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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1589fa21cf6bf20ff3bdb8c933f5a4a5b7255471
| 35,350
|
py
|
Python
|
tests/test_all_step_predator_prey.py
|
Leonardo767/Abmarl
|
9fada5447b09174c6a70b6032b4a8d08b66c4589
|
[
"Apache-2.0"
] | null | null | null |
tests/test_all_step_predator_prey.py
|
Leonardo767/Abmarl
|
9fada5447b09174c6a70b6032b4a8d08b66c4589
|
[
"Apache-2.0"
] | null | null | null |
tests/test_all_step_predator_prey.py
|
Leonardo767/Abmarl
|
9fada5447b09174c6a70b6032b4a8d08b66c4589
|
[
"Apache-2.0"
] | null | null | null |
import numpy as np
import pytest
from abmarl.sim.predator_prey import PredatorPreySimulation, Predator, Prey
from abmarl.managers import AllStepManager
def test_turn_based_predator_prey_distance():
np.random.seed(24)
predators = [Predator(id=f'predator{i}', attack=1) for i in range(2)]
prey = [Prey(id=f'prey{i}') for i in range(7)]
agents = predators + prey
sim_config = {
'region': 6,
'observation_mode': PredatorPreySimulation.ObservationMode.DISTANCE,
'agents': agents,
}
sim = PredatorPreySimulation.build(sim_config)
sim = AllStepManager(sim)
# Little hackish here because I have to explicitly set their values
obs = sim.reset()
sim.agents['predator0'].position = np.array([2, 3])
sim.agents['predator1'].position = np.array([0, 1])
sim.agents['prey0'].position = np.array([1, 1])
sim.agents['prey1'].position = np.array([4, 3])
sim.agents['prey2'].position = np.array([4, 3])
sim.agents['prey3'].position = np.array([2, 3])
sim.agents['prey4'].position = np.array([3, 3])
sim.agents['prey5'].position = np.array([3, 1])
sim.agents['prey6'].position = np.array([2, 1])
obs = {agent_id: sim.sim.get_obs(agent_id) for agent_id in sim.agents}
np.testing.assert_array_equal(obs['predator0']['predator1'], np.array([-2, -2, 2]))
np.testing.assert_array_equal(obs['predator0']['prey0'], np.array([-1, -2, 1]))
np.testing.assert_array_equal(obs['predator0']['prey1'], np.array([2, 0, 1]))
np.testing.assert_array_equal(obs['predator0']['prey2'], np.array([2, 0, 1]))
np.testing.assert_array_equal(obs['predator0']['prey3'], np.array([0, 0, 1]))
np.testing.assert_array_equal(obs['predator0']['prey4'], np.array([1, 0, 1]))
np.testing.assert_array_equal(obs['predator0']['prey5'], np.array([1, -2, 1]))
np.testing.assert_array_equal(obs['predator0']['prey6'], np.array([0, -2, 1]))
np.testing.assert_array_equal(obs['predator1']['predator0'], np.array([2, 2, 2]))
np.testing.assert_array_equal(obs['predator1']['prey0'], np.array([1, 0, 1]))
np.testing.assert_array_equal(obs['predator1']['prey1'], np.array([4, 2, 1]))
np.testing.assert_array_equal(obs['predator1']['prey2'], np.array([4, 2, 1]))
np.testing.assert_array_equal(obs['predator1']['prey3'], np.array([2, 2, 1]))
np.testing.assert_array_equal(obs['predator1']['prey4'], np.array([3, 2, 1]))
np.testing.assert_array_equal(obs['predator1']['prey5'], np.array([3, 0, 1]))
np.testing.assert_array_equal(obs['predator1']['prey6'], np.array([2, 0, 1]))
np.testing.assert_array_equal(obs['prey0']['predator0'], np.array([1, 2, 2]))
np.testing.assert_array_equal(obs['prey0']['predator1'], np.array([-1, 0, 2]))
np.testing.assert_array_equal(obs['prey0']['prey1'], np.array([3, 2, 1]))
np.testing.assert_array_equal(obs['prey0']['prey2'], np.array([3, 2, 1]))
np.testing.assert_array_equal(obs['prey0']['prey3'], np.array([1, 2, 1]))
np.testing.assert_array_equal(obs['prey0']['prey4'], np.array([2, 2, 1]))
np.testing.assert_array_equal(obs['prey0']['prey5'], np.array([2, 0, 1]))
np.testing.assert_array_equal(obs['prey0']['prey6'], np.array([1, 0, 1]))
np.testing.assert_array_equal(obs['prey1']['predator0'], np.array([-2, 0, 2]))
np.testing.assert_array_equal(obs['prey1']['predator1'], np.array([-4, -2, 2]))
np.testing.assert_array_equal(obs['prey1']['prey0'], np.array([-3, -2, 1]))
np.testing.assert_array_equal(obs['prey1']['prey2'], np.array([0, 0, 1]))
np.testing.assert_array_equal(obs['prey1']['prey3'], np.array([-2, 0, 1]))
np.testing.assert_array_equal(obs['prey1']['prey4'], np.array([-1, 0, 1]))
np.testing.assert_array_equal(obs['prey1']['prey5'], np.array([-1, -2, 1]))
np.testing.assert_array_equal(obs['prey1']['prey6'], np.array([-2, -2, 1]))
np.testing.assert_array_equal(obs['prey2']['predator0'], np.array([-2, 0, 2]))
np.testing.assert_array_equal(obs['prey2']['predator1'], np.array([-4, -2, 2]))
np.testing.assert_array_equal(obs['prey2']['prey0'], np.array([-3, -2, 1]))
np.testing.assert_array_equal(obs['prey2']['prey1'], np.array([0, 0, 1]))
np.testing.assert_array_equal(obs['prey2']['prey3'], np.array([-2, 0, 1]))
np.testing.assert_array_equal(obs['prey2']['prey4'], np.array([-1, 0, 1]))
np.testing.assert_array_equal(obs['prey2']['prey5'], np.array([-1, -2, 1]))
np.testing.assert_array_equal(obs['prey2']['prey6'], np.array([-2, -2, 1]))
np.testing.assert_array_equal(obs['prey3']['predator0'], np.array([0, 0, 2]))
np.testing.assert_array_equal(obs['prey3']['predator1'], np.array([-2, -2, 2]))
np.testing.assert_array_equal(obs['prey3']['prey0'], np.array([-1, -2, 1]))
np.testing.assert_array_equal(obs['prey3']['prey1'], np.array([2, 0, 1]))
np.testing.assert_array_equal(obs['prey3']['prey2'], np.array([2, 0, 1]))
np.testing.assert_array_equal(obs['prey3']['prey4'], np.array([1, 0, 1]))
np.testing.assert_array_equal(obs['prey3']['prey5'], np.array([1, -2, 1]))
np.testing.assert_array_equal(obs['prey3']['prey6'], np.array([0, -2, 1]))
np.testing.assert_array_equal(obs['prey4']['predator0'], np.array([-1, 0, 2]))
np.testing.assert_array_equal(obs['prey4']['predator1'], np.array([-3, -2, 2]))
np.testing.assert_array_equal(obs['prey4']['prey0'], np.array([-2, -2, 1]))
np.testing.assert_array_equal(obs['prey4']['prey1'], np.array([1, 0, 1]))
np.testing.assert_array_equal(obs['prey4']['prey2'], np.array([1, 0, 1]))
np.testing.assert_array_equal(obs['prey4']['prey3'], np.array([-1, 0, 1]))
np.testing.assert_array_equal(obs['prey4']['prey5'], np.array([0, -2, 1]))
np.testing.assert_array_equal(obs['prey4']['prey6'], np.array([-1, -2, 1]))
np.testing.assert_array_equal(obs['prey5']['predator0'], np.array([-1, 2, 2]))
np.testing.assert_array_equal(obs['prey5']['predator1'], np.array([-3, 0, 2]))
np.testing.assert_array_equal(obs['prey5']['prey0'], np.array([-2, 0, 1]))
np.testing.assert_array_equal(obs['prey5']['prey1'], np.array([1, 2, 1]))
np.testing.assert_array_equal(obs['prey5']['prey2'], np.array([1, 2, 1]))
np.testing.assert_array_equal(obs['prey5']['prey3'], np.array([-1, 2, 1]))
np.testing.assert_array_equal(obs['prey5']['prey4'], np.array([0, 2, 1]))
np.testing.assert_array_equal(obs['prey5']['prey6'], np.array([-1, 0, 1]))
np.testing.assert_array_equal(obs['prey6']['predator0'], np.array([0, 2, 2]))
np.testing.assert_array_equal(obs['prey6']['predator1'], np.array([-2, 0, 2]))
np.testing.assert_array_equal(obs['prey6']['prey0'], np.array([-1, 0, 1]))
np.testing.assert_array_equal(obs['prey6']['prey1'], np.array([2, 2, 1]))
np.testing.assert_array_equal(obs['prey6']['prey2'], np.array([2, 2, 1]))
np.testing.assert_array_equal(obs['prey6']['prey3'], np.array([0, 2, 1]))
np.testing.assert_array_equal(obs['prey6']['prey4'], np.array([1, 2, 1]))
np.testing.assert_array_equal(obs['prey6']['prey5'], np.array([1, 0, 1]))
obs, reward, done, info = sim.step({
'predator0': {'attack': 1, 'move': np.array([0, 0])},
'predator1': {'attack': 1, 'move': np.array([0, 0])},
'prey0': np.array([-1, 1]),
'prey1': np.array([0, -1]),
'prey2': np.array([1, 1]),
'prey3': np.array([1, -1]),
'prey4': np.array([-1, 1]),
'prey5': np.array([1, 1]),
'prey6': np.array([0, 0]),
})
np.testing.assert_array_equal(obs['predator0']['predator1'], np.array([-2, -2, 2]))
np.testing.assert_array_equal(obs['predator0']['prey0'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['predator0']['prey1'], np.array([2, -1, 1]))
np.testing.assert_array_equal(obs['predator0']['prey2'], np.array([3, 1, 1]))
np.testing.assert_array_equal(obs['predator0']['prey3'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['predator0']['prey4'], np.array([0, 1, 1]))
np.testing.assert_array_equal(obs['predator0']['prey5'], np.array([2, -1, 1]))
np.testing.assert_array_equal(obs['predator0']['prey6'], np.array([0, -2, 1]))
np.testing.assert_array_equal(obs['predator1']['predator0'], np.array([2, 2, 2]))
np.testing.assert_array_equal(obs['predator1']['prey0'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['predator1']['prey1'], np.array([4, 1, 1]))
np.testing.assert_array_equal(obs['predator1']['prey2'], np.array([5, 3, 1]))
np.testing.assert_array_equal(obs['predator1']['prey3'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['predator1']['prey4'], np.array([2, 3, 1]))
np.testing.assert_array_equal(obs['predator1']['prey5'], np.array([4, 1, 1]))
np.testing.assert_array_equal(obs['predator1']['prey6'], np.array([2, 0, 1]))
np.testing.assert_array_equal(obs['prey0']['predator0'], np.array([1, 2, 2]))
np.testing.assert_array_equal(obs['prey0']['predator1'], np.array([-1, 0, 2]))
np.testing.assert_array_equal(obs['prey0']['prey1'], np.array([3, 1, 1]))
np.testing.assert_array_equal(obs['prey0']['prey2'], np.array([4, 3, 1]))
np.testing.assert_array_equal(obs['prey0']['prey3'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey0']['prey4'], np.array([1, 3, 1]))
np.testing.assert_array_equal(obs['prey0']['prey5'], np.array([3, 1, 1]))
np.testing.assert_array_equal(obs['prey0']['prey6'], np.array([1, 0, 1]))
np.testing.assert_array_equal(obs['prey1']['predator0'], np.array([-2, 1, 2]))
np.testing.assert_array_equal(obs['prey1']['predator1'], np.array([-4, -1, 2]))
np.testing.assert_array_equal(obs['prey1']['prey0'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey1']['prey2'], np.array([1, 2, 1]))
np.testing.assert_array_equal(obs['prey1']['prey3'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey1']['prey4'], np.array([-2, 2, 1]))
np.testing.assert_array_equal(obs['prey1']['prey5'], np.array([0, 0, 1]))
np.testing.assert_array_equal(obs['prey1']['prey6'], np.array([-2, -1, 1]))
np.testing.assert_array_equal(obs['prey2']['predator0'], np.array([-3, -1, 2]))
np.testing.assert_array_equal(obs['prey2']['predator1'], np.array([-5, -3, 2]))
np.testing.assert_array_equal(obs['prey2']['prey0'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey2']['prey1'], np.array([-1, -2, 1]))
np.testing.assert_array_equal(obs['prey2']['prey3'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey2']['prey4'], np.array([-3, 0, 1]))
np.testing.assert_array_equal(obs['prey2']['prey5'], np.array([-1, -2, 1]))
np.testing.assert_array_equal(obs['prey2']['prey6'], np.array([-3, -3, 1]))
np.testing.assert_array_equal(obs['prey3']['predator0'], np.array([0, 0, 2]))
np.testing.assert_array_equal(obs['prey3']['predator1'], np.array([-2, -2, 2]))
np.testing.assert_array_equal(obs['prey3']['prey0'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey3']['prey1'], np.array([2, -1, 1]))
np.testing.assert_array_equal(obs['prey3']['prey2'], np.array([3, 1, 1]))
np.testing.assert_array_equal(obs['prey3']['prey4'], np.array([0, 1, 1]))
np.testing.assert_array_equal(obs['prey3']['prey5'], np.array([2, -1, 1]))
np.testing.assert_array_equal(obs['prey3']['prey6'], np.array([0, -2, 1]))
np.testing.assert_array_equal(obs['prey4']['predator0'], np.array([0, -1, 2]))
np.testing.assert_array_equal(obs['prey4']['predator1'], np.array([-2, -3, 2]))
np.testing.assert_array_equal(obs['prey4']['prey0'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey4']['prey1'], np.array([2, -2, 1]))
np.testing.assert_array_equal(obs['prey4']['prey2'], np.array([3, 0, 1]))
np.testing.assert_array_equal(obs['prey4']['prey3'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey4']['prey5'], np.array([2, -2, 1]))
np.testing.assert_array_equal(obs['prey4']['prey6'], np.array([0, -3, 1]))
np.testing.assert_array_equal(obs['prey5']['predator0'], np.array([-2, 1, 2]))
np.testing.assert_array_equal(obs['prey5']['predator1'], np.array([-4, -1, 2]))
np.testing.assert_array_equal(obs['prey5']['prey0'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey5']['prey1'], np.array([0, 0, 1]))
np.testing.assert_array_equal(obs['prey5']['prey2'], np.array([1, 2, 1]))
np.testing.assert_array_equal(obs['prey5']['prey3'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey5']['prey4'], np.array([-2, 2, 1]))
np.testing.assert_array_equal(obs['prey5']['prey6'], np.array([-2, -1, 1]))
np.testing.assert_array_equal(obs['prey6']['predator0'], np.array([0, 2, 2]))
np.testing.assert_array_equal(obs['prey6']['predator1'], np.array([-2, 0, 2]))
np.testing.assert_array_equal(obs['prey6']['prey0'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey6']['prey1'], np.array([2, 1, 1]))
np.testing.assert_array_equal(obs['prey6']['prey2'], np.array([3, 3, 1]))
np.testing.assert_array_equal(obs['prey6']['prey3'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey6']['prey4'], np.array([0, 3, 1]))
np.testing.assert_array_equal(obs['prey6']['prey5'], np.array([2, 1, 1]))
assert reward == {
'predator0': 36,
'predator1': 36,
'prey0': -36,
'prey1': -1,
'prey2': -1,
'prey3': -36,
'prey4': -1,
'prey5': -1,
'prey6': 0,
}
assert done == {
'predator0': False,
'predator1': False,
'prey0': True,
'prey1': False,
'prey2': False,
'prey3': True,
'prey4': False,
'prey5': False,
'prey6': False,
'__all__': False}
with pytest.raises(AssertionError):
obs, reward, done, info = sim.step({
'predator0': {'attack': 1, 'move': np.array([0, 0])},
'predator1': {'attack': 1, 'move': np.array([0, 0])},
'prey0': np.array([-1, 1]),
'prey1': np.array([0, -1]),
'prey2': np.array([1, 1]),
'prey3': np.array([1, -1]),
'prey4': np.array([-1, 1]),
'prey5': np.array([1, 1]),
'prey6': np.array([0, 0]),
})
obs, reward, done, info = sim.step({
'predator0': {'attack': 1, 'move': np.array([0, 0])},
'predator1': {'attack': 0, 'move': np.array([1, 0])},
'prey1': np.array([-1, -1]),
'prey2': np.array([-1, 0]),
'prey4': np.array([-1, 0]),
'prey5': np.array([-1, 0]),
'prey6': np.array([0, -1]),
})
np.testing.assert_array_equal(obs['predator0']['predator1'], np.array([-1, -2, 2]))
np.testing.assert_array_equal(obs['predator0']['prey0'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['predator0']['prey1'], np.array([1, -2, 1]))
np.testing.assert_array_equal(obs['predator0']['prey2'], np.array([2, 1, 1]))
np.testing.assert_array_equal(obs['predator0']['prey3'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['predator0']['prey4'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['predator0']['prey5'], np.array([1, -1, 1]))
np.testing.assert_array_equal(obs['predator0']['prey6'], np.array([0, -3, 1]))
np.testing.assert_array_equal(obs['predator1']['predator0'], np.array([1, 2, 2]))
np.testing.assert_array_equal(obs['predator1']['prey0'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['predator1']['prey1'], np.array([2, 0, 1]))
np.testing.assert_array_equal(obs['predator1']['prey2'], np.array([3, 3, 1]))
np.testing.assert_array_equal(obs['predator1']['prey3'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['predator1']['prey4'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['predator1']['prey5'], np.array([2, 1, 1]))
np.testing.assert_array_equal(obs['predator1']['prey6'], np.array([1, -1, 1]))
np.testing.assert_array_equal(obs['prey1']['predator0'], np.array([-1, 2, 2]))
np.testing.assert_array_equal(obs['prey1']['predator1'], np.array([-2, 0, 2]))
np.testing.assert_array_equal(obs['prey1']['prey0'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey1']['prey2'], np.array([1, 3, 1]))
np.testing.assert_array_equal(obs['prey1']['prey3'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey1']['prey4'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey1']['prey5'], np.array([0, 1, 1]))
np.testing.assert_array_equal(obs['prey1']['prey6'], np.array([-1, -1, 1]))
np.testing.assert_array_equal(obs['prey2']['predator0'], np.array([-2, -1, 2]))
np.testing.assert_array_equal(obs['prey2']['predator1'], np.array([-3, -3, 2]))
np.testing.assert_array_equal(obs['prey2']['prey0'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey2']['prey1'], np.array([-1, -3, 1]))
np.testing.assert_array_equal(obs['prey2']['prey3'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey2']['prey4'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey2']['prey5'], np.array([-1, -2, 1]))
np.testing.assert_array_equal(obs['prey2']['prey6'], np.array([-2, -4, 1]))
np.testing.assert_array_equal(obs['prey4']['predator0'], np.array([0, -1, 2]))
np.testing.assert_array_equal(obs['prey4']['predator1'], np.array([-1, -3, 2]))
np.testing.assert_array_equal(obs['prey4']['prey0'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey4']['prey1'], np.array([1, -3, 1]))
np.testing.assert_array_equal(obs['prey4']['prey2'], np.array([2, 0, 1]))
np.testing.assert_array_equal(obs['prey4']['prey3'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey4']['prey5'], np.array([1, -2, 1]))
np.testing.assert_array_equal(obs['prey4']['prey6'], np.array([0, -4, 1]))
np.testing.assert_array_equal(obs['prey5']['predator0'], np.array([-1, 1, 2]))
np.testing.assert_array_equal(obs['prey5']['predator1'], np.array([-2, -1, 2]))
np.testing.assert_array_equal(obs['prey5']['prey0'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey5']['prey1'], np.array([0, -1, 1]))
np.testing.assert_array_equal(obs['prey5']['prey2'], np.array([1, 2, 1]))
np.testing.assert_array_equal(obs['prey5']['prey3'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey5']['prey4'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey5']['prey6'], np.array([-1, -2, 1]))
np.testing.assert_array_equal(obs['prey6']['predator0'], np.array([0, 3, 2]))
np.testing.assert_array_equal(obs['prey6']['predator1'], np.array([-1, 1, 2]))
np.testing.assert_array_equal(obs['prey6']['prey0'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey6']['prey1'], np.array([1, 1, 1]))
np.testing.assert_array_equal(obs['prey6']['prey2'], np.array([2, 4, 1]))
np.testing.assert_array_equal(obs['prey6']['prey3'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey6']['prey4'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey6']['prey5'], np.array([1, 2, 1]))
assert reward == {
'predator0': 36,
'predator1': -1,
'prey1': -1,
'prey2': -1,
'prey4': -36,
'prey5': -1,
'prey6': -1,
}
assert done == {
'predator0': False,
'predator1': False,
'prey1': False,
'prey2': False,
'prey4': True,
'prey5': False,
'prey6': False,
'__all__': False}
with pytest.raises(AssertionError):
obs, reward, done, info = sim.step({
'predator0': {'attack': 1, 'move': np.array([0, 0])},
'predator1': {'attack': 1, 'move': np.array([0, 0])},
'prey1': np.array([0, -1]),
'prey2': np.array([1, 1]),
'prey4': np.array([-1, 1]),
'prey5': np.array([1, 1]),
'prey6': np.array([0, 0]),
})
obs, reward, done, info = sim.step({
'predator0': {'attack': 1, 'move': np.array([0, 0])},
'predator1': {'attack': 1, 'move': np.array([0, 0])},
'prey1': np.array([-1, 0]),
'prey2': np.array([-1, 0]),
'prey5': np.array([0, 1]),
'prey6': np.array([-1, 0]),
})
np.testing.assert_array_equal(obs['predator0']['predator1'], np.array([-1, -2, 2]))
np.testing.assert_array_equal(obs['predator0']['prey0'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['predator0']['prey1'], np.array([0, -2, 1]))
np.testing.assert_array_equal(obs['predator0']['prey2'], np.array([1, 1, 1]))
np.testing.assert_array_equal(obs['predator0']['prey3'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['predator0']['prey4'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['predator0']['prey5'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['predator0']['prey6'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['predator1']['predator0'], np.array([1, 2, 2]))
np.testing.assert_array_equal(obs['predator1']['prey0'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['predator1']['prey1'], np.array([1, 0, 1]))
np.testing.assert_array_equal(obs['predator1']['prey2'], np.array([2, 3, 1]))
np.testing.assert_array_equal(obs['predator1']['prey3'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['predator1']['prey4'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['predator1']['prey5'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['predator1']['prey6'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey1']['predator0'], np.array([0, 2, 2]))
np.testing.assert_array_equal(obs['prey1']['predator1'], np.array([-1, 0, 2]))
np.testing.assert_array_equal(obs['prey1']['prey0'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey1']['prey2'], np.array([1, 3, 1]))
np.testing.assert_array_equal(obs['prey1']['prey3'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey1']['prey4'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey1']['prey5'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey1']['prey6'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey2']['predator0'], np.array([-1, -1, 2]))
np.testing.assert_array_equal(obs['prey2']['predator1'], np.array([-2, -3, 2]))
np.testing.assert_array_equal(obs['prey2']['prey0'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey2']['prey1'], np.array([-1, -3, 1]))
np.testing.assert_array_equal(obs['prey2']['prey3'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey2']['prey4'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey2']['prey5'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey2']['prey6'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey5']['predator0'], np.array([-1, 1, 2]))
np.testing.assert_array_equal(obs['prey5']['predator1'], np.array([-2, -1, 2]))
np.testing.assert_array_equal(obs['prey5']['prey0'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey5']['prey1'], np.array([-1, -1, 1]))
np.testing.assert_array_equal(obs['prey5']['prey2'], np.array([0, 2, 1]))
np.testing.assert_array_equal(obs['prey5']['prey3'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey5']['prey4'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey5']['prey6'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey6']['predator0'], np.array([0, 3, 2]))
np.testing.assert_array_equal(obs['prey6']['predator1'], np.array([-1, 1, 2]))
np.testing.assert_array_equal(obs['prey6']['prey0'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey6']['prey1'], np.array([0, 1, 1]))
np.testing.assert_array_equal(obs['prey6']['prey2'], np.array([1, 4, 1]))
np.testing.assert_array_equal(obs['prey6']['prey3'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey6']['prey4'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey6']['prey5'], np.array([0, 0, 0]))
assert reward == {
'predator0': 36,
'predator1': 36,
'prey1': -1,
'prey2': -1,
'prey5': -36,
'prey6': -36
}
assert done == {
'predator0': False,
'predator1': False,
'prey1': False,
'prey2': False,
'prey5': True,
'prey6': True,
'__all__': False}
with pytest.raises(AssertionError):
obs, reward, done, info = sim.step({
'predator0': {'attack': 1, 'move': np.array([0, 0])},
'predator1': {'attack': 1, 'move': np.array([0, 0])},
'prey1': np.array([0, -1]),
'prey2': np.array([1, 1]),
'prey5': np.array([1, 1]),
'prey6': np.array([0, 0]),
})
obs, reward, done, info = sim.step({
'predator0': {'attack': 1, 'move': np.array([0, 0])},
'predator1': {'attack': 1, 'move': np.array([0, 0])},
'prey1': np.array([-1, 0]),
'prey2': np.array([-1, 0]),
})
np.testing.assert_array_equal(obs['predator0']['predator1'], np.array([-1, -2, 2]))
np.testing.assert_array_equal(obs['predator0']['prey0'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['predator0']['prey1'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['predator0']['prey2'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['predator0']['prey3'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['predator0']['prey4'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['predator0']['prey5'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['predator0']['prey6'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['predator1']['predator0'], np.array([1, 2, 2]))
np.testing.assert_array_equal(obs['predator1']['prey0'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['predator1']['prey1'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['predator1']['prey2'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['predator1']['prey3'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['predator1']['prey4'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['predator1']['prey5'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['predator1']['prey6'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey1']['predator0'], np.array([0, 2, 2]))
np.testing.assert_array_equal(obs['prey1']['predator1'], np.array([-1, 0, 2]))
np.testing.assert_array_equal(obs['prey1']['prey0'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey1']['prey2'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey1']['prey3'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey1']['prey4'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey1']['prey5'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey1']['prey6'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey2']['predator0'], np.array([-1, -1, 2]))
np.testing.assert_array_equal(obs['prey2']['predator1'], np.array([-2, -3, 2]))
np.testing.assert_array_equal(obs['prey2']['prey0'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey2']['prey1'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey2']['prey3'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey2']['prey4'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey2']['prey5'], np.array([0, 0, 0]))
np.testing.assert_array_equal(obs['prey2']['prey6'], np.array([0, 0, 0]))
assert reward == {
'predator0': 36,
'predator1': 36,
'prey1': -36,
'prey2': -36,
}
assert done == {
'predator0': False,
'predator1': False,
'prey1': True,
'prey2': True,
'__all__': True}
def test_turn_based_predator_prey_grid():
np.random.seed(24)
predators = [Predator(id=f'predator{i}', attack=1, view=0) for i in range(2)]
prey = [Prey(id=f'prey{i}', view=0) for i in range(7)]
agents = predators + prey
sim_config = {
'region': 6,
'observation_mode': PredatorPreySimulation.ObservationMode.GRID,
'agents': agents,
}
sim = PredatorPreySimulation.build(sim_config)
sim = AllStepManager(sim)
# Little hackish here because I have to explicitly set their values
obs = sim.reset()
sim.agents['predator0'].position = np.array([2, 3])
sim.agents['predator1'].position = np.array([0, 1])
sim.agents['prey0'].position = np.array([1, 1])
sim.agents['prey1'].position = np.array([4, 3])
sim.agents['prey2'].position = np.array([4, 3])
sim.agents['prey3'].position = np.array([2, 3])
sim.agents['prey4'].position = np.array([3, 3])
sim.agents['prey5'].position = np.array([3, 1])
sim.agents['prey6'].position = np.array([2, 1])
obs = {agent_id: sim.sim.get_obs(agent_id) for agent_id in sim.agents}
assert 'predator0' in obs
assert 'predator0' in obs
assert 'prey0' in obs
assert 'prey1' in obs
assert 'prey2' in obs
assert 'prey3' in obs
assert 'prey4' in obs
assert 'prey5' in obs
assert 'prey6' in obs
obs, reward, done, info = sim.step({
'predator0': {'attack': 1, 'move': np.array([0, 0])},
'predator1': {'attack': 1, 'move': np.array([0, 0])},
'prey0': {'move': np.array([1, 1]), 'harvest': 0},
'prey1': {'move': np.array([0, -1]), 'harvest': 0},
'prey2': {'move': np.array([1, 1]), 'harvest': 0},
'prey3': {'move': np.array([0, 0]), 'harvest': 0},
'prey4': {'move': np.array([-1, 1]), 'harvest': 0},
'prey5': {'move': np.array([1, 1]), 'harvest': 0},
'prey6': {'move': np.array([0, 0]), 'harvest': 0},
})
assert 'predator0' in obs
assert 'predator0' in obs
assert 'prey0' in obs
assert 'prey1' in obs
assert 'prey2' in obs
assert 'prey3' in obs
assert 'prey4' in obs
assert 'prey5' in obs
assert 'prey6' in obs
assert reward == {
'predator0': 36,
'predator1': 36,
'prey0': -36,
'prey1': -1,
'prey2': -1,
'prey3': -36,
'prey4': -1,
'prey5': -1,
'prey6': 0,
}
assert done == {
'predator0': False,
'predator1': False,
'prey0': True,
'prey1': False,
'prey2': False,
'prey3': True,
'prey4': False,
'prey5': False,
'prey6': False,
'__all__': False}
with pytest.raises(AssertionError):
obs, reward, done, info = sim.step({
'predator0': {'attack': 1, 'move': np.array([0, 0])},
'predator1': {'attack': 1, 'move': np.array([0, 0])},
'prey0': {'move': np.array([0, -1]), 'harvest': 0},
'prey1': {'move': np.array([0, -1]), 'harvest': 0},
'prey2': {'move': np.array([1, 1]), 'harvest': 0},
'prey3': {'move': np.array([0, -1]), 'harvest': 0},
'prey4': {'move': np.array([0, -1]), 'harvest': 0},
'prey5': {'move': np.array([1, 1]), 'harvest': 0},
'prey6': {'move': np.array([0, 0]), 'harvest': 0},
})
obs, reward, done, info = sim.step({
'predator0': {'attack': 1, 'move': np.array([0, 0])},
'predator1': {'attack': 0, 'move': np.array([1, 0])},
'prey1': {'move': np.array([-1, -1]), 'harvest': 0},
'prey2': {'move': np.array([-1, 0]), 'harvest': 0},
'prey4': {'move': np.array([0, -1]), 'harvest': 0},
'prey5': {'move': np.array([-1, 0]), 'harvest': 0},
'prey6': {'move': np.array([0, -1]), 'harvest': 0},
})
assert 'predator0' in obs
assert 'predator0' in obs
assert 'prey1' in obs
assert 'prey2' in obs
assert 'prey4' in obs
assert 'prey5' in obs
assert 'prey6' in obs
assert reward == {
'predator0': 36,
'predator1': -1,
'prey1': -1,
'prey2': -1,
'prey4': -36,
'prey5': -1,
'prey6': -1,
}
assert done == {
'predator0': False,
'predator1': False,
'prey1': False,
'prey2': False,
'prey4': True,
'prey5': False,
'prey6': False,
'__all__': False}
with pytest.raises(AssertionError):
obs, reward, done, info = sim.step({
'predator0': {'attack': 1, 'move': np.array([0, 0])},
'predator1': {'attack': 1, 'move': np.array([0, 0])},
'prey1': {'move': np.array([0, -1]), 'harvest': 0},
'prey2': {'move': np.array([1, 1]), 'harvest': 0},
'prey4': {'move': np.array([0, -1]), 'harvest': 0},
'prey5': {'move': np.array([1, 1]), 'harvest': 0},
'prey6': {'move': np.array([0, 0]), 'harvest': 0},
})
obs, reward, done, info = sim.step({
'predator0': {'attack': 1, 'move': np.array([0, 0])},
'predator1': {'attack': 1, 'move': np.array([0, 0])},
'prey1': {'move': np.array([-1, 0]), 'harvest': 0},
'prey2': {'move': np.array([-1, 0]), 'harvest': 0},
'prey5': {'move': np.array([-1, 0]), 'harvest': 0},
'prey6': {'move': np.array([1, -1]), 'harvest': 0},
})
assert 'predator0' in obs
assert 'predator0' in obs
assert 'prey1' in obs
assert 'prey2' in obs
assert 'prey5' in obs
assert 'prey6' in obs
assert reward == {
'predator0': 36,
'predator1': 36,
'prey1': -1,
'prey2': -1,
'prey5': -36,
'prey6': -36,
}
assert done == {
'predator0': False,
'predator1': False,
'prey1': False,
'prey2': False,
'prey5': True,
'prey6': True,
'__all__': False}
with pytest.raises(AssertionError):
obs, reward, done, info = sim.step({
'predator0': {'attack': 1, 'move': np.array([0, 0])},
'predator1': {'attack': 1, 'move': np.array([0, 0])},
'prey1': {'move': np.array([0, -1]), 'harvest': 0},
'prey2': {'move': np.array([1, 1]), 'harvest': 0},
'prey5': {'move': np.array([1, 1]), 'harvest': 0},
'prey6': {'move': np.array([0, 0]), 'harvest': 0},
})
obs, reward, done, info = sim.step({
'predator0': {'attack': 1, 'move': np.array([0, 0])},
'predator1': {'attack': 1, 'move': np.array([0, 0])},
'prey1': {'move': np.array([-1, 0]), 'harvest': 0},
'prey2': {'move': np.array([-1, 0]), 'harvest': 0},
})
assert 'predator0' in obs
assert 'predator0' in obs
assert 'prey1' in obs
assert 'prey2' in obs
assert reward == {
'predator0': 36,
'predator1': 36,
'prey1': -36,
'prey2': -36,
}
assert done == {
'predator0': False,
'predator1': False,
'prey1': True,
'prey2': True,
'__all__': True}
| 49.440559
| 88
| 0.583847
| 5,150
| 35,350
| 3.887767
| 0.016893
| 0.137748
| 0.209769
| 0.279692
| 0.99126
| 0.989312
| 0.985766
| 0.982619
| 0.982419
| 0.98227
| 0
| 0.07001
| 0.176917
| 35,350
| 714
| 89
| 49.509804
| 0.618126
| 0.003706
| 0
| 0.714739
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| 0.162511
| 0
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| 0.534073
| 1
| 0.00317
| false
| 0
| 0.006339
| 0
| 0.009509
| 0
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| 0
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| null | 0
| 1
| 1
| 1
| 1
| 1
| 1
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0
| 10
|
ecd10a6748d120d96fbe6c06e4392a30a0fa9a69
| 24,086
|
py
|
Python
|
sdk/python/pulumi_azure/synapse/workspace_key.py
|
henriktao/pulumi-azure
|
f1cbcf100b42b916da36d8fe28be3a159abaf022
|
[
"ECL-2.0",
"Apache-2.0"
] | null | null | null |
sdk/python/pulumi_azure/synapse/workspace_key.py
|
henriktao/pulumi-azure
|
f1cbcf100b42b916da36d8fe28be3a159abaf022
|
[
"ECL-2.0",
"Apache-2.0"
] | null | null | null |
sdk/python/pulumi_azure/synapse/workspace_key.py
|
henriktao/pulumi-azure
|
f1cbcf100b42b916da36d8fe28be3a159abaf022
|
[
"ECL-2.0",
"Apache-2.0"
] | null | null | null |
# coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from .. import _utilities
__all__ = ['WorkspaceKeyArgs', 'WorkspaceKey']
@pulumi.input_type
class WorkspaceKeyArgs:
def __init__(__self__, *,
active: pulumi.Input[bool],
synapse_workspace_id: pulumi.Input[str],
cusomter_managed_key_name: Optional[pulumi.Input[str]] = None,
customer_managed_key_name: Optional[pulumi.Input[str]] = None,
customer_managed_key_versionless_id: Optional[pulumi.Input[str]] = None):
"""
The set of arguments for constructing a WorkspaceKey resource.
"""
pulumi.set(__self__, "active", active)
pulumi.set(__self__, "synapse_workspace_id", synapse_workspace_id)
if cusomter_managed_key_name is not None:
warnings.warn("""As this property name contained a typo originally, please switch to using 'customer_managed_key_name' instead.""", DeprecationWarning)
pulumi.log.warn("""cusomter_managed_key_name is deprecated: As this property name contained a typo originally, please switch to using 'customer_managed_key_name' instead.""")
if cusomter_managed_key_name is not None:
pulumi.set(__self__, "cusomter_managed_key_name", cusomter_managed_key_name)
if customer_managed_key_name is not None:
pulumi.set(__self__, "customer_managed_key_name", customer_managed_key_name)
if customer_managed_key_versionless_id is not None:
pulumi.set(__self__, "customer_managed_key_versionless_id", customer_managed_key_versionless_id)
@property
@pulumi.getter
def active(self) -> pulumi.Input[bool]:
return pulumi.get(self, "active")
@active.setter
def active(self, value: pulumi.Input[bool]):
pulumi.set(self, "active", value)
@property
@pulumi.getter(name="synapseWorkspaceId")
def synapse_workspace_id(self) -> pulumi.Input[str]:
return pulumi.get(self, "synapse_workspace_id")
@synapse_workspace_id.setter
def synapse_workspace_id(self, value: pulumi.Input[str]):
pulumi.set(self, "synapse_workspace_id", value)
@property
@pulumi.getter(name="cusomterManagedKeyName")
def cusomter_managed_key_name(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "cusomter_managed_key_name")
@cusomter_managed_key_name.setter
def cusomter_managed_key_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "cusomter_managed_key_name", value)
@property
@pulumi.getter(name="customerManagedKeyName")
def customer_managed_key_name(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "customer_managed_key_name")
@customer_managed_key_name.setter
def customer_managed_key_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "customer_managed_key_name", value)
@property
@pulumi.getter(name="customerManagedKeyVersionlessId")
def customer_managed_key_versionless_id(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "customer_managed_key_versionless_id")
@customer_managed_key_versionless_id.setter
def customer_managed_key_versionless_id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "customer_managed_key_versionless_id", value)
@pulumi.input_type
class _WorkspaceKeyState:
def __init__(__self__, *,
active: Optional[pulumi.Input[bool]] = None,
cusomter_managed_key_name: Optional[pulumi.Input[str]] = None,
customer_managed_key_name: Optional[pulumi.Input[str]] = None,
customer_managed_key_versionless_id: Optional[pulumi.Input[str]] = None,
synapse_workspace_id: Optional[pulumi.Input[str]] = None):
"""
Input properties used for looking up and filtering WorkspaceKey resources.
"""
if active is not None:
pulumi.set(__self__, "active", active)
if cusomter_managed_key_name is not None:
warnings.warn("""As this property name contained a typo originally, please switch to using 'customer_managed_key_name' instead.""", DeprecationWarning)
pulumi.log.warn("""cusomter_managed_key_name is deprecated: As this property name contained a typo originally, please switch to using 'customer_managed_key_name' instead.""")
if cusomter_managed_key_name is not None:
pulumi.set(__self__, "cusomter_managed_key_name", cusomter_managed_key_name)
if customer_managed_key_name is not None:
pulumi.set(__self__, "customer_managed_key_name", customer_managed_key_name)
if customer_managed_key_versionless_id is not None:
pulumi.set(__self__, "customer_managed_key_versionless_id", customer_managed_key_versionless_id)
if synapse_workspace_id is not None:
pulumi.set(__self__, "synapse_workspace_id", synapse_workspace_id)
@property
@pulumi.getter
def active(self) -> Optional[pulumi.Input[bool]]:
return pulumi.get(self, "active")
@active.setter
def active(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "active", value)
@property
@pulumi.getter(name="cusomterManagedKeyName")
def cusomter_managed_key_name(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "cusomter_managed_key_name")
@cusomter_managed_key_name.setter
def cusomter_managed_key_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "cusomter_managed_key_name", value)
@property
@pulumi.getter(name="customerManagedKeyName")
def customer_managed_key_name(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "customer_managed_key_name")
@customer_managed_key_name.setter
def customer_managed_key_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "customer_managed_key_name", value)
@property
@pulumi.getter(name="customerManagedKeyVersionlessId")
def customer_managed_key_versionless_id(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "customer_managed_key_versionless_id")
@customer_managed_key_versionless_id.setter
def customer_managed_key_versionless_id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "customer_managed_key_versionless_id", value)
@property
@pulumi.getter(name="synapseWorkspaceId")
def synapse_workspace_id(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "synapse_workspace_id")
@synapse_workspace_id.setter
def synapse_workspace_id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "synapse_workspace_id", value)
class WorkspaceKey(pulumi.CustomResource):
@overload
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
active: Optional[pulumi.Input[bool]] = None,
cusomter_managed_key_name: Optional[pulumi.Input[str]] = None,
customer_managed_key_name: Optional[pulumi.Input[str]] = None,
customer_managed_key_versionless_id: Optional[pulumi.Input[str]] = None,
synapse_workspace_id: Optional[pulumi.Input[str]] = None,
__props__=None):
"""
Manages a Synapse Workspace.
## Example Usage
```python
import pulumi
import pulumi_azure as azure
example_resource_group = azure.core.ResourceGroup("exampleResourceGroup", location="West Europe")
example_account = azure.storage.Account("exampleAccount",
resource_group_name=example_resource_group.name,
location=example_resource_group.location,
account_tier="Standard",
account_replication_type="LRS",
account_kind="StorageV2",
is_hns_enabled=True)
example_data_lake_gen2_filesystem = azure.storage.DataLakeGen2Filesystem("exampleDataLakeGen2Filesystem", storage_account_id=example_account.id)
example_workspace = azure.synapse.Workspace("exampleWorkspace",
resource_group_name=example_resource_group.name,
location=example_resource_group.location,
storage_data_lake_gen2_filesystem_id=example_data_lake_gen2_filesystem.id,
sql_administrator_login="sqladminuser",
sql_administrator_login_password="H@Sh1CoR3!",
aad_admin=azure.synapse.WorkspaceAadAdminArgs(
login="AzureAD Admin",
object_id="00000000-0000-0000-0000-000000000000",
tenant_id="00000000-0000-0000-0000-000000000000",
),
tags={
"Env": "production",
})
```
### Creating A Workspace With Customer Managed Key And Azure AD Admin
```python
import pulumi
import pulumi_azure as azure
current = azure.core.get_client_config()
example_resource_group = azure.core.ResourceGroup("exampleResourceGroup", location="West Europe")
example_account = azure.storage.Account("exampleAccount",
resource_group_name=example_resource_group.name,
location=example_resource_group.location,
account_tier="Standard",
account_replication_type="LRS",
account_kind="StorageV2",
is_hns_enabled=True)
example_data_lake_gen2_filesystem = azure.storage.DataLakeGen2Filesystem("exampleDataLakeGen2Filesystem", storage_account_id=example_account.id)
example_key_vault = azure.keyvault.KeyVault("exampleKeyVault",
location=example_resource_group.location,
resource_group_name=example_resource_group.name,
tenant_id=current.tenant_id,
sku_name="standard",
purge_protection_enabled=True)
deployer = azure.keyvault.AccessPolicy("deployer",
key_vault_id=example_key_vault.id,
tenant_id=current.tenant_id,
object_id=current.object_id,
key_permissions=[
"create",
"get",
"delete",
"purge",
])
example_key = azure.keyvault.Key("exampleKey",
key_vault_id=example_key_vault.id,
key_type="RSA",
key_size=2048,
key_opts=[
"unwrapKey",
"wrapKey",
],
opts=pulumi.ResourceOptions(depends_on=[deployer]))
example_workspace = azure.synapse.Workspace("exampleWorkspace",
resource_group_name=example_resource_group.name,
location=example_resource_group.location,
storage_data_lake_gen2_filesystem_id=example_data_lake_gen2_filesystem.id,
sql_administrator_login="sqladminuser",
sql_administrator_login_password="H@Sh1CoR3!",
customer_managed_key=azure.synapse.WorkspaceCustomerManagedKeyArgs(
key_versionless_id=example_key.versionless_id,
key_name="enckey",
),
tags={
"Env": "production",
})
workspace_policy = azure.keyvault.AccessPolicy("workspacePolicy",
key_vault_id=example_key_vault.id,
tenant_id=example_workspace.identities[0].tenant_id,
object_id=example_workspace.identities[0].principal_id,
key_permissions=[
"Get",
"WrapKey",
"UnwrapKey",
])
example_workspace_key = azure.synapse.WorkspaceKey("exampleWorkspaceKey",
customer_managed_key_versionless_id=example_key.versionless_id,
synapse_workspace_id=example_workspace.id,
active=True,
customer_managed_key_name="enckey",
opts=pulumi.ResourceOptions(depends_on=[workspace_policy]))
example_workspace_aad_admin = azure.synapse.WorkspaceAadAdmin("exampleWorkspaceAadAdmin",
synapse_workspace_id=example_workspace.id,
login="AzureAD Admin",
object_id="00000000-0000-0000-0000-000000000000",
tenant_id="00000000-0000-0000-0000-000000000000",
opts=pulumi.ResourceOptions(depends_on=[example_workspace_key]))
```
## Import
Synapse Workspace can be imported using the `resource id`, e.g.
```sh
$ pulumi import azure:synapse/workspaceKey:WorkspaceKey example /subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/group1/providers/Microsoft.Synapse/workspaces/workspace1
```
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
...
@overload
def __init__(__self__,
resource_name: str,
args: WorkspaceKeyArgs,
opts: Optional[pulumi.ResourceOptions] = None):
"""
Manages a Synapse Workspace.
## Example Usage
```python
import pulumi
import pulumi_azure as azure
example_resource_group = azure.core.ResourceGroup("exampleResourceGroup", location="West Europe")
example_account = azure.storage.Account("exampleAccount",
resource_group_name=example_resource_group.name,
location=example_resource_group.location,
account_tier="Standard",
account_replication_type="LRS",
account_kind="StorageV2",
is_hns_enabled=True)
example_data_lake_gen2_filesystem = azure.storage.DataLakeGen2Filesystem("exampleDataLakeGen2Filesystem", storage_account_id=example_account.id)
example_workspace = azure.synapse.Workspace("exampleWorkspace",
resource_group_name=example_resource_group.name,
location=example_resource_group.location,
storage_data_lake_gen2_filesystem_id=example_data_lake_gen2_filesystem.id,
sql_administrator_login="sqladminuser",
sql_administrator_login_password="H@Sh1CoR3!",
aad_admin=azure.synapse.WorkspaceAadAdminArgs(
login="AzureAD Admin",
object_id="00000000-0000-0000-0000-000000000000",
tenant_id="00000000-0000-0000-0000-000000000000",
),
tags={
"Env": "production",
})
```
### Creating A Workspace With Customer Managed Key And Azure AD Admin
```python
import pulumi
import pulumi_azure as azure
current = azure.core.get_client_config()
example_resource_group = azure.core.ResourceGroup("exampleResourceGroup", location="West Europe")
example_account = azure.storage.Account("exampleAccount",
resource_group_name=example_resource_group.name,
location=example_resource_group.location,
account_tier="Standard",
account_replication_type="LRS",
account_kind="StorageV2",
is_hns_enabled=True)
example_data_lake_gen2_filesystem = azure.storage.DataLakeGen2Filesystem("exampleDataLakeGen2Filesystem", storage_account_id=example_account.id)
example_key_vault = azure.keyvault.KeyVault("exampleKeyVault",
location=example_resource_group.location,
resource_group_name=example_resource_group.name,
tenant_id=current.tenant_id,
sku_name="standard",
purge_protection_enabled=True)
deployer = azure.keyvault.AccessPolicy("deployer",
key_vault_id=example_key_vault.id,
tenant_id=current.tenant_id,
object_id=current.object_id,
key_permissions=[
"create",
"get",
"delete",
"purge",
])
example_key = azure.keyvault.Key("exampleKey",
key_vault_id=example_key_vault.id,
key_type="RSA",
key_size=2048,
key_opts=[
"unwrapKey",
"wrapKey",
],
opts=pulumi.ResourceOptions(depends_on=[deployer]))
example_workspace = azure.synapse.Workspace("exampleWorkspace",
resource_group_name=example_resource_group.name,
location=example_resource_group.location,
storage_data_lake_gen2_filesystem_id=example_data_lake_gen2_filesystem.id,
sql_administrator_login="sqladminuser",
sql_administrator_login_password="H@Sh1CoR3!",
customer_managed_key=azure.synapse.WorkspaceCustomerManagedKeyArgs(
key_versionless_id=example_key.versionless_id,
key_name="enckey",
),
tags={
"Env": "production",
})
workspace_policy = azure.keyvault.AccessPolicy("workspacePolicy",
key_vault_id=example_key_vault.id,
tenant_id=example_workspace.identities[0].tenant_id,
object_id=example_workspace.identities[0].principal_id,
key_permissions=[
"Get",
"WrapKey",
"UnwrapKey",
])
example_workspace_key = azure.synapse.WorkspaceKey("exampleWorkspaceKey",
customer_managed_key_versionless_id=example_key.versionless_id,
synapse_workspace_id=example_workspace.id,
active=True,
customer_managed_key_name="enckey",
opts=pulumi.ResourceOptions(depends_on=[workspace_policy]))
example_workspace_aad_admin = azure.synapse.WorkspaceAadAdmin("exampleWorkspaceAadAdmin",
synapse_workspace_id=example_workspace.id,
login="AzureAD Admin",
object_id="00000000-0000-0000-0000-000000000000",
tenant_id="00000000-0000-0000-0000-000000000000",
opts=pulumi.ResourceOptions(depends_on=[example_workspace_key]))
```
## Import
Synapse Workspace can be imported using the `resource id`, e.g.
```sh
$ pulumi import azure:synapse/workspaceKey:WorkspaceKey example /subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/group1/providers/Microsoft.Synapse/workspaces/workspace1
```
:param str resource_name: The name of the resource.
:param WorkspaceKeyArgs args: The arguments to use to populate this resource's properties.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
...
def __init__(__self__, resource_name: str, *args, **kwargs):
resource_args, opts = _utilities.get_resource_args_opts(WorkspaceKeyArgs, pulumi.ResourceOptions, *args, **kwargs)
if resource_args is not None:
__self__._internal_init(resource_name, opts, **resource_args.__dict__)
else:
__self__._internal_init(resource_name, *args, **kwargs)
def _internal_init(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
active: Optional[pulumi.Input[bool]] = None,
cusomter_managed_key_name: Optional[pulumi.Input[str]] = None,
customer_managed_key_name: Optional[pulumi.Input[str]] = None,
customer_managed_key_versionless_id: Optional[pulumi.Input[str]] = None,
synapse_workspace_id: Optional[pulumi.Input[str]] = None,
__props__=None):
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = _utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = WorkspaceKeyArgs.__new__(WorkspaceKeyArgs)
if active is None and not opts.urn:
raise TypeError("Missing required property 'active'")
__props__.__dict__["active"] = active
if cusomter_managed_key_name is not None and not opts.urn:
warnings.warn("""As this property name contained a typo originally, please switch to using 'customer_managed_key_name' instead.""", DeprecationWarning)
pulumi.log.warn("""cusomter_managed_key_name is deprecated: As this property name contained a typo originally, please switch to using 'customer_managed_key_name' instead.""")
__props__.__dict__["cusomter_managed_key_name"] = cusomter_managed_key_name
__props__.__dict__["customer_managed_key_name"] = customer_managed_key_name
__props__.__dict__["customer_managed_key_versionless_id"] = customer_managed_key_versionless_id
if synapse_workspace_id is None and not opts.urn:
raise TypeError("Missing required property 'synapse_workspace_id'")
__props__.__dict__["synapse_workspace_id"] = synapse_workspace_id
super(WorkspaceKey, __self__).__init__(
'azure:synapse/workspaceKey:WorkspaceKey',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None,
active: Optional[pulumi.Input[bool]] = None,
cusomter_managed_key_name: Optional[pulumi.Input[str]] = None,
customer_managed_key_name: Optional[pulumi.Input[str]] = None,
customer_managed_key_versionless_id: Optional[pulumi.Input[str]] = None,
synapse_workspace_id: Optional[pulumi.Input[str]] = None) -> 'WorkspaceKey':
"""
Get an existing WorkspaceKey resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = _WorkspaceKeyState.__new__(_WorkspaceKeyState)
__props__.__dict__["active"] = active
__props__.__dict__["cusomter_managed_key_name"] = cusomter_managed_key_name
__props__.__dict__["customer_managed_key_name"] = customer_managed_key_name
__props__.__dict__["customer_managed_key_versionless_id"] = customer_managed_key_versionless_id
__props__.__dict__["synapse_workspace_id"] = synapse_workspace_id
return WorkspaceKey(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter
def active(self) -> pulumi.Output[bool]:
return pulumi.get(self, "active")
@property
@pulumi.getter(name="cusomterManagedKeyName")
def cusomter_managed_key_name(self) -> pulumi.Output[str]:
return pulumi.get(self, "cusomter_managed_key_name")
@property
@pulumi.getter(name="customerManagedKeyName")
def customer_managed_key_name(self) -> pulumi.Output[str]:
return pulumi.get(self, "customer_managed_key_name")
@property
@pulumi.getter(name="customerManagedKeyVersionlessId")
def customer_managed_key_versionless_id(self) -> pulumi.Output[Optional[str]]:
return pulumi.get(self, "customer_managed_key_versionless_id")
@property
@pulumi.getter(name="synapseWorkspaceId")
def synapse_workspace_id(self) -> pulumi.Output[str]:
return pulumi.get(self, "synapse_workspace_id")
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0
| 7
|
01e93bd20aaf8e412f3e3e02acf44e5c0f0a2002
| 19,439
|
py
|
Python
|
generator_gru.py
|
accentgan/acl2018
|
d1fc5ad6e48f8fe77b14562a6044a2d2faf59aef
|
[
"MIT"
] | null | null | null |
generator_gru.py
|
accentgan/acl2018
|
d1fc5ad6e48f8fe77b14562a6044a2d2faf59aef
|
[
"MIT"
] | null | null | null |
generator_gru.py
|
accentgan/acl2018
|
d1fc5ad6e48f8fe77b14562a6044a2d2faf59aef
|
[
"MIT"
] | 1
|
2017-10-31T19:31:46.000Z
|
2017-10-31T19:31:46.000Z
|
from __future__ import print_function
import tensorflow as tf
from tensorflow.contrib.layers import batch_norm, fully_connected, flatten
from tensorflow.contrib.layers import xavier_initializer
from ops import *
import numpy as np
class ActionGenerator(object):
def __init__(self, segan):
self.segan = segan
self.grucell = tf.contrib.rnn.GRUCell(256+self.segan.accent_class)
def zero(self,batch_size):
if self.cell_type == "grucell" or not hasattr(self, "grucell"):
grucell = self.grucell
else :
return ValueError("No such implemented Cell for action sampling")
return grucell.zero_state(batch_size, tf.float32)
def __call__(self, noisy_w,hidden_state, is_ref, spk=None, z_on=False, do_prelu=False):
# TODO: remove c_vec
""" Build the graph propagating (noisy_w) --> x
On first pass will make variables.
"""
segan = self.segan
def make_z(shape, mean=0., std=1., name='z'):
if is_ref:
with tf.variable_scope(name) as scope:
z_init = tf.random_normal_initializer(mean=mean, stddev=std)
z = tf.get_variable("z", shape,
initializer=z_init,
trainable=False
)
if z.device != "/device:GPU:0":
# this has to be created into gpu0
print('z.device is {}'.format(z.device))
assert False
else:
z = tf.random_normal(shape, mean=mean, stddev=std,
name=name, dtype=tf.float32)
return z
if hasattr(segan, 'generator_built'):
tf.get_variable_scope().reuse_variables()
make_vars = False
else:
make_vars = True
if is_ref:
print('*** Building Generator ***')
in_dims = noisy_w.get_shape().as_list()
h_i = noisy_w
if len(in_dims) == 2:
h_i = tf.expand_dims(noisy_w, -1)
elif len(in_dims) < 2 or len(in_dims) > 3:
raise ValueError('Generator input must be 2-D or 3-D')
kwidth = 31
enc_layers = 7
skips = []
if is_ref and do_prelu:
#keep track of prelu activations
alphas = []
with tf.variable_scope('g_e'):
#AE to be built is shaped:
# enc ~ [16384x1, 8192x16, 4096x32, 2048x32, 1024x64, 512x64, 256x128, 128x128, 64x256, 32x256, 16x512, 8x1024]
# dec ~ [8x2048, 16x1024, 32x512, 64x512, 8x256, 256x256, 512x128, 1024x128, 2048x64, 4096x64, 8192x32, 16384x1]
#FIRST ENCODER
for layer_idx, layer_depth in enumerate(segan.g_enc_depths):
bias_init = None
if segan.bias_downconv:
if is_ref:
print('Biasing downconv in G')
bias_init = tf.constant_initializer(0.)
h_i_dwn = downconv(h_i, layer_depth, kwidth=kwidth,
init=tf.truncated_normal_initializer(stddev=0.02),
bias_init=bias_init,
name='enc_{}'.format(layer_idx))
if is_ref:
print('Downconv {} -> {}'.format(h_i.get_shape(),
h_i_dwn.get_shape()))
h_i = h_i_dwn
if layer_idx < len(segan.g_enc_depths) - 1:
if is_ref:
print('Adding skip connection downconv '
'{}'.format(layer_idx))
# store skip connection
# last one is not stored cause it's the code
skips.append(h_i)
if do_prelu:
if is_ref:
print('-- Enc: prelu activation --')
h_i = prelu(h_i, ref=is_ref, name='enc_prelu_{}'.format(layer_idx))
if is_ref:
# split h_i into its components
alpha_i = h_i[1]
h_i = h_i[0]
alphas.append(alpha_i)
else:
if is_ref:
print('-- Enc: leakyrelu activation --')
h_i = leakyrelu(h_i)
with tf.variable_scope("g_gru"):
zmid = h_i
encode_z = zmid[:,:,:256]
h_i, hidden_state = self.grucell(tf.squeeze(zmid),hidden_state)
h_i = tf.expand_dims(h_i, [-2])
z = tf.nn.softmax(h_i[:,:,256:])
zdim = z.get_shape().as_list()[-1]
zstack = tf.reshape(z,shape=[segan.batch_size, 1, zdim])
real_z = h_i[:,:,:256]
h_i = gaussian_noise_layer(h_i[:,:,:256],1e-2)
zmid = h_i
#SECOND DECODER (reverse order)
with tf.variable_scope("g_d") as scope:
g_dec_depths = segan.g_enc_depths[:-1][::-1] + [1]
if is_ref:
print('g_dec_depths: ', g_dec_depths)
for layer_idx, layer_depth in enumerate(g_dec_depths):
h_i_dim = h_i.get_shape().as_list()
dimension = h_i.get_shape().as_list()[1]
zconcat = zstack*tf.ones([segan.batch_size, dimension, zdim])
h_i = tf.concat(values=[h_i, zconcat], axis=2)
out_shape = [h_i_dim[0], h_i_dim[1] * 2, layer_depth]
bias_init = None
# deconv
if segan.deconv_type == 'deconv':
if is_ref:
print('-- Transposed deconvolution type --')
if segan.bias_deconv:
print('Biasing deconv in G')
if segan.bias_deconv:
bias_init = tf.constant_initializer(0.)
h_i_dcv = deconv(h_i, out_shape, kwidth=kwidth, dilation=2,
init=tf.truncated_normal_initializer(stddev=0.02),
bias_init=bias_init,
name='dec_{}'.format(layer_idx))
elif segan.deconv_type == 'nn_deconv':
if is_ref:
print('-- NN interpolated deconvolution type --')
if segan.bias_deconv:
print('Biasing deconv in G')
if segan.bias_deconv:
bias_init = 0.
h_i_dcv = nn_deconv(h_i, kwidth=kwidth, dilation=2,
init=tf.truncated_normal_initializer(stddev=0.02),
bias_init=bias_init,
name='dec_{}'.format(layer_idx))
else:
raise ValueError('Unknown deconv type {}'.format(segan.deconv_type))
if is_ref:
print('Deconv {} -> {}'.format(h_i.get_shape(),
h_i_dcv.get_shape()))
h_i = h_i_dcv
if layer_idx < len(g_dec_depths) - 1:
if do_prelu:
if is_ref:
print('-- Dec: prelu activation --')
h_i = prelu(h_i, ref=is_ref,
name='dec_prelu_{}'.format(layer_idx))
if is_ref:
# split h_i into its components
alpha_i = h_i[1]
h_i = h_i[0]
alphas.append(alpha_i)
else:
if is_ref:
print('-- Dec: leakyrelu activation --')
h_i = leakyrelu(h_i)
# fuse skip connection
skip_ = skips[-(layer_idx + 1)]
if is_ref:
print('Fusing skip connection of '
'shape {}'.format(skip_.get_shape()))
h_i = tf.concat(axis=2, values=[h_i, skip_])
else:
if is_ref:
print('-- Dec: tanh activation --')
h_i = tf.tanh(h_i)
wave = h_i
if is_ref and do_prelu:
print('Amount of alpha vectors: ', len(alphas))
segan.gen_wave_summ = histogram_summary('gen_wave', wave)
if is_ref:
print('Amount of skip connections: ', len(skips))
print('Last wave shape: ', wave.get_shape())
print('*************************')
segan.generator_built = True
# ret feats contains the features refs to be returned
ret_feats = [wave]
ret_feats.append(z)
ret_feats.append(zmid)
ret_feats.append(hidden_state)
ret_feats.append(real_z)
ret_feats.append(encode_z)
if is_ref and do_prelu:
ret_feats += alphas
return ret_feats
class MultiGenerator(object):
def __init__(self, segan):
self.segan = segan
self.grucell = tf.contrib.rnn.GRUCell(256+self.segan.accent_class)
def zero(self,batch_size):
grucell = self.grucell
return grucell.zero_state(batch_size, tf.float32)
def __call__(self, noisy_w,hidden_state, is_ref, h_i=None, modus=0,
spk=None, z_on=False, do_prelu=False):
# TODO: remove c_vec
""" Build the graph propagating (noisy_w) --> x
On first pass will make variables.
"""
segan = self.segan
def make_z(shape, mean=0., std=1., name='z'):
if is_ref:
with tf.variable_scope(name) as scope:
z_init = tf.random_normal_initializer(mean=mean, stddev=std)
z = tf.get_variable("z", shape,
initializer=z_init,
trainable=False
)
if z.device != "/device:GPU:0":
# this has to be created into gpu0
print('z.device is {}'.format(z.device))
assert False
else:
z = tf.random_normal(shape, mean=mean, stddev=std,
name=name, dtype=tf.float32)
return z
if hasattr(segan, 'generator_built'):
tf.get_variable_scope().reuse_variables()
make_vars = False
else:
make_vars = True
if is_ref:
print('*** Building Generator ***')
in_dims = noisy_w.get_shape().as_list()
if modus == 0:
h_i = noisy_w
if len(in_dims) == 2:
h_i = tf.expand_dims(noisy_w, -1)
elif len(in_dims) < 2 or len(in_dims) > 3:
raise ValueError('Generator input must be 2-D or 3-D')
kwidth = 31
enc_layers = 7
skips = []
if is_ref and do_prelu:
#keep track of prelu activations
alphas = []
if modus == 0 :
with tf.variable_scope('g_e'):
#AE to be built is shaped:
# enc ~ [16384x1, 8192x16, 4096x32, 2048x32, 1024x64, 512x64, 256x128, 128x128, 64x256, 32x256, 16x512, 8x1024]
# dec ~ [8x2048, 16x1024, 32x512, 64x512, 8x256, 256x256, 512x128, 1024x128, 2048x64, 4096x64, 8192x32, 16384x1]
#FIRST ENCODER
for layer_idx, layer_depth in enumerate(segan.g_enc_depths):
bias_init = None
if segan.bias_downconv:
if is_ref:
print('Biasing downconv in G')
bias_init = tf.constant_initializer(0.)
h_i_dwn = downconv(h_i, layer_depth, kwidth=kwidth,
init=tf.truncated_normal_initializer(stddev=0.02),
bias_init=bias_init,
name='enc_{}'.format(layer_idx))
if is_ref:
print('Downconv {} -> {}'.format(h_i.get_shape(),
h_i_dwn.get_shape()))
h_i = h_i_dwn
if layer_idx < len(segan.g_enc_depths) - 1:
if is_ref:
print('Adding skip connection downconv '
'{}'.format(layer_idx))
# store skip connection
# last one is not stored cause it's the code
skips.append(h_i)
if do_prelu:
if is_ref:
print('-- Enc: prelu activation --')
h_i = prelu(h_i, ref=is_ref, name='enc_prelu_{}'.format(layer_idx))
if is_ref:
# split h_i into its components
alpha_i = h_i[1]
h_i = h_i[0]
alphas.append(alpha_i)
else:
if is_ref:
print('-- Enc: leakyrelu activation --')
h_i = leakyrelu(h_i)
with tf.variable_scope("g_gru"):
zmid = h_i
encode_z = zmid[:,:,:256]
if modus != 2:
h_i, hidden_state = self.grucell(tf.squeeze(zmid),hidden_state)
h_i = tf.expand_dims(h_i, [-2])
z = tf.nn.softmax(h_i[:,:,256:])
zdim = z.get_shape().as_list()[-1]
zstack = tf.reshape(z,shape=[segan.batch_size, 1, zdim])
real_z = h_i[:,:,:256]
h_i = tf.concat([gaussian_noise_layer(h_i[:,:,:256],1e-1),
h_i[:,:,256:]],axis=2)
zmid = h_i
h_i = h_i[:,:,:256]
#SECOND DECODER (reverse order)
with tf.variable_scope("g_d") as scope:
g_dec_depths = segan.g_enc_depths[:-1][::-1] + [1]
if is_ref:
print('g_dec_depths: ', g_dec_depths)
for layer_idx, layer_depth in enumerate(g_dec_depths):
h_i_dim = h_i.get_shape().as_list()
dimension = h_i.get_shape().as_list()[1]
zconcat = zstack*tf.ones([segan.batch_size, dimension, zdim])
out_shape = [h_i_dim[0], h_i_dim[1] * 2, layer_depth]
h_i = tf.concat(values=[h_i, zconcat], axis=2)
bias_init = None
# deconv
if segan.deconv_type == 'deconv':
if is_ref:
print('-- Transposed deconvolution type --')
if segan.bias_deconv:
print('Biasing deconv in G')
if segan.bias_deconv:
bias_init = tf.constant_initializer(0.)
h_i_dcv = deconv(h_i, out_shape, kwidth=kwidth, dilation=2,
init=tf.truncated_normal_initializer(stddev=0.02),
bias_init=bias_init,
name='dec_{}'.format(layer_idx))
elif segan.deconv_type == 'nn_deconv':
if is_ref:
print('-- NN interpolated deconvolution type --')
if segan.bias_deconv:
print('Biasing deconv in G')
if segan.bias_deconv:
bias_init = 0.
h_i_dcv = nn_deconv(h_i, kwidth=kwidth, dilation=2,
init=tf.truncated_normal_initializer(stddev=0.02),
bias_init=bias_init,
name='dec_{}'.format(layer_idx))
else:
raise ValueError('Unknown deconv type {}'.format(segan.deconv_type))
if is_ref:
print('Deconv {} -> {}'.format(h_i.get_shape(),
h_i_dcv.get_shape()))
h_i = h_i_dcv
if layer_idx < len(g_dec_depths) - 1:
if do_prelu:
if is_ref:
print('-- Dec: prelu activation --')
h_i = prelu(h_i, ref=is_ref,
name='dec_prelu_{}'.format(layer_idx))
if is_ref:
# split h_i into its components
alpha_i = h_i[1]
h_i = h_i[0]
alphas.append(alpha_i)
else:
if is_ref:
print('-- Dec: leakyrelu activation --')
h_i = leakyrelu(h_i)
# fuse skip connection
if modus == 0:
if not hasattr(self, "skip"):
self.skip = {}
skip_ = skips[-(layer_idx + 1)]
if is_ref:
print('Fusing skip connection of '
'shape {}'.format(skip_.get_shape()))
h_i = tf.concat(axis=2, values=[h_i, skip_])
self.skip["layer_%d"%(layer_idx)] = skip_.get_shape().as_list()
else :
dimension = h_i.get_shape().as_list()[1]
shape = h_i.get_shape().as_list()
shape[2] /= 2
zconcat = zstack*tf.ones([segan.batch_size, dimension, zdim])
t_i = tf.zeros(shape=self.skip["layer_%d"%(layer_idx)])
h_i = tf.concat(axis=2, values=[h_i,t_i])
else:
if is_ref:
print('-- Dec: tanh activation --')
h_i = tf.tanh(h_i)
wave = h_i
if is_ref and do_prelu:
print('Amount of alpha vectors: ', len(alphas))
segan.gen_wave_summ = histogram_summary('gen_wave', wave)
if is_ref:
print('Amount of skip connections: ', len(skips))
print('Last wave shape: ', wave.get_shape())
print('*************************')
segan.generator_built = True
# ret feats contains the features refs to be returned
ret_feats = [wave]
ret_feats.append(z)
ret_feats.append(zmid)
ret_feats.append(hidden_state)
ret_feats.append(real_z)
ret_feats.append(encode_z)
if is_ref and do_prelu:
ret_feats += alphas
return ret_feats
| 48.476309
| 126
| 0.450023
| 2,114
| 19,439
| 3.882214
| 0.116367
| 0.028269
| 0.035823
| 0.043865
| 0.943828
| 0.935787
| 0.928476
| 0.922261
| 0.919093
| 0.910686
| 0
| 0.039507
| 0.45311
| 19,439
| 400
| 127
| 48.5975
| 0.73248
| 0.067493
| 0
| 0.924157
| 0
| 0
| 0.082262
| 0.002833
| 0
| 0
| 0
| 0.005
| 0.005618
| 1
| 0.022472
| false
| 0
| 0.016854
| 0
| 0.064607
| 0.120787
| 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
|
01ea08ca353ba165010886d2db63ee670dcf38d5
| 958
|
py
|
Python
|
Frontend_Scripts/Django_view_methods.py
|
jeromjoy/Correct-Project
|
ce9ab7dc3171ddd38cd43d59b0589b7865306be7
|
[
"MIT"
] | null | null | null |
Frontend_Scripts/Django_view_methods.py
|
jeromjoy/Correct-Project
|
ce9ab7dc3171ddd38cd43d59b0589b7865306be7
|
[
"MIT"
] | null | null | null |
Frontend_Scripts/Django_view_methods.py
|
jeromjoy/Correct-Project
|
ce9ab7dc3171ddd38cd43d59b0589b7865306be7
|
[
"MIT"
] | null | null | null |
def upload (request):
cursor = connection.cursor()
cursor.execute('SELECT * FROM webapp_news')
News = cursor.fetchone() # fetchall() may not be the right call here?
return render(request, 'webapp/upload.html', {'News':News})
def upload (request):
cursor = connection.cursor()
URL = 'http://www.bbc.com/news'
cursor.execute('SELECT count(*)FROM webapp_news WHERE article = %s', [URL])
News = cursor.fetchone() # fetchall() may not be the right call here?
return render(request, 'webapp/upload.html', {'News':News})
def upload (request):
cursor = connection.cursor()
url = 'http://www.bbc.com/news'
cursor.execute('SELECT last_name FROM webapp_news WHERE articleUrl = %s', [])
News = cursor.fetchone() # fetchall() may not be the right call here?
return render(request, 'webapp/upload.html', {'News':News})
| 35.481481
| 85
| 0.606472
| 115
| 958
| 5.017391
| 0.295652
| 0.086655
| 0.083189
| 0.114385
| 0.816291
| 0.816291
| 0.750433
| 0.750433
| 0.750433
| 0.750433
| 0
| 0
| 0.256785
| 958
| 26
| 86
| 36.846154
| 0.810393
| 0.133612
| 0
| 0.705882
| 0
| 0
| 0.293689
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.176471
| false
| 0
| 0
| 0
| 0.352941
| 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
|
1770d252318449b9843a66b3a391e94403b83727
| 6,487
|
py
|
Python
|
test/client/test_read_memory_by_address.py
|
martinjthompson/python-udsoncan
|
fd89262785b968eb4a1aab15af86dbbd7353488b
|
[
"MIT"
] | 326
|
2017-08-11T10:23:13.000Z
|
2022-03-30T09:03:27.000Z
|
test/client/test_read_memory_by_address.py
|
martinjthompson/python-udsoncan
|
fd89262785b968eb4a1aab15af86dbbd7353488b
|
[
"MIT"
] | 105
|
2018-04-17T13:26:57.000Z
|
2022-03-30T09:00:34.000Z
|
test/client/test_read_memory_by_address.py
|
martinjthompson/python-udsoncan
|
fd89262785b968eb4a1aab15af86dbbd7353488b
|
[
"MIT"
] | 142
|
2018-02-20T19:52:18.000Z
|
2022-03-10T00:39:06.000Z
|
from test.ClientServerTest import ClientServerTest
from udsoncan import MemoryLocation
from udsoncan.exceptions import *
# Note :
# MemoryLocation object is unit tested in a separate file (test_helper_class).
# As it is the only parameter to be passed, no need to push this test too far for nothing.
class TestReadMemoryByAddress(ClientServerTest):
def test_4byte_block(self):
request = self.conn.touserqueue.get(timeout=0.2)
self.assertEqual(request, b"\x23\x12\x12\x34\x04")
self.conn.fromuserqueue.put(b"\x63\x99\x88\x77\x66")
def _test_4byte_block(self):
response = self.udsclient.read_memory_by_address(MemoryLocation(address=0x1234, memorysize=4, address_format=16, memorysize_format=8))
self.assertEqual(response.service_data.memory_block, b'\x99\x88\x77\x66')
def test_4byte_block_spr_no_effect(self):
request = self.conn.touserqueue.get(timeout=0.2)
self.assertEqual(request, b"\x23\x12\x12\x34\x04")
self.conn.fromuserqueue.put(b"\x63\x99\x88\x77\x66")
def _test_4byte_block_spr_no_effect(self):
with self.udsclient.suppress_positive_response:
response = self.udsclient.read_memory_by_address(MemoryLocation(address=0x1234, memorysize=4, address_format=16, memorysize_format=8))
self.assertEqual(response.service_data.memory_block, b'\x99\x88\x77\x66')
def test_config_format(self):
request = self.conn.touserqueue.get(timeout=0.2)
self.assertEqual(request, b"\x23\x24\x00\x00\x12\x34\x00\x04")
self.conn.fromuserqueue.put(b"\x63\x99\x88\x77\x66")
def _test_config_format(self):
self.udsclient.config['server_address_format'] = 32
self.udsclient.config['server_memorysize_format'] = 16
self.udsclient.read_memory_by_address(MemoryLocation(address=0x1234, memorysize=4))
def test_4byte_block_zeropadding_ok(self):
data = b"\x63\x99\x88\x77\x66"
for i in range(8):
self.wait_request_and_respond(data + b'\x00'*(i+1))
def _test_4byte_block_zeropadding_ok(self):
self.udsclient.config['tolerate_zero_padding'] = True
for i in range(8):
response = self.udsclient.read_memory_by_address(MemoryLocation(address=0x1234, memorysize=4, address_format=16, memorysize_format=8))
self.assertEqual(response.service_data.memory_block, b'\x99\x88\x77\x66')
def test_4byte_block_zeropadding_not_ok_exception(self):
data = b"\x63\x99\x88\x77\x66"
for i in range(8):
self.wait_request_and_respond(data + b'\x00'*(i+1))
def _test_4byte_block_zeropadding_not_ok_exception(self):
self.udsclient.config['tolerate_zero_padding'] = False
for i in range(8):
with self.assertRaises(UnexpectedResponseException):
self.udsclient.read_memory_by_address(MemoryLocation(address=0x1234, memorysize=4, address_format=16, memorysize_format=8))
def test_4byte_block_zeropadding_not_ok_no_exception(self):
data = b"\x63\x99\x88\x77\x66"
for i in range(8):
self.wait_request_and_respond(data + b'\x00'*(i+1))
def _test_4byte_block_zeropadding_not_ok_no_exception(self):
self.udsclient.config['tolerate_zero_padding'] = False
self.udsclient.config['exception_on_unexpected_response'] = False
for i in range(8):
response = self.udsclient.read_memory_by_address(MemoryLocation(address=0x1234, memorysize=4, address_format=16, memorysize_format=8))
self.assertTrue(response.valid)
self.assertTrue(response.unexpected)
def test_request_denied_exception(self):
self.wait_request_and_respond(b"\x7F\x23\x45") #Request Out Of Range
def _test_request_denied_exception(self):
with self.assertRaises(NegativeResponseException) as handle:
self.udsclient.read_memory_by_address(MemoryLocation(address=0x1234, memorysize=4, address_format=16, memorysize_format=8))
def test_request_denied_no_exception(self):
self.wait_request_and_respond(b"\x7F\x23\x45") #Request Out Of Range
def _test_request_denied_no_exception(self):
self.udsclient.config['exception_on_negative_response'] = False
response = self.udsclient.read_memory_by_address(MemoryLocation(address=0x1234, memorysize=4, address_format=16, memorysize_format=8))
self.assertTrue(response.valid)
self.assertFalse(response.positive)
def test_request_invalid_service_exception(self):
self.wait_request_and_respond(b"\x00\x45") #Inexistent Service
def _test_request_invalid_service_exception(self):
with self.assertRaises(InvalidResponseException) as handle:
self.udsclient.read_memory_by_address(MemoryLocation(address=0x1234, memorysize=4, address_format=16, memorysize_format=8))
def test_request_invalid_service_no_exception(self):
self.wait_request_and_respond(b"\x00\x45") #Inexistent Service
def _test_request_invalid_service_no_exception(self):
self.udsclient.config['exception_on_invalid_response'] = False
response = self.udsclient.read_memory_by_address(MemoryLocation(address=0x1234, memorysize=4, address_format=16, memorysize_format=8))
self.assertFalse(response.valid)
def test_wrong_service_exception(self):
self.wait_request_and_respond(b"\x7E\x99\x88\x77\x66") # Valid but wrong service (Tester Present)
def _test_wrong_service_exception(self):
with self.assertRaises(UnexpectedResponseException) as handle:
self.udsclient.read_memory_by_address(MemoryLocation(address=0x1234, memorysize=4, address_format=16, memorysize_format=8))
def test_wrong_service_no_exception(self):
self.wait_request_and_respond(b"\x7E\x99\x88\x77\x66") # Valid but wrong service (Tester Present)
def _test_wrong_service_no_exception(self):
self.udsclient.config['exception_on_unexpected_response'] = False
response = self.udsclient.read_memory_by_address(MemoryLocation(address=0x1234, memorysize=4, address_format=16, memorysize_format=8))
self.assertTrue(response.valid)
self.assertTrue(response.unexpected)
def test_bad_param(self):
pass
def _test_bad_param(self):
with self.assertRaises(ValueError):
self.udsclient.read_memory_by_address(1)
with self.assertRaises(ValueError):
self.udsclient.read_memory_by_address('aaa')
| 49.519084
| 146
| 0.735625
| 860
| 6,487
| 5.272093
| 0.146512
| 0.040141
| 0.052492
| 0.071019
| 0.861712
| 0.845832
| 0.823114
| 0.799294
| 0.773048
| 0.716586
| 0
| 0.054807
| 0.164637
| 6,487
| 130
| 147
| 49.9
| 0.781879
| 0.051179
| 0
| 0.520408
| 0
| 0
| 0.092107
| 0.042799
| 0
| 0
| 0.011717
| 0
| 0.193878
| 1
| 0.265306
| false
| 0.010204
| 0.030612
| 0
| 0.306122
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
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0
| 7
|
bd7bd889815c46f4a27262054cc530f1f9d4fde4
| 39,155
|
py
|
Python
|
sdk/python/pulumi_aws/emr/_inputs.py
|
mdop-wh/pulumi-aws
|
05bb32e9d694dde1c3b76d440fd2cd0344d23376
|
[
"ECL-2.0",
"Apache-2.0"
] | null | null | null |
sdk/python/pulumi_aws/emr/_inputs.py
|
mdop-wh/pulumi-aws
|
05bb32e9d694dde1c3b76d440fd2cd0344d23376
|
[
"ECL-2.0",
"Apache-2.0"
] | null | null | null |
sdk/python/pulumi_aws/emr/_inputs.py
|
mdop-wh/pulumi-aws
|
05bb32e9d694dde1c3b76d440fd2cd0344d23376
|
[
"ECL-2.0",
"Apache-2.0"
] | null | null | null |
# coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Dict, List, Mapping, Optional, Tuple, Union
from .. import _utilities, _tables
__all__ = [
'ClusterBootstrapActionArgs',
'ClusterCoreInstanceGroupArgs',
'ClusterCoreInstanceGroupEbsConfigArgs',
'ClusterEc2AttributesArgs',
'ClusterKerberosAttributesArgs',
'ClusterMasterInstanceGroupArgs',
'ClusterMasterInstanceGroupEbsConfigArgs',
'ClusterStepArgs',
'ClusterStepHadoopJarStepArgs',
'InstanceGroupEbsConfigArgs',
]
@pulumi.input_type
class ClusterBootstrapActionArgs:
def __init__(__self__, *,
name: pulumi.Input[str],
path: pulumi.Input[str],
args: Optional[pulumi.Input[List[pulumi.Input[str]]]] = None):
"""
:param pulumi.Input[str] name: The name of the step.
:param pulumi.Input[str] path: Location of the script to run during a bootstrap action. Can be either a location in Amazon S3 or on a local file system
:param pulumi.Input[List[pulumi.Input[str]]] args: List of command line arguments passed to the JAR file's main function when executed.
"""
pulumi.set(__self__, "name", name)
pulumi.set(__self__, "path", path)
if args is not None:
pulumi.set(__self__, "args", args)
@property
@pulumi.getter
def name(self) -> pulumi.Input[str]:
"""
The name of the step.
"""
return pulumi.get(self, "name")
@name.setter
def name(self, value: pulumi.Input[str]):
pulumi.set(self, "name", value)
@property
@pulumi.getter
def path(self) -> pulumi.Input[str]:
"""
Location of the script to run during a bootstrap action. Can be either a location in Amazon S3 or on a local file system
"""
return pulumi.get(self, "path")
@path.setter
def path(self, value: pulumi.Input[str]):
pulumi.set(self, "path", value)
@property
@pulumi.getter
def args(self) -> Optional[pulumi.Input[List[pulumi.Input[str]]]]:
"""
List of command line arguments passed to the JAR file's main function when executed.
"""
return pulumi.get(self, "args")
@args.setter
def args(self, value: Optional[pulumi.Input[List[pulumi.Input[str]]]]):
pulumi.set(self, "args", value)
@pulumi.input_type
class ClusterCoreInstanceGroupArgs:
def __init__(__self__, *,
instance_type: pulumi.Input[str],
autoscaling_policy: Optional[pulumi.Input[str]] = None,
bid_price: Optional[pulumi.Input[str]] = None,
ebs_configs: Optional[pulumi.Input[List[pulumi.Input['ClusterCoreInstanceGroupEbsConfigArgs']]]] = None,
id: Optional[pulumi.Input[str]] = None,
instance_count: Optional[pulumi.Input[float]] = None,
name: Optional[pulumi.Input[str]] = None):
"""
:param pulumi.Input[str] instance_type: EC2 instance type for all instances in the instance group.
:param pulumi.Input[str] autoscaling_policy: String containing the [EMR Auto Scaling Policy](https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-automatic-scaling.html) JSON.
:param pulumi.Input[str] bid_price: Bid price for each EC2 instance in the instance group, expressed in USD. By setting this attribute, the instance group is being declared as a Spot Instance, and will implicitly create a Spot request. Leave this blank to use On-Demand Instances.
:param pulumi.Input[List[pulumi.Input['ClusterCoreInstanceGroupEbsConfigArgs']]] ebs_configs: Configuration block(s) for EBS volumes attached to each instance in the instance group. Detailed below.
:param pulumi.Input[str] id: The ID of the EMR Cluster
:param pulumi.Input[float] instance_count: Target number of instances for the instance group. Must be 1 or 3. Defaults to 1. Launching with multiple master nodes is only supported in EMR version 5.23.0+, and requires this resource's `core_instance_group` to be configured. Public (Internet accessible) instances must be created in VPC subnets that have `map public IP on launch` enabled. Termination protection is automatically enabled when launched with multiple master nodes and this provider must have the `termination_protection = false` configuration applied before destroying this resource.
:param pulumi.Input[str] name: The name of the step.
"""
pulumi.set(__self__, "instance_type", instance_type)
if autoscaling_policy is not None:
pulumi.set(__self__, "autoscaling_policy", autoscaling_policy)
if bid_price is not None:
pulumi.set(__self__, "bid_price", bid_price)
if ebs_configs is not None:
pulumi.set(__self__, "ebs_configs", ebs_configs)
if id is not None:
pulumi.set(__self__, "id", id)
if instance_count is not None:
pulumi.set(__self__, "instance_count", instance_count)
if name is not None:
pulumi.set(__self__, "name", name)
@property
@pulumi.getter(name="instanceType")
def instance_type(self) -> pulumi.Input[str]:
"""
EC2 instance type for all instances in the instance group.
"""
return pulumi.get(self, "instance_type")
@instance_type.setter
def instance_type(self, value: pulumi.Input[str]):
pulumi.set(self, "instance_type", value)
@property
@pulumi.getter(name="autoscalingPolicy")
def autoscaling_policy(self) -> Optional[pulumi.Input[str]]:
"""
String containing the [EMR Auto Scaling Policy](https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-automatic-scaling.html) JSON.
"""
return pulumi.get(self, "autoscaling_policy")
@autoscaling_policy.setter
def autoscaling_policy(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "autoscaling_policy", value)
@property
@pulumi.getter(name="bidPrice")
def bid_price(self) -> Optional[pulumi.Input[str]]:
"""
Bid price for each EC2 instance in the instance group, expressed in USD. By setting this attribute, the instance group is being declared as a Spot Instance, and will implicitly create a Spot request. Leave this blank to use On-Demand Instances.
"""
return pulumi.get(self, "bid_price")
@bid_price.setter
def bid_price(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "bid_price", value)
@property
@pulumi.getter(name="ebsConfigs")
def ebs_configs(self) -> Optional[pulumi.Input[List[pulumi.Input['ClusterCoreInstanceGroupEbsConfigArgs']]]]:
"""
Configuration block(s) for EBS volumes attached to each instance in the instance group. Detailed below.
"""
return pulumi.get(self, "ebs_configs")
@ebs_configs.setter
def ebs_configs(self, value: Optional[pulumi.Input[List[pulumi.Input['ClusterCoreInstanceGroupEbsConfigArgs']]]]):
pulumi.set(self, "ebs_configs", value)
@property
@pulumi.getter
def id(self) -> Optional[pulumi.Input[str]]:
"""
The ID of the EMR Cluster
"""
return pulumi.get(self, "id")
@id.setter
def id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "id", value)
@property
@pulumi.getter(name="instanceCount")
def instance_count(self) -> Optional[pulumi.Input[float]]:
"""
Target number of instances for the instance group. Must be 1 or 3. Defaults to 1. Launching with multiple master nodes is only supported in EMR version 5.23.0+, and requires this resource's `core_instance_group` to be configured. Public (Internet accessible) instances must be created in VPC subnets that have `map public IP on launch` enabled. Termination protection is automatically enabled when launched with multiple master nodes and this provider must have the `termination_protection = false` configuration applied before destroying this resource.
"""
return pulumi.get(self, "instance_count")
@instance_count.setter
def instance_count(self, value: Optional[pulumi.Input[float]]):
pulumi.set(self, "instance_count", value)
@property
@pulumi.getter
def name(self) -> Optional[pulumi.Input[str]]:
"""
The name of the step.
"""
return pulumi.get(self, "name")
@name.setter
def name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "name", value)
@pulumi.input_type
class ClusterCoreInstanceGroupEbsConfigArgs:
def __init__(__self__, *,
size: pulumi.Input[float],
type: pulumi.Input[str],
iops: Optional[pulumi.Input[float]] = None,
volumes_per_instance: Optional[pulumi.Input[float]] = None):
"""
:param pulumi.Input[float] size: The volume size, in gibibytes (GiB).
:param pulumi.Input[str] type: The volume type. Valid options are `gp2`, `io1`, `standard` and `st1`. See [EBS Volume Types](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/EBSVolumeTypes.html).
:param pulumi.Input[float] iops: The number of I/O operations per second (IOPS) that the volume supports
:param pulumi.Input[float] volumes_per_instance: The number of EBS volumes with this configuration to attach to each EC2 instance in the instance group (default is 1)
"""
pulumi.set(__self__, "size", size)
pulumi.set(__self__, "type", type)
if iops is not None:
pulumi.set(__self__, "iops", iops)
if volumes_per_instance is not None:
pulumi.set(__self__, "volumes_per_instance", volumes_per_instance)
@property
@pulumi.getter
def size(self) -> pulumi.Input[float]:
"""
The volume size, in gibibytes (GiB).
"""
return pulumi.get(self, "size")
@size.setter
def size(self, value: pulumi.Input[float]):
pulumi.set(self, "size", value)
@property
@pulumi.getter
def type(self) -> pulumi.Input[str]:
"""
The volume type. Valid options are `gp2`, `io1`, `standard` and `st1`. See [EBS Volume Types](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/EBSVolumeTypes.html).
"""
return pulumi.get(self, "type")
@type.setter
def type(self, value: pulumi.Input[str]):
pulumi.set(self, "type", value)
@property
@pulumi.getter
def iops(self) -> Optional[pulumi.Input[float]]:
"""
The number of I/O operations per second (IOPS) that the volume supports
"""
return pulumi.get(self, "iops")
@iops.setter
def iops(self, value: Optional[pulumi.Input[float]]):
pulumi.set(self, "iops", value)
@property
@pulumi.getter(name="volumesPerInstance")
def volumes_per_instance(self) -> Optional[pulumi.Input[float]]:
"""
The number of EBS volumes with this configuration to attach to each EC2 instance in the instance group (default is 1)
"""
return pulumi.get(self, "volumes_per_instance")
@volumes_per_instance.setter
def volumes_per_instance(self, value: Optional[pulumi.Input[float]]):
pulumi.set(self, "volumes_per_instance", value)
@pulumi.input_type
class ClusterEc2AttributesArgs:
def __init__(__self__, *,
instance_profile: pulumi.Input[str],
additional_master_security_groups: Optional[pulumi.Input[str]] = None,
additional_slave_security_groups: Optional[pulumi.Input[str]] = None,
emr_managed_master_security_group: Optional[pulumi.Input[str]] = None,
emr_managed_slave_security_group: Optional[pulumi.Input[str]] = None,
key_name: Optional[pulumi.Input[str]] = None,
service_access_security_group: Optional[pulumi.Input[str]] = None,
subnet_id: Optional[pulumi.Input[str]] = None):
"""
:param pulumi.Input[str] instance_profile: Instance Profile for EC2 instances of the cluster assume this role
:param pulumi.Input[str] additional_master_security_groups: String containing a comma separated list of additional Amazon EC2 security group IDs for the master node
:param pulumi.Input[str] additional_slave_security_groups: String containing a comma separated list of additional Amazon EC2 security group IDs for the slave nodes as a comma separated string
:param pulumi.Input[str] emr_managed_master_security_group: Identifier of the Amazon EC2 EMR-Managed security group for the master node
:param pulumi.Input[str] emr_managed_slave_security_group: Identifier of the Amazon EC2 EMR-Managed security group for the slave nodes
:param pulumi.Input[str] key_name: Amazon EC2 key pair that can be used to ssh to the master node as the user called `hadoop`
:param pulumi.Input[str] service_access_security_group: Identifier of the Amazon EC2 service-access security group - required when the cluster runs on a private subnet
:param pulumi.Input[str] subnet_id: VPC subnet id where you want the job flow to launch. Cannot specify the `cc1.4xlarge` instance type for nodes of a job flow launched in a Amazon VPC
"""
pulumi.set(__self__, "instance_profile", instance_profile)
if additional_master_security_groups is not None:
pulumi.set(__self__, "additional_master_security_groups", additional_master_security_groups)
if additional_slave_security_groups is not None:
pulumi.set(__self__, "additional_slave_security_groups", additional_slave_security_groups)
if emr_managed_master_security_group is not None:
pulumi.set(__self__, "emr_managed_master_security_group", emr_managed_master_security_group)
if emr_managed_slave_security_group is not None:
pulumi.set(__self__, "emr_managed_slave_security_group", emr_managed_slave_security_group)
if key_name is not None:
pulumi.set(__self__, "key_name", key_name)
if service_access_security_group is not None:
pulumi.set(__self__, "service_access_security_group", service_access_security_group)
if subnet_id is not None:
pulumi.set(__self__, "subnet_id", subnet_id)
@property
@pulumi.getter(name="instanceProfile")
def instance_profile(self) -> pulumi.Input[str]:
"""
Instance Profile for EC2 instances of the cluster assume this role
"""
return pulumi.get(self, "instance_profile")
@instance_profile.setter
def instance_profile(self, value: pulumi.Input[str]):
pulumi.set(self, "instance_profile", value)
@property
@pulumi.getter(name="additionalMasterSecurityGroups")
def additional_master_security_groups(self) -> Optional[pulumi.Input[str]]:
"""
String containing a comma separated list of additional Amazon EC2 security group IDs for the master node
"""
return pulumi.get(self, "additional_master_security_groups")
@additional_master_security_groups.setter
def additional_master_security_groups(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "additional_master_security_groups", value)
@property
@pulumi.getter(name="additionalSlaveSecurityGroups")
def additional_slave_security_groups(self) -> Optional[pulumi.Input[str]]:
"""
String containing a comma separated list of additional Amazon EC2 security group IDs for the slave nodes as a comma separated string
"""
return pulumi.get(self, "additional_slave_security_groups")
@additional_slave_security_groups.setter
def additional_slave_security_groups(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "additional_slave_security_groups", value)
@property
@pulumi.getter(name="emrManagedMasterSecurityGroup")
def emr_managed_master_security_group(self) -> Optional[pulumi.Input[str]]:
"""
Identifier of the Amazon EC2 EMR-Managed security group for the master node
"""
return pulumi.get(self, "emr_managed_master_security_group")
@emr_managed_master_security_group.setter
def emr_managed_master_security_group(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "emr_managed_master_security_group", value)
@property
@pulumi.getter(name="emrManagedSlaveSecurityGroup")
def emr_managed_slave_security_group(self) -> Optional[pulumi.Input[str]]:
"""
Identifier of the Amazon EC2 EMR-Managed security group for the slave nodes
"""
return pulumi.get(self, "emr_managed_slave_security_group")
@emr_managed_slave_security_group.setter
def emr_managed_slave_security_group(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "emr_managed_slave_security_group", value)
@property
@pulumi.getter(name="keyName")
def key_name(self) -> Optional[pulumi.Input[str]]:
"""
Amazon EC2 key pair that can be used to ssh to the master node as the user called `hadoop`
"""
return pulumi.get(self, "key_name")
@key_name.setter
def key_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "key_name", value)
@property
@pulumi.getter(name="serviceAccessSecurityGroup")
def service_access_security_group(self) -> Optional[pulumi.Input[str]]:
"""
Identifier of the Amazon EC2 service-access security group - required when the cluster runs on a private subnet
"""
return pulumi.get(self, "service_access_security_group")
@service_access_security_group.setter
def service_access_security_group(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "service_access_security_group", value)
@property
@pulumi.getter(name="subnetId")
def subnet_id(self) -> Optional[pulumi.Input[str]]:
"""
VPC subnet id where you want the job flow to launch. Cannot specify the `cc1.4xlarge` instance type for nodes of a job flow launched in a Amazon VPC
"""
return pulumi.get(self, "subnet_id")
@subnet_id.setter
def subnet_id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "subnet_id", value)
@pulumi.input_type
class ClusterKerberosAttributesArgs:
def __init__(__self__, *,
kdc_admin_password: pulumi.Input[str],
realm: pulumi.Input[str],
ad_domain_join_password: Optional[pulumi.Input[str]] = None,
ad_domain_join_user: Optional[pulumi.Input[str]] = None,
cross_realm_trust_principal_password: Optional[pulumi.Input[str]] = None):
"""
:param pulumi.Input[str] kdc_admin_password: The password used within the cluster for the kadmin service on the cluster-dedicated KDC, which maintains Kerberos principals, password policies, and keytabs for the cluster. This provider cannot perform drift detection of this configuration.
:param pulumi.Input[str] realm: The name of the Kerberos realm to which all nodes in a cluster belong. For example, `EC2.INTERNAL`
:param pulumi.Input[str] ad_domain_join_password: The Active Directory password for `ad_domain_join_user`. This provider cannot perform drift detection of this configuration.
:param pulumi.Input[str] ad_domain_join_user: Required only when establishing a cross-realm trust with an Active Directory domain. A user with sufficient privileges to join resources to the domain. This provider cannot perform drift detection of this configuration.
:param pulumi.Input[str] cross_realm_trust_principal_password: Required only when establishing a cross-realm trust with a KDC in a different realm. The cross-realm principal password, which must be identical across realms. This provider cannot perform drift detection of this configuration.
"""
pulumi.set(__self__, "kdc_admin_password", kdc_admin_password)
pulumi.set(__self__, "realm", realm)
if ad_domain_join_password is not None:
pulumi.set(__self__, "ad_domain_join_password", ad_domain_join_password)
if ad_domain_join_user is not None:
pulumi.set(__self__, "ad_domain_join_user", ad_domain_join_user)
if cross_realm_trust_principal_password is not None:
pulumi.set(__self__, "cross_realm_trust_principal_password", cross_realm_trust_principal_password)
@property
@pulumi.getter(name="kdcAdminPassword")
def kdc_admin_password(self) -> pulumi.Input[str]:
"""
The password used within the cluster for the kadmin service on the cluster-dedicated KDC, which maintains Kerberos principals, password policies, and keytabs for the cluster. This provider cannot perform drift detection of this configuration.
"""
return pulumi.get(self, "kdc_admin_password")
@kdc_admin_password.setter
def kdc_admin_password(self, value: pulumi.Input[str]):
pulumi.set(self, "kdc_admin_password", value)
@property
@pulumi.getter
def realm(self) -> pulumi.Input[str]:
"""
The name of the Kerberos realm to which all nodes in a cluster belong. For example, `EC2.INTERNAL`
"""
return pulumi.get(self, "realm")
@realm.setter
def realm(self, value: pulumi.Input[str]):
pulumi.set(self, "realm", value)
@property
@pulumi.getter(name="adDomainJoinPassword")
def ad_domain_join_password(self) -> Optional[pulumi.Input[str]]:
"""
The Active Directory password for `ad_domain_join_user`. This provider cannot perform drift detection of this configuration.
"""
return pulumi.get(self, "ad_domain_join_password")
@ad_domain_join_password.setter
def ad_domain_join_password(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "ad_domain_join_password", value)
@property
@pulumi.getter(name="adDomainJoinUser")
def ad_domain_join_user(self) -> Optional[pulumi.Input[str]]:
"""
Required only when establishing a cross-realm trust with an Active Directory domain. A user with sufficient privileges to join resources to the domain. This provider cannot perform drift detection of this configuration.
"""
return pulumi.get(self, "ad_domain_join_user")
@ad_domain_join_user.setter
def ad_domain_join_user(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "ad_domain_join_user", value)
@property
@pulumi.getter(name="crossRealmTrustPrincipalPassword")
def cross_realm_trust_principal_password(self) -> Optional[pulumi.Input[str]]:
"""
Required only when establishing a cross-realm trust with a KDC in a different realm. The cross-realm principal password, which must be identical across realms. This provider cannot perform drift detection of this configuration.
"""
return pulumi.get(self, "cross_realm_trust_principal_password")
@cross_realm_trust_principal_password.setter
def cross_realm_trust_principal_password(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "cross_realm_trust_principal_password", value)
@pulumi.input_type
class ClusterMasterInstanceGroupArgs:
def __init__(__self__, *,
instance_type: pulumi.Input[str],
bid_price: Optional[pulumi.Input[str]] = None,
ebs_configs: Optional[pulumi.Input[List[pulumi.Input['ClusterMasterInstanceGroupEbsConfigArgs']]]] = None,
id: Optional[pulumi.Input[str]] = None,
instance_count: Optional[pulumi.Input[float]] = None,
name: Optional[pulumi.Input[str]] = None):
"""
:param pulumi.Input[str] instance_type: EC2 instance type for all instances in the instance group.
:param pulumi.Input[str] bid_price: Bid price for each EC2 instance in the instance group, expressed in USD. By setting this attribute, the instance group is being declared as a Spot Instance, and will implicitly create a Spot request. Leave this blank to use On-Demand Instances.
:param pulumi.Input[List[pulumi.Input['ClusterMasterInstanceGroupEbsConfigArgs']]] ebs_configs: Configuration block(s) for EBS volumes attached to each instance in the instance group. Detailed below.
:param pulumi.Input[str] id: The ID of the EMR Cluster
:param pulumi.Input[float] instance_count: Target number of instances for the instance group. Must be 1 or 3. Defaults to 1. Launching with multiple master nodes is only supported in EMR version 5.23.0+, and requires this resource's `core_instance_group` to be configured. Public (Internet accessible) instances must be created in VPC subnets that have `map public IP on launch` enabled. Termination protection is automatically enabled when launched with multiple master nodes and this provider must have the `termination_protection = false` configuration applied before destroying this resource.
:param pulumi.Input[str] name: The name of the step.
"""
pulumi.set(__self__, "instance_type", instance_type)
if bid_price is not None:
pulumi.set(__self__, "bid_price", bid_price)
if ebs_configs is not None:
pulumi.set(__self__, "ebs_configs", ebs_configs)
if id is not None:
pulumi.set(__self__, "id", id)
if instance_count is not None:
pulumi.set(__self__, "instance_count", instance_count)
if name is not None:
pulumi.set(__self__, "name", name)
@property
@pulumi.getter(name="instanceType")
def instance_type(self) -> pulumi.Input[str]:
"""
EC2 instance type for all instances in the instance group.
"""
return pulumi.get(self, "instance_type")
@instance_type.setter
def instance_type(self, value: pulumi.Input[str]):
pulumi.set(self, "instance_type", value)
@property
@pulumi.getter(name="bidPrice")
def bid_price(self) -> Optional[pulumi.Input[str]]:
"""
Bid price for each EC2 instance in the instance group, expressed in USD. By setting this attribute, the instance group is being declared as a Spot Instance, and will implicitly create a Spot request. Leave this blank to use On-Demand Instances.
"""
return pulumi.get(self, "bid_price")
@bid_price.setter
def bid_price(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "bid_price", value)
@property
@pulumi.getter(name="ebsConfigs")
def ebs_configs(self) -> Optional[pulumi.Input[List[pulumi.Input['ClusterMasterInstanceGroupEbsConfigArgs']]]]:
"""
Configuration block(s) for EBS volumes attached to each instance in the instance group. Detailed below.
"""
return pulumi.get(self, "ebs_configs")
@ebs_configs.setter
def ebs_configs(self, value: Optional[pulumi.Input[List[pulumi.Input['ClusterMasterInstanceGroupEbsConfigArgs']]]]):
pulumi.set(self, "ebs_configs", value)
@property
@pulumi.getter
def id(self) -> Optional[pulumi.Input[str]]:
"""
The ID of the EMR Cluster
"""
return pulumi.get(self, "id")
@id.setter
def id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "id", value)
@property
@pulumi.getter(name="instanceCount")
def instance_count(self) -> Optional[pulumi.Input[float]]:
"""
Target number of instances for the instance group. Must be 1 or 3. Defaults to 1. Launching with multiple master nodes is only supported in EMR version 5.23.0+, and requires this resource's `core_instance_group` to be configured. Public (Internet accessible) instances must be created in VPC subnets that have `map public IP on launch` enabled. Termination protection is automatically enabled when launched with multiple master nodes and this provider must have the `termination_protection = false` configuration applied before destroying this resource.
"""
return pulumi.get(self, "instance_count")
@instance_count.setter
def instance_count(self, value: Optional[pulumi.Input[float]]):
pulumi.set(self, "instance_count", value)
@property
@pulumi.getter
def name(self) -> Optional[pulumi.Input[str]]:
"""
The name of the step.
"""
return pulumi.get(self, "name")
@name.setter
def name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "name", value)
@pulumi.input_type
class ClusterMasterInstanceGroupEbsConfigArgs:
def __init__(__self__, *,
size: pulumi.Input[float],
type: pulumi.Input[str],
iops: Optional[pulumi.Input[float]] = None,
volumes_per_instance: Optional[pulumi.Input[float]] = None):
"""
:param pulumi.Input[float] size: The volume size, in gibibytes (GiB).
:param pulumi.Input[str] type: The volume type. Valid options are `gp2`, `io1`, `standard` and `st1`. See [EBS Volume Types](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/EBSVolumeTypes.html).
:param pulumi.Input[float] iops: The number of I/O operations per second (IOPS) that the volume supports
:param pulumi.Input[float] volumes_per_instance: The number of EBS volumes with this configuration to attach to each EC2 instance in the instance group (default is 1)
"""
pulumi.set(__self__, "size", size)
pulumi.set(__self__, "type", type)
if iops is not None:
pulumi.set(__self__, "iops", iops)
if volumes_per_instance is not None:
pulumi.set(__self__, "volumes_per_instance", volumes_per_instance)
@property
@pulumi.getter
def size(self) -> pulumi.Input[float]:
"""
The volume size, in gibibytes (GiB).
"""
return pulumi.get(self, "size")
@size.setter
def size(self, value: pulumi.Input[float]):
pulumi.set(self, "size", value)
@property
@pulumi.getter
def type(self) -> pulumi.Input[str]:
"""
The volume type. Valid options are `gp2`, `io1`, `standard` and `st1`. See [EBS Volume Types](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/EBSVolumeTypes.html).
"""
return pulumi.get(self, "type")
@type.setter
def type(self, value: pulumi.Input[str]):
pulumi.set(self, "type", value)
@property
@pulumi.getter
def iops(self) -> Optional[pulumi.Input[float]]:
"""
The number of I/O operations per second (IOPS) that the volume supports
"""
return pulumi.get(self, "iops")
@iops.setter
def iops(self, value: Optional[pulumi.Input[float]]):
pulumi.set(self, "iops", value)
@property
@pulumi.getter(name="volumesPerInstance")
def volumes_per_instance(self) -> Optional[pulumi.Input[float]]:
"""
The number of EBS volumes with this configuration to attach to each EC2 instance in the instance group (default is 1)
"""
return pulumi.get(self, "volumes_per_instance")
@volumes_per_instance.setter
def volumes_per_instance(self, value: Optional[pulumi.Input[float]]):
pulumi.set(self, "volumes_per_instance", value)
@pulumi.input_type
class ClusterStepArgs:
def __init__(__self__, *,
action_on_failure: pulumi.Input[str],
hadoop_jar_step: pulumi.Input['ClusterStepHadoopJarStepArgs'],
name: pulumi.Input[str]):
"""
:param pulumi.Input[str] action_on_failure: The action to take if the step fails. Valid values: `TERMINATE_JOB_FLOW`, `TERMINATE_CLUSTER`, `CANCEL_AND_WAIT`, and `CONTINUE`
:param pulumi.Input['ClusterStepHadoopJarStepArgs'] hadoop_jar_step: The JAR file used for the step. Defined below.
:param pulumi.Input[str] name: The name of the step.
"""
pulumi.set(__self__, "action_on_failure", action_on_failure)
pulumi.set(__self__, "hadoop_jar_step", hadoop_jar_step)
pulumi.set(__self__, "name", name)
@property
@pulumi.getter(name="actionOnFailure")
def action_on_failure(self) -> pulumi.Input[str]:
"""
The action to take if the step fails. Valid values: `TERMINATE_JOB_FLOW`, `TERMINATE_CLUSTER`, `CANCEL_AND_WAIT`, and `CONTINUE`
"""
return pulumi.get(self, "action_on_failure")
@action_on_failure.setter
def action_on_failure(self, value: pulumi.Input[str]):
pulumi.set(self, "action_on_failure", value)
@property
@pulumi.getter(name="hadoopJarStep")
def hadoop_jar_step(self) -> pulumi.Input['ClusterStepHadoopJarStepArgs']:
"""
The JAR file used for the step. Defined below.
"""
return pulumi.get(self, "hadoop_jar_step")
@hadoop_jar_step.setter
def hadoop_jar_step(self, value: pulumi.Input['ClusterStepHadoopJarStepArgs']):
pulumi.set(self, "hadoop_jar_step", value)
@property
@pulumi.getter
def name(self) -> pulumi.Input[str]:
"""
The name of the step.
"""
return pulumi.get(self, "name")
@name.setter
def name(self, value: pulumi.Input[str]):
pulumi.set(self, "name", value)
@pulumi.input_type
class ClusterStepHadoopJarStepArgs:
def __init__(__self__, *,
jar: pulumi.Input[str],
args: Optional[pulumi.Input[List[pulumi.Input[str]]]] = None,
main_class: Optional[pulumi.Input[str]] = None,
properties: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None):
"""
:param pulumi.Input[str] jar: Path to a JAR file run during the step.
:param pulumi.Input[List[pulumi.Input[str]]] args: List of command line arguments passed to the JAR file's main function when executed.
:param pulumi.Input[str] main_class: Name of the main class in the specified Java file. If not specified, the JAR file should specify a Main-Class in its manifest file.
:param pulumi.Input[Mapping[str, pulumi.Input[str]]] properties: Key-Value map of Java properties that are set when the step runs. You can use these properties to pass key value pairs to your main function.
"""
pulumi.set(__self__, "jar", jar)
if args is not None:
pulumi.set(__self__, "args", args)
if main_class is not None:
pulumi.set(__self__, "main_class", main_class)
if properties is not None:
pulumi.set(__self__, "properties", properties)
@property
@pulumi.getter
def jar(self) -> pulumi.Input[str]:
"""
Path to a JAR file run during the step.
"""
return pulumi.get(self, "jar")
@jar.setter
def jar(self, value: pulumi.Input[str]):
pulumi.set(self, "jar", value)
@property
@pulumi.getter
def args(self) -> Optional[pulumi.Input[List[pulumi.Input[str]]]]:
"""
List of command line arguments passed to the JAR file's main function when executed.
"""
return pulumi.get(self, "args")
@args.setter
def args(self, value: Optional[pulumi.Input[List[pulumi.Input[str]]]]):
pulumi.set(self, "args", value)
@property
@pulumi.getter(name="mainClass")
def main_class(self) -> Optional[pulumi.Input[str]]:
"""
Name of the main class in the specified Java file. If not specified, the JAR file should specify a Main-Class in its manifest file.
"""
return pulumi.get(self, "main_class")
@main_class.setter
def main_class(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "main_class", value)
@property
@pulumi.getter
def properties(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]:
"""
Key-Value map of Java properties that are set when the step runs. You can use these properties to pass key value pairs to your main function.
"""
return pulumi.get(self, "properties")
@properties.setter
def properties(self, value: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]):
pulumi.set(self, "properties", value)
@pulumi.input_type
class InstanceGroupEbsConfigArgs:
def __init__(__self__, *,
size: pulumi.Input[float],
type: pulumi.Input[str],
iops: Optional[pulumi.Input[float]] = None,
volumes_per_instance: Optional[pulumi.Input[float]] = None):
"""
:param pulumi.Input[float] size: The volume size, in gibibytes (GiB). This can be a number from 1 - 1024. If the volume type is EBS-optimized, the minimum value is 10.
:param pulumi.Input[str] type: The volume type. Valid options are 'gp2', 'io1' and 'standard'.
:param pulumi.Input[float] iops: The number of I/O operations per second (IOPS) that the volume supports.
:param pulumi.Input[float] volumes_per_instance: The number of EBS Volumes to attach per instance.
"""
pulumi.set(__self__, "size", size)
pulumi.set(__self__, "type", type)
if iops is not None:
pulumi.set(__self__, "iops", iops)
if volumes_per_instance is not None:
pulumi.set(__self__, "volumes_per_instance", volumes_per_instance)
@property
@pulumi.getter
def size(self) -> pulumi.Input[float]:
"""
The volume size, in gibibytes (GiB). This can be a number from 1 - 1024. If the volume type is EBS-optimized, the minimum value is 10.
"""
return pulumi.get(self, "size")
@size.setter
def size(self, value: pulumi.Input[float]):
pulumi.set(self, "size", value)
@property
@pulumi.getter
def type(self) -> pulumi.Input[str]:
"""
The volume type. Valid options are 'gp2', 'io1' and 'standard'.
"""
return pulumi.get(self, "type")
@type.setter
def type(self, value: pulumi.Input[str]):
pulumi.set(self, "type", value)
@property
@pulumi.getter
def iops(self) -> Optional[pulumi.Input[float]]:
"""
The number of I/O operations per second (IOPS) that the volume supports.
"""
return pulumi.get(self, "iops")
@iops.setter
def iops(self, value: Optional[pulumi.Input[float]]):
pulumi.set(self, "iops", value)
@property
@pulumi.getter(name="volumesPerInstance")
def volumes_per_instance(self) -> Optional[pulumi.Input[float]]:
"""
The number of EBS Volumes to attach per instance.
"""
return pulumi.get(self, "volumes_per_instance")
@volumes_per_instance.setter
def volumes_per_instance(self, value: Optional[pulumi.Input[float]]):
pulumi.set(self, "volumes_per_instance", value)
| 46.337278
| 604
| 0.677947
| 5,008
| 39,155
| 5.135383
| 0.067093
| 0.094953
| 0.074034
| 0.046193
| 0.898476
| 0.849911
| 0.824753
| 0.774438
| 0.751069
| 0.712808
| 0
| 0.003378
| 0.2213
| 39,155
| 844
| 605
| 46.39218
| 0.840112
| 0.35574
| 0
| 0.596078
| 1
| 0
| 0.119069
| 0.059662
| 0
| 0
| 0
| 0
| 0
| 1
| 0.207843
| false
| 0.05098
| 0.009804
| 0
| 0.331373
| 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
| 1
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 9
|
bd86bf450c22926890dd3e00aca5632f9c858486
| 185
|
py
|
Python
|
evaluation_framework/Classification/__init__.py
|
nheist/Evaluation-Framework
|
0561fcbca5025f280624c02f6fad24a888c653ab
|
[
"Apache-2.0"
] | 2
|
2020-08-01T07:12:00.000Z
|
2022-02-10T10:19:11.000Z
|
evaluation_framework/Classification/__init__.py
|
nheist/Evaluation-Framework
|
0561fcbca5025f280624c02f6fad24a888c653ab
|
[
"Apache-2.0"
] | null | null | null |
evaluation_framework/Classification/__init__.py
|
nheist/Evaluation-Framework
|
0561fcbca5025f280624c02f6fad24a888c653ab
|
[
"Apache-2.0"
] | null | null | null |
from evaluation_framework.Classification.classification_model import ClassificationModel
from evaluation_framework.Classification.classification_taskManager import ClassificationManager
| 92.5
| 96
| 0.940541
| 16
| 185
| 10.625
| 0.5625
| 0.164706
| 0.270588
| 0.435294
| 0.6
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.037838
| 185
| 2
| 96
| 92.5
| 0.955056
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 1
| null | 0
| 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
|
da83e8ab16fc871f4059c9091181a7c3a1d8e9f0
| 115
|
py
|
Python
|
src/models/utils.py
|
arijitmondal-94/app-review-sentiment-anslysis-using-bert
|
78edceb6c2077348d6b3f2904477d6cad00c1671
|
[
"MIT"
] | null | null | null |
src/models/utils.py
|
arijitmondal-94/app-review-sentiment-anslysis-using-bert
|
78edceb6c2077348d6b3f2904477d6cad00c1671
|
[
"MIT"
] | null | null | null |
src/models/utils.py
|
arijitmondal-94/app-review-sentiment-anslysis-using-bert
|
78edceb6c2077348d6b3f2904477d6cad00c1671
|
[
"MIT"
] | null | null | null |
import transformers
def get_tokenizer():
return transformers.BertTokenizer.from_pretrained('bert-base-cased')
| 23
| 72
| 0.808696
| 13
| 115
| 7
| 0.923077
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.095652
| 115
| 5
| 72
| 23
| 0.875
| 0
| 0
| 0
| 0
| 0
| 0.12931
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| true
| 0
| 0.333333
| 0.333333
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 1
| 1
| 0
|
0
| 7
|
e526ba9034575784c4f2b4f0a06c6c5fecf35813
| 4,658
|
py
|
Python
|
tests/test_metrics.py
|
jaluebbe/ahrs
|
4b4a33b1006e0d455a71ac8379a2697202361758
|
[
"MIT"
] | 1
|
2022-01-11T20:10:48.000Z
|
2022-01-11T20:10:48.000Z
|
tests/test_metrics.py
|
geoKinga/ahrs
|
87f9210cfcf6c545d86ae8588a93f012020164ee
|
[
"MIT"
] | null | null | null |
tests/test_metrics.py
|
geoKinga/ahrs
|
87f9210cfcf6c545d86ae8588a93f012020164ee
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python3
import unittest
import numpy as np
import ahrs
class TestEuclidean(unittest.TestCase):
def test_correct_values(self):
self.assertEqual(ahrs.utils.euclidean(np.zeros(3), np.zeros(3)), 0.0)
self.assertEqual(ahrs.utils.euclidean(np.zeros(3), np.ones(3)), np.sqrt(3))
self.assertEqual(ahrs.utils.euclidean(np.ones(3), -np.ones(3)), 2.0*np.sqrt(3))
self.assertEqual(ahrs.utils.euclidean(np.array([1, 2, 3]), np.array([4, 5, 6])), 5.196152422706632)
self.assertGreaterEqual(ahrs.utils.euclidean(np.random.random(3)-0.5, np.random.random(3)-0.5), 0.0)
def test_guard_clauses(self):
self.assertRaises(ValueError, ahrs.utils.euclidean, np.zeros(3), np.zeros(2))
class TestChordal(unittest.TestCase):
def setUp(self):
self.R1 = ahrs.DCM(rpy=[10.0, -20.0, 30.0])
self.R2 = ahrs.DCM(rpy=[-10.0, 20.0, -30.0])
def test_correct_values(self):
self.assertEqual(ahrs.utils.chordal(np.identity(3), np.identity(3)), 0.0)
self.assertEqual(ahrs.utils.chordal(np.identity(3), -np.identity(3)), 2.0*np.sqrt(3))
self.assertGreaterEqual(ahrs.utils.euclidean(np.random.random((3, 3))-0.5, np.random.random((3, 3))-0.5), 0.0)
self.assertEqual(ahrs.utils.chordal(self.R1, self.R2), 1.6916338074634352)
self.assertEqual(ahrs.utils.chordal(self.R1.tolist(), self.R2.tolist()), 1.6916338074634352)
def test_guard_clauses(self):
self.assertRaises(TypeError, ahrs.utils.chordal, np.identity(3), 3.0)
self.assertRaises(TypeError, ahrs.utils.chordal, 3.0, np.identity(3))
self.assertRaises(TypeError, ahrs.utils.chordal, "np.identity(3)", np.identity(3))
self.assertRaises(TypeError, ahrs.utils.chordal, np.identity(3), "np.identity(3)")
self.assertRaises(ValueError, ahrs.utils.chordal, np.identity(3), np.identity(2))
self.assertRaises(ValueError, ahrs.utils.chordal, np.tile(np.identity(3), (2, 1, 1)), np.tile(np.identity(3), (3, 1, 1)))
class TestIdentityDeviation(unittest.TestCase):
def setUp(self):
self.R1 = ahrs.DCM(rpy=[10.0, -20.0, 30.0])
self.R2 = ahrs.DCM(rpy=[-10.0, 20.0, -30.0])
def test_correct_values(self):
self.assertEqual(ahrs.utils.identity_deviation(np.identity(3), np.identity(3)), 0.0)
self.assertEqual(ahrs.utils.identity_deviation(np.identity(3), -np.identity(3)), 2.0*np.sqrt(3))
self.assertGreaterEqual(ahrs.utils.identity_deviation(np.random.random((3, 3))-0.5, np.random.random((3, 3))-0.5), 0.0)
self.assertEqual(ahrs.utils.identity_deviation(self.R1, self.R2), 1.6916338074634352)
self.assertEqual(ahrs.utils.identity_deviation(self.R1.tolist(), self.R2.tolist()), 1.6916338074634352)
def test_guard_clauses(self):
self.assertRaises(TypeError, ahrs.utils.identity_deviation, np.identity(3), 3.0)
self.assertRaises(TypeError, ahrs.utils.identity_deviation, 3.0, np.identity(3))
self.assertRaises(TypeError, ahrs.utils.identity_deviation, "np.identity(3)", np.identity(3))
self.assertRaises(TypeError, ahrs.utils.identity_deviation, np.identity(3), "np.identity(3)")
self.assertRaises(ValueError, ahrs.utils.identity_deviation, np.identity(3), np.identity(2))
self.assertRaises(ValueError, ahrs.utils.identity_deviation, np.zeros((3, 3)), np.zeros((2, 2)))
class TestAngularDistance(unittest.TestCase):
def setUp(self):
self.R1 = ahrs.DCM(rpy=[10.0, -20.0, 30.0])
self.R2 = ahrs.DCM(rpy=[-10.0, 20.0, -30.0])
def test_correct_values(self):
self.assertEqual(ahrs.utils.angular_distance(np.identity(3), np.identity(3)), 0.0)
self.assertGreaterEqual(ahrs.utils.angular_distance(np.random.random((3, 3))-0.5, np.random.random((3, 3))-0.5), 0.0)
self.assertEqual(ahrs.utils.angular_distance(self.R1, self.R2), 1.282213683073497)
self.assertEqual(ahrs.utils.angular_distance(self.R1.tolist(), self.R2.tolist()), 1.282213683073497)
def test_guard_clauses(self):
self.assertRaises(TypeError, ahrs.utils.angular_distance, np.identity(3), 3.0)
self.assertRaises(TypeError, ahrs.utils.angular_distance, 3.0, np.identity(3))
self.assertRaises(TypeError, ahrs.utils.angular_distance, "np.identity(3)", np.identity(3))
self.assertRaises(TypeError, ahrs.utils.angular_distance, np.identity(3), "np.identity(3)")
self.assertRaises(ValueError, ahrs.utils.angular_distance, np.identity(3), np.identity(2))
self.assertRaises(ValueError, ahrs.utils.angular_distance, np.zeros((3, 3)), np.zeros((2, 2)))
if __name__ == "__main__":
unittest.main()
| 59.717949
| 129
| 0.689137
| 690
| 4,658
| 4.586957
| 0.091304
| 0.108057
| 0.114692
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| 0.9109
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| 0.878989
| 0.836019
| 0.76872
| 0.672986
| 0
| 0.07989
| 0.137398
| 4,658
| 77
| 130
| 60.493506
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| 0.004508
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| 0
| 0.019845
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| 0
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| 0
| 0
| 0.59375
| 1
| 0.171875
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| 0
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| null | 0
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| 0
| 0
|
0
| 7
|
e5874ae1c7c42915e8cd9d0bff8febd39ede0361
| 18,978
|
py
|
Python
|
src/conductor/client/http/api/workflow_bulk_resource_api.py
|
conductor-sdk/conductor-python
|
b3e4e0ae196f9963316a829fe42d9e7e01a390e2
|
[
"Apache-2.0"
] | 3
|
2022-03-10T18:24:46.000Z
|
2022-03-22T20:49:30.000Z
|
src/conductor/client/http/api/workflow_bulk_resource_api.py
|
conductor-sdk/conductor-python
|
b3e4e0ae196f9963316a829fe42d9e7e01a390e2
|
[
"Apache-2.0"
] | 6
|
2022-03-08T17:48:28.000Z
|
2022-03-30T00:39:22.000Z
|
src/conductor/client/http/api/workflow_bulk_resource_api.py
|
conductor-sdk/conductor-python
|
b3e4e0ae196f9963316a829fe42d9e7e01a390e2
|
[
"Apache-2.0"
] | null | null | null |
from __future__ import absolute_import
import re # noqa: F401
# python 2 and python 3 compatibility library
import six
from conductor.client.http.api_client import ApiClient
class WorkflowBulkResourceApi(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 pause_workflow1(self, body, **kwargs): # noqa: E501
"""Pause the list of workflows # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.pause_workflow1(body, async_req=True)
>>> result = thread.get()
:param async_req bool
:param list[str] body: (required)
:return: BulkResponse
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.pause_workflow1_with_http_info(body, **kwargs) # noqa: E501
else:
(data) = self.pause_workflow1_with_http_info(body, **kwargs) # noqa: E501
return data
def pause_workflow1_with_http_info(self, body, **kwargs): # noqa: E501
"""Pause the list of workflows # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.pause_workflow1_with_http_info(body, async_req=True)
>>> result = thread.get()
:param async_req bool
:param list[str] body: (required)
:return: BulkResponse
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 pause_workflow1" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'body' is set
if ('body' not in params or
params['body'] is None):
raise ValueError("Missing the required parameter `body` when calling `pause_workflow1`") # noqa: E501
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(
['*/*']) # 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 = [] # noqa: E501
return self.api_client.call_api(
'/api/workflow/bulk/pause', 'PUT',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='BulkResponse', # 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 restart1(self, body, **kwargs): # noqa: E501
"""Restart the list of completed workflow # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.restart1(body, async_req=True)
>>> result = thread.get()
:param async_req bool
:param list[str] body: (required)
:param bool use_latest_definitions:
:return: BulkResponse
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.restart1_with_http_info(body, **kwargs) # noqa: E501
else:
(data) = self.restart1_with_http_info(body, **kwargs) # noqa: E501
return data
def restart1_with_http_info(self, body, **kwargs): # noqa: E501
"""Restart the list of completed workflow # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.restart1_with_http_info(body, async_req=True)
>>> result = thread.get()
:param async_req bool
:param list[str] body: (required)
:param bool use_latest_definitions:
:return: BulkResponse
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['body', 'use_latest_definitions'] # 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 restart1" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'body' is set
if ('body' not in params or
params['body'] is None):
raise ValueError("Missing the required parameter `body` when calling `restart1`") # noqa: E501
collection_formats = {}
path_params = {}
query_params = []
if 'use_latest_definitions' in params:
query_params.append(('useLatestDefinitions', params['use_latest_definitions'])) # 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(
['*/*']) # 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 = [] # noqa: E501
return self.api_client.call_api(
'/api/workflow/bulk/restart', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='BulkResponse', # 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 resume_workflow1(self, body, **kwargs): # noqa: E501
"""Resume the list of workflows # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.resume_workflow1(body, async_req=True)
>>> result = thread.get()
:param async_req bool
:param list[str] body: (required)
:return: BulkResponse
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.resume_workflow1_with_http_info(body, **kwargs) # noqa: E501
else:
(data) = self.resume_workflow1_with_http_info(body, **kwargs) # noqa: E501
return data
def resume_workflow1_with_http_info(self, body, **kwargs): # noqa: E501
"""Resume the list of workflows # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.resume_workflow1_with_http_info(body, async_req=True)
>>> result = thread.get()
:param async_req bool
:param list[str] body: (required)
:return: BulkResponse
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 resume_workflow1" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'body' is set
if ('body' not in params or
params['body'] is None):
raise ValueError("Missing the required parameter `body` when calling `resume_workflow1`") # noqa: E501
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(
['*/*']) # 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 = [] # noqa: E501
return self.api_client.call_api(
'/api/workflow/bulk/resume', 'PUT',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='BulkResponse', # 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 retry1(self, body, **kwargs): # noqa: E501
"""Retry the last failed task for each workflow from the list # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.retry1(body, async_req=True)
>>> result = thread.get()
:param async_req bool
:param list[str] body: (required)
:return: BulkResponse
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.retry1_with_http_info(body, **kwargs) # noqa: E501
else:
(data) = self.retry1_with_http_info(body, **kwargs) # noqa: E501
return data
def retry1_with_http_info(self, body, **kwargs): # noqa: E501
"""Retry the last failed task for each workflow from the list # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.retry1_with_http_info(body, async_req=True)
>>> result = thread.get()
:param async_req bool
:param list[str] body: (required)
:return: BulkResponse
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 retry1" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'body' is set
if ('body' not in params or
params['body'] is None):
raise ValueError("Missing the required parameter `body` when calling `retry1`") # noqa: E501
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(
['*/*']) # 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 = [] # noqa: E501
return self.api_client.call_api(
'/api/workflow/bulk/retry', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='BulkResponse', # 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 terminate(self, body, **kwargs): # noqa: E501
"""Terminate workflows execution # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.terminate(body, async_req=True)
>>> result = thread.get()
:param async_req bool
:param list[str] body: (required)
:param str reason:
:return: BulkResponse
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.terminate_with_http_info(body, **kwargs) # noqa: E501
else:
(data) = self.terminate_with_http_info(body, **kwargs) # noqa: E501
return data
def terminate_with_http_info(self, body, **kwargs): # noqa: E501
"""Terminate workflows execution # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.terminate_with_http_info(body, async_req=True)
>>> result = thread.get()
:param async_req bool
:param list[str] body: (required)
:param str reason:
:return: BulkResponse
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['body', 'reason'] # 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 terminate" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'body' is set
if ('body' not in params or
params['body'] is None):
raise ValueError("Missing the required parameter `body` when calling `terminate`") # noqa: E501
collection_formats = {}
path_params = {}
query_params = []
if 'reason' in params:
query_params.append(('reason', params['reason'])) # 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(
['*/*']) # 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 = [] # noqa: E501
return self.api_client.call_api(
'/api/workflow/bulk/terminate', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='BulkResponse', # 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)
| 36.850485
| 115
| 0.593371
| 2,123
| 18,978
| 5.072068
| 0.073481
| 0.049777
| 0.026003
| 0.033432
| 0.942793
| 0.933414
| 0.931742
| 0.931742
| 0.928399
| 0.911311
| 0
| 0.018228
| 0.311993
| 18,978
| 514
| 116
| 36.922179
| 0.806464
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| 0
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| 0
| 0
| 0.169092
| 0.042764
| 0
| 0
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| 0
| 1
| 0.038869
| false
| 0
| 0.014134
| 0
| 0.109541
| 0
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| 0
| 0
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| 0
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| 1
| 1
| 1
| 1
| 1
| 1
| 0
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| 0
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| null | 0
| 0
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| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
e590707d49634a0cb8d2ffdb1eba9e26c922160f
| 1,363
|
py
|
Python
|
pycspr/factory/__init__.py
|
hboshnak/casper-python-sdk
|
19db9bf3b4720d5b4e133463e5a32fd64f1c33ed
|
[
"Apache-2.0"
] | 11
|
2021-09-27T08:41:18.000Z
|
2022-03-24T11:25:20.000Z
|
pycspr/factory/__init__.py
|
hboshnak/casper-python-sdk
|
19db9bf3b4720d5b4e133463e5a32fd64f1c33ed
|
[
"Apache-2.0"
] | 13
|
2021-09-13T19:08:45.000Z
|
2022-02-08T10:01:12.000Z
|
pycspr/factory/__init__.py
|
hboshnak/casper-python-sdk
|
19db9bf3b4720d5b4e133463e5a32fd64f1c33ed
|
[
"Apache-2.0"
] | 14
|
2021-07-12T10:46:33.000Z
|
2022-03-01T08:25:07.000Z
|
from pycspr.factory.accounts import create_private_key
from pycspr.factory.accounts import create_public_key
from pycspr.factory.accounts import create_public_key_from_account_key
from pycspr.factory.accounts import parse_private_key
from pycspr.factory.accounts import parse_private_key_bytes
from pycspr.factory.accounts import parse_public_key
from pycspr.factory.accounts import parse_public_key_bytes
from pycspr.factory.deploys import create_deploy
from pycspr.factory.deploys import create_deploy_approval
from pycspr.factory.deploys import create_deploy_arguments
from pycspr.factory.deploys import create_deploy_body
from pycspr.factory.deploys import create_deploy_header
from pycspr.factory.deploys import create_deploy_parameters
from pycspr.factory.deploys import create_deploy_ttl
from pycspr.factory.deploys import create_transfer
from pycspr.factory.deploys import create_transfer_session
from pycspr.factory.deploys import create_standard_payment
from pycspr.factory.deploys import create_validator_auction_bid
from pycspr.factory.deploys import create_validator_auction_bid_withdrawal
from pycspr.factory.deploys import create_validator_delegation
from pycspr.factory.deploys import create_validator_delegation_withdrawal
from pycspr.factory.digests import create_digest_of_deploy
from pycspr.factory.digests import create_digest_of_deploy_body
| 56.791667
| 74
| 0.898753
| 194
| 1,363
| 6.025773
| 0.164948
| 0.196749
| 0.334474
| 0.287425
| 0.920445
| 0.911891
| 0.849444
| 0.516681
| 0.345595
| 0.084688
| 0
| 0
| 0.067498
| 1,363
| 23
| 75
| 59.26087
| 0.919748
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| 0
|
0
| 8
|
e5ece634fbf2957d2b0497bc43bc87d22c7d8b81
| 10,962
|
py
|
Python
|
src/jlauto/models/load_premade.py
|
AllaVinner/JL-ML
|
9d0bbbd324fd59ee812144ef0b4cff88d339ee76
|
[
"MIT"
] | null | null | null |
src/jlauto/models/load_premade.py
|
AllaVinner/JL-ML
|
9d0bbbd324fd59ee812144ef0b4cff88d339ee76
|
[
"MIT"
] | null | null | null |
src/jlauto/models/load_premade.py
|
AllaVinner/JL-ML
|
9d0bbbd324fd59ee812144ef0b4cff88d339ee76
|
[
"MIT"
] | null | null | null |
import tensorflow as tf
from tensorflow import keras
import numpy as np
from collections import defaultdict
from jlauto.models.autoencoder import Autoencoder
from jlauto.models.variational_autoencoder import VariationalAutoencoder
def load_premade_model(model_type = None, model_name = None, **kwargs):
load = defaultdict(dict)
# Autoencoder models
load['autoencoder']['dense'] = autoencoder_dense
load['autoencoder']['mnist_cnn_deep'] = autoencoder_mnist_cnn_deep
load['autoencoder']['mnist_cnn_shallow'] = autoencoder_mnist_cnn_shallow
# Variational autoencoder models
load['variational_autoencoder']['mnist_dense'] = variational_autoencoder_dense
load['variational_autoencoder']['mnist_cnn_deep'] = variational_autoencoder_mnist_cnn_deep
load['variational_autoencoder']['mnist_cnn_shallow'] = variational_autoencoder_mnist_cnn_shallow
return load[model_type][model_name](**kwargs)
### Architectures ###
#-----------------------------------------------------------------------------
# Autoencoders
def autoencoder_mnist_cnn_deep(latent_dim = 10, input_shape = (28,28,1), **kwargs):
# OUTPUT_SHAPE = (28,28,1) or (28,28)
# Variational autoencoder
encoder = keras.Sequential([
keras.layers.InputLayer(input_shape=input_shape),
keras.layers.Reshape((28,28,1)),
keras.layers.Conv2D(8, (3,3), padding = "same", activation = "relu"),
keras.layers.Conv2D(8, (3,3), padding = "same", activation = "relu"),
keras.layers.MaxPool2D((2,2)),
keras.layers.Conv2D(16, (3,3), padding = "same", activation = "relu"),
keras.layers.Conv2D(16, (3,3), padding = "same", activation = "relu"),
keras.layers.MaxPool2D((2,2)),
keras.layers.Conv2D(32, (3,3), padding = "same", activation = "relu"),
keras.layers.Conv2D(32, (3,3), padding = "same", activation = "relu"),
keras.layers.MaxPool2D((2,2)),
keras.layers.Conv2D(64, (3,3), padding = "same", activation = "relu"),
keras.layers.Conv2D(64, (3,3), padding = "same", activation = "relu"),
keras.layers.Flatten(),
keras.layers.Dense(200, activation = 'relu'),
keras.layers.Dense(100, activation = 'relu'),
keras.layers.Dense(latent_dim),
keras.layers.Reshape((latent_dim,)),
], name = "Encoder" )
decoder = keras.Sequential([
keras.layers.InputLayer(input_shape = (latent_dim,)),
keras.layers.Dense(200, activation = "relu"),
keras.layers.Dense(3*3*64, activation = "relu"),
keras.layers.Reshape((3,3,64)),
keras.layers.Conv2DTranspose(32, (3,3), activation = "relu", padding = "same"),
keras.layers.Conv2DTranspose(32, (3,3), activation = "relu", strides = 2),
keras.layers.UpSampling2D((2,2)),
keras.layers.Conv2DTranspose(16, (3,3), activation = "relu", padding = "same"),
keras.layers.Conv2DTranspose(16, (3,3), activation = "relu", padding = "same"),
keras.layers.Conv2DTranspose(16, (3,3), activation = "relu", padding = "same"),
keras.layers.UpSampling2D((2,2)),
keras.layers.Conv2DTranspose(16, (3,3), activation = "relu", padding = "same"),
keras.layers.Conv2DTranspose(1, (3,3), activation = "sigmoid", padding = "same"),
keras.layers.Reshape(input_shape),
], name = "Decoder")
model = Autoencoder(encoder, decoder)
return model
def autoencoder_mnist_cnn_shallow(latent_dim = 10, input_shape = (28,28,1), **kwargs):
# OUTPUT_SHAPE = (28,28,1) or (28,28)
# Variational autoencoder
encoder = tf.keras.Sequential(
[
tf.keras.layers.InputLayer(input_shape=input_shape),
tf.keras.layers.Reshape((28,28,1)),
tf.keras.layers.Conv2D(
filters=32, kernel_size=3, strides=(2, 2), activation='relu'),
tf.keras.layers.Conv2D(
filters=64, kernel_size=3, strides=(2, 2), activation='relu'),
tf.keras.layers.Flatten(),
# No activation
tf.keras.layers.Dense(latent_dim),
tf.keras.layers.Reshape((latent_dim,)),
]
)
decoder = tf.keras.Sequential(
[
tf.keras.layers.InputLayer(input_shape=(latent_dim,)),
tf.keras.layers.Dense(units=7*7*32, activation=tf.nn.relu),
tf.keras.layers.Reshape(target_shape=(7, 7, 32)),
tf.keras.layers.Conv2DTranspose(
filters=64, kernel_size=3, strides=2, padding='same',
activation='relu'),
tf.keras.layers.Conv2DTranspose(
filters=32, kernel_size=3, strides=2, padding='same',
activation='relu'),
# No activation
tf.keras.layers.Conv2DTranspose(
filters=1, kernel_size=3, strides=1, padding='same', activation='sigmoid'),
tf.keras.layers.Reshape(input_shape)
]
)
# Initiate model
model = Autoencoder(encoder, decoder)
return model
def autoencoder_dense(latent_dim = 10, input_shape = None,
intermediat_dim = 100, **kwargs):
encoder = keras.Sequential([
keras.layers.InputLayer(input_shape=input_shape),
keras.layers.Flatten(),
keras.layers.Dense(intermediat_dim, activation="relu"),
keras.layers.Dense(latent_dim),
keras.layers.Reshape((latent_dim,)),
], name = "Encoder")
decoder = keras.Sequential([
keras.layers.InputLayer(input_shape=(latent_dim,)),
keras.layers.Dense(intermediat_dim, activation = "relu"),
keras.layers.Dense(np.prod(input_shape), activation = "sigmoid"),
keras.layers.Reshape(input_shape),
], name = "Decoder")
# Initiate model
model = Autoencoder(encoder, decoder)
return model
#-----------------------------------------------------------------------------
# Variational autoencoders
def variational_autoencoder_mnist_cnn_deep(input_shape = (28,28,1),
latent_dim = 10, **kwargs):
# OUTPUT_SHAPE = (28,28,1)
# Variational autoencoder
encoder = keras.Sequential([
keras.layers.InputLayer(input_shape=input_shape),
keras.layers.Reshape((28,28,1)),
keras.layers.Conv2D(8, (3,3), padding = "same", activation = "relu"),
keras.layers.Conv2D(8, (3,3), padding = "same", activation = "relu"),
keras.layers.MaxPool2D((2,2)),
keras.layers.Conv2D(16, (3,3), padding = "same", activation = "relu"),
keras.layers.Conv2D(16, (3,3), padding = "same", activation = "relu"),
keras.layers.MaxPool2D((2,2)),
keras.layers.Conv2D(32, (3,3), padding = "same", activation = "relu"),
keras.layers.Conv2D(32, (3,3), padding = "same", activation = "relu"),
keras.layers.MaxPool2D((2,2)),
keras.layers.Conv2D(64, (3,3), padding = "same", activation = "relu"),
keras.layers.Conv2D(64, (3,3), padding = "same", activation = "relu"),
keras.layers.Flatten(),
keras.layers.Dense(200, activation = 'relu'),
keras.layers.Dense(100, activation = 'relu'),
keras.layers.Dense(2*latent_dim),
keras.layers.Reshape((2,latent_dim)),
], name = "Encoder" )
decoder = keras.Sequential([
keras.layers.InputLayer(input_shape=(latent_dim,)),
keras.layers.Dense(200, activation = "relu"),
keras.layers.Dense(3*3*64, activation = "relu"),
keras.layers.Reshape((3,3,64)),
keras.layers.Conv2DTranspose(32, (3,3), activation = "relu", padding = "same"),
keras.layers.Conv2DTranspose(32, (3,3), activation = "relu", strides = 2),
keras.layers.UpSampling2D((2,2)),
keras.layers.Conv2DTranspose(16, (3,3), activation = "relu", padding = "same"),
keras.layers.Conv2DTranspose(16, (3,3), activation = "relu", padding = "same"),
keras.layers.Conv2DTranspose(16, (3,3), activation = "relu", padding = "same"),
keras.layers.UpSampling2D((2,2)),
keras.layers.Conv2DTranspose(16, (3,3), activation = "relu", padding = "same"),
keras.layers.Conv2DTranspose(1, (3,3), activation = "sigmoid", padding = "same"),
keras.layers.Reshape(input_shape),
], name = "Decoder")
model = VariationalAutoencoder(encoder, decoder)
return model
def variational_autoencoder_mnist_cnn_shallow(input_shape = (28,28,1),
latent_dim = 10, **kwargs):
encoder = tf.keras.Sequential(
[
tf.keras.layers.InputLayer(input_shape=input_shape),
tf.keras.layers.Reshape((28,28,1)),
tf.keras.layers.Conv2D(
filters=32, kernel_size=3, strides=(2, 2), activation='relu'),
tf.keras.layers.Conv2D(
filters=64, kernel_size=3, strides=(2, 2), activation='relu'),
tf.keras.layers.Flatten(),
# No activation
tf.keras.layers.Dense(2*latent_dim),
tf.keras.layers.Reshape((2,latent_dim)),
]
)
decoder = tf.keras.Sequential(
[
tf.keras.layers.InputLayer(input_shape=(latent_dim,)),
tf.keras.layers.Dense(units=7*7*32, activation=tf.nn.relu),
tf.keras.layers.Reshape(target_shape=(7, 7, 32)),
tf.keras.layers.Conv2DTranspose(
filters=64, kernel_size=3, strides=2, padding='same',
activation='relu'),
tf.keras.layers.Conv2DTranspose(
filters=32, kernel_size=3, strides=2, padding='same',
activation='relu'),
# No activation
tf.keras.layers.Conv2DTranspose(
filters=1, kernel_size=3, strides=1, padding='same',
activation = "sigmoid"),
tf.keras.layers.Reshape(input_shape),
]
)
# Initiate model
model = VariationalAutoencoder(encoder, decoder)
return model
def variational_autoencoder_dense(input_shape = None, latent_dim = 10,
intermediat_dim = 100, **kwargs):
encoder = keras.Sequential([
keras.layers.InputLayer(input_shape=input_shape),
keras.layers.Flatten(),
keras.layers.Dense(intermediat_dim, activation="relu"),
keras.layers.Dense(2*latent_dim),
keras.layers.Reshape((2,latent_dim)),
], name = "Encoder")
decoder = keras.Sequential([
keras.layers.InputLayer(input_shape=(latent_dim,)),
keras.layers.Dense(intermediat_dim, activation = "relu"),
keras.layers.Dense(np.prod(input_shape), activation = "sigmoid"),
keras.layers.Reshape(input_shape),
], name = "Decoder")
# Initiate model
model = VariationalAutoencoder(encoder, decoder)
return model
#-----------------------------------------------------------------------------
| 42.653696
| 101
| 0.599161
| 1,216
| 10,962
| 5.294408
| 0.063322
| 0.187947
| 0.082634
| 0.108729
| 0.905405
| 0.874029
| 0.847313
| 0.847313
| 0.818888
| 0.78658
| 0
| 0.04353
| 0.235085
| 10,962
| 256
| 102
| 42.820313
| 0.72427
| 0.056377
| 0
| 0.783505
| 0
| 0
| 0.058766
| 0.006691
| 0
| 0
| 0
| 0
| 0
| 1
| 0.036082
| false
| 0
| 0.030928
| 0
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| 0
| null | 0
| 0
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| 1
| 1
| 1
| 1
| 1
| 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
|
e5fcdd809462d257927bcca5d2a664f24d1cb0d5
| 874
|
py
|
Python
|
ddtn/run_many.py
|
DukeGonzo/ddtn
|
156cf5fb2f2e46619c0243a5accfddbe3567f109
|
[
"MIT"
] | 51
|
2018-03-25T07:18:21.000Z
|
2022-02-11T12:05:52.000Z
|
ddtn/run_many.py
|
DukeGonzo/ddtn
|
156cf5fb2f2e46619c0243a5accfddbe3567f109
|
[
"MIT"
] | 2
|
2018-10-26T06:43:44.000Z
|
2018-12-20T02:05:31.000Z
|
ddtn/run_many.py
|
DukeGonzo/ddtn
|
156cf5fb2f2e46619c0243a5accfddbe3567f109
|
[
"MIT"
] | 7
|
2018-04-11T20:34:27.000Z
|
2021-07-19T17:57:40.000Z
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon May 28 14:18:21 2018
@author: nsde
"""
#%%
import os
#%%
ne = '-ne 50 '
bs = '-bs 100 '
lr = '-lr 1e-5 '
#%%
os.system("PYTHONPATH='/home/nsde/Documents/ddtn' python mnist_classifier.py -tt no "
+ ne + bs + lr)
os.system("PYTHONPATH='/home/nsde/Documents/ddtn' python mnist_classifier.py -tt affine "
+ ne + bs + lr)
os.system("PYTHONPATH='/home/nsde/Documents/ddtn' python mnist_classifier.py -tt affinediffeo "
+ ne + bs + lr)
os.system("PYTHONPATH='/home/nsde/Documents/ddtn' python mnist_classifier.py -tt homografy "
+ ne + bs + lr)
os.system("PYTHONPATH='/home/nsde/Documents/ddtn' python mnist_classifier.py -tt TPS "
+ ne + bs + lr)
os.system("PYTHONPATH='/home/nsde/Documents/ddtn' python mnist_classifier.py -tt CPAB "
+ ne + bs + lr)
| 30.137931
| 95
| 0.632723
| 126
| 874
| 4.34127
| 0.34127
| 0.087751
| 0.197441
| 0.241316
| 0.756856
| 0.756856
| 0.756856
| 0.756856
| 0.756856
| 0.756856
| 0
| 0.029957
| 0.197941
| 874
| 29
| 96
| 30.137931
| 0.750357
| 0.114416
| 0
| 0.375
| 0
| 0
| 0.636959
| 0.29882
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.0625
| 0
| 0.0625
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 1
| 1
| 1
| 1
| 1
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
00640b1be1be8bf2e594bb79ca0146d1db973f66
| 1,560
|
py
|
Python
|
cloudformation/test_cf.py
|
DYeag/AWS-Shell
|
b5318e72373b1a948ac6aced1c0bb4566d5ae46f
|
[
"0BSD"
] | 3
|
2016-08-22T07:14:56.000Z
|
2018-03-16T07:31:44.000Z
|
cloudformation/test_cf.py
|
QualiSystemsLab/AWS-Shell-ext
|
bf7b62640d8d97a5e9199edb7a1ada0b98aac6fb
|
[
"0BSD"
] | 470
|
2016-03-24T13:38:08.000Z
|
2022-02-05T01:14:05.000Z
|
cloudformation/test_cf.py
|
QualiSystemsLab/AWS-Shell-ext
|
bf7b62640d8d97a5e9199edb7a1ada0b98aac6fb
|
[
"0BSD"
] | 9
|
2016-06-20T11:41:54.000Z
|
2020-11-21T00:42:45.000Z
|
from unittest import TestCase
import json
class TestCloudFormation(TestCase):
def setUp(self):
pass
def test_main_json_valid(self):
json_file = open('0_Main.json', 'r')
json_string = json_file.read()
json.loads(json_string)
def test_main_ex_json_valid(self):
json_file = open('0_Main_EX.json', 'r')
json_string = json_file.read()
json.loads(json_string)
def test_main_ex_no_vpn_json_valid(self):
json_file = open('0_Main_EX_No_VPN.json', 'r')
json_string = json_file.read()
json.loads(json_string)
def test_vpc_json_valid(self):
json_file = open('1_VPC.json', 'r')
json_string = json_file.read()
json.loads(json_string)
def test_vpc_ex_json_valid(self):
json_file = open('1_VPC_EX.json', 'r')
json_string = json_file.read()
json.loads(json_string)
def test_vpc_ex__no_vpn_json_valid(self):
json_file = open('1_VPC_EX_No_VPN.json', 'r')
json_string = json_file.read()
json.loads(json_string)
def test_ec2_json_valid(self):
json_file = open('2_EC2.json', 'r')
json_string = json_file.read()
json.loads(json_string)
def test_ec2_ex_json_valid(self):
json_file = open('2_EC2_EX.json', 'r')
json_string = json_file.read()
json.loads(json_string)
def test_ec2_ex__no_vpn_json_valid(self):
json_file = open('2_EC2_EX_No_VPN.json', 'r')
json_string = json_file.read()
json.loads(json_string)
| 28.888889
| 54
| 0.644872
| 235
| 1,560
| 3.86383
| 0.119149
| 0.15859
| 0.128855
| 0.168502
| 0.896476
| 0.896476
| 0.896476
| 0.896476
| 0.799559
| 0.589207
| 0
| 0.01268
| 0.241667
| 1,560
| 53
| 55
| 29.433962
| 0.754861
| 0
| 0
| 0.439024
| 0
| 0
| 0.090385
| 0.013462
| 0
| 0
| 0
| 0
| 0
| 1
| 0.243902
| false
| 0.02439
| 0.04878
| 0
| 0.317073
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
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| 0
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| 0
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| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
00db7ec563c22288a9920b45af3e41147620391d
| 282
|
py
|
Python
|
src/genie/libs/parser/iosxe/tests/ShowBootvar/cli/equal/golden_output1_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/iosxe/tests/ShowBootvar/cli/equal/golden_output1_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/iosxe/tests/ShowBootvar/cli/equal/golden_output1_expected.py
|
balmasea/genieparser
|
d1e71a96dfb081e0a8591707b9d4872decd5d9d3
|
[
"Apache-2.0"
] | 309
|
2019-01-16T20:21:07.000Z
|
2022-03-30T12:56:41.000Z
|
expected_output = {
"active": {
"boot_variable": "harddisk:/ISSUCleanGolden,12;bootflash:12351822-iedge-asr-uut,12;",
"configuration_register": "0x2",
},
"next_reload_boot_variable": "harddisk:/ISSUCleanGolden,12;bootflash:12351822-iedge-asr-uut,12;",
}
| 35.25
| 101
| 0.687943
| 30
| 282
| 6.266667
| 0.6
| 0.12766
| 0.212766
| 0.37234
| 0.712766
| 0.712766
| 0.712766
| 0.712766
| 0.712766
| 0.712766
| 0
| 0.107884
| 0.14539
| 282
| 7
| 102
| 40.285714
| 0.672199
| 0
| 0
| 0
| 0
| 0
| 0.705674
| 0.62766
| 0
| 0
| 0.010638
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 10
|
00e04f82f30d8a9e4efd91f4afa8ee177322df18
| 112
|
py
|
Python
|
MiscContests/Code Galdiators/Semifinal/gen.py
|
Mindjolt2406/Competitive-Programming
|
d000d98bf7005ee4fb809bcea2f110e4c4793b80
|
[
"MIT"
] | 2
|
2018-12-11T14:37:24.000Z
|
2022-01-23T18:11:54.000Z
|
MiscContests/Code Galdiators/Semifinal/gen.py
|
Mindjolt2406/Competitive-Programming
|
d000d98bf7005ee4fb809bcea2f110e4c4793b80
|
[
"MIT"
] | null | null | null |
MiscContests/Code Galdiators/Semifinal/gen.py
|
Mindjolt2406/Competitive-Programming
|
d000d98bf7005ee4fb809bcea2f110e4c4793b80
|
[
"MIT"
] | null | null | null |
print 1
print 100000,100000
for i in range(100000): print 1,
print ""
for i in range(100000): print 1,
print ""
| 16
| 32
| 0.705357
| 21
| 112
| 3.761905
| 0.333333
| 0.227848
| 0.417722
| 0.278481
| 0.708861
| 0.708861
| 0.708861
| 0.708861
| 0
| 0
| 0
| 0.293478
| 0.178571
| 112
| 6
| 33
| 18.666667
| 0.565217
| 0
| 0
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 1
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 11
|
dab2e81c8710a956681a93975d3427ecfb8f0a04
| 150
|
py
|
Python
|
src/test/boostrap/salle/salles.py
|
stormi/tsunami
|
bdc853229834b52b2ee8ed54a3161a1a3133d926
|
[
"BSD-3-Clause"
] | 14
|
2015-08-21T19:15:21.000Z
|
2017-11-26T13:59:17.000Z
|
src/test/boostrap/salle/salles.py
|
stormi/tsunami
|
bdc853229834b52b2ee8ed54a3161a1a3133d926
|
[
"BSD-3-Clause"
] | 20
|
2015-09-29T20:50:45.000Z
|
2018-06-21T12:58:30.000Z
|
src/test/boostrap/salle/salles.py
|
stormi/tsunami
|
bdc853229834b52b2ee8ed54a3161a1a3133d926
|
[
"BSD-3-Clause"
] | 3
|
2015-05-02T19:42:03.000Z
|
2018-09-06T10:55:00.000Z
|
importeur.salle.creer_salle("autre", "1", 10, 10)
importeur.salle.creer_salle("autre", "2", 10, 11)
importeur.salle.creer_salle("autre", "3", 10, 12)
| 37.5
| 49
| 0.7
| 24
| 150
| 4.25
| 0.416667
| 0.411765
| 0.558824
| 0.705882
| 0.852941
| 0
| 0
| 0
| 0
| 0
| 0
| 0.108696
| 0.08
| 150
| 3
| 50
| 50
| 0.630435
| 0
| 0
| 0
| 0
| 0
| 0.12
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 9
|
975948a23edd2bc842a406dbf7332a58c0adf99f
| 9,118
|
py
|
Python
|
tests/cli/env/test_remove.py
|
ashemedai/hatch
|
9ec00d5e027c992efbc16dd777b1f6926368b6bf
|
[
"MIT"
] | null | null | null |
tests/cli/env/test_remove.py
|
ashemedai/hatch
|
9ec00d5e027c992efbc16dd777b1f6926368b6bf
|
[
"MIT"
] | null | null | null |
tests/cli/env/test_remove.py
|
ashemedai/hatch
|
9ec00d5e027c992efbc16dd777b1f6926368b6bf
|
[
"MIT"
] | null | null | null |
from hatch.config.constants import AppEnvVars
from hatch.project.core import Project
def test_unknown(hatch, temp_dir, helpers, config_file):
project_name = 'My App'
cache_path = temp_dir / 'cache'
config_file.model.dirs.env = str(cache_path)
config_file.save()
with temp_dir.as_cwd():
result = hatch('new', project_name)
assert result.exit_code == 0, result.output
project_path = temp_dir / 'my-app'
with project_path.as_cwd():
result = hatch('env', 'remove', 'foo')
assert result.exit_code == 1
assert result.output == helpers.dedent(
"""
Unknown environment: foo
"""
)
def test_nonexistent(hatch, temp_dir, config_file):
project_name = 'My App'
cache_path = temp_dir / 'cache'
config_file.model.dirs.env = str(cache_path)
config_file.save()
with temp_dir.as_cwd():
result = hatch('new', project_name)
assert result.exit_code == 0, result.output
project_path = temp_dir / 'my-app'
with project_path.as_cwd():
result = hatch('env', 'remove', 'default')
assert result.exit_code == 0, result.output
assert not result.output
def test_single(hatch, helpers, temp_dir, config_file):
project_name = 'My App'
cache_path = temp_dir / 'cache'
config_file.model.dirs.env = str(cache_path)
config_file.save()
with temp_dir.as_cwd():
result = hatch('new', project_name)
assert result.exit_code == 0, result.output
project_path = temp_dir / 'my-app'
project = Project(project_path)
helpers.update_project_environment(project, 'default', {'skip-install': True, **project.config.envs['default']})
helpers.update_project_environment(project, 'foo', {})
helpers.update_project_environment(project, 'bar', {})
with project_path.as_cwd():
result = hatch('env', 'create', 'foo')
assert result.exit_code == 0, result.output
with project_path.as_cwd():
result = hatch('env', 'create', 'bar')
assert result.exit_code == 0, result.output
env_cache_path = cache_path / 'env' / 'virtual'
assert env_cache_path.is_dir()
storage_dirs = list(env_cache_path.iterdir())
assert len(storage_dirs) == 1
storage_path = storage_dirs[0]
project_part = f'{project_path.name}-'
assert storage_path.name.startswith(project_part)
hash_part = storage_path.name[len(project_part) :]
assert len(hash_part) == 8
env_dirs = list(storage_path.iterdir())
assert len(env_dirs) == 2
foo_env_path = storage_path / 'foo'
bar_env_path = storage_path / 'bar'
assert foo_env_path.is_dir()
assert bar_env_path.is_dir()
with project_path.as_cwd():
result = hatch('env', 'remove', 'bar')
assert result.exit_code == 0, result.output
assert not result.output
assert foo_env_path.is_dir()
assert not bar_env_path.is_dir()
def test_all(hatch, helpers, temp_dir, config_file):
project_name = 'My App'
cache_path = temp_dir / 'cache'
config_file.model.dirs.env = str(cache_path)
config_file.save()
with temp_dir.as_cwd():
result = hatch('new', project_name)
assert result.exit_code == 0, result.output
project_path = temp_dir / 'my-app'
project = Project(project_path)
helpers.update_project_environment(project, 'default', {'skip-install': True, **project.config.envs['default']})
helpers.update_project_environment(project, 'foo', {})
helpers.update_project_environment(project, 'bar', {})
with project_path.as_cwd():
result = hatch('env', 'create', 'foo')
assert result.exit_code == 0, result.output
with project_path.as_cwd():
result = hatch('env', 'create', 'bar')
assert result.exit_code == 0, result.output
env_cache_path = cache_path / 'env' / 'virtual'
assert env_cache_path.is_dir()
storage_dirs = list(env_cache_path.iterdir())
assert len(storage_dirs) == 1
storage_path = storage_dirs[0]
project_part = f'{project_path.name}-'
assert storage_path.name.startswith(project_part)
hash_part = storage_path.name[len(project_part) :]
assert len(hash_part) == 8
env_dirs = list(storage_path.iterdir())
assert len(env_dirs) == 2
foo_env_path = storage_path / 'foo'
bar_env_path = storage_path / 'bar'
assert foo_env_path.is_dir()
assert bar_env_path.is_dir()
with project_path.as_cwd():
result = hatch('env', 'remove', 'foo')
assert result.exit_code == 0, result.output
assert not result.output
with project_path.as_cwd():
result = hatch('env', 'remove', 'bar')
assert result.exit_code == 0, result.output
assert not result.output
assert not storage_path.is_dir()
def test_matrix_all(hatch, helpers, temp_dir, config_file):
project_name = 'My App'
cache_path = temp_dir / 'cache'
config_file.model.dirs.env = str(cache_path)
config_file.save()
with temp_dir.as_cwd():
result = hatch('new', project_name)
assert result.exit_code == 0, result.output
project_path = temp_dir / 'my-app'
project = Project(project_path)
helpers.update_project_environment(project, 'default', {'skip-install': True, **project.config.envs['default']})
helpers.update_project_environment(project, 'foo', {'matrix': [{'version': ['9000', '42']}]})
with project_path.as_cwd():
result = hatch('env', 'create', 'foo')
assert result.exit_code == 0, result.output
env_cache_path = cache_path / 'env' / 'virtual'
assert env_cache_path.is_dir()
storage_dirs = list(env_cache_path.iterdir())
assert len(storage_dirs) == 1
storage_path = storage_dirs[0]
project_part = f'{project_path.name}-'
assert storage_path.name.startswith(project_part)
hash_part = storage_path.name[len(project_part) :]
assert len(hash_part) == 8
env_dirs = list(storage_path.iterdir())
assert len(env_dirs) == 2
foo_env_path = storage_path / 'foo.42'
bar_env_path = storage_path / 'foo.9000'
assert foo_env_path.is_dir()
assert bar_env_path.is_dir()
with project_path.as_cwd():
result = hatch('env', 'remove', 'foo')
assert result.exit_code == 0, result.output
assert not result.output
assert not storage_path.is_dir()
def test_incompatible_ok(hatch, helpers, temp_dir, config_file):
project_name = 'My App'
cache_path = temp_dir / 'cache'
config_file.model.dirs.env = str(cache_path)
config_file.save()
with temp_dir.as_cwd():
result = hatch('new', project_name)
assert result.exit_code == 0, result.output
project_path = temp_dir / 'my-app'
project = Project(project_path)
helpers.update_project_environment(
project, 'default', {'skip-install': True, 'platforms': ['foo'], **project.config.envs['default']}
)
with project_path.as_cwd():
result = hatch('env', 'remove')
assert result.exit_code == 0, result.output
assert not result.output
def test_active(hatch, temp_dir, helpers, config_file):
project_name = 'My App'
cache_path = temp_dir / 'cache'
config_file.model.dirs.env = str(cache_path)
config_file.save()
with temp_dir.as_cwd():
result = hatch('new', project_name)
assert result.exit_code == 0, result.output
project_path = temp_dir / 'my-app'
with project_path.as_cwd(env_vars={AppEnvVars.ENV_ACTIVE: 'default'}):
result = hatch('env', 'remove')
assert result.exit_code == 1
assert result.output == helpers.dedent(
"""
Cannot remove active environment: default
"""
)
def test_active_override(hatch, helpers, temp_dir, config_file):
project_name = 'My App'
cache_path = temp_dir / 'cache'
config_file.model.dirs.env = str(cache_path)
config_file.save()
with temp_dir.as_cwd():
result = hatch('new', project_name)
assert result.exit_code == 0, result.output
project_path = temp_dir / 'my-app'
project = Project(project_path)
helpers.update_project_environment(project, 'default', {'skip-install': True, **project.config.envs['default']})
helpers.update_project_environment(project, 'foo', {})
with project_path.as_cwd():
result = hatch('env', 'create')
assert result.exit_code == 0, result.output
env_cache_path = cache_path / 'env' / 'virtual'
assert env_cache_path.is_dir()
storage_dirs = list(env_cache_path.iterdir())
assert len(storage_dirs) == 1
storage_path = storage_dirs[0]
project_part = f'{project_path.name}-'
assert storage_path.name.startswith(project_part)
hash_part = storage_path.name[len(project_part) :]
assert len(hash_part) == 8
env_dirs = list(storage_path.iterdir())
assert len(env_dirs) == 1
(storage_path / 'default').is_dir()
with project_path.as_cwd(env_vars={AppEnvVars.ENV_ACTIVE: 'foo'}):
result = hatch('env', 'remove', 'default')
assert result.exit_code == 0, result.output
assert not result.output
assert not storage_path.is_dir()
| 27.21791
| 116
| 0.670432
| 1,241
| 9,118
| 4.651088
| 0.060435
| 0.038808
| 0.063756
| 0.079695
| 0.945773
| 0.937803
| 0.937803
| 0.933299
| 0.929834
| 0.915974
| 0
| 0.007008
| 0.201908
| 9,118
| 334
| 117
| 27.299401
| 0.786176
| 0
| 0
| 0.885714
| 0
| 0
| 0.075625
| 0
| 0
| 0
| 0
| 0
| 0.3
| 1
| 0.038095
| false
| 0
| 0.009524
| 0
| 0.047619
| 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
|
97a5a9b25dfa5067e639f7c0b5076832ed97be6a
| 2,499
|
py
|
Python
|
config.py
|
jkhu29/Deblurring-by-Realistic-Blurring
|
fef7041f96127e415912ae62975ca0be01797204
|
[
"MIT"
] | 10
|
2021-07-30T08:33:03.000Z
|
2021-12-03T07:04:53.000Z
|
config.py
|
jkhu29/Deblurring-by-Realistic-Blurring
|
fef7041f96127e415912ae62975ca0be01797204
|
[
"MIT"
] | 4
|
2021-09-15T08:16:05.000Z
|
2022-02-14T06:35:12.000Z
|
config.py
|
jkhu29/Deblurring-by-Realistic-Blurring
|
fef7041f96127e415912ae62975ca0be01797204
|
[
"MIT"
] | 1
|
2022-02-17T09:59:36.000Z
|
2022-02-17T09:59:36.000Z
|
import argparse
def get_cyclegan_options(parser=argparse.ArgumentParser()):
parser.add_argument('--train_file', type=str, required=True)
parser.add_argument('--valid_file', type=str, required=True)
parser.add_argument('--output_dir', type=str, default="/home/jkhu29/img-edit/deblur")
parser.add_argument('--workers', type=int, default=4, help='number of data loading workers, you had better put it '
'4 times of your gpu')
parser.add_argument('--batch_size', type=int, default=64, help='input batch size, default=64')
parser.add_argument('--batch_scale', type=int, default=4, help='input batch size, default=64')
parser.add_argument('--niter', type=int, default=10, help='number of epochs to train for, default=10')
parser.add_argument('--lr', type=float, default=1e-3, help='select the learning rate, default=1e-4')
parser.add_argument('--adam', action='store_true', default=True, help='whether to use adam')
parser.add_argument('--cuda', action='store_true', default=True, help='enables cuda')
parser.add_argument('--seed', type=int, default=118, help="random seed")
opt = parser.parse_args()
return opt
def get_dbgan_options(parser=argparse.ArgumentParser()):
parser.add_argument('--train_file', type=str, required=True)
parser.add_argument('--valid_file', type=str, required=True)
parser.add_argument('--output_dir', type=str, default="/home/jkhu29/img-edit/deblur")
parser.add_argument('--workers', type=int, default=4, help='number of data loading workers, you had better put it '
'4 times of your gpu')
parser.add_argument('--batch_size', type=int, default=64, help='input batch size, default=64')
parser.add_argument('--batch_scale', type=int, default=4, help='input batch size, default=64')
parser.add_argument('--niter', type=int, default=10, help='number of epochs to train for, default=10')
parser.add_argument('--lr', type=float, default=1e-4, help='select the learning rate, default=1e-4')
parser.add_argument('--adam', action='store_true', default=True, help='whether to use adam')
parser.add_argument('--cuda', action='store_true', default=True, help='enables cuda')
parser.add_argument('--seed', type=int, default=118, help="random seed")
parser.add_argument('--blur_model_path', type=str, required=True)
opt = parser.parse_args()
return opt
| 64.076923
| 119
| 0.679872
| 350
| 2,499
| 4.725714
| 0.22
| 0.125151
| 0.236397
| 0.057437
| 0.945586
| 0.945586
| 0.912938
| 0.912938
| 0.912938
| 0.912938
| 0
| 0.021093
| 0.165266
| 2,499
| 38
| 120
| 65.763158
| 0.771812
| 0
| 0
| 0.8125
| 0
| 0
| 0.32453
| 0.022409
| 0
| 0
| 0
| 0
| 0
| 1
| 0.0625
| false
| 0
| 0.03125
| 0
| 0.15625
| 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
|
97ac4aaa302b8266e188a1227e9d754f647d9ffb
| 3,338
|
py
|
Python
|
tests/data.py
|
BolunThompson/PyLox
|
afcdebf44ace3c032d7247d79704d60791f605ef
|
[
"MIT"
] | 2
|
2021-09-07T06:50:46.000Z
|
2021-11-15T22:54:42.000Z
|
tests/data.py
|
BolunThompson/PyLox
|
afcdebf44ace3c032d7247d79704d60791f605ef
|
[
"MIT"
] | null | null | null |
tests/data.py
|
BolunThompson/PyLox
|
afcdebf44ace3c032d7247d79704d60791f605ef
|
[
"MIT"
] | 1
|
2021-03-26T20:05:13.000Z
|
2021-03-26T20:05:13.000Z
|
from pylox.lox_types import nil
from pylox.token_classes import Token, TokenType
#TODO: Use mocking?
_source_text = (
"2+2;\n"
" /* 𐀀 ⍅*/ // \r\n"
"\tif (nil) {var x = 3/2; print x+-2.1;} fun test(a, b) {;}\n"
"true==false\n"
'"test"=="test"'
)
SOURCE = (
_source_text,
(
Token(type=TokenType("NUMBER"), lexeme="2", literal=2.0, line=1),
Token(type=TokenType("PLUS"), lexeme="+", literal=None, line=1),
Token(type=TokenType("NUMBER"), lexeme="2", literal=2.0, line=1),
Token(type=TokenType("SEMICOLON"), lexeme=";", literal=None, line=1),
Token(type=TokenType("IF"), lexeme="if", literal=None, line=2),
Token(type=TokenType("LEFT_PAREN"), lexeme="(", literal=None, line=2),
Token(type=TokenType("NIL"), lexeme="nil", literal=nil, line=2),
Token(type=TokenType("RIGHT_PAREN"), lexeme=")", literal=None, line=2),
Token(type=TokenType("LEFT_BRACE"), lexeme="{", literal=None, line=2),
Token(type=TokenType("VAR"), lexeme="var", literal=None, line=2),
Token(type=TokenType("IDENTIFIER"), lexeme="x", literal=None, line=2),
Token(type=TokenType("EQUAL"), lexeme="=", literal=None, line=2),
Token(type=TokenType("NUMBER"), lexeme="3", literal=3.0, line=2),
Token(type=TokenType("SLASH"), lexeme="/", literal=None, line=2),
Token(type=TokenType("NUMBER"), lexeme="2", literal=2.0, line=2),
Token(type=TokenType("SEMICOLON"), lexeme=";", literal=None, line=2),
Token(type=TokenType("PRINT"), lexeme="print", literal=None, line=2),
Token(type=TokenType("IDENTIFIER"), lexeme="x", literal=None, line=2),
Token(type=TokenType("PLUS"), lexeme="+", literal=None, line=2),
Token(type=TokenType("MINUS"), lexeme="-", literal=None, line=2),
Token(type=TokenType("NUMBER"), lexeme="2.1", literal=2.1, line=2),
Token(type=TokenType("SEMICOLON"), lexeme=";", literal=None, line=2),
Token(type=TokenType("RIGHT_BRACE"), lexeme="}", literal=None, line=2),
Token(type=TokenType("FUN"), lexeme="fun", literal=None, line=2),
Token(type=TokenType("IDENTIFIER"), lexeme="test", literal=None, line=2),
Token(type=TokenType("LEFT_PAREN"), lexeme="(", literal=None, line=2),
Token(type=TokenType("IDENTIFIER"), lexeme="a", literal=None, line=2),
Token(type=TokenType("COMMA"), lexeme=",", literal=None, line=2),
Token(type=TokenType("IDENTIFIER"), lexeme="b", literal=None, line=2),
Token(type=TokenType("RIGHT_PAREN"), lexeme=")", literal=None, line=2),
Token(type=TokenType("LEFT_BRACE"), lexeme="{", literal=None, line=2),
Token(type=TokenType("SEMICOLON"), lexeme=";", literal=None, line=2),
Token(type=TokenType("RIGHT_BRACE"), lexeme="}", literal=None, line=2),
Token(type=TokenType("TRUE"), lexeme="true", literal=True, line=3),
Token(type=TokenType("EQUAL_EQUAL"), lexeme="==", literal=None, line=3),
Token(type=TokenType("FALSE"), lexeme="false", literal=False, line=3),
Token(type=TokenType("STRING"), lexeme='"test"', literal="test", line=4),
Token(type=TokenType("EQUAL_EQUAL"), lexeme="==", literal=None, line=4),
Token(type=TokenType("STRING"), lexeme='"test"', literal="test", line=4),
),
)
EXPRS = ()
| 53.83871
| 81
| 0.615938
| 435
| 3,338
| 4.691954
| 0.121839
| 0.171975
| 0.343949
| 0.198922
| 0.837335
| 0.820186
| 0.808427
| 0.791769
| 0.728074
| 0.655561
| 0
| 0.021903
| 0.165668
| 3,338
| 61
| 82
| 54.721311
| 0.710592
| 0.005392
| 0
| 0.314815
| 0
| 0.018519
| 0.144321
| 0
| 0
| 0
| 0
| 0.016393
| 0
| 1
| 0
| false
| 0
| 0.037037
| 0
| 0.037037
| 0.037037
| 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 9
|
c141576a2d7baa0c86ff9f62da60c2294afdcf27
| 9,825
|
py
|
Python
|
tests/components/azure_devops/test_config_flow.py
|
pcaston/core
|
e74d946cef7a9d4e232ae9e0ba150d18018cfe33
|
[
"Apache-2.0"
] | 1
|
2021-07-08T20:09:55.000Z
|
2021-07-08T20:09:55.000Z
|
tests/components/azure_devops/test_config_flow.py
|
pcaston/core
|
e74d946cef7a9d4e232ae9e0ba150d18018cfe33
|
[
"Apache-2.0"
] | 47
|
2021-02-21T23:43:07.000Z
|
2022-03-31T06:07:10.000Z
|
tests/components/azure_devops/test_config_flow.py
|
OpenPeerPower/core
|
f673dfac9f2d0c48fa30af37b0a99df9dd6640ee
|
[
"Apache-2.0"
] | null | null | null |
"""Test the Azure DevOps config flow."""
from unittest.mock import patch
from aioazuredevops.core import DevOpsProject
import aiohttp
from openpeerpower import config_entries, data_entry_flow
from openpeerpower.components.azure_devops.const import (
CONF_ORG,
CONF_PAT,
CONF_PROJECT,
DOMAIN,
)
from openpeerpower.core import OpenPeerPower
from tests.common import MockConfigEntry
FIXTURE_REAUTH_INPUT = {CONF_PAT: "abc123"}
FIXTURE_USER_INPUT = {CONF_ORG: "random", CONF_PROJECT: "project", CONF_PAT: "abc123"}
UNIQUE_ID = "random_project"
async def test_show_user_form(opp: OpenPeerPower) -> None:
"""Test that the setup form is served."""
result = await opp.config_entries.flow.async_init(
DOMAIN, context={"source": config_entries.SOURCE_USER}
)
assert result["type"] == data_entry_flow.RESULT_TYPE_FORM
assert result["step_id"] == "user"
async def test_authorization_error(opp: OpenPeerPower) -> None:
"""Test we show user form on Azure DevOps authorization error."""
with patch(
"openpeerpower.components.azure_devops.config_flow.DevOpsClient.authorize",
return_value=False,
):
result = await opp.config_entries.flow.async_init(
DOMAIN, context={"source": config_entries.SOURCE_USER}
)
assert result["type"] == data_entry_flow.RESULT_TYPE_FORM
assert result["step_id"] == "user"
result2 = await opp.config_entries.flow.async_configure(
result["flow_id"],
FIXTURE_USER_INPUT,
)
await opp.async_block_till_done()
assert result2["type"] == data_entry_flow.RESULT_TYPE_FORM
assert result2["step_id"] == "user"
assert result2["errors"] == {"base": "invalid_auth"}
async def test_reauth_authorization_error(opp: OpenPeerPower) -> None:
"""Test we show user form on Azure DevOps authorization error."""
with patch(
"openpeerpower.components.azure_devops.config_flow.DevOpsClient.authorize",
return_value=False,
):
result = await opp.config_entries.flow.async_init(
DOMAIN,
context={"source": config_entries.SOURCE_REAUTH},
data=FIXTURE_USER_INPUT,
)
assert result["type"] == data_entry_flow.RESULT_TYPE_FORM
assert result["step_id"] == "reauth"
result2 = await opp.config_entries.flow.async_configure(
result["flow_id"],
FIXTURE_REAUTH_INPUT,
)
await opp.async_block_till_done()
assert result2["type"] == data_entry_flow.RESULT_TYPE_FORM
assert result2["step_id"] == "reauth"
assert result2["errors"] == {"base": "invalid_auth"}
async def test_connection_error(opp: OpenPeerPower) -> None:
"""Test we show user form on Azure DevOps connection error."""
with patch(
"openpeerpower.components.azure_devops.config_flow.DevOpsClient.authorize",
side_effect=aiohttp.ClientError,
):
result = await opp.config_entries.flow.async_init(
DOMAIN, context={"source": config_entries.SOURCE_USER}
)
assert result["type"] == data_entry_flow.RESULT_TYPE_FORM
assert result["step_id"] == "user"
result2 = await opp.config_entries.flow.async_configure(
result["flow_id"],
FIXTURE_USER_INPUT,
)
await opp.async_block_till_done()
assert result2["type"] == data_entry_flow.RESULT_TYPE_FORM
assert result2["step_id"] == "user"
assert result2["errors"] == {"base": "cannot_connect"}
async def test_reauth_connection_error(opp: OpenPeerPower) -> None:
"""Test we show user form on Azure DevOps connection error."""
with patch(
"openpeerpower.components.azure_devops.config_flow.DevOpsClient.authorize",
side_effect=aiohttp.ClientError,
):
result = await opp.config_entries.flow.async_init(
DOMAIN,
context={"source": config_entries.SOURCE_REAUTH},
data=FIXTURE_USER_INPUT,
)
assert result["type"] == data_entry_flow.RESULT_TYPE_FORM
assert result["step_id"] == "reauth"
result2 = await opp.config_entries.flow.async_configure(
result["flow_id"],
FIXTURE_REAUTH_INPUT,
)
await opp.async_block_till_done()
assert result2["type"] == data_entry_flow.RESULT_TYPE_FORM
assert result2["step_id"] == "reauth"
assert result2["errors"] == {"base": "cannot_connect"}
async def test_project_error(opp: OpenPeerPower) -> None:
"""Test we show user form on Azure DevOps connection error."""
with patch(
"openpeerpower.components.azure_devops.config_flow.DevOpsClient.authorized",
return_value=True,
), patch(
"openpeerpower.components.azure_devops.config_flow.DevOpsClient.authorize",
), patch(
"openpeerpower.components.azure_devops.config_flow.DevOpsClient.get_project",
return_value=None,
):
result = await opp.config_entries.flow.async_init(
DOMAIN, context={"source": config_entries.SOURCE_USER}
)
assert result["type"] == data_entry_flow.RESULT_TYPE_FORM
assert result["step_id"] == "user"
result2 = await opp.config_entries.flow.async_configure(
result["flow_id"],
FIXTURE_USER_INPUT,
)
await opp.async_block_till_done()
assert result2["type"] == data_entry_flow.RESULT_TYPE_FORM
assert result2["step_id"] == "user"
assert result2["errors"] == {"base": "project_error"}
async def test_reauth_project_error(opp: OpenPeerPower) -> None:
"""Test we show user form on Azure DevOps project error."""
with patch(
"openpeerpower.components.azure_devops.config_flow.DevOpsClient.authorize",
), patch(
"openpeerpower.components.azure_devops.config_flow.DevOpsClient.authorized",
return_value=True,
), patch(
"openpeerpower.components.azure_devops.config_flow.DevOpsClient.get_project",
return_value=None,
):
result = await opp.config_entries.flow.async_init(
DOMAIN,
context={"source": config_entries.SOURCE_REAUTH},
data=FIXTURE_USER_INPUT,
)
assert result["type"] == data_entry_flow.RESULT_TYPE_FORM
assert result["step_id"] == "reauth"
result2 = await opp.config_entries.flow.async_configure(
result["flow_id"],
FIXTURE_REAUTH_INPUT,
)
await opp.async_block_till_done()
assert result2["type"] == data_entry_flow.RESULT_TYPE_FORM
assert result2["step_id"] == "reauth"
assert result2["errors"] == {"base": "project_error"}
async def test_reauth_flow(opp: OpenPeerPower) -> None:
"""Test reauth works."""
with patch(
"openpeerpower.components.azure_devops.config_flow.DevOpsClient.authorize",
return_value=False,
):
mock_config = MockConfigEntry(
domain=DOMAIN, unique_id=UNIQUE_ID, data=FIXTURE_USER_INPUT
)
mock_config.add_to_opp(opp)
result = await opp.config_entries.flow.async_init(
DOMAIN,
context={"source": config_entries.SOURCE_REAUTH},
data=FIXTURE_USER_INPUT,
)
assert result["type"] == data_entry_flow.RESULT_TYPE_FORM
assert result["step_id"] == "reauth"
assert result["errors"] == {"base": "invalid_auth"}
with patch(
"openpeerpower.components.azure_devops.config_flow.DevOpsClient.authorize",
), patch(
"openpeerpower.components.azure_devops.config_flow.DevOpsClient.authorized",
return_value=True,
), patch(
"openpeerpower.components.azure_devops.config_flow.DevOpsClient.get_project",
return_value=DevOpsProject(
"abcd-abcd-abcd-abcd", FIXTURE_USER_INPUT[CONF_PROJECT]
),
):
result2 = await opp.config_entries.flow.async_configure(
result["flow_id"],
FIXTURE_REAUTH_INPUT,
)
await opp.async_block_till_done()
assert result2["type"] == data_entry_flow.RESULT_TYPE_ABORT
assert result2["reason"] == "reauth_successful"
async def test_full_flow_implementation(opp: OpenPeerPower) -> None:
"""Test registering an integration and finishing flow works."""
with patch(
"openpeerpower.components.azure_devops.async_setup_entry",
return_value=True,
) as mock_setup_entry, patch(
"openpeerpower.components.azure_devops.config_flow.DevOpsClient.authorized",
return_value=True,
), patch(
"openpeerpower.components.azure_devops.config_flow.DevOpsClient.authorize",
), patch(
"openpeerpower.components.azure_devops.config_flow.DevOpsClient.get_project",
return_value=DevOpsProject(
"abcd-abcd-abcd-abcd", FIXTURE_USER_INPUT[CONF_PROJECT]
),
):
result = await opp.config_entries.flow.async_init(
DOMAIN, context={"source": config_entries.SOURCE_USER}
)
assert result["type"] == data_entry_flow.RESULT_TYPE_FORM
assert result["step_id"] == "user"
result2 = await opp.config_entries.flow.async_configure(
result["flow_id"],
FIXTURE_USER_INPUT,
)
await opp.async_block_till_done()
assert len(mock_setup_entry.mock_calls) == 1
assert result2["type"] == data_entry_flow.RESULT_TYPE_CREATE_ENTRY
assert (
result2["title"]
== f"{FIXTURE_USER_INPUT[CONF_ORG]}/{FIXTURE_USER_INPUT[CONF_PROJECT]}"
)
assert result2["data"][CONF_ORG] == FIXTURE_USER_INPUT[CONF_ORG]
assert result2["data"][CONF_PROJECT] == FIXTURE_USER_INPUT[CONF_PROJECT]
| 35.989011
| 86
| 0.663511
| 1,115
| 9,825
| 5.552466
| 0.093274
| 0.056695
| 0.085931
| 0.104345
| 0.846067
| 0.833791
| 0.825069
| 0.816508
| 0.810047
| 0.79454
| 0
| 0.005147
| 0.228804
| 9,825
| 272
| 87
| 36.121324
| 0.811931
| 0.003461
| 0
| 0.732394
| 0
| 0
| 0.213963
| 0.146089
| 0
| 0
| 0
| 0
| 0.206573
| 1
| 0
| false
| 0
| 0.032864
| 0
| 0.032864
| 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
|
c16f4b9fbb0eac79b4625155736f17d80bf10449
| 102
|
py
|
Python
|
tests/test_analyze.py
|
lbsx/gov-purchase-analyzer
|
e6bf01289fef3ed35e493868617a9e6b26064dde
|
[
"MIT"
] | null | null | null |
tests/test_analyze.py
|
lbsx/gov-purchase-analyzer
|
e6bf01289fef3ed35e493868617a9e6b26064dde
|
[
"MIT"
] | null | null | null |
tests/test_analyze.py
|
lbsx/gov-purchase-analyzer
|
e6bf01289fef3ed35e493868617a9e6b26064dde
|
[
"MIT"
] | null | null | null |
from analyze import starts_with_wan
def test_starts_with_wan():
assert(starts_with_wan('万abc'))
| 17
| 35
| 0.784314
| 16
| 102
| 4.5625
| 0.625
| 0.410959
| 0.534247
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.127451
| 102
| 5
| 36
| 20.4
| 0.820225
| 0
| 0
| 0
| 0
| 0
| 0.039216
| 0
| 0
| 0
| 0
| 0
| 0.333333
| 1
| 0.333333
| true
| 0
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 8
|
c1e53b1a9dae7b7da023cffa31a8cc4957eeb231
| 1,561
|
py
|
Python
|
Final_QCNN/Angular_hybrid.py
|
magelead/QCNN
|
611750f1529b361713dcf5a4792e901295077688
|
[
"Apache-2.0"
] | 9
|
2021-11-10T07:06:00.000Z
|
2022-03-10T18:15:29.000Z
|
Final_QCNN/Angular_hybrid.py
|
magelead/QCNN
|
611750f1529b361713dcf5a4792e901295077688
|
[
"Apache-2.0"
] | 1
|
2022-03-08T03:13:57.000Z
|
2022-03-22T20:33:17.000Z
|
Final_QCNN/Angular_hybrid.py
|
magelead/QCNN
|
611750f1529b361713dcf5a4792e901295077688
|
[
"Apache-2.0"
] | 6
|
2021-11-04T01:17:44.000Z
|
2022-03-05T14:16:24.000Z
|
# This is an implementation of an alternative Mottonen State Preparation to avoid normalization problem.
import pennylane as qml
# 3 bits of information is embedded in 2 wires
def Angular_Hybrid_2(X, wires):
qml.RY(X[0], wires=wires[0])
qml.PauliX(wires=wires[0])
qml.CRY(X[1], wires=[wires[0], wires[1]])
qml.PauliX(wires=wires[0])
qml.CRY(X[2], wires=[wires[0], wires[1]])
# 15 bits of information is embedded in 4 wires
def Angular_Hybrid_4(X, wires):
qml.RY(X[0], wires=wires[0])
qml.PauliX(wires=wires[0])
qml.CRY(X[1], wires=[wires[0], wires[1]])
qml.PauliX(wires=wires[0])
qml.CRY(X[2], wires=[wires[0], wires[1]])
qml.RY(X[3], wires=wires[2])
qml.CNOT(wires=[wires[1], wires[2]])
qml.RY(X[4], wires=wires[2])
qml.CNOT(wires=[wires[0], wires[2]])
qml.RY(X[5], wires=wires[2])
qml.CNOT(wires=[wires[1], wires[2]])
qml.RY(X[6], wires=wires[2])
qml.CNOT(wires=[wires[0], wires[2]])
qml.RY(X[7], wires=wires[3])
qml.CNOT(wires=[wires[2], wires[3]])
qml.RY(X[8], wires=wires[3])
qml.CNOT(wires=[wires[1], wires[3]])
qml.RY(X[9], wires=wires[3])
qml.CNOT(wires=[wires[2], wires[3]])
qml.RY(X[10], wires=wires[3])
qml.CNOT(wires=[wires[0], wires[3]])
qml.RY(X[11], wires=wires[3])
qml.CNOT(wires=[wires[2], wires[3]])
qml.RY(X[12], wires=wires[3])
qml.CNOT(wires=[wires[1], wires[3]])
qml.RY(X[13], wires=wires[3])
qml.CNOT(wires=[wires[2], wires[3]])
qml.RY(X[14], wires=wires[3])
qml.CNOT(wires=[wires[0], wires[3]])
| 33.212766
| 104
| 0.612428
| 286
| 1,561
| 3.328671
| 0.160839
| 0.357143
| 0.141807
| 0.214286
| 0.814076
| 0.807773
| 0.743697
| 0.743697
| 0.743697
| 0.743697
| 0
| 0.061256
| 0.163357
| 1,561
| 46
| 105
| 33.934783
| 0.667688
| 0.123639
| 0
| 0.594595
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.054054
| false
| 0
| 0.027027
| 0
| 0.081081
| 0
| 0
| 0
| 0
| null | 1
| 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
| 8
|
a9f4130bef02146210579f91f74128009efc1dc3
| 160
|
py
|
Python
|
neat_code_backup/neatcode_backup_20190317/nn/__init__.py
|
felix0901/NEAT
|
a1f25608c98ff1003c1525a291577fa59dec2469
|
[
"BSD-3-Clause"
] | null | null | null |
neat_code_backup/neatcode_backup_20190317/nn/__init__.py
|
felix0901/NEAT
|
a1f25608c98ff1003c1525a291577fa59dec2469
|
[
"BSD-3-Clause"
] | null | null | null |
neat_code_backup/neatcode_backup_20190317/nn/__init__.py
|
felix0901/NEAT
|
a1f25608c98ff1003c1525a291577fa59dec2469
|
[
"BSD-3-Clause"
] | null | null | null |
from neat.nn.feed_forward import FeedForwardNetwork
from neat.nn.feed_forward_fpga import FeedForwardNetworkFPGA
from neat.nn.recurrent import RecurrentNetwork
| 40
| 60
| 0.8875
| 21
| 160
| 6.619048
| 0.52381
| 0.172662
| 0.215827
| 0.201439
| 0.302158
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.075
| 160
| 3
| 61
| 53.333333
| 0.939189
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
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| 1
| 0
| 1
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
e73e17bfb5e1f3ecd7be9aa8f9c0cd97969a3b30
| 102
|
py
|
Python
|
python/testData/postfix/main/severalStatements_after.py
|
truthiswill/intellij-community
|
fff88cfb0dc168eea18ecb745d3e5b93f57b0b95
|
[
"Apache-2.0"
] | 2
|
2019-04-28T07:48:50.000Z
|
2020-12-11T14:18:08.000Z
|
python/testData/postfix/main/severalStatements_after.py
|
truthiswill/intellij-community
|
fff88cfb0dc168eea18ecb745d3e5b93f57b0b95
|
[
"Apache-2.0"
] | 173
|
2018-07-05T13:59:39.000Z
|
2018-08-09T01:12:03.000Z
|
python/testData/postfix/main/severalStatements_after.py
|
truthiswill/intellij-community
|
fff88cfb0dc168eea18ecb745d3e5b93f57b0b95
|
[
"Apache-2.0"
] | 2
|
2020-03-15T08:57:37.000Z
|
2020-04-07T04:48:14.000Z
|
if __name__ == '__main__':
print("I want to be inside main")
print("I want to be inside main too")
| 34
| 37
| 0.676471
| 18
| 102
| 3.388889
| 0.555556
| 0.295082
| 0.327869
| 0.459016
| 0.852459
| 0.852459
| 0.852459
| 0.852459
| 0
| 0
| 0
| 0
| 0.196078
| 102
| 3
| 38
| 34
| 0.743902
| 0
| 0
| 0
| 0
| 0
| 0.582524
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 0.666667
| 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
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 11
|
e7a713da77059cbfee5cf0fbd349c080f3fe49df
| 93
|
py
|
Python
|
stubs/esp32_1_10_0/ssl.py
|
jmannau/micropython-stubber
|
8930e8a0038192fd259b31a193d1da3b2501256a
|
[
"MIT"
] | null | null | null |
stubs/esp32_1_10_0/ssl.py
|
jmannau/micropython-stubber
|
8930e8a0038192fd259b31a193d1da3b2501256a
|
[
"MIT"
] | null | null | null |
stubs/esp32_1_10_0/ssl.py
|
jmannau/micropython-stubber
|
8930e8a0038192fd259b31a193d1da3b2501256a
|
[
"MIT"
] | null | null | null |
"Module 'ssl' on firmware 'v1.10-247-g0fb15fc3f on 2019-03-29'"
def wrap_socket():
pass
| 18.6
| 63
| 0.688172
| 16
| 93
| 3.9375
| 0.9375
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.230769
| 0.16129
| 93
| 4
| 64
| 23.25
| 0.576923
| 0.655914
| 0
| 0
| 0
| 0.333333
| 0.663043
| 0.228261
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| true
| 0.333333
| 0
| 0
| 0.333333
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 8
|
e7daf06d2b0e2ee5dc0d54c0b6253d4d3b0a3c2c
| 30,715
|
py
|
Python
|
src/controller/wamdamAPI/GetValues.py
|
WamdamProject/WaMDaM_Wizard
|
f8f5a830464f3c8f45e4eb0557833eefb267d7b2
|
[
"BSD-3-Clause"
] | null | null | null |
src/controller/wamdamAPI/GetValues.py
|
WamdamProject/WaMDaM_Wizard
|
f8f5a830464f3c8f45e4eb0557833eefb267d7b2
|
[
"BSD-3-Clause"
] | 3
|
2018-11-17T05:49:18.000Z
|
2020-12-31T15:57:14.000Z
|
src/controller/wamdamAPI/GetValues.py
|
WamdamProject/WaMDaM_Wizard
|
f8f5a830464f3c8f45e4eb0557833eefb267d7b2
|
[
"BSD-3-Clause"
] | null | null | null |
from ..ConnectDB_ParseExcel import DB_Setup
from ..ConnectDB_ParseExcel import SqlAlchemy as sq
'''
This class is used to get result that query to get data of values in sqlite db.
'''
class GetValues(object):
def __init__(self, pathOfSqlite=''):
self.setup = DB_Setup()
if self.setup.get_session() == None and pathOfSqlite != '':
self.setup.connect(pathOfSqlite, db_type='sqlite')
self.session = self.setup.get_session()
self.excel_pointer = None
def getNumericValue(self, selectedType='', selectedAttribute='', selectedInstance=''):
'''
This method is used to get data making NumericValues_table.
:param selectedType: selected Object Type
:param selectedAttribute: controlled Attribute
:param selectedInstance: controlled Instance Name
:param excelPath: full path of excel file to export data
:return: None
'''
try:
if selectedType == '' and selectedAttribute == '' and selectedInstance == '':
sql = 'SELECT "ResourceTypes"."ResourceType", ObjectType,AttributeName, SourceName, InstanceName,MasterNetworkName,' \
'ScenarioName,MethodName, NumericValue ' \
'FROM "ResourceTypes" '\
'Left JOIN "ObjectTypes" ON "ObjectTypes"."ResourceTypeID"="ResourceTypes"."ResourceTypeID" '\
'Left JOIN "Attributes" ON "Attributes"."ObjectTypeID"="ObjectTypes"."ObjectTypeID" '\
'Left JOIN "Mappings" ON "Mappings"."AttributeID"= "Attributes"."AttributeID" '\
'Left JOIN "ValuesMapper" ON "ValuesMapper"."ValuesMapperID"="Mappings"."ValuesMapperID" '\
'Left JOIN "ScenarioMappings" ON "ScenarioMappings"."MappingID"="Mappings"."MappingID" '\
'Left JOIN "Scenarios" ON "Scenarios"."ScenarioID"="ScenarioMappings"."ScenarioID" '\
'Left JOIN "MasterNetworks" ON "MasterNetworks"."MasterNetworkID"="Scenarios"."MasterNetworkID" '\
'Left JOIN "Methods" ON "Methods"."MethodID"="Mappings"."MethodID" '\
'Left JOIN "Sources" ON "Sources"."SourceID"="Mappings"."SourceID" '\
'Left JOIN "Instances" ON "Instances"."InstanceID"="Mappings"."InstanceID" '\
'LEFT JOIN "NumericValues" ON "NumericValues"."ValuesMapperID" = "ValuesMapper"."ValuesMapperID" '\
'WHERE "AttributeDataTypeCV"="Parameter"'
else:
sql = 'SELECT "ResourceTypes"."ResourceType", ObjectType,AttributeName, SourceName, InstanceName,MasterNetworkName,' \
'ScenarioName,MethodName, NumericValue ' \
'FROM "ResourceTypes" '\
'Left JOIN "ObjectTypes" ON "ObjectTypes"."ResourceTypeID"="ResourceTypes"."ResourceTypeID" '\
'Left JOIN "Attributes" ON "Attributes"."ObjectTypeID"="ObjectTypes"."ObjectTypeID" '\
'Left JOIN "Mappings" ON "Mappings"."AttributeID"= "Attributes"."AttributeID" '\
'Left JOIN "ValuesMapper" ON "ValuesMapper"."ValuesMapperID"="Mappings"."ValuesMapperID" '\
'Left JOIN "ScenarioMappings" ON "ScenarioMappings"."MappingID"="Mappings"."MappingID" '\
'Left JOIN "Scenarios" ON "Scenarios"."ScenarioID"="ScenarioMappings"."ScenarioID" '\
'Left JOIN "MasterNetworks" ON "MasterNetworks"."MasterNetworkID"="Scenarios"."MasterNetworkID" '\
'Left JOIN "Methods" ON "Methods"."MethodID"="Mappings"."MethodID" '\
'Left JOIN "Sources" ON "Sources"."SourceID"="Mappings"."SourceID" '\
'Left JOIN "Instances" ON "Instances"."InstanceID"="Mappings"."InstanceID" '\
'LEFT JOIN "NumericValues" ON "NumericValues"."ValuesMapperID" = "ValuesMapper"."ValuesMapperID" '\
'WHERE "AttributeDataTypeCV"="Parameter" AND "ObjectTypeCV" = "{}" AND "InstanceNameCV" = "{}" AND "AttributeNameCV" = "{}"'\
.format(selectedType, selectedInstance, selectedAttribute)
result = self.session.execute(sql)
# nameResult = list()
complete_result = list()
for row in result:
# isExisting = False
# for name in nameResult:
# if name == row.InstanceName:
# isExisting = True
# break
# if not isExisting:
# nameResult.append(row.InstanceName)
complete_result.append([row.ObjectType, row.InstanceName, row.ScenarioName,
row.AttributeName, row.SourceName, row.MethodName,
row.NumericValue])
return complete_result
except Exception as e:
print e
raise Exception('Erro occure in reading Data Structure.\n' + e.message)
def getFreeText(self, selectedType = '', selectedAttribute='', selectedInstance=''):
'''
This method is used to get data making FreeTextSheet.
:param selectedType: selected Object Type
:param selectedAttribute: controlled Attribute
:param selectedInstance: controlled Instance Name
:param excelPath: full path of excel file to export data
:return: None
'''
try:
if selectedType == '' and selectedAttribute == '' and selectedInstance == '':
sql = 'SELECT ResourceType, ObjectType, AttributeName, SourceName, InstanceName,FreeTextValue,' \
'ScenarioName,MethodName ' \
'FROM "ResourceTypes" '\
'Left JOIN "ObjectTypes" ON "ObjectTypes"."ResourceTypeID"="ResourceTypes"."ResourceTypeID" '\
'Left JOIN "Attributes" ON "Attributes"."ObjectTypeID"="ObjectTypes"."ObjectTypeID" '\
'Left JOIN "Mappings" ON "Mappings"."AttributeID"= "Attributes"."AttributeID" '\
'Left JOIN "ValuesMapper" ON "ValuesMapper"."ValuesMapperID"="Mappings"."ValuesMapperID" '\
'Left JOIN "ScenarioMappings" ON "ScenarioMappings"."MappingID"="Mappings"."MappingID" '\
'Left JOIN "Scenarios" ON "Scenarios"."ScenarioID"="ScenarioMappings"."ScenarioID" '\
'Left JOIN "MasterNetworks" ON "MasterNetworks"."MasterNetworkID"="Scenarios"."MasterNetworkID" '\
'Left JOIN "Methods" ON "Methods"."MethodID"="Mappings"."MethodID" '\
'Left JOIN "Sources" ON "Sources"."SourceID"="Mappings"."SourceID" '\
'Left JOIN "Instances" ON "Instances"."InstanceID"="Mappings"."InstanceID" '\
'LEFT JOIN "FreeText" ON "FreeText"."ValuesMapperID" = "ValuesMapper"."ValuesMapperID" '\
'WHERE AttributeDataTypeCV="FreeText" '
else:
sql = 'SELECT ResourceType, ObjectType, AttributeName, SourceName, InstanceName,FreeTextValue,' \
'ScenarioName,MethodName ' \
'FROM "ResourceTypes" '\
'Left JOIN "ObjectTypes" ON "ObjectTypes"."ResourceTypeID"="ResourceTypes"."ResourceTypeID" '\
'Left JOIN "Attributes" ON "Attributes"."ObjectTypeID"="ObjectTypes"."ObjectTypeID" '\
'Left JOIN "Mappings" ON "Mappings"."AttributeID"= "Attributes"."AttributeID" '\
'Left JOIN "ValuesMapper" ON "ValuesMapper"."ValuesMapperID"="Mappings"."ValuesMapperID" '\
'Left JOIN "ScenarioMappings" ON "ScenarioMappings"."MappingID"="Mappings"."MappingID" '\
'Left JOIN "Scenarios" ON "Scenarios"."ScenarioID"="ScenarioMappings"."ScenarioID" '\
'Left JOIN "MasterNetworks" ON "MasterNetworks"."MasterNetworkID"="Scenarios"."MasterNetworkID" '\
'Left JOIN "Methods" ON "Methods"."MethodID"="Mappings"."MethodID" '\
'Left JOIN "Sources" ON "Sources"."SourceID"="Mappings"."SourceID" '\
'Left JOIN "Instances" ON "Instances"."InstanceID"="Mappings"."InstanceID" '\
'LEFT JOIN "FreeText" ON "FreeText"."ValuesMapperID" = "ValuesMapper"."ValuesMapperID" '\
'WHERE AttributeDataTypeCV="FreeText" ' \
' AND "ObjectTypeCV" = "{}" AND "InstanceNameCV" = "{}" AND "AttributeNameCV" = "{}"'\
.format(selectedType, selectedInstance, selectedAttribute)
result = self.session.execute(sql)
# nameResult = list()
complete_result = list()
for row in result:
complete_result.append([row.ObjectType, row.InstanceName, row.ScenarioName,
row.AttributeName, row.SourceName, row.MethodName,row.FreeTextValue])
return complete_result
except Exception as e:
print e
raise Exception('Erro occure in reading Data Structure.\n' + e.message)
def getSeasonaNumericValues(self, selectedResourceType='', selectedNetwork='', selectedScenarior=''):
'''
This method is used to get data making SeasonalParameter.
:param selectedResourceType: selected Model name
:param selectedNetwork: selected Master Network name
:param selectedScenarior: selected scenario Name
:param excelPath: full path of excel file to export data
:return: None
'''
try:
if selectedResourceType == '' and selectedNetwork == '' and selectedScenarior == '':
sql = 'SELECT ObjectType, AttributeName, SourceName, InstanceName,MasterNetworkName,' \
'ScenarioName,MethodName,SeasonName, SeasonNumericValue, SeasonNameCV, SeasonDateFormate ' \
'FROM "Attributes" '\
'Left JOIN "ObjectTypes" ON "Attributes"."ObjectTypeID"="ObjectTypes"."ObjectTypeID" '\
'Left JOIN "Mappings" ON "Mappings"."AttributeID"= "Attributes"."AttributeID" '\
'Left JOIN "ValuesMapper" ON "ValuesMapper"."ValuesMapperID"="Mappings"."ValuesMapperID" '\
'Left JOIN "ScenarioMappings" ON "ScenarioMappings"."MappingID"="Mappings"."MappingID" '\
'Left JOIN "Scenarios" ON "Scenarios"."ScenarioID"="ScenarioMappings"."ScenarioID" '\
'Left JOIN "MasterNetworks" ON "MasterNetworks"."MasterNetworkID"="Scenarios"."MasterNetworkID" '\
'Left JOIN "Methods" ON "Methods"."MethodID"="Mappings"."MethodID" '\
'Left JOIN "Sources" ON "Sources"."SourceID"="Mappings"."SourceID" '\
'Left JOIN "Instances" ON "Instances"."InstanceID"="Mappings"."InstanceID" '\
'LEFT JOIN "SeasonalNumericValues" ON "SeasonalNumericValues"."ValuesMapperID" = "ValuesMapper"."ValuesMapperID" '\
'WHERE "AttributeDataTypeCV"="SeasonaNumericValues" '
else:
sql = 'SELECT ObjectType, AttributeName, SourceName, InstanceName,MasterNetworkName,' \
'ScenarioName,MethodName,SeasonName, SeasonNumericValue, SeasonNameCV, SeasonDateFormate ' \
'FROM "Attributes" '\
'Left JOIN "ObjectTypes" ON "Attributes"."ObjectTypeID"="ObjectTypes"."ObjectTypeID" '\
'Left JOIN "Mappings" ON "Mappings"."AttributeID"= "Attributes"."AttributeID" '\
'Left JOIN "ValuesMapper" ON "ValuesMapper"."ValuesMapperID"="Mappings"."ValuesMapperID" '\
'Left JOIN "ScenarioMappings" ON "ScenarioMappings"."MappingID"="Mappings"."MappingID" '\
'Left JOIN "Scenarios" ON "Scenarios"."ScenarioID"="ScenarioMappings"."ScenarioID" '\
'Left JOIN "MasterNetworks" ON "MasterNetworks"."MasterNetworkID"="Scenarios"."MasterNetworkID" '\
'Left JOIN "Methods" ON "Methods"."MethodID"="Mappings"."MethodID" '\
'Left JOIN "Sources" ON "Sources"."SourceID"="Mappings"."SourceID" '\
'Left JOIN "Instances" ON "Instances"."InstanceID"="Mappings"."InstanceID" '\
'LEFT JOIN "SeasonalNumericValues" ON "SeasonalNumericValues"."ValuesMapperID" = "ValuesMapper"."ValuesMapperID" '\
'WHERE "AttributeDataTypeCV"="SeasonaNumericValues" AND "MasterNetworkName" = "{}" AND "ScenarioName" = "{}"'\
.format(selectedNetwork, selectedScenarior)
result = self.session.execute(sql)
# nameResult = list()
complete_result = list()
for row in result:
complete_result.append([row.ObjectType, row.InstanceName, row.ScenarioName,
row.AttributeName, row.SourceName, row.MethodName,
row.SeasonName, row.SeasonNameCV, row.SeasonNumericValue,
row.SeasonDateFormate])
return complete_result
except Exception as e:
print e
raise Exception('Erro occure in reading Data Structure.\n' + e.message)
def gettTimeSeriesValues(self, selectedResourceType='', selectedNetwork='', selectedScenarior=''):
'''
This method is used to get data making TimeSeries.
:param selectedResourceType: selected Model name
:param selectedNetwork: selected Master Network name
:param selectedScenarior: selected scenario Name
:param excelPath: full path of excel file to export data
:return: None
'''
try:
if selectedResourceType == '' and selectedNetwork == '' and selectedScenarior == '':
sql = 'SELECT ResourceType ObjectType, AttributeName, SourceName, InstanceName,YearType,' \
'ScenarioName,MethodName,AggregationStatisticCV, AggregationInterval, IntervalTimeUnitCV,' \
'IsRegular, NoDataValue, "TimeSeries"."Description", "TimeSeriesValues"."DataValue", "TimeSeriesValues"."DataTimeStamp"' \
'FROM "ResourceTypes" '\
'Left JOIN "ObjectTypes" ON "ObjectTypes"."ResourceTypeID"="ResourceTypes"."ResourceTypeID" '\
'Left JOIN "Attributes" ON "Attributes"."ObjectTypeID"="ObjectTypes"."ObjectTypeID" '\
'Left JOIN "Mappings" ON "Mappings"."AttributeID"= "Attributes"."AttributeID" '\
'Left JOIN "ValuesMapper" ON "ValuesMapper"."ValuesMapperID"="Mappings"."ValuesMapperID" '\
'Left JOIN "ScenarioMappings" ON "ScenarioMappings"."MappingID"="Mappings"."MappingID" '\
'Left JOIN "Scenarios" ON "Scenarios"."ScenarioID"="ScenarioMappings"."ScenarioID" '\
'Left JOIN "MasterNetworks" ON "MasterNetworks"."MasterNetworkID"="Scenarios"."MasterNetworkID" '\
'Left JOIN "Methods" ON "Methods"."MethodID"="Mappings"."MethodID" '\
'Left JOIN "Sources" ON "Sources"."SourceID"="Mappings"."SourceID" '\
'Left JOIN "Instances" ON "Instances"."InstanceID"="Mappings"."InstanceID" '\
'LEFT JOIN "TimeSeries" ON "TimeSeries"."ValuesMapperID" = "ValuesMapper"."ValuesMapperID" '\
'LEFT JOIN "TimeSeriesValues" ON "TimeSeriesValues"."TimeSeriesID" = "TimeSeries"."TimeSeriesID" '\
'WHERE AttributeName!="ObjectInstances" AND AttributeDataTypeCV="TimeSeries" '
else:
sql = 'SELECT ResourceType ObjectType, AttributeName, SourceName, InstanceName,YearType,' \
'ScenarioName,MethodName,AggregationStatisticCV, AggregationInterval, IntervalTimeUnitCV,' \
'IsRegular, NoDataValue, "TimeSeries"."Description", "TimeSeriesValues"."DataValue", "TimeSeriesValues"."DataTimeStamp" ' \
'FROM "ResourceTypes" '\
'Left JOIN "ObjectTypes" ON "ObjectTypes"."ResourceTypeID"="ResourceTypes"."ResourceTypeID" '\
'Left JOIN "Attributes" ON "Attributes"."ObjectTypeID"="ObjectTypes"."ObjectTypeID" '\
'Left JOIN "Mappings" ON "Mappings"."AttributeID"= "Attributes"."AttributeID" '\
'Left JOIN "ValuesMapper" ON "ValuesMapper"."ValuesMapperID"="Mappings"."ValuesMapperID" '\
'Left JOIN "ScenarioMappings" ON "ScenarioMappings"."MappingID"="Mappings"."MappingID" '\
'Left JOIN "Scenarios" ON "Scenarios"."ScenarioID"="ScenarioMappings"."ScenarioID" '\
'Left JOIN "MasterNetworks" ON "MasterNetworks"."MasterNetworkID"="Scenarios"."MasterNetworkID" '\
'Left JOIN "Methods" ON "Methods"."MethodID"="Mappings"."MethodID" '\
'Left JOIN "Sources" ON "Sources"."SourceID"="Mappings"."SourceID" '\
'Left JOIN "Instances" ON "Instances"."InstanceID"="Mappings"."InstanceID" '\
'LEFT JOIN "TimeSeries" ON "TimeSeries"."ValuesMapperID" = "ValuesMapper"."ValuesMapperID" '\
'LEFT JOIN "TimeSeriesValues" ON "TimeSeriesValues"."TimeSeriesID" = "TimeSeries"."TimeSeriesID" '\
'WHERE AttributeName!="ObjectInstances" AND AttributeDataTypeCV="TimeSeries" ' \
'AND "ResourceTypeAcronym" = "{}" AND "MasterNetworkName" = "{}" AND "ScenarioName" = "{}"'\
.format(selectedResourceType, selectedNetwork, selectedScenarior)
result = self.session.execute(sql)
# nameResult = list()
complete_result = list()
for row in result:
# isExisting = False
# for name in nameResult:
# if name == row.InstanceName:
# isExisting = True
# break
# if not isExisting:
# nameResult.append(row.InstanceName)
complete_result.append([row.ObjectType, row.InstanceName, row.ScenarioName,
row.AttributeName, row.DataTimeStamp, row.DataValue])
return complete_result
except Exception as e:
print e
raise Exception('Erro occure in reading Data Structure.\n' + e.message)
def getMultiAttributeSeries(self, selectedResourceType='', selectedNetwork='', selectedScenarior=''):
'''
This method is used to get data making MultiVariableSeries.
:param selectedResourceType: selected Model name
:param selectedNetwork: selected Master Network name
:param selectedScenarior: selected scenario Name
:param excelPath: full path of excel file to export data
:return: None
'''
try:
if selectedResourceType == '' and selectedNetwork == '' and selectedScenarior == '':
sql = """
SELECT "ObjectTypes"."ObjectType",
"Instances"."InstanceName",ScenarioName,"Attributes"."AttributeName" AS MultiAttributeName,"Attributes".AttributeDataTypeCV,
SourceName,MethodName,
"AttributesColumns"."AttributeName" AS "AttributeName",
"AttributesColumns"."AttributeNameCV",
"AttributesColumns"."UnitNameCV" AS "AttributeNameUnitName",
"ValueOrder","DataValue"
FROM ResourceTypes
Left JOIN "ObjectTypes"
ON "ObjectTypes"."ResourceTypeID"="ResourceTypes"."ResourceTypeID"
-- Join the Object types to get their attributes
LEFT JOIN "Attributes"
ON "Attributes"."ObjectTypeID"="ObjectTypes"."ObjectTypeID"
-- Join the Attributes to get their Mappings
LEFT JOIN "Mappings"
ON Mappings.AttributeID= Attributes.AttributeID
-- Join the Mappings to get their Instances
LEFT JOIN "Instances"
ON "Instances"."InstanceID"="Mappings"."InstanceID"
-- Join the Mappings to get their ScenarioMappings
LEFT JOIN "ScenarioMappings"
ON "ScenarioMappings"."MappingID"="Mappings"."MappingID"
-- Join the ScenarioMappings to get their Scenarios
LEFT JOIN "Scenarios"
ON "Scenarios"."ScenarioID"="ScenarioMappings"."ScenarioID"
-- Join the Scenarios to get their MasterNetworks
LEFT JOIN "MasterNetworks"
ON "MasterNetworks"."MasterNetworkID"="Scenarios"."MasterNetworkID"
-- Join the Mappings to get their Methods
LEFT JOIN "Methods"
ON "Methods"."MethodID"="Mappings"."MethodID"
-- Join the Mappings to get their Sources
LEFT JOIN "Sources"
ON "Sources"."SourceID"="Mappings"."SourceID"
-- Join the Mappings to get their DataValuesMappers
LEFT JOIN "ValuesMapper"
ON "ValuesMapper"."ValuesMapperID"="Mappings"."ValuesMapperID"
-- Join the DataValuesMapper to get their MultiAttributeSeries
LEFT JOIN "MultiAttributeSeries"
ON "MultiAttributeSeries" ."ValuesMapperID"="ValuesMapper"."ValuesMapperID"
/*This is an extra join to get to each column name within the MultiColumn Array */
-- Join the MultiAttributeSeries to get to their specific DataValuesMapper, now called DataValuesMapperColumn
LEFT JOIN "ValuesMapper" As "ValuesMapperColumn"
ON "ValuesMapperColumn"."ValuesMapperID"="MultiAttributeSeries"."MappingID_Attribute"
-- Join the DataValuesMapperColumn to get back to their specific Mapping, now called MappingColumns
LEFT JOIN "Mappings" As "MappingColumns"
ON "MappingColumns"."ValuesMapperID"="ValuesMapperColumn"."ValuesMapperID"
-- Join the MappingColumns to get back to their specific Attribute, now called AttributeColumns
LEFT JOIN "Attributes" AS "AttributesColumns"
ON "AttributesColumns"."AttributeID"="MappingColumns"."AttributeID"
/* Finishes here */
-- Join the MultiAttributeSeries to get access to their MultiAttributeSeriesValues
LEFT JOIN "MultiAttributeSeriesValues"
ON "MultiAttributeSeriesValues"."MultiAttributeSeriesID"="MultiAttributeSeries"."MultiAttributeSeriesID"
-- Select one InstanceName and restrict the query AttributeDataTypeCV that is MultiAttributeSeries
WHERE
"Attributes".AttributeDataTypeCV='MultiAttributeSeries'
"""
else:
sql = """
SELECT "ObjectTypes"."ObjectType",
"Instances"."InstanceName",ScenarioName,"Attributes"."AttributeName" AS MultiAttributeName,"Attributes".AttributeDataTypeCV,
SourceName,MethodName,
"AttributesColumns"."AttributeName" AS "AttributeName",
"AttributesColumns"."AttributeNameCV",
"AttributesColumns"."UnitNameCV" AS "AttributeNameUnitName",
"ValueOrder","DataValue"
FROM ResourceTypes
Left JOIN "ObjectTypes"
ON "ObjectTypes"."ResourceTypeID"="ResourceTypes"."ResourceTypeID"
-- Join the Object types to get their attributes
LEFT JOIN "Attributes"
ON "Attributes"."ObjectTypeID"="ObjectTypes"."ObjectTypeID"
-- Join the Attributes to get their Mappings
LEFT JOIN "Mappings"
ON Mappings.AttributeID= Attributes.AttributeID
-- Join the Mappings to get their Instances
LEFT JOIN "Instances"
ON "Instances"."InstanceID"="Mappings"."InstanceID"
-- Join the Mappings to get their ScenarioMappings
LEFT JOIN "ScenarioMappings"
ON "ScenarioMappings"."MappingID"="Mappings"."MappingID"
-- Join the ScenarioMappings to get their Scenarios
LEFT JOIN "Scenarios"
ON "Scenarios"."ScenarioID"="ScenarioMappings"."ScenarioID"
-- Join the Scenarios to get their MasterNetworks
LEFT JOIN "MasterNetworks"
ON "MasterNetworks"."MasterNetworkID"="Scenarios"."MasterNetworkID"
-- Join the Mappings to get their Methods
LEFT JOIN "Methods"
ON "Methods"."MethodID"="Mappings"."MethodID"
-- Join the Mappings to get their Sources
LEFT JOIN "Sources"
ON "Sources"."SourceID"="Mappings"."SourceID"
-- Join the Mappings to get their DataValuesMappers
LEFT JOIN "ValuesMapper"
ON "ValuesMapper"."ValuesMapperID"="Mappings"."ValuesMapperID"
-- Join the DataValuesMapper to get their MultiAttributeSeries
LEFT JOIN "MultiAttributeSeries"
ON "MultiAttributeSeries" ."ValuesMapperID"="ValuesMapper"."ValuesMapperID"
/*This is an extra join to get to each column name within the MultiColumn Array */
-- Join the MultiAttributeSeries to get to their specific DataValuesMapper, now called DataValuesMapperColumn
LEFT JOIN "ValuesMapper" As "ValuesMapperColumn"
ON "ValuesMapperColumn"."ValuesMapperID"="MultiAttributeSeries"."MappingID_Attribute"
-- Join the DataValuesMapperColumn to get back to their specific Mapping, now called MappingColumns
LEFT JOIN "Mappings" As "MappingColumns"
ON "MappingColumns"."ValuesMapperID"="ValuesMapperColumn"."ValuesMapperID"
-- Join the MappingColumns to get back to their specific Attribute, now called AttributeColumns
LEFT JOIN "Attributes" AS "AttributesColumns"
ON "AttributesColumns"."AttributeID"="MappingColumns"."AttributeID"
/* Finishes here */
-- Join the MultiAttributeSeries to get access to their MultiAttributeSeriesValues
LEFT JOIN "MultiAttributeSeriesValues"
ON "MultiAttributeSeriesValues"."MultiAttributeSeriesID"="MultiAttributeSeries"."MultiAttributeSeriesID"
-- Select one InstanceName and restrict the query AttributeDataTypeCV that is MultiAttributeSeries
WHERE
"Attributes".AttributeDataTypeCV='MultiAttributeSeries'
AND "ResourceTypeAcronym"="{}"
AND "MasterNetworkName"= "{}"
AND "ScenarioName" ="{}"
Order By ScenarioName, AttributeName,ValueOrder asc
""".format(selectedResourceType, selectedNetwork, selectedScenarior)
result = self.session.execute(sql)
'''Down Table(MultiVariableSeries_table Table) write'''
complete_result = list()
strAtrributName = ''
valueOrder = None
AttributeName = ''
tempColumn = {}
sourceName = ''
i = 0
currentrow = 0
setNumber = 0
for row in result:
if row.AttributeName == None or row.AttributeName == "":
continue
if strAtrributName != row.AttributeName:
strAtrributName = row.AttributeName
tempColumn[row.AttributeName] = []
tempColumn[row.AttributeName].append(row.AttributeName)
AttributeName = row.AttributeName
if sourceName != row.ScenarioName:
sourceName = row.ScenarioName
setNumber = i
currentrow = 0
if AttributeName != row.AttributeName:
AttributeName = row.AttributeName
currentrow = 0
if row.AttributeName in tempColumn[row.AttributeName]:
index = tempColumn[row.AttributeName].index(row.AttributeName)
if index == 0:
complete_result.append([row.ObjectType, row.InstanceName, row.ScenarioName, row.AttributeName, row.SourceName,
row.MethodName, row.DataValue])
i += 1
else:
complete_result[setNumber + currentrow].append(row.DataValue)
currentrow += 1
else:
currentrow = 0
tempColumn[row.AttributeName].append(row.AttributeName)
index = tempColumn[row.AttributeName].index(row.AttributeName)
if index == 0:
complete_result.append([row.ObjectType, row.InstanceName, row.ScenarioName, row.AttributeName, row.SourceName,
row.MethodName, row.DataValue])
i += 1
else:
complete_result[setNumber + currentrow].append(row.DataValue)
currentrow += 1
return complete_result
except Exception as e:
print e
raise Exception('Error occured in reading Data Structure.\n' + e.message)
| 68.104213
| 148
| 0.572554
| 2,284
| 30,715
| 7.684764
| 0.092382
| 0.053783
| 0.011395
| 0.011964
| 0.93807
| 0.929125
| 0.918072
| 0.916306
| 0.91585
| 0.909526
| 0
| 0.00058
| 0.326909
| 30,715
| 451
| 149
| 68.104213
| 0.848409
| 0.013967
| 0
| 0.850136
| 0
| 0.002725
| 0.656386
| 0.310487
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0.00545
| null | null | 0.013624
| 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
| 1
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 9
|
99b5acc26a3a3b7b1e534cd5f516ad60d34cd65e
| 4,042
|
py
|
Python
|
src/bert_models/training/at_training.py
|
roronoayhd/2021daguan
|
132380c55c54de08ec44c2c4161f962312c50a29
|
[
"Apache-2.0"
] | 24
|
2021-09-02T10:50:13.000Z
|
2021-11-03T10:06:36.000Z
|
src/bert_models/training/at_training.py
|
roronoayhd/2021daguan
|
132380c55c54de08ec44c2c4161f962312c50a29
|
[
"Apache-2.0"
] | 2
|
2021-09-16T02:12:06.000Z
|
2021-12-03T06:50:18.000Z
|
src/bert_models/training/at_training.py
|
roronoayhd/2021daguan
|
132380c55c54de08ec44c2c4161f962312c50a29
|
[
"Apache-2.0"
] | 7
|
2021-09-02T15:25:21.000Z
|
2021-09-18T17:09:24.000Z
|
import logging
import re
import torch
logger = logging.getLogger(__name__)
class FGM(object):
"""Reference: https://arxiv.org/pdf/1605.07725.pdf"""
def __init__(self,
model,
emb_names=['word_embeddings', "encoder.layer.0"],
epsilon=1.0):
self.model = model
# emb_names 这个参数要换成你模型中embedding的参数名
# 可以是多组参数
self.emb_names = emb_names
self.epsilon = epsilon
self.emb_backup = {}
self.grad_backup = {}
def attack(self):
"""Add adversity."""
for name, param in self.model.named_parameters():
if param.requires_grad and re.search("|".join(self.emb_names), name):
# 把真实参数保存起来
self.emb_backup[name] = param.data.clone()
norm = torch.norm(param.grad)
if norm != 0 and not torch.isnan(norm):
r_adv = self.epsilon * param.grad / norm
param.data.add_(r_adv)
def restore(self):
""" restore embedding """
for name, param in self.model.named_parameters():
if param.requires_grad and re.search("|".join(self.emb_names), name):
assert name in self.emb_backup
param.data = self.emb_backup[name]
self.emb_backup = {}
def backup_grad(self):
for name, param in self.model.named_parameters():
if param.requires_grad and param.grad is not None:
self.grad_backup[name] = param.grad.clone()
def restore_grad(self):
for name, param in self.model.named_parameters():
if param.requires_grad and param.grad is not None:
if re.search("|".join(self.emb_names), name):
param.grad = self.grad_backup[name]
else:
param.grad += self.grad_backup[name]
class PGD(object):
"""Reference: https://arxiv.org/pdf/1706.06083.pdf"""
def __init__(self,
model,
emb_names=['word_embeddings', "encoder.layer.0"],
epsilon=1.0,
alpha=0.3):
self.model = model
self.emb_names = emb_names
self.epsilon = epsilon
self.alpha = alpha
self.emb_backup = {}
self.grad_backup = {}
def attack(self, is_first_attack=False):
"""Add adversity."""
for name, param in self.model.named_parameters():
if param.requires_grad and re.search("|".join(self.emb_names), name):
if is_first_attack:
self.emb_backup[name] = param.data.clone()
norm = torch.norm(param.grad)
if norm != 0 and not torch.isnan(norm):
r_adv = self.alpha * param.grad / norm
param.data.add_(r_adv)
param.data = self.project(name, param.data)
def restore(self):
"""restore embedding"""
for name, param in self.model.named_parameters():
if param.requires_grad and re.search("|".join(self.emb_names), name):
assert name in self.emb_backup
param.data = self.emb_backup[name]
self.emb_backup = {}
def project(self, param_name, param_data):
r_adv = param_data - self.emb_backup[param_name]
if torch.norm(r_adv) > self.epsilon:
r_adv_0 = self.epsilon * r_adv / torch.norm(r_adv)
return self.emb_backup[param_name] + r_adv
def backup_grad(self):
for name, param in self.model.named_parameters():
if param.requires_grad and param.grad is not None:
self.grad_backup[name] = param.grad.clone()
def restore_grad(self):
for name, param in self.model.named_parameters():
if param.requires_grad and param.grad is not None:
if re.search("|".join(self.emb_names), name):
param.grad = self.grad_backup[name]
else:
param.grad += self.grad_backup[name]
| 36.089286
| 81
| 0.563582
| 500
| 4,042
| 4.378
| 0.14
| 0.063956
| 0.071265
| 0.051165
| 0.852901
| 0.809045
| 0.780722
| 0.780722
| 0.754226
| 0.681133
| 0
| 0.010627
| 0.324839
| 4,042
| 112
| 82
| 36.089286
| 0.791499
| 0.053439
| 0
| 0.746988
| 0
| 0
| 0.017405
| 0
| 0
| 0
| 0
| 0
| 0.024096
| 1
| 0.13253
| false
| 0
| 0.036145
| 0
| 0.204819
| 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
|
99bfb3c9c7d380b43de31eb120bad28acfbe40c8
| 7,443
|
py
|
Python
|
assignments/assignment2/model.py
|
MorrisNein/dlcourse_ai
|
9458921dc60ae56579793e295eb3b28f95eda1c2
|
[
"MIT"
] | null | null | null |
assignments/assignment2/model.py
|
MorrisNein/dlcourse_ai
|
9458921dc60ae56579793e295eb3b28f95eda1c2
|
[
"MIT"
] | null | null | null |
assignments/assignment2/model.py
|
MorrisNein/dlcourse_ai
|
9458921dc60ae56579793e295eb3b28f95eda1c2
|
[
"MIT"
] | null | null | null |
import numpy as np
from layers import FullyConnectedLayer, ReLULayer, softmax_with_cross_entropy, l2_regularization, softmax
from collections import OrderedDict
class TwoLayerNet:
""" Neural network with two fully connected layers """
def __init__(self, n_input, n_output, hidden_layer_size, reg):
"""
Initializes the neural network
Arguments:
n_input, int - dimension of the model input
n_output, int - number of classes to predict
hidden_layer_size, int - number of neurons in the hidden layer
reg, float - L2 regularization strength
"""
self.reg = reg
# TODO Create necessary layers
self.layers = OrderedDict({
"linear_1": FullyConnectedLayer(n_input, hidden_layer_size),
"relu_1": ReLULayer(),
"linear_2": FullyConnectedLayer(hidden_layer_size, n_output)
})
def compute_loss_and_gradients(self, X, y):
"""
Computes total loss and updates parameter gradients
on a batch of training examples
Arguments:
X, np array (batch_size, input_features) - input data
y, np array of int (batch_size) - classes
"""
# Before running forward and backward pass through the model,
# clear parameter gradients aggregated from the previous pass
# TODO Set parameter gradient to zeros
# Hint: using self.params() might be useful!
params = self.params().values()
for par in params:
par.grad = np.zeros_like(par.grad)
# TODO Compute loss and fill param gradients
# by running forward and backward passes through the model
# Forward pass
for n_lay, lay in enumerate(self.layers.values()):
if n_lay == 0:
# print(X)
current_X = lay.forward(X)
else:
# print(current_X)
current_X = lay.forward(current_X)
# print(current_X)
# print(f"{n_lay}, {lay}")
clf_output = current_X
CE_loss, dpredictions = softmax_with_cross_entropy(clf_output, y)
# Backward pass
for n_lay, lay in enumerate(reversed(self.layers.values())):
# print(f"{n_lay}")
if n_lay == 0:
# print(dpredictions)
current_dX = lay.backward(dpredictions)
else:
# print(current_dX)
current_dX = lay.backward(current_dX)
# print(current_dX)
# After that, implement l2 regularization on all params
# Hint: self.params() is useful again!
reg_loss_accumulated = 0
for par in params:
reg_loss, dpar = l2_regularization(par.value, self.reg)
par.grad += dpar
reg_loss_accumulated += reg_loss
loss = CE_loss + reg_loss_accumulated
return loss
def predict(self, X):
"""
Produces classifier predictions on the set
Arguments:
X, np array (test_samples, num_features)
Returns:
y_pred, np.array of int (test_samples)
"""
# TODO: Implement predict
# Hint: some of the code of the compute_loss_and_gradients
# can be reused
y_pred = np.zeros(X.shape[0], np.int)
for n_lay, lay in enumerate(self.layers.values()):
if n_lay == 0:
current_X = lay.forward(X)
else:
current_X = lay.forward(current_X)
clf_output = current_X
probs = softmax(clf_output)
# print(probs)
y_pred = np.argmax(probs, axis=-1)
# print(y_pred)
# raise Exception("Not implemented!")
return y_pred
def params(self):
result = {}
# TODO Implement aggregating all of the params
for layname, lay in self.layers.items():
result.update({f"{layname}_{parname}" : par for parname, par in lay.params().items()})
return result
class OneLayerNet:
""" Neural network with two fully connected layers """
def __init__(self, n_input, n_output, hidden_layer_size, reg):
"""
Initializes the neural network
Arguments:
n_input, int - dimension of the model input
n_output, int - number of classes to predict
hidden_layer_size, int - number of neurons in the hidden layer
reg, float - L2 regularization strength
"""
self.reg = reg
# TODO Create necessary layers
self.layers = OrderedDict({
"linear_1": FullyConnectedLayer(n_input, n_output),
})
def compute_loss_and_gradients(self, X, y):
"""
Computes total loss and updates parameter gradients
on a batch of training examples
Arguments:
X, np array (batch_size, input_features) - input data
y, np array of int (batch_size) - classes
"""
# Before running forward and backward pass through the model,
# clear parameter gradients aggregated from the previous pass
# TODO Set parameter gradient to zeros
# Hint: using self.params() might be useful!
params = self.params().values()
for par in params:
par.grad = np.zeros_like(par.grad)
# TODO Compute loss and fill param gradients
# by running forward and backward passes through the model
# Forward pass
for n_lay, lay in enumerate(self.layers.values()):
if n_lay == 0:
current_X = lay.forward(X)
else:
current_X = lay.forward(current_X)
clf_output = current_X
CE_loss, dpredictions = softmax_with_cross_entropy(clf_output, y)
# Backward pass
for n_lay, lay in enumerate(reversed(self.layers.values())):
if n_lay == 0:
current_dX = lay.backward(dpredictions)
else:
current_dX = lay.backward(current_dX)
# After that, implement l2 regularization on all params
# Hint: self.params() is useful again!
reg_loss_accumulated = 0
for par in params:
reg_loss, dpar = l2_regularization(par.value, self.reg)
par.grad += dpar
reg_loss_accumulated += reg_loss
loss = CE_loss + reg_loss_accumulated
return loss
def predict(self, X):
"""
Produces classifier predictions on the set
Arguments:
X, np array (test_samples, num_features)
Returns:
y_pred, np.array of int (test_samples)
"""
# TODO: Implement predict
# Hint: some of the code of the compute_loss_and_gradients
# can be reused
y_pred = np.zeros(X.shape[0], np.int)
for n_lay, lay in enumerate(self.layers.values()):
if n_lay == 0:
current_X = lay.forward(X)
else:
current_X = lay.forward(current_X)
clf_output = current_X
probs = softmax(clf_output)
# print(probs)
y_pred = np.argmax(probs, axis=-1)
# print(y_pred)
# raise Exception("Not implemented!")
return y_pred
def params(self):
result = {}
# TODO Implement aggregating all of the params
for layname, lay in self.layers.items():
result.update({f"{layname}_{parname}" : par for parname, par in lay.params().items()})
return result
| 33.678733
| 105
| 0.595593
| 913
| 7,443
| 4.686747
| 0.164294
| 0.033653
| 0.020566
| 0.033653
| 0.925684
| 0.921243
| 0.88315
| 0.88315
| 0.879878
| 0.879878
| 0
| 0.004569
| 0.32366
| 7,443
| 220
| 106
| 33.831818
| 0.845451
| 0.361413
| 0
| 0.905263
| 0
| 0
| 0.015629
| 0
| 0
| 0
| 0
| 0.027273
| 0
| 1
| 0.084211
| false
| 0
| 0.031579
| 0
| 0.2
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
99eaae6aa3d3914f71d2af6c29dc34d918cf7454
| 15,872
|
py
|
Python
|
tests/tasks/kubernetes/test_service.py
|
concreted/prefect
|
dd732f5990ee2b0f3d816adb285168fd63b239e4
|
[
"Apache-2.0"
] | 8,633
|
2019-03-23T17:51:03.000Z
|
2022-03-31T22:17:42.000Z
|
tests/tasks/kubernetes/test_service.py
|
concreted/prefect
|
dd732f5990ee2b0f3d816adb285168fd63b239e4
|
[
"Apache-2.0"
] | 3,903
|
2019-03-23T19:11:21.000Z
|
2022-03-31T23:21:23.000Z
|
tests/tasks/kubernetes/test_service.py
|
concreted/prefect
|
dd732f5990ee2b0f3d816adb285168fd63b239e4
|
[
"Apache-2.0"
] | 937
|
2019-03-23T18:49:44.000Z
|
2022-03-31T21:45:13.000Z
|
from unittest.mock import MagicMock
import pytest
import prefect
from prefect.tasks.kubernetes import (
CreateNamespacedService,
DeleteNamespacedService,
ListNamespacedService,
PatchNamespacedService,
ReadNamespacedService,
ReplaceNamespacedService,
)
from prefect.utilities.configuration import set_temporary_config
@pytest.fixture
def kube_secret():
with set_temporary_config({"cloud.use_local_secrets": True}):
with prefect.context(secrets=dict(KUBERNETES_API_KEY="test_key")):
yield
@pytest.fixture
def api_client(monkeypatch):
client = MagicMock()
monkeypatch.setattr(
"prefect.tasks.kubernetes.service.get_kubernetes_client",
MagicMock(return_value=client),
)
return client
class TestCreateNamespacedServiceTask:
def test_empty_initialization(self, kube_secret):
task = CreateNamespacedService()
assert task.body == {}
assert task.namespace == "default"
assert task.kube_kwargs == {}
assert task.kubernetes_api_key_secret == "KUBERNETES_API_KEY"
def test_filled_initialization(self, kube_secret):
task = CreateNamespacedService(
body={"test": "test"},
namespace="test",
kube_kwargs={"test": "test"},
kubernetes_api_key_secret="test",
)
assert task.body == {"test": "test"}
assert task.namespace == "test"
assert task.kube_kwargs == {"test": "test"}
assert task.kubernetes_api_key_secret == "test"
def test_empty_body_raises_error(self, kube_secret):
task = CreateNamespacedService()
with pytest.raises(ValueError):
task.run()
def test_invalid_body_raises_error(self, kube_secret):
task = CreateNamespacedService()
with pytest.raises(ValueError):
task.run(body=None)
def test_body_value_is_replaced(self, kube_secret, api_client):
task = CreateNamespacedService(body={"test": "a"})
task.run(body={"test": "b"})
assert api_client.create_namespaced_service.call_args[1]["body"] == {
"test": "b"
}
def test_body_value_is_appended(self, kube_secret, api_client):
task = CreateNamespacedService(body={"test": "a"})
task.run(body={"a": "test"})
assert api_client.create_namespaced_service.call_args[1]["body"] == {
"a": "test",
"test": "a",
}
def test_empty_body_value_is_updated(self, kube_secret, api_client):
task = CreateNamespacedService()
task.run(body={"test": "a"})
assert api_client.create_namespaced_service.call_args[1]["body"] == {
"test": "a"
}
def test_kube_kwargs_value_is_replaced(self, kube_secret, api_client):
task = CreateNamespacedService(body={"test": "a"}, kube_kwargs={"test": "a"})
task.run(kube_kwargs={"test": "b"})
assert api_client.create_namespaced_service.call_args[1]["test"] == "b"
def test_kube_kwargs_value_is_appended(self, kube_secret, api_client):
task = CreateNamespacedService(body={"test": "a"}, kube_kwargs={"test": "a"})
task.run(kube_kwargs={"a": "test"})
assert api_client.create_namespaced_service.call_args[1]["a"] == "test"
assert api_client.create_namespaced_service.call_args[1]["test"] == "a"
def test_empty_kube_kwargs_value_is_updated(self, kube_secret, api_client):
task = CreateNamespacedService(body={"test": "a"})
task.run(kube_kwargs={"test": "a"})
assert api_client.create_namespaced_service.call_args[1]["test"] == "a"
class TestDeleteNamespacedServiceTask:
def test_empty_initialization(self, kube_secret):
task = DeleteNamespacedService()
assert not task.service_name
assert task.namespace == "default"
assert task.kube_kwargs == {}
assert task.kubernetes_api_key_secret == "KUBERNETES_API_KEY"
def test_filled_initialization(self, kube_secret):
task = DeleteNamespacedService(
service_name="test",
namespace="test",
kube_kwargs={"test": "test"},
kubernetes_api_key_secret="test",
)
assert task.service_name == "test"
assert task.namespace == "test"
assert task.kube_kwargs == {"test": "test"}
assert task.kubernetes_api_key_secret == "test"
def test_empty_name_raises_error(self, kube_secret):
task = DeleteNamespacedService()
with pytest.raises(ValueError):
task.run()
def test_invalid_body_raises_error(self, kube_secret):
task = DeleteNamespacedService()
with pytest.raises(ValueError):
task.run(service_name=None)
def test_kube_kwargs_value_is_replaced(self, kube_secret, api_client):
task = DeleteNamespacedService(service_name="test", kube_kwargs={"test": "a"})
task.run(kube_kwargs={"test": "b"})
assert api_client.delete_namespaced_service.call_args[1]["test"] == "b"
def test_kube_kwargs_value_is_appended(self, kube_secret, api_client):
task = DeleteNamespacedService(service_name="test", kube_kwargs={"test": "a"})
task.run(kube_kwargs={"a": "test"})
assert api_client.delete_namespaced_service.call_args[1]["a"] == "test"
assert api_client.delete_namespaced_service.call_args[1]["test"] == "a"
def test_empty_kube_kwargs_value_is_updated(self, kube_secret, api_client):
task = DeleteNamespacedService(service_name="test")
task.run(kube_kwargs={"test": "a"})
assert api_client.delete_namespaced_service.call_args[1]["test"] == "a"
class TestListNamespacedServiceTask:
def test_empty_initialization(self, kube_secret):
task = ListNamespacedService()
assert task.namespace == "default"
assert task.kube_kwargs == {}
assert task.kubernetes_api_key_secret == "KUBERNETES_API_KEY"
def test_filled_initialization(self, kube_secret):
task = ListNamespacedService(
namespace="test",
kube_kwargs={"test": "test"},
kubernetes_api_key_secret="test",
)
assert task.namespace == "test"
assert task.kube_kwargs == {"test": "test"}
assert task.kubernetes_api_key_secret == "test"
def test_kube_kwargs_value_is_replaced(self, kube_secret, api_client):
task = ListNamespacedService(kube_kwargs={"test": "a"})
task.run(kube_kwargs={"test": "b"})
assert api_client.list_namespaced_service.call_args[1]["test"] == "b"
def test_kube_kwargs_value_is_appended(self, kube_secret, api_client):
task = ListNamespacedService(kube_kwargs={"test": "a"})
task.run(kube_kwargs={"a": "test"})
assert api_client.list_namespaced_service.call_args[1]["a"] == "test"
assert api_client.list_namespaced_service.call_args[1]["test"] == "a"
def test_empty_kube_kwargs_value_is_updated(self, kube_secret, api_client):
task = ListNamespacedService()
task.run(kube_kwargs={"test": "a"})
assert api_client.list_namespaced_service.call_args[1]["test"] == "a"
class TestPatchNamespacedServiceTask:
def test_empty_initialization(self, kube_secret):
task = PatchNamespacedService()
assert not task.service_name
assert task.body == {}
assert task.namespace == "default"
assert task.kube_kwargs == {}
assert task.kubernetes_api_key_secret == "KUBERNETES_API_KEY"
def test_filled_initialization(self, kube_secret):
task = PatchNamespacedService(
service_name="test",
body={"test": "test"},
namespace="test",
kube_kwargs={"test": "test"},
kubernetes_api_key_secret="test",
)
assert task.service_name == "test"
assert task.body == {"test": "test"}
assert task.namespace == "test"
assert task.kube_kwargs == {"test": "test"}
assert task.kubernetes_api_key_secret == "test"
def test_empty_body_raises_error(self, kube_secret):
task = PatchNamespacedService()
with pytest.raises(ValueError):
task.run()
def test_invalid_body_raises_error(self, kube_secret):
task = PatchNamespacedService()
with pytest.raises(ValueError):
task.run(body=None)
def test_invalid_service_name_raises_error(self, kube_secret):
task = PatchNamespacedService()
with pytest.raises(ValueError):
task.run(body={"test": "test"}, service_name=None)
def test_body_value_is_replaced(self, kube_secret, api_client):
task = PatchNamespacedService(body={"test": "a"}, service_name="test")
task.run(body={"test": "b"})
assert api_client.patch_namespaced_service.call_args[1]["body"] == {"test": "b"}
def test_body_value_is_appended(self, kube_secret, api_client):
task = PatchNamespacedService(body={"test": "a"}, service_name="test")
task.run(body={"a": "test"})
assert api_client.patch_namespaced_service.call_args[1]["body"] == {
"a": "test",
"test": "a",
}
def test_empty_body_value_is_updated(self, kube_secret, api_client):
task = PatchNamespacedService(service_name="test")
task.run(body={"test": "a"})
assert api_client.patch_namespaced_service.call_args[1]["body"] == {"test": "a"}
def test_kube_kwargs_value_is_replaced(self, kube_secret, api_client):
task = PatchNamespacedService(
body={"test": "a"}, kube_kwargs={"test": "a"}, service_name="test"
)
task.run(kube_kwargs={"test": "b"})
assert api_client.patch_namespaced_service.call_args[1]["test"] == "b"
def test_kube_kwargs_value_is_appended(self, kube_secret, api_client):
task = PatchNamespacedService(
body={"test": "a"}, kube_kwargs={"test": "a"}, service_name="test"
)
task.run(kube_kwargs={"a": "test"})
assert api_client.patch_namespaced_service.call_args[1]["a"] == "test"
assert api_client.patch_namespaced_service.call_args[1]["test"] == "a"
def test_empty_kube_kwargs_value_is_updated(self, kube_secret, api_client):
task = PatchNamespacedService(body={"test": "a"}, service_name="test")
task.run(kube_kwargs={"test": "a"})
assert api_client.patch_namespaced_service.call_args[1]["test"] == "a"
class TestReadNamespacedServiceTask:
def test_empty_initialization(self, kube_secret):
task = ReadNamespacedService()
assert not task.service_name
assert task.namespace == "default"
assert task.kube_kwargs == {}
assert task.kubernetes_api_key_secret == "KUBERNETES_API_KEY"
def test_filled_initialization(self, kube_secret):
task = ReadNamespacedService(
service_name="test",
namespace="test",
kube_kwargs={"test": "test"},
kubernetes_api_key_secret="test",
)
assert task.service_name == "test"
assert task.namespace == "test"
assert task.kube_kwargs == {"test": "test"}
assert task.kubernetes_api_key_secret == "test"
def test_empty_name_raises_error(self, kube_secret):
task = ReadNamespacedService()
with pytest.raises(ValueError):
task.run()
def test_invalid_body_raises_error(self, kube_secret):
task = ReadNamespacedService()
with pytest.raises(ValueError):
task.run(service_name=None)
def test_kube_kwargs_value_is_replaced(self, kube_secret, api_client):
task = ReadNamespacedService(service_name="test", kube_kwargs={"test": "a"})
task.run(kube_kwargs={"test": "b"})
assert api_client.read_namespaced_service.call_args[1]["test"] == "b"
def test_kube_kwargs_value_is_appended(self, kube_secret, api_client):
task = ReadNamespacedService(service_name="test", kube_kwargs={"test": "a"})
task.run(kube_kwargs={"a": "test"})
assert api_client.read_namespaced_service.call_args[1]["a"] == "test"
assert api_client.read_namespaced_service.call_args[1]["test"] == "a"
def test_empty_kube_kwargs_value_is_updated(self, kube_secret, api_client):
task = ReadNamespacedService(service_name="test")
task.run(kube_kwargs={"test": "a"})
assert api_client.read_namespaced_service.call_args[1]["test"] == "a"
class TestReplaceNamespacedServiceTask:
def test_empty_initialization(self, kube_secret):
task = ReplaceNamespacedService()
assert not task.service_name
assert task.body == {}
assert task.namespace == "default"
assert task.kube_kwargs == {}
assert task.kubernetes_api_key_secret == "KUBERNETES_API_KEY"
def test_filled_initialization(self, kube_secret):
task = ReplaceNamespacedService(
service_name="test",
body={"test": "test"},
namespace="test",
kube_kwargs={"test": "test"},
kubernetes_api_key_secret="test",
)
assert task.service_name == "test"
assert task.body == {"test": "test"}
assert task.namespace == "test"
assert task.kube_kwargs == {"test": "test"}
assert task.kubernetes_api_key_secret == "test"
def test_empty_body_raises_error(self, kube_secret):
task = ReplaceNamespacedService()
with pytest.raises(ValueError):
task.run()
def test_invalid_body_raises_error(self, kube_secret):
task = ReplaceNamespacedService()
with pytest.raises(ValueError):
task.run(body=None)
def test_invalid_service_name_raises_error(self, kube_secret):
task = ReplaceNamespacedService()
with pytest.raises(ValueError):
task.run(body={"test": "test"}, service_name=None)
def test_body_value_is_replaced(self, kube_secret, api_client):
task = ReplaceNamespacedService(body={"test": "a"}, service_name="test")
task.run(body={"test": "b"})
assert api_client.replace_namespaced_service.call_args[1]["body"] == {
"test": "b"
}
def test_body_value_is_appended(self, kube_secret, api_client):
task = ReplaceNamespacedService(body={"test": "a"}, service_name="test")
task.run(body={"a": "test"})
assert api_client.replace_namespaced_service.call_args[1]["body"] == {
"a": "test",
"test": "a",
}
def test_empty_body_value_is_updated(self, kube_secret, api_client):
task = ReplaceNamespacedService(service_name="test")
task.run(body={"test": "a"})
assert api_client.replace_namespaced_service.call_args[1]["body"] == {
"test": "a"
}
def test_kube_kwargs_value_is_replaced(self, kube_secret, api_client):
task = ReplaceNamespacedService(
body={"test": "a"}, kube_kwargs={"test": "a"}, service_name="test"
)
task.run(kube_kwargs={"test": "b"})
assert api_client.replace_namespaced_service.call_args[1]["test"] == "b"
def test_kube_kwargs_value_is_appended(self, kube_secret, api_client):
task = ReplaceNamespacedService(
body={"test": "a"}, kube_kwargs={"test": "a"}, service_name="test"
)
task.run(kube_kwargs={"a": "test"})
assert api_client.replace_namespaced_service.call_args[1]["a"] == "test"
assert api_client.replace_namespaced_service.call_args[1]["test"] == "a"
def test_empty_kube_kwargs_value_is_updated(self, kube_secret, api_client):
task = ReplaceNamespacedService(body={"test": "a"}, service_name="test")
task.run(kube_kwargs={"test": "a"})
assert api_client.replace_namespaced_service.call_args[1]["test"] == "a"
| 38.245783
| 88
| 0.654612
| 1,837
| 15,872
| 5.339684
| 0.045727
| 0.067285
| 0.07279
| 0.084106
| 0.92354
| 0.92354
| 0.896218
| 0.894077
| 0.863493
| 0.86023
| 0
| 0.002657
| 0.217427
| 15,872
| 414
| 89
| 38.338164
| 0.787054
| 0
| 0
| 0.786378
| 0
| 0
| 0.068107
| 0.004851
| 0
| 0
| 0
| 0
| 0.256966
| 1
| 0.164087
| false
| 0
| 0.01548
| 0
| 0.201238
| 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
|
99f96715495e4a2b1e0641cb6a394b2a6f575da5
| 131
|
py
|
Python
|
python/testData/joinLines/CommentProducesTooLongLineAfterJoin.py
|
jnthn/intellij-community
|
8fa7c8a3ace62400c838e0d5926a7be106aa8557
|
[
"Apache-2.0"
] | 2
|
2019-04-28T07:48:50.000Z
|
2020-12-11T14:18:08.000Z
|
python/testData/joinLines/CommentProducesTooLongLineAfterJoin.py
|
jnthn/intellij-community
|
8fa7c8a3ace62400c838e0d5926a7be106aa8557
|
[
"Apache-2.0"
] | 173
|
2018-07-05T13:59:39.000Z
|
2018-08-09T01:12:03.000Z
|
python/testData/joinLines/CommentProducesTooLongLineAfterJoin.py
|
jnthn/intellij-community
|
8fa7c8a3ace62400c838e0d5926a7be106aa8557
|
[
"Apache-2.0"
] | 2
|
2020-03-15T08:57:37.000Z
|
2020-04-07T04:48:14.000Z
|
# this comment is very very very very very very long.
# And this is the second line of this very long comment
def test():
pass
| 32.75
| 56
| 0.725191
| 24
| 131
| 3.958333
| 0.541667
| 0.421053
| 0.505263
| 0.505263
| 0.252632
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.229008
| 131
| 4
| 57
| 32.75
| 0.940594
| 0.80916
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| true
| 0.5
| 0
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 9
|
41579165f191d47071c14d731a8728016fa78030
| 6,815
|
py
|
Python
|
fabfile.py
|
errord/sputnik
|
b83c635a9a160dcd5809265c0d9d231ade33e5ea
|
[
"BSD-3-Clause"
] | null | null | null |
fabfile.py
|
errord/sputnik
|
b83c635a9a160dcd5809265c0d9d231ade33e5ea
|
[
"BSD-3-Clause"
] | null | null | null |
fabfile.py
|
errord/sputnik
|
b83c635a9a160dcd5809265c0d9d231ade33e5ea
|
[
"BSD-3-Clause"
] | 1
|
2018-03-04T04:48:44.000Z
|
2018-03-04T04:48:44.000Z
|
#!/usr/bin/env python
#coding:utf8
# author: zhizhimama
# date: 2014-10-09
from fabric.api import *
from fabric.colors import *
import os
import sys
import time
fab_time = time.strftime('%m%d_%H%M%S')
env.roledefs = {
'web_server': ['xxxx@xx.xx.xx.xx'],
'service_server': ['xxxx@xx.xx.xx.xx'],
'all_server': ['xxxx@xx.xx.xx.xx',
'xxxx@xx.xx.xx.xx'],
}
target_path='/home/msx/pip_local'
@roles('service_server')
def _upload_service(level):
"""
upload project to the service server, for service server
"""
pwd = local('pwd', capture=True)
localpath, project = os.path.split(pwd)
with lcd(localpath):
local('rm -rf {0}_temp'.format(project))
local('rm -rf {0}_temp.tar.bz'.format(project))
local('cp -rf {0} {0}_temp'.format(project))
local("find ./{0}_temp -type f -name '*.pyc' | xargs rm -rf".format(project))
local("rm -rf {0}_temp/tags".format(project))
local("rm -rf {0}_temp/.git".format(project))
local('tar jcvf {0}_temp.tar.bz {0}_temp'.format(project))
print green('上传到 msx@{host}:{path}/{level}/{dir}'.format(host=env.host, path=target_path, dir=project, level=level))
local("scp {filename}_temp.tar.bz msx@{host}:{path}/{level}/{dir}".format(filename=project, host=env.host, path=target_path, dir=project, level=level))
@roles('all_server')
def _upload_all(level):
"""
upload project to all the server
"""
pwd = local('pwd', capture=True)
localpath, project = os.path.split(pwd)
with lcd(localpath):
local('rm -rf {0}_temp'.format(project))
local('rm -rf {0}_temp.tar.bz'.format(project))
local('cp -rf {0} {0}_temp'.format(project))
local("find ./{0}_temp -type f -name '*.pyc' | xargs rm -rf".format(project))
local("rm -rf {0}_temp/tags".format(project))
local("rm -rf {0}_temp/.git".format(project))
local('tar jcvf {0}_temp.tar.bz {0}_temp'.format(project))
print green('上传到 msx@{host}:{path}/{level}/{dir}'.format(host=env.host, path=target_path, dir=project, level=level))
local("scp {filename}_temp.tar.bz msx@{host}:{path}/{level}/{dir}".format(filename=project, host=env.host, path=target_path, dir=project, level=level))
@roles('service_server')
def _install_service(workon=None, level=''):
"""
install on the service server, need to specify env and level
"""
if not (workon and level):
print red('please specify a environment:\nuseage: fab install:env_name,level')
sys.exit(0)
pwd = local('pwd', capture=True)
localpath, project = os.path.split(pwd)
with prefix('workon {0}'.format(workon)):
with cd('/'.join((target_path, level, project))):
run('mv {0} {0}.bak/{0}_{1}'.format(project, fab_time))
run('tar jxf {0}_temp.tar.bz'.format(project))
run('mv {0}_temp {0}'.format (project))
with cd('./{0}'.format (project)):
run('python setup.py install')
@roles('all_server')
def _install_all(workon=None, level=''):
"""
install on the service server, need to specify env and level
"""
if not (workon and level):
print red('please specify a environment:\nuseage: fab install:env_name,level')
sys.exit(0)
pwd = local('pwd', capture=True)
localpath, project = os.path.split(pwd)
with prefix('workon {0}'.format(workon)):
with cd('/'.join((target_path, level, project))):
run('mv {0} {0}.bak/{0}_{1}'.format(project, fab_time))
run('tar jxf {0}_temp.tar.bz'.format(project))
run('mv {0}_temp {0}'.format (project))
with cd('./{0}'.format (project)):
run('python setup.py install')
@roles('service_server')
def _start(level=None, workon=None):
"""
start mode under env, userage: fab start:mode,env
"""
if not (level and workon):
print red('please specify a environment:\nuseage: fab start:level,env_name')
sys.exit(0)
pwd = local('pwd', capture=True)
localpath, project = os.path.split(pwd)
with prefix('workon {0}'.format(workon)):
with cd('/'.join((target_path, level, project,'{0}', 'server')).format(project)):
service = 'spumaster'
print yellow('new restart {}'.format(service))
run('./run_{0}.sh stop {1}'.format(service, level))
run('./run_{0}.sh start {1}'.format(service, level))
run('sleep 5')
run('./run_{0}.sh list {1}'.format(service, level))
service = 'fastmq'
print yellow('new restart {}'.format(service))
run('./run_{0}.sh stop {1}'.format(service, level))
run('./run_{0}.sh start {1}'.format(service, level))
run('sleep 5')
run('./run_{0}.sh list {1}'.format(service, level))
@roles('service_server')
def _show_status(level=None, workon=None):
"""
show status, userage: fab start:mode,env
"""
if not (level and workon):
print red('please specify a environment:\nuseage: fab start:level,env_name')
sys.exit(0)
pwd = local('pwd', capture=True)
localpath, project = os.path.split(pwd)
with prefix('workon {0}'.format(workon)):
with cd('/'.join((target_path, level, project,'{0}', 'server')).format(project)):
service = 'spumaster'
print yellow('new show {}'.format(service))
run('./run_{0}.sh list {1}'.format(service, level))
service = 'fastmq'
print yellow('new show {}'.format(service))
run('./run_{0}.sh list {1}'.format(service, level))
def dev(level='dev', env='leo_dev'):
execute(_upload_service, level=level)
execute(_install_service, workon=env, level=level)
execute(_start, level=level, workon=env)
def pre(level='pre', env='leo_pre'):
execute(_upload_service, level=level)
execute(_install_service, workon=env, level=level)
execute(_start, level=level, workon=env)
def online(level='online', env='leo_online'):
execute(_upload_all, level=level)
execute(_install_all, workon=env, level=level)
execute(_start, level=level, workon=env)
def restart_dev(level='dev', env='leo_dev'):
execute(_start, level=level, workon=env)
def restart_pre(level='pre', env='leo_pre'):
execute(_start, level=level, workon=env)
def restart_online(level='online', env='leo_online'):
execute(_start, level=level, workon=env)
def show_dev_status(level='dev', env='leo_dev'):
execute(_show_status, level=level, workon=env)
def show_pre_status(level='pre', env='leo_pre'):
execute(_show_status, level=level, workon=env)
def show_online_status(level='online', env='leo_online'):
execute(_show_status, level=level, workon=env)
| 39.166667
| 159
| 0.618782
| 948
| 6,815
| 4.330169
| 0.130802
| 0.076005
| 0.052619
| 0.041657
| 0.872838
| 0.860901
| 0.832887
| 0.780512
| 0.758831
| 0.737881
| 0
| 0.013078
| 0.203375
| 6,815
| 173
| 160
| 39.393064
| 0.743047
| 0.009831
| 0
| 0.767442
| 0
| 0
| 0.257597
| 0.046607
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0.03876
| null | null | 0.077519
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
416e6c67df7a90e642f6631bf3630440f1383f14
| 123
|
py
|
Python
|
trends/filters.py
|
marissapang/covid19-Django
|
d29cd1f751dd8d0914492c2bfa1310ab8275cde0
|
[
"Apache-2.0"
] | null | null | null |
trends/filters.py
|
marissapang/covid19-Django
|
d29cd1f751dd8d0914492c2bfa1310ab8275cde0
|
[
"Apache-2.0"
] | 7
|
2020-04-12T22:42:55.000Z
|
2021-09-22T18:48:51.000Z
|
trends/filters.py
|
marissapang/covid19-Django
|
d29cd1f751dd8d0914492c2bfa1310ab8275cde0
|
[
"Apache-2.0"
] | null | null | null |
from django.contrib.auth.models import User
import django_filters
class CountryFilter(django_filters.FilterSet):
pass
| 20.5
| 46
| 0.829268
| 16
| 123
| 6.25
| 0.75
| 0.26
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.113821
| 123
| 5
| 47
| 24.6
| 0.917431
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.25
| 0.5
| 0
| 0.75
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 7
|
4180cbfade79f931f22d4b36181aa1f37f7269ee
| 192
|
py
|
Python
|
ada_loss/chainer_impl/__init__.py
|
kumasento/gradient-scaling
|
0ca435433b9953e33656173c4d60ebd61c5c5e87
|
[
"MIT"
] | 7
|
2020-08-12T12:04:28.000Z
|
2021-11-22T15:56:08.000Z
|
ada_loss/chainer_impl/__init__.py
|
kumasento/gradient-scaling
|
0ca435433b9953e33656173c4d60ebd61c5c5e87
|
[
"MIT"
] | 1
|
2021-10-07T08:37:39.000Z
|
2021-10-08T02:41:39.000Z
|
ada_loss/chainer_impl/__init__.py
|
kumasento/gradient-scaling
|
0ca435433b9953e33656173c4d60ebd61c5c5e87
|
[
"MIT"
] | null | null | null |
from ada_loss.chainer_impl.ada_loss_scaled import AdaLossScaled
# all the transformations
from ada_loss.chainer_impl.ada_loss_transforms import *
from ada_loss.chainer_impl import transforms
| 32
| 63
| 0.875
| 29
| 192
| 5.448276
| 0.413793
| 0.221519
| 0.208861
| 0.341772
| 0.506329
| 0.367089
| 0.367089
| 0
| 0
| 0
| 0
| 0
| 0.088542
| 192
| 5
| 64
| 38.4
| 0.902857
| 0.119792
| 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
|
68fda1df1d75d3352993a629c68dbc2a010871af
| 151
|
py
|
Python
|
tests/test_async_rust_sleep.py
|
Pure-Peace/peace-performance-python
|
51bac7c346aeaac5b62b602ff0ec16acced87e7b
|
[
"MIT"
] | 8
|
2021-08-07T19:43:17.000Z
|
2022-02-02T11:51:42.000Z
|
tests/test_async_rust_sleep.py
|
Pure-Peace/peace-performance-python
|
51bac7c346aeaac5b62b602ff0ec16acced87e7b
|
[
"MIT"
] | 1
|
2021-08-08T08:38:50.000Z
|
2021-08-08T08:38:50.000Z
|
tests/test_async_rust_sleep.py
|
Pure-Peace/peace-performance-python
|
51bac7c346aeaac5b62b602ff0ec16acced87e7b
|
[
"MIT"
] | 3
|
2021-08-08T04:30:29.000Z
|
2021-08-18T22:52:05.000Z
|
from peace_performance_python.functions import rust_sleep
from . import async_run
def test_async_rust_sleep() -> None:
async_run(rust_sleep(0))
| 18.875
| 57
| 0.794702
| 23
| 151
| 4.826087
| 0.608696
| 0.243243
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.007634
| 0.13245
| 151
| 7
| 58
| 21.571429
| 0.839695
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 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
| 0
| 0
| 1
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
ec1ccd348f2ec84603fa123aa453f1e8592a72b7
| 2,100
|
py
|
Python
|
layout/misc.py
|
euxhenh/cellar
|
679387216043f3d287ea29a15f78868f412d2948
|
[
"MIT"
] | 9
|
2021-09-08T16:56:45.000Z
|
2021-12-12T03:13:29.000Z
|
layout/misc.py
|
euxhenh/cellar
|
679387216043f3d287ea29a15f78868f412d2948
|
[
"MIT"
] | null | null | null |
layout/misc.py
|
euxhenh/cellar
|
679387216043f3d287ea29a15f78868f412d2948
|
[
"MIT"
] | 1
|
2022-01-20T03:04:44.000Z
|
2022-01-20T03:04:44.000Z
|
empty_figure = {
"layout": {
"xaxis": {
"visible": False
},
"yaxis": {
"visible": False
},
"annotations": [
{
"text": "Nothing to show. Load the data and "
"run dimensionality reduction.",
"xref": "paper",
"yref": "paper",
"showarrow": False,
"font": {
"size": 14
}
}
],
"height": "700"
}
}
empty_analysis_figure = {
"layout": {
"xaxis": {
"visible": False
},
"yaxis": {
"visible": False
},
"annotations": [
{
"text": "No heatmap or violin plot to show. " +
"Select genes first.",
"xref": "paper",
"yref": "paper",
"showarrow": False,
"font": {
"size": 14
}
}
]
# "height": "650"
}
}
empty_colocalization_figure = {
"layout": {
"xaxis": {
"visible": False
},
"yaxis": {
"visible": False
},
"annotations": [
{
"text": "Nothing to show. Load a spatial tile first.",
"xref": "paper",
"yref": "paper",
"showarrow": False,
"font": {
"size": 14
}
}
]
# "height": "650"
}
}
empty_spatial_figure = {
"layout": {
"xaxis": {
"visible": False
},
"yaxis": {
"visible": False
},
"annotations": [
{
"text": "Nothing to show. Load a spatial tile first.",
"xref": "paper",
"yref": "paper",
"showarrow": False,
"font": {
"size": 14
}
}
],
"width": "1000"
}
}
| 21.649485
| 70
| 0.318095
| 127
| 2,100
| 5.204724
| 0.346457
| 0.145234
| 0.102874
| 0.145234
| 0.810893
| 0.810893
| 0.810893
| 0.810893
| 0.810893
| 0.810893
| 0
| 0.021944
| 0.544286
| 2,100
| 96
| 71
| 21.875
| 0.668757
| 0.014762
| 0
| 0.545455
| 0
| 0
| 0.262343
| 0
| 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
|
ec238809b37030fa483f39d83baea2f7036c8dcc
| 756
|
py
|
Python
|
IMS/ims_users/permissions.py
|
AyushPaudel/Inventory-Management-System
|
04e57b0d02b1b7cade992b959569e750ca339c8e
|
[
"MIT"
] | 2
|
2021-09-01T13:00:24.000Z
|
2021-11-19T12:16:52.000Z
|
IMS/ims_users/permissions.py
|
aadarshadhakalg/Inventory-Management-System-1
|
075ec49b9d4abebb7d9a0b150a6cb70f6cbf5144
|
[
"MIT"
] | null | null | null |
IMS/ims_users/permissions.py
|
aadarshadhakalg/Inventory-Management-System-1
|
075ec49b9d4abebb7d9a0b150a6cb70f6cbf5144
|
[
"MIT"
] | 1
|
2021-12-23T23:41:20.000Z
|
2021-12-23T23:41:20.000Z
|
from rest_framework.permissions import BasePermission
class adminPermission(BasePermission):
def has_permission(self, request, view):
return request.user.is_authenticated and request.user.user_type == 'AD'
class staffPermission(BasePermission):
def has_permission(self, request, view):
return request.user.is_authenticated and request.user.user_type == 'ST'
class StaffOrAdmin(BasePermission):
def has_permission(self, request, view):
return request.user.is_authenticated and (request.user.user_type == 'ST' or request.user.user_type == 'AD')
class customerPermission(BasePermission):
def has_permission(self, request, view):
return request.user.is_authenticated and request.user.user_type == 'CU'
| 31.5
| 115
| 0.752646
| 92
| 756
| 6.032609
| 0.293478
| 0.178378
| 0.135135
| 0.171171
| 0.78018
| 0.78018
| 0.720721
| 0.720721
| 0.720721
| 0.720721
| 0
| 0
| 0.150794
| 756
| 23
| 116
| 32.869565
| 0.864486
| 0
| 0
| 0.307692
| 0
| 0
| 0.013228
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.307692
| false
| 0
| 0.076923
| 0.307692
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
|
0
| 8
|
6be2a74246dfad622fae84a189603f927215bc57
| 47,452
|
py
|
Python
|
billforward/apis/products_api.py
|
billforward/bf-python
|
d2b812329ca3ed1fd94364d7f46f69ad74665596
|
[
"Apache-2.0"
] | 2
|
2016-11-23T17:32:37.000Z
|
2022-02-24T05:13:20.000Z
|
billforward/apis/products_api.py
|
billforward/bf-python
|
d2b812329ca3ed1fd94364d7f46f69ad74665596
|
[
"Apache-2.0"
] | null | null | null |
billforward/apis/products_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 ProductsApi(object):
"""
NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
Ref: https://github.com/swagger-api/swagger-codegen
"""
def __init__(self, api_client=None):
config = Configuration()
if api_client:
self.api_client = api_client
else:
if not config.api_client:
config.api_client = ApiClient()
self.api_client = config.api_client
def create_product(self, product, **kwargs):
"""
Create a product.
{\"nickname\":\"Create a new product\",\"request\":\"createProductRequest.html\",\"response\":\"createProductResponse.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.create_product(product, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param Product product: The product object to be updated. (required)
:return: ProductPagedMetadata
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('callback'):
return self.create_product_with_http_info(product, **kwargs)
else:
(data) = self.create_product_with_http_info(product, **kwargs)
return data
def create_product_with_http_info(self, product, **kwargs):
"""
Create a product.
{\"nickname\":\"Create a new product\",\"request\":\"createProductRequest.html\",\"response\":\"createProductResponse.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.create_product_with_http_info(product, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param Product product: The product object to be updated. (required)
:return: ProductPagedMetadata
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['product']
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 create_product" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'product' is set
if ('product' not in params) or (params['product'] is None):
raise ValueError("Missing the required parameter `product` when calling `create_product`")
resource_path = '/products'.replace('{format}', 'json')
path_params = {}
query_params = {}
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'product' in params:
body_params = params['product']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['text/xml', 'application/xml', '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='ProductPagedMetadata',
auth_settings=auth_settings,
callback=params.get('callback'),
_return_http_data_only=params.get('_return_http_data_only'))
def delete_metadata_for_product(self, product_id, **kwargs):
"""
Remove any associated metadata.
{\"nickname\":\"Clear metadata from product\",\"request\" :\"deleteProductMetadataRequest.html\",\"response\":\"deleteProductMetadataResponse.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_for_product(product_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param str product_id: (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.delete_metadata_for_product_with_http_info(product_id, **kwargs)
else:
(data) = self.delete_metadata_for_product_with_http_info(product_id, **kwargs)
return data
def delete_metadata_for_product_with_http_info(self, product_id, **kwargs):
"""
Remove any associated metadata.
{\"nickname\":\"Clear metadata from product\",\"request\" :\"deleteProductMetadataRequest.html\",\"response\":\"deleteProductMetadataResponse.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_for_product_with_http_info(product_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param str product_id: (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 = ['product_id', '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_for_product" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'product_id' is set
if ('product_id' not in params) or (params['product_id'] is None):
raise ValueError("Missing the required parameter `product_id` when calling `delete_metadata_for_product`")
resource_path = '/products/{product-ID}/metadata'.replace('{format}', 'json')
path_params = {}
if 'product_id' in params:
path_params['product-ID'] = params['product_id']
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', 'application/json'])
# 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_all_products(self, **kwargs):
"""
Returns a collection of products. By default 10 values are returned. Records are returned in natural order.
{\"nickname\":\"Get all products\",\"response\":\"getProductAll.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_all_products(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.
:param int offset: The offset from the first product to return.
:param int records: The maximum number of products to return.
:param str order_by: Specify a field used to order the result set.
:param str order: Ihe direction of any ordering, either ASC or DESC.
:param bool include_retired: Whether retired products should be returned.
:param str metadata:
:return: ProductPagedMetadata
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('callback'):
return self.get_all_products_with_http_info(**kwargs)
else:
(data) = self.get_all_products_with_http_info(**kwargs)
return data
def get_all_products_with_http_info(self, **kwargs):
"""
Returns a collection of products. By default 10 values are returned. Records are returned in natural order.
{\"nickname\":\"Get all products\",\"response\":\"getProductAll.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_all_products_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.
:param int offset: The offset from the first product to return.
:param int records: The maximum number of products to return.
:param str order_by: Specify a field used to order the result set.
:param str order: Ihe direction of any ordering, either ASC or DESC.
:param bool include_retired: Whether retired products should be returned.
:param str metadata:
:return: ProductPagedMetadata
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['organizations', 'offset', 'records', 'order_by', 'order', 'include_retired', 'metadata']
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_all_products" % key
)
params[key] = val
del params['kwargs']
resource_path = '/products'.replace('{format}', 'json')
path_params = {}
query_params = {}
if 'organizations' in params:
query_params['organizations'] = params['organizations']
if 'offset' in params:
query_params['offset'] = params['offset']
if 'records' in params:
query_params['records'] = params['records']
if 'order_by' in params:
query_params['order_by'] = params['order_by']
if 'order' in params:
query_params['order'] = params['order']
if 'include_retired' in params:
query_params['include_retired'] = params['include_retired']
if 'metadata' in params:
query_params['metadata'] = params['metadata']
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json', 'text/plain'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type([])
# 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='ProductPagedMetadata',
auth_settings=auth_settings,
callback=params.get('callback'),
_return_http_data_only=params.get('_return_http_data_only'))
def get_metadata_for_product(self, product_id, **kwargs):
"""
Retrieve any associated metadata.
{\"nickname\":\"Retrieve metadata on product\",\"request\":\"getProductMetadataRequest.html\",\"response\":\"getProductMetadataResponse.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_for_product(product_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param str product_id: (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.get_metadata_for_product_with_http_info(product_id, **kwargs)
else:
(data) = self.get_metadata_for_product_with_http_info(product_id, **kwargs)
return data
def get_metadata_for_product_with_http_info(self, product_id, **kwargs):
"""
Retrieve any associated metadata.
{\"nickname\":\"Retrieve metadata on product\",\"request\":\"getProductMetadataRequest.html\",\"response\":\"getProductMetadataResponse.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_for_product_with_http_info(product_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param str product_id: (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 = ['product_id', '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_for_product" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'product_id' is set
if ('product_id' not in params) or (params['product_id'] is None):
raise ValueError("Missing the required parameter `product_id` when calling `get_metadata_for_product`")
resource_path = '/products/{product-ID}/metadata'.replace('{format}', 'json')
path_params = {}
if 'product_id' in params:
path_params['product-ID'] = params['product_id']
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(['application/json', '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 get_product_by_id(self, product_id, **kwargs):
"""
Returns a single product, specified by the product-ID parameter.
{\"nickname\":\"Retrieve an existing product\",\"response\":\"getProductByID.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_product_by_id(product_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param str product_id: ID or name of the product. (required)
:param list[str] organizations: A list of organization-IDs used to restrict the scope of API calls.
:param int offset: The offset from the first product-rate-plan to return.
:param int records: The maximum number of product-rate-plans to return.
:param str order_by: Specify a field used to order the result set.
:param str order: Ihe direction of any ordering, either ASC or DESC.
:param bool include_retired: Whether retired products should be returned.
:return: ProductPagedMetadata
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('callback'):
return self.get_product_by_id_with_http_info(product_id, **kwargs)
else:
(data) = self.get_product_by_id_with_http_info(product_id, **kwargs)
return data
def get_product_by_id_with_http_info(self, product_id, **kwargs):
"""
Returns a single product, specified by the product-ID parameter.
{\"nickname\":\"Retrieve an existing product\",\"response\":\"getProductByID.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_product_by_id_with_http_info(product_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param str product_id: ID or name of the product. (required)
:param list[str] organizations: A list of organization-IDs used to restrict the scope of API calls.
:param int offset: The offset from the first product-rate-plan to return.
:param int records: The maximum number of product-rate-plans to return.
:param str order_by: Specify a field used to order the result set.
:param str order: Ihe direction of any ordering, either ASC or DESC.
:param bool include_retired: Whether retired products should be returned.
:return: ProductPagedMetadata
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['product_id', 'organizations', 'offset', 'records', 'order_by', 'order', 'include_retired']
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_product_by_id" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'product_id' is set
if ('product_id' not in params) or (params['product_id'] is None):
raise ValueError("Missing the required parameter `product_id` when calling `get_product_by_id`")
resource_path = '/products/{product-ID}'.replace('{format}', 'json')
path_params = {}
if 'product_id' in params:
path_params['product-ID'] = params['product_id']
query_params = {}
if 'organizations' in params:
query_params['organizations'] = params['organizations']
if 'offset' in params:
query_params['offset'] = params['offset']
if 'records' in params:
query_params['records'] = params['records']
if 'order_by' in params:
query_params['order_by'] = params['order_by']
if 'order' in params:
query_params['order'] = params['order']
if 'include_retired' in params:
query_params['include_retired'] = params['include_retired']
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', 'application/json'])
# 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='ProductPagedMetadata',
auth_settings=auth_settings,
callback=params.get('callback'),
_return_http_data_only=params.get('_return_http_data_only'))
def retire_product(self, product_id, **kwargs):
"""
Deletes the product specified by the product-ID parameter. Any existing subscriptions will continue; it is a soft delete.
{\"nickname\":\"Delete a product\",\"response\":\"deleteProduct.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.retire_product(product_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param str product_id: ID of the Product. (required)
:param list[str] organizations: A list of organization-IDs used to restrict the scope of API calls.
:return: ProductPagedMetadata
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('callback'):
return self.retire_product_with_http_info(product_id, **kwargs)
else:
(data) = self.retire_product_with_http_info(product_id, **kwargs)
return data
def retire_product_with_http_info(self, product_id, **kwargs):
"""
Deletes the product specified by the product-ID parameter. Any existing subscriptions will continue; it is a soft delete.
{\"nickname\":\"Delete a product\",\"response\":\"deleteProduct.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.retire_product_with_http_info(product_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param str product_id: ID of the Product. (required)
:param list[str] organizations: A list of organization-IDs used to restrict the scope of API calls.
:return: ProductPagedMetadata
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['product_id', '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 retire_product" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'product_id' is set
if ('product_id' not in params) or (params['product_id'] is None):
raise ValueError("Missing the required parameter `product_id` when calling `retire_product`")
resource_path = '/products/{product-ID}'.replace('{format}', 'json')
path_params = {}
if 'product_id' in params:
path_params['product-ID'] = params['product_id']
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', 'application/json'])
# 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='ProductPagedMetadata',
auth_settings=auth_settings,
callback=params.get('callback'),
_return_http_data_only=params.get('_return_http_data_only'))
def set_metadata_for_product(self, metadata, product_id, **kwargs):
"""
Remove any existing metadata keys and create the provided data.
{\"nickname\":\"Set metadata on product\",\"request\":\"setProductMetadataRequest.html\",\"response\":\"setProductMetadataResponse.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_for_product(metadata, product_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param DynamicMetadata metadata: (required)
:param str product_id: (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_for_product_with_http_info(metadata, product_id, **kwargs)
else:
(data) = self.set_metadata_for_product_with_http_info(metadata, product_id, **kwargs)
return data
def set_metadata_for_product_with_http_info(self, metadata, product_id, **kwargs):
"""
Remove any existing metadata keys and create the provided data.
{\"nickname\":\"Set metadata on product\",\"request\":\"setProductMetadataRequest.html\",\"response\":\"setProductMetadataResponse.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_for_product_with_http_info(metadata, product_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param DynamicMetadata metadata: (required)
:param str product_id: (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', 'product_id', '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_for_product" % 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_for_product`")
# verify the required parameter 'product_id' is set
if ('product_id' not in params) or (params['product_id'] is None):
raise ValueError("Missing the required parameter `product_id` when calling `set_metadata_for_product`")
resource_path = '/products/{product-ID}/metadata'.replace('{format}', 'json')
path_params = {}
if 'product_id' in params:
path_params['product-ID'] = params['product_id']
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 update_product(self, product, **kwargs):
"""
Update a product.
{\"nickname\":\"Update a product\",\"request\":\"updateProductRequest.html\",\"response\":\"updateProductResponse.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.update_product(product, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param Product product: The product object to be updated. (required)
:return: ProductPagedMetadata
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('callback'):
return self.update_product_with_http_info(product, **kwargs)
else:
(data) = self.update_product_with_http_info(product, **kwargs)
return data
def update_product_with_http_info(self, product, **kwargs):
"""
Update a product.
{\"nickname\":\"Update a product\",\"request\":\"updateProductRequest.html\",\"response\":\"updateProductResponse.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.update_product_with_http_info(product, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param Product product: The product object to be updated. (required)
:return: ProductPagedMetadata
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['product']
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 update_product" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'product' is set
if ('product' not in params) or (params['product'] is None):
raise ValueError("Missing the required parameter `product` when calling `update_product`")
resource_path = '/products'.replace('{format}', 'json')
path_params = {}
query_params = {}
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'product' in params:
body_params = params['product']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['text/xml', 'application/xml', '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='ProductPagedMetadata',
auth_settings=auth_settings,
callback=params.get('callback'),
_return_http_data_only=params.get('_return_http_data_only'))
def upsert_metadata_for_product(self, metadata, product_id, **kwargs):
"""
Update any existing metadata key-values and insert any new key-values, no keys will be removed.
{\"nickname\":\"Upsert metadata on product\",\"request\":\"upsertProductMetadataRequest.html\",\"response\":\"upsertProductMetadataResponse.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_for_product(metadata, product_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param DynamicMetadata metadata: (required)
:param str product_id: (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_for_product_with_http_info(metadata, product_id, **kwargs)
else:
(data) = self.upsert_metadata_for_product_with_http_info(metadata, product_id, **kwargs)
return data
def upsert_metadata_for_product_with_http_info(self, metadata, product_id, **kwargs):
"""
Update any existing metadata key-values and insert any new key-values, no keys will be removed.
{\"nickname\":\"Upsert metadata on product\",\"request\":\"upsertProductMetadataRequest.html\",\"response\":\"upsertProductMetadataResponse.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_for_product_with_http_info(metadata, product_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param DynamicMetadata metadata: (required)
:param str product_id: (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', 'product_id', '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_for_product" % 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_for_product`")
# verify the required parameter 'product_id' is set
if ('product_id' not in params) or (params['product_id'] is None):
raise ValueError("Missing the required parameter `product_id` when calling `upsert_metadata_for_product`")
resource_path = '/products/{product-ID}/metadata'.replace('{format}', 'json')
path_params = {}
if 'product_id' in params:
path_params['product-ID'] = params['product_id']
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'))
| 44.472352
| 157
| 0.588869
| 4,942
| 47,452
| 5.461149
| 0.055443
| 0.035348
| 0.018674
| 0.02401
| 0.959835
| 0.955389
| 0.953055
| 0.944978
| 0.93627
| 0.92923
| 0
| 0.000436
| 0.323274
| 47,452
| 1,066
| 158
| 44.514071
| 0.840029
| 0.386917
| 0
| 0.828974
| 1
| 0
| 0.180726
| 0.039509
| 0
| 0
| 0
| 0
| 0
| 1
| 0.038229
| false
| 0
| 0.014085
| 0
| 0.108652
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
d41e32ea1110c67d567c5cf35a4ee2adf609e70f
| 155
|
py
|
Python
|
privatekube/privatekube/platform/__init__.py
|
DelphianCalamity/PrivateKube
|
14f575e77021ab7baca30f4061140ec83bdc96a7
|
[
"Apache-2.0"
] | 9
|
2021-06-16T00:22:45.000Z
|
2021-11-25T07:19:11.000Z
|
privatekube/privatekube/platform/__init__.py
|
DelphianCalamity/PrivateKube
|
14f575e77021ab7baca30f4061140ec83bdc96a7
|
[
"Apache-2.0"
] | 2
|
2021-11-14T10:42:43.000Z
|
2022-03-16T03:43:22.000Z
|
privatekube/privatekube/platform/__init__.py
|
DelphianCalamity/PrivateKube
|
14f575e77021ab7baca30f4061140ec83bdc96a7
|
[
"Apache-2.0"
] | 3
|
2021-04-08T08:08:48.000Z
|
2021-12-24T01:42:20.000Z
|
import privatekube.platform.stoppable_thread, privatekube.platform.privacy_budget, privatekube.platform.privacy_resource_client, privatekube.platform.timer
| 155
| 155
| 0.903226
| 17
| 155
| 8
| 0.588235
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| 155
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| 155
| 0.900662
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| true
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| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 8
|
2e432d7b01df2fb8ca3fd3aff489ace421db7504
| 155
|
py
|
Python
|
xrdtools/__init__.py
|
monkeyclass/xrdtools
|
c462bf71709c71f9600c916353f62f0d24e995b6
|
[
"MIT"
] | 2
|
2017-02-27T20:25:47.000Z
|
2019-12-18T22:31:10.000Z
|
xrdtools/__init__.py
|
monkeyclass/xrdtools
|
c462bf71709c71f9600c916353f62f0d24e995b6
|
[
"MIT"
] | 5
|
2015-10-17T00:09:06.000Z
|
2018-04-13T22:17:12.000Z
|
xrdtools/__init__.py
|
monkeyclass/xrdtools
|
c462bf71709c71f9600c916353f62f0d24e995b6
|
[
"MIT"
] | 6
|
2016-08-02T23:28:00.000Z
|
2021-04-23T12:30:21.000Z
|
from xrdtools.io import read_xrdml # noqa: F401
from xrdtools import utils # noqa: F401
from xrdtools import tools # noqa: F401
__version__ = '0.1.1'
| 22.142857
| 48
| 0.735484
| 24
| 155
| 4.541667
| 0.541667
| 0.330275
| 0.220183
| 0.366972
| 0.477064
| 0
| 0
| 0
| 0
| 0
| 0
| 0.095238
| 0.187097
| 155
| 6
| 49
| 25.833333
| 0.769841
| 0.206452
| 0
| 0
| 0
| 0
| 0.042017
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.75
| 0
| 0.75
| 0
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| 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
|
2e79be134aa47099d74d0f78b55c089f17e9369d
| 7,129
|
py
|
Python
|
tests/test_spaces.py
|
neuroio/neuroio-python
|
160f96515877e5e2ee0e888b7424c77cb2d7496a
|
[
"MIT"
] | null | null | null |
tests/test_spaces.py
|
neuroio/neuroio-python
|
160f96515877e5e2ee0e888b7424c77cb2d7496a
|
[
"MIT"
] | 6
|
2021-09-06T08:23:09.000Z
|
2021-11-10T16:19:20.000Z
|
tests/test_spaces.py
|
neuroio/neuroio-python
|
160f96515877e5e2ee0e888b7424c77cb2d7496a
|
[
"MIT"
] | null | null | null |
import pytest
import respx
from neuroio.constants import IAM_BASE_URL
from tests.utils import mock_query_params_all_combos
@respx.mock
def test_create_ok(client):
request = respx.post(f"{IAM_BASE_URL}/v1/spaces/").respond(
status_code=201,
json={"id": 1, "name": "name"},
)
response = client.spaces.create(name="name")
assert request.called
assert response.status_code == 201
assert response.json()["name"] == "name"
@respx.mock
def test_create_failed(client):
request = respx.post(f"{IAM_BASE_URL}/v1/spaces/").respond(status_code=400)
response = client.spaces.create(name="name")
assert request.called
assert response.status_code == 400
@respx.mock
@pytest.mark.asyncio
async def test_async_create_ok(async_client):
request = respx.post(f"{IAM_BASE_URL}/v1/spaces/").respond(
status_code=201,
json={"id": 1, "name": "name"},
)
response = await async_client.spaces.create(name="name")
assert request.called
assert response.status_code == 201
assert response.json()["name"] == "name"
@respx.mock
@pytest.mark.asyncio
async def test_async_create_failed(async_client):
request = respx.post(f"{IAM_BASE_URL}/v1/spaces/").respond(status_code=400)
response = await async_client.spaces.create(name="name")
assert request.called
assert response.status_code == 400
@respx.mock
def test_list_without_params(client):
requests = mock_query_params_all_combos(
f"{IAM_BASE_URL}/v1/spaces",
"limit=20",
"offset=0",
"q=",
json={"results": [{"id": 1, "name": "name"}]},
)
response = client.spaces.list()
assert any([request.called for request in requests])
assert response.status_code == 200
assert response.json()["results"][0]["id"] == 1
@respx.mock
def test_list_with_params(client):
requests = mock_query_params_all_combos(
f"{IAM_BASE_URL}/v1/spaces",
"limit=20",
"offset=20",
"q=test",
json={"results": [{"id": 1, "name": "name"}]},
)
response = client.spaces.list(q="test", offset=20)
assert any([request.called for request in requests])
assert response.status_code == 200
assert response.json()["results"][0]["id"] == 1
@respx.mock
@pytest.mark.asyncio
async def test_async_list_without_params(async_client):
requests = mock_query_params_all_combos(
f"{IAM_BASE_URL}/v1/spaces",
"limit=20",
"offset=0",
"q=",
json={"results": [{"id": 1, "name": "name"}]},
)
response = await async_client.spaces.list()
assert any([request.called for request in requests])
assert response.status_code == 200
assert response.json()["results"][0]["id"] == 1
@respx.mock
@pytest.mark.asyncio
async def test_async_list_with_params(async_client):
requests = mock_query_params_all_combos(
f"{IAM_BASE_URL}/v1/spaces",
"limit=20",
"offset=20",
"q=test",
json={"results": [{"id": 1, "name": "name"}]},
)
response = await async_client.spaces.list(q="test", offset=20)
assert any([request.called for request in requests])
assert response.status_code == 200
assert response.json()["results"][0]["id"] == 1
@respx.mock
def test_get_ok(client):
request = respx.get(f"{IAM_BASE_URL}/v1/spaces/1/").respond(
status_code=200,
json={"id": 1, "name": "name"},
)
response = client.spaces.get(id=1)
assert request.called
assert response.status_code == 200
assert response.json()["id"] == 1
@respx.mock
def test_get_not_found(client):
request = respx.get(f"{IAM_BASE_URL}/v1/spaces/1/").respond(
status_code=404
)
response = client.spaces.get(id=1)
assert request.called
assert response.status_code == 404
@respx.mock
@pytest.mark.asyncio
async def test_async_get_ok(async_client):
request = respx.get(f"{IAM_BASE_URL}/v1/spaces/1/").respond(
status_code=200,
json={"id": 1, "name": "name"},
)
response = await async_client.spaces.get(id=1)
assert request.called
assert response.status_code == 200
assert response.json()["id"] == 1
@respx.mock
@pytest.mark.asyncio
async def test_async_get_not_found(async_client):
request = respx.get(f"{IAM_BASE_URL}/v1/spaces/1/").respond(
status_code=404
)
response = await async_client.spaces.get(id=1)
assert request.called
assert response.status_code == 404
@respx.mock
def test_update_ok(client):
request = respx.patch(f"{IAM_BASE_URL}/v1/spaces/1/").respond(
status_code=200,
json={"id": 1, "name": "new_name"},
)
response = client.spaces.update(id=1, name="new_name")
assert request.called
assert response.status_code == 200
assert response.json()["name"] == "new_name"
@respx.mock
@pytest.mark.asyncio
async def test_async_update_ok(async_client):
request = respx.patch(f"{IAM_BASE_URL}/v1/spaces/1/").respond(
status_code=200,
json={"id": 1, "name": "new_name"},
)
response = await async_client.spaces.update(id=1, name="new_name")
assert request.called
assert response.status_code == 200
assert response.json()["name"] == "new_name"
@respx.mock
def test_delete_ok(client):
request = respx.delete(f"{IAM_BASE_URL}/v1/spaces/1/").respond(
status_code=202
)
response = client.spaces.delete(id=1)
assert request.called
assert response.status_code == 202
@respx.mock
@pytest.mark.asyncio
async def test_async_delete_ok(async_client):
request = respx.delete(f"{IAM_BASE_URL}/v1/spaces/1/").respond(
status_code=202
)
response = await async_client.spaces.delete(id=1)
assert request.called
assert response.status_code == 202
@respx.mock
def test_token_create_ok(client):
request = respx.post(f"{IAM_BASE_URL}/v1/spaces/1/tokens/").respond(
status_code=201,
json={"is_active": True, "key": "key"},
)
response = client.spaces.token(id=1)
assert request.called
assert response.status_code == 201
assert response.json()["key"] == "key"
@respx.mock
def test_token_create_failed(client):
request = respx.post(f"{IAM_BASE_URL}/v1/spaces/1/tokens/").respond(
status_code=400
)
response = client.spaces.token(id=1)
assert request.called
assert response.status_code == 400
@respx.mock
@pytest.mark.asyncio
async def test_async_token_create(async_client):
request = respx.post(f"{IAM_BASE_URL}/v1/spaces/1/tokens/").respond(
status_code=201,
json={"is_active": True, "key": "key"},
)
response = await async_client.spaces.token(id=1)
assert request.called
assert response.status_code == 201
assert response.json()["key"] == "key"
@respx.mock
@pytest.mark.asyncio
async def test_async_token_create_failed(async_client):
request = respx.post(f"{IAM_BASE_URL}/v1/spaces/1/tokens/").respond(
status_code=400
)
response = await async_client.spaces.token(id=1)
assert request.called
assert response.status_code == 400
| 26.600746
| 79
| 0.663768
| 987
| 7,129
| 4.60689
| 0.072948
| 0.079173
| 0.046184
| 0.048384
| 0.966132
| 0.944799
| 0.937321
| 0.934462
| 0.934462
| 0.926985
| 0
| 0.032861
| 0.188947
| 7,129
| 267
| 80
| 26.700375
| 0.753545
| 0
| 0
| 0.757282
| 0
| 0
| 0.130173
| 0.076869
| 0
| 0
| 0
| 0
| 0.252427
| 1
| 0.048544
| false
| 0
| 0.019417
| 0
| 0.067961
| 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
|
2e83c34a899798597eb8c8f98a9a569bc917f23c
| 1,457
|
py
|
Python
|
tests/test_order.py
|
nikoheikkila/semmy
|
cca9efcd65c6c4006bc0405780dcaca919d84b73
|
[
"MIT"
] | 1
|
2022-02-13T18:07:10.000Z
|
2022-02-13T18:07:10.000Z
|
tests/test_order.py
|
nikoheikkila/semmy
|
cca9efcd65c6c4006bc0405780dcaca919d84b73
|
[
"MIT"
] | null | null | null |
tests/test_order.py
|
nikoheikkila/semmy
|
cca9efcd65c6c4006bc0405780dcaca919d84b73
|
[
"MIT"
] | null | null | null |
from pytest import mark
from semmy import Semver
@mark.parametrize(
("a", "b"),
[
[Semver(0, 1, 1), Semver(0, 1, 0)],
[Semver(1, 2, 0), Semver(1, 1, 0)],
[Semver(2, 0, 0), Semver(1, 0, 0)],
],
)
def test_greater(a: Semver, b: Semver) -> None:
assert a > b
assert not a < b
@mark.parametrize(
("a", "b"),
[
[Semver(0, 1, 1), Semver(0, 1, 0)],
[Semver(0, 1, 1), Semver(0, 1, 1)],
[Semver(1, 2, 0), Semver(1, 1, 0)],
[Semver(1, 2, 0), Semver(1, 2, 0)],
[Semver(2, 0, 0), Semver(1, 0, 0)],
[Semver(2, 0, 0), Semver(2, 0, 0)],
],
)
def test_greater_or_equal(a: Semver, b: Semver) -> None:
assert a >= b
def test_not_greater_than_object() -> None:
assert not Semver().__gt__(object())
@mark.parametrize(
("a", "b"),
[
[Semver(0, 1, 1), Semver(0, 1, 2)],
[Semver(1, 2, 0), Semver(1, 3, 0)],
[Semver(2, 0, 0), Semver(3, 0, 0)],
],
)
def test_lesser(a: Semver, b: Semver) -> None:
assert a < b
assert not a > b
@mark.parametrize(
("a", "b"),
[
[Semver(0, 1, 1), Semver(0, 1, 2)],
[Semver(0, 1, 1), Semver(0, 1, 1)],
[Semver(1, 2, 0), Semver(1, 3, 0)],
[Semver(1, 2, 0), Semver(1, 2, 0)],
[Semver(2, 0, 0), Semver(3, 0, 0)],
[Semver(2, 0, 0), Semver(2, 0, 0)],
],
)
def test_lesser_or_equal(a: Semver, b: Semver) -> None:
assert a <= b
| 22.765625
| 56
| 0.47838
| 236
| 1,457
| 2.885593
| 0.105932
| 0.226138
| 0.140969
| 0.105727
| 0.860499
| 0.801762
| 0.801762
| 0.801762
| 0.801762
| 0.707783
| 0
| 0.105366
| 0.2965
| 1,457
| 63
| 57
| 23.126984
| 0.559024
| 0
| 0
| 0.576923
| 0
| 0
| 0.005491
| 0
| 0
| 0
| 0
| 0
| 0.134615
| 1
| 0.096154
| false
| 0
| 0.038462
| 0
| 0.134615
| 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
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
cf48616dd3a7f79ed19588c2adca9da9f8ffde7a
| 4,078
|
py
|
Python
|
display3D/image_resizer_fields.py
|
seVenVo1d/General-Relativity-Tensorial-Calculations
|
6c07823f74840352253c235af2e4dbe60044941a
|
[
"MIT"
] | 1
|
2021-06-16T07:29:30.000Z
|
2021-06-16T07:29:30.000Z
|
display3D/image_resizer_fields.py
|
seVenVo1d/General-Relativity-Tensorial-Calculations
|
6c07823f74840352253c235af2e4dbe60044941a
|
[
"MIT"
] | null | null | null |
display3D/image_resizer_fields.py
|
seVenVo1d/General-Relativity-Tensorial-Calculations
|
6c07823f74840352253c235af2e4dbe60044941a
|
[
"MIT"
] | 1
|
2021-12-02T15:11:06.000Z
|
2021-12-02T15:11:06.000Z
|
from PIL import Image
def resize_cd_image3d(field_object):
"""
Re-sizing the image of covariant derivative for a given field object
for the case of 3D
Args:
field_object [str]: The name of the field object (scalar, vector or tensor)
"""
if field_object == 'Scalar Field':
im = Image.open(r'display3D\output images\cd_scalar_field.png')
size = (500, 500)
im.thumbnail(size, Image.ANTIALIAS)
out_dim = im.size
out_name = r'display3D\output images\cd_scalar_field.png'
elif field_object == 'Type (1,0) Vector Field':
im = Image.open(r'display3D\output images\cd_vector_field_10.png')
size = (800, 600)
im.thumbnail(size, Image.ANTIALIAS)
out_dim = im.size
out_name = r'display3D\output images\cd_vector_field_10.png'
elif field_object == 'Type (0,1) Vector Field':
im = Image.open(r'display3D\output images\cd_vector_field_01.png')
size = (800, 600)
im.thumbnail(size, Image.ANTIALIAS)
out_dim = im.size
out_name = r'display3D\output images\cd_vector_field_01.png'
elif field_object == 'Type (2,0) Tensor Field':
im = Image.open(r'display3D\output images\cd_tensor_field_20.png')
size = (1200, 650)
im.thumbnail(size, Image.ANTIALIAS)
out_dim = im.size
out_name = r'display3D\output images\cd_tensor_field_20.png'
elif field_object == 'Type (1,1) Tensor Field':
im = Image.open(r'display3D\output images\cd_tensor_field_11.png')
size = (1200, 650)
im.thumbnail(size, Image.ANTIALIAS)
out_dim = im.size
out_name = r'display3D\output images\cd_tensor_field_11.png'
elif field_object == 'Type (0,2) Tensor Field':
im = Image.open(r'display3D\output images\cd_tensor_field_02.png')
size = (1200, 650)
im.thumbnail(size, Image.ANTIALIAS)
out_dim = im.size
out_name = r'display3D\output images\cd_tensor_field_02.png'
im.save(out_name, "PNG")
im.close()
def resize_ld_image3d(field_object):
"""
Re-sizing the image of lie derivative for a given field object
for the case of 3D
Args:
field_object [str]: The name of the field object (scalar, vector or tensor)
"""
if field_object == 'Scalar Field':
im = Image.open(r'display3D\output images\ld_scalar_field.png')
size = (500, 500)
im.thumbnail(size, Image.ANTIALIAS)
out_dim = im.size
out_name = r'display3D\output images\ld_scalar_field.png'
elif field_object == 'Type (1,0) Vector Field':
im = Image.open(r'display3D\output images\ld_vector_field_10.png')
size = (800, 600)
im.thumbnail(size, Image.ANTIALIAS)
out_dim = im.size
out_name = r'display3D\output images\ld_vector_field_10.png'
elif field_object == 'Type (0,1) Vector Field':
im = Image.open(r'display3D\output images\ld_vector_field_01.png')
size = (800, 600)
im.thumbnail(size, Image.ANTIALIAS)
out_dim = im.size
out_name = r'display3D\output images\ld_vector_field_01.png'
elif field_object == 'Type (2,0) Tensor Field':
im = Image.open(r'display3D\output images\ld_tensor_field_20.png')
size = (1200, 650)
im.thumbnail(size, Image.ANTIALIAS)
out_dim = im.size
out_name = r'display3D\output images\ld_tensor_field_20.png'
elif field_object == 'Type (1,1) Tensor Field':
im = Image.open(r'display3D\output images\ld_tensor_field_11.png')
size = (1200, 650)
im.thumbnail(size, Image.ANTIALIAS)
out_dim = im.size
out_name = r'display3D\output images\ld_tensor_field_11.png'
elif field_object == 'Type (0,2) Tensor Field':
im = Image.open(r'display3D\output images\ld_tensor_field_02.png')
size = (1200, 650)
im.thumbnail(size, Image.ANTIALIAS)
out_dim = im.size
out_name = r'display3D\output images\ld_tensor_field_02.png'
im.save(out_name, "PNG")
im.close()
| 37.759259
| 83
| 0.649338
| 598
| 4,078
| 4.237458
| 0.100334
| 0.094712
| 0.151539
| 0.208366
| 0.979479
| 0.979479
| 0.979479
| 0.979479
| 0.951066
| 0.949487
| 0
| 0.053514
| 0.239333
| 4,078
| 107
| 84
| 38.11215
| 0.763378
| 0.08411
| 0
| 0.658228
| 0
| 0
| 0.367391
| 0.18587
| 0
| 0
| 0
| 0
| 0
| 1
| 0.025316
| false
| 0
| 0.012658
| 0
| 0.037975
| 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
|
d8765cbacb4cd3662f8b64ee70c8aabcb7936b8c
| 41
|
py
|
Python
|
sabueso/tools/database_RCSB_PDB/__init__.py
|
dprada/sabueso
|
14843cf3522b5b89db5b61c1541a7015f114dd53
|
[
"MIT"
] | null | null | null |
sabueso/tools/database_RCSB_PDB/__init__.py
|
dprada/sabueso
|
14843cf3522b5b89db5b61c1541a7015f114dd53
|
[
"MIT"
] | 2
|
2022-01-31T21:22:17.000Z
|
2022-02-04T20:20:12.000Z
|
sabueso/tools/database_RCSB_PDB/__init__.py
|
dprada/sabueso
|
14843cf3522b5b89db5b61c1541a7015f114dd53
|
[
"MIT"
] | 1
|
2021-07-20T15:01:14.000Z
|
2021-07-20T15:01:14.000Z
|
from .is_accessible import is_accessible
| 20.5
| 40
| 0.878049
| 6
| 41
| 5.666667
| 0.666667
| 0.705882
| 0
| 0
| 0
| 0
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0
| 7
|
d87f811db9a17bd199ebfea46bac9fb3d8e84824
| 30,926
|
py
|
Python
|
model_VAE.py
|
se7endragon/cnn_model
|
21d76edfa73bd679182430341979f8d17f7b2940
|
[
"MIT"
] | null | null | null |
model_VAE.py
|
se7endragon/cnn_model
|
21d76edfa73bd679182430341979f8d17f7b2940
|
[
"MIT"
] | null | null | null |
model_VAE.py
|
se7endragon/cnn_model
|
21d76edfa73bd679182430341979f8d17f7b2940
|
[
"MIT"
] | null | null | null |
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import logging
import os
import utils
logging.basicConfig(level=logging.INFO, format='%(message)s')
class VAE_mnist():
def __init__(self, sess, batch_size=100, report_period=100, learning_rate=1e-3,
epoch_number=20, # this is used when we train without using random batch
num_iteration=2e+4, # this is used when we train with using random batch
middle_man_dim_1=1000,
middle_man_dim_2=1000,
latent_space_dim=10,
activation_function=tf.nn.relu,
alpha=0.5):
"""
original_img ---> middle_man_1 ---> middle_man_2 ---> latent_space ---> middle_man_2 ---> middle_man_1 ---> reconstruced_img:
self.middle_man_dim_1 : dimension of middle_man_1 space
self.middle_man_dim_2 : dimension of middle_man_2 space
self.latent_space_dim : dimension of latent_man space
"""
self.sess = sess
self.batch_size = int(batch_size)
self.report_period = int(report_period)
self.learning_rate = float(learning_rate)
self.epoch_number = int(epoch_number)
self.num_iteration = int(num_iteration)
self.middle_man_dim_1 = int(middle_man_dim_1)
self.middle_man_dim_2 = int(middle_man_dim_2)
self.latent_space_dim = int(latent_space_dim)
self.activation_function = activation_function
self.alpha = float(alpha)
def data_loading(self, data):
self.data = data
# data pre-processing
self.x_train, self.x_test, self.y_train, self.y_test, self.y_train_cls, self.y_test_cls = self.data
self.class_names = [0,1,2,3,4,5,6,7,8,9]
self.num_test = self.y_test.shape[0]
def encoding(self, x, E_W1, E_b1, E_W2, E_b2, E_W3_mu, E_b3_mu, E_W3_log_var, E_b3_log_var):
h1 = self.activation_function(tf.matmul(x, E_W1) + E_b1)
h2 = self.activation_function(tf.matmul(h1, E_W2) + E_b2)
mu = tf.matmul(h2, E_W3_mu) + E_b3_mu
log_var = tf.matmul(h2, E_W3_log_var) + E_b3_log_var
return mu, log_var
def sampling(self, mu, log_var):
eps = tf.random_normal(shape=tf.shape(mu))
z = mu + tf.exp(log_var / 2) * eps
return z
def decoding(self, z, D_W1, D_b1, D_W2, D_b2, D_W3, D_b3):
h1 = self.activation_function(tf.matmul(z, D_W1) + D_b1)
h2 = self.activation_function(tf.matmul(h1, D_W2) + D_b2)
logits = tf.matmul(h2, D_W3) + D_b3
probs = tf.nn.sigmoid(logits)
return logits, probs
def graph_construction(self):
# data dimension
self.img_size = 28
self.img_size_flat = 784
self.img_shape = (28, 28)
self.num_classes = 10
# placeholders
self.x = tf.placeholder(tf.float32, shape=[None, self.img_size_flat], name='x')
self.z = tf.placeholder(tf.float32, shape=[None, self.latent_space_dim], name='z')
# weights
self.E_W1 = tf.get_variable("E_W1",
shape=(self.img_size_flat, self.middle_man_dim_1),
initializer=tf.contrib.layers.variance_scaling_initializer(mode="FAN_AVG"))
#initializer=tf.truncated_normal_initializer(stddev=0.1))
self.E_b1 = tf.get_variable("E_b1",
shape=(self.middle_man_dim_1, ),
initializer=tf.constant_initializer(0.0))
self.E_W2 = tf.get_variable("E_W2",
shape=(self.middle_man_dim_1, self.middle_man_dim_2),
initializer=tf.contrib.layers.variance_scaling_initializer(mode="FAN_AVG"))
#initializer=tf.truncated_normal_initializer(stddev=0.1))
self.E_b2 = tf.get_variable("E_b2",
shape=(self.middle_man_dim_2, ),
initializer=tf.constant_initializer(0.0))
self.E_W3_mu = tf.get_variable("E_W3_mu",
shape=(self.middle_man_dim_2, self.latent_space_dim),
initializer=tf.contrib.layers.variance_scaling_initializer(mode="FAN_AVG"))
#initializer=tf.truncated_normal_initializer(stddev=0.1))
self.E_b3_mu = tf.get_variable("E_b3_mu",
shape=(self.latent_space_dim, ),
initializer=tf.constant_initializer(0.0))
self.E_W3_log_var = tf.get_variable("E_W3_log_var",
shape=(self.middle_man_dim_2, self.latent_space_dim),
initializer=tf.contrib.layers.variance_scaling_initializer(mode="FAN_AVG"))
#initializer=tf.truncated_normal_initializer(stddev=0.1))
self.E_b3_log_var = tf.get_variable("E_b3_log_var",
shape=(self.latent_space_dim, ),
initializer=tf.constant_initializer(0.0))
self.D_W1 = tf.get_variable("D_W1",
shape=(self.latent_space_dim, self.middle_man_dim_2),
initializer=tf.contrib.layers.variance_scaling_initializer(mode="FAN_AVG"))
#initializer=tf.truncated_normal_initializer(stddev=0.1))
self.D_b1 = tf.get_variable("D_b1",
shape=(self.middle_man_dim_2, ),
initializer=tf.constant_initializer(0.0))
self.D_W2 = tf.get_variable("D_W2",
shape=(self.middle_man_dim_2, self.middle_man_dim_1),
initializer=tf.contrib.layers.variance_scaling_initializer(mode="FAN_AVG"))
#initializer=tf.truncated_normal_initializer(stddev=0.1))
self.D_b2 = tf.get_variable("D_b2",
shape=(self.middle_man_dim_1, ),
initializer=tf.constant_initializer(0.0))
self.D_W3 = tf.get_variable("D_W3",
shape=(self.middle_man_dim_1, self.img_size_flat),
initializer=tf.contrib.layers.variance_scaling_initializer(mode="FAN_AVG"))
#initializer=tf.truncated_normal_initializer(stddev=0.1))
self.D_b3 = tf.get_variable("D_b3",
shape=(self.img_size_flat, ),
initializer=tf.constant_initializer(0.0))
# encoding, sampling, and decoding of x
self.mu_x, self.log_var_x = self.encoding(self.x, self.E_W1, self.E_b1, self.E_W2, self.E_b2, self.E_W3_mu, self.E_b3_mu, self.E_W3_log_var, self.E_b3_log_var)
self.z_x = self.sampling(self.mu_x, self.log_var_x)
self.logits_x, self.probs_x = self.decoding(self.z_x, self.D_W1, self.D_b1, self.D_W2, self.D_b2, self.D_W3, self.D_b3)
# reconstructed images
self.x_reconstructed = self.probs_x
# cost and optimizer
self.cross_entropy = tf.reduce_sum(tf.nn.sigmoid_cross_entropy_with_logits(logits=self.logits_x, labels=self.x), axis=1)
self.cost_ce = tf.reduce_mean(self.cross_entropy)
self.kl_divergence = tf.reduce_sum(tf.exp(self.log_var_x) + self.mu_x**2 - 1. - self.log_var_x, axis=1)
self.cost_kl = tf.reduce_mean(self.kl_divergence)
self.cost = self.cost_ce + self.alpha * self.cost_kl
self.optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate).minimize(self.cost)
# decoding of z
self.logits_z, self.probs_z = self.decoding(self.z, self.D_W1, self.D_b1, self.D_W2, self.D_b2, self.D_W3, self.D_b3)
# generated images
self.x_generated = self.probs_z
def train(self):
for i in range(self.epoch_number):
start_time = time.time() # start time of this epoch
training_batch = zip(range(0, len(self.y_train), self.batch_size),
range(self.batch_size, len(self.y_train), self.batch_size))
idx_for_print_cost = 0
for start, end in training_batch:
feed_dict = {self.x: self.x_train[start:end, :]}
self.sess.run(self.optimizer, feed_dict=feed_dict)
if idx_for_print_cost % self.report_period == 0: # for every self.report_period train
cost_now = self.sess.run(self.cost, feed_dict=feed_dict) # we compute cost now
print(idx_for_print_cost, cost_now) # we print cost now
idx_for_print_cost += 1
end_time = time.time() # end time of this epoch
print("==========================================================")
print("Epoch:", i)
time_dif = end_time - start_time # we check computing time for each epoch
print("Time Usage: " + str(timedelta(seconds=int(round(time_dif))))) # and print it
self.plot_16_generated_images(figure_save_dir='./img', figure_index=i)
def train_random_batch(self):
for i in range(self.num_iteration):
idx = np.random.choice(self.x_train.shape[0], size=self.batch_size, replace=False) # random_batch
x_batch = self.x_train[idx] # random_batch
feed_dict = {self.x: x_batch}
self.sess.run(self.optimizer, feed_dict=feed_dict)
if (i % self.report_period == 0) or (i == self.num_iteration - 1):
loss = self.sess.run(self.cost, feed_dict=feed_dict)
logging.info('train iter : {:6d} | loss : {:.6f}'.format(i, loss))
self.plot_16_generated_images(figure_save_dir='./img', figure_index=i)
def plot_16_generated_images(self, figure_save_dir, figure_index):
if not os.path.exists(figure_save_dir):
os.makedirs(figure_save_dir)
feed_dict = {self.z: np.random.normal(0, 1, size=(16, self.latent_space_dim))}
images = self.sess.run(self.x_generated, feed_dict=feed_dict)
fig = utils.plot_16_images_2d_and_returen(images, img_shape=self.img_shape)
plt.savefig(figure_save_dir + '/{}.png'.format(figure_index), bbox_inches='tight')
plt.close(fig)
def visualization_of_reconstruction(self):
imgs_original = self.x_test[0:16, :]
feed_dict = {self.x: imgs_original}
imgs_recon = self.sess.run(self.x_reconstructed, feed_dict=feed_dict)
fig = utils.plot_16_images_2d_and_returen(imgs_original, img_shape=self.img_shape)
plt.show(fig)
fig = utils.plot_16_images_2d_and_returen(imgs_recon, img_shape=self.img_shape)
plt.show(fig)
def visualization_of_16_loading_vectors(self):
z_batch = np.zeros(shape=(16, self.latent_space_dim))
for i in range(16):
if i < self.latent_space_dim:
z_batch[i, i] = 1
feed_dict = {self.z: z_batch}
images = self.sess.run(self.x_generated, feed_dict=feed_dict)
fig = utils.plot_16_images_2d_and_returen(images, img_shape=self.img_shape)
plt.show(fig)
def visualization_of_zero_vector_in_latent_space(self):
z_batch = np.zeros(shape=(1, self.latent_space_dim))
feed_dict = {self.z: z_batch}
img = self.sess.run(self.x_generated, feed_dict=feed_dict)
fig = utils.plot_one_image(img, self.img_shape)
plt.show(fig)
def save(self, sess, save_path):
self.saver = tf.train.Saver()
self.sess = sess
self.save_path = save_path
self.save_dir = self.save_path.split('/')[0]
if not os.path.isdir(self.save_dir):
os.makedirs(self.save_dir)
self.saver.save(sess=self.sess, save_path=self.save_path)
print("Graph Saved")
def restore(self, sess, save_path):
self.saver = tf.train.Saver()
self.sess = sess
self.save_path = save_path
self.save_dir = self.save_path.split('/')[0]
if not os.path.isdir(self.save_dir):
os.makedirs(self.save_dir)
self.saver.restore(sess=self.sess, save_path=self.save_path)
print("Graph Restored")
class DVAE_mnist(VAE_mnist):
def __init__(self, sess, batch_size=100, report_period=100, learning_rate=1e-3,
epoch_number=20, # this is used when we train without using random batch
num_iteration=int(2e+4), # this is used when we train with using random batch
middle_man_dim_1=int(1000),
middle_man_dim_2=int(1000),
latent_space_dim=int(10),
activation_function=tf.nn.relu,
alpha=0.5,
noise_factor=0.1):
super().__init__(sess, batch_size, report_period, learning_rate, epoch_number, num_iteration, middle_man_dim_1,
middle_man_dim_2, latent_space_dim, activation_function, alpha)
self.noise_factor = float(noise_factor)
def graph_construction(self):
# data dimension
self.img_size = 28
self.img_size_flat = 784
self.img_shape = (28, 28)
self.num_classes = 10
# placeholders
self.x = tf.placeholder(tf.float32, shape=[None, self.img_size_flat], name='x')
self.z = tf.placeholder(tf.float32, shape=[None, self.latent_space_dim], name='z')
# weights
self.E_W1 = tf.get_variable("E_W1",
shape=(self.img_size_flat, self.middle_man_dim_1),
initializer=tf.contrib.layers.variance_scaling_initializer(mode="FAN_AVG"))
#initializer=tf.truncated_normal_initializer(stddev=0.1))
self.E_b1 = tf.get_variable("E_b1",
shape=(self.middle_man_dim_1, ),
initializer=tf.constant_initializer(0.0))
self.E_W2 = tf.get_variable("E_W2",
shape=(self.middle_man_dim_1, self.middle_man_dim_2),
initializer=tf.contrib.layers.variance_scaling_initializer(mode="FAN_AVG"))
#initializer=tf.truncated_normal_initializer(stddev=0.1))
self.E_b2 = tf.get_variable("E_b2",
shape=(self.middle_man_dim_2, ),
initializer=tf.constant_initializer(0.0))
self.E_W3_mu = tf.get_variable("E_W3_mu",
shape=(self.middle_man_dim_2, self.latent_space_dim),
initializer=tf.contrib.layers.variance_scaling_initializer(mode="FAN_AVG"))
#initializer=tf.truncated_normal_initializer(stddev=0.1))
self.E_b3_mu = tf.get_variable("E_b3_mu",
shape=(self.latent_space_dim, ),
initializer=tf.constant_initializer(0.0))
self.E_W3_log_var = tf.get_variable("E_W3_log_var",
shape=(self.middle_man_dim_2, self.latent_space_dim),
initializer=tf.contrib.layers.variance_scaling_initializer(mode="FAN_AVG"))
#initializer=tf.truncated_normal_initializer(stddev=0.1))
self.E_b3_log_var = tf.get_variable("E_b3_log_var",
shape=(self.latent_space_dim, ),
initializer=tf.constant_initializer(0.0))
self.D_W1 = tf.get_variable("D_W1",
shape=(self.latent_space_dim, self.middle_man_dim_2),
initializer=tf.contrib.layers.variance_scaling_initializer(mode="FAN_AVG"))
#initializer=tf.truncated_normal_initializer(stddev=0.1))
self.D_b1 = tf.get_variable("D_b1",
shape=(self.middle_man_dim_2, ),
initializer=tf.constant_initializer(0.0))
self.D_W2 = tf.get_variable("D_W2",
shape=(self.middle_man_dim_2, self.middle_man_dim_1),
initializer=tf.contrib.layers.variance_scaling_initializer(mode="FAN_AVG"))
#initializer=tf.truncated_normal_initializer(stddev=0.1))
self.D_b2 = tf.get_variable("D_b2",
shape=(self.middle_man_dim_1, ),
initializer=tf.constant_initializer(0.0))
self.D_W3 = tf.get_variable("D_W3",
shape=(self.middle_man_dim_1, self.img_size_flat),
initializer=tf.contrib.layers.variance_scaling_initializer(mode="FAN_AVG"))
#initializer=tf.truncated_normal_initializer(stddev=0.1))
self.D_b3 = tf.get_variable("D_b3",
shape=(self.img_size_flat, ),
initializer=tf.constant_initializer(0.0))
# encoding, sampling, and decoding of x
# Add noise to X
self.x_noise = self.x + self.noise_factor * tf.random_normal(tf.shape(self.x))
self.x_noise_clipped = tf.clip_by_value(self.x_noise, 0., 1.)
self.mu_x, self.log_var_x = self.encoding(self.x_noise_clipped, self.E_W1, self.E_b1, self.E_W2, self.E_b2, self.E_W3_mu, self.E_b3_mu, self.E_W3_log_var, self.E_b3_log_var)
self.z_x = self.sampling(self.mu_x, self.log_var_x)
self.logits_x, self.probs_x = self.decoding(self.z_x, self.D_W1, self.D_b1, self.D_W2, self.D_b2, self.D_W3, self.D_b3)
# reconstructed images
self.x_reconstructed = self.probs_x
# cost and optimizer
self.cross_entropy = tf.reduce_sum(tf.nn.sigmoid_cross_entropy_with_logits(logits=self.logits_x, labels=self.x), axis=1)
self.cost_ce = tf.reduce_mean(self.cross_entropy)
self.kl_divergence = tf.reduce_sum(tf.exp(self.log_var_x) + self.mu_x**2 - 1. - self.log_var_x, axis=1)
self.cost_kl = tf.reduce_mean(self.kl_divergence)
self.cost = self.cost_ce + self.alpha * self.cost_kl
self.optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate).minimize(self.cost)
# decoding of z
self.logits_z, self.probs_z = self.decoding(self.z, self.D_W1, self.D_b1, self.D_W2, self.D_b2, self.D_W3, self.D_b3)
# generated images
self.x_generated = self.probs_z
class CVAE_mnist(VAE_mnist):
def __init__(self, sess, batch_size=100, report_period=100, learning_rate=1e-3,
epoch_number=20, # this is used when we train without using random batch
num_iteration=int(2e+4), # this is used when we train with using random batch
middle_man_dim_1=int(1000),
middle_man_dim_2=int(1000),
latent_space_dim=int(10),
activation_function=tf.nn.relu,
alpha=0.5):
super().__init__(sess, batch_size, report_period, learning_rate, epoch_number, num_iteration, middle_man_dim_1,
middle_man_dim_2, latent_space_dim, activation_function, alpha)
def encoding(self, x, y, E_W1, E_b1, E_W2, E_b2, E_W3_mu, E_b3_mu, E_W3_log_var, E_b3_log_var):
inputs = tf.concat(axis=1, values=[x, y])
h1 = self.activation_function(tf.matmul(inputs, E_W1) + E_b1)
h2 = self.activation_function(tf.matmul(h1, E_W2) + E_b2)
mu = tf.matmul(h2, E_W3_mu) + E_b3_mu
log_var = tf.matmul(h2, E_W3_log_var) + E_b3_log_var
return mu, log_var
def sampling(self, mu, log_var):
eps = tf.random_normal(shape=tf.shape(mu))
z = mu + tf.exp(log_var / 2) * eps
return z
def decoding(self, z, y, D_W1, D_b1, D_W2, D_b2, D_W3, D_b3):
inputs = tf.concat(axis=1, values=[z, y])
h1 = self.activation_function(tf.matmul(inputs, D_W1) + D_b1)
h2 = self.activation_function(tf.matmul(h1, D_W2) + D_b2)
logits = tf.matmul(h2, D_W3) + D_b3
probs = tf.nn.sigmoid(logits)
return logits, probs
def graph_construction(self):
# data dimension
self.img_size = 28
self.img_size_flat = 784
self.img_shape = (28, 28)
self.num_classes = 10
# placeholders
self.x = tf.placeholder(tf.float32, shape=[None, self.img_size_flat], name='x')
self.y = tf.placeholder(tf.float32, shape=[None, self.num_classes], name='y')
self.z = tf.placeholder(tf.float32, shape=[None, self.latent_space_dim], name='z')
# weights
self.E_W1 = tf.get_variable("E_W1",
shape=(self.img_size_flat + self.num_classes, self.middle_man_dim_1),
initializer=tf.contrib.layers.variance_scaling_initializer(mode="FAN_AVG"))
#initializer=tf.truncated_normal_initializer(stddev=0.1))
self.E_b1 = tf.get_variable("E_b1",
shape=(self.middle_man_dim_1, ),
initializer=tf.constant_initializer(0.0))
self.E_W2 = tf.get_variable("E_W2",
shape=(self.middle_man_dim_1, self.middle_man_dim_2),
initializer=tf.contrib.layers.variance_scaling_initializer(mode="FAN_AVG"))
#initializer=tf.truncated_normal_initializer(stddev=0.1))
self.E_b2 = tf.get_variable("E_b2",
shape=(self.middle_man_dim_2, ),
initializer=tf.constant_initializer(0.0))
self.E_W3_mu = tf.get_variable("E_W3_mu",
shape=(self.middle_man_dim_2, self.latent_space_dim),
initializer=tf.contrib.layers.variance_scaling_initializer(mode="FAN_AVG"))
#initializer=tf.truncated_normal_initializer(stddev=0.1))
self.E_b3_mu = tf.get_variable("E_b3_mu",
shape=(self.latent_space_dim, ),
initializer=tf.constant_initializer(0.0))
self.E_W3_log_var = tf.get_variable("E_W3_log_var",
shape=(self.middle_man_dim_2, self.latent_space_dim),
initializer=tf.contrib.layers.variance_scaling_initializer(mode="FAN_AVG"))
#initializer=tf.truncated_normal_initializer(stddev=0.1))
self.E_b3_log_var = tf.get_variable("E_b3_log_var",
shape=(self.latent_space_dim, ),
initializer=tf.constant_initializer(0.0))
self.D_W1 = tf.get_variable("D_W1",
shape=(self.latent_space_dim + self.num_classes, self.middle_man_dim_2),
initializer=tf.contrib.layers.variance_scaling_initializer(mode="FAN_AVG"))
#initializer=tf.truncated_normal_initializer(stddev=0.1))
self.D_b1 = tf.get_variable("D_b1",
shape=(self.middle_man_dim_2, ),
initializer=tf.constant_initializer(0.0))
self.D_W2 = tf.get_variable("D_W2",
shape=(self.middle_man_dim_2, self.middle_man_dim_1),
initializer=tf.contrib.layers.variance_scaling_initializer(mode="FAN_AVG"))
#initializer=tf.truncated_normal_initializer(stddev=0.1))
self.D_b2 = tf.get_variable("D_b2",
shape=(self.middle_man_dim_1, ),
initializer=tf.constant_initializer(0.0))
self.D_W3 = tf.get_variable("D_W3",
shape=(self.middle_man_dim_1, self.img_size_flat),
initializer=tf.contrib.layers.variance_scaling_initializer(mode="FAN_AVG"))
#initializer=tf.truncated_normal_initializer(stddev=0.1))
self.D_b3 = tf.get_variable("D_b3",
shape=(self.img_size_flat, ),
initializer=tf.constant_initializer(0.0))
# encoding, sampling, and decoding of x
self.mu_x, self.log_var_x = self.encoding(self.x, self.y, self.E_W1, self.E_b1, self.E_W2, self.E_b2, self.E_W3_mu, self.E_b3_mu, self.E_W3_log_var, self.E_b3_log_var)
self.z_x = self.sampling(self.mu_x, self.log_var_x)
self.logits_x, self.probs_x = self.decoding(self.z_x, self.y, self.D_W1, self.D_b1, self.D_W2, self.D_b2, self.D_W3, self.D_b3)
# reconstructed images
self.x_reconstructed = self.probs_x
# cost and optimizer
self.cross_entropy = tf.reduce_sum(tf.nn.sigmoid_cross_entropy_with_logits(logits=self.logits_x, labels=self.x), axis=1)
self.cost_ce = tf.reduce_mean(self.cross_entropy)
self.kl_divergence = tf.reduce_sum(tf.exp(self.log_var_x) + self.mu_x**2 - 1. - self.log_var_x, axis=1)
self.cost_kl = tf.reduce_mean(self.kl_divergence)
self.cost = self.cost_ce + self.alpha * self.cost_kl
self.optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate).minimize(self.cost)
# decoding of z
self.logits_z, self.probs_z = self.decoding(self.z, self.y, self.D_W1, self.D_b1, self.D_W2, self.D_b2, self.D_W3, self.D_b3)
# generated images
self.x_generated = self.probs_z
def train(self):
for i in range(self.epoch_number):
start_time = time.time() # start time of this epoch
training_batch = zip(range(0, len(self.y_train), self.batch_size),
range(self.batch_size, len(self.y_train), self.batch_size))
idx_for_print_cost = 0
for start, end in training_batch:
feed_dict = {self.x: self.x_train[start:end, :],
self.y: self.y_train[start:end, :]}
self.sess.run(self.optimizer, feed_dict=feed_dict)
if idx_for_print_cost % self.report_period == 0: # for every self.report_period train
cost_now = self.sess.run(self.cost, feed_dict=feed_dict) # we compute cost now
print(idx_for_print_cost, cost_now) # we print cost now
idx_for_print_cost += 1
end_time = time.time() # end time of this epoch
print("==========================================================")
print("Epoch:", i)
time_dif = end_time - start_time # we check computing time for each epoch
print("Time Usage: " + str(timedelta(seconds=int(round(time_dif))))) # and print it
self.plot_16_generated_images(figure_save_dir='./img', figure_index=i)
def train_random_batch(self):
for i in range(self.num_iteration):
idx = np.random.choice(self.x_train.shape[0], size=self.batch_size, replace=False) # random_batch
x_batch = self.x_train[idx]
y_batch = self.y_train[idx] # random_batch
feed_dict = {self.x: x_batch,
self.y: y_batch}
self.sess.run(self.optimizer, feed_dict=feed_dict)
if (i % self.report_period == 0) or (i == self.num_iteration - 1):
loss = self.sess.run(self.cost, feed_dict=feed_dict)
logging.info('train iter : {:6d} | loss : {:.6f}'.format(i, loss))
def visualization_of_reconstruction(self):
imgs_original = self.x_test[0:16, :]
labels_original = self.y_test[0:16, :]
feed_dict = {self.x: imgs_original,
self.y: labels_original}
imgs_recon = self.sess.run(self.x_reconstructed, feed_dict=feed_dict)
fig = utils.plot_16_images_2d_and_returen(imgs_original, img_shape=self.img_shape)
plt.show(fig)
fig = utils.plot_16_images_2d_and_returen(imgs_recon, img_shape=self.img_shape)
plt.show(fig)
def visualization_of_16_loading_vectors(self):
z_batch = np.zeros(shape=(16, self.latent_space_dim))
y_batch = np.array([[1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0]]).astype(np.float32)
for i in range(16):
if i < self.latent_space_dim:
z_batch[i, i] = 1
feed_dict = {self.z: z_batch,
self.y: y_batch}
images = self.sess.run(self.x_generated, feed_dict=feed_dict)
fig = utils.plot_16_images_2d_and_returen(images, img_shape=self.img_shape)
plt.show(fig)
def visualization_of_zero_vector_in_latent_space(self):
z_batch = np.zeros(shape=(1, self.latent_space_dim))
y_batch = np.array([[1, 0, 0, 0, 0, 0, 0, 0, 0, 0]]).astype(np.float32)
feed_dict = {self.z: z_batch,
self.y: y_batch}
img = self.sess.run(self.x_generated, feed_dict=feed_dict)
fig = utils.plot_one_image(img, self.img_shape)
plt.show(fig)
| 53.137457
| 181
| 0.566481
| 4,185
| 30,926
| 3.88411
| 0.058542
| 0.01944
| 0.022332
| 0.025838
| 0.922055
| 0.915964
| 0.90809
| 0.902676
| 0.89597
| 0.888957
| 0
| 0.036538
| 0.322997
| 30,926
| 581
| 182
| 53.228916
| 0.739839
| 0.08585
| 0
| 0.806527
| 0
| 0
| 0.023911
| 0.004121
| 0
| 0
| 0
| 0
| 0
| 1
| 0.060606
| false
| 0
| 0.013986
| 0
| 0.095571
| 0.037296
| 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
|
d8bd398339ef38d751d18de4656b7c2165543483
| 19,783
|
py
|
Python
|
sdk/python/pulumi_alicloud/arms/alert_contact.py
|
pulumi/pulumi-alicloud
|
9c34d84b4588a7c885c6bec1f03b5016e5a41683
|
[
"ECL-2.0",
"Apache-2.0"
] | 42
|
2019-03-18T06:34:37.000Z
|
2022-03-24T07:08:57.000Z
|
sdk/python/pulumi_alicloud/arms/alert_contact.py
|
pulumi/pulumi-alicloud
|
9c34d84b4588a7c885c6bec1f03b5016e5a41683
|
[
"ECL-2.0",
"Apache-2.0"
] | 152
|
2019-04-15T21:03:44.000Z
|
2022-03-29T18:00:57.000Z
|
sdk/python/pulumi_alicloud/arms/alert_contact.py
|
pulumi/pulumi-alicloud
|
9c34d84b4588a7c885c6bec1f03b5016e5a41683
|
[
"ECL-2.0",
"Apache-2.0"
] | 3
|
2020-08-26T17:30:07.000Z
|
2021-07-05T01:37:45.000Z
|
# coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from .. import _utilities
__all__ = ['AlertContactArgs', 'AlertContact']
@pulumi.input_type
class AlertContactArgs:
def __init__(__self__, *,
alert_contact_name: Optional[pulumi.Input[str]] = None,
ding_robot_webhook_url: Optional[pulumi.Input[str]] = None,
email: Optional[pulumi.Input[str]] = None,
phone_num: Optional[pulumi.Input[str]] = None,
system_noc: Optional[pulumi.Input[bool]] = None):
"""
The set of arguments for constructing a AlertContact resource.
:param pulumi.Input[str] alert_contact_name: The name of the alert contact.
:param pulumi.Input[str] ding_robot_webhook_url: The webhook URL of the DingTalk chatbot. For more information about how to obtain the URL, see Configure a DingTalk chatbot to send alert notifications: https://www.alibabacloud.com/help/en/doc-detail/106247.htm. You must specify at least one of the following parameters: PhoneNum, Email, and DingRobotWebhookUrl.
:param pulumi.Input[str] email: The email address of the alert contact. You must specify at least one of the following parameters: PhoneNum, Email, and DingRobotWebhookUrl.
:param pulumi.Input[str] phone_num: The mobile number of the alert contact. You must specify at least one of the following parameters: PhoneNum, Email, and DingRobotWebhookUrl.
:param pulumi.Input[bool] system_noc: Specifies whether the alert contact receives system notifications. Valid values: true: receives system notifications. false: does not receive system notifications.
"""
if alert_contact_name is not None:
pulumi.set(__self__, "alert_contact_name", alert_contact_name)
if ding_robot_webhook_url is not None:
pulumi.set(__self__, "ding_robot_webhook_url", ding_robot_webhook_url)
if email is not None:
pulumi.set(__self__, "email", email)
if phone_num is not None:
pulumi.set(__self__, "phone_num", phone_num)
if system_noc is not None:
pulumi.set(__self__, "system_noc", system_noc)
@property
@pulumi.getter(name="alertContactName")
def alert_contact_name(self) -> Optional[pulumi.Input[str]]:
"""
The name of the alert contact.
"""
return pulumi.get(self, "alert_contact_name")
@alert_contact_name.setter
def alert_contact_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "alert_contact_name", value)
@property
@pulumi.getter(name="dingRobotWebhookUrl")
def ding_robot_webhook_url(self) -> Optional[pulumi.Input[str]]:
"""
The webhook URL of the DingTalk chatbot. For more information about how to obtain the URL, see Configure a DingTalk chatbot to send alert notifications: https://www.alibabacloud.com/help/en/doc-detail/106247.htm. You must specify at least one of the following parameters: PhoneNum, Email, and DingRobotWebhookUrl.
"""
return pulumi.get(self, "ding_robot_webhook_url")
@ding_robot_webhook_url.setter
def ding_robot_webhook_url(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "ding_robot_webhook_url", value)
@property
@pulumi.getter
def email(self) -> Optional[pulumi.Input[str]]:
"""
The email address of the alert contact. You must specify at least one of the following parameters: PhoneNum, Email, and DingRobotWebhookUrl.
"""
return pulumi.get(self, "email")
@email.setter
def email(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "email", value)
@property
@pulumi.getter(name="phoneNum")
def phone_num(self) -> Optional[pulumi.Input[str]]:
"""
The mobile number of the alert contact. You must specify at least one of the following parameters: PhoneNum, Email, and DingRobotWebhookUrl.
"""
return pulumi.get(self, "phone_num")
@phone_num.setter
def phone_num(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "phone_num", value)
@property
@pulumi.getter(name="systemNoc")
def system_noc(self) -> Optional[pulumi.Input[bool]]:
"""
Specifies whether the alert contact receives system notifications. Valid values: true: receives system notifications. false: does not receive system notifications.
"""
return pulumi.get(self, "system_noc")
@system_noc.setter
def system_noc(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "system_noc", value)
@pulumi.input_type
class _AlertContactState:
def __init__(__self__, *,
alert_contact_name: Optional[pulumi.Input[str]] = None,
ding_robot_webhook_url: Optional[pulumi.Input[str]] = None,
email: Optional[pulumi.Input[str]] = None,
phone_num: Optional[pulumi.Input[str]] = None,
system_noc: Optional[pulumi.Input[bool]] = None):
"""
Input properties used for looking up and filtering AlertContact resources.
:param pulumi.Input[str] alert_contact_name: The name of the alert contact.
:param pulumi.Input[str] ding_robot_webhook_url: The webhook URL of the DingTalk chatbot. For more information about how to obtain the URL, see Configure a DingTalk chatbot to send alert notifications: https://www.alibabacloud.com/help/en/doc-detail/106247.htm. You must specify at least one of the following parameters: PhoneNum, Email, and DingRobotWebhookUrl.
:param pulumi.Input[str] email: The email address of the alert contact. You must specify at least one of the following parameters: PhoneNum, Email, and DingRobotWebhookUrl.
:param pulumi.Input[str] phone_num: The mobile number of the alert contact. You must specify at least one of the following parameters: PhoneNum, Email, and DingRobotWebhookUrl.
:param pulumi.Input[bool] system_noc: Specifies whether the alert contact receives system notifications. Valid values: true: receives system notifications. false: does not receive system notifications.
"""
if alert_contact_name is not None:
pulumi.set(__self__, "alert_contact_name", alert_contact_name)
if ding_robot_webhook_url is not None:
pulumi.set(__self__, "ding_robot_webhook_url", ding_robot_webhook_url)
if email is not None:
pulumi.set(__self__, "email", email)
if phone_num is not None:
pulumi.set(__self__, "phone_num", phone_num)
if system_noc is not None:
pulumi.set(__self__, "system_noc", system_noc)
@property
@pulumi.getter(name="alertContactName")
def alert_contact_name(self) -> Optional[pulumi.Input[str]]:
"""
The name of the alert contact.
"""
return pulumi.get(self, "alert_contact_name")
@alert_contact_name.setter
def alert_contact_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "alert_contact_name", value)
@property
@pulumi.getter(name="dingRobotWebhookUrl")
def ding_robot_webhook_url(self) -> Optional[pulumi.Input[str]]:
"""
The webhook URL of the DingTalk chatbot. For more information about how to obtain the URL, see Configure a DingTalk chatbot to send alert notifications: https://www.alibabacloud.com/help/en/doc-detail/106247.htm. You must specify at least one of the following parameters: PhoneNum, Email, and DingRobotWebhookUrl.
"""
return pulumi.get(self, "ding_robot_webhook_url")
@ding_robot_webhook_url.setter
def ding_robot_webhook_url(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "ding_robot_webhook_url", value)
@property
@pulumi.getter
def email(self) -> Optional[pulumi.Input[str]]:
"""
The email address of the alert contact. You must specify at least one of the following parameters: PhoneNum, Email, and DingRobotWebhookUrl.
"""
return pulumi.get(self, "email")
@email.setter
def email(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "email", value)
@property
@pulumi.getter(name="phoneNum")
def phone_num(self) -> Optional[pulumi.Input[str]]:
"""
The mobile number of the alert contact. You must specify at least one of the following parameters: PhoneNum, Email, and DingRobotWebhookUrl.
"""
return pulumi.get(self, "phone_num")
@phone_num.setter
def phone_num(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "phone_num", value)
@property
@pulumi.getter(name="systemNoc")
def system_noc(self) -> Optional[pulumi.Input[bool]]:
"""
Specifies whether the alert contact receives system notifications. Valid values: true: receives system notifications. false: does not receive system notifications.
"""
return pulumi.get(self, "system_noc")
@system_noc.setter
def system_noc(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "system_noc", value)
class AlertContact(pulumi.CustomResource):
@overload
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
alert_contact_name: Optional[pulumi.Input[str]] = None,
ding_robot_webhook_url: Optional[pulumi.Input[str]] = None,
email: Optional[pulumi.Input[str]] = None,
phone_num: Optional[pulumi.Input[str]] = None,
system_noc: Optional[pulumi.Input[bool]] = None,
__props__=None):
"""
Provides a Application Real-Time Monitoring Service (ARMS) Alert Contact resource.
For information about Application Real-Time Monitoring Service (ARMS) Alert Contact and how to use it, see [What is Alert Contact](https://www.alibabacloud.com/help/en/doc-detail/42953.htm).
> **NOTE:** Available in v1.129.0+.
## Example Usage
Basic Usage
```python
import pulumi
import pulumi_alicloud as alicloud
example = alicloud.arms.AlertContact("example",
alert_contact_name="example_value",
ding_robot_webhook_url="https://oapi.dingtalk.com/robot/send?access_token=91f2f6****",
email="someone@example.com",
phone_num="1381111****")
```
## Import
Application Real-Time Monitoring Service (ARMS) Alert Contact can be imported using the id, e.g.
```sh
$ pulumi import alicloud:arms/alertContact:AlertContact example <id>
```
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] alert_contact_name: The name of the alert contact.
:param pulumi.Input[str] ding_robot_webhook_url: The webhook URL of the DingTalk chatbot. For more information about how to obtain the URL, see Configure a DingTalk chatbot to send alert notifications: https://www.alibabacloud.com/help/en/doc-detail/106247.htm. You must specify at least one of the following parameters: PhoneNum, Email, and DingRobotWebhookUrl.
:param pulumi.Input[str] email: The email address of the alert contact. You must specify at least one of the following parameters: PhoneNum, Email, and DingRobotWebhookUrl.
:param pulumi.Input[str] phone_num: The mobile number of the alert contact. You must specify at least one of the following parameters: PhoneNum, Email, and DingRobotWebhookUrl.
:param pulumi.Input[bool] system_noc: Specifies whether the alert contact receives system notifications. Valid values: true: receives system notifications. false: does not receive system notifications.
"""
...
@overload
def __init__(__self__,
resource_name: str,
args: Optional[AlertContactArgs] = None,
opts: Optional[pulumi.ResourceOptions] = None):
"""
Provides a Application Real-Time Monitoring Service (ARMS) Alert Contact resource.
For information about Application Real-Time Monitoring Service (ARMS) Alert Contact and how to use it, see [What is Alert Contact](https://www.alibabacloud.com/help/en/doc-detail/42953.htm).
> **NOTE:** Available in v1.129.0+.
## Example Usage
Basic Usage
```python
import pulumi
import pulumi_alicloud as alicloud
example = alicloud.arms.AlertContact("example",
alert_contact_name="example_value",
ding_robot_webhook_url="https://oapi.dingtalk.com/robot/send?access_token=91f2f6****",
email="someone@example.com",
phone_num="1381111****")
```
## Import
Application Real-Time Monitoring Service (ARMS) Alert Contact can be imported using the id, e.g.
```sh
$ pulumi import alicloud:arms/alertContact:AlertContact example <id>
```
:param str resource_name: The name of the resource.
:param AlertContactArgs args: The arguments to use to populate this resource's properties.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
...
def __init__(__self__, resource_name: str, *args, **kwargs):
resource_args, opts = _utilities.get_resource_args_opts(AlertContactArgs, pulumi.ResourceOptions, *args, **kwargs)
if resource_args is not None:
__self__._internal_init(resource_name, opts, **resource_args.__dict__)
else:
__self__._internal_init(resource_name, *args, **kwargs)
def _internal_init(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
alert_contact_name: Optional[pulumi.Input[str]] = None,
ding_robot_webhook_url: Optional[pulumi.Input[str]] = None,
email: Optional[pulumi.Input[str]] = None,
phone_num: Optional[pulumi.Input[str]] = None,
system_noc: Optional[pulumi.Input[bool]] = None,
__props__=None):
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = _utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = AlertContactArgs.__new__(AlertContactArgs)
__props__.__dict__["alert_contact_name"] = alert_contact_name
__props__.__dict__["ding_robot_webhook_url"] = ding_robot_webhook_url
__props__.__dict__["email"] = email
__props__.__dict__["phone_num"] = phone_num
__props__.__dict__["system_noc"] = system_noc
super(AlertContact, __self__).__init__(
'alicloud:arms/alertContact:AlertContact',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None,
alert_contact_name: Optional[pulumi.Input[str]] = None,
ding_robot_webhook_url: Optional[pulumi.Input[str]] = None,
email: Optional[pulumi.Input[str]] = None,
phone_num: Optional[pulumi.Input[str]] = None,
system_noc: Optional[pulumi.Input[bool]] = None) -> 'AlertContact':
"""
Get an existing AlertContact resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] alert_contact_name: The name of the alert contact.
:param pulumi.Input[str] ding_robot_webhook_url: The webhook URL of the DingTalk chatbot. For more information about how to obtain the URL, see Configure a DingTalk chatbot to send alert notifications: https://www.alibabacloud.com/help/en/doc-detail/106247.htm. You must specify at least one of the following parameters: PhoneNum, Email, and DingRobotWebhookUrl.
:param pulumi.Input[str] email: The email address of the alert contact. You must specify at least one of the following parameters: PhoneNum, Email, and DingRobotWebhookUrl.
:param pulumi.Input[str] phone_num: The mobile number of the alert contact. You must specify at least one of the following parameters: PhoneNum, Email, and DingRobotWebhookUrl.
:param pulumi.Input[bool] system_noc: Specifies whether the alert contact receives system notifications. Valid values: true: receives system notifications. false: does not receive system notifications.
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = _AlertContactState.__new__(_AlertContactState)
__props__.__dict__["alert_contact_name"] = alert_contact_name
__props__.__dict__["ding_robot_webhook_url"] = ding_robot_webhook_url
__props__.__dict__["email"] = email
__props__.__dict__["phone_num"] = phone_num
__props__.__dict__["system_noc"] = system_noc
return AlertContact(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter(name="alertContactName")
def alert_contact_name(self) -> pulumi.Output[Optional[str]]:
"""
The name of the alert contact.
"""
return pulumi.get(self, "alert_contact_name")
@property
@pulumi.getter(name="dingRobotWebhookUrl")
def ding_robot_webhook_url(self) -> pulumi.Output[Optional[str]]:
"""
The webhook URL of the DingTalk chatbot. For more information about how to obtain the URL, see Configure a DingTalk chatbot to send alert notifications: https://www.alibabacloud.com/help/en/doc-detail/106247.htm. You must specify at least one of the following parameters: PhoneNum, Email, and DingRobotWebhookUrl.
"""
return pulumi.get(self, "ding_robot_webhook_url")
@property
@pulumi.getter
def email(self) -> pulumi.Output[Optional[str]]:
"""
The email address of the alert contact. You must specify at least one of the following parameters: PhoneNum, Email, and DingRobotWebhookUrl.
"""
return pulumi.get(self, "email")
@property
@pulumi.getter(name="phoneNum")
def phone_num(self) -> pulumi.Output[Optional[str]]:
"""
The mobile number of the alert contact. You must specify at least one of the following parameters: PhoneNum, Email, and DingRobotWebhookUrl.
"""
return pulumi.get(self, "phone_num")
@property
@pulumi.getter(name="systemNoc")
def system_noc(self) -> pulumi.Output[Optional[bool]]:
"""
Specifies whether the alert contact receives system notifications. Valid values: true: receives system notifications. false: does not receive system notifications.
"""
return pulumi.get(self, "system_noc")
| 50.725641
| 370
| 0.680433
| 2,453
| 19,783
| 5.283734
| 0.083979
| 0.05856
| 0.058329
| 0.061106
| 0.87717
| 0.866445
| 0.857804
| 0.851709
| 0.847774
| 0.840367
| 0
| 0.00556
| 0.227165
| 19,783
| 389
| 371
| 50.856041
| 0.842174
| 0.454886
| 0
| 0.777228
| 1
| 0
| 0.098776
| 0.024184
| 0
| 0
| 0
| 0
| 0
| 1
| 0.158416
| false
| 0.004951
| 0.024752
| 0
| 0.277228
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
d8e11a73fc4299a6cfae0ea508320adada8bb8a1
| 10,431
|
py
|
Python
|
db_adapter/curw_sim/grids/flo2d_grid_utils.py
|
CUrW-SL/curw_db_adapter
|
9d9ef24f42080910e0bd251bc7f001b0a4b0ab31
|
[
"MIT"
] | 2
|
2019-04-26T07:50:33.000Z
|
2019-09-28T20:15:33.000Z
|
db_adapter/curw_sim/grids/flo2d_grid_utils.py
|
CUrW-SL/curw_db_adapter
|
9d9ef24f42080910e0bd251bc7f001b0a4b0ab31
|
[
"MIT"
] | 1
|
2019-04-03T09:30:38.000Z
|
2019-04-20T18:11:59.000Z
|
db_adapter/curw_sim/grids/flo2d_grid_utils.py
|
shadhini/curw_db_adapter
|
4db8e1ea8794ffbd0dce29ac954a13315e83d843
|
[
"MIT"
] | null | null | null |
import traceback
import csv
import pkg_resources
from db_adapter.logger import logger
def add_flo2d_raincell_grid_mappings(pool, grid_interpolation, flo2d_model, obs_map_file_path, d03_map_file_path=None):
"""
Add flo2d grid mappings to the database
:param pool: database connection pool
:param grid_interpolation: grid interpolation method
:param flo2d_model: string: flo2d model (e.g. FLO2D_250, FLO2D_150, FLO2D_30)
:param obs_map_file_path: path to file containing flo2d grids to rainfall observational stations mapping
:param d03_map_file_path: path to file containing flo2d grids to d03 stations mapping
:return: True if the insertion is successful, else False
"""
# [flo2d_250_station_id,ob_1_id,ob_1_dist,ob_2_id,ob_2_dist,ob_3_id,ob_3_dist]
with open(obs_map_file_path, 'r') as f2:
flo2d_obs_mapping=[line for line in csv.reader(f2)][1:]
grid_mappings_list = []
if d03_map_file_path is not None:
# [flo2d_grid_id,nearest_d03_station_id,dist]
with open(d03_map_file_path, 'r') as f1:
flo2d_d03_mapping=[line for line in csv.reader(f1)][1:]
for index in range(len(flo2d_obs_mapping)):
cell_id = flo2d_obs_mapping[index][0]
obs1 = flo2d_obs_mapping[index][1]
obs2 = flo2d_obs_mapping[index][3]
obs3 = flo2d_obs_mapping[index][5]
fcst = flo2d_d03_mapping[index][1]
grid_mapping = ['{}_{}_{}'.format(flo2d_model, grid_interpolation, (str(cell_id)).zfill(10)),
obs1, obs2, obs3, fcst]
grid_mappings_list.append(tuple(grid_mapping))
sql_statement = "INSERT INTO `grid_map_flo2d_raincell` (`grid_id`, `obs1`, `obs2`, `obs3`, `fcst`)" \
" VALUES ( %s, %s, %s, %s, %s) " \
"ON DUPLICATE KEY UPDATE `obs1`=VALUES(`obs1`), `obs2`=VALUES(`obs2`), " \
"`obs3`=VALUES(`obs3`), `fcst`=VALUES(`fcst`);"
else:
for index in range(len(flo2d_obs_mapping)):
cell_id = flo2d_obs_mapping[index][0]
obs1 = flo2d_obs_mapping[index][1]
obs2 = flo2d_obs_mapping[index][3]
obs3 = flo2d_obs_mapping[index][5]
grid_mapping = ['{}_{}_{}'.format(flo2d_model, grid_interpolation, (str(cell_id)).zfill(10)),
obs1, obs2, obs3]
grid_mappings_list.append(tuple(grid_mapping))
sql_statement = "INSERT INTO `grid_map_flo2d_raincell` (`grid_id`, `obs1`, `obs2`, `obs3`)" \
" VALUES ( %s, %s, %s, %s) " \
"ON DUPLICATE KEY UPDATE `obs1`=VALUES(`obs1`), `obs2`=VALUES(`obs2`), " \
"`obs3`=VALUES(`obs3`);"
connection = pool.connection()
try:
with connection.cursor() as cursor:
row_count = cursor.executemany(sql_statement, grid_mappings_list)
connection.commit()
return row_count
except Exception as exception:
connection.rollback()
error_message = "Insertion of flo2d raincell grid mappings failed."
logger.error(error_message)
traceback.print_exc()
raise exception
finally:
if connection is not None:
connection.close()
def get_flo2d_cells_to_obs_grid_mappings(pool, grid_interpolation, flo2d_model):
"""
Retrieve flo2d to obs grid mappings
:param pool: database connection pool
:param grid_interpolation: grid interpolation method
:param flo2d_model: string: flo2d model (e.g. FLO2D_250, FLO2D_150, FLO2D_30)
:return: dictionary with grid ids as keys and corresponding obs1, obs2, obs3 station ids as a list
"""
flo2d_grid_mappings = {}
connection = pool.connection()
try:
with connection.cursor() as cursor:
sql_statement = "SELECT * FROM `grid_map_flo2d_raincell` WHERE `grid_id` like %s ESCAPE '$'"
row_count = cursor.execute(sql_statement, "flo2d$_{}$_{}$_%".format('$_'.join(flo2d_model.split('_')[1:]), grid_interpolation))
if row_count > 0:
results = cursor.fetchall()
for dict in results:
flo2d_grid_mappings[dict.get("grid_id")] = [dict.get("obs1"), dict.get("obs2"), dict.get("obs3")]
return flo2d_grid_mappings
else:
return None
except Exception as exception:
error_message = "Retrieving flo2d cells to obs grid mappings failed"
logger.error(error_message)
traceback.print_exc()
raise exception
finally:
if connection is not None:
connection.close()
def get_flo2d_cells_to_wrf_grid_mappings(pool, grid_interpolation, flo2d_model):
"""
Retrieve flo2d to wrf stations mappings
:param pool: database connection pool
:param grid_interpolation: grid interpolation method
:param flo2d_model: string: flo2d model (e.g. FLO2D_250, FLO2D_150, FLO2D_30)
:return: dictionary with grid ids as keys and corresponding wrf station ids as values
"""
flo2d_grid_mappings = {}
connection = pool.connection()
try:
with connection.cursor() as cursor:
sql_statement = "SELECT `grid_id`, `fcst` FROM `grid_map_flo2d_raincell` WHERE `grid_id` like %s ESCAPE '$'"
row_count = cursor.execute(sql_statement, "flo2d$_{}$_{}$_%".format('$_'.join(flo2d_model.split('_')[1:]), grid_interpolation))
if row_count > 0:
results = cursor.fetchall()
for dict in results:
flo2d_grid_mappings[dict.get("grid_id")] = dict.get("fcst")
return flo2d_grid_mappings
else:
return None
except Exception as exception:
error_message = "Retrieving flo2d cells to obs grid mappings failed"
logger.error(error_message)
traceback.print_exc()
raise exception
finally:
if connection is not None:
connection.close()
def add_flo2d_initial_conditions(pool, flo2d_model, initial_condition_file_path):
"""
Add flo2d grid mappings to the database
:param pool: database connection pool
:param flo2d_model: string: flo2d model (e.g. enum values of FLO2D_250, FLO2D_150, FLO2D_30)
:param initial_condition_file_path: path to the file with flo2d initial conditions
:return: True if the insertion is successful, else False
"""
with open(initial_condition_file_path, 'r') as f1:
flo2d_init_cond=[line for line in csv.reader(f1)][1:]
grid_mappings_list = []
for index in range(len(flo2d_init_cond)):
upstrm = flo2d_init_cond[index][0]
downstrm = flo2d_init_cond[index][1]
obs_wl = flo2d_init_cond[index][2]
canal = flo2d_init_cond[index][3]
grid_mapping = ['{}_{}_{}'.format(flo2d_model, upstrm, downstrm),
upstrm, downstrm, canal, obs_wl]
grid_mappings_list.append(tuple(grid_mapping))
connection = pool.connection()
try:
with connection.cursor() as cursor:
sql_statement = "INSERT INTO `grid_map_flo2d_initial_cond` (`grid_id`, `up_strm`, `down_strm`, `canal_seg`, `obs_wl`)" \
" VALUES ( %s, %s, %s, %s, %s) "\
"ON DUPLICATE KEY UPDATE `up_strm`=VALUES(`up_strm`), `down_strm`=VALUES(`down_strm`), " \
"`canal_seg`=VALUES(`canal_seg`), `obs_wl`=VALUES(`obs_wl`);"
row_count = cursor.executemany(sql_statement, grid_mappings_list)
connection.commit()
return row_count
except Exception as exception:
connection.rollback()
error_message = "Insertion of {} initial conditions failed.".format(flo2d_model)
logger.error(error_message)
traceback.print_exc()
raise exception
finally:
if connection is not None:
connection.close()
def get_flo2d_initial_conditions(pool, flo2d_model):
"""
Retrieve flo2d initial conditions
:param pool: database connection pool
:param flo2d_model: string: flo2d model (e.g. FLO2D_250, FLO2D_150, FLO2D_30)
:return: dictionary with grid ids as keys and corresponding up_strm, down_strm, canal_seg, and obs_wl as a list
"""
initial_conditions = {}
connection = pool.connection()
try:
with connection.cursor() as cursor:
sql_statement = "SELECT `grid_id`,`up_strm`,`down_strm`,`obs_wl`, `obs_wl_down_strm` FROM `grid_map_flo2d_initial_cond` " \
"WHERE `grid_id` like %s ESCAPE '$'"
row_count = cursor.execute(sql_statement, "{}$_%".format(flo2d_model))
if row_count > 0:
results = cursor.fetchall()
for dict in results:
initial_conditions[dict.get("grid_id")] = [dict.get("up_strm"), dict.get("down_strm"),
dict.get("obs_wl"), dict.get("obs_wl_down_strm")]
return initial_conditions
else:
return None
except Exception as exception:
error_message = "Retrieving {} initial conditions failed".format(flo2d_model)
logger.error(error_message)
traceback.print_exc()
raise exception
finally:
if connection is not None:
connection.close()
def clear_initial_conditions(pool, flo2d_model):
"""
Clear existing initial conditions of a given flo2d model from database
:param pool: database connection pool
:param flo2d_model: string: flo2d model (e.g. FLO2D_250, FLO2D_150, FLO2D_30)
:return: affected row count if successful
"""
connection = pool.connection()
try:
with connection.cursor() as cursor:
sql_statement = "DELETