assertEqual(
reg.metadata.get_metadata(
'gauge_dim=hello_gauge.test_gauge_with_dim'),
{
'dimensions': {'gauge_dim': 'hello_gauge'},
'metric': 'test_gauge_with_dim',
})
self.assertEqual(reg.dump_metrics(), {
'test_gauge': {'value': 1},
'gauge_dim=hello_gauge.test_gauge_with_dim': {'value': 2},
}
)
reg.clear()
self.assertEqual(reg.dump_metrics(), {})
self.assertEqual(len(reg.metadata._metadata), 0)
def test_global_gauge(self):
pyf.gauge('test_gauge').set_value(1)
pyf.gauge('test_gauge_with_dim', default=3,
gauge_dim='hello_gauge').set_value(2)
self.assertEqual(
pyf.global_registry().metadata.get_metadata(
'gauge_dim=hello_gauge.test_gauge_with_dim'),
{
'dimensions': {'gauge_dim': 'hello_gauge'},
'metric': 'test_gauge_with_dim',
})
self.assertEqual(pyf.dump_metrics(), {
'test_gauge': {'value': 1},
'gauge_dim=hello_gauge.test_gauge_with_dim': {'value': 2},
}
)
def test_counter(self):
reg = pyf.MetricsRegistry()
reg.counter('test_counter').inc()
reg.counter('test_counter_with_dim',
counter_dim='hello_counter').inc()
self.assertEqual(
reg.metadata.get_metadata(
'counter_dim=hello_counter.test_counter_with_dim'),
{
'dimensions': {'counter_dim': 'hello_counter'},
'metric': 'test_counter_with_dim',
})
self.assertEqual(reg.dump_metrics(), {
'test_counter': {'count': 1},
'counter_dim=hello_counter.test_counter_with_dim': {'count': 1},
})
reg.clear()
self.assertEqual(reg.dump_metrics(), {})
self.assertEqual(len(reg.metadata._metadata), 0)
def test_global_counter(self):
pyf.counter('test_counter').inc()
pyf.counter('test_counter_with_dim',
counter_dim='hello_counter').inc()
self.assertEqual(
pyf.global_registry().metadata.get_metadata(
'counter_dim=hello_counter.test_counter_with_dim'),
{
'dimensions': {'counter_dim': 'hello_counter'},
'metric': 'test_counter_with_dim',
})
self.assertEqual(pyf.dump_metrics(), {
'test_counter': {'count': 1},
'counter_dim=hello_counter.test_counter_with_dim': {'count': 1},
})
def test_counter_decorator(self):
@pyf.count_calls
def callme():
pass
qcallme = get_qualname(callme)
@pyf.count_calls_with_dims(counter_dim='hello_counter')
def callme_with_dims():
pass
qcallme_with_dims = get_qualname(callme_with_dims)
callme()
callme_with_dims()
if sys.version_info[0] < 3:
self.assertEqual(
pyf.global_registry().metadata.get_metadata(
'counter_dim=hello_counter.{0}_calls'.format(
qcallme_with_dims)),
{
'dimensions': {'counter_dim': 'hello_counter'},
'metric': '{0}_calls'.format(qcallme_with_dims),
})
self.assertEqual(pyf.dump_metrics(), {
'{0}_calls'.format(qcallme): {'count': 1},
'counter_dim=hello_counter.{0}_calls'.format(
qcallme_with_dims):
{'count': 1},
})
def test_histogram(self):
reg = pyf.MetricsRegistry()
h1 = reg.histogram('test_histogram')
h1.add(1)
h1.add(1)
h1.add(1)
h2 = reg.histogram('test_histogram_with_dim',
histogram_dim='hello_histogram')
h2.add(1)
h2.add(1)
h2.add(1)
metrics = reg.dump_metrics()
self.assertEqual(metrics, {
'test_histogram': {'count': 3, '999_percentile': 1,
'99_percentile': 1, 'min': 1,
'95_percentile': 1, '75_percentile': 1,
'std_dev': 0.0, 'max': 1, 'avg': 1.0},
'histogram_dim=hello_histogram.test_histogram_with_dim':
{'count': 3, '999_percentile': 1, '99_percentile': 1, 'min': 1,
'95_percentile': 1, '75_percentile': 1, 'std_dev': 0.0,
'max': 1, 'avg': 1.0},
})
reg.clear()
self.assertEqual(reg.dump_metrics(), {})
self.assertEqual(len(reg.metadata._metadata), 0)
def test_global_histogram(self):
h1 = pyf.histogram('test_histogram')
h1.add(1)
h1.add(1)
h1.add(1)
h2 = pyf.histogram('test_histogram_with_dim',
histogram_dim='hello_histogram')
h2.add(1)
h2.add(1)
h2.add(1)
self.assertEqual(
pyf.global_registry().metadata.get_metadata(
'histogram_dim=hello_histogram.test_histogram_with_dim'),
{
'dimensions': {'histogram_dim': 'hello_histogram'},
'metric': 'test_histogram_with_dim',
})
self.assertEqual(pyf.dump_metrics(), {
'test_histogram': {'count': 3, '999_percentile': 1,
'99_percentile': 1, 'min': 1,
'95_percentile': 1, '75_percentile': 1,
'std_dev': 0.0, 'max': 1, 'avg': 1.0},
'histogram_dim=hello_histogram.test_histogram_with_dim':
{'count': 3, '999_percentile': 1, '99_percentile': 1, 'min': 1,
'95_percentile': 1, '75_percentile': 1, 'std_dev': 0.0,
'max': 1, 'avg': 1.0},
})
def test_histogram_decorator(self):
@pyf.hist_calls
def callme():
return 1
qcallme = get_qualname(callme)
@pyf.hist_calls_with_dims(histogram_dim='hello_histogram')
def callme_with_dims():
return 1
qcallme_with_dims = get_qualname(callme_with_dims)
callme()
callme()
callme()
callme_with_dims()
callme_with_dims()
callme_with_dims()
self.assertEqual(
pyf.global_registry().metadata.get_metadata(
'histogram_dim=hello_histogram.{0}_calls'.format(
qcallme_with_dims)),
{
'dimensions': {'histogram_dim': 'hello_histogram'},
'metric': '{0}_calls'.format(qcallme_with_dims),
})
self.assertEqual(pyf.dump_metrics(), {
'{0}_calls'.format(qcallme): {
'count': 3, '999_percentile': 1,
'99_percentile': 1, 'min': 1,
'95_percentile': 1, '75_percentile': 1,
'std_dev': 0.0, 'max': 1, 'avg': 1.0},
'histogram_dim=hello_histogram.{0}_calls'.format(
qcallme_with_dims):
{'count': 3, '999_percentile': 1, '99_percentile': 1, 'min': 1,
'95_percentile': 1, '75_percentile': 1, 'std_dev': 0.0,
'max': 1, 'avg': 1.0},
})
def test_meter(self):
reg = pyf.MetricsRegistry()
reg.meter('test_meter')
reg.meter('test_meter_with_dim',
meter_dim='hello_meter')
self.assertEqual(
reg.metadata.get_metadata(
'meter_dim=hello_meter.test_meter_with_dim'),
{
'dimensions': {'meter_dim': 'hello_meter'},
'metric': 'test_meter_with_dim',
})
self.assertEqual(len(reg.dump_metrics()), 2)
reg.clear()
self.assertEqual(reg.dump_metrics(), {})
self.assertEqual(len(reg.metadata._metadata), 0)
def test_global_meter(self):
pyf.meter('test_meter')
pyf.meter('test_meter_with_dim', meter_dim='hello_meter')
self.assertEqual(
pyf.global_registry().metadata.get_metadata(
'meter_dim=hello_meter.test_meter_with_dim'),
{
'dimensions': {'meter_dim': 'hello_meter'},
'metric': 'test_meter_with_dim',
})
self.assertEqual(len(pyf.dump_metrics()), 2)
def test_meter_decorator(self):
@pyf.meter_calls
def callme():
return 1
@pyf.meter_calls_with_dims(meter_dim='hello_meter')
def callme_with_dims():
return 1
qcallme_with_dims = get_qualname(callme_with_dims)
callme()
callme()
callme()
callme_with_dims()
callme_with_dims()
callme_with_dims()
self.assertEqual(
pyf.global_registry().metadata.get_metadata(
'meter_dim=hello_meter.{0}_calls'.format(qcallme_with_dims)),
{
'dimensions': {'meter_dim': 'hello_meter'},
'metric': '{0}_calls'.format(qcallme_with_dims),
})
self.assertEqual(len(pyf.dump_metrics()), 2)
def test_timer(self):
reg = pyf.MetricsRegistry()
reg.timer('test_timer')
reg.timer('test_timer_with_dim',
timer_dim='hello_timer')
self.assertEqual(
reg.metadata.get_metadata(
'timer_dim=hello_timer.test_timer_with_dim'),
{
'dimensions': {'timer_dim': 'hello_timer'},
'metric': 'test_timer_with_dim',
})
self.assertEqual(len(reg.dump_metrics()), 2)
reg.clear()
self.assertEqual(reg.dump_metrics(), {})
self.assertEqual(len(reg.metadata._metadata), 0)
def test_global_timer(self):
pyf.timer('test_timer')
pyf.timer('test_timer_with_dim', timer_dim='hello_timer')
self.assertEqual(
pyf.global_registry().metadata.get_metadata(
'timer_dim=hello_timer.test_timer_with_dim'),
{
'dimensions': {'timer_dim': 'hello_timer'},
'metric': 'test_timer_with_dim',
})
self.assertEqual(len(pyf.dump_metrics()), 2)
def test_timer_decorator(self):
@pyf.time_calls
def callme():
return 1
@pyf.time_calls_with_dims(timer_dim='hello_timer')
def callme_with_dims():
return 1
qcallme_with_dims = get_qualname(callme_with_dims)
callme()
callme()
callme()
callme_with_dims()
callme_with_dims()
callme_with_dims()
self.assertEqual(
pyf.global_registry().metadata.get_metadata(
'timer_dim=hello_timer.{0}_calls'.format(qcallme_with_dims)),
{
'dimensions': {'timer_dim': 'hello_timer'},
'metric': '{0}_calls'.format(qcallme_with_dims),
})
self.assertEqual(len(pyf.dump_metrics()), 2)
if __name__ == '__main__':
unittest.main()
| 34.991098
| 77
| 0.545794
| 1,273
| 11,792
| 4.718775
| 0.073841
| 0.063925
| 0.041951
| 0.043949
| 0.841685
| 0.805727
| 0.766939
| 0.750458
| 0.737473
| 0.737473
| 0
| 0.02511
| 0.324542
| 11,792
| 336
| 78
| 35.095238
| 0.729065
| 0.010092
| 0
| 0.713311
| 0
| 0
| 0.229326
| 0.088954
| 0
| 0
| 0
| 0
| 0.12628
| 1
| 0.078498
| false
| 0.006826
| 0.017065
| 0.020478
| 0.119454
| 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
|
86d6f20744ccd21888ff3f18391753887eb5eb6e
| 4,123
|
py
|
Python
|
examples/jaqal/single_qubit_gst.py
|
haikusw/jaqalpaq
|
d507e894cb897756a1e51c99582b736254995b4e
|
[
"Apache-2.0"
] | 8
|
2021-02-19T23:25:28.000Z
|
2021-09-24T20:11:13.000Z
|
examples/jaqal/single_qubit_gst.py
|
haikusw/jaqalpaq
|
d507e894cb897756a1e51c99582b736254995b4e
|
[
"Apache-2.0"
] | null | null | null |
examples/jaqal/single_qubit_gst.py
|
haikusw/jaqalpaq
|
d507e894cb897756a1e51c99582b736254995b4e
|
[
"Apache-2.0"
] | null | null | null |
(
"circuit",
("register", "q", 1),
("macro", "F0", "qubit", ("sequential_block",)),
("macro", "F1", "qubit", ("sequential_block", ("gate", "Sx", "qubit"))),
("macro", "F2", "qubit", ("sequential_block", ("gate", "Sy", "qubit"))),
(
"macro",
"F3",
"qubit",
("sequential_block", ("gate", "Sx", "qubit"), ("gate", "Sx", "qubit")),
),
(
"macro",
"F4",
"qubit",
(
"sequential_block",
("gate", "Sx", "qubit"),
("gate", "Sx", "qubit"),
("gate", "Sx", "qubit"),
),
),
(
"macro",
"F5",
"qubit",
(
"sequential_block",
("gate", "Sy", "qubit"),
("gate", "Sy", "qubit"),
("gate", "Sy", "qubit"),
),
),
("macro", "G0", "qubit", ("sequential_block", ("gate", "Sx", "qubit"))),
("macro", "G1", "qubit", ("sequential_block", ("gate", "Sy", "qubit"))),
("macro", "G2", "qubit", ("sequential_block", ("gate", "I_Sx", "qubit"))),
(
"macro",
"G3",
"qubit",
("sequential_block", ("gate", "Sx", "qubit"), ("gate", "Sy", "qubit")),
),
(
"macro",
"G4",
"qubit",
(
"sequential_block",
("gate", "Sx", "qubit"),
("gate", "Sy", "qubit"),
("gate", "I_Sx", "qubit"),
),
),
(
"macro",
"G5",
"qubit",
(
"sequential_block",
("gate", "Sx", "qubit"),
("gate", "I_Sx", "qubit"),
("gate", "Sy", "qubit"),
),
),
(
"macro",
"G6",
"qubit",
(
"sequential_block",
("gate", "Sx", "qubit"),
("gate", "I_Sx", "qubit"),
("gate", "I_Sx", "qubit"),
),
),
(
"macro",
"G7",
"qubit",
(
"sequential_block",
("gate", "Sy", "qubit"),
("gate", "I_Sx", "qubit"),
("gate", "I_Sx", "qubit"),
),
),
(
"macro",
"G8",
"qubit",
(
"sequential_block",
("gate", "Sx", "qubit"),
("gate", "Sx", "qubit"),
("gate", "I_Sx", "qubit"),
("gate", "Sy", "qubit"),
),
),
(
"macro",
"G9",
"qubit",
(
"sequential_block",
("gate", "Sx", "qubit"),
("gate", "Sy", "qubit"),
("gate", "Sy", "qubit"),
("gate", "I_Sx", "qubit"),
),
),
(
"macro",
"G10",
"qubit",
(
"sequential_block",
("gate", "Sx", "qubit"),
("gate", "Sx", "qubit"),
("gate", "Sy", "qubit"),
("gate", "Sx", "qubit"),
("gate", "Sy", "qubit"),
("gate", "Sy", "qubit"),
),
),
("gate", "prepare_all"),
("gate", "F0", ("array_item", "q", 0)),
("gate", "measure_all"),
("gate", "prepare_all"),
("gate", "F1", ("array_item", "q", 0)),
("gate", "measure_all"),
("gate", "prepare_all"),
("gate", "F2", ("array_item", "q", 0)),
("gate", "measure_all"),
("gate", "prepare_all"),
("gate", "F3", ("array_item", "q", 0)),
("gate", "measure_all"),
("gate", "prepare_all"),
("gate", "F4", ("array_item", "q", 0)),
("gate", "measure_all"),
("gate", "prepare_all"),
("gate", "F5", ("array_item", "q", 0)),
("gate", "measure_all"),
("gate", "prepare_all"),
("gate", "F1", ("array_item", "q", 0)),
("gate", "F1", ("array_item", "q", 0)),
("gate", "measure_all"),
("gate", "prepare_all"),
("gate", "F1", ("array_item", "q", 0)),
("gate", "F2", ("array_item", "q", 0)),
("gate", "measure_all"),
("gate", "prepare_all"),
("gate", "F1", ("array_item", "q", 0)),
("loop", 8, ("sequential_block", ("gate", "G1", ("array_item", "q", 0)))),
("gate", "F1", ("array_item", "q", 0)),
("gate", "measure_all"),
)
| 26.094937
| 79
| 0.360902
| 357
| 4,123
| 4.005602
| 0.103641
| 0.127273
| 0.237762
| 0.268531
| 0.895105
| 0.87972
| 0.876224
| 0.708392
| 0.703497
| 0.626573
| 0
| 0.017544
| 0.364055
| 4,123
| 157
| 80
| 26.261147
| 0.527841
| 0
| 0
| 0.694268
| 0
| 0
| 0.357264
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 9
|
86f6aa7336abb75cd61c8b8fbecbbeee2a5dc4ba
| 3,708
|
py
|
Python
|
tests/test_process_collector.py
|
vmarkovtsev/client_python
|
dd93abe3b1d20bf8ac0ea07080e1c961dc8e44bd
|
[
"Apache-2.0"
] | 2,729
|
2015-02-12T13:13:24.000Z
|
2022-03-30T10:33:12.000Z
|
tests/test_process_collector.py
|
vmarkovtsev/client_python
|
dd93abe3b1d20bf8ac0ea07080e1c961dc8e44bd
|
[
"Apache-2.0"
] | 668
|
2015-02-10T22:57:50.000Z
|
2022-03-30T06:25:49.000Z
|
tests/test_process_collector.py
|
vmarkovtsev/client_python
|
dd93abe3b1d20bf8ac0ea07080e1c961dc8e44bd
|
[
"Apache-2.0"
] | 767
|
2015-02-10T22:51:46.000Z
|
2022-03-26T01:11:58.000Z
|
from __future__ import unicode_literals
import os
import unittest
from prometheus_client import CollectorRegistry, ProcessCollector
class TestProcessCollector(unittest.TestCase):
def setUp(self):
self.registry = CollectorRegistry()
self.test_proc = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'proc')
def test_working(self):
collector = ProcessCollector(proc=self.test_proc, pid=lambda: 26231, registry=self.registry)
collector._ticks = 100
collector._pagesize = 4096
self.assertEqual(17.21, self.registry.get_sample_value('process_cpu_seconds_total'))
self.assertEqual(56274944.0, self.registry.get_sample_value('process_virtual_memory_bytes'))
self.assertEqual(8114176, self.registry.get_sample_value('process_resident_memory_bytes'))
self.assertEqual(1418184099.75, self.registry.get_sample_value('process_start_time_seconds'))
self.assertEqual(2048.0, self.registry.get_sample_value('process_max_fds'))
self.assertEqual(5.0, self.registry.get_sample_value('process_open_fds'))
self.assertEqual(None, self.registry.get_sample_value('process_fake_namespace'))
def test_namespace(self):
collector = ProcessCollector(proc=self.test_proc, pid=lambda: 26231, registry=self.registry, namespace='n')
collector._ticks = 100
collector._pagesize = 4096
self.assertEqual(17.21, self.registry.get_sample_value('n_process_cpu_seconds_total'))
self.assertEqual(56274944.0, self.registry.get_sample_value('n_process_virtual_memory_bytes'))
self.assertEqual(8114176, self.registry.get_sample_value('n_process_resident_memory_bytes'))
self.assertEqual(1418184099.75, self.registry.get_sample_value('n_process_start_time_seconds'))
self.assertEqual(2048.0, self.registry.get_sample_value('n_process_max_fds'))
self.assertEqual(5.0, self.registry.get_sample_value('n_process_open_fds'))
self.assertEqual(None, self.registry.get_sample_value('process_cpu_seconds_total'))
def test_working_584(self):
collector = ProcessCollector(proc=self.test_proc, pid=lambda: "584\n", registry=self.registry)
collector._ticks = 100
collector._pagesize = 4096
self.assertEqual(0.0, self.registry.get_sample_value('process_cpu_seconds_total'))
self.assertEqual(10395648.0, self.registry.get_sample_value('process_virtual_memory_bytes'))
self.assertEqual(634880, self.registry.get_sample_value('process_resident_memory_bytes'))
self.assertEqual(1418291667.75, self.registry.get_sample_value('process_start_time_seconds'))
self.assertEqual(None, self.registry.get_sample_value('process_max_fds'))
self.assertEqual(None, self.registry.get_sample_value('process_open_fds'))
def test_working_fake_pid(self):
collector = ProcessCollector(proc=self.test_proc, pid=lambda: 123, registry=self.registry)
collector._ticks = 100
collector._pagesize = 4096
self.assertEqual(None, self.registry.get_sample_value('process_cpu_seconds_total'))
self.assertEqual(None, self.registry.get_sample_value('process_virtual_memory_bytes'))
self.assertEqual(None, self.registry.get_sample_value('process_resident_memory_bytes'))
self.assertEqual(None, self.registry.get_sample_value('process_start_time_seconds'))
self.assertEqual(None, self.registry.get_sample_value('process_max_fds'))
self.assertEqual(None, self.registry.get_sample_value('process_open_fds'))
self.assertEqual(None, self.registry.get_sample_value('process_fake_namespace'))
if __name__ == '__main__':
unittest.main()
| 54.529412
| 115
| 0.755933
| 471
| 3,708
| 5.592357
| 0.152866
| 0.145786
| 0.153759
| 0.215262
| 0.854594
| 0.854594
| 0.854594
| 0.853075
| 0.851936
| 0.809415
| 0
| 0.048095
| 0.136462
| 3,708
| 67
| 116
| 55.343284
| 0.774516
| 0
| 0
| 0.301887
| 0
| 0
| 0.176645
| 0.137271
| 0
| 0
| 0
| 0
| 0.509434
| 1
| 0.09434
| false
| 0
| 0.075472
| 0
| 0.188679
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
86ff1581adfdbc6ba1c6c689e6a0d1e115b08d15
| 161
|
py
|
Python
|
Lib/Scripts/glyphs/actions/copy & paste.py
|
gferreira/hTools2
|
a75a671b81a0f4ce5c82b2ad3e2f971ca3e3d98c
|
[
"BSD-3-Clause"
] | 11
|
2015-01-06T15:43:56.000Z
|
2019-07-27T00:35:20.000Z
|
Lib/Scripts/glyphs/actions/copy & paste.py
|
gferreira/hTools2
|
a75a671b81a0f4ce5c82b2ad3e2f971ca3e3d98c
|
[
"BSD-3-Clause"
] | 2
|
2017-05-17T10:11:46.000Z
|
2018-11-21T21:43:43.000Z
|
Lib/Scripts/glyphs/actions/copy & paste.py
|
gferreira/hTools2
|
a75a671b81a0f4ce5c82b2ad3e2f971ca3e3d98c
|
[
"BSD-3-Clause"
] | 4
|
2015-01-10T13:58:50.000Z
|
2019-12-18T15:40:14.000Z
|
# [h] copy / paste
import hTools2.dialogs.glyphs.copy_paste
reload(hTools2.dialogs.glyphs.copy_paste)
hTools2.dialogs.glyphs.copy_paste.copyPasteGlyphDialog()
| 23
| 56
| 0.819876
| 21
| 161
| 6.142857
| 0.428571
| 0.27907
| 0.465116
| 0.55814
| 0.674419
| 0
| 0
| 0
| 0
| 0
| 0
| 0.02
| 0.068323
| 161
| 6
| 57
| 26.833333
| 0.84
| 0.099379
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.333333
| 0
| 0.333333
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 7
|
810d025d92882a4f6972fa75975ddd07cb8d6801
| 2,460
|
py
|
Python
|
beer-song/beer_song_test.py
|
mambocab/xpython
|
be4aacc18aafee449fa2ce0f515ee03b0c8ae4d9
|
[
"MIT"
] | null | null | null |
beer-song/beer_song_test.py
|
mambocab/xpython
|
be4aacc18aafee449fa2ce0f515ee03b0c8ae4d9
|
[
"MIT"
] | null | null | null |
beer-song/beer_song_test.py
|
mambocab/xpython
|
be4aacc18aafee449fa2ce0f515ee03b0c8ae4d9
|
[
"MIT"
] | 1
|
2020-06-10T23:33:20.000Z
|
2020-06-10T23:33:20.000Z
|
import unittest
from beer import song, verse
class BeerTest(unittest.TestCase):
def test_a_verse(self):
self.assertEqual(
verse(8),
"8 bottles of beer on the wall, 8 bottles of beer.\n"
"Take one down and pass it around, 7 bottles of beer on the wall.\n"
)
def test_verse_1(self):
self.assertEqual(
verse(1),
"1 bottle of beer on the wall, 1 bottle of beer.\n"
"Take it down and pass it around, no more bottles of beer on the wall.\n"
)
def test_verse_2(self):
self.assertEqual(
verse(2),
"2 bottles of beer on the wall, 2 bottles of beer.\n"
"Take one down and pass it around, 1 bottle of beer on the wall.\n"
)
def test_verse_0(self):
self.assertEqual(
verse(0),
"No more bottles of beer on the wall, no more bottles of beer.\n"
"Go to the store and buy some more, 99 bottles of beer on the wall.\n"
)
def test_songing_several_verses(self):
self.assertEqual(
song(8, 6),
"8 bottles of beer on the wall, 8 bottles of beer.\n"
"Take one down and pass it around, 7 bottles of beer on the wall.\n"
"\n"
"7 bottles of beer on the wall, 7 bottles of beer.\n"
"Take one down and pass it around, 6 bottles of beer on the wall.\n"
"\n"
"6 bottles of beer on the wall, 6 bottles of beer.\n"
"Take one down and pass it around, 5 bottles of beer on the wall.\n"
"\n"
)
def test_song_all_the_rest_of_the_verses(self):
self.assertEqual(
song(3),
"3 bottles of beer on the wall, 3 bottles of beer.\n"
"Take one down and pass it around, 2 bottles of beer on the wall.\n"
"\n"
"2 bottles of beer on the wall, 2 bottles of beer.\n"
"Take one down and pass it around, 1 bottle of beer on the wall.\n"
"\n"
"1 bottle of beer on the wall, 1 bottle of beer.\n"
"Take it down and pass it around, no more bottles of beer on the wall.\n"
"\n"
"No more bottles of beer on the wall, no more bottles of beer.\n"
"Go to the store and buy some more, 99 bottles of beer on the wall.\n"
"\n"
)
if __name__ == '__main__':
unittest.main()
| 36.176471
| 85
| 0.55813
| 389
| 2,460
| 3.462725
| 0.133676
| 0.146993
| 0.260579
| 0.179659
| 0.812918
| 0.769859
| 0.753526
| 0.717149
| 0.66147
| 0.647365
| 0
| 0.024809
| 0.360976
| 2,460
| 67
| 86
| 36.716418
| 0.832061
| 0
| 0
| 0.5
| 0
| 0
| 0.545122
| 0
| 0
| 0
| 0
| 0
| 0.103448
| 1
| 0.103448
| false
| 0.155172
| 0.034483
| 0
| 0.155172
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 8
|
d4b8cad13f3f663e5b8fa574dc0bfdd109f5614f
| 136
|
py
|
Python
|
scripts/create_db.py
|
abrookins/siren
|
8e85d35e01e804ce962ea3ffe88885270b3bd573
|
[
"MIT"
] | 2
|
2015-01-12T10:04:29.000Z
|
2018-07-09T16:56:27.000Z
|
scripts/create_db.py
|
abrookins/siren
|
8e85d35e01e804ce962ea3ffe88885270b3bd573
|
[
"MIT"
] | null | null | null |
scripts/create_db.py
|
abrookins/siren
|
8e85d35e01e804ce962ea3ffe88885270b3bd573
|
[
"MIT"
] | null | null | null |
import os
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
import util
util.make_crimes_db()
| 15.111111
| 77
| 0.779412
| 23
| 136
| 4.347826
| 0.521739
| 0.18
| 0.26
| 0.3
| 0.32
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.080882
| 136
| 8
| 78
| 17
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.6
| 0
| 0.6
| 0
| 1
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
d4bf4c78ad21229345e1f693919c458914f989b7
| 107
|
py
|
Python
|
config.py
|
e-io/anticovirus
|
893c3746e2e3471b75ff5e8f2fcdcddbfcdd834e
|
[
"Apache-2.0"
] | 1
|
2020-05-18T17:26:04.000Z
|
2020-05-18T17:26:04.000Z
|
config.py
|
e-io/anticovirus
|
893c3746e2e3471b75ff5e8f2fcdcddbfcdd834e
|
[
"Apache-2.0"
] | null | null | null |
config.py
|
e-io/anticovirus
|
893c3746e2e3471b75ff5e8f2fcdcddbfcdd834e
|
[
"Apache-2.0"
] | null | null | null |
vk_token="7fc711c235c1904d191b6a80b7c440b66dc90e76919d7e006cad3922b599016a32226736c5bbcf91555c5"
lang="ru"
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0
| 8
|
d4c0293a1d5cce797deb06ef44cce236502d021a
| 5,958
|
py
|
Python
|
tests/python/test_sparse_matrix.py
|
josephgalestian/taichiV2-master
|
12a63a05fdccc824205b1ee6545e4706bf473405
|
[
"MIT"
] | null | null | null |
tests/python/test_sparse_matrix.py
|
josephgalestian/taichiV2-master
|
12a63a05fdccc824205b1ee6545e4706bf473405
|
[
"MIT"
] | null | null | null |
tests/python/test_sparse_matrix.py
|
josephgalestian/taichiV2-master
|
12a63a05fdccc824205b1ee6545e4706bf473405
|
[
"MIT"
] | null | null | null |
import taichi as ti
from tests import test_utils
@test_utils.test(arch=ti.cpu)
def test_sparse_matrix_builder():
n = 8
Abuilder = ti.linalg.SparseMatrixBuilder(n, n, max_num_triplets=100)
@ti.kernel
def fill(Abuilder: ti.linalg.sparse_matrix_builder()):
for i, j in ti.ndrange(n, n):
Abuilder[i, j] += i + j
fill(Abuilder)
A = Abuilder.build()
for i in range(n):
for j in range(n):
assert A[i, j] == i + j
@test_utils.test(arch=ti.cpu)
def test_sparse_matrix_shape():
n, m = 8, 9
Abuilder = ti.linalg.SparseMatrixBuilder(n, m, max_num_triplets=100)
@ti.kernel
def fill(Abuilder: ti.linalg.sparse_matrix_builder()):
for i, j in ti.ndrange(n, m):
Abuilder[i, j] += i + j
fill(Abuilder)
A = Abuilder.build()
assert A.shape() == (n, m)
@test_utils.test(arch=ti.cpu)
def test_sparse_matrix_element_access():
n = 8
Abuilder = ti.linalg.SparseMatrixBuilder(n, n, max_num_triplets=100)
@ti.kernel
def fill(Abuilder: ti.linalg.sparse_matrix_builder()):
for i in range(n):
Abuilder[i, i] += i
fill(Abuilder)
A = Abuilder.build()
for i in range(n):
assert A[i, i] == i
@test_utils.test(arch=ti.cpu)
def test_sparse_matrix_element_modify():
n = 8
Abuilder = ti.linalg.SparseMatrixBuilder(n, n, max_num_triplets=100)
@ti.kernel
def fill(Abuilder: ti.linalg.sparse_matrix_builder()):
for i in range(n):
Abuilder[i, i] += i
fill(Abuilder)
A = Abuilder.build()
A[0, 0] = 1024.0
assert A[0, 0] == 1024.0
@test_utils.test(arch=ti.cpu)
def test_sparse_matrix_addition():
n = 8
Abuilder = ti.linalg.SparseMatrixBuilder(n, n, max_num_triplets=100)
Bbuilder = ti.linalg.SparseMatrixBuilder(n, n, max_num_triplets=100)
@ti.kernel
def fill(Abuilder: ti.linalg.sparse_matrix_builder(),
Bbuilder: ti.linalg.sparse_matrix_builder()):
for i, j in ti.ndrange(n, n):
Abuilder[i, j] += i + j
Bbuilder[i, j] += i - j
fill(Abuilder, Bbuilder)
A = Abuilder.build()
B = Bbuilder.build()
C = A + B
for i in range(n):
for j in range(n):
assert C[i, j] == 2 * i
@test_utils.test(arch=ti.cpu)
def test_sparse_matrix_subtraction():
n = 8
Abuilder = ti.linalg.SparseMatrixBuilder(n, n, max_num_triplets=100)
Bbuilder = ti.linalg.SparseMatrixBuilder(n, n, max_num_triplets=100)
@ti.kernel
def fill(Abuilder: ti.linalg.sparse_matrix_builder(),
Bbuilder: ti.linalg.sparse_matrix_builder()):
for i, j in ti.ndrange(n, n):
Abuilder[i, j] += i + j
Bbuilder[i, j] += i - j
fill(Abuilder, Bbuilder)
A = Abuilder.build()
B = Bbuilder.build()
C = A - B
for i in range(n):
for j in range(n):
assert C[i, j] == 2 * j
@test_utils.test(arch=ti.cpu)
def test_sparse_matrix_scalar_multiplication():
n = 8
Abuilder = ti.linalg.SparseMatrixBuilder(n, n, max_num_triplets=100)
@ti.kernel
def fill(Abuilder: ti.linalg.sparse_matrix_builder()):
for i, j in ti.ndrange(n, n):
Abuilder[i, j] += i + j
fill(Abuilder)
A = Abuilder.build()
B = A * 3.0
for i in range(n):
for j in range(n):
assert B[i, j] == 3 * (i + j)
@test_utils.test(arch=ti.cpu)
def test_sparse_matrix_transpose():
n = 8
Abuilder = ti.linalg.SparseMatrixBuilder(n, n, max_num_triplets=100)
@ti.kernel
def fill(Abuilder: ti.linalg.sparse_matrix_builder()):
for i, j in ti.ndrange(n, n):
Abuilder[i, j] += i + j
fill(Abuilder)
A = Abuilder.build()
B = A.transpose()
for i in range(n):
for j in range(n):
assert B[i, j] == A[j, i]
@test_utils.test(arch=ti.cpu)
def test_sparse_matrix_elementwise_multiplication():
n = 8
Abuilder = ti.linalg.SparseMatrixBuilder(n, n, max_num_triplets=100)
Bbuilder = ti.linalg.SparseMatrixBuilder(n, n, max_num_triplets=100)
@ti.kernel
def fill(Abuilder: ti.linalg.sparse_matrix_builder(),
Bbuilder: ti.linalg.sparse_matrix_builder()):
for i, j in ti.ndrange(n, n):
Abuilder[i, j] += i + j
Bbuilder[i, j] += i - j
fill(Abuilder, Bbuilder)
A = Abuilder.build()
B = Bbuilder.build()
C = A * B
for i in range(n):
for j in range(n):
assert C[i, j] == (i + j) * (i - j)
@test_utils.test(arch=ti.cpu)
def test_sparse_matrix_multiplication():
n = 2
Abuilder = ti.linalg.SparseMatrixBuilder(n, n, max_num_triplets=100)
Bbuilder = ti.linalg.SparseMatrixBuilder(n, n, max_num_triplets=100)
@ti.kernel
def fill(Abuilder: ti.linalg.sparse_matrix_builder(),
Bbuilder: ti.linalg.sparse_matrix_builder()):
for i, j in ti.ndrange(n, n):
Abuilder[i, j] += i + j
Bbuilder[i, j] += i - j
fill(Abuilder, Bbuilder)
A = Abuilder.build()
B = Bbuilder.build()
C = A @ B
assert C[0, 0] == 1.0
assert C[0, 1] == 0.0
assert C[1, 0] == 2.0
assert C[1, 1] == -1.0
@test_utils.test(arch=ti.cpu)
def test_sparse_matrix_nonsymmetric_multiplication():
n, k, m = 2, 3, 4
Abuilder = ti.linalg.SparseMatrixBuilder(n, k, max_num_triplets=100)
Bbuilder = ti.linalg.SparseMatrixBuilder(k, m, max_num_triplets=100)
@ti.kernel
def fill(Abuilder: ti.linalg.sparse_matrix_builder(),
Bbuilder: ti.linalg.sparse_matrix_builder()):
for i, j in ti.ndrange(n, k):
Abuilder[i, j] += i + j
for i, j in ti.ndrange(k, m):
Bbuilder[i, j] -= i + j
fill(Abuilder, Bbuilder)
A = Abuilder.build()
B = Bbuilder.build()
C = A @ B
GT = [[-5, -8, -11, -14], [-8, -14, -20, -26]]
for i in range(n):
for j in range(m):
assert C[i, j] == GT[i][j]
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| 0
|
0
| 7
|
d4d3eb3ecdc6bc63b9fb323f4f6cbb2ee4137d98
| 45,387
|
py
|
Python
|
odoo-13.0/addons/sale_stock/tests/test_anglo_saxon_valuation.py
|
VaibhavBhujade/Blockchain-ERP-interoperability
|
b5190a037fb6615386f7cbad024d51b0abd4ba03
|
[
"MIT"
] | 12
|
2021-03-26T08:39:40.000Z
|
2022-03-16T02:20:10.000Z
|
odoo-13.0/addons/sale_stock/tests/test_anglo_saxon_valuation.py
|
VaibhavBhujade/Blockchain-ERP-interoperability
|
b5190a037fb6615386f7cbad024d51b0abd4ba03
|
[
"MIT"
] | 13
|
2020-12-20T16:00:21.000Z
|
2022-03-14T14:55:30.000Z
|
odoo-13.0/addons/sale_stock/tests/test_anglo_saxon_valuation.py
|
VaibhavBhujade/Blockchain-ERP-interoperability
|
b5190a037fb6615386f7cbad024d51b0abd4ba03
|
[
"MIT"
] | 17
|
2020-08-31T11:18:49.000Z
|
2022-02-09T05:57:31.000Z
|
# -*- coding: utf-8 -*-
# Part of Odoo. See LICENSE file for full copyright and licensing details.
from odoo.tests import Form
from odoo.tests.common import SavepointCase
from odoo.exceptions import UserError
class TestAngloSaxonValuation(SavepointCase):
@classmethod
def setUpClass(cls):
super(TestAngloSaxonValuation, cls).setUpClass()
cls.env.user.company_id.anglo_saxon_accounting = True
cls.product = cls.env['product.product'].create({
'name': 'product',
'type': 'product',
'categ_id': cls.env.ref('product.product_category_all').id,
})
cls.stock_input_account = cls.env['account.account'].create({
'name': 'Stock Input',
'code': 'StockIn',
'user_type_id': cls.env.ref('account.data_account_type_current_assets').id,
})
cls.stock_output_account = cls.env['account.account'].create({
'name': 'Stock Output',
'code': 'StockOut',
'reconcile': True,
'user_type_id': cls.env.ref('account.data_account_type_current_assets').id,
})
cls.stock_valuation_account = cls.env['account.account'].create({
'name': 'Stock Valuation',
'code': 'StockVal',
'user_type_id': cls.env.ref('account.data_account_type_current_assets').id,
})
cls.expense_account = cls.env['account.account'].create({
'name': 'Expense Account',
'code': 'Exp',
'user_type_id': cls.env.ref('account.data_account_type_expenses').id,
})
cls.income_account = cls.env['account.account'].create({
'name': 'Income Account',
'code': 'Inc',
'user_type_id': cls.env.ref('account.data_account_type_expenses').id,
})
cls.stock_journal = cls.env['account.journal'].create({
'name': 'Stock Journal',
'code': 'STJTEST',
'type': 'general',
})
cls.product.write({
'property_account_expense_id': cls.expense_account.id,
'property_account_income_id': cls.income_account.id,
})
cls.product.categ_id.write({
'property_stock_account_input_categ_id': cls.stock_input_account.id,
'property_stock_account_output_categ_id': cls.stock_output_account.id,
'property_stock_valuation_account_id': cls.stock_valuation_account.id,
'property_stock_journal': cls.stock_journal.id,
'property_valuation': 'real_time',
})
cls.stock_location = cls.env['stock.warehouse'].search([], limit=1).lot_stock_id
cls.recv_account = cls.env['account.account'].create({
'name': 'account receivable',
'code': 'RECV',
'user_type_id': cls.env.ref('account.data_account_type_receivable').id,
'reconcile': True,
})
cls.pay_account = cls.env['account.account'].create({
'name': 'account payable',
'code': 'PAY',
'user_type_id': cls.env.ref('account.data_account_type_payable').id,
'reconcile': True,
})
cls.customer = cls.env['res.partner'].create({
'name': 'customer',
'property_account_receivable_id': cls.recv_account.id,
'property_account_payable_id': cls.pay_account.id,
})
cls.journal_sale = cls.env['account.journal'].create({
'name': 'Sale Journal - Test',
'code': 'AJ-SALE',
'type': 'sale',
'company_id': cls.env.user.company_id.id,
})
cls.counterpart_account = cls.env['account.account'].create({
'name': 'Counterpart account',
'code': 'Count',
'user_type_id': cls.env.ref('account.data_account_type_expenses').id,
})
def _inv_adj_two_units(self):
inventory = self.env['stock.inventory'].create({
'name': 'test',
'location_ids': [(4, self.stock_location.id)],
'product_ids': [(4, self.product.id)],
})
inventory.action_start()
self.env['stock.inventory.line'].create({
'inventory_id': inventory.id,
'location_id': self.stock_location.id,
'product_id': self.product.id,
'product_qty': 2,
})
inventory.action_validate()
def _so_and_confirm_two_units(self):
sale_order = self.env['sale.order'].create({
'partner_id': self.customer.id,
'order_line': [
(0, 0, {
'name': self.product.name,
'product_id': self.product.id,
'product_uom_qty': 2.0,
'product_uom': self.product.uom_id.id,
'price_unit': 12,
'tax_id': False, # no love taxes amls
})],
})
sale_order.action_confirm()
return sale_order
def _fifo_in_one_eight_one_ten(self):
# Put two items in stock.
in_move_1 = self.env['stock.move'].create({
'name': 'a',
'product_id': self.product.id,
'location_id': self.env.ref('stock.stock_location_suppliers').id,
'location_dest_id': self.stock_location.id,
'product_uom': self.product.uom_id.id,
'product_uom_qty': 1,
'price_unit': 8,
})
in_move_1._action_confirm()
in_move_1.quantity_done = 1
in_move_1._action_done()
in_move_2 = self.env['stock.move'].create({
'name': 'a',
'product_id': self.product.id,
'location_id': self.env.ref('stock.stock_location_suppliers').id,
'location_dest_id': self.stock_location.id,
'product_uom': self.product.uom_id.id,
'product_uom_qty': 1,
'price_unit': 10,
})
in_move_2._action_confirm()
in_move_2.quantity_done = 1
in_move_2._action_done()
# -------------------------------------------------------------------------
# Standard Ordered
# -------------------------------------------------------------------------
def test_standard_ordered_invoice_pre_delivery(self):
"""Standard price set to 10. Get 2 units in stock. Sale order 2@12. Standard price set
to 14. Invoice 2 without delivering. The amount in Stock OUT and COGS should be 14*2.
"""
self.product.categ_id.property_cost_method = 'standard'
self.product.invoice_policy = 'order'
self.product._change_standard_price(10.0, counterpart_account_id=self.counterpart_account.id)
# Put two items in stock.
self._inv_adj_two_units()
# Create and confirm a sale order for 2@12
sale_order = self._so_and_confirm_two_units()
# standard price to 14
self.product._change_standard_price(14.0, counterpart_account_id=self.counterpart_account.id)
# Invoice the sale order.
invoice = sale_order.with_context(default_journal_id=self.journal_sale.id)._create_invoices()
invoice.post()
# Check the resulting accounting entries
amls = invoice.line_ids
self.assertEqual(len(amls), 4)
stock_out_aml = amls.filtered(lambda aml: aml.account_id == self.stock_output_account)
self.assertEqual(stock_out_aml.debit, 0)
self.assertEqual(stock_out_aml.credit, 28)
cogs_aml = amls.filtered(lambda aml: aml.account_id == self.expense_account)
self.assertEqual(cogs_aml.debit, 28)
self.assertEqual(cogs_aml.credit, 0)
receivable_aml = amls.filtered(lambda aml: aml.account_id == self.recv_account)
self.assertEqual(receivable_aml.debit, 24)
self.assertEqual(receivable_aml.credit, 0)
income_aml = amls.filtered(lambda aml: aml.account_id == self.income_account)
self.assertEqual(income_aml.debit, 0)
self.assertEqual(income_aml.credit, 24)
def test_standard_ordered_invoice_post_partial_delivery_1(self):
"""Standard price set to 10. Get 2 units in stock. Sale order 2@12. Deliver 1, invoice 1,
change the standard price to 14, deliver one, change the standard price to 16, invoice 1.
The amounts used in Stock OUT and COGS should be 10 then 14."""
self.product.categ_id.property_cost_method = 'standard'
self.product.invoice_policy = 'order'
self.product._change_standard_price(10.0, counterpart_account_id=self.counterpart_account.id)
# Put two items in stock.
sale_order = self._so_and_confirm_two_units()
# Create and confirm a sale order for 2@12
sale_order = self._so_and_confirm_two_units()
# Deliver one.
sale_order.picking_ids.move_lines.quantity_done = 1
wiz = sale_order.picking_ids.button_validate()
wiz = self.env[wiz['res_model']].browse(wiz['res_id'])
wiz.process()
# Invoice 1
invoice = sale_order.with_context(default_journal_id=self.journal_sale.id)._create_invoices()
invoice_form = Form(invoice)
with invoice_form.invoice_line_ids.edit(0) as invoice_line:
invoice_line.quantity = 1
invoice_form.save()
invoice.post()
# Check the resulting accounting entries
amls = invoice.line_ids
self.assertEqual(len(amls), 4)
stock_out_aml = amls.filtered(lambda aml: aml.account_id == self.stock_output_account)
self.assertEqual(stock_out_aml.debit, 0)
self.assertEqual(stock_out_aml.credit, 10)
cogs_aml = amls.filtered(lambda aml: aml.account_id == self.expense_account)
self.assertEqual(cogs_aml.debit, 10)
self.assertEqual(cogs_aml.credit, 0)
receivable_aml = amls.filtered(lambda aml: aml.account_id == self.recv_account)
self.assertEqual(receivable_aml.debit, 12)
self.assertEqual(receivable_aml.credit, 0)
income_aml = amls.filtered(lambda aml: aml.account_id == self.income_account)
self.assertEqual(income_aml.debit, 0)
self.assertEqual(income_aml.credit, 12)
# change the standard price to 14
self.product._change_standard_price(14.0, counterpart_account_id=self.counterpart_account.id)
# deliver the backorder
sale_order.picking_ids[0].move_lines.quantity_done = 1
sale_order.picking_ids[0].button_validate()
# change the standard price to 16
self.product._change_standard_price(16.0, counterpart_account_id=self.counterpart_account.id)
# invoice 1
invoice2 = sale_order.with_context(default_journal_id=self.journal_sale.id)._create_invoices()
invoice2.post()
amls = invoice2.line_ids
self.assertEqual(len(amls), 4)
stock_out_aml = amls.filtered(lambda aml: aml.account_id == self.stock_output_account)
self.assertEqual(stock_out_aml.debit, 0)
self.assertEqual(stock_out_aml.credit, 14)
cogs_aml = amls.filtered(lambda aml: aml.account_id == self.expense_account)
self.assertEqual(cogs_aml.debit, 14)
self.assertEqual(cogs_aml.credit, 0)
receivable_aml = amls.filtered(lambda aml: aml.account_id == self.recv_account)
self.assertEqual(receivable_aml.debit, 12)
self.assertEqual(receivable_aml.credit, 0)
income_aml = amls.filtered(lambda aml: aml.account_id == self.income_account)
self.assertEqual(income_aml.debit, 0)
self.assertEqual(income_aml.credit, 12)
def test_standard_ordered_invoice_post_delivery(self):
"""Standard price set to 10. Get 2 units in stock. Sale order 2@12. Deliver 1, change the
standard price to 14, deliver one, invoice 2. The amounts used in Stock OUT and COGS should
be 12*2."""
self.product.categ_id.property_cost_method = 'standard'
self.product.invoice_policy = 'order'
self.product.standard_price = 10
# Put two items in stock.
self._inv_adj_two_units()
# Create and confirm a sale order for 2@12
sale_order = self._so_and_confirm_two_units()
# Deliver one.
sale_order.picking_ids.move_lines.quantity_done = 1
wiz = sale_order.picking_ids.button_validate()
wiz = self.env[wiz['res_model']].browse(wiz['res_id'])
wiz.process()
# change the standard price to 14
self.product._change_standard_price(14.0, counterpart_account_id=self.counterpart_account.id)
# deliver the backorder
sale_order.picking_ids.filtered('backorder_id').move_lines.quantity_done = 1
sale_order.picking_ids.filtered('backorder_id').button_validate()
# Invoice the sale order.
invoice = sale_order.with_context(default_journal_id=self.journal_sale.id)._create_invoices()
invoice.post()
# Check the resulting accounting entries
amls = invoice.line_ids
self.assertEqual(len(amls), 4)
stock_out_aml = amls.filtered(lambda aml: aml.account_id == self.stock_output_account)
self.assertEqual(stock_out_aml.debit, 0)
self.assertEqual(stock_out_aml.credit, 24)
cogs_aml = amls.filtered(lambda aml: aml.account_id == self.expense_account)
self.assertEqual(cogs_aml.debit, 24)
self.assertEqual(cogs_aml.credit, 0)
receivable_aml = amls.filtered(lambda aml: aml.account_id == self.recv_account)
self.assertEqual(receivable_aml.debit, 24)
self.assertEqual(receivable_aml.credit, 0)
income_aml = amls.filtered(lambda aml: aml.account_id == self.income_account)
self.assertEqual(income_aml.debit, 0)
self.assertEqual(income_aml.credit, 24)
# -------------------------------------------------------------------------
# Standard Delivered
# -------------------------------------------------------------------------
def test_standard_delivered_invoice_pre_delivery(self):
"""Not possible to invoice pre delivery."""
self.product.categ_id.property_cost_method = 'standard'
self.product.invoice_policy = 'delivery'
self.product.standard_price = 10
# Put two items in stock.
self._inv_adj_two_units()
# Create and confirm a sale order for 2@12
sale_order = self._so_and_confirm_two_units()
# Invoice the sale order.
# Nothing delivered = nothing to invoice.
with self.assertRaises(UserError):
sale_order._create_invoices()
def test_standard_delivered_invoice_post_partial_delivery(self):
"""Standard price set to 10. Get 2 units in stock. Sale order 2@12. Deliver 1, invoice 1,
change the standard price to 14, deliver one, change the standard price to 16, invoice 1.
The amounts used in Stock OUT and COGS should be 10 then 14."""
self.product.categ_id.property_cost_method = 'standard'
self.product.invoice_policy = 'delivery'
self.product.standard_price = 10
# Put two items in stock.
sale_order = self._so_and_confirm_two_units()
# Create and confirm a sale order for 2@12
sale_order = self._so_and_confirm_two_units()
# Deliver one.
sale_order.picking_ids.move_lines.quantity_done = 1
wiz = sale_order.picking_ids.button_validate()
wiz = self.env[wiz['res_model']].browse(wiz['res_id'])
wiz.process()
# Invoice 1
invoice = sale_order.with_context(default_journal_id=self.journal_sale.id)._create_invoices()
invoice_form = Form(invoice)
with invoice_form.invoice_line_ids.edit(0) as invoice_line:
invoice_line.quantity = 1
invoice_form.save()
invoice.post()
# Check the resulting accounting entries
amls = invoice.line_ids
self.assertEqual(len(amls), 4)
stock_out_aml = amls.filtered(lambda aml: aml.account_id == self.stock_output_account)
self.assertEqual(stock_out_aml.debit, 0)
self.assertEqual(stock_out_aml.credit, 10)
cogs_aml = amls.filtered(lambda aml: aml.account_id == self.expense_account)
self.assertEqual(cogs_aml.debit, 10)
self.assertEqual(cogs_aml.credit, 0)
receivable_aml = amls.filtered(lambda aml: aml.account_id == self.recv_account)
self.assertEqual(receivable_aml.debit, 12)
self.assertEqual(receivable_aml.credit, 0)
income_aml = amls.filtered(lambda aml: aml.account_id == self.income_account)
self.assertEqual(income_aml.debit, 0)
self.assertEqual(income_aml.credit, 12)
# change the standard price to 14
self.product._change_standard_price(14.0, counterpart_account_id=self.counterpart_account.id)
# deliver the backorder
sale_order.picking_ids[0].move_lines.quantity_done = 1
sale_order.picking_ids[0].button_validate()
# change the standard price to 16
self.product._change_standard_price(16.0, counterpart_account_id=self.counterpart_account.id)
# invoice 1
invoice2 = sale_order.with_context(default_journal_id=self.journal_sale.id)._create_invoices()
invoice2.post()
amls = invoice2.line_ids
self.assertEqual(len(amls), 4)
stock_out_aml = amls.filtered(lambda aml: aml.account_id == self.stock_output_account)
self.assertEqual(stock_out_aml.debit, 0)
self.assertEqual(stock_out_aml.credit, 14)
cogs_aml = amls.filtered(lambda aml: aml.account_id == self.expense_account)
self.assertEqual(cogs_aml.debit, 14)
self.assertEqual(cogs_aml.credit, 0)
receivable_aml = amls.filtered(lambda aml: aml.account_id == self.recv_account)
self.assertEqual(receivable_aml.debit, 12)
self.assertEqual(receivable_aml.credit, 0)
income_aml = amls.filtered(lambda aml: aml.account_id == self.income_account)
self.assertEqual(income_aml.debit, 0)
self.assertEqual(income_aml.credit, 12)
def test_standard_delivered_invoice_post_delivery(self):
"""Standard price set to 10. Get 2 units in stock. Sale order 2@12. Deliver 1, change the
standard price to 14, deliver one, invoice 2. The amounts used in Stock OUT and COGS should
be 12*2."""
self.product.categ_id.property_cost_method = 'standard'
self.product.invoice_policy = 'delivery'
self.product.standard_price = 10
# Put two items in stock.
self._inv_adj_two_units()
# Create and confirm a sale order for 2@12
sale_order = self._so_and_confirm_two_units()
# Deliver one.
sale_order.picking_ids.move_lines.quantity_done = 1
wiz = sale_order.picking_ids.button_validate()
wiz = self.env[wiz['res_model']].browse(wiz['res_id'])
wiz.process()
# change the standard price to 14
self.product._change_standard_price(14.0, counterpart_account_id=self.counterpart_account.id)
# deliver the backorder
sale_order.picking_ids.filtered('backorder_id').move_lines.quantity_done = 1
sale_order.picking_ids.filtered('backorder_id').button_validate()
# Invoice the sale order.
invoice = sale_order.with_context(default_journal_id=self.journal_sale.id)._create_invoices()
invoice.post()
# Check the resulting accounting entries
amls = invoice.line_ids
self.assertEqual(len(amls), 4)
stock_out_aml = amls.filtered(lambda aml: aml.account_id == self.stock_output_account)
self.assertEqual(stock_out_aml.debit, 0)
self.assertEqual(stock_out_aml.credit, 24)
cogs_aml = amls.filtered(lambda aml: aml.account_id == self.expense_account)
self.assertEqual(cogs_aml.debit, 24)
self.assertEqual(cogs_aml.credit, 0)
receivable_aml = amls.filtered(lambda aml: aml.account_id == self.recv_account)
self.assertEqual(receivable_aml.debit, 24)
self.assertEqual(receivable_aml.credit, 0)
income_aml = amls.filtered(lambda aml: aml.account_id == self.income_account)
self.assertEqual(income_aml.debit, 0)
self.assertEqual(income_aml.credit, 24)
# -------------------------------------------------------------------------
# AVCO Ordered
# -------------------------------------------------------------------------
def test_avco_ordered_invoice_pre_delivery(self):
"""Standard price set to 10. Sale order 2@12. Invoice without delivering."""
self.product.categ_id.property_cost_method = 'average'
self.product.invoice_policy = 'order'
self.product.standard_price = 10
# Put two items in stock.
self._inv_adj_two_units()
# Create and confirm a sale order for 2@12
sale_order = self._so_and_confirm_two_units()
# Invoice the sale order.
invoice = sale_order.with_context(default_journal_id=self.journal_sale.id)._create_invoices()
invoice.post()
# Check the resulting accounting entries
amls = invoice.line_ids
self.assertEqual(len(amls), 4)
stock_out_aml = amls.filtered(lambda aml: aml.account_id == self.stock_output_account)
self.assertEqual(stock_out_aml.debit, 0)
self.assertEqual(stock_out_aml.credit, 20)
cogs_aml = amls.filtered(lambda aml: aml.account_id == self.expense_account)
self.assertEqual(cogs_aml.debit, 20)
self.assertEqual(cogs_aml.credit, 0)
receivable_aml = amls.filtered(lambda aml: aml.account_id == self.recv_account)
self.assertEqual(receivable_aml.debit, 24)
self.assertEqual(receivable_aml.credit, 0)
income_aml = amls.filtered(lambda aml: aml.account_id == self.income_account)
self.assertEqual(income_aml.debit, 0)
self.assertEqual(income_aml.credit, 24)
def test_avco_ordered_invoice_post_partial_delivery(self):
"""Standard price set to 10. Sale order 2@12. Invoice after delivering 1."""
self.product.categ_id.property_cost_method = 'average'
self.product.invoice_policy = 'order'
self.product.standard_price = 10
# Put two items in stock.
self._inv_adj_two_units()
# Create and confirm a sale order for 2@12
sale_order = self._so_and_confirm_two_units()
# Deliver one.
sale_order.picking_ids.move_lines.quantity_done = 1
wiz = sale_order.picking_ids.button_validate()
wiz = self.env[wiz['res_model']].browse(wiz['res_id'])
wiz.process()
# Invoice the sale order.
invoice = sale_order.with_context(default_journal_id=self.journal_sale.id)._create_invoices()
invoice.post()
# Check the resulting accounting entries
amls = invoice.line_ids
self.assertEqual(len(amls), 4)
stock_out_aml = amls.filtered(lambda aml: aml.account_id == self.stock_output_account)
self.assertEqual(stock_out_aml.debit, 0)
self.assertEqual(stock_out_aml.credit, 20)
cogs_aml = amls.filtered(lambda aml: aml.account_id == self.expense_account)
self.assertEqual(cogs_aml.debit, 20)
self.assertEqual(cogs_aml.credit, 0)
receivable_aml = amls.filtered(lambda aml: aml.account_id == self.recv_account)
self.assertEqual(receivable_aml.debit, 24)
self.assertEqual(receivable_aml.credit, 0)
income_aml = amls.filtered(lambda aml: aml.account_id == self.income_account)
self.assertEqual(income_aml.debit, 0)
self.assertEqual(income_aml.credit, 24)
def test_avco_ordered_invoice_post_delivery(self):
"""Standard price set to 10. Sale order 2@12. Invoice after full delivery."""
self.product.categ_id.property_cost_method = 'average'
self.product.invoice_policy = 'order'
self.product.standard_price = 10
# Put two items in stock.
self._inv_adj_two_units()
# Create and confirm a sale order for 2@12
sale_order = self._so_and_confirm_two_units()
# Deliver one.
sale_order.picking_ids.move_lines.quantity_done = 2
sale_order.picking_ids.button_validate()
# Invoice the sale order.
invoice = sale_order.with_context(default_journal_id=self.journal_sale.id)._create_invoices()
invoice.post()
# Check the resulting accounting entries
amls = invoice.line_ids
self.assertEqual(len(amls), 4)
stock_out_aml = amls.filtered(lambda aml: aml.account_id == self.stock_output_account)
self.assertEqual(stock_out_aml.debit, 0)
self.assertEqual(stock_out_aml.credit, 20)
cogs_aml = amls.filtered(lambda aml: aml.account_id == self.expense_account)
self.assertEqual(cogs_aml.debit, 20)
self.assertEqual(cogs_aml.credit, 0)
receivable_aml = amls.filtered(lambda aml: aml.account_id == self.recv_account)
self.assertEqual(receivable_aml.debit, 24)
self.assertEqual(receivable_aml.credit, 0)
income_aml = amls.filtered(lambda aml: aml.account_id == self.income_account)
self.assertEqual(income_aml.debit, 0)
self.assertEqual(income_aml.credit, 24)
# -------------------------------------------------------------------------
# AVCO Delivered
# -------------------------------------------------------------------------
def test_avco_delivered_invoice_pre_delivery(self):
"""Standard price set to 10. Sale order 2@12. Invoice without delivering. """
self.product.categ_id.property_cost_method = 'average'
self.product.invoice_policy = 'delivery'
self.product.standard_price = 10
# Put two items in stock.
self._inv_adj_two_units()
# Create and confirm a sale order for 2@12
sale_order = self._so_and_confirm_two_units()
# Invoice the sale order.
# Nothing delivered = nothing to invoice.
with self.assertRaises(UserError):
sale_order._create_invoices()
def test_avco_delivered_invoice_post_partial_delivery(self):
"""Standard price set to 10. Sale order 2@12. Invoice after delivering 1."""
self.product.categ_id.property_cost_method = 'average'
self.product.invoice_policy = 'delivery'
self.product.standard_price = 10
# Put two items in stock.
self._inv_adj_two_units()
# Create and confirm a sale order for 2@12
sale_order = self._so_and_confirm_two_units()
# Deliver one.
sale_order.picking_ids.move_lines.quantity_done = 1
wiz = sale_order.picking_ids.button_validate()
wiz = self.env[wiz['res_model']].browse(wiz['res_id'])
wiz.process()
# Invoice the sale order.
invoice = sale_order.with_context(default_journal_id=self.journal_sale.id)._create_invoices()
invoice.post()
# Check the resulting accounting entries
amls = invoice.line_ids
self.assertEqual(len(amls), 4)
stock_out_aml = amls.filtered(lambda aml: aml.account_id == self.stock_output_account)
self.assertEqual(stock_out_aml.debit, 0)
self.assertEqual(stock_out_aml.credit, 10)
cogs_aml = amls.filtered(lambda aml: aml.account_id == self.expense_account)
self.assertEqual(cogs_aml.debit, 10)
self.assertEqual(cogs_aml.credit, 0)
receivable_aml = amls.filtered(lambda aml: aml.account_id == self.recv_account)
self.assertEqual(receivable_aml.debit, 12)
self.assertEqual(receivable_aml.credit, 0)
income_aml = amls.filtered(lambda aml: aml.account_id == self.income_account)
self.assertEqual(income_aml.debit, 0)
self.assertEqual(income_aml.credit, 12)
def test_avco_delivered_invoice_post_delivery(self):
"""Standard price set to 10. Sale order 2@12. Invoice after full delivery."""
self.product.categ_id.property_cost_method = 'average'
self.product.invoice_policy = 'delivery'
self.product.standard_price = 10
# Put two items in stock.
self._inv_adj_two_units()
# Create and confirm a sale order for 2@12
sale_order = self._so_and_confirm_two_units()
# Deliver one.
sale_order.picking_ids.move_lines.quantity_done = 2
sale_order.picking_ids.button_validate()
# Invoice the sale order.
invoice = sale_order.with_context(default_journal_id=self.journal_sale.id)._create_invoices()
invoice.post()
# Check the resulting accounting entries
amls = invoice.line_ids
self.assertEqual(len(amls), 4)
stock_out_aml = amls.filtered(lambda aml: aml.account_id == self.stock_output_account)
self.assertEqual(stock_out_aml.debit, 0)
self.assertEqual(stock_out_aml.credit, 20)
cogs_aml = amls.filtered(lambda aml: aml.account_id == self.expense_account)
self.assertEqual(cogs_aml.debit, 20)
self.assertEqual(cogs_aml.credit, 0)
receivable_aml = amls.filtered(lambda aml: aml.account_id == self.recv_account)
self.assertEqual(receivable_aml.debit, 24)
self.assertEqual(receivable_aml.credit, 0)
income_aml = amls.filtered(lambda aml: aml.account_id == self.income_account)
self.assertEqual(income_aml.debit, 0)
self.assertEqual(income_aml.credit, 24)
# -------------------------------------------------------------------------
# FIFO Ordered
# -------------------------------------------------------------------------
def test_fifo_ordered_invoice_pre_delivery(self):
"""Receive at 8 then at 10. Sale order 2@12. Invoice without delivering.
As no standard price is set, the Stock OUT and COGS amounts are 0."""
self.product.categ_id.property_cost_method = 'fifo'
self.product.invoice_policy = 'order'
self._fifo_in_one_eight_one_ten()
# Create and confirm a sale order for 2@12
sale_order = self._so_and_confirm_two_units()
# Invoice the sale order.
invoice = sale_order.with_context(default_journal_id=self.journal_sale.id)._create_invoices()
invoice.post()
# Check the resulting accounting entries
amls = invoice.line_ids
self.assertEqual(len(amls), 4)
stock_out_aml = amls.filtered(lambda aml: aml.account_id == self.stock_output_account)
self.assertEqual(stock_out_aml.debit, 0)
self.assertEqual(stock_out_aml.credit, 0)
cogs_aml = amls.filtered(lambda aml: aml.account_id == self.expense_account)
self.assertEqual(cogs_aml.debit, 0)
self.assertEqual(cogs_aml.credit, 0)
receivable_aml = amls.filtered(lambda aml: aml.account_id == self.recv_account)
self.assertEqual(receivable_aml.debit, 24)
self.assertEqual(receivable_aml.credit, 0)
income_aml = amls.filtered(lambda aml: aml.account_id == self.income_account)
self.assertEqual(income_aml.debit, 0)
self.assertEqual(income_aml.credit, 24)
def test_fifo_ordered_invoice_post_partial_delivery(self):
"""Receive 1@8, 1@10, so 2@12, standard price 12, deliver 1, invoice 2: the COGS amount
should be 20: 1 really delivered at 10 and the other valued at the standard price 10."""
self.product.categ_id.property_cost_method = 'fifo'
self.product.invoice_policy = 'order'
self._fifo_in_one_eight_one_ten()
# Create and confirm a sale order for 2@12
sale_order = self._so_and_confirm_two_units()
# Deliver one.
sale_order.picking_ids.move_lines.quantity_done = 1
wiz = sale_order.picking_ids.button_validate()
wiz = self.env[wiz['res_model']].browse(wiz['res_id'])
wiz.process()
# upate the standard price to 12
self.product.standard_price = 12
# Invoice 2
invoice = sale_order.with_context(default_journal_id=self.journal_sale.id)._create_invoices()
invoice_form = Form(invoice)
with invoice_form.invoice_line_ids.edit(0) as invoice_line:
invoice_line.quantity = 2
invoice_form.save()
invoice.post()
# Check the resulting accounting entries
amls = invoice.line_ids
self.assertEqual(len(amls), 4)
stock_out_aml = amls.filtered(lambda aml: aml.account_id == self.stock_output_account)
self.assertEqual(stock_out_aml.debit, 0)
self.assertEqual(stock_out_aml.credit, 20)
cogs_aml = amls.filtered(lambda aml: aml.account_id == self.expense_account)
self.assertEqual(cogs_aml.debit, 20)
self.assertEqual(cogs_aml.credit, 0)
receivable_aml = amls.filtered(lambda aml: aml.account_id == self.recv_account)
self.assertEqual(receivable_aml.debit, 24)
self.assertEqual(receivable_aml.credit, 0)
income_aml = amls.filtered(lambda aml: aml.account_id == self.income_account)
self.assertEqual(income_aml.debit, 0)
self.assertEqual(income_aml.credit, 24)
def test_fifo_ordered_invoice_post_delivery(self):
"""Receive at 8 then at 10. Sale order 2@12. Invoice after delivering everything."""
self.product.categ_id.property_cost_method = 'fifo'
self.product.invoice_policy = 'order'
self._fifo_in_one_eight_one_ten()
# Create and confirm a sale order for 2@12
sale_order = self._so_and_confirm_two_units()
# Deliver one.
sale_order.picking_ids.move_lines.quantity_done = 2
sale_order.picking_ids.button_validate()
# Invoice the sale order.
invoice = sale_order.with_context(default_journal_id=self.journal_sale.id)._create_invoices()
invoice.post()
# Check the resulting accounting entries
amls = invoice.line_ids
self.assertEqual(len(amls), 4)
stock_out_aml = amls.filtered(lambda aml: aml.account_id == self.stock_output_account)
self.assertEqual(stock_out_aml.debit, 0)
self.assertEqual(stock_out_aml.credit, 18)
cogs_aml = amls.filtered(lambda aml: aml.account_id == self.expense_account)
self.assertEqual(cogs_aml.debit, 18)
self.assertEqual(cogs_aml.credit, 0)
receivable_aml = amls.filtered(lambda aml: aml.account_id == self.recv_account)
self.assertEqual(receivable_aml.debit, 24)
self.assertEqual(receivable_aml.credit, 0)
income_aml = amls.filtered(lambda aml: aml.account_id == self.income_account)
self.assertEqual(income_aml.debit, 0)
self.assertEqual(income_aml.credit, 24)
# -------------------------------------------------------------------------
# FIFO Delivered
# -------------------------------------------------------------------------
def test_fifo_delivered_invoice_pre_delivery(self):
self.product.categ_id.property_cost_method = 'fifo'
self.product.invoice_policy = 'delivery'
self.product.standard_price = 10
self._fifo_in_one_eight_one_ten()
# Create and confirm a sale order for 2@12
sale_order = self._so_and_confirm_two_units()
# Invoice the sale order.
# Nothing delivered = nothing to invoice.
with self.assertRaises(UserError):
invoice_id = sale_order._create_invoices()
def test_fifo_delivered_invoice_post_partial_delivery(self):
"""Receive 1@8, 1@10, so 2@12, standard price 12, deliver 1, invoice 2: the price used should be 10:
one at 8 and one at 10."""
self.product.categ_id.property_cost_method = 'fifo'
self.product.invoice_policy = 'delivery'
self._fifo_in_one_eight_one_ten()
# Create and confirm a sale order for 2@12
sale_order = self._so_and_confirm_two_units()
# Deliver one.
sale_order.picking_ids.move_lines.quantity_done = 1
wiz = sale_order.picking_ids.button_validate()
wiz = self.env[wiz['res_model']].browse(wiz['res_id'])
wiz.process()
# upate the standard price to 12
self.product.standard_price = 12
# Invoice 2
invoice = sale_order.with_context(default_journal_id=self.journal_sale.id)._create_invoices()
invoice_form = Form(invoice)
with invoice_form.invoice_line_ids.edit(0) as invoice_line:
invoice_line.quantity = 2
invoice_form.save()
invoice.post()
# Check the resulting accounting entries
amls = invoice.line_ids
self.assertEqual(len(amls), 4)
stock_out_aml = amls.filtered(lambda aml: aml.account_id == self.stock_output_account)
self.assertEqual(stock_out_aml.debit, 0)
self.assertEqual(stock_out_aml.credit, 20)
cogs_aml = amls.filtered(lambda aml: aml.account_id == self.expense_account)
self.assertEqual(cogs_aml.debit, 20)
self.assertEqual(cogs_aml.credit, 0)
receivable_aml = amls.filtered(lambda aml: aml.account_id == self.recv_account)
self.assertEqual(receivable_aml.debit, 24)
self.assertEqual(receivable_aml.credit, 0)
income_aml = amls.filtered(lambda aml: aml.account_id == self.income_account)
self.assertEqual(income_aml.debit, 0)
self.assertEqual(income_aml.credit, 24)
def test_fifo_delivered_invoice_post_delivery(self):
"""Receive at 8 then at 10. Sale order 2@12. Invoice after delivering everything."""
self.product.categ_id.property_cost_method = 'fifo'
self.product.invoice_policy = 'delivery'
self.product.standard_price = 10
self._fifo_in_one_eight_one_ten()
# Create and confirm a sale order for 2@12
sale_order = self._so_and_confirm_two_units()
# Deliver one.
sale_order.picking_ids.move_lines.quantity_done = 2
sale_order.picking_ids.button_validate()
# Invoice the sale order.
invoice = sale_order.with_context(default_journal_id=self.journal_sale.id)._create_invoices()
invoice.post()
# Check the resulting accounting entries
amls = invoice.line_ids
self.assertEqual(len(amls), 4)
stock_out_aml = amls.filtered(lambda aml: aml.account_id == self.stock_output_account)
self.assertEqual(stock_out_aml.debit, 0)
self.assertEqual(stock_out_aml.credit, 18)
cogs_aml = amls.filtered(lambda aml: aml.account_id == self.expense_account)
self.assertEqual(cogs_aml.debit, 18)
self.assertEqual(cogs_aml.credit, 0)
receivable_aml = amls.filtered(lambda aml: aml.account_id == self.recv_account)
self.assertEqual(receivable_aml.debit, 24)
self.assertEqual(receivable_aml.credit, 0)
income_aml = amls.filtered(lambda aml: aml.account_id == self.income_account)
self.assertEqual(income_aml.debit, 0)
self.assertEqual(income_aml.credit, 24)
def test_fifo_delivered_invoice_post_delivery_2(self):
"""Receive at 8 then at 10. Sale order 10@12 and deliver without receiving the 2 missing.
receive 2@12. Invoice."""
self.product.categ_id.property_cost_method = 'fifo'
self.product.invoice_policy = 'delivery'
self.product.standard_price = 10
in_move_1 = self.env['stock.move'].create({
'name': 'a',
'product_id': self.product.id,
'location_id': self.env.ref('stock.stock_location_suppliers').id,
'location_dest_id': self.stock_location.id,
'product_uom': self.product.uom_id.id,
'product_uom_qty': 8,
'price_unit': 10,
})
in_move_1._action_confirm()
in_move_1.quantity_done = 8
in_move_1._action_done()
# Create and confirm a sale order for 2@12
sale_order = self.env['sale.order'].create({
'partner_id': self.customer.id,
'order_line': [
(0, 0, {
'name': self.product.name,
'product_id': self.product.id,
'product_uom_qty': 10.0,
'product_uom': self.product.uom_id.id,
'price_unit': 12,
'tax_id': False, # no love taxes amls
})],
})
sale_order.action_confirm()
# Deliver 10
sale_order.picking_ids.move_lines.quantity_done = 10
sale_order.picking_ids.button_validate()
# Make the second receipt
in_move_2 = self.env['stock.move'].create({
'name': 'a',
'product_id': self.product.id,
'location_id': self.env.ref('stock.stock_location_suppliers').id,
'location_dest_id': self.stock_location.id,
'product_uom': self.product.uom_id.id,
'product_uom_qty': 2,
'price_unit': 12,
})
in_move_2._action_confirm()
in_move_2.quantity_done = 2
in_move_2._action_done()
self.assertEqual(self.product.stock_valuation_layer_ids[-1].value, -4) # we sent two at 10 but they should have been sent at 12
self.assertEqual(self.product.stock_valuation_layer_ids[-1].quantity, 0)
self.assertEqual(sale_order.order_line.move_ids.stock_valuation_layer_ids[-1].quantity, 0)
# Invoice the sale order.
invoice = sale_order.with_context(default_journal_id=self.journal_sale.id)._create_invoices()
invoice.post()
# Check the resulting accounting entries
amls = invoice.line_ids
self.assertEqual(len(amls), 4)
stock_out_aml = amls.filtered(lambda aml: aml.account_id == self.stock_output_account)
self.assertEqual(stock_out_aml.debit, 0)
self.assertEqual(stock_out_aml.credit, 104)
cogs_aml = amls.filtered(lambda aml: aml.account_id == self.expense_account)
self.assertEqual(cogs_aml.debit, 104)
self.assertEqual(cogs_aml.credit, 0)
receivable_aml = amls.filtered(lambda aml: aml.account_id == self.recv_account)
self.assertEqual(receivable_aml.debit, 120)
self.assertEqual(receivable_aml.credit, 0)
income_aml = amls.filtered(lambda aml: aml.account_id == self.income_account)
self.assertEqual(income_aml.debit, 0)
self.assertEqual(income_aml.credit, 120)
def test_fifo_delivered_invoice_post_delivery_3(self):
"""Receive 5@8, receive 8@12, sale 1@20, deliver, sale 6@20, deliver. Make sure no rouding
issues appear on the second invoice."""
self.product.categ_id.property_cost_method = 'fifo'
self.product.invoice_policy = 'delivery'
# +5@8
in_move_1 = self.env['stock.move'].create({
'name': 'a',
'product_id': self.product.id,
'location_id': self.env.ref('stock.stock_location_suppliers').id,
'location_dest_id': self.stock_location.id,
'product_uom': self.product.uom_id.id,
'product_uom_qty': 5,
'price_unit': 8,
})
in_move_1._action_confirm()
in_move_1.quantity_done = 5
in_move_1._action_done()
# +8@12
in_move_2 = self.env['stock.move'].create({
'name': 'a',
'product_id': self.product.id,
'location_id': self.env.ref('stock.stock_location_suppliers').id,
'location_dest_id': self.stock_location.id,
'product_uom': self.product.uom_id.id,
'product_uom_qty': 8,
'price_unit': 12,
})
in_move_2._action_confirm()
in_move_2.quantity_done = 8
in_move_2._action_done()
# sale 1@20, deliver, invoice
sale_order = self.env['sale.order'].create({
'partner_id': self.customer.id,
'order_line': [
(0, 0, {
'name': self.product.name,
'product_id': self.product.id,
'product_uom_qty': 1,
'product_uom': self.product.uom_id.id,
'price_unit': 20,
'tax_id': False,
})],
})
sale_order.action_confirm()
sale_order.picking_ids.move_lines.quantity_done = 1
sale_order.picking_ids.button_validate()
invoice = sale_order.with_context(default_journal_id=self.journal_sale.id)._create_invoices()
invoice.post()
# sale 6@20, deliver, invoice
sale_order = self.env['sale.order'].create({
'partner_id': self.customer.id,
'order_line': [
(0, 0, {
'name': self.product.name,
'product_id': self.product.id,
'product_uom_qty': 6,
'product_uom': self.product.uom_id.id,
'price_unit': 20,
'tax_id': False,
})],
})
sale_order.action_confirm()
sale_order.picking_ids.move_lines.quantity_done = 6
sale_order.picking_ids.button_validate()
invoice = sale_order.with_context(default_journal_id=self.journal_sale.id)._create_invoices()
invoice.post()
# check the last anglo saxon invoice line
amls = invoice.line_ids
cogs_aml = amls.filtered(lambda aml: aml.account_id == self.expense_account)
self.assertEqual(cogs_aml.debit, 56)
self.assertEqual(cogs_aml.credit, 0)
| 44.893175
| 136
| 0.644634
| 5,810
| 45,387
| 4.762823
| 0.039931
| 0.090525
| 0.038523
| 0.055399
| 0.923966
| 0.90597
| 0.89596
| 0.888154
| 0.878072
| 0.873374
| 0
| 0.020098
| 0.237006
| 45,387
| 1,010
| 137
| 44.937624
| 0.778978
| 0.140921
| 0
| 0.831944
| 0
| 0
| 0.073587
| 0.019173
| 0
| 0
| 0
| 0
| 0.236111
| 1
| 0.033333
| false
| 0
| 0.004167
| 0
| 0.040278
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
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|
0
| 7
|
d4d55b01e0778ee5f76e2fc56148b185058a54a8
| 118,842
|
py
|
Python
|
ai2thor/tests/test_event.py
|
ekolve/ai2thor-lgtm
|
0a8d5cf961134ee31f5410d4aa2f3f9f750d6911
|
[
"Apache-2.0"
] | null | null | null |
ai2thor/tests/test_event.py
|
ekolve/ai2thor-lgtm
|
0a8d5cf961134ee31f5410d4aa2f3f9f750d6911
|
[
"Apache-2.0"
] | 2
|
2021-04-26T16:29:22.000Z
|
2021-04-26T16:34:39.000Z
|
ai2thor/tests/test_event.py
|
ekolve/ai2thor-lgtm
|
0a8d5cf961134ee31f5410d4aa2f3f9f750d6911
|
[
"Apache-2.0"
] | null | null | null |
import os
from ai2thor.server import Event
import numpy as np
import pytest
from ai2thor.tests.constants import TESTS_DATA_DIR
metadata_complex = {
"agent": {
"bounds3D": [],
"cameraHorizon": 0.0,
"distance": 0.0,
"isopen": False,
"name": "agent",
"objectId": "",
"objectType": "",
"openable": False,
"parentReceptacle": "",
"pickupable": False,
"pivotSimObjs": [],
"position": {"x": -0.75, "y": 0.9799995422363281, "z": -0.25},
"receptacle": False,
"receptacleCount": 0,
"receptacleObjectIds": [],
"rotation": {"x": 0.0, "y": 180.0, "z": 0.0},
"visible": False,
},
"thirdPartyCameras": [],
"agentId": 0,
"collided": False,
"collidedObjects": [],
"colorBounds": [
{"bounds": [0, 0, 119, 299], "color": [138, 235, 7]},
{"bounds": [116, 0, 299, 99], "color": [127, 29, 203]},
{"bounds": [116, 0, 137, 64], "color": [237, 189, 33]},
{"bounds": [131, 0, 143, 55], "color": [97, 134, 44]},
{"bounds": [139, 0, 169, 71], "color": [193, 44, 202]},
{"bounds": [141, 0, 146, 30], "color": [96, 50, 133]},
{"bounds": [133, 0, 299, 85], "color": [89, 77, 61]},
{"bounds": [143, 0, 297, 34], "color": [214, 15, 78]},
{"bounds": [116, 0, 299, 99], "color": [115, 3, 101]},
{"bounds": [258, 12, 299, 84], "color": [96, 140, 59]},
{"bounds": [116, 14, 120, 28], "color": [162, 203, 153]},
{"bounds": [195, 15, 255, 85], "color": [108, 174, 95]},
{"bounds": [172, 17, 194, 71], "color": [168, 12, 250]},
{"bounds": [121, 18, 132, 30], "color": [246, 16, 151]},
{"bounds": [124, 29, 133, 40], "color": [116, 220, 170]},
{"bounds": [117, 31, 125, 63], "color": [115, 78, 181]},
{"bounds": [258, 35, 289, 43], "color": [241, 134, 252]},
{"bounds": [126, 39, 135, 49], "color": [114, 84, 146]},
{"bounds": [119, 44, 299, 203], "color": [73, 64, 168]},
{"bounds": [128, 48, 136, 57], "color": [185, 225, 171]},
{"bounds": [223, 54, 233, 69], "color": [14, 97, 183]},
{"bounds": [135, 56, 138, 74], "color": [96, 48, 36]},
{"bounds": [126, 69, 127, 69], "color": [66, 225, 0]},
{"bounds": [172, 72, 194, 84], "color": [191, 227, 85]},
{"bounds": [117, 77, 121, 78], "color": [92, 3, 233]},
{"bounds": [116, 81, 170, 96], "color": [177, 60, 44]},
{"bounds": [284, 91, 299, 123], "color": [110, 132, 248]},
{"bounds": [192, 92, 197, 97], "color": [36, 91, 74]},
{"bounds": [218, 92, 224, 97], "color": [56, 51, 197]},
{"bounds": [118, 93, 133, 101], "color": [72, 78, 219]},
{"bounds": [205, 93, 212, 99], "color": [178, 18, 13]},
{"bounds": [116, 95, 117, 106], "color": [60, 103, 95]},
{"bounds": [184, 95, 203, 106], "color": [42, 54, 156]},
{"bounds": [210, 95, 217, 103], "color": [214, 68, 168]},
{"bounds": [121, 96, 124, 118], "color": [226, 66, 148]},
{"bounds": [160, 96, 177, 101], "color": [135, 13, 200]},
{"bounds": [233, 96, 237, 103], "color": [127, 73, 96]},
{"bounds": [246, 96, 253, 102], "color": [18, 240, 113]},
{"bounds": [118, 97, 133, 120], "color": [110, 250, 103]},
{"bounds": [149, 97, 154, 105], "color": [44, 186, 193]},
{"bounds": [201, 97, 209, 115], "color": [118, 102, 24]},
{"bounds": [213, 97, 221, 115], "color": [182, 114, 149]},
{"bounds": [224, 97, 231, 103], "color": [20, 107, 195]},
{"bounds": [233, 97, 242, 110], "color": [219, 74, 174]},
{"bounds": [120, 98, 125, 106], "color": [202, 218, 132]},
{"bounds": [133, 98, 138, 110], "color": [122, 156, 16]},
{"bounds": [245, 99, 253, 112], "color": [216, 69, 22]},
{"bounds": [186, 107, 189, 108], "color": [34, 152, 164]},
{"bounds": [257, 107, 260, 108], "color": [48, 42, 241]},
{"bounds": [167, 108, 219, 187], "color": [92, 62, 94]},
{"bounds": [145, 109, 152, 113], "color": [17, 67, 188]},
{"bounds": [55, 134, 160, 298], "color": [216, 148, 75]},
{"bounds": [115, 136, 146, 203], "color": [181, 237, 187]},
{"bounds": [109, 189, 113, 210], "color": [104, 199, 254]},
{"bounds": [103, 195, 108, 219], "color": [238, 221, 39]},
{"bounds": [92, 201, 102, 239], "color": [36, 61, 25]},
{"bounds": [117, 202, 137, 208], "color": [143, 211, 227]},
{"bounds": [55, 202, 299, 299], "color": [55, 223, 207]},
{"bounds": [107, 210, 112, 218], "color": [135, 101, 149]},
{"bounds": [73, 213, 91, 269], "color": [1, 209, 145]},
{"bounds": [46, 234, 72, 299], "color": [215, 152, 183]},
{"bounds": [11, 263, 45, 299], "color": [45, 75, 161]},
],
"colors": [
{"color": [58, 205, 56], "name": "Bowl|-00.16|+01.50|-01.45"},
{"color": [209, 182, 193], "name": "Bowl"},
{"color": [226, 29, 217], "name": "Container|-00.16|+00.93|-02.94"},
{"color": [14, 114, 120], "name": "Container"},
{"color": [219, 14, 164], "name": "Ladel1.001"},
{"color": [138, 235, 7], "name": "Fridge|-00.22|00.00|-00.83"},
{"color": [91, 156, 207], "name": "Fridge1"},
{"color": [181, 237, 187], "name": "Cabinet|-00.35|+01.89|-03.29"},
{"color": [210, 149, 89], "name": "Drawer"},
{"color": [237, 189, 33], "name": "StoveBase1"},
{"color": [216, 148, 75], "name": "Cube.090"},
{"color": [117, 7, 236], "name": "Toaster|-00.16|+00.93|-01.45"},
{"color": [55, 33, 114], "name": "Toaster1"},
{"color": [215, 152, 183], "name": "Cabinet|-00.34|+01.89|-01.29"},
{"color": [44, 186, 193], "name": "Mug|-00.78|+00.93|-03.85"},
{"color": [8, 94, 186], "name": "CoffeeCup1"},
{"color": [122, 156, 16], "name": "Bottle5.001"},
{"color": [116, 220, 170], "name": "StoveKnob|-00.62|+00.90|-01.98"},
{"color": [106, 252, 95], "name": "StoveKnob2_Range4"},
{"color": [41, 198, 116], "name": "Spatula2.001"},
{"color": [119, 173, 49], "name": "Torus"},
{"color": [168, 12, 250], "name": "Cabinet|-01.01|+00.39|-03.37"},
{"color": [61, 44, 125], "name": "Microwave|-00.17|+01.49|-02.06"},
{"color": [54, 96, 202], "name": "Microwave4"},
{"color": [240, 130, 222], "name": "StoveBurner|-00.23|+00.93|-01.85"},
{"color": [156, 249, 101], "name": "GasStoveTop_Range1"},
{"color": [72, 78, 219], "name": "Sphere.010"},
{"color": [255, 102, 152], "name": "StoveBurner|-00.42|+00.93|-02.26"},
{"color": [248, 115, 142], "name": "StoveBurner|-00.23|+00.93|-02.26"},
{"color": [135, 13, 200], "name": "TurkeyPan.005"},
{"color": [45, 75, 161], "name": "Cabinet|-00.34|+02.11|-01.27"},
{"color": [92, 3, 233], "name": "Spatula1.002"},
{"color": [96, 50, 133], "name": "Towl1 (1)"},
{"color": [143, 211, 227], "name": "Cylinder.028"},
{"color": [108, 174, 95], "name": "Cube.085"},
{"color": [34, 152, 164], "name": "SugarJar.005"},
{"color": [96, 48, 36], "name": "Cabinet|-00.48|+00.78|-02.74"},
{"color": [131, 29, 70], "name": "Ladel3.001"},
{"color": [55, 223, 207], "name": "Ceiling"},
{"color": [102, 49, 87], "name": "Knife|-00.14|+01.12|-02.75"},
{"color": [211, 157, 122], "name": "Knife1"},
{"color": [177, 60, 44], "name": "Cube.100"},
{"color": [114, 84, 146], "name": "StoveKnob|-00.62|+00.90|-02.13"},
{"color": [60, 103, 95], "name": "Bottle3.001"},
{"color": [186, 206, 150], "name": "PaperRoll1"},
{"color": [164, 253, 150], "name": "Sphere.012"},
{"color": [77, 4, 136], "name": "Spatula1.001"},
{"color": [135, 101, 149], "name": "TurkeyPan.006"},
{"color": [237, 39, 71], "name": "Decals.002"},
{"color": [226, 66, 148], "name": "Bottle4.001"},
{"color": [246, 16, 151], "name": "StoveKnob|-00.62|+00.90|-01.83"},
{"color": [36, 91, 74], "name": "Tomato|-01.32|+00.93|-03.53"},
{"color": [119, 189, 121], "name": "Tomato"},
{"color": [193, 44, 202], "name": "Cabinet|-00.63|+00.39|-03.01"},
{"color": [118, 102, 24], "name": "SugarJar.004"},
{"color": [92, 62, 94], "name": "VenetianFrame"},
{"color": [14, 97, 183], "name": "Towl1"},
{"color": [87, 195, 41], "name": "GarbageCan|-00.36|00.00|-00.21"},
{"color": [225, 40, 55], "name": "GarbageCan"},
{"color": [110, 132, 248], "name": "CoffeeMachine|-02.65|+00.93|-03.57"},
{"color": [147, 71, 238], "name": "CoffeeMachine2"},
{"color": [214, 15, 78], "name": "Floor"},
{"color": [73, 64, 168], "name": "Room"},
{"color": [89, 77, 61], "name": "Cube.086"},
{"color": [127, 29, 203], "name": "Cube.082"},
{"color": [97, 134, 44], "name": "StoveTopDoor1"},
{"color": [140, 135, 166], "name": "Fork|-00.48|+00.81|-02.74"},
{"color": [54, 200, 25], "name": "Fork1"},
{"color": [185, 225, 171], "name": "StoveKnob|-00.62|+00.90|-02.29"},
{"color": [91, 94, 10], "name": "Egg|-00.21|+00.27|-00.83"},
{"color": [240, 75, 163], "name": "Egg"},
{"color": [162, 203, 153], "name": "Mug|-00.53|+00.93|-01.58"},
{"color": [1, 209, 145], "name": "Cabinet|-00.34|+02.11|-01.63"},
{"color": [104, 199, 254], "name": "Cabinet|-00.33|+01.89|-03.24"},
{"color": [29, 84, 249], "name": "Spoon|-00.50|+00.78|-01.45"},
{"color": [235, 57, 90], "name": "Spoon"},
{"color": [115, 3, 101], "name": "Decals.003"},
{"color": [71, 3, 53], "name": "Sphere.008"},
{"color": [191, 227, 85], "name": "Cabinet|-01.15|+00.78|-03.50"},
{"color": [238, 221, 39], "name": "Cabinet|-00.33|+01.89|-02.51"},
{"color": [18, 240, 113], "name": "SugarFill.006"},
{"color": [36, 61, 25], "name": "Cabinet|-00.34|+02.11|-02.50"},
{"color": [214, 68, 168], "name": "Mug|-01.63|+00.92|-03.74"},
{"color": [17, 67, 188], "name": "Outlet (1)"},
{"color": [66, 225, 0], "name": "ButterKnife|-00.43|+00.93|-02.60"},
{"color": [135, 147, 55], "name": "butterKnife"},
{"color": [115, 78, 181], "name": "StoveTopGas"},
{"color": [182, 114, 149], "name": "SugarJar.001"},
{"color": [139, 56, 140], "name": "StoveBottomDoor1"},
{"color": [202, 218, 132], "name": "Cube.109"},
{"color": [178, 18, 13], "name": "Apple|-01.49|+00.93|-03.50"},
{"color": [159, 98, 144], "name": "Apple"},
{"color": [20, 107, 195], "name": "SugarFill.001"},
{"color": [193, 221, 101], "name": "Plate|-00.15|+01.49|-02.73"},
{"color": [188, 154, 128], "name": "Plate"},
{"color": [55, 176, 84], "name": "Cabinet|-00.63|+00.39|-01.61"},
{"color": [145, 107, 85], "name": "Cabinet|-00.34|+02.11|-00.39"},
{"color": [138, 185, 132], "name": "SugarJar.003"},
{"color": [202, 210, 177], "name": "Bottle2.001"},
{"color": [141, 139, 54], "name": "Cabinet|-00.63|+00.39|-02.51"},
{"color": [96, 140, 59], "name": "Chair|-02.35|00.00|-03.60"},
{"color": [166, 13, 176], "name": "Chair5"},
{"color": [199, 148, 125], "name": "Bottle1.001"},
{"color": [34, 126, 70], "name": "ladel2.001"},
{"color": [48, 42, 241], "name": "SugarJar.006"},
{"color": [127, 73, 96], "name": "SugarFill.004"},
{"color": [219, 74, 174], "name": "Sugar.001"},
{"color": [216, 69, 22], "name": "SugarJar.002"},
{"color": [31, 88, 95], "name": "StoveBurner|-00.42|+00.93|-01.85"},
{"color": [193, 143, 140], "name": "Outlet"},
{"color": [97, 114, 178], "name": "Sphere.001"},
{"color": [56, 51, 197], "name": "Potato|-01.63|+00.93|-03.48"},
{"color": [187, 142, 9], "name": "Potato"},
{"color": [42, 54, 156], "name": "Bread|-01.33|+00.93|-03.71"},
{"color": [18, 150, 252], "name": "Bread"},
{"color": [195, 218, 223], "name": "Cabinet|-00.50|+00.78|-01.45"},
{"color": [34, 130, 237], "name": "Pot|-00.47|+00.08|-02.74"},
{"color": [132, 237, 87], "name": "Pot1"},
{"color": [110, 250, 103], "name": "Bottles.001"},
{"color": [4, 93, 193], "name": "Lettuce|-00.33|+00.74|-00.69"},
{"color": [203, 156, 88], "name": "Lettuce1"},
{"color": [241, 134, 252], "name": "Baseboard.020"},
{"color": [127, 127, 189], "name": "Pan|-00.68|+00.08|-03.27"},
{"color": [246, 212, 161], "name": "Pan1"},
{"color": [207, 119, 70], "name": "Spatula3.001"},
],
"errorCode": "",
"errorMessage": "",
"inventoryObjects": [],
"lastAction": "RotateRight",
"lastActionSuccess": True,
"objects": [
{
"bounds3D": [
-2.5750010013580322,
0.8563164472579956,
-3.647000312805176,
-1.5749990940093994,
0.9563164710998535,
-3.3069992065429688,
],
"cameraHorizon": 0.0,
"distance": 3.6240997314453125,
"isopen": False,
"name": "Tabletop",
"objectId": "TableTop|-02.08|+00.94|-03.62",
"objectType": "TableTop",
"openable": False,
"parentReceptacle": "",
"pickupable": False,
"pivotSimObjs": [],
"position": {
"x": -2.075000047683716,
"y": 0.9433164596557617,
"z": -3.622999906539917,
},
"receptacle": True,
"receptacleCount": 4,
"receptacleObjectIds": [],
"rotation": {"x": 0.0, "y": 90.0, "z": 0.0},
"visible": False,
},
{
"bounds3D": [
-0.2521742284297943,
1.4949759244918823,
-2.831829071044922,
-0.05024271458387375,
1.5067294836044312,
-2.6298975944519043,
],
"cameraHorizon": 0.0,
"distance": 2.6035001277923584,
"isopen": False,
"name": "Plate",
"objectId": "Plate|-00.15|+01.49|-02.73",
"objectType": "Plate",
"openable": False,
"parentReceptacle": "Cabinet|-00.33|+01.89|-02.51",
"pickupable": True,
"pivotSimObjs": [],
"position": {
"x": -0.15120847523212433,
"y": 1.494760513305664,
"z": -2.730863332748413,
},
"receptacle": False,
"receptacleCount": 0,
"receptacleObjectIds": [],
"rotation": {"x": -1.0245284101983998e-05, "y": 0.0, "z": 0.0},
"visible": False,
},
{
"bounds3D": [
-0.6563448309898376,
0.8580825328826904,
-2.015467643737793,
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"x": -0.1746000051498413,
"y": 1.485553503036499,
"z": -2.055999994277954,
},
"receptacle": True,
"receptacleCount": 1,
"receptacleObjectIds": [],
"rotation": {"x": 0.0, "y": 0.0, "z": 0.0},
"visible": False,
},
],
"sceneName": "FloorPlan28",
"screenHeight": 300,
"screenWidth": 300,
}
@pytest.fixture
def event_complex():
return Event(metadata_complex)
@pytest.fixture
def event():
return Event(metadata_simple)
@pytest.fixture
def event_with_frame(event):
e = event
with open(os.path.join(TESTS_DATA_DIR, "rgb-image.raw"), "rb") as f:
raw_image = memoryview(f.read())
e.add_image(raw_image)
return e
def test_get_object(event):
microwave = {
"bounds3D": [
-0.3505246043205261,
1.5073667764663696,
-2.2319486141204834,
0.009090721607208252,
1.8599165678024292,
-1.720513105392456,
],
"cameraHorizon": 0.0,
"distance": 1.9566510915756226,
"isopen": False,
"name": "Microwave4",
"objectId": "Microwave|-00.17|+01.49|-02.06",
"objectType": "Microwave",
"openable": True,
"parentReceptacle": "",
"pickupable": False,
"pivotSimObjs": [],
"position": {
"x": -0.1746000051498413,
"y": 1.485553503036499,
"z": -2.055999994277954,
},
"receptacle": True,
"receptacleCount": 1,
"receptacleObjectIds": [],
"rotation": {"x": 0.0, "y": 0.0, "z": 0.0},
"visible": False,
}
assert event.get_object("Microwave|-00.17|+01.49|-02.06") == microwave
assert event.get_object("FOOO") is None
def test_cv2img(event_with_frame):
cvf = np.load(os.path.join(TESTS_DATA_DIR, "test-image1-bgr.npy"))
assert event_with_frame.cv2img.shape == event_with_frame.frame.shape
assert np.all(cvf == event_with_frame.cv2img)
assert not np.all(event_with_frame.frame == event_with_frame.cv2img)
def test_add_image(event):
with open(os.path.join(TESTS_DATA_DIR, "rgb-image.raw"), "rb") as f:
raw_image = memoryview(f.read())
f = np.load(os.path.join(TESTS_DATA_DIR, "test-image1-rgb.npy"))
assert event.frame is None
event.add_image(raw_image)
assert event.frame.shape == (300, 300, 3)
assert np.all(f == event.frame)
def test_metadata(event):
assert event.screen_height == 300
assert event.screen_width == 300
assert event.pose == (-750, -250, 0, 0)
def test_objets_by_test(event):
all_mugs = [o["objectId"] for o in event.objects_by_type("Mug")]
mug_object_ids = [
"Mug|-00.78|+00.93|-03.85",
"Mug|-01.63|+00.92|-03.74",
"Mug|-00.53|+00.93|-01.58",
]
assert all_mugs == mug_object_ids
assert event.objects_by_type("FOO") == []
def test_process_colors(event_complex):
event_complex.process_colors
assert len(event_complex.color_to_object_id.keys()) == 125
assert event_complex.color_to_object_id[(207, 119, 70)] == "Spatula3.001"
assert (
event_complex.color_to_object_id[(141, 139, 54)]
== "Cabinet|-00.63|+00.39|-02.51"
)
assert (
event_complex.color_to_object_id[(29, 84, 249)] == "Spoon|-00.50|+00.78|-01.45"
)
assert event_complex.color_to_object_id[(235, 57, 90)] == "Spoon"
assert event_complex.object_id_to_color["Spatula3.001"] == (207, 119, 70)
assert event_complex.object_id_to_color["Cabinet|-00.63|+00.39|-02.51"] == (
141,
139,
54,
)
assert event_complex.object_id_to_color["Spoon|-00.50|+00.78|-01.45"] == (
29,
84,
249,
)
assert event_complex.object_id_to_color["Spoon"] == (235, 57, 90)
| 35.464637
| 87
| 0.430589
| 9,143
| 118,842
| 5.584053
| 0.102592
| 0.012261
| 0.029674
| 0.0455
| 0.856214
| 0.855822
| 0.853648
| 0.829086
| 0.827598
| 0.827598
| 0
| 0.334787
| 0.403898
| 118,842
| 3,350
| 88
| 35.475224
| 0.385901
| 0
| 0
| 0.801143
| 0
| 0
| 0.236743
| 0.046473
| 0
| 0
| 0
| 0
| 0.006619
| 1
| 0.002708
| false
| 0
| 0.001504
| 0.000602
| 0.005114
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 9
|
be2670d8565e9eaf449d500cde28ee99708be4a1
| 8,533
|
py
|
Python
|
prior_utils/prepare_prior_cgenie.py
|
frodre/LMR
|
4c00d3f9db96447e69bd3f426d59524f7b5f3ef5
|
[
"BSD-3-Clause"
] | 17
|
2018-08-27T18:50:36.000Z
|
2021-03-17T22:48:55.000Z
|
prior_utils/prepare_prior_cgenie.py
|
mingsongli/LMR
|
4c00d3f9db96447e69bd3f426d59524f7b5f3ef5
|
[
"BSD-3-Clause"
] | 5
|
2018-10-15T22:13:27.000Z
|
2019-04-26T11:45:58.000Z
|
prior_utils/prepare_prior_cgenie.py
|
mingsongli/LMR
|
4c00d3f9db96447e69bd3f426d59524f7b5f3ef5
|
[
"BSD-3-Clause"
] | 11
|
2018-10-11T19:35:34.000Z
|
2021-08-17T12:08:11.000Z
|
"""
Module: prepare_prior_cgenie.py
Purpose: Extract data from a set of variables from a cGENIE climate model
simulation to generate files formatted for input into the
LMR data assimilation system.
Originator: Robert Tardif - University of Washington : August 2017
"""
import os
# ==============================================================================
input_data_directory = '/home/disk/ekman/rtardif/kalman3/LMR/data/model/cgenie_petm/orig_files/'
output_data_directory = '/home/disk/ekman/rtardif/kalman3/LMR/data/model/cgenie_petm/'
# GENIE output files, input to this script
file_2d_fields = input_data_directory+'fields_biogem_2d.nc'
file_3d_fields = input_data_directory+'fields_biogem_3d.nc'
#time_interval = 'ann' # for files with data every year
time_interval = 'dec' # for files with data every decade
#time_interval = 'cen' # for files with data every century
# ==============================================================================
FillVal = 9.96920996839e+36
# ---------------------------------------------------------------------
# 1) extract near-surface air temperature from GENIE 2D file (atm_temp)
# ---------------------------------------------------------------------
lmr_file = output_data_directory+'tas_sfc_A%s_cgenie_petm.nc' %(time_interval)
genie_variable = 'atm_temp'
# extract the variable
command = 'ncks -v %s %s %s' %(genie_variable, file_2d_fields,lmr_file)
status = os.system(command)
if status == 0:
# rename to LMR variable
command = 'ncrename -O -v atm_temp,tas %s' %(lmr_file)
status = os.system(command)
# convert deg C to Kelvins
# mv data file to temporary file
command = 'mv -f %s tmp.nc' %(lmr_file)
status = os.system(command)
# rename "missing_value" as _FillValue (recognized by NCO)
command = 'ncrename -a .missing_value,_FillValue tmp.nc tmp2.nc'
status = os.system(command)
# perform conversion and put results in lmr_file
command = 'ncap -O -s "tas=(tas+273.15)" tmp2.nc %s' %(lmr_file)
status = os.system(command)
# delete temporary files
command = 'rm -f tmp.nc tmp2.nc'
status = os.system(command)
# re-add variable attributes
command = 'ncatted -O -a long_name,tas,c,c,"surface air temperature" %s' %(lmr_file)
status = os.system(command)
command = 'ncatted -O -a units,tas,c,c,"K" %s' %(lmr_file)
status = os.system(command)
command = 'ncatted -a _FillValue,,m,f,%f %s' %(FillVal,lmr_file)
status = os.system(command)
command = 'ncatted -a missing_value,,c,f,%f %s' %(FillVal,lmr_file)
status = os.system(command)
# add necessary attributes to time variable
command = 'ncatted -O -a calendar,time,c,c,"noleap" %s' %(lmr_file)
status = os.system(command)
command = 'ncatted -O -a units,time,o,c,"year mid-point" %s' %(lmr_file)
status = os.system(command)
# ------------------------------------------------
# 2) extract SST from GENIE 2D file (ocn_sur_temp)
# ------------------------------------------------
lmr_file = output_data_directory+'tos_sfc_O%s_cgenie_petm.nc' %(time_interval)
genie_variable = 'ocn_sur_temp'
# extract the variable
command = 'ncks -v %s %s %s' %(genie_variable,file_2d_fields,lmr_file)
status = os.system(command)
if status == 0:
# rename to LMR variable
command = 'ncrename -O -v ocn_sur_temp,tos %s' %(lmr_file)
status = os.system(command)
# convert deg C to Kelvins
# mv data file to temporary file
command = 'mv -f %s tmp.nc' %(lmr_file)
status = os.system(command)
# rename "missing_value" as _FillValue (recognized by NCO)
command = 'ncrename -a .missing_value,_FillValue tmp.nc tmp2.nc'
status = os.system(command)
# perform conversion and put results in lmr_file
command = 'ncap -O -s "tos=(tos+273.15)" tmp2.nc %s' %(lmr_file)
status = os.system(command)
# delete temporary files
command = 'rm -f tmp.nc tmp2.nc'
status = os.system(command)
# re-add variable attributes
command = 'ncatted -O -a long_name,tos,c,c,"surface-water temp" %s' %(lmr_file)
status = os.system(command)
command = 'ncatted -O -a units,tos,c,c,"K" %s' %(lmr_file)
status = os.system(command)
command = 'ncatted -a _FillValue,,m,f,%f %s' %(FillVal,lmr_file)
status = os.system(command)
command = 'ncatted -a missing_value,,c,f,%f %s' %(FillVal,lmr_file)
status = os.system(command)
# add necessary attributes to time variable
command = 'ncatted -O -a calendar,time,c,c,"noleap" %s' %(lmr_file)
status = os.system(command)
command = 'ncatted -O -a units,time,o,c,"year mid-point" %s' %(lmr_file)
status = os.system(command)
# -----------------------------------------------
# 3) extract SSS from GENIE 2D file (ocn_sur_sal)
# ------------------------------------------------
lmr_file = output_data_directory+'sos_sfc_O%s_cgenie_petm.nc' %(time_interval)
genie_variable = 'ocn_sur_sal'
# extract the variable
command = 'ncks -v %s %s %s' %(genie_variable,file_2d_fields,lmr_file)
status = os.system(command)
if status == 0:
# rename to LMR variable
command = 'ncrename -O -v ocn_sur_sal,sos %s' %(lmr_file)
status = os.system(command)
# rename "missing_value" as _FillValue (recognized by NCO)
command = 'ncrename -a .missing_value,_FillValue %s' %(lmr_file)
status = os.system(command)
# re-add variable attributes
command = 'ncatted -a _FillValue,,m,f,%f %s' %(FillVal,lmr_file)
status = os.system(command)
command = 'ncatted -a missing_value,,c,f,%f %s' %(FillVal,lmr_file)
status = os.system(command)
# add necessary attributes to time variable
command = 'ncatted -O -a calendar,time,c,c,"noleap" %s' %(lmr_file)
status = os.system(command)
command = 'ncatted -O -a units,time,o,c,"year mid-point" %s' %(lmr_file)
status = os.system(command)
# ---------------------------------------------------------
# 4) extract sea-ice cover from GENIE 2D file (phys_seaice)
# ---------------------------------------------------------
lmr_file = output_data_directory+'sic_sfc_OI%s_cgenie_petm.nc' %(time_interval)
genie_variable = 'phys_seaice'
# extract the variable
command = 'ncks -v %s %s %s' %(genie_variable,file_2d_fields,lmr_file)
status = os.system(command)
if status == 0:
# rename to LMR variable
command = 'ncrename -O -v phys_seaice,sic %s' %(lmr_file)
status = os.system(command)
# rename "missing_value" as _FillValue (recognized by NCO)
command = 'ncrename -a .missing_value,_FillValue %s' %(lmr_file)
status = os.system(command)
# re-add variable attributes
command = 'ncatted -a _FillValue,,m,f,%f %s' %(FillVal,lmr_file)
status = os.system(command)
command = 'ncatted -a missing_value,,c,f,%f %s' %(FillVal,lmr_file)
status = os.system(command)
# add necessary attributes to time variable
command = 'ncatted -O -a calendar,time,c,c,"noleap" %s' %(lmr_file)
status = os.system(command)
command = 'ncatted -O -a units,time,o,c,"year mid-point" %s' %(lmr_file)
status = os.system(command)
# modify units attribute to sea-ice cover
command = 'ncatted -O -a units,sic,o,c,"percent" %s' %(lmr_file)
status = os.system(command)
# ----------------------------------------------------------------
# 5) extract sea-ice thickness from GENIE 2D file (phys_seaice_th)
# ----------------------------------------------------------------
lmr_file = output_data_directory+'sit_sfc_OI%s_cgenie_petm.nc' %(time_interval)
genie_variable = 'phys_seaice_th'
# extract the variable
command = 'ncks -v %s %s %s' %(genie_variable,file_2d_fields,lmr_file)
status = os.system(command)
if status == 0:
# rename to LMR variable
command = 'ncrename -O -v phys_seaice_th,sit %s' %(lmr_file)
status = os.system(command)
# rename "missing_value" as _FillValue (recognized by NCO)
command = 'ncrename -a .missing_value,_FillValue %s' %(lmr_file)
status = os.system(command)
# re-add variable attributes
command = 'ncatted -a _FillValue,,m,f,%f %s' %(FillVal,lmr_file)
status = os.system(command)
command = 'ncatted -a missing_value,,c,f,%f %s' %(FillVal,lmr_file)
status = os.system(command)
# add necessary attributes to time variable
command = 'ncatted -O -a calendar,time,c,c,"noleap" %s' %(lmr_file)
status = os.system(command)
command = 'ncatted -O -a units,time,o,c,"year mid-point" %s' %(lmr_file)
status = os.system(command)
| 37.924444
| 97
| 0.624517
| 1,182
| 8,533
| 4.352792
| 0.130288
| 0.066667
| 0.12517
| 0.187755
| 0.87036
| 0.838873
| 0.795335
| 0.789699
| 0.782313
| 0.782313
| 0
| 0.008487
| 0.171452
| 8,533
| 224
| 98
| 38.09375
| 0.719236
| 0.309153
| 0
| 0.745614
| 1
| 0.008772
| 0.34197
| 0.120796
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.008772
| 0
| 0.008772
| 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
|
be28c6bcd0585c61f4d6f3ff8e3447dc5d801e96
| 15,379
|
py
|
Python
|
ctpbee/record.py
|
yutiansut/ctpbee
|
02ceb3d4456a54b1b4f8066a2662c4b8fac1027f
|
[
"MIT"
] | null | null | null |
ctpbee/record.py
|
yutiansut/ctpbee
|
02ceb3d4456a54b1b4f8066a2662c4b8fac1027f
|
[
"MIT"
] | null | null | null |
ctpbee/record.py
|
yutiansut/ctpbee
|
02ceb3d4456a54b1b4f8066a2662c4b8fac1027f
|
[
"MIT"
] | 3
|
2019-11-21T03:38:14.000Z
|
2022-02-14T08:09:11.000Z
|
from copy import deepcopy
from datetime import datetime
from ctpbee.constant import EVENT_TICK, EVENT_ORDER, EVENT_TRADE, EVENT_POSITION, EVENT_ACCOUNT, \
EVENT_CONTRACT, EVENT_BAR, EVENT_LOG, EVENT_ERROR, EVENT_SHARED
from ctpbee.data_handle import generator
from ctpbee.data_handle.local_position import LocalPositionManager
from ctpbee.event_engine import Event
class Recorder(object):
"""
data center
"""
def __init__(self, app, event_engine):
""""""
self.bar = {}
self.ticks = {}
self.orders = {}
self.trades = {}
self.positions = {}
self.accounts = {}
self.contracts = {}
self.logs = {}
self.errors = []
self.shared = {}
self.generators = {}
self.active_orders = {}
self.event_engine = event_engine
self.register_event()
self.app = app
self.position_manager = LocalPositionManager(app=self.app)
@staticmethod
def get_local_time():
from datetime import datetime
return datetime.now().strftime('%Y-%m-%d %H:%M:%S')
def register_event(self):
"""bind process function"""
self.event_engine.register(EVENT_TICK, self.process_tick_event)
self.event_engine.register(EVENT_ORDER, self.process_order_event)
self.event_engine.register(EVENT_TRADE, self.process_trade_event)
self.event_engine.register(EVENT_POSITION, self.process_position_event)
self.event_engine.register(EVENT_ACCOUNT, self.process_account_event)
self.event_engine.register(EVENT_CONTRACT, self.process_contract_event)
self.event_engine.register(EVENT_BAR, self.process_bar_event)
self.event_engine.register(EVENT_LOG, self.process_log_event)
self.event_engine.register(EVENT_ERROR, self.process_error_event)
self.event_engine.register(EVENT_SHARED, self.process_shared_event)
def process_shared_event(self, event):
if self.shared.get(event.data.local_symbol, None) is not None:
self.shared[event.data.local_symbol].append(event.data)
else:
self.shared[event.data.local_symbol] = []
for value in self.app.extensions.values():
value(deepcopy(event))
def process_error_event(self, event: Event):
self.errors.append({"time": self.get_local_time(), "data": event.data})
print(self.get_local_time() + ": ", event.data)
def process_log_event(self, event: Event):
self.logs[self.get_local_time()] = event.data
if self.app.config.get("LOG_OUTPUT"):
print(self.get_local_time() + ": ", event.data)
for value in self.app.extensions.values():
value(deepcopy(event))
def process_bar_event(self, event: Event):
bar = event.data
local = self.bar.get(bar.local_symbol)
if local is None:
self.bar[bar.local_symbol] = {bar.interval: []}
else:
if self.bar[bar.local_symbol].get(bar.interval) is None:
self.bar[bar.local_symbol] = {bar.interval: []}
self.bar[bar.local_symbol][bar.interval].append(bar)
for value in self.app.extensions.values():
value(deepcopy(event))
def process_tick_event(self, event: Event):
""""""
tick = event.data
self.ticks[tick.local_symbol] = tick
symbol = tick.symbol
self.position_manager.update_tick(tick)
# 生成datetime对象
if not tick.datetime:
if '.' in tick.time:
tick.datetime = datetime.strptime(' '.join([tick.date, tick.time]), '%Y%m%d %H:%M:%S.%f')
else:
tick.datetime = datetime.strptime(' '.join([tick.date, tick.time]), '%Y%m%d %H:%M:%S')
bm = self.generators.get(symbol, None)
if bm:
bm.update_tick(tick)
if not bm:
self.generators[symbol] = generator(self.event_engine)
for value in self.app.extensions.values():
value(deepcopy(event))
def process_order_event(self, event: Event):
""""""
order = event.data
self.orders[order.local_order_id] = order
# If order is active, then update data in dict.
if order._is_active():
self.active_orders[order.local_order_id] = order
# Otherwise, pop inactive order from in dict
elif order.local_order_id in self.active_orders:
self.active_orders.pop(order.local_order_id)
self.position_manager.update_order(order)
for value in self.app.extensions.values():
value(deepcopy(event))
def process_trade_event(self, event: Event):
""""""
trade = event.data
self.trades[trade.local_trade_id] = trade
self.position_manager.update_trade(trade)
for value in self.app.extensions.values():
value(deepcopy(event))
def process_position_event(self, event: Event):
""""""
position = event.data
self.positions[position.local_position_id] = position
self.position_manager.update_position(position)
for value in self.app.extensions.values():
value(deepcopy(event))
def process_account_event(self, event: Event):
""""""
account = event.data
self.accounts[account.local_account_id] = account
for value in self.app.extensions.values():
value(deepcopy(event))
def process_contract_event(self, event: Event):
""""""
contract = event.data
self.contracts[contract.local_symbol] = contract
for value in self.app.extensions.values():
value(deepcopy(event))
def get_shared(self, symbol):
return self.shared.get(symbol, None)
def get_all_shared(self):
return self.shared
def get_bar(self, local_symbol):
return self.bar.get(local_symbol, None)
def get_all_bar(self):
return self.bar
def get_tick(self, local_symbol):
return self.ticks.get(local_symbol, None)
def get_order(self, local_order_id):
return self.orders.get(local_order_id, None)
def get_trade(self, local_trade_id):
return self.trades.get(local_trade_id, None)
def get_position(self, local_position_id):
return self.positions.get(local_position_id, None)
def get_account(self, local_account_id):
return self.accounts.get(local_account_id, None)
def get_contract(self, local_symbol):
return self.contracts.get(local_symbol, None)
def get_all_ticks(self):
"""
Get all tick data.
"""
return list(self.ticks.values())
def get_all_orders(self):
"""
Get all order data.
"""
return list(self.orders.values())
def get_all_trades(self):
"""
Get all trade data.
"""
return list(self.trades.values())
def get_all_positions(self):
"""
Get all position data.
"""
return self.position_manager.get_all_positions()
def get_errors(self):
return self.errors
def get_new_error(self):
return self.errors[-1]
def get_all_accounts(self):
"""
Get all account data.
"""
return list(self.accounts.values())
def get_all_contracts(self):
"""
Get all contract data.
"""
return list(self.contracts.values())
def get_all_active_orders(self, local_symbol: str = ""):
if not local_symbol:
return list(self.active_orders.values())
else:
active_orders = [
order
for order in self.active_orders.values()
if order.local_symbol == local_symbol
]
return active_orders
class AsyncRecorder(object):
"""
data center
"""
def __init__(self, app, event_engine):
""""""
self.bar = {}
self.ticks = {}
self.orders = {}
self.trades = {}
self.positions = {}
self.accounts = {}
self.contracts = {}
self.logs = {}
self.errors = []
self.shared = {}
self.generators = {}
self.active_orders = {}
self.event_engine = event_engine
self.register_event()
self.app = app
self.position_manager = LocalPositionManager(app=self.app)
@staticmethod
def get_local_time():
from datetime import datetime
return datetime.now().strftime('%Y-%m-%d %H:%M:%S')
def register_event(self):
"""bind process function"""
self.event_engine.register(EVENT_TICK, self.process_tick_event)
self.event_engine.register(EVENT_ORDER, self.process_order_event)
self.event_engine.register(EVENT_TRADE, self.process_trade_event)
self.event_engine.register(EVENT_POSITION, self.process_position_event)
self.event_engine.register(EVENT_ACCOUNT, self.process_account_event)
self.event_engine.register(EVENT_CONTRACT, self.process_contract_event)
self.event_engine.register(EVENT_BAR, self.process_bar_event)
self.event_engine.register(EVENT_LOG, self.process_log_event)
self.event_engine.register(EVENT_ERROR, self.process_error_event)
self.event_engine.register(EVENT_SHARED, self.process_shared_event)
async def process_shared_event(self, event):
if self.shared.get(event.data.local_symbol, None) is not None:
self.shared[event.data.local_symbol].append(event.data)
else:
self.shared[event.data.local_symbol] = []
for value in self.app.extensions.values():
await value(deepcopy(event))
async def process_error_event(self, event: Event):
self.errors.append({"time": self.get_local_time(), "data": event.data})
print(self.get_local_time() + ": ", event.data)
async def process_log_event(self, event: Event):
self.logs[self.get_local_time()] = event.data
if self.app.config.get("LOG_OUTPUT"):
print(self.get_local_time() + ": ", event.data)
for value in self.app.extensions.values():
await value(deepcopy(event))
async def process_bar_event(self, event: Event):
bar = event.data
local = self.bar.get(bar.local_symbol)
if local is None:
self.bar[bar.local_symbol] = {bar.interval: []}
else:
if self.bar[bar.local_symbol].get(bar.interval) is None:
self.bar[bar.local_symbol] = {bar.interval: []}
self.bar[bar.local_symbol][bar.interval].append(bar)
for value in self.app.extensions.values():
await value(deepcopy(event))
async def process_tick_event(self, event: Event):
""""""
tick = event.data
self.ticks[tick.local_symbol] = tick
symbol = tick.symbol
self.position_manager.update_tick(tick)
# 生成datetime对象
if not tick.datetime:
if '.' in tick.time:
tick.datetime = datetime.strptime(' '.join([tick.date, tick.time]), '%Y%m%d %H:%M:%S.%f')
else:
tick.datetime = datetime.strptime(' '.join([tick.date, tick.time]), '%Y%m%d %H:%M:%S')
bm = self.generators.get(symbol, None)
if bm:
bm.update_tick(tick)
if not bm:
self.generators[symbol] = generator(self.event_engine)
for value in self.app.extensions.values():
await value(deepcopy(event))
async def process_order_event(self, event: Event):
""""""
order = event.data
self.orders[order.local_order_id] = order
# If order is active, then update data in dict.
if order._is_active():
self.active_orders[order.local_order_id] = order
# Otherwise, pop inactive order from in dict
elif order.local_order_id in self.active_orders:
self.active_orders.pop(order.local_order_id)
self.position_manager.update_order(order)
for value in self.app.extensions.values():
await value(deepcopy(event))
async def process_trade_event(self, event: Event):
""""""
trade = event.data
self.trades[trade.local_trade_id] = trade
self.position_manager.update_trade(trade)
for value in self.app.extensions.values():
await value(deepcopy(event))
async def process_position_event(self, event: Event):
""""""
position = event.data
self.positions[position.local_position_id] = position
self.position_manager.update_position(position)
for value in self.app.extensions.values():
await value(deepcopy(event))
async def process_account_event(self, event: Event):
""""""
account = event.data
self.accounts[account.local_account_id] = account
for value in self.app.extensions.values():
await value(deepcopy(event))
async def process_contract_event(self, event: Event):
""""""
contract = event.data
self.contracts[contract.local_symbol] = contract
for value in self.app.extensions.values():
await value(deepcopy(event))
def get_shared(self, symbol):
return self.shared.get(symbol, None)
def get_all_shared(self):
return self.shared
def get_bar(self, local_symbol):
return self.bar.get(local_symbol, None)
def get_all_bar(self):
return self.bar
def get_tick(self, local_symbol):
return self.ticks.get(local_symbol, None)
def get_order(self, local_order_id):
return self.orders.get(local_order_id, None)
def get_trade(self, local_trade_id):
return self.trades.get(local_trade_id, None)
def get_position(self, local_position_id):
return self.positions.get(local_position_id, None)
def get_account(self, local_account_id):
return self.accounts.get(local_account_id, None)
def get_contract(self, local_symbol):
return self.contracts.get(local_symbol, None)
def get_all_ticks(self):
"""
Get all tick data.
"""
return list(self.ticks.values())
def get_all_orders(self):
"""
Get all order data.
"""
return list(self.orders.values())
def get_all_trades(self):
"""
Get all trade data.
"""
return list(self.trades.values())
def get_all_positions(self):
"""
Get all position data.
"""
return self.position_manager.get_all_positions()
def get_errors(self):
return self.errors
def get_new_error(self):
return self.errors[-1]
def get_all_accounts(self):
"""
Get all account data.
"""
return list(self.accounts.values())
def get_all_contracts(self):
"""
Get all contract data.
"""
return list(self.contracts.values())
def get_all_active_orders(self, local_symbol: str = ""):
if not local_symbol:
return list(self.active_orders.values())
else:
active_orders = [
order
for order in self.active_orders.values()
if order.local_symbol == local_symbol
]
return active_orders
| 33.360087
| 105
| 0.621367
| 1,903
| 15,379
| 4.820809
| 0.050447
| 0.045128
| 0.05799
| 0.050142
| 0.962612
| 0.962612
| 0.962612
| 0.962612
| 0.962612
| 0.962612
| 0
| 0.000177
| 0.266727
| 15,379
| 460
| 106
| 33.432609
| 0.813337
| 0.034202
| 0
| 0.905363
| 0
| 0
| 0.010383
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.170347
| false
| 0
| 0.025237
| 0.07571
| 0.334385
| 0.012618
| 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
|
077ef5a456f4ef84d1fb90d05e9ac1302b4a87c9
| 5,722
|
py
|
Python
|
ElectroWeakAnalysis/ZMuMu/python/zSelection_cfi.py
|
SWuchterl/cmssw
|
769b4a7ef81796579af7d626da6039dfa0347b8e
|
[
"Apache-2.0"
] | 6
|
2017-09-08T14:12:56.000Z
|
2022-03-09T23:57:01.000Z
|
ElectroWeakAnalysis/ZMuMu/python/zSelection_cfi.py
|
SWuchterl/cmssw
|
769b4a7ef81796579af7d626da6039dfa0347b8e
|
[
"Apache-2.0"
] | 545
|
2017-09-19T17:10:19.000Z
|
2022-03-07T16:55:27.000Z
|
ElectroWeakAnalysis/ZMuMu/python/zSelection_cfi.py
|
SWuchterl/cmssw
|
769b4a7ef81796579af7d626da6039dfa0347b8e
|
[
"Apache-2.0"
] | 14
|
2017-10-04T09:47:21.000Z
|
2019-10-23T18:04:45.000Z
|
import FWCore.ParameterSet.Config as cms
zSelectionLoose = cms.PSet(
cut = cms.string("charge = 0 & daughter(0).pt > 15 & daughter(1).pt > 15 & abs(daughter(0).eta)<2.4 & abs(daughter(1).eta)<2.4 & mass > 0"),
isoCut = cms.double(1000.),
ptThreshold = cms.untracked.double(1.5),
etEcalThreshold = cms.untracked.double(0.2),
etHcalThreshold = cms.untracked.double(0.5),
deltaRVetoTrk = cms.untracked.double(0.015),
deltaRTrk = cms.untracked.double(0.3),
deltaREcal = cms.untracked.double(0.25),
deltaRHcal = cms.untracked.double(0.25),
alpha = cms.untracked.double(0.),
beta = cms.untracked.double(-0.75),
relativeIsolation = cms.bool(False)
# For standard isolation (I_Tkr<3GeV) choose this configuration:
# isoCut = cms.double(3.),
# ptThreshold = cms.untracked.double(1.5),
# etEcalThreshold = cms.untracked.double(0.2),
# etHcalThreshold = cms.untracked.double(0.5),
# deltaRVetoTrk = cms.untracked.double(0.015),
# deltaRTrk = cms.untracked.double(0.3),
# deltaREcal = cms.untracked.double(0.25),
# deltaRHcal = cms.untracked.double(0.25),
# alpha = cms.untracked.double(0.),
# beta = cms.untracked.double(-0.75),
# relativeIsolation = cms.bool(False)
)
##### I = alpha /2 (( 1 + beta) HCal + (1 - beta) Ecal ) + (1 - alpha)Trk
####### combined isolation
#zSelection = cms.PSet(
# cut = cms.string("charge = 0 & daughter(0).pt > 20. & daughter(1).pt > 20. & abs(daughter(0).eta)<2.1 & abs(daughter(1).eta)<2.1 & mass > 0"),
# isoCut = cms.double(.45), ### with alpha = 2/3 and beta =0, so 0.45 is equivalent to 0.15......
# ptThreshold = cms.untracked.double(0.),
# etEcalThreshold = cms.untracked.double(0.),
# etHcalThreshold = cms.untracked.double(0.),
# deltaRVetoTrk = cms.untracked.double(0.01),
# deltaRTrk = cms.untracked.double(0.3),
# deltaREcal = cms.untracked.double(0.3),
# deltaRHcal = cms.untracked.double(0.3),
# alpha = cms.untracked.double(0.666667),
# beta = cms.untracked.double(0.0),
# relativeIsolation = cms.bool(True)
# )
#### tracker isolation
zSelection = cms.PSet(
cut = cms.string("charge = 0 & daughter(0).pt > 20. & daughter(1).pt > 20. & abs(daughter(0).eta)<2.1 & abs(daughter(1).eta)<2.1 & mass > 0"),
isoCut = cms.double(3.00),
ptThreshold = cms.untracked.double(0.),
etEcalThreshold = cms.untracked.double(0.),
etHcalThreshold = cms.untracked.double(0.),
deltaRVetoTrk = cms.untracked.double(0.01),
deltaRTrk = cms.untracked.double(0.3),
deltaREcal = cms.untracked.double(0.3),
deltaRHcal = cms.untracked.double(0.3),
alpha = cms.untracked.double(0.),
beta = cms.untracked.double(0.0),
relativeIsolation = cms.bool(False)
)
### region A: |eta|<2.1, region B: 2.1< |eta| <2.4
zSelectionABLoose = cms.PSet(
cut = cms.string("charge = 0 & daughter(0).pt > 15 & daughter(1).pt > 15 & ( (abs(daughter(0).eta)<2.1 & 2.1< abs(daughter(1).eta)<2.4 ) || (abs(daughter(1).eta)<2.1 & 2.1< abs(daughter(0).eta)<2.4 ) ) & mass > 0"),
isoCut = cms.double(1000.),
ptThreshold = cms.untracked.double(1.5),
etEcalThreshold = cms.untracked.double(0.2),
etHcalThreshold = cms.untracked.double(0.5),
deltaRVetoTrk = cms.untracked.double(0.015),
deltaRTrk = cms.untracked.double(0.3),
deltaREcal = cms.untracked.double(0.25),
deltaRHcal = cms.untracked.double(0.25),
alpha = cms.untracked.double(0.),
beta = cms.untracked.double(-0.75),
relativeIsolation = cms.bool(False)
)
zSelectionAB = cms.PSet(
cut = cms.string("charge = 0 & daughter(0).pt > 20. & daughter(1).pt > 20. & ( (abs(daughter(0).eta)<2.1 & 2.1< abs(daughter(1).eta)<2.4 ) || (abs(daughter(1).eta)<2.1 & 2.1< abs(daughter(0).eta)<2.4 ) ) & mass > 0"),
isoCut = cms.double(1000.),
ptThreshold = cms.untracked.double(1.5),
etEcalThreshold = cms.untracked.double(0.2),
etHcalThreshold = cms.untracked.double(0.5),
deltaRVetoTrk = cms.untracked.double(0.015),
deltaRTrk = cms.untracked.double(0.3),
deltaREcal = cms.untracked.double(0.25),
deltaRHcal = cms.untracked.double(0.25),
alpha = cms.untracked.double(0.),
beta = cms.untracked.double(-0.75),
relativeIsolation = cms.bool(False)
)
zSelectionBBLoose = cms.PSet(
cut = cms.string("charge = 0 & daughter(0).pt > 15 & daughter(1).pt > 15 & ( 2.1< abs(daughter(0).eta)<2.4 & 2.1< abs(daughter(1).eta)<2.4 ) & mass > 0"),
isoCut = cms.double(1000.),
ptThreshold = cms.untracked.double(1.5),
etEcalThreshold = cms.untracked.double(0.2),
etHcalThreshold = cms.untracked.double(0.5),
deltaRVetoTrk = cms.untracked.double(0.015),
deltaRTrk = cms.untracked.double(0.3),
deltaREcal = cms.untracked.double(0.25),
deltaRHcal = cms.untracked.double(0.25),
alpha = cms.untracked.double(0.),
beta = cms.untracked.double(-0.75),
relativeIsolation = cms.bool(False)
)
zSelectionBB = cms.PSet(
cut = cms.string("charge = 0 & daughter(0).pt > 20 & daughter(1).pt > 20 & ( 2.1< abs(daughter(0).eta)<2.4 & 2.1< abs(daughter(1).eta)<2.4 ) & mass > 0"),
isoCut = cms.double(1000.),
ptThreshold = cms.untracked.double(1.5),
etEcalThreshold = cms.untracked.double(0.2),
etHcalThreshold = cms.untracked.double(0.5),
deltaRVetoTrk = cms.untracked.double(0.015),
deltaRTrk = cms.untracked.double(0.3),
deltaREcal = cms.untracked.double(0.25),
deltaRHcal = cms.untracked.double(0.25),
alpha = cms.untracked.double(0.),
beta = cms.untracked.double(-0.75),
relativeIsolation = cms.bool(False)
)
goodZTight = cms.EDFilter(
"ZToMuMuIsolatedIDSelector",
zSelection,
src = cms.InputTag("goodZ"),
filter = cms.bool(True)
)
| 39.462069
| 222
| 0.647326
| 805
| 5,722
| 4.6
| 0.103106
| 0.233324
| 0.349987
| 0.338644
| 0.891169
| 0.891169
| 0.891169
| 0.891169
| 0.890899
| 0.871186
| 0
| 0.071444
| 0.168298
| 5,722
| 144
| 223
| 39.736111
| 0.706661
| 0.237854
| 0
| 0.637363
| 0
| 0.065934
| 0.215727
| 0.095801
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.010989
| 0
| 0.010989
| 0
| 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
|
07861f3ea99112ced4f3d202f67e445824038884
| 15,099
|
py
|
Python
|
gluoncv/data/pascal_voc/detection.py
|
JiangongWang/mean-teacher-cross-domain-detection
|
c52b8b2e22e8ff30ead1bef82409d41f52883ccd
|
[
"Apache-2.0"
] | 36
|
2019-12-25T04:59:49.000Z
|
2022-03-17T07:24:49.000Z
|
gluoncv/data/pascal_voc/detection.py
|
JiangongWang/mean-teacher-cross-domain-detection
|
c52b8b2e22e8ff30ead1bef82409d41f52883ccd
|
[
"Apache-2.0"
] | 1
|
2020-02-25T05:56:19.000Z
|
2020-05-15T17:03:59.000Z
|
gluoncv/data/pascal_voc/detection.py
|
JiangongWang/mean-teacher-cross-domain-detection
|
c52b8b2e22e8ff30ead1bef82409d41f52883ccd
|
[
"Apache-2.0"
] | 9
|
2019-12-25T05:00:33.000Z
|
2021-10-01T14:23:51.000Z
|
"""Pascal VOC object detection dataset."""
from __future__ import absolute_import
from __future__ import division
import os
import logging
import numpy as np
try:
import xml.etree.cElementTree as ET
except ImportError:
import xml.etree.ElementTree as ET
import mxnet as mx
from ..base import VisionDataset
class VOCDetection(VisionDataset):
"""Pascal VOC detection Dataset.
Parameters
----------
root : str, default '~/mxnet/datasets/voc'
Path to folder storing the dataset.
splits : list of tuples, default ((2007, 'trainval'), (2012, 'trainval'))
List of combinations of (year, name)
For years, candidates can be: 2007, 2012.
For names, candidates can be: 'train', 'val', 'trainval', 'test'.
transform : callable, defaut None
A function that takes data and label and transforms them. Refer to
:doc:`./transforms` for examples.
A transform function for object detection should take label into consideration,
because any geometric modification will require label to be modified.
index_map : dict, default None
In default, the 20 classes are mapped into indices from 0 to 19. We can
customize it by providing a str to int dict specifying how to map class
names to indicies. Use by advanced users only, when you want to swap the orders
of class labels.
preload_label : bool, default True
If True, then parse and load all labels into memory during
initialization. It often accelerate speed but require more memory
usage. Typical preloaded labels took tens of MB. You only need to disable it
when your dataset is extreamly large.
"""
CLASSES = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car',
'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike',
'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor')
def __init__(self, root=os.path.join('~', '.mxnet', 'datasets', 'voc'),
splits=((2007, 'trainval'), (2012, 'trainval')),
transform=None, index_map=None, preload_label=True):
super(VOCDetection, self).__init__(root)
self._im_shapes = {}
self._root = os.path.expanduser(root)
self._transform = transform
self._splits = splits
self._items = self._load_items(splits)
self._anno_path = os.path.join('{}', 'Annotations', '{}.xml')
self._image_path = os.path.join('{}', 'JPEGImages', '{}.jpg')
self.index_map = index_map or dict(zip(self.classes, range(self.num_class)))
self._label_cache = self._preload_labels() if preload_label else None
def __str__(self):
detail = ','.join([str(s[0]) + s[1] for s in self._splits])
return self.__class__.__name__ + '(' + detail + ')'
@property
def classes(self):
"""Category names."""
return type(self).CLASSES
def __len__(self):
return len(self._items)
def __getitem__(self, idx):
img_id = self._items[idx]
img_path = self._image_path.format(*img_id)
label = self._label_cache[idx] if self._label_cache else self._load_label(idx)
img = mx.image.imread(img_path, 1)
if self._transform is not None:
return self._transform(img, label)
return img, label
def _load_items(self, splits):
"""Load individual image indices from splits."""
ids = []
for year, name in splits:
root = os.path.join(self._root, 'VOC' + str(year))
lf = os.path.join(root, 'ImageSets', 'Main', name + '.txt')
with open(lf, 'r') as f:
ids += [(root, line.strip()) for line in f.readlines()]
return ids
def _load_label(self, idx):
"""Parse xml file and return labels."""
img_id = self._items[idx]
anno_path = self._anno_path.format(*img_id)
root = ET.parse(anno_path).getroot()
size = root.find('size')
width = float(size.find('width').text)
height = float(size.find('height').text)
if idx not in self._im_shapes:
# store the shapes for later usage
self._im_shapes[idx] = (width, height)
label = []
for obj in root.iter('object'):
difficult = int(obj.find('difficult').text)
cls_name = obj.find('name').text.strip().lower()
if cls_name not in self.classes:
continue
cls_id = self.index_map[cls_name]
xml_box = obj.find('bndbox')
xmin = (float(xml_box.find('xmin').text) - 1)
ymin = (float(xml_box.find('ymin').text) - 1)
xmax = (float(xml_box.find('xmax').text) - 1)
ymax = (float(xml_box.find('ymax').text) - 1)
try:
self._validate_label(xmin, ymin, xmax, ymax, width, height)
except AssertionError as e:
raise RuntimeError("Invalid label at {}, {}".format(anno_path, e))
label.append([xmin, ymin, xmax, ymax, cls_id, difficult])
return np.array(label)
def _validate_label(self, xmin, ymin, xmax, ymax, width, height):
"""Validate labels."""
assert 0 <= xmin < width, "xmin must in [0, {}), given {}".format(width, xmin)
assert 0 <= ymin < height, "ymin must in [0, {}), given {}".format(height, ymin)
assert xmin < xmax <= width, "xmax must in (xmin, {}], given {}".format(width, xmax)
assert ymin < ymax <= height, "ymax must in (ymin, {}], given {}".format(height, ymax)
def _preload_labels(self):
"""Preload all labels into memory."""
logging.debug("Preloading %s labels into memory...", str(self))
return [self._load_label(idx) for idx in range(len(self))]
class CityScapeDetection(VisionDataset):
"""Pascal VOC detection Dataset.
Parameters
----------
root : str, default '~/mxnet/datasets/voc'
Path to folder storing the dataset.
splits : list of tuples, default ((2007, 'trainval'), (2012, 'trainval'))
List of combinations of (year, name)
For years, candidates can be: 2007, 2012.
For names, candidates can be: 'train', 'val', 'trainval', 'test'.
transform : callable, defaut None
A function that takes data and label and transforms them. Refer to
:doc:`./transforms` for examples.
A transform function for object detection should take label into consideration,
because any geometric modification will require label to be modified.
index_map : dict, default None
In default, the 20 classes are mapped into indices from 0 to 19. We can
customize it by providing a str to int dict specifying how to map class
names to indicies. Use by advanced users only, when you want to swap the orders
of class labels.
preload_label : bool, default True
If True, then parse and load all labels into memory during
initialization. It often accelerate speed but require more memory
usage. Typical preloaded labels took tens of MB. You only need to disable it
when your dataset is extreamly large.
"""
CLASSES = ["person", "rider", "car", "truck", "bus", "train", "motorcycle", "bicycle"]
def __init__(self, root=os.path.join('~', '.mxnet', 'datasets', 'voc'),
splits="",
transform=None, index_map=None, preload_label=True, min_dataset_size=-1):
super(CityScapeDetection, self).__init__(root)
self._im_shapes = {}
self.min_dataset_size = min_dataset_size
self._root = os.path.expanduser(root)
self._transform = transform
self._splits = splits
self._items = self._load_items(splits)
# self._image_path = os.path.join('{}')
self.index_map = index_map or dict(zip(self.classes, range(self.num_class)))
self._label_cache = self._preload_labels() if preload_label else None
def __str__(self):
detail = self._splits
return self.__class__.__name__ + '(' + detail + ')'
@property
def classes(self):
"""Category names."""
return type(self).CLASSES
def __len__(self):
return len(self._items)
def __getitem__(self, idx):
img_id = self._items[idx]
# img_path = self._image_path.format(*img_id)
img_path = img_id[0]
label = self._label_cache[idx] if self._label_cache else self._load_label(idx)
img = mx.image.imread(img_path, 1)
if self._transform is not None:
return self._transform(img, label)
return img, label
def _load_items(self, splits):
"""Load individual image indices from splits."""
ids = []
with open(os.path.join(self._root, splits), 'r') as f:
for line in f.readlines():
line = line.strip().split(" ")
path = os.path.join(self._root, line[0])
others = line[1:]
ids.append([path] + others)
if self.min_dataset_size > 0:
print("{}: padding from : {} to {}".format(self._splits, len(ids), self.min_dataset_size))
while (len(ids)) < self.min_dataset_size:
ids = ids + ids
ids = ids[:self.min_dataset_size]
return ids
def _load_label(self, idx):
"""Parse xml file and return labels."""
img_id = self._items[idx]
annotation_data = img_id[1:]
annotation_data = np.array([float(k) for k in annotation_data])
annotation_data = np.reshape(annotation_data, newshape=(-1, 5))
annotation_data = np.concatenate((annotation_data, np.zeros(shape=(annotation_data.shape[0], 1))), axis=1)
return np.array(annotation_data)
def _validate_label(self, xmin, ymin, xmax, ymax, width, height):
"""Validate labels."""
assert 0 <= xmin < width, "xmin must in [0, {}), given {}".format(width, xmin)
assert 0 <= ymin < height, "ymin must in [0, {}), given {}".format(height, ymin)
assert xmin < xmax <= width, "xmax must in (xmin, {}], given {}".format(width, xmax)
assert ymin < ymax <= height, "ymax must in (ymin, {}], given {}".format(height, ymax)
def _preload_labels(self):
"""Preload all labels into memory."""
logging.debug("Preloading %s labels into memory...", str(self))
return [self._load_label(idx) for idx in range(len(self))]
class SIM10kDetection(VisionDataset):
"""Pascal VOC detection Dataset.
Parameters
----------
root : str, default '~/mxnet/datasets/voc'
Path to folder storing the dataset.
splits : list of tuples, default ((2007, 'trainval'), (2012, 'trainval'))
List of combinations of (year, name)
For years, candidates can be: 2007, 2012.
For names, candidates can be: 'train', 'val', 'trainval', 'test'.
transform : callable, defaut None
A function that takes data and label and transforms them. Refer to
:doc:`./transforms` for examples.
A transform function for object detection should take label into consideration,
because any geometric modification will require label to be modified.
index_map : dict, default None
In default, the 20 classes are mapped into indices from 0 to 19. We can
customize it by providing a str to int dict specifying how to map class
names to indicies. Use by advanced users only, when you want to swap the orders
of class labels.
preload_label : bool, default True
If True, then parse and load all labels into memory during
initialization. It often accelerate speed but require more memory
usage. Typical preloaded labels took tens of MB. You only need to disable it
when your dataset is extreamly large.
"""
CLASSES = ["car", ]
def __init__(self, root=os.path.join('~', '.mxnet', 'datasets', 'voc'),
splits="",
transform=None, index_map=None, preload_label=True, min_dataset_size=-1):
super(SIM10kDetection, self).__init__(root)
self._im_shapes = {}
self.min_dataset_size = min_dataset_size
self._root = os.path.expanduser(root)
self._transform = transform
self._splits = splits
self._items = self._load_items(splits)
# self._image_path = os.path.join('{}')
self.index_map = index_map or dict(zip(self.classes, range(self.num_class)))
self._label_cache = self._preload_labels() if preload_label else None
def __str__(self):
detail = self._splits
return self.__class__.__name__ + '(' + detail + ')'
@property
def classes(self):
"""Category names."""
return type(self).CLASSES
def __len__(self):
return len(self._items)
def __getitem__(self, idx):
img_id = self._items[idx]
# img_path = self._image_path.format(*img_id)
img_path = img_id[0]
label = self._label_cache[idx] if self._label_cache else self._load_label(idx)
img = mx.image.imread(img_path, 1)
if self._transform is not None:
return self._transform(img, label)
return img, label
def _load_items(self, splits):
"""Load individual image indices from splits."""
ids = []
with open(os.path.join(self._root, splits), 'r') as f:
for line in f.readlines():
line = line.strip().split(" ")
path = os.path.join(self._root, line[0])
others = line[1:]
ids.append([path] + others)
if self.min_dataset_size > 0:
print("{}: padding from : {} to {}".format(self._splits, len(ids), self.min_dataset_size))
while (len(ids)) < self.min_dataset_size:
ids = ids + ids
ids = ids[:self.min_dataset_size]
return ids
def _load_label(self, idx):
"""Parse xml file and return labels."""
img_id = self._items[idx]
annotation_data = img_id[1:]
annotation_data = np.array([float(k) for k in annotation_data])
annotation_data = np.reshape(annotation_data, newshape=(-1, 5))
annotation_data = np.concatenate((annotation_data, np.zeros(shape=(annotation_data.shape[0], 1))), axis=1)
return np.array(annotation_data)
def _validate_label(self, xmin, ymin, xmax, ymax, width, height):
"""Validate labels."""
assert 0 <= xmin < width, "xmin must in [0, {}), given {}".format(width, xmin)
assert 0 <= ymin < height, "ymin must in [0, {}), given {}".format(height, ymin)
assert xmin < xmax <= width, "xmax must in (xmin, {}], given {}".format(width, xmax)
assert ymin < ymax <= height, "ymax must in (ymin, {}], given {}".format(height, ymax)
def _preload_labels(self):
"""Preload all labels into memory."""
logging.debug("Preloading %s labels into memory...", str(self))
return [self._load_label(idx) for idx in range(len(self))]
| 43.892442
| 114
| 0.615074
| 1,952
| 15,099
| 4.588627
| 0.13627
| 0.028134
| 0.021882
| 0.020096
| 0.852629
| 0.848498
| 0.842916
| 0.840237
| 0.835659
| 0.835659
| 0
| 0.010608
| 0.263262
| 15,099
| 343
| 115
| 44.020408
| 0.794588
| 0.291675
| 0
| 0.726829
| 0
| 0
| 0.088995
| 0
| 0
| 0
| 0
| 0
| 0.063415
| 1
| 0.131707
| false
| 0
| 0.04878
| 0.014634
| 0.326829
| 0.009756
| 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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| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
078e788beb4e94e509d9cf9c9979a01ac4f4fffa
| 359
|
py
|
Python
|
pyaedt/edb_core/__init__.py
|
beliaev-maksim/pyaedt
|
c549de1d0c80f3598afc5475817a332bb6d6df57
|
[
"MIT"
] | 12
|
2021-07-01T06:35:12.000Z
|
2021-09-22T15:53:07.000Z
|
pyaedt/edb_core/__init__.py
|
beliaev-maksim/pyaedt
|
c549de1d0c80f3598afc5475817a332bb6d6df57
|
[
"MIT"
] | 111
|
2021-07-01T16:02:36.000Z
|
2021-09-29T12:36:44.000Z
|
pyaedt/edb_core/__init__.py
|
beliaev-maksim/pyaedt
|
c549de1d0c80f3598afc5475817a332bb6d6df57
|
[
"MIT"
] | 5
|
2021-07-09T14:24:59.000Z
|
2021-09-07T12:42:03.000Z
|
from __future__ import absolute_import
from pyaedt.edb_core.components import Components
from pyaedt.edb_core.hfss import EdbHfss
from pyaedt.edb_core.nets import EdbNets
from pyaedt.edb_core.padstack import EdbPadstacks
from pyaedt.edb_core.siwave import EdbSiwave
from pyaedt.edb_core.stackup import EdbStackup
from pyaedt.edb_core.layout import EdbLayout
| 35.9
| 49
| 0.869081
| 54
| 359
| 5.555556
| 0.37037
| 0.233333
| 0.303333
| 0.396667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.091922
| 359
| 9
| 50
| 39.888889
| 0.920245
| 0
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| 0
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| 0
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| 1
| 0
| true
| 0
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| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
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| 0
| 0
| 0
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| null | 0
| 0
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| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
07b6c66d0922c5b856f358ab41ac7c95ea96458f
| 74,170
|
py
|
Python
|
azure/storage/blob/pageblobservice.py
|
RobertoPrevato/azure-storage-python
|
fae8ed9916095cc1fc17ada44e6406f96f7bd11d
|
[
"Apache-2.0"
] | 5
|
2018-03-21T12:59:53.000Z
|
2020-11-30T12:24:18.000Z
|
azure/storage/blob/pageblobservice.py
|
RobertoPrevato/azure-storage-python
|
fae8ed9916095cc1fc17ada44e6406f96f7bd11d
|
[
"Apache-2.0"
] | null | null | null |
azure/storage/blob/pageblobservice.py
|
RobertoPrevato/azure-storage-python
|
fae8ed9916095cc1fc17ada44e6406f96f7bd11d
|
[
"Apache-2.0"
] | 3
|
2018-10-09T18:35:19.000Z
|
2019-03-13T09:43:02.000Z
|
# -------------------------------------------------------------------------
# Copyright (c) Microsoft. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# --------------------------------------------------------------------------
import sys
from os import path
from azure.storage.common._common_conversion import (
_int_to_str,
_to_str,
_datetime_to_utc_string,
_get_content_md5,
)
from azure.storage.common._constants import (
SERVICE_HOST_BASE,
DEFAULT_PROTOCOL,
)
from azure.storage.common._error import (
_validate_not_none,
_validate_type_bytes,
_validate_encryption_required,
_validate_encryption_unsupported,
_ERROR_VALUE_NEGATIVE,
)
from azure.storage.common._http import HTTPRequest
from azure.storage.common._serialization import (
_get_data_bytes_only,
_add_metadata_headers,
)
from ._deserialization import (
_convert_xml_to_page_ranges,
_parse_page_properties,
_parse_base_properties,
)
from ._encryption import _generate_blob_encryption_data
from ._error import (
_ERROR_PAGE_BLOB_SIZE_ALIGNMENT,
)
from ._serialization import (
_get_path,
_validate_and_format_range_headers,
)
from ._upload_chunking import (
_PageBlobChunkUploader,
_upload_blob_chunks,
)
from .baseblobservice import BaseBlobService
from .models import (
_BlobTypes,
ResourceProperties)
from io import BytesIO
# Keep this value sync with _ERROR_PAGE_BLOB_SIZE_ALIGNMENT
_PAGE_ALIGNMENT = 512
class PageBlobService(BaseBlobService):
'''
Page blobs are a collection of 512-byte pages optimized for random read and
write operations. To create a page blob, you initialize the page blob and
specify the maximum size the page blob will grow. To add or update the
contents of a page blob, you write a page or pages by specifying an offset
and a range that align to 512-byte page boundaries. A write to a page blob
can overwrite just one page, some pages, or up to 4 MB of the page blob.
Writes to page blobs happen in-place and are immediately committed to the
blob. The maximum size for a page blob is 1 TB.
:ivar int MAX_PAGE_SIZE:
The size of the pages put by create_blob_from_* methods. Smaller pages
may be put if there is less data provided. The maximum page size the service
supports is 4MB.
'''
MAX_PAGE_SIZE = 4 * 1024 * 1024
def __init__(self, account_name=None, account_key=None, sas_token=None,
is_emulated=False, protocol=DEFAULT_PROTOCOL, endpoint_suffix=SERVICE_HOST_BASE,
custom_domain=None, request_session=None, connection_string=None, socket_timeout=None):
'''
:param str account_name:
The storage account name. This is used to authenticate requests
signed with an account key and to construct the storage endpoint. It
is required unless a connection string is given, or if a custom
domain is used with anonymous authentication.
:param str account_key:
The storage account key. This is used for shared key authentication.
If neither account key or sas token is specified, anonymous access
will be used.
:param str sas_token:
A shared access signature token to use to authenticate requests
instead of the account key. If account key and sas token are both
specified, account key will be used to sign. If neither are
specified, anonymous access will be used.
:param bool is_emulated:
Whether to use the emulator. Defaults to False. If specified, will
override all other parameters besides connection string and request
session.
:param str protocol:
The protocol to use for requests. Defaults to https.
:param str endpoint_suffix:
The host base component of the url, minus the account name. Defaults
to Azure (core.windows.net). Override this to use the China cloud
(core.chinacloudapi.cn).
:param str custom_domain:
The custom domain to use. This can be set in the Azure Portal. For
example, 'www.mydomain.com'.
:param requests.Session request_session:
The session object to use for http requests.
:param str connection_string:
If specified, this will override all other parameters besides
request session. See
http://azure.microsoft.com/en-us/documentation/articles/storage-configure-connection-string/
for the connection string format.
:param int socket_timeout:
If specified, this will override the default socket timeout. The timeout specified is in seconds.
See DEFAULT_SOCKET_TIMEOUT in _constants.py for the default value.
'''
self.blob_type = _BlobTypes.PageBlob
super(PageBlobService, self).__init__(
account_name, account_key, sas_token, is_emulated, protocol, endpoint_suffix,
custom_domain, request_session, connection_string, socket_timeout)
async def create_blob(
self, container_name, blob_name, content_length, content_settings=None,
sequence_number=None, metadata=None, lease_id=None, if_modified_since=None,
if_unmodified_since=None, if_match=None, if_none_match=None, timeout=None, premium_page_blob_tier=None):
'''
Creates a new Page Blob.
See create_blob_from_* for high level functions that handle the
creation and upload of large blobs with automatic chunking and
progress notifications.
:param str container_name:
Name of existing container.
:param str blob_name:
Name of blob to create or update.
:param int content_length:
Required. This header specifies the maximum size
for the page blob, up to 1 TB. The page blob size must be aligned
to a 512-byte boundary.
:param ~azure.storage.blob.models.ContentSettings content_settings:
ContentSettings object used to set properties on the blob.
:param int sequence_number:
The sequence number is a user-controlled value that you can use to
track requests. The value of the sequence number must be between 0
and 2^63 - 1.The default value is 0.
:param metadata:
Name-value pairs associated with the blob as metadata.
:type metadata: dict(str, str)
:param str lease_id:
Required if the blob has an active lease.
:param datetime if_modified_since:
A DateTime value. Azure expects the date value passed in to be UTC.
If timezone is included, any non-UTC datetimes will be converted to UTC.
If a date is passed in without timezone info, it is assumed to be UTC.
Specify this header to perform the operation only
if the resource has been modified since the specified time.
:param datetime if_unmodified_since:
A DateTime value. Azure expects the date value passed in to be UTC.
If timezone is included, any non-UTC datetimes will be converted to UTC.
If a date is passed in without timezone info, it is assumed to be UTC.
Specify this header to perform the operation only if
the resource has not been modified since the specified date/time.
:param str if_match:
An ETag value, or the wildcard character (*). Specify this header to perform
the operation only if the resource's ETag matches the value specified.
:param str if_none_match:
An ETag value, or the wildcard character (*). Specify this header
to perform the operation only if the resource's ETag does not match
the value specified. Specify the wildcard character (*) to perform
the operation only if the resource does not exist, and fail the
operation if it does exist.
:param int timeout:
The timeout parameter is expressed in seconds.
:param PremiumPageBlobTier premium_page_blob_tier:
A page blob tier value to set the blob to. The tier correlates to the size of the
blob and number of allowed IOPS. This is only applicable to page blobs on
premium storage accounts.
:return: ETag and last modified properties for the new Page Blob
:rtype: :class:`~azure.storage.blob.models.ResourceProperties`
'''
_validate_encryption_unsupported(self.require_encryption, self.key_encryption_key)
return await self._create_blob(
container_name,
blob_name,
content_length,
content_settings=content_settings,
sequence_number=sequence_number,
metadata=metadata,
lease_id=lease_id,
premium_page_blob_tier=premium_page_blob_tier,
if_modified_since=if_modified_since,
if_unmodified_since=if_unmodified_since,
if_match=if_match,
if_none_match=if_none_match,
timeout=timeout
)
async def incremental_copy_blob(self, container_name, blob_name, copy_source,
metadata=None, destination_if_modified_since=None, destination_if_unmodified_since=None,
destination_if_match=None, destination_if_none_match=None, destination_lease_id=None,
source_lease_id=None, timeout=None):
'''
Copies an incremental copy of a blob asynchronously. This operation returns a copy operation
properties object, including a copy ID you can use to check or abort the
copy operation. The Blob service copies blobs on a best-effort basis.
The source blob for an incremental copy operation must be a page blob.
Call get_blob_properties on the destination blob to check the status of the copy operation.
The final blob will be committed when the copy completes.
:param str container_name:
Name of the destination container. The container must exist.
:param str blob_name:
Name of the destination blob. If the destination blob exists, it will
be overwritten. Otherwise, it will be created.
:param str copy_source:
A URL of up to 2 KB in length that specifies an Azure page blob.
The value should be URL-encoded as it would appear in a request URI.
The copy source must be a snapshot and include a valid SAS token or be public.
Example:
https://myaccount.blob.core.windows.net/mycontainer/myblob?snapshot=<DateTime>&sastoken
:param metadata:
Name-value pairs associated with the blob as metadata. If no name-value
pairs are specified, the operation will copy the metadata from the
source blob or file to the destination blob. If one or more name-value
pairs are specified, the destination blob is created with the specified
metadata, and metadata is not copied from the source blob or file.
:type metadata: dict(str, str).
:param datetime destination_if_modified_since:
A DateTime value. Azure expects the date value passed in to be UTC.
If timezone is included, any non-UTC datetimes will be converted to UTC.
If a date is passed in without timezone info, it is assumed to be UTC.
Specify this conditional header to copy the blob only
if the destination blob has been modified since the specified date/time.
If the destination blob has not been modified, the Blob service returns
status code 412 (Precondition Failed).
:param datetime destination_if_unmodified_since:
A DateTime value. Azure expects the date value passed in to be UTC.
If timezone is included, any non-UTC datetimes will be converted to UTC.
If a date is passed in without timezone info, it is assumed to be UTC.
Specify this conditional header to copy the blob only if the destination blob
has not been modified since the specified ate/time. If the destination blob
has been modified, the Blob service returns status code 412 (Precondition Failed).
:param ETag destination_if_match:
An ETag value, or the wildcard character (*). Specify an ETag value for
this conditional header to copy the blob only if the specified ETag value
matches the ETag value for an existing destination blob. If the ETag for
the destination blob does not match the ETag specified for If-Match, the
Blob service returns status code 412 (Precondition Failed).
:param ETag destination_if_none_match:
An ETag value, or the wildcard character (*). Specify an ETag value for
this conditional header to copy the blob only if the specified ETag value
does not match the ETag value for the destination blob. Specify the wildcard
character (*) to perform the operation only if the destination blob does not
exist. If the specified condition isn't met, the Blob service returns status
code 412 (Precondition Failed).
:param str destination_lease_id:
The lease ID specified for this header must match the lease ID of the
destination blob. If the request does not include the lease ID or it is not
valid, the operation fails with status code 412 (Precondition Failed).
:param str source_lease_id:
Specify this to perform the Copy Blob operation only if
the lease ID given matches the active lease ID of the source blob.
:param int timeout:
The timeout parameter is expressed in seconds.
:return: Copy operation properties such as status, source, and ID.
:rtype: :class:`~azure.storage.blob.models.CopyProperties`
'''
return await self._copy_blob(container_name, blob_name, copy_source,
metadata,
source_if_modified_since=None, source_if_unmodified_since=None,
source_if_match=None, source_if_none_match=None,
destination_if_modified_since=destination_if_modified_since,
destination_if_unmodified_since=destination_if_unmodified_since,
destination_if_match=destination_if_match,
destination_if_none_match=destination_if_none_match,
destination_lease_id=destination_lease_id,
source_lease_id=source_lease_id, timeout=timeout,
incremental_copy=True)
async def update_page(
self, container_name, blob_name, page, start_range, end_range,
validate_content=False, lease_id=None, if_sequence_number_lte=None,
if_sequence_number_lt=None, if_sequence_number_eq=None,
if_modified_since=None, if_unmodified_since=None,
if_match=None, if_none_match=None, timeout=None):
'''
Updates a range of pages.
:param str container_name:
Name of existing container.
:param str blob_name:
Name of existing blob.
:param bytes page:
Content of the page.
:param int start_range:
Start of byte range to use for writing to a section of the blob.
Pages must be aligned with 512-byte boundaries, the start offset
must be a modulus of 512 and the end offset must be a modulus of
512-1. Examples of valid byte ranges are 0-511, 512-1023, etc.
:param int end_range:
End of byte range to use for writing to a section of the blob.
Pages must be aligned with 512-byte boundaries, the start offset
must be a modulus of 512 and the end offset must be a modulus of
512-1. Examples of valid byte ranges are 0-511, 512-1023, etc.
:param bool validate_content:
If true, calculates an MD5 hash of the page content. The storage
service checks the hash of the content that has arrived
with the hash that was sent. This is primarily valuable for detecting
bitflips on the wire if using http instead of https as https (the default)
will already validate. Note that this MD5 hash is not stored with the
blob.
:param str lease_id:
Required if the blob has an active lease.
:param int if_sequence_number_lte:
If the blob's sequence number is less than or equal to
the specified value, the request proceeds; otherwise it fails.
:param int if_sequence_number_lt:
If the blob's sequence number is less than the specified
value, the request proceeds; otherwise it fails.
:param int if_sequence_number_eq:
If the blob's sequence number is equal to the specified
value, the request proceeds; otherwise it fails.
:param datetime if_modified_since:
A DateTime value. Azure expects the date value passed in to be UTC.
If timezone is included, any non-UTC datetimes will be converted to UTC.
If a date is passed in without timezone info, it is assumed to be UTC.
Specify this header to perform the operation only
if the resource has been modified since the specified time.
:param datetime if_unmodified_since:
A DateTime value. Azure expects the date value passed in to be UTC.
If timezone is included, any non-UTC datetimes will be converted to UTC.
If a date is passed in without timezone info, it is assumed to be UTC.
Specify this header to perform the operation only if
the resource has not been modified since the specified date/time.
:param str if_match:
An ETag value, or the wildcard character (*). Specify an ETag value for this conditional
header to write the page only if the blob's ETag value matches the
value specified. If the values do not match, the Blob service fails.
:param str if_none_match:
An ETag value, or the wildcard character (*). Specify an ETag value for this conditional
header to write the page only if the blob's ETag value does not
match the value specified. If the values are identical, the Blob
service fails.
:param int timeout:
The timeout parameter is expressed in seconds.
:return: ETag and last modified properties for the updated Page Blob
:rtype: :class:`~azure.storage.blob.models.ResourceProperties`
'''
_validate_encryption_unsupported(self.require_encryption, self.key_encryption_key)
return await self._update_page(
container_name,
blob_name,
page,
start_range,
end_range,
validate_content=validate_content,
lease_id=lease_id,
if_sequence_number_lte=if_sequence_number_lte,
if_sequence_number_lt=if_sequence_number_lt,
if_sequence_number_eq=if_sequence_number_eq,
if_modified_since=if_modified_since,
if_unmodified_since=if_unmodified_since,
if_match=if_match,
if_none_match=if_none_match,
timeout=timeout
)
async def clear_page(
self, container_name, blob_name, start_range, end_range,
lease_id=None, if_sequence_number_lte=None,
if_sequence_number_lt=None, if_sequence_number_eq=None,
if_modified_since=None, if_unmodified_since=None,
if_match=None, if_none_match=None, timeout=None):
'''
Clears a range of pages.
:param str container_name:
Name of existing container.
:param str blob_name:
Name of existing blob.
:param int start_range:
Start of byte range to use for writing to a section of the blob.
Pages must be aligned with 512-byte boundaries, the start offset
must be a modulus of 512 and the end offset must be a modulus of
512-1. Examples of valid byte ranges are 0-511, 512-1023, etc.
:param int end_range:
End of byte range to use for writing to a section of the blob.
Pages must be aligned with 512-byte boundaries, the start offset
must be a modulus of 512 and the end offset must be a modulus of
512-1. Examples of valid byte ranges are 0-511, 512-1023, etc.
:param str lease_id:
Required if the blob has an active lease.
:param int if_sequence_number_lte:
If the blob's sequence number is less than or equal to
the specified value, the request proceeds; otherwise it fails.
:param int if_sequence_number_lt:
If the blob's sequence number is less than the specified
value, the request proceeds; otherwise it fails.
:param int if_sequence_number_eq:
If the blob's sequence number is equal to the specified
value, the request proceeds; otherwise it fails.
:param datetime if_modified_since:
A DateTime value. Azure expects the date value passed in to be UTC.
If timezone is included, any non-UTC datetimes will be converted to UTC.
If a date is passed in without timezone info, it is assumed to be UTC.
Specify this header to perform the operation only
if the resource has been modified since the specified time.
:param datetime if_unmodified_since:
A DateTime value. Azure expects the date value passed in to be UTC.
If timezone is included, any non-UTC datetimes will be converted to UTC.
If a date is passed in without timezone info, it is assumed to be UTC.
Specify this header to perform the operation only if
the resource has not been modified since the specified date/time.
:param str if_match:
An ETag value, or the wildcard character (*). Specify an ETag value for this conditional
header to write the page only if the blob's ETag value matches the
value specified. If the values do not match, the Blob service fails.
:param str if_none_match:
An ETag value, or the wildcard character (*). Specify an ETag value for this conditional
header to write the page only if the blob's ETag value does not
match the value specified. If the values are identical, the Blob
service fails.
:param int timeout:
The timeout parameter is expressed in seconds.
:return: ETag and last modified properties for the updated Page Blob
:rtype: :class:`~azure.storage.blob.models.ResourceProperties`
'''
_validate_not_none('container_name', container_name)
_validate_not_none('blob_name', blob_name)
request = HTTPRequest()
request.method = 'PUT'
request.host_locations = self._get_host_locations()
request.path = _get_path(container_name, blob_name)
request.query = {
'comp': 'page',
'timeout': _int_to_str(timeout),
}
request.headers = {
'x-ms-page-write': 'clear',
'x-ms-lease-id': _to_str(lease_id),
'x-ms-if-sequence-number-le': _to_str(if_sequence_number_lte),
'x-ms-if-sequence-number-lt': _to_str(if_sequence_number_lt),
'x-ms-if-sequence-number-eq': _to_str(if_sequence_number_eq),
'If-Modified-Since': _datetime_to_utc_string(if_modified_since),
'If-Unmodified-Since': _datetime_to_utc_string(if_unmodified_since),
'If-Match': _to_str(if_match),
'If-None-Match': _to_str(if_none_match)
}
_validate_and_format_range_headers(
request,
start_range,
end_range,
align_to_page=True)
return await self._perform_request(request, _parse_page_properties)
async def get_page_ranges(
self, container_name, blob_name, snapshot=None, start_range=None,
end_range=None, lease_id=None, if_modified_since=None,
if_unmodified_since=None, if_match=None, if_none_match=None, timeout=None):
'''
Returns the list of valid page ranges for a Page Blob or snapshot
of a page blob.
:param str container_name:
Name of existing container.
:param str blob_name:
Name of existing blob.
:param str snapshot:
The snapshot parameter is an opaque DateTime value that,
when present, specifies the blob snapshot to retrieve information
from.
:param int start_range:
Start of byte range to use for getting valid page ranges.
If no end_range is given, all bytes after the start_range will be searched.
Pages must be aligned with 512-byte boundaries, the start offset
must be a modulus of 512 and the end offset must be a modulus of
512-1. Examples of valid byte ranges are 0-511, 512-, etc.
:param int end_range:
End of byte range to use for getting valid page ranges.
If end_range is given, start_range must be provided.
This range will return valid page ranges for from the offset start up to
offset end.
Pages must be aligned with 512-byte boundaries, the start offset
must be a modulus of 512 and the end offset must be a modulus of
512-1. Examples of valid byte ranges are 0-511, 512-, etc.
:param str lease_id:
Required if the blob has an active lease.
:param datetime if_modified_since:
A DateTime value. Azure expects the date value passed in to be UTC.
If timezone is included, any non-UTC datetimes will be converted to UTC.
If a date is passed in without timezone info, it is assumed to be UTC.
Specify this header to perform the operation only
if the resource has been modified since the specified time.
:param datetime if_unmodified_since:
A DateTime value. Azure expects the date value passed in to be UTC.
If timezone is included, any non-UTC datetimes will be converted to UTC.
If a date is passed in without timezone info, it is assumed to be UTC.
Specify this header to perform the operation only if
the resource has not been modified since the specified date/time.
:param str if_match:
An ETag value, or the wildcard character (*). Specify this header to perform
the operation only if the resource's ETag matches the value specified.
:param str if_none_match:
An ETag value, or the wildcard character (*). Specify this header
to perform the operation only if the resource's ETag does not match
the value specified. Specify the wildcard character (*) to perform
the operation only if the resource does not exist, and fail the
operation if it does exist.
:param int timeout:
The timeout parameter is expressed in seconds.
:return: A list of valid Page Ranges for the Page Blob.
:rtype: list(:class:`~azure.storage.blob.models.PageRange`)
'''
_validate_not_none('container_name', container_name)
_validate_not_none('blob_name', blob_name)
request = HTTPRequest()
request.method = 'GET'
request.host_locations = self._get_host_locations(secondary=True)
request.path = _get_path(container_name, blob_name)
request.query = {
'comp': 'pagelist',
'snapshot': _to_str(snapshot),
'timeout': _int_to_str(timeout),
}
request.headers = {
'x-ms-lease-id': _to_str(lease_id),
'If-Modified-Since': _datetime_to_utc_string(if_modified_since),
'If-Unmodified-Since': _datetime_to_utc_string(if_unmodified_since),
'If-Match': _to_str(if_match),
'If-None-Match': _to_str(if_none_match),
}
if start_range is not None:
_validate_and_format_range_headers(
request,
start_range,
end_range,
start_range_required=False,
end_range_required=False,
align_to_page=True)
return await self._perform_request(request, _convert_xml_to_page_ranges)
async def get_page_ranges_diff(
self, container_name, blob_name, previous_snapshot, snapshot=None,
start_range=None, end_range=None, lease_id=None, if_modified_since=None,
if_unmodified_since=None, if_match=None, if_none_match=None, timeout=None):
'''
The response will include only the pages that are different between either a
recent snapshot or the current blob and a previous snapshot, including pages
that were cleared.
:param str container_name:
Name of existing container.
:param str blob_name:
Name of existing blob.
:param str previous_snapshot:
The snapshot parameter is an opaque DateTime value that
specifies a previous blob snapshot to be compared
against a more recent snapshot or the current blob.
:param str snapshot:
The snapshot parameter is an opaque DateTime value that
specifies a more recent blob snapshot to be compared
against a previous snapshot (previous_snapshot).
:param int start_range:
Start of byte range to use for getting different page ranges.
If no end_range is given, all bytes after the start_range will be searched.
Pages must be aligned with 512-byte boundaries, the start offset
must be a modulus of 512 and the end offset must be a modulus of
512-1. Examples of valid byte ranges are 0-511, 512-, etc.
:param int end_range:
End of byte range to use for getting different page ranges.
If end_range is given, start_range must be provided.
This range will return valid page ranges for from the offset start up to
offset end.
Pages must be aligned with 512-byte boundaries, the start offset
must be a modulus of 512 and the end offset must be a modulus of
512-1. Examples of valid byte ranges are 0-511, 512-, etc.
:param str lease_id:
Required if the blob has an active lease.
:param datetime if_modified_since:
A DateTime value. Azure expects the date value passed in to be UTC.
If timezone is included, any non-UTC datetimes will be converted to UTC.
If a date is passed in without timezone info, it is assumed to be UTC.
Specify this header to perform the operation only
if the resource has been modified since the specified time.
:param datetime if_unmodified_since:
A DateTime value. Azure expects the date value passed in to be UTC.
If timezone is included, any non-UTC datetimes will be converted to UTC.
If a date is passed in without timezone info, it is assumed to be UTC.
Specify this header to perform the operation only if
the resource has not been modified since the specified date/time.
:param str if_match:
An ETag value, or the wildcard character (*). Specify this header to perform
the operation only if the resource's ETag matches the value specified.
:param str if_none_match:
An ETag value, or the wildcard character (*). Specify this header
to perform the operation only if the resource's ETag does not match
the value specified. Specify the wildcard character (*) to perform
the operation only if the resource does not exist, and fail the
operation if it does exist.
:param int timeout:
The timeout parameter is expressed in seconds.
:return: A list of different Page Ranges for the Page Blob.
:rtype: list(:class:`~azure.storage.blob.models.PageRange`)
'''
_validate_not_none('container_name', container_name)
_validate_not_none('blob_name', blob_name)
_validate_not_none('previous_snapshot', previous_snapshot)
request = HTTPRequest()
request.method = 'GET'
request.host_locations = self._get_host_locations(secondary=True)
request.path = _get_path(container_name, blob_name)
request.query = {
'comp': 'pagelist',
'snapshot': _to_str(snapshot),
'prevsnapshot': _to_str(previous_snapshot),
'timeout': _int_to_str(timeout),
}
request.headers = {
'x-ms-lease-id': _to_str(lease_id),
'If-Modified-Since': _datetime_to_utc_string(if_modified_since),
'If-Unmodified-Since': _datetime_to_utc_string(if_unmodified_since),
'If-Match': _to_str(if_match),
'If-None-Match': _to_str(if_none_match),
}
if start_range is not None:
_validate_and_format_range_headers(
request,
start_range,
end_range,
start_range_required=False,
end_range_required=False,
align_to_page=True)
return await self._perform_request(request, _convert_xml_to_page_ranges)
async def set_sequence_number(
self, container_name, blob_name, sequence_number_action, sequence_number=None,
lease_id=None, if_modified_since=None, if_unmodified_since=None,
if_match=None, if_none_match=None, timeout=None):
'''
Sets the blob sequence number.
:param str container_name:
Name of existing container.
:param str blob_name:
Name of existing blob.
:param str sequence_number_action:
This property indicates how the service should modify the blob's sequence
number. See :class:`~azure.storage.blob.models.SequenceNumberAction` for more information.
:param str sequence_number:
This property sets the blob's sequence number. The sequence number is a
user-controlled property that you can use to track requests and manage
concurrency issues.
:param str lease_id:
Required if the blob has an active lease.
:param datetime if_modified_since:
A DateTime value. Azure expects the date value passed in to be UTC.
If timezone is included, any non-UTC datetimes will be converted to UTC.
If a date is passed in without timezone info, it is assumed to be UTC.
Specify this header to perform the operation only
if the resource has been modified since the specified time.
:param datetime if_unmodified_since:
A DateTime value. Azure expects the date value passed in to be UTC.
If timezone is included, any non-UTC datetimes will be converted to UTC.
If a date is passed in without timezone info, it is assumed to be UTC.
Specify this header to perform the operation only if
the resource has not been modified since the specified date/time.
:param str if_match:
An ETag value, or the wildcard character (*). Specify this header to perform
the operation only if the resource's ETag matches the value specified.
:param str if_none_match:
An ETag value, or the wildcard character (*). Specify this header
to perform the operation only if the resource's ETag does not match
the value specified. Specify the wildcard character (*) to perform
the operation only if the resource does not exist, and fail the
operation if it does exist.
:param int timeout:
The timeout parameter is expressed in seconds.
:return: ETag and last modified properties for the updated Page Blob
:rtype: :class:`~azure.storage.blob.models.ResourceProperties`
'''
_validate_not_none('container_name', container_name)
_validate_not_none('blob_name', blob_name)
_validate_not_none('sequence_number_action', sequence_number_action)
request = HTTPRequest()
request.method = 'PUT'
request.host_locations = self._get_host_locations()
request.path = _get_path(container_name, blob_name)
request.query = {
'comp': 'properties',
'timeout': _int_to_str(timeout),
}
request.headers = {
'x-ms-blob-sequence-number': _to_str(sequence_number),
'x-ms-sequence-number-action': _to_str(sequence_number_action),
'x-ms-lease-id': _to_str(lease_id),
'If-Modified-Since': _datetime_to_utc_string(if_modified_since),
'If-Unmodified-Since': _datetime_to_utc_string(if_unmodified_since),
'If-Match': _to_str(if_match),
'If-None-Match': _to_str(if_none_match),
}
return await self._perform_request(request, _parse_page_properties)
async def resize_blob(
self, container_name, blob_name, content_length,
lease_id=None, if_modified_since=None, if_unmodified_since=None,
if_match=None, if_none_match=None, timeout=None):
'''
Resizes a page blob to the specified size. If the specified value is less
than the current size of the blob, then all pages above the specified value
are cleared.
:param str container_name:
Name of existing container.
:param str blob_name:
Name of existing blob.
:param int content_length:
Size to resize blob to.
:param str lease_id:
Required if the blob has an active lease.
:param datetime if_modified_since:
A DateTime value. Azure expects the date value passed in to be UTC.
If timezone is included, any non-UTC datetimes will be converted to UTC.
If a date is passed in without timezone info, it is assumed to be UTC.
Specify this header to perform the operation only
if the resource has been modified since the specified time.
:param datetime if_unmodified_since:
A DateTime value. Azure expects the date value passed in to be UTC.
If timezone is included, any non-UTC datetimes will be converted to UTC.
If a date is passed in without timezone info, it is assumed to be UTC.
Specify this header to perform the operation only if
the resource has not been modified since the specified date/time.
:param str if_match:
An ETag value, or the wildcard character (*). Specify this header to perform
the operation only if the resource's ETag matches the value specified.
:param str if_none_match:
An ETag value, or the wildcard character (*). Specify this header
to perform the operation only if the resource's ETag does not match
the value specified. Specify the wildcard character (*) to perform
the operation only if the resource does not exist, and fail the
operation if it does exist.
:param int timeout:
The timeout parameter is expressed in seconds.
:return: ETag and last modified properties for the updated Page Blob
:rtype: :class:`~azure.storage.blob.models.ResourceProperties`
'''
_validate_not_none('container_name', container_name)
_validate_not_none('blob_name', blob_name)
_validate_not_none('content_length', content_length)
request = HTTPRequest()
request.method = 'PUT'
request.host_locations = self._get_host_locations()
request.path = _get_path(container_name, blob_name)
request.query = {
'comp': 'properties',
'timeout': _int_to_str(timeout),
}
request.headers = {
'x-ms-blob-content-length': _to_str(content_length),
'x-ms-lease-id': _to_str(lease_id),
'If-Modified-Since': _datetime_to_utc_string(if_modified_since),
'If-Unmodified-Since': _datetime_to_utc_string(if_unmodified_since),
'If-Match': _to_str(if_match),
'If-None-Match': _to_str(if_none_match),
}
return await self._perform_request(request, _parse_page_properties)
# ----Convenience APIs-----------------------------------------------------
async def create_blob_from_path(
self, container_name, blob_name, file_path, content_settings=None,
metadata=None, validate_content=False, progress_callback=None, max_connections=2,
lease_id=None, if_modified_since=None, if_unmodified_since=None,
if_match=None, if_none_match=None, timeout=None, premium_page_blob_tier=None):
'''
Creates a new blob from a file path, or updates the content of an
existing blob, with automatic chunking and progress notifications.
:param str container_name:
Name of existing container.
:param str blob_name:
Name of blob to create or update.
:param str file_path:
Path of the file to upload as the blob content.
:param ~azure.storage.blob.models.ContentSettings content_settings:
ContentSettings object used to set blob properties.
:param metadata:
Name-value pairs associated with the blob as metadata.
:type metadata: dict(str, str)
:param bool validate_content:
If true, calculates an MD5 hash for each page of the blob. The storage
service checks the hash of the content that has arrived with the hash
that was sent. This is primarily valuable for detecting bitflips on
the wire if using http instead of https as https (the default) will
already validate. Note that this MD5 hash is not stored with the
blob.
:param progress_callback:
Callback for progress with signature function(current, total) where
current is the number of bytes transfered so far, and total is the
size of the blob, or None if the total size is unknown.
:type progress_callback: func(current, total)
:param int max_connections:
Maximum number of parallel connections to use.
:param str lease_id:
Required if the blob has an active lease.
:param datetime if_modified_since:
A DateTime value. Azure expects the date value passed in to be UTC.
If timezone is included, any non-UTC datetimes will be converted to UTC.
If a date is passed in without timezone info, it is assumed to be UTC.
Specify this header to perform the operation only
if the resource has been modified since the specified time.
:param datetime if_unmodified_since:
A DateTime value. Azure expects the date value passed in to be UTC.
If timezone is included, any non-UTC datetimes will be converted to UTC.
If a date is passed in without timezone info, it is assumed to be UTC.
Specify this header to perform the operation only if
the resource has not been modified since the specified date/time.
:param str if_match:
An ETag value, or the wildcard character (*). Specify this header to perform
the operation only if the resource's ETag matches the value specified.
:param str if_none_match:
An ETag value, or the wildcard character (*). Specify this header
to perform the operation only if the resource's ETag does not match
the value specified. Specify the wildcard character (*) to perform
the operation only if the resource does not exist, and fail the
operation if it does exist.
:param int timeout:
The timeout parameter is expressed in seconds. This method may make
multiple calls to the Azure service and the timeout will apply to
each call individually.
:param premium_page_blob_tier:
A page blob tier value to set the blob to. The tier correlates to the size of the
blob and number of allowed IOPS. This is only applicable to page blobs on
premium storage accounts.
:return: ETag and last modified properties for the Page Blob
:rtype: :class:`~azure.storage.blob.models.ResourceProperties`
'''
_validate_not_none('container_name', container_name)
_validate_not_none('blob_name', blob_name)
_validate_not_none('file_path', file_path)
count = path.getsize(file_path)
with open(file_path, 'rb') as stream:
return await self.create_blob_from_stream(
container_name=container_name,
blob_name=blob_name,
stream=stream,
count=count,
content_settings=content_settings,
metadata=metadata,
validate_content=validate_content,
progress_callback=progress_callback,
max_connections=max_connections,
lease_id=lease_id,
if_modified_since=if_modified_since,
if_unmodified_since=if_unmodified_since,
if_match=if_match,
if_none_match=if_none_match,
timeout=timeout,
premium_page_blob_tier=premium_page_blob_tier)
async def create_blob_from_stream(
self, container_name, blob_name, stream, count, content_settings=None,
metadata=None, validate_content=False, progress_callback=None,
max_connections=2, lease_id=None, if_modified_since=None,
if_unmodified_since=None, if_match=None, if_none_match=None, timeout=None,
premium_page_blob_tier=None):
'''
Creates a new blob from a file/stream, or updates the content of an
existing blob, with automatic chunking and progress notifications.
:param str container_name:
Name of existing container.
:param str blob_name:
Name of blob to create or update.
:param io.IOBase stream:
Opened file/stream to upload as the blob content.
:param int count:
Number of bytes to read from the stream. This is required, a page
blob cannot be created if the count is unknown.
:param ~azure.storage.blob.models.ContentSettings content_settings:
ContentSettings object used to set the blob properties.
:param metadata:
Name-value pairs associated with the blob as metadata.
:type metadata: dict(str, str)
:param bool validate_content:
If true, calculates an MD5 hash for each page of the blob. The storage
service checks the hash of the content that has arrived with the hash
that was sent. This is primarily valuable for detecting bitflips on
the wire if using http instead of https as https (the default) will
already validate. Note that this MD5 hash is not stored with the
blob.
:param progress_callback:
Callback for progress with signature function(current, total) where
current is the number of bytes transfered so far, and total is the
size of the blob, or None if the total size is unknown.
:type progress_callback: func(current, total)
:param int max_connections:
Maximum number of parallel connections to use. Note that parallel upload
requires the stream to be seekable.
:param str lease_id:
Required if the blob has an active lease.
:param datetime if_modified_since:
A DateTime value. Azure expects the date value passed in to be UTC.
If timezone is included, any non-UTC datetimes will be converted to UTC.
If a date is passed in without timezone info, it is assumed to be UTC.
Specify this header to perform the operation only
if the resource has been modified since the specified time.
:param datetime if_unmodified_since:
A DateTime value. Azure expects the date value passed in to be UTC.
If timezone is included, any non-UTC datetimes will be converted to UTC.
If a date is passed in without timezone info, it is assumed to be UTC.
Specify this header to perform the operation only if
the resource has not been modified since the specified date/time.
:param str if_match:
An ETag value, or the wildcard character (*). Specify this header to perform
the operation only if the resource's ETag matches the value specified.
:param str if_none_match:
An ETag value, or the wildcard character (*). Specify this header
to perform the operation only if the resource's ETag does not match
the value specified. Specify the wildcard character (*) to perform
the operation only if the resource does not exist, and fail the
operation if it does exist.
:param int timeout:
The timeout parameter is expressed in seconds. This method may make
multiple calls to the Azure service and the timeout will apply to
each call individually.
:param premium_page_blob_tier:
A page blob tier value to set the blob to. The tier correlates to the size of the
blob and number of allowed IOPS. This is only applicable to page blobs on
premium storage accounts.
:return: ETag and last modified properties for the Page Blob
:rtype: :class:`~azure.storage.blob.models.ResourceProperties`
'''
_validate_not_none('container_name', container_name)
_validate_not_none('blob_name', blob_name)
_validate_not_none('stream', stream)
_validate_not_none('count', count)
_validate_encryption_required(self.require_encryption, self.key_encryption_key)
if count < 0:
raise ValueError(_ERROR_VALUE_NEGATIVE.format('count'))
if count % _PAGE_ALIGNMENT != 0:
raise ValueError(_ERROR_PAGE_BLOB_SIZE_ALIGNMENT.format(count))
cek, iv, encryption_data = None, None, None
if self.key_encryption_key is not None:
cek, iv, encryption_data = _generate_blob_encryption_data(self.key_encryption_key)
response = await self._create_blob(
container_name=container_name,
blob_name=blob_name,
content_length=count,
content_settings=content_settings,
metadata=metadata,
lease_id=lease_id,
premium_page_blob_tier=premium_page_blob_tier,
if_modified_since=if_modified_since,
if_unmodified_since=if_unmodified_since,
if_match=if_match,
if_none_match=if_none_match,
timeout=timeout,
encryption_data=encryption_data
)
if count == 0:
return response
# _upload_blob_chunks returns the block ids for block blobs so resource_properties
# is passed as a parameter to get the last_modified and etag for page and append blobs.
# this info is not needed for block_blobs since _put_block_list is called after which gets this info
resource_properties = ResourceProperties()
await _upload_blob_chunks(
blob_service=self,
container_name=container_name,
blob_name=blob_name,
blob_size=count,
block_size=self.MAX_PAGE_SIZE,
stream=stream,
max_connections=max_connections,
progress_callback=progress_callback,
validate_content=validate_content,
lease_id=lease_id,
uploader_class=_PageBlobChunkUploader,
if_match=response.etag,
timeout=timeout,
content_encryption_key=cek,
initialization_vector=iv,
resource_properties=resource_properties
)
return resource_properties
async def create_blob_from_bytes(
self, container_name, blob_name, blob, index=0, count=None,
content_settings=None, metadata=None, validate_content=False,
progress_callback=None, max_connections=2, lease_id=None,
if_modified_since=None, if_unmodified_since=None, if_match=None,
if_none_match=None, timeout=None, premium_page_blob_tier=None):
'''
Creates a new blob from an array of bytes, or updates the content
of an existing blob, with automatic chunking and progress
notifications.
:param str container_name:
Name of existing container.
:param str blob_name:
Name of blob to create or update.
:param bytes blob:
Content of blob as an array of bytes.
:param int index:
Start index in the byte array.
:param int count:
Number of bytes to upload. Set to None or negative value to upload
all bytes starting from index.
:param ~azure.storage.blob.models.ContentSettings content_settings:
ContentSettings object used to set blob properties.
:param metadata:
Name-value pairs associated with the blob as metadata.
:type metadata: dict(str, str)
:param bool validate_content:
If true, calculates an MD5 hash for each page of the blob. The storage
service checks the hash of the content that has arrived with the hash
that was sent. This is primarily valuable for detecting bitflips on
the wire if using http instead of https as https (the default) will
already validate. Note that this MD5 hash is not stored with the
blob.
:param progress_callback:
Callback for progress with signature function(current, total) where
current is the number of bytes transfered so far, and total is the
size of the blob, or None if the total size is unknown.
:type progress_callback: func(current, total)
:param int max_connections:
Maximum number of parallel connections to use.
:param str lease_id:
Required if the blob has an active lease.
:param datetime if_modified_since:
A DateTime value. Azure expects the date value passed in to be UTC.
If timezone is included, any non-UTC datetimes will be converted to UTC.
If a date is passed in without timezone info, it is assumed to be UTC.
Specify this header to perform the operation only
if the resource has been modified since the specified time.
:param datetime if_unmodified_since:
A DateTime value. Azure expects the date value passed in to be UTC.
If timezone is included, any non-UTC datetimes will be converted to UTC.
If a date is passed in without timezone info, it is assumed to be UTC.
Specify this header to perform the operation only if
the resource has not been modified since the specified date/time.
:param str if_match:
An ETag value, or the wildcard character (*). Specify this header to perform
the operation only if the resource's ETag matches the value specified.
:param str if_none_match:
An ETag value, or the wildcard character (*). Specify this header
to perform the operation only if the resource's ETag does not match
the value specified. Specify the wildcard character (*) to perform
the operation only if the resource does not exist, and fail the
operation if it does exist.
:param int timeout:
The timeout parameter is expressed in seconds. This method may make
multiple calls to the Azure service and the timeout will apply to
each call individually.
:param premium_page_blob_tier:
A page blob tier value to set the blob to. The tier correlates to the size of the
blob and number of allowed IOPS. This is only applicable to page blobs on
premium storage accounts.
:return: ETag and last modified properties for the Page Blob
:rtype: :class:`~azure.storage.blob.models.ResourceProperties`
'''
_validate_not_none('container_name', container_name)
_validate_not_none('blob_name', blob_name)
_validate_not_none('blob', blob)
_validate_type_bytes('blob', blob)
if index < 0:
raise IndexError(_ERROR_VALUE_NEGATIVE.format('index'))
if count is None or count < 0:
count = len(blob) - index
stream = BytesIO(blob)
stream.seek(index)
return await self.create_blob_from_stream(
container_name=container_name,
blob_name=blob_name,
stream=stream,
count=count,
content_settings=content_settings,
metadata=metadata,
validate_content=validate_content,
lease_id=lease_id,
progress_callback=progress_callback,
max_connections=max_connections,
if_modified_since=if_modified_since,
if_unmodified_since=if_unmodified_since,
if_match=if_match,
if_none_match=if_none_match,
timeout=timeout,
premium_page_blob_tier=premium_page_blob_tier)
async def set_premium_page_blob_tier(
self, container_name, blob_name, premium_page_blob_tier,
timeout=None):
'''
Sets the page blob tiers on the blob. This API is only supported for page blobs on premium accounts.
:param str container_name:
Name of existing container.
:param str blob_name:
Name of blob to update.
:param PremiumPageBlobTier premium_page_blob_tier:
A page blob tier value to set the blob to. The tier correlates to the size of the
blob and number of allowed IOPS. This is only applicable to page blobs on
premium storage accounts.
:param int timeout:
The timeout parameter is expressed in seconds. This method may make
multiple calls to the Azure service and the timeout will apply to
each call individually.
'''
_validate_not_none('container_name', container_name)
_validate_not_none('blob_name', blob_name)
_validate_not_none('premium_page_blob_tier', premium_page_blob_tier)
request = HTTPRequest()
request.method = 'PUT'
request.host_locations = self._get_host_locations()
request.path = _get_path(container_name, blob_name)
request.query = {
'comp': 'tier',
'timeout': _int_to_str(timeout),
}
request.headers = {
'x-ms-access-tier': _to_str(premium_page_blob_tier)
}
await self._perform_request(request)
async def copy_blob(self, container_name, blob_name, copy_source,
metadata=None,
source_if_modified_since=None,
source_if_unmodified_since=None,
source_if_match=None, source_if_none_match=None,
destination_if_modified_since=None,
destination_if_unmodified_since=None,
destination_if_match=None,
destination_if_none_match=None,
destination_lease_id=None,
source_lease_id=None, timeout=None,
premium_page_blob_tier=None):
'''
Copies a blob asynchronously. This operation returns a copy operation
properties object, including a copy ID you can use to check or abort the
copy operation. The Blob service copies blobs on a best-effort basis.
The source blob for a copy operation must be a page blob. If the destination
blob already exists, it must be of the same blob type as the source blob.
Any existing destination blob will be overwritten.
The destination blob cannot be modified while a copy operation is in progress.
When copying from a page blob, the Blob service creates a destination page
blob of the source blob's length, initially containing all zeroes. Then
the source page ranges are enumerated, and non-empty ranges are copied.
If the tier on the source blob is larger than the tier being passed to this
copy operation or if the size of the blob exceeds the tier being passed to
this copy operation then the operation will fail.
You can call get_blob_properties on the destination
blob to check the status of the copy operation. The final blob will be
committed when the copy completes.
:param str container_name:
Name of the destination container. The container must exist.
:param str blob_name:
Name of the destination blob. If the destination blob exists, it will
be overwritten. Otherwise, it will be created.
:param str copy_source:
A URL of up to 2 KB in length that specifies an Azure file or blob.
The value should be URL-encoded as it would appear in a request URI.
If the source is in another account, the source must either be public
or must be authenticated via a shared access signature. If the source
is public, no authentication is required.
Examples:
https://myaccount.blob.core.windows.net/mycontainer/myblob
https://myaccount.blob.core.windows.net/mycontainer/myblob?snapshot=<DateTime>
https://otheraccount.blob.core.windows.net/mycontainer/myblob?sastoken
:param metadata:
Name-value pairs associated with the blob as metadata. If no name-value
pairs are specified, the operation will copy the metadata from the
source blob or file to the destination blob. If one or more name-value
pairs are specified, the destination blob is created with the specified
metadata, and metadata is not copied from the source blob or file.
:type metadata: dict(str, str).
:param datetime source_if_modified_since:
A DateTime value. Azure expects the date value passed in to be UTC.
If timezone is included, any non-UTC datetimes will be converted to UTC.
If a date is passed in without timezone info, it is assumed to be UTC.
Specify this conditional header to copy the blob only if the source
blob has been modified since the specified date/time.
:param datetime source_if_unmodified_since:
A DateTime value. Azure expects the date value passed in to be UTC.
If timezone is included, any non-UTC datetimes will be converted to UTC.
If a date is passed in without timezone info, it is assumed to be UTC.
Specify this conditional header to copy the blob only if the source blob
has not been modified since the specified date/time.
:param ETag source_if_match:
An ETag value, or the wildcard character (*). Specify this conditional
header to copy the source blob only if its ETag matches the value
specified. If the ETag values do not match, the Blob service returns
status code 412 (Precondition Failed). This header cannot be specified
if the source is an Azure File.
:param ETag source_if_none_match:
An ETag value, or the wildcard character (*). Specify this conditional
header to copy the blob only if its ETag does not match the value
specified. If the values are identical, the Blob service returns status
code 412 (Precondition Failed). This header cannot be specified if the
source is an Azure File.
:param datetime destination_if_modified_since:
A DateTime value. Azure expects the date value passed in to be UTC.
If timezone is included, any non-UTC datetimes will be converted to UTC.
If a date is passed in without timezone info, it is assumed to be UTC.
Specify this conditional header to copy the blob only
if the destination blob has been modified since the specified date/time.
If the destination blob has not been modified, the Blob service returns
status code 412 (Precondition Failed).
:param datetime destination_if_unmodified_since:
A DateTime value. Azure expects the date value passed in to be UTC.
If timezone is included, any non-UTC datetimes will be converted to UTC.
If a date is passed in without timezone info, it is assumed to be UTC.
Specify this conditional header to copy the blob only
if the destination blob has not been modified since the specified
date/time. If the destination blob has been modified, the Blob service
returns status code 412 (Precondition Failed).
:param ETag destination_if_match:
An ETag value, or the wildcard character (*). Specify an ETag value for
this conditional header to copy the blob only if the specified ETag value
matches the ETag value for an existing destination blob. If the ETag for
the destination blob does not match the ETag specified for If-Match, the
Blob service returns status code 412 (Precondition Failed).
:param ETag destination_if_none_match:
An ETag value, or the wildcard character (*). Specify an ETag value for
this conditional header to copy the blob only if the specified ETag value
does not match the ETag value for the destination blob. Specify the wildcard
character (*) to perform the operation only if the destination blob does not
exist. If the specified condition isn't met, the Blob service returns status
code 412 (Precondition Failed).
:param str destination_lease_id:
The lease ID specified for this header must match the lease ID of the
destination blob. If the request does not include the lease ID or it is not
valid, the operation fails with status code 412 (Precondition Failed).
:param str source_lease_id:
Specify this to perform the Copy Blob operation only if
the lease ID given matches the active lease ID of the source blob.
:param int timeout:
The timeout parameter is expressed in seconds.
:param PageBlobTier premium_page_blob_tier:
A page blob tier value to set on the destination blob. The tier correlates to
the size of the blob and number of allowed IOPS. This is only applicable to
page blobs on premium storage accounts.
If the tier on the source blob is larger than the tier being passed to this
copy operation or if the size of the blob exceeds the tier being passed to
this copy operation then the operation will fail.
:return: Copy operation properties such as status, source, and ID.
:rtype: :class:`~azure.storage.blob.models.CopyProperties`
'''
return await self._copy_blob(container_name, blob_name, copy_source,
metadata, premium_page_blob_tier,
source_if_modified_since, source_if_unmodified_since,
source_if_match, source_if_none_match,
destination_if_modified_since,
destination_if_unmodified_since,
destination_if_match,
destination_if_none_match,
destination_lease_id,
source_lease_id, timeout,
False)
# -----Helper methods-----------------------------------------------------
async def _create_blob(
self, container_name, blob_name, content_length, content_settings=None,
sequence_number=None, metadata=None, lease_id=None, premium_page_blob_tier=None, if_modified_since=None,
if_unmodified_since=None, if_match=None, if_none_match=None, timeout=None,
encryption_data=None):
'''
See create_blob for more details. This helper method
allows for encryption or other such special behavior because
it is safely handled by the library. These behaviors are
prohibited in the public version of this function.
:param str encryption_data:
The JSON formatted encryption metadata to upload as a part of the blob.
This should only be passed internally from other methods and only applied
when uploading entire blob contents immediately follows creation of the blob.
'''
_validate_not_none('container_name', container_name)
_validate_not_none('blob_name', blob_name)
_validate_not_none('content_length', content_length)
request = HTTPRequest()
request.method = 'PUT'
request.host_locations = self._get_host_locations()
request.path = _get_path(container_name, blob_name)
request.query = {'timeout': _int_to_str(timeout)}
request.headers = {
'x-ms-blob-type': _to_str(self.blob_type),
'x-ms-blob-content-length': _to_str(content_length),
'x-ms-lease-id': _to_str(lease_id),
'x-ms-blob-sequence-number': _to_str(sequence_number),
'x-ms-access-tier': _to_str(premium_page_blob_tier),
'If-Modified-Since': _datetime_to_utc_string(if_modified_since),
'If-Unmodified-Since': _datetime_to_utc_string(if_unmodified_since),
'If-Match': _to_str(if_match),
'If-None-Match': _to_str(if_none_match)
}
_add_metadata_headers(metadata, request)
if content_settings is not None:
request.headers.update(content_settings._to_headers())
if encryption_data is not None:
request.headers['x-ms-meta-encryptiondata'] = encryption_data
return await self._perform_request(request, _parse_base_properties)
async def _update_page(
self, container_name, blob_name, page, start_range, end_range,
validate_content=False, lease_id=None, if_sequence_number_lte=None,
if_sequence_number_lt=None, if_sequence_number_eq=None,
if_modified_since=None, if_unmodified_since=None,
if_match=None, if_none_match=None, timeout=None):
'''
See update_page for more details. This helper method
allows for encryption or other such special behavior because
it is safely handled by the library. These behaviors are
prohibited in the public version of this function.
'''
request = HTTPRequest()
request.method = 'PUT'
request.host_locations = self._get_host_locations()
request.path = _get_path(container_name, blob_name)
request.query = {
'comp': 'page',
'timeout': _int_to_str(timeout),
}
request.headers = {
'x-ms-page-write': 'update',
'x-ms-lease-id': _to_str(lease_id),
'x-ms-if-sequence-number-le': _to_str(if_sequence_number_lte),
'x-ms-if-sequence-number-lt': _to_str(if_sequence_number_lt),
'x-ms-if-sequence-number-eq': _to_str(if_sequence_number_eq),
'If-Modified-Since': _datetime_to_utc_string(if_modified_since),
'If-Unmodified-Since': _datetime_to_utc_string(if_unmodified_since),
'If-Match': _to_str(if_match),
'If-None-Match': _to_str(if_none_match)
}
_validate_and_format_range_headers(
request,
start_range,
end_range,
align_to_page=True)
request.body = _get_data_bytes_only('page', page)
if validate_content:
computed_md5 = _get_content_md5(request.body)
request.headers['Content-MD5'] = _to_str(computed_md5)
return await self._perform_request(request, _parse_page_properties)
| 53.244795
| 118
| 0.65478
| 9,930
| 74,170
| 4.733031
| 0.055891
| 0.011702
| 0.011873
| 0.018383
| 0.833741
| 0.818443
| 0.810549
| 0.797825
| 0.785251
| 0.780485
| 0
| 0.004713
| 0.293447
| 74,170
| 1,392
| 119
| 53.283046
| 0.89211
| 0.01599
| 0
| 0.556017
| 0
| 0
| 0.061935
| 0.015011
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0.03112
| null | null | 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
07c0b3a5dcad5bf45c1b4f7f05c6ebb3d468b438
| 191
|
py
|
Python
|
platform/hwconf_data/efr32mg12p/modules/PIN/PIN_Snippets.py
|
lenloe1/v2.7
|
9ac9c4a7bb37987af382c80647f42d84db5f2e1d
|
[
"Zlib"
] | null | null | null |
platform/hwconf_data/efr32mg12p/modules/PIN/PIN_Snippets.py
|
lenloe1/v2.7
|
9ac9c4a7bb37987af382c80647f42d84db5f2e1d
|
[
"Zlib"
] | 1
|
2020-08-25T02:36:22.000Z
|
2020-08-25T02:36:22.000Z
|
platform/hwconf_data/efr32mg12p/modules/PIN/PIN_Snippets.py
|
lenloe1/v2.7
|
9ac9c4a7bb37987af382c80647f42d84db5f2e1d
|
[
"Zlib"
] | 1
|
2020-08-25T01:56:04.000Z
|
2020-08-25T01:56:04.000Z
|
"""
Generated from a template
"""
import efr32mg12p.PythonSnippet.RuntimeModel as RuntimeModel
from efr32mg12p.modules.PIN.PIN_Defs import PORT_PINS
def activate_runtime():
pass
| 11.235294
| 60
| 0.769634
| 23
| 191
| 6.26087
| 0.782609
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.049689
| 0.157068
| 191
| 16
| 61
| 11.9375
| 0.844721
| 0.13089
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| true
| 0.25
| 0.5
| 0
| 0.75
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 7
|
07c430d528140be09b3a3537f23179d26e201762
| 22,475
|
py
|
Python
|
RecoJets/JetProducers/python/PileupJetIDParams_cfi.py
|
rmanzoni/cmssw
|
53286dbc96455754b882e6668ed6a33a6186def2
|
[
"Apache-2.0"
] | null | null | null |
RecoJets/JetProducers/python/PileupJetIDParams_cfi.py
|
rmanzoni/cmssw
|
53286dbc96455754b882e6668ed6a33a6186def2
|
[
"Apache-2.0"
] | null | null | null |
RecoJets/JetProducers/python/PileupJetIDParams_cfi.py
|
rmanzoni/cmssw
|
53286dbc96455754b882e6668ed6a33a6186def2
|
[
"Apache-2.0"
] | 1
|
2015-05-08T02:08:04.000Z
|
2015-05-08T02:08:04.000Z
|
import FWCore.ParameterSet.Config as cms
from RecoJets.JetProducers.PileupJetIDCutParams_cfi import *
####################################################################################################################
full_81x_chs = cms.PSet(
impactParTkThreshold = cms.double(1.),
cutBased = cms.bool(False),
etaBinnedWeights = cms.bool(True),
tmvaMethod = cms.string("JetIDMVAHighPt"),
version = cms.int32(-1),
nEtaBins = cms.int32(4),
trainings = cms.VPSet(
cms.PSet(
jEtaMin = cms.double(0.),
jEtaMax = cms.double(2.5),
tmvaWeights = cms.FileInPath("RecoJets/JetProducers/data/pileupJetId_80XvarFix_Eta0to2p5_BDT.weights.xml.gz"),
tmvaVariables = cms.vstring(
"nvtx",
"dR2Mean" ,
"nParticles" ,
"nCharged" ,
"majW" ,
"minW",
"frac01" ,
"frac02" ,
"frac03" ,
"frac04" ,
"ptD" ,
"beta" ,
"pull" ,
"jetR" ,
"jetRchg" ,
)
),
cms.PSet(
jEtaMin = cms.double(2.5),
jEtaMax = cms.double(2.75),
tmvaWeights = cms.FileInPath("RecoJets/JetProducers/data/pileupJetId_80XvarFix_Eta2p5to2p75_BDT.weights.xml.gz"),
tmvaVariables = cms.vstring(
"nvtx",
"dR2Mean" ,
"nParticles" ,
"nCharged" ,
"majW" ,
"minW",
"frac01" ,
"frac02" ,
"frac03" ,
"frac04" ,
"ptD" ,
"beta" ,
"pull" ,
"jetR" ,
"jetRchg" ,
)
),
cms.PSet(
jEtaMin = cms.double(2.75),
jEtaMax = cms.double(3.),
tmvaWeights = cms.FileInPath("RecoJets/JetProducers/data/pileupJetId_80XvarFix_Eta2p75to3_BDT.weights.xml.gz"),
tmvaVariables = cms.vstring(
"nvtx",
"dR2Mean" ,
"nParticles" ,
"nCharged" ,
"majW" ,
"minW",
"frac01" ,
"frac02" ,
"frac03" ,
"frac04" ,
"ptD" ,
"beta" ,
"pull" ,
"jetR" ,
"jetRchg" ,
)
),
cms.PSet(
jEtaMin = cms.double(3.),
jEtaMax = cms.double(5.),
tmvaWeights = cms.FileInPath("RecoJets/JetProducers/data/pileupJetId_80XvarFix_Eta3to5_BDT.weights.xml.gz"),
tmvaVariables = cms.vstring(
"nvtx",
"dR2Mean" ,
"nParticles" ,
"majW" ,
"minW",
"frac01" ,
"frac02" ,
"frac03" ,
"frac04" ,
"ptD" ,
"pull" ,
"jetR" ,
)
),
),
tmvaSpectators = cms.vstring(
"jetPt" ,
"jetEta" ,
),
JetIdParams = full_81x_chs_wp,
label = cms.string("full")
)
####################################################################################################################
trainingVariables_102X_Eta0To3 = [
"nvtx" ,
"beta" ,
"dR2Mean" ,
"frac01" ,
"frac02" ,
"frac03" ,
"frac04" ,
"majW" ,
"minW" ,
"jetR" ,
"jetRchg" ,
"nParticles",
"nCharged" ,
"ptD" ,
"pull" ,
]
trainingVariables_102X_Eta3To5 = list(trainingVariables_102X_Eta0To3)
trainingVariables_102X_Eta3To5.remove('beta')
trainingVariables_102X_Eta3To5.remove('jetRchg')
trainingVariables_102X_Eta3To5.remove('nCharged')
full_102x_chs = full_81x_chs.clone(JetIdParams = full_102x_chs_wp)
full_102x_chs.trainings[0].tmvaWeights = "RecoJets/JetProducers/data/pileupJetId_102X_Eta0p0To2p5_chs_BDT.weights.xml.gz"
full_102x_chs.trainings[0].tmvaVariables = trainingVariables_102X_Eta0To3
full_102x_chs.trainings[1].tmvaWeights = "RecoJets/JetProducers/data/pileupJetId_102X_Eta2p5To2p75_chs_BDT.weights.xml.gz"
full_102x_chs.trainings[1].tmvaVariables = trainingVariables_102X_Eta0To3
full_102x_chs.trainings[2].tmvaWeights = "RecoJets/JetProducers/data/pileupJetId_102X_Eta2p75To3p0_chs_BDT.weights.xml.gz"
full_102x_chs.trainings[2].tmvaVariables = trainingVariables_102X_Eta0To3
full_102x_chs.trainings[3].tmvaWeights = "RecoJets/JetProducers/data/pileupJetId_102X_Eta3p0To5p0_chs_BDT.weights.xml.gz"
full_102x_chs.trainings[3].tmvaVariables = trainingVariables_102X_Eta3To5
####################################################################################################################
trainingVariables_94X_Eta0To3 = list(trainingVariables_102X_Eta0To3)
trainingVariables_94X_Eta3To5 = list(trainingVariables_102X_Eta3To5)
full_94x_chs = full_81x_chs.clone(JetIdParams = full_94x_chs_wp)
full_94x_chs.trainings[0].tmvaWeights = "RecoJets/JetProducers/data/pileupJetId_94X_Eta0p0To2p5_chs_BDT.weights.xml.gz"
full_94x_chs.trainings[0].tmvaVariables = trainingVariables_94X_Eta0To3
full_94x_chs.trainings[1].tmvaWeights = "RecoJets/JetProducers/data/pileupJetId_94X_Eta2p5To2p75_chs_BDT.weights.xml.gz"
full_94x_chs.trainings[1].tmvaVariables = trainingVariables_94X_Eta0To3
full_94x_chs.trainings[2].tmvaWeights = "RecoJets/JetProducers/data/pileupJetId_94X_Eta2p75To3p0_chs_BDT.weights.xml.gz"
full_94x_chs.trainings[2].tmvaVariables = trainingVariables_94X_Eta0To3
full_94x_chs.trainings[3].tmvaWeights = "RecoJets/JetProducers/data/pileupJetId_94X_Eta3p0To5p0_chs_BDT.weights.xml.gz"
full_94x_chs.trainings[3].tmvaVariables = trainingVariables_94X_Eta3To5
####################################################################################################################
full_80x_chs = cms.PSet(
impactParTkThreshold = cms.double(1.),
cutBased = cms.bool(False),
etaBinnedWeights = cms.bool(True),
tmvaMethod = cms.string("JetIDMVAHighPt"),
version = cms.int32(-1),
nEtaBins = cms.int32(4),
trainings = cms.VPSet(
cms.PSet(
jEtaMin = cms.double(0.),
jEtaMax = cms.double(2.5),
tmvaWeights = cms.FileInPath("RecoJets/JetProducers/data/pileupJetId_80X_Eta0to2p5_BDT.weights.xml.gz"),
tmvaVariables = cms.vstring(
"nvtx",
"dR2Mean" ,
"nParticles" ,
"nCharged" ,
"majW" ,
"minW",
"frac01" ,
"frac02" ,
"frac03" ,
"frac04" ,
"ptD" ,
"beta" ,
"pull" ,
"jetR" ,
"jetRchg" ,
)
),
cms.PSet(
jEtaMin = cms.double(2.5),
jEtaMax = cms.double(2.75),
tmvaWeights = cms.FileInPath("RecoJets/JetProducers/data/pileupJetId_80X_Eta2p5to2p75_BDT.weights.xml.gz"),
tmvaVariables = cms.vstring(
"nvtx",
"dR2Mean" ,
"nParticles" ,
"nCharged" ,
"majW" ,
"minW",
"frac01" ,
"frac02" ,
"frac03" ,
"frac04" ,
"ptD" ,
"beta" ,
"pull" ,
"jetR" ,
"jetRchg" ,
)
),
cms.PSet(
jEtaMin = cms.double(2.75),
jEtaMax = cms.double(3.),
tmvaWeights = cms.FileInPath("RecoJets/JetProducers/data/pileupJetId_80X_Eta2p75to3_BDT.weights.xml.gz"),
tmvaVariables = cms.vstring(
"nvtx",
"dR2Mean" ,
"nParticles" ,
"nCharged" ,
"majW" ,
"minW",
"frac01" ,
"frac02" ,
"frac03" ,
"frac04" ,
"ptD" ,
"beta" ,
"pull" ,
"jetR" ,
"jetRchg" ,
)
),
cms.PSet(
jEtaMin = cms.double(3.),
jEtaMax = cms.double(5.),
tmvaWeights = cms.FileInPath("RecoJets/JetProducers/data/pileupJetId_80X_Eta3to5_BDT.weights.xml.gz"),
tmvaVariables = cms.vstring(
"nvtx",
"dR2Mean" ,
"nParticles" ,
"majW" ,
"minW",
"frac01" ,
"frac02" ,
"frac03" ,
"frac04" ,
"ptD" ,
"pull" ,
"jetR" ,
)
),
),
tmvaSpectators = cms.vstring(
"jetPt" ,
"jetEta" ,
),
JetIdParams = full_80x_chs_wp,
label = cms.string("full")
)
####################################################################################################################
full_76x_chs = cms.PSet(
impactParTkThreshold = cms.double(1.) ,
cutBased = cms.bool(False),
etaBinnedWeights = cms.bool(True),
nEtaBins = cms.int32(4),
trainings = cms.VPSet(
cms.PSet(
jEtaMin = cms.double(0.),
jEtaMax = cms.double(2.5),
tmvaWeights = cms.FileInPath("RecoJets/JetProducers/data/pileupJetId_76x_Eta0to2p5_BDT.weights.xml.gz"),
tmvaVariables = cms.vstring(
"nvtx",
"dR2Mean" ,
"nParticles" ,
"nCharged" ,
"majW" ,
"minW",
"frac01" ,
"frac02" ,
"frac03" ,
"frac04" ,
"ptD" ,
"beta" ,
"pull" ,
"jetR" ,
"jetRchg" ,
)
),
cms.PSet(
jEtaMin = cms.double(2.5),
jEtaMax = cms.double(2.75),
tmvaWeights = cms.FileInPath("RecoJets/JetProducers/data/pileupJetId_76x_Eta2p5to2p75_BDT.weights.xml.gz"),
tmvaVariables = cms.vstring(
"nvtx",
"dR2Mean" ,
"nParticles" ,
"nCharged" ,
"majW" ,
"minW",
"frac01" ,
"frac02" ,
"frac03" ,
"frac04" ,
"ptD" ,
"beta" ,
"pull" ,
"jetR" ,
"jetRchg" ,
)
),
cms.PSet(
jEtaMin = cms.double(2.75),
jEtaMax = cms.double(3.),
tmvaWeights = cms.FileInPath("RecoJets/JetProducers/data/pileupJetId_76x_Eta2p75to3_BDT.weights.xml.gz"),
tmvaVariables = cms.vstring(
"nvtx",
"dR2Mean" ,
"nParticles" ,
"nCharged" ,
"majW" ,
"minW",
"frac01" ,
"frac02" ,
"frac03" ,
"frac04" ,
"ptD" ,
"beta" ,
"pull" ,
"jetR" ,
"jetRchg" ,
)
),
cms.PSet(
jEtaMin = cms.double(3.),
jEtaMax = cms.double(5.),
tmvaWeights = cms.FileInPath("RecoJets/JetProducers/data/pileupJetId_76x_Eta3to5_BDT.weights.xml.gz"),
tmvaVariables = cms.vstring(
"nvtx",
"dR2Mean" ,
"nParticles" ,
"majW" ,
"minW",
"frac01" ,
"frac02" ,
"frac03" ,
"frac04" ,
"ptD" ,
"pull" ,
"jetR" ,
)
),
),
tmvaMethod = cms.string("JetIDMVAHighPt"),
version = cms.int32(-1),
tmvaSpectators = cms.vstring(
"jetPt" ,
"jetEta" ,
),
JetIdParams = full_76x_chs_wp,
label = cms.string("full")
)
####################################################################################################################
full_74x_chs = cms.PSet(
impactParTkThreshold = cms.double(1.) ,
cutBased = cms.bool(False),
etaBinnedWeights = cms.bool(True),
nEtaBins = cms.int32(4),
trainings = cms.VPSet(
cms.PSet(
jEtaMin = cms.double(0.),
jEtaMax = cms.double(2.),
tmvaWeights = cms.FileInPath("RecoJets/JetProducers/data/TMVAClassificationCategory_BDTG.weights_jteta_0_2_newNames.xml.gz"),
tmvaVariables = cms.vstring(
"dR2Mean" ,
"rho" ,
"nParticles" ,
"nCharged" ,
"majW" ,
"minW",
"frac01" ,
"frac02" ,
"frac03" ,
"frac04" ,
"ptD" ,
"beta" ,
"betaStar" ,
"pull" ,
"jetR" ,
"jetRchg" ,
)
),
cms.PSet(
jEtaMin = cms.double(2.),
jEtaMax = cms.double(2.5),
tmvaWeights = cms.FileInPath("RecoJets/JetProducers/data/TMVAClassificationCategory_BDTG.weights_jteta_2_2p5_newNames.xml.gz"),
tmvaVariables = cms.vstring(
"dR2Mean" ,
"rho" ,
"nParticles" ,
"nCharged" ,
"majW" ,
"minW",
"frac01" ,
"frac02" ,
"frac03" ,
"frac04" ,
"ptD" ,
"beta" ,
"betaStar" ,
"pull" ,
"jetR" ,
"jetRchg" ,
)
),
cms.PSet(
jEtaMin = cms.double(2.5),
jEtaMax = cms.double(3.),
tmvaWeights = cms.FileInPath("RecoJets/JetProducers/data/TMVAClassificationCategory_BDTG.weights_jteta_2p5_3_newNames.xml.gz"),
tmvaVariables = cms.vstring(
"dR2Mean" ,
"rho" ,
"nParticles" ,
"nCharged" ,
"majW" ,
"minW",
"frac01" ,
"frac02" ,
"frac03" ,
"frac04" ,
"ptD" ,
"beta" ,
"betaStar" ,
"pull" ,
"jetR" ,
"jetRchg" ,
)
),
cms.PSet(
jEtaMin = cms.double(3.),
jEtaMax = cms.double(5.),
tmvaWeights = cms.FileInPath("RecoJets/JetProducers/data/TMVAClassificationCategory_BDTG.weights_jteta_3_5_newNames.xml.gz"),
tmvaVariables = cms.vstring(
"dR2Mean" ,
"rho" ,
"nParticles" ,
"majW" ,
"minW",
"frac01" ,
"frac02" ,
"frac03" ,
"frac04" ,
"ptD" ,
"pull" ,
"jetR" ,
)
),
),
version = cms.int32(-1),
tmvaSpectators = cms.vstring(
"jetPt" ,
"jetEta" ,
"nTrueInt" ,
"dRMatch" ,
),
JetIdParams = full_74x_chs_wp,
label = cms.string("full")
)
####################################################################################################################
full_53x = cms.PSet(
impactParTkThreshold = cms.double(1.) ,
cutBased = cms.bool(False),
etaBinnedWeights = cms.bool(False),
tmvaWeights = cms.FileInPath("CondFormats/JetMETObjects/data/TMVAClassificationCategory_JetID_53X_Dec2012.weights.xml"),
tmvaMethod = cms.string("JetIDMVAHighPt"),
version = cms.int32(-1),
tmvaVariables = cms.vstring(
"nvtx" ,
"dZ" ,
"beta" ,
"betaStar" ,
"nCharged" ,
"nNeutrals",
"dR2Mean" ,
"ptD" ,
"frac01" ,
"frac02" ,
"frac03" ,
"frac04" ,
"frac05" ,
),
tmvaSpectators = cms.vstring(
"jetPt",
"jetEta",
"jetPhi"
),
JetIdParams = full_53x_wp,
label = cms.string("full53x")
)
####################################################################################################################
full_53x_chs = cms.PSet(
impactParTkThreshold = cms.double(1.) ,
cutBased = cms.bool(False),
etaBinnedWeights = cms.bool(False),
tmvaWeights = cms.FileInPath("CondFormats/JetMETObjects/data/TMVAClassificationCategory_JetID_53X_chs_Dec2012.weights.xml"),
#tmvaWeights = cms.FileInPath("RecoJets/JetProducers/data/TMVAClassificationCategory_JetID_53X_chs_Dec2012.weights.xml"),
tmvaMethod = cms.string("JetIDMVAHighPt"),
version = cms.int32(-1),
tmvaVariables = cms.vstring(
"nvtx" ,
"dZ" ,
"beta" ,
"betaStar" ,
"nCharged" ,
"nNeutrals",
"dR2Mean" ,
"ptD" ,
"frac01" ,
"frac02" ,
"frac03" ,
"frac04" ,
"frac05" ,
),
tmvaSpectators = cms.vstring(
"jetPt",
"jetEta",
"jetPhi"
),
JetIdParams = full_53x_chs_wp,
label = cms.string("full")
)
####################################################################################################################
met_53x = cms.PSet(
impactParTkThreshold = cms.double(1.) ,
cutBased = cms.bool(False),
etaBinnedWeights = cms.bool(False),
tmvaWeights = cms.FileInPath("RecoJets/JetProducers/data/TMVAClassificationCategory_JetID_MET_53X_Dec2012.weights.xml.gz"),
tmvaMethod = cms.string("JetIDMVAMET"),
version = cms.int32(-1),
tmvaVariables = cms.vstring(
"nvtx" ,
"jetPt" ,
"jetEta" ,
"jetPhi" ,
"dZ" ,
"beta" ,
"betaStar" ,
"nCharged" ,
"nNeutrals",
"dR2Mean" ,
"ptD" ,
"frac01" ,
"frac02" ,
"frac03" ,
"frac04" ,
"frac05" ,
),
tmvaSpectators = cms.vstring(),
JetIdParams = met_53x_wp,
label = cms.string("met53x")
)
##################################################################################################################
full_5x = cms.PSet(
impactParTkThreshold = cms.double(1.) ,
cutBased = cms.bool(False),
etaBinnedWeights = cms.bool(False),
tmvaWeights = cms.FileInPath("RecoJets/JetProducers/data/TMVAClassificationCategory_JetID_MET_53X_Dec2012.weights.xml.gz"),
tmvaMethod = cms.string("BDT_fullPlusRMS"),
version = cms.int32(-1),
tmvaVariables = cms.vstring(
"frac01",
"frac02",
"frac03",
"frac04",
"frac05",
"dR2Mean",
"nvtx",
"nNeutrals",
"beta",
"betaStar",
"dZ",
"nCharged",
),
tmvaSpectators = cms.vstring(
"jetPt",
"jetEta",
),
JetIdParams = full_5x_wp,
label = cms.string("full")
)
##################################################################################################################
full_5x_chs = cms.PSet(
impactParTkThreshold = cms.double(1.) ,
cutBased = cms.bool(False),
etaBinnedWeights = cms.bool(False),
tmvaWeights = cms.FileInPath("RecoJets/JetProducers/data/TMVAClassification_5x_BDT_chsFullPlusRMS.weights.xml.gz"),
tmvaMethod = cms.string("BDT_chsFullPlusRMS"),
version = cms.int32(-1),
tmvaVariables = cms.vstring(
"frac01",
"frac02",
"frac03",
"frac04",
"frac05",
"dR2Mean",
"nvtx",
"nNeutrals",
"beta",
"betaStar",
"dZ",
"nCharged",
),
tmvaSpectators = cms.vstring(
"jetPt",
"jetEta",
),
JetIdParams = full_5x_chs_wp,
label = cms.string("full")
)
####################################################################################################################
cutbased = cms.PSet(
impactParTkThreshold = cms.double(1.),
cutBased = cms.bool(True),
JetIdParams = PuJetIdCutBased_wp,
label = cms.string("cutbased")
)
####################################################################################################################
PhilV1 = cms.PSet(
impactParTkThreshold = cms.double(1.) ,
cutBased = cms.bool(False),
etaBinnedWeights = cms.bool(False),
tmvaWeights = cms.FileInPath("RecoJets/JetProducers/data/mva_JetID_v1.weights.xml.gz"),
tmvaMethod = cms.string("JetID"),
version = cms.int32(-1),
tmvaVariables = cms.vstring(
"nvtx",
"jetPt",
"jetEta",
"jetPhi",
"dZ",
"d0",
"beta",
"betaStar",
"nCharged",
"nNeutrals",
"dRMean",
"frac01",
"frac02",
"frac03",
"frac04",
"frac05",
),
tmvaSpectators = cms.vstring(),
JetIdParams = JetIdParams,
label = cms.string("philv1")
)
| 34.208524
| 141
| 0.427408
| 1,581
| 22,475
| 5.925996
| 0.075901
| 0.041306
| 0.074288
| 0.058918
| 0.916213
| 0.893158
| 0.876935
| 0.845875
| 0.746825
| 0.682143
| 0
| 0.050367
| 0.399288
| 22,475
| 656
| 142
| 34.260671
| 0.643582
| 0.005339
| 0
| 0.822504
| 0
| 0
| 0.216365
| 0.11377
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.00317
| 0
| 0.00317
| 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
|
07d63f8a948065297bfdea497d199d76805779a1
| 165,434
|
py
|
Python
|
sympy/integrals/rubi/rubi_tests/tests/test_sine.py
|
Michal-Gagala/sympy
|
3cc756c2af73b5506102abaeefd1b654e286e2c8
|
[
"MIT"
] | null | null | null |
sympy/integrals/rubi/rubi_tests/tests/test_sine.py
|
Michal-Gagala/sympy
|
3cc756c2af73b5506102abaeefd1b654e286e2c8
|
[
"MIT"
] | null | null | null |
sympy/integrals/rubi/rubi_tests/tests/test_sine.py
|
Michal-Gagala/sympy
|
3cc756c2af73b5506102abaeefd1b654e286e2c8
|
[
"MIT"
] | null | null | null |
import sys
from sympy.external import import_module
matchpy = import_module("matchpy")
if not matchpy:
#bin/test will not execute any tests now
disabled = True
if sys.version_info[:2] < (3, 6):
disabled = True
from sympy.integrals.rubi.utility_function import (
sympy_op_factory, Int, Sum, Set, With, Module, Scan, MapAnd, FalseQ,
ZeroQ, NegativeQ, NonzeroQ, FreeQ, NFreeQ, List, Log, PositiveQ,
PositiveIntegerQ, NegativeIntegerQ, IntegerQ, IntegersQ,
ComplexNumberQ, PureComplexNumberQ, RealNumericQ, PositiveOrZeroQ,
NegativeOrZeroQ, FractionOrNegativeQ, NegQ, Equal, Unequal, IntPart,
FracPart, RationalQ, ProductQ, SumQ, NonsumQ, Subst, First, Rest,
SqrtNumberQ, SqrtNumberSumQ, LinearQ, Sqrt, ArcCosh, Coefficient,
Denominator, Hypergeometric2F1, Not, Simplify, FractionalPart,
IntegerPart, AppellF1, EllipticPi, EllipticE, EllipticF, ArcTan,
ArcCot, ArcCoth, ArcTanh, ArcSin, ArcSinh, ArcCos, ArcCsc, ArcSec,
ArcCsch, ArcSech, Sinh, Tanh, Cosh, Sech, Csch, Coth, LessEqual, Less,
Greater, GreaterEqual, FractionQ, IntLinearcQ, Expand, IndependentQ,
PowerQ, IntegerPowerQ, PositiveIntegerPowerQ, FractionalPowerQ, AtomQ,
ExpQ, LogQ, Head, MemberQ, TrigQ, SinQ, CosQ, TanQ, CotQ, SecQ, CscQ,
Sin, Cos, Tan, Cot, Sec, Csc, HyperbolicQ, SinhQ, CoshQ, TanhQ, CothQ,
SechQ, CschQ, InverseTrigQ, SinCosQ, SinhCoshQ, LeafCount, Numerator,
NumberQ, NumericQ, Length, ListQ, Im, Re, InverseHyperbolicQ,
InverseFunctionQ, TrigHyperbolicFreeQ, InverseFunctionFreeQ, RealQ,
EqQ, FractionalPowerFreeQ, ComplexFreeQ, PolynomialQ, FactorSquareFree,
PowerOfLinearQ, Exponent, QuadraticQ, LinearPairQ, BinomialParts,
TrinomialParts, PolyQ, EvenQ, OddQ, PerfectSquareQ, NiceSqrtAuxQ,
NiceSqrtQ, Together, PosAux, PosQ, CoefficientList, ReplaceAll,
ExpandLinearProduct, GCD, ContentFactor, NumericFactor,
NonnumericFactors, MakeAssocList, GensymSubst, KernelSubst,
ExpandExpression, Apart, SmartApart, MatchQ,
PolynomialQuotientRemainder, FreeFactors, NonfreeFactors,
RemoveContentAux, RemoveContent, FreeTerms, NonfreeTerms,
ExpandAlgebraicFunction, CollectReciprocals, ExpandCleanup,
AlgebraicFunctionQ, Coeff, LeadTerm, RemainingTerms, LeadFactor,
RemainingFactors, LeadBase, LeadDegree, Numer, Denom, hypergeom, Expon,
MergeMonomials, PolynomialDivide, BinomialQ, TrinomialQ,
GeneralizedBinomialQ, GeneralizedTrinomialQ, FactorSquareFreeList,
PerfectPowerTest, SquareFreeFactorTest, RationalFunctionQ,
RationalFunctionFactors, NonrationalFunctionFactors, Reverse,
RationalFunctionExponents, RationalFunctionExpand, ExpandIntegrand,
SimplerQ, SimplerSqrtQ, SumSimplerQ, BinomialDegree, TrinomialDegree,
CancelCommonFactors, SimplerIntegrandQ, GeneralizedBinomialDegree,
GeneralizedBinomialParts, GeneralizedTrinomialDegree,
GeneralizedTrinomialParts, MonomialQ, MonomialSumQ,
MinimumMonomialExponent, MonomialExponent, LinearMatchQ,
PowerOfLinearMatchQ, QuadraticMatchQ, CubicMatchQ, BinomialMatchQ,
TrinomialMatchQ, GeneralizedBinomialMatchQ, GeneralizedTrinomialMatchQ,
QuotientOfLinearsMatchQ, PolynomialTermQ, PolynomialTerms,
NonpolynomialTerms, PseudoBinomialParts, NormalizePseudoBinomial,
PseudoBinomialPairQ, PseudoBinomialQ, PolynomialGCD, PolyGCD,
AlgebraicFunctionFactors, NonalgebraicFunctionFactors,
QuotientOfLinearsP, QuotientOfLinearsParts, QuotientOfLinearsQ,
Flatten, Sort, AbsurdNumberQ, AbsurdNumberFactors,
NonabsurdNumberFactors, SumSimplerAuxQ, Prepend, Drop,
CombineExponents, FactorInteger, FactorAbsurdNumber,
SubstForInverseFunction, SubstForFractionalPower,
SubstForFractionalPowerOfQuotientOfLinears,
FractionalPowerOfQuotientOfLinears, SubstForFractionalPowerQ,
SubstForFractionalPowerAuxQ, FractionalPowerOfSquareQ,
FractionalPowerSubexpressionQ, Apply, FactorNumericGcd,
MergeableFactorQ, MergeFactor, MergeFactors, TrigSimplifyQ,
TrigSimplify, TrigSimplifyRecur, Order, FactorOrder, Smallest,
OrderedQ, MinimumDegree, PositiveFactors, Sign, NonpositiveFactors,
PolynomialInAuxQ, PolynomialInQ, ExponentInAux, ExponentIn,
PolynomialInSubstAux, PolynomialInSubst, Distrib, DistributeDegree,
FunctionOfPower, DivideDegreesOfFactors, MonomialFactor, FullSimplify,
FunctionOfLinearSubst, FunctionOfLinear, NormalizeIntegrand,
NormalizeIntegrandAux, NormalizeIntegrandFactor,
NormalizeIntegrandFactorBase, NormalizeTogether,
NormalizeLeadTermSigns, AbsorbMinusSign, NormalizeSumFactors,
SignOfFactor, NormalizePowerOfLinear, SimplifyIntegrand, SimplifyTerm,
TogetherSimplify, SmartSimplify, SubstForExpn, ExpandToSum, UnifySum,
UnifyTerms, UnifyTerm, CalculusQ, FunctionOfInverseLinear,
PureFunctionOfSinhQ, PureFunctionOfTanhQ, PureFunctionOfCoshQ,
IntegerQuotientQ, OddQuotientQ, EvenQuotientQ, FindTrigFactor,
FunctionOfSinhQ, FunctionOfCoshQ, OddHyperbolicPowerQ, FunctionOfTanhQ,
FunctionOfTanhWeight, FunctionOfHyperbolicQ, SmartNumerator,
SmartDenominator, SubstForAux, ActivateTrig, ExpandTrig, TrigExpand,
SubstForTrig, SubstForHyperbolic, InertTrigFreeQ, LCM,
SubstForFractionalPowerOfLinear, FractionalPowerOfLinear,
InverseFunctionOfLinear, InertTrigQ, InertReciprocalQ, DeactivateTrig,
FixInertTrigFunction, DeactivateTrigAux, PowerOfInertTrigSumQ,
PiecewiseLinearQ, KnownTrigIntegrandQ, KnownSineIntegrandQ,
KnownTangentIntegrandQ, KnownCotangentIntegrandQ,
KnownSecantIntegrandQ, TryPureTanSubst, TryTanhSubst, TryPureTanhSubst,
AbsurdNumberGCD, AbsurdNumberGCDList, ExpandTrigExpand,
ExpandTrigReduce, ExpandTrigReduceAux, NormalizeTrig, TrigToExp,
ExpandTrigToExp, TrigReduce, FunctionOfTrig, AlgebraicTrigFunctionQ,
FunctionOfHyperbolic, FunctionOfQ, FunctionOfExpnQ, PureFunctionOfSinQ,
PureFunctionOfCosQ, PureFunctionOfTanQ, PureFunctionOfCotQ,
FunctionOfCosQ, FunctionOfSinQ, OddTrigPowerQ, FunctionOfTanQ,
FunctionOfTanWeight, FunctionOfTrigQ, FunctionOfDensePolynomialsQ,
FunctionOfLog, PowerVariableExpn, PowerVariableDegree,
PowerVariableSubst, EulerIntegrandQ, FunctionOfSquareRootOfQuadratic,
SquareRootOfQuadraticSubst, Divides, EasyDQ, ProductOfLinearPowersQ,
Rt, NthRoot, AtomBaseQ, SumBaseQ, NegSumBaseQ, AllNegTermQ,
SomeNegTermQ, TrigSquareQ, RtAux, TrigSquare, IntSum, IntTerm, Map2,
ConstantFactor, SameQ, ReplacePart, CommonFactors,
MostMainFactorPosition, FunctionOfExponentialQ, FunctionOfExponential,
FunctionOfExponentialFunction, FunctionOfExponentialFunctionAux,
FunctionOfExponentialTest, FunctionOfExponentialTestAux, stdev,
rubi_test, If, IntQuadraticQ, IntBinomialQ, RectifyTangent,
RectifyCotangent, Inequality, Condition, Simp, SimpHelp, SplitProduct,
SplitSum, SubstFor, SubstForAux, FresnelS, FresnelC, Erfc, Erfi, Gamma,
FunctionOfTrigOfLinearQ, ElementaryFunctionQ, Complex, UnsameQ,
_SimpFixFactor, SimpFixFactor, _FixSimplify, FixSimplify,
_SimplifyAntiderivativeSum, SimplifyAntiderivativeSum,
_SimplifyAntiderivative, SimplifyAntiderivative, _TrigSimplifyAux,
TrigSimplifyAux, Cancel, Part, PolyLog, D, Dist, Sum_doit, PolynomialQuotient, Floor,
PolynomialRemainder, Factor, PolyLog, CosIntegral, SinIntegral, LogIntegral, SinhIntegral,
CoshIntegral, Rule, Erf, PolyGamma, ExpIntegralEi, ExpIntegralE, LogGamma , UtilityOperator, Factorial,
Zeta, ProductLog, DerivativeDivides, HypergeometricPFQ, IntHide, OneQ
)
from sympy.core.add import Add
from sympy.core.mod import Mod
from sympy.core.mul import Mul
from sympy.core.numbers import (Float, I, Integer)
from sympy.core.power import Pow
from sympy.core.singleton import S
from sympy.functions.elementary.complexes import Abs
from sympy.functions.elementary.miscellaneous import sqrt
from sympy.integrals.integrals import Integral
from sympy.logic.boolalg import (And, Or)
from sympy.simplify.simplify import simplify
from sympy.integrals.rubi.symbol import WC
from sympy.core.symbol import symbols, Symbol
from sympy.functions import (sin, cos, tan, cot, csc, sec, sqrt, erf, exp, log)
from sympy.functions.elementary.hyperbolic import (acosh, asinh, atanh, acoth, acsch, asech, cosh, sinh, tanh, coth, sech, csch)
from sympy.functions.elementary.trigonometric import (atan, acsc, asin, acot, acos, asec)
from sympy.integrals.rubi.rubimain import rubi_integrate
from sympy.core.numbers import pi as Pi
a, b, c, d, e, f, m, n, x, u , k, p, r, s, t, i, j= symbols('a b c d e f m n x u k p r s t i j')
A, B, C, D, a, b, c, d, e, f, g, h, y, z, m, n, p, q, u, v, w, F = symbols('A B C D a b c d e f g h y z m n p q u v w F', )
def test_1():
assert rubi_test(rubi_integrate(sin(a + b*x), x), x, -cos(a + b*x)/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(2), x), x, x/S(2) - sin(a + b*x)*cos(a + b*x)/(S(2)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(3), x), x, cos(a + b*x)**S(3)/(S(3)*b) - cos(a + b*x)/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(4), x), x, S(3)*x/S(8) - sin(a + b*x)**S(3)*cos(a + b*x)/(S(4)*b) - S(3)*sin(a + b*x)*cos(a + b*x)/(S(8)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(5), x), x, -cos(a + b*x)**S(5)/(S(5)*b) + S(2)*cos(a + b*x)**S(3)/(S(3)*b) - cos(a + b*x)/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(6), x), x, S(5)*x/S(16) - sin(a + b*x)**S(5)*cos(a + b*x)/(S(6)*b) - S(5)*sin(a + b*x)**S(3)*cos(a + b*x)/(S(24)*b) - S(5)*sin(a + b*x)*cos(a + b*x)/(S(16)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(7), x), x, cos(a + b*x)**S(7)/(S(7)*b) - S(3)*cos(a + b*x)**S(5)/(S(5)*b) + cos(a + b*x)**S(3)/b - cos(a + b*x)/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(8), x), x, S(35)*x/S(128) - sin(a + b*x)**S(7)*cos(a + b*x)/(S(8)*b) - S(7)*sin(a + b*x)**S(5)*cos(a + b*x)/(S(48)*b) - S(35)*sin(a + b*x)**S(3)*cos(a + b*x)/(S(192)*b) - S(35)*sin(a + b*x)*cos(a + b*x)/(S(128)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**(S(7)/2), x), x, S(10)*EllipticF(-Pi/S(4) + a/S(2) + b*x/S(2), S(2))/(S(21)*b) - S(2)*sin(a + b*x)**(S(5)/2)*cos(a + b*x)/(S(7)*b) - S(10)*sqrt(sin(a + b*x))*cos(a + b*x)/(S(21)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**(S(5)/2), x), x, S(6)*EllipticE(-Pi/S(4) + a/S(2) + b*x/S(2), S(2))/(S(5)*b) - S(2)*sin(a + b*x)**(S(3)/2)*cos(a + b*x)/(S(5)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**(S(3)/2), x), x, S(2)*EllipticF(-Pi/S(4) + a/S(2) + b*x/S(2), S(2))/(S(3)*b) - S(2)*sqrt(sin(a + b*x))*cos(a + b*x)/(S(3)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(sin(a + b*x)), x), x, S(2)*EllipticE(-Pi/S(4) + a/S(2) + b*x/S(2), S(2))/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(S(1)/sqrt(sin(a + b*x)), x), x, S(2)*EllipticF(-Pi/S(4) + a/S(2) + b*x/S(2), S(2))/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**(S(-3)/2), x), x, -S(2)*EllipticE(-Pi/S(4) + a/S(2) + b*x/S(2), S(2))/b - S(2)*cos(a + b*x)/(b*sqrt(sin(a + b*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**(S(-5)/2), x), x, S(2)*EllipticF(-Pi/S(4) + a/S(2) + b*x/S(2), S(2))/(S(3)*b) - S(2)*cos(a + b*x)/(S(3)*b*sin(a + b*x)**(S(3)/2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**(S(-7)/2), x), x, -S(6)*EllipticE(-Pi/S(4) + a/S(2) + b*x/S(2), S(2))/(S(5)*b) - S(6)*cos(a + b*x)/(S(5)*b*sqrt(sin(a + b*x))) - S(2)*cos(a + b*x)/(S(5)*b*sin(a + b*x)**(S(5)/2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**(S(7)/2), x), x, S(10)*c**S(4)*EllipticF(-Pi/S(4) + a/S(2) + b*x/S(2), S(2))*sqrt(sin(a + b*x))/(S(21)*b*sqrt(c*sin(a + b*x))) - S(10)*c**S(3)*sqrt(c*sin(a + b*x))*cos(a + b*x)/(S(21)*b) - S(2)*c*(c*sin(a + b*x))**(S(5)/2)*cos(a + b*x)/(S(7)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**(S(5)/2), x), x, S(6)*c**S(2)*sqrt(c*sin(a + b*x))*EllipticE(-Pi/S(4) + a/S(2) + b*x/S(2), S(2))/(S(5)*b*sqrt(sin(a + b*x))) - S(2)*c*(c*sin(a + b*x))**(S(3)/2)*cos(a + b*x)/(S(5)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**(S(3)/2), x), x, S(2)*c**S(2)*EllipticF(-Pi/S(4) + a/S(2) + b*x/S(2), S(2))*sqrt(sin(a + b*x))/(S(3)*b*sqrt(c*sin(a + b*x))) - S(2)*c*sqrt(c*sin(a + b*x))*cos(a + b*x)/(S(3)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(c*sin(a + b*x)), x), x, S(2)*sqrt(c*sin(a + b*x))*EllipticE(-Pi/S(4) + a/S(2) + b*x/S(2), S(2))/(b*sqrt(sin(a + b*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(S(1)/sqrt(c*sin(a + b*x)), x), x, S(2)*EllipticF(-Pi/S(4) + a/S(2) + b*x/S(2), S(2))*sqrt(sin(a + b*x))/(b*sqrt(c*sin(a + b*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**(S(-3)/2), x), x, -S(2)*cos(a + b*x)/(b*c*sqrt(c*sin(a + b*x))) - S(2)*sqrt(c*sin(a + b*x))*EllipticE(-Pi/S(4) + a/S(2) + b*x/S(2), S(2))/(b*c**S(2)*sqrt(sin(a + b*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**(S(-5)/2), x), x, -S(2)*cos(a + b*x)/(S(3)*b*c*(c*sin(a + b*x))**(S(3)/2)) + S(2)*EllipticF(-Pi/S(4) + a/S(2) + b*x/S(2), S(2))*sqrt(sin(a + b*x))/(S(3)*b*c**S(2)*sqrt(c*sin(a + b*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**(S(-7)/2), x), x, -S(2)*cos(a + b*x)/(S(5)*b*c*(c*sin(a + b*x))**(S(5)/2)) - S(6)*cos(a + b*x)/(S(5)*b*c**S(3)*sqrt(c*sin(a + b*x))) - S(6)*sqrt(c*sin(a + b*x))*EllipticE(-Pi/S(4) + a/S(2) + b*x/S(2), S(2))/(S(5)*b*c**S(4)*sqrt(sin(a + b*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**(S(4)/3), x), x, S(3)*(c*sin(a + b*x))**(S(7)/3)*Hypergeometric2F1(S(1)/2, S(7)/6, S(13)/6, sin(a + b*x)**S(2))*cos(a + b*x)/(S(7)*b*c*sqrt(cos(a + b*x)**S(2))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**(S(2)/3), x), x, S(3)*(c*sin(a + b*x))**(S(5)/3)*Hypergeometric2F1(S(1)/2, S(5)/6, S(11)/6, sin(a + b*x)**S(2))*cos(a + b*x)/(S(5)*b*c*sqrt(cos(a + b*x)**S(2))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**(S(1)/3), x), x, -S(3)*c**(S(1)/3)*sqrt(S(1) - (c*sin(a + b*x))**(S(2)/3)/c**(S(2)/3))*sqrt(S(9)/2 - S(3)*sqrt(S(3))*I/S(2))*sqrt((-sqrt(S(3)) + I)/(-sqrt(S(3)) + S(3)*I) + S(2)*(c*sin(a + b*x))**(S(2)/3)/(c**(S(2)/3)*(S(3) + sqrt(S(3))*I)))*sqrt((sqrt(S(3)) + I)/(sqrt(S(3)) + S(3)*I) + S(2)*(c*sin(a + b*x))**(S(2)/3)/(c**(S(2)/3)*(S(3) - sqrt(S(3))*I)))*EllipticE(asin(sqrt(S(2))*sqrt(S(1) - (c*sin(a + b*x))**(S(2)/3)/c**(S(2)/3))/sqrt(S(3) + sqrt(S(3))*I)), (-sqrt(S(3)) + S(3)*I)/(sqrt(S(3)) + S(3)*I))*sec(a + b*x)/b + S(3)*sqrt(S(2))*c**(S(1)/3)*(S(1) - sqrt(S(3))*I)*sqrt(S(1) - (c*sin(a + b*x))**(S(2)/3)/c**(S(2)/3))*sqrt(S(3) - sqrt(S(3))*I)*sqrt((-sqrt(S(3)) + I)/(-sqrt(S(3)) + S(3)*I) + S(2)*(c*sin(a + b*x))**(S(2)/3)/(c**(S(2)/3)*(S(3) + sqrt(S(3))*I)))*sqrt((sqrt(S(3)) + I)/(sqrt(S(3)) + S(3)*I) + S(2)*(c*sin(a + b*x))**(S(2)/3)/(c**(S(2)/3)*(S(3) - sqrt(S(3))*I)))*EllipticF(asin(sqrt(S(2))*sqrt(S(1) - (c*sin(a + b*x))**(S(2)/3)/c**(S(2)/3))/sqrt(S(3) - sqrt(S(3))*I)), (sqrt(S(3)) + S(3)*I)/(-sqrt(S(3)) + S(3)*I))*sec(a + b*x)/(S(4)*b), expand=True, _diff=True, _numerical=True) or rubi_test(rubi_integrate((c*sin(a + b*x))**(S(1)/3), x), x, S(3)*(c*sin(a + b*x))**(S(4)/3)*Hypergeometric2F1(S(1)/2, S(2)/3, S(5)/3, sin(a + b*x)**S(2))*cos(a + b*x)/(S(4)*b*c*sqrt(cos(a + b*x)**S(2))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**(S(-1)/3), x), x, -S(3)*sqrt(S(2))*sqrt(S(1) - (c*sin(a + b*x))**(S(2)/3)/c**(S(2)/3))*sqrt(S(3) - sqrt(S(3))*I)*sqrt((-sqrt(S(3)) + I)/(-sqrt(S(3)) + S(3)*I) + S(2)*(c*sin(a + b*x))**(S(2)/3)/(c**(S(2)/3)*(S(3) + sqrt(S(3))*I)))*sqrt((sqrt(S(3)) + I)/(sqrt(S(3)) + S(3)*I) + S(2)*(c*sin(a + b*x))**(S(2)/3)/(c**(S(2)/3)*(S(3) - sqrt(S(3))*I)))*EllipticF(asin(sqrt(S(2))*sqrt(S(1) - (c*sin(a + b*x))**(S(2)/3)/c**(S(2)/3))/sqrt(S(3) - sqrt(S(3))*I)), (sqrt(S(3)) + S(3)*I)/(-sqrt(S(3)) + S(3)*I))*sec(a + b*x)/(S(2)*b*c**(S(1)/3)), expand=True, _diff=True, _numerical=True) or rubi_test(rubi_integrate((c*sin(a + b*x))**(S(-1)/3), x), x, S(3)*(c*sin(a + b*x))**(S(2)/3)*Hypergeometric2F1(S(1)/3, S(1)/2, S(4)/3, sin(a + b*x)**S(2))*cos(a + b*x)/(S(2)*b*c*sqrt(cos(a + b*x)**S(2))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**(S(-2)/3), x), x, S(3)**(S(3)/4)*(c*sin(a + b*x))**(S(1)/3)*sqrt(c**(S(4)/3)*(S(1) + (c*sin(a + b*x))**(S(2)/3)/c**(S(2)/3) + (c*sin(a + b*x))**(S(4)/3)/c**(S(4)/3))/(c**(S(2)/3) - (c*sin(a + b*x))**(S(2)/3)*(S(1) + sqrt(S(3))))**S(2))*(c**(S(2)/3) - (c*sin(a + b*x))**(S(2)/3))*EllipticF(acos((c**(S(2)/3) - (c*sin(a + b*x))**(S(2)/3)*(-sqrt(S(3)) + S(1)))/(c**(S(2)/3) - (c*sin(a + b*x))**(S(2)/3)*(S(1) + sqrt(S(3))))), sqrt(S(3))/S(4) + S(1)/2)*sec(a + b*x)/(S(2)*b*c**(S(5)/3)*sqrt(-(c*sin(a + b*x))**(S(2)/3)*(c**(S(2)/3) - (c*sin(a + b*x))**(S(2)/3))/(c**(S(2)/3) - (c*sin(a + b*x))**(S(2)/3)*(S(1) + sqrt(S(3))))**S(2))), expand=True, _diff=True, _numerical=True) or rubi_test(rubi_integrate((c*sin(a + b*x))**(S(-2)/3), x), x, S(3)*(c*sin(a + b*x))**(S(1)/3)*Hypergeometric2F1(S(1)/6, S(1)/2, S(7)/6, sin(a + b*x)**S(2))*cos(a + b*x)/(b*c*sqrt(cos(a + b*x)**S(2))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**(S(-4)/3), x), x, -S(3)*Hypergeometric2F1(S(-1)/6, S(1)/2, S(5)/6, sin(a + b*x)**S(2))*cos(a + b*x)/(b*c*(c*sin(a + b*x))**(S(1)/3)*sqrt(cos(a + b*x)**S(2))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**n, x), x, Hypergeometric2F1(S(1)/2, n/S(2) + S(1)/2, n/S(2) + S(3)/2, sin(a + b*x)**S(2))*sin(a + b*x)**(n + S(1))*cos(a + b*x)/(b*(n + S(1))*sqrt(cos(a + b*x)**S(2))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**n, x), x, (c*sin(a + b*x))**(n + S(1))*Hypergeometric2F1(S(1)/2, n/S(2) + S(1)/2, n/S(2) + S(3)/2, sin(a + b*x)**S(2))*cos(a + b*x)/(b*c*(n + S(1))*sqrt(cos(a + b*x)**S(2))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((a*sin(x)**S(2))**(S(5)/2), x), x, -S(8)*a**S(2)*sqrt(a*sin(x)**S(2))*cot(x)/S(15) - S(4)*a*(a*sin(x)**S(2))**(S(3)/2)*cot(x)/S(15) - (a*sin(x)**S(2))**(S(5)/2)*cot(x)/S(5), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((a*sin(x)**S(2))**(S(3)/2), x), x, -S(2)*a*sqrt(a*sin(x)**S(2))*cot(x)/S(3) - (a*sin(x)**S(2))**(S(3)/2)*cot(x)/S(3), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(a*sin(x)**S(2)), x), x, -sqrt(a*sin(x)**S(2))*cot(x), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(S(1)/sqrt(a*sin(x)**S(2)), x), x, -sin(x)*atanh(cos(x))/sqrt(a*sin(x)**S(2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((a*sin(x)**S(2))**(S(-3)/2), x), x, -sin(x)*atanh(cos(x))/(S(2)*a*sqrt(a*sin(x)**S(2))) - cot(x)/(S(2)*a*sqrt(a*sin(x)**S(2))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((a*sin(x)**S(2))**(S(-5)/2), x), x, -cot(x)/(S(4)*a*(a*sin(x)**S(2))**(S(3)/2)) - S(3)*sin(x)*atanh(cos(x))/(S(8)*a**S(2)*sqrt(a*sin(x)**S(2))) - S(3)*cot(x)/(S(8)*a**S(2)*sqrt(a*sin(x)**S(2))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((a*sin(x)**S(3))**(S(5)/2), x), x, -S(26)*a**S(2)*sqrt(a*sin(x)**S(3))*EllipticF(Pi/S(4) - x/S(2), S(2))/(S(77)*sin(x)**(S(3)/2)) - S(2)*a**S(2)*sqrt(a*sin(x)**S(3))*sin(x)**S(5)*cos(x)/S(15) - S(26)*a**S(2)*sqrt(a*sin(x)**S(3))*sin(x)**S(3)*cos(x)/S(165) - S(78)*a**S(2)*sqrt(a*sin(x)**S(3))*sin(x)*cos(x)/S(385) - S(26)*a**S(2)*sqrt(a*sin(x)**S(3))*cot(x)/S(77), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((a*sin(x)**S(3))**(S(3)/2), x), x, -S(14)*a*sqrt(a*sin(x)**S(3))*EllipticE(Pi/S(4) - x/S(2), S(2))/(S(15)*sin(x)**(S(3)/2)) - S(2)*a*sqrt(a*sin(x)**S(3))*sin(x)**S(2)*cos(x)/S(9) - S(14)*a*sqrt(a*sin(x)**S(3))*cos(x)/S(45), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(a*sin(x)**S(3)), x), x, -S(2)*sqrt(a*sin(x)**S(3))*EllipticF(Pi/S(4) - x/S(2), S(2))/(S(3)*sin(x)**(S(3)/2)) - S(2)*sqrt(a*sin(x)**S(3))*cot(x)/S(3), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(S(1)/sqrt(a*sin(x)**S(3)), x), x, S(2)*EllipticE(Pi/S(4) - x/S(2), S(2))*sin(x)**(S(3)/2)/sqrt(a*sin(x)**S(3)) - S(2)*sin(x)*cos(x)/sqrt(a*sin(x)**S(3)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((a*sin(x)**S(3))**(S(-3)/2), x), x, -S(10)*EllipticF(Pi/S(4) - x/S(2), S(2))*sin(x)**(S(3)/2)/(S(21)*a*sqrt(a*sin(x)**S(3))) - S(10)*cos(x)/(S(21)*a*sqrt(a*sin(x)**S(3))) - S(2)*cot(x)*csc(x)/(S(7)*a*sqrt(a*sin(x)**S(3))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((a*sin(x)**S(3))**(S(-5)/2), x), x, S(154)*EllipticE(Pi/S(4) - x/S(2), S(2))*sin(x)**(S(3)/2)/(S(195)*a**S(2)*sqrt(a*sin(x)**S(3))) - S(154)*sin(x)*cos(x)/(S(195)*a**S(2)*sqrt(a*sin(x)**S(3))) - S(2)*cot(x)*csc(x)**S(4)/(S(13)*a**S(2)*sqrt(a*sin(x)**S(3))) - S(22)*cot(x)*csc(x)**S(2)/(S(117)*a**S(2)*sqrt(a*sin(x)**S(3))) - S(154)*cot(x)/(S(585)*a**S(2)*sqrt(a*sin(x)**S(3))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((a*sin(x)**S(4))**(S(5)/2), x), x, S(63)*a**S(2)*x*sqrt(a*sin(x)**S(4))*csc(x)**S(2)/S(256) - a**S(2)*sqrt(a*sin(x)**S(4))*sin(x)**S(7)*cos(x)/S(10) - S(9)*a**S(2)*sqrt(a*sin(x)**S(4))*sin(x)**S(5)*cos(x)/S(80) - S(21)*a**S(2)*sqrt(a*sin(x)**S(4))*sin(x)**S(3)*cos(x)/S(160) - S(21)*a**S(2)*sqrt(a*sin(x)**S(4))*sin(x)*cos(x)/S(128) - S(63)*a**S(2)*sqrt(a*sin(x)**S(4))*cot(x)/S(256), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((a*sin(x)**S(4))**(S(3)/2), x), x, S(5)*a*x*sqrt(a*sin(x)**S(4))*csc(x)**S(2)/S(16) - a*sqrt(a*sin(x)**S(4))*sin(x)**S(3)*cos(x)/S(6) - S(5)*a*sqrt(a*sin(x)**S(4))*sin(x)*cos(x)/S(24) - S(5)*a*sqrt(a*sin(x)**S(4))*cot(x)/S(16), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(a*sin(x)**S(4)), x), x, x*sqrt(a*sin(x)**S(4))*csc(x)**S(2)/S(2) - sqrt(a*sin(x)**S(4))*cot(x)/S(2), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(S(1)/sqrt(a*sin(x)**S(4)), x), x, -sin(x)*cos(x)/sqrt(a*sin(x)**S(4)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((a*sin(x)**S(4))**(S(-3)/2), x), x, -sin(x)*cos(x)/(a*sqrt(a*sin(x)**S(4))) - cos(x)**S(2)*cot(x)**S(3)/(S(5)*a*sqrt(a*sin(x)**S(4))) - S(2)*cos(x)**S(2)*cot(x)/(S(3)*a*sqrt(a*sin(x)**S(4))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((a*sin(x)**S(4))**(S(-5)/2), x), x, -sin(x)*cos(x)/(a**S(2)*sqrt(a*sin(x)**S(4))) - cos(x)**S(2)*cot(x)**S(7)/(S(9)*a**S(2)*sqrt(a*sin(x)**S(4))) - S(4)*cos(x)**S(2)*cot(x)**S(5)/(S(7)*a**S(2)*sqrt(a*sin(x)**S(4))) - S(6)*cos(x)**S(2)*cot(x)**S(3)/(S(5)*a**S(2)*sqrt(a*sin(x)**S(4))) - S(4)*cos(x)**S(2)*cot(x)/(S(3)*a**S(2)*sqrt(a*sin(x)**S(4))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sin(c + d*x)**p)**n, x), x, (b*sin(c + d*x)**p)**n*Hypergeometric2F1(S(1)/2, n*p/S(2) + S(1)/2, n*p/S(2) + S(3)/2, sin(c + d*x)**S(2))*sin(c + d*x)*cos(c + d*x)/(d*(n*p + S(1))*sqrt(cos(c + d*x)**S(2))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x)**S(2))**n, x), x, (c*sin(a + b*x)**S(2))**n*Hypergeometric2F1(S(1)/2, n + S(1)/2, n + S(3)/2, sin(a + b*x)**S(2))*sin(a + b*x)*cos(a + b*x)/(b*(S(2)*n + S(1))*sqrt(cos(a + b*x)**S(2))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x)**S(3))**n, x), x, (c*sin(a + b*x)**S(3))**n*Hypergeometric2F1(S(1)/2, S(3)*n/S(2) + S(1)/2, S(3)*n/S(2) + S(3)/2, sin(a + b*x)**S(2))*sin(a + b*x)*cos(a + b*x)/(b*(S(3)*n + S(1))*sqrt(cos(a + b*x)**S(2))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x)**S(4))**n, x), x, (c*sin(a + b*x)**S(4))**n*Hypergeometric2F1(S(1)/2, S(2)*n + S(1)/2, S(2)*n + S(3)/2, sin(a + b*x)**S(2))*sin(a + b*x)*cos(a + b*x)/(b*(S(4)*n + S(1))*sqrt(cos(a + b*x)**S(2))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x)**m)**(S(5)/2), x), x, S(2)*c**S(2)*sqrt(c*sin(a + b*x)**m)*Hypergeometric2F1(S(1)/2, S(5)*m/S(4) + S(1)/2, S(5)*m/S(4) + S(3)/2, sin(a + b*x)**S(2))*sin(a + b*x)**(S(2)*m + S(1))*cos(a + b*x)/(b*(S(5)*m + S(2))*sqrt(cos(a + b*x)**S(2))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x)**m)**(S(3)/2), x), x, S(2)*c*sqrt(c*sin(a + b*x)**m)*Hypergeometric2F1(S(1)/2, S(3)*m/S(4) + S(1)/2, S(3)*m/S(4) + S(3)/2, sin(a + b*x)**S(2))*sin(a + b*x)**(m + S(1))*cos(a + b*x)/(b*(S(3)*m + S(2))*sqrt(cos(a + b*x)**S(2))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(c*sin(a + b*x)**m), x), x, S(2)*sqrt(c*sin(a + b*x)**m)*Hypergeometric2F1(S(1)/2, m/S(4) + S(1)/2, m/S(4) + S(3)/2, sin(a + b*x)**S(2))*sin(a + b*x)*cos(a + b*x)/(b*(m + S(2))*sqrt(cos(a + b*x)**S(2))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(S(1)/sqrt(c*sin(a + b*x)**m), x), x, S(2)*Hypergeometric2F1(S(1)/2, -m/S(4) + S(1)/2, -m/S(4) + S(3)/2, sin(a + b*x)**S(2))*sin(a + b*x)*cos(a + b*x)/(b*sqrt(c*sin(a + b*x)**m)*(-m + S(2))*sqrt(cos(a + b*x)**S(2))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x)**m)**(S(-3)/2), x), x, S(2)*Hypergeometric2F1(S(1)/2, -S(3)*m/S(4) + S(1)/2, -S(3)*m/S(4) + S(3)/2, sin(a + b*x)**S(2))*sin(a + b*x)**(-m + S(1))*cos(a + b*x)/(b*c*sqrt(c*sin(a + b*x)**m)*(-S(3)*m + S(2))*sqrt(cos(a + b*x)**S(2))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x)**m)**(S(-5)/2), x), x, S(2)*Hypergeometric2F1(S(1)/2, -S(5)*m/S(4) + S(1)/2, -S(5)*m/S(4) + S(3)/2, sin(a + b*x)**S(2))*sin(a + b*x)**(-S(2)*m + S(1))*cos(a + b*x)/(b*c**S(2)*sqrt(c*sin(a + b*x)**m)*(-S(5)*m + S(2))*sqrt(cos(a + b*x)**S(2))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x)**m)**(S(1)/m), x), x, -(c*sin(a + b*x)**m)**(S(1)/m)*cot(a + b*x)/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((a*(b*sin(c + d*x))**p)**n, x), x, (a*(b*sin(c + d*x))**p)**n*Hypergeometric2F1(S(1)/2, n*p/S(2) + S(1)/2, n*p/S(2) + S(3)/2, sin(c + d*x)**S(2))*sin(c + d*x)*cos(c + d*x)/(d*(n*p + S(1))*sqrt(cos(c + d*x)**S(2))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((a*sin(e + f*x))**m*(b*sin(e + f*x))**n, x), x, (a*sin(e + f*x))**(m + S(1))*(b*sin(e + f*x))**n*Hypergeometric2F1(S(1)/2, m/S(2) + n/S(2) + S(1)/2, m/S(2) + n/S(2) + S(3)/2, sin(e + f*x)**S(2))*cos(e + f*x)/(a*f*(m + n + S(1))*sqrt(cos(e + f*x)**S(2))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)*cos(a + b*x)**S(3), x), x, -cos(a + b*x)**S(4)/(S(4)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)*cos(a + b*x)**S(2), x), x, -cos(a + b*x)**S(3)/(S(3)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)*cos(a + b*x), x), x, sin(a + b*x)**S(2)/(S(2)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)*sec(a + b*x), x), x, -log(cos(a + b*x))/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)*sec(a + b*x)**S(2), x), x, sec(a + b*x)/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)*sec(a + b*x)**S(3), x), x, sec(a + b*x)**S(2)/(S(2)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)*sec(a + b*x)**S(4), x), x, sec(a + b*x)**S(3)/(S(3)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(2)*cos(a + b*x)**S(7), x), x, -sin(a + b*x)**S(9)/(S(9)*b) + S(3)*sin(a + b*x)**S(7)/(S(7)*b) - S(3)*sin(a + b*x)**S(5)/(S(5)*b) + sin(a + b*x)**S(3)/(S(3)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(2)*cos(a + b*x)**S(5), x), x, sin(a + b*x)**S(7)/(S(7)*b) - S(2)*sin(a + b*x)**S(5)/(S(5)*b) + sin(a + b*x)**S(3)/(S(3)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(2)*cos(a + b*x)**S(3), x), x, -sin(a + b*x)**S(5)/(S(5)*b) + sin(a + b*x)**S(3)/(S(3)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(2)*cos(a + b*x), x), x, sin(a + b*x)**S(3)/(S(3)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(2)*sec(a + b*x)**S(2), x), x, -x + tan(a + b*x)/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(2)*sec(a + b*x)**S(4), x), x, tan(a + b*x)**S(3)/(S(3)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(2)*sec(a + b*x)**S(6), x), x, tan(a + b*x)**S(5)/(S(5)*b) + tan(a + b*x)**S(3)/(S(3)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(2)*sec(a + b*x)**S(8), x), x, tan(a + b*x)**S(7)/(S(7)*b) + S(2)*tan(a + b*x)**S(5)/(S(5)*b) + tan(a + b*x)**S(3)/(S(3)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(2)*sec(a + b*x)**S(10), x), x, tan(a + b*x)**S(9)/(S(9)*b) + S(3)*tan(a + b*x)**S(7)/(S(7)*b) + S(3)*tan(a + b*x)**S(5)/(S(5)*b) + tan(a + b*x)**S(3)/(S(3)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(2)*cos(a + b*x)**S(6), x), x, S(5)*x/S(128) - sin(a + b*x)*cos(a + b*x)**S(7)/(S(8)*b) + sin(a + b*x)*cos(a + b*x)**S(5)/(S(48)*b) + S(5)*sin(a + b*x)*cos(a + b*x)**S(3)/(S(192)*b) + S(5)*sin(a + b*x)*cos(a + b*x)/(S(128)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(2)*cos(a + b*x)**S(4), x), x, x/S(16) - sin(a + b*x)*cos(a + b*x)**S(5)/(S(6)*b) + sin(a + b*x)*cos(a + b*x)**S(3)/(S(24)*b) + sin(a + b*x)*cos(a + b*x)/(S(16)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(2)*cos(a + b*x)**S(2), x), x, x/S(8) - sin(a + b*x)*cos(a + b*x)**S(3)/(S(4)*b) + sin(a + b*x)*cos(a + b*x)/(S(8)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(2), x), x, x/S(2) - sin(a + b*x)*cos(a + b*x)/(S(2)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(2)*sec(a + b*x), x), x, -sin(a + b*x)/b + atanh(sin(a + b*x))/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(2)*sec(a + b*x)**S(3), x), x, tan(a + b*x)*sec(a + b*x)/(S(2)*b) - atanh(sin(a + b*x))/(S(2)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(2)*sec(a + b*x)**S(5), x), x, tan(a + b*x)*sec(a + b*x)**S(3)/(S(4)*b) - tan(a + b*x)*sec(a + b*x)/(S(8)*b) - atanh(sin(a + b*x))/(S(8)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(2)*sec(a + b*x)**S(7), x), x, tan(a + b*x)*sec(a + b*x)**S(5)/(S(6)*b) - tan(a + b*x)*sec(a + b*x)**S(3)/(S(24)*b) - tan(a + b*x)*sec(a + b*x)/(S(16)*b) - atanh(sin(a + b*x))/(S(16)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(3)*cos(a + b*x)**S(5), x), x, cos(a + b*x)**S(8)/(S(8)*b) - cos(a + b*x)**S(6)/(S(6)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(3)*cos(a + b*x)**S(4), x), x, cos(a + b*x)**S(7)/(S(7)*b) - cos(a + b*x)**S(5)/(S(5)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(3)*cos(a + b*x)**S(3), x), x, -sin(a + b*x)**S(6)/(S(6)*b) + sin(a + b*x)**S(4)/(S(4)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(3)*cos(a + b*x)**S(2), x), x, cos(a + b*x)**S(5)/(S(5)*b) - cos(a + b*x)**S(3)/(S(3)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(3)*cos(a + b*x), x), x, sin(a + b*x)**S(4)/(S(4)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(3)*sec(a + b*x), x), x, -log(cos(a + b*x))/b + cos(a + b*x)**S(2)/(S(2)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(3)*sec(a + b*x)**S(2), x), x, cos(a + b*x)/b + sec(a + b*x)/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(3)*sec(a + b*x)**S(3), x), x, log(cos(a + b*x))/b + tan(a + b*x)**S(2)/(S(2)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(3)*sec(a + b*x)**S(4), x), x, sec(a + b*x)**S(3)/(S(3)*b) - sec(a + b*x)/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(3)*sec(a + b*x)**S(5), x), x, tan(a + b*x)**S(4)/(S(4)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(3)*sec(a + b*x)**S(6), x), x, sec(a + b*x)**S(5)/(S(5)*b) - sec(a + b*x)**S(3)/(S(3)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(3)*sec(a + b*x)**S(7), x), x, sec(a + b*x)**S(6)/(S(6)*b) - sec(a + b*x)**S(4)/(S(4)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(3)*sec(a + b*x)**S(8), x), x, sec(a + b*x)**S(7)/(S(7)*b) - sec(a + b*x)**S(5)/(S(5)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(3)*sec(a + b*x)**S(9), x), x, sec(a + b*x)**S(8)/(S(8)*b) - sec(a + b*x)**S(6)/(S(6)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(4)*cos(a + b*x)**S(7), x), x, -sin(a + b*x)**S(11)/(S(11)*b) + sin(a + b*x)**S(9)/(S(3)*b) - S(3)*sin(a + b*x)**S(7)/(S(7)*b) + sin(a + b*x)**S(5)/(S(5)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(4)*cos(a + b*x)**S(5), x), x, sin(a + b*x)**S(9)/(S(9)*b) - S(2)*sin(a + b*x)**S(7)/(S(7)*b) + sin(a + b*x)**S(5)/(S(5)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(4)*cos(a + b*x)**S(3), x), x, -sin(a + b*x)**S(7)/(S(7)*b) + sin(a + b*x)**S(5)/(S(5)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(4)*cos(a + b*x), x), x, sin(a + b*x)**S(5)/(S(5)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(4)*sec(a + b*x)**S(2), x), x, -S(3)*x/S(2) - sin(a + b*x)**S(2)*tan(a + b*x)/(S(2)*b) + S(3)*tan(a + b*x)/(S(2)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(4)*sec(a + b*x)**S(4), x), x, x + tan(a + b*x)**S(3)/(S(3)*b) - tan(a + b*x)/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(4)*sec(a + b*x)**S(6), x), x, tan(a + b*x)**S(5)/(S(5)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(4)*sec(a + b*x)**S(8), x), x, tan(a + b*x)**S(7)/(S(7)*b) + tan(a + b*x)**S(5)/(S(5)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(4)*sec(a + b*x)**S(10), x), x, tan(a + b*x)**S(9)/(S(9)*b) + S(2)*tan(a + b*x)**S(7)/(S(7)*b) + tan(a + b*x)**S(5)/(S(5)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(4)*cos(a + b*x)**S(6), x), x, S(3)*x/S(256) - sin(a + b*x)**S(3)*cos(a + b*x)**S(7)/(S(10)*b) - S(3)*sin(a + b*x)*cos(a + b*x)**S(7)/(S(80)*b) + sin(a + b*x)*cos(a + b*x)**S(5)/(S(160)*b) + sin(a + b*x)*cos(a + b*x)**S(3)/(S(128)*b) + S(3)*sin(a + b*x)*cos(a + b*x)/(S(256)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(4)*cos(a + b*x)**S(4), x), x, S(3)*x/S(128) - sin(a + b*x)**S(3)*cos(a + b*x)**S(5)/(S(8)*b) - sin(a + b*x)*cos(a + b*x)**S(5)/(S(16)*b) + sin(a + b*x)*cos(a + b*x)**S(3)/(S(64)*b) + S(3)*sin(a + b*x)*cos(a + b*x)/(S(128)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(4)*cos(a + b*x)**S(2), x), x, x/S(16) - sin(a + b*x)**S(3)*cos(a + b*x)**S(3)/(S(6)*b) - sin(a + b*x)*cos(a + b*x)**S(3)/(S(8)*b) + sin(a + b*x)*cos(a + b*x)/(S(16)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(4), x), x, S(3)*x/S(8) - sin(a + b*x)**S(3)*cos(a + b*x)/(S(4)*b) - S(3)*sin(a + b*x)*cos(a + b*x)/(S(8)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(4)*sec(a + b*x), x), x, -sin(a + b*x)**S(3)/(S(3)*b) - sin(a + b*x)/b + atanh(sin(a + b*x))/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(4)*sec(a + b*x)**S(3), x), x, sin(a + b*x)*tan(a + b*x)**S(2)/(S(2)*b) + S(3)*sin(a + b*x)/(S(2)*b) - S(3)*atanh(sin(a + b*x))/(S(2)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(4)*sec(a + b*x)**S(5), x), x, tan(a + b*x)**S(3)*sec(a + b*x)/(S(4)*b) - S(3)*tan(a + b*x)*sec(a + b*x)/(S(8)*b) + S(3)*atanh(sin(a + b*x))/(S(8)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(4)*sec(a + b*x)**S(7), x), x, tan(a + b*x)**S(3)*sec(a + b*x)**S(3)/(S(6)*b) - tan(a + b*x)*sec(a + b*x)**S(3)/(S(8)*b) + tan(a + b*x)*sec(a + b*x)/(S(16)*b) + atanh(sin(a + b*x))/(S(16)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(4)*sec(a + b*x)**S(9), x), x, tan(a + b*x)**S(3)*sec(a + b*x)**S(5)/(S(8)*b) - tan(a + b*x)*sec(a + b*x)**S(5)/(S(16)*b) + tan(a + b*x)*sec(a + b*x)**S(3)/(S(64)*b) + S(3)*tan(a + b*x)*sec(a + b*x)/(S(128)*b) + S(3)*atanh(sin(a + b*x))/(S(128)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(5)*cos(a + b*x)**S(7), x), x, -cos(a + b*x)**S(12)/(S(12)*b) + cos(a + b*x)**S(10)/(S(5)*b) - cos(a + b*x)**S(8)/(S(8)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(5)*cos(a + b*x)**S(6), x), x, -cos(a + b*x)**S(11)/(S(11)*b) + S(2)*cos(a + b*x)**S(9)/(S(9)*b) - cos(a + b*x)**S(7)/(S(7)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(5)*cos(a + b*x)**S(5), x), x, sin(a + b*x)**S(10)/(S(10)*b) - sin(a + b*x)**S(8)/(S(4)*b) + sin(a + b*x)**S(6)/(S(6)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(5)*cos(a + b*x)**S(4), x), x, -cos(a + b*x)**S(9)/(S(9)*b) + S(2)*cos(a + b*x)**S(7)/(S(7)*b) - cos(a + b*x)**S(5)/(S(5)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(5)*cos(a + b*x)**S(3), x), x, -sin(a + b*x)**S(8)/(S(8)*b) + sin(a + b*x)**S(6)/(S(6)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(5)*cos(a + b*x)**S(2), x), x, -cos(a + b*x)**S(7)/(S(7)*b) + S(2)*cos(a + b*x)**S(5)/(S(5)*b) - cos(a + b*x)**S(3)/(S(3)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(5)*cos(a + b*x), x), x, sin(a + b*x)**S(6)/(S(6)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(5)*sec(a + b*x), x), x, -log(cos(a + b*x))/b - cos(a + b*x)**S(4)/(S(4)*b) + cos(a + b*x)**S(2)/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(5)*sec(a + b*x)**S(2), x), x, -cos(a + b*x)**S(3)/(S(3)*b) + S(2)*cos(a + b*x)/b + sec(a + b*x)/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(5)*sec(a + b*x)**S(3), x), x, S(2)*log(cos(a + b*x))/b - cos(a + b*x)**S(2)/(S(2)*b) + sec(a + b*x)**S(2)/(S(2)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(5)*sec(a + b*x)**S(4), x), x, -cos(a + b*x)/b + sec(a + b*x)**S(3)/(S(3)*b) - S(2)*sec(a + b*x)/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(5)*sec(a + b*x)**S(5), x), x, -log(cos(a + b*x))/b + tan(a + b*x)**S(4)/(S(4)*b) - tan(a + b*x)**S(2)/(S(2)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(5)*sec(a + b*x)**S(6), x), x, sec(a + b*x)**S(5)/(S(5)*b) - S(2)*sec(a + b*x)**S(3)/(S(3)*b) + sec(a + b*x)/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(5)*sec(a + b*x)**S(7), x), x, tan(a + b*x)**S(6)/(S(6)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(5)*sec(a + b*x)**S(8), x), x, sec(a + b*x)**S(7)/(S(7)*b) - S(2)*sec(a + b*x)**S(5)/(S(5)*b) + sec(a + b*x)**S(3)/(S(3)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(5)*sec(a + b*x)**S(9), x), x, tan(a + b*x)**S(8)/(S(8)*b) + tan(a + b*x)**S(6)/(S(6)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(5)*sec(a + b*x)**S(10), x), x, sec(a + b*x)**S(9)/(S(9)*b) - S(2)*sec(a + b*x)**S(7)/(S(7)*b) + sec(a + b*x)**S(5)/(S(5)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(5)*sec(a + b*x)**S(11), x), x, sec(a + b*x)**S(10)/(S(10)*b) - sec(a + b*x)**S(8)/(S(4)*b) + sec(a + b*x)**S(6)/(S(6)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(5)*sec(a + b*x)**S(12), x), x, sec(a + b*x)**S(11)/(S(11)*b) - S(2)*sec(a + b*x)**S(9)/(S(9)*b) + sec(a + b*x)**S(7)/(S(7)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(5)*sec(a + b*x)**S(13), x), x, sec(a + b*x)**S(12)/(S(12)*b) - sec(a + b*x)**S(10)/(S(5)*b) + sec(a + b*x)**S(8)/(S(8)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(6)*sec(a + b*x)**S(3), x), x, sin(a + b*x)**S(3)*tan(a + b*x)**S(2)/(S(2)*b) + S(5)*sin(a + b*x)**S(3)/(S(6)*b) + S(5)*sin(a + b*x)/(S(2)*b) - S(5)*atanh(sin(a + b*x))/(S(2)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(7)*sec(a + b*x)**S(6), x), x, cos(a + b*x)/b + sec(a + b*x)**S(5)/(S(5)*b) - sec(a + b*x)**S(3)/b + S(3)*sec(a + b*x)/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)**S(6)/sin(a + b*x), x), x, cos(a + b*x)**S(5)/(S(5)*b) + cos(a + b*x)**S(3)/(S(3)*b) + cos(a + b*x)/b - atanh(cos(a + b*x))/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)**S(5)/sin(a + b*x), x), x, log(sin(a + b*x))/b + sin(a + b*x)**S(4)/(S(4)*b) - sin(a + b*x)**S(2)/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)**S(4)/sin(a + b*x), x), x, cos(a + b*x)**S(3)/(S(3)*b) + cos(a + b*x)/b - atanh(cos(a + b*x))/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)**S(3)/sin(a + b*x), x), x, log(sin(a + b*x))/b - sin(a + b*x)**S(2)/(S(2)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)**S(2)/sin(a + b*x), x), x, cos(a + b*x)/b - atanh(cos(a + b*x))/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)/sin(a + b*x), x), x, log(sin(a + b*x))/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sec(a + b*x)/sin(a + b*x), x), x, log(tan(a + b*x))/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sec(a + b*x)**S(2)/sin(a + b*x), x), x, -atanh(cos(a + b*x))/b + sec(a + b*x)/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sec(a + b*x)**S(3)/sin(a + b*x), x), x, log(tan(a + b*x))/b + tan(a + b*x)**S(2)/(S(2)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sec(a + b*x)**S(4)/sin(a + b*x), x), x, -atanh(cos(a + b*x))/b + sec(a + b*x)**S(3)/(S(3)*b) + sec(a + b*x)/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sec(a + b*x)**S(5)/sin(a + b*x), x), x, log(tan(a + b*x))/b + tan(a + b*x)**S(4)/(S(4)*b) + tan(a + b*x)**S(2)/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sec(a + b*x)**S(6)/sin(a + b*x), x), x, -atanh(cos(a + b*x))/b + sec(a + b*x)**S(5)/(S(5)*b) + sec(a + b*x)**S(3)/(S(3)*b) + sec(a + b*x)/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sec(a + b*x)**S(7)/sin(a + b*x), x), x, log(tan(a + b*x))/b + tan(a + b*x)**S(6)/(S(6)*b) + S(3)*tan(a + b*x)**S(4)/(S(4)*b) + S(3)*tan(a + b*x)**S(2)/(S(2)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)**S(7)/sin(a + b*x)**S(2), x), x, -sin(a + b*x)**S(5)/(S(5)*b) + sin(a + b*x)**S(3)/b - S(3)*sin(a + b*x)/b - csc(a + b*x)/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)**S(6)/sin(a + b*x)**S(2), x), x, -S(15)*x/S(8) + cos(a + b*x)**S(4)*cot(a + b*x)/(S(4)*b) + S(5)*cos(a + b*x)**S(2)*cot(a + b*x)/(S(8)*b) - S(15)*cot(a + b*x)/(S(8)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)**S(5)/sin(a + b*x)**S(2), x), x, sin(a + b*x)**S(3)/(S(3)*b) - S(2)*sin(a + b*x)/b - csc(a + b*x)/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)**S(4)/sin(a + b*x)**S(2), x), x, -S(3)*x/S(2) + cos(a + b*x)**S(2)*cot(a + b*x)/(S(2)*b) - S(3)*cot(a + b*x)/(S(2)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)**S(3)/sin(a + b*x)**S(2), x), x, -sin(a + b*x)/b - csc(a + b*x)/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)**S(2)/sin(a + b*x)**S(2), x), x, -x - cot(a + b*x)/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)/sin(a + b*x)**S(2), x), x, -csc(a + b*x)/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sec(a + b*x)/sin(a + b*x)**S(2), x), x, atanh(sin(a + b*x))/b - csc(a + b*x)/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sec(a + b*x)**S(2)/sin(a + b*x)**S(2), x), x, tan(a + b*x)/b - cot(a + b*x)/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sec(a + b*x)**S(3)/sin(a + b*x)**S(2), x), x, S(3)*atanh(sin(a + b*x))/(S(2)*b) + csc(a + b*x)*sec(a + b*x)**S(2)/(S(2)*b) - S(3)*csc(a + b*x)/(S(2)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sec(a + b*x)**S(4)/sin(a + b*x)**S(2), x), x, tan(a + b*x)**S(3)/(S(3)*b) + S(2)*tan(a + b*x)/b - cot(a + b*x)/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sec(a + b*x)**S(5)/sin(a + b*x)**S(2), x), x, S(15)*atanh(sin(a + b*x))/(S(8)*b) + csc(a + b*x)*sec(a + b*x)**S(4)/(S(4)*b) + S(5)*csc(a + b*x)*sec(a + b*x)**S(2)/(S(8)*b) - S(15)*csc(a + b*x)/(S(8)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)**S(7)/sin(a + b*x)**S(3), x), x, -S(3)*log(sin(a + b*x))/b - sin(a + b*x)**S(4)/(S(4)*b) + S(3)*sin(a + b*x)**S(2)/(S(2)*b) - csc(a + b*x)**S(2)/(S(2)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)**S(6)/sin(a + b*x)**S(3), x), x, -cos(a + b*x)**S(3)*cot(a + b*x)**S(2)/(S(2)*b) - S(5)*cos(a + b*x)**S(3)/(S(6)*b) - S(5)*cos(a + b*x)/(S(2)*b) + S(5)*atanh(cos(a + b*x))/(S(2)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)**S(5)/sin(a + b*x)**S(3), x), x, -S(2)*log(sin(a + b*x))/b + sin(a + b*x)**S(2)/(S(2)*b) - csc(a + b*x)**S(2)/(S(2)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)**S(4)/sin(a + b*x)**S(3), x), x, -cos(a + b*x)*cot(a + b*x)**S(2)/(S(2)*b) - S(3)*cos(a + b*x)/(S(2)*b) + S(3)*atanh(cos(a + b*x))/(S(2)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)**S(3)/sin(a + b*x)**S(3), x), x, -log(sin(a + b*x))/b - cot(a + b*x)**S(2)/(S(2)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)**S(2)/sin(a + b*x)**S(3), x), x, -cot(a + b*x)*csc(a + b*x)/(S(2)*b) + atanh(cos(a + b*x))/(S(2)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)/sin(a + b*x)**S(3), x), x, -csc(a + b*x)**S(2)/(S(2)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sec(a + b*x)/sin(a + b*x)**S(3), x), x, log(tan(a + b*x))/b - cot(a + b*x)**S(2)/(S(2)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sec(a + b*x)**S(2)/sin(a + b*x)**S(3), x), x, -S(3)*atanh(cos(a + b*x))/(S(2)*b) - csc(a + b*x)**S(2)*sec(a + b*x)/(S(2)*b) + S(3)*sec(a + b*x)/(S(2)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sec(a + b*x)**S(3)/sin(a + b*x)**S(3), x), x, S(2)*log(tan(a + b*x))/b + tan(a + b*x)**S(2)/(S(2)*b) - cot(a + b*x)**S(2)/(S(2)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sec(a + b*x)**S(4)/sin(a + b*x)**S(3), x), x, -S(5)*atanh(cos(a + b*x))/(S(2)*b) - csc(a + b*x)**S(2)*sec(a + b*x)**S(3)/(S(2)*b) + S(5)*sec(a + b*x)**S(3)/(S(6)*b) + S(5)*sec(a + b*x)/(S(2)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sec(a + b*x)**S(5)/sin(a + b*x)**S(3), x), x, S(3)*log(tan(a + b*x))/b + tan(a + b*x)**S(4)/(S(4)*b) + S(3)*tan(a + b*x)**S(2)/(S(2)*b) - cot(a + b*x)**S(2)/(S(2)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)**S(9)/sin(a + b*x)**S(4), x), x, sin(a + b*x)**S(5)/(S(5)*b) - S(4)*sin(a + b*x)**S(3)/(S(3)*b) + S(6)*sin(a + b*x)/b - csc(a + b*x)**S(3)/(S(3)*b) + S(4)*csc(a + b*x)/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)**S(8)/sin(a + b*x)**S(4), x), x, S(35)*x/S(8) + cos(a + b*x)**S(4)*cot(a + b*x)**S(3)/(S(4)*b) + S(7)*cos(a + b*x)**S(2)*cot(a + b*x)**S(3)/(S(8)*b) - S(35)*cot(a + b*x)**S(3)/(S(24)*b) + S(35)*cot(a + b*x)/(S(8)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)**S(7)/sin(a + b*x)**S(4), x), x, -sin(a + b*x)**S(3)/(S(3)*b) + S(3)*sin(a + b*x)/b - csc(a + b*x)**S(3)/(S(3)*b) + S(3)*csc(a + b*x)/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)**S(6)/sin(a + b*x)**S(4), x), x, S(5)*x/S(2) + cos(a + b*x)**S(2)*cot(a + b*x)**S(3)/(S(2)*b) - S(5)*cot(a + b*x)**S(3)/(S(6)*b) + S(5)*cot(a + b*x)/(S(2)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)**S(5)/sin(a + b*x)**S(4), x), x, sin(a + b*x)/b - csc(a + b*x)**S(3)/(S(3)*b) + S(2)*csc(a + b*x)/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)**S(4)/sin(a + b*x)**S(4), x), x, x - cot(a + b*x)**S(3)/(S(3)*b) + cot(a + b*x)/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)**S(3)/sin(a + b*x)**S(4), x), x, -csc(a + b*x)**S(3)/(S(3)*b) + csc(a + b*x)/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)**S(2)/sin(a + b*x)**S(4), x), x, -cot(a + b*x)**S(3)/(S(3)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)/sin(a + b*x)**S(4), x), x, -csc(a + b*x)**S(3)/(S(3)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sec(a + b*x)/sin(a + b*x)**S(4), x), x, atanh(sin(a + b*x))/b - csc(a + b*x)**S(3)/(S(3)*b) - csc(a + b*x)/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sec(a + b*x)**S(2)/sin(a + b*x)**S(4), x), x, tan(a + b*x)/b - cot(a + b*x)**S(3)/(S(3)*b) - S(2)*cot(a + b*x)/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sec(a + b*x)**S(3)/sin(a + b*x)**S(4), x), x, S(5)*atanh(sin(a + b*x))/(S(2)*b) + csc(a + b*x)**S(3)*sec(a + b*x)**S(2)/(S(2)*b) - S(5)*csc(a + b*x)**S(3)/(S(6)*b) - S(5)*csc(a + b*x)/(S(2)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sec(a + b*x)**S(4)/sin(a + b*x)**S(4), x), x, tan(a + b*x)**S(3)/(S(3)*b) + S(3)*tan(a + b*x)/b - cot(a + b*x)**S(3)/(S(3)*b) - S(3)*cot(a + b*x)/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sec(a + b*x)**S(5)/sin(a + b*x)**S(4), x), x, S(35)*atanh(sin(a + b*x))/(S(8)*b) + csc(a + b*x)**S(3)*sec(a + b*x)**S(4)/(S(4)*b) + S(7)*csc(a + b*x)**S(3)*sec(a + b*x)**S(2)/(S(8)*b) - S(35)*csc(a + b*x)**S(3)/(S(24)*b) - S(35)*csc(a + b*x)/(S(8)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)**S(9)/sin(a + b*x)**S(5), x), x, S(6)*log(sin(a + b*x))/b + sin(a + b*x)**S(4)/(S(4)*b) - S(2)*sin(a + b*x)**S(2)/b - csc(a + b*x)**S(4)/(S(4)*b) + S(2)*csc(a + b*x)**S(2)/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)**S(8)/sin(a + b*x)**S(5), x), x, -cos(a + b*x)**S(3)*cot(a + b*x)**S(4)/(S(4)*b) + S(7)*cos(a + b*x)**S(3)*cot(a + b*x)**S(2)/(S(8)*b) + S(35)*cos(a + b*x)**S(3)/(S(24)*b) + S(35)*cos(a + b*x)/(S(8)*b) - S(35)*atanh(cos(a + b*x))/(S(8)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)**S(7)/sin(a + b*x)**S(5), x), x, S(3)*log(sin(a + b*x))/b - sin(a + b*x)**S(2)/(S(2)*b) - csc(a + b*x)**S(4)/(S(4)*b) + S(3)*csc(a + b*x)**S(2)/(S(2)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)**S(6)/sin(a + b*x)**S(5), x), x, -cos(a + b*x)*cot(a + b*x)**S(4)/(S(4)*b) + S(5)*cos(a + b*x)*cot(a + b*x)**S(2)/(S(8)*b) + S(15)*cos(a + b*x)/(S(8)*b) - S(15)*atanh(cos(a + b*x))/(S(8)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)**S(5)/sin(a + b*x)**S(5), x), x, log(sin(a + b*x))/b - cot(a + b*x)**S(4)/(S(4)*b) + cot(a + b*x)**S(2)/(S(2)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)**S(4)/sin(a + b*x)**S(5), x), x, -cot(a + b*x)**S(3)*csc(a + b*x)/(S(4)*b) + S(3)*cot(a + b*x)*csc(a + b*x)/(S(8)*b) - S(3)*atanh(cos(a + b*x))/(S(8)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)**S(3)/sin(a + b*x)**S(5), x), x, -cot(a + b*x)**S(4)/(S(4)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)**S(2)/sin(a + b*x)**S(5), x), x, -cot(a + b*x)*csc(a + b*x)**S(3)/(S(4)*b) + cot(a + b*x)*csc(a + b*x)/(S(8)*b) + atanh(cos(a + b*x))/(S(8)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)/sin(a + b*x)**S(5), x), x, -csc(a + b*x)**S(4)/(S(4)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sec(a + b*x)/sin(a + b*x)**S(5), x), x, log(tan(a + b*x))/b - cot(a + b*x)**S(4)/(S(4)*b) - cot(a + b*x)**S(2)/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sec(a + b*x)**S(2)/sin(a + b*x)**S(5), x), x, -S(15)*atanh(cos(a + b*x))/(S(8)*b) - csc(a + b*x)**S(4)*sec(a + b*x)/(S(4)*b) - S(5)*csc(a + b*x)**S(2)*sec(a + b*x)/(S(8)*b) + S(15)*sec(a + b*x)/(S(8)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sec(a + b*x)**S(3)/sin(a + b*x)**S(5), x), x, S(3)*log(tan(a + b*x))/b + tan(a + b*x)**S(2)/(S(2)*b) - cot(a + b*x)**S(4)/(S(4)*b) - S(3)*cot(a + b*x)**S(2)/(S(2)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sec(a + b*x)**S(4)/sin(a + b*x)**S(5), x), x, -S(35)*atanh(cos(a + b*x))/(S(8)*b) - csc(a + b*x)**S(4)*sec(a + b*x)**S(3)/(S(4)*b) - S(7)*csc(a + b*x)**S(2)*sec(a + b*x)**S(3)/(S(8)*b) + S(35)*sec(a + b*x)**S(3)/(S(24)*b) + S(35)*sec(a + b*x)/(S(8)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sec(a + b*x)**S(5)/sin(a + b*x)**S(5), x), x, S(6)*log(tan(a + b*x))/b + tan(a + b*x)**S(4)/(S(4)*b) + S(2)*tan(a + b*x)**S(2)/b - cot(a + b*x)**S(4)/(S(4)*b) - S(2)*cot(a + b*x)**S(2)/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(x)**S(2)/sin(x)**S(6), x), x, -cot(x)**S(5)/S(5) - cot(x)**S(3)/S(3), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(x)**S(3)/sin(x)**S(7), x), x, -csc(x)**S(6)/S(6) + csc(x)**S(4)/S(4), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*cos(a + b*x))**(S(3)/2)*sin(a + b*x), x), x, -S(2)*(d*cos(a + b*x))**(S(5)/2)/(S(5)*b*d), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(d*cos(a + b*x))*sin(a + b*x), x), x, -S(2)*(d*cos(a + b*x))**(S(3)/2)/(S(3)*b*d), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)/sqrt(d*cos(a + b*x)), x), x, -S(2)*sqrt(d*cos(a + b*x))/(b*d), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)/(d*cos(a + b*x))**(S(3)/2), x), x, S(2)/(b*d*sqrt(d*cos(a + b*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)/(d*cos(a + b*x))**(S(5)/2), x), x, S(2)/(S(3)*b*d*(d*cos(a + b*x))**(S(3)/2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)/(d*cos(a + b*x))**(S(7)/2), x), x, S(2)/(S(5)*b*d*(d*cos(a + b*x))**(S(5)/2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)/(d*cos(a + b*x))**(S(9)/2), x), x, S(2)/(S(7)*b*d*(d*cos(a + b*x))**(S(7)/2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*cos(a + b*x))**(S(9)/2)*sin(a + b*x)**S(2), x), x, S(28)*d**S(4)*sqrt(d*cos(a + b*x))*EllipticE(a/S(2) + b*x/S(2), S(2))/(S(195)*b*sqrt(cos(a + b*x))) + S(28)*d**S(3)*(d*cos(a + b*x))**(S(3)/2)*sin(a + b*x)/(S(585)*b) + S(4)*d*(d*cos(a + b*x))**(S(7)/2)*sin(a + b*x)/(S(117)*b) - S(2)*(d*cos(a + b*x))**(S(11)/2)*sin(a + b*x)/(S(13)*b*d), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*cos(a + b*x))**(S(7)/2)*sin(a + b*x)**S(2), x), x, S(20)*d**S(4)*EllipticF(a/S(2) + b*x/S(2), S(2))*sqrt(cos(a + b*x))/(S(231)*b*sqrt(d*cos(a + b*x))) + S(20)*d**S(3)*sqrt(d*cos(a + b*x))*sin(a + b*x)/(S(231)*b) + S(4)*d*(d*cos(a + b*x))**(S(5)/2)*sin(a + b*x)/(S(77)*b) - S(2)*(d*cos(a + b*x))**(S(9)/2)*sin(a + b*x)/(S(11)*b*d), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*cos(a + b*x))**(S(5)/2)*sin(a + b*x)**S(2), x), x, S(4)*d**S(2)*sqrt(d*cos(a + b*x))*EllipticE(a/S(2) + b*x/S(2), S(2))/(S(15)*b*sqrt(cos(a + b*x))) + S(4)*d*(d*cos(a + b*x))**(S(3)/2)*sin(a + b*x)/(S(45)*b) - S(2)*(d*cos(a + b*x))**(S(7)/2)*sin(a + b*x)/(S(9)*b*d), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*cos(a + b*x))**(S(3)/2)*sin(a + b*x)**S(2), x), x, S(4)*d**S(2)*EllipticF(a/S(2) + b*x/S(2), S(2))*sqrt(cos(a + b*x))/(S(21)*b*sqrt(d*cos(a + b*x))) + S(4)*d*sqrt(d*cos(a + b*x))*sin(a + b*x)/(S(21)*b) - S(2)*(d*cos(a + b*x))**(S(5)/2)*sin(a + b*x)/(S(7)*b*d), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(d*cos(a + b*x))*sin(a + b*x)**S(2), x), x, S(4)*sqrt(d*cos(a + b*x))*EllipticE(a/S(2) + b*x/S(2), S(2))/(S(5)*b*sqrt(cos(a + b*x))) - S(2)*(d*cos(a + b*x))**(S(3)/2)*sin(a + b*x)/(S(5)*b*d), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(2)/sqrt(d*cos(a + b*x)), x), x, S(4)*EllipticF(a/S(2) + b*x/S(2), S(2))*sqrt(cos(a + b*x))/(S(3)*b*sqrt(d*cos(a + b*x))) - S(2)*sqrt(d*cos(a + b*x))*sin(a + b*x)/(S(3)*b*d), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(2)/(d*cos(a + b*x))**(S(3)/2), x), x, S(2)*sin(a + b*x)/(b*d*sqrt(d*cos(a + b*x))) - S(4)*sqrt(d*cos(a + b*x))*EllipticE(a/S(2) + b*x/S(2), S(2))/(b*d**S(2)*sqrt(cos(a + b*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(2)/(d*cos(a + b*x))**(S(5)/2), x), x, S(2)*sin(a + b*x)/(S(3)*b*d*(d*cos(a + b*x))**(S(3)/2)) - S(4)*EllipticF(a/S(2) + b*x/S(2), S(2))*sqrt(cos(a + b*x))/(S(3)*b*d**S(2)*sqrt(d*cos(a + b*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(2)/(d*cos(a + b*x))**(S(7)/2), x), x, S(2)*sin(a + b*x)/(S(5)*b*d*(d*cos(a + b*x))**(S(5)/2)) - S(4)*sin(a + b*x)/(S(5)*b*d**S(3)*sqrt(d*cos(a + b*x))) + S(4)*sqrt(d*cos(a + b*x))*EllipticE(a/S(2) + b*x/S(2), S(2))/(S(5)*b*d**S(4)*sqrt(cos(a + b*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(2)/(d*cos(a + b*x))**(S(9)/2), x), x, S(2)*sin(a + b*x)/(S(7)*b*d*(d*cos(a + b*x))**(S(7)/2)) - S(4)*sin(a + b*x)/(S(21)*b*d**S(3)*(d*cos(a + b*x))**(S(3)/2)) - S(4)*EllipticF(a/S(2) + b*x/S(2), S(2))*sqrt(cos(a + b*x))/(S(21)*b*d**S(4)*sqrt(d*cos(a + b*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(d*cos(a + b*x))*sin(a + b*x)**S(3), x), x, -S(2)*(d*cos(a + b*x))**(S(3)/2)/(S(3)*b*d) + S(2)*(d*cos(a + b*x))**(S(7)/2)/(S(7)*b*d**S(3)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(3)/sqrt(d*cos(a + b*x)), x), x, -S(2)*sqrt(d*cos(a + b*x))/(b*d) + S(2)*(d*cos(a + b*x))**(S(5)/2)/(S(5)*b*d**S(3)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(3)/(d*cos(a + b*x))**(S(3)/2), x), x, S(2)/(b*d*sqrt(d*cos(a + b*x))) + S(2)*(d*cos(a + b*x))**(S(3)/2)/(S(3)*b*d**S(3)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(3)/(d*cos(a + b*x))**(S(5)/2), x), x, S(2)/(S(3)*b*d*(d*cos(a + b*x))**(S(3)/2)) + S(2)*sqrt(d*cos(a + b*x))/(b*d**S(3)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(3)/(d*cos(a + b*x))**(S(7)/2), x), x, S(2)/(S(5)*b*d*(d*cos(a + b*x))**(S(5)/2)) - S(2)/(b*d**S(3)*sqrt(d*cos(a + b*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(3)/(d*cos(a + b*x))**(S(9)/2), x), x, S(2)/(S(7)*b*d*(d*cos(a + b*x))**(S(7)/2)) - S(2)/(S(3)*b*d**S(3)*(d*cos(a + b*x))**(S(3)/2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(3)/(d*cos(a + b*x))**(S(11)/2), x), x, S(2)/(S(9)*b*d*(d*cos(a + b*x))**(S(9)/2)) - S(2)/(S(5)*b*d**S(3)*(d*cos(a + b*x))**(S(5)/2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*cos(a + b*x))**(S(9)/2)*sin(a + b*x)**S(4), x), x, S(56)*d**S(4)*sqrt(d*cos(a + b*x))*EllipticE(a/S(2) + b*x/S(2), S(2))/(S(1105)*b*sqrt(cos(a + b*x))) + S(56)*d**S(3)*(d*cos(a + b*x))**(S(3)/2)*sin(a + b*x)/(S(3315)*b) + S(8)*d*(d*cos(a + b*x))**(S(7)/2)*sin(a + b*x)/(S(663)*b) - S(2)*(d*cos(a + b*x))**(S(11)/2)*sin(a + b*x)**S(3)/(S(17)*b*d) - S(12)*(d*cos(a + b*x))**(S(11)/2)*sin(a + b*x)/(S(221)*b*d), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*cos(a + b*x))**(S(7)/2)*sin(a + b*x)**S(4), x), x, S(8)*d**S(4)*EllipticF(a/S(2) + b*x/S(2), S(2))*sqrt(cos(a + b*x))/(S(231)*b*sqrt(d*cos(a + b*x))) + S(8)*d**S(3)*sqrt(d*cos(a + b*x))*sin(a + b*x)/(S(231)*b) + S(8)*d*(d*cos(a + b*x))**(S(5)/2)*sin(a + b*x)/(S(385)*b) - S(2)*(d*cos(a + b*x))**(S(9)/2)*sin(a + b*x)**S(3)/(S(15)*b*d) - S(4)*(d*cos(a + b*x))**(S(9)/2)*sin(a + b*x)/(S(55)*b*d), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*cos(a + b*x))**(S(5)/2)*sin(a + b*x)**S(4), x), x, S(8)*d**S(2)*sqrt(d*cos(a + b*x))*EllipticE(a/S(2) + b*x/S(2), S(2))/(S(65)*b*sqrt(cos(a + b*x))) + S(8)*d*(d*cos(a + b*x))**(S(3)/2)*sin(a + b*x)/(S(195)*b) - S(2)*(d*cos(a + b*x))**(S(7)/2)*sin(a + b*x)**S(3)/(S(13)*b*d) - S(4)*(d*cos(a + b*x))**(S(7)/2)*sin(a + b*x)/(S(39)*b*d), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*cos(a + b*x))**(S(3)/2)*sin(a + b*x)**S(4), x), x, S(8)*d**S(2)*EllipticF(a/S(2) + b*x/S(2), S(2))*sqrt(cos(a + b*x))/(S(77)*b*sqrt(d*cos(a + b*x))) + S(8)*d*sqrt(d*cos(a + b*x))*sin(a + b*x)/(S(77)*b) - S(2)*(d*cos(a + b*x))**(S(5)/2)*sin(a + b*x)**S(3)/(S(11)*b*d) - S(12)*(d*cos(a + b*x))**(S(5)/2)*sin(a + b*x)/(S(77)*b*d), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(d*cos(a + b*x))*sin(a + b*x)**S(4), x), x, S(8)*sqrt(d*cos(a + b*x))*EllipticE(a/S(2) + b*x/S(2), S(2))/(S(15)*b*sqrt(cos(a + b*x))) - S(2)*(d*cos(a + b*x))**(S(3)/2)*sin(a + b*x)**S(3)/(S(9)*b*d) - S(4)*(d*cos(a + b*x))**(S(3)/2)*sin(a + b*x)/(S(15)*b*d), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(4)/sqrt(d*cos(a + b*x)), x), x, S(8)*EllipticF(a/S(2) + b*x/S(2), S(2))*sqrt(cos(a + b*x))/(S(7)*b*sqrt(d*cos(a + b*x))) - S(2)*sqrt(d*cos(a + b*x))*sin(a + b*x)**S(3)/(S(7)*b*d) - S(4)*sqrt(d*cos(a + b*x))*sin(a + b*x)/(S(7)*b*d), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(4)/(d*cos(a + b*x))**(S(3)/2), x), x, S(2)*sin(a + b*x)**S(3)/(b*d*sqrt(d*cos(a + b*x))) - S(24)*sqrt(d*cos(a + b*x))*EllipticE(a/S(2) + b*x/S(2), S(2))/(S(5)*b*d**S(2)*sqrt(cos(a + b*x))) + S(12)*(d*cos(a + b*x))**(S(3)/2)*sin(a + b*x)/(S(5)*b*d**S(3)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(4)/(d*cos(a + b*x))**(S(5)/2), x), x, S(2)*sin(a + b*x)**S(3)/(S(3)*b*d*(d*cos(a + b*x))**(S(3)/2)) - S(8)*EllipticF(a/S(2) + b*x/S(2), S(2))*sqrt(cos(a + b*x))/(S(3)*b*d**S(2)*sqrt(d*cos(a + b*x))) + S(4)*sqrt(d*cos(a + b*x))*sin(a + b*x)/(S(3)*b*d**S(3)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(4)/(d*cos(a + b*x))**(S(7)/2), x), x, S(2)*sin(a + b*x)**S(3)/(S(5)*b*d*(d*cos(a + b*x))**(S(5)/2)) - S(12)*sin(a + b*x)/(S(5)*b*d**S(3)*sqrt(d*cos(a + b*x))) + S(24)*sqrt(d*cos(a + b*x))*EllipticE(a/S(2) + b*x/S(2), S(2))/(S(5)*b*d**S(4)*sqrt(cos(a + b*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(4)/(d*cos(a + b*x))**(S(9)/2), x), x, S(2)*sin(a + b*x)**S(3)/(S(7)*b*d*(d*cos(a + b*x))**(S(7)/2)) - S(4)*sin(a + b*x)/(S(7)*b*d**S(3)*(d*cos(a + b*x))**(S(3)/2)) + S(8)*EllipticF(a/S(2) + b*x/S(2), S(2))*sqrt(cos(a + b*x))/(S(7)*b*d**S(4)*sqrt(d*cos(a + b*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**S(5)*cos(a + b*x)**(S(3)/2), x), x, -S(2)*cos(a + b*x)**(S(13)/2)/(S(13)*b) + S(4)*cos(a + b*x)**(S(9)/2)/(S(9)*b) - S(2)*cos(a + b*x)**(S(5)/2)/(S(5)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*cos(a + b*x))**(S(9)/2)*csc(a + b*x), x), x, d**(S(9)/2)*ArcTan(sqrt(d*cos(a + b*x))/sqrt(d))/b - d**(S(9)/2)*atanh(sqrt(d*cos(a + b*x))/sqrt(d))/b + S(2)*d**S(3)*(d*cos(a + b*x))**(S(3)/2)/(S(3)*b) + S(2)*d*(d*cos(a + b*x))**(S(7)/2)/(S(7)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*cos(a + b*x))**(S(7)/2)*csc(a + b*x), x), x, -d**(S(7)/2)*ArcTan(sqrt(d*cos(a + b*x))/sqrt(d))/b - d**(S(7)/2)*atanh(sqrt(d*cos(a + b*x))/sqrt(d))/b + S(2)*d**S(3)*sqrt(d*cos(a + b*x))/b + S(2)*d*(d*cos(a + b*x))**(S(5)/2)/(S(5)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*cos(a + b*x))**(S(5)/2)*csc(a + b*x), x), x, d**(S(5)/2)*ArcTan(sqrt(d*cos(a + b*x))/sqrt(d))/b - d**(S(5)/2)*atanh(sqrt(d*cos(a + b*x))/sqrt(d))/b + S(2)*d*(d*cos(a + b*x))**(S(3)/2)/(S(3)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*cos(a + b*x))**(S(3)/2)*csc(a + b*x), x), x, -d**(S(3)/2)*ArcTan(sqrt(d*cos(a + b*x))/sqrt(d))/b - d**(S(3)/2)*atanh(sqrt(d*cos(a + b*x))/sqrt(d))/b + S(2)*d*sqrt(d*cos(a + b*x))/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(d*cos(a + b*x))*csc(a + b*x), x), x, sqrt(d)*ArcTan(sqrt(d*cos(a + b*x))/sqrt(d))/b - sqrt(d)*atanh(sqrt(d*cos(a + b*x))/sqrt(d))/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(csc(a + b*x)/sqrt(d*cos(a + b*x)), x), x, -ArcTan(sqrt(d*cos(a + b*x))/sqrt(d))/(b*sqrt(d)) - atanh(sqrt(d*cos(a + b*x))/sqrt(d))/(b*sqrt(d)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(csc(a + b*x)/(d*cos(a + b*x))**(S(3)/2), x), x, S(2)/(b*d*sqrt(d*cos(a + b*x))) + ArcTan(sqrt(d*cos(a + b*x))/sqrt(d))/(b*d**(S(3)/2)) - atanh(sqrt(d*cos(a + b*x))/sqrt(d))/(b*d**(S(3)/2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(csc(a + b*x)/(d*cos(a + b*x))**(S(5)/2), x), x, S(2)/(S(3)*b*d*(d*cos(a + b*x))**(S(3)/2)) - ArcTan(sqrt(d*cos(a + b*x))/sqrt(d))/(b*d**(S(5)/2)) - atanh(sqrt(d*cos(a + b*x))/sqrt(d))/(b*d**(S(5)/2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(csc(a + b*x)/(d*cos(a + b*x))**(S(7)/2), x), x, S(2)/(S(5)*b*d*(d*cos(a + b*x))**(S(5)/2)) + S(2)/(b*d**S(3)*sqrt(d*cos(a + b*x))) + ArcTan(sqrt(d*cos(a + b*x))/sqrt(d))/(b*d**(S(7)/2)) - atanh(sqrt(d*cos(a + b*x))/sqrt(d))/(b*d**(S(7)/2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(csc(a + b*x)/(d*cos(a + b*x))**(S(9)/2), x), x, S(2)/(S(7)*b*d*(d*cos(a + b*x))**(S(7)/2)) + S(2)/(S(3)*b*d**S(3)*(d*cos(a + b*x))**(S(3)/2)) - ArcTan(sqrt(d*cos(a + b*x))/sqrt(d))/(b*d**(S(9)/2)) - atanh(sqrt(d*cos(a + b*x))/sqrt(d))/(b*d**(S(9)/2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*cos(a + b*x))**(S(11)/2)*csc(a + b*x)**S(2), x), x, -S(15)*d**S(6)*EllipticF(a/S(2) + b*x/S(2), S(2))*sqrt(cos(a + b*x))/(S(7)*b*sqrt(d*cos(a + b*x))) - S(15)*d**S(5)*sqrt(d*cos(a + b*x))*sin(a + b*x)/(S(7)*b) - S(9)*d**S(3)*(d*cos(a + b*x))**(S(5)/2)*sin(a + b*x)/(S(7)*b) - d*(d*cos(a + b*x))**(S(9)/2)*csc(a + b*x)/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*cos(a + b*x))**(S(9)/2)*csc(a + b*x)**S(2), x), x, -S(21)*d**S(4)*sqrt(d*cos(a + b*x))*EllipticE(a/S(2) + b*x/S(2), S(2))/(S(5)*b*sqrt(cos(a + b*x))) - S(7)*d**S(3)*(d*cos(a + b*x))**(S(3)/2)*sin(a + b*x)/(S(5)*b) - d*(d*cos(a + b*x))**(S(7)/2)*csc(a + b*x)/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*cos(a + b*x))**(S(7)/2)*csc(a + b*x)**S(2), x), x, -S(5)*d**S(4)*EllipticF(a/S(2) + b*x/S(2), S(2))*sqrt(cos(a + b*x))/(S(3)*b*sqrt(d*cos(a + b*x))) - S(5)*d**S(3)*sqrt(d*cos(a + b*x))*sin(a + b*x)/(S(3)*b) - d*(d*cos(a + b*x))**(S(5)/2)*csc(a + b*x)/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*cos(a + b*x))**(S(5)/2)*csc(a + b*x)**S(2), x), x, -S(3)*d**S(2)*sqrt(d*cos(a + b*x))*EllipticE(a/S(2) + b*x/S(2), S(2))/(b*sqrt(cos(a + b*x))) - d*(d*cos(a + b*x))**(S(3)/2)*csc(a + b*x)/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*cos(a + b*x))**(S(3)/2)*csc(a + b*x)**S(2), x), x, -d**S(2)*EllipticF(a/S(2) + b*x/S(2), S(2))*sqrt(cos(a + b*x))/(b*sqrt(d*cos(a + b*x))) - d*sqrt(d*cos(a + b*x))*csc(a + b*x)/b, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(d*cos(a + b*x))*csc(a + b*x)**S(2), x), x, -sqrt(d*cos(a + b*x))*EllipticE(a/S(2) + b*x/S(2), S(2))/(b*sqrt(cos(a + b*x))) - (d*cos(a + b*x))**(S(3)/2)*csc(a + b*x)/(b*d), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(csc(a + b*x)**S(2)/sqrt(d*cos(a + b*x)), x), x, EllipticF(a/S(2) + b*x/S(2), S(2))*sqrt(cos(a + b*x))/(b*sqrt(d*cos(a + b*x))) - sqrt(d*cos(a + b*x))*csc(a + b*x)/(b*d), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(csc(a + b*x)**S(2)/(d*cos(a + b*x))**(S(3)/2), x), x, S(3)*sin(a + b*x)/(b*d*sqrt(d*cos(a + b*x))) - csc(a + b*x)/(b*d*sqrt(d*cos(a + b*x))) - S(3)*sqrt(d*cos(a + b*x))*EllipticE(a/S(2) + b*x/S(2), S(2))/(b*d**S(2)*sqrt(cos(a + b*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(csc(a + b*x)**S(2)/(d*cos(a + b*x))**(S(5)/2), x), x, S(5)*sin(a + b*x)/(S(3)*b*d*(d*cos(a + b*x))**(S(3)/2)) - csc(a + b*x)/(b*d*(d*cos(a + b*x))**(S(3)/2)) + S(5)*EllipticF(a/S(2) + b*x/S(2), S(2))*sqrt(cos(a + b*x))/(S(3)*b*d**S(2)*sqrt(d*cos(a + b*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(csc(a + b*x)**S(2)/(d*cos(a + b*x))**(S(7)/2), x), x, S(7)*sin(a + b*x)/(S(5)*b*d*(d*cos(a + b*x))**(S(5)/2)) - csc(a + b*x)/(b*d*(d*cos(a + b*x))**(S(5)/2)) + S(21)*sin(a + b*x)/(S(5)*b*d**S(3)*sqrt(d*cos(a + b*x))) - S(21)*sqrt(d*cos(a + b*x))*EllipticE(a/S(2) + b*x/S(2), S(2))/(S(5)*b*d**S(4)*sqrt(cos(a + b*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*cos(a + b*x))**(S(11)/2)*csc(a + b*x)**S(3), x), x, S(9)*d**(S(11)/2)*ArcTan(sqrt(d*cos(a + b*x))/sqrt(d))/(S(4)*b) + S(9)*d**(S(11)/2)*atanh(sqrt(d*cos(a + b*x))/sqrt(d))/(S(4)*b) - S(9)*d**S(5)*sqrt(d*cos(a + b*x))/(S(2)*b) - S(9)*d**S(3)*(d*cos(a + b*x))**(S(5)/2)/(S(10)*b) - d*(d*cos(a + b*x))**(S(9)/2)*csc(a + b*x)**S(2)/(S(2)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*cos(a + b*x))**(S(9)/2)*csc(a + b*x)**S(3), x), x, -S(7)*d**(S(9)/2)*ArcTan(sqrt(d*cos(a + b*x))/sqrt(d))/(S(4)*b) + S(7)*d**(S(9)/2)*atanh(sqrt(d*cos(a + b*x))/sqrt(d))/(S(4)*b) - S(7)*d**S(3)*(d*cos(a + b*x))**(S(3)/2)/(S(6)*b) - d*(d*cos(a + b*x))**(S(7)/2)*csc(a + b*x)**S(2)/(S(2)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*cos(a + b*x))**(S(7)/2)*csc(a + b*x)**S(3), x), x, S(5)*d**(S(7)/2)*ArcTan(sqrt(d*cos(a + b*x))/sqrt(d))/(S(4)*b) + S(5)*d**(S(7)/2)*atanh(sqrt(d*cos(a + b*x))/sqrt(d))/(S(4)*b) - S(5)*d**S(3)*sqrt(d*cos(a + b*x))/(S(2)*b) - d*(d*cos(a + b*x))**(S(5)/2)*csc(a + b*x)**S(2)/(S(2)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*cos(a + b*x))**(S(5)/2)*csc(a + b*x)**S(3), x), x, -S(3)*d**(S(5)/2)*ArcTan(sqrt(d*cos(a + b*x))/sqrt(d))/(S(4)*b) + S(3)*d**(S(5)/2)*atanh(sqrt(d*cos(a + b*x))/sqrt(d))/(S(4)*b) - d*(d*cos(a + b*x))**(S(3)/2)*csc(a + b*x)**S(2)/(S(2)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*cos(a + b*x))**(S(3)/2)*csc(a + b*x)**S(3), x), x, d**(S(3)/2)*ArcTan(sqrt(d*cos(a + b*x))/sqrt(d))/(S(4)*b) + d**(S(3)/2)*atanh(sqrt(d*cos(a + b*x))/sqrt(d))/(S(4)*b) - d*sqrt(d*cos(a + b*x))*csc(a + b*x)**S(2)/(S(2)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(d*cos(a + b*x))*csc(a + b*x)**S(3), x), x, sqrt(d)*ArcTan(sqrt(d*cos(a + b*x))/sqrt(d))/(S(4)*b) - sqrt(d)*atanh(sqrt(d*cos(a + b*x))/sqrt(d))/(S(4)*b) - (d*cos(a + b*x))**(S(3)/2)*csc(a + b*x)**S(2)/(S(2)*b*d), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(csc(a + b*x)**S(3)/sqrt(d*cos(a + b*x)), x), x, -sqrt(d*cos(a + b*x))*csc(a + b*x)**S(2)/(S(2)*b*d) - S(3)*ArcTan(sqrt(d*cos(a + b*x))/sqrt(d))/(S(4)*b*sqrt(d)) - S(3)*atanh(sqrt(d*cos(a + b*x))/sqrt(d))/(S(4)*b*sqrt(d)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(csc(a + b*x)**S(3)/(d*cos(a + b*x))**(S(3)/2), x), x, -csc(a + b*x)**S(2)/(S(2)*b*d*sqrt(d*cos(a + b*x))) + S(5)/(S(2)*b*d*sqrt(d*cos(a + b*x))) + S(5)*ArcTan(sqrt(d*cos(a + b*x))/sqrt(d))/(S(4)*b*d**(S(3)/2)) - S(5)*atanh(sqrt(d*cos(a + b*x))/sqrt(d))/(S(4)*b*d**(S(3)/2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(csc(a + b*x)**S(3)/(d*cos(a + b*x))**(S(5)/2), x), x, -csc(a + b*x)**S(2)/(S(2)*b*d*(d*cos(a + b*x))**(S(3)/2)) + S(7)/(S(6)*b*d*(d*cos(a + b*x))**(S(3)/2)) - S(7)*ArcTan(sqrt(d*cos(a + b*x))/sqrt(d))/(S(4)*b*d**(S(5)/2)) - S(7)*atanh(sqrt(d*cos(a + b*x))/sqrt(d))/(S(4)*b*d**(S(5)/2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(csc(a + b*x)**S(3)/(d*cos(a + b*x))**(S(7)/2), x), x, -csc(a + b*x)**S(2)/(S(2)*b*d*(d*cos(a + b*x))**(S(5)/2)) + S(9)/(S(10)*b*d*(d*cos(a + b*x))**(S(5)/2)) + S(9)/(S(2)*b*d**S(3)*sqrt(d*cos(a + b*x))) + S(9)*ArcTan(sqrt(d*cos(a + b*x))/sqrt(d))/(S(4)*b*d**(S(7)/2)) - S(9)*atanh(sqrt(d*cos(a + b*x))/sqrt(d))/(S(4)*b*d**(S(7)/2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*cos(a + b*x))**(S(1)/5)*sin(a + b*x), x), x, -S(5)*(d*cos(a + b*x))**(S(6)/5)/(S(6)*b*d), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(sin(x))*cos(x)**S(3), x), x, -S(2)*sin(x)**(S(7)/2)/S(7) + S(2)*sin(x)**(S(3)/2)/S(3), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(x)**(S(3)/2)*cos(x)**S(3), x), x, -S(2)*sin(x)**(S(9)/2)/S(9) + S(2)*sin(x)**(S(5)/2)/S(5), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(x)**(S(5)/2)*cos(x)**S(3), x), x, -S(2)*sin(x)**(S(11)/2)/S(11) + S(2)*sin(x)**(S(7)/2)/S(7), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(x)**S(3)/sqrt(sin(x)), x), x, -S(2)*sin(x)**(S(5)/2)/S(5) + S(2)*sqrt(sin(x)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(c*sin(a + b*x))*(d*cos(a + b*x))**(S(9)/2), x), x, S(7)*d**S(4)*sqrt(c*sin(a + b*x))*sqrt(d*cos(a + b*x))*EllipticE(-Pi/S(4) + a + b*x, S(2))/(S(20)*b*sqrt(sin(S(2)*a + S(2)*b*x))) + S(7)*d**S(3)*(c*sin(a + b*x))**(S(3)/2)*(d*cos(a + b*x))**(S(3)/2)/(S(30)*b*c) + d*(c*sin(a + b*x))**(S(3)/2)*(d*cos(a + b*x))**(S(7)/2)/(S(5)*b*c), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(c*sin(a + b*x))*(d*cos(a + b*x))**(S(5)/2), x), x, d**S(2)*sqrt(c*sin(a + b*x))*sqrt(d*cos(a + b*x))*EllipticE(-Pi/S(4) + a + b*x, S(2))/(S(2)*b*sqrt(sin(S(2)*a + S(2)*b*x))) + d*(c*sin(a + b*x))**(S(3)/2)*(d*cos(a + b*x))**(S(3)/2)/(S(3)*b*c), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(c*sin(a + b*x))*sqrt(d*cos(a + b*x)), x), x, sqrt(c*sin(a + b*x))*sqrt(d*cos(a + b*x))*EllipticE(-Pi/S(4) + a + b*x, S(2))/(b*sqrt(sin(S(2)*a + S(2)*b*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(c*sin(a + b*x))/(d*cos(a + b*x))**(S(3)/2), x), x, -S(2)*sqrt(c*sin(a + b*x))*sqrt(d*cos(a + b*x))*EllipticE(-Pi/S(4) + a + b*x, S(2))/(b*d**S(2)*sqrt(sin(S(2)*a + S(2)*b*x))) + S(2)*(c*sin(a + b*x))**(S(3)/2)/(b*c*d*sqrt(d*cos(a + b*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(c*sin(a + b*x))/(d*cos(a + b*x))**(S(7)/2), x), x, -S(4)*sqrt(c*sin(a + b*x))*sqrt(d*cos(a + b*x))*EllipticE(-Pi/S(4) + a + b*x, S(2))/(S(5)*b*d**S(4)*sqrt(sin(S(2)*a + S(2)*b*x))) + S(2)*(c*sin(a + b*x))**(S(3)/2)/(S(5)*b*c*d*(d*cos(a + b*x))**(S(5)/2)) + S(4)*(c*sin(a + b*x))**(S(3)/2)/(S(5)*b*c*d**S(3)*sqrt(d*cos(a + b*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(c*sin(a + b*x))*(d*cos(a + b*x))**(S(3)/2), x), x, -sqrt(S(2))*sqrt(c)*d**(S(3)/2)*ArcTan(S(1) - sqrt(S(2))*sqrt(d)*sqrt(c*sin(a + b*x))/(sqrt(c)*sqrt(d*cos(a + b*x))))/(S(8)*b) + sqrt(S(2))*sqrt(c)*d**(S(3)/2)*ArcTan(S(1) + sqrt(S(2))*sqrt(d)*sqrt(c*sin(a + b*x))/(sqrt(c)*sqrt(d*cos(a + b*x))))/(S(8)*b) + sqrt(S(2))*sqrt(c)*d**(S(3)/2)*log(sqrt(c)*tan(a + b*x) + sqrt(c) - sqrt(S(2))*sqrt(d)*sqrt(c*sin(a + b*x))/sqrt(d*cos(a + b*x)))/(S(16)*b) - sqrt(S(2))*sqrt(c)*d**(S(3)/2)*log(sqrt(c)*tan(a + b*x) + sqrt(c) + sqrt(S(2))*sqrt(d)*sqrt(c*sin(a + b*x))/sqrt(d*cos(a + b*x)))/(S(16)*b) + d*(c*sin(a + b*x))**(S(3)/2)*sqrt(d*cos(a + b*x))/(S(2)*b*c), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(c*sin(a + b*x))/sqrt(d*cos(a + b*x)), x), x, -sqrt(S(2))*sqrt(c)*ArcTan(S(1) - sqrt(S(2))*sqrt(d)*sqrt(c*sin(a + b*x))/(sqrt(c)*sqrt(d*cos(a + b*x))))/(S(2)*b*sqrt(d)) + sqrt(S(2))*sqrt(c)*ArcTan(S(1) + sqrt(S(2))*sqrt(d)*sqrt(c*sin(a + b*x))/(sqrt(c)*sqrt(d*cos(a + b*x))))/(S(2)*b*sqrt(d)) + sqrt(S(2))*sqrt(c)*log(sqrt(c)*tan(a + b*x) + sqrt(c) - sqrt(S(2))*sqrt(d)*sqrt(c*sin(a + b*x))/sqrt(d*cos(a + b*x)))/(S(4)*b*sqrt(d)) - sqrt(S(2))*sqrt(c)*log(sqrt(c)*tan(a + b*x) + sqrt(c) + sqrt(S(2))*sqrt(d)*sqrt(c*sin(a + b*x))/sqrt(d*cos(a + b*x)))/(S(4)*b*sqrt(d)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(c*sin(a + b*x))/(d*cos(a + b*x))**(S(5)/2), x), x, S(2)*(c*sin(a + b*x))**(S(3)/2)/(S(3)*b*c*d*(d*cos(a + b*x))**(S(3)/2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(c*sin(a + b*x))/(d*cos(a + b*x))**(S(9)/2), x), x, S(2)*(c*sin(a + b*x))**(S(3)/2)/(S(7)*b*c*d*(d*cos(a + b*x))**(S(7)/2)) + S(8)*(c*sin(a + b*x))**(S(3)/2)/(S(21)*b*c*d**S(3)*(d*cos(a + b*x))**(S(3)/2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(c*sin(a + b*x))/(d*cos(a + b*x))**(S(13)/2), x), x, S(2)*(c*sin(a + b*x))**(S(3)/2)/(S(11)*b*c*d*(d*cos(a + b*x))**(S(11)/2)) + S(16)*(c*sin(a + b*x))**(S(3)/2)/(S(77)*b*c*d**S(3)*(d*cos(a + b*x))**(S(7)/2)) + S(64)*(c*sin(a + b*x))**(S(3)/2)/(S(231)*b*c*d**S(5)*(d*cos(a + b*x))**(S(3)/2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**(S(3)/2)*(d*cos(a + b*x))**(S(3)/2), x), x, c**S(2)*d**S(2)*EllipticF(-Pi/S(4) + a + b*x, S(2))*sqrt(sin(S(2)*a + S(2)*b*x))/(S(12)*b*sqrt(c*sin(a + b*x))*sqrt(d*cos(a + b*x))) + c*d*sqrt(c*sin(a + b*x))*sqrt(d*cos(a + b*x))/(S(6)*b) - c*sqrt(c*sin(a + b*x))*(d*cos(a + b*x))**(S(5)/2)/(S(3)*b*d), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**(S(3)/2)/sqrt(d*cos(a + b*x)), x), x, c**S(2)*EllipticF(-Pi/S(4) + a + b*x, S(2))*sqrt(sin(S(2)*a + S(2)*b*x))/(S(2)*b*sqrt(c*sin(a + b*x))*sqrt(d*cos(a + b*x))) - c*sqrt(c*sin(a + b*x))*sqrt(d*cos(a + b*x))/(b*d), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**(S(3)/2)/(d*cos(a + b*x))**(S(5)/2), x), x, -c**S(2)*EllipticF(-Pi/S(4) + a + b*x, S(2))*sqrt(sin(S(2)*a + S(2)*b*x))/(S(3)*b*d**S(2)*sqrt(c*sin(a + b*x))*sqrt(d*cos(a + b*x))) + S(2)*c*sqrt(c*sin(a + b*x))/(S(3)*b*d*(d*cos(a + b*x))**(S(3)/2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**(S(3)/2)/(d*cos(a + b*x))**(S(9)/2), x), x, -S(2)*c**S(2)*EllipticF(-Pi/S(4) + a + b*x, S(2))*sqrt(sin(S(2)*a + S(2)*b*x))/(S(21)*b*d**S(4)*sqrt(c*sin(a + b*x))*sqrt(d*cos(a + b*x))) + S(2)*c*sqrt(c*sin(a + b*x))/(S(7)*b*d*(d*cos(a + b*x))**(S(7)/2)) - S(2)*c*sqrt(c*sin(a + b*x))/(S(21)*b*d**S(3)*(d*cos(a + b*x))**(S(3)/2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**(S(3)/2)*sqrt(d*cos(a + b*x)), x), x, sqrt(S(2))*c**(S(3)/2)*sqrt(d)*ArcTan(-sqrt(S(2))*sqrt(c)*sqrt(d*cos(a + b*x))/(sqrt(d)*sqrt(c*sin(a + b*x))) + S(1))/(S(8)*b) - sqrt(S(2))*c**(S(3)/2)*sqrt(d)*ArcTan(sqrt(S(2))*sqrt(c)*sqrt(d*cos(a + b*x))/(sqrt(d)*sqrt(c*sin(a + b*x))) + S(1))/(S(8)*b) - sqrt(S(2))*c**(S(3)/2)*sqrt(d)*log(-sqrt(S(2))*sqrt(c)*sqrt(d*cos(a + b*x))/sqrt(c*sin(a + b*x)) + sqrt(d)*cot(a + b*x) + sqrt(d))/(S(16)*b) + sqrt(S(2))*c**(S(3)/2)*sqrt(d)*log(sqrt(S(2))*sqrt(c)*sqrt(d*cos(a + b*x))/sqrt(c*sin(a + b*x)) + sqrt(d)*cot(a + b*x) + sqrt(d))/(S(16)*b) - c*sqrt(c*sin(a + b*x))*(d*cos(a + b*x))**(S(3)/2)/(S(2)*b*d), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**(S(3)/2)/(d*cos(a + b*x))**(S(3)/2), x), x, -sqrt(S(2))*c**(S(3)/2)*ArcTan(-sqrt(S(2))*sqrt(c)*sqrt(d*cos(a + b*x))/(sqrt(d)*sqrt(c*sin(a + b*x))) + S(1))/(S(2)*b*d**(S(3)/2)) + sqrt(S(2))*c**(S(3)/2)*ArcTan(sqrt(S(2))*sqrt(c)*sqrt(d*cos(a + b*x))/(sqrt(d)*sqrt(c*sin(a + b*x))) + S(1))/(S(2)*b*d**(S(3)/2)) + sqrt(S(2))*c**(S(3)/2)*log(-sqrt(S(2))*sqrt(c)*sqrt(d*cos(a + b*x))/sqrt(c*sin(a + b*x)) + sqrt(d)*cot(a + b*x) + sqrt(d))/(S(4)*b*d**(S(3)/2)) - sqrt(S(2))*c**(S(3)/2)*log(sqrt(S(2))*sqrt(c)*sqrt(d*cos(a + b*x))/sqrt(c*sin(a + b*x)) + sqrt(d)*cot(a + b*x) + sqrt(d))/(S(4)*b*d**(S(3)/2)) + S(2)*c*sqrt(c*sin(a + b*x))/(b*d*sqrt(d*cos(a + b*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**(S(3)/2)/(d*cos(a + b*x))**(S(7)/2), x), x, S(2)*(c*sin(a + b*x))**(S(5)/2)/(S(5)*b*c*d*(d*cos(a + b*x))**(S(5)/2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**(S(3)/2)/(d*cos(a + b*x))**(S(11)/2), x), x, S(2)*c*sqrt(c*sin(a + b*x))/(S(9)*b*d*(d*cos(a + b*x))**(S(9)/2)) - S(2)*c*sqrt(c*sin(a + b*x))/(S(45)*b*d**S(3)*(d*cos(a + b*x))**(S(5)/2)) - S(8)*c*sqrt(c*sin(a + b*x))/(S(45)*b*d**S(5)*sqrt(d*cos(a + b*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**(S(3)/2)/(d*cos(a + b*x))**(S(15)/2), x), x, S(2)*c*sqrt(c*sin(a + b*x))/(S(13)*b*d*(d*cos(a + b*x))**(S(13)/2)) - S(2)*c*sqrt(c*sin(a + b*x))/(S(117)*b*d**S(3)*(d*cos(a + b*x))**(S(9)/2)) - S(16)*c*sqrt(c*sin(a + b*x))/(S(585)*b*d**S(5)*(d*cos(a + b*x))**(S(5)/2)) - S(64)*c*sqrt(c*sin(a + b*x))/(S(585)*b*d**S(7)*sqrt(d*cos(a + b*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**(S(5)/2)*(d*cos(a + b*x))**(S(9)/2), x), x, S(3)*c**S(2)*d**S(4)*sqrt(c*sin(a + b*x))*sqrt(d*cos(a + b*x))*EllipticE(-Pi/S(4) + a + b*x, S(2))/(S(40)*b*sqrt(sin(S(2)*a + S(2)*b*x))) + c*d**S(3)*(c*sin(a + b*x))**(S(3)/2)*(d*cos(a + b*x))**(S(3)/2)/(S(20)*b) + S(3)*c*d*(c*sin(a + b*x))**(S(3)/2)*(d*cos(a + b*x))**(S(7)/2)/(S(70)*b) - c*(c*sin(a + b*x))**(S(3)/2)*(d*cos(a + b*x))**(S(11)/2)/(S(7)*b*d), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**(S(5)/2)*(d*cos(a + b*x))**(S(5)/2), x), x, S(3)*c**S(2)*d**S(2)*sqrt(c*sin(a + b*x))*sqrt(d*cos(a + b*x))*EllipticE(-Pi/S(4) + a + b*x, S(2))/(S(20)*b*sqrt(sin(S(2)*a + S(2)*b*x))) + c*d*(c*sin(a + b*x))**(S(3)/2)*(d*cos(a + b*x))**(S(3)/2)/(S(10)*b) - c*(c*sin(a + b*x))**(S(3)/2)*(d*cos(a + b*x))**(S(7)/2)/(S(5)*b*d), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**(S(5)/2)*sqrt(d*cos(a + b*x)), x), x, c**S(2)*sqrt(c*sin(a + b*x))*sqrt(d*cos(a + b*x))*EllipticE(-Pi/S(4) + a + b*x, S(2))/(S(2)*b*sqrt(sin(S(2)*a + S(2)*b*x))) - c*(c*sin(a + b*x))**(S(3)/2)*(d*cos(a + b*x))**(S(3)/2)/(S(3)*b*d), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**(S(5)/2)/(d*cos(a + b*x))**(S(3)/2), x), x, -S(3)*c**S(2)*sqrt(c*sin(a + b*x))*sqrt(d*cos(a + b*x))*EllipticE(-Pi/S(4) + a + b*x, S(2))/(b*d**S(2)*sqrt(sin(S(2)*a + S(2)*b*x))) + S(2)*c*(c*sin(a + b*x))**(S(3)/2)/(b*d*sqrt(d*cos(a + b*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**(S(5)/2)/(d*cos(a + b*x))**(S(7)/2), x), x, S(6)*c**S(2)*sqrt(c*sin(a + b*x))*sqrt(d*cos(a + b*x))*EllipticE(-Pi/S(4) + a + b*x, S(2))/(S(5)*b*d**S(4)*sqrt(sin(S(2)*a + S(2)*b*x))) + S(2)*c*(c*sin(a + b*x))**(S(3)/2)/(S(5)*b*d*(d*cos(a + b*x))**(S(5)/2)) - S(6)*c*(c*sin(a + b*x))**(S(3)/2)/(S(5)*b*d**S(3)*sqrt(d*cos(a + b*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**(S(5)/2)/(d*cos(a + b*x))**(S(11)/2), x), x, S(4)*c**S(2)*sqrt(c*sin(a + b*x))*sqrt(d*cos(a + b*x))*EllipticE(-Pi/S(4) + a + b*x, S(2))/(S(15)*b*d**S(6)*sqrt(sin(S(2)*a + S(2)*b*x))) + S(2)*c*(c*sin(a + b*x))**(S(3)/2)/(S(9)*b*d*(d*cos(a + b*x))**(S(9)/2)) - S(2)*c*(c*sin(a + b*x))**(S(3)/2)/(S(15)*b*d**S(3)*(d*cos(a + b*x))**(S(5)/2)) - S(4)*c*(c*sin(a + b*x))**(S(3)/2)/(S(15)*b*d**S(5)*sqrt(d*cos(a + b*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**(S(5)/2)/sqrt(d*cos(a + b*x)), x), x, -S(3)*sqrt(S(2))*c**(S(5)/2)*ArcTan(S(1) - sqrt(S(2))*sqrt(d)*sqrt(c*sin(a + b*x))/(sqrt(c)*sqrt(d*cos(a + b*x))))/(S(8)*b*sqrt(d)) + S(3)*sqrt(S(2))*c**(S(5)/2)*ArcTan(S(1) + sqrt(S(2))*sqrt(d)*sqrt(c*sin(a + b*x))/(sqrt(c)*sqrt(d*cos(a + b*x))))/(S(8)*b*sqrt(d)) + S(3)*sqrt(S(2))*c**(S(5)/2)*log(sqrt(c)*tan(a + b*x) + sqrt(c) - sqrt(S(2))*sqrt(d)*sqrt(c*sin(a + b*x))/sqrt(d*cos(a + b*x)))/(S(16)*b*sqrt(d)) - S(3)*sqrt(S(2))*c**(S(5)/2)*log(sqrt(c)*tan(a + b*x) + sqrt(c) + sqrt(S(2))*sqrt(d)*sqrt(c*sin(a + b*x))/sqrt(d*cos(a + b*x)))/(S(16)*b*sqrt(d)) - c*(c*sin(a + b*x))**(S(3)/2)*sqrt(d*cos(a + b*x))/(S(2)*b*d), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**(S(5)/2)/(d*cos(a + b*x))**(S(5)/2), x), x, sqrt(S(2))*c**(S(5)/2)*ArcTan(S(1) - sqrt(S(2))*sqrt(d)*sqrt(c*sin(a + b*x))/(sqrt(c)*sqrt(d*cos(a + b*x))))/(S(2)*b*d**(S(5)/2)) - sqrt(S(2))*c**(S(5)/2)*ArcTan(S(1) + sqrt(S(2))*sqrt(d)*sqrt(c*sin(a + b*x))/(sqrt(c)*sqrt(d*cos(a + b*x))))/(S(2)*b*d**(S(5)/2)) - sqrt(S(2))*c**(S(5)/2)*log(sqrt(c)*tan(a + b*x) + sqrt(c) - sqrt(S(2))*sqrt(d)*sqrt(c*sin(a + b*x))/sqrt(d*cos(a + b*x)))/(S(4)*b*d**(S(5)/2)) + sqrt(S(2))*c**(S(5)/2)*log(sqrt(c)*tan(a + b*x) + sqrt(c) + sqrt(S(2))*sqrt(d)*sqrt(c*sin(a + b*x))/sqrt(d*cos(a + b*x)))/(S(4)*b*d**(S(5)/2)) + S(2)*c*(c*sin(a + b*x))**(S(3)/2)/(S(3)*b*d*(d*cos(a + b*x))**(S(3)/2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**(S(5)/2)/(d*cos(a + b*x))**(S(9)/2), x), x, S(2)*(c*sin(a + b*x))**(S(7)/2)/(S(7)*b*c*d*(d*cos(a + b*x))**(S(7)/2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**(S(5)/2)/(d*cos(a + b*x))**(S(13)/2), x), x, S(2)*c*(c*sin(a + b*x))**(S(3)/2)/(S(11)*b*d*(d*cos(a + b*x))**(S(11)/2)) - S(6)*c*(c*sin(a + b*x))**(S(3)/2)/(S(77)*b*d**S(3)*(d*cos(a + b*x))**(S(7)/2)) - S(8)*c*(c*sin(a + b*x))**(S(3)/2)/(S(77)*b*d**S(5)*(d*cos(a + b*x))**(S(3)/2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**(S(5)/2)/(d*cos(a + b*x))**(S(17)/2), x), x, S(2)*c*(c*sin(a + b*x))**(S(3)/2)/(S(15)*b*d*(d*cos(a + b*x))**(S(15)/2)) - S(2)*c*(c*sin(a + b*x))**(S(3)/2)/(S(55)*b*d**S(3)*(d*cos(a + b*x))**(S(11)/2)) - S(16)*c*(c*sin(a + b*x))**(S(3)/2)/(S(385)*b*d**S(5)*(d*cos(a + b*x))**(S(7)/2)) - S(64)*c*(c*sin(a + b*x))**(S(3)/2)/(S(1155)*b*d**S(7)*(d*cos(a + b*x))**(S(3)/2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**(S(7)/2)/cos(a + b*x)**(S(7)/2), x), x, sqrt(S(2))*ArcTan(S(1) - sqrt(S(2))*sqrt(cos(a + b*x))/sqrt(sin(a + b*x)))/(S(2)*b) - sqrt(S(2))*ArcTan(S(1) + sqrt(S(2))*sqrt(cos(a + b*x))/sqrt(sin(a + b*x)))/(S(2)*b) - sqrt(S(2))*log(cot(a + b*x) + S(1) - sqrt(S(2))*sqrt(cos(a + b*x))/sqrt(sin(a + b*x)))/(S(4)*b) + sqrt(S(2))*log(cot(a + b*x) + S(1) + sqrt(S(2))*sqrt(cos(a + b*x))/sqrt(sin(a + b*x)))/(S(4)*b) + S(2)*sin(a + b*x)**(S(5)/2)/(S(5)*b*cos(a + b*x)**(S(5)/2)) - S(2)*sqrt(sin(a + b*x))/(b*sqrt(cos(a + b*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(x)**(S(3)/2)/cos(x)**(S(7)/2), x), x, S(2)*sin(x)**(S(5)/2)/(S(5)*cos(x)**(S(5)/2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(sin(x))/sqrt(cos(x)), x), x, -sqrt(S(2))*ArcTan(-sqrt(S(2))*sqrt(sin(x))/sqrt(cos(x)) + S(1))/S(2) + sqrt(S(2))*ArcTan(sqrt(S(2))*sqrt(sin(x))/sqrt(cos(x)) + S(1))/S(2) + sqrt(S(2))*log(-sqrt(S(2))*sqrt(sin(x))/sqrt(cos(x)) + tan(x) + S(1))/S(4) - sqrt(S(2))*log(sqrt(S(2))*sqrt(sin(x))/sqrt(cos(x)) + tan(x) + S(1))/S(4), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(x)**(S(5)/2)/sqrt(cos(x)), x), x, -S(3)*sqrt(S(2))*ArcTan(-sqrt(S(2))*sqrt(sin(x))/sqrt(cos(x)) + S(1))/S(8) + S(3)*sqrt(S(2))*ArcTan(sqrt(S(2))*sqrt(sin(x))/sqrt(cos(x)) + S(1))/S(8) + S(3)*sqrt(S(2))*log(-sqrt(S(2))*sqrt(sin(x))/sqrt(cos(x)) + tan(x) + S(1))/S(16) - S(3)*sqrt(S(2))*log(sqrt(S(2))*sqrt(sin(x))/sqrt(cos(x)) + tan(x) + S(1))/S(16) - sin(x)**(S(3)/2)*sqrt(cos(x))/S(2), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*cos(a + b*x))**(S(7)/2)/sqrt(c*sin(a + b*x)), x), x, S(5)*d**S(4)*EllipticF(-Pi/S(4) + a + b*x, S(2))*sqrt(sin(S(2)*a + S(2)*b*x))/(S(12)*b*sqrt(c*sin(a + b*x))*sqrt(d*cos(a + b*x))) + S(5)*d**S(3)*sqrt(c*sin(a + b*x))*sqrt(d*cos(a + b*x))/(S(6)*b*c) + d*sqrt(c*sin(a + b*x))*(d*cos(a + b*x))**(S(5)/2)/(S(3)*b*c), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*cos(a + b*x))**(S(3)/2)/sqrt(c*sin(a + b*x)), x), x, d**S(2)*EllipticF(-Pi/S(4) + a + b*x, S(2))*sqrt(sin(S(2)*a + S(2)*b*x))/(S(2)*b*sqrt(c*sin(a + b*x))*sqrt(d*cos(a + b*x))) + d*sqrt(c*sin(a + b*x))*sqrt(d*cos(a + b*x))/(b*c), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(S(1)/(sqrt(c*sin(a + b*x))*sqrt(d*cos(a + b*x))), x), x, EllipticF(-Pi/S(4) + a + b*x, S(2))*sqrt(sin(S(2)*a + S(2)*b*x))/(b*sqrt(c*sin(a + b*x))*sqrt(d*cos(a + b*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(S(1)/(sqrt(c*sin(a + b*x))*(d*cos(a + b*x))**(S(5)/2)), x), x, S(2)*EllipticF(-Pi/S(4) + a + b*x, S(2))*sqrt(sin(S(2)*a + S(2)*b*x))/(S(3)*b*d**S(2)*sqrt(c*sin(a + b*x))*sqrt(d*cos(a + b*x))) + S(2)*sqrt(c*sin(a + b*x))/(S(3)*b*c*d*(d*cos(a + b*x))**(S(3)/2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(S(1)/(sqrt(c*sin(a + b*x))*(d*cos(a + b*x))**(S(9)/2)), x), x, S(4)*EllipticF(-Pi/S(4) + a + b*x, S(2))*sqrt(sin(S(2)*a + S(2)*b*x))/(S(7)*b*d**S(4)*sqrt(c*sin(a + b*x))*sqrt(d*cos(a + b*x))) + S(2)*sqrt(c*sin(a + b*x))/(S(7)*b*c*d*(d*cos(a + b*x))**(S(7)/2)) + S(4)*sqrt(c*sin(a + b*x))/(S(7)*b*c*d**S(3)*(d*cos(a + b*x))**(S(3)/2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(d*cos(a + b*x))/sqrt(c*sin(a + b*x)), x), x, sqrt(S(2))*sqrt(d)*ArcTan(-sqrt(S(2))*sqrt(c)*sqrt(d*cos(a + b*x))/(sqrt(d)*sqrt(c*sin(a + b*x))) + S(1))/(S(2)*b*sqrt(c)) - sqrt(S(2))*sqrt(d)*ArcTan(sqrt(S(2))*sqrt(c)*sqrt(d*cos(a + b*x))/(sqrt(d)*sqrt(c*sin(a + b*x))) + S(1))/(S(2)*b*sqrt(c)) - sqrt(S(2))*sqrt(d)*log(-sqrt(S(2))*sqrt(c)*sqrt(d*cos(a + b*x))/sqrt(c*sin(a + b*x)) + sqrt(d)*cot(a + b*x) + sqrt(d))/(S(4)*b*sqrt(c)) + sqrt(S(2))*sqrt(d)*log(sqrt(S(2))*sqrt(c)*sqrt(d*cos(a + b*x))/sqrt(c*sin(a + b*x)) + sqrt(d)*cot(a + b*x) + sqrt(d))/(S(4)*b*sqrt(c)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(S(1)/(sqrt(c*sin(a + b*x))*(d*cos(a + b*x))**(S(3)/2)), x), x, S(2)*sqrt(c*sin(a + b*x))/(b*c*d*sqrt(d*cos(a + b*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(S(1)/(sqrt(c*sin(a + b*x))*(d*cos(a + b*x))**(S(7)/2)), x), x, S(2)*sqrt(c*sin(a + b*x))/(S(5)*b*c*d*(d*cos(a + b*x))**(S(5)/2)) + S(8)*sqrt(c*sin(a + b*x))/(S(5)*b*c*d**S(3)*sqrt(d*cos(a + b*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(S(1)/(sqrt(c*sin(a + b*x))*(d*cos(a + b*x))**(S(11)/2)), x), x, S(2)*sqrt(c*sin(a + b*x))/(S(9)*b*c*d*(d*cos(a + b*x))**(S(9)/2)) + S(16)*sqrt(c*sin(a + b*x))/(S(45)*b*c*d**S(3)*(d*cos(a + b*x))**(S(5)/2)) + S(64)*sqrt(c*sin(a + b*x))/(S(45)*b*c*d**S(5)*sqrt(d*cos(a + b*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(cos(a + b*x))/sqrt(sin(a + b*x)), x), x, sqrt(S(2))*ArcTan(S(1) - sqrt(S(2))*sqrt(cos(a + b*x))/sqrt(sin(a + b*x)))/(S(2)*b) - sqrt(S(2))*ArcTan(S(1) + sqrt(S(2))*sqrt(cos(a + b*x))/sqrt(sin(a + b*x)))/(S(2)*b) - sqrt(S(2))*log(cot(a + b*x) + S(1) - sqrt(S(2))*sqrt(cos(a + b*x))/sqrt(sin(a + b*x)))/(S(4)*b) + sqrt(S(2))*log(cot(a + b*x) + S(1) + sqrt(S(2))*sqrt(cos(a + b*x))/sqrt(sin(a + b*x)))/(S(4)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)**(S(3)/2)/sin(a + b*x)**(S(3)/2), x), x, sqrt(S(2))*ArcTan(-sqrt(S(2))*sqrt(sin(a + b*x))/sqrt(cos(a + b*x)) + S(1))/(S(2)*b) - sqrt(S(2))*ArcTan(sqrt(S(2))*sqrt(sin(a + b*x))/sqrt(cos(a + b*x)) + S(1))/(S(2)*b) - sqrt(S(2))*log(-sqrt(S(2))*sqrt(sin(a + b*x))/sqrt(cos(a + b*x)) + tan(a + b*x) + S(1))/(S(4)*b) + sqrt(S(2))*log(sqrt(S(2))*sqrt(sin(a + b*x))/sqrt(cos(a + b*x)) + tan(a + b*x) + S(1))/(S(4)*b) - S(2)*sqrt(cos(a + b*x))/(b*sqrt(sin(a + b*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)**(S(5)/2)/sin(a + b*x)**(S(5)/2), x), x, -sqrt(S(2))*ArcTan(S(1) - sqrt(S(2))*sqrt(cos(a + b*x))/sqrt(sin(a + b*x)))/(S(2)*b) + sqrt(S(2))*ArcTan(S(1) + sqrt(S(2))*sqrt(cos(a + b*x))/sqrt(sin(a + b*x)))/(S(2)*b) + sqrt(S(2))*log(cot(a + b*x) + S(1) - sqrt(S(2))*sqrt(cos(a + b*x))/sqrt(sin(a + b*x)))/(S(4)*b) - sqrt(S(2))*log(cot(a + b*x) + S(1) + sqrt(S(2))*sqrt(cos(a + b*x))/sqrt(sin(a + b*x)))/(S(4)*b) - S(2)*cos(a + b*x)**(S(3)/2)/(S(3)*b*sin(a + b*x)**(S(3)/2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)**(S(7)/2)/sin(a + b*x)**(S(7)/2), x), x, -sqrt(S(2))*ArcTan(-sqrt(S(2))*sqrt(sin(a + b*x))/sqrt(cos(a + b*x)) + S(1))/(S(2)*b) + sqrt(S(2))*ArcTan(sqrt(S(2))*sqrt(sin(a + b*x))/sqrt(cos(a + b*x)) + S(1))/(S(2)*b) + sqrt(S(2))*log(-sqrt(S(2))*sqrt(sin(a + b*x))/sqrt(cos(a + b*x)) + tan(a + b*x) + S(1))/(S(4)*b) - sqrt(S(2))*log(sqrt(S(2))*sqrt(sin(a + b*x))/sqrt(cos(a + b*x)) + tan(a + b*x) + S(1))/(S(4)*b) + S(2)*sqrt(cos(a + b*x))/(b*sqrt(sin(a + b*x))) - S(2)*cos(a + b*x)**(S(5)/2)/(S(5)*b*sin(a + b*x)**(S(5)/2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sin(e + f*x))**(S(1)/3)*cos(e + f*x)**S(4), x), x, S(3)*(b*sin(e + f*x))**(S(4)/3)*Hypergeometric2F1(S(-3)/2, S(2)/3, S(5)/3, sin(e + f*x)**S(2))*cos(e + f*x)/(S(4)*b*f*sqrt(cos(e + f*x)**S(2))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sin(e + f*x))**(S(1)/3)*cos(e + f*x)**S(2), x), x, S(3)*(b*sin(e + f*x))**(S(4)/3)*Hypergeometric2F1(S(-1)/2, S(2)/3, S(5)/3, sin(e + f*x)**S(2))*cos(e + f*x)/(S(4)*b*f*sqrt(cos(e + f*x)**S(2))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sin(e + f*x))**(S(1)/3), x), x, S(3)*(b*sin(e + f*x))**(S(4)/3)*Hypergeometric2F1(S(1)/2, S(2)/3, S(5)/3, sin(e + f*x)**S(2))*cos(e + f*x)/(S(4)*b*f*sqrt(cos(e + f*x)**S(2))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sin(e + f*x))**(S(1)/3)*sec(e + f*x)**S(2), x), x, S(3)*(b*sin(e + f*x))**(S(4)/3)*sqrt(cos(e + f*x)**S(2))*Hypergeometric2F1(S(2)/3, S(3)/2, S(5)/3, sin(e + f*x)**S(2))*sec(e + f*x)/(S(4)*b*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sin(e + f*x))**(S(1)/3)*sec(e + f*x)**S(4), x), x, S(3)*(b*sin(e + f*x))**(S(4)/3)*sqrt(cos(e + f*x)**S(2))*Hypergeometric2F1(S(2)/3, S(5)/2, S(5)/3, sin(e + f*x)**S(2))*sec(e + f*x)/(S(4)*b*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sin(e + f*x))**(S(5)/3)*cos(e + f*x)**S(4), x), x, S(3)*(b*sin(e + f*x))**(S(8)/3)*Hypergeometric2F1(S(-3)/2, S(4)/3, S(7)/3, sin(e + f*x)**S(2))*cos(e + f*x)/(S(8)*b*f*sqrt(cos(e + f*x)**S(2))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sin(e + f*x))**(S(5)/3)*cos(e + f*x)**S(2), x), x, S(3)*(b*sin(e + f*x))**(S(8)/3)*Hypergeometric2F1(S(-1)/2, S(4)/3, S(7)/3, sin(e + f*x)**S(2))*cos(e + f*x)/(S(8)*b*f*sqrt(cos(e + f*x)**S(2))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sin(e + f*x))**(S(5)/3), x), x, S(3)*(b*sin(e + f*x))**(S(8)/3)*Hypergeometric2F1(S(1)/2, S(4)/3, S(7)/3, sin(e + f*x)**S(2))*cos(e + f*x)/(S(8)*b*f*sqrt(cos(e + f*x)**S(2))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sin(e + f*x))**(S(5)/3)*sec(e + f*x)**S(2), x), x, S(3)*(b*sin(e + f*x))**(S(8)/3)*sqrt(cos(e + f*x)**S(2))*Hypergeometric2F1(S(4)/3, S(3)/2, S(7)/3, sin(e + f*x)**S(2))*sec(e + f*x)/(S(8)*b*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sin(e + f*x))**(S(5)/3)*sec(e + f*x)**S(4), x), x, S(3)*(b*sin(e + f*x))**(S(8)/3)*sqrt(cos(e + f*x)**S(2))*Hypergeometric2F1(S(4)/3, S(5)/2, S(7)/3, sin(e + f*x)**S(2))*sec(e + f*x)/(S(8)*b*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(e + f*x)**S(4)/(b*sin(e + f*x))**(S(1)/3), x), x, S(3)*(b*sin(e + f*x))**(S(2)/3)*Hypergeometric2F1(S(-3)/2, S(1)/3, S(4)/3, sin(e + f*x)**S(2))*cos(e + f*x)/(S(2)*b*f*sqrt(cos(e + f*x)**S(2))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(e + f*x)**S(2)/(b*sin(e + f*x))**(S(1)/3), x), x, S(3)*(b*sin(e + f*x))**(S(2)/3)*Hypergeometric2F1(S(-1)/2, S(1)/3, S(4)/3, sin(e + f*x)**S(2))*cos(e + f*x)/(S(2)*b*f*sqrt(cos(e + f*x)**S(2))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sin(e + f*x))**(S(-1)/3), x), x, S(3)*(b*sin(e + f*x))**(S(2)/3)*Hypergeometric2F1(S(1)/3, S(1)/2, S(4)/3, sin(e + f*x)**S(2))*cos(e + f*x)/(S(2)*b*f*sqrt(cos(e + f*x)**S(2))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sec(e + f*x)**S(2)/(b*sin(e + f*x))**(S(1)/3), x), x, S(3)*(b*sin(e + f*x))**(S(2)/3)*sqrt(cos(e + f*x)**S(2))*Hypergeometric2F1(S(1)/3, S(3)/2, S(4)/3, sin(e + f*x)**S(2))*sec(e + f*x)/(S(2)*b*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sec(e + f*x)**S(4)/(b*sin(e + f*x))**(S(1)/3), x), x, S(3)*(b*sin(e + f*x))**(S(2)/3)*sqrt(cos(e + f*x)**S(2))*Hypergeometric2F1(S(1)/3, S(5)/2, S(4)/3, sin(e + f*x)**S(2))*sec(e + f*x)/(S(2)*b*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(e + f*x)**S(4)/(b*sin(e + f*x))**(S(5)/3), x), x, -S(3)*Hypergeometric2F1(S(-3)/2, S(-1)/3, S(2)/3, sin(e + f*x)**S(2))*cos(e + f*x)/(S(2)*b*f*(b*sin(e + f*x))**(S(2)/3)*sqrt(cos(e + f*x)**S(2))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(e + f*x)**S(2)/(b*sin(e + f*x))**(S(5)/3), x), x, -S(3)*Hypergeometric2F1(S(-1)/2, S(-1)/3, S(2)/3, sin(e + f*x)**S(2))*cos(e + f*x)/(S(2)*b*f*(b*sin(e + f*x))**(S(2)/3)*sqrt(cos(e + f*x)**S(2))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sin(e + f*x))**(S(-5)/3), x), x, -S(3)*Hypergeometric2F1(S(-1)/3, S(1)/2, S(2)/3, sin(e + f*x)**S(2))*cos(e + f*x)/(S(2)*b*f*(b*sin(e + f*x))**(S(2)/3)*sqrt(cos(e + f*x)**S(2))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sec(e + f*x)**S(2)/(b*sin(e + f*x))**(S(5)/3), x), x, -S(3)*sqrt(cos(e + f*x)**S(2))*Hypergeometric2F1(S(-1)/3, S(3)/2, S(2)/3, sin(e + f*x)**S(2))*sec(e + f*x)/(S(2)*b*f*(b*sin(e + f*x))**(S(2)/3)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sec(e + f*x)**S(4)/(b*sin(e + f*x))**(S(5)/3), x), x, -S(3)*sqrt(cos(e + f*x)**S(2))*Hypergeometric2F1(S(-1)/3, S(5)/2, S(2)/3, sin(e + f*x)**S(2))*sec(e + f*x)/(S(2)*b*f*(b*sin(e + f*x))**(S(2)/3)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**(S(1)/3)/cos(a + b*x)**(S(1)/3), x), x, -sqrt(S(3))*ArcTan(sqrt(S(3))*(-S(2)*sin(a + b*x)**(S(2)/3)/cos(a + b*x)**(S(2)/3) + S(1))/S(3))/(S(2)*b) - log(sin(a + b*x)**(S(2)/3)/cos(a + b*x)**(S(2)/3) + S(1))/(S(2)*b) + log(sin(a + b*x)**(S(4)/3)/cos(a + b*x)**(S(4)/3) - sin(a + b*x)**(S(2)/3)/cos(a + b*x)**(S(2)/3) + S(1))/(S(4)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**(S(2)/3)/cos(a + b*x)**(S(2)/3), x), x, ArcTan(sin(a + b*x)**(S(1)/3)/cos(a + b*x)**(S(1)/3))/b - ArcTan(-S(2)*sin(a + b*x)**(S(1)/3)/cos(a + b*x)**(S(1)/3) + sqrt(S(3)))/(S(2)*b) + ArcTan(S(2)*sin(a + b*x)**(S(1)/3)/cos(a + b*x)**(S(1)/3) + sqrt(S(3)))/(S(2)*b) + sqrt(S(3))*log(sin(a + b*x)**(S(2)/3)/cos(a + b*x)**(S(2)/3) - sqrt(S(3))*sin(a + b*x)**(S(1)/3)/cos(a + b*x)**(S(1)/3) + S(1))/(S(4)*b) - sqrt(S(3))*log(sin(a + b*x)**(S(2)/3)/cos(a + b*x)**(S(2)/3) + sqrt(S(3))*sin(a + b*x)**(S(1)/3)/cos(a + b*x)**(S(1)/3) + S(1))/(S(4)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**(S(4)/3)/cos(a + b*x)**(S(4)/3), x), x, ArcTan(cos(a + b*x)**(S(1)/3)/sin(a + b*x)**(S(1)/3))/b - ArcTan(sqrt(S(3)) - S(2)*cos(a + b*x)**(S(1)/3)/sin(a + b*x)**(S(1)/3))/(S(2)*b) + ArcTan(sqrt(S(3)) + S(2)*cos(a + b*x)**(S(1)/3)/sin(a + b*x)**(S(1)/3))/(S(2)*b) + sqrt(S(3))*log(S(1) - sqrt(S(3))*cos(a + b*x)**(S(1)/3)/sin(a + b*x)**(S(1)/3) + cos(a + b*x)**(S(2)/3)/sin(a + b*x)**(S(2)/3))/(S(4)*b) - sqrt(S(3))*log(S(1) + sqrt(S(3))*cos(a + b*x)**(S(1)/3)/sin(a + b*x)**(S(1)/3) + cos(a + b*x)**(S(2)/3)/sin(a + b*x)**(S(2)/3))/(S(4)*b) + S(3)*sin(a + b*x)**(S(1)/3)/(b*cos(a + b*x)**(S(1)/3)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**(S(5)/3)/cos(a + b*x)**(S(5)/3), x), x, -sqrt(S(3))*ArcTan(sqrt(S(3))*(S(1) - S(2)*cos(a + b*x)**(S(2)/3)/sin(a + b*x)**(S(2)/3))/S(3))/(S(2)*b) - log(S(1) + cos(a + b*x)**(S(2)/3)/sin(a + b*x)**(S(2)/3))/(S(2)*b) + log(S(1) - cos(a + b*x)**(S(2)/3)/sin(a + b*x)**(S(2)/3) + cos(a + b*x)**(S(4)/3)/sin(a + b*x)**(S(4)/3))/(S(4)*b) + S(3)*sin(a + b*x)**(S(2)/3)/(S(2)*b*cos(a + b*x)**(S(2)/3)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(a + b*x)**(S(7)/3)/cos(a + b*x)**(S(7)/3), x), x, sqrt(S(3))*ArcTan(sqrt(S(3))*(-S(2)*sin(a + b*x)**(S(2)/3)/cos(a + b*x)**(S(2)/3) + S(1))/S(3))/(S(2)*b) + log(sin(a + b*x)**(S(2)/3)/cos(a + b*x)**(S(2)/3) + S(1))/(S(2)*b) - log(sin(a + b*x)**(S(4)/3)/cos(a + b*x)**(S(4)/3) - sin(a + b*x)**(S(2)/3)/cos(a + b*x)**(S(2)/3) + S(1))/(S(4)*b) + S(3)*sin(a + b*x)**(S(4)/3)/(S(4)*b*cos(a + b*x)**(S(4)/3)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)**(S(1)/3)/sin(a + b*x)**(S(1)/3), x), x, sqrt(S(3))*ArcTan(sqrt(S(3))*(S(1) - S(2)*cos(a + b*x)**(S(2)/3)/sin(a + b*x)**(S(2)/3))/S(3))/(S(2)*b) + log(S(1) + cos(a + b*x)**(S(2)/3)/sin(a + b*x)**(S(2)/3))/(S(2)*b) - log(S(1) - cos(a + b*x)**(S(2)/3)/sin(a + b*x)**(S(2)/3) + cos(a + b*x)**(S(4)/3)/sin(a + b*x)**(S(4)/3))/(S(4)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)**(S(2)/3)/sin(a + b*x)**(S(2)/3), x), x, -ArcTan(cos(a + b*x)**(S(1)/3)/sin(a + b*x)**(S(1)/3))/b + ArcTan(sqrt(S(3)) - S(2)*cos(a + b*x)**(S(1)/3)/sin(a + b*x)**(S(1)/3))/(S(2)*b) - ArcTan(sqrt(S(3)) + S(2)*cos(a + b*x)**(S(1)/3)/sin(a + b*x)**(S(1)/3))/(S(2)*b) - sqrt(S(3))*log(S(1) - sqrt(S(3))*cos(a + b*x)**(S(1)/3)/sin(a + b*x)**(S(1)/3) + cos(a + b*x)**(S(2)/3)/sin(a + b*x)**(S(2)/3))/(S(4)*b) + sqrt(S(3))*log(S(1) + sqrt(S(3))*cos(a + b*x)**(S(1)/3)/sin(a + b*x)**(S(1)/3) + cos(a + b*x)**(S(2)/3)/sin(a + b*x)**(S(2)/3))/(S(4)*b), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)**(S(4)/3)/sin(a + b*x)**(S(4)/3), x), x, -ArcTan(sin(a + b*x)**(S(1)/3)/cos(a + b*x)**(S(1)/3))/b + ArcTan(-S(2)*sin(a + b*x)**(S(1)/3)/cos(a + b*x)**(S(1)/3) + sqrt(S(3)))/(S(2)*b) - ArcTan(S(2)*sin(a + b*x)**(S(1)/3)/cos(a + b*x)**(S(1)/3) + sqrt(S(3)))/(S(2)*b) - sqrt(S(3))*log(sin(a + b*x)**(S(2)/3)/cos(a + b*x)**(S(2)/3) - sqrt(S(3))*sin(a + b*x)**(S(1)/3)/cos(a + b*x)**(S(1)/3) + S(1))/(S(4)*b) + sqrt(S(3))*log(sin(a + b*x)**(S(2)/3)/cos(a + b*x)**(S(2)/3) + sqrt(S(3))*sin(a + b*x)**(S(1)/3)/cos(a + b*x)**(S(1)/3) + S(1))/(S(4)*b) - S(3)*cos(a + b*x)**(S(1)/3)/(b*sin(a + b*x)**(S(1)/3)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)**(S(5)/3)/sin(a + b*x)**(S(5)/3), x), x, sqrt(S(3))*ArcTan(sqrt(S(3))*(-S(2)*sin(a + b*x)**(S(2)/3)/cos(a + b*x)**(S(2)/3) + S(1))/S(3))/(S(2)*b) + log(sin(a + b*x)**(S(2)/3)/cos(a + b*x)**(S(2)/3) + S(1))/(S(2)*b) - log(sin(a + b*x)**(S(4)/3)/cos(a + b*x)**(S(4)/3) - sin(a + b*x)**(S(2)/3)/cos(a + b*x)**(S(2)/3) + S(1))/(S(4)*b) - S(3)*cos(a + b*x)**(S(2)/3)/(S(2)*b*sin(a + b*x)**(S(2)/3)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(a + b*x)**(S(7)/3)/sin(a + b*x)**(S(7)/3), x), x, -sqrt(S(3))*ArcTan(sqrt(S(3))*(S(1) - S(2)*cos(a + b*x)**(S(2)/3)/sin(a + b*x)**(S(2)/3))/S(3))/(S(2)*b) - log(S(1) + cos(a + b*x)**(S(2)/3)/sin(a + b*x)**(S(2)/3))/(S(2)*b) + log(S(1) - cos(a + b*x)**(S(2)/3)/sin(a + b*x)**(S(2)/3) + cos(a + b*x)**(S(4)/3)/sin(a + b*x)**(S(4)/3))/(S(4)*b) - S(3)*cos(a + b*x)**(S(4)/3)/(S(4)*b*sin(a + b*x)**(S(4)/3)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(cos(x)**(S(2)/3)/sin(x)**(S(8)/3), x), x, -S(3)*cos(x)**(S(5)/3)/(S(5)*sin(x)**(S(5)/3)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(x)**(S(2)/3)/cos(x)**(S(8)/3), x), x, S(3)*sin(x)**(S(5)/3)/(S(5)*cos(x)**(S(5)/3)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(e + f*x)**m*cos(e + f*x)**n, x), x, (cos(e + f*x)**S(2))**(-n/S(2) + S(1)/2)*Hypergeometric2F1(m/S(2) + S(1)/2, -n/S(2) + S(1)/2, m/S(2) + S(3)/2, sin(e + f*x)**S(2))*sin(e + f*x)**(m + S(1))*cos(e + f*x)**(n + S(-1))/(f*(m + S(1))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*cos(e + f*x))**n*sin(e + f*x)**m, x), x, -(d*cos(e + f*x))**(n + S(1))*(sin(e + f*x)**S(2))**(-m/S(2) + S(1)/2)*Hypergeometric2F1(-m/S(2) + S(1)/2, n/S(2) + S(1)/2, n/S(2) + S(3)/2, cos(e + f*x)**S(2))*sin(e + f*x)**(m + S(-1))/(d*f*(n + S(1))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sin(e + f*x))**m*cos(e + f*x)**n, x), x, (b*sin(e + f*x))**(m + S(1))*(cos(e + f*x)**S(2))**(-n/S(2) + S(1)/2)*Hypergeometric2F1(m/S(2) + S(1)/2, -n/S(2) + S(1)/2, m/S(2) + S(3)/2, sin(e + f*x)**S(2))*cos(e + f*x)**(n + S(-1))/(b*f*(m + S(1))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sin(e + f*x))**m*(d*cos(e + f*x))**n, x), x, d*(b*sin(e + f*x))**(m + S(1))*(d*cos(e + f*x))**(n + S(-1))*(cos(e + f*x)**S(2))**(-n/S(2) + S(1)/2)*Hypergeometric2F1(m/S(2) + S(1)/2, -n/S(2) + S(1)/2, m/S(2) + S(3)/2, sin(e + f*x)**S(2))/(b*f*(m + S(1))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**m*cos(a + b*x)**S(5), x), x, (c*sin(a + b*x))**(m + S(1))/(b*c*(m + S(1))) - S(2)*(c*sin(a + b*x))**(m + S(3))/(b*c**S(3)*(m + S(3))) + (c*sin(a + b*x))**(m + S(5))/(b*c**S(5)*(m + S(5))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**m*cos(a + b*x)**S(3), x), x, (c*sin(a + b*x))**(m + S(1))/(b*c*(m + S(1))) - (c*sin(a + b*x))**(m + S(3))/(b*c**S(3)*(m + S(3))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**m*cos(a + b*x), x), x, (c*sin(a + b*x))**(m + S(1))/(b*c*(m + S(1))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**m*sec(a + b*x), x), x, (c*sin(a + b*x))**(m + S(1))*Hypergeometric2F1(S(1), m/S(2) + S(1)/2, m/S(2) + S(3)/2, sin(a + b*x)**S(2))/(b*c*(m + S(1))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**m*sec(a + b*x)**S(3), x), x, (c*sin(a + b*x))**(m + S(1))*Hypergeometric2F1(S(2), m/S(2) + S(1)/2, m/S(2) + S(3)/2, sin(a + b*x)**S(2))/(b*c*(m + S(1))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**m*cos(a + b*x)**S(4), x), x, (c*sin(a + b*x))**(m + S(1))*Hypergeometric2F1(S(-3)/2, m/S(2) + S(1)/2, m/S(2) + S(3)/2, sin(a + b*x)**S(2))*cos(a + b*x)/(b*c*(m + S(1))*sqrt(cos(a + b*x)**S(2))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**m*cos(a + b*x)**S(2), x), x, (c*sin(a + b*x))**(m + S(1))*Hypergeometric2F1(S(-1)/2, m/S(2) + S(1)/2, m/S(2) + S(3)/2, sin(a + b*x)**S(2))*cos(a + b*x)/(b*c*(m + S(1))*sqrt(cos(a + b*x)**S(2))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**m, x), x, (c*sin(a + b*x))**(m + S(1))*Hypergeometric2F1(S(1)/2, m/S(2) + S(1)/2, m/S(2) + S(3)/2, sin(a + b*x)**S(2))*cos(a + b*x)/(b*c*(m + S(1))*sqrt(cos(a + b*x)**S(2))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**m*sec(a + b*x)**S(2), x), x, (c*sin(a + b*x))**(m + S(1))*sqrt(cos(a + b*x)**S(2))*Hypergeometric2F1(S(3)/2, m/S(2) + S(1)/2, m/S(2) + S(3)/2, sin(a + b*x)**S(2))*sec(a + b*x)/(b*c*(m + S(1))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**m*sec(a + b*x)**S(4), x), x, (c*sin(a + b*x))**(m + S(1))*sqrt(cos(a + b*x)**S(2))*Hypergeometric2F1(S(5)/2, m/S(2) + S(1)/2, m/S(2) + S(3)/2, sin(a + b*x)**S(2))*sec(a + b*x)/(b*c*(m + S(1))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**m*(d*cos(a + b*x))**(S(3)/2), x), x, d*(c*sin(a + b*x))**(m + S(1))*sqrt(d*cos(a + b*x))*Hypergeometric2F1(S(-1)/4, m/S(2) + S(1)/2, m/S(2) + S(3)/2, sin(a + b*x)**S(2))/(b*c*(m + S(1))*(cos(a + b*x)**S(2))**(S(1)/4)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**m*sqrt(d*cos(a + b*x)), x), x, d*(c*sin(a + b*x))**(m + S(1))*(cos(a + b*x)**S(2))**(S(1)/4)*Hypergeometric2F1(S(1)/4, m/S(2) + S(1)/2, m/S(2) + S(3)/2, sin(a + b*x)**S(2))/(b*c*sqrt(d*cos(a + b*x))*(m + S(1))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**m/sqrt(d*cos(a + b*x)), x), x, d*(c*sin(a + b*x))**(m + S(1))*(cos(a + b*x)**S(2))**(S(3)/4)*Hypergeometric2F1(S(3)/4, m/S(2) + S(1)/2, m/S(2) + S(3)/2, sin(a + b*x)**S(2))/(b*c*(d*cos(a + b*x))**(S(3)/2)*(m + S(1))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**m/(d*cos(a + b*x))**(S(3)/2), x), x, (c*sin(a + b*x))**(m + S(1))*(cos(a + b*x)**S(2))**(S(1)/4)*Hypergeometric2F1(S(5)/4, m/S(2) + S(1)/2, m/S(2) + S(3)/2, sin(a + b*x)**S(2))/(b*c*d*sqrt(d*cos(a + b*x))*(m + S(1))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**m/(d*cos(a + b*x))**(S(5)/2), x), x, (c*sin(a + b*x))**(m + S(1))*(cos(a + b*x)**S(2))**(S(3)/4)*Hypergeometric2F1(S(7)/4, m/S(2) + S(1)/2, m/S(2) + S(3)/2, sin(a + b*x)**S(2))/(b*c*d*(d*cos(a + b*x))**(S(3)/2)*(m + S(1))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*cos(a + b*x))**n*sin(a + b*x)**S(5), x), x, -(d*cos(a + b*x))**(n + S(1))/(b*d*(n + S(1))) + S(2)*(d*cos(a + b*x))**(n + S(3))/(b*d**S(3)*(n + S(3))) - (d*cos(a + b*x))**(n + S(5))/(b*d**S(5)*(n + S(5))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*cos(a + b*x))**n*sin(a + b*x)**S(3), x), x, -(d*cos(a + b*x))**(n + S(1))/(b*d*(n + S(1))) + (d*cos(a + b*x))**(n + S(3))/(b*d**S(3)*(n + S(3))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*cos(a + b*x))**n*sin(a + b*x), x), x, -(d*cos(a + b*x))**(n + S(1))/(b*d*(n + S(1))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*cos(a + b*x))**n*csc(a + b*x), x), x, -(d*cos(a + b*x))**(n + S(1))*Hypergeometric2F1(S(1), n/S(2) + S(1)/2, n/S(2) + S(3)/2, cos(a + b*x)**S(2))/(b*d*(n + S(1))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*cos(a + b*x))**n*csc(a + b*x)**S(3), x), x, -(d*cos(a + b*x))**(n + S(1))*Hypergeometric2F1(S(2), n/S(2) + S(1)/2, n/S(2) + S(3)/2, cos(a + b*x)**S(2))/(b*d*(n + S(1))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*cos(a + b*x))**n*csc(a + b*x)**S(5), x), x, -(d*cos(a + b*x))**(n + S(1))*Hypergeometric2F1(S(3), n/S(2) + S(1)/2, n/S(2) + S(3)/2, cos(a + b*x)**S(2))/(b*d*(n + S(1))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*cos(a + b*x))**n*sin(a + b*x)**S(4), x), x, -(d*cos(a + b*x))**(n + S(1))*Hypergeometric2F1(S(-3)/2, n/S(2) + S(1)/2, n/S(2) + S(3)/2, cos(a + b*x)**S(2))*sin(a + b*x)/(b*d*(n + S(1))*sqrt(sin(a + b*x)**S(2))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*cos(a + b*x))**n*sin(a + b*x)**S(2), x), x, -(d*cos(a + b*x))**(n + S(1))*Hypergeometric2F1(S(-1)/2, n/S(2) + S(1)/2, n/S(2) + S(3)/2, cos(a + b*x)**S(2))*sin(a + b*x)/(b*d*(n + S(1))*sqrt(sin(a + b*x)**S(2))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*cos(a + b*x))**n, x), x, -(d*cos(a + b*x))**(n + S(1))*Hypergeometric2F1(S(1)/2, n/S(2) + S(1)/2, n/S(2) + S(3)/2, cos(a + b*x)**S(2))*sin(a + b*x)/(b*d*(n + S(1))*sqrt(sin(a + b*x)**S(2))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*cos(a + b*x))**n*csc(a + b*x)**S(2), x), x, -(d*cos(a + b*x))**(n + S(1))*sqrt(sin(a + b*x)**S(2))*Hypergeometric2F1(S(3)/2, n/S(2) + S(1)/2, n/S(2) + S(3)/2, cos(a + b*x)**S(2))*csc(a + b*x)/(b*d*(n + S(1))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*cos(a + b*x))**n*csc(a + b*x)**S(4), x), x, -(d*cos(a + b*x))**(n + S(1))*sqrt(sin(a + b*x)**S(2))*Hypergeometric2F1(S(5)/2, n/S(2) + S(1)/2, n/S(2) + S(3)/2, cos(a + b*x)**S(2))*csc(a + b*x)/(b*d*(n + S(1))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**(S(5)/2)*(d*cos(a + b*x))**n, x), x, -c*(c*sin(a + b*x))**(S(3)/2)*(d*cos(a + b*x))**(n + S(1))*Hypergeometric2F1(S(-3)/4, n/S(2) + S(1)/2, n/S(2) + S(3)/2, cos(a + b*x)**S(2))/(b*d*(n + S(1))*(sin(a + b*x)**S(2))**(S(3)/4)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**(S(3)/2)*(d*cos(a + b*x))**n, x), x, -c*sqrt(c*sin(a + b*x))*(d*cos(a + b*x))**(n + S(1))*Hypergeometric2F1(S(-1)/4, n/S(2) + S(1)/2, n/S(2) + S(3)/2, cos(a + b*x)**S(2))/(b*d*(n + S(1))*(sin(a + b*x)**S(2))**(S(1)/4)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(c*sin(a + b*x))*(d*cos(a + b*x))**n, x), x, -c*(d*cos(a + b*x))**(n + S(1))*(sin(a + b*x)**S(2))**(S(1)/4)*Hypergeometric2F1(S(1)/4, n/S(2) + S(1)/2, n/S(2) + S(3)/2, cos(a + b*x)**S(2))/(b*d*sqrt(c*sin(a + b*x))*(n + S(1))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*cos(a + b*x))**n/sqrt(c*sin(a + b*x)), x), x, -c*(d*cos(a + b*x))**(n + S(1))*(sin(a + b*x)**S(2))**(S(3)/4)*Hypergeometric2F1(S(3)/4, n/S(2) + S(1)/2, n/S(2) + S(3)/2, cos(a + b*x)**S(2))/(b*d*(c*sin(a + b*x))**(S(3)/2)*(n + S(1))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*cos(a + b*x))**n/(c*sin(a + b*x))**(S(3)/2), x), x, -(d*cos(a + b*x))**(n + S(1))*(sin(a + b*x)**S(2))**(S(1)/4)*Hypergeometric2F1(S(5)/4, n/S(2) + S(1)/2, n/S(2) + S(3)/2, cos(a + b*x)**S(2))/(b*c*d*sqrt(c*sin(a + b*x))*(n + S(1))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(b*sec(e + f*x))*sin(e + f*x)**S(7), x), x, S(2)*b**S(7)/(S(13)*f*(b*sec(e + f*x))**(S(13)/2)) - S(2)*b**S(5)/(S(3)*f*(b*sec(e + f*x))**(S(9)/2)) + S(6)*b**S(3)/(S(5)*f*(b*sec(e + f*x))**(S(5)/2)) - S(2)*b/(f*sqrt(b*sec(e + f*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(b*sec(e + f*x))*sin(e + f*x)**S(5), x), x, -S(2)*b**S(5)/(S(9)*f*(b*sec(e + f*x))**(S(9)/2)) + S(4)*b**S(3)/(S(5)*f*(b*sec(e + f*x))**(S(5)/2)) - S(2)*b/(f*sqrt(b*sec(e + f*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(b*sec(e + f*x))*sin(e + f*x)**S(3), x), x, S(2)*b**S(3)/(S(5)*f*(b*sec(e + f*x))**(S(5)/2)) - S(2)*b/(f*sqrt(b*sec(e + f*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(b*sec(e + f*x))*sin(e + f*x), x), x, -S(2)*b/(f*sqrt(b*sec(e + f*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(b*sec(e + f*x))*csc(e + f*x), x), x, sqrt(b)*ArcTan(sqrt(b*sec(e + f*x))/sqrt(b))/f - sqrt(b)*atanh(sqrt(b*sec(e + f*x))/sqrt(b))/f, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(b*sec(e + f*x))*csc(e + f*x)**S(3), x), x, S(3)*sqrt(b)*ArcTan(sqrt(b*sec(e + f*x))/sqrt(b))/(S(4)*f) - S(3)*sqrt(b)*atanh(sqrt(b*sec(e + f*x))/sqrt(b))/(S(4)*f) - (b*sec(e + f*x))**(S(3)/2)*cot(e + f*x)**S(2)/(S(2)*b*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(b*sec(e + f*x))*csc(e + f*x)**S(5), x), x, S(21)*sqrt(b)*ArcTan(sqrt(b*sec(e + f*x))/sqrt(b))/(S(32)*f) - S(21)*sqrt(b)*atanh(sqrt(b*sec(e + f*x))/sqrt(b))/(S(32)*f) - S(7)*(b*sec(e + f*x))**(S(3)/2)*cot(e + f*x)**S(2)/(S(16)*b*f) - (b*sec(e + f*x))**(S(7)/2)*cot(e + f*x)**S(4)/(S(4)*b**S(3)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(b*sec(e + f*x))*sin(e + f*x)**S(6), x), x, -S(2)*b*sin(e + f*x)**S(5)/(S(11)*f*sqrt(b*sec(e + f*x))) - S(20)*b*sin(e + f*x)**S(3)/(S(77)*f*sqrt(b*sec(e + f*x))) - S(40)*b*sin(e + f*x)/(S(77)*f*sqrt(b*sec(e + f*x))) + S(80)*sqrt(b*sec(e + f*x))*EllipticF(e/S(2) + f*x/S(2), S(2))*sqrt(cos(e + f*x))/(S(77)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(b*sec(e + f*x))*sin(e + f*x)**S(4), x), x, -S(2)*b*sin(e + f*x)**S(3)/(S(7)*f*sqrt(b*sec(e + f*x))) - S(4)*b*sin(e + f*x)/(S(7)*f*sqrt(b*sec(e + f*x))) + S(8)*sqrt(b*sec(e + f*x))*EllipticF(e/S(2) + f*x/S(2), S(2))*sqrt(cos(e + f*x))/(S(7)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(b*sec(e + f*x))*sin(e + f*x)**S(2), x), x, -S(2)*b*sin(e + f*x)/(S(3)*f*sqrt(b*sec(e + f*x))) + S(4)*sqrt(b*sec(e + f*x))*EllipticF(e/S(2) + f*x/S(2), S(2))*sqrt(cos(e + f*x))/(S(3)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(b*sec(e + f*x)), x), x, S(2)*sqrt(b*sec(e + f*x))*EllipticF(e/S(2) + f*x/S(2), S(2))*sqrt(cos(e + f*x))/f, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(b*sec(e + f*x))*csc(e + f*x)**S(2), x), x, -b*csc(e + f*x)/(f*sqrt(b*sec(e + f*x))) + sqrt(b*sec(e + f*x))*EllipticF(e/S(2) + f*x/S(2), S(2))*sqrt(cos(e + f*x))/f, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(b*sec(e + f*x))*csc(e + f*x)**S(4), x), x, -b*csc(e + f*x)**S(3)/(S(3)*f*sqrt(b*sec(e + f*x))) - S(5)*b*csc(e + f*x)/(S(6)*f*sqrt(b*sec(e + f*x))) + S(5)*sqrt(b*sec(e + f*x))*EllipticF(e/S(2) + f*x/S(2), S(2))*sqrt(cos(e + f*x))/(S(6)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(b*sec(e + f*x))*csc(e + f*x)**S(6), x), x, -b*csc(e + f*x)**S(5)/(S(5)*f*sqrt(b*sec(e + f*x))) - S(3)*b*csc(e + f*x)**S(3)/(S(10)*f*sqrt(b*sec(e + f*x))) - S(3)*b*csc(e + f*x)/(S(4)*f*sqrt(b*sec(e + f*x))) + S(3)*sqrt(b*sec(e + f*x))*EllipticF(e/S(2) + f*x/S(2), S(2))*sqrt(cos(e + f*x))/(S(4)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sec(e + f*x))**(S(3)/2)*sin(e + f*x)**S(7), x), x, S(2)*b**S(7)/(S(11)*f*(b*sec(e + f*x))**(S(11)/2)) - S(6)*b**S(5)/(S(7)*f*(b*sec(e + f*x))**(S(7)/2)) + S(2)*b**S(3)/(f*(b*sec(e + f*x))**(S(3)/2)) + S(2)*b*sqrt(b*sec(e + f*x))/f, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sec(e + f*x))**(S(3)/2)*sin(e + f*x)**S(5), x), x, -S(2)*b**S(5)/(S(7)*f*(b*sec(e + f*x))**(S(7)/2)) + S(4)*b**S(3)/(S(3)*f*(b*sec(e + f*x))**(S(3)/2)) + S(2)*b*sqrt(b*sec(e + f*x))/f, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sec(e + f*x))**(S(3)/2)*sin(e + f*x)**S(3), x), x, S(2)*b**S(3)/(S(3)*f*(b*sec(e + f*x))**(S(3)/2)) + S(2)*b*sqrt(b*sec(e + f*x))/f, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sec(e + f*x))**(S(3)/2)*sin(e + f*x), x), x, S(2)*b*sqrt(b*sec(e + f*x))/f, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sec(e + f*x))**(S(3)/2)*csc(e + f*x), x), x, -b**(S(3)/2)*ArcTan(sqrt(b*sec(e + f*x))/sqrt(b))/f - b**(S(3)/2)*atanh(sqrt(b*sec(e + f*x))/sqrt(b))/f + S(2)*b*sqrt(b*sec(e + f*x))/f, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sec(e + f*x))**(S(3)/2)*csc(e + f*x)**S(3), x), x, -S(5)*b**(S(3)/2)*ArcTan(sqrt(b*sec(e + f*x))/sqrt(b))/(S(4)*f) - S(5)*b**(S(3)/2)*atanh(sqrt(b*sec(e + f*x))/sqrt(b))/(S(4)*f) + S(5)*b*sqrt(b*sec(e + f*x))/(S(2)*f) - (b*sec(e + f*x))**(S(5)/2)*cot(e + f*x)**S(2)/(S(2)*b*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sec(e + f*x))**(S(3)/2)*sin(e + f*x)**S(6), x), x, S(20)*b**S(3)*sin(e + f*x)**S(3)/(S(9)*f*(b*sec(e + f*x))**(S(3)/2)) + S(8)*b**S(3)*sin(e + f*x)/(S(3)*f*(b*sec(e + f*x))**(S(3)/2)) - S(16)*b**S(2)*EllipticE(e/S(2) + f*x/S(2), S(2))/(S(3)*f*sqrt(b*sec(e + f*x))*sqrt(cos(e + f*x))) + S(2)*b*sqrt(b*sec(e + f*x))*sin(e + f*x)**S(5)/f, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sec(e + f*x))**(S(3)/2)*sin(e + f*x)**S(4), x), x, S(12)*b**S(3)*sin(e + f*x)/(S(5)*f*(b*sec(e + f*x))**(S(3)/2)) - S(24)*b**S(2)*EllipticE(e/S(2) + f*x/S(2), S(2))/(S(5)*f*sqrt(b*sec(e + f*x))*sqrt(cos(e + f*x))) + S(2)*b*sqrt(b*sec(e + f*x))*sin(e + f*x)**S(3)/f, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sec(e + f*x))**(S(3)/2)*sin(e + f*x)**S(2), x), x, -S(4)*b**S(2)*EllipticE(e/S(2) + f*x/S(2), S(2))/(f*sqrt(b*sec(e + f*x))*sqrt(cos(e + f*x))) + S(2)*b*sqrt(b*sec(e + f*x))*sin(e + f*x)/f, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sec(e + f*x))**(S(3)/2), x), x, -S(2)*b**S(2)*EllipticE(e/S(2) + f*x/S(2), S(2))/(f*sqrt(b*sec(e + f*x))*sqrt(cos(e + f*x))) + S(2)*b*sqrt(b*sec(e + f*x))*sin(e + f*x)/f, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sec(e + f*x))**(S(3)/2)*csc(e + f*x)**S(2), x), x, -S(3)*b**S(2)*EllipticE(e/S(2) + f*x/S(2), S(2))/(f*sqrt(b*sec(e + f*x))*sqrt(cos(e + f*x))) + S(3)*b*sqrt(b*sec(e + f*x))*sin(e + f*x)/f - b*sqrt(b*sec(e + f*x))*csc(e + f*x)/f, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sec(e + f*x))**(S(3)/2)*csc(e + f*x)**S(4), x), x, -S(7)*b**S(2)*EllipticE(e/S(2) + f*x/S(2), S(2))/(S(2)*f*sqrt(b*sec(e + f*x))*sqrt(cos(e + f*x))) + S(7)*b*sqrt(b*sec(e + f*x))*sin(e + f*x)/(S(2)*f) - b*sqrt(b*sec(e + f*x))*csc(e + f*x)**S(3)/(S(3)*f) - S(7)*b*sqrt(b*sec(e + f*x))*csc(e + f*x)/(S(6)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sec(e + f*x))**(S(5)/2)*sin(e + f*x)**S(7), x), x, S(2)*b**S(7)/(S(9)*f*(b*sec(e + f*x))**(S(9)/2)) - S(6)*b**S(5)/(S(5)*f*(b*sec(e + f*x))**(S(5)/2)) + S(6)*b**S(3)/(f*sqrt(b*sec(e + f*x))) + S(2)*b*(b*sec(e + f*x))**(S(3)/2)/(S(3)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sec(e + f*x))**(S(5)/2)*sin(e + f*x)**S(5), x), x, -S(2)*b**S(5)/(S(5)*f*(b*sec(e + f*x))**(S(5)/2)) + S(4)*b**S(3)/(f*sqrt(b*sec(e + f*x))) + S(2)*b*(b*sec(e + f*x))**(S(3)/2)/(S(3)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sec(e + f*x))**(S(5)/2)*sin(e + f*x)**S(3), x), x, S(2)*b**S(3)/(f*sqrt(b*sec(e + f*x))) + S(2)*b*(b*sec(e + f*x))**(S(3)/2)/(S(3)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sec(e + f*x))**(S(5)/2)*sin(e + f*x), x), x, S(2)*b*(b*sec(e + f*x))**(S(3)/2)/(S(3)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sec(e + f*x))**(S(5)/2)*csc(e + f*x), x), x, b**(S(5)/2)*ArcTan(sqrt(b*sec(e + f*x))/sqrt(b))/f - b**(S(5)/2)*atanh(sqrt(b*sec(e + f*x))/sqrt(b))/f + S(2)*b*(b*sec(e + f*x))**(S(3)/2)/(S(3)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sec(e + f*x))**(S(5)/2)*csc(e + f*x)**S(3), x), x, S(7)*b**(S(5)/2)*ArcTan(sqrt(b*sec(e + f*x))/sqrt(b))/(S(4)*f) - S(7)*b**(S(5)/2)*atanh(sqrt(b*sec(e + f*x))/sqrt(b))/(S(4)*f) + S(7)*b*(b*sec(e + f*x))**(S(3)/2)/(S(6)*f) - (b*sec(e + f*x))**(S(7)/2)*cot(e + f*x)**S(2)/(S(2)*b*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sec(e + f*x))**(S(5)/2)*csc(e + f*x)**S(5), x), x, S(77)*b**(S(5)/2)*ArcTan(sqrt(b*sec(e + f*x))/sqrt(b))/(S(32)*f) - S(77)*b**(S(5)/2)*atanh(sqrt(b*sec(e + f*x))/sqrt(b))/(S(32)*f) + S(77)*b*(b*sec(e + f*x))**(S(3)/2)/(S(48)*f) - S(11)*(b*sec(e + f*x))**(S(7)/2)*cot(e + f*x)**S(2)/(S(16)*b*f) - (b*sec(e + f*x))**(S(11)/2)*cot(e + f*x)**S(4)/(S(4)*b**S(3)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sec(e + f*x))**(S(5)/2)*sin(e + f*x)**S(6), x), x, S(20)*b**S(3)*sin(e + f*x)**S(3)/(S(21)*f*sqrt(b*sec(e + f*x))) + S(40)*b**S(3)*sin(e + f*x)/(S(21)*f*sqrt(b*sec(e + f*x))) - S(80)*b**S(2)*sqrt(b*sec(e + f*x))*EllipticF(e/S(2) + f*x/S(2), S(2))*sqrt(cos(e + f*x))/(S(21)*f) + S(2)*b*(b*sec(e + f*x))**(S(3)/2)*sin(e + f*x)**S(5)/(S(3)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sec(e + f*x))**(S(5)/2)*sin(e + f*x)**S(4), x), x, S(4)*b**S(3)*sin(e + f*x)/(S(3)*f*sqrt(b*sec(e + f*x))) - S(8)*b**S(2)*sqrt(b*sec(e + f*x))*EllipticF(e/S(2) + f*x/S(2), S(2))*sqrt(cos(e + f*x))/(S(3)*f) + S(2)*b*(b*sec(e + f*x))**(S(3)/2)*sin(e + f*x)**S(3)/(S(3)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sec(e + f*x))**(S(5)/2)*sin(e + f*x)**S(2), x), x, -S(4)*b**S(2)*sqrt(b*sec(e + f*x))*EllipticF(e/S(2) + f*x/S(2), S(2))*sqrt(cos(e + f*x))/(S(3)*f) + S(2)*b*(b*sec(e + f*x))**(S(3)/2)*sin(e + f*x)/(S(3)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sec(e + f*x))**(S(5)/2), x), x, S(2)*b**S(2)*sqrt(b*sec(e + f*x))*EllipticF(e/S(2) + f*x/S(2), S(2))*sqrt(cos(e + f*x))/(S(3)*f) + S(2)*b*(b*sec(e + f*x))**(S(3)/2)*sin(e + f*x)/(S(3)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sec(e + f*x))**(S(5)/2)*csc(e + f*x)**S(2), x), x, S(5)*b**S(2)*sqrt(b*sec(e + f*x))*EllipticF(e/S(2) + f*x/S(2), S(2))*sqrt(cos(e + f*x))/(S(3)*f) + S(5)*b*(b*sec(e + f*x))**(S(3)/2)*sin(e + f*x)/(S(3)*f) - b*(b*sec(e + f*x))**(S(3)/2)*csc(e + f*x)/f, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sec(e + f*x))**(S(5)/2)*csc(e + f*x)**S(4), x), x, S(5)*b**S(2)*sqrt(b*sec(e + f*x))*EllipticF(e/S(2) + f*x/S(2), S(2))*sqrt(cos(e + f*x))/(S(2)*f) + S(5)*b*(b*sec(e + f*x))**(S(3)/2)*sin(e + f*x)/(S(2)*f) - b*(b*sec(e + f*x))**(S(3)/2)*csc(e + f*x)**S(3)/(S(3)*f) - S(3)*b*(b*sec(e + f*x))**(S(3)/2)*csc(e + f*x)/(S(2)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(e + f*x)**S(7)/sqrt(b*sec(e + f*x)), x), x, S(2)*b**S(7)/(S(15)*f*(b*sec(e + f*x))**(S(15)/2)) - S(6)*b**S(5)/(S(11)*f*(b*sec(e + f*x))**(S(11)/2)) + S(6)*b**S(3)/(S(7)*f*(b*sec(e + f*x))**(S(7)/2)) - S(2)*b/(S(3)*f*(b*sec(e + f*x))**(S(3)/2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(e + f*x)**S(5)/sqrt(b*sec(e + f*x)), x), x, -S(2)*b**S(5)/(S(11)*f*(b*sec(e + f*x))**(S(11)/2)) + S(4)*b**S(3)/(S(7)*f*(b*sec(e + f*x))**(S(7)/2)) - S(2)*b/(S(3)*f*(b*sec(e + f*x))**(S(3)/2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(e + f*x)**S(3)/sqrt(b*sec(e + f*x)), x), x, S(2)*b**S(3)/(S(7)*f*(b*sec(e + f*x))**(S(7)/2)) - S(2)*b/(S(3)*f*(b*sec(e + f*x))**(S(3)/2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(e + f*x)/sqrt(b*sec(e + f*x)), x), x, -S(2)*b/(S(3)*f*(b*sec(e + f*x))**(S(3)/2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(csc(e + f*x)/sqrt(b*sec(e + f*x)), x), x, -ArcTan(sqrt(b*sec(e + f*x))/sqrt(b))/(sqrt(b)*f) - atanh(sqrt(b*sec(e + f*x))/sqrt(b))/(sqrt(b)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(csc(e + f*x)**S(3)/sqrt(b*sec(e + f*x)), x), x, -sqrt(b*sec(e + f*x))*cot(e + f*x)**S(2)/(S(2)*b*f) - ArcTan(sqrt(b*sec(e + f*x))/sqrt(b))/(S(4)*sqrt(b)*f) - atanh(sqrt(b*sec(e + f*x))/sqrt(b))/(S(4)*sqrt(b)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(csc(e + f*x)**S(5)/sqrt(b*sec(e + f*x)), x), x, -S(5)*sqrt(b*sec(e + f*x))*cot(e + f*x)**S(2)/(S(16)*b*f) - (b*sec(e + f*x))**(S(5)/2)*cot(e + f*x)**S(4)/(S(4)*b**S(3)*f) - S(5)*ArcTan(sqrt(b*sec(e + f*x))/sqrt(b))/(S(32)*sqrt(b)*f) - S(5)*atanh(sqrt(b*sec(e + f*x))/sqrt(b))/(S(32)*sqrt(b)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(e + f*x)**S(6)/sqrt(b*sec(e + f*x)), x), x, -S(2)*b*sin(e + f*x)**S(5)/(S(13)*f*(b*sec(e + f*x))**(S(3)/2)) - S(20)*b*sin(e + f*x)**S(3)/(S(117)*f*(b*sec(e + f*x))**(S(3)/2)) - S(8)*b*sin(e + f*x)/(S(39)*f*(b*sec(e + f*x))**(S(3)/2)) + S(16)*EllipticE(e/S(2) + f*x/S(2), S(2))/(S(39)*f*sqrt(b*sec(e + f*x))*sqrt(cos(e + f*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(e + f*x)**S(4)/sqrt(b*sec(e + f*x)), x), x, -S(2)*b*sin(e + f*x)**S(3)/(S(9)*f*(b*sec(e + f*x))**(S(3)/2)) - S(4)*b*sin(e + f*x)/(S(15)*f*(b*sec(e + f*x))**(S(3)/2)) + S(8)*EllipticE(e/S(2) + f*x/S(2), S(2))/(S(15)*f*sqrt(b*sec(e + f*x))*sqrt(cos(e + f*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(e + f*x)**S(2)/sqrt(b*sec(e + f*x)), x), x, -S(2)*b*sin(e + f*x)/(S(5)*f*(b*sec(e + f*x))**(S(3)/2)) + S(4)*EllipticE(e/S(2) + f*x/S(2), S(2))/(S(5)*f*sqrt(b*sec(e + f*x))*sqrt(cos(e + f*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(S(1)/sqrt(b*sec(e + f*x)), x), x, S(2)*EllipticE(e/S(2) + f*x/S(2), S(2))/(f*sqrt(b*sec(e + f*x))*sqrt(cos(e + f*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(csc(e + f*x)**S(2)/sqrt(b*sec(e + f*x)), x), x, -b*csc(e + f*x)/(f*(b*sec(e + f*x))**(S(3)/2)) - EllipticE(e/S(2) + f*x/S(2), S(2))/(f*sqrt(b*sec(e + f*x))*sqrt(cos(e + f*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(csc(e + f*x)**S(4)/sqrt(b*sec(e + f*x)), x), x, -b*csc(e + f*x)**S(3)/(S(3)*f*(b*sec(e + f*x))**(S(3)/2)) - b*csc(e + f*x)/(S(2)*f*(b*sec(e + f*x))**(S(3)/2)) - EllipticE(e/S(2) + f*x/S(2), S(2))/(S(2)*f*sqrt(b*sec(e + f*x))*sqrt(cos(e + f*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(csc(e + f*x)**S(6)/sqrt(b*sec(e + f*x)), x), x, -b*csc(e + f*x)**S(5)/(S(5)*f*(b*sec(e + f*x))**(S(3)/2)) - S(7)*b*csc(e + f*x)**S(3)/(S(30)*f*(b*sec(e + f*x))**(S(3)/2)) - S(7)*b*csc(e + f*x)/(S(20)*f*(b*sec(e + f*x))**(S(3)/2)) - S(7)*EllipticE(e/S(2) + f*x/S(2), S(2))/(S(20)*f*sqrt(b*sec(e + f*x))*sqrt(cos(e + f*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(e + f*x)**S(7)/(b*sec(e + f*x))**(S(3)/2), x), x, S(2)*b**S(7)/(S(17)*f*(b*sec(e + f*x))**(S(17)/2)) - S(6)*b**S(5)/(S(13)*f*(b*sec(e + f*x))**(S(13)/2)) + S(2)*b**S(3)/(S(3)*f*(b*sec(e + f*x))**(S(9)/2)) - S(2)*b/(S(5)*f*(b*sec(e + f*x))**(S(5)/2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(e + f*x)**S(5)/(b*sec(e + f*x))**(S(3)/2), x), x, -S(2)*b**S(5)/(S(13)*f*(b*sec(e + f*x))**(S(13)/2)) + S(4)*b**S(3)/(S(9)*f*(b*sec(e + f*x))**(S(9)/2)) - S(2)*b/(S(5)*f*(b*sec(e + f*x))**(S(5)/2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(e + f*x)**S(3)/(b*sec(e + f*x))**(S(3)/2), x), x, S(2)*b**S(3)/(S(9)*f*(b*sec(e + f*x))**(S(9)/2)) - S(2)*b/(S(5)*f*(b*sec(e + f*x))**(S(5)/2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(e + f*x)/(b*sec(e + f*x))**(S(3)/2), x), x, -S(2)*b/(S(5)*f*(b*sec(e + f*x))**(S(5)/2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(csc(e + f*x)/(b*sec(e + f*x))**(S(3)/2), x), x, S(2)/(b*f*sqrt(b*sec(e + f*x))) + ArcTan(sqrt(b*sec(e + f*x))/sqrt(b))/(b**(S(3)/2)*f) - atanh(sqrt(b*sec(e + f*x))/sqrt(b))/(b**(S(3)/2)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(csc(e + f*x)**S(3)/(b*sec(e + f*x))**(S(3)/2), x), x, -(b*sec(e + f*x))**(S(3)/2)*cot(e + f*x)**S(2)/(S(2)*b**S(3)*f) - ArcTan(sqrt(b*sec(e + f*x))/sqrt(b))/(S(4)*b**(S(3)/2)*f) + atanh(sqrt(b*sec(e + f*x))/sqrt(b))/(S(4)*b**(S(3)/2)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(csc(e + f*x)**S(5)/(b*sec(e + f*x))**(S(3)/2), x), x, -(b*sec(e + f*x))**(S(3)/2)*cot(e + f*x)**S(4)/(S(4)*b**S(3)*f) - S(3)*(b*sec(e + f*x))**(S(3)/2)*cot(e + f*x)**S(2)/(S(16)*b**S(3)*f) - S(3)*ArcTan(sqrt(b*sec(e + f*x))/sqrt(b))/(S(32)*b**(S(3)/2)*f) + S(3)*atanh(sqrt(b*sec(e + f*x))/sqrt(b))/(S(32)*b**(S(3)/2)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(e + f*x)**S(4)/(b*sec(e + f*x))**(S(3)/2), x), x, -S(2)*b*sin(e + f*x)**S(3)/(S(11)*f*(b*sec(e + f*x))**(S(5)/2)) - S(12)*b*sin(e + f*x)/(S(77)*f*(b*sec(e + f*x))**(S(5)/2)) + S(8)*sin(e + f*x)/(S(77)*b*f*sqrt(b*sec(e + f*x))) + S(8)*sqrt(b*sec(e + f*x))*EllipticF(e/S(2) + f*x/S(2), S(2))*sqrt(cos(e + f*x))/(S(77)*b**S(2)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(e + f*x)**S(2)/(b*sec(e + f*x))**(S(3)/2), x), x, -S(2)*b*sin(e + f*x)/(S(7)*f*(b*sec(e + f*x))**(S(5)/2)) + S(4)*sin(e + f*x)/(S(21)*b*f*sqrt(b*sec(e + f*x))) + S(4)*sqrt(b*sec(e + f*x))*EllipticF(e/S(2) + f*x/S(2), S(2))*sqrt(cos(e + f*x))/(S(21)*b**S(2)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sec(e + f*x))**(S(-3)/2), x), x, S(2)*sin(e + f*x)/(S(3)*b*f*sqrt(b*sec(e + f*x))) + S(2)*sqrt(b*sec(e + f*x))*EllipticF(e/S(2) + f*x/S(2), S(2))*sqrt(cos(e + f*x))/(S(3)*b**S(2)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(csc(e + f*x)**S(2)/(b*sec(e + f*x))**(S(3)/2), x), x, -csc(e + f*x)/(b*f*sqrt(b*sec(e + f*x))) - sqrt(b*sec(e + f*x))*EllipticF(e/S(2) + f*x/S(2), S(2))*sqrt(cos(e + f*x))/(b**S(2)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(csc(e + f*x)**S(4)/(b*sec(e + f*x))**(S(3)/2), x), x, -csc(e + f*x)**S(3)/(S(3)*b*f*sqrt(b*sec(e + f*x))) + csc(e + f*x)/(S(6)*b*f*sqrt(b*sec(e + f*x))) - sqrt(b*sec(e + f*x))*EllipticF(e/S(2) + f*x/S(2), S(2))*sqrt(cos(e + f*x))/(S(6)*b**S(2)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(csc(e + f*x)**S(6)/(b*sec(e + f*x))**(S(3)/2), x), x, -csc(e + f*x)**S(5)/(S(5)*b*f*sqrt(b*sec(e + f*x))) + csc(e + f*x)**S(3)/(S(30)*b*f*sqrt(b*sec(e + f*x))) + csc(e + f*x)/(S(12)*b*f*sqrt(b*sec(e + f*x))) - sqrt(b*sec(e + f*x))*EllipticF(e/S(2) + f*x/S(2), S(2))*sqrt(cos(e + f*x))/(S(12)*b**S(2)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(e + f*x)**S(7)/(b*sec(e + f*x))**(S(5)/2), x), x, S(2)*b**S(7)/(S(19)*f*(b*sec(e + f*x))**(S(19)/2)) - S(2)*b**S(5)/(S(5)*f*(b*sec(e + f*x))**(S(15)/2)) + S(6)*b**S(3)/(S(11)*f*(b*sec(e + f*x))**(S(11)/2)) - S(2)*b/(S(7)*f*(b*sec(e + f*x))**(S(7)/2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(e + f*x)**S(5)/(b*sec(e + f*x))**(S(5)/2), x), x, -S(2)*b**S(5)/(S(15)*f*(b*sec(e + f*x))**(S(15)/2)) + S(4)*b**S(3)/(S(11)*f*(b*sec(e + f*x))**(S(11)/2)) - S(2)*b/(S(7)*f*(b*sec(e + f*x))**(S(7)/2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(e + f*x)**S(3)/(b*sec(e + f*x))**(S(5)/2), x), x, S(2)*b**S(3)/(S(11)*f*(b*sec(e + f*x))**(S(11)/2)) - S(2)*b/(S(7)*f*(b*sec(e + f*x))**(S(7)/2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(e + f*x)/(b*sec(e + f*x))**(S(5)/2), x), x, -S(2)*b/(S(7)*f*(b*sec(e + f*x))**(S(7)/2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(csc(e + f*x)/(b*sec(e + f*x))**(S(5)/2), x), x, S(2)/(S(3)*b*f*(b*sec(e + f*x))**(S(3)/2)) - ArcTan(sqrt(b*sec(e + f*x))/sqrt(b))/(b**(S(5)/2)*f) - atanh(sqrt(b*sec(e + f*x))/sqrt(b))/(b**(S(5)/2)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(csc(e + f*x)**S(3)/(b*sec(e + f*x))**(S(5)/2), x), x, -sqrt(b*sec(e + f*x))*cot(e + f*x)**S(2)/(S(2)*b**S(3)*f) + S(3)*ArcTan(sqrt(b*sec(e + f*x))/sqrt(b))/(S(4)*b**(S(5)/2)*f) + S(3)*atanh(sqrt(b*sec(e + f*x))/sqrt(b))/(S(4)*b**(S(5)/2)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(csc(e + f*x)**S(5)/(b*sec(e + f*x))**(S(5)/2), x), x, -sqrt(b*sec(e + f*x))*cot(e + f*x)**S(4)/(S(4)*b**S(3)*f) - sqrt(b*sec(e + f*x))*cot(e + f*x)**S(2)/(S(16)*b**S(3)*f) + S(3)*ArcTan(sqrt(b*sec(e + f*x))/sqrt(b))/(S(32)*b**(S(5)/2)*f) + S(3)*atanh(sqrt(b*sec(e + f*x))/sqrt(b))/(S(32)*b**(S(5)/2)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(e + f*x)**S(4)/(b*sec(e + f*x))**(S(5)/2), x), x, -S(2)*b*sin(e + f*x)**S(3)/(S(13)*f*(b*sec(e + f*x))**(S(7)/2)) - S(4)*b*sin(e + f*x)/(S(39)*f*(b*sec(e + f*x))**(S(7)/2)) + S(8)*sin(e + f*x)/(S(195)*b*f*(b*sec(e + f*x))**(S(3)/2)) + S(8)*EllipticE(e/S(2) + f*x/S(2), S(2))/(S(65)*b**S(2)*f*sqrt(b*sec(e + f*x))*sqrt(cos(e + f*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(e + f*x)**S(2)/(b*sec(e + f*x))**(S(5)/2), x), x, -S(2)*b*sin(e + f*x)/(S(9)*f*(b*sec(e + f*x))**(S(7)/2)) + S(4)*sin(e + f*x)/(S(45)*b*f*(b*sec(e + f*x))**(S(3)/2)) + S(4)*EllipticE(e/S(2) + f*x/S(2), S(2))/(S(15)*b**S(2)*f*sqrt(b*sec(e + f*x))*sqrt(cos(e + f*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sec(e + f*x))**(S(-5)/2), x), x, S(2)*sin(e + f*x)/(S(5)*b*f*(b*sec(e + f*x))**(S(3)/2)) + S(6)*EllipticE(e/S(2) + f*x/S(2), S(2))/(S(5)*b**S(2)*f*sqrt(b*sec(e + f*x))*sqrt(cos(e + f*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(csc(e + f*x)**S(2)/(b*sec(e + f*x))**(S(5)/2), x), x, -csc(e + f*x)/(b*f*(b*sec(e + f*x))**(S(3)/2)) - S(3)*EllipticE(e/S(2) + f*x/S(2), S(2))/(b**S(2)*f*sqrt(b*sec(e + f*x))*sqrt(cos(e + f*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(csc(e + f*x)**S(4)/(b*sec(e + f*x))**(S(5)/2), x), x, -csc(e + f*x)**S(3)/(S(3)*b*f*(b*sec(e + f*x))**(S(3)/2)) + csc(e + f*x)/(S(2)*b*f*(b*sec(e + f*x))**(S(3)/2)) + EllipticE(e/S(2) + f*x/S(2), S(2))/(S(2)*b**S(2)*f*sqrt(b*sec(e + f*x))*sqrt(cos(e + f*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(csc(e + f*x)**S(6)/(b*sec(e + f*x))**(S(5)/2), x), x, -csc(e + f*x)**S(5)/(S(5)*b*f*(b*sec(e + f*x))**(S(3)/2)) + csc(e + f*x)**S(3)/(S(10)*b*f*(b*sec(e + f*x))**(S(3)/2)) + S(3)*csc(e + f*x)/(S(20)*b*f*(b*sec(e + f*x))**(S(3)/2)) + S(3)*EllipticE(e/S(2) + f*x/S(2), S(2))/(S(20)*b**S(2)*f*sqrt(b*sec(e + f*x))*sqrt(cos(e + f*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(e + f*x)**(S(9)/2)/sqrt(b*sec(e + f*x)), x), x, -b*sin(e + f*x)**(S(7)/2)/(S(5)*f*(b*sec(e + f*x))**(S(3)/2)) - S(7)*b*sin(e + f*x)**(S(3)/2)/(S(30)*f*(b*sec(e + f*x))**(S(3)/2)) + S(7)*sqrt(b*sec(e + f*x))*EllipticE(-Pi/S(4) + e + f*x, S(2))*sqrt(sin(e + f*x))*cos(e + f*x)/(S(20)*b*f*sqrt(sin(S(2)*e + S(2)*f*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(e + f*x)**(S(5)/2)/sqrt(b*sec(e + f*x)), x), x, -b*sin(e + f*x)**(S(3)/2)/(S(3)*f*(b*sec(e + f*x))**(S(3)/2)) + sqrt(b*sec(e + f*x))*EllipticE(-Pi/S(4) + e + f*x, S(2))*sqrt(sin(e + f*x))*cos(e + f*x)/(S(2)*b*f*sqrt(sin(S(2)*e + S(2)*f*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(sin(e + f*x))/sqrt(b*sec(e + f*x)), x), x, sqrt(b*sec(e + f*x))*EllipticE(-Pi/S(4) + e + f*x, S(2))*sqrt(sin(e + f*x))*cos(e + f*x)/(b*f*sqrt(sin(S(2)*e + S(2)*f*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(S(1)/(sqrt(b*sec(e + f*x))*sin(e + f*x)**(S(3)/2)), x), x, -S(2)*b/(f*(b*sec(e + f*x))**(S(3)/2)*sqrt(sin(e + f*x))) - S(2)*sqrt(b*sec(e + f*x))*EllipticE(-Pi/S(4) + e + f*x, S(2))*sqrt(sin(e + f*x))*cos(e + f*x)/(b*f*sqrt(sin(S(2)*e + S(2)*f*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(S(1)/(sqrt(b*sec(e + f*x))*sin(e + f*x)**(S(7)/2)), x), x, -S(4)*b/(S(5)*f*(b*sec(e + f*x))**(S(3)/2)*sqrt(sin(e + f*x))) - S(2)*b/(S(5)*f*(b*sec(e + f*x))**(S(3)/2)*sin(e + f*x)**(S(5)/2)) - S(4)*sqrt(b*sec(e + f*x))*EllipticE(-Pi/S(4) + e + f*x, S(2))*sqrt(sin(e + f*x))*cos(e + f*x)/(S(5)*b*f*sqrt(sin(S(2)*e + S(2)*f*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(e + f*x)**(S(3)/2)/sqrt(b*sec(e + f*x)), x), x, -b*sqrt(sin(e + f*x))/(S(2)*f*(b*sec(e + f*x))**(S(3)/2)) + sqrt(S(2))*sqrt(cos(e + f*x)/b)*sqrt(b*sec(e + f*x))*ArcTan(-sqrt(S(2))*sqrt(b)*sqrt(cos(e + f*x)/b)/sqrt(sin(e + f*x)) + S(1))/(S(8)*sqrt(b)*f) - sqrt(S(2))*sqrt(cos(e + f*x)/b)*sqrt(b*sec(e + f*x))*ArcTan(sqrt(S(2))*sqrt(b)*sqrt(cos(e + f*x)/b)/sqrt(sin(e + f*x)) + S(1))/(S(8)*sqrt(b)*f) - sqrt(S(2))*sqrt(cos(e + f*x)/b)*sqrt(b*sec(e + f*x))*log(-sqrt(S(2))*sqrt(b)*sqrt(cos(e + f*x)/b)/sqrt(sin(e + f*x)) + cot(e + f*x) + S(1))/(S(16)*sqrt(b)*f) + sqrt(S(2))*sqrt(cos(e + f*x)/b)*sqrt(b*sec(e + f*x))*log(sqrt(S(2))*sqrt(b)*sqrt(cos(e + f*x)/b)/sqrt(sin(e + f*x)) + cot(e + f*x) + S(1))/(S(16)*sqrt(b)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(S(1)/(sqrt(b*sec(e + f*x))*sqrt(sin(e + f*x))), x), x, sqrt(S(2))*sqrt(cos(e + f*x)/b)*sqrt(b*sec(e + f*x))*ArcTan(-sqrt(S(2))*sqrt(b)*sqrt(cos(e + f*x)/b)/sqrt(sin(e + f*x)) + S(1))/(S(2)*sqrt(b)*f) - sqrt(S(2))*sqrt(cos(e + f*x)/b)*sqrt(b*sec(e + f*x))*ArcTan(sqrt(S(2))*sqrt(b)*sqrt(cos(e + f*x)/b)/sqrt(sin(e + f*x)) + S(1))/(S(2)*sqrt(b)*f) - sqrt(S(2))*sqrt(cos(e + f*x)/b)*sqrt(b*sec(e + f*x))*log(-sqrt(S(2))*sqrt(b)*sqrt(cos(e + f*x)/b)/sqrt(sin(e + f*x)) + cot(e + f*x) + S(1))/(S(4)*sqrt(b)*f) + sqrt(S(2))*sqrt(cos(e + f*x)/b)*sqrt(b*sec(e + f*x))*log(sqrt(S(2))*sqrt(b)*sqrt(cos(e + f*x)/b)/sqrt(sin(e + f*x)) + cot(e + f*x) + S(1))/(S(4)*sqrt(b)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(S(1)/(sqrt(b*sec(e + f*x))*sin(e + f*x)**(S(5)/2)), x), x, -S(2)*b/(S(3)*f*(b*sec(e + f*x))**(S(3)/2)*sin(e + f*x)**(S(3)/2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(S(1)/(sqrt(b*sec(e + f*x))*sin(e + f*x)**(S(9)/2)), x), x, -S(8)*b/(S(21)*f*(b*sec(e + f*x))**(S(3)/2)*sin(e + f*x)**(S(3)/2)) - S(2)*b/(S(7)*f*(b*sec(e + f*x))**(S(3)/2)*sin(e + f*x)**(S(7)/2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(S(1)/(sqrt(b*sec(e + f*x))*sin(e + f*x)**(S(13)/2)), x), x, -S(64)*b/(S(231)*f*(b*sec(e + f*x))**(S(3)/2)*sin(e + f*x)**(S(3)/2)) - S(16)*b/(S(77)*f*(b*sec(e + f*x))**(S(3)/2)*sin(e + f*x)**(S(7)/2)) - S(2)*b/(S(11)*f*(b*sec(e + f*x))**(S(3)/2)*sin(e + f*x)**(S(11)/2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(S(1)/(sqrt(b*sec(e + f*x))*sin(e + f*x)**(S(17)/2)), x), x, -S(256)*b/(S(1155)*f*(b*sec(e + f*x))**(S(3)/2)*sin(e + f*x)**(S(3)/2)) - S(64)*b/(S(385)*f*(b*sec(e + f*x))**(S(3)/2)*sin(e + f*x)**(S(7)/2)) - S(8)*b/(S(55)*f*(b*sec(e + f*x))**(S(3)/2)*sin(e + f*x)**(S(11)/2)) - S(2)*b/(S(15)*f*(b*sec(e + f*x))**(S(3)/2)*sin(e + f*x)**(S(15)/2)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**m*(d*sec(a + b*x))**(S(5)/2), x), x, d**S(2)*(c*sin(a + b*x))**(m + S(1))*sqrt(d*sec(a + b*x))*(cos(a + b*x)**S(2))**(S(3)/4)*Hypergeometric2F1(S(7)/4, m/S(2) + S(1)/2, m/S(2) + S(3)/2, sin(a + b*x)**S(2))*sec(a + b*x)/(b*c*(m + S(1))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**m*(d*sec(a + b*x))**(S(3)/2), x), x, d*(c*sin(a + b*x))**(m + S(1))*sqrt(d*sec(a + b*x))*(cos(a + b*x)**S(2))**(S(1)/4)*Hypergeometric2F1(S(5)/4, m/S(2) + S(1)/2, m/S(2) + S(3)/2, sin(a + b*x)**S(2))/(b*c*(m + S(1))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**m*sqrt(d*sec(a + b*x)), x), x, (c*sin(a + b*x))**(m + S(1))*sqrt(d*sec(a + b*x))*(cos(a + b*x)**S(2))**(S(3)/4)*Hypergeometric2F1(S(3)/4, m/S(2) + S(1)/2, m/S(2) + S(3)/2, sin(a + b*x)**S(2))*sec(a + b*x)/(b*c*(m + S(1))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**m/sqrt(d*sec(a + b*x)), x), x, (c*sin(a + b*x))**(m + S(1))*sqrt(d*sec(a + b*x))*(cos(a + b*x)**S(2))**(S(1)/4)*Hypergeometric2F1(S(1)/4, m/S(2) + S(1)/2, m/S(2) + S(3)/2, sin(a + b*x)**S(2))/(b*c*d*(m + S(1))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((c*sin(a + b*x))**m/(d*sec(a + b*x))**(S(3)/2), x), x, (c*sin(a + b*x))**(m + S(1))*sqrt(d*sec(a + b*x))*Hypergeometric2F1(S(-1)/4, m/S(2) + S(1)/2, m/S(2) + S(3)/2, sin(a + b*x)**S(2))*cos(a + b*x)/(b*c*d**S(2)*(m + S(1))*(cos(a + b*x)**S(2))**(S(1)/4)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(e + f*x)**m*sec(e + f*x)**n, x), x, (cos(e + f*x)**S(2))**(n/S(2) + S(1)/2)*Hypergeometric2F1(m/S(2) + S(1)/2, n/S(2) + S(1)/2, m/S(2) + S(3)/2, sin(e + f*x)**S(2))*sin(e + f*x)**(m + S(1))*sec(e + f*x)**(n + S(1))/(f*(m + S(1))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((a*sin(e + f*x))**m*sec(e + f*x)**n, x), x, (a*sin(e + f*x))**(m + S(1))*(cos(e + f*x)**S(2))**(n/S(2) + S(1)/2)*Hypergeometric2F1(m/S(2) + S(1)/2, n/S(2) + S(1)/2, m/S(2) + S(3)/2, sin(e + f*x)**S(2))*sec(e + f*x)**(n + S(1))/(a*f*(m + S(1))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sec(e + f*x))**n*sin(e + f*x)**m, x), x, -b*(b*sec(e + f*x))**(n + S(-1))*(sin(e + f*x)**S(2))**(-m/S(2) + S(1)/2)*Hypergeometric2F1(-m/S(2) + S(1)/2, -n/S(2) + S(1)/2, -n/S(2) + S(3)/2, cos(e + f*x)**S(2))*sin(e + f*x)**(m + S(-1))/(f*(-n + S(1))), expand=True, _diff=True, _numerical=True) or rubi_test(rubi_integrate((b*sec(e + f*x))**n*sin(e + f*x)**m, x), x, -(b*sec(e + f*x))**n*(sin(e + f*x)**S(2))**(-m/S(2) + S(1)/2)*Hypergeometric2F1(-m/S(2) + S(1)/2, -n/S(2) + S(1)/2, -n/S(2) + S(3)/2, cos(e + f*x)**S(2))*sin(e + f*x)**(m + S(-1))*cos(e + f*x)/(f*(-n + S(1))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((a*sin(e + f*x))**m*(b*sec(e + f*x))**n, x), x, (a*sin(e + f*x))**(m + S(1))*(b*sec(e + f*x))**(n + S(1))*(cos(e + f*x)**S(2))**(n/S(2) + S(1)/2)*Hypergeometric2F1(m/S(2) + S(1)/2, n/S(2) + S(1)/2, m/S(2) + S(3)/2, sin(e + f*x)**S(2))/(a*b*f*(m + S(1))), expand=True, _diff=True, _numerical=True) or rubi_test(rubi_integrate((a*sin(e + f*x))**m*(b*sec(e + f*x))**n, x), x, (a*sin(e + f*x))**(m + S(1))*(b*sec(e + f*x))**n*(cos(e + f*x)**S(2))**(n/S(2) + S(1)/2)*Hypergeometric2F1(m/S(2) + S(1)/2, n/S(2) + S(1)/2, m/S(2) + S(3)/2, sin(e + f*x)**S(2))*sec(e + f*x)/(a*f*(m + S(1))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sec(e + f*x))**n*sin(e + f*x)**S(5), x), x, -b**S(5)*(b*sec(e + f*x))**(n + S(-5))/(f*(-n + S(5))) + S(2)*b**S(3)*(b*sec(e + f*x))**(n + S(-3))/(f*(-n + S(3))) - b*(b*sec(e + f*x))**(n + S(-1))/(f*(-n + S(1))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sec(e + f*x))**n*sin(e + f*x)**S(3), x), x, b**S(3)*(b*sec(e + f*x))**(n + S(-3))/(f*(-n + S(3))) - b*(b*sec(e + f*x))**(n + S(-1))/(f*(-n + S(1))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sec(e + f*x))**n*sin(e + f*x), x), x, -b*(b*sec(e + f*x))**(n + S(-1))/(f*(-n + S(1))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sec(e + f*x))**n*csc(e + f*x), x), x, -(b*sec(e + f*x))**(n + S(1))*Hypergeometric2F1(S(1), n/S(2) + S(1)/2, n/S(2) + S(3)/2, sec(e + f*x)**S(2))/(b*f*(n + S(1))), expand=True, _diff=True, _numerical=True)
# long time assert rubi_test(rubi_integrate((b*sec(e + f*x))**n*csc(e + f*x)**S(3), x), x, (b*sec(e + f*x))**(n + S(3))*Hypergeometric2F1(S(2), n/S(2) + S(3)/2, n/S(2) + S(5)/2, sec(e + f*x)**S(2))/(b**S(3)*f*(n + S(3))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sec(e + f*x))**n*sin(e + f*x)**S(6), x), x, -(b*sec(e + f*x))**n*Hypergeometric2F1(S(-5)/2, -n/S(2) + S(1)/2, -n/S(2) + S(3)/2, cos(e + f*x)**S(2))*sin(e + f*x)*cos(e + f*x)/(f*(-n + S(1))*sqrt(sin(e + f*x)**S(2))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sec(e + f*x))**n*sin(e + f*x)**S(4), x), x, -(b*sec(e + f*x))**n*Hypergeometric2F1(S(-3)/2, -n/S(2) + S(1)/2, -n/S(2) + S(3)/2, cos(e + f*x)**S(2))*sin(e + f*x)*cos(e + f*x)/(f*(-n + S(1))*sqrt(sin(e + f*x)**S(2))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sec(e + f*x))**n*sin(e + f*x)**S(2), x), x, -(b*sec(e + f*x))**n*Hypergeometric2F1(S(-1)/2, -n/S(2) + S(1)/2, -n/S(2) + S(3)/2, cos(e + f*x)**S(2))*sin(e + f*x)*cos(e + f*x)/(f*(-n + S(1))*sqrt(sin(e + f*x)**S(2))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sec(e + f*x))**n, x), x, -b*(b*sec(e + f*x))**(n + S(-1))*Hypergeometric2F1(S(1)/2, -n/S(2) + S(1)/2, -n/S(2) + S(3)/2, cos(e + f*x)**S(2))*sin(e + f*x)/(f*(-n + S(1))*sqrt(sin(e + f*x)**S(2))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sec(e + f*x))**n*csc(e + f*x)**S(2), x), x, -(b*sec(e + f*x))**n*sqrt(sin(e + f*x)**S(2))*Hypergeometric2F1(S(3)/2, -n/S(2) + S(1)/2, -n/S(2) + S(3)/2, cos(e + f*x)**S(2))*cot(e + f*x)/(f*(-n + S(1))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sec(e + f*x))**n*csc(e + f*x)**S(4), x), x, -(b*sec(e + f*x))**n*sqrt(sin(e + f*x)**S(2))*Hypergeometric2F1(S(5)/2, -n/S(2) + S(1)/2, -n/S(2) + S(3)/2, cos(e + f*x)**S(2))*cot(e + f*x)/(f*(-n + S(1))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sec(a + b*x))**n*(c*sin(a + b*x))**(S(3)/2), x), x, -c*(b*sec(a + b*x))**n*sqrt(c*sin(a + b*x))*Hypergeometric2F1(S(-1)/4, -n/S(2) + S(1)/2, -n/S(2) + S(3)/2, cos(a + b*x)**S(2))*cos(a + b*x)/(b*(-n + S(1))*(sin(a + b*x)**S(2))**(S(1)/4)), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sec(a + b*x))**n*sqrt(c*sin(a + b*x)), x), x, -c*(b*sec(a + b*x))**n*(sin(a + b*x)**S(2))**(S(1)/4)*Hypergeometric2F1(S(1)/4, -n/S(2) + S(1)/2, -n/S(2) + S(3)/2, cos(a + b*x)**S(2))*cos(a + b*x)/(b*sqrt(c*sin(a + b*x))*(-n + S(1))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sec(a + b*x))**n/sqrt(c*sin(a + b*x)), x), x, -c*(b*sec(a + b*x))**n*(sin(a + b*x)**S(2))**(S(3)/4)*Hypergeometric2F1(S(3)/4, -n/S(2) + S(1)/2, -n/S(2) + S(3)/2, cos(a + b*x)**S(2))*cos(a + b*x)/(b*(c*sin(a + b*x))**(S(3)/2)*(-n + S(1))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((b*sec(a + b*x))**n/(c*sin(a + b*x))**(S(3)/2), x), x, -(b*sec(a + b*x))**n*(sin(a + b*x)**S(2))**(S(1)/4)*Hypergeometric2F1(S(5)/4, -n/S(2) + S(1)/2, -n/S(2) + S(3)/2, cos(a + b*x)**S(2))*cos(a + b*x)/(b*c*sqrt(c*sin(a + b*x))*(-n + S(1))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(d*csc(e + f*x))*sin(e + f*x)**S(4), x), x, -S(2)*d**S(3)*cos(e + f*x)/(S(7)*f*(d*csc(e + f*x))**(S(5)/2)) - S(10)*d*cos(e + f*x)/(S(21)*f*sqrt(d*csc(e + f*x))) + S(10)*sqrt(d*csc(e + f*x))*EllipticF(-Pi/S(4) + e/S(2) + f*x/S(2), S(2))*sqrt(sin(e + f*x))/(S(21)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(d*csc(e + f*x))*sin(e + f*x)**S(3), x), x, -S(2)*d**S(2)*cos(e + f*x)/(S(5)*f*(d*csc(e + f*x))**(S(3)/2)) + S(6)*d*EllipticE(-Pi/S(4) + e/S(2) + f*x/S(2), S(2))/(S(5)*f*sqrt(d*csc(e + f*x))*sqrt(sin(e + f*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(d*csc(e + f*x))*sin(e + f*x)**S(2), x), x, -S(2)*d*cos(e + f*x)/(S(3)*f*sqrt(d*csc(e + f*x))) + S(2)*sqrt(d*csc(e + f*x))*EllipticF(-Pi/S(4) + e/S(2) + f*x/S(2), S(2))*sqrt(sin(e + f*x))/(S(3)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(d*csc(e + f*x))*sin(e + f*x), x), x, S(2)*d*EllipticE(-Pi/S(4) + e/S(2) + f*x/S(2), S(2))/(f*sqrt(d*csc(e + f*x))*sqrt(sin(e + f*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(d*csc(e + f*x)), x), x, S(2)*sqrt(d*csc(e + f*x))*EllipticF(-Pi/S(4) + e/S(2) + f*x/S(2), S(2))*sqrt(sin(e + f*x))/f, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(d*csc(e + f*x))*csc(e + f*x), x), x, -S(2)*d*EllipticE(-Pi/S(4) + e/S(2) + f*x/S(2), S(2))/(f*sqrt(d*csc(e + f*x))*sqrt(sin(e + f*x))) - S(2)*sqrt(d*csc(e + f*x))*cos(e + f*x)/f, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(d*csc(e + f*x))*csc(e + f*x)**S(2), x), x, S(2)*sqrt(d*csc(e + f*x))*EllipticF(-Pi/S(4) + e/S(2) + f*x/S(2), S(2))*sqrt(sin(e + f*x))/(S(3)*f) - S(2)*(d*csc(e + f*x))**(S(3)/2)*cos(e + f*x)/(S(3)*d*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sqrt(d*csc(e + f*x))*csc(e + f*x)**S(3), x), x, -S(6)*d*EllipticE(-Pi/S(4) + e/S(2) + f*x/S(2), S(2))/(S(5)*f*sqrt(d*csc(e + f*x))*sqrt(sin(e + f*x))) - S(6)*sqrt(d*csc(e + f*x))*cos(e + f*x)/(S(5)*f) - S(2)*(d*csc(e + f*x))**(S(5)/2)*cos(e + f*x)/(S(5)*d**S(2)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*csc(e + f*x))**(S(3)/2)*sin(e + f*x)**S(5), x), x, -S(2)*d**S(4)*cos(e + f*x)/(S(7)*f*(d*csc(e + f*x))**(S(5)/2)) - S(10)*d**S(2)*cos(e + f*x)/(S(21)*f*sqrt(d*csc(e + f*x))) + S(10)*d*sqrt(d*csc(e + f*x))*EllipticF(-Pi/S(4) + e/S(2) + f*x/S(2), S(2))*sqrt(sin(e + f*x))/(S(21)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*csc(e + f*x))**(S(3)/2)*sin(e + f*x)**S(4), x), x, -S(2)*d**S(3)*cos(e + f*x)/(S(5)*f*(d*csc(e + f*x))**(S(3)/2)) + S(6)*d**S(2)*EllipticE(-Pi/S(4) + e/S(2) + f*x/S(2), S(2))/(S(5)*f*sqrt(d*csc(e + f*x))*sqrt(sin(e + f*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*csc(e + f*x))**(S(3)/2)*sin(e + f*x)**S(3), x), x, -S(2)*d**S(2)*cos(e + f*x)/(S(3)*f*sqrt(d*csc(e + f*x))) + S(2)*d*sqrt(d*csc(e + f*x))*EllipticF(-Pi/S(4) + e/S(2) + f*x/S(2), S(2))*sqrt(sin(e + f*x))/(S(3)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*csc(e + f*x))**(S(3)/2)*sin(e + f*x)**S(2), x), x, S(2)*d**S(2)*EllipticE(-Pi/S(4) + e/S(2) + f*x/S(2), S(2))/(f*sqrt(d*csc(e + f*x))*sqrt(sin(e + f*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*csc(e + f*x))**(S(3)/2)*sin(e + f*x), x), x, S(2)*d*sqrt(d*csc(e + f*x))*EllipticF(-Pi/S(4) + e/S(2) + f*x/S(2), S(2))*sqrt(sin(e + f*x))/f, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*csc(e + f*x))**(S(3)/2), x), x, -S(2)*d**S(2)*EllipticE(-Pi/S(4) + e/S(2) + f*x/S(2), S(2))/(f*sqrt(d*csc(e + f*x))*sqrt(sin(e + f*x))) - S(2)*d*sqrt(d*csc(e + f*x))*cos(e + f*x)/f, expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*csc(e + f*x))**(S(3)/2)*csc(e + f*x), x), x, S(2)*d*sqrt(d*csc(e + f*x))*EllipticF(-Pi/S(4) + e/S(2) + f*x/S(2), S(2))*sqrt(sin(e + f*x))/(S(3)*f) - S(2)*(d*csc(e + f*x))**(S(3)/2)*cos(e + f*x)/(S(3)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*csc(e + f*x))**(S(3)/2)*csc(e + f*x)**S(2), x), x, -S(6)*d**S(2)*EllipticE(-Pi/S(4) + e/S(2) + f*x/S(2), S(2))/(S(5)*f*sqrt(d*csc(e + f*x))*sqrt(sin(e + f*x))) - S(6)*d*sqrt(d*csc(e + f*x))*cos(e + f*x)/(S(5)*f) - S(2)*(d*csc(e + f*x))**(S(5)/2)*cos(e + f*x)/(S(5)*d*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(e + f*x)**S(3)/sqrt(d*csc(e + f*x)), x), x, -S(2)*d**S(2)*cos(e + f*x)/(S(7)*f*(d*csc(e + f*x))**(S(5)/2)) - S(10)*cos(e + f*x)/(S(21)*f*sqrt(d*csc(e + f*x))) + S(10)*sqrt(d*csc(e + f*x))*EllipticF(-Pi/S(4) + e/S(2) + f*x/S(2), S(2))*sqrt(sin(e + f*x))/(S(21)*d*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(e + f*x)**S(2)/sqrt(d*csc(e + f*x)), x), x, -S(2)*d*cos(e + f*x)/(S(5)*f*(d*csc(e + f*x))**(S(3)/2)) + S(6)*EllipticE(-Pi/S(4) + e/S(2) + f*x/S(2), S(2))/(S(5)*f*sqrt(d*csc(e + f*x))*sqrt(sin(e + f*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(e + f*x)/sqrt(d*csc(e + f*x)), x), x, -S(2)*cos(e + f*x)/(S(3)*f*sqrt(d*csc(e + f*x))) + S(2)*sqrt(d*csc(e + f*x))*EllipticF(-Pi/S(4) + e/S(2) + f*x/S(2), S(2))*sqrt(sin(e + f*x))/(S(3)*d*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(S(1)/sqrt(d*csc(e + f*x)), x), x, S(2)*EllipticE(-Pi/S(4) + e/S(2) + f*x/S(2), S(2))/(f*sqrt(d*csc(e + f*x))*sqrt(sin(e + f*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(csc(e + f*x)/sqrt(d*csc(e + f*x)), x), x, S(2)*sqrt(d*csc(e + f*x))*EllipticF(-Pi/S(4) + e/S(2) + f*x/S(2), S(2))*sqrt(sin(e + f*x))/(d*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(csc(e + f*x)**S(2)/sqrt(d*csc(e + f*x)), x), x, -S(2)*EllipticE(-Pi/S(4) + e/S(2) + f*x/S(2), S(2))/(f*sqrt(d*csc(e + f*x))*sqrt(sin(e + f*x))) - S(2)*sqrt(d*csc(e + f*x))*cos(e + f*x)/(d*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(csc(e + f*x)**S(3)/sqrt(d*csc(e + f*x)), x), x, S(2)*sqrt(d*csc(e + f*x))*EllipticF(-Pi/S(4) + e/S(2) + f*x/S(2), S(2))*sqrt(sin(e + f*x))/(S(3)*d*f) - S(2)*(d*csc(e + f*x))**(S(3)/2)*cos(e + f*x)/(S(3)*d**S(2)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(e + f*x)**S(2)/(d*csc(e + f*x))**(S(3)/2), x), x, -S(2)*d*cos(e + f*x)/(S(7)*f*(d*csc(e + f*x))**(S(5)/2)) - S(10)*cos(e + f*x)/(S(21)*d*f*sqrt(d*csc(e + f*x))) + S(10)*sqrt(d*csc(e + f*x))*EllipticF(-Pi/S(4) + e/S(2) + f*x/S(2), S(2))*sqrt(sin(e + f*x))/(S(21)*d**S(2)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(sin(e + f*x)/(d*csc(e + f*x))**(S(3)/2), x), x, -S(2)*cos(e + f*x)/(S(5)*f*(d*csc(e + f*x))**(S(3)/2)) + S(6)*EllipticE(-Pi/S(4) + e/S(2) + f*x/S(2), S(2))/(S(5)*d*f*sqrt(d*csc(e + f*x))*sqrt(sin(e + f*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((d*csc(e + f*x))**(S(-3)/2), x), x, -S(2)*cos(e + f*x)/(S(3)*d*f*sqrt(d*csc(e + f*x))) + S(2)*sqrt(d*csc(e + f*x))*EllipticF(-Pi/S(4) + e/S(2) + f*x/S(2), S(2))*sqrt(sin(e + f*x))/(S(3)*d**S(2)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(csc(e + f*x)/(d*csc(e + f*x))**(S(3)/2), x), x, S(2)*EllipticE(-Pi/S(4) + e/S(2) + f*x/S(2), S(2))/(d*f*sqrt(d*csc(e + f*x))*sqrt(sin(e + f*x))), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(csc(e + f*x)**S(2)/(d*csc(e + f*x))**(S(3)/2), x), x, S(2)*sqrt(d*csc(e + f*x))*EllipticF(-Pi/S(4) + e/S(2) + f*x/S(2), S(2))*sqrt(sin(e + f*x))/(d**S(2)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(csc(e + f*x)**S(3)/(d*csc(e + f*x))**(S(3)/2), x), x, -S(2)*EllipticE(-Pi/S(4) + e/S(2) + f*x/S(2), S(2))/(d*f*sqrt(d*csc(e + f*x))*sqrt(sin(e + f*x))) - S(2)*sqrt(d*csc(e + f*x))*cos(e + f*x)/(d**S(2)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(csc(e + f*x)**S(4)/(d*csc(e + f*x))**(S(3)/2), x), x, S(2)*sqrt(d*csc(e + f*x))*EllipticF(-Pi/S(4) + e/S(2) + f*x/S(2), S(2))*sqrt(sin(e + f*x))/(S(3)*d**S(2)*f) - S(2)*(d*csc(e + f*x))**(S(3)/2)*cos(e + f*x)/(S(3)*d**S(3)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate(csc(e + f*x)**S(5)/(d*csc(e + f*x))**(S(3)/2), x), x, -S(6)*EllipticE(-Pi/S(4) + e/S(2) + f*x/S(2), S(2))/(S(5)*d*f*sqrt(d*csc(e + f*x))*sqrt(sin(e + f*x))) - S(6)*sqrt(d*csc(e + f*x))*cos(e + f*x)/(S(5)*d**S(2)*f) - S(2)*(d*csc(e + f*x))**(S(5)/2)*cos(e + f*x)/(S(5)*d**S(4)*f), expand=True, _diff=True, _numerical=True)
assert rubi_test(rubi_integrate((a*sin(e + f*x))**m*(b*csc(e + f*x))**n, x), x, (a*sin(e + f*x))**(m + S(1))*(b*csc(e + f*x))**n*Hypergeometric2F1(S(1)/2, m/S(2) - n/S(2) + S(1)/2, m/S(2) - n/S(2) + S(3)/2, sin(e + f*x)**S(2))*cos(e + f*x)/(a*f*(m - n + S(1))*sqrt(cos(e + f*x)**S(2))), expand=True, _diff=True, _numerical=True)
| 240.107402
| 1,406
| 0.535374
| 39,656
| 165,434
| 2.178283
| 0.015382
| 0.063601
| 0.074703
| 0.068811
| 0.918038
| 0.914739
| 0.911556
| 0.90718
| 0.899643
| 0.889005
| 0
| 0.049768
| 0.112873
| 165,434
| 688
| 1,407
| 240.456395
| 0.538822
| 0.001819
| 0
| 0.008902
| 0
| 0.001484
| 0.000505
| 0
| 0
| 0
| 0
| 0
| 0.796736
| 1
| 0.001484
| false
| 0
| 0.032641
| 0
| 0.034125
| 0
| 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
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 9
|
ed560a4fccf0d8a26b6cf41e1e69866aa0233ac6
| 363
|
py
|
Python
|
backup_codes/generated_xor_data.py
|
shridharmishra4/Artificial-neural-netowrks-in-C
|
40f89aa3da9b2cc036df372e5bf5b6e7031d0d87
|
[
"Apache-2.0"
] | null | null | null |
backup_codes/generated_xor_data.py
|
shridharmishra4/Artificial-neural-netowrks-in-C
|
40f89aa3da9b2cc036df372e5bf5b6e7031d0d87
|
[
"Apache-2.0"
] | null | null | null |
backup_codes/generated_xor_data.py
|
shridharmishra4/Artificial-neural-netowrks-in-C
|
40f89aa3da9b2cc036df372e5bf5b6e7031d0d87
|
[
"Apache-2.0"
] | null | null | null |
with open("validate.txt","w") as f:
for i in range(25):
f.writelines("{0} {1} {2}\n".format(0,0,0^0))
for i in range(25):
f.writelines("{0} {1} {2}\n".format(0, 1, 0 ^ 1))
for i in range(25):
f.writelines("{0} {1} {2}\n".format(1, 0, 1 ^ 0))
for i in range(25):
f.writelines("{0} {1} {2}\n".format(1, 1, 1 ^ 1))
| 27.923077
| 57
| 0.482094
| 71
| 363
| 2.464789
| 0.253521
| 0.08
| 0.137143
| 0.251429
| 0.811429
| 0.811429
| 0.811429
| 0.811429
| 0.811429
| 0.811429
| 0
| 0.134328
| 0.261708
| 363
| 12
| 58
| 30.25
| 0.518657
| 0
| 0
| 0.444444
| 0
| 0
| 0.180055
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 1
| 1
| 1
| 1
| 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
|
9c1dc3417d4b0cae09f5728d8a9a1215e29755db
| 5,608
|
py
|
Python
|
tests/test_syntax/inline/images.py
|
rossant/Python-Markdown
|
8c0698b013edeb82586290e637df7c30ede81b5a
|
[
"BSD-3-Clause"
] | null | null | null |
tests/test_syntax/inline/images.py
|
rossant/Python-Markdown
|
8c0698b013edeb82586290e637df7c30ede81b5a
|
[
"BSD-3-Clause"
] | null | null | null |
tests/test_syntax/inline/images.py
|
rossant/Python-Markdown
|
8c0698b013edeb82586290e637df7c30ede81b5a
|
[
"BSD-3-Clause"
] | null | null | null |
from markdown.test_tools import TestCase
class TestAdvancedImages(TestCase):
def test_nested_square_brackets(self):
self.assertMarkdownRenders(
"""![Text[[[[[[[]]]]]]][]](http://link.com/image.png) more text""",
"""<p><img alt="Text[[[[[[[]]]]]]][]" src="http://link.com/image.png" /> more text</p>"""
)
def test_nested_round_brackets(self):
self.assertMarkdownRenders(
"""))))))()).png) more text""",
"""<p><img alt="Text" src="http://link.com/(((((((()))))))()).png" /> more text</p>"""
)
def test_uneven_brackets_with_titles1(self):
self.assertMarkdownRenders(
"""![Text](http://link.com/(.png"title") more text""",
"""<p><img alt="Text" src="http://link.com/(.png" title="title" /> more text</p>"""
)
def test_uneven_brackets_with_titles2(self):
self.assertMarkdownRenders(
"""![Text](http://link.com/('.png"title") more text""",
"""<p><img alt="Text" src="http://link.com/('.png" title="title" /> more text</p>"""
)
def test_uneven_brackets_with_titles3(self):
self.assertMarkdownRenders(
"""![Text](http://link.com/(.png"title)") more text""",
"""<p><img alt="Text" src="http://link.com/(.png" title="title)" /> more text</p>"""
)
def test_uneven_brackets_with_titles4(self):
self.assertMarkdownRenders(
""" more text""",
"""<p><img alt="Text" src="http://link.com/(.png" title="title" /> more text</p>"""
)
def test_uneven_brackets_with_titles5(self):
self.assertMarkdownRenders(
""" more text""",
"""<p><img alt="Text" src="http://link.com/(.png" title="title)" /> more text</p>"""
)
def test_mixed_title_quotes1(self):
self.assertMarkdownRenders(
"""![Text](http://link.com/'.png"title") more text""",
"""<p><img alt="Text" src="http://link.com/'.png" title="title" /> more text</p>"""
)
def test_mixed_title_quotes2(self):
self.assertMarkdownRenders(
"""![Text](http://link.com/".png'title') more text""",
"""<p><img alt="Text" src="http://link.com/".png" title="title" /> more text</p>"""
)
def test_mixed_title_quotes3(self):
self.assertMarkdownRenders(
"""![Text](http://link.com/with spaces.png'"and quotes" 'and title') more text""",
"""<p><img alt="Text" src="http://link.com/with spaces.png" title=""and quotes" 'and title" />"""
""" more text</p>"""
)
def test_mixed_title_quotes4(self):
self.assertMarkdownRenders(
"""![Text](http://link.com/with spaces'.png"and quotes" 'and title") more text""",
"""<p><img alt="Text" src="http://link.com/with spaces'.png" title="and quotes" 'and title" />"""
""" more text</p>"""
)
def test_mixed_title_quotes5(self):
self.assertMarkdownRenders(
"""![Text](http://link.com/with spaces .png'"and quotes" 'and title') more text""",
"""<p><img alt="Text" src="http://link.com/with spaces .png" title=""and quotes""""
""" 'and title" /> more text</p>"""
)
def test_mixed_title_quotes6(self):
self.assertMarkdownRenders(
"""![Text](http://link.com/with spaces "and quotes".png 'and title') more text""",
"""<p><img alt="Text" src="http://link.com/with spaces "and quotes".png" title="and title" />"""
""" more text</p>"""
)
def test_single_quote(self):
self.assertMarkdownRenders(
"""![test](link"notitle.png)""",
"""<p><img alt="test" src="link"notitle.png" /></p>"""
)
def test_angle_with_mixed_title_quotes(self):
self.assertMarkdownRenders(
"""![Text](<http://link.com/with spaces '"and quotes".png> 'and title') more text""",
"""<p><img alt="Text" src="http://link.com/with spaces '"and quotes".png" title="and title" />"""
""" more text</p>"""
)
def test_misc(self):
self.assertMarkdownRenders(
"""""",
"""<p><img alt="Poster" src="http://humane_man.jpg" title="The most humane man." /></p>"""
)
def test_misc_ref(self):
self.assertMarkdownRenders(
self.dedent(
"""
![Poster][]
[Poster]:http://humane_man.jpg "The most humane man."
"""
),
self.dedent(
"""
<p><img alt="Poster" src="http://humane_man.jpg" title="The most humane man." /></p>
"""
)
)
def test_misc_blank(self):
self.assertMarkdownRenders(
"""![Blank]()""",
"""<p><img alt="Blank" src="" /></p>"""
)
def test_misc_img_title(self):
self.assertMarkdownRenders(
"""""",
"""<p><img alt="Image" src="http://humane man.jpg" title="The most humane man." /></p>"""
)
def test_misc_img(self):
self.assertMarkdownRenders(
"""""",
"""<p><img alt="Image" src="http://humane man.jpg" /></p>"""
)
| 40.057143
| 119
| 0.526034
| 647
| 5,608
| 4.457496
| 0.09119
| 0.07767
| 0.106796
| 0.116505
| 0.843967
| 0.837379
| 0.834951
| 0.834951
| 0.750347
| 0.722607
| 0
| 0.00264
| 0.256954
| 5,608
| 139
| 120
| 40.345324
| 0.689465
| 0
| 0
| 0.258824
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.235294
| 1
| 0.235294
| false
| 0
| 0.011765
| 0
| 0.258824
| 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
|
9c2352932d298fc00d115f31134fdaff41eb9e22
| 101
|
py
|
Python
|
Python/Tests/TestData/Grammar/ClassDef3x.py
|
nanshuiyu/pytools
|
9f9271fe8cf564b4f94e9456d400f4306ea77c23
|
[
"Apache-2.0"
] | null | null | null |
Python/Tests/TestData/Grammar/ClassDef3x.py
|
nanshuiyu/pytools
|
9f9271fe8cf564b4f94e9456d400f4306ea77c23
|
[
"Apache-2.0"
] | null | null | null |
Python/Tests/TestData/Grammar/ClassDef3x.py
|
nanshuiyu/pytools
|
9f9271fe8cf564b4f94e9456d400f4306ea77c23
|
[
"Apache-2.0"
] | null | null | null |
class C(metaclass=1): pass
class C(object, metaclass=1): pass
class C(list, object, fob=1): pass
| 25.25
| 35
| 0.693069
| 19
| 101
| 3.736842
| 0.473684
| 0.253521
| 0.394366
| 0.535211
| 0.56338
| 0
| 0
| 0
| 0
| 0
| 0
| 0.034884
| 0.148515
| 101
| 3
| 36
| 33.666667
| 0.77907
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 1
| 0
| null | null | 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
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 7
|
9c7a54f24104737efcd8b6d83b4fad34015e1189
| 2,233
|
py
|
Python
|
miniconda3-lnx/pkgs/cryptography-2.9.2-py37h1ba5d50_0/info/test/run_test.py
|
Thibaut-Kovaltchouk/MultiPyzo
|
a15ecf77e31ebeb195e70385f5ac132f6ab4504d
|
[
"CC0-1.0"
] | null | null | null |
miniconda3-lnx/pkgs/cryptography-2.9.2-py37h1ba5d50_0/info/test/run_test.py
|
Thibaut-Kovaltchouk/MultiPyzo
|
a15ecf77e31ebeb195e70385f5ac132f6ab4504d
|
[
"CC0-1.0"
] | null | null | null |
miniconda3-lnx/pkgs/cryptography-2.9.2-py37h1ba5d50_0/info/test/run_test.py
|
Thibaut-Kovaltchouk/MultiPyzo
|
a15ecf77e31ebeb195e70385f5ac132f6ab4504d
|
[
"CC0-1.0"
] | null | null | null |
# tests for cryptography-2.9.2-py37h1ba5d50_0 (this is a generated file);
print('===== testing package: cryptography-2.9.2-py37h1ba5d50_0 =====');
print('running run_test.py');
# --- run_test.py (begin) ---
import subprocess
import time
from cryptography.hazmat.backends.openssl import backend
# the version that cryptography uses
linked_version = backend.openssl_version_text()
# the version present in the conda environment
env_version = subprocess.check_output('openssl version', shell=True).decode('utf8').strip()
print('Version used by cryptography:\n{linked_version}'.format(linked_version=linked_version))
print('Version in conda environment:\n{env_version}'.format(env_version=env_version))
# avoid race condition between print and (possible) AssertionError
time.sleep(1)
# linking problems have appeared on windows before (see issue #38),
# and were only caught by lucky accident through the test suite.
# This is intended to ensure it does not happen again.
assert linked_version == env_version
# --- run_test.py (end) ---
print('===== cryptography-2.9.2-py37h1ba5d50_0 OK =====');
print("import: 'cryptography'")
import cryptography
print("import: 'cryptography.fernet'")
import cryptography.fernet
print("import: 'cryptography.hazmat'")
import cryptography.hazmat
print("import: 'cryptography.hazmat.backends'")
import cryptography.hazmat.backends
print("import: 'cryptography.hazmat.backends.openssl'")
import cryptography.hazmat.backends.openssl
print("import: 'cryptography.hazmat.bindings'")
import cryptography.hazmat.bindings
print("import: 'cryptography.hazmat.bindings.openssl'")
import cryptography.hazmat.bindings.openssl
print("import: 'cryptography.hazmat.primitives'")
import cryptography.hazmat.primitives
print("import: 'cryptography.hazmat.primitives.asymmetric'")
import cryptography.hazmat.primitives.asymmetric
print("import: 'cryptography.hazmat.primitives.ciphers'")
import cryptography.hazmat.primitives.ciphers
print("import: 'cryptography.hazmat.primitives.kdf'")
import cryptography.hazmat.primitives.kdf
print("import: 'cryptography.hazmat.primitives.twofactor'")
import cryptography.hazmat.primitives.twofactor
print("import: 'cryptography.x509'")
import cryptography.x509
| 33.833333
| 94
| 0.788177
| 276
| 2,233
| 6.307971
| 0.344203
| 0.268811
| 0.275704
| 0.166571
| 0.431936
| 0.048248
| 0
| 0
| 0
| 0
| 0
| 0.019569
| 0.08464
| 2,233
| 65
| 95
| 34.353846
| 0.832192
| 0.202866
| 0
| 0
| 1
| 0
| 0.42275
| 0.280136
| 0
| 0
| 0
| 0
| 0.026316
| 1
| 0
| false
| 0
| 0.763158
| 0
| 0.763158
| 0.473684
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 1
|
0
| 7
|
135585d421a2730371b73ae0165bf864988ddb30
| 415
|
py
|
Python
|
star4.py
|
anmol1455/python
|
8e3858bdebb21ec3c9e8147ceef17a82b4a36926
|
[
"bzip2-1.0.6"
] | null | null | null |
star4.py
|
anmol1455/python
|
8e3858bdebb21ec3c9e8147ceef17a82b4a36926
|
[
"bzip2-1.0.6"
] | null | null | null |
star4.py
|
anmol1455/python
|
8e3858bdebb21ec3c9e8147ceef17a82b4a36926
|
[
"bzip2-1.0.6"
] | null | null | null |
for i in range(1,4,1):
print()
for j in range(1,3):
print("*",end=" ")
for k in range(1,3):
print(" ",end=" ")
for j in range(1,3):
print("*",end=" ")
for i in range(1,3):
print()
for j in range(1,7):
print("*",end=" ")
for i in range(1,8,1):
print()
for j in range(1,3):
print(" ",end=" ")
for k in range(1,3):
print("*",end=" ")
| 21.842105
| 26
| 0.438554
| 71
| 415
| 2.56338
| 0.183099
| 0.346154
| 0.395604
| 0.296703
| 0.983516
| 0.912088
| 0.818681
| 0.659341
| 0.659341
| 0.527473
| 0
| 0.071942
| 0.33012
| 415
| 18
| 27
| 23.055556
| 0.582734
| 0
| 0
| 0.777778
| 0
| 0
| 0.028916
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.5
| 0
| 0
| 0
| null | 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 8
|
b93e078ae56275ddb2657451a96fcc0471f0fadb
| 57
|
py
|
Python
|
src/compiler.py
|
Mikerah/py-circom
|
5ea4c788342301a24b9245feb6421746876df630
|
[
"MIT"
] | 2
|
2020-04-18T12:58:44.000Z
|
2020-04-18T13:05:32.000Z
|
src/compiler.py
|
Mikerah/py-circom
|
5ea4c788342301a24b9245feb6421746876df630
|
[
"MIT"
] | null | null | null |
src/compiler.py
|
Mikerah/py-circom
|
5ea4c788342301a24b9245feb6421746876df630
|
[
"MIT"
] | null | null | null |
from Lark import Lark
def circom_compiler():
pass
| 8.142857
| 22
| 0.701754
| 8
| 57
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| 57
| 6
| 23
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| 0.333333
| true
| 0.333333
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| 0
| null | 0
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| null | 0
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| 1
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| 1
| 0
| 1
| 0
|
0
| 7
|
b94dcc36e2d626e18ccbca2731d79a8cf25be612
| 85
|
py
|
Python
|
src/stitch_m/scripts/commandline.py
|
iandobbie/StitchM
|
73e6692562c22106bf48454876050d14a6d52213
|
[
"BSD-3-Clause"
] | null | null | null |
src/stitch_m/scripts/commandline.py
|
iandobbie/StitchM
|
73e6692562c22106bf48454876050d14a6d52213
|
[
"BSD-3-Clause"
] | 5
|
2021-02-01T20:49:13.000Z
|
2021-09-09T21:20:35.000Z
|
src/stitch_m/scripts/commandline.py
|
iandobbie/StitchM
|
73e6692562c22106bf48454876050d14a6d52213
|
[
"BSD-3-Clause"
] | 1
|
2021-02-03T14:39:17.000Z
|
2021-02-03T14:39:17.000Z
|
from stitch_m import argparse_entrypoint
def main():
argparse_entrypoint.main()
| 17
| 40
| 0.788235
| 11
| 85
| 5.818182
| 0.727273
| 0.5625
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.141176
| 85
| 5
| 41
| 17
| 0.876712
| 0
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| 1
| 0.333333
| true
| 0
| 0.333333
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| 0.666667
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| 0
| null | 1
| 0
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| 0
| 1
| 0
|
0
| 7
|
b95bc40a653e805035b33629732db365d61193cf
| 26,566
|
py
|
Python
|
txdav/caldav/datastore/test/test_sql_external.py
|
eventable/CalendarServer
|
384444edb1966b530bc391789afbe3fb9cd6fd3e
|
[
"Apache-2.0"
] | 1
|
2017-02-18T19:22:19.000Z
|
2017-02-18T19:22:19.000Z
|
txdav/caldav/datastore/test/test_sql_external.py
|
eventable/CalendarServer
|
384444edb1966b530bc391789afbe3fb9cd6fd3e
|
[
"Apache-2.0"
] | null | null | null |
txdav/caldav/datastore/test/test_sql_external.py
|
eventable/CalendarServer
|
384444edb1966b530bc391789afbe3fb9cd6fd3e
|
[
"Apache-2.0"
] | null | null | null |
##
# Copyright (c) 2013-2015 Apple Inc. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
##
from twisted.internet.defer import inlineCallbacks
from twext.python.clsprop import classproperty
from txdav.common.datastore.test.util import populateCalendarsFrom
from txdav.common.datastore.sql_tables import _BIND_MODE_READ, \
_BIND_STATUS_INVITED, _BIND_MODE_DIRECT, _BIND_STATUS_ACCEPTED
from txdav.common.datastore.podding.test.util import MultiStoreConduitTest
class BaseSharingTests(MultiStoreConduitTest):
"""
Test store-based calendar sharing.
"""
@inlineCallbacks
def setUp(self):
yield super(BaseSharingTests, self).setUp()
yield self.populate()
@inlineCallbacks
def populate(self):
yield populateCalendarsFrom(self.requirements, self.theStoreUnderTest(0))
self.notifierFactory.reset()
cal1 = """BEGIN:VCALENDAR
VERSION:2.0
CALSCALE:GREGORIAN
PRODID:-//CALENDARSERVER.ORG//NONSGML Version 1//EN
BEGIN:VEVENT
UID:uid1
DTSTART:20131122T140000
DURATION:PT1H
CREATED:20060102T190000Z
DTSTAMP:20051222T210507Z
SUMMARY:event 1
END:VEVENT
END:VCALENDAR
"""
@classproperty(cache=False)
def requirements(cls): #@NoSelf
return {
"user01": {
"calendar": {
"cal1.ics": (cls.cal1, None,),
},
"inbox": {
},
},
"user02": {
"calendar": {
},
"inbox": {
},
},
"user03": {
"calendar": {
},
"inbox": {
},
},
}
class CalendarSharing(BaseSharingTests):
@inlineCallbacks
def test_no_shares(self):
"""
Test that initially there are no shares.
"""
calendar = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar")
invites = yield calendar.sharingInvites()
self.assertEqual(len(invites), 0)
self.assertFalse(calendar.isShared())
@inlineCallbacks
def test_invite_sharee(self):
"""
Test invite/uninvite creates/removes shares and notifications.
"""
# Invite
calendar = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar")
invites = yield calendar.sharingInvites()
self.assertEqual(len(invites), 0)
self.assertFalse(calendar.isShared())
shareeView = yield calendar.inviteUIDToShare("puser02", _BIND_MODE_READ, "summary")
invites = yield calendar.sharingInvites()
self.assertEqual(len(invites), 1)
self.assertEqual(invites[0].uid, shareeView.shareUID())
self.assertEqual(invites[0].ownerUID, "user01")
self.assertEqual(invites[0].shareeUID, "puser02")
self.assertEqual(invites[0].mode, _BIND_MODE_READ)
self.assertEqual(invites[0].status, _BIND_STATUS_INVITED)
self.assertEqual(invites[0].summary, "summary")
inviteUID = shareeView.shareUID()
sharedName = shareeView.name()
self.assertTrue(calendar.isShared())
yield self.commitTransaction(0)
shared = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(1), home="puser02", name=sharedName)
self.assertTrue(shared is None)
notifyHome = yield self.theTransactionUnderTest(1).notificationsWithUID("puser02")
notifications = yield notifyHome.listNotificationObjects()
self.assertEqual(notifications, [inviteUID, ])
yield self.commitTransaction(1)
# Uninvite
calendar = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar")
invites = yield calendar.sharingInvites()
self.assertEqual(len(invites), 1)
yield calendar.uninviteUIDFromShare("puser02")
invites = yield calendar.sharingInvites()
self.assertEqual(len(invites), 0)
self.assertTrue(calendar.isShared())
yield self.commitTransaction(0)
notifyHome = yield self.theTransactionUnderTest(1).notificationsWithUID("puser02")
notifications = yield notifyHome.listNotificationObjects()
self.assertEqual(notifications, [])
yield self.commitTransaction(1)
calendar = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar")
self.assertTrue(calendar.isShared())
yield calendar.setShared(False)
self.assertFalse(calendar.isShared())
@inlineCallbacks
def test_accept_share(self):
"""
Test that invite+accept creates shares and notifications.
"""
# Invite
calendar = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar")
invites = yield calendar.sharingInvites()
self.assertEqual(len(invites), 0)
self.assertFalse(calendar.isShared())
shareeView = yield calendar.inviteUIDToShare("puser02", _BIND_MODE_READ, "summary")
invites = yield calendar.sharingInvites()
self.assertEqual(len(invites), 1)
inviteUID = shareeView.shareUID()
sharedName = shareeView.name()
self.assertTrue(calendar.isShared())
yield self.commitTransaction(0)
shared = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(1), home="puser02", name=sharedName)
self.assertTrue(shared is None)
notifyHome = yield self.theTransactionUnderTest(1).notificationsWithUID("puser02")
notifications = yield notifyHome.listNotificationObjects()
self.assertEqual(len(notifications), 1)
yield self.commitTransaction(1)
# Accept
txn2 = self.theTransactionUnderTest(1)
shareeHome = yield self.homeUnderTest(txn=txn2, name="puser02")
yield shareeHome.acceptShare(inviteUID)
shared = yield self.calendarUnderTest(txn=txn2, home="puser02", name=sharedName)
self.assertTrue(shared is not None)
yield self.commitTransaction(1)
notifyHome = yield self.theTransactionUnderTest(0).notificationsWithUID("user01")
notifications = yield notifyHome.listNotificationObjects()
self.assertEqual(notifications, [inviteUID + "-reply", ])
calendar = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar")
self.assertTrue(calendar.isShared())
yield self.commitTransaction(0)
# Re-accept
txn2 = self.theTransactionUnderTest(1)
shareeHome = yield self.homeUnderTest(txn=txn2, name="puser02")
yield shareeHome.acceptShare(inviteUID)
shared = yield self.calendarUnderTest(txn=txn2, home="puser02", name=sharedName)
self.assertTrue(shared is not None)
yield self.commitTransaction(1)
notifyHome = yield self.theTransactionUnderTest(0).notificationsWithUID("user01")
notifications = yield notifyHome.listNotificationObjects()
self.assertEqual(notifications, [inviteUID + "-reply", ])
calendar = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar")
self.assertTrue(calendar.isShared())
@inlineCallbacks
def test_decline_share(self):
"""
Test that invite+decline does not create shares but does create notifications.
"""
# Invite
calendar = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar")
invites = yield calendar.sharingInvites()
self.assertEqual(len(invites), 0)
self.assertFalse(calendar.isShared())
shareeView = yield calendar.inviteUIDToShare("puser02", _BIND_MODE_READ, "summary")
invites = yield calendar.sharingInvites()
self.assertEqual(len(invites), 1)
inviteUID = shareeView.shareUID()
sharedName = shareeView.name()
self.assertTrue(calendar.isShared())
yield self.commitTransaction(0)
txn2 = self.theTransactionUnderTest(1)
shared = yield self.calendarUnderTest(txn=txn2, home="puser02", name=sharedName)
self.assertTrue(shared is None)
notifyHome = yield txn2.notificationsWithUID("puser02")
notifications = yield notifyHome.listNotificationObjects()
self.assertEqual(len(notifications), 1)
yield self.commitTransaction(1)
# Decline
txn2 = self.theTransactionUnderTest(1)
shareeHome = yield self.homeUnderTest(txn=txn2, name="puser02")
yield shareeHome.declineShare(inviteUID)
shared = yield self.calendarUnderTest(txn=txn2, home="puser02", name=sharedName)
self.assertTrue(shared is None)
yield self.commitTransaction(1)
notifyHome = yield self.theTransactionUnderTest(0).notificationsWithUID("user01")
notifications = yield notifyHome.listNotificationObjects()
self.assertEqual(notifications, [inviteUID + "-reply", ])
calendar = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar")
self.assertTrue(calendar.isShared())
yield self.commitTransaction(0)
# Redecline
txn2 = self.theTransactionUnderTest(1)
shareeHome = yield self.homeUnderTest(txn=txn2, name="puser02")
yield shareeHome.declineShare(inviteUID)
shared = yield self.calendarUnderTest(txn=txn2, home="puser02", name=sharedName)
self.assertTrue(shared is None)
yield self.commitTransaction(1)
notifyHome = yield self.theTransactionUnderTest(0).notificationsWithUID("user01")
notifications = yield notifyHome.listNotificationObjects()
self.assertEqual(notifications, [inviteUID + "-reply", ])
calendar = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar")
self.assertTrue(calendar.isShared())
@inlineCallbacks
def test_accept_decline_share(self):
"""
Test that invite+accept/decline creates/removes shares and notifications.
Decline via the home.
"""
# Invite
calendar = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar")
invites = yield calendar.sharingInvites()
self.assertEqual(len(invites), 0)
self.assertFalse(calendar.isShared())
shareeView = yield calendar.inviteUIDToShare("puser02", _BIND_MODE_READ, "summary")
invites = yield calendar.sharingInvites()
self.assertEqual(len(invites), 1)
inviteUID = shareeView.shareUID()
sharedName = shareeView.name()
self.assertTrue(calendar.isShared())
yield self.commitTransaction(0)
txn2 = self.theTransactionUnderTest(1)
shared = yield self.calendarUnderTest(txn=txn2, home="puser02", name=sharedName)
self.assertTrue(shared is None)
notifyHome = yield txn2.notificationsWithUID("puser02")
notifications = yield notifyHome.listNotificationObjects()
self.assertEqual(len(notifications), 1)
yield self.commitTransaction(1)
# Accept
txn2 = self.theTransactionUnderTest(1)
shareeHome = yield self.homeUnderTest(txn=txn2, name="puser02")
yield shareeHome.acceptShare(inviteUID)
shared = yield self.calendarUnderTest(txn=txn2, home="puser02", name=sharedName)
self.assertTrue(shared is not None)
yield self.commitTransaction(1)
notifyHome = yield self.theTransactionUnderTest(0).notificationsWithUID("user01")
notifications = yield notifyHome.listNotificationObjects()
self.assertEqual(notifications, [inviteUID + "-reply", ])
calendar = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar")
self.assertTrue(calendar.isShared())
yield self.commitTransaction(0)
# Decline
txn2 = self.theTransactionUnderTest(1)
shareeHome = yield self.homeUnderTest(txn=txn2, name="puser02")
yield shareeHome.declineShare(inviteUID)
shared = yield self.calendarUnderTest(txn=txn2, home="puser02", name=sharedName)
self.assertTrue(shared is None)
yield self.commitTransaction(1)
notifyHome = yield self.theTransactionUnderTest(0).notificationsWithUID("user01")
notifications = yield notifyHome.listNotificationObjects()
self.assertEqual(notifications, [inviteUID + "-reply", ])
calendar = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar")
self.assertTrue(calendar.isShared())
@inlineCallbacks
def test_accept_remove_share(self):
"""
Test that invite+accept/decline creates/removes shares and notifications.
Decline via the shared collection (removal).
"""
# Invite
calendar = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar")
invites = yield calendar.sharingInvites()
self.assertEqual(len(invites), 0)
shareeView = yield calendar.inviteUIDToShare("puser02", _BIND_MODE_READ, "summary")
invites = yield calendar.sharingInvites()
self.assertEqual(len(invites), 1)
inviteUID = shareeView.shareUID()
sharedName = shareeView.name()
yield self.commitTransaction(0)
txn2 = self.theTransactionUnderTest(1)
shared = yield self.calendarUnderTest(txn=txn2, home="puser02", name=sharedName)
self.assertTrue(shared is None)
notifyHome = yield txn2.notificationsWithUID("puser02")
notifications = yield notifyHome.listNotificationObjects()
self.assertEqual(len(notifications), 1)
yield self.commitTransaction(1)
# Accept
txn2 = self.theTransactionUnderTest(1)
shareeHome = yield self.homeUnderTest(txn=txn2, name="puser02")
yield shareeHome.acceptShare(inviteUID)
shared = yield self.calendarUnderTest(txn=txn2, home="puser02", name=sharedName)
self.assertTrue(shared is not None)
yield self.commitTransaction(1)
notifyHome = yield self.theTransactionUnderTest(0).notificationsWithUID("user01")
notifications = yield notifyHome.listNotificationObjects()
self.assertEqual(notifications, [inviteUID + "-reply", ])
yield self.commitTransaction(0)
# Delete
txn2 = self.theTransactionUnderTest(1)
shared = yield self.calendarUnderTest(txn=txn2, home="puser02", name=sharedName)
yield shared.deleteShare()
yield self.commitTransaction(1)
txn2 = self.theTransactionUnderTest(1)
shared = yield self.calendarUnderTest(txn=txn2, home="puser02", name=sharedName)
self.assertTrue(shared is None)
yield self.commitTransaction(1)
notifyHome = yield self.theTransactionUnderTest(0).notificationsWithUID("user01")
notifications = yield notifyHome.listNotificationObjects()
self.assertEqual(notifications, [inviteUID + "-reply", ])
@inlineCallbacks
def test_accept_remove_accept(self):
yield self.createShare()
yield self.removeShare()
shared_name = yield self.createShare()
txn2 = self.theTransactionUnderTest(1)
otherCal = yield self.calendarUnderTest(txn=txn2, home="puser02", name=shared_name)
self.assertTrue(otherCal is not None)
yield self.commitTransaction(1)
@inlineCallbacks
def test_accept_remove_accept_newcalendar(self):
"""
Test that deleting and re-creating a share with the same sharer name works.
"""
home = yield self.homeUnderTest(txn=self.theTransactionUnderTest(0), name="user01", create=True)
yield home.createCalendarWithName("shared")
yield self.commitTransaction(0)
shared_name = yield self.createShare(name="shared")
txn2 = self.theTransactionUnderTest(1)
otherCal = yield self.calendarUnderTest(txn=txn2, home="puser02", name=shared_name)
self.assertTrue(otherCal is not None)
yield self.commitTransaction(1)
yield self.removeShare(name="shared")
home = yield self.homeUnderTest(txn=self.theTransactionUnderTest(0), name="user01", create=True)
yield home.removeCalendarWithName("shared")
yield self.commitTransaction(0)
txn2 = self.theTransactionUnderTest(1)
otherCal = yield self.calendarUnderTest(txn=txn2, home="puser02", name=shared_name)
self.assertTrue(otherCal is None)
yield self.commitTransaction(1)
home = yield self.homeUnderTest(txn=self.theTransactionUnderTest(0), name="user01", create=True)
yield home.createCalendarWithName("shared")
yield self.commitTransaction(0)
shared_name = yield self.createShare(name="shared")
txn2 = self.theTransactionUnderTest(1)
otherCal = yield self.calendarUnderTest(txn=txn2, home="puser02", name=shared_name)
self.assertTrue(otherCal is not None)
yield self.commitTransaction(1)
@inlineCallbacks
def test_inviteProperties(self):
calendar = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar")
yield calendar.setUsedForFreeBusy(True)
yield self.commitTransaction(0)
shared_name = yield self.createShare()
txn2 = self.theTransactionUnderTest(1)
shared = yield self.calendarUnderTest(txn=txn2, home="puser02", name=shared_name)
self.assertFalse(shared.isUsedForFreeBusy())
@inlineCallbacks
def test_direct_sharee(self):
"""
Test invite/uninvite creates/removes shares and notifications.
"""
# Invite
calendar = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar")
invites = yield calendar.sharingInvites()
self.assertEqual(len(invites), 0)
self.assertFalse(calendar.isShared())
shareeView = yield calendar.directShareWithUser("puser02")
invites = yield calendar.sharingInvites()
self.assertEqual(len(invites), 1)
self.assertEqual(invites[0].uid, shareeView.shareUID())
self.assertEqual(invites[0].ownerUID, "user01")
self.assertEqual(invites[0].shareeUID, "puser02")
self.assertEqual(invites[0].mode, _BIND_MODE_DIRECT)
self.assertEqual(invites[0].status, _BIND_STATUS_ACCEPTED)
sharedName = shareeView.name()
yield self.commitTransaction(0)
txn2 = self.theTransactionUnderTest(1)
shared = yield self.calendarUnderTest(txn=txn2, home="user02", name=sharedName)
self.assertTrue(shared is not None)
notifyHome = yield txn2.notificationsWithUID("user02")
notifications = yield notifyHome.listNotificationObjects()
self.assertEqual(len(notifications), 0)
yield self.commitTransaction(1)
# Remove
txn2 = self.theTransactionUnderTest(1)
shared = yield self.calendarUnderTest(txn=txn2, home="puser02", name=sharedName)
yield shared.deleteShare()
yield self.commitTransaction(1)
calendar = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar")
invites = yield calendar.sharingInvites()
self.assertEqual(len(invites), 0)
notifyHome = yield self.theTransactionUnderTest(0).notificationsWithUID("user01")
notifications = yield notifyHome.listNotificationObjects()
self.assertEqual(len(notifications), 0)
test_direct_sharee.skip = True
@inlineCallbacks
def test_sharedNotifierID(self):
shared_name = yield self.createShare()
home = yield self.homeUnderTest(txn=self.theTransactionUnderTest(0), name="user01")
self.assertEquals(home.notifierID(), ("CalDAV", "user01",))
calendar = yield home.calendarWithName("calendar")
self.assertEquals(calendar.notifierID(), ("CalDAV", "user01/calendar",))
yield self.commitTransaction(0)
txn2 = self.theTransactionUnderTest(1)
home = yield self.homeUnderTest(txn=txn2, name="puser02")
self.assertEquals(home.notifierID(), ("CalDAV", "puser02",))
calendar = yield home.calendarWithName(shared_name)
self.assertEquals(calendar.notifierID(), ("CalDAV", "user01/calendar",))
@inlineCallbacks
def test_sharedWithTwo(self):
shared_name1 = yield self.createShare(shareeGUID="puser02")
shared_name2 = yield self.createShare(shareeGUID="puser03")
txn2 = self.theTransactionUnderTest(1)
otherCal = yield self.calendarUnderTest(txn=txn2, home="puser02", name=shared_name1)
self.assertTrue(otherCal is not None)
yield self.commitTransaction(1)
txn2 = self.theTransactionUnderTest(1)
otherCal = yield self.calendarUnderTest(txn=txn2, home="puser03", name=shared_name2)
self.assertTrue(otherCal is not None)
yield self.commitTransaction(1)
class SharingRevisions(BaseSharingTests):
"""
Test store-based sharing and interaction with revision table.
"""
@inlineCallbacks
def test_shareWithRevision(self):
"""
Verify that bindRevision on calendars and shared calendars has the correct value.
"""
sharedName = yield self.createShare()
normalCal = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar")
self.assertEqual(normalCal._bindRevision, 0)
txn2 = self.theTransactionUnderTest(1)
otherCal = yield self.calendarUnderTest(txn=txn2, home="puser02", name=sharedName)
self.assertNotEqual(otherCal._bindRevision, 0)
@inlineCallbacks
def test_updateShareRevision(self):
"""
Verify that bindRevision on calendars and shared calendars has the correct value.
"""
# Invite
calendar = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar")
invites = yield calendar.sharingInvites()
self.assertEqual(len(invites), 0)
shareeView = yield calendar.inviteUIDToShare("puser02", _BIND_MODE_READ, "summary")
newCalName = shareeView.shareUID()
yield self.commitTransaction(0)
normalCal = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar")
self.assertEqual(normalCal._bindRevision, 0)
yield self.commitTransaction(0)
txn2 = self.theTransactionUnderTest(1)
otherHome = yield self.homeUnderTest(txn=txn2, name="puser02")
otherCal = yield otherHome.anyObjectWithShareUID(newCalName)
self.assertEqual(otherCal._bindRevision, 0)
yield self.commitTransaction(1)
txn2 = self.theTransactionUnderTest(1)
shareeHome = yield self.homeUnderTest(txn=txn2, name="puser02")
shareeView = yield shareeHome.acceptShare(newCalName)
sharedName = shareeView.name()
yield self.commitTransaction(1)
normalCal = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar")
self.assertEqual(normalCal._bindRevision, 0)
txn2 = self.theTransactionUnderTest(1)
otherCal = yield self.calendarUnderTest(txn=txn2, home="puser02", name=sharedName)
self.assertNotEqual(otherCal._bindRevision, 0)
@inlineCallbacks
def test_sharedRevisions(self):
"""
Verify that resourceNamesSinceRevision returns all resources after initial bind and sync.
"""
sharedName = yield self.createShare()
normalCal = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar")
self.assertEqual(normalCal._bindRevision, 0)
txn2 = self.theTransactionUnderTest(1)
otherHome = yield self.homeUnderTest(txn=txn2, name="puser02")
otherCal = yield self.calendarUnderTest(txn=txn2, home="puser02", name=sharedName)
self.assertNotEqual(otherCal._bindRevision, 0)
sync_token = yield otherCal.syncToken()
revision = otherCal.revisionFromToken(sync_token)
changed, deleted, invalid = yield otherCal.resourceNamesSinceRevision(0)
self.assertNotEqual(len(changed), 0)
self.assertEqual(len(deleted), 0)
self.assertEqual(len(invalid), 0)
changed, deleted, invalid = yield otherCal.resourceNamesSinceRevision(revision)
self.assertEqual(len(changed), 0)
self.assertEqual(len(deleted), 0)
self.assertEqual(len(invalid), 0)
sync_token = yield otherHome.syncToken()
revision = otherHome.revisionFromToken(sync_token)
for depth in ("1", "infinity",):
changed, deleted, invalid = yield otherHome.resourceNamesSinceRevision(revision - 1, depth)
self.assertEqual(len(changed), 0 if depth == "infinity" else 1)
self.assertEqual(len(deleted), 0)
self.assertEqual(len(invalid), 1 if depth == "infinity" else 0)
changed, deleted, invalid = yield otherHome.resourceNamesSinceRevision(revision, depth)
self.assertEqual(len(changed), 0)
self.assertEqual(len(deleted), 0)
self.assertEqual(len(invalid), 1 if depth == "infinity" else 0)
yield self.commitTransaction(1)
yield self.removeShare()
txn2 = self.theTransactionUnderTest(1)
otherHome = yield self.homeUnderTest(txn=txn2, name="puser02")
for depth in ("1", "infinity",):
changed, deleted, invalid = yield otherHome.resourceNamesSinceRevision(revision, depth)
self.assertEqual(len(changed), 0)
self.assertEqual(len(deleted), 1)
self.assertEqual(len(invalid), 0)
| 38.896047
| 117
| 0.682414
| 2,578
| 26,566
| 6.993018
| 0.103569
| 0.066896
| 0.069226
| 0.077213
| 0.834813
| 0.821611
| 0.801309
| 0.780896
| 0.767417
| 0.756213
| 0
| 0.024622
| 0.214184
| 26,566
| 682
| 118
| 38.953079
| 0.838954
| 0.062335
| 0
| 0.763514
| 0
| 0
| 0.055339
| 0.004395
| 0
| 0
| 0
| 0
| 0.25
| 1
| 0.040541
| false
| 0
| 0.011261
| 0.002252
| 0.063063
| 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
|
b97ac8897802026797a1a65c6bbac982555d92e9
| 11,393
|
py
|
Python
|
api_1.3/containerd/services/images/v1/images_pb2_grpc.py
|
siemens/pycontainerd
|
9b1184ecbcc91144ad6903403818b5b8989a32f3
|
[
"Apache-2.0"
] | 24
|
2019-12-16T12:38:51.000Z
|
2022-02-16T18:44:20.000Z
|
api_1.5/containerd/services/images/v1/images_pb2_grpc.py
|
siemens/pycontainerd
|
9b1184ecbcc91144ad6903403818b5b8989a32f3
|
[
"Apache-2.0"
] | 9
|
2020-03-03T07:42:40.000Z
|
2021-09-01T10:11:18.000Z
|
api_1.4/containerd/services/images/v1/images_pb2_grpc.py
|
siemens/pycontainerd
|
9b1184ecbcc91144ad6903403818b5b8989a32f3
|
[
"Apache-2.0"
] | 10
|
2019-12-16T11:20:23.000Z
|
2022-01-24T01:53:13.000Z
|
# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT!
"""Client and server classes corresponding to protobuf-defined services."""
import grpc
from containerd.services.images.v1 import images_pb2 as containerd_dot_services_dot_images_dot_v1_dot_images__pb2
from google.protobuf import empty_pb2 as google_dot_protobuf_dot_empty__pb2
class ImagesStub(object):
"""Images is a service that allows one to register images with containerd.
In containerd, an image is merely the mapping of a name to a content root,
described by a descriptor. The behavior and state of image is purely
dictated by the type of the descriptor.
From the perspective of this service, these references are mostly shallow,
in that the existence of the required content won't be validated until
required by consuming services.
As such, this can really be considered a "metadata service".
"""
def __init__(self, channel):
"""Constructor.
Args:
channel: A grpc.Channel.
"""
self.Get = channel.unary_unary(
'/containerd.services.images.v1.Images/Get',
request_serializer=containerd_dot_services_dot_images_dot_v1_dot_images__pb2.GetImageRequest.SerializeToString,
response_deserializer=containerd_dot_services_dot_images_dot_v1_dot_images__pb2.GetImageResponse.FromString,
)
self.List = channel.unary_unary(
'/containerd.services.images.v1.Images/List',
request_serializer=containerd_dot_services_dot_images_dot_v1_dot_images__pb2.ListImagesRequest.SerializeToString,
response_deserializer=containerd_dot_services_dot_images_dot_v1_dot_images__pb2.ListImagesResponse.FromString,
)
self.Create = channel.unary_unary(
'/containerd.services.images.v1.Images/Create',
request_serializer=containerd_dot_services_dot_images_dot_v1_dot_images__pb2.CreateImageRequest.SerializeToString,
response_deserializer=containerd_dot_services_dot_images_dot_v1_dot_images__pb2.CreateImageResponse.FromString,
)
self.Update = channel.unary_unary(
'/containerd.services.images.v1.Images/Update',
request_serializer=containerd_dot_services_dot_images_dot_v1_dot_images__pb2.UpdateImageRequest.SerializeToString,
response_deserializer=containerd_dot_services_dot_images_dot_v1_dot_images__pb2.UpdateImageResponse.FromString,
)
self.Delete = channel.unary_unary(
'/containerd.services.images.v1.Images/Delete',
request_serializer=containerd_dot_services_dot_images_dot_v1_dot_images__pb2.DeleteImageRequest.SerializeToString,
response_deserializer=google_dot_protobuf_dot_empty__pb2.Empty.FromString,
)
class ImagesServicer(object):
"""Images is a service that allows one to register images with containerd.
In containerd, an image is merely the mapping of a name to a content root,
described by a descriptor. The behavior and state of image is purely
dictated by the type of the descriptor.
From the perspective of this service, these references are mostly shallow,
in that the existence of the required content won't be validated until
required by consuming services.
As such, this can really be considered a "metadata service".
"""
def Get(self, request, context):
"""Get returns an image by name.
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def List(self, request, context):
"""List returns a list of all images known to containerd.
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def Create(self, request, context):
"""Create an image record in the metadata store.
The name of the image must be unique.
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def Update(self, request, context):
"""Update assigns the name to a given target image based on the provided
image.
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def Delete(self, request, context):
"""Delete deletes the image by name.
"""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def add_ImagesServicer_to_server(servicer, server):
rpc_method_handlers = {
'Get': grpc.unary_unary_rpc_method_handler(
servicer.Get,
request_deserializer=containerd_dot_services_dot_images_dot_v1_dot_images__pb2.GetImageRequest.FromString,
response_serializer=containerd_dot_services_dot_images_dot_v1_dot_images__pb2.GetImageResponse.SerializeToString,
),
'List': grpc.unary_unary_rpc_method_handler(
servicer.List,
request_deserializer=containerd_dot_services_dot_images_dot_v1_dot_images__pb2.ListImagesRequest.FromString,
response_serializer=containerd_dot_services_dot_images_dot_v1_dot_images__pb2.ListImagesResponse.SerializeToString,
),
'Create': grpc.unary_unary_rpc_method_handler(
servicer.Create,
request_deserializer=containerd_dot_services_dot_images_dot_v1_dot_images__pb2.CreateImageRequest.FromString,
response_serializer=containerd_dot_services_dot_images_dot_v1_dot_images__pb2.CreateImageResponse.SerializeToString,
),
'Update': grpc.unary_unary_rpc_method_handler(
servicer.Update,
request_deserializer=containerd_dot_services_dot_images_dot_v1_dot_images__pb2.UpdateImageRequest.FromString,
response_serializer=containerd_dot_services_dot_images_dot_v1_dot_images__pb2.UpdateImageResponse.SerializeToString,
),
'Delete': grpc.unary_unary_rpc_method_handler(
servicer.Delete,
request_deserializer=containerd_dot_services_dot_images_dot_v1_dot_images__pb2.DeleteImageRequest.FromString,
response_serializer=google_dot_protobuf_dot_empty__pb2.Empty.SerializeToString,
),
}
generic_handler = grpc.method_handlers_generic_handler(
'containerd.services.images.v1.Images', rpc_method_handlers)
server.add_generic_rpc_handlers((generic_handler,))
# This class is part of an EXPERIMENTAL API.
class Images(object):
"""Images is a service that allows one to register images with containerd.
In containerd, an image is merely the mapping of a name to a content root,
described by a descriptor. The behavior and state of image is purely
dictated by the type of the descriptor.
From the perspective of this service, these references are mostly shallow,
in that the existence of the required content won't be validated until
required by consuming services.
As such, this can really be considered a "metadata service".
"""
@staticmethod
def Get(request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
insecure=False,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None):
return grpc.experimental.unary_unary(request, target, '/containerd.services.images.v1.Images/Get',
containerd_dot_services_dot_images_dot_v1_dot_images__pb2.GetImageRequest.SerializeToString,
containerd_dot_services_dot_images_dot_v1_dot_images__pb2.GetImageResponse.FromString,
options, channel_credentials,
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
@staticmethod
def List(request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
insecure=False,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None):
return grpc.experimental.unary_unary(request, target, '/containerd.services.images.v1.Images/List',
containerd_dot_services_dot_images_dot_v1_dot_images__pb2.ListImagesRequest.SerializeToString,
containerd_dot_services_dot_images_dot_v1_dot_images__pb2.ListImagesResponse.FromString,
options, channel_credentials,
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
@staticmethod
def Create(request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
insecure=False,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None):
return grpc.experimental.unary_unary(request, target, '/containerd.services.images.v1.Images/Create',
containerd_dot_services_dot_images_dot_v1_dot_images__pb2.CreateImageRequest.SerializeToString,
containerd_dot_services_dot_images_dot_v1_dot_images__pb2.CreateImageResponse.FromString,
options, channel_credentials,
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
@staticmethod
def Update(request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
insecure=False,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None):
return grpc.experimental.unary_unary(request, target, '/containerd.services.images.v1.Images/Update',
containerd_dot_services_dot_images_dot_v1_dot_images__pb2.UpdateImageRequest.SerializeToString,
containerd_dot_services_dot_images_dot_v1_dot_images__pb2.UpdateImageResponse.FromString,
options, channel_credentials,
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
@staticmethod
def Delete(request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
insecure=False,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None):
return grpc.experimental.unary_unary(request, target, '/containerd.services.images.v1.Images/Delete',
containerd_dot_services_dot_images_dot_v1_dot_images__pb2.DeleteImageRequest.SerializeToString,
google_dot_protobuf_dot_empty__pb2.Empty.FromString,
options, channel_credentials,
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
| 47.273859
| 136
| 0.698762
| 1,260
| 11,393
| 5.980952
| 0.119841
| 0.066879
| 0.078025
| 0.089172
| 0.857086
| 0.85284
| 0.842489
| 0.812898
| 0.770303
| 0.770303
| 0
| 0.008531
| 0.238655
| 11,393
| 240
| 137
| 47.470833
| 0.860272
| 0.178355
| 0
| 0.490798
| 1
| 0
| 0.078789
| 0.050923
| 0
| 0
| 0
| 0
| 0
| 1
| 0.07362
| false
| 0
| 0.018405
| 0.030675
| 0.141104
| 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
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
b987499f9b20405c786ea15f917a86bdeebc41e0
| 1,548
|
py
|
Python
|
code/sm_matrices.py
|
naoufal51/orion
|
0beb3020a3ca0fa7a4113db23ef4767ac3d63624
|
[
"MIT"
] | 13
|
2017-04-15T16:46:34.000Z
|
2019-05-07T06:33:23.000Z
|
code/sm_matrices.py
|
naoufal51/orion
|
0beb3020a3ca0fa7a4113db23ef4767ac3d63624
|
[
"MIT"
] | 3
|
2017-07-26T09:01:26.000Z
|
2020-10-01T16:25:08.000Z
|
code/sm_matrices.py
|
naoufal51/orion
|
0beb3020a3ca0fa7a4113db23ef4767ac3d63624
|
[
"MIT"
] | 8
|
2017-08-08T21:15:35.000Z
|
2021-08-07T09:12:54.000Z
|
"""sm_matrices.py
sm matrices used by the intel 5300 agn
Naoufal Mahfoudi (c) 2016 mohamed-naoufal.mahfoudi@inria.fr
"""
import numpy as np
# sm_1 = 1
#
# sm_2_20 = np.matrix('1 1; 1 -1') / np.sqrt(2)
#
# sm_2_40 = np.matrix('1 1; 1j 1') / np.sqrt(2)
#
# sm_3_20 = np.matrix('-2*np.pi/16 -2*np.pi/(80/33) 2*np.pi/(80/3);'
# '2*np.pi/(80/23) 2*np.pi/(48/13) 2*np.pi/(240/13);'
# '-2*np.pi/(80/13) 2*np.pi/(240/37) 2*np.pi/(48/13)')
# sm_3_20 = np.exp(1j * sm_3_20) / np.sqrt(3)
#
# sm_3_40 = np.matrix('-2*np.pi/16 -2*np.pi/(80/13) 2*np.pi/(80/23);'
# '2*np.pi/(80/37) 2*np.pi/(48/11) 2*np.pi/(240/107);'
# '-2*np.pi/(80/7) 2*np.pi/(240/83) 2*np.pi/(48/11)')
# sm_3_20 = np.exp(1j * sm_3_40) / np.sqrt(3)
sm_1 = 1
sm_2_20 = np.matrix([[1, 1], [1, -1]]) / np.sqrt(2)
sm_2_40 = np.matrix([[1, 1], [1j, 1]]) / np.sqrt(2)
sm_3_20 = np.matrix([[-2 * np.pi / 16, -2 * np.pi / (80 / 33), 2 * np.pi / (80 / 3)],
[2 * np.pi / (80 / 23), 2 * np.pi / (48 / 13), 2 * np.pi / (240 / 13)],
[-2 * np.pi / (80 / 13), 2 * np.pi / (240 / 37), 2 * np.pi / (48 / 13)]])
sm_3_20 = np.exp(1j * sm_3_20) / np.sqrt(3)
sm_3_40 = np.matrix([[-2 * np.pi / 16, -2 * np.pi / (80 / 13), 2 * np.pi / (80 / 23)],
[-2 * np.pi / (80 / 37), -2 * np.pi / (48 / 11), -2 * np.pi / (240 / 107)],
[2 * np.pi / (80 / 7), -2 * np.pi / (240 / 83), -2 * np.pi / (48 / 11)]])
sm_3_40 = np.exp(1j * sm_3_40) / np.sqrt(3)
| 36.857143
| 96
| 0.463824
| 322
| 1,548
| 2.121118
| 0.130435
| 0.158126
| 0.263543
| 0.163982
| 0.84041
| 0.84041
| 0.84041
| 0.84041
| 0.837482
| 0.781845
| 0
| 0.236234
| 0.27261
| 1,548
| 41
| 97
| 37.756098
| 0.370337
| 0.471576
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.083333
| 0
| 0.083333
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 10
|
b99aeec863c277d8b0ead15aec523a9ccb9ea599
| 17,685
|
py
|
Python
|
tests/api/crud/test_trigger_tags.py
|
bossjones/ultron8
|
45db73d32542a844570d44bc83defa935e15803f
|
[
"Apache-2.0",
"MIT"
] | null | null | null |
tests/api/crud/test_trigger_tags.py
|
bossjones/ultron8
|
45db73d32542a844570d44bc83defa935e15803f
|
[
"Apache-2.0",
"MIT"
] | 43
|
2019-06-01T23:08:32.000Z
|
2022-02-07T22:24:53.000Z
|
tests/api/crud/test_trigger_tags.py
|
bossjones/ultron8
|
45db73d32542a844570d44bc83defa935e15803f
|
[
"Apache-2.0",
"MIT"
] | null | null | null |
from fastapi.encoders import jsonable_encoder
from freezegun import freeze_time
import pytest
from sqlalchemy.orm import Session
from ultron8.api import crud
from ultron8.api.db_models.trigger import TriggerDB
from ultron8.api.models.packs import PacksCreate
from ultron8.api.models.trigger import (
TriggerBaseDB,
TriggerBaseInDB,
TriggerCreate,
TriggerInstanceBaseDB,
TriggerInstanceBaseInDB,
TriggerInstanceCreate,
TriggerInstanceUpdate,
TriggerTagsBase,
TriggerTagsBaseInDB,
TriggerTagsCreate,
TriggerTagsUpdate,
TriggerTypeBase,
TriggerTypeBaseInDB,
TriggerTypeCreate,
TriggerTypeUpdate,
TriggerUpdate,
)
from tests.utils.utils import random_lower_string
TRIGGER_0 = {
"name": "ultron8.test.trigger0",
"pack": "dummy_pack_1",
"description": "test trigger",
"type": "dummy_pack_1.ultron8.test.triggertype0",
"parameters": {},
}
TRIGGER_1 = {
"name": "ultron8.test.trigger1",
"pack": "dummy_pack_1",
"description": "test trigger",
"type": "dummy_pack_1.ultron8.test.triggertype1",
"parameters": {},
}
TRIGGER_2 = {
"name": "ultron8.test.trigger2",
"pack": "dummy_pack_1",
"description": "test trigger",
"type": "dummy_pack_1.ultron8.test.triggertype2",
"parameters": {"param1": {"foo": "bar"}},
}
TRIGGERTYPE_0 = {
"name": "ultron8.test.triggertype0",
"pack": "dummy_pack_1",
"description": "test trigger",
"payload_schema": {"tp1": None, "tp2": None, "tp3": None},
"parameters_schema": {},
}
TRIGGERTYPE_1 = {
"name": "ultron8.test.triggertype1",
"pack": "dummy_pack_1",
"description": "test trigger",
"payload_schema": {"tp1": None, "tp2": None, "tp3": None},
}
TRIGGERTYPE_2 = {
"name": "ultron8.test.triggertype2",
"pack": "dummy_pack_1",
"description": "test trigger",
"payload_schema": {"tp1": None, "tp2": None, "tp3": None},
"parameters_schema": {"param1": {"type": "object"}},
}
@freeze_time("2019-07-25 01:11:00.740428")
@pytest.mark.triggeronly
@pytest.mark.unittest
def test_create_trigger_tags(db: Session) -> None:
packs_shared_name = random_lower_string()
packs_name = packs_shared_name
packs_description = random_lower_string()
packs_keywords = random_lower_string()
packs_version = random_lower_string()
packs_python_versions = random_lower_string()
packs_author = random_lower_string()
packs_email = "info@theblacktonystark.com"
packs_contributors = random_lower_string()
packs_files = random_lower_string()
packs_path = random_lower_string()
packs_ref = packs_shared_name
trigger_name = TRIGGER_0["name"]
trigger_packs_name = packs_name
trigger_description = TRIGGER_0["description"]
trigger_type = TRIGGER_0["type"]
trigger_parameters = TRIGGER_0["parameters"]
packs_in = PacksCreate(
name=packs_name,
description=packs_description,
keywords=packs_keywords,
version=packs_version,
python_versions=packs_python_versions,
author=packs_author,
email=packs_email,
contributors=packs_contributors,
files=packs_files,
path=packs_path,
ref=packs_ref,
)
packs = crud.packs.create(db, packs_in=packs_in)
trigger_in = TriggerCreate(
name=trigger_name,
packs_name=trigger_packs_name,
description=trigger_description,
type=trigger_type,
parameters=trigger_parameters,
)
trigger = crud.trigger.create(db, trigger_in=trigger_in, packs_id=packs.id)
assert trigger.name == trigger_name
assert trigger.packs_name == trigger_packs_name
assert trigger.description == trigger_description
assert trigger.type == trigger_type
assert trigger.parameters == trigger_parameters
@freeze_time("2019-07-25 01:11:00.740428")
@pytest.mark.triggeronly
@pytest.mark.unittest
def test_get_trigger_tags(db: Session) -> None:
packs_shared_name = random_lower_string()
packs_name = packs_shared_name
packs_description = random_lower_string()
packs_keywords = random_lower_string()
packs_version = random_lower_string()
packs_python_versions = random_lower_string()
packs_author = random_lower_string()
packs_email = "info@theblacktonystark.com"
packs_contributors = random_lower_string()
packs_files = random_lower_string()
packs_path = random_lower_string()
packs_ref = packs_shared_name
trigger_name = TRIGGER_0["name"]
trigger_packs_name = packs_name
trigger_description = TRIGGER_0["description"]
trigger_type = TRIGGER_0["type"]
trigger_parameters = TRIGGER_0["parameters"]
packs_in = PacksCreate(
name=packs_name,
description=packs_description,
keywords=packs_keywords,
version=packs_version,
python_versions=packs_python_versions,
author=packs_author,
email=packs_email,
contributors=packs_contributors,
files=packs_files,
path=packs_path,
ref=packs_ref,
)
packs = crud.packs.create(db, packs_in=packs_in)
trigger_in = TriggerCreate(
name=trigger_name,
packs_name=trigger_packs_name,
description=trigger_description,
type=trigger_type,
parameters=trigger_parameters,
)
trigger = crud.trigger.create(db, trigger_in=trigger_in, packs_id=packs.id)
trigger_2 = crud.trigger.get(db, trigger_id=trigger.id)
assert jsonable_encoder(trigger) == jsonable_encoder(trigger_2)
@freeze_time("2019-07-25 01:11:00.740428")
@pytest.mark.triggeronly
@pytest.mark.unittest
def test_get_by_ref_trigger_tags(db: Session) -> None:
pack_shared_name = random_lower_string()
packs_name = pack_shared_name
packs_description = random_lower_string()
packs_keywords = random_lower_string()
packs_version = random_lower_string()
packs_python_versions = random_lower_string()
packs_author = random_lower_string()
packs_email = "info@theblacktonystark.com"
packs_contributors = random_lower_string()
packs_files = random_lower_string()
packs_path = random_lower_string()
packs_ref = pack_shared_name
trigger_name = TRIGGER_0["name"]
trigger_packs_name = packs_name
trigger_description = TRIGGER_0["description"]
trigger_type = TRIGGER_0["type"]
trigger_parameters = TRIGGER_0["parameters"]
packs_in = PacksCreate(
name=packs_name,
description=packs_description,
keywords=packs_keywords,
version=packs_version,
python_versions=packs_python_versions,
author=packs_author,
email=packs_email,
contributors=packs_contributors,
files=packs_files,
path=packs_path,
ref=packs_ref,
)
packs = crud.packs.create(db, packs_in=packs_in)
trigger_in = TriggerCreate(
name=trigger_name,
packs_name=trigger_packs_name,
description=trigger_description,
type=trigger_type,
parameters=trigger_parameters,
)
trigger = crud.trigger.create(db, trigger_in=trigger_in, packs_id=packs.id)
ref_lookup = "{}.{}".format(packs_name, trigger.name)
trigger_2 = crud.trigger.get_by_ref(db, ref=ref_lookup)
assert jsonable_encoder(trigger) == jsonable_encoder(trigger_2)
@freeze_time("2019-07-25 01:11:00.740428")
@pytest.mark.triggeronly
@pytest.mark.unittest
def test_get_by_name_trigger_tags(db: Session) -> None:
pack_shared_name = random_lower_string()
packs_name = pack_shared_name
packs_description = random_lower_string()
packs_keywords = random_lower_string()
packs_version = random_lower_string()
packs_python_versions = random_lower_string()
packs_author = random_lower_string()
packs_email = "info@theblacktonystark.com"
packs_contributors = random_lower_string()
packs_files = random_lower_string()
packs_path = random_lower_string()
packs_ref = pack_shared_name
trigger_name = random_lower_string()
trigger_packs_name = packs_name
trigger_description = TRIGGER_0["description"]
trigger_type = TRIGGER_0["type"]
trigger_parameters = TRIGGER_0["parameters"]
packs_in = PacksCreate(
name=packs_name,
description=packs_description,
keywords=packs_keywords,
version=packs_version,
python_versions=packs_python_versions,
author=packs_author,
email=packs_email,
contributors=packs_contributors,
files=packs_files,
path=packs_path,
ref=packs_ref,
)
packs = crud.packs.create(db, packs_in=packs_in)
trigger_in = TriggerCreate(
name=trigger_name,
packs_name=trigger_packs_name,
description=trigger_description,
type=trigger_type,
parameters=trigger_parameters,
)
trigger = crud.trigger.create(db, trigger_in=trigger_in, packs_id=packs.id)
trigger_2 = crud.trigger.get_by_name(db, name=trigger_name)
assert jsonable_encoder(trigger) == jsonable_encoder(trigger_2)
@freeze_time("2019-07-25 01:11:00.740428")
@pytest.mark.triggeronly
@pytest.mark.unittest
def test_get_multi_trigger_tags(db: Session) -> None:
pack_shared_name = random_lower_string()
packs_name = pack_shared_name
packs_description = random_lower_string()
packs_keywords = random_lower_string()
packs_version = random_lower_string()
packs_python_versions = random_lower_string()
packs_author = random_lower_string()
packs_email = "info@theblacktonystark.com"
packs_contributors = random_lower_string()
packs_files = random_lower_string()
packs_path = random_lower_string()
packs_ref = pack_shared_name
trigger_name0 = random_lower_string()
trigger_packs_name0 = packs_name
trigger_description0 = TRIGGER_0["description"]
trigger_type0 = TRIGGER_0["type"]
trigger_parameters0 = TRIGGER_0["parameters"]
trigger_name1 = random_lower_string()
trigger_packs_name1 = packs_name
trigger_description1 = TRIGGER_1["description"]
trigger_type1 = TRIGGER_1["type"]
trigger_parameters1 = TRIGGER_1["parameters"]
packs_in = PacksCreate(
name=packs_name,
description=packs_description,
keywords=packs_keywords,
version=packs_version,
python_versions=packs_python_versions,
author=packs_author,
email=packs_email,
contributors=packs_contributors,
files=packs_files,
path=packs_path,
ref=packs_ref,
)
packs = crud.packs.create(db, packs_in=packs_in)
trigger_in0 = TriggerCreate(
name=trigger_name0,
packs_name=trigger_packs_name0,
description=trigger_description0,
type=trigger_type0,
parameters=trigger_parameters0,
)
trigger_in1 = TriggerCreate(
name=trigger_name1,
packs_name=trigger_packs_name1,
description=trigger_description1,
type=trigger_type1,
parameters=trigger_parameters1,
)
trigger0 = crud.trigger.create(db, trigger_in=trigger_in0, packs_id=packs.id)
trigger1 = crud.trigger.create(db, trigger_in=trigger_in1, packs_id=packs.id)
trigger_2 = crud.trigger.get_multi(db)
for t in trigger_2:
assert type(t) == TriggerDB
@freeze_time("2019-07-25 01:11:00.740428")
@pytest.mark.triggeronly
@pytest.mark.unittest
def test_get_multi_by_packs_id_trigger_tags(db: Session) -> None:
pack_shared_name = random_lower_string()
packs_name = pack_shared_name
packs_description = random_lower_string()
packs_keywords = random_lower_string()
packs_version = random_lower_string()
packs_python_versions = random_lower_string()
packs_author = random_lower_string()
packs_email = "info@theblacktonystark.com"
packs_contributors = random_lower_string()
packs_files = random_lower_string()
packs_path = random_lower_string()
packs_ref = pack_shared_name
trigger_name0 = random_lower_string()
trigger_packs_name0 = packs_name
trigger_description0 = TRIGGER_0["description"]
trigger_type0 = TRIGGER_0["type"]
trigger_parameters0 = TRIGGER_0["parameters"]
trigger_name1 = random_lower_string()
trigger_packs_name1 = packs_name
trigger_description1 = TRIGGER_1["description"]
trigger_type1 = TRIGGER_1["type"]
trigger_parameters1 = TRIGGER_1["parameters"]
packs_in = PacksCreate(
name=packs_name,
description=packs_description,
keywords=packs_keywords,
version=packs_version,
python_versions=packs_python_versions,
author=packs_author,
email=packs_email,
contributors=packs_contributors,
files=packs_files,
path=packs_path,
ref=packs_ref,
)
packs = crud.packs.create(db, packs_in=packs_in)
trigger_in0 = TriggerCreate(
name=trigger_name0,
packs_name=trigger_packs_name0,
description=trigger_description0,
type=trigger_type0,
parameters=trigger_parameters0,
)
trigger_in1 = TriggerCreate(
name=trigger_name1,
packs_name=trigger_packs_name1,
description=trigger_description1,
type=trigger_type1,
parameters=trigger_parameters1,
)
trigger0 = crud.trigger.create(db, trigger_in=trigger_in0, packs_id=packs.id)
trigger1 = crud.trigger.create(db, trigger_in=trigger_in1, packs_id=packs.id)
trigger_2 = crud.trigger.get_multi_by_packs_id(db, packs_id=packs.id, limit=2)
for t in trigger_2:
assert type(t) == TriggerDB
assert t.packs_id == packs.id
@freeze_time("2019-07-25 01:11:00.740428")
@pytest.mark.triggeronly
@pytest.mark.unittest
def test_update_trigger_tags(db: Session) -> None:
packs_shared_name = random_lower_string()
packs_name = packs_shared_name
packs_description = random_lower_string()
packs_keywords = random_lower_string()
packs_version = random_lower_string()
packs_python_versions = random_lower_string()
packs_author = random_lower_string()
packs_email = "info@theblacktonystark.com"
packs_contributors = random_lower_string()
packs_files = random_lower_string()
packs_path = random_lower_string()
packs_ref = packs_shared_name
trigger_name = TRIGGER_0["name"]
trigger_packs_name = packs_name
trigger_description = TRIGGER_0["description"]
trigger_type = TRIGGER_0["type"]
trigger_parameters = TRIGGER_0["parameters"]
packs_in = PacksCreate(
name=packs_name,
description=packs_description,
keywords=packs_keywords,
version=packs_version,
python_versions=packs_python_versions,
author=packs_author,
email=packs_email,
contributors=packs_contributors,
files=packs_files,
path=packs_path,
ref=packs_ref,
)
packs = crud.packs.create(db, packs_in=packs_in)
trigger_in = TriggerCreate(
name=trigger_name,
packs_name=trigger_packs_name,
description=trigger_description,
type=trigger_type,
parameters=trigger_parameters,
)
trigger = crud.trigger.create(db, trigger_in=trigger_in, packs_id=packs.id)
description2 = random_lower_string()
trigger_update = TriggerUpdate(description=description2)
trigger2 = crud.trigger.update(
db_session=db, trigger=trigger, trigger_in=trigger_update
)
assert trigger.name == trigger2.name
assert trigger.packs_name == trigger2.packs_name
assert trigger.description == description2
assert trigger.type == trigger2.type
assert trigger.parameters == trigger2.parameters
@freeze_time("2019-07-25 01:11:00.740428")
@pytest.mark.triggeronly
@pytest.mark.unittest
def test_delete_trigger_tags(db: Session) -> None:
packs_shared_name = random_lower_string()
packs_name = packs_shared_name
packs_description = random_lower_string()
packs_keywords = random_lower_string()
packs_version = random_lower_string()
packs_python_versions = random_lower_string()
packs_author = random_lower_string()
packs_email = "info@theblacktonystark.com"
packs_contributors = random_lower_string()
packs_files = random_lower_string()
packs_path = random_lower_string()
packs_ref = packs_shared_name
trigger_name = TRIGGER_0["name"]
trigger_packs_name = packs_name
trigger_description = TRIGGER_0["description"]
trigger_type = TRIGGER_0["type"]
trigger_parameters = TRIGGER_0["parameters"]
packs_in = PacksCreate(
name=packs_name,
description=packs_description,
keywords=packs_keywords,
version=packs_version,
python_versions=packs_python_versions,
author=packs_author,
email=packs_email,
contributors=packs_contributors,
files=packs_files,
path=packs_path,
ref=packs_ref,
)
packs = crud.packs.create(db, packs_in=packs_in)
trigger_in = TriggerCreate(
name=trigger_name,
packs_name=trigger_packs_name,
description=trigger_description,
type=trigger_type,
parameters=trigger_parameters,
)
trigger = crud.trigger.create(db, trigger_in=trigger_in, packs_id=packs.id)
trigger2 = crud.trigger.remove(db_session=db, trigger_id=trigger.id)
trigger3 = crud.trigger.get(db_session=db, trigger_id=trigger.id)
assert trigger3 is None
assert trigger2.id == trigger.id
assert trigger2.name == trigger.name
assert trigger2.packs_name == trigger.packs_name
assert trigger2.description == trigger2.description
assert trigger2.type == trigger.type
assert trigger2.parameters == trigger.parameters
| 32.096189
| 82
| 0.715069
| 2,092
| 17,685
| 5.677342
| 0.059273
| 0.073167
| 0.113076
| 0.133367
| 0.86436
| 0.853498
| 0.8439
| 0.839017
| 0.839017
| 0.833291
| 0
| 0.022732
| 0.194063
| 17,685
| 550
| 83
| 32.154545
| 0.810566
| 0
| 0
| 0.772632
| 0
| 0
| 0.077693
| 0.026011
| 0
| 0
| 0
| 0
| 0.048421
| 1
| 0.016842
| false
| 0
| 0.018947
| 0
| 0.035789
| 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
|
b9f227157f9ae198fc5fb6787602709419f32850
| 1,351
|
py
|
Python
|
data/datasets/7b/voices.inc.py
|
CherokeeLanguage/Cherokee-TTS
|
de034392ba8934fd8468617ebd7fcd8dace91162
|
[
"MIT"
] | 5
|
2020-10-12T16:07:53.000Z
|
2022-01-12T17:52:43.000Z
|
data/datasets/5f/voices.inc.py
|
CherokeeLanguage/Cherokee-TTS
|
de034392ba8934fd8468617ebd7fcd8dace91162
|
[
"MIT"
] | null | null | null |
data/datasets/5f/voices.inc.py
|
CherokeeLanguage/Cherokee-TTS
|
de034392ba8934fd8468617ebd7fcd8dace91162
|
[
"MIT"
] | 1
|
2020-10-30T06:53:18.000Z
|
2020-10-30T06:53:18.000Z
|
voices: list[str] = ["03-chr", "10-chr", "02-ru", "01-m-ssw-chr", "04-fr", "05-ru", "360-en-m", "329-en-f", "361-en-f", "308-en-f", "311-en-m", "334-en-m", "362-en-f", "330-en-f", "339-en-f", "294-en-f", "310-en-f", "318-en-f", "333-en-f", "305-en-f", "297-en-f", "301-en-f", "27-de", "341-en-f", "299-en-f", "300-en-f", "345-en-m", "11-fr", "13-de", "01-nl", "04-ru", "306-en-f", "08-nl", "cno-f-chr_2", "21-fr", "52-de", "24-de", "03-nl", "36-de", "09-fr", "14-de", "19-fr", "21-de", "20-fr", "01-de", "01-m-walc1", "22-de", "16-fr", "cno-m-chr_2", "04-m-walc1", "37-de", "49-de", "07-fr", "06-ru", "07-zh", "03-zh", "06-de", "17-de", "02-m-walc1", "51-de", "06-f-walc1", "06-fr", "02-m-df-chr", "01-m-wwacc", "45-de", "12-de", "02-de", "41-de", "25-fr", "04-f-walc1", "03-ru", "06-nl", "07-de", "47-de", "04-de", "04-nl", "06-zh", "08-fr", "01-m-df-chr", "50-de", "40-de", "02-zh", "19-de", "48-de", "01-ru", "23-de", "03-f-walc1", "32-de", "cno-m-chr_1", "17-fr", "46-de", "09-nl", "cno-f-chr_5", "44-de", "14-fr", "18-fr", "10-fr", "43-de", "26-de", "01-fr", "09-de", "10-nl", "31-de", "02-fr", "12-nl", "02-nl", "10-de", "29-de", "13-fr", "05-fr", "05-zh", "05-de", "26-fr", "07-nl", "11-nl", "cno-f-chr_3", "25-de", "11-de", "33-de", "22-fr", "15-fr", "01-zh", "04-chr", "01-f-walc1", "cno-f-chr_1", "02-f-walc1", "05-f-walc1", "01-f-wwacc", ]
| 675.5
| 1,350
| 0.486306
| 309
| 1,351
| 2.106796
| 0.281553
| 0.078341
| 0.043011
| 0.041475
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.229696
| 0.097705
| 1,351
| 1
| 1,351
| 1,351
| 0.304348
| 0
| 0
| 0
| 0
| 0
| 0.603997
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
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| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
b9f4604b081f940cee670899b0f3a63167bc3e8c
| 92
|
py
|
Python
|
wildfirepy/net/__init__.py
|
ram-nad/wildfirepy
|
7c449357ddbafa9ef9797b58fabf44d8a1f54137
|
[
"MIT"
] | null | null | null |
wildfirepy/net/__init__.py
|
ram-nad/wildfirepy
|
7c449357ddbafa9ef9797b58fabf44d8a1f54137
|
[
"MIT"
] | 4
|
2020-05-07T07:04:55.000Z
|
2020-05-08T12:47:41.000Z
|
wildfirepy/net/__init__.py
|
ram-nad/wildfirepy
|
7c449357ddbafa9ef9797b58fabf44d8a1f54137
|
[
"MIT"
] | null | null | null |
from wildfirepy.net import util
from wildfirepy.net import usgs
__all__ = ['usgs', 'util']
| 18.4
| 31
| 0.75
| 13
| 92
| 5
| 0.538462
| 0.430769
| 0.523077
| 0.707692
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.141304
| 92
| 4
| 32
| 23
| 0.822785
| 0
| 0
| 0
| 0
| 0
| 0.086957
| 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
|
e01793be5f44283b451c12c8958c07362787f353
| 4,174
|
py
|
Python
|
tests/test_io.py
|
rahulbhadani/hcipy
|
b52726cb9502b5225ddff9d7b1ff417f2350cda8
|
[
"MIT"
] | null | null | null |
tests/test_io.py
|
rahulbhadani/hcipy
|
b52726cb9502b5225ddff9d7b1ff417f2350cda8
|
[
"MIT"
] | null | null | null |
tests/test_io.py
|
rahulbhadani/hcipy
|
b52726cb9502b5225ddff9d7b1ff417f2350cda8
|
[
"MIT"
] | null | null | null |
import numpy as np
import os
from hcipy import *
"""
def test_write_mode_basis():
# grid for the test mode basis
pupil_grid = make_pupil_grid(128)
# generating a test mode basis
test_mode_basis = make_zernike_basis(num_modes=20, D=1, grid=pupil_grid, starting_mode=1, ansi=False, radial_cutoff=True)
file_name = 'write_mode_basis_test.fits'
# saving the mode basis
write_mode_basis(test_mode_basis, file_name)
# loading it
test_mode_basis_array_read = read_fits(file_name)
# comparing the arrays
test_mode_basis_array = np.array([test_mode_basis])
test_mode_basis_array = np.reshape(test_mode_basis_array, [20,128,128])
assert np.isclose(test_mode_basis_array, test_mode_basis_array_read, rtol=1e-02, atol=1e-05).all()
# Remove temporary file.
os.remove(file_name)
def test_read_mode_basis_1():
#-------------------------------
# testing a square mode basis that we read without providing a grid
#-------------------------------
# grid for the test mode basis
pupil_grid = make_pupil_grid(128)
# testing a square mode basis defined in the pupil plane
test_mode_basis = make_zernike_basis(num_modes=20, D=1, grid=pupil_grid, starting_mode=1, ansi=False, radial_cutoff=True)
# writing the mode basis
file_name = 'read_mode_basis_test_1.fits'
write_mode_basis(test_mode_basis, file_name)
# and loading it again
test_mode_basis_read = read_mode_basis(file_name, grid=None)
# checking if the modes are still the same
for mode, mode_read in zip(test_mode_basis, test_mode_basis_read):
assert np.isclose(mode, mode_read, rtol=1e-02, atol=1e-05).all()
# checking if the grid is correct
assert np.isclose(pupil_grid.x, test_mode_basis_read[0].grid.x, rtol=1e-02, atol=1e-05).all()
assert np.isclose(pupil_grid.y, test_mode_basis_read[0].grid.y, rtol=1e-02, atol=1e-05).all()
# Remove temporary file.
os.remove(file_name)
def test_read_mode_basis_2():
#-------------------------------
# testing a square mode basis that we read with providing a grid
#-------------------------------
# grid for the test mode basis
pupil_grid = make_pupil_grid(128, 3)
# testing a square mode basis defined in the pupil plane
test_mode_basis = make_zernike_basis(num_modes=20, D=3, grid=pupil_grid, starting_mode=1, ansi=False, radial_cutoff=True)
# writing the mode basis
file_name = 'read_mode_basis_test_2.fits'
write_mode_basis(test_mode_basis, file_name)
# and loading it again
test_mode_basis_read = read_mode_basis(file_name, grid=pupil_grid)
# checking if the modes are still the same
for mode, mode_read in zip(test_mode_basis, test_mode_basis_read):
assert np.isclose(mode, mode_read, rtol=1e-02, atol=1e-05).all()
# checking if the grid is correct
assert np.isclose(pupil_grid.x, test_mode_basis_read[0].grid.x, rtol=1e-02, atol=1e-05).all()
assert np.isclose(pupil_grid.y, test_mode_basis_read[0].grid.y, rtol=1e-02, atol=1e-05).all()
# Remove temporary file.
os.remove(file_name)
def test_read_mode_basis_3():
#-------------------------------
# testing a non-square mode basis that we read with providing a grid
#-------------------------------
# grid for the test mode basis
pupil_grid = make_uniform_grid([128,256], [128,256], center=0, has_center=False)
# testing a square mode basis defined in the pupil plane
test_mode_basis = []
for i in np.arange(20):
test_mode_basis.append(Field(np.random.rand(128*256), pupil_grid))
test_mode_basis = ModeBasis(test_mode_basis)
# writing the mode basis
file_name = 'read_mode_basis_test_3.fits'
write_mode_basis(test_mode_basis, file_name)
# and loading it again
test_mode_basis_read = read_mode_basis(file_name, grid=pupil_grid)
# checking if the modes are still the same
for mode, mode_read in zip(test_mode_basis, test_mode_basis_read):
assert np.isclose(mode, mode_read, rtol=1e-02, atol=1e-05).all()
# checking if the grid is correct
assert np.isclose(pupil_grid.x, test_mode_basis_read[0].grid.x, rtol=1e-02, atol=1e-05).all()
assert np.isclose(pupil_grid.y, test_mode_basis_read[0].grid.y, rtol=1e-02, atol=1e-05).all()
# Remove temporary file.
os.remove(file_name)
"""
| 35.675214
| 122
| 0.723527
| 704
| 4,174
| 4.015625
| 0.130682
| 0.200566
| 0.174744
| 0.072161
| 0.855677
| 0.831977
| 0.818182
| 0.818182
| 0.789883
| 0.789883
| 0
| 0.034157
| 0.137278
| 4,174
| 117
| 123
| 35.675214
| 0.750903
| 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
| 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
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 10
|
e0297dda2f8aa945c1bdc99cb0ae6de51836f3a5
| 77
|
py
|
Python
|
CodeUp/1534.py
|
chae-heechan/Algorithm_Study
|
183a77e2cfe352cd82fb5e988b493082529a73dd
|
[
"MIT"
] | null | null | null |
CodeUp/1534.py
|
chae-heechan/Algorithm_Study
|
183a77e2cfe352cd82fb5e988b493082529a73dd
|
[
"MIT"
] | null | null | null |
CodeUp/1534.py
|
chae-heechan/Algorithm_Study
|
183a77e2cfe352cd82fb5e988b493082529a73dd
|
[
"MIT"
] | null | null | null |
# 함수로 실수(double) 3.1415926535897 리턴하기
def f():
print(3.1415926535897)
f()
| 19.25
| 37
| 0.688312
| 12
| 77
| 4.416667
| 0.75
| 0.528302
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.430769
| 0.155844
| 77
| 4
| 38
| 19.25
| 0.384615
| 0.454545
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| true
| 0
| 0
| 0
| 0.333333
| 0.333333
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
e0529bb59750569b4010e063ae86f405220f24ca
| 90
|
py
|
Python
|
principal.py
|
7RogerIkeda/Travis_teste
|
034d625f32f3487c4c4923ea2d868d37bf0677d2
|
[
"Apache-2.0"
] | null | null | null |
principal.py
|
7RogerIkeda/Travis_teste
|
034d625f32f3487c4c4923ea2d868d37bf0677d2
|
[
"Apache-2.0"
] | null | null | null |
principal.py
|
7RogerIkeda/Travis_teste
|
034d625f32f3487c4c4923ea2d868d37bf0677d2
|
[
"Apache-2.0"
] | null | null | null |
def somar(x,y):
return x+ y
def subtrair (x,y):
return x- y
def mult():
pass
| 11.25
| 19
| 0.555556
| 17
| 90
| 2.941176
| 0.470588
| 0.16
| 0.32
| 0.36
| 0.52
| 0.52
| 0
| 0
| 0
| 0
| 0
| 0
| 0.3
| 90
| 7
| 20
| 12.857143
| 0.793651
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| false
| 0.166667
| 0
| 0.333333
| 0.833333
| 0
| 1
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
|
0
| 8
|
e07216ae0a66acc75116aaf06810abd64420d527
| 18,981
|
py
|
Python
|
pymove/tests/test_semantic.py
|
JoaoCarabetta/PyMove
|
0b712a9b65e0a5666db4bfecee3cd038ed155f7d
|
[
"MIT"
] | 1
|
2022-01-25T19:57:23.000Z
|
2022-01-25T19:57:23.000Z
|
pymove/tests/test_semantic.py
|
safarzadeh-reza/PyMove
|
c04f365499cc201c14d4fcf86e40e8fce43e2906
|
[
"MIT"
] | null | null | null |
pymove/tests/test_semantic.py
|
safarzadeh-reza/PyMove
|
c04f365499cc201c14d4fcf86e40e8fce43e2906
|
[
"MIT"
] | null | null | null |
from numpy import nan
from pandas import DataFrame, Timestamp
from pandas.testing import assert_frame_equal
from pymove import MoveDataFrame, semantic
from pymove.utils.constants import (
BLOCK,
DATETIME,
LATITUDE,
LONGITUDE,
SEGMENT_STOP,
TRAJ_ID,
)
list_data = [
[39.984094, 116.319236, '2008-10-23 05:53:05', 1],
[39.984198, 116.319322, '2008-10-23 05:53:06', 1],
[39.984224, 116.319402, '2008-10-23 05:53:11', 1],
[39.984211, 116.319389, '2008-10-23 05:53:16', 1],
[39.984217, 116.319422, '2008-10-23 05:53:21', 1],
[39.984710, 116.319865, '2008-10-23 05:53:23', 1],
[39.984674, 116.319810, '2008-10-23 05:53:28', 1],
[39.984623, 116.319773, '2008-10-23 05:53:33', 1],
[39.984606, 116.319732, '2008-10-23 05:53:38', 1],
[39.984555, 116.319728, '2008-10-23 05:53:43', 1]
]
list_data_2 = [
[39.984094, 116.319236, '2008-10-23 05:53:03', 1],
[39.984710, 116.319865, '2008-10-23 05:53:13', 1],
[39.984710, 116.319865, '2008-10-23 05:53:23', 1],
[39.984710, 116.319865, '2008-10-23 05:53:33', 1],
[39.984710, 116.319865, '2008-10-23 05:53:43', 1],
[39.984674, 116.319810, '2008-10-23 05:53:53', 1],
[39.984710, 116.319865, '2008-10-23 05:54:03', 1],
[39.984710, 116.319865, '2008-10-23 05:54:13', 1],
[39.984710, 116.319865, '2008-10-23 05:54:23', 1],
[39.984555, 116.319728, '2008-10-23 05:54:33', 1]
]
def _default_move_df(data=None):
if data is None:
data = list_data
return MoveDataFrame(
data=data,
latitude=LATITUDE,
longitude=LONGITUDE,
datetime=DATETIME,
traj_id=TRAJ_ID,
)
def test_end_create_operation():
move_df = _default_move_df()
expected = DataFrame(
data=[
[39.984094, 116.319236, Timestamp('2008-10-23 05:53:05'), 1],
[39.984198, 116.319322, Timestamp('2008-10-23 05:53:06'), 1],
[39.984224, 116.319402, Timestamp('2008-10-23 05:53:11'), 1],
[39.984211, 116.319389, Timestamp('2008-10-23 05:53:16'), 1],
[39.984217, 116.319422, Timestamp('2008-10-23 05:53:21'), 1],
[39.984710, 116.319865, Timestamp('2008-10-23 05:53:23'), 1],
[39.984674, 116.319810, Timestamp('2008-10-23 05:53:28'), 1],
[39.984623, 116.319773, Timestamp('2008-10-23 05:53:33'), 1],
[39.984606, 116.319732, Timestamp('2008-10-23 05:53:38'), 1],
[39.984555, 116.319728, Timestamp('2008-10-23 05:53:43'), 1]
],
columns=['lat', 'lon', 'datetime', 'id'],
index=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
)
new_move_df = semantic._end_create_operation(move_df, 'lat', False)
assert_frame_equal(new_move_df, expected)
semantic._end_create_operation(move_df, 'lat', True)
assert_frame_equal(move_df, expected)
def test_process_simple_filter():
move_df = _default_move_df()
expected = DataFrame(
data=[
[39.984094, 116.319236, Timestamp('2008-10-23 05:53:05'), 1, False],
[39.984198, 116.319322, Timestamp('2008-10-23 05:53:06'), 1, True],
[39.984224, 116.319402, Timestamp('2008-10-23 05:53:11'), 1, True],
[39.984211, 116.319389, Timestamp('2008-10-23 05:53:16'), 1, True],
[39.984217, 116.319422, Timestamp('2008-10-23 05:53:21'), 1, True],
[39.984710, 116.319865, Timestamp('2008-10-23 05:53:23'), 1, True],
[39.984674, 116.319810, Timestamp('2008-10-23 05:53:28'), 1, True],
[39.984623, 116.319773, Timestamp('2008-10-23 05:53:33'), 1, True],
[39.984606, 116.319732, Timestamp('2008-10-23 05:53:38'), 1, True],
[39.984555, 116.319728, Timestamp('2008-10-23 05:53:43'), 1, True]
],
columns=['lat', 'lon', 'datetime', 'id', 'new_label'],
index=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
)
new_move_df = semantic._process_simple_filter(move_df,
'new_label',
'lat',
39.984217,
False
)
assert_frame_equal(new_move_df, expected)
semantic._process_simple_filter(move_df,
'new_label',
'lat',
39.984217,
True)
assert_frame_equal(move_df, expected)
def test_create_or_update_out_of_the_bbox():
bbox = (39.984217, 116.319236, 39.98471, 116.319865)
move_df = _default_move_df()
expected = DataFrame(
data=[
[39.984094, 116.319236, Timestamp('2008-10-23 05:53:05'), 1, True],
[39.984198, 116.319322, Timestamp('2008-10-23 05:53:06'), 1, True],
[39.984224, 116.319402, Timestamp('2008-10-23 05:53:11'), 1, False],
[39.984211, 116.319389, Timestamp('2008-10-23 05:53:16'), 1, True],
[39.984217, 116.319422, Timestamp('2008-10-23 05:53:21'), 1, False],
[39.984710, 116.319865, Timestamp('2008-10-23 05:53:23'), 1, False],
[39.984674, 116.319810, Timestamp('2008-10-23 05:53:28'), 1, False],
[39.984623, 116.319773, Timestamp('2008-10-23 05:53:33'), 1, False],
[39.984606, 116.319732, Timestamp('2008-10-23 05:53:38'), 1, False],
[39.984555, 116.319728, Timestamp('2008-10-23 05:53:43'), 1, False]
],
columns=['lat', 'lon', 'datetime', 'id', 'out_bbox'],
index=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
)
semantic.create_or_update_out_of_the_bbox(move_df, bbox)
assert_frame_equal(move_df, expected)
def test_create_or_update_gps_deactivated_signal():
move_df = _default_move_df()
expected = DataFrame(
data=[
[1, 39.984094, 116.319236, Timestamp('2008-10-23 05:53:05'),
nan, 1.0, nan, False],
[1, 39.984198, 116.319322, Timestamp('2008-10-23 05:53:06'),
1.0, 5.0, 6.0, True],
[1, 39.984224, 116.319402, Timestamp('2008-10-23 05:53:11'),
5.0, 5.0, 10.0, True],
[1, 39.984211, 116.319389, Timestamp('2008-10-23 05:53:16'),
5.0, 5.0, 10.0, True],
[1, 39.984217, 116.319422, Timestamp('2008-10-23 05:53:21'),
5.0, 2.0, 7.0, True],
[1, 39.984710, 116.319865, Timestamp('2008-10-23 05:53:23'),
2.0, 5.0, 7.0, True],
[1, 39.984674, 116.319810, Timestamp('2008-10-23 05:53:28'),
5.0, 5.0, 10.0, True],
[1, 39.984623, 116.319773, Timestamp('2008-10-23 05:53:33'),
5.0, 5.0, 10.0, True],
[1, 39.984606, 116.319732, Timestamp('2008-10-23 05:53:38'),
5.0, 5.0, 10.0, True],
[1, 39.984555, 116.319728, Timestamp('2008-10-23 05:53:43'),
5.0, nan, nan, True]
],
columns=['id',
'lat',
'lon',
'datetime',
'time_to_prev',
'time_to_next',
'time_prev_to_next',
'deactivated_signal'],
index=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
)
new_move_df = semantic.create_or_update_gps_deactivated_signal(
move_df, max_time_between_adj_points=5.0, inplace=False)
assert_frame_equal(new_move_df, expected)
semantic.create_or_update_gps_deactivated_signal(move_df,
max_time_between_adj_points=5.0)
assert_frame_equal(move_df, expected)
def test_create_or_update_gps_jump():
move_df = _default_move_df()
expected = DataFrame(
data=[
[1, 39.984094, 116.319236, Timestamp('2008-10-23 05:53:05'),
nan, 13.690153, nan, True],
[1, 39.984198, 116.319322, Timestamp('2008-10-23 05:53:06'),
13.690153, 7.403788, 20.223428, True],
[1, 39.984224, 116.319402, Timestamp('2008-10-23 05:53:11'),
7.403788, 1.821083, 5.888579, True],
[1, 39.984211, 116.319389, Timestamp('2008-10-23 05:53:16'),
1.821083, 2.889671, 1.873356, False],
[1, 39.984217, 116.319422, Timestamp('2008-10-23 05:53:21'),
2.889671, 66.555997, 68.727260, True],
[1, 39.984710, 116.319865, Timestamp('2008-10-23 05:53:23'),
66.555997, 6.162987, 60.622358, True],
[1, 39.984674, 116.319810, Timestamp('2008-10-23 05:53:28'),
6.162987, 6.488225, 12.450907, True],
[1, 39.984623, 116.319773, Timestamp('2008-10-23 05:53:33'),
6.488225, 3.971848, 10.066577, True],
[1, 39.984606, 116.319732, Timestamp('2008-10-23 05:53:38'),
3.971848, 5.681172, 8.477733, True],
[1, 39.984555, 116.319728, Timestamp('2008-10-23 05:53:43'),
5.681172, nan, nan, True]
],
columns=['id',
'lat',
'lon',
'datetime',
'dist_to_prev',
'dist_to_next',
'dist_prev_to_next',
'gps_jump'],
index=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
)
new_move_df = semantic.create_or_update_gps_jump(move_df,
max_dist_between_adj_points=5.0,
inplace=False)
assert_frame_equal(new_move_df, expected)
semantic.create_or_update_gps_jump(move_df, max_dist_between_adj_points=5.0)
assert_frame_equal(move_df, expected)
def test_create_or_update_short_trajectory():
move_df = _default_move_df()
move_df.at[[6, 7, 8, 9], 'id'] = 2
expected = DataFrame(
data=[
[1, 39.984094, 116.319236, Timestamp('2008-10-23 05:53:05'),
nan, nan, nan, 1, False],
[1, 39.984198, 116.319322, Timestamp('2008-10-23 05:53:06'),
13.690153, 1.0, 13.690153, 1, False],
[1, 39.984224, 116.319402, Timestamp('2008-10-23 05:53:11'),
7.403788, 5.0, 1.480758, 1, False],
[1, 39.984211, 116.319389, Timestamp('2008-10-23 05:53:16'),
1.821083, 5.0, 0.364217, 1, False],
[1, 39.984217, 116.319422, Timestamp('2008-10-23 05:53:21'),
2.889671, 5.0, 0.577934, 1, False],
[1, 39.984710, 116.319865, Timestamp('2008-10-23 05:53:23'),
66.555997, 2.0, 33.277998, 1, False],
[2, 39.984674, 116.319810, Timestamp('2008-10-23 05:53:28'),
nan, nan, nan, 2, True],
[2, 39.984623, 116.319773, Timestamp('2008-10-23 05:53:33'),
6.488225, 5.0, 1.297645, 2, True],
[2, 39.984606, 116.319732, Timestamp('2008-10-23 05:53:38'),
3.971848, 5.0, 0.794370, 2, True],
[2, 39.984555, 116.319728, Timestamp('2008-10-23 05:53:43'),
5.681172, 5.0, 1.136234, 2, True]
],
columns=['id',
'lat',
'lon',
'datetime',
'dist_to_prev',
'time_to_prev',
'speed_to_prev',
'tid_part',
'short_traj'],
index=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
)
new_move_df = semantic.create_or_update_short_trajectory(move_df,
k_segment_max=4,
inplace=False)
assert_frame_equal(new_move_df, expected)
assert ('short_traj' not in move_df)
semantic.create_or_update_short_trajectory(move_df, k_segment_max=4)
assert_frame_equal(move_df, expected)
def test_create_or_update_gps_block_signal():
move_df = _default_move_df(list_data_2)
cols = [
'tid_part', 'id', 'lat', 'lon', 'datetime',
'dist_to_prev', 'time_to_prev', 'speed_to_prev', 'block_signal'
]
expected = DataFrame(data=[
[1, 1, 39.984094, 116.319236, Timestamp('2008-10-23 05:53:03'),
nan, nan, nan, False],
[2, 1, 39.98471, 116.319865, Timestamp('2008-10-23 05:53:13'),
nan, nan, nan, True],
[2, 1, 39.98471, 116.319865, Timestamp('2008-10-23 05:53:23'),
0.0, 10.0, 0.0, True],
[2, 1, 39.98471, 116.319865, Timestamp('2008-10-23 05:53:33'),
0.0, 10.0, 0.0, True],
[2, 1, 39.98471, 116.319865, Timestamp('2008-10-23 05:53:43'),
0.0, 10.0, 0.0, True],
[3, 1, 39.984674, 116.31981, Timestamp('2008-10-23 05:53:53'),
nan, nan, nan, False],
[4, 1, 39.98471, 116.319865, Timestamp('2008-10-23 05:54:03'),
nan, nan, nan, True],
[4, 1, 39.98471, 116.319865, Timestamp('2008-10-23 05:54:13'),
0.0, 10.0, 0.0, True],
[4, 1, 39.98471, 116.319865, Timestamp('2008-10-23 05:54:23'),
0.0, 10.0, 0.0, True],
[5, 1, 39.984555, 116.319728, Timestamp('2008-10-23 05:54:33'),
nan, nan, nan, False],
], columns=cols, index=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
)
new_move_df = semantic.create_or_update_gps_block_signal(
move_df, max_time_stop=15, inplace=False
)
assert_frame_equal(new_move_df, expected)
assert BLOCK not in move_df
semantic.create_or_update_gps_block_signal(move_df, max_time_stop=15)
assert_frame_equal(move_df, expected)
def test_filter_block_signal_by_repeated_amount_of_points():
move_df = _default_move_df(list_data_2)
cols = [
'tid_part', 'id', 'lat', 'lon', 'datetime',
'dist_to_prev', 'time_to_prev', 'speed_to_prev', 'block_signal'
]
expected = DataFrame(data=[
[1, 1, 39.984094, 116.319236, Timestamp('2008-10-23 05:53:03'),
nan, nan, nan, False],
[3, 1, 39.984674, 116.31981, Timestamp('2008-10-23 05:53:53'),
nan, nan, nan, False],
[4, 1, 39.98471, 116.319865, Timestamp('2008-10-23 05:54:03'),
nan, nan, nan, True],
[4, 1, 39.98471, 116.319865, Timestamp('2008-10-23 05:54:13'),
0.0, 10.0, 0.0, True],
[4, 1, 39.98471, 116.319865, Timestamp('2008-10-23 05:54:23'),
0.0, 10.0, 0.0, True],
[5, 1, 39.984555, 116.319728, Timestamp('2008-10-23 05:54:33'),
nan, nan, nan, False],
], columns=cols, index=[0, 5, 6, 7, 8, 9]
)
new_move_df = semantic.filter_block_signal_by_repeated_amount_of_points(
move_df, max_time_stop=15, amount_max_of_points_stop=3, inplace=False
)
assert_frame_equal(new_move_df, expected)
assert BLOCK not in move_df
semantic.filter_block_signal_by_repeated_amount_of_points(
move_df, max_time_stop=15, amount_max_of_points_stop=3,
filter_out=True, inplace=True
)
expected = DataFrame(data=[
[2, 1, 39.98471, 116.319865, Timestamp('2008-10-23 05:53:13'),
nan, nan, nan, True],
[2, 1, 39.98471, 116.319865, Timestamp('2008-10-23 05:53:23'),
0.0, 10.0, 0.0, True],
[2, 1, 39.98471, 116.319865, Timestamp('2008-10-23 05:53:33'),
0.0, 10.0, 0.0, True],
[2, 1, 39.98471, 116.319865, Timestamp('2008-10-23 05:53:43'),
0.0, 10.0, 0.0, True],
], columns=cols, index=[1, 2, 3, 4]
)
assert_frame_equal(move_df, expected)
def test_filter_block_signal_by_time():
move_df = _default_move_df(list_data_2)
cols = [
'tid_part', 'id', 'lat', 'lon', 'datetime',
'dist_to_prev', 'time_to_prev', 'speed_to_prev', 'block_signal'
]
expected = DataFrame(data=[
[1, 1, 39.984094, 116.319236, Timestamp('2008-10-23 05:53:03'), nan, nan, nan,
False],
[3, 1, 39.984674, 116.31981, Timestamp('2008-10-23 05:53:53'), nan, nan, nan,
False],
[5, 1, 39.984555, 116.319728, Timestamp('2008-10-23 05:54:33'), nan, nan, nan,
False],
], columns=cols, index=[0, 5, 9]
)
new_move_df = semantic.filter_block_signal_by_time(
move_df, max_time_stop=15, inplace=False
)
# assert False
assert_frame_equal(new_move_df, expected)
assert BLOCK not in move_df
semantic.filter_block_signal_by_time(
move_df, max_time_stop=15, filter_out=True, inplace=True
)
expected = DataFrame(data=[
[2, 1, 39.98471, 116.319865, Timestamp('2008-10-23 05:53:13'), nan, nan, nan,
True],
[2, 1, 39.98471, 116.319865, Timestamp('2008-10-23 05:53:23'), 0.0, 10.0, 0.0,
True],
[2, 1, 39.98471, 116.319865, Timestamp('2008-10-23 05:53:33'), 0.0, 10.0, 0.0,
True],
[2, 1, 39.98471, 116.319865, Timestamp('2008-10-23 05:53:43'), 0.0, 10.0, 0.0,
True],
[4, 1, 39.98471, 116.319865, Timestamp('2008-10-23 05:54:03'), nan, nan, nan,
True],
[4, 1, 39.98471, 116.319865, Timestamp('2008-10-23 05:54:13'), 0.0, 10.0, 0.0,
True],
[4, 1, 39.98471, 116.319865, Timestamp('2008-10-23 05:54:23'), 0.0, 10.0, 0.0,
True],
], columns=cols, index=[1, 2, 3, 4, 6, 7, 8]
)
assert_frame_equal(move_df, expected)
def test_filter_longer_time_to_stop_segment_by_id():
move_df = _default_move_df(list_data_2)
cols = [
'segment_stop', 'id', 'lat', 'lon', 'datetime',
'dist_to_prev', 'time_to_prev', 'speed_to_prev', 'stop'
]
expected = DataFrame(data=[
[1, 1, 39.984094, 116.319236, Timestamp('2008-10-23 05:53:03'), nan, nan, nan,
False],
[3, 1, 39.984674, 116.31981, Timestamp('2008-10-23 05:53:53'), nan, nan, nan,
False],
[4, 1, 39.98471, 116.319865, Timestamp('2008-10-23 05:54:03'), nan, nan, nan,
True],
[4, 1, 39.98471, 116.319865, Timestamp('2008-10-23 05:54:13'), 0.0, 10.0, 0.0,
True],
[4, 1, 39.98471, 116.319865, Timestamp('2008-10-23 05:54:23'), 0.0, 10.0, 0.0,
True],
[5, 1, 39.984555, 116.319728, Timestamp('2008-10-23 05:54:33'), nan, nan, nan,
False],
], columns=cols, index=[0, 5, 6, 7, 8, 9]
)
new_move_df = semantic.filter_longer_time_to_stop_segment_by_id(
move_df, dist_radius=5, time_radius=10, inplace=False
)
assert_frame_equal(new_move_df, expected)
assert SEGMENT_STOP not in move_df
expected = DataFrame(data=[
[2, 1, 39.98471, 116.319865, Timestamp('2008-10-23 05:53:13'),
nan, nan, nan, True],
[2, 1, 39.98471, 116.319865, Timestamp('2008-10-23 05:53:23'),
0.0, 10.0, 0.0, True],
[2, 1, 39.98471, 116.319865, Timestamp('2008-10-23 05:53:33'),
0.0, 10.0, 0.0, True],
[2, 1, 39.98471, 116.319865, Timestamp('2008-10-23 05:53:43'),
0.0, 10.0, 0.0, True],
], columns=cols, index=[1, 2, 3, 4]
)
semantic.filter_longer_time_to_stop_segment_by_id(
move_df, dist_radius=5, time_radius=10, filter_out=True, inplace=True
)
assert_frame_equal(move_df, expected)
| 39.626305
| 86
| 0.552026
| 2,912
| 18,981
| 3.45261
| 0.056662
| 0.071613
| 0.095484
| 0.119355
| 0.905411
| 0.890591
| 0.889198
| 0.873085
| 0.865725
| 0.827432
| 0
| 0.334236
| 0.280122
| 18,981
| 478
| 87
| 39.709205
| 0.401566
| 0.000632
| 0
| 0.53202
| 0
| 0
| 0.152167
| 0
| 0
| 0
| 0
| 0
| 0.061576
| 1
| 0.027094
| false
| 0
| 0.012315
| 0
| 0.041872
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
0ebf3ba7a7f17a15f702da54be665980146f4e40
| 262
|
py
|
Python
|
Sources/Workflows/Command-Help/alp/__init__.py
|
yagosys/AlfredWorkflow.com
|
9e5087e61fb89640a7a6ca89ba554303aec0b037
|
[
"MIT"
] | 2,177
|
2015-01-02T09:56:51.000Z
|
2022-03-27T01:48:37.000Z
|
Sources/Workflows/Command-Help/alp/__init__.py
|
yagosys/AlfredWorkflow.com
|
9e5087e61fb89640a7a6ca89ba554303aec0b037
|
[
"MIT"
] | 24
|
2015-01-02T19:11:51.000Z
|
2021-01-27T07:20:33.000Z
|
Sources/Workflows/Command-Help/alp/__init__.py
|
yagosys/AlfredWorkflow.com
|
9e5087e61fb89640a7a6ca89ba554303aec0b037
|
[
"MIT"
] | 516
|
2015-01-02T18:48:29.000Z
|
2022-01-26T07:12:35.000Z
|
from .core import *
try:
from .item import *
from .keychain import *
from .settings import *
from .mail import *
from alp.request.request import *
except ImportError:
pass
try:
from .notification import *
except ImportError:
pass
| 18.714286
| 37
| 0.667939
| 31
| 262
| 5.645161
| 0.451613
| 0.228571
| 0.262857
| 0.308571
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.259542
| 262
| 13
| 38
| 20.153846
| 0.902062
| 0
| 0
| 0.461538
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.153846
| 0.692308
| 0
| 0.692308
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 7
|
0ed0dd673451109629565023694e7e4eff651b32
| 452,672
|
py
|
Python
|
tests/rsc/schemas/input_json_schemas_shopify.py
|
hotgluexyz/target-bigquery
|
8b9f6f0ca652dd1ac408965a4e5af06cc1cd344f
|
[
"BSD-3-Clause"
] | 10
|
2020-09-28T15:12:17.000Z
|
2021-12-03T12:39:23.000Z
|
tests/rsc/schemas/input_json_schemas_shopify.py
|
hotgluexyz/target-bigquery
|
8b9f6f0ca652dd1ac408965a4e5af06cc1cd344f
|
[
"BSD-3-Clause"
] | 26
|
2021-01-04T14:01:07.000Z
|
2022-03-27T22:53:34.000Z
|
tests/rsc/schemas/input_json_schemas_shopify.py
|
hotgluexyz/target-bigquery
|
8b9f6f0ca652dd1ac408965a4e5af06cc1cd344f
|
[
"BSD-3-Clause"
] | 39
|
2020-10-01T18:16:20.000Z
|
2022-03-11T16:14:41.000Z
|
"""
shopify_orders_malformed:
this schema is from here: https://bitbucket.org/analyticspros/dt-singerio-shopify/commits/
it has three instances of this:
,
{
"properties": {},
"type": [
"null",
"object"
]
}
These instances are inside anyOf.
it's breaking the pipeline, if it's branch feature/schema-translation
it's running fine if it's master branch
This is incorrect schema because:
1) it has empty properties
2) if you remove this bit, new method schema conversion (simplify and convert) runs.
No data loss happens. No schema change happens. So therefore, this bit doesn't really add anything.
3) Simplification step taken from target-postgres should remove all instances of anyOf.
This anyOf persists. Ths is not normal, anyOf should be removed in this case.
"""
shopify_orders_malformed = """{"type":"SCHEMA",
"stream": "orders",
"tap_stream_id": "orders",
"schema": {
"properties": {
"presentment_currency": {
"type": [
"null",
"string"
]
},
"subtotal_price_set": {
"properties": {
"shop_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
},
"presentment_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
}
},
"type": [
"null",
"object"
]
},
"total_discounts_set": {
"properties": {
"shop_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
},
"presentment_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
}
},
"type": [
"null",
"object"
]
},
"total_line_items_price_set": {
"properties": {
"shop_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
},
"presentment_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
}
},
"type": [
"null",
"object"
]
},
"total_price_set": {
"properties": {
"shop_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
},
"presentment_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
}
},
"type": [
"null",
"object"
]
},
"total_shipping_price_set": {
"properties": {
"shop_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
},
"presentment_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
}
},
"type": [
"null",
"object"
]
},
"total_tax_set": {
"properties": {
"shop_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
},
"presentment_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
}
},
"type": [
"null",
"object"
]
},
"total_price": {
"type": [
"null",
"number"
],
"multipleOf": 1e-10
},
"line_items": {
"items": {
"properties": {
"applied_discounts": {
"type": [
"null",
"array"
],
"items": {
"properties": {
"title": {
"type": [
"null",
"string"
]
},
"code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
},
"savings": {
"type": [
"null",
"number"
]
},
"type": {
"type": [
"null",
"string"
]
}
},
"type": [
"null",
"object"
]
}
},
"total_discount_set": {
"properties": {
"shop_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
},
"presentment_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
}
},
"type": [
"null",
"object"
]
},
"pre_tax_price_set": {
"properties": {
"shop_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
},
"presentment_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
}
},
"type": [
"null",
"object"
]
},
"price_set": {
"properties": {
"shop_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
},
"presentment_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
}
},
"type": [
"null",
"object"
]
},
"grams": {
"type": [
"null",
"integer"
]
},
"compare_at_price": {
"type": [
"null",
"number"
]
},
"destination_location_id": {
"type": [
"null",
"integer"
]
},
"key": {
"type": [
"null",
"string"
]
},
"line_price": {
"type": [
"null",
"string"
]
},
"origin_location_id": {
"type": [
"null",
"integer"
]
},
"applied_discount": {
"type": [
"null",
"integer"
]
},
"fulfillable_quantity": {
"type": [
"null",
"integer"
]
},
"variant_title": {
"type": [
"null",
"string"
]
},
"properties": {
"anyOf": [
{
"items": {
"properties": {
"name": {
"type": [
"null",
"string"
]
},
"value": {
"type": [
"null",
"string"
]
}
},
"type": [
"null",
"object"
]
},
"type": [
"null",
"array"
]
},
{
"properties": {},
"type": [
"null",
"object"
]
}
]
},
"tax_code": {
"type": [
"null",
"string"
]
},
"discount_allocations": {
"items": {
"properties": {
"discount_application_index": {
"type": [
"null",
"integer"
]
},
"amount_set": {
"properties": {
"shop_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
},
"presentment_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"string"
]
}
},
"type": [
"null",
"object"
]
}
},
"type": [
"null",
"object"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
},
"type": [
"null",
"array"
]
},
"admin_graphql_api_id": {
"type": [
"null",
"string"
]
},
"pre_tax_price": {
"type": [
"null",
"number"
]
},
"sku": {
"type": [
"null",
"string"
]
},
"product_exists": {
"type": [
"null",
"boolean"
]
},
"total_discount": {
"type": [
"null",
"number"
],
"multipleOf": 1e-10
},
"name": {
"type": [
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"fulfillment_service": {
"type": [
"null",
"string"
]
},
"variant_inventory_management": {
"type": [
"null",
"string"
]
},
"title": {
"type": [
"null",
"string"
]
},
"destination_location": {
"properties": {
"country_code": {
"type": [
"null",
"string"
]
},
"name": {
"type": [
"null",
"string"
]
},
"address1": {
"type": [
"null",
"string"
]
},
"city": {
"type": [
"null",
"string"
]
},
"id": {
"type": [
"null",
"string"
]
},
"address2": {
"type": [
"null",
"string"
]
},
"province_code": {
"type": [
"null",
"string"
]
},
"zip": {
"type": [
"null",
"string"
]
}
},
"type": [
"null",
"object"
]
},
"quantity": {
"type": [
"null",
"integer"
]
},
"product_id": {
"type": [
"null",
"string"
]
},
"variant_id": {
"type": [
"null",
"string"
]
}
},
"type": [
"null",
"object"
]
},
"location_id": {
"type": [
"null",
"string"
]
},
"line_item_id": {
"type": [
"null",
"string"
]
},
"quantity": {
"type": [
"null",
"integer"
]
},
"id": {
"type": [
"null",
"string"
]
},
"total_tax": {
"type": [
"null",
"number"
]
},
"restock_type": {
"type": [
"null",
"string"
]
},
"subtotal": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
},
"type": [
"null",
"array"
]
},
"restock": {
"type": [
"null",
"boolean"
]
},
"note": {
"type": [
"null",
"string"
]
},
"id": {
"type": [
"null",
"string"
]
},
"user_id": {
"type": [
"null",
"string"
]
},
"created_at": {
"type": [
"null",
"string"
],
"format": "date-time"
},
"processed_at": {
"type": [
"null",
"string"
],
"format": "date-time"
},
"order_adjustments": {
"items": {
"properties": {
"order_id": {
"type": [
"null",
"string"
]
},
"tax_amount": {
"type": [
"null",
"number"
],
"multipleOf": 1e-10
},
"refund_id": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
],
"multipleOf": 1e-10
},
"kind": {
"type": [
"null",
"string"
]
},
"id": {
"type": [
"null",
"string"
]
},
"reason": {
"type": [
"null",
"string"
]
}
},
"type": [
"null",
"object"
]
},
"type": [
"null",
"array"
]
}
},
"type": [
"null",
"object"
]
},
"type": [
"null",
"array"
]
},
"created_at": {
"type": [
"null",
"string"
],
"format": "date-time"
},
"updated_at": {
"type": [
"null",
"string"
],
"format": "date-time"
},
"reference": {
"type": [
"null",
"string"
]
}
},
"type": "object"
},
"metadata": [
{
"breadcrumb": [],
"metadata": {
"table-key-properties": [
"id"
],
"forced-replication-method": "INCREMENTAL",
"valid-replication-keys": [
"updated_at"
],
"selected": true
}
},
{
"breadcrumb": [
"properties",
"presentment_currency"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"subtotal_price_set"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"total_discounts_set"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"total_line_items_price_set"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"total_price_set"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"total_shipping_price_set"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"total_tax_set"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"total_price"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"line_items"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"processing_method"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"order_number"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"confirmed"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"total_discounts"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"total_line_items_price"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"order_adjustments"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"shipping_lines"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"admin_graphql_api_id"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"device_id"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"cancel_reason"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"currency"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"payment_gateway_names"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"source_identifier"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"id"
],
"metadata": {
"inclusion": "automatic"
}
},
{
"breadcrumb": [
"properties",
"processed_at"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"referring_site"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"contact_email"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"location_id"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"fulfillments"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"customer"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"test"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"total_tax"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"payment_details"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"number"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"email"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"source_name"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"landing_site_ref"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"shipping_address"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"total_price_usd"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"closed_at"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"discount_applications"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"name"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"note"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"user_id"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"source_url"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"subtotal_price"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"billing_address"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"landing_site"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"taxes_included"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"token"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"app_id"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"total_tip_received"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"browser_ip"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"discount_codes"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"tax_lines"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"phone"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"note_attributes"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"fulfillment_status"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"order_status_url"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"client_details"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"buyer_accepts_marketing"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"checkout_token"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"tags"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"financial_status"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"customer_locale"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"checkout_id"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"total_weight"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"gateway"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"cart_token"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"cancelled_at"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"refunds"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"created_at"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"updated_at"
],
"metadata": {
"inclusion": "automatic"
}
},
{
"breadcrumb": [
"properties",
"reference"
],
"metadata": {
"inclusion": "available",
"selected": true
}
}
],
"key_properties": [
"id"
],
"replication_key": "updated_at",
"replication_method": "INCREMENTAL"
}"""
# removed the object/dict with emppty properties
shopify_orders_fixed = """
{"type":"SCHEMA",
"stream": "orders",
"tap_stream_id": "orders",
"schema": {
"properties": {
"presentment_currency": {
"type": [
"null",
"string"
]
},
"subtotal_price_set": {
"properties": {
"shop_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
},
"presentment_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
}
},
"type": [
"null",
"object"
]
},
"total_discounts_set": {
"properties": {
"shop_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
},
"presentment_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
}
},
"type": [
"null",
"object"
]
},
"total_line_items_price_set": {
"properties": {
"shop_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
},
"presentment_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
}
},
"type": [
"null",
"object"
]
},
"total_price_set": {
"properties": {
"shop_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
},
"presentment_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
}
},
"type": [
"null",
"object"
]
},
"total_shipping_price_set": {
"properties": {
"shop_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
},
"presentment_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
}
},
"type": [
"null",
"object"
]
},
"total_tax_set": {
"properties": {
"shop_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
},
"presentment_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
}
},
"type": [
"null",
"object"
]
},
"total_price": {
"type": [
"null",
"number"
],
"multipleOf": 1e-10
},
"line_items": {
"items": {
"properties": {
"applied_discounts": {
"type": [
"null",
"array"
],
"items": {
"properties": {
"title": {
"type": [
"null",
"string"
]
},
"code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
},
"savings": {
"type": [
"null",
"number"
]
},
"type": {
"type": [
"null",
"string"
]
}
},
"type": [
"null",
"object"
]
}
},
"total_discount_set": {
"properties": {
"shop_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
},
"presentment_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
}
},
"type": [
"null",
"object"
]
},
"pre_tax_price_set": {
"properties": {
"shop_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
},
"presentment_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
}
},
"type": [
"null",
"object"
]
},
"price_set": {
"properties": {
"shop_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
},
"presentment_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
}
},
"type": [
"null",
"object"
]
},
"grams": {
"type": [
"null",
"integer"
]
},
"compare_at_price": {
"type": [
"null",
"number"
]
},
"destination_location_id": {
"type": [
"null",
"integer"
]
},
"key": {
"type": [
"null",
"string"
]
},
"line_price": {
"type": [
"null",
"string"
]
},
"origin_location_id": {
"type": [
"null",
"integer"
]
},
"applied_discount": {
"type": [
"null",
"integer"
]
},
"fulfillable_quantity": {
"type": [
"null",
"integer"
]
},
"variant_title": {
"type": [
"null",
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"object"
]
}
},
"type": [
"null",
"object"
]
},
"price": {
"type": [
"null",
"number"
],
"multipleOf": 1e-10
},
"title": {
"type": [
"null",
"string"
]
},
"rate": {
"type": [
"null",
"number"
],
"multipleOf": 1e-10
},
"compare_at": {
"type": [
"null",
"number"
]
},
"position": {
"type": [
"null",
"integer"
]
},
"source": {
"type": [
"null",
"string"
]
},
"zone": {
"type": [
"null",
"string"
]
}
},
"type": [
"null",
"object"
]
},
"type": [
"null",
"array"
]
},
"origin_location": {
"properties": {
"country_code": {
"type": [
"null",
"string"
]
},
"name": {
"type": [
"null",
"string"
]
},
"address1": {
"type": [
"null",
"string"
]
},
"city": {
"type": [
"null",
"string"
]
},
"id": {
"type": [
"null",
"string"
]
},
"address2": {
"type": [
"null",
"string"
]
},
"province_code": {
"type": [
"null",
"string"
]
},
"zip": {
"type": [
"null",
"string"
]
}
},
"type": [
"null",
"object"
]
},
"price": {
"type": [
"null",
"number"
],
"multipleOf": 1e-10
},
"requires_shipping": {
"type": [
"null",
"boolean"
]
},
"fulfillment_service": {
"type": [
"null",
"string"
]
},
"variant_inventory_management": {
"type": [
"null",
"string"
]
},
"title": {
"type": [
"null",
"string"
]
},
"destination_location": {
"properties": {
"country_code": {
"type": [
"null",
"string"
]
},
"name": {
"type": [
"null",
"string"
]
},
"address1": {
"type": [
"null",
"string"
]
},
"city": {
"type": [
"null",
"string"
]
},
"id": {
"type": [
"null",
"string"
]
},
"address2": {
"type": [
"null",
"string"
]
},
"province_code": {
"type": [
"null",
"string"
]
},
"zip": {
"type": [
"null",
"string"
]
}
},
"type": [
"null",
"object"
]
},
"quantity": {
"type": [
"null",
"integer"
]
},
"product_id": {
"type": [
"null",
"string"
]
},
"variant_id": {
"type": [
"null",
"string"
]
}
},
"type": [
"null",
"object"
]
},
"location_id": {
"type": [
"null",
"string"
]
},
"line_item_id": {
"type": [
"null",
"string"
]
},
"quantity": {
"type": [
"null",
"integer"
]
},
"id": {
"type": [
"null",
"string"
]
},
"total_tax": {
"type": [
"null",
"number"
]
},
"restock_type": {
"type": [
"null",
"string"
]
},
"subtotal": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
},
"type": [
"null",
"array"
]
},
"restock": {
"type": [
"null",
"boolean"
]
},
"note": {
"type": [
"null",
"string"
]
},
"id": {
"type": [
"null",
"string"
]
},
"user_id": {
"type": [
"null",
"string"
]
},
"created_at": {
"type": [
"null",
"string"
],
"format": "date-time"
},
"processed_at": {
"type": [
"null",
"string"
],
"format": "date-time"
},
"order_adjustments": {
"items": {
"properties": {
"order_id": {
"type": [
"null",
"string"
]
},
"tax_amount": {
"type": [
"null",
"number"
],
"multipleOf": 1e-10
},
"refund_id": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
],
"multipleOf": 1e-10
},
"kind": {
"type": [
"null",
"string"
]
},
"id": {
"type": [
"null",
"string"
]
},
"reason": {
"type": [
"null",
"string"
]
}
},
"type": [
"null",
"object"
]
},
"type": [
"null",
"array"
]
}
},
"type": [
"null",
"object"
]
},
"type": [
"null",
"array"
]
},
"created_at": {
"type": [
"null",
"string"
],
"format": "date-time"
},
"updated_at": {
"type": [
"null",
"string"
],
"format": "date-time"
},
"reference": {
"type": [
"null",
"string"
]
}
},
"type": "object"
},
"metadata": [
{
"breadcrumb": [],
"metadata": {
"table-key-properties": [
"id"
],
"forced-replication-method": "INCREMENTAL",
"valid-replication-keys": [
"updated_at"
],
"selected": true
}
},
{
"breadcrumb": [
"properties",
"presentment_currency"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"subtotal_price_set"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"total_discounts_set"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"total_line_items_price_set"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"total_price_set"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"total_shipping_price_set"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"total_tax_set"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"total_price"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"line_items"
],
"metadata": {
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"selected": true
}
},
{
"breadcrumb": [
"properties",
"processing_method"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"order_number"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"confirmed"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"total_discounts"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"total_line_items_price"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"order_adjustments"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"shipping_lines"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"admin_graphql_api_id"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"device_id"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"cancel_reason"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"currency"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"payment_gateway_names"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"source_identifier"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"id"
],
"metadata": {
"inclusion": "automatic"
}
},
{
"breadcrumb": [
"properties",
"processed_at"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"referring_site"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"contact_email"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"location_id"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"fulfillments"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"customer"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"test"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"total_tax"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"payment_details"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"number"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"email"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"source_name"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"landing_site_ref"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"shipping_address"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"total_price_usd"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"closed_at"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"discount_applications"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"name"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"note"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"user_id"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"source_url"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"subtotal_price"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"billing_address"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"landing_site"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"taxes_included"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"token"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"app_id"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"total_tip_received"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"browser_ip"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"discount_codes"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"tax_lines"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"phone"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"note_attributes"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"fulfillment_status"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"order_status_url"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"client_details"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"buyer_accepts_marketing"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"checkout_token"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"tags"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"financial_status"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"customer_locale"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"checkout_id"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"total_weight"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"gateway"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"cart_token"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"cancelled_at"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"refunds"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"created_at"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"updated_at"
],
"metadata": {
"inclusion": "automatic"
}
},
{
"breadcrumb": [
"properties",
"reference"
],
"metadata": {
"inclusion": "available",
"selected": true
}
}
],
"key_properties": [
"id"
],
"replication_key": "updated_at",
"replication_method": "INCREMENTAL"
}
"""
"""old schema conversion test is failing on this one
new schema conversion is handling it
"""
shopify_customers = """{"type":"SCHEMA",
"stream": "customers",
"tap_stream_id": "customers",
"schema": {
"type": "object",
"properties": {
"last_order_name": {
"type": [
"null",
"string"
]
},
"currency": {
"type": [
"null",
"string"
]
},
"email": {
"type": [
"null",
"string"
]
},
"multipass_identifier": {
"type": [
"null",
"string"
]
},
"default_address": {
"type": [
"null",
"object"
],
"properties": {
"city": {
"type": [
"null",
"string"
]
},
"address1": {
"type": [
"null",
"string"
]
},
"zip": {
"type": [
"null",
"string"
]
},
"id": {
"type": [
"null",
"string"
]
},
"country_name": {
"type": [
"null",
"string"
]
},
"province": {
"type": [
"null",
"string"
]
},
"phone": {
"type": [
"null",
"string"
]
},
"country": {
"type": [
"null",
"string"
]
},
"first_name": {
"type": [
"null",
"string"
]
},
"customer_id": {
"type": [
"null",
"string"
]
},
"default": {
"type": [
"null",
"boolean"
]
},
"last_name": {
"type": [
"null",
"string"
]
},
"country_code": {
"type": [
"null",
"string"
]
},
"name": {
"type": [
"null",
"string"
]
},
"province_code": {
"type": [
"null",
"string"
]
},
"address2": {
"type": [
"null",
"string"
]
},
"company": {
"type": [
"null",
"string"
]
}
}
},
"orders_count": {
"type": [
"null",
"integer"
]
},
"state": {
"type": [
"null",
"string"
]
},
"verified_email": {
"type": [
"null",
"boolean"
]
},
"total_spent": {
"type": [
"null",
"string"
]
},
"last_order_id": {
"type": [
"null",
"string"
]
},
"first_name": {
"type": [
"null",
"string"
]
},
"updated_at": {
"type": [
"null",
"string"
],
"format": "date-time"
},
"note": {
"type": [
"null",
"string"
]
},
"phone": {
"type": [
"null",
"string"
]
},
"admin_graphql_api_id": {
"type": [
"null",
"string"
]
},
"addresses": {
"type": [
"null",
"array"
],
"items": {
"type": [
"null",
"object"
],
"properties": {
"city": {
"type": [
"null",
"string"
]
},
"address1": {
"type": [
"null",
"string"
]
},
"zip": {
"type": [
"null",
"string"
]
},
"id": {
"type": [
"null",
"string"
]
},
"country_name": {
"type": [
"null",
"string"
]
},
"province": {
"type": [
"null",
"string"
]
},
"phone": {
"type": [
"null",
"string"
]
},
"country": {
"type": [
"null",
"string"
]
},
"first_name": {
"type": [
"null",
"string"
]
},
"customer_id": {
"type": [
"null",
"string"
]
},
"default": {
"type": [
"null",
"boolean"
]
},
"last_name": {
"type": [
"null",
"string"
]
},
"country_code": {
"type": [
"null",
"string"
]
},
"name": {
"type": [
"null",
"string"
]
},
"province_code": {
"type": [
"null",
"string"
]
},
"address2": {
"type": [
"null",
"string"
]
},
"company": {
"type": [
"null",
"string"
]
}
}
}
},
"last_name": {
"type": [
"null",
"string"
]
},
"tags": {
"type": [
"null",
"string"
]
},
"tax_exempt": {
"type": [
"null",
"boolean"
]
},
"id": {
"type": [
"null",
"string"
]
},
"accepts_marketing": {
"type": [
"null",
"boolean"
]
},
"accepts_marketing_updated_at": {
"anyOf": [
{
"type": "string",
"format": "date-time"
},
{
"type": "string"
},
{
"type": "null"
}
]
},
"created_at": {
"type": [
"null",
"string"
],
"format": "date-time"
}
}
},
"metadata": [
{
"breadcrumb": [],
"metadata": {
"table-key-properties": [
"id"
],
"forced-replication-method": "INCREMENTAL",
"valid-replication-keys": [
"updated_at"
],
"selected": true
}
},
{
"breadcrumb": [
"properties",
"last_order_name"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"currency"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"email"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"multipass_identifier"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"default_address"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"orders_count"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"state"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"verified_email"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"total_spent"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"last_order_id"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"first_name"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"updated_at"
],
"metadata": {
"inclusion": "automatic"
}
},
{
"breadcrumb": [
"properties",
"note"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"phone"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"admin_graphql_api_id"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"addresses"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"last_name"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"tags"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"tax_exempt"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"id"
],
"metadata": {
"inclusion": "automatic"
}
},
{
"breadcrumb": [
"properties",
"accepts_marketing"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"accepts_marketing_updated_at"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"created_at"
],
"metadata": {
"inclusion": "available",
"selected": true
}
}
],
"key_properties": [
"id"
],
"replication_key": "updated_at",
"replication_method": "INCREMENTAL"
}"""
shopify_custom_collections = """{"type":"SCHEMA",
"stream": "custom_collections",
"tap_stream_id": "custom_collections",
"schema": {
"properties": {
"handle": {
"type": [
"null",
"string"
]
},
"sort_order": {
"type": [
"null",
"string"
]
},
"body_html": {
"type": [
"null",
"string"
]
},
"title": {
"type": [
"null",
"string"
]
},
"id": {
"type": [
"null",
"string"
]
},
"published_scope": {
"type": [
"null",
"string"
]
},
"admin_graphql_api_id": {
"type": [
"null",
"string"
]
},
"updated_at": {
"type": [
"null",
"string"
],
"format": "date-time"
},
"image": {
"properties": {
"alt": {
"type": [
"null",
"string"
]
},
"src": {
"type": [
"null",
"string"
]
},
"width": {
"type": [
"null",
"integer"
]
},
"created_at": {
"type": [
"null",
"string"
],
"format": "date-time"
},
"height": {
"type": [
"null",
"integer"
]
}
},
"type": [
"null",
"object"
]
},
"published_at": {
"type": [
"null",
"string"
],
"format": "date-time"
},
"template_suffix": {
"type": [
"null",
"string"
]
}
},
"type": "object"
},
"metadata": [
{
"breadcrumb": [],
"metadata": {
"table-key-properties": [
"id"
],
"forced-replication-method": "INCREMENTAL",
"valid-replication-keys": [
"updated_at"
],
"selected": true
}
},
{
"breadcrumb": [
"properties",
"handle"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"sort_order"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"body_html"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"title"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"id"
],
"metadata": {
"inclusion": "automatic"
}
},
{
"breadcrumb": [
"properties",
"published_scope"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"admin_graphql_api_id"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"updated_at"
],
"metadata": {
"inclusion": "automatic"
}
},
{
"breadcrumb": [
"properties",
"image"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"published_at"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"template_suffix"
],
"metadata": {
"inclusion": "available",
"selected": true
}
}
],
"key_properties": [
"id"
],
"replication_key": "updated_at",
"replication_method": "INCREMENTAL"}
"""
"""
shopify_abandoned_checkouts_malformed
it also has empty properties
,
{
"properties": {},
"type": [
"null",
"object"
]
}
"""
shopify_abandoned_checkouts_fixed = """{"type":"SCHEMA",
"stream": "abandoned_checkouts",
"tap_stream_id": "abandoned_checkouts",
"schema": {
"type": "object",
"properties": {
"note_attributes": {
"items": {
"properties": {
"name": {
"type": [
"null",
"string"
]
},
"value": {
"type": [
"null",
"string"
]
}
},
"type": [
"null",
"object"
]
},
"type": [
"null",
"array"
]
},
"location_id": {
"type": [
"null",
"string"
]
},
"buyer_accepts_marketing": {
"type": [
"null",
"boolean"
]
},
"currency": {
"type": [
"null",
"string"
]
},
"completed_at": {
"type": [
"null",
"string"
],
"format": "date-time"
},
"token": {
"type": [
"null",
"string"
]
},
"billing_address": {
"type": [
"null",
"object"
],
"properties": {
"phone": {
"type": [
"null",
"string"
]
},
"country": {
"type": [
"null",
"string"
]
},
"first_name": {
"type": [
"null",
"string"
]
},
"name": {
"type": [
"null",
"string"
]
},
"latitude": {
"type": [
"null",
"number"
]
},
"zip": {
"type": [
"null",
"string"
]
},
"last_name": {
"type": [
"null",
"string"
]
},
"province": {
"type": [
"null",
"string"
]
},
"address2": {
"type": [
"null",
"string"
]
},
"address1": {
"type": [
"null",
"string"
]
},
"country_code": {
"type": [
"null",
"string"
]
},
"city": {
"type": [
"null",
"string"
]
},
"company": {
"type": [
"null",
"string"
]
},
"province_code": {
"type": [
"null",
"string"
]
},
"longitude": {
"type": [
"null",
"number"
]
}
}
},
"email": {
"type": [
"null",
"string"
]
},
"discount_codes": {
"type": [
"null",
"array"
],
"items": {
"type": [
"null",
"object"
],
"properties": {
"type": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
],
"multipleOf": 1e-10
},
"code": {
"type": [
"null",
"string"
]
}
}
}
},
"customer_locale": {
"type": [
"null",
"string"
]
},
"created_at": {
"type": [
"null",
"string"
],
"format": "date-time"
},
"updated_at": {
"type": [
"null",
"string"
],
"format": "date-time"
},
"gateway": {
"type": [
"null",
"string"
]
},
"referring_site": {
"type": [
"null",
"string"
]
},
"source_identifier": {
"type": [
"null",
"string"
]
},
"total_weight": {
"type": [
"null",
"integer"
]
},
"tax_lines": {
"items": {
"properties": {
"price_set": {
"properties": {
"shop_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
},
"presentment_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
}
},
"type": [
"null",
"object"
]
},
"price": {
"type": [
"null",
"number"
],
"multipleOf": 1e-10
},
"title": {
"type": [
"null",
"string"
]
},
"rate": {
"type": [
"null",
"number"
],
"multipleOf": 1e-10
},
"compare_at": {
"type": [
"null",
"number"
]
},
"position": {
"type": [
"null",
"integer"
]
},
"source": {
"type": [
"null",
"string"
]
},
"zone": {
"type": [
"null",
"string"
]
}
},
"type": [
"null",
"object"
]
},
"type": [
"null",
"array"
]
},
"total_line_items_price": {
"type": [
"null",
"number"
],
"multipleOf": 1e-10
},
"closed_at": {
"type": [
"null",
"string"
],
"format": "date-time"
},
"device_id": {
"type": [
"null",
"string"
]
},
"phone": {
"type": [
"null",
"string"
]
},
"source_name": {
"type": [
"null",
"string"
]
},
"id": {
"type": [
"null",
"string"
]
},
"name": {
"type": [
"null",
"string"
]
},
"total_tax": {
"type": [
"null",
"number"
],
"multipleOf": 1e-10
},
"subtotal_price": {
"type": [
"null",
"number"
],
"multipleOf": 1e-10
},
"line_items": {
"items": {
"properties": {
"applied_discounts": {
"type": [
"null",
"array"
],
"items": {
"properties": {
"title": {
"type": [
"null",
"string"
]
},
"code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
},
"savings": {
"type": [
"null",
"number"
]
},
"type": {
"type": [
"null",
"string"
]
}
},
"type": [
"null",
"object"
]
}
},
"total_discount_set": {
"properties": {
"shop_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
},
"presentment_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
}
},
"type": [
"null",
"object"
]
},
"pre_tax_price_set": {
"properties": {
"shop_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
},
"presentment_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
}
},
"type": [
"null",
"object"
]
},
"price_set": {
"properties": {
"shop_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
},
"presentment_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
}
},
"type": [
"null",
"object"
]
},
"grams": {
"type": [
"null",
"integer"
]
},
"compare_at_price": {
"type": [
"null",
"number"
]
},
"destination_location_id": {
"type": [
"null",
"string"
]
},
"key": {
"type": [
"null",
"string"
]
},
"line_price": {
"type": [
"null",
"string"
]
},
"origin_location_id": {
"type": [
"null",
"string"
]
},
"applied_discount": {
"type": [
"null",
"integer"
]
},
"fulfillable_quantity": {
"type": [
"null",
"integer"
]
},
"variant_title": {
"type": [
"null",
"string"
]
},
"properties": {
"anyOf": [
{
"items": {
"properties": {
"name": {
"type": [
"null",
"string"
]
},
"value": {
"type": [
"null",
"string"
]
}
},
"type": [
"null",
"object"
]
},
"type": [
"null",
"array"
]
}
]
},
"tax_code": {
"type": [
"null",
"string"
]
},
"discount_allocations": {
"items": {
"properties": {
"discount_application_index": {
"type": [
"null",
"integer"
]
},
"amount_set": {
"properties": {
"shop_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
},
"presentment_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
}
},
"type": [
"null",
"object"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
},
"type": [
"null",
"array"
]
},
"admin_graphql_api_id": {
"type": [
"null",
"string"
]
},
"pre_tax_price": {
"type": [
"null",
"number"
]
},
"sku": {
"type": [
"null",
"string"
]
},
"product_exists": {
"type": [
"null",
"boolean"
]
},
"total_discount": {
"type": [
"null",
"number"
],
"multipleOf": 1e-10
},
"name": {
"type": [
"null",
"string"
]
},
"fulfillment_status": {
"type": [
"null",
"string"
]
},
"gift_card": {
"type": [
"null",
"boolean"
]
},
"id": {
"type": [
"null",
"string"
]
},
"taxable": {
"type": [
"null",
"boolean"
]
},
"vendor": {
"type": [
"null",
"string"
]
},
"tax_lines": {
"items": {
"properties": {
"price_set": {
"properties": {
"shop_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
},
"presentment_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
}
},
"type": [
"null",
"object"
]
},
"price": {
"type": [
"null",
"number"
],
"multipleOf": 1e-10
},
"title": {
"type": [
"null",
"string"
]
},
"rate": {
"type": [
"null",
"number"
],
"multipleOf": 1e-10
},
"compare_at": {
"type": [
"null",
"number"
]
},
"position": {
"type": [
"null",
"integer"
]
},
"source": {
"type": [
"null",
"string"
]
},
"zone": {
"type": [
"null",
"string"
]
}
},
"type": [
"null",
"object"
]
},
"type": [
"null",
"array"
]
},
"origin_location": {
"properties": {
"country_code": {
"type": [
"null",
"string"
]
},
"name": {
"type": [
"null",
"string"
]
},
"address1": {
"type": [
"null",
"string"
]
},
"city": {
"type": [
"null",
"string"
]
},
"id": {
"type": [
"null",
"string"
]
},
"address2": {
"type": [
"null",
"string"
]
},
"province_code": {
"type": [
"null",
"string"
]
},
"zip": {
"type": [
"null",
"string"
]
}
},
"type": [
"null",
"object"
]
},
"price": {
"type": [
"null",
"number"
],
"multipleOf": 1e-10
},
"requires_shipping": {
"type": [
"null",
"boolean"
]
},
"fulfillment_service": {
"type": [
"null",
"string"
]
},
"variant_inventory_management": {
"type": [
"null",
"string"
]
},
"title": {
"type": [
"null",
"string"
]
},
"destination_location": {
"properties": {
"country_code": {
"type": [
"null",
"string"
]
},
"name": {
"type": [
"null",
"string"
]
},
"address1": {
"type": [
"null",
"string"
]
},
"city": {
"type": [
"null",
"string"
]
},
"id": {
"type": [
"null",
"string"
]
},
"address2": {
"type": [
"null",
"string"
]
},
"province_code": {
"type": [
"null",
"string"
]
},
"zip": {
"type": [
"null",
"string"
]
}
},
"type": [
"null",
"object"
]
},
"quantity": {
"type": [
"null",
"integer"
]
},
"product_id": {
"type": [
"null",
"string"
]
},
"variant_id": {
"type": [
"null",
"string"
]
}
},
"type": [
"null",
"object"
]
},
"type": [
"null",
"array"
]
},
"source_url": {
"type": [
"null",
"string"
]
},
"total_discounts": {
"type": [
"null",
"number"
],
"multipleOf": 1e-10
},
"note": {
"type": [
"null",
"string"
]
},
"presentment_currency": {
"type": [
"null",
"string"
]
},
"shipping_lines": {
"type": [
"null",
"array"
],
"items": {
"type": [
"null",
"object"
],
"properties": {
"applied_discounts": {
"type": [
"null",
"array"
],
"items": {
"properties": {
"title": {
"type": [
"null",
"string"
]
},
"code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
},
"savings": {
"type": [
"null",
"number"
]
},
"type": {
"type": [
"null",
"string"
]
}
},
"type": [
"null",
"object"
]
}
},
"custom_tax_lines": {
"items": {
"properties": {
"price": {
"type": [
"null",
"number"
],
"multipleOf": 1e-10
},
"title": {
"type": [
"null",
"string"
]
},
"rate": {
"type": [
"null",
"number"
],
"multipleOf": 1e-10
},
"compare_at": {
"type": [
"null",
"number"
]
},
"position": {
"type": [
"null",
"integer"
]
},
"source": {
"type": [
"null",
"string"
]
},
"zone": {
"type": [
"null",
"string"
]
}
},
"type": [
"null",
"object"
]
},
"type": [
"null",
"array"
]
},
"phone": {
"type": [
"null",
"string"
]
},
"validation_context": {
"type": [
"null",
"string"
]
},
"id": {
"type": [
"null",
"string"
]
},
"carrier_identifier": {
"type": [
"null",
"string"
]
},
"api_client_id": {
"type": [
"null",
"string"
]
},
"price": {
"type": [
"null",
"number"
],
"multipleOf": 1e-10
},
"requested_fulfillment_service_id": {
"type": [
"null",
"string"
]
},
"title": {
"type": [
"null",
"string"
]
},
"code": {
"type": [
"null",
"string"
]
},
"tax_lines": {
"items": {
"properties": {
"price_set": {
"properties": {
"shop_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
},
"presentment_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
}
},
"type": [
"null",
"object"
]
},
"price": {
"type": [
"null",
"number"
],
"multipleOf": 1e-10
},
"title": {
"type": [
"null",
"string"
]
},
"rate": {
"type": [
"null",
"number"
],
"multipleOf": 1e-10
},
"compare_at": {
"type": [
"null",
"number"
]
},
"position": {
"type": [
"null",
"integer"
]
},
"source": {
"type": [
"null",
"string"
]
},
"zone": {
"type": [
"null",
"string"
]
}
},
"type": [
"null",
"object"
]
},
"type": [
"null",
"array"
]
},
"carrier_service_id": {
"type": [
"null",
"string"
]
},
"delivery_category": {
"type": [
"null",
"string"
]
},
"markup": {
"type": [
"null",
"string"
]
},
"source": {
"type": [
"null",
"string"
]
}
}
}
},
"user_id": {
"type": [
"null",
"string"
]
},
"source": {
"type": [
"null",
"string"
]
},
"shipping_address": {
"type": [
"null",
"object"
],
"properties": {
"phone": {
"type": [
"null",
"string"
]
},
"country": {
"type": [
"null",
"string"
]
},
"first_name": {
"type": [
"null",
"string"
]
},
"name": {
"type": [
"null",
"string"
]
},
"latitude": {
"type": [
"null",
"number"
]
},
"zip": {
"type": [
"null",
"string"
]
},
"last_name": {
"type": [
"null",
"string"
]
},
"province": {
"type": [
"null",
"string"
]
},
"address2": {
"type": [
"null",
"string"
]
},
"address1": {
"type": [
"null",
"string"
]
},
"country_code": {
"type": [
"null",
"string"
]
},
"city": {
"type": [
"null",
"string"
]
},
"company": {
"type": [
"null",
"string"
]
},
"province_code": {
"type": [
"null",
"string"
]
},
"longitude": {
"type": [
"null",
"number"
]
}
}
},
"abandoned_checkout_url": {
"type": [
"null",
"string"
]
},
"landing_site": {
"type": [
"null",
"string"
]
},
"customer": {
"type": "object",
"properties": {
"last_order_name": {
"type": [
"null",
"string"
]
},
"currency": {
"type": [
"null",
"string"
]
},
"email": {
"type": [
"null",
"string"
]
},
"multipass_identifier": {
"type": [
"null",
"string"
]
},
"default_address": {
"type": [
"null",
"object"
],
"properties": {
"city": {
"type": [
"null",
"string"
]
},
"address1": {
"type": [
"null",
"string"
]
},
"zip": {
"type": [
"null",
"string"
]
},
"id": {
"type": [
"null",
"string"
]
},
"country_name": {
"type": [
"null",
"string"
]
},
"province": {
"type": [
"null",
"string"
]
},
"phone": {
"type": [
"null",
"string"
]
},
"country": {
"type": [
"null",
"string"
]
},
"first_name": {
"type": [
"null",
"string"
]
},
"customer_id": {
"type": [
"null",
"string"
]
},
"default": {
"type": [
"null",
"boolean"
]
},
"last_name": {
"type": [
"null",
"string"
]
},
"country_code": {
"type": [
"null",
"string"
]
},
"name": {
"type": [
"null",
"string"
]
},
"province_code": {
"type": [
"null",
"string"
]
},
"address2": {
"type": [
"null",
"string"
]
},
"company": {
"type": [
"null",
"string"
]
}
}
},
"orders_count": {
"type": [
"null",
"integer"
]
},
"state": {
"type": [
"null",
"string"
]
},
"verified_email": {
"type": [
"null",
"boolean"
]
},
"total_spent": {
"type": [
"null",
"string"
]
},
"last_order_id": {
"type": [
"null",
"string"
]
},
"first_name": {
"type": [
"null",
"string"
]
},
"updated_at": {
"type": [
"null",
"string"
],
"format": "date-time"
},
"note": {
"type": [
"null",
"string"
]
},
"phone": {
"type": [
"null",
"string"
]
},
"admin_graphql_api_id": {
"type": [
"null",
"string"
]
},
"addresses": {
"type": [
"null",
"array"
],
"items": {
"type": [
"null",
"object"
],
"properties": {
"city": {
"type": [
"null",
"string"
]
},
"address1": {
"type": [
"null",
"string"
]
},
"zip": {
"type": [
"null",
"string"
]
},
"id": {
"type": [
"null",
"string"
]
},
"country_name": {
"type": [
"null",
"string"
]
},
"province": {
"type": [
"null",
"string"
]
},
"phone": {
"type": [
"null",
"string"
]
},
"country": {
"type": [
"null",
"string"
]
},
"first_name": {
"type": [
"null",
"string"
]
},
"customer_id": {
"type": [
"null",
"string"
]
},
"default": {
"type": [
"null",
"boolean"
]
},
"last_name": {
"type": [
"null",
"string"
]
},
"country_code": {
"type": [
"null",
"string"
]
},
"name": {
"type": [
"null",
"string"
]
},
"province_code": {
"type": [
"null",
"string"
]
},
"address2": {
"type": [
"null",
"string"
]
},
"company": {
"type": [
"null",
"string"
]
}
}
}
},
"last_name": {
"type": [
"null",
"string"
]
},
"tags": {
"type": [
"null",
"string"
]
},
"tax_exempt": {
"type": [
"null",
"boolean"
]
},
"id": {
"type": [
"null",
"string"
]
},
"accepts_marketing": {
"type": [
"null",
"boolean"
]
},
"accepts_marketing_updated_at": {
"anyOf": [
{
"type": "string",
"format": "date-time"
},
{
"type": "string"
},
{
"type": "null"
}
]
},
"created_at": {
"type": [
"null",
"string"
],
"format": "date-time"
}
}
},
"total_price": {
"type": [
"null",
"number"
],
"multipleOf": 1e-10
},
"cart_token": {
"type": [
"null",
"string"
]
},
"taxes_included": {
"type": [
"null",
"boolean"
]
}
}
},
"metadata": [
{
"breadcrumb": [],
"metadata": {
"table-key-properties": [
"id"
],
"forced-replication-method": "INCREMENTAL",
"valid-replication-keys": [
"updated_at"
],
"selected": true
}
},
{
"breadcrumb": [
"properties",
"note_attributes"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"location_id"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"buyer_accepts_marketing"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"currency"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"completed_at"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"token"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"billing_address"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"email"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"discount_codes"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"customer_locale"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"created_at"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"updated_at"
],
"metadata": {
"inclusion": "automatic"
}
},
{
"breadcrumb": [
"properties",
"gateway"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"referring_site"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"source_identifier"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"total_weight"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"tax_lines"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"total_line_items_price"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"closed_at"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"device_id"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"phone"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"source_name"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"id"
],
"metadata": {
"inclusion": "automatic"
}
},
{
"breadcrumb": [
"properties",
"name"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"total_tax"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"subtotal_price"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"line_items"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"source_url"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"total_discounts"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"note"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"presentment_currency"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"shipping_lines"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"user_id"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"source"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"shipping_address"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"abandoned_checkout_url"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"landing_site"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"customer"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"total_price"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"cart_token"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"taxes_included"
],
"metadata": {
"inclusion": "available",
"selected": true
}
}
],
"key_properties": [
"id"
],
"replication_key": "updated_at",
"replication_method": "INCREMENTAL"
}"""
shopify_products = """{"type":"SCHEMA",
"stream": "products",
"tap_stream_id": "products",
"schema": {
"properties": {
"published_at": {
"type": [
"null",
"string"
],
"format": "date-time"
},
"created_at": {
"type": [
"null",
"string"
],
"format": "date-time"
},
"published_scope": {
"type": [
"null",
"string"
]
},
"vendor": {
"type": [
"null",
"string"
]
},
"updated_at": {
"type": [
"null",
"string"
],
"format": "date-time"
},
"body_html": {
"type": [
"null",
"string"
]
},
"product_type": {
"type": [
"null",
"string"
]
},
"tags": {
"type": [
"null",
"string"
]
},
"options": {
"type": [
"null",
"array"
],
"items": {
"properties": {
"name": {
"type": [
"null",
"string"
]
},
"product_id": {
"type": [
"null",
"string"
]
},
"values": {
"type": [
"null",
"array"
],
"items": {
"type": [
"null",
"string"
]
}
},
"id": {
"type": [
"null",
"string"
]
},
"position": {
"type": [
"null",
"integer"
]
}
},
"type": [
"null",
"object"
]
}
},
"image": {
"properties": {
"updated_at": {
"type": [
"null",
"string"
],
"format": "date-time"
},
"created_at": {
"type": [
"null",
"string"
],
"format": "date-time"
},
"variant_ids": {
"type": [
"null",
"array"
],
"items": {
"type": [
"null",
"string"
]
}
},
"height": {
"type": [
"null",
"integer"
]
},
"alt": {
"type": [
"null",
"string"
]
},
"src": {
"type": [
"null",
"string"
]
},
"position": {
"type": [
"null",
"integer"
]
},
"id": {
"type": [
"null",
"string"
]
},
"admin_graphql_api_id": {
"type": [
"null",
"string"
]
},
"width": {
"type": [
"null",
"integer"
]
}
},
"type": [
"null",
"object"
]
},
"handle": {
"type": [
"null",
"string"
]
},
"images": {
"type": [
"null",
"array"
],
"items": {
"properties": {
"updated_at": {
"type": [
"null",
"string"
],
"format": "date-time"
},
"created_at": {
"type": [
"null",
"string"
],
"format": "date-time"
},
"variant_ids": {
"type": [
"null",
"array"
],
"items": {
"type": [
"null",
"string"
]
}
},
"height": {
"type": [
"null",
"integer"
]
},
"alt": {
"type": [
"null",
"string"
]
},
"src": {
"type": [
"null",
"string"
]
},
"position": {
"type": [
"null",
"integer"
]
},
"id": {
"type": [
"null",
"string"
]
},
"admin_graphql_api_id": {
"type": [
"null",
"string"
]
},
"width": {
"type": [
"null",
"integer"
]
}
},
"type": [
"null",
"object"
]
}
},
"template_suffix": {
"type": [
"null",
"string"
]
},
"title": {
"type": [
"null",
"string"
]
},
"variants": {
"type": [
"null",
"array"
],
"items": {
"properties": {
"barcode": {
"type": [
"null",
"string"
]
},
"tax_code": {
"type": [
"null",
"string"
]
},
"created_at": {
"type": [
"null",
"string"
],
"format": "date-time"
},
"weight_unit": {
"type": [
"null",
"string"
]
},
"id": {
"type": [
"null",
"string"
]
},
"position": {
"type": [
"null",
"integer"
]
},
"price": {
"type": [
"null",
"number"
],
"multipleOf": 1e-10
},
"image_id": {
"type": [
"null",
"string"
]
},
"inventory_policy": {
"type": [
"null",
"string"
]
},
"sku": {
"type": [
"null",
"string"
]
},
"inventory_item_id": {
"type": [
"null",
"string"
]
},
"fulfillment_service": {
"type": [
"null",
"string"
]
},
"title": {
"type": [
"null",
"string"
]
},
"weight": {
"type": [
"null",
"number"
]
},
"inventory_management": {
"type": [
"null",
"string"
]
},
"taxable": {
"type": [
"null",
"boolean"
]
},
"admin_graphql_api_id": {
"type": [
"null",
"string"
]
},
"option1": {
"type": [
"null",
"string"
]
},
"compare_at_price": {
"type": [
"null",
"number"
],
"multipleOf": 1e-10
},
"updated_at": {
"type": [
"null",
"string"
],
"format": "date-time"
},
"option2": {
"type": [
"null",
"string"
]
},
"old_inventory_quantity": {
"type": [
"null",
"integer"
]
},
"requires_shipping": {
"type": [
"null",
"boolean"
]
},
"inventory_quantity": {
"type": [
"null",
"integer"
]
},
"grams": {
"type": [
"null",
"integer"
]
},
"option3": {
"type": [
"null",
"string"
]
}
},
"type": [
"null",
"object"
]
}
},
"admin_graphql_api_id": {
"type": [
"null",
"string"
]
},
"id": {
"type": [
"null",
"string"
]
}
},
"type": "object"
},
"metadata": [
{
"breadcrumb": [],
"metadata": {
"table-key-properties": [
"id"
],
"forced-replication-method": "INCREMENTAL",
"valid-replication-keys": [
"updated_at"
],
"selected": true
}
},
{
"breadcrumb": [
"properties",
"published_at"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"created_at"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"published_scope"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"vendor"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"updated_at"
],
"metadata": {
"inclusion": "automatic"
}
},
{
"breadcrumb": [
"properties",
"body_html"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"product_type"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"tags"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"options"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"image"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"handle"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"images"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"template_suffix"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"title"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"variants"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"admin_graphql_api_id"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"id"
],
"metadata": {
"inclusion": "automatic"
}
}
],
"key_properties": [
"id"
],
"replication_key": "updated_at",
"replication_method": "INCREMENTAL"
}"""
shopify_transactions = """{"type":"SCHEMA",
"stream": "transactions",
"tap_stream_id": "transactions",
"schema": {
"properties": {
"error_code": {
"type": [
"null",
"string"
]
},
"device_id": {
"type": [
"null",
"string"
]
},
"user_id": {
"type": [
"null",
"string"
]
},
"parent_id": {
"type": [
"null",
"string"
]
},
"test": {
"type": [
"null",
"boolean"
]
},
"kind": {
"type": [
"null",
"string"
]
},
"order_id": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
],
"multipleOf": 1e-10
},
"authorization": {
"type": [
"null",
"string"
]
},
"currency": {
"type": [
"null",
"string"
]
},
"source_name": {
"type": [
"null",
"string"
]
},
"message": {
"type": [
"null",
"string"
]
},
"id": {
"type": [
"null",
"string"
]
},
"created_at": {
"type": [
"null",
"string"
],
"format": "date-time"
},
"status": {
"type": [
"null",
"string"
]
},
"payment_details": {
"properties": {
"cvv_result_code": {
"type": [
"null",
"string"
]
},
"credit_card_bin": {
"type": [
"null",
"string"
]
},
"credit_card_company": {
"type": [
"null",
"string"
]
},
"credit_card_number": {
"type": [
"null",
"string"
]
},
"avs_result_code": {
"type": [
"null",
"string"
]
}
},
"type": [
"null",
"object"
]
},
"gateway": {
"type": [
"null",
"string"
]
},
"admin_graphql_api_id": {
"type": [
"null",
"string"
]
},
"receipt": {
"type": [
"null",
"object"
],
"properties": {
"fee_amount": {
"type": [
"null",
"number"
],
"multipleOf": 1e-10
},
"gross_amount": {
"type": [
"null",
"number"
],
"multipleOf": 1e-10
},
"tax_amount": {
"type": [
"null",
"number"
],
"multipleOf": 1e-10
}
},
"patternProperties": {
".+": {}
}
},
"location_id": {
"type": [
"null",
"string"
]
}
},
"type": "object"
},
"metadata": [
{
"breadcrumb": [],
"metadata": {
"table-key-properties": [
"id"
],
"forced-replication-method": "INCREMENTAL",
"valid-replication-keys": [
"created_at"
],
"selected": true
}
},
{
"breadcrumb": [
"properties",
"error_code"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"device_id"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"user_id"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"parent_id"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"test"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"kind"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"order_id"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"amount"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"authorization"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"currency"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"source_name"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"message"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"id"
],
"metadata": {
"inclusion": "automatic"
}
},
{
"breadcrumb": [
"properties",
"created_at"
],
"metadata": {
"inclusion": "automatic"
}
},
{
"breadcrumb": [
"properties",
"status"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"payment_details"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"gateway"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"admin_graphql_api_id"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"receipt"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"location_id"
],
"metadata": {
"inclusion": "available",
"selected": true
}
}
],
"key_properties": [
"id"
],
"replication_key": "created_at",
"replication_method": "INCREMENTAL"
}"""
shopify_metafields_malformed = """{"type":"SCHEMA",
"stream": "metafields",
"tap_stream_id": "metafields",
"schema": {
"properties": {
"owner_id": {
"type": [
"null",
"string"
]
},
"admin_graphql_api_id": {
"type": [
"null",
"string"
]
},
"owner_resource": {
"type": [
"null",
"string"
]
},
"value_type": {
"type": [
"null",
"string"
]
},
"key": {
"type": [
"null",
"string"
]
},
"created_at": {
"type": [
"null",
"string"
],
"format": "date-time"
},
"id": {
"type": [
"null",
"string"
]
},
"namespace": {
"type": [
"null",
"string"
]
},
"description": {
"type": [
"null",
"string"
]
},
"value": {
"type": [
"null",
"integer",
"object",
"string"
],
"properties": {}
},
"updated_at": {
"type": [
"null",
"string"
],
"format": "date-time"
}
},
"type": "object"
},
"metadata": [
{
"breadcrumb": [],
"metadata": {
"table-key-properties": [
"id"
],
"forced-replication-method": "INCREMENTAL",
"valid-replication-keys": [
"updated_at"
],
"selected": false
}
},
{
"breadcrumb": [
"properties",
"owner_id"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"admin_graphql_api_id"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"owner_resource"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"value_type"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"key"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"created_at"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"id"
],
"metadata": {
"inclusion": "automatic"
}
},
{
"breadcrumb": [
"properties",
"namespace"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"description"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"value"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"updated_at"
],
"metadata": {
"inclusion": "automatic"
}
}
],
"key_properties": [
"id"
],
"replication_key": "updated_at",
"replication_method": "INCREMENTAL"
}"""
"""
replaced this :
"value": {
"type": [
"null",
"integer",
"object",
"string"
],
"properties": {}
}
with this:
"value": {
"type": [
"null",
"integer",
"object",
"string"
]
}
removed "properties": {}
"""
shopify_metafields_fixed = """{"type":"SCHEMA",
"stream": "metafields",
"tap_stream_id": "metafields",
"schema": {
"properties": {
"owner_id": {
"type": [
"null",
"string"
]
},
"admin_graphql_api_id": {
"type": [
"null",
"string"
]
},
"owner_resource": {
"type": [
"null",
"string"
]
},
"value_type": {
"type": [
"null",
"string"
]
},
"key": {
"type": [
"null",
"string"
]
},
"created_at": {
"type": [
"null",
"string"
],
"format": "date-time"
},
"id": {
"type": [
"null",
"string"
]
},
"namespace": {
"type": [
"null",
"string"
]
},
"description": {
"type": [
"null",
"string"
]
},
"value": {
"type": [
"null",
"integer",
"object",
"string"
]
},
"updated_at": {
"type": [
"null",
"string"
],
"format": "date-time"
}
},
"type": "object"
},
"metadata": [
{
"breadcrumb": [],
"metadata": {
"table-key-properties": [
"id"
],
"forced-replication-method": "INCREMENTAL",
"valid-replication-keys": [
"updated_at"
],
"selected": false
}
},
{
"breadcrumb": [
"properties",
"owner_id"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"admin_graphql_api_id"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"owner_resource"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"value_type"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"key"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"created_at"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"id"
],
"metadata": {
"inclusion": "automatic"
}
},
{
"breadcrumb": [
"properties",
"namespace"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"description"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"value"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"updated_at"
],
"metadata": {
"inclusion": "automatic"
}
}
],
"key_properties": [
"id"
],
"replication_key": "updated_at",
"replication_method": "INCREMENTAL"
}"""
shopify_order_refunds = """{"type":"SCHEMA",
"stream": "order_refunds",
"tap_stream_id": "order_refunds",
"schema": {
"type": "object",
"properties": {
"order_id": {
"type": [
"null",
"string"
]
},
"restock": {
"type": [
"null",
"boolean"
]
},
"order_adjustments": {
"items": {
"properties": {
"order_id": {
"type": [
"null",
"string"
]
},
"tax_amount": {
"type": [
"null",
"number"
],
"multipleOf": 1e-10
},
"refund_id": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
],
"multipleOf": 1e-10
},
"kind": {
"type": [
"null",
"string"
]
},
"id": {
"type": [
"null",
"string"
]
},
"reason": {
"type": [
"null",
"string"
]
}
},
"type": [
"null",
"object"
]
},
"type": [
"null",
"array"
]
},
"processed_at": {
"type": [
"null",
"string"
],
"format": "date-time"
},
"user_id": {
"type": [
"null",
"string"
]
},
"note": {
"type": [
"null",
"string"
]
},
"id": {
"type": [
"null",
"string"
]
},
"created_at": {
"type": [
"null",
"string"
],
"format": "date-time"
},
"admin_graphql_api_id": {
"type": [
"null",
"string"
]
},
"refund_line_items": {
"type": [
"null",
"array"
],
"items": {
"properties": {
"location_id": {
"type": [
"null",
"string"
]
},
"subtotal_set": {
"properties": {
"shop_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
},
"presentment_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
}
},
"type": [
"null",
"object"
]
},
"total_tax_set": {
"properties": {
"shop_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
},
"presentment_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
}
},
"type": [
"null",
"object"
]
},
"line_item_id": {
"type": [
"null",
"string"
]
},
"total_tax": {
"type": [
"null",
"number"
]
},
"quantity": {
"type": [
"null",
"integer"
]
},
"id": {
"type": [
"null",
"string"
]
},
"line_item": {
"properties": {
"gift_card": {
"type": [
"null",
"boolean"
]
},
"price": {
"type": [
"null",
"string"
]
},
"tax_lines": {
"type": [
"null",
"array"
],
"items": {
"properties": {
"price_set": {
"properties": {
"shop_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
},
"presentment_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
}
},
"type": [
"null",
"object"
]
},
"price": {
"type": [
"null",
"string"
]
},
"title": {
"type": [
"null",
"string"
]
},
"rate": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
}
},
"fulfillment_service": {
"type": [
"null",
"string"
]
},
"sku": {
"type": [
"null",
"string"
]
},
"fulfillment_status": {
"type": [
"null",
"string"
]
},
"properties": {
"type": [
"null",
"array"
],
"items": {
"properties": {
"name": {
"type": [
"null",
"string"
]
},
"value": {
"type": [
"null",
"string"
]
}
},
"type": [
"null",
"object"
]
}
},
"quantity": {
"type": [
"null",
"integer"
]
},
"variant_id": {
"type": [
"null",
"string"
]
},
"grams": {
"type": [
"null",
"integer"
]
},
"requires_shipping": {
"type": [
"null",
"boolean"
]
},
"vendor": {
"type": [
"null",
"string"
]
},
"price_set": {
"properties": {
"shop_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
},
"presentment_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
}
},
"type": [
"null",
"object"
]
},
"variant_inventory_management": {
"type": [
"null",
"string"
]
},
"pre_tax_price": {
"type": [
"null",
"string"
]
},
"variant_title": {
"type": [
"null",
"string"
]
},
"total_discount_set": {
"properties": {
"shop_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
},
"presentment_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
}
},
"type": [
"null",
"object"
]
},
"discount_allocations": {
"type": [
"null",
"array"
],
"items": {
"properties": {
"amount": {
"type": [
"null",
"number"
]
},
"amount_set": {
"properties": {
"shop_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
},
"presentment_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
}
},
"type": [
"null",
"object"
]
},
"discount_application_index": {
"type": [
"null",
"integer"
]
}
},
"type": [
"null",
"object"
]
}
},
"pre_tax_price_set": {
"properties": {
"shop_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
},
"presentment_money": {
"properties": {
"currency_code": {
"type": [
"null",
"string"
]
},
"amount": {
"type": [
"null",
"number"
]
}
},
"type": [
"null",
"object"
]
}
},
"type": [
"null",
"object"
]
},
"fulfillable_quantity": {
"type": [
"null",
"integer"
]
},
"id": {
"type": [
"null",
"string"
]
},
"admin_graphql_api_id": {
"type": [
"null",
"string"
]
},
"total_discount": {
"type": [
"null",
"string"
]
},
"name": {
"type": [
"null",
"string"
]
},
"product_exists": {
"type": [
"null",
"boolean"
]
},
"taxable": {
"type": [
"null",
"boolean"
]
},
"product_id": {
"type": [
"null",
"string"
]
},
"title": {
"type": [
"null",
"string"
]
}
},
"type": [
"null",
"object"
]
},
"subtotal": {
"type": [
"null",
"number"
]
},
"restock_type": {
"type": [
"null",
"string"
]
}
},
"type": [
"null",
"object"
]
}
}
}
},
"metadata": [
{
"breadcrumb": [],
"metadata": {
"table-key-properties": [
"id"
],
"forced-replication-method": "INCREMENTAL",
"valid-replication-keys": [
"created_at"
],
"selected": true
}
},
{
"breadcrumb": [
"properties",
"order_id"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"restock"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"order_adjustments"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"processed_at"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"user_id"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"note"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"id"
],
"metadata": {
"inclusion": "automatic"
}
},
{
"breadcrumb": [
"properties",
"created_at"
],
"metadata": {
"inclusion": "automatic"
}
},
{
"breadcrumb": [
"properties",
"admin_graphql_api_id"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"refund_line_items"
],
"metadata": {
"inclusion": "available",
"selected": true
}
}
],
"key_properties": [
"id"
],
"replication_key": "created_at",
"replication_method": "INCREMENTAL"
}"""
shopify_collects = """{"type":"SCHEMA",
"stream": "collects",
"tap_stream_id": "collects",
"schema": {
"type": "object",
"properties": {
"id": {
"type": [
"null",
"string"
]
},
"collection_id": {
"type": [
"null",
"string"
]
},
"created_at": {
"type": [
"null",
"string"
],
"format": "date-time"
},
"position": {
"type": [
"null",
"integer"
]
},
"product_id": {
"type": [
"null",
"string"
]
},
"sort_value": {
"type": [
"null",
"string"
]
},
"updated_at": {
"type": [
"null",
"string"
],
"format": "date-time"
}
}
},
"metadata": [
{
"breadcrumb": [],
"metadata": {
"table-key-properties": [
"id"
],
"forced-replication-method": "INCREMENTAL",
"valid-replication-keys": [
"updated_at"
],
"selected": true
}
},
{
"breadcrumb": [
"properties",
"id"
],
"metadata": {
"inclusion": "automatic"
}
},
{
"breadcrumb": [
"properties",
"collection_id"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"created_at"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"position"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"product_id"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"sort_value"
],
"metadata": {
"inclusion": "available",
"selected": true
}
},
{
"breadcrumb": [
"properties",
"updated_at"
],
"metadata": {
"inclusion": "automatic"
}
}
],
"key_properties": [
"id"
],
"replication_key": "updated_at",
"replication_method": "INCREMENTAL"
}"""
| 28.565154
| 99
| 0.167704
| 13,005
| 452,672
| 5.713725
| 0.025221
| 0.209509
| 0.20706
| 0.119289
| 0.966033
| 0.955361
| 0.951741
| 0.936789
| 0.91445
| 0.90838
| 0
| 0.00256
| 0.73684
| 452,672
| 15,847
| 100
| 28.565154
| 0.621213
| 0.002136
| 0
| 0.675557
| 0
| 0
| 0.999151
| 0.012802
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0.000318
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 1
| 1
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 10
|
0ed51f28ba67a1d02a10a4fc54a5773f0a350f91
| 58,747
|
py
|
Python
|
hs_core/tests/api/rest/test_resource_science_meta.py
|
kjlippold/hydroshare
|
a82c6a3ce2aee3f00d1a1022f7c328e6a610fc3f
|
[
"BSD-3-Clause"
] | null | null | null |
hs_core/tests/api/rest/test_resource_science_meta.py
|
kjlippold/hydroshare
|
a82c6a3ce2aee3f00d1a1022f7c328e6a610fc3f
|
[
"BSD-3-Clause"
] | null | null | null |
hs_core/tests/api/rest/test_resource_science_meta.py
|
kjlippold/hydroshare
|
a82c6a3ce2aee3f00d1a1022f7c328e6a610fc3f
|
[
"BSD-3-Clause"
] | null | null | null |
from rest_framework import status
from hs_core.hydroshare import resource
from hs_core.hydroshare.utils import resource_post_create_actions
from .base import HSRESTTestCase
class TestResourceScienceMetadata(HSRESTTestCase):
def setUp(self):
super(TestResourceScienceMetadata, self).setUp()
self.rtype = 'GenericResource'
self.title = 'My Test resource'
res = resource.create_resource(self.rtype,
self.user,
self.title)
self.resource = res
self.pid = res.short_id
self.resources_to_delete.append(self.pid)
# create another resource for testing relation metadata
another_res = resource.create_resource('GenericResource',
self.user,
'My another Test resource')
self.pid2 = another_res.short_id
self.resources_to_delete.append(self.pid2)
def test_get_scimeta(self):
# Get the resource system metadata
sysmeta_url = "/hsapi/resource/{res_id}/scimeta/elements/".format(res_id=self.pid)
response = self.client.get(sysmeta_url)
self.assertEqual(response.status_code, status.HTTP_200_OK)
# content = json.loads(response.content)
def test_put_scimeta_generic_resource(self):
sysmeta_url = "/hsapi/resource/{res_id}/scimeta/elements/".format(res_id=self.pid)
put_data = {
"title": "New Title",
"description": "New Description",
"subjects": [
{"value": "subject1"},
{"value": "subject2"},
{"value": "subject3"}
],
"contributors": [{
"name": "Test Name 1",
"organization": "Org 1",
"identifiers": {"ORCID": "https://orcid.org/012",
"ResearchGateID": "https://www.researchgate.net/002"}
}, {
"name": None,
"organization": "Org 2"
}],
"creators": [{
"name": "Creator 1",
"organization": None
}, {
"name": "Creator 2",
"organization": "USU",
"identifiers": {"ORCID": "https://orcid.org/011",
"ResearchGateID": "https://www.researchgate.net/001"}
}],
"coverages": [{
"type": "box",
"value": {
"northlimit": 43.19716728247476,
"projection": "WGS 84 EPSG:4326",
"name": "A whole bunch of the atlantic ocean",
"units": "Decimal degrees",
"southlimit": 23.8858376999,
"eastlimit": -19.16015625,
"westlimit": -62.75390625
}
}],
"dates": [
{
"type": "valid",
"start_date": "2016-12-07T00:00:00Z",
"end_date": "2018-12-07T00:00:00Z"
}
],
"language": "fre",
"rights": {"statement": "CCC", "url": "http://www.hydroshare.org"},
"sources": [
{
"derived_from": "Source 3"
},
{
"derived_from": "Source 2"
}
],
"relations": [
{
"type": "isCopiedFrom",
"value": "https://www.hydroshare.org/resource/{}/".format(self.pid2)
},
{
"type": "isExecutedBy",
"value": "https://www.hydroshare.org/resource/{}/".format(self.pid2)
}
],
"funding_agencies": [
{
"agency_name": "NSF",
"award_title": "Cyber Infrastructure",
"award_number": "NSF-101-20-6789",
"agency_url": "https://www.nsf.gov",
},
{
"agency_name": "NSF2",
"award_title": "Cyber Infrastructure2",
"award_number": "NSF-123",
"agency_url": "https://www.google.com",
}
]
}
response = self.client.put(sysmeta_url, put_data, format='json')
self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)
self.assertEqual(self.resource.metadata.dates.all().count(), 3)
self.assertEqual(self.resource.metadata.sources.all().count(), 2)
self.assertEqual(self.resource.metadata.relations.all().count(), 2)
self.assertEqual(self.resource.metadata.funding_agencies.all().count(), 2)
self.assertEqual(str(self.resource.metadata.rights), "CCC http://www.hydroshare.org")
self.assertEqual(str(self.resource.metadata.language), "fre")
self.assertEqual(self.resource.metadata.coverages.all().count(), 1)
self.assertEqual(self.resource.metadata.creators.all().count(), 2)
self.assertEqual(self.resource.metadata.contributors.all().count(), 2)
self.assertEqual(self.resource.metadata.subjects.all().count(), 3)
self.assertEqual(str(self.resource.metadata.description), "New Description")
self.assertEqual(str(self.resource.metadata.title), "New Title")
def test_put_scimeta_generic_resource_double_none(self):
sysmeta_url = "/hsapi/resource/{res_id}/scimeta/elements/".format(res_id=self.pid)
put_data = {
"title": "New Title",
"description": "New Description",
"subjects": [
{"value": "subject1"},
{"value": "subject2"},
{"value": "subject3"}
],
"contributors": [{
"name": "Test Name 1",
"organization": "Org 1"
}, {
"name": None,
"organization": "Org 2"
}],
"creators": [
{
"name": "Creator",
"organization": None
},
{
"name": None,
"organization": None
}
],
"coverages": [{
"type": "box",
"value": {
"northlimit": 43.19716728247476,
"projection": "WGS 84 EPSG:4326",
"name": "A whole bunch of the atlantic ocean",
"units": "Decimal degrees",
"southlimit": 23.8858376999,
"eastlimit": -19.16015625,
"westlimit": -62.75390625
}
}],
"dates": [
{
"type": "valid",
"start_date": "2016-12-07T00:00:00Z",
"end_date": "2018-12-07T00:00:00Z"
}
],
"language": "fre",
"rights": {"statement": "CCC", "url": "http://www.hydroshare.org"},
"sources": [
{
"derived_from": "Source 3"
},
{
"derived_from": "Source 2"
}
]
}
response = self.client.put(sysmeta_url, put_data, format='json')
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
def test_put_scimeta_composite_resource_with_core_metadata(self):
# testing bulk metadata update that includes only core metadata
# create a composite resource
self._create_resource(resource_type="CompositeResource")
sysmeta_url = "/hsapi/resource/{res_id}/scimeta/elements/".format(
res_id=self.resource.short_id)
put_data = {
"title": "New Title",
"description": "New Description",
"subjects": [
{"value": "subject1"},
{"value": "subject2"},
{"value": "subject3"}
],
"contributors": [{
"name": "Test Name 1",
"organization": "Org 1"
}, {
"name": "Test Name 2",
"organization": "Org 2"
}],
"creators": [{
"name": "Creator",
"organization": None,
"identifiers": {"ORCID": "https://orcid.org/011",
"ResearchGateID": "https://www.researchgate.net/001"}
}],
"dates": [
{
"type": "valid",
"start_date": "2016-12-07T00:00:00Z",
"end_date": "2018-12-07T00:00:00Z"
}
],
"language": "fre",
"rights": {"statement": "CCC", "url": "http://www.hydroshare.org"},
"sources": [
{
"derived_from": "Source 3"
},
{
"derived_from": "Source 2"
}
],
"relations": [
{
"type": "isCopiedFrom",
"value": "https://www.hydroshare.org/resource/{}/".format(self.pid2)
},
{
"type": "isExecutedBy",
"value": "https://www.hydroshare.org/resource/{}/".format(self.pid2)
}
],
"funding_agencies": [
{
"agency_name": "NSF",
"award_title": "Cyber Infrastructure",
"award_number": "NSF-101-20-6789",
"agency_url": "https://www.nsf.gov",
},
{
"agency_name": "NSF2",
"award_title": "Cyber Infrastructure2",
"award_number": "NSF-123",
"agency_url": "https://www.google.com",
}
]
}
response = self.client.put(sysmeta_url, put_data, format='json')
self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)
self.assertEqual(self.resource.metadata.dates.all().count(), 3)
self.assertEqual(self.resource.metadata.sources.all().count(), 2)
self.assertEqual(self.resource.metadata.relations.all().count(), 2)
self.assertEqual(self.resource.metadata.funding_agencies.all().count(), 2)
self.assertEqual(str(self.resource.metadata.rights), "CCC http://www.hydroshare.org")
self.assertEqual(str(self.resource.metadata.language), "fre")
self.assertEqual(self.resource.metadata.creators.all().count(), 1)
self.assertEqual(self.resource.metadata.contributors.all().count(), 2)
self.assertEqual(self.resource.metadata.subjects.all().count(), 3)
self.assertEqual(str(self.resource.metadata.description), "New Description")
self.assertEqual(str(self.resource.metadata.title), "New Title")
self.resource.delete()
def test_put_scimeta_composite_resource_with_core_metadata_and_coverage(self):
# testing bulk metadata update with only core metadata that includes coverage metadata
# create a composite resource
self._create_resource(resource_type="CompositeResource")
sysmeta_url = "/hsapi/resource/{res_id}/scimeta/elements/".format(
res_id=self.resource.short_id)
put_data = {
"title": "New Title",
"description": "New Description",
"subjects": [
{"value": "subject1"},
{"value": "subject2"},
{"value": "subject3"}
],
"contributors": [{
"name": "Test Name 1",
"organization": "Org 1"
}, {
"name": "Test Name 2",
"organization": "Org 2"
}],
"creators": [{
"name": "Creator",
"organization": None
}],
"coverages": [{
"type": "box",
"value": {
"northlimit": 43.19716728247476,
"projection": "WGS 84 EPSG:4326",
"name": "A whole bunch of the atlantic ocean",
"units": "Decimal degrees",
"southlimit": 23.8858376999,
"eastlimit": -19.16015625,
"westlimit": -62.75390625
}
}],
"dates": [
{
"type": "valid",
"start_date": "2016-12-07T00:00:00Z",
"end_date": "2018-12-07T00:00:00Z"
}
],
"language": "fre",
"rights": {"statement": "CCC", "url": "http://www.hydroshare.org"},
"sources": [
{
"derived_from": "Source 3"
},
{
"derived_from": "Source 2"
}
]
}
response = self.client.put(sysmeta_url, put_data, format='json')
self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)
self.resource.delete()
def test_put_scimeta_timeseries_resource_with_core_metadata(self):
# testing bulk metadata update that includes only core metadata
# create a composite resource
self._create_resource(resource_type="TimeSeriesResource")
sysmeta_url = "/hsapi/resource/{res_id}/scimeta/elements/".format(
res_id=self.resource.short_id)
put_data = {
"title": "New Title",
"description": "New Description",
"subjects": [
{"value": "subject1"},
{"value": "subject2"},
{"value": "subject3"}
],
"contributors": [{
"name": "Test Name 1",
"organization": "Org 1"
}, {
"name": "Test Name 2",
"organization": "Org 2"
}],
"creators": [{
"name": "Creator",
"organization": None,
"identifiers": {"ORCID": "https://orcid.org/011",
"ResearchGateID": "https://www.researchgate.net/001"}
}],
"dates": [
{
"type": "valid",
"start_date": "2016-12-07T00:00:00Z",
"end_date": "2018-12-07T00:00:00Z"
}
],
"language": "fre",
"rights": {"statement": "CCC", "url": "http://www.hydroshare.org"},
"sources": [
{
"derived_from": "Source 3"
},
{
"derived_from": "Source 2"
}
]
}
response = self.client.put(sysmeta_url, put_data, format='json')
self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)
self.resource.delete()
def test_put_scimeta_timeseries_resource_with_core_metadata_failure(self):
# testing bulk metadata update with only core metadata that includes coverage metadata
# coverage metadata can't be updated for time series resource - this bulk update should fail
# create a composite resource
self._create_resource(resource_type="TimeSeriesResource")
sysmeta_url = "/hsapi/resource/{res_id}/scimeta/elements/".format(
res_id=self.resource.short_id)
put_data = {
"title": "New Title",
"description": "New Description",
"subjects": [
{"value": "subject1"},
{"value": "subject2"},
{"value": "subject3"}
],
"contributors": [{
"name": "Test Name 1",
"organization": "Org 1"
}, {
"name": "Test Name 2",
"organization": "Org 2"
}],
"creators": [{
"name": "Creator",
"organization": None
}],
"coverages": [{
"type": "box",
"value": {
"northlimit": 43.19716728247476,
"projection": "WGS 84 EPSG:4326",
"name": "A whole bunch of the atlantic ocean",
"units": "Decimal degrees",
"southlimit": 23.8858376999,
"eastlimit": -19.16015625,
"westlimit": -62.75390625
}
}],
"dates": [
{
"type": "valid",
"start_date": "2016-12-07T00:00:00Z",
"end_date": "2018-12-07T00:00:00Z"
}
],
"language": "fre",
"rights": {"statement": "CCC", "url": "http://www.hydroshare.org"},
"sources": [
{
"derived_from": "Source 3"
},
{
"derived_from": "Source 2"
}
]
}
response = self.client.put(sysmeta_url, put_data, format='json')
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
self.resource.delete()
def test_put_scimeta_netcdf_resource_with_core_metadata(self):
# testing bulk metadata update that includes both core metadata and resource specific
# metadata update
# create a netcdf resource
netcdf_file = 'hs_core/tests/data/netcdf_valid.nc'
file_to_upload = open(netcdf_file, "r")
self._create_resource(resource_type="NetcdfResource", file_to_upload=file_to_upload)
sysmeta_url = "/hsapi/resource/{res_id}/scimeta/elements/".format(
res_id=self.resource.short_id)
put_data = {
"title": "New Title",
"description": "New Description",
"subjects": [
{"value": "subject1"},
{"value": "subject2"},
{"value": "subject3"}
],
"contributors": [{
"name": "Test Name 1",
"organization": "Org 1"
}, {
"name": "Test Name 2",
"organization": "Org 2"
}],
"creators": [{
"name": "Creator",
"organization": None,
"identifiers": {"ORCID": "https://orcid.org/011",
"ResearchGateID": "https://www.researchgate.net/001"}
}],
"coverages": [{
"type": "box",
"value": {
"northlimit": 43.19716728247476,
"projection": "WGS 84 EPSG:4326",
"name": "A whole bunch of the atlantic ocean",
"units": "Decimal degrees",
"southlimit": 23.8858376999,
"eastlimit": -19.16015625,
"westlimit": -62.75390625
}
}],
"dates": [
{
"type": "valid",
"start_date": "2016-12-07T00:00:00Z",
"end_date": "2018-12-07T00:00:00Z"
}
],
"language": "fre",
"rights": {"statement": "CCC", "url": "http://www.hydroshare.org"},
"sources": [
{
"derived_from": "Source 3"
},
{
"derived_from": "Source 2"
}
],
"originalcoverage": {
"value": {
"northlimit": '12', "projection": "transverse_mercator",
"units": "meter", "southlimit": '10',
"eastlimit": '23', "westlimit": '2'
},
"projection_string_text": '+proj=tmerc +lon_0=-111.0 +lat_0=0.0 +x_0=500000.0 '
'+y_0=0.0 +k_0=0.9996',
"projection_string_type": 'Proj4 String'
},
"variables": [
{
"name": "SWE",
"type": "Float",
"shape": "y,x,time",
"unit": "m",
"missing_value": "-9999",
"descriptive_name": "Snow water equivalent",
"method": "model simulation of UEB"
},
{
"name": "x",
"unit": "Centimeter"
}
]
}
response = self.client.put(sysmeta_url, put_data, format='json')
self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)
self.resource.delete()
def test_put_scimeta_netcdf_resource_without_core_metadata(self):
# testing bulk metadata update that only updates resource specific metadata
# create a netcdf resource
netcdf_file = 'hs_core/tests/data/netcdf_valid.nc'
file_to_upload = open(netcdf_file, "r")
self._create_resource(resource_type="NetcdfResource", file_to_upload=file_to_upload)
sysmeta_url = "/hsapi/resource/{res_id}/scimeta/elements/".format(
res_id=self.resource.short_id)
put_data = {
"originalcoverage": {
"value": {
"northlimit": '12', "projection": "transverse_mercator",
"units": "meter", "southlimit": '10',
"eastlimit": '23', "westlimit": '2'
},
"projection_string_text": '+proj=tmerc +lon_0=-111.0 +lat_0=0.0 +x_0=500000.0 '
'+y_0=0.0 +k_0=0.9996',
"projection_string_type": 'Proj4 String'
},
"variables": [
{
"name": "SWE",
"type": "Float",
"shape": "y,x,time",
"unit": "m",
"missing_value": "-9999",
"descriptive_name": "Snow water equivalent",
"method": "model simulation of UEB"
},
{
"name": "x",
"unit": "Centimeter"
}
]
}
response = self.client.put(sysmeta_url, put_data, format='json')
self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)
self.resource.delete()
def test_put_scimeta_raster_resource_with_core_metadata(self):
# testing bulk metadata update that includes both core metadata and resource specific
# metadata update (Note: the only resource specific metadata element that can be updated
# is BandInformation)
# create a raster resource
raster_file = 'hs_core/tests/data/cea.tif'
file_to_upload = open(raster_file, "r")
self._create_resource(resource_type="RasterResource", file_to_upload=file_to_upload)
sysmeta_url = "/hsapi/resource/{res_id}/scimeta/elements/".format(
res_id=self.resource.short_id)
put_data = {
"title": "New Title",
"description": "New Description",
"subjects": [
{"value": "subject1"},
{"value": "subject2"},
{"value": "subject3"}
],
"contributors": [{
"name": "Test Name 1",
"organization": "Org 1"
}, {
"name": "Test Name 2",
"organization": "Org 2"
}],
"creators": [{
"name": "Creator",
"organization": None,
"identifiers": {"ORCID": "https://orcid.org/011",
"ResearchGateID": "https://www.researchgate.net/001"}
}],
"coverages": [{
"type": "box",
"value": {
"northlimit": 43.19716728247476,
"projection": "WGS 84 EPSG:4326",
"name": "A whole bunch of the atlantic ocean",
"units": "Decimal degrees",
"southlimit": 23.8858376999,
"eastlimit": -19.16015625,
"westlimit": -62.75390625
}
}],
"dates": [
{
"type": "valid",
"start_date": "2016-12-07T00:00:00Z",
"end_date": "2018-12-07T00:00:00Z"
}
],
"language": "fre",
"rights": {"statement": "CCC", "url": "http://www.hydroshare.org"},
"sources": [
{
"derived_from": "Source 3"
},
{
"derived_from": "Source 2"
}
],
"bandinformations": [
{'original_band_name': 'Band_1',
'name': 'Band_2',
'variableName': 'digital elevation',
'variableUnit': 'meter',
'method': 'this is method',
'comment': 'this is comment',
'maximumValue': 1000,
'minimumValue': 0,
'noDataValue': -9999
}
]
}
response = self.client.put(sysmeta_url, put_data, format='json')
self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)
self.resource.delete()
def test_put_scimeta_raster_resource_without_core_metadata(self):
# testing bulk metadata update that includes only resource specific
# metadata update (Note: the only resource specific metadata element that can be updated
# is BandInformation)
# create a raster resource
raster_file = 'hs_core/tests/data/cea.tif'
file_to_upload = open(raster_file, "r")
self._create_resource(resource_type="RasterResource", file_to_upload=file_to_upload)
sysmeta_url = "/hsapi/resource/{res_id}/scimeta/elements/".format(
res_id=self.resource.short_id)
put_data = {
"bandinformations": [
{'original_band_name': 'Band_1',
'name': 'Band_2',
'variableName': 'digital elevation',
'variableUnit': 'meter',
'method': 'this is method',
'comment': 'this is comment',
'maximumValue': 1000,
'minimumValue': 0,
'noDataValue': -9999
}
]
}
response = self.client.put(sysmeta_url, put_data, format='json')
self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)
self.resource.delete()
def test_put_scimeta_modelprogram_resource_with_core_metadata(self):
# testing bulk metadata update that includes both core metadata and resource specific
# metadata update
# create a model program resource
some_file = 'hs_core/tests/data/cea.tif'
file_to_upload = open(some_file, "r")
self._create_resource(resource_type="ModelProgramResource", file_to_upload=file_to_upload)
sysmeta_url = "/hsapi/resource/{res_id}/scimeta/elements/".format(
res_id=self.resource.short_id)
put_data = {
"title": "New Title",
"description": "New Description",
"subjects": [
{"value": "subject1"},
{"value": "subject2"},
{"value": "subject3"}
],
"contributors": [{
"name": "Test Name 1",
"organization": "Org 1"
}, {
"name": "Test Name 2",
"organization": "Org 2"
}],
"creators": [{
"name": "Creator",
"organization": None,
"identifiers": {"ORCID": "https://orcid.org/011",
"ResearchGateID": "https://www.researchgate.net/001"}
}],
"coverages": [{
"type": "box",
"value": {
"northlimit": 43.19716728247476,
"projection": "WGS 84 EPSG:4326",
"name": "A whole bunch of the atlantic ocean",
"units": "Decimal degrees",
"southlimit": 23.8858376999,
"eastlimit": -19.16015625,
"westlimit": -62.75390625
}
}],
"dates": [
{
"type": "valid",
"start_date": "2016-12-07T00:00:00Z",
"end_date": "2018-12-07T00:00:00Z"
}
],
"language": "fre",
"rights": {"statement": "CCC", "url": "http://www.hydroshare.org"},
"sources": [
{
"derived_from": "Source 3"
},
{
"derived_from": "Source 2"
}
],
"mpmetadata": {
"modelVersion": "5.1.011",
"modelProgramLanguage": "Fortran",
"modelOperatingSystem": "Windows",
"modelReleaseDate": "2016-10-24T21:05:00.315907+00:00",
"modelWebsite": "http://www.hydroshare.org",
"modelCodeRepository": "http://www.github.com",
"modelReleaseNotes": "releaseNote.pdf",
"modelDocumentation": "manual.pdf",
"modelSoftware": "utilities.exe",
"modelEngine": "sourceCode.zip"
}
}
response = self.client.put(sysmeta_url, put_data, format='json')
self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)
self.resource.delete()
def test_put_scimeta_modelprogram_resource_without_core_metadata(self):
# testing bulk metadata update that only updates resource specific
# metadata
# create a model program resource
some_file = 'hs_core/tests/data/cea.tif'
file_to_upload = open(some_file, "r")
self._create_resource(resource_type="ModelProgramResource", file_to_upload=file_to_upload)
sysmeta_url = "/hsapi/resource/{res_id}/scimeta/elements/".format(
res_id=self.resource.short_id)
put_data = {
"mpmetadata": {
"modelVersion": "5.1.011",
"modelProgramLanguage": "Fortran",
"modelOperatingSystem": "Windows",
"modelReleaseDate": "2016-10-24T21:05:00.315907+00:00",
"modelWebsite": "http://www.hydroshare.org",
"modelCodeRepository": "http://www.github.com",
"modelReleaseNotes": "releaseNote.pdf",
"modelDocumentation": "manual.pdf",
"modelSoftware": "utilities.exe",
"modelEngine": "sourceCode.zip"
}
}
response = self.client.put(sysmeta_url, put_data, format='json')
self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)
self.resource.delete()
def test_put_scimeta_modelinstance_resource_with_core_metadata(self):
# testing bulk metadata update that includes both core metadata and resource specific
# metadata update
# create a model instance resource
some_file = 'hs_core/tests/data/cea.tif'
file_to_upload = open(some_file, "r")
self._create_resource(resource_type="ModelInstanceResource", file_to_upload=file_to_upload)
sysmeta_url = "/hsapi/resource/{res_id}/scimeta/elements/".format(
res_id=self.resource.short_id)
put_data = {
"title": "New Title",
"description": "New Description",
"subjects": [
{"value": "subject1"},
{"value": "subject2"},
{"value": "subject3"}
],
"contributors": [{
"name": "Test Name 1",
"organization": "Org 1"
}, {
"name": "Test Name 2",
"organization": "Org 2",
"identifiers": {"ORCID": "https://orcid.org/011",
"ResearchGateID": "https://www.researchgate.net/001"}
}],
"creators": [{
"name": "Creator",
"organization": None,
"identifiers": {"ORCID": "https://orcid.org/011",
"ResearchGateID": "https://www.researchgate.net/001"}
}],
"coverages": [{
"type": "box",
"value": {
"northlimit": 43.19716728247476,
"projection": "WGS 84 EPSG:4326",
"name": "A whole bunch of the atlantic ocean",
"units": "Decimal degrees",
"southlimit": 23.8858376999,
"eastlimit": -19.16015625,
"westlimit": -62.75390625
}
}],
"dates": [
{
"type": "valid",
"start_date": "2016-12-07T00:00:00Z",
"end_date": "2018-12-07T00:00:00Z"
}
],
"language": "fre",
"rights": {"statement": "CCC", "url": "http://www.hydroshare.org"},
"sources": [
{
"derived_from": "Source 3"
},
{
"derived_from": "Source 2"
}
],
"modeloutput": {"includes_output": False},
"executedby": {"model_name": "id of a an existing model program resource"}
}
response = self.client.put(sysmeta_url, put_data, format='json')
self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)
self.resource.delete()
def test_put_scimeta_modelinstance_resource_without_core_metadata(self):
# testing bulk metadata update updates only resource specific metadata
# create a model instance resource
some_file = 'hs_core/tests/data/cea.tif'
file_to_upload = open(some_file, "r")
self._create_resource(resource_type="ModelInstanceResource", file_to_upload=file_to_upload)
# create a model program resource to link as executed by
model_program_resource = resource.create_resource(
resource_type="ModelProgramResource",
owner=self.user,
title="A model program resource",
files=(file_to_upload,)
)
sysmeta_url = "/hsapi/resource/{res_id}/scimeta/elements/".format(
res_id=self.resource.short_id)
put_data = {
"modeloutput": {"includes_output": True},
"executedby": {"model_name": model_program_resource.short_id}
}
response = self.client.put(sysmeta_url, put_data, format='json')
self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)
self.resource.delete()
model_program_resource.delete()
def test_put_scimeta_modflowinstance_resource_with_core_metadata(self):
# testing bulk metadata update that includes both core metadata and resource specific
# metadata update
# create a MODFLOW model instance resource
some_file = 'hs_core/tests/data/cea.tif'
file_to_upload = open(some_file, "r")
self._create_resource(resource_type="MODFLOWModelInstanceResource",
file_to_upload=file_to_upload)
sysmeta_url = "/hsapi/resource/{res_id}/scimeta/elements/".format(
res_id=self.resource.short_id)
put_data = {
"title": "New Title",
"description": "New Description",
"subjects": [
{"value": "subject1"},
{"value": "subject2"},
{"value": "subject3"}
],
"contributors": [{
"name": "Test Name 1",
"organization": "Org 1",
"identifiers": {"ORCID": "https://orcid.org/011",
"ResearchGateID": "https://www.researchgate.net/001"}
}, {
"name": "Test Name 2",
"organization": "Org 2"
}],
"creators": [{
"name": "Creator",
"organization": None,
"identifiers": {"ORCID": "https://orcid.org/011",
"ResearchGateID": "https://www.researchgate.net/001"}
}],
"coverages": [{
"type": "box",
"value": {
"northlimit": 43.19716728247476,
"projection": "WGS 84 EPSG:4326",
"name": "A whole bunch of the atlantic ocean",
"units": "Decimal degrees",
"southlimit": 23.8858376999,
"eastlimit": -19.16015625,
"westlimit": -62.75390625
}
}],
"dates": [
{
"type": "valid",
"start_date": "2016-12-07T00:00:00Z",
"end_date": "2018-12-07T00:00:00Z"
}
],
"language": "fre",
"rights": {"statement": "CCC", "url": "http://www.hydroshare.org"},
"sources": [
{
"derived_from": "Source 3"
},
{
"derived_from": "Source 2"
}
],
"modeloutput": {"includes_output": False},
"executedby": {"model_name": "id of a an existing model program resource"},
"studyarea": {
"totalLength": 1111,
"totalWidth": 2222,
"maximumElevation": 3333,
"minimumElevation": 4444
},
"griddimensions": {
"numberOfLayers": 5555,
"typeOfRows": "Irregular",
"numberOfRows": 6666,
"typeOfColumns": "Regular",
"numberOfColumns": 7777
},
"stressperiod": {
"stressPeriodType": "Steady and Transient",
"steadyStateValue": 8888,
"transientStateValueType": "Monthly",
"transientStateValue": 9999
},
"groundwaterflow": {
"flowPackage": "LPF",
"flowParameter": "Hydraulic Conductivity"
},
"boundarycondition": {
"specified_head_boundary_packages": ["CHD", "FHB"],
"specified_flux_boundary_packages": ["FHB", "WEL"],
"head_dependent_flux_boundary_packages": ["RIV", "MNW1"]
},
"modelcalibration": {
"calibratedParameter": "test parameter",
"observationType": "test observation type",
"observationProcessPackage": "GBOB",
"calibrationMethod": "test calibration method"
},
"modelinputs": [
{
"inputType": "test input type",
"inputSourceName": "test source name",
"inputSourceURL": "http://www.test.com"
}
],
"generalelements": {
"modelParameter": "test model parameter",
"modelSolver": "SIP",
"output_control_package": ["HYD", "OC"],
"subsidencePackage": "SWT"
}
}
response = self.client.put(sysmeta_url, put_data, format='json')
self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)
self.resource.delete()
def test_put_scimeta_modflowinstance_resource_without_core_metadata(self):
# testing bulk metadata update that updates onlt the resource specific
# metadata
# create a MODFLOW model instance resource
some_file = 'hs_core/tests/data/cea.tif'
file_to_upload = open(some_file, "r")
self._create_resource(resource_type="MODFLOWModelInstanceResource",
file_to_upload=file_to_upload)
sysmeta_url = "/hsapi/resource/{res_id}/scimeta/elements/".format(
res_id=self.resource.short_id)
put_data = {
"modeloutput": {"includes_output": False},
"executedby": {"model_name": "id of a an existing model program resource"},
"studyarea": {
"totalLength": 1111,
"totalWidth": 2222,
"maximumElevation": 3333,
"minimumElevation": 4444
},
"griddimensions": {
"numberOfLayers": 5555,
"typeOfRows": "Irregular",
"numberOfRows": 6666,
"typeOfColumns": "Regular",
"numberOfColumns": 7777
},
"stressperiod": {
"stressPeriodType": "Steady and Transient",
"steadyStateValue": 8888,
"transientStateValueType": "Monthly",
"transientStateValue": 9999
},
"groundwaterflow": {
"flowPackage": "LPF",
"flowParameter": "Hydraulic Conductivity"
},
"boundarycondition": {
"specified_head_boundary_packages": ["CHD", "FHB"],
"specified_flux_boundary_packages": ["FHB", "WEL"],
"head_dependent_flux_boundary_packages": ["RIV", "MNW1"]
},
"modelcalibration": {
"calibratedParameter": "test parameter",
"observationType": "test observation type",
"observationProcessPackage": "GBOB",
"calibrationMethod": "test calibration method"
},
"modelinputs": [
{
"inputType": "test input type-1",
"inputSourceName": "test source name-1",
"inputSourceURL": "http://www.test-1.com"
},
{
"inputType": "test input type-2",
"inputSourceName": "test source name-2",
"inputSourceURL": "http://www.test-2.com"
}
],
"generalelements": {
"modelParameter": "test model parameter",
"modelSolver": "SIP",
"output_control_package": ["HYD", "OC"],
"subsidencePackage": "SWT"
}
}
response = self.client.put(sysmeta_url, put_data, format='json')
self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)
self.resource.delete()
def test_put_scimeta_script_resource_with_core_metadata(self):
# testing bulk metadata update that includes both core metadata and resource specific
# metadata update
# create a script resource
self._create_resource(resource_type="ScriptResource")
sysmeta_url = "/hsapi/resource/{res_id}/scimeta/elements/".format(
res_id=self.resource.short_id)
put_data = {
"title": "New Title",
"description": "New Description",
"subjects": [
{"value": "subject1"},
{"value": "subject2"},
{"value": "subject3"}
],
"contributors": [{
"name": "Test Name 1",
"organization": "Org 1",
"identifiers": {"ORCID": "https://orcid.org/011",
"ResearchGateID": "https://www.researchgate.net/001"}
}, {
"name": "Test Name 2",
"organization": "Org 2"
}],
"creators": [{
"name": "Creator",
"organization": None,
"identifiers": {"ORCID": "https://orcid.org/011",
"ResearchGateID": "https://www.researchgate.net/001"}
}],
"coverages": [{
"type": "box",
"value": {
"northlimit": 43.19716728247476,
"projection": "WGS 84 EPSG:4326",
"name": "A whole bunch of the atlantic ocean",
"units": "Decimal degrees",
"southlimit": 23.8858376999,
"eastlimit": -19.16015625,
"westlimit": -62.75390625
}
}],
"dates": [
{
"type": "valid",
"start_date": "2016-12-07T00:00:00Z",
"end_date": "2018-12-07T00:00:00Z"
}
],
"language": "fre",
"rights": {"statement": "CCC", "url": "http://www.hydroshare.org"},
"sources": [
{
"derived_from": "Source 3"
},
{
"derived_from": "Source 2"
}
],
"scriptspecificmetadata": {
"scriptLanguage": "R",
"languageVersion": "3.5",
"scriptVersion": "1.0",
"scriptDependencies": "None",
"scriptReleaseDate": "2015-12-01 00:00",
"scriptCodeRepository": "http://www.google.com"
}
}
response = self.client.put(sysmeta_url, put_data, format='json')
self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)
self.resource.delete()
def test_put_scimeta_script_resource_without_core_metadata(self):
# testing bulk metadata update for resource specific
# metadata only
# create a script resource
self._create_resource(resource_type="ScriptResource")
sysmeta_url = "/hsapi/resource/{res_id}/scimeta/elements/".format(
res_id=self.resource.short_id)
put_data = {
"scriptspecificmetadata": {
"scriptLanguage": "R",
"languageVersion": "3.5",
"scriptVersion": "1.0",
"scriptDependencies": "None",
"scriptReleaseDate": "2015-12-01 00:00",
"scriptCodeRepository": "http://www.google.com"
}
}
response = self.client.put(sysmeta_url, put_data, format='json')
self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)
self.resource.delete()
def test_put_scimeta_SWATModelInstance_resource_with_core_metadata(self):
# testing bulk metadata update that includes both core metadata and resource specific
# metadata update
# create a SWAT model resource
self._create_resource(resource_type="SWATModelInstanceResource")
sysmeta_url = "/hsapi/resource/{res_id}/scimeta/elements/".format(
res_id=self.resource.short_id)
put_data = {
"title": "New Title",
"description": "New Description",
"subjects": [
{"value": "subject1"},
{"value": "subject2"},
{"value": "subject3"}
],
"contributors": [{
"name": "Test Name 1",
"organization": "Org 1"
}, {
"name": "Test Name 2",
"organization": "Org 2",
"identifiers": {"ORCID": "https://orcid.org/011",
"ResearchGateID": "https://www.researchgate.net/001"}
}],
"creators": [{
"name": "Creator",
"organization": None,
"identifiers": {"ORCID": "https://orcid.org/011",
"ResearchGateID": "https://www.researchgate.net/001"}
}],
"coverages": [{
"type": "box",
"value": {
"northlimit": 43.19716728247476,
"projection": "WGS 84 EPSG:4326",
"name": "A whole bunch of the atlantic ocean",
"units": "Decimal degrees",
"southlimit": 23.8858376999,
"eastlimit": -19.16015625,
"westlimit": -62.75390625
}
}],
"dates": [
{
"type": "valid",
"start_date": "2016-12-07T00:00:00Z",
"end_date": "2018-12-07T00:00:00Z"
}
],
"language": "fre",
"rights": {"statement": "CCC", "url": "http://www.hydroshare.org"},
"sources": [
{
"derived_from": "Source 3"
},
{
"derived_from": "Source 2"
}
],
"modeloutput": {"includes_output": False},
"executedby": {"model_name": "id of a an existing model program resource"},
"modelobjective": {
"swat_model_objectives": ["BMPs", "Hydrology", "Water quality"],
"other_objectives": "some other objectives"
},
"simulationtype": {
"simulation_type_name": "Normal Simulation"
},
"modelmethod": {
"runoffCalculationMethod": "A test calculation method",
"flowRoutingMethod": "A test flow routing method",
"petEstimationMethod": "A test estimation method"
},
"modelparameter": {
"model_parameters": ["Crop rotation", "Tillage operation"],
"other_parameters": "some other model parameters"
},
"modelinput": {
"warmupPeriodValue": 10,
"rainfallTimeStepType": "Daily",
"rainfallTimeStepValue": 5,
"routingTimeStepType": "Daily",
"routingTimeStepValue": 2,
"simulationTimeStepType": "Hourly",
"simulationTimeStepValue": 1,
"watershedArea": 1000,
"numberOfSubbasins": 200,
"numberOfHRUs": 10000,
"demResolution": 30,
"demSourceName": "Unknown",
"demSourceURL": "http://dem-source.org",
"landUseDataSourceName": "Unknown",
"landUseDataSourceURL": "http://land-data.org",
"soilDataSourceName": "Unknown",
"soilDataSourceURL": "http://soil-data.org"
}
}
response = self.client.put(sysmeta_url, put_data, format='json')
self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)
self.resource.delete()
def test_put_scimeta_SWATModelInstance_resource_without_core_metadata(self):
# testing bulk metadata update that includes only resource specific
# metadata update
# create a SWAT model resource
self._create_resource(resource_type="SWATModelInstanceResource")
sysmeta_url = "/hsapi/resource/{res_id}/scimeta/elements/".format(
res_id=self.resource.short_id)
put_data = {
"modeloutput": {"includes_output": False},
"executedby": {"model_name": "id of a an existing model program resource"},
"modelobjective": {
"swat_model_objectives": ["BMPs", "Hydrology", "Water quality"],
"other_objectives": "some other objectives"
},
"simulationtype": {
"simulation_type_name": "Normal Simulation"
},
"modelmethod": {
"runoffCalculationMethod": "A test calculation method",
"flowRoutingMethod": "A test flow routing method",
"petEstimationMethod": "A test estimation method"
},
"modelparameter": {
"model_parameters": ["Crop rotation", "Tillage operation"],
"other_parameters": "some other model parameters"
},
"modelinput": {
"warmupPeriodValue": 10,
"rainfallTimeStepType": "Daily",
"rainfallTimeStepValue": 5,
"routingTimeStepType": "Daily",
"routingTimeStepValue": 2,
"simulationTimeStepType": "Hourly",
"simulationTimeStepValue": 1,
"watershedArea": 1000,
"numberOfSubbasins": 200,
"numberOfHRUs": 10000,
"demResolution": 30,
"demSourceName": "Unknown",
"demSourceURL": "http://dem-source.org",
"landUseDataSourceName": "Unknown",
"landUseDataSourceURL": "http://land-data.org",
"soilDataSourceName": "Unknown",
"soilDataSourceURL": "http://soil-data.org"
}
}
response = self.client.put(sysmeta_url, put_data, format='json')
self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)
self.resource.delete()
def test_put_web_app_resource_with_core_metadata(self):
# testing bulk metadata update that includes both core metadata and resource specific
# metadata update
# create a web app resource
self._create_resource(resource_type="ToolResource")
sysmeta_url = "/hsapi/resource/{res_id}/scimeta/elements/".format(
res_id=self.resource.short_id)
put_data = {
"title": "New Title",
"description": "New Description",
"subjects": [
{"value": "subject1"},
{"value": "subject2"},
{"value": "subject3"}
],
"contributors": [{
"name": "Test Name 1",
"organization": "Org 1"
}, {
"name": "Test Name 2",
"organization": "Org 2",
"identifiers": {"ORCID": "https://orcid.org/011",
"ResearchGateID": "https://www.researchgate.net/001"}
}],
"creators": [{
"name": "Creator",
"organization": None,
"identifiers": {"ORCID": "https://orcid.org/011",
"ResearchGateID": "https://www.researchgate.net/001"}
}],
"coverages": [{
"type": "box",
"value": {
"northlimit": 43.19716728247476,
"projection": "WGS 84 EPSG:4326",
"name": "A whole bunch of the atlantic ocean",
"units": "Decimal degrees",
"southlimit": 23.8858376999,
"eastlimit": -19.16015625,
"westlimit": -62.75390625
}
}],
"dates": [
{
"type": "valid",
"start_date": "2016-12-07T00:00:00Z",
"end_date": "2018-12-07T00:00:00Z"
}
],
"language": "fre",
"rights": {"statement": "CCC", "url": "http://www.hydroshare.org"},
"sources": [
{
"derived_from": "Source 3"
},
{
"derived_from": "Source 2"
}
],
"requesturlbase": {
"value": "https://www.google.com"
},
"toolversion": {
"value": "1.12"
},
"supportedrestypes": {
"supported_res_types": ["NetcdfResource", "TimeSeriesResource"]
},
"supportedsharingstatuses": {
"sharing_status": ["Public", "Discoverable"]
},
"toolicon": {
"value": "https://www.hydroshare.org/static/img/logo-sm.png"
},
"apphomepageurl": {
"value": "https://mywebapp.com"
}
}
response = self.client.put(sysmeta_url, put_data, format='json')
self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)
self.resource.delete()
def test_put_web_app_resource_without_core_metadata(self):
# testing bulk metadata update that includes only resource specific
# metadata update
# create a web app resource
self._create_resource(resource_type="ToolResource")
sysmeta_url = "/hsapi/resource/{res_id}/scimeta/elements/".format(
res_id=self.resource.short_id)
put_data = {
"requesturlbase": {
"value": "https://www.google.com"
},
"toolversion": {
"value": "1.12"
},
"supportedrestypes": {
"supported_res_types": ["NetcdfResource", "TimeSeriesResource"]
},
"supportedsharingstatuses": {
"sharing_status": ["Public", "Discoverable"]
},
"toolicon": {
"value": "https://www.hydroshare.org/static/img/logo-sm.png"
},
"apphomepageurl": {
"value": "https://mywebapp.com"
}
}
response = self.client.put(sysmeta_url, put_data, format='json')
self.assertEqual(response.status_code, status.HTTP_202_ACCEPTED)
self.resource.delete()
def _create_resource(self, resource_type, file_to_upload=None):
files = ()
if file_to_upload is not None:
files = (file_to_upload,)
self.resource = resource.create_resource(
resource_type=resource_type,
owner=self.user,
title="Testing bulk metadata update for resource type - {}".format(resource_type),
files=files
)
resource_post_create_actions(resource=self.resource, user=self.user,
metadata=self.resource.metadata)
| 40.543133
| 100
| 0.477795
| 4,794
| 58,747
| 5.700459
| 0.09637
| 0.029859
| 0.01493
| 0.012295
| 0.948331
| 0.944379
| 0.936366
| 0.934133
| 0.925461
| 0.914447
| 0
| 0.047756
| 0.395476
| 58,747
| 1,448
| 101
| 40.571133
| 0.721744
| 0.04659
| 0
| 0.78518
| 0
| 0.001497
| 0.313112
| 0.040001
| 0
| 0
| 0
| 0
| 0.034431
| 1
| 0.018713
| false
| 0
| 0.002994
| 0
| 0.022455
| 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
|
1623521771de97b8ec1428ee8e1cf9b7f82faa6f
| 188
|
py
|
Python
|
src/simplify/__init__.py
|
kratsg/simplify
|
9b9245dce6ff433f10f9e471e94d321106fdfaba
|
[
"BSD-3-Clause"
] | 4
|
2020-11-25T16:02:07.000Z
|
2021-11-13T13:05:09.000Z
|
src/simplify/__init__.py
|
kratsg/simplify
|
9b9245dce6ff433f10f9e471e94d321106fdfaba
|
[
"BSD-3-Clause"
] | 19
|
2020-11-25T16:05:17.000Z
|
2021-11-10T18:12:24.000Z
|
src/simplify/__init__.py
|
kratsg/simplify
|
9b9245dce6ff433f10f9e471e94d321106fdfaba
|
[
"BSD-3-Clause"
] | 2
|
2020-11-25T16:10:48.000Z
|
2021-11-08T15:20:59.000Z
|
from . import model_tools # NOQA
from . import plot # NOQA
from . import fitter # NOQA
from . import configuration # NOQA
from . import yields # NOQA
from . import simplified # NOQA
| 26.857143
| 35
| 0.712766
| 25
| 188
| 5.32
| 0.4
| 0.451128
| 0.526316
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.223404
| 188
| 6
| 36
| 31.333333
| 0.910959
| 0.154255
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| 1
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| true
| 0
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| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
1646741defe104df90b88616f54259d3160f0c8a
| 75,967
|
py
|
Python
|
imageme.py
|
gimmeyoon/imageme
|
fdf83ecfc3ae28e5de32c315f95bd31f2b15cc3f
|
[
"MIT"
] | null | null | null |
imageme.py
|
gimmeyoon/imageme
|
fdf83ecfc3ae28e5de32c315f95bd31f2b15cc3f
|
[
"MIT"
] | null | null | null |
imageme.py
|
gimmeyoon/imageme
|
fdf83ecfc3ae28e5de32c315f95bd31f2b15cc3f
|
[
"MIT"
] | null | null | null |
#!/usr/bin/python
"""
imageMe is a super simple image gallery server.
Run imageme.py from the top level of an image directory to generate gallery
index HTML and run a SimpleHTTPServer on the localhost.
Imported as a module, use imageme.serve_dir(your_path) to do the same for any
directory programmatically. When run as entry point, imageme.serve_dir('.') is
what's called.
"""
# Dependencies
import base64, io, os, re, sys, threading, http.server, socketserver
# Attempt to import PIL - if it doesn't exist we won't be able to make use of
# some performance enhancing goodness, but imageMe will still work fine
PIL_ENABLED = False
try:
print('Attempting to import from PIL...')
from PIL import Image
PIL_ENABLED = True
print('Success! Enjoy your supercharged imageMe.')
except ImportError:
print(
'WARNING: \'PIL\' module not found, so you won\'t get all the ' +\
'performance you could out of imageMe. Install Pillow (' +\
'https://github.com/python-pillow/Pillow) to enable support.'
)
# Constants / configuration
## Filename of the generated index files
INDEX_FILE_NAME = 'imageme.html'
## Regex for matching only image files
IMAGE_FILE_REGEX = '^.+\.(png|jpg|jpeg|tif|tiff|gif|bmp)$'
## Images per row of the gallery tables
IMAGES_PER_ROW = 3
## Resampling mode to use when thumbnailing
RESAMPLE = None if not PIL_ENABLED else Image.NEAREST
## Width in pixels of thumnbails generated with PIL
THUMBNAIL_WIDTH = 800
## Base64 data for an image notifying user of an unsupported image type
UNSUPPORTED_IMAGE_TYPE_DATA = 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class BackgroundIndexFileGenerator:
def __init__(self, dir_path):
self.dir_path = dir_path
self.thread = threading.Thread(target=self._process, args=())
self.thread.daemon = True
def _process(self):
_create_index_files(self.dir_path)
def run(self):
self.thread.start()
def _clean_up(paths):
"""
Clean up after ourselves, removing created files.
@param {[String]} A list of file paths specifying the files we've created
during run. Will all be deleted.
@return {None}
"""
print('Cleaning up')
# Iterate over the given paths, unlinking them
for path in paths:
print('Removing %s' % path)
os.unlink(path)
def _create_index_file(
root_dir, location, image_files, dirs, force_no_processing=False):
"""
Create an index file in the given location, supplying known lists of
present image files and subdirectories.
@param {String} root_dir - The root directory of the entire crawl. Used to
ascertain whether the given location is the top level.
@param {String} location - The current directory of the crawl. The index
file will be created here.
@param {[String]} image_files - A list of image file names in the location.
These will be displayed in the index file's gallery.
@param {[String]} dirs - The subdirectories of the location directory.
These will be displayed as links further down the file structure.
@param {Boolean=False} force_no_processing - If True, do not attempt to
actually process thumbnails, PIL images or anything. Simply index
<img> tags with original file src attributes.
@return {String} The full path (location plus filename) of the newly
created index file. Intended for usage cleaning up created files.
"""
# Put together HTML as a list of the lines we'll want to include
# Issue #2 exists to do this better than HTML in-code
header_text = \
'imageMe: ' + location + ' [' + str(len(image_files)) + ' image(s)]'
html = [
'<!DOCTYPE html>',
'<html>',
' <head>',
' <title>imageMe</title>'
' <style>',
' html, body {margin: 0;padding: 0;}',
' .header {text-align: right;}',
' .content {',
' padding: 3em;',
' padding-left: 4em;',
' padding-right: 4em;',
' }',
' .image {max-width: 100%; border-radius: 0.3em;}',
' td {width: ' + str(100.0 / IMAGES_PER_ROW) + '%;}',
' </style>',
' </head>',
' <body>',
' <div class="content">',
' <h2 class="header">' + header_text + '</h2>'
]
# Populate the present subdirectories - this includes '..' unless we're at
# the top level
directories = []
if root_dir != location:
directories = ['..']
directories += dirs
if len(directories) > 0:
html.append('<hr>')
# For each subdirectory, include a link to its index file
for directory in directories:
link = directory + '/' + INDEX_FILE_NAME
html += [
' <h3 class="header">',
' <a href="' + link + '">' + directory + '</a>',
' </h3>'
]
# Populate the image gallery table
# Counter to cycle down through table rows
table_row_count = 1
html += ['<hr>', '<table>']
# For each image file, potentially create a new <tr> and create a new <td>
for image_file in image_files:
if table_row_count == 1:
html.append('<tr>')
img_src = _get_thumbnail_src_from_file(
location, image_file, force_no_processing
)
link_target = _get_image_link_target_from_file(
location, image_file, force_no_processing
)
html += [
' <td>',
' <a href="' + link_target + '">',
' <img class="image" src="' + img_src + '">',
' </a>',
' </td>'
]
if table_row_count == IMAGES_PER_ROW:
table_row_count = 0
html.append('</tr>')
table_row_count += 1
html += ['</tr>', '</table>']
html += [
' </div>',
' </body>',
'</html>'
]
# Actually create the file, now we've put together the HTML content
index_file_path = _get_index_file_path(location)
print('Creating index file %s' % index_file_path)
index_file = open(index_file_path, 'w')
index_file.write('\n'.join(html))
index_file.close()
# Return the path for cleaning up later
return index_file_path
def _create_index_files(root_dir, force_no_processing=False):
"""
Crawl the root directory downwards, generating an index HTML file in each
directory on the way down.
@param {String} root_dir - The top level directory to crawl down from. In
normal usage, this will be '.'.
@param {Boolean=False} force_no_processing - If True, do not attempt to
actually process thumbnails, PIL images or anything. Simply index
<img> tags with original file src attributes.
@return {[String]} Full file paths of all created files.
"""
# Initialise list of created file paths to build up as we make them
created_files = []
# Walk the root dir downwards, creating index files as we go
for here, dirs, files in os.walk(root_dir):
print('Processing %s' % here)
# Sort the subdirectories by name
dirs = sorted(dirs)
# Get image files - all files in the directory matching IMAGE_FILE_REGEX
image_files = [f for f in files if re.match(IMAGE_FILE_REGEX, f)]
# Sort the image files by name
image_files = sorted(image_files)
# Create this directory's index file and add its name to the created
# files list
created_files.append(
_create_index_file(
root_dir, here, image_files, dirs, force_no_processing
)
)
# Return the list of created files
return created_files
def _get_image_from_file(dir_path, image_file):
"""
Get an instance of PIL.Image from the given file.
@param {String} dir_path - The directory containing the image file
@param {String} image_file - The filename of the image file within dir_path
@return {PIL.Image} An instance of the image file as a PIL Image, or None
if the functionality is not available. This could be because PIL is not
present, or because it can't process the given file type.
"""
# Save ourselves the effort if PIL is not present, and return None now
if not PIL_ENABLED:
return None
# Put together full path
path = os.path.join(dir_path, image_file)
# Try to read the image
img = None
try:
img = Image.open(path)
except IOError as exptn:
print('Error loading image file %s: %s' % (path, exptn))
# Return image or None
return img
def _get_image_link_target_from_file(dir_path, image_file, force_no_processing=False):
"""
Get the value to be used as the href for links from thumbnail images. For
most image formats this will simply be the image file name itself. However,
some image formats (tif) are not natively displayable by many browsers and
therefore we must link to image data in another format.
@param {String} dir_path - The directory containing the image file
@param {String} image_file - The filename of the image file within dir_path
@param {Boolean=False} force_no_processing - If True, do not attempt to
actually process a thumbnail, PIL image or anything. Simply return the
image filename as src.
@return {String} The href to use.
"""
# If we've specified to force no processing, just return the image filename
if force_no_processing:
return image_file
# First try to get an image
img = _get_image_from_file(dir_path, image_file)
# If format is directly displayable in-browser, just return the filename
# Else, we need to return a full-sized chunk of displayable image data
if img.format.lower() in ['tif', 'tiff']:
return _get_image_src_from_file(
dir_path, image_file, force_no_processing
)
return image_file
def _get_image_src_from_file(dir_path, image_file, force_no_processing=False):
"""
Get base-64 encoded data as a string for the given image file's full image,
for use directly in HTML <img> tags, or a path to the original if image
scaling is not supported.
This is a full-sized version of _get_thumbnail_src_from_file, for use in
image formats which cannot be displayed directly in-browser, and therefore
need processed versions even at full size.
@param {String} dir_path - The directory containing the image file
@param {String} image_file - The filename of the image file within dir_path
@param {Boolean=False} force_no_processing - If True, do not attempt to
actually process a thumbnail, PIL image or anything. Simply return the
image filename as src.
@return {String} The base-64 encoded image data string, or path to the file
itself if not supported.
"""
# If we've specified to force no processing, just return the image filename
if force_no_processing:
if image_file.endswith('tif') or image_file.endswith('tiff'):
return UNSUPPORTED_IMAGE_TYPE_DATA
return image_file
# First try to get an image
img = _get_image_from_file(dir_path, image_file)
return _get_src_from_image(img, image_file)
def _get_index_file_path(location):
"""
Get the full file path to be used for an index file in the given location.
Yields location plus the constant INDEX_FILE_NAME.
@param {String} location - A directory location in which we want to create
a new index file.
@return {String} A file path for usage with a new index file.
"""
return os.path.join(location, INDEX_FILE_NAME)
def _get_server_port():
"""
Get the port specified for the server to run on. If given as the first
command line argument, we'll use that. Else we'll default to 8000.
@return {Integer} The port to run the server on. Default 8000, overridden
by first command line argument.
"""
return int(sys.argv[1]) if len(sys.argv) >= 2 else 8000
def _get_src_from_image(img, fallback_image_file):
"""
Get base-64 encoded data as a string for the given image. Fallback to return
fallback_image_file if cannot get the image data or img is None.
@param {Image} img - The PIL Image to get src data for
@param {String} fallback_image_file - The filename of the image file,
to be used when image data capture fails
@return {String} The base-64 encoded image data string, or path to the file
itself if not supported.
"""
# If the image is None, then we can't process, so we should return the
# path to the file itself
if img is None:
return fallback_image_file
# Target format should be the same as the original image format, unless it's
# a TIF/TIFF, which can't be displayed by most browsers; we convert these
# to jpeg
target_format = img.format
if target_format.lower() in ['tif', 'tiff']:
target_format = 'JPEG'
# If we have an actual Image, great - put together the base64 image string
try:
bytesio = io.BytesIO()
img.save(bytesio, target_format)
byte_value = bytesio.getvalue()
b64 = base64.b64encode(byte_value)
return 'data:image/%s;base64,%s' % (target_format.lower(), b64)
except IOError as exptn:
print('IOError while saving image bytes: %s' % exptn)
return fallback_image_file
def _get_thumbnail_image_from_file(dir_path, image_file):
"""
Get a PIL.Image from the given image file which has been scaled down to
THUMBNAIL_WIDTH wide.
@param {String} dir_path - The directory containing the image file
@param {String} image_file - The filename of the image file within dir_path
@return {PIL.Image} An instance of the thumbnail as a PIL Image, or None
if the functionality is not available. See _get_image_from_file for
details.
"""
# Get image
img = _get_image_from_file(dir_path, image_file)
# If it's not supported, exit now
if img is None:
return None
if img.format.lower() == 'gif':
return None
# Get image dimensions
img_width, img_height = img.size
# We need to perform a resize - first, work out the scale ratio to take the
# image width to THUMBNAIL_WIDTH (THUMBNAIL_WIDTH:img_width ratio)
scale_ratio = THUMBNAIL_WIDTH / float(img_width)
# Work out target image height based on the scale ratio
target_height = int(scale_ratio * img_height)
# Perform the resize
try:
img.thumbnail((THUMBNAIL_WIDTH, target_height), resample=RESAMPLE)
except IOError as exptn:
print('WARNING: IOError when thumbnailing %s/%s: %s' % (
dir_path, image_file, exptn
))
return None
# Return the resized image
return img
def _get_thumbnail_src_from_file(dir_path, image_file, force_no_processing=False):
"""
Get base-64 encoded data as a string for the given image file's thumbnail,
for use directly in HTML <img> tags, or a path to the original if image
scaling is not supported.
@param {String} dir_path - The directory containing the image file
@param {String} image_file - The filename of the image file within dir_path
@param {Boolean=False} force_no_processing - If True, do not attempt to
actually process a thumbnail, PIL image or anything. Simply return the
image filename as src.
@return {String} The base-64 encoded image data string, or path to the file
itself if not supported.
"""
# If we've specified to force no processing, just return the image filename
if force_no_processing:
if image_file.endswith('tif') or image_file.endswith('tiff'):
return UNSUPPORTED_IMAGE_TYPE_DATA
return image_file
# First try to get a thumbnail image
img = _get_thumbnail_image_from_file(dir_path, image_file)
return _get_src_from_image(img, image_file)
def _run_server():
"""
Run the image server. This is blocking. Will handle user KeyboardInterrupt
and other exceptions appropriately and return control once the server is
stopped.
@return {None}
"""
# Get the port to run on
port = _get_server_port()
# Configure allow_reuse_address to make re-runs of the script less painful -
# if this is not True then waiting for the address to be freed after the
# last run can block a subsequent run
socketserver.TCPServer.allow_reuse_address = True
# Create the server instance
server = socketserver.TCPServer(
('', port),
http.server.SimpleHTTPRequestHandler
)
# Print out before actually running the server (cheeky / optimistic, however
# you want to look at it)
print('Your images are at http://127.0.0.1:%d/%s' % (
port,
INDEX_FILE_NAME
))
# Try to run the server
try:
# Run it - this call blocks until the server is killed
server.serve_forever()
except KeyboardInterrupt:
# This is the expected way of the server being killed, since imageMe is
# intended for ad-hoc running from command line
print('User interrupted, stopping')
except Exception as exptn:
# Catch everything else - this will handle shutdowns via other signals
# and faults actually starting the server in the first place
print(exptn)
print('Unhandled exception in server, stopping')
def serve_dir(dir_path):
"""
Generate indexes and run server from the given directory downwards.
@param {String} dir_path - The directory path (absolute, or relative to CWD)
@return {None}
"""
# Create index files, and store the list of their paths for cleanup later
# This time, force no processing - this gives us a fast first-pass in terms
# of page generation, but potentially slow serving for large image files
print('Performing first pass index file generation')
created_files = _create_index_files(dir_path, True)
if (PIL_ENABLED):
# If PIL is enabled, we'd like to process the HTML indexes to include
# generated thumbnails - this slows down generation so we don't do it
# first time around, but now we're serving it's good to do in the
# background
print('Performing PIL-enchanced optimised index file generation in background')
background_indexer = BackgroundIndexFileGenerator(dir_path)
background_indexer.run()
# Run the server in the current location - this blocks until it's stopped
_run_server()
# Clean up the index files created earlier so we don't make a mess of
# the image directories
_clean_up(created_files)
if __name__ == '__main__':
# Generate indices and serve from the current directory downwards when run
# as the entry point
serve_dir('.')
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0
| 7
|
166b0e9fcf52d892b054f980b0113351603fa15c
| 12,723
|
py
|
Python
|
tests/resampler_batch_test.py
|
tdml13/NiftyNet
|
b35fa19ca307e81d229e2fe8269a417724833da2
|
[
"Apache-2.0"
] | 1,403
|
2017-08-30T11:49:45.000Z
|
2022-03-31T11:44:05.000Z
|
tests/resampler_batch_test.py
|
tdml13/NiftyNet
|
b35fa19ca307e81d229e2fe8269a417724833da2
|
[
"Apache-2.0"
] | 360
|
2017-10-03T15:33:53.000Z
|
2021-03-17T06:27:38.000Z
|
tests/resampler_batch_test.py
|
tdml13/NiftyNet
|
b35fa19ca307e81d229e2fe8269a417724833da2
|
[
"Apache-2.0"
] | 464
|
2017-09-13T20:56:32.000Z
|
2022-02-11T20:33:47.000Z
|
from __future__ import absolute_import, print_function, division
import numpy as np
import tensorflow as tf
from niftynet.layer.resampler import ResamplerLayer
from tests.niftynet_testcase import NiftyNetTestCase
class ResamplerTest(NiftyNetTestCase):
def test_shape_interface(self):
test_input = tf.zeros((2, 10, 10, 10, 3))
test_coords = tf.zeros((3, 5, 5, 5, 3))
# bad batch sizes
with self.assertRaisesRegexp(ValueError, ''):
out = ResamplerLayer()(test_input, test_coords)
test_input = tf.zeros((2, 10, 10, 10, 3))
test_coords = tf.zeros((5, 5, 5, 3))
# bad batch sizes
with self.assertRaisesRegexp(ValueError, ''):
out = ResamplerLayer()(test_input, test_coords)
test_input = tf.zeros((1, 10, 10, 3))
test_coords = tf.zeros((1, 5, 5, 3))
# bad n coordinates
with self.assertRaisesRegexp(ValueError, ''):
out = ResamplerLayer()(test_input, test_coords)
def test_linear_shape(self):
# 3D
test_input = np.zeros((2, 8, 8, 8, 2))
test_input[0, 0, 0, 0, 0] = 1.0
test_input = tf.constant(test_input)
test_coords = tf.ones((1, 5, 5, 5, 3)) * 0.1
out = ResamplerLayer("LINEAR")(test_input, test_coords)
with self.cached_session() as sess:
out_value = sess.run(out)
self.assertTrue(
np.all(np.isclose(out_value[0, ..., 0], 0.9**3, atol=1e-5)))
self.assertTrue(np.all(out_value[1, ...]==0))
self.assertEqual(out_value.shape, (2, 5, 5, 5, 2))
# 2D
test_input = np.zeros((2, 8, 8, 2))
test_input[0, 0, 0, 0] = 1.0
test_input = tf.constant(test_input)
test_coords = tf.ones((1, 5, 5, 2)) * 0.1
out = ResamplerLayer("LINEAR")(test_input, test_coords)
with self.cached_session() as sess:
out_value = sess.run(out)
self.assertTrue(np.all(out_value[1, ...]==0))
self.assertTrue(
np.all(np.isclose(out_value[0, ..., 0], 0.9**2, atol=1e-5)))
self.assertEqual(out_value.shape, (2, 5, 5, 2))
# 1D
test_input = np.zeros((2, 8, 2))
test_input[0, 0, 0] = 1.0
test_input = tf.constant(test_input)
test_coords = tf.ones((1, 5, 1)) * 0.1
out = ResamplerLayer("LINEAR")(test_input, test_coords)
with self.cached_session() as sess:
out_value = sess.run(out)
self.assertTrue(np.all(out_value[1, ...]==0))
self.assertTrue(
np.all(np.isclose(out_value[0, ..., 0], 0.9, atol=1e-5)))
self.assertEqual(out_value.shape, (2, 5, 2))
def test_linear_no_broadcasting(self):
# 3D
test_input = np.zeros((2, 8, 8, 8, 2))
test_input[:, 0, 0, 0, 0] = 1.0
test_input = tf.constant(test_input)
test_coords = tf.concat([tf.ones((1, 5, 5, 5, 3)) * 0.1,
tf.ones((1, 5, 5, 5, 3)) * 0.2], axis=0)
out = ResamplerLayer("LINEAR")(test_input, test_coords)
with self.cached_session() as sess:
out_value = sess.run(out)
self.assertTrue(
np.all(np.isclose(out_value[0, ..., 0], 0.9**3, atol=1e-5)))
self.assertTrue(
np.all(np.isclose(out_value[1, ..., 0], 0.8**3, atol=1e-5)))
self.assertEqual(out_value.shape, (2, 5, 5, 5, 2))
# 2D
test_input = np.zeros((2, 8, 8, 2))
test_input[:, 0, 0, 0] = 1.0
test_input = tf.constant(test_input)
test_coords = tf.concat([tf.ones((1, 5, 5, 2)) * 0.1,
tf.ones((1, 5, 5, 2)) * 0.2], axis=0)
out = ResamplerLayer("LINEAR")(test_input, test_coords)
with self.cached_session() as sess:
out_value = sess.run(out)
self.assertTrue(
np.all(np.isclose(out_value[0, ..., 0], 0.9**2, atol=1e-5)))
self.assertTrue(
np.all(np.isclose(out_value[1, ..., 0], 0.8**2, atol=1e-5)))
self.assertEqual(out_value.shape, (2, 5, 5, 2))
# 1D
test_input = np.zeros((2, 8, 2))
test_input[:, 0, 0] = 1.0
test_input = tf.constant(test_input)
test_coords = tf.concat([tf.ones((1, 5, 1)) * 0.1,
tf.ones((1, 5, 1)) * 0.2], axis=0)
out = ResamplerLayer("LINEAR")(test_input, test_coords)
with self.cached_session() as sess:
out_value = sess.run(out)
self.assertTrue(
np.all(np.isclose(out_value[0, ..., 0], 0.9, atol=1e-5)))
self.assertTrue(
np.all(np.isclose(out_value[1, ..., 0], 0.8, atol=1e-5)))
self.assertEqual(out_value.shape, (2, 5, 2))
def test_nearest_shape(self):
# 3D
test_input = np.zeros((2, 8, 8, 8, 2))
test_input[0, 0, 0, 0, 0] = 1.0
test_input = tf.constant(test_input)
test_coords = tf.ones((1, 5, 5, 5, 3)) * 0.1
out = ResamplerLayer("NEAREST")(test_input, test_coords)
with self.cached_session() as sess:
out_value = sess.run(out)
self.assertTrue(
np.all(np.isclose(out_value[0, ..., 0], 1.0, atol=1e-5)))
self.assertTrue(np.all(out_value[1, ...]==0))
self.assertEqual(out_value.shape, (2, 5, 5, 5, 2))
# 2D
test_input = np.zeros((2, 8, 8, 2))
test_input[0, 0, 0, 0] = 1.0
test_input = tf.constant(test_input)
test_coords = tf.ones((1, 5, 5, 2)) * 0.1
out = ResamplerLayer("NEAREST")(test_input, test_coords)
with self.cached_session() as sess:
out_value = sess.run(out)
self.assertTrue(np.all(out_value[1, ...]==0))
self.assertTrue(
np.all(np.isclose(out_value[0, ..., 0], 1.0, atol=1e-5)))
self.assertEqual(out_value.shape, (2, 5, 5, 2))
# 1D
test_input = np.zeros((2, 8, 2))
test_input[0, 0, 0] = 1.0
test_input = tf.constant(test_input)
test_coords = tf.ones((1, 5, 1)) * 0.1
out = ResamplerLayer("NEAREST")(test_input, test_coords)
with self.cached_session() as sess:
out_value = sess.run(out)
self.assertTrue(np.all(out_value[1, ...]==0))
self.assertTrue(
np.all(np.isclose(out_value[0, ..., 0], 1.0, atol=1e-5)))
self.assertEqual(out_value.shape, (2, 5, 2))
def test_nearest_no_broadcasting(self):
# 3D
test_input = np.zeros((2, 3, 3, 3, 2))
test_input[:, 0, 0, 0, 0] = 1.0
test_input = tf.constant(test_input)
test_coords = tf.concat([tf.ones((1, 5, 5, 5, 3)) * 0.1,
tf.ones((1, 5, 5, 5, 3)) * 1.2], axis=0)
out = ResamplerLayer("NEAREST")(test_input, test_coords)
with self.cached_session() as sess:
out_value = sess.run(out)
self.assertTrue(
np.all(np.isclose(out_value[0, ..., 0], 1.0, atol=1e-5)))
self.assertTrue(
np.all(np.isclose(out_value[1, ..., 0], 0.0, atol=1e-5)))
self.assertEqual(out_value.shape, (2, 5, 5, 5, 2))
# 2D
test_input = np.zeros((2, 3, 3, 2))
test_input[:, 0, 0, 0] = 1.0
test_input = tf.constant(test_input)
test_coords = tf.concat([tf.ones((1, 5, 5, 2)) * 0.1,
tf.ones((1, 5, 5, 2)) * 1.2], axis=0)
out = ResamplerLayer("NEAREST")(test_input, test_coords)
with self.cached_session() as sess:
out_value = sess.run(out)
self.assertTrue(
np.all(np.isclose(out_value[0, ..., 0], 1.0, atol=1e-5)))
self.assertTrue(
np.all(np.isclose(out_value[1, ..., 0], 0.0, atol=1e-5)))
self.assertEqual(out_value.shape, (2, 5, 5, 2))
# 1D
test_input = np.zeros((2, 3, 2))
test_input[:, 0, 0] = 1.0
test_input = tf.constant(test_input)
test_coords = tf.concat([tf.ones((1, 5, 1)) * 0.1,
tf.ones((1, 5, 1)) * 1.2], axis=0)
out = ResamplerLayer("NEAREST")(test_input, test_coords)
with self.cached_session() as sess:
out_value = sess.run(out)
self.assertTrue(
np.all(np.isclose(out_value[0, ..., 0], 1.0, atol=1e-5)))
self.assertTrue(
np.all(np.isclose(out_value[1, ..., 0], 0.0, atol=1e-5)))
self.assertEqual(out_value.shape, (2, 5, 2))
def test_idw_shape(self):
# 3D
test_input = np.zeros((2, 8, 8, 8, 2))
test_input[0, 0, 0, 0, 0] = 1.0
test_input = tf.constant(test_input)
test_coords = tf.ones((1, 5, 5, 5, 3)) * 0.1
out = ResamplerLayer("IDW")(test_input, test_coords)
with self.cached_session() as sess:
out_value = sess.run(out)
self.assertTrue(
np.all(np.isclose(out_value[0, ..., 0],
1.0/(1. + 9./83 + 9./163 + 3./243), atol=1e-5)))
self.assertTrue(np.all(out_value[1, ...]==0))
self.assertEqual(out_value.shape, (2, 5, 5, 5, 2))
# 2D
test_input = np.zeros((2, 8, 8, 2))
test_input[0, 0, 0, 0] = 1.0
test_input = tf.constant(test_input)
test_coords = tf.ones((1, 5, 5, 2)) * 0.1
out = ResamplerLayer("IDW")(test_input, test_coords)
with self.cached_session() as sess:
out_value = sess.run(out)
self.assertTrue(np.all(out_value[1, ...]==0))
self.assertTrue(
np.all(np.isclose(out_value[0, ..., 0],
1./(2./41. + 1./81.0 + 1.0), atol=1e-5)))
self.assertEqual(out_value.shape, (2, 5, 5, 2))
# 1D
test_input = np.zeros((2, 8, 2))
test_input[0, 0, 0] = 1.0
test_input = tf.constant(test_input)
test_coords = tf.ones((1, 5, 1)) * 0.1
out = ResamplerLayer("IDW")(test_input, test_coords)
with self.cached_session() as sess:
out_value = sess.run(out)
self.assertTrue(np.all(out_value[1, ...]==0))
self.assertTrue(
np.all(np.isclose(out_value[0, ..., 0],
100.0/(100.0+1/0.81), atol=1e-5)))
self.assertEqual(out_value.shape, (2, 5, 2))
def test_idw_no_broadcasting(self):
# 3D
test_input = np.zeros((2, 3, 3, 3, 2))
test_input[:, 0, 0, 0, 0] = 1.0
test_input = tf.constant(test_input)
test_coords = tf.concat([tf.ones((1, 5, 5, 5, 3)) * 0.2,
tf.ones((1, 5, 5, 5, 3)) * 1.2], axis=0)
out = ResamplerLayer("IDW")(test_input, test_coords)
with self.cached_session() as sess:
out_value = sess.run(out)
self.assertTrue(
np.all(np.isclose(out_value[0, ..., 0],
1.0/(1. + 1./2. + 36./132. + 12./192.), atol=1e-5)))
self.assertTrue(
np.all(np.isclose(out_value[1, ..., 0], 0.0, atol=1e-5)))
self.assertEqual(out_value.shape, (2, 5, 5, 5, 2))
# 2D
test_input = np.zeros((2, 3, 3, 2))
test_input[:, 0, 0, 0] = 1.0
test_input = tf.constant(test_input)
test_coords = tf.concat([tf.ones((1, 5, 5, 2)) * 0.2,
tf.ones((1, 5, 5, 2)) * 1.2], axis=0)
out = ResamplerLayer("IDW")(test_input, test_coords)
with self.cached_session() as sess:
out_value = sess.run(out)
self.assertTrue(
np.all(np.isclose(out_value[0, ..., 0],
1.0/(1.0 + 1.0/16.0 + 16./68.), atol=1e-5)))
self.assertTrue(
np.all(np.isclose(out_value[1, ..., 0], 0.0, atol=1e-5)))
self.assertEqual(out_value.shape, (2, 5, 5, 2))
# 1D
test_input = np.zeros((2, 3, 2))
test_input[:, 0, 0] = 1.0
test_input = tf.constant(test_input)
test_coords = tf.concat([tf.ones((1, 5, 1)) * 0.2,
tf.ones((1, 5, 1)) * 1.2], axis=0)
out = ResamplerLayer("IDW")(test_input, test_coords)
with self.cached_session() as sess:
out_value = sess.run(out)
self.assertTrue(
np.all(np.isclose(out_value[0, ..., 0],
1.0/(1.0 + 1/16.0), atol=1e-5)))
self.assertTrue(
np.all(np.isclose(out_value[1, ..., 0], 0.0, atol=1e-5)))
self.assertEqual(out_value.shape, (2, 5, 2))
if __name__ == "__main__":
tf.test.main()
| 43.571918
| 76
| 0.516545
| 1,865
| 12,723
| 3.38445
| 0.04504
| 0.136882
| 0.080323
| 0.117395
| 0.944867
| 0.94455
| 0.94455
| 0.940906
| 0.940748
| 0.938371
| 0
| 0.081992
| 0.316513
| 12,723
| 291
| 77
| 43.721649
| 0.643859
| 0.008096
| 0
| 0.863454
| 0
| 0
| 0.008255
| 0
| 0
| 0
| 0
| 0
| 0.228916
| 1
| 0.028112
| false
| 0
| 0.02008
| 0
| 0.052209
| 0.004016
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
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|
0
| 7
|
167b34c312d1a59bc27b2a2321cd692d9e8b7a5c
| 214,271
|
py
|
Python
|
msgraph-cli-extensions/v1_0/planner_v1_0/azext_planner_v1_0/generated/custom.py
|
thewahome/msgraph-cli
|
33127d9efa23a0e5f5303c93242fbdbb73348671
|
[
"MIT"
] | null | null | null |
msgraph-cli-extensions/v1_0/planner_v1_0/azext_planner_v1_0/generated/custom.py
|
thewahome/msgraph-cli
|
33127d9efa23a0e5f5303c93242fbdbb73348671
|
[
"MIT"
] | null | null | null |
msgraph-cli-extensions/v1_0/planner_v1_0/azext_planner_v1_0/generated/custom.py
|
thewahome/msgraph-cli
|
33127d9efa23a0e5f5303c93242fbdbb73348671
|
[
"MIT"
] | null | null | null |
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
#
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may cause incorrect behavior and will be lost if the code is
# regenerated.
# --------------------------------------------------------------------------
# pylint: disable=too-many-lines
def planner_group_delete_planner(client,
group_id,
if_match=None):
return client.delete_planner(group_id=group_id,
if_match=if_match)
def planner_group_show_planner(client,
group_id,
select=None,
expand=None):
return client.get_planner(group_id=group_id,
select=select,
expand=expand)
def planner_group_update_planner(client,
group_id,
id_=None,
plans=None):
body = {}
body['id'] = id_
body['plans'] = plans
return client.update_planner(group_id=group_id,
body=body)
def planner_group_planner_create_plan(client,
group_id,
id_=None,
created_date_time=None,
owner=None,
title=None,
buckets=None,
tasks=None,
microsoft_graph_entity_id=None,
category_descriptions=None,
shared_with=None,
application=None,
device=None,
user=None):
body = {}
body['id'] = id_
body['created_date_time'] = created_date_time
body['owner'] = owner
body['title'] = title
body['buckets'] = buckets
body['tasks'] = tasks
body['details'] = {}
body['details']['id'] = microsoft_graph_entity_id
body['details']['category_descriptions'] = category_descriptions
body['details']['shared_with'] = shared_with
body['created_by'] = {}
body['created_by']['application'] = application
body['created_by']['device'] = device
body['created_by']['user'] = user
return client.create_plans(group_id=group_id,
body=body)
def planner_group_planner_delete_plan(client,
group_id,
planner_plan_id,
if_match=None):
return client.delete_plans(group_id=group_id,
planner_plan_id=planner_plan_id,
if_match=if_match)
def planner_group_planner_list_plan(client,
group_id,
orderby=None,
select=None,
expand=None):
return client.list_plans(group_id=group_id,
orderby=orderby,
select=select,
expand=expand)
def planner_group_planner_show_plan(client,
group_id,
planner_plan_id,
select=None,
expand=None):
return client.get_plans(group_id=group_id,
planner_plan_id=planner_plan_id,
select=select,
expand=expand)
def planner_group_planner_update_plan(client,
group_id,
planner_plan_id,
id_=None,
created_date_time=None,
owner=None,
title=None,
buckets=None,
tasks=None,
microsoft_graph_entity_id=None,
category_descriptions=None,
shared_with=None,
application=None,
device=None,
user=None):
body = {}
body['id'] = id_
body['created_date_time'] = created_date_time
body['owner'] = owner
body['title'] = title
body['buckets'] = buckets
body['tasks'] = tasks
body['details'] = {}
body['details']['id'] = microsoft_graph_entity_id
body['details']['category_descriptions'] = category_descriptions
body['details']['shared_with'] = shared_with
body['created_by'] = {}
body['created_by']['application'] = application
body['created_by']['device'] = device
body['created_by']['user'] = user
return client.update_plans(group_id=group_id,
planner_plan_id=planner_plan_id,
body=body)
def planner_group_planner_plan_create_bucket(client,
group_id,
planner_plan_id,
id_=None,
name=None,
order_hint=None,
plan_id=None,
tasks=None):
body = {}
body['id'] = id_
body['name'] = name
body['order_hint'] = order_hint
body['plan_id'] = plan_id
body['tasks'] = tasks
return client.create_buckets(group_id=group_id,
planner_plan_id=planner_plan_id,
body=body)
def planner_group_planner_plan_create_task(client,
group_id,
planner_plan_id,
id_=None,
active_checklist_item_count=None,
applied_categories=None,
assignee_priority=None,
assignments=None,
bucket_id=None,
checklist_item_count=None,
completed_date_time=None,
conversation_thread_id=None,
created_date_time=None,
due_date_time=None,
has_description=None,
order_hint=None,
percent_complete=None,
plan_id=None,
preview_type=None,
reference_count=None,
start_date_time=None,
title=None,
bucket_task_board_format=None,
progress_task_board_format=None,
microsoft_graph_entity_id=None,
checklist=None,
description=None,
microsoft_graph_planner_preview_type=None,
references=None,
id1=None,
order_hints_by_assignee=None,
unassigned_order_hint=None,
application=None,
device=None,
user=None,
microsoft_graph_identity_application=None,
microsoft_graph_identity_device=None,
microsoft_graph_identity_user=None):
body = {}
body['id'] = id_
body['active_checklist_item_count'] = active_checklist_item_count
body['applied_categories'] = applied_categories
body['assignee_priority'] = assignee_priority
body['assignments'] = assignments
body['bucket_id'] = bucket_id
body['checklist_item_count'] = checklist_item_count
body['completed_date_time'] = completed_date_time
body['conversation_thread_id'] = conversation_thread_id
body['created_date_time'] = created_date_time
body['due_date_time'] = due_date_time
body['has_description'] = has_description
body['order_hint'] = order_hint
body['percent_complete'] = percent_complete
body['plan_id'] = plan_id
body['preview_type'] = preview_type
body['reference_count'] = reference_count
body['start_date_time'] = start_date_time
body['title'] = title
body['bucket_task_board_format'] = bucket_task_board_format
body['progress_task_board_format'] = progress_task_board_format
body['details'] = {}
body['details']['id'] = microsoft_graph_entity_id
body['details']['checklist'] = checklist
body['details']['description'] = description
body['details']['preview_type'] = microsoft_graph_planner_preview_type
body['details']['references'] = references
body['assigned_to_task_board_format'] = {}
body['assigned_to_task_board_format']['id'] = id1
body['assigned_to_task_board_format']['order_hints_by_assignee'] = order_hints_by_assignee
body['assigned_to_task_board_format']['unassigned_order_hint'] = unassigned_order_hint
body['created_by'] = {}
body['created_by']['application'] = application
body['created_by']['device'] = device
body['created_by']['user'] = user
body['completed_by'] = {}
body['completed_by']['application'] = microsoft_graph_identity_application
body['completed_by']['device'] = microsoft_graph_identity_device
body['completed_by']['user'] = microsoft_graph_identity_user
return client.create_tasks(group_id=group_id,
planner_plan_id=planner_plan_id,
body=body)
def planner_group_planner_plan_delete_bucket(client,
group_id,
planner_plan_id,
planner_bucket_id,
if_match=None):
return client.delete_buckets(group_id=group_id,
planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
if_match=if_match)
def planner_group_planner_plan_delete_detail(client,
group_id,
planner_plan_id,
if_match=None):
return client.delete_details(group_id=group_id,
planner_plan_id=planner_plan_id,
if_match=if_match)
def planner_group_planner_plan_delete_task(client,
group_id,
planner_plan_id,
planner_task_id,
if_match=None):
return client.delete_tasks(group_id=group_id,
planner_plan_id=planner_plan_id,
planner_task_id=planner_task_id,
if_match=if_match)
def planner_group_planner_plan_list_bucket(client,
group_id,
planner_plan_id,
orderby=None,
select=None,
expand=None):
return client.list_buckets(group_id=group_id,
planner_plan_id=planner_plan_id,
orderby=orderby,
select=select,
expand=expand)
def planner_group_planner_plan_list_task(client,
group_id,
planner_plan_id,
orderby=None,
select=None,
expand=None):
return client.list_tasks(group_id=group_id,
planner_plan_id=planner_plan_id,
orderby=orderby,
select=select,
expand=expand)
def planner_group_planner_plan_show_bucket(client,
group_id,
planner_plan_id,
planner_bucket_id,
select=None,
expand=None):
return client.get_buckets(group_id=group_id,
planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
select=select,
expand=expand)
def planner_group_planner_plan_show_detail(client,
group_id,
planner_plan_id,
select=None,
expand=None):
return client.get_details(group_id=group_id,
planner_plan_id=planner_plan_id,
select=select,
expand=expand)
def planner_group_planner_plan_show_task(client,
group_id,
planner_plan_id,
planner_task_id,
select=None,
expand=None):
return client.get_tasks(group_id=group_id,
planner_plan_id=planner_plan_id,
planner_task_id=planner_task_id,
select=select,
expand=expand)
def planner_group_planner_plan_update_bucket(client,
group_id,
planner_plan_id,
planner_bucket_id,
id_=None,
name=None,
order_hint=None,
plan_id=None,
tasks=None):
body = {}
body['id'] = id_
body['name'] = name
body['order_hint'] = order_hint
body['plan_id'] = plan_id
body['tasks'] = tasks
return client.update_buckets(group_id=group_id,
planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
body=body)
def planner_group_planner_plan_update_detail(client,
group_id,
planner_plan_id,
id_=None,
category_descriptions=None,
shared_with=None):
body = {}
body['id'] = id_
body['category_descriptions'] = category_descriptions
body['shared_with'] = shared_with
return client.update_details(group_id=group_id,
planner_plan_id=planner_plan_id,
body=body)
def planner_group_planner_plan_update_task(client,
group_id,
planner_plan_id,
planner_task_id,
id_=None,
active_checklist_item_count=None,
applied_categories=None,
assignee_priority=None,
assignments=None,
bucket_id=None,
checklist_item_count=None,
completed_date_time=None,
conversation_thread_id=None,
created_date_time=None,
due_date_time=None,
has_description=None,
order_hint=None,
percent_complete=None,
plan_id=None,
preview_type=None,
reference_count=None,
start_date_time=None,
title=None,
bucket_task_board_format=None,
progress_task_board_format=None,
microsoft_graph_entity_id=None,
checklist=None,
description=None,
microsoft_graph_planner_preview_type=None,
references=None,
id1=None,
order_hints_by_assignee=None,
unassigned_order_hint=None,
application=None,
device=None,
user=None,
microsoft_graph_identity_application=None,
microsoft_graph_identity_device=None,
microsoft_graph_identity_user=None):
body = {}
body['id'] = id_
body['active_checklist_item_count'] = active_checklist_item_count
body['applied_categories'] = applied_categories
body['assignee_priority'] = assignee_priority
body['assignments'] = assignments
body['bucket_id'] = bucket_id
body['checklist_item_count'] = checklist_item_count
body['completed_date_time'] = completed_date_time
body['conversation_thread_id'] = conversation_thread_id
body['created_date_time'] = created_date_time
body['due_date_time'] = due_date_time
body['has_description'] = has_description
body['order_hint'] = order_hint
body['percent_complete'] = percent_complete
body['plan_id'] = plan_id
body['preview_type'] = preview_type
body['reference_count'] = reference_count
body['start_date_time'] = start_date_time
body['title'] = title
body['bucket_task_board_format'] = bucket_task_board_format
body['progress_task_board_format'] = progress_task_board_format
body['details'] = {}
body['details']['id'] = microsoft_graph_entity_id
body['details']['checklist'] = checklist
body['details']['description'] = description
body['details']['preview_type'] = microsoft_graph_planner_preview_type
body['details']['references'] = references
body['assigned_to_task_board_format'] = {}
body['assigned_to_task_board_format']['id'] = id1
body['assigned_to_task_board_format']['order_hints_by_assignee'] = order_hints_by_assignee
body['assigned_to_task_board_format']['unassigned_order_hint'] = unassigned_order_hint
body['created_by'] = {}
body['created_by']['application'] = application
body['created_by']['device'] = device
body['created_by']['user'] = user
body['completed_by'] = {}
body['completed_by']['application'] = microsoft_graph_identity_application
body['completed_by']['device'] = microsoft_graph_identity_device
body['completed_by']['user'] = microsoft_graph_identity_user
return client.update_tasks(group_id=group_id,
planner_plan_id=planner_plan_id,
planner_task_id=planner_task_id,
body=body)
def planner_group_planner_plan_bucket_create_task(client,
group_id,
planner_plan_id,
planner_bucket_id,
id_=None,
active_checklist_item_count=None,
applied_categories=None,
assignee_priority=None,
assignments=None,
bucket_id=None,
checklist_item_count=None,
completed_date_time=None,
conversation_thread_id=None,
created_date_time=None,
due_date_time=None,
has_description=None,
order_hint=None,
percent_complete=None,
plan_id=None,
preview_type=None,
reference_count=None,
start_date_time=None,
title=None,
bucket_task_board_format=None,
progress_task_board_format=None,
microsoft_graph_entity_id=None,
checklist=None,
description=None,
microsoft_graph_planner_preview_type=None,
references=None,
id1=None,
order_hints_by_assignee=None,
unassigned_order_hint=None,
application=None,
device=None,
user=None,
microsoft_graph_identity_application=None,
microsoft_graph_identity_device=None,
microsoft_graph_identity_user=None):
body = {}
body['id'] = id_
body['active_checklist_item_count'] = active_checklist_item_count
body['applied_categories'] = applied_categories
body['assignee_priority'] = assignee_priority
body['assignments'] = assignments
body['bucket_id'] = bucket_id
body['checklist_item_count'] = checklist_item_count
body['completed_date_time'] = completed_date_time
body['conversation_thread_id'] = conversation_thread_id
body['created_date_time'] = created_date_time
body['due_date_time'] = due_date_time
body['has_description'] = has_description
body['order_hint'] = order_hint
body['percent_complete'] = percent_complete
body['plan_id'] = plan_id
body['preview_type'] = preview_type
body['reference_count'] = reference_count
body['start_date_time'] = start_date_time
body['title'] = title
body['bucket_task_board_format'] = bucket_task_board_format
body['progress_task_board_format'] = progress_task_board_format
body['details'] = {}
body['details']['id'] = microsoft_graph_entity_id
body['details']['checklist'] = checklist
body['details']['description'] = description
body['details']['preview_type'] = microsoft_graph_planner_preview_type
body['details']['references'] = references
body['assigned_to_task_board_format'] = {}
body['assigned_to_task_board_format']['id'] = id1
body['assigned_to_task_board_format']['order_hints_by_assignee'] = order_hints_by_assignee
body['assigned_to_task_board_format']['unassigned_order_hint'] = unassigned_order_hint
body['created_by'] = {}
body['created_by']['application'] = application
body['created_by']['device'] = device
body['created_by']['user'] = user
body['completed_by'] = {}
body['completed_by']['application'] = microsoft_graph_identity_application
body['completed_by']['device'] = microsoft_graph_identity_device
body['completed_by']['user'] = microsoft_graph_identity_user
return client.create_tasks(group_id=group_id,
planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
body=body)
def planner_group_planner_plan_bucket_delete_task(client,
group_id,
planner_plan_id,
planner_bucket_id,
planner_task_id,
if_match=None):
return client.delete_tasks(group_id=group_id,
planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
if_match=if_match)
def planner_group_planner_plan_bucket_list_task(client,
group_id,
planner_plan_id,
planner_bucket_id,
orderby=None,
select=None,
expand=None):
return client.list_tasks(group_id=group_id,
planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
orderby=orderby,
select=select,
expand=expand)
def planner_group_planner_plan_bucket_show_task(client,
group_id,
planner_plan_id,
planner_bucket_id,
planner_task_id,
select=None,
expand=None):
return client.get_tasks(group_id=group_id,
planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
select=select,
expand=expand)
def planner_group_planner_plan_bucket_update_task(client,
group_id,
planner_plan_id,
planner_bucket_id,
planner_task_id,
id_=None,
active_checklist_item_count=None,
applied_categories=None,
assignee_priority=None,
assignments=None,
bucket_id=None,
checklist_item_count=None,
completed_date_time=None,
conversation_thread_id=None,
created_date_time=None,
due_date_time=None,
has_description=None,
order_hint=None,
percent_complete=None,
plan_id=None,
preview_type=None,
reference_count=None,
start_date_time=None,
title=None,
bucket_task_board_format=None,
progress_task_board_format=None,
microsoft_graph_entity_id=None,
checklist=None,
description=None,
microsoft_graph_planner_preview_type=None,
references=None,
id1=None,
order_hints_by_assignee=None,
unassigned_order_hint=None,
application=None,
device=None,
user=None,
microsoft_graph_identity_application=None,
microsoft_graph_identity_device=None,
microsoft_graph_identity_user=None):
body = {}
body['id'] = id_
body['active_checklist_item_count'] = active_checklist_item_count
body['applied_categories'] = applied_categories
body['assignee_priority'] = assignee_priority
body['assignments'] = assignments
body['bucket_id'] = bucket_id
body['checklist_item_count'] = checklist_item_count
body['completed_date_time'] = completed_date_time
body['conversation_thread_id'] = conversation_thread_id
body['created_date_time'] = created_date_time
body['due_date_time'] = due_date_time
body['has_description'] = has_description
body['order_hint'] = order_hint
body['percent_complete'] = percent_complete
body['plan_id'] = plan_id
body['preview_type'] = preview_type
body['reference_count'] = reference_count
body['start_date_time'] = start_date_time
body['title'] = title
body['bucket_task_board_format'] = bucket_task_board_format
body['progress_task_board_format'] = progress_task_board_format
body['details'] = {}
body['details']['id'] = microsoft_graph_entity_id
body['details']['checklist'] = checklist
body['details']['description'] = description
body['details']['preview_type'] = microsoft_graph_planner_preview_type
body['details']['references'] = references
body['assigned_to_task_board_format'] = {}
body['assigned_to_task_board_format']['id'] = id1
body['assigned_to_task_board_format']['order_hints_by_assignee'] = order_hints_by_assignee
body['assigned_to_task_board_format']['unassigned_order_hint'] = unassigned_order_hint
body['created_by'] = {}
body['created_by']['application'] = application
body['created_by']['device'] = device
body['created_by']['user'] = user
body['completed_by'] = {}
body['completed_by']['application'] = microsoft_graph_identity_application
body['completed_by']['device'] = microsoft_graph_identity_device
body['completed_by']['user'] = microsoft_graph_identity_user
return client.update_tasks(group_id=group_id,
planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
body=body)
def planner_group_planner_plan_bucket_task_delete_assigned_to_task_board_format(client,
group_id,
planner_plan_id,
planner_bucket_id,
planner_task_id,
if_match=None):
return client.delete_assigned_to_task_board_format(group_id=group_id,
planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
if_match=if_match)
def planner_group_planner_plan_bucket_task_delete_bucket_task_board_format(client,
group_id,
planner_plan_id,
planner_bucket_id,
planner_task_id,
if_match=None):
return client.delete_bucket_task_board_format(group_id=group_id,
planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
if_match=if_match)
def planner_group_planner_plan_bucket_task_delete_detail(client,
group_id,
planner_plan_id,
planner_bucket_id,
planner_task_id,
if_match=None):
return client.delete_details(group_id=group_id,
planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
if_match=if_match)
def planner_group_planner_plan_bucket_task_delete_progress_task_board_format(client,
group_id,
planner_plan_id,
planner_bucket_id,
planner_task_id,
if_match=None):
return client.delete_progress_task_board_format(group_id=group_id,
planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
if_match=if_match)
def planner_group_planner_plan_bucket_task_show_assigned_to_task_board_format(client,
group_id,
planner_plan_id,
planner_bucket_id,
planner_task_id,
select=None,
expand=None):
return client.get_assigned_to_task_board_format(group_id=group_id,
planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
select=select,
expand=expand)
def planner_group_planner_plan_bucket_task_show_bucket_task_board_format(client,
group_id,
planner_plan_id,
planner_bucket_id,
planner_task_id,
select=None,
expand=None):
return client.get_bucket_task_board_format(group_id=group_id,
planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
select=select,
expand=expand)
def planner_group_planner_plan_bucket_task_show_detail(client,
group_id,
planner_plan_id,
planner_bucket_id,
planner_task_id,
select=None,
expand=None):
return client.get_details(group_id=group_id,
planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
select=select,
expand=expand)
def planner_group_planner_plan_bucket_task_show_progress_task_board_format(client,
group_id,
planner_plan_id,
planner_bucket_id,
planner_task_id,
select=None,
expand=None):
return client.get_progress_task_board_format(group_id=group_id,
planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
select=select,
expand=expand)
def planner_group_planner_plan_bucket_task_update_assigned_to_task_board_format(client,
group_id,
planner_plan_id,
planner_bucket_id,
planner_task_id,
id_=None,
order_hints_by_assignee=None,
unassigned_order_hint=None):
body = {}
body['id'] = id_
body['order_hints_by_assignee'] = order_hints_by_assignee
body['unassigned_order_hint'] = unassigned_order_hint
return client.update_assigned_to_task_board_format(group_id=group_id,
planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
body=body)
def planner_group_planner_plan_bucket_task_update_bucket_task_board_format(client,
group_id,
planner_plan_id,
planner_bucket_id,
planner_task_id,
id_=None,
order_hint=None):
body = {}
body['id'] = id_
body['order_hint'] = order_hint
return client.update_bucket_task_board_format(group_id=group_id,
planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
body=body)
def planner_group_planner_plan_bucket_task_update_detail(client,
group_id,
planner_plan_id,
planner_bucket_id,
planner_task_id,
id_=None,
checklist=None,
description=None,
preview_type=None,
references=None):
body = {}
body['id'] = id_
body['checklist'] = checklist
body['description'] = description
body['preview_type'] = preview_type
body['references'] = references
return client.update_details(group_id=group_id,
planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
body=body)
def planner_group_planner_plan_bucket_task_update_progress_task_board_format(client,
group_id,
planner_plan_id,
planner_bucket_id,
planner_task_id,
id_=None,
order_hint=None):
body = {}
body['id'] = id_
body['order_hint'] = order_hint
return client.update_progress_task_board_format(group_id=group_id,
planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
body=body)
def planner_group_planner_plan_task_delete_assigned_to_task_board_format(client,
group_id,
planner_plan_id,
planner_task_id,
if_match=None):
return client.delete_assigned_to_task_board_format(group_id=group_id,
planner_plan_id=planner_plan_id,
planner_task_id=planner_task_id,
if_match=if_match)
def planner_group_planner_plan_task_delete_bucket_task_board_format(client,
group_id,
planner_plan_id,
planner_task_id,
if_match=None):
return client.delete_bucket_task_board_format(group_id=group_id,
planner_plan_id=planner_plan_id,
planner_task_id=planner_task_id,
if_match=if_match)
def planner_group_planner_plan_task_delete_detail(client,
group_id,
planner_plan_id,
planner_task_id,
if_match=None):
return client.delete_details(group_id=group_id,
planner_plan_id=planner_plan_id,
planner_task_id=planner_task_id,
if_match=if_match)
def planner_group_planner_plan_task_delete_progress_task_board_format(client,
group_id,
planner_plan_id,
planner_task_id,
if_match=None):
return client.delete_progress_task_board_format(group_id=group_id,
planner_plan_id=planner_plan_id,
planner_task_id=planner_task_id,
if_match=if_match)
def planner_group_planner_plan_task_show_assigned_to_task_board_format(client,
group_id,
planner_plan_id,
planner_task_id,
select=None,
expand=None):
return client.get_assigned_to_task_board_format(group_id=group_id,
planner_plan_id=planner_plan_id,
planner_task_id=planner_task_id,
select=select,
expand=expand)
def planner_group_planner_plan_task_show_bucket_task_board_format(client,
group_id,
planner_plan_id,
planner_task_id,
select=None,
expand=None):
return client.get_bucket_task_board_format(group_id=group_id,
planner_plan_id=planner_plan_id,
planner_task_id=planner_task_id,
select=select,
expand=expand)
def planner_group_planner_plan_task_show_detail(client,
group_id,
planner_plan_id,
planner_task_id,
select=None,
expand=None):
return client.get_details(group_id=group_id,
planner_plan_id=planner_plan_id,
planner_task_id=planner_task_id,
select=select,
expand=expand)
def planner_group_planner_plan_task_show_progress_task_board_format(client,
group_id,
planner_plan_id,
planner_task_id,
select=None,
expand=None):
return client.get_progress_task_board_format(group_id=group_id,
planner_plan_id=planner_plan_id,
planner_task_id=planner_task_id,
select=select,
expand=expand)
def planner_group_planner_plan_task_update_assigned_to_task_board_format(client,
group_id,
planner_plan_id,
planner_task_id,
id_=None,
order_hints_by_assignee=None,
unassigned_order_hint=None):
body = {}
body['id'] = id_
body['order_hints_by_assignee'] = order_hints_by_assignee
body['unassigned_order_hint'] = unassigned_order_hint
return client.update_assigned_to_task_board_format(group_id=group_id,
planner_plan_id=planner_plan_id,
planner_task_id=planner_task_id,
body=body)
def planner_group_planner_plan_task_update_bucket_task_board_format(client,
group_id,
planner_plan_id,
planner_task_id,
id_=None,
order_hint=None):
body = {}
body['id'] = id_
body['order_hint'] = order_hint
return client.update_bucket_task_board_format(group_id=group_id,
planner_plan_id=planner_plan_id,
planner_task_id=planner_task_id,
body=body)
def planner_group_planner_plan_task_update_detail(client,
group_id,
planner_plan_id,
planner_task_id,
id_=None,
checklist=None,
description=None,
preview_type=None,
references=None):
body = {}
body['id'] = id_
body['checklist'] = checklist
body['description'] = description
body['preview_type'] = preview_type
body['references'] = references
return client.update_details(group_id=group_id,
planner_plan_id=planner_plan_id,
planner_task_id=planner_task_id,
body=body)
def planner_group_planner_plan_task_update_progress_task_board_format(client,
group_id,
planner_plan_id,
planner_task_id,
id_=None,
order_hint=None):
body = {}
body['id'] = id_
body['order_hint'] = order_hint
return client.update_progress_task_board_format(group_id=group_id,
planner_plan_id=planner_plan_id,
planner_task_id=planner_task_id,
body=body)
def planner_planner_update(client,
id_=None,
buckets=None,
plans=None,
tasks=None):
body = {}
body['id'] = id_
body['buckets'] = buckets
body['plans'] = plans
body['tasks'] = tasks
return client.update_planner(body=body)
def planner_planner_show_planner(client,
select=None,
expand=None):
return client.get_planner(select=select,
expand=expand)
def planner_planner_create_bucket(client,
id_=None,
name=None,
order_hint=None,
plan_id=None,
tasks=None):
body = {}
body['id'] = id_
body['name'] = name
body['order_hint'] = order_hint
body['plan_id'] = plan_id
body['tasks'] = tasks
return client.create_buckets(body=body)
def planner_planner_create_plan(client,
id_=None,
created_date_time=None,
owner=None,
title=None,
buckets=None,
tasks=None,
microsoft_graph_entity_id=None,
category_descriptions=None,
shared_with=None,
application=None,
device=None,
user=None):
body = {}
body['id'] = id_
body['created_date_time'] = created_date_time
body['owner'] = owner
body['title'] = title
body['buckets'] = buckets
body['tasks'] = tasks
body['details'] = {}
body['details']['id'] = microsoft_graph_entity_id
body['details']['category_descriptions'] = category_descriptions
body['details']['shared_with'] = shared_with
body['created_by'] = {}
body['created_by']['application'] = application
body['created_by']['device'] = device
body['created_by']['user'] = user
return client.create_plans(body=body)
def planner_planner_create_task(client,
id_=None,
active_checklist_item_count=None,
applied_categories=None,
assignee_priority=None,
assignments=None,
bucket_id=None,
checklist_item_count=None,
completed_date_time=None,
conversation_thread_id=None,
created_date_time=None,
due_date_time=None,
has_description=None,
order_hint=None,
percent_complete=None,
plan_id=None,
preview_type=None,
reference_count=None,
start_date_time=None,
title=None,
bucket_task_board_format=None,
progress_task_board_format=None,
microsoft_graph_entity_id=None,
checklist=None,
description=None,
microsoft_graph_planner_preview_type=None,
references=None,
id1=None,
order_hints_by_assignee=None,
unassigned_order_hint=None,
application=None,
device=None,
user=None,
microsoft_graph_identity_application=None,
microsoft_graph_identity_device=None,
microsoft_graph_identity_user=None):
body = {}
body['id'] = id_
body['active_checklist_item_count'] = active_checklist_item_count
body['applied_categories'] = applied_categories
body['assignee_priority'] = assignee_priority
body['assignments'] = assignments
body['bucket_id'] = bucket_id
body['checklist_item_count'] = checklist_item_count
body['completed_date_time'] = completed_date_time
body['conversation_thread_id'] = conversation_thread_id
body['created_date_time'] = created_date_time
body['due_date_time'] = due_date_time
body['has_description'] = has_description
body['order_hint'] = order_hint
body['percent_complete'] = percent_complete
body['plan_id'] = plan_id
body['preview_type'] = preview_type
body['reference_count'] = reference_count
body['start_date_time'] = start_date_time
body['title'] = title
body['bucket_task_board_format'] = bucket_task_board_format
body['progress_task_board_format'] = progress_task_board_format
body['details'] = {}
body['details']['id'] = microsoft_graph_entity_id
body['details']['checklist'] = checklist
body['details']['description'] = description
body['details']['preview_type'] = microsoft_graph_planner_preview_type
body['details']['references'] = references
body['assigned_to_task_board_format'] = {}
body['assigned_to_task_board_format']['id'] = id1
body['assigned_to_task_board_format']['order_hints_by_assignee'] = order_hints_by_assignee
body['assigned_to_task_board_format']['unassigned_order_hint'] = unassigned_order_hint
body['created_by'] = {}
body['created_by']['application'] = application
body['created_by']['device'] = device
body['created_by']['user'] = user
body['completed_by'] = {}
body['completed_by']['application'] = microsoft_graph_identity_application
body['completed_by']['device'] = microsoft_graph_identity_device
body['completed_by']['user'] = microsoft_graph_identity_user
return client.create_tasks(body=body)
def planner_planner_delete_bucket(client,
planner_bucket_id,
if_match=None):
return client.delete_buckets(planner_bucket_id=planner_bucket_id,
if_match=if_match)
def planner_planner_delete_plan(client,
planner_plan_id,
if_match=None):
return client.delete_plans(planner_plan_id=planner_plan_id,
if_match=if_match)
def planner_planner_delete_task(client,
planner_task_id,
if_match=None):
return client.delete_tasks(planner_task_id=planner_task_id,
if_match=if_match)
def planner_planner_list_bucket(client,
orderby=None,
select=None,
expand=None):
return client.list_buckets(orderby=orderby,
select=select,
expand=expand)
def planner_planner_list_plan(client,
orderby=None,
select=None,
expand=None):
return client.list_plans(orderby=orderby,
select=select,
expand=expand)
def planner_planner_list_task(client,
orderby=None,
select=None,
expand=None):
return client.list_tasks(orderby=orderby,
select=select,
expand=expand)
def planner_planner_show_bucket(client,
planner_bucket_id,
select=None,
expand=None):
return client.get_buckets(planner_bucket_id=planner_bucket_id,
select=select,
expand=expand)
def planner_planner_show_plan(client,
planner_plan_id,
select=None,
expand=None):
return client.get_plans(planner_plan_id=planner_plan_id,
select=select,
expand=expand)
def planner_planner_show_task(client,
planner_task_id,
select=None,
expand=None):
return client.get_tasks(planner_task_id=planner_task_id,
select=select,
expand=expand)
def planner_planner_update_bucket(client,
planner_bucket_id,
id_=None,
name=None,
order_hint=None,
plan_id=None,
tasks=None):
body = {}
body['id'] = id_
body['name'] = name
body['order_hint'] = order_hint
body['plan_id'] = plan_id
body['tasks'] = tasks
return client.update_buckets(planner_bucket_id=planner_bucket_id,
body=body)
def planner_planner_update_plan(client,
planner_plan_id,
id_=None,
created_date_time=None,
owner=None,
title=None,
buckets=None,
tasks=None,
microsoft_graph_entity_id=None,
category_descriptions=None,
shared_with=None,
application=None,
device=None,
user=None):
body = {}
body['id'] = id_
body['created_date_time'] = created_date_time
body['owner'] = owner
body['title'] = title
body['buckets'] = buckets
body['tasks'] = tasks
body['details'] = {}
body['details']['id'] = microsoft_graph_entity_id
body['details']['category_descriptions'] = category_descriptions
body['details']['shared_with'] = shared_with
body['created_by'] = {}
body['created_by']['application'] = application
body['created_by']['device'] = device
body['created_by']['user'] = user
return client.update_plans(planner_plan_id=planner_plan_id,
body=body)
def planner_planner_update_task(client,
planner_task_id,
id_=None,
active_checklist_item_count=None,
applied_categories=None,
assignee_priority=None,
assignments=None,
bucket_id=None,
checklist_item_count=None,
completed_date_time=None,
conversation_thread_id=None,
created_date_time=None,
due_date_time=None,
has_description=None,
order_hint=None,
percent_complete=None,
plan_id=None,
preview_type=None,
reference_count=None,
start_date_time=None,
title=None,
bucket_task_board_format=None,
progress_task_board_format=None,
microsoft_graph_entity_id=None,
checklist=None,
description=None,
microsoft_graph_planner_preview_type=None,
references=None,
id1=None,
order_hints_by_assignee=None,
unassigned_order_hint=None,
application=None,
device=None,
user=None,
microsoft_graph_identity_application=None,
microsoft_graph_identity_device=None,
microsoft_graph_identity_user=None):
body = {}
body['id'] = id_
body['active_checklist_item_count'] = active_checklist_item_count
body['applied_categories'] = applied_categories
body['assignee_priority'] = assignee_priority
body['assignments'] = assignments
body['bucket_id'] = bucket_id
body['checklist_item_count'] = checklist_item_count
body['completed_date_time'] = completed_date_time
body['conversation_thread_id'] = conversation_thread_id
body['created_date_time'] = created_date_time
body['due_date_time'] = due_date_time
body['has_description'] = has_description
body['order_hint'] = order_hint
body['percent_complete'] = percent_complete
body['plan_id'] = plan_id
body['preview_type'] = preview_type
body['reference_count'] = reference_count
body['start_date_time'] = start_date_time
body['title'] = title
body['bucket_task_board_format'] = bucket_task_board_format
body['progress_task_board_format'] = progress_task_board_format
body['details'] = {}
body['details']['id'] = microsoft_graph_entity_id
body['details']['checklist'] = checklist
body['details']['description'] = description
body['details']['preview_type'] = microsoft_graph_planner_preview_type
body['details']['references'] = references
body['assigned_to_task_board_format'] = {}
body['assigned_to_task_board_format']['id'] = id1
body['assigned_to_task_board_format']['order_hints_by_assignee'] = order_hints_by_assignee
body['assigned_to_task_board_format']['unassigned_order_hint'] = unassigned_order_hint
body['created_by'] = {}
body['created_by']['application'] = application
body['created_by']['device'] = device
body['created_by']['user'] = user
body['completed_by'] = {}
body['completed_by']['application'] = microsoft_graph_identity_application
body['completed_by']['device'] = microsoft_graph_identity_device
body['completed_by']['user'] = microsoft_graph_identity_user
return client.update_tasks(planner_task_id=planner_task_id,
body=body)
def planner_planner_bucket_create_task(client,
planner_bucket_id,
id_=None,
active_checklist_item_count=None,
applied_categories=None,
assignee_priority=None,
assignments=None,
bucket_id=None,
checklist_item_count=None,
completed_date_time=None,
conversation_thread_id=None,
created_date_time=None,
due_date_time=None,
has_description=None,
order_hint=None,
percent_complete=None,
plan_id=None,
preview_type=None,
reference_count=None,
start_date_time=None,
title=None,
bucket_task_board_format=None,
progress_task_board_format=None,
microsoft_graph_entity_id=None,
checklist=None,
description=None,
microsoft_graph_planner_preview_type=None,
references=None,
id1=None,
order_hints_by_assignee=None,
unassigned_order_hint=None,
application=None,
device=None,
user=None,
microsoft_graph_identity_application=None,
microsoft_graph_identity_device=None,
microsoft_graph_identity_user=None):
body = {}
body['id'] = id_
body['active_checklist_item_count'] = active_checklist_item_count
body['applied_categories'] = applied_categories
body['assignee_priority'] = assignee_priority
body['assignments'] = assignments
body['bucket_id'] = bucket_id
body['checklist_item_count'] = checklist_item_count
body['completed_date_time'] = completed_date_time
body['conversation_thread_id'] = conversation_thread_id
body['created_date_time'] = created_date_time
body['due_date_time'] = due_date_time
body['has_description'] = has_description
body['order_hint'] = order_hint
body['percent_complete'] = percent_complete
body['plan_id'] = plan_id
body['preview_type'] = preview_type
body['reference_count'] = reference_count
body['start_date_time'] = start_date_time
body['title'] = title
body['bucket_task_board_format'] = bucket_task_board_format
body['progress_task_board_format'] = progress_task_board_format
body['details'] = {}
body['details']['id'] = microsoft_graph_entity_id
body['details']['checklist'] = checklist
body['details']['description'] = description
body['details']['preview_type'] = microsoft_graph_planner_preview_type
body['details']['references'] = references
body['assigned_to_task_board_format'] = {}
body['assigned_to_task_board_format']['id'] = id1
body['assigned_to_task_board_format']['order_hints_by_assignee'] = order_hints_by_assignee
body['assigned_to_task_board_format']['unassigned_order_hint'] = unassigned_order_hint
body['created_by'] = {}
body['created_by']['application'] = application
body['created_by']['device'] = device
body['created_by']['user'] = user
body['completed_by'] = {}
body['completed_by']['application'] = microsoft_graph_identity_application
body['completed_by']['device'] = microsoft_graph_identity_device
body['completed_by']['user'] = microsoft_graph_identity_user
return client.create_tasks(planner_bucket_id=planner_bucket_id,
body=body)
def planner_planner_bucket_delete_task(client,
planner_bucket_id,
planner_task_id,
if_match=None):
return client.delete_tasks(planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
if_match=if_match)
def planner_planner_bucket_list_task(client,
planner_bucket_id,
orderby=None,
select=None,
expand=None):
return client.list_tasks(planner_bucket_id=planner_bucket_id,
orderby=orderby,
select=select,
expand=expand)
def planner_planner_bucket_show_task(client,
planner_bucket_id,
planner_task_id,
select=None,
expand=None):
return client.get_tasks(planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
select=select,
expand=expand)
def planner_planner_bucket_update_task(client,
planner_bucket_id,
planner_task_id,
id_=None,
active_checklist_item_count=None,
applied_categories=None,
assignee_priority=None,
assignments=None,
bucket_id=None,
checklist_item_count=None,
completed_date_time=None,
conversation_thread_id=None,
created_date_time=None,
due_date_time=None,
has_description=None,
order_hint=None,
percent_complete=None,
plan_id=None,
preview_type=None,
reference_count=None,
start_date_time=None,
title=None,
bucket_task_board_format=None,
progress_task_board_format=None,
microsoft_graph_entity_id=None,
checklist=None,
description=None,
microsoft_graph_planner_preview_type=None,
references=None,
id1=None,
order_hints_by_assignee=None,
unassigned_order_hint=None,
application=None,
device=None,
user=None,
microsoft_graph_identity_application=None,
microsoft_graph_identity_device=None,
microsoft_graph_identity_user=None):
body = {}
body['id'] = id_
body['active_checklist_item_count'] = active_checklist_item_count
body['applied_categories'] = applied_categories
body['assignee_priority'] = assignee_priority
body['assignments'] = assignments
body['bucket_id'] = bucket_id
body['checklist_item_count'] = checklist_item_count
body['completed_date_time'] = completed_date_time
body['conversation_thread_id'] = conversation_thread_id
body['created_date_time'] = created_date_time
body['due_date_time'] = due_date_time
body['has_description'] = has_description
body['order_hint'] = order_hint
body['percent_complete'] = percent_complete
body['plan_id'] = plan_id
body['preview_type'] = preview_type
body['reference_count'] = reference_count
body['start_date_time'] = start_date_time
body['title'] = title
body['bucket_task_board_format'] = bucket_task_board_format
body['progress_task_board_format'] = progress_task_board_format
body['details'] = {}
body['details']['id'] = microsoft_graph_entity_id
body['details']['checklist'] = checklist
body['details']['description'] = description
body['details']['preview_type'] = microsoft_graph_planner_preview_type
body['details']['references'] = references
body['assigned_to_task_board_format'] = {}
body['assigned_to_task_board_format']['id'] = id1
body['assigned_to_task_board_format']['order_hints_by_assignee'] = order_hints_by_assignee
body['assigned_to_task_board_format']['unassigned_order_hint'] = unassigned_order_hint
body['created_by'] = {}
body['created_by']['application'] = application
body['created_by']['device'] = device
body['created_by']['user'] = user
body['completed_by'] = {}
body['completed_by']['application'] = microsoft_graph_identity_application
body['completed_by']['device'] = microsoft_graph_identity_device
body['completed_by']['user'] = microsoft_graph_identity_user
return client.update_tasks(planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
body=body)
def planner_planner_bucket_task_delete_assigned_to_task_board_format(client,
planner_bucket_id,
planner_task_id,
if_match=None):
return client.delete_assigned_to_task_board_format(planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
if_match=if_match)
def planner_planner_bucket_task_delete_bucket_task_board_format(client,
planner_bucket_id,
planner_task_id,
if_match=None):
return client.delete_bucket_task_board_format(planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
if_match=if_match)
def planner_planner_bucket_task_delete_detail(client,
planner_bucket_id,
planner_task_id,
if_match=None):
return client.delete_details(planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
if_match=if_match)
def planner_planner_bucket_task_delete_progress_task_board_format(client,
planner_bucket_id,
planner_task_id,
if_match=None):
return client.delete_progress_task_board_format(planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
if_match=if_match)
def planner_planner_bucket_task_show_assigned_to_task_board_format(client,
planner_bucket_id,
planner_task_id,
select=None,
expand=None):
return client.get_assigned_to_task_board_format(planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
select=select,
expand=expand)
def planner_planner_bucket_task_show_bucket_task_board_format(client,
planner_bucket_id,
planner_task_id,
select=None,
expand=None):
return client.get_bucket_task_board_format(planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
select=select,
expand=expand)
def planner_planner_bucket_task_show_detail(client,
planner_bucket_id,
planner_task_id,
select=None,
expand=None):
return client.get_details(planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
select=select,
expand=expand)
def planner_planner_bucket_task_show_progress_task_board_format(client,
planner_bucket_id,
planner_task_id,
select=None,
expand=None):
return client.get_progress_task_board_format(planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
select=select,
expand=expand)
def planner_planner_bucket_task_update_assigned_to_task_board_format(client,
planner_bucket_id,
planner_task_id,
id_=None,
order_hints_by_assignee=None,
unassigned_order_hint=None):
body = {}
body['id'] = id_
body['order_hints_by_assignee'] = order_hints_by_assignee
body['unassigned_order_hint'] = unassigned_order_hint
return client.update_assigned_to_task_board_format(planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
body=body)
def planner_planner_bucket_task_update_bucket_task_board_format(client,
planner_bucket_id,
planner_task_id,
id_=None,
order_hint=None):
body = {}
body['id'] = id_
body['order_hint'] = order_hint
return client.update_bucket_task_board_format(planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
body=body)
def planner_planner_bucket_task_update_detail(client,
planner_bucket_id,
planner_task_id,
id_=None,
checklist=None,
description=None,
preview_type=None,
references=None):
body = {}
body['id'] = id_
body['checklist'] = checklist
body['description'] = description
body['preview_type'] = preview_type
body['references'] = references
return client.update_details(planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
body=body)
def planner_planner_bucket_task_update_progress_task_board_format(client,
planner_bucket_id,
planner_task_id,
id_=None,
order_hint=None):
body = {}
body['id'] = id_
body['order_hint'] = order_hint
return client.update_progress_task_board_format(planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
body=body)
def planner_planner_plan_create_bucket(client,
planner_plan_id,
id_=None,
name=None,
order_hint=None,
plan_id=None,
tasks=None):
body = {}
body['id'] = id_
body['name'] = name
body['order_hint'] = order_hint
body['plan_id'] = plan_id
body['tasks'] = tasks
return client.create_buckets(planner_plan_id=planner_plan_id,
body=body)
def planner_planner_plan_create_task(client,
planner_plan_id,
id_=None,
active_checklist_item_count=None,
applied_categories=None,
assignee_priority=None,
assignments=None,
bucket_id=None,
checklist_item_count=None,
completed_date_time=None,
conversation_thread_id=None,
created_date_time=None,
due_date_time=None,
has_description=None,
order_hint=None,
percent_complete=None,
plan_id=None,
preview_type=None,
reference_count=None,
start_date_time=None,
title=None,
bucket_task_board_format=None,
progress_task_board_format=None,
microsoft_graph_entity_id=None,
checklist=None,
description=None,
microsoft_graph_planner_preview_type=None,
references=None,
id1=None,
order_hints_by_assignee=None,
unassigned_order_hint=None,
application=None,
device=None,
user=None,
microsoft_graph_identity_application=None,
microsoft_graph_identity_device=None,
microsoft_graph_identity_user=None):
body = {}
body['id'] = id_
body['active_checklist_item_count'] = active_checklist_item_count
body['applied_categories'] = applied_categories
body['assignee_priority'] = assignee_priority
body['assignments'] = assignments
body['bucket_id'] = bucket_id
body['checklist_item_count'] = checklist_item_count
body['completed_date_time'] = completed_date_time
body['conversation_thread_id'] = conversation_thread_id
body['created_date_time'] = created_date_time
body['due_date_time'] = due_date_time
body['has_description'] = has_description
body['order_hint'] = order_hint
body['percent_complete'] = percent_complete
body['plan_id'] = plan_id
body['preview_type'] = preview_type
body['reference_count'] = reference_count
body['start_date_time'] = start_date_time
body['title'] = title
body['bucket_task_board_format'] = bucket_task_board_format
body['progress_task_board_format'] = progress_task_board_format
body['details'] = {}
body['details']['id'] = microsoft_graph_entity_id
body['details']['checklist'] = checklist
body['details']['description'] = description
body['details']['preview_type'] = microsoft_graph_planner_preview_type
body['details']['references'] = references
body['assigned_to_task_board_format'] = {}
body['assigned_to_task_board_format']['id'] = id1
body['assigned_to_task_board_format']['order_hints_by_assignee'] = order_hints_by_assignee
body['assigned_to_task_board_format']['unassigned_order_hint'] = unassigned_order_hint
body['created_by'] = {}
body['created_by']['application'] = application
body['created_by']['device'] = device
body['created_by']['user'] = user
body['completed_by'] = {}
body['completed_by']['application'] = microsoft_graph_identity_application
body['completed_by']['device'] = microsoft_graph_identity_device
body['completed_by']['user'] = microsoft_graph_identity_user
return client.create_tasks(planner_plan_id=planner_plan_id,
body=body)
def planner_planner_plan_delete_bucket(client,
planner_plan_id,
planner_bucket_id,
if_match=None):
return client.delete_buckets(planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
if_match=if_match)
def planner_planner_plan_delete_detail(client,
planner_plan_id,
if_match=None):
return client.delete_details(planner_plan_id=planner_plan_id,
if_match=if_match)
def planner_planner_plan_delete_task(client,
planner_plan_id,
planner_task_id,
if_match=None):
return client.delete_tasks(planner_plan_id=planner_plan_id,
planner_task_id=planner_task_id,
if_match=if_match)
def planner_planner_plan_list_bucket(client,
planner_plan_id,
orderby=None,
select=None,
expand=None):
return client.list_buckets(planner_plan_id=planner_plan_id,
orderby=orderby,
select=select,
expand=expand)
def planner_planner_plan_list_task(client,
planner_plan_id,
orderby=None,
select=None,
expand=None):
return client.list_tasks(planner_plan_id=planner_plan_id,
orderby=orderby,
select=select,
expand=expand)
def planner_planner_plan_show_bucket(client,
planner_plan_id,
planner_bucket_id,
select=None,
expand=None):
return client.get_buckets(planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
select=select,
expand=expand)
def planner_planner_plan_show_detail(client,
planner_plan_id,
select=None,
expand=None):
return client.get_details(planner_plan_id=planner_plan_id,
select=select,
expand=expand)
def planner_planner_plan_show_task(client,
planner_plan_id,
planner_task_id,
select=None,
expand=None):
return client.get_tasks(planner_plan_id=planner_plan_id,
planner_task_id=planner_task_id,
select=select,
expand=expand)
def planner_planner_plan_update_bucket(client,
planner_plan_id,
planner_bucket_id,
id_=None,
name=None,
order_hint=None,
plan_id=None,
tasks=None):
body = {}
body['id'] = id_
body['name'] = name
body['order_hint'] = order_hint
body['plan_id'] = plan_id
body['tasks'] = tasks
return client.update_buckets(planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
body=body)
def planner_planner_plan_update_detail(client,
planner_plan_id,
id_=None,
category_descriptions=None,
shared_with=None):
body = {}
body['id'] = id_
body['category_descriptions'] = category_descriptions
body['shared_with'] = shared_with
return client.update_details(planner_plan_id=planner_plan_id,
body=body)
def planner_planner_plan_update_task(client,
planner_plan_id,
planner_task_id,
id_=None,
active_checklist_item_count=None,
applied_categories=None,
assignee_priority=None,
assignments=None,
bucket_id=None,
checklist_item_count=None,
completed_date_time=None,
conversation_thread_id=None,
created_date_time=None,
due_date_time=None,
has_description=None,
order_hint=None,
percent_complete=None,
plan_id=None,
preview_type=None,
reference_count=None,
start_date_time=None,
title=None,
bucket_task_board_format=None,
progress_task_board_format=None,
microsoft_graph_entity_id=None,
checklist=None,
description=None,
microsoft_graph_planner_preview_type=None,
references=None,
id1=None,
order_hints_by_assignee=None,
unassigned_order_hint=None,
application=None,
device=None,
user=None,
microsoft_graph_identity_application=None,
microsoft_graph_identity_device=None,
microsoft_graph_identity_user=None):
body = {}
body['id'] = id_
body['active_checklist_item_count'] = active_checklist_item_count
body['applied_categories'] = applied_categories
body['assignee_priority'] = assignee_priority
body['assignments'] = assignments
body['bucket_id'] = bucket_id
body['checklist_item_count'] = checklist_item_count
body['completed_date_time'] = completed_date_time
body['conversation_thread_id'] = conversation_thread_id
body['created_date_time'] = created_date_time
body['due_date_time'] = due_date_time
body['has_description'] = has_description
body['order_hint'] = order_hint
body['percent_complete'] = percent_complete
body['plan_id'] = plan_id
body['preview_type'] = preview_type
body['reference_count'] = reference_count
body['start_date_time'] = start_date_time
body['title'] = title
body['bucket_task_board_format'] = bucket_task_board_format
body['progress_task_board_format'] = progress_task_board_format
body['details'] = {}
body['details']['id'] = microsoft_graph_entity_id
body['details']['checklist'] = checklist
body['details']['description'] = description
body['details']['preview_type'] = microsoft_graph_planner_preview_type
body['details']['references'] = references
body['assigned_to_task_board_format'] = {}
body['assigned_to_task_board_format']['id'] = id1
body['assigned_to_task_board_format']['order_hints_by_assignee'] = order_hints_by_assignee
body['assigned_to_task_board_format']['unassigned_order_hint'] = unassigned_order_hint
body['created_by'] = {}
body['created_by']['application'] = application
body['created_by']['device'] = device
body['created_by']['user'] = user
body['completed_by'] = {}
body['completed_by']['application'] = microsoft_graph_identity_application
body['completed_by']['device'] = microsoft_graph_identity_device
body['completed_by']['user'] = microsoft_graph_identity_user
return client.update_tasks(planner_plan_id=planner_plan_id,
planner_task_id=planner_task_id,
body=body)
def planner_planner_plan_bucket_create_task(client,
planner_plan_id,
planner_bucket_id,
id_=None,
active_checklist_item_count=None,
applied_categories=None,
assignee_priority=None,
assignments=None,
bucket_id=None,
checklist_item_count=None,
completed_date_time=None,
conversation_thread_id=None,
created_date_time=None,
due_date_time=None,
has_description=None,
order_hint=None,
percent_complete=None,
plan_id=None,
preview_type=None,
reference_count=None,
start_date_time=None,
title=None,
bucket_task_board_format=None,
progress_task_board_format=None,
microsoft_graph_entity_id=None,
checklist=None,
description=None,
microsoft_graph_planner_preview_type=None,
references=None,
id1=None,
order_hints_by_assignee=None,
unassigned_order_hint=None,
application=None,
device=None,
user=None,
microsoft_graph_identity_application=None,
microsoft_graph_identity_device=None,
microsoft_graph_identity_user=None):
body = {}
body['id'] = id_
body['active_checklist_item_count'] = active_checklist_item_count
body['applied_categories'] = applied_categories
body['assignee_priority'] = assignee_priority
body['assignments'] = assignments
body['bucket_id'] = bucket_id
body['checklist_item_count'] = checklist_item_count
body['completed_date_time'] = completed_date_time
body['conversation_thread_id'] = conversation_thread_id
body['created_date_time'] = created_date_time
body['due_date_time'] = due_date_time
body['has_description'] = has_description
body['order_hint'] = order_hint
body['percent_complete'] = percent_complete
body['plan_id'] = plan_id
body['preview_type'] = preview_type
body['reference_count'] = reference_count
body['start_date_time'] = start_date_time
body['title'] = title
body['bucket_task_board_format'] = bucket_task_board_format
body['progress_task_board_format'] = progress_task_board_format
body['details'] = {}
body['details']['id'] = microsoft_graph_entity_id
body['details']['checklist'] = checklist
body['details']['description'] = description
body['details']['preview_type'] = microsoft_graph_planner_preview_type
body['details']['references'] = references
body['assigned_to_task_board_format'] = {}
body['assigned_to_task_board_format']['id'] = id1
body['assigned_to_task_board_format']['order_hints_by_assignee'] = order_hints_by_assignee
body['assigned_to_task_board_format']['unassigned_order_hint'] = unassigned_order_hint
body['created_by'] = {}
body['created_by']['application'] = application
body['created_by']['device'] = device
body['created_by']['user'] = user
body['completed_by'] = {}
body['completed_by']['application'] = microsoft_graph_identity_application
body['completed_by']['device'] = microsoft_graph_identity_device
body['completed_by']['user'] = microsoft_graph_identity_user
return client.create_tasks(planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
body=body)
def planner_planner_plan_bucket_delete_task(client,
planner_plan_id,
planner_bucket_id,
planner_task_id,
if_match=None):
return client.delete_tasks(planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
if_match=if_match)
def planner_planner_plan_bucket_list_task(client,
planner_plan_id,
planner_bucket_id,
orderby=None,
select=None,
expand=None):
return client.list_tasks(planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
orderby=orderby,
select=select,
expand=expand)
def planner_planner_plan_bucket_show_task(client,
planner_plan_id,
planner_bucket_id,
planner_task_id,
select=None,
expand=None):
return client.get_tasks(planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
select=select,
expand=expand)
def planner_planner_plan_bucket_update_task(client,
planner_plan_id,
planner_bucket_id,
planner_task_id,
id_=None,
active_checklist_item_count=None,
applied_categories=None,
assignee_priority=None,
assignments=None,
bucket_id=None,
checklist_item_count=None,
completed_date_time=None,
conversation_thread_id=None,
created_date_time=None,
due_date_time=None,
has_description=None,
order_hint=None,
percent_complete=None,
plan_id=None,
preview_type=None,
reference_count=None,
start_date_time=None,
title=None,
bucket_task_board_format=None,
progress_task_board_format=None,
microsoft_graph_entity_id=None,
checklist=None,
description=None,
microsoft_graph_planner_preview_type=None,
references=None,
id1=None,
order_hints_by_assignee=None,
unassigned_order_hint=None,
application=None,
device=None,
user=None,
microsoft_graph_identity_application=None,
microsoft_graph_identity_device=None,
microsoft_graph_identity_user=None):
body = {}
body['id'] = id_
body['active_checklist_item_count'] = active_checklist_item_count
body['applied_categories'] = applied_categories
body['assignee_priority'] = assignee_priority
body['assignments'] = assignments
body['bucket_id'] = bucket_id
body['checklist_item_count'] = checklist_item_count
body['completed_date_time'] = completed_date_time
body['conversation_thread_id'] = conversation_thread_id
body['created_date_time'] = created_date_time
body['due_date_time'] = due_date_time
body['has_description'] = has_description
body['order_hint'] = order_hint
body['percent_complete'] = percent_complete
body['plan_id'] = plan_id
body['preview_type'] = preview_type
body['reference_count'] = reference_count
body['start_date_time'] = start_date_time
body['title'] = title
body['bucket_task_board_format'] = bucket_task_board_format
body['progress_task_board_format'] = progress_task_board_format
body['details'] = {}
body['details']['id'] = microsoft_graph_entity_id
body['details']['checklist'] = checklist
body['details']['description'] = description
body['details']['preview_type'] = microsoft_graph_planner_preview_type
body['details']['references'] = references
body['assigned_to_task_board_format'] = {}
body['assigned_to_task_board_format']['id'] = id1
body['assigned_to_task_board_format']['order_hints_by_assignee'] = order_hints_by_assignee
body['assigned_to_task_board_format']['unassigned_order_hint'] = unassigned_order_hint
body['created_by'] = {}
body['created_by']['application'] = application
body['created_by']['device'] = device
body['created_by']['user'] = user
body['completed_by'] = {}
body['completed_by']['application'] = microsoft_graph_identity_application
body['completed_by']['device'] = microsoft_graph_identity_device
body['completed_by']['user'] = microsoft_graph_identity_user
return client.update_tasks(planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
body=body)
def planner_planner_plan_bucket_task_delete_assigned_to_task_board_format(client,
planner_plan_id,
planner_bucket_id,
planner_task_id,
if_match=None):
return client.delete_assigned_to_task_board_format(planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
if_match=if_match)
def planner_planner_plan_bucket_task_delete_bucket_task_board_format(client,
planner_plan_id,
planner_bucket_id,
planner_task_id,
if_match=None):
return client.delete_bucket_task_board_format(planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
if_match=if_match)
def planner_planner_plan_bucket_task_delete_detail(client,
planner_plan_id,
planner_bucket_id,
planner_task_id,
if_match=None):
return client.delete_details(planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
if_match=if_match)
def planner_planner_plan_bucket_task_delete_progress_task_board_format(client,
planner_plan_id,
planner_bucket_id,
planner_task_id,
if_match=None):
return client.delete_progress_task_board_format(planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
if_match=if_match)
def planner_planner_plan_bucket_task_show_assigned_to_task_board_format(client,
planner_plan_id,
planner_bucket_id,
planner_task_id,
select=None,
expand=None):
return client.get_assigned_to_task_board_format(planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
select=select,
expand=expand)
def planner_planner_plan_bucket_task_show_bucket_task_board_format(client,
planner_plan_id,
planner_bucket_id,
planner_task_id,
select=None,
expand=None):
return client.get_bucket_task_board_format(planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
select=select,
expand=expand)
def planner_planner_plan_bucket_task_show_detail(client,
planner_plan_id,
planner_bucket_id,
planner_task_id,
select=None,
expand=None):
return client.get_details(planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
select=select,
expand=expand)
def planner_planner_plan_bucket_task_show_progress_task_board_format(client,
planner_plan_id,
planner_bucket_id,
planner_task_id,
select=None,
expand=None):
return client.get_progress_task_board_format(planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
select=select,
expand=expand)
def planner_planner_plan_bucket_task_update_assigned_to_task_board_format(client,
planner_plan_id,
planner_bucket_id,
planner_task_id,
id_=None,
order_hints_by_assignee=None,
unassigned_order_hint=None):
body = {}
body['id'] = id_
body['order_hints_by_assignee'] = order_hints_by_assignee
body['unassigned_order_hint'] = unassigned_order_hint
return client.update_assigned_to_task_board_format(planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
body=body)
def planner_planner_plan_bucket_task_update_bucket_task_board_format(client,
planner_plan_id,
planner_bucket_id,
planner_task_id,
id_=None,
order_hint=None):
body = {}
body['id'] = id_
body['order_hint'] = order_hint
return client.update_bucket_task_board_format(planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
body=body)
def planner_planner_plan_bucket_task_update_detail(client,
planner_plan_id,
planner_bucket_id,
planner_task_id,
id_=None,
checklist=None,
description=None,
preview_type=None,
references=None):
body = {}
body['id'] = id_
body['checklist'] = checklist
body['description'] = description
body['preview_type'] = preview_type
body['references'] = references
return client.update_details(planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
body=body)
def planner_planner_plan_bucket_task_update_progress_task_board_format(client,
planner_plan_id,
planner_bucket_id,
planner_task_id,
id_=None,
order_hint=None):
body = {}
body['id'] = id_
body['order_hint'] = order_hint
return client.update_progress_task_board_format(planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
body=body)
def planner_planner_plan_task_delete_assigned_to_task_board_format(client,
planner_plan_id,
planner_task_id,
if_match=None):
return client.delete_assigned_to_task_board_format(planner_plan_id=planner_plan_id,
planner_task_id=planner_task_id,
if_match=if_match)
def planner_planner_plan_task_delete_bucket_task_board_format(client,
planner_plan_id,
planner_task_id,
if_match=None):
return client.delete_bucket_task_board_format(planner_plan_id=planner_plan_id,
planner_task_id=planner_task_id,
if_match=if_match)
def planner_planner_plan_task_delete_detail(client,
planner_plan_id,
planner_task_id,
if_match=None):
return client.delete_details(planner_plan_id=planner_plan_id,
planner_task_id=planner_task_id,
if_match=if_match)
def planner_planner_plan_task_delete_progress_task_board_format(client,
planner_plan_id,
planner_task_id,
if_match=None):
return client.delete_progress_task_board_format(planner_plan_id=planner_plan_id,
planner_task_id=planner_task_id,
if_match=if_match)
def planner_planner_plan_task_show_assigned_to_task_board_format(client,
planner_plan_id,
planner_task_id,
select=None,
expand=None):
return client.get_assigned_to_task_board_format(planner_plan_id=planner_plan_id,
planner_task_id=planner_task_id,
select=select,
expand=expand)
def planner_planner_plan_task_show_bucket_task_board_format(client,
planner_plan_id,
planner_task_id,
select=None,
expand=None):
return client.get_bucket_task_board_format(planner_plan_id=planner_plan_id,
planner_task_id=planner_task_id,
select=select,
expand=expand)
def planner_planner_plan_task_show_detail(client,
planner_plan_id,
planner_task_id,
select=None,
expand=None):
return client.get_details(planner_plan_id=planner_plan_id,
planner_task_id=planner_task_id,
select=select,
expand=expand)
def planner_planner_plan_task_show_progress_task_board_format(client,
planner_plan_id,
planner_task_id,
select=None,
expand=None):
return client.get_progress_task_board_format(planner_plan_id=planner_plan_id,
planner_task_id=planner_task_id,
select=select,
expand=expand)
def planner_planner_plan_task_update_assigned_to_task_board_format(client,
planner_plan_id,
planner_task_id,
id_=None,
order_hints_by_assignee=None,
unassigned_order_hint=None):
body = {}
body['id'] = id_
body['order_hints_by_assignee'] = order_hints_by_assignee
body['unassigned_order_hint'] = unassigned_order_hint
return client.update_assigned_to_task_board_format(planner_plan_id=planner_plan_id,
planner_task_id=planner_task_id,
body=body)
def planner_planner_plan_task_update_bucket_task_board_format(client,
planner_plan_id,
planner_task_id,
id_=None,
order_hint=None):
body = {}
body['id'] = id_
body['order_hint'] = order_hint
return client.update_bucket_task_board_format(planner_plan_id=planner_plan_id,
planner_task_id=planner_task_id,
body=body)
def planner_planner_plan_task_update_detail(client,
planner_plan_id,
planner_task_id,
id_=None,
checklist=None,
description=None,
preview_type=None,
references=None):
body = {}
body['id'] = id_
body['checklist'] = checklist
body['description'] = description
body['preview_type'] = preview_type
body['references'] = references
return client.update_details(planner_plan_id=planner_plan_id,
planner_task_id=planner_task_id,
body=body)
def planner_planner_plan_task_update_progress_task_board_format(client,
planner_plan_id,
planner_task_id,
id_=None,
order_hint=None):
body = {}
body['id'] = id_
body['order_hint'] = order_hint
return client.update_progress_task_board_format(planner_plan_id=planner_plan_id,
planner_task_id=planner_task_id,
body=body)
def planner_planner_task_delete_assigned_to_task_board_format(client,
planner_task_id,
if_match=None):
return client.delete_assigned_to_task_board_format(planner_task_id=planner_task_id,
if_match=if_match)
def planner_planner_task_delete_bucket_task_board_format(client,
planner_task_id,
if_match=None):
return client.delete_bucket_task_board_format(planner_task_id=planner_task_id,
if_match=if_match)
def planner_planner_task_delete_detail(client,
planner_task_id,
if_match=None):
return client.delete_details(planner_task_id=planner_task_id,
if_match=if_match)
def planner_planner_task_delete_progress_task_board_format(client,
planner_task_id,
if_match=None):
return client.delete_progress_task_board_format(planner_task_id=planner_task_id,
if_match=if_match)
def planner_planner_task_show_assigned_to_task_board_format(client,
planner_task_id,
select=None,
expand=None):
return client.get_assigned_to_task_board_format(planner_task_id=planner_task_id,
select=select,
expand=expand)
def planner_planner_task_show_bucket_task_board_format(client,
planner_task_id,
select=None,
expand=None):
return client.get_bucket_task_board_format(planner_task_id=planner_task_id,
select=select,
expand=expand)
def planner_planner_task_show_detail(client,
planner_task_id,
select=None,
expand=None):
return client.get_details(planner_task_id=planner_task_id,
select=select,
expand=expand)
def planner_planner_task_show_progress_task_board_format(client,
planner_task_id,
select=None,
expand=None):
return client.get_progress_task_board_format(planner_task_id=planner_task_id,
select=select,
expand=expand)
def planner_planner_task_update_assigned_to_task_board_format(client,
planner_task_id,
id_=None,
order_hints_by_assignee=None,
unassigned_order_hint=None):
body = {}
body['id'] = id_
body['order_hints_by_assignee'] = order_hints_by_assignee
body['unassigned_order_hint'] = unassigned_order_hint
return client.update_assigned_to_task_board_format(planner_task_id=planner_task_id,
body=body)
def planner_planner_task_update_bucket_task_board_format(client,
planner_task_id,
id_=None,
order_hint=None):
body = {}
body['id'] = id_
body['order_hint'] = order_hint
return client.update_bucket_task_board_format(planner_task_id=planner_task_id,
body=body)
def planner_planner_task_update_detail(client,
planner_task_id,
id_=None,
checklist=None,
description=None,
preview_type=None,
references=None):
body = {}
body['id'] = id_
body['checklist'] = checklist
body['description'] = description
body['preview_type'] = preview_type
body['references'] = references
return client.update_details(planner_task_id=planner_task_id,
body=body)
def planner_planner_task_update_progress_task_board_format(client,
planner_task_id,
id_=None,
order_hint=None):
body = {}
body['id'] = id_
body['order_hint'] = order_hint
return client.update_progress_task_board_format(planner_task_id=planner_task_id,
body=body)
def planner_user_delete_planner(client,
user_id,
if_match=None):
return client.delete_planner(user_id=user_id,
if_match=if_match)
def planner_user_show_planner(client,
user_id,
select=None,
expand=None):
return client.get_planner(user_id=user_id,
select=select,
expand=expand)
def planner_user_update_planner(client,
user_id,
id_=None,
plans=None,
tasks=None):
body = {}
body['id'] = id_
body['plans'] = plans
body['tasks'] = tasks
return client.update_planner(user_id=user_id,
body=body)
def planner_user_planner_create_plan(client,
user_id,
id_=None,
created_date_time=None,
owner=None,
title=None,
buckets=None,
tasks=None,
microsoft_graph_entity_id=None,
category_descriptions=None,
shared_with=None,
application=None,
device=None,
user=None):
body = {}
body['id'] = id_
body['created_date_time'] = created_date_time
body['owner'] = owner
body['title'] = title
body['buckets'] = buckets
body['tasks'] = tasks
body['details'] = {}
body['details']['id'] = microsoft_graph_entity_id
body['details']['category_descriptions'] = category_descriptions
body['details']['shared_with'] = shared_with
body['created_by'] = {}
body['created_by']['application'] = application
body['created_by']['device'] = device
body['created_by']['user'] = user
return client.create_plans(user_id=user_id,
body=body)
def planner_user_planner_create_task(client,
user_id,
id_=None,
active_checklist_item_count=None,
applied_categories=None,
assignee_priority=None,
assignments=None,
bucket_id=None,
checklist_item_count=None,
completed_date_time=None,
conversation_thread_id=None,
created_date_time=None,
due_date_time=None,
has_description=None,
order_hint=None,
percent_complete=None,
plan_id=None,
preview_type=None,
reference_count=None,
start_date_time=None,
title=None,
bucket_task_board_format=None,
progress_task_board_format=None,
microsoft_graph_entity_id=None,
checklist=None,
description=None,
microsoft_graph_planner_preview_type=None,
references=None,
id1=None,
order_hints_by_assignee=None,
unassigned_order_hint=None,
application=None,
device=None,
user=None,
microsoft_graph_identity_application=None,
microsoft_graph_identity_device=None,
microsoft_graph_identity_user=None):
body = {}
body['id'] = id_
body['active_checklist_item_count'] = active_checklist_item_count
body['applied_categories'] = applied_categories
body['assignee_priority'] = assignee_priority
body['assignments'] = assignments
body['bucket_id'] = bucket_id
body['checklist_item_count'] = checklist_item_count
body['completed_date_time'] = completed_date_time
body['conversation_thread_id'] = conversation_thread_id
body['created_date_time'] = created_date_time
body['due_date_time'] = due_date_time
body['has_description'] = has_description
body['order_hint'] = order_hint
body['percent_complete'] = percent_complete
body['plan_id'] = plan_id
body['preview_type'] = preview_type
body['reference_count'] = reference_count
body['start_date_time'] = start_date_time
body['title'] = title
body['bucket_task_board_format'] = bucket_task_board_format
body['progress_task_board_format'] = progress_task_board_format
body['details'] = {}
body['details']['id'] = microsoft_graph_entity_id
body['details']['checklist'] = checklist
body['details']['description'] = description
body['details']['preview_type'] = microsoft_graph_planner_preview_type
body['details']['references'] = references
body['assigned_to_task_board_format'] = {}
body['assigned_to_task_board_format']['id'] = id1
body['assigned_to_task_board_format']['order_hints_by_assignee'] = order_hints_by_assignee
body['assigned_to_task_board_format']['unassigned_order_hint'] = unassigned_order_hint
body['created_by'] = {}
body['created_by']['application'] = application
body['created_by']['device'] = device
body['created_by']['user'] = user
body['completed_by'] = {}
body['completed_by']['application'] = microsoft_graph_identity_application
body['completed_by']['device'] = microsoft_graph_identity_device
body['completed_by']['user'] = microsoft_graph_identity_user
return client.create_tasks(user_id=user_id,
body=body)
def planner_user_planner_delete_plan(client,
user_id,
planner_plan_id,
if_match=None):
return client.delete_plans(user_id=user_id,
planner_plan_id=planner_plan_id,
if_match=if_match)
def planner_user_planner_delete_task(client,
user_id,
planner_task_id,
if_match=None):
return client.delete_tasks(user_id=user_id,
planner_task_id=planner_task_id,
if_match=if_match)
def planner_user_planner_list_plan(client,
user_id,
orderby=None,
select=None,
expand=None):
return client.list_plans(user_id=user_id,
orderby=orderby,
select=select,
expand=expand)
def planner_user_planner_list_task(client,
user_id,
orderby=None,
select=None,
expand=None):
return client.list_tasks(user_id=user_id,
orderby=orderby,
select=select,
expand=expand)
def planner_user_planner_show_plan(client,
user_id,
planner_plan_id,
select=None,
expand=None):
return client.get_plans(user_id=user_id,
planner_plan_id=planner_plan_id,
select=select,
expand=expand)
def planner_user_planner_show_task(client,
user_id,
planner_task_id,
select=None,
expand=None):
return client.get_tasks(user_id=user_id,
planner_task_id=planner_task_id,
select=select,
expand=expand)
def planner_user_planner_update_plan(client,
user_id,
planner_plan_id,
id_=None,
created_date_time=None,
owner=None,
title=None,
buckets=None,
tasks=None,
microsoft_graph_entity_id=None,
category_descriptions=None,
shared_with=None,
application=None,
device=None,
user=None):
body = {}
body['id'] = id_
body['created_date_time'] = created_date_time
body['owner'] = owner
body['title'] = title
body['buckets'] = buckets
body['tasks'] = tasks
body['details'] = {}
body['details']['id'] = microsoft_graph_entity_id
body['details']['category_descriptions'] = category_descriptions
body['details']['shared_with'] = shared_with
body['created_by'] = {}
body['created_by']['application'] = application
body['created_by']['device'] = device
body['created_by']['user'] = user
return client.update_plans(user_id=user_id,
planner_plan_id=planner_plan_id,
body=body)
def planner_user_planner_update_task(client,
user_id,
planner_task_id,
id_=None,
active_checklist_item_count=None,
applied_categories=None,
assignee_priority=None,
assignments=None,
bucket_id=None,
checklist_item_count=None,
completed_date_time=None,
conversation_thread_id=None,
created_date_time=None,
due_date_time=None,
has_description=None,
order_hint=None,
percent_complete=None,
plan_id=None,
preview_type=None,
reference_count=None,
start_date_time=None,
title=None,
bucket_task_board_format=None,
progress_task_board_format=None,
microsoft_graph_entity_id=None,
checklist=None,
description=None,
microsoft_graph_planner_preview_type=None,
references=None,
id1=None,
order_hints_by_assignee=None,
unassigned_order_hint=None,
application=None,
device=None,
user=None,
microsoft_graph_identity_application=None,
microsoft_graph_identity_device=None,
microsoft_graph_identity_user=None):
body = {}
body['id'] = id_
body['active_checklist_item_count'] = active_checklist_item_count
body['applied_categories'] = applied_categories
body['assignee_priority'] = assignee_priority
body['assignments'] = assignments
body['bucket_id'] = bucket_id
body['checklist_item_count'] = checklist_item_count
body['completed_date_time'] = completed_date_time
body['conversation_thread_id'] = conversation_thread_id
body['created_date_time'] = created_date_time
body['due_date_time'] = due_date_time
body['has_description'] = has_description
body['order_hint'] = order_hint
body['percent_complete'] = percent_complete
body['plan_id'] = plan_id
body['preview_type'] = preview_type
body['reference_count'] = reference_count
body['start_date_time'] = start_date_time
body['title'] = title
body['bucket_task_board_format'] = bucket_task_board_format
body['progress_task_board_format'] = progress_task_board_format
body['details'] = {}
body['details']['id'] = microsoft_graph_entity_id
body['details']['checklist'] = checklist
body['details']['description'] = description
body['details']['preview_type'] = microsoft_graph_planner_preview_type
body['details']['references'] = references
body['assigned_to_task_board_format'] = {}
body['assigned_to_task_board_format']['id'] = id1
body['assigned_to_task_board_format']['order_hints_by_assignee'] = order_hints_by_assignee
body['assigned_to_task_board_format']['unassigned_order_hint'] = unassigned_order_hint
body['created_by'] = {}
body['created_by']['application'] = application
body['created_by']['device'] = device
body['created_by']['user'] = user
body['completed_by'] = {}
body['completed_by']['application'] = microsoft_graph_identity_application
body['completed_by']['device'] = microsoft_graph_identity_device
body['completed_by']['user'] = microsoft_graph_identity_user
return client.update_tasks(user_id=user_id,
planner_task_id=planner_task_id,
body=body)
def planner_user_planner_plan_create_bucket(client,
user_id,
planner_plan_id,
id_=None,
name=None,
order_hint=None,
plan_id=None,
tasks=None):
body = {}
body['id'] = id_
body['name'] = name
body['order_hint'] = order_hint
body['plan_id'] = plan_id
body['tasks'] = tasks
return client.create_buckets(user_id=user_id,
planner_plan_id=planner_plan_id,
body=body)
def planner_user_planner_plan_create_task(client,
user_id,
planner_plan_id,
id_=None,
active_checklist_item_count=None,
applied_categories=None,
assignee_priority=None,
assignments=None,
bucket_id=None,
checklist_item_count=None,
completed_date_time=None,
conversation_thread_id=None,
created_date_time=None,
due_date_time=None,
has_description=None,
order_hint=None,
percent_complete=None,
plan_id=None,
preview_type=None,
reference_count=None,
start_date_time=None,
title=None,
bucket_task_board_format=None,
progress_task_board_format=None,
microsoft_graph_entity_id=None,
checklist=None,
description=None,
microsoft_graph_planner_preview_type=None,
references=None,
id1=None,
order_hints_by_assignee=None,
unassigned_order_hint=None,
application=None,
device=None,
user=None,
microsoft_graph_identity_application=None,
microsoft_graph_identity_device=None,
microsoft_graph_identity_user=None):
body = {}
body['id'] = id_
body['active_checklist_item_count'] = active_checklist_item_count
body['applied_categories'] = applied_categories
body['assignee_priority'] = assignee_priority
body['assignments'] = assignments
body['bucket_id'] = bucket_id
body['checklist_item_count'] = checklist_item_count
body['completed_date_time'] = completed_date_time
body['conversation_thread_id'] = conversation_thread_id
body['created_date_time'] = created_date_time
body['due_date_time'] = due_date_time
body['has_description'] = has_description
body['order_hint'] = order_hint
body['percent_complete'] = percent_complete
body['plan_id'] = plan_id
body['preview_type'] = preview_type
body['reference_count'] = reference_count
body['start_date_time'] = start_date_time
body['title'] = title
body['bucket_task_board_format'] = bucket_task_board_format
body['progress_task_board_format'] = progress_task_board_format
body['details'] = {}
body['details']['id'] = microsoft_graph_entity_id
body['details']['checklist'] = checklist
body['details']['description'] = description
body['details']['preview_type'] = microsoft_graph_planner_preview_type
body['details']['references'] = references
body['assigned_to_task_board_format'] = {}
body['assigned_to_task_board_format']['id'] = id1
body['assigned_to_task_board_format']['order_hints_by_assignee'] = order_hints_by_assignee
body['assigned_to_task_board_format']['unassigned_order_hint'] = unassigned_order_hint
body['created_by'] = {}
body['created_by']['application'] = application
body['created_by']['device'] = device
body['created_by']['user'] = user
body['completed_by'] = {}
body['completed_by']['application'] = microsoft_graph_identity_application
body['completed_by']['device'] = microsoft_graph_identity_device
body['completed_by']['user'] = microsoft_graph_identity_user
return client.create_tasks(user_id=user_id,
planner_plan_id=planner_plan_id,
body=body)
def planner_user_planner_plan_delete_bucket(client,
user_id,
planner_plan_id,
planner_bucket_id,
if_match=None):
return client.delete_buckets(user_id=user_id,
planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
if_match=if_match)
def planner_user_planner_plan_delete_detail(client,
user_id,
planner_plan_id,
if_match=None):
return client.delete_details(user_id=user_id,
planner_plan_id=planner_plan_id,
if_match=if_match)
def planner_user_planner_plan_delete_task(client,
user_id,
planner_plan_id,
planner_task_id,
if_match=None):
return client.delete_tasks(user_id=user_id,
planner_plan_id=planner_plan_id,
planner_task_id=planner_task_id,
if_match=if_match)
def planner_user_planner_plan_list_bucket(client,
user_id,
planner_plan_id,
orderby=None,
select=None,
expand=None):
return client.list_buckets(user_id=user_id,
planner_plan_id=planner_plan_id,
orderby=orderby,
select=select,
expand=expand)
def planner_user_planner_plan_list_task(client,
user_id,
planner_plan_id,
orderby=None,
select=None,
expand=None):
return client.list_tasks(user_id=user_id,
planner_plan_id=planner_plan_id,
orderby=orderby,
select=select,
expand=expand)
def planner_user_planner_plan_show_bucket(client,
user_id,
planner_plan_id,
planner_bucket_id,
select=None,
expand=None):
return client.get_buckets(user_id=user_id,
planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
select=select,
expand=expand)
def planner_user_planner_plan_show_detail(client,
user_id,
planner_plan_id,
select=None,
expand=None):
return client.get_details(user_id=user_id,
planner_plan_id=planner_plan_id,
select=select,
expand=expand)
def planner_user_planner_plan_show_task(client,
user_id,
planner_plan_id,
planner_task_id,
select=None,
expand=None):
return client.get_tasks(user_id=user_id,
planner_plan_id=planner_plan_id,
planner_task_id=planner_task_id,
select=select,
expand=expand)
def planner_user_planner_plan_update_bucket(client,
user_id,
planner_plan_id,
planner_bucket_id,
id_=None,
name=None,
order_hint=None,
plan_id=None,
tasks=None):
body = {}
body['id'] = id_
body['name'] = name
body['order_hint'] = order_hint
body['plan_id'] = plan_id
body['tasks'] = tasks
return client.update_buckets(user_id=user_id,
planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
body=body)
def planner_user_planner_plan_update_detail(client,
user_id,
planner_plan_id,
id_=None,
category_descriptions=None,
shared_with=None):
body = {}
body['id'] = id_
body['category_descriptions'] = category_descriptions
body['shared_with'] = shared_with
return client.update_details(user_id=user_id,
planner_plan_id=planner_plan_id,
body=body)
def planner_user_planner_plan_update_task(client,
user_id,
planner_plan_id,
planner_task_id,
id_=None,
active_checklist_item_count=None,
applied_categories=None,
assignee_priority=None,
assignments=None,
bucket_id=None,
checklist_item_count=None,
completed_date_time=None,
conversation_thread_id=None,
created_date_time=None,
due_date_time=None,
has_description=None,
order_hint=None,
percent_complete=None,
plan_id=None,
preview_type=None,
reference_count=None,
start_date_time=None,
title=None,
bucket_task_board_format=None,
progress_task_board_format=None,
microsoft_graph_entity_id=None,
checklist=None,
description=None,
microsoft_graph_planner_preview_type=None,
references=None,
id1=None,
order_hints_by_assignee=None,
unassigned_order_hint=None,
application=None,
device=None,
user=None,
microsoft_graph_identity_application=None,
microsoft_graph_identity_device=None,
microsoft_graph_identity_user=None):
body = {}
body['id'] = id_
body['active_checklist_item_count'] = active_checklist_item_count
body['applied_categories'] = applied_categories
body['assignee_priority'] = assignee_priority
body['assignments'] = assignments
body['bucket_id'] = bucket_id
body['checklist_item_count'] = checklist_item_count
body['completed_date_time'] = completed_date_time
body['conversation_thread_id'] = conversation_thread_id
body['created_date_time'] = created_date_time
body['due_date_time'] = due_date_time
body['has_description'] = has_description
body['order_hint'] = order_hint
body['percent_complete'] = percent_complete
body['plan_id'] = plan_id
body['preview_type'] = preview_type
body['reference_count'] = reference_count
body['start_date_time'] = start_date_time
body['title'] = title
body['bucket_task_board_format'] = bucket_task_board_format
body['progress_task_board_format'] = progress_task_board_format
body['details'] = {}
body['details']['id'] = microsoft_graph_entity_id
body['details']['checklist'] = checklist
body['details']['description'] = description
body['details']['preview_type'] = microsoft_graph_planner_preview_type
body['details']['references'] = references
body['assigned_to_task_board_format'] = {}
body['assigned_to_task_board_format']['id'] = id1
body['assigned_to_task_board_format']['order_hints_by_assignee'] = order_hints_by_assignee
body['assigned_to_task_board_format']['unassigned_order_hint'] = unassigned_order_hint
body['created_by'] = {}
body['created_by']['application'] = application
body['created_by']['device'] = device
body['created_by']['user'] = user
body['completed_by'] = {}
body['completed_by']['application'] = microsoft_graph_identity_application
body['completed_by']['device'] = microsoft_graph_identity_device
body['completed_by']['user'] = microsoft_graph_identity_user
return client.update_tasks(user_id=user_id,
planner_plan_id=planner_plan_id,
planner_task_id=planner_task_id,
body=body)
def planner_user_planner_plan_bucket_create_task(client,
user_id,
planner_plan_id,
planner_bucket_id,
id_=None,
active_checklist_item_count=None,
applied_categories=None,
assignee_priority=None,
assignments=None,
bucket_id=None,
checklist_item_count=None,
completed_date_time=None,
conversation_thread_id=None,
created_date_time=None,
due_date_time=None,
has_description=None,
order_hint=None,
percent_complete=None,
plan_id=None,
preview_type=None,
reference_count=None,
start_date_time=None,
title=None,
bucket_task_board_format=None,
progress_task_board_format=None,
microsoft_graph_entity_id=None,
checklist=None,
description=None,
microsoft_graph_planner_preview_type=None,
references=None,
id1=None,
order_hints_by_assignee=None,
unassigned_order_hint=None,
application=None,
device=None,
user=None,
microsoft_graph_identity_application=None,
microsoft_graph_identity_device=None,
microsoft_graph_identity_user=None):
body = {}
body['id'] = id_
body['active_checklist_item_count'] = active_checklist_item_count
body['applied_categories'] = applied_categories
body['assignee_priority'] = assignee_priority
body['assignments'] = assignments
body['bucket_id'] = bucket_id
body['checklist_item_count'] = checklist_item_count
body['completed_date_time'] = completed_date_time
body['conversation_thread_id'] = conversation_thread_id
body['created_date_time'] = created_date_time
body['due_date_time'] = due_date_time
body['has_description'] = has_description
body['order_hint'] = order_hint
body['percent_complete'] = percent_complete
body['plan_id'] = plan_id
body['preview_type'] = preview_type
body['reference_count'] = reference_count
body['start_date_time'] = start_date_time
body['title'] = title
body['bucket_task_board_format'] = bucket_task_board_format
body['progress_task_board_format'] = progress_task_board_format
body['details'] = {}
body['details']['id'] = microsoft_graph_entity_id
body['details']['checklist'] = checklist
body['details']['description'] = description
body['details']['preview_type'] = microsoft_graph_planner_preview_type
body['details']['references'] = references
body['assigned_to_task_board_format'] = {}
body['assigned_to_task_board_format']['id'] = id1
body['assigned_to_task_board_format']['order_hints_by_assignee'] = order_hints_by_assignee
body['assigned_to_task_board_format']['unassigned_order_hint'] = unassigned_order_hint
body['created_by'] = {}
body['created_by']['application'] = application
body['created_by']['device'] = device
body['created_by']['user'] = user
body['completed_by'] = {}
body['completed_by']['application'] = microsoft_graph_identity_application
body['completed_by']['device'] = microsoft_graph_identity_device
body['completed_by']['user'] = microsoft_graph_identity_user
return client.create_tasks(user_id=user_id,
planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
body=body)
def planner_user_planner_plan_bucket_delete_task(client,
user_id,
planner_plan_id,
planner_bucket_id,
planner_task_id,
if_match=None):
return client.delete_tasks(user_id=user_id,
planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
if_match=if_match)
def planner_user_planner_plan_bucket_list_task(client,
user_id,
planner_plan_id,
planner_bucket_id,
orderby=None,
select=None,
expand=None):
return client.list_tasks(user_id=user_id,
planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
orderby=orderby,
select=select,
expand=expand)
def planner_user_planner_plan_bucket_show_task(client,
user_id,
planner_plan_id,
planner_bucket_id,
planner_task_id,
select=None,
expand=None):
return client.get_tasks(user_id=user_id,
planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
select=select,
expand=expand)
def planner_user_planner_plan_bucket_update_task(client,
user_id,
planner_plan_id,
planner_bucket_id,
planner_task_id,
id_=None,
active_checklist_item_count=None,
applied_categories=None,
assignee_priority=None,
assignments=None,
bucket_id=None,
checklist_item_count=None,
completed_date_time=None,
conversation_thread_id=None,
created_date_time=None,
due_date_time=None,
has_description=None,
order_hint=None,
percent_complete=None,
plan_id=None,
preview_type=None,
reference_count=None,
start_date_time=None,
title=None,
bucket_task_board_format=None,
progress_task_board_format=None,
microsoft_graph_entity_id=None,
checklist=None,
description=None,
microsoft_graph_planner_preview_type=None,
references=None,
id1=None,
order_hints_by_assignee=None,
unassigned_order_hint=None,
application=None,
device=None,
user=None,
microsoft_graph_identity_application=None,
microsoft_graph_identity_device=None,
microsoft_graph_identity_user=None):
body = {}
body['id'] = id_
body['active_checklist_item_count'] = active_checklist_item_count
body['applied_categories'] = applied_categories
body['assignee_priority'] = assignee_priority
body['assignments'] = assignments
body['bucket_id'] = bucket_id
body['checklist_item_count'] = checklist_item_count
body['completed_date_time'] = completed_date_time
body['conversation_thread_id'] = conversation_thread_id
body['created_date_time'] = created_date_time
body['due_date_time'] = due_date_time
body['has_description'] = has_description
body['order_hint'] = order_hint
body['percent_complete'] = percent_complete
body['plan_id'] = plan_id
body['preview_type'] = preview_type
body['reference_count'] = reference_count
body['start_date_time'] = start_date_time
body['title'] = title
body['bucket_task_board_format'] = bucket_task_board_format
body['progress_task_board_format'] = progress_task_board_format
body['details'] = {}
body['details']['id'] = microsoft_graph_entity_id
body['details']['checklist'] = checklist
body['details']['description'] = description
body['details']['preview_type'] = microsoft_graph_planner_preview_type
body['details']['references'] = references
body['assigned_to_task_board_format'] = {}
body['assigned_to_task_board_format']['id'] = id1
body['assigned_to_task_board_format']['order_hints_by_assignee'] = order_hints_by_assignee
body['assigned_to_task_board_format']['unassigned_order_hint'] = unassigned_order_hint
body['created_by'] = {}
body['created_by']['application'] = application
body['created_by']['device'] = device
body['created_by']['user'] = user
body['completed_by'] = {}
body['completed_by']['application'] = microsoft_graph_identity_application
body['completed_by']['device'] = microsoft_graph_identity_device
body['completed_by']['user'] = microsoft_graph_identity_user
return client.update_tasks(user_id=user_id,
planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
body=body)
def planner_user_planner_plan_bucket_task_delete_assigned_to_task_board_format(client,
user_id,
planner_plan_id,
planner_bucket_id,
planner_task_id,
if_match=None):
return client.delete_assigned_to_task_board_format(user_id=user_id,
planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
if_match=if_match)
def planner_user_planner_plan_bucket_task_delete_bucket_task_board_format(client,
user_id,
planner_plan_id,
planner_bucket_id,
planner_task_id,
if_match=None):
return client.delete_bucket_task_board_format(user_id=user_id,
planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
if_match=if_match)
def planner_user_planner_plan_bucket_task_delete_detail(client,
user_id,
planner_plan_id,
planner_bucket_id,
planner_task_id,
if_match=None):
return client.delete_details(user_id=user_id,
planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
if_match=if_match)
def planner_user_planner_plan_bucket_task_delete_progress_task_board_format(client,
user_id,
planner_plan_id,
planner_bucket_id,
planner_task_id,
if_match=None):
return client.delete_progress_task_board_format(user_id=user_id,
planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
if_match=if_match)
def planner_user_planner_plan_bucket_task_show_assigned_to_task_board_format(client,
user_id,
planner_plan_id,
planner_bucket_id,
planner_task_id,
select=None,
expand=None):
return client.get_assigned_to_task_board_format(user_id=user_id,
planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
select=select,
expand=expand)
def planner_user_planner_plan_bucket_task_show_bucket_task_board_format(client,
user_id,
planner_plan_id,
planner_bucket_id,
planner_task_id,
select=None,
expand=None):
return client.get_bucket_task_board_format(user_id=user_id,
planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
select=select,
expand=expand)
def planner_user_planner_plan_bucket_task_show_detail(client,
user_id,
planner_plan_id,
planner_bucket_id,
planner_task_id,
select=None,
expand=None):
return client.get_details(user_id=user_id,
planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
select=select,
expand=expand)
def planner_user_planner_plan_bucket_task_show_progress_task_board_format(client,
user_id,
planner_plan_id,
planner_bucket_id,
planner_task_id,
select=None,
expand=None):
return client.get_progress_task_board_format(user_id=user_id,
planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
select=select,
expand=expand)
def planner_user_planner_plan_bucket_task_update_assigned_to_task_board_format(client,
user_id,
planner_plan_id,
planner_bucket_id,
planner_task_id,
id_=None,
order_hints_by_assignee=None,
unassigned_order_hint=None):
body = {}
body['id'] = id_
body['order_hints_by_assignee'] = order_hints_by_assignee
body['unassigned_order_hint'] = unassigned_order_hint
return client.update_assigned_to_task_board_format(user_id=user_id,
planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
body=body)
def planner_user_planner_plan_bucket_task_update_bucket_task_board_format(client,
user_id,
planner_plan_id,
planner_bucket_id,
planner_task_id,
id_=None,
order_hint=None):
body = {}
body['id'] = id_
body['order_hint'] = order_hint
return client.update_bucket_task_board_format(user_id=user_id,
planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
body=body)
def planner_user_planner_plan_bucket_task_update_detail(client,
user_id,
planner_plan_id,
planner_bucket_id,
planner_task_id,
id_=None,
checklist=None,
description=None,
preview_type=None,
references=None):
body = {}
body['id'] = id_
body['checklist'] = checklist
body['description'] = description
body['preview_type'] = preview_type
body['references'] = references
return client.update_details(user_id=user_id,
planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
body=body)
def planner_user_planner_plan_bucket_task_update_progress_task_board_format(client,
user_id,
planner_plan_id,
planner_bucket_id,
planner_task_id,
id_=None,
order_hint=None):
body = {}
body['id'] = id_
body['order_hint'] = order_hint
return client.update_progress_task_board_format(user_id=user_id,
planner_plan_id=planner_plan_id,
planner_bucket_id=planner_bucket_id,
planner_task_id=planner_task_id,
body=body)
def planner_user_planner_plan_task_delete_assigned_to_task_board_format(client,
user_id,
planner_plan_id,
planner_task_id,
if_match=None):
return client.delete_assigned_to_task_board_format(user_id=user_id,
planner_plan_id=planner_plan_id,
planner_task_id=planner_task_id,
if_match=if_match)
def planner_user_planner_plan_task_delete_bucket_task_board_format(client,
user_id,
planner_plan_id,
planner_task_id,
if_match=None):
return client.delete_bucket_task_board_format(user_id=user_id,
planner_plan_id=planner_plan_id,
planner_task_id=planner_task_id,
if_match=if_match)
def planner_user_planner_plan_task_delete_detail(client,
user_id,
planner_plan_id,
planner_task_id,
if_match=None):
return client.delete_details(user_id=user_id,
planner_plan_id=planner_plan_id,
planner_task_id=planner_task_id,
if_match=if_match)
def planner_user_planner_plan_task_delete_progress_task_board_format(client,
user_id,
planner_plan_id,
planner_task_id,
if_match=None):
return client.delete_progress_task_board_format(user_id=user_id,
planner_plan_id=planner_plan_id,
planner_task_id=planner_task_id,
if_match=if_match)
def planner_user_planner_plan_task_show_assigned_to_task_board_format(client,
user_id,
planner_plan_id,
planner_task_id,
select=None,
expand=None):
return client.get_assigned_to_task_board_format(user_id=user_id,
planner_plan_id=planner_plan_id,
planner_task_id=planner_task_id,
select=select,
expand=expand)
def planner_user_planner_plan_task_show_bucket_task_board_format(client,
user_id,
planner_plan_id,
planner_task_id,
select=None,
expand=None):
return client.get_bucket_task_board_format(user_id=user_id,
planner_plan_id=planner_plan_id,
planner_task_id=planner_task_id,
select=select,
expand=expand)
def planner_user_planner_plan_task_show_detail(client,
user_id,
planner_plan_id,
planner_task_id,
select=None,
expand=None):
return client.get_details(user_id=user_id,
planner_plan_id=planner_plan_id,
planner_task_id=planner_task_id,
select=select,
expand=expand)
def planner_user_planner_plan_task_show_progress_task_board_format(client,
user_id,
planner_plan_id,
planner_task_id,
select=None,
expand=None):
return client.get_progress_task_board_format(user_id=user_id,
planner_plan_id=planner_plan_id,
planner_task_id=planner_task_id,
select=select,
expand=expand)
def planner_user_planner_plan_task_update_assigned_to_task_board_format(client,
user_id,
planner_plan_id,
planner_task_id,
id_=None,
order_hints_by_assignee=None,
unassigned_order_hint=None):
body = {}
body['id'] = id_
body['order_hints_by_assignee'] = order_hints_by_assignee
body['unassigned_order_hint'] = unassigned_order_hint
return client.update_assigned_to_task_board_format(user_id=user_id,
planner_plan_id=planner_plan_id,
planner_task_id=planner_task_id,
body=body)
def planner_user_planner_plan_task_update_bucket_task_board_format(client,
user_id,
planner_plan_id,
planner_task_id,
id_=None,
order_hint=None):
body = {}
body['id'] = id_
body['order_hint'] = order_hint
return client.update_bucket_task_board_format(user_id=user_id,
planner_plan_id=planner_plan_id,
planner_task_id=planner_task_id,
body=body)
def planner_user_planner_plan_task_update_detail(client,
user_id,
planner_plan_id,
planner_task_id,
id_=None,
checklist=None,
description=None,
preview_type=None,
references=None):
body = {}
body['id'] = id_
body['checklist'] = checklist
body['description'] = description
body['preview_type'] = preview_type
body['references'] = references
return client.update_details(user_id=user_id,
planner_plan_id=planner_plan_id,
planner_task_id=planner_task_id,
body=body)
def planner_user_planner_plan_task_update_progress_task_board_format(client,
user_id,
planner_plan_id,
planner_task_id,
id_=None,
order_hint=None):
body = {}
body['id'] = id_
body['order_hint'] = order_hint
return client.update_progress_task_board_format(user_id=user_id,
planner_plan_id=planner_plan_id,
planner_task_id=planner_task_id,
body=body)
def planner_user_planner_task_delete_assigned_to_task_board_format(client,
user_id,
planner_task_id,
if_match=None):
return client.delete_assigned_to_task_board_format(user_id=user_id,
planner_task_id=planner_task_id,
if_match=if_match)
def planner_user_planner_task_delete_bucket_task_board_format(client,
user_id,
planner_task_id,
if_match=None):
return client.delete_bucket_task_board_format(user_id=user_id,
planner_task_id=planner_task_id,
if_match=if_match)
def planner_user_planner_task_delete_detail(client,
user_id,
planner_task_id,
if_match=None):
return client.delete_details(user_id=user_id,
planner_task_id=planner_task_id,
if_match=if_match)
def planner_user_planner_task_delete_progress_task_board_format(client,
user_id,
planner_task_id,
if_match=None):
return client.delete_progress_task_board_format(user_id=user_id,
planner_task_id=planner_task_id,
if_match=if_match)
def planner_user_planner_task_show_assigned_to_task_board_format(client,
user_id,
planner_task_id,
select=None,
expand=None):
return client.get_assigned_to_task_board_format(user_id=user_id,
planner_task_id=planner_task_id,
select=select,
expand=expand)
def planner_user_planner_task_show_bucket_task_board_format(client,
user_id,
planner_task_id,
select=None,
expand=None):
return client.get_bucket_task_board_format(user_id=user_id,
planner_task_id=planner_task_id,
select=select,
expand=expand)
def planner_user_planner_task_show_detail(client,
user_id,
planner_task_id,
select=None,
expand=None):
return client.get_details(user_id=user_id,
planner_task_id=planner_task_id,
select=select,
expand=expand)
def planner_user_planner_task_show_progress_task_board_format(client,
user_id,
planner_task_id,
select=None,
expand=None):
return client.get_progress_task_board_format(user_id=user_id,
planner_task_id=planner_task_id,
select=select,
expand=expand)
def planner_user_planner_task_update_assigned_to_task_board_format(client,
user_id,
planner_task_id,
id_=None,
order_hints_by_assignee=None,
unassigned_order_hint=None):
body = {}
body['id'] = id_
body['order_hints_by_assignee'] = order_hints_by_assignee
body['unassigned_order_hint'] = unassigned_order_hint
return client.update_assigned_to_task_board_format(user_id=user_id,
planner_task_id=planner_task_id,
body=body)
def planner_user_planner_task_update_bucket_task_board_format(client,
user_id,
planner_task_id,
id_=None,
order_hint=None):
body = {}
body['id'] = id_
body['order_hint'] = order_hint
return client.update_bucket_task_board_format(user_id=user_id,
planner_task_id=planner_task_id,
body=body)
def planner_user_planner_task_update_detail(client,
user_id,
planner_task_id,
id_=None,
checklist=None,
description=None,
preview_type=None,
references=None):
body = {}
body['id'] = id_
body['checklist'] = checklist
body['description'] = description
body['preview_type'] = preview_type
body['references'] = references
return client.update_details(user_id=user_id,
planner_task_id=planner_task_id,
body=body)
def planner_user_planner_task_update_progress_task_board_format(client,
user_id,
planner_task_id,
id_=None,
order_hint=None):
body = {}
body['id'] = id_
body['order_hint'] = order_hint
return client.update_progress_task_board_format(user_id=user_id,
planner_task_id=planner_task_id,
body=body)
| 53.972544
| 110
| 0.422288
| 16,306
| 214,271
| 5.066356
| 0.007114
| 0.096959
| 0.063732
| 0.068089
| 0.992967
| 0.98873
| 0.986382
| 0.984457
| 0.976468
| 0.972316
| 0
| 0.000352
| 0.523253
| 214,271
| 3,969
| 111
| 53.986143
| 0.808356
| 0.002193
| 0
| 0.920259
| 0
| 0
| 0.071194
| 0.025007
| 0
| 0
| 0
| 0
| 0
| 1
| 0.057763
| false
| 0
| 0
| 0.036912
| 0.115526
| 0
| 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
|
1682a7be2337c1b4eb7b2b00ef83ea6214262534
| 6,221
|
py
|
Python
|
tensorflow-keras-mnist-demonstration.py
|
Xiaoyu-Xing/learning-machine-learning
|
92fc86a3b18b34d7d91e24d3e1693f27611cb08e
|
[
"MIT"
] | 2
|
2019-02-16T21:41:30.000Z
|
2019-02-17T17:43:42.000Z
|
tensorflow-keras-mnist-demonstration.py
|
Xiaoyu-Xing/learning-machine-learning
|
92fc86a3b18b34d7d91e24d3e1693f27611cb08e
|
[
"MIT"
] | null | null | null |
tensorflow-keras-mnist-demonstration.py
|
Xiaoyu-Xing/learning-machine-learning
|
92fc86a3b18b34d7d91e24d3e1693f27611cb08e
|
[
"MIT"
] | null | null | null |
import tensorflow as tf
import time
mnist = tf.keras.datasets.mnist
# Train 60000, test 10000
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# Normalize
x_train, x_test = x_train / 255.0, x_test / 255.0
# Naive NN with 3 layers, SGD 0.01 lr, cross entropy loss
def test1():
start = time.time()
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(512, activation=tf.nn.relu),
tf.keras.layers.Dense(128, activation=tf.nn.relu),
tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
sgd = tf.keras.optimizers.SGD(lr=0.01)
model.compile(optimizer=sgd,
validation_split=0.1,
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=64, epochs=20)
result = model.evaluate(x_test, y_test)
end = time.time()
print(result, end - start)
# NN with 3 layers, Adam 0.01 lr, cross entropy loss
def test2():
start = time.time()
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(512, activation=tf.nn.relu),
tf.keras.layers.Dense(128, activation=tf.nn.relu),
tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
print(model)
adam = tf.keras.optimizers.Adam(lr=0.01)
model.compile(optimizer=adam,
validation_split=0.1,
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=64, epochs=2)
result = model.evaluate(x_test, y_test)
end = time.time()
print(result, end - start)
# Compare to test1, config changed to: lr = 0.1, epochs = 5
def test3():
start = time.time()
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(512, activation=tf.nn.relu),
tf.keras.layers.Dense(128, activation=tf.nn.relu),
tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
sgd = tf.keras.optimizers.SGD(lr=0.1)
model.compile(optimizer=sgd,
validation_split=0.1,
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=64, epochs=5)
result = model.evaluate(x_test, y_test)
end = time.time()
print(result, end - start)
# Reduce layers: only one hidden layer
def test4():
start = time.time()
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(128, activation=tf.nn.relu),
tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
sgd = tf.keras.optimizers.SGD(lr=0.1)
model.compile(optimizer=sgd,
validation_split=0.1,
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=64, epochs=5)
result = model.evaluate(x_test, y_test)
end = time.time()
print(result, end - start)
# Change hidden layer to 8 nodes
def test5():
start = time.time()
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(8, activation=tf.nn.relu),
tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
sgd = tf.keras.optimizers.SGD(lr=0.1)
model.compile(optimizer=sgd,
validation_split=0.1,
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=64, epochs=5)
result = model.evaluate(x_test, y_test)
end = time.time()
print(result, end - start)
# No hidden layer
def test6():
start = time.time()
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
sgd = tf.keras.optimizers.SGD(lr=0.1)
model.compile(optimizer=sgd,
validation_split=0.1,
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=64, epochs=5)
result = model.evaluate(x_test, y_test)
end = time.time()
print(result, end - start)
# Try to overfit it.
def test7():
start = time.time()
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(2048, activation=tf.nn.relu),
tf.keras.layers.Dense(4096, activation=tf.nn.relu),
tf.keras.layers.Dense(10240, activation=tf.nn.relu),
tf.keras.layers.Dense(4096, activation=tf.nn.relu),
tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
sgd = tf.keras.optimizers.SGD(lr=0.1)
model.compile(optimizer=sgd,
validation_split=0.1,
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=64, epochs=5)
result = model.evaluate(x_test, y_test)
end = time.time()
print(result, end - start)
# Add regularizations.
def test8():
start = time.time()
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(2048, activation=tf.nn.relu,
kernel_regularizer=tf.keras.regularizers.l2(0.01)),
tf.keras.layers.Dense(4096, activation=tf.nn.relu,
kernel_regularizer=tf.keras.regularizers.l2(0.01)),
tf.keras.layers.Dense(10240, activation=tf.nn.relu,
kernel_regularizer=tf.keras.regularizers.l2(0.01)),
tf.keras.layers.Dense(4096, activation=tf.nn.relu,
kernel_regularizer=tf.keras.regularizers.l2(0.01)),
tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
sgd = tf.keras.optimizers.SGD(lr=0.1)
model.compile(optimizer=sgd,
validation_split=0.1,
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=64, epochs=5)
result = model.evaluate(x_test, y_test)
end = time.time()
print(result, end - start)
test1()
| 34.949438
| 81
| 0.618229
| 833
| 6,221
| 4.52461
| 0.120048
| 0.098435
| 0.110374
| 0.114619
| 0.885381
| 0.885381
| 0.877952
| 0.865216
| 0.865216
| 0.862563
| 0
| 0.039804
| 0.244816
| 6,221
| 177
| 82
| 35.146893
| 0.762452
| 0.05176
| 0
| 0.829932
| 0
| 0
| 0.052989
| 0.04212
| 0
| 0
| 0
| 0
| 0
| 1
| 0.054422
| false
| 0
| 0.013605
| 0
| 0.068027
| 0.061224
| 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
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
169f25a6eaf477c587eec04cd7cb0c755def08e1
| 15,920
|
py
|
Python
|
alad/svhn_utilities.py
|
jzhao23/Adversarially-Learned-Anomaly-Detection
|
d01ce7fb15265d36de7550c92dbacbbb071fb9d2
|
[
"MIT"
] | null | null | null |
alad/svhn_utilities.py
|
jzhao23/Adversarially-Learned-Anomaly-Detection
|
d01ce7fb15265d36de7550c92dbacbbb071fb9d2
|
[
"MIT"
] | null | null | null |
alad/svhn_utilities.py
|
jzhao23/Adversarially-Learned-Anomaly-Detection
|
d01ce7fb15265d36de7550c92dbacbbb071fb9d2
|
[
"MIT"
] | null | null | null |
"""
CIFAR10 ALAD architecture.
Generator (decoder), encoder and discriminator.
"""
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
from utils import sn
learning_rate = 0.0002
batch_size = 32
latent_dim = 100
init_kernel = tf.random_normal_initializer(mean=0.0, stddev=0.01)
def leakyReLu(x, alpha=0.2, name=None):
if name:
with tf.variable_scope(name):
return tf.nn.relu(x) - (alpha * tf.nn.relu(-x))
else:
return tf.nn.relu(x) - (alpha * tf.nn.relu(-x))
def encoder(x_inp, is_training=False, getter=None, reuse=False,
do_spectral_norm=True):
""" Encoder architecture in tensorflow
Maps the data into the latent space
Args:
x_inp (tensor): input data for the encoder.
is_training (bool): for batch norms and dropouts
getter: for exponential moving average during inference
reuse (bool): sharing variables or not
Returns:
net (tensor): last activation layer of the encoder
"""
layers = sn if do_spectral_norm else tf.layers
with tf.variable_scope('encoder', reuse=reuse, custom_getter=getter):
x_inp = tf.reshape(x_inp, [-1, 32, 32, 3])
name_net = 'layer_1'
with tf.variable_scope(name_net):
net = layers.conv2d(x_inp,
128,
kernel_size=4,
padding='SAME',
strides=2,
kernel_initializer=init_kernel,
name='conv')
net = tf.layers.batch_normalization(net,
training=is_training)
net = leakyReLu(net, name='leaky_relu')
name_net = 'layer_2'
with tf.variable_scope(name_net):
net = layers.conv2d(net,
256,
kernel_size=4,
padding='SAME',
strides=2,
kernel_initializer=init_kernel,
name='conv')
net = tf.layers.batch_normalization(net,
training=is_training)
net = leakyReLu(net, name='leaky_relu')
name_net = 'layer_3'
with tf.variable_scope(name_net):
net = layers.conv2d(net,
512,
kernel_size=4,
padding='SAME',
strides=2,
kernel_initializer=init_kernel,
name='conv')
net = tf.layers.batch_normalization(net,
training=is_training)
net = leakyReLu(net, name='leaky_relu')
name_net = 'layer_4'
with tf.variable_scope(name_net):
net = tf.layers.conv2d(net,
latent_dim,
kernel_size=4,
strides=1,
padding='VALID',
kernel_initializer=init_kernel,
name='conv')
net = tf.squeeze(net, [1, 2])
return net
def decoder(z_inp, is_training=False, getter=None, reuse=False):
""" Generator architecture in tensorflow
Generates data from the latent space
Args:
z_inp (tensor): input variable in the latent space
is_training (bool): for batch norms and dropouts
getter: for exponential moving average during inference
reuse (bool): sharing variables or not
Returns:
net (tensor): last activation layer of the generator
"""
with tf.variable_scope('generator', reuse=reuse, custom_getter=getter):
net = tf.reshape(z_inp, [-1, 1, 1, latent_dim])
name_net = 'layer_1'
with tf.variable_scope(name_net):
net = tf.layers.conv2d_transpose(net,
filters=512,
kernel_size=4,
strides=2,
padding='VALID',
kernel_initializer=init_kernel,
name='tconv1')
net = tf.layers.batch_normalization(net,
training=is_training,
name='tconv1/batch_normalization')
net = tf.nn.relu(net, name='tconv1/relu')
name_net = 'layer_2'
with tf.variable_scope(name_net):
net = tf.layers.conv2d_transpose(net,
filters=256,
kernel_size=4,
strides=2,
padding='SAME',
kernel_initializer=init_kernel,
name='tconv2')
net = tf.layers.batch_normalization(net,
training=is_training,
name='tconv2/batch_normalization')
net = tf.nn.relu(net, name='tconv2/relu')
name_net = 'layer_3'
with tf.variable_scope(name_net):
net = tf.layers.conv2d_transpose(net,
filters=128,
kernel_size=4,
strides=2,
padding='SAME',
kernel_initializer=init_kernel,
name='tconv3')
net = tf.layers.batch_normalization(net,
training=is_training,
name='tconv3/batch_normalization')
net = tf.nn.relu(net, name='tconv3/relu')
name_net = 'layer_4'
with tf.variable_scope(name_net):
net = tf.layers.conv2d_transpose(net,
filters=3,
kernel_size=4,
strides=2,
padding='SAME',
kernel_initializer=init_kernel,
name='tconv4')
net = tf.tanh(net, name='tconv4/tanh')
return net
def discriminator_xz(x_inp, z_inp, is_training=False, getter=None, reuse=False,
do_spectral_norm=True):
""" Discriminator architecture in tensorflow
Discriminates between pairs (E(x), x) and (z, G(z))
Args:
x_inp (tensor): input data for the discriminator.
z_inp (tensor): input variable in the latent space
is_training (bool): for batch norms and dropouts
getter: for exponential moving average during inference
reuse (bool): sharing variables or not
Returns:
logits (tensor): last activation layer of the discriminator (shape 1)
intermediate_layer (tensor): intermediate layer for feature matching
"""
layers = sn if do_spectral_norm else tf.layers
with tf.variable_scope('discriminator_xz', reuse=reuse, custom_getter=getter):
name_net = 'x_layer_1'
with tf.variable_scope(name_net):
x = layers.conv2d(x_inp,
filters=128,
kernel_size=4,
strides=2,
padding='SAME',
kernel_initializer=init_kernel,
name='conv1')
x = leakyReLu(x, 0.2, name='conv1/leaky_relu')
name_net = 'x_layer_2'
with tf.variable_scope(name_net):
x = layers.conv2d(x,
filters=256,
kernel_size=4,
strides=2,
padding='SAME',
kernel_initializer=init_kernel,
name='conv2')
x = tf.layers.batch_normalization(x,
training=is_training,
name='conv2/batch_normalization')
x = leakyReLu(x, 0.2, name='conv2/leaky_relu')
name_net = 'x_layer_3'
with tf.variable_scope(name_net):
x = layers.conv2d(x,
filters=512,
kernel_size=4,
strides=2,
padding='SAME',
kernel_initializer=init_kernel,
name='conv3')
x = tf.layers.batch_normalization(x,
training=is_training,
name='conv3/batch_normalization')
x = leakyReLu(x, 0.2, name='conv3/leaky_relu')
x = tf.reshape(x, [-1,1,1,512*4*4])
z = tf.reshape(z_inp, [-1, 1, 1, latent_dim])
name_net = 'z_layer_1'
with tf.variable_scope(name_net):
z = layers.conv2d(z,
filters=512,
kernel_size=1,
strides=1,
padding='SAME',
kernel_initializer=init_kernel,
name='conv')
z = leakyReLu(z)
z = tf.layers.dropout(z, rate=0.2, training=is_training,
name='dropout')
name_net = 'z_layer_2'
with tf.variable_scope(name_net):
z = layers.conv2d(z,
filters=512,
kernel_size=1,
strides=1,
padding='SAME',
kernel_initializer=init_kernel,
name='conv')
z = leakyReLu(z)
z = tf.layers.dropout(z, rate=0.2, training=is_training,
name='dropout')
y = tf.concat([x, z], axis=-1)
name_net = 'y_layer_1'
with tf.variable_scope(name_net):
y = layers.conv2d(y,
filters=1024,
kernel_size=1,
strides=1,
padding='SAME',
kernel_initializer=init_kernel,
name='conv')
y = leakyReLu(y)
y = tf.layers.dropout(y, rate=0.2, training=is_training,
name='dropout')
intermediate_layer = y
name_net = 'y_layer_2'
with tf.variable_scope(name_net):
y = tf.layers.conv2d(y,
filters=1,
kernel_size=1,
strides=1,
padding='SAME',
kernel_initializer=init_kernel,
name='conv')
logits = tf.squeeze(y)
return logits, intermediate_layer
def discriminator_xx(x, rec_x, is_training=False, getter=None, reuse=False,
do_spectral_norm=True):
""" Discriminator architecture in tensorflow
Discriminates between (x,x) and (x,rec_x)
Args:
x (tensor): input from the data space
rec_x (tensor): reconstructed data
is_training (bool): for batch norms and dropouts
getter: for exponential moving average during inference
reuse (bool): sharing variables or not
Returns:
logits (tensor): last activation layer of the discriminator
intermediate_layer (tensor): intermediate layer for feature matching
"""
layers = sn if do_spectral_norm else tf.layers
with tf.variable_scope('discriminator_xx', reuse=reuse, custom_getter=getter):
net = tf.concat([x, rec_x], axis=1)
name_net = 'layer_1'
with tf.variable_scope(name_net):
net = layers.conv2d(net,
filters=64,
kernel_size=5,
strides=2,
padding='SAME',
kernel_initializer=init_kernel,
name='conv1')
net = leakyReLu(net, 0.2, name='conv1/leaky_relu')
net = tf.layers.dropout(net, rate=0.2, training=is_training,
name='dropout')
with tf.variable_scope(name_net, reuse=True):
weights = tf.get_variable('conv1/kernel')
name_net = 'layer_2'
with tf.variable_scope(name_net):
net = layers.conv2d(net,
filters=128,
kernel_size=5,
strides=2,
padding='SAME',
kernel_initializer=init_kernel,
name='conv2')
net = leakyReLu(net, 0.2, name='conv2/leaky_relu')
net = tf.layers.dropout(net, rate=0.2, training=is_training,
name='dropout')
net = tf.layers.flatten(net)
intermediate_layer = net
name_net = 'layer_3'
with tf.variable_scope(name_net):
net = tf.layers.dense(net,
units=1,
kernel_initializer=init_kernel,
name='fc')
logits = tf.squeeze(net)
return logits, intermediate_layer
def discriminator_zz(z, rec_z, is_training=False, getter=None, reuse=False,
do_spectral_norm=True):
""" Discriminator architecture in tensorflow
Discriminates between (z,z) and (z,rec_z)
Args:
z (tensor): input from the latent space
rec_z (tensor): reconstructed data
is_training (bool): for batch norms and dropouts
getter: for exponential moving average during inference
reuse (bool): sharing variables or not
Returns:
logits (tensor): last activation layer of the discriminator
intermediate_layer (tensor): intermediate layer for feature matching
"""
layers = sn if do_spectral_norm else tf.layers
with tf.variable_scope('discriminator_zz', reuse=reuse,
custom_getter=getter):
y = tf.concat([z, rec_z], axis=-1)
name_net = 'y_layer_1'
with tf.variable_scope(name_net):
y = layers.dense(y, units=64, kernel_initializer=init_kernel,
name='fc')
y = leakyReLu(y)
y = tf.layers.dropout(y, rate=0.2, training=is_training,
name='dropout')
name_net = 'y_layer_2'
with tf.variable_scope(name_net):
y = layers.dense(y,
units=32,
kernel_initializer=init_kernel,
name='fc')
y = leakyReLu(y)
y = tf.layers.dropout(y, rate=0.2, training=is_training,
name='dropout')
intermediate_layer = y
name_net = 'y_layer_3'
with tf.variable_scope(name_net):
y = tf.layers.dense(y,
units=1,
kernel_initializer=init_kernel,
name='fc')
logits = tf.squeeze(y)
return logits, intermediate_layer
| 37.370892
| 82
| 0.468467
| 1,564
| 15,920
| 4.588875
| 0.095269
| 0.04194
| 0.054619
| 0.074126
| 0.853421
| 0.838094
| 0.810227
| 0.794622
| 0.736937
| 0.725373
| 0
| 0.024794
| 0.450251
| 15,920
| 426
| 83
| 37.370892
| 0.795247
| 0.141834
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| 0.72242
| 0
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| 0.054665
| 0.009546
| 0
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| 0
| 0
| 0
| 1
| 0.021352
| false
| 0
| 0.007117
| 0
| 0.053381
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
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| 0
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
16a6913c3581e2f794a3996d00e10562614169eb
| 158
|
py
|
Python
|
src/monitoring/notifications/__init__.py
|
gkovacs81/argus_server
|
97ebf705ed3e61a69bd561faf495e2c19bda510d
|
[
"MIT"
] | null | null | null |
src/monitoring/notifications/__init__.py
|
gkovacs81/argus_server
|
97ebf705ed3e61a69bd561faf495e2c19bda510d
|
[
"MIT"
] | 3
|
2021-06-02T04:07:35.000Z
|
2021-12-27T20:21:46.000Z
|
src/monitoring/notifications/__init__.py
|
gkovacs81/argus_server
|
97ebf705ed3e61a69bd561faf495e2c19bda510d
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
# @Author: Gábor Kovács
# @Date: 2021-02-25 20:08:34
# @Last Modified by: Gábor Kovács
# @Last Modified time: 2021-02-25 20:08:35
| 26.333333
| 42
| 0.632911
| 27
| 158
| 3.703704
| 0.666667
| 0.22
| 0.16
| 0.2
| 0.24
| 0
| 0
| 0
| 0
| 0
| 0
| 0.224806
| 0.183544
| 158
| 5
| 43
| 31.6
| 0.550388
| 0.93038
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 0
| 0
| null | 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
bc56c2f2d9b6ad38f3a61ca4dd5ca6f490c4d6fd
| 6,068
|
py
|
Python
|
Whop.py
|
xliquid808/WhoopSongInfoGrabber
|
f7a6fa5c929182094e1a5433b88c956d99ff870e
|
[
"Apache-2.0"
] | null | null | null |
Whop.py
|
xliquid808/WhoopSongInfoGrabber
|
f7a6fa5c929182094e1a5433b88c956d99ff870e
|
[
"Apache-2.0"
] | null | null | null |
Whop.py
|
xliquid808/WhoopSongInfoGrabber
|
f7a6fa5c929182094e1a5433b88c956d99ff870e
|
[
"Apache-2.0"
] | null | null | null |
import glob
import os
import time
from mp3_tagger import MP3File
def search_for_song(filename):
import mp3_tagger
from selenium import webdriver
webdriver = webdriver.Chrome()
webdriver.get("https://genius.com/search?q=" + file_query[0])
time.sleep(5)
webdriver.find_element_by_xpath('//*[@id="onetrust-accept-btn-handler"]').click()
correct = input("Is the displayed song correct? (Y/n) ")
if correct == "Y":
try:
webdriver.find_element_by_xpath("/html/body/routable-page/ng-outlet/search-results-page/div/div[2]/div[1]/div[2]/search-result-section/div/div[2]/search-result-items/div[1]/search-result-item/div/mini-song-card/a").click()
song_name = webdriver.find_element_by_xpath('//*[@id="application"]/main/div[1]/div[3]/div/h1').text
artist = webdriver.find_element_by_xpath('//*[@id="application"]/main/div[1]/div[3]/div/a').text
try:
if webdriver.find_element_by_xpath('//*[@id="application"]/main/div[1]/div[4]/div/div[1]/div[1]/p').text == "Featuring":
featuring_artist = webdriver.find_element_by_xpath('//*[@id="application"]/main/div[1]/div[4]/div/div[1]/div[1]/a').text
song_name = song_name + " (feat. " + featuring_artist + ")"
print(song_name + " by " + artist)
except:
print(song_name + " by " + artist)
mp3 = MP3File("songs/" + filename[0] + ".mp3")
mp3.song = song_name
mp3.artist = artist
mp3.save()
print("Set tags!")
except:
time.sleep(3)
song_name = webdriver.find_element_by_xpath('/html/body/routable-page/ng-outlet/song-page/div/div/header-with-cover-art/div/div/div[1]/div[2]/div/h1').text
artist = webdriver.find_element_by_xpath('/html/body/routable-page/ng-outlet/song-page/div/div/header-with-cover-art/div/div/div[1]/div[2]/div/h2/span/a').text
try:
if webdriver.find_element_by_xpath('/html/body/routable-page/ng-outlet/song-page/div/div/header-with-cover-art/div/div/div[1]/div[2]/div/ng-transclude/metadata/h3[1]/expandable-list/div/span[1]').text == "Featuring":
featuring_artist = webdriver.find_element_by_xpath('/html/body/routable-page/ng-outlet/song-page/div/div/header-with-cover-art/div/div/div[1]/div[2]/div/ng-transclude/metadata/h3[1]/expandable-list/div/span[2]/span/a').text
song_name = song_name + " (feat. " + featuring_artist + ")"
print(song_name + " by " + artist)
except:
print(song_name + " by " + artist)
mp3 = MP3File("songs/" + filename[0] + ".mp3")
mp3.song = song_name
mp3.artist = artist
mp3.save()
print("Set tags!")
else:
song_query_manual = input("Please enter your query: ")
webdriver.get("https://genius.com/search?q=" + song_query_manual)
time.sleep(5)
webdriver.find_element_by_xpath("/html/body/routable-page/ng-outlet/search-results-page/div/div[2]/div[1]/div[2]/search-result-section/div/div[2]/search-result-items/div[1]/search-result-item/div/mini-song-card/a").click()
try:
song_name = webdriver.find_element_by_xpath('//*[@id="application"]/main/div[1]/div[3]/div/h1').text
artist = webdriver.find_element_by_xpath('//*[@id="application"]/main/div[1]/div[3]/div/a').text
try:
if webdriver.find_element_by_xpath('//*[@id="application"]/main/div[1]/div[4]/div/div[1]/div[1]/p').text == "Featuring":
featuring_artist = webdriver.find_element_by_xpath('//*[@id="application"]/main/div[1]/div[4]/div/div[1]/div[1]/a').text
song_name = song_name + " (feat. " + featuring_artist + ")"
print(song_name + " by " + artist)
except:
print(song_name + " by " + artist)
mp3 = MP3File("songs/" + filename[0] + ".mp3")
mp3.song = song_name
mp3.artist = artist
mp3.save()
print("Set tags!")
except:
time.sleep(3)
song_name = webdriver.find_element_by_xpath('/html/body/routable-page/ng-outlet/song-page/div/div/header-with-cover-art/div/div/div[1]/div[2]/div/h1').text
artist = webdriver.find_element_by_xpath('/html/body/routable-page/ng-outlet/song-page/div/div/header-with-cover-art/div/div/div[1]/div[2]/div/h2/span/a').text
try:
try:
if webdriver.find_element_by_xpath('/html/body/routable-page/ng-outlet/song-page/div/div/header-with-cover-art/div/div/div[1]/div[2]/div/ng-transclude/metadata/h3[1]/expandable-list/div/span[1]').text == "Featuring":
featuring_artist = webdriver.find_element_by_xpath('/html/body/routable-page/ng-outlet/song-page/div/div/header-with-cover-art/div/div/div[1]/div[2]/div/ng-transclude/metadata/h3[1]/expandable-list/div/span[2]/span/a').text
song_name = song_name + " (feat. " + featuring_artist + ")"
print(song_name + " by " + artist)
except:
print(song_name + " by " + artist)
mp3 = MP3File("songs/" + filename[0] + ".mp3")
mp3.song = song_name
mp3.artist = artist
mp3.save()
print("Set tags!")
except:
print(song_name + " by " + artist)
mp3 = MP3File("songs/" + filename[0] + ".mp3")
mp3.song = song_name
mp3.artist = artist
mp3.save()
print("Set tags!")
for file_path in glob.iglob(r'songs\*.mp3'):
file_query = os.path.basename(file_path)
print(file_query)
file_query = file_query.split(".mp3")
print(file_query[0])
search_for_song(file_query)
| 45.62406
| 247
| 0.582564
| 815
| 6,068
| 4.202454
| 0.123926
| 0.056058
| 0.044964
| 0.122044
| 0.872701
| 0.872701
| 0.872117
| 0.852847
| 0.839124
| 0.839124
| 0
| 0.025193
| 0.254285
| 6,068
| 132
| 248
| 45.969697
| 0.731713
| 0
| 0
| 0.778947
| 0
| 0.147368
| 0.368326
| 0.312788
| 0
| 0
| 0
| 0
| 0
| 1
| 0.010526
| false
| 0
| 0.063158
| 0
| 0.073684
| 0.168421
| 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
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| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
bc6d333b2480b8c74cc013e8fb1025b883c44024
| 8,056
|
py
|
Python
|
day22_pong/draw_number.py
|
frnkvsk/python100days
|
70d607ca58a526f0d66544ed65405b2425718108
|
[
"Unlicense"
] | null | null | null |
day22_pong/draw_number.py
|
frnkvsk/python100days
|
70d607ca58a526f0d66544ed65405b2425718108
|
[
"Unlicense"
] | null | null | null |
day22_pong/draw_number.py
|
frnkvsk/python100days
|
70d607ca58a526f0d66544ed65405b2425718108
|
[
"Unlicense"
] | null | null | null |
from turtle import Turtle
class DrawScore:
def __init__(self, num, x, y):
self.segments = [[] for _ in range(5)]
self.create_segments(x, y)
self.draw_number(num)
def create_segments(self, x, y):
for i in range(0, 5):
for j in range(0, 3):
t = Turtle('square')
t.shapesize(stretch_wid=.5, stretch_len=.5)
t.goto(x + (j * 10), y - (i * 10))
self.segments[i].append(t)
def draw_number(self, num):
if num == 0:
self.segments[0][0].color('white')
self.segments[0][1].color('white')
self.segments[0][2].color('white')
self.segments[1][0].color('white')
self.segments[1][1].color('black')
self.segments[1][2].color('white')
self.segments[2][0].color('white')
self.segments[2][1].color('black')
self.segments[2][2].color('white')
self.segments[3][0].color('white')
self.segments[3][1].color('black')
self.segments[3][2].color('white')
self.segments[4][0].color('white')
self.segments[4][1].color('white')
self.segments[4][2].color('white')
elif num == 1:
self.segments[0][0].color('black')
self.segments[0][1].color('black')
self.segments[0][2].color('white')
self.segments[1][0].color('black')
self.segments[1][1].color('black')
self.segments[1][2].color('white')
self.segments[2][0].color('black')
self.segments[2][1].color('black')
self.segments[2][2].color('white')
self.segments[3][0].color('black')
self.segments[3][1].color('black')
self.segments[3][2].color('white')
self.segments[4][0].color('black')
self.segments[4][1].color('black')
self.segments[4][2].color('white')
elif num == 2:
self.segments[0][0].color('white')
self.segments[0][1].color('white')
self.segments[0][2].color('white')
self.segments[1][0].color('black')
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0
| 10
|
bc7ab283bc3faf06f35ab56f7c809e433a119308
| 48,192
|
py
|
Python
|
Segmentation_UGSCNN/Projected_ResNet/MyDataLoader.py
|
Abdulah-Fawaz/Benchmarking-Surface-DL
|
9693379f26d57f9aabf28b973f40a9f6f627d26f
|
[
"MIT"
] | 2
|
2021-12-04T07:04:56.000Z
|
2021-12-13T16:28:50.000Z
|
Segmentation_UGSCNN/Projected_ResNet/MyDataLoader.py
|
Abdulah-Fawaz/Benchmarking-Surface-DL
|
9693379f26d57f9aabf28b973f40a9f6f627d26f
|
[
"MIT"
] | 1
|
2021-12-21T09:36:11.000Z
|
2022-01-25T10:26:43.000Z
|
Segmentation_UGSCNN/Projected_ResNet/MyDataLoader.py
|
Abdulah-Fawaz/Benchmarking-Surface-DL
|
9693379f26d57f9aabf28b973f40a9f6f627d26f
|
[
"MIT"
] | 1
|
2022-02-27T17:38:19.000Z
|
2022-02-27T17:38:19.000Z
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Sep 15 17:29:27 2020
@author: fa19
"""
import nibabel as nb
import numpy as np
import torch
import random
import torch.nn.functional as F
means = np.load('../dHCP_mean_seg.npy')
std = np.load('../dHCP_std_seg.npy')
means = torch.from_numpy(means)
stds = torch.from_numpy(std)
### SEGMENTATION FILES ###
unwarped_files_directory = '/data/Data/benchmarking/fsaverage_32k_30_01_2021/ico6'
unwarped_labels_directory = '/data/Data/dHCP/M-CRIB-S/template_space/ico6L'
warped_files_directory = '/data/Data/benchmarking/fsaverage_32k_30_01_2021/ico6_warped'
warped_labels_directory = '/data/Data/dHCP/M-CRIB-S/template_space/ico6L_warp'
#unwarped_files_directory='/data/Data/derivatives_native_ico6_seg/features'
#warped_files_directory='/data/Data/derivatives_native_ico6_seg/features_warp'
#unwarped_labels_directory ='/data/Data/derivatives_native_ico6_seg/labels'
#warped_labels_directory ='/data/Data/derivatives_native_ico6_seg/labels_warp'
#means = torch.Tensor([0.0345])
#stds = torch.Tensor([0.1906])
test_rotation_arr = np.load('../GraphMethods/data/unseen_rots.npy').astype(int)
rotation_arr = np.load('../GraphMethods/data/rotations_array.npy').astype(int)
reversing_arr = np.load('../GraphMethods/data/reversing_arr.npy')
#smoothing_arr = [[0, 10], [-12,34], [11,200], [-1,1]]
# minima and maxima defines as mean +/- 4*std
lower_bound = torch.Tensor([ -0.2819, -0.7279, -0.5199, -16.1347])
upper_bound = torch.Tensor([ 2.5354, 0.7970, 2.5550, 16.2459])
minima = torch.Tensor([ 0.0000, -0.7279, -0.3271, -14.8748])
maxima = torch.Tensor([ 2.5354, 0.7970, 2.5550, 12.1209])
xy_points = np.load('equirectangular_ico_6_points.npy')
xy_points[:,0] = (xy_points[:,0] + 0.1)%1
grid = np.load('grid_170_square.npy')
grid_x, grid_y = np.meshgrid(np.linspace(0.02, 0.98, 170), np.linspace(0.02, 0.98, 170))
grid[:,0] = grid_x.flatten()
grid[:,1] = grid_y.flatten()
from scipy.interpolate import griddata
"""
unwarped_files_directory: the directory of all the files in input_arr. BOTH L and R
aarped_files_directory: the directory of all the warped files.
warped directory could be the same as unwarped directory
"""
class My_dHCP_Data(torch.utils.data.Dataset):
def __init__(self, input_arr, rotations = False,
number_of_warps = 0, parity_choice = 'left', smoothing = False, normalisation = None, sample_only = True, output_as_torch = True ):
"""
A Full Dataset for the dHCP Data. Can include warps, rotations and parity flips.
Fileanme style:
in the array: only 'sub-X-ses-Y'
but for the filenames themselves
Left = 'sub-X_ses-Y_L'
Right = 'sub-X_ses-Y_R'
if warped:
'sub-X_ses-Y_L_W1'
INPUT ARGS:
1. input_arr:
Numpy array size Nx2
FIRST index MUST be the filename (excluding directory AND L or R ) of MERGED nibabel files
LAST index must be the (float) label
(OPTIONAL) Middle index if size 3 (optional) is any confounding metadata (Also float, e.g scan age for predicting birth age)
2 . rotations - boolean: to add rotations or not to add rotations
3. number of warps to include - INT
NB WARPED AR INCLUDED AS FILENAME CHANGES. WARP NUMBER X IS WRITTEN AS filename_WX
NUMBER OF WARPS CANNOT EXCEED NUMBER OF WARPES PRESENT IN FILES
4. Particy Choice (JMPORTANT!) - defines left and right-ness
If: 'left'- will output ONLY LEFT
If: 'both' - will randomly choose L or R
If 'combined' - will output a combined array (left first), will be eventually read as a file with twice the number of input channels. as they will be stacked together
5. smoothing - boolean, will clip extremal values according to the smoothing_array
6. normalisation - str. Will normalise according to 'range', 'std' or 'None'
Range is from -1 to 1
Std is mean = 0, std = 1
7. output_as_torch - boolean:
outputs values as torch Tensors if you want (usually yes)
"""
self.input_arr = input_arr
self.image_files = input_arr[:,0]
self.label = input_arr[:,-1]
self.sample_only = sample_only
self.rotations = rotations
self.number_of_warps = number_of_warps
self.parity = parity_choice
self.smoothing = smoothing
self.normalisation = normalisation
self.output_as_torch = output_as_torch
if self.number_of_warps != 0 and self.number_of_warps != None:
self.directory = warped_files_directory
else:
self.directory = unwarped_files_directory
def __len__(self):
L = len(self.input_arr)
if self.number_of_warps !=0:
if self.sample_only == False:
L = L*self.number_of_warps
return L
def __test_input_params__(self):
assert self.input_arr.shape[1] >=2, 'check your input array is a nunpy array of files and labels'
assert type(self.number_of_warps) == int, "number of warps must be an in integer (can be 0)"
assert self.parity in ['left', 'both', 'combined'], "parity choice must be either left, combined or both"
if self.number_of_rotations != 0:
assert self.rotation_arr != None,'Must specify a rotation file containing rotation vertex ids if rotations are non-zero'
assert self.rotations == bool, 'rotations must be boolean'
assert self.normalisation in [None, 'none', 'std', 'range'], 'Normalisation must be either std or range'
def __genfilename__(self,idx):
"""
gets the appropriate file based on input parameters on PARITY and on WARPS
"""
# grab raw filename
if self.number_of_warps != 0:
warp_choice = str(1 + idx//len(self.input_arr))
idx = idx%len(self.input_arr)
raw_filename = self.image_files[idx]
# add parity to it. IN THE FORM OF A LIST! If requries both will output a list of length 2
filename = []
if self.parity == 'left':
filename.append(raw_filename + '_L')
elif self.parity == 'both':
coin_flip = random.randint(0,1)
if coin_flip == 0:
filename.append(raw_filename + '_L')
elif coin_flip == 1:
filename.append(raw_filename + '_R')
elif self.parity == 'combined':
filename.append(raw_filename + '_L')
filename.append(raw_filename+'_R')
# filename is now a list of the correct filenames.
# now add warps if required
if self.number_of_warps != 0:
filename = [s + '_W'+warp_choice for s in filename ]
return filename
def __getitem__(self, idx):
"""
First load the images and collect them as numpy arrays
then collect the label
then collect the metadata (though might be None)
"""
filename = self.__genfilename__(idx)
image_gifti = [nb.load(self.directory + '/'+individual_filename+'.shape.gii').darrays for individual_filename in filename]
image = []
if self.rotations == True:
rotation_choice = random.randint(0, len(rotation_arr)-1)
if rotation_choice !=0:
for file in image_gifti:
image.extend(item.data[rotation_arr[rotation_choice]] for item in file)
else:
for file in image_gifti:
image.extend(item.data for item in file)
else:
for file in image_gifti:
image.extend(item.data for item in file)
### labels
if self.number_of_warps != 0:
idx = idx%len(self.input_arr)
label = self.label[idx]
###### metadata grabbing if necessary
if self.input_arr.shape[1] > 2:
self.metadata = input_arr[:,1:-1]
else:
self.metadata = None
if self.smoothing != False:
for i in range(len(image)):
image[i] = np.clip(image[i], lower_bound[i%len(lower_bound)].item(), upper_bound[i%len(upper_bound)].item())
# torchify if required:
if self.normalisation != None:
if self.normalisation == 'std':
for i in range(len(image)):
image[i] = ( image[i] - means[i%len(means)].item( )) / stds[i%len(stds)].item()
elif self.normalisation == 'range':
for i in range(len(image)):
image[i] = image[i] - minima[i%len(minima)].item()
image[i] = image[i] / (maxima[i%len(maxima)].item()- minima[i%len(minima)].item())
if self.output_as_torch:
image = torch.Tensor( image )
label = torch.Tensor( [label] )
if self.metadata != None:
metadata = torch.Tensor( [self.metadata] )
if self.metadata != None:
sample = {'image': image, 'metadata' : self.metadata, 'label': label}
else:
sample = {'image': image,'label': label}
return sample
"""
examples:
file_arr = np.load('/home/fa19/Documents/dHCP_Data_merged/scan_age_regression_full_shuffled_18-08-2020.npy', allow_pickle = True)
My_dHCP_Data(file_arr, rotations=True, smoothing = True, parity_choice='both')
My_dHCP_Data(file_arr, rotations=True, smoothing = True, parity_choice='combined')
My_dHCP_Data(file_arr, number_of_warps = 5, rotations=True, smoothing = False, parity_choice='left')
"""
def get_global_mean_and_std_from_ds(ds):
nb_samples = 0
num_channels = ds[0]['image'].size(0)
channel_mean = torch.zeros(num_channels)
channel_var = torch.zeros(num_channels)
#channel_std = torch.Tensor([0., 0., 0.])
for samples in ds:
# scale image to be between 0 and 1
images = samples['image']
channel_mean += images.mean(1)
channel_var += images.var(1)
nb_samples += 1
channel_mean /= nb_samples
channel_var /= nb_samples
channel_std = np.sqrt(channel_var)
return channel_mean, channel_std
def get_global_min_and_max_from_ds(ds):
num_channels = ds[0]['image'].size(0)
running_minima = torch.ones(num_channels)*100
running_maxima = torch.ones(num_channels)*-100
for samples in ds:
# scale image to be between 0 and 1
images = samples['image']
image_minima = torch.min(images, dim=1)[0]
image_maxima = torch.max(images, dim=1)[0]
for i in range(len(image_minima)):
if image_minima[i] < running_minima[i]:
running_minima[i] = image_minima[i].item()
if image_maxima[i] > running_maxima[i]:
running_maxima[i] = image_maxima[i].item()
return running_minima, running_maxima
class My_Projected_dHCP_Data(torch.utils.data.Dataset):
def __init__(self, input_arr, rotations = False,
number_of_warps = 0, parity_choice = 'left', smoothing = False, normalisation = None, projected =False, sample_only = True, output_as_torch = True ):
"""
A Full Dataset for the dHCP Data. Can include warps, rotations and parity flips.
Fileanme style:
in the array: only 'sub-X-ses-Y'
but for the filenames themselves
Left = 'sub-X_ses-Y_L'
Right = 'sub-X_ses-Y_R'
if warped:
'sub-X_ses-Y_L_W1'
INPUT ARGS:
1. input_arr:
Numpy array size Nx2
FIRST index MUST be the filename (excluding directory AND L or R ) of MERGED nibabel files
LAST index must be the (float) label
(OPTIONAL) Middle index if size 3 (optional) is any confounding metadata (Also float, e.g scan age for predicting birth age)
2 . rotations - boolean: to add rotations or not to add rotations
3. number of warps to include - INT
NB WARPED AR INCLUDED AS FILENAME CHANGES. WARP NUMBER X IS WRITTEN AS filename_WX
NUMBER OF WARPS CANNOT EXCEED NUMBER OF WARPES PRESENT IN FILES
4. Particy Choice (JMPORTANT!) - defines left and right-ness
If: 'left'- will output ONLY LEFT
If: 'both' - will randomly choose L or R
If 'combined' - will output a combined array (left first), will be eventually read as a file with twice the number of input channels. as they will be stacked together
5. smoothing - boolean, will clip extremal values according to the smoothing_array
6. normalisation - str. Will normalise according to 'range', 'std' or 'None'
Range is from -1 to 1
Std is mean = 0, std = 1
7. output_as_torch - boolean:
outputs values as torch Tensors if you want (usually yes)
"""
self.input_arr = input_arr
self.image_files = input_arr[:,0]
self.label = input_arr[:,-1]
self.rotations = rotations
self.projected = projected
self.number_of_warps = number_of_warps
self.parity = parity_choice
self.smoothing = smoothing
self.normalisation = normalisation
self.sample_only = sample_only
self.output_as_torch = output_as_torch
if self.number_of_warps != 0 and self.number_of_warps != None:
self.directory = warped_files_directory
else:
self.directory = unwarped_files_directory
def __len__(self):
L = len(self.input_arr)
if self.number_of_warps !=0:
if self.sample_only == False:
L = L*self.number_of_warps
return L
def __test_input_params__(self):
assert self.input_arr.shape[1] >=2, 'check your input array is a nunpy array of files and labels'
assert type(self.number_of_warps) == int, "number of warps must be an in integer (can be 0)"
assert self.parity in ['left', 'both', 'combined'], "parity choice must be either left, combined or both"
if self.number_of_rotations != 0:
assert self.rotation_arr != None,'Must specify a rotation file containing rotation vertex ids if rotations are non-zero'
assert self.rotations == bool, 'rotations must be boolean'
assert self.normalisation in [None, 'none', 'std', 'range'], 'Normalisation must be either std or range'
def __genfilename__(self,idx):
"""
gets the appropriate file based on input parameters on PARITY and on WARPS
"""
# grab raw filename
raw_filename = self.image_files[idx]
# add parity to it. IN THE FORM OF A LIST! If requries both will output a list of length 2
filename = []
if self.parity == 'left':
filename.append(raw_filename + '_L')
elif self.parity == 'both':
coin_flip = random.randint(0,1)
if coin_flip == 0:
filename.append(raw_filename + '_L')
elif coin_flip == 1:
filename.append(raw_filename + '_R')
elif self.parity == 'combined':
filename.append(raw_filename + '_L')
filename.append(raw_filename+'_R')
# filename is now a list of the correct filenames.
# now add warps if required
if self.number_of_warps != 0:
warp_choice = str(random.randint(1,self.number_of_warps))
filename = [s + '_W'+warp_choice for s in filename ]
return filename
def __getitem__(self, idx):
"""
First load the images and collect them as numpy arrays
then collect the label
then collect the metadata (though might be None)
"""
filename = self.__genfilename__(idx)
image_gifti = [nb.load(self.directory + '/'+individual_filename+'.shape.gii').darrays for individual_filename in filename]
image = []
if self.rotations == True:
rotation_choice = random.randint(0, len(rotation_arr)-1)
if rotation_choice !=0:
for file in image_gifti:
image.extend(item.data[rotation_arr[rotation_choice]] for item in file)
else:
for file in image_gifti:
image.extend(item.data for item in file)
else:
for file in image_gifti:
image.extend(item.data for item in file)
### labels
if self.number_of_warps != 0:
idx = idx%len(self.input_arr)
label = self.label[idx]
###### metadata grabbing if necessary
if self.input_arr.shape[1] > 2:
self.metadata = input_arr[:,1:-1]
else:
self.metadata = None
if self.smoothing != False:
for i in range(len(image)):
image[i] = np.clip(image[i], lower_bound[i%len(lower_bound)].item(), upper_bound[i%len(upper_bound)].item())
# torchify if required:
if self.normalisation != None:
if self.normalisation == 'std':
for i in range(len(image)):
image[i] = ( image[i] - means[i%len(means)].item( )) / stds[i%len(stds)].item()
elif self.normalisation == 'range':
for i in range(len(image)):
image[i] = image[i] - minima[i%len(minima)].item()
image[i] = image[i] / (maxima[i%len(maxima)].item()- minima[i%len(minima)].item())
if self.output_as_torch:
image = torch.Tensor( image )
label = torch.Tensor( [label] )
if self.metadata != None:
metadata = torch.Tensor( [self.metadata] )
if self.projected == True:
image = griddata(xy_points, image.T, grid, 'linear')
image = torch.Tensor(image.reshape(170,170,4)).permute(2,0,1)
if self.metadata != None:
sample = {'image': image, 'metadata' : self.metadata, 'label': label}
else:
sample = {'image': image,'label': label}
return sample
class My_Projected_dHCP_Data_Segmentation(torch.utils.data.Dataset):
def __init__(self, input_arr, rotations = False,
number_of_warps = 0, parity_choice = 'left', smoothing = False, normalisation = None, projected =False, sample_only = True, output_as_torch = True ):
"""
A Full Dataset for the dHCP Data. Can include warps, rotations and parity flips.
Fileanme style:
in the array: only 'sub-X-ses-Y'
but for the filenames themselves
Left = 'sub-X_ses-Y_L'
Right = 'sub-X_ses-Y_R'
if warped:
'sub-X_ses-Y_L_W1'
INPUT ARGS:
1. input_arr:
Numpy array size Nx2
FIRST index MUST be the filename (excluding directory AND L or R ) of MERGED nibabel files
LAST index must be the (float) label
(OPTIONAL) Middle index if size 3 (optional) is any confounding metadata (Also float, e.g scan age for predicting birth age)
2 . rotations - boolean: to add rotations or not to add rotations
3. number of warps to include - INT
NB WARPED AR INCLUDED AS FILENAME CHANGES. WARP NUMBER X IS WRITTEN AS filename_WX
NUMBER OF WARPS CANNOT EXCEED NUMBER OF WARPES PRESENT IN FILES
4. Particy Choice (JMPORTANT!) - defines left and right-ness
If: 'left'- will output ONLY LEFT
If: 'both' - will randomly choose L or R
If 'combined' - will output a combined array (left first), will be eventually read as a file with twice the number of input channels. as they will be stacked together
5. smoothing - boolean, will clip extremal values according to the smoothing_array
6. normalisation - str. Will normalise according to 'range', 'std' or 'None'
Range is from -1 to 1
Std is mean = 0, std = 1
7. output_as_torch - boolean:
outputs values as torch Tensors if you want (usually yes)
"""
self.input_arr = input_arr
self.image_files = input_arr[:,0]
self.rotations = rotations
self.projected = projected
self.number_of_warps = number_of_warps
self.parity = parity_choice
self.smoothing = smoothing
self.normalisation = normalisation
self.sample_only = sample_only
self.output_as_torch = output_as_torch
if self.number_of_warps != 0 and self.number_of_warps != None:
self.directory = warped_files_directory
else:
self.directory = unwarped_files_directory
if self.number_of_warps != 0 and self.number_of_warps != None:
self.label_directory = warped_labels_directory
else:
self.label_directory = unwarped_labels_directory
def __len__(self):
L = len(self.input_arr)
if self.number_of_warps !=0:
if self.sample_only == False:
L = L*self.number_of_warps
return L
def __test_input_params__(self):
assert self.input_arr.shape[1] >=2, 'check your input array is a nunpy array of files and labels'
assert type(self.number_of_warps) == int, "number of warps must be an in integer (can be 0)"
assert self.parity in ['left', 'both', 'combined'], "parity choice must be either left, combined or both"
if self.number_of_rotations != 0:
assert self.rotation_arr != None,'Must specify a rotation file containing rotation vertex ids if rotations are non-zero'
assert self.rotations == bool, 'rotations must be boolean'
assert self.normalisation in [None, 'none', 'std', 'range'], 'Normalisation must be either std or range'
def __genfilename__(self,idx, right):
"""
gets the appropriate file based on input parameters on PARITY and on WARPS
"""
# grab raw filename
raw_filename = self.image_files[idx]
# add parity to it. IN THE FORM OF A LIST! If requries both will output a list of length 2
filename = []
if self.parity != 'combined':
if right == True:
filename.append(raw_filename + '_R')
else:
filename.append(raw_filename + '_L')
# if self.parity == 'left':
# filename.append(raw_filename + '_L')
#
# elif self.parity == 'both':
# coin_flip = random.randint(0,1)
# if coin_flip == 0:
# filename.append(raw_filename + '_L')
# elif coin_flip == 1:
# filename.append(raw_filename + '_R')
# right = True
if self.parity == 'combined':
filename.append(raw_filename + '_L')
filename.append(raw_filename+'_R')
# filename is now a list of the correct filenames.
# now add warps if required
if self.number_of_warps != 0:
warp_choice = str(random.randint(0,self.number_of_warps))
if warp_choice !='0':
filename = [s + '_W'+warp_choice for s in filename ]
return filename
def __getitem__(self, idx):
"""
First load the images and collect them as numpy arrays
then collect the label
then collect the metadata (though might be None)
"""
if self.parity == 'both':
T = self.__len__()//2
idx, right = idx % T, idx // T
filename = self.__genfilename__(idx, right)
else:
right = False
filename = self.__genfilename__(idx, right)
image_gifti = [nb.load(self.directory + '/'+individual_filename+'.shape.gii').darrays for individual_filename in filename]
label_gifti = [nb.load(self.label_directory + '/'+individual_filename+'.label.gii').darrays for individual_filename in filename]
image = []
label = []
if self.rotations == True:
rotation_choice = random.randint(0, len(rotation_arr)-1)
if rotation_choice !=0:
for file in image_gifti:
image.extend(item.data[rotation_arr[rotation_choice]] for item in file)
for file in label_gifti:
label.extend(item.data[rotation_arr[rotation_choice]] for item in file)
else:
for file in image_gifti:
image.extend(item.data for item in file)
for file in label_gifti:
label.extend(item.data for item in file)
else:
for file in image_gifti:
image.extend(item.data for item in file)
for file in label_gifti:
label.extend(item.data for item in file)
if right == True:
image = [item[reversing_arr] for item in image]
label = [item[reversing_arr] for item in label]
###### metadata grabbing if necessary
if self.input_arr.shape[1] > 2:
self.metadata = input_arr[:,1:-1]
else:
self.metadata = None
if self.smoothing != False:
for i in range(len(image)):
image[i] = np.clip(image[i], lower_bound[i%len(lower_bound)].item(), upper_bound[i%len(upper_bound)].item())
# torchify if required:
if self.normalisation != None:
if self.normalisation == 'std':
for i in range(len(image)):
image[i] = ( image[i] - means[i%len(means)].item( )) / stds[i%len(stds)].item()
elif self.normalisation == 'range':
for i in range(len(image)):
image[i] = image[i] - minima[i%len(minima)].item()
image[i] = image[i] / (maxima[i%len(maxima)].item()- minima[i%len(minima)].item())
if self.output_as_torch:
image = torch.Tensor( image )
label = torch.Tensor( label )
if self.metadata != None:
metadata = torch.Tensor( [self.metadata] )
if self.projected == True:
image = griddata(xy_points, image.T, grid, 'nearest')
image = torch.Tensor(image.reshape(170,170,4)).permute(2,0,1)
label = griddata(xy_points, label.T, grid, 'nearest')
label = torch.Tensor(label.reshape(170,170,1))#.permute(2,0,1)
label = F.one_hot(label.to(torch.int64), 37).contiguous()
label = label.squeeze()
label = label.permute(2,0,1)
if self.metadata != None:
sample = {'image': image, 'metadata' : self.metadata, 'label': label}
else:
sample = {'image': image,'label': label}
return sample
class My_Projected_dHCP_Data_Segmentation_Test(torch.utils.data.Dataset):
def __init__(self, input_arr, rotations = False,
number_of_warps = 0, parity_choice = 'left', smoothing = False, normalisation = None, projected =False, sample_only = True, output_as_torch = True ):
"""
A Full Dataset for the dHCP Data. Can include warps, rotations and parity flips.
Fileanme style:
in the array: only 'sub-X-ses-Y'
but for the filenames themselves
Left = 'sub-X_ses-Y_L'
Right = 'sub-X_ses-Y_R'
if warped:
'sub-X_ses-Y_L_W1'
INPUT ARGS:
1. input_arr:
Numpy array size Nx2
FIRST index MUST be the filename (excluding directory AND L or R ) of MERGED nibabel files
LAST index must be the (float) label
(OPTIONAL) Middle index if size 3 (optional) is any confounding metadata (Also float, e.g scan age for predicting birth age)
2 . rotations - boolean: to add rotations or not to add rotations
3. number of warps to include - INT
NB WARPED AR INCLUDED AS FILENAME CHANGES. WARP NUMBER X IS WRITTEN AS filename_WX
NUMBER OF WARPS CANNOT EXCEED NUMBER OF WARPES PRESENT IN FILES
4. Particy Choice (JMPORTANT!) - defines left and right-ness
If: 'left'- will output ONLY LEFT
If: 'both' - will randomly choose L or R
If 'combined' - will output a combined array (left first), will be eventually read as a file with twice the number of input channels. as they will be stacked together
5. smoothing - boolean, will clip extremal values according to the smoothing_array
6. normalisation - str. Will normalise according to 'range', 'std' or 'None'
Range is from -1 to 1
Std is mean = 0, std = 1
7. output_as_torch - boolean:
outputs values as torch Tensors if you want (usually yes)
"""
self.input_arr = input_arr
self.image_files = input_arr[:,0]
self.rotations = rotations
self.projected = projected
self.number_of_warps = number_of_warps
self.parity = parity_choice
self.smoothing = smoothing
self.normalisation = normalisation
self.sample_only = sample_only
self.output_as_torch = output_as_torch
if self.number_of_warps != 0 and self.number_of_warps != None:
self.directory = warped_files_directory
else:
self.directory = unwarped_files_directory
if self.number_of_warps != 0 and self.number_of_warps != None:
self.label_directory = warped_labels_directory
else:
self.label_directory = unwarped_labels_directory
def __len__(self):
L = len(self.input_arr)
if self.number_of_warps !=0:
if self.sample_only == False:
L = L*self.number_of_warps
return L
def __test_input_params__(self):
assert self.input_arr.shape[1] >=2, 'check your input array is a nunpy array of files and labels'
assert type(self.number_of_warps) == int, "number of warps must be an in integer (can be 0)"
assert self.parity in ['left', 'both', 'combined'], "parity choice must be either left, combined or both"
if self.number_of_rotations != 0:
assert self.rotation_arr != None,'Must specify a rotation file containing rotation vertex ids if rotations are non-zero'
assert self.rotations == bool, 'rotations must be boolean'
assert self.normalisation in [None, 'none', 'std', 'range'], 'Normalisation must be either std or range'
def __genfilename__(self,idx, right):
"""
gets the appropriate file based on input parameters on PARITY and on WARPS
"""
# grab raw filename
raw_filename = self.image_files[idx]
# add parity to it. IN THE FORM OF A LIST! If requries both will output a list of length 2
filename = []
if self.parity != 'combined':
if right == True:
filename.append(raw_filename + '_R')
else:
filename.append(raw_filename + '_L')
# if self.parity == 'left':
# filename.append(raw_filename + '_L')
#
# elif self.parity == 'both':
# coin_flip = random.randint(0,1)
# if coin_flip == 0:
# filename.append(raw_filename + '_L')
# elif coin_flip == 1:
# filename.append(raw_filename + '_R')
# right = True
if self.parity == 'combined':
filename.append(raw_filename + '_L')
filename.append(raw_filename+'_R')
# filename is now a list of the correct filenames.
# now add warps if required
if self.number_of_warps != 0:
warp_choice = str(random.randint(0,self.number_of_warps))
if warp_choice !='0':
filename = [s + '_W'+warp_choice for s in filename ]
return filename
def __getitem__(self, idx):
"""
First load the images and collect them as numpy arrays
then collect the label
then collect the metadata (though might be None)
"""
if self.parity == 'both':
T = self.__len__()//2
idx, right = idx % T, idx // T
filename = self.__genfilename__(idx, right)
else:
right = False
filename = self.__genfilename__(idx, right)
image_gifti = [nb.load(self.directory + '/'+individual_filename+'.shape.gii').darrays for individual_filename in filename]
label_gifti = [nb.load(self.label_directory + '/'+individual_filename+'.label.gii').darrays for individual_filename in filename]
image = []
label = []
if self.rotations == True:
rotation_choice = random.randint(1, len(test_rotation_arr)-1)
if rotation_choice !=0:
for file in image_gifti:
image.extend(item.data[test_rotation_arr[rotation_choice]] for item in file)
for file in label_gifti:
label.extend(item.data[test_rotation_arr[rotation_choice]] for item in file)
else:
for file in image_gifti:
image.extend(item.data for item in file)
for file in label_gifti:
label.extend(item.data for item in file)
else:
for file in image_gifti:
image.extend(item.data for item in file)
for file in label_gifti:
label.extend(item.data for item in file)
if right == True:
image = [item[reversing_arr] for item in image]
label = [item[reversing_arr] for item in label]
###### metadata grabbing if necessary
if self.input_arr.shape[1] > 2:
self.metadata = input_arr[:,1:-1]
else:
self.metadata = None
if self.smoothing != False:
for i in range(len(image)):
image[i] = np.clip(image[i], lower_bound[i%len(lower_bound)].item(), upper_bound[i%len(upper_bound)].item())
# torchify if required:
if self.normalisation != None:
if self.normalisation == 'std':
for i in range(len(image)):
image[i] = ( image[i] - means[i%len(means)].item( )) / stds[i%len(stds)].item()
elif self.normalisation == 'range':
for i in range(len(image)):
image[i] = image[i] - minima[i%len(minima)].item()
image[i] = image[i] / (maxima[i%len(maxima)].item()- minima[i%len(minima)].item())
if self.output_as_torch:
image = torch.Tensor( image )
label = torch.Tensor( label )
if self.metadata != None:
metadata = torch.Tensor( [self.metadata] )
if self.projected == True:
image = griddata(xy_points, image.T, grid, 'nearest')
image = torch.Tensor(image.reshape(170,170,4)).permute(2,0,1)
label = griddata(xy_points, label.T, grid, 'nearest')
label = torch.Tensor(label.reshape(170,170,1))#.permute(2,0,1)
label = F.one_hot(label.to(torch.int64), 37).contiguous()
label = label.squeeze()
label = label.permute(2,0,1)
if self.metadata != None:
sample = {'image': image, 'metadata' : self.metadata, 'label': label}
else:
sample = {'image': image,'label': label}
return sample
class My_Linear_Projected_dHCP_Data(torch.utils.data.Dataset):
def __init__(self, input_arr, rotations = False,
number_of_warps = 0, parity_choice = 'left', smoothing = False, normalisation = None, projected =False, output_as_torch = True ):
"""
A Full Dataset for the dHCP Data. Can include warps, rotations and parity flips.
Fileanme style:
in the array: only 'sub-X-ses-Y'
but for the filenames themselves
Left = 'sub-X_ses-Y_L'
Right = 'sub-X_ses-Y_R'
if warped:
'sub-X_ses-Y_L_W1'
INPUT ARGS:
1. input_arr:
Numpy array size Nx2
FIRST index MUST be the filename (excluding directory AND L or R ) of MERGED nibabel files
LAST index must be the (float) label
(OPTIONAL) Middle index if size 3 (optional) is any confounding metadata (Also float, e.g scan age for predicting birth age)
2 . rotations - boolean: to add rotations or not to add rotations
3. number of warps to include - INT
NB WARPED AR INCLUDED AS FILENAME CHANGES. WARP NUMBER X IS WRITTEN AS filename_WX
NUMBER OF WARPS CANNOT EXCEED NUMBER OF WARPES PRESENT IN FILES
4. Particy Choice (JMPORTANT!) - defines left and right-ness
If: 'left'- will output ONLY LEFT
If: 'both' - will randomly choose L or R
If 'combined' - will output a combined array (left first), will be eventually read as a file with twice the number of input channels. as they will be stacked together
5. smoothing - boolean, will clip extremal values according to the smoothing_array
6. normalisation - str. Will normalise according to 'range', 'std' or 'None'
Range is from -1 to 1
Std is mean = 0, std = 1
7. output_as_torch - boolean:
outputs values as torch Tensors if you want (usually yes)
"""
self.input_arr = input_arr
self.image_files = input_arr[:,0]
self.label = input_arr[:,-1]
self.rotations = rotations
self.projected = projected
self.number_of_warps = number_of_warps
self.parity = parity_choice
self.smoothing = smoothing
self.normalisation = normalisation
self.output_as_torch = output_as_torch
if self.number_of_warps != 0 and self.number_of_warps != None:
self.directory = warped_files_directory
else:
self.directory = unwarped_files_directory
def __len__(self):
L = len(self.input_arr)
if self.number_of_warps !=0:
L = L*self.number_of_warps
return L
def __test_input_params__(self):
assert self.input_arr.shape[1] >=2, 'check your input array is a nunpy array of files and labels'
assert type(self.number_of_warps) == int, "number of warps must be an in integer (can be 0)"
assert self.parity in ['left', 'both', 'combined'], "parity choice must be either left, combined or both"
if self.number_of_rotations != 0:
assert self.rotation_arr != None,'Must specify a rotation file containing rotation vertex ids if rotations are non-zero'
assert self.rotations == bool, 'rotations must be boolean'
assert self.normalisation in [None, 'none', 'std', 'range'], 'Normalisation must be either std or range'
def __genfilename__(self,idx):
"""
gets the appropriate file based on input parameters on PARITY and on WARPS
"""
# grab raw filename
raw_filename = self.image_files[idx]
# add parity to it. IN THE FORM OF A LIST! If requries both will output a list of length 2
filename = []
if self.parity == 'left':
filename.append(raw_filename + '_L')
elif self.parity == 'both':
coin_flip = random.randint(0,1)
if coin_flip == 0:
filename.append(raw_filename + '_L')
elif coin_flip == 1:
filename.append(raw_filename + '_R')
elif self.parity == 'combined':
filename.append(raw_filename + '_L')
filename.append(raw_filename+'_R')
# filename is now a list of the correct filenames.
# now add warps if required
if self.number_of_warps != 0:
warp_choice = str(random.randint(1,self.number_of_warps))
filename = [s + '_W'+warp_choice for s in filename ]
return filename
def __getitem__(self, idx):
"""
First load the images and collect them as numpy arrays
then collect the label
then collect the metadata (though might be None)
"""
filename = self.__genfilename__(idx)
image_gifti = [nb.load(self.directory + '/'+individual_filename+'.shape.gii').darrays for individual_filename in filename]
image = []
if self.rotations == True:
rotation_choice = random.randint(0, len(rotation_arr)-1)
if rotation_choice !=0:
for file in image_gifti:
image.extend(item.data[rotation_arr[rotation_choice]] for item in file)
else:
for file in image_gifti:
image.extend(item.data for item in file)
else:
for file in image_gifti:
image.extend(item.data for item in file)
### labels
if self.number_of_warps != 0:
idx = idx%len(self.input_arr)
label = self.label[idx]
###### metadata grabbing if necessary
if self.input_arr.shape[1] > 2:
self.metadata = input_arr[:,1:-1]
else:
self.metadata = None
if self.smoothing != False:
for i in range(len(image)):
image[i] = np.clip(image[i], lower_bound[i%len(lower_bound)].item(), upper_bound[i%len(upper_bound)].item())
# torchify if required:
if self.normalisation != None:
if self.normalisation == 'std':
for i in range(len(image)):
image[i] = ( image[i] - means[i%len(means)].item( )) / stds[i%len(stds)].item()
elif self.normalisation == 'range':
for i in range(len(image)):
image[i] = image[i] - minima[i%len(minima)].item()
image[i] = image[i] / (maxima[i%len(maxima)].item()- minima[i%len(minima)].item())
if self.output_as_torch:
image = torch.Tensor( image )
label = torch.Tensor( [label] )
if self.metadata != None:
metadata = torch.Tensor( [self.metadata] )
if self.projected == True:
image = torch.Tensor(image.reshape(170,170,4)).permute(2,0,1)
if self.metadata != None:
sample = {'image': image, 'metadata' : self.metadata, 'label': label}
else:
sample = {'image': image,'label': label}
return sample
| 34.033898
| 182
| 0.530441
| 5,596
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| 0.928641
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| 0
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| 0.382823
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| 183
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| 0
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| 0.010651
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| 1
| 0.047368
| false
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| 0.010526
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| 0.096491
| 0
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| null | 0
| 0
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| 1
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|
0
| 7
|
bce0ed0aa53c5a99a6da90b880ed1dc0f5e37446
| 54,303
|
py
|
Python
|
trankit/pipeline.py
|
wdhxek/trankit
|
8d1ce9f1a00a86a3d4c87d2e9bfd17daba098bfc
|
[
"Apache-2.0"
] | 1
|
2021-04-07T04:35:47.000Z
|
2021-04-07T04:35:47.000Z
|
trankit/pipeline.py
|
wdhxek/trankit
|
8d1ce9f1a00a86a3d4c87d2e9bfd17daba098bfc
|
[
"Apache-2.0"
] | null | null | null |
trankit/pipeline.py
|
wdhxek/trankit
|
8d1ce9f1a00a86a3d4c87d2e9bfd17daba098bfc
|
[
"Apache-2.0"
] | null | null | null |
from .config import config as master_config
from .models.base_models import Multilingual_Embedding
from .models.classifiers import TokenizerClassifier, PosDepClassifier, NERClassifier
from .models.mwt_model import MWTWrapper
from .models.lemma_model import LemmaWrapper
from .iterators.tokenizer_iterators import TokenizeDatasetLive
from .iterators.tagger_iterators import TaggerDatasetLive
from .iterators.ner_iterators import NERDatasetLive
from .utils.tokenizer_utils import *
from collections import defaultdict
from .utils.conll import *
from .utils.tbinfo import tbname2training_id, lang2treebank
from .utils.chuliu_edmonds import *
from .adapter_transformers import XLMRobertaTokenizer
from datetime import datetime
import langid
def is_string(input):
if type(input) == str and len(input.strip()) > 0:
return True
return False
def is_list_strings(input):
if type(input) == list and len(input) > 0:
for element in input:
if not (type(element) == str and not element.isspace()):
return False
return True
return False
def is_list_list_strings(input):
if type(input) == list and len(input) > 0 and type(input[0]) == list and len(input[0]) > 0:
for element in input[0]:
if not (type(element) == str and not element.isspace()):
return False
return True
return False
class Pipeline:
def __init__(self, lang, cache_dir=None, gpu=True, embedding='xlm-roberta-base'):
super(Pipeline, self).__init__()
# auto detection of lang
if lang == 'auto':
lang = list(code2lang.values())[0]
self.auto_mode = True
else:
self.auto_mode = False
# set the embedding type
assert embedding in supported_embeddings, '{} has not been supported.\nSupported embeddings: {}'.format(
embedding, supported_embeddings)
master_config.embedding_name = embedding
self._cache_dir = cache_dir
self._gpu = gpu
self._use_gpu = gpu
self._ud_eval = False
self._setup_config(lang)
self._config.training = False
self.added_langs = [lang]
for lang in self.added_langs:
assert lang in lang2treebank, '{} has not been supported. Currently supported languages: {}'.format(lang,
list(
lang2treebank.keys()))
# download saved model for initial language
download(
cache_dir=self._config._cache_dir,
language=lang,
saved_model_version='v1.0.0', # manually set this to avoid duplicated storage
embedding_name=master_config.embedding_name
)
# load ALL vocabs
self._load_vocabs()
# shared multilingual embeddings
print('Loading pretrained XLM-Roberta, this may take a while...')
self._embedding_layers = Multilingual_Embedding(self._config)
self._embedding_layers.to(self._config.device)
if self._use_gpu:
self._embedding_layers.half()
self._embedding_layers.eval()
# tokenizers
self._tokenizer = {}
for lang in self.added_langs:
self._tokenizer[lang] = TokenizerClassifier(self._config, treebank_name=lang2treebank[lang])
self._tokenizer[lang].to(self._config.device)
if self._use_gpu:
self._tokenizer[lang].half()
self._tokenizer[lang].eval()
# taggers
self._tagger = {}
for lang in self.added_langs:
self._tagger[lang] = PosDepClassifier(self._config, treebank_name=lang2treebank[lang])
self._tagger[lang].to(self._config.device)
if self._use_gpu:
self._tagger[lang].half()
self._tagger[lang].eval()
# - mwt and lemma:
self._mwt_model = {}
for lang in self.added_langs:
treebank_name = lang2treebank[lang]
if tbname2training_id[treebank_name] % 2 == 1:
self._mwt_model[lang] = MWTWrapper(self._config, treebank_name=treebank_name, use_gpu=self._use_gpu)
self._lemma_model = {}
for lang in self.added_langs:
treebank_name = lang2treebank[lang]
self._lemma_model[lang] = LemmaWrapper(self._config, treebank_name=treebank_name, use_gpu=self._use_gpu)
# ner if possible
self._ner_model = {}
for lang in self.added_langs:
if lang in langwithner:
self._ner_model[lang] = NERClassifier(self._config, lang)
self._ner_model[lang].to(self._config.device)
if self._use_gpu:
self._ner_model[lang].half()
self._ner_model[lang].eval()
# load and hold the pretrained weights
self._embedding_weights = self._embedding_layers.state_dict()
if self.auto_mode:
for l in code2lang.values():
if l not in self.added_langs:
self.add(l)
# constrain the language set for auto mode
langid.set_languages([lang2code[l] for l in self.added_langs])
self.code2lang = code2lang
print('=' * 50)
print('Trankit is in auto mode!\nAvailable languages: {}'.format(self.added_langs))
print('=' * 50)
else:
self.set_active(lang)
def _setup_config(self, lang):
torch.cuda.empty_cache()
# decide whether to run on GPU or CPU
if self._gpu and torch.cuda.is_available():
self._use_gpu = True
master_config.device = torch.device('cuda')
self._tokbatchsize = 6
self._tagbatchsize = 24
else:
self._use_gpu = False
master_config.device = torch.device('cpu')
self._tokbatchsize = 2
self._tagbatchsize = 12
if self._cache_dir is None:
master_config._cache_dir = 'cache/trankit'
else:
master_config._cache_dir = self._cache_dir
if not os.path.exists(master_config._cache_dir):
os.makedirs(master_config._cache_dir, exist_ok=True)
master_config.wordpiece_splitter = XLMRobertaTokenizer.from_pretrained(master_config.embedding_name,
cache_dir=os.path.join(
master_config._cache_dir,
master_config.embedding_name))
self._config = master_config
self._config.max_input_length = tbname2max_input_length.get(lang2treebank[lang],
400) # this is for tokenizer only
def set_auto(self, state):
assert type(state) == bool
if state is True:
print('Turning on auto mode for {} ...'.format(self.added_langs))
self.auto_mode = True
cls_codes = []
self.code2lang = {}
for l in self.added_langs:
if l in extra_lang2code:
cls_codes.append(extra_lang2code[l])
self.code2lang[extra_lang2code[l]] = l
langid.set_languages(cls_codes)
print('=' * 50)
print('Trankit is in auto mode!')
print('=' * 50)
else:
self.auto_mode = False
lang = self.added_langs[0]
self._config.active_lang = lang
self.active_lang = lang
self._config.treebank_name = lang2treebank[lang]
self._config.max_input_length = tbname2max_input_length.get(lang2treebank[lang],
400) # this is for tokenizer only
print('=' * 50)
print('Trankit is in normal mode!')
print('=' * 50)
print('Active language: {}'.format(self._config.active_lang))
print('Available languages: {}'.format(self.added_langs))
print('=' * 50)
def set_active(self, lang):
assert not self.auto_mode, 'Cannot set a particular language as active in auto mode.\nPlease consider using Trankit in the normal mode to use this function.'
assert is_string(
lang) and lang in self.added_langs, 'Specified language must be added before being activated.\nCurrent added languages: {}'.format(
self.added_langs)
self._config.active_lang = lang
self.active_lang = lang
self._config.treebank_name = lang2treebank[lang]
self._config.max_input_length = tbname2max_input_length.get(lang2treebank[lang],
400) # this is for tokenizer only
print('=' * 50)
print('Active language: {}'.format(self._config.active_lang))
print('=' * 50)
def add(self, lang):
assert is_string(
lang) and lang in supported_langs, 'Specified language must be one of the supported languages: {}'.format(
supported_langs)
# download saved models
download(
cache_dir=self._config._cache_dir,
language=lang,
saved_model_version='v1.0.0', # manually set this to avoid duplicated storage
embedding_name=master_config.embedding_name
)
# update vocabs
treebank_name = lang2treebank[lang]
with open(os.path.join(self._config._cache_dir, master_config.embedding_name,
'{}/{}.vocabs.json'.format(treebank2lang[treebank_name],
treebank2lang[treebank_name]))) as f:
vocabs = json.load(f)
self._config.vocabs[treebank_name] = vocabs
if lang in langwithner:
with open(os.path.join(self._config._cache_dir, master_config.embedding_name,
'{}/{}.ner-vocab.json'.format(lang, lang))) as f:
self._config.ner_vocabs[lang] = json.load(f)
self._config.itos[lang][UPOS] = {v: k for k, v in vocabs[UPOS].items()}
self._config.itos[lang][XPOS] = {v: k for k, v in vocabs[XPOS].items()}
self._config.itos[lang][FEATS] = {v: k for k, v in vocabs[FEATS].items()}
self._config.itos[lang][DEPREL] = {v: k for k, v in vocabs[DEPREL].items()}
# add tokenizer
self._tokenizer[lang] = TokenizerClassifier(self._config, treebank_name=lang2treebank[lang])
self._tokenizer[lang].to(self._config.device)
if self._use_gpu:
self._tokenizer[lang].half()
self._tokenizer[lang].eval()
# add tagger
self._tagger[lang] = PosDepClassifier(self._config, treebank_name=lang2treebank[lang])
self._tagger[lang].to(self._config.device)
if self._use_gpu:
self._tagger[lang].half()
self._tagger[lang].eval()
# mwt if needed
treebank_name = lang2treebank[lang]
if tbname2training_id[treebank_name] % 2 == 1:
self._mwt_model[lang] = MWTWrapper(self._config, treebank_name=treebank_name, use_gpu=self._use_gpu)
# lemma
self._lemma_model[lang] = LemmaWrapper(self._config, treebank_name=treebank_name, use_gpu=self._use_gpu)
# ner if possible
if lang in langwithner:
self._ner_model[lang] = NERClassifier(self._config, lang)
self._ner_model[lang].to(self._config.device)
if self._use_gpu:
self._ner_model[lang].half()
self._ner_model[lang].eval()
self.added_langs.append(lang)
def _load_vocabs(self):
self._config.vocabs = {}
self._config.ner_vocabs = {}
self._config.itos = defaultdict(dict)
for lang in self.added_langs:
treebank_name = lang2treebank[lang]
with open(os.path.join(self._config._cache_dir, master_config.embedding_name,
'{}/{}.vocabs.json'.format(lang, lang))) as f:
vocabs = json.load(f)
self._config.vocabs[treebank_name] = vocabs
self._config.itos[lang][UPOS] = {v: k for k, v in vocabs[UPOS].items()}
self._config.itos[lang][XPOS] = {v: k for k, v in vocabs[XPOS].items()}
self._config.itos[lang][FEATS] = {v: k for k, v in vocabs[FEATS].items()}
self._config.itos[lang][DEPREL] = {v: k for k, v in vocabs[DEPREL].items()}
# ner vocabs
if lang in langwithner:
with open(os.path.join(self._config._cache_dir, master_config.embedding_name,
'{}/{}.ner-vocab.json'.format(lang, lang))) as f:
self._config.ner_vocabs[lang] = json.load(f)
def _load_adapter_weights(self, model_name):
assert model_name in ['tokenizer', 'tagger', 'ner']
if model_name == 'tokenizer':
pretrained_weights = self._tokenizer[self._config.active_lang].pretrained_tokenizer_weights
elif model_name == 'tagger':
pretrained_weights = self._tagger[self._config.active_lang].pretrained_tagger_weights
else:
assert model_name == 'ner'
pretrained_weights = self._ner_model[self._config.active_lang].pretrained_ner_weights
for name, value in pretrained_weights.items():
if 'adapters.{}.adapter'.format(model_name) in name:
target_name = name.replace('adapters.{}.adapter'.format(model_name), 'adapters.embedding.adapter')
self._embedding_weights[target_name] = value
self._embedding_layers.load_state_dict(self._embedding_weights)
def _detect_lang_and_switch(self, text):
detected_code = langid.classify(text)[0]
assert detected_code in self.code2lang, 'Detected code "{}" must be in {}'.format(detected_code,
self.code2lang.keys())
lang = self.code2lang[detected_code]
assert is_string(
lang) and lang in self.added_langs, 'Specified language must be added before being activated.\nCurrent added languages: {}'.format(
self.added_langs)
self._config.active_lang = lang
self.active_lang = lang
self._config.treebank_name = lang2treebank[lang]
self._config.max_input_length = tbname2max_input_length.get(lang2treebank[lang],
400) # this is for tokenizer only
# print('=' * 50)
# print('Switching to {}'.format(lang))
# print('=' * 50)
def ssplit(self, in_doc): # assuming input is a document
assert is_string(in_doc), 'Input must be a non-empty string.'
# switch to detected lang if auto mode is on
if self.auto_mode:
self._detect_lang_and_switch(text=in_doc)
eval_batch_size = tbname2tokbatchsize.get(lang2treebank[self.active_lang], self._tokbatchsize)
# load input text
config = self._config
test_set = TokenizeDatasetLive(config, in_doc, max_input_length=tbname2max_input_length.get(
lang2treebank[self.active_lang], 400))
test_set.numberize(config.wordpiece_splitter)
# load weights of tokenizer into the combined model
self._load_adapter_weights(model_name='tokenizer')
# make predictions
wordpiece_pred_labels, wordpiece_ends, paragraph_indexes = [], [], []
for batch in DataLoader(test_set, batch_size=eval_batch_size,
shuffle=False, collate_fn=test_set.collate_fn):
wordpiece_reprs = self._embedding_layers.get_tokenizer_inputs(batch)
predictions = self._tokenizer[self._config.active_lang].predict(batch, wordpiece_reprs)
wp_pred_labels, wp_ends, para_ids = predictions[0], predictions[1], predictions[2]
wp_pred_labels = wp_pred_labels.data.cpu().numpy().tolist()
for i in range(len(wp_pred_labels)):
wordpiece_pred_labels.append(wp_pred_labels[i][: len(wp_ends[i])])
wordpiece_ends.extend(wp_ends)
paragraph_indexes.extend(para_ids)
torch.cuda.empty_cache()
# mapping
para_id_to_wp_pred_labels = defaultdict(list)
for wp_pred_ls, wp_es, p_index in zip(wordpiece_pred_labels, wordpiece_ends,
paragraph_indexes):
para_id_to_wp_pred_labels[p_index].extend([(pred, char_position) for pred, char_position in
zip(wp_pred_ls, wp_es)])
# get predictions
corpus_text = in_doc
paragraphs = [pt.rstrip() for pt in
NEWLINE_WHITESPACE_RE.split(corpus_text) if
len(pt.rstrip()) > 0]
all_wp_preds = []
all_para_texts = []
all_para_starts = []
##############
cloned_raw_text = deepcopy(in_doc)
global_offset = 0
for para_index, para_text in enumerate(paragraphs):
cloned_raw_text, start_char_idx = get_start_char_idx(para_text, cloned_raw_text)
start_char_idx += global_offset
global_offset = start_char_idx + len(para_text)
all_para_starts.append(start_char_idx)
para_wp_preds = [0 for _ in para_text]
for wp_l, end_position in para_id_to_wp_pred_labels[para_index]:
para_wp_preds[end_position] = wp_l
all_wp_preds.append(para_wp_preds)
all_para_texts.append(para_text)
###########################
sentences = []
for j in range(len(paragraphs)):
para_text = all_para_texts[j]
wp_pred = all_wp_preds[j]
para_start = all_para_starts[j]
current_tok = ''
current_sent = []
local_position = 0
for t, wp_p in zip(para_text, wp_pred):
local_position += 1
current_tok += t
if wp_p >= 1:
tok = normalize_token(test_set.treebank_name, current_tok, ud_eval=self._ud_eval)
assert '\t' not in tok, tok
if len(tok) <= 0:
current_tok = ''
continue
additional_info = {DSPAN: (para_start + local_position - len(tok),
para_start + local_position)}
current_sent += [(tok, wp_p, additional_info)]
current_tok = ''
if (wp_p == 2 or wp_p == 4):
sent_span = (current_sent[0][2][DSPAN][0], current_sent[-1][2][DSPAN][1])
sentences.append(
{ID: len(sentences) + 1, TEXT: in_doc[sent_span[0]: sent_span[1]],
DSPAN: (sent_span[0], sent_span[1])})
current_sent = []
if len(current_tok):
tok = normalize_token(test_set.treebank_name, current_tok, ud_eval=self._ud_eval)
assert '\t' not in tok, tok
if len(tok) > 0:
additional_info = {DSPAN: (para_start + local_position - len(tok),
para_start + local_position)}
current_sent += [(tok, 2, additional_info)]
if len(current_sent):
sent_span = (current_sent[0][2][DSPAN][0], current_sent[-1][2][DSPAN][1])
sentences.append(
{ID: len(sentences) + 1, TEXT: in_doc[sent_span[0]: sent_span[1]],
DSPAN: (sent_span[0], sent_span[1])})
torch.cuda.empty_cache()
return {TEXT: in_doc, SENTENCES: sentences, LANG: self.active_lang}
def tokenize(self, input, is_sent=False):
assert is_string(input), 'Input must be a non-empty string.'
# switch to detected lang if auto mode is on
if self.auto_mode:
self._detect_lang_and_switch(text=input)
if type(input) == str and input.isspace():
return []
ori_text = deepcopy(input)
if is_sent:
return {TEXT: ori_text, TOKENS: self._tokenize_sent(in_sent=input), LANG: self.active_lang}
else:
return {TEXT: ori_text, SENTENCES: self._tokenize_doc(in_doc=input), LANG: self.active_lang}
def _tokenize_sent(self, in_sent): # assuming input is a sentence
eval_batch_size = tbname2tokbatchsize.get(lang2treebank[self.active_lang], self._tokbatchsize)
if self._config.embedding_name == 'xlm-roberta-large':
eval_batch_size = int(eval_batch_size / 2)
# load input text
config = self._config
test_set = TokenizeDatasetLive(config, in_sent, max_input_length=tbname2max_input_length.get(
lang2treebank[self.active_lang], 400))
test_set.numberize(config.wordpiece_splitter)
# load weights of tokenizer into the combined model
self._load_adapter_weights(model_name='tokenizer')
# make predictions
wordpiece_pred_labels, wordpiece_ends, paragraph_indexes = [], [], []
for batch in DataLoader(test_set, batch_size=eval_batch_size,
shuffle=False, collate_fn=test_set.collate_fn):
wordpiece_reprs = self._embedding_layers.get_tokenizer_inputs(batch)
predictions = self._tokenizer[self._config.active_lang].predict(batch, wordpiece_reprs)
wp_pred_labels, wp_ends, para_ids = predictions[0], predictions[1], predictions[2]
wp_pred_labels = wp_pred_labels.data.cpu().numpy().tolist()
for i in range(len(wp_pred_labels)):
wordpiece_pred_labels.append(wp_pred_labels[i][: len(wp_ends[i])])
wordpiece_ends.extend(wp_ends)
paragraph_indexes.extend(para_ids)
# mapping
para_id_to_wp_pred_labels = defaultdict(list)
for wp_pred_ls, wp_es, p_index in zip(wordpiece_pred_labels, wordpiece_ends,
paragraph_indexes):
para_id_to_wp_pred_labels[p_index].extend([(pred, char_position) for pred, char_position in
zip(wp_pred_ls, wp_es)])
# get predictions
corpus_text = in_sent
paragraphs = [pt.rstrip() for pt in
NEWLINE_WHITESPACE_RE.split(corpus_text) if
len(pt.rstrip()) > 0]
all_wp_preds = []
all_para_texts = []
all_para_starts = []
##############
cloned_raw_text = deepcopy(in_sent)
global_offset = 0
for para_index, para_text in enumerate(paragraphs):
cloned_raw_text, start_char_idx = get_start_char_idx(para_text, cloned_raw_text)
start_char_idx += global_offset
global_offset = start_char_idx + len(para_text)
all_para_starts.append(start_char_idx)
para_wp_preds = [0 for _ in para_text]
for wp_l, end_position in para_id_to_wp_pred_labels[para_index]:
para_wp_preds[end_position] = wp_l
all_wp_preds.append(para_wp_preds)
all_para_texts.append(para_text)
###########################
tokens = []
for j in range(len(paragraphs)):
para_text = all_para_texts[j]
wp_pred = all_wp_preds[j]
para_start = all_para_starts[j]
current_tok = ''
current_sent = []
local_position = 0
for t, wp_p in zip(para_text, wp_pred):
local_position += 1
current_tok += t
if wp_p >= 1:
tok = normalize_token(test_set.treebank_name, current_tok, ud_eval=self._ud_eval)
assert '\t' not in tok, tok
if len(tok) <= 0:
current_tok = ''
continue
additional_info = {'current_len': len(tokens),
SSPAN: (para_start + local_position - len(tok),
para_start + local_position)}
current_sent += [(tok, wp_p, additional_info)]
current_tok = ''
if (wp_p == 2 or wp_p == 4):
tokens += get_output_sentence(current_sent)
current_sent = []
if len(current_tok):
tok = normalize_token(test_set.treebank_name, current_tok, ud_eval=self._ud_eval)
assert '\t' not in tok, tok
if len(tok) > 0:
additional_info = {'current_len': len(tokens),
SSPAN: (para_start + local_position - len(tok),
para_start + local_position)}
current_sent += [(tok, 2, additional_info)]
if len(current_sent):
tokens += get_output_sentence(current_sent)
# multi-word expansion if required
if tbname2training_id[self._config.treebank_name] % 2 == 1:
tokens = self._mwt_expand([{TOKENS: tokens}])[0][TOKENS]
torch.cuda.empty_cache()
return tokens
def _tokenize_doc(self, in_doc): # assuming input is a document
eval_batch_size = tbname2tokbatchsize.get(lang2treebank[self.active_lang], self._tokbatchsize)
if self._config.embedding_name == 'xlm-roberta-large':
eval_batch_size = int(eval_batch_size / 2)
# load input text
config = self._config
test_set = TokenizeDatasetLive(config, in_doc, max_input_length=tbname2max_input_length.get(
lang2treebank[self.active_lang], 400))
test_set.numberize(config.wordpiece_splitter)
# load weights of tokenizer into the combined model
self._load_adapter_weights(model_name='tokenizer')
# make predictions
wordpiece_pred_labels, wordpiece_ends, paragraph_indexes = [], [], []
for batch in DataLoader(test_set, batch_size=eval_batch_size,
shuffle=False, collate_fn=test_set.collate_fn):
wordpiece_reprs = self._embedding_layers.get_tokenizer_inputs(batch)
predictions = self._tokenizer[self._config.active_lang].predict(batch, wordpiece_reprs)
wp_pred_labels, wp_ends, para_ids = predictions[0], predictions[1], predictions[2]
wp_pred_labels = wp_pred_labels.data.cpu().numpy().tolist()
for i in range(len(wp_pred_labels)):
wordpiece_pred_labels.append(wp_pred_labels[i][: len(wp_ends[i])])
wordpiece_ends.extend(wp_ends)
paragraph_indexes.extend(para_ids)
# mapping
para_id_to_wp_pred_labels = defaultdict(list)
for wp_pred_ls, wp_es, p_index in zip(wordpiece_pred_labels, wordpiece_ends,
paragraph_indexes):
para_id_to_wp_pred_labels[p_index].extend([(pred, char_position) for pred, char_position in
zip(wp_pred_ls, wp_es)])
# get predictions
corpus_text = in_doc
paragraphs = [pt.rstrip() for pt in
NEWLINE_WHITESPACE_RE.split(corpus_text) if
len(pt.rstrip()) > 0]
all_wp_preds = []
all_para_texts = []
all_para_starts = []
##############
cloned_raw_text = deepcopy(in_doc)
global_offset = 0
for para_index, para_text in enumerate(paragraphs):
cloned_raw_text, start_char_idx = get_start_char_idx(para_text, cloned_raw_text)
start_char_idx += global_offset
global_offset = start_char_idx + len(para_text)
all_para_starts.append(start_char_idx)
para_wp_preds = [0 for _ in para_text]
for wp_l, end_position in para_id_to_wp_pred_labels[para_index]:
para_wp_preds[end_position] = wp_l
all_wp_preds.append(para_wp_preds)
all_para_texts.append(para_text)
###########################
doc = []
for j in range(len(paragraphs)):
para_text = all_para_texts[j]
wp_pred = all_wp_preds[j]
para_start = all_para_starts[j]
current_tok = ''
current_sent = []
local_position = 0
for t, wp_p in zip(para_text, wp_pred):
local_position += 1
current_tok += t
if wp_p >= 1:
tok = normalize_token(test_set.treebank_name, current_tok, ud_eval=self._ud_eval)
assert '\t' not in tok, tok
if len(tok) <= 0:
current_tok = ''
continue
additional_info = {DSPAN: (para_start + local_position - len(tok),
para_start + local_position)}
current_sent += [(tok, wp_p, additional_info)]
current_tok = ''
if (wp_p == 2 or wp_p == 4):
processed_sent = get_output_sentence(current_sent)
doc.append({
ID: len(doc) + 1,
TEXT: in_doc[processed_sent[0][DSPAN][0]: processed_sent[-1][DSPAN][
1]],
TOKENS: processed_sent,
DSPAN: (processed_sent[0][DSPAN][0], processed_sent[-1][DSPAN][1])
})
current_sent = []
if len(current_tok):
tok = normalize_token(test_set.treebank_name, current_tok, ud_eval=self._ud_eval)
assert '\t' not in tok, tok
if len(tok) > 0:
additional_info = {DSPAN: (para_start + local_position - len(tok),
para_start + local_position)}
current_sent += [(tok, 2, additional_info)]
if len(current_sent):
processed_sent = get_output_sentence(current_sent)
doc.append({
ID: len(doc) + 1,
TEXT: in_doc[
processed_sent[0][DSPAN][0]: processed_sent[-1][DSPAN][1]],
TOKENS: processed_sent,
DSPAN: (processed_sent[0][DSPAN][0], processed_sent[-1][DSPAN][1])
})
# multi-word expansion if required
if tbname2training_id[self._config.treebank_name] % 2 == 1:
doc = self._mwt_expand(doc)
torch.cuda.empty_cache()
return doc
def posdep(self, input, is_sent=False):
if is_sent:
assert is_string(input) or is_list_strings(
input), 'Input must be one of the following:\n(i) A non-empty string.\n(ii) A list of non-empty strings.'
if is_list_strings(input):
# switch to detected lang if auto mode is on
if self.auto_mode:
self._detect_lang_and_switch(text=' '.join(input))
input = [{ID: k + 1, TEXT: w} for k, w in enumerate(input)]
return {TOKENS: self._posdep_sent(in_sent=input), LANG: self.active_lang}
else:
# switch to detected lang if auto mode is on
if self.auto_mode:
self._detect_lang_and_switch(text=input)
ori_text = deepcopy(input)
return {TEXT: ori_text, TOKENS: self._posdep_sent(in_sent=input), LANG: self.active_lang}
else:
assert is_string(input) or is_list_list_strings(
input), 'Input must be one of the following:\n(i) A non-empty string.\n(ii) A list of lists of non-empty strings.'
if is_list_list_strings(input):
# switch to detected lang if auto mode is on
if self.auto_mode:
self._detect_lang_and_switch(text='\n'.join([' '.join(sent) for sent in input]))
input = [{ID: sid + 1, TOKENS: [{ID: tid + 1, TEXT: w} for tid, w in enumerate(sent)]} for sid, sent in
enumerate(input)]
return {SENTENCES: self._posdep_doc(in_doc=input), LANG: self.active_lang}
else:
# switch to detected lang if auto mode is on
if self.auto_mode:
self._detect_lang_and_switch(text=input)
ori_text = deepcopy(input)
return {TEXT: ori_text, SENTENCES: self._posdep_doc(in_doc=input), LANG: self.active_lang}
def _posdep_sent(self, in_sent): # assuming input is a sentence
if type(in_sent) == str: # input sentence is an untokenized string in this case
in_sent = self._tokenize_sent(in_sent)
posdep_sent = deepcopy(in_sent)
posdep_sent = [{ID: 1, TOKENS: posdep_sent}]
# load outputs of tokenizer
config = self._config
test_set = TaggerDatasetLive(
tokenized_doc=posdep_sent,
wordpiece_splitter=config.wordpiece_splitter,
config=config
)
test_set.numberize()
# load weights of tagger into the combined model
self._load_adapter_weights(model_name='tagger')
# make predictions
eval_batch_size = tbname2tagbatchsize.get(self._config.treebank_name, self._tagbatchsize)
if self._config.embedding_name == 'xlm-roberta-large':
eval_batch_size = int(eval_batch_size / 3)
for batch in DataLoader(test_set,
batch_size=eval_batch_size,
shuffle=False, collate_fn=test_set.collate_fn):
batch_size = len(batch.word_num)
word_reprs, cls_reprs = self._embedding_layers.get_tagger_inputs(batch)
predictions = self._tagger[self._config.active_lang].predict(batch, word_reprs, cls_reprs)
predicted_upos = predictions[0]
predicted_xpos = predictions[1]
predicted_feats = predictions[2]
predicted_upos = predicted_upos.data.cpu().numpy().tolist()
predicted_xpos = predicted_xpos.data.cpu().numpy().tolist()
predicted_feats = predicted_feats.data.cpu().numpy().tolist()
# head, deprel
predicted_dep = predictions[3]
sentlens = [l + 1 for l in batch.word_num]
head_seqs = [chuliu_edmonds_one_root(adj[:l, :l])[1:] for adj, l in
zip(predicted_dep[0], sentlens)] # remove attachment for the root
deprel_seqs = [
[self._config.itos[self._config.active_lang][DEPREL][predicted_dep[1][i][j + 1][h]] for j, h in
enumerate(hs)] for
i, hs
in
enumerate(head_seqs)]
pred_tokens = [[[head_seqs[i][j], deprel_seqs[i][j]] for j in range(sentlens[i] - 1)] for i in
range(batch_size)]
for bid in range(batch_size):
sentid = batch.sent_index[bid]
for i in range(batch.word_num[bid]):
wordid = batch.word_ids[bid][i]
# upos
pred_upos_id = predicted_upos[bid][i]
upos_name = self._config.itos[self._config.active_lang][UPOS][pred_upos_id]
test_set.conllu_doc[sentid][wordid][UPOS] = upos_name
# xpos
pred_xpos_id = predicted_xpos[bid][i]
xpos_name = self._config.itos[self._config.active_lang][XPOS][pred_xpos_id]
test_set.conllu_doc[sentid][wordid][XPOS] = xpos_name
# feats
pred_feats_id = predicted_feats[bid][i]
feats_name = self._config.itos[self._config.active_lang][FEATS][pred_feats_id]
test_set.conllu_doc[sentid][wordid][FEATS] = feats_name
# head
test_set.conllu_doc[sentid][wordid][HEAD] = int(pred_tokens[bid][i][0])
# deprel
test_set.conllu_doc[sentid][wordid][DEPREL] = pred_tokens[bid][i][1]
tagged_doc = get_output_doc(posdep_sent, test_set.conllu_doc)
torch.cuda.empty_cache()
return tagged_doc[0][TOKENS]
def _posdep_doc(self, in_doc): # assuming input is a document
if type(in_doc) == str: # in_doc is an untokenized string in this case
in_doc = self._tokenize_doc(in_doc)
dposdep_doc = deepcopy(in_doc)
# load outputs of tokenizer
config = self._config
test_set = TaggerDatasetLive(
tokenized_doc=dposdep_doc,
wordpiece_splitter=config.wordpiece_splitter,
config=config
)
test_set.numberize()
# load weights of tagger into the combined model
self._load_adapter_weights(model_name='tagger')
# make predictions
eval_batch_size = tbname2tagbatchsize.get(self._config.treebank_name, self._tagbatchsize)
if self._config.embedding_name == 'xlm-roberta-large':
eval_batch_size = int(eval_batch_size / 3)
for batch in DataLoader(test_set,
batch_size=eval_batch_size,
shuffle=False, collate_fn=test_set.collate_fn):
batch_size = len(batch.word_num)
word_reprs, cls_reprs = self._embedding_layers.get_tagger_inputs(batch)
predictions = self._tagger[self._config.active_lang].predict(batch, word_reprs, cls_reprs)
predicted_upos = predictions[0]
predicted_xpos = predictions[1]
predicted_feats = predictions[2]
predicted_upos = predicted_upos.data.cpu().numpy().tolist()
predicted_xpos = predicted_xpos.data.cpu().numpy().tolist()
predicted_feats = predicted_feats.data.cpu().numpy().tolist()
# head, deprel
predicted_dep = predictions[3]
sentlens = [l + 1 for l in batch.word_num]
head_seqs = [chuliu_edmonds_one_root(adj[:l, :l])[1:] for adj, l in
zip(predicted_dep[0], sentlens)] # remove attachment for the root
deprel_seqs = [
[self._config.itos[self._config.active_lang][DEPREL][predicted_dep[1][i][j + 1][h]] for j, h in
enumerate(hs)] for
i, hs
in
enumerate(head_seqs)]
pred_tokens = [[[head_seqs[i][j], deprel_seqs[i][j]] for j in range(sentlens[i] - 1)] for i in
range(batch_size)]
for bid in range(batch_size):
sentid = batch.sent_index[bid]
for i in range(batch.word_num[bid]):
wordid = batch.word_ids[bid][i]
# upos
pred_upos_id = predicted_upos[bid][i]
upos_name = self._config.itos[self._config.active_lang][UPOS][pred_upos_id]
test_set.conllu_doc[sentid][wordid][UPOS] = upos_name
# xpos
pred_xpos_id = predicted_xpos[bid][i]
xpos_name = self._config.itos[self._config.active_lang][XPOS][pred_xpos_id]
test_set.conllu_doc[sentid][wordid][XPOS] = xpos_name
# feats
pred_feats_id = predicted_feats[bid][i]
feats_name = self._config.itos[self._config.active_lang][FEATS][pred_feats_id]
test_set.conllu_doc[sentid][wordid][FEATS] = feats_name
# head
test_set.conllu_doc[sentid][wordid][HEAD] = int(pred_tokens[bid][i][0])
# deprel
test_set.conllu_doc[sentid][wordid][DEPREL] = pred_tokens[bid][i][1]
tagged_doc = get_output_doc(dposdep_doc, test_set.conllu_doc)
torch.cuda.empty_cache()
return tagged_doc
def lemmatize(self, input, is_sent=False):
if is_sent:
assert is_string(input) or is_list_strings(
input), 'Input must be one of the following:\n(i) A non-empty string.\n(ii) A list of non-empty strings.'
if is_list_strings(input):
# switch to detected lang if auto mode is on
if self.auto_mode:
self._detect_lang_and_switch(text=' '.join(input))
input = [{ID: k + 1, TEXT: w} for k, w in enumerate(input)]
return {TOKENS: self._lemmatize_sent(in_sent=input, obmit_tag=True), LANG: self.active_lang}
else:
# switch to detected lang if auto mode is on
if self.auto_mode:
self._detect_lang_and_switch(text=input)
ori_text = deepcopy(input)
return {TEXT: ori_text, TOKENS: self._lemmatize_sent(in_sent=input, obmit_tag=True), LANG: self.active_lang}
else:
assert is_string(input) or is_list_list_strings(
input), 'Input must be one of the following:\n(i) A non-empty string.\n(ii) A list of lists of non-empty strings.'
if is_list_list_strings(input):
# switch to detected lang if auto mode is on
if self.auto_mode:
self._detect_lang_and_switch(text='\n'.join([' '.join(sent) for sent in input]))
input = [{ID: sid + 1, TOKENS: [{ID: tid + 1, TEXT: w} for tid, w in enumerate(sent)]} for sid, sent in
enumerate(input)]
return {SENTENCES: self._lemmatize_doc(in_doc=input, obmit_tag=True), LANG: self.active_lang}
else:
# switch to detected lang if auto mode is on
if self.auto_mode:
self._detect_lang_and_switch(text=input)
ori_text = deepcopy(input)
return {TEXT: ori_text, SENTENCES: self._lemmatize_doc(in_doc=input, obmit_tag=True), LANG: self.active_lang}
def _lemmatize_sent(self, in_sent, obmit_tag=False):
if type(in_sent) == str:
in_sent = self._tokenize_sent(in_sent)
in_sent = self._posdep_sent(in_sent)
dlemmatize_sent = deepcopy(in_sent)
lemmatized_sent = \
self._lemma_model[self._config.active_lang].predict([{ID: 1, TOKENS: dlemmatize_sent}], obmit_tag)[0][
TOKENS]
return lemmatized_sent
def _lemmatize_doc(self, in_doc, obmit_tag=False): # assuming input is a document
if type(in_doc) == str: # in_doc is a raw string in this case
in_doc = self._tokenize_doc(in_doc)
in_doc = self._posdep_doc(in_doc)
dlemmatize_doc = deepcopy(in_doc)
lemmatized_doc = self._lemma_model[self._config.active_lang].predict(dlemmatize_doc, obmit_tag)
return lemmatized_doc
def _mwt_expand(self, tokenized_doc):
expanded_doc = self._mwt_model[self._config.active_lang].predict(tokenized_doc)
return expanded_doc
def ner(self, input, is_sent=False):
if is_sent:
assert is_string(input) or is_list_strings(
input), 'Input must be one of the following:\n(i) A non-empty string.\n(ii) A list of non-empty strings.'
if is_list_strings(input):
# switch to detected lang if auto mode is on
if self.auto_mode:
self._detect_lang_and_switch(text=' '.join(input))
assert self.active_lang in langwithner, 'NER module is not available for "{}"'.format(self.active_lang)
input = [{ID: k + 1, TEXT: w} for k, w in enumerate(input)]
return {TOKENS: self._ner_sent(in_sent=input), LANG: self.active_lang}
else:
# switch to detected lang if auto mode is on
if self.auto_mode:
self._detect_lang_and_switch(text=input)
assert self.active_lang in langwithner, 'NER module is not available for "{}"'.format(self.active_lang)
ori_text = deepcopy(input)
return {TEXT: ori_text, TOKENS: self._ner_sent(in_sent=input), LANG: self.active_lang}
else:
assert is_string(input) or is_list_list_strings(
input), 'Input must be one of the following:\n(i) A non-empty string.\n(ii) A list of lists of non-empty strings.'
if is_list_list_strings(input):
# switch to detected lang if auto mode is on
if self.auto_mode:
self._detect_lang_and_switch(text='\n'.join([' '.join(sent) for sent in input]))
assert self.active_lang in langwithner, 'NER module is not available for "{}"'.format(self.active_lang)
input = [{ID: sid + 1, TOKENS: [{ID: tid + 1, TEXT: w} for tid, w in enumerate(sent)]} for sid, sent in
enumerate(input)]
return {SENTENCES: self._ner_doc(in_doc=input), LANG: self.active_lang}
else:
# switch to detected lang if auto mode is on
if self.auto_mode:
self._detect_lang_and_switch(text=input)
assert self.active_lang in langwithner, 'NER module is not available for "{}"'.format(self.active_lang)
ori_text = deepcopy(input)
return {TEXT: ori_text, SENTENCES: self._ner_doc(in_doc=input), LANG: self.active_lang}
def _ner_sent(self, in_sent): # assuming input is a document
if type(in_sent) == str:
in_sent = self._tokenize_sent(in_sent)
dner_doc = [{ID: 1, TOKENS: deepcopy(in_sent)}]
sentences = [[t[TEXT] for t in sentence[TOKENS]] for sentence in dner_doc]
test_set = NERDatasetLive(
config=self._config,
tokenized_sentences=sentences
)
test_set.numberize()
# load ner adapter weights
self._load_adapter_weights(model_name='ner')
eval_batch_size = tbname2tagbatchsize.get(self._config.treebank_name, self._tagbatchsize)
if self._config.embedding_name == 'xlm-roberta-large':
eval_batch_size = int(eval_batch_size / 3)
for batch in DataLoader(test_set,
batch_size=eval_batch_size,
shuffle=False, collate_fn=test_set.collate_fn):
word_reprs, cls_reprs = self._embedding_layers.get_tagger_inputs(batch)
pred_entity_labels = self._ner_model[self._config.active_lang].predict(batch, word_reprs)
batch_size = len(batch.word_num)
for bid in range(batch_size):
sentid = batch.sent_index[bid]
for i in range(batch.word_num[bid]):
wordid = batch.word_ids[bid][i]
# NER tag
dner_doc[sentid][TOKENS][wordid][NER] = pred_entity_labels[bid][i]
torch.cuda.empty_cache()
return dner_doc[0][TOKENS]
def _ner_doc(self, in_doc): # assuming input is a document
if type(in_doc) == str:
in_doc = self._tokenize_doc(in_doc)
dner_doc = deepcopy(in_doc)
sentences = [[t[TEXT] for t in sentence[TOKENS]] for sentence in dner_doc]
test_set = NERDatasetLive(
config=self._config,
tokenized_sentences=sentences
)
test_set.numberize()
# load ner adapter weights
self._load_adapter_weights(model_name='ner')
eval_batch_size = tbname2tagbatchsize.get(self._config.treebank_name, self._tagbatchsize)
if self._config.embedding_name == 'xlm-roberta-large':
eval_batch_size = int(eval_batch_size / 3)
for batch in DataLoader(test_set,
batch_size=eval_batch_size,
shuffle=False, collate_fn=test_set.collate_fn):
word_reprs, cls_reprs = self._embedding_layers.get_tagger_inputs(batch)
pred_entity_labels = self._ner_model[self._config.active_lang].predict(batch, word_reprs)
batch_size = len(batch.word_num)
for bid in range(batch_size):
sentid = batch.sent_index[bid]
for i in range(batch.word_num[bid]):
wordid = batch.word_ids[bid][i]
# NER tag
dner_doc[sentid][TOKENS][wordid][NER] = pred_entity_labels[bid][i]
torch.cuda.empty_cache()
return dner_doc
def __call__(self, input, is_sent=False):
if is_sent:
assert is_string(input) or is_list_strings(
input), 'Input must be one of the following:\n(i) A non-empty string.\n(ii) A list of non-empty strings.'
if is_list_strings(input):
# switch to detected lang if auto mode is on
if self.auto_mode:
self._detect_lang_and_switch(text=' '.join(input))
tokenized_sent = [{ID: k + 1, TEXT: w} for k, w in enumerate(input)]
tagged_sent = self._posdep_sent(tokenized_sent)
out = self._lemmatize_sent(tagged_sent)
if self._config.active_lang in langwithner: # ner if possible
out = self._ner_sent(out)
final = {TOKENS: out, LANG: self.active_lang}
else:
# switch to detected lang if auto mode is on
if self.auto_mode:
self._detect_lang_and_switch(text=input)
ori_text = deepcopy(input)
tagged_sent = self._posdep_sent(input)
out = self._lemmatize_sent(tagged_sent)
if self._config.active_lang in langwithner: # ner if possible
out = self._ner_sent(out)
final = {TEXT: ori_text, TOKENS: out, LANG: self.active_lang}
else:
assert is_string(input) or is_list_list_strings(
input), 'Input must be one of the following:\n(i) A non-empty string.\n(ii) A list of lists of non-empty strings.'
if is_list_list_strings(input):
# switch to detected lang if auto mode is on
if self.auto_mode:
self._detect_lang_and_switch(text='\n'.join([' '.join(sent) for sent in input]))
input = [{ID: sid + 1, TOKENS: [{ID: tid + 1, TEXT: w} for tid, w in enumerate(sent)]} for sid, sent in
enumerate(input)]
tagged_doc = self._posdep_doc(input)
out = self._lemmatize_doc(tagged_doc)
if self._config.active_lang in langwithner: # ner if possible
out = self._ner_doc(out)
final = {SENTENCES: out, LANG: self.active_lang}
else:
# switch to detected lang if auto mode is on
if self.auto_mode:
self._detect_lang_and_switch(text=input)
ori_text = deepcopy(input)
tagged_doc = self._posdep_doc(in_doc=input)
out = self._lemmatize_doc(tagged_doc)
if self._config.active_lang in langwithner: # ner if possible
out = self._ner_doc(out)
final = {TEXT: ori_text, SENTENCES: out, LANG: self.active_lang}
return final
def _conllu_predict(self, text_fpath):
print('Running the pipeline on device={}'.format(self._config.device))
with open(text_fpath) as f:
raw_text = f.read()
print('Beginning tokenization')
tokenized_doc = self._tokenize_doc(raw_text)
print('Beginning pos tagging and dependency parsing')
tagged_doc = self._posdep_doc(tokenized_doc)
print('Beginning lemmatization')
lemmatized_doc = self._lemmatize_doc(tagged_doc)
conllu_doc = []
for sentence in lemmatized_doc:
conllu_sentence = []
for token in sentence[TOKENS]:
if type(token[ID]) == int or len(token[ID]) == 1:
conllu_sentence.append(token)
else:
conllu_sentence.append(token)
for word in token[EXPANDED]:
conllu_sentence.append(word)
conllu_doc.append(conllu_sentence)
pred_lemma_fpath = text_fpath + '.pred'
CoNLL.dict2conll(conllu_doc, pred_lemma_fpath)
return pred_lemma_fpath
| 48.226465
| 166
| 0.565936
| 6,399
| 54,303
| 4.514455
| 0.058134
| 0.036693
| 0.017447
| 0.02077
| 0.825845
| 0.807844
| 0.788978
| 0.777209
| 0.768174
| 0.760454
| 0
| 0.007422
| 0.342449
| 54,303
| 1,125
| 167
| 48.269333
| 0.801608
| 0.04904
| 0
| 0.732584
| 0
| 0.010112
| 0.046035
| 0.000935
| 0
| 0
| 0
| 0
| 0.033708
| 1
| 0.030337
| false
| 0
| 0.017978
| 0
| 0.088764
| 0.023596
| 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
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| null | 0
| 0
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| 0
| 0
|
0
| 7
|
4c196fa1c178a8046261245a00e427b5e1eecea2
| 3,490
|
py
|
Python
|
flatland/database/population/drawing/closed_shape_fill_instances.py
|
erik-soederholm/flatland-model-diagram-editor
|
088e27cded9eca2cacba2c6168c03caf4b43ef72
|
[
"MIT"
] | 10
|
2021-01-03T16:47:34.000Z
|
2022-03-30T18:47:07.000Z
|
flatland/database/population/drawing/closed_shape_fill_instances.py
|
modelint/flatland-model-diagram-editor
|
088e27cded9eca2cacba2c6168c03caf4b43ef72
|
[
"MIT"
] | 91
|
2021-01-09T02:14:13.000Z
|
2022-02-24T10:24:10.000Z
|
flatland/database/population/drawing/closed_shape_fill_instances.py
|
erik-soederholm/flatland-model-diagram-editor
|
088e27cded9eca2cacba2c6168c03caf4b43ef72
|
[
"MIT"
] | 1
|
2021-01-13T22:13:19.000Z
|
2021-01-13T22:13:19.000Z
|
"""
closed_shape_fill_instances.py
"""
population = [
# Title block boxes
{'Asset': 'Block border', 'Presentation': 'default', 'Drawing type': 'OS Engineer large frame', 'Fill': 'white'},
{'Asset': 'Block border', 'Presentation': 'default', 'Drawing type': 'OS Engineer medium frame', 'Fill': 'white'},
{'Asset': 'Block border', 'Presentation': 'default', 'Drawing type': 'TRI MBSE large frame', 'Fill': 'white'},
{'Asset': 'Block border', 'Presentation': 'default', 'Drawing type': 'TRI MBSE medium frame', 'Fill': 'white'},
# Starr/default symbols
{'Asset': 'solid arrow', 'Presentation': 'default', 'Drawing type': 'Starr class diagram', 'Fill': 'black'},
{'Asset': 'hollow arrow', 'Presentation': 'default', 'Drawing type': 'Starr class diagram', 'Fill': 'white'},
{'Asset': 'gen arrow', 'Presentation': 'default', 'Drawing type': 'Starr class diagram', 'Fill': 'white'},
{'Asset': 'class name compartment', 'Presentation': 'default', 'Drawing type': 'Starr class diagram', 'Fill': 'white'},
{'Asset': 'class attribute compartment', 'Presentation': 'default', 'Drawing type': 'Starr class diagram', 'Fill': 'white'},
{'Asset': 'class method compartment', 'Presentation': 'default', 'Drawing type': 'Starr class diagram', 'Fill': 'white'},
{'Asset': 'imported class name compartment', 'Presentation': 'default', 'Drawing type': 'Starr class diagram',
'Fill': 'white'},
{'Asset': 'imported class attribute compartment', 'Presentation': 'default', 'Drawing type': 'Starr class diagram',
'Fill': 'white'},
# Shlaer-Mellor/default symbols
{'Asset': 'class name compartment', 'Presentation': 'default', 'Drawing type': 'Shlaer-Mellor class diagram',
'Fill': 'white'},
{'Asset': 'class attribute compartment', 'Presentation': 'default', 'Drawing type': 'Shlaer-Mellor class diagram',
'Fill': 'white'},
{'Asset': 'class method compartment', 'Presentation': 'default', 'Drawing type': 'Shlaer-Mellor class diagram',
'Fill': 'white'},
{'Asset': 'imported class compartment', 'Presentation': 'default', 'Drawing type': 'Shlaer-Mellor class diagram',
'Fill': 'white'},
# xUML/default
{'Asset': 'class name compartment', 'Presentation': 'default', 'Drawing type': 'xUML class diagram', 'Fill': 'white'},
{'Asset': 'class attribute compartment', 'Presentation': 'default', 'Drawing type': 'xUML class diagram', 'Fill': 'white'},
{'Asset': 'class method compartment', 'Presentation': 'default', 'Drawing type': 'xUML class diagram', 'Fill': 'white'},
{'Asset': 'imported class compartment', 'Presentation': 'default', 'Drawing type': 'xUML class diagram', 'Fill': 'white'},
{'Asset': 'state name compartment', 'Presentation': 'default', 'Drawing type': 'xUML state machine diagram',
'Fill': 'white'},
{'Asset': 'state name only compartment', 'Presentation': 'default', 'Drawing type': 'xUML state machine diagram',
'Fill': 'white'},
{'Asset': 'state activity compartment', 'Presentation': 'default', 'Drawing type': 'xUML state machine diagram',
'Fill': 'white'},
{'Asset': 'solid arrow', 'Presentation': 'default', 'Drawing type': 'xUML state machine diagram', 'Fill': 'black'},
{'Asset': 'solid small dot', 'Presentation': 'default', 'Drawing type': 'xUML state machine diagram', 'Fill': 'black'},
{'Asset': 'hollow large circle', 'Presentation': 'default', 'Drawing type': 'xUML state machine diagram',
'Fill': 'white'},
]
| 68.431373
| 128
| 0.646132
| 365
| 3,490
| 6.169863
| 0.128767
| 0.219361
| 0.300178
| 0.346359
| 0.923623
| 0.913854
| 0.909858
| 0.899201
| 0.886767
| 0.832593
| 0
| 0
| 0.153868
| 3,490
| 50
| 129
| 69.8
| 0.762614
| 0.032665
| 0
| 0.263158
| 0
| 0
| 0.678752
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.105263
| 0
| 0.105263
| 0
| 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
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 10
|
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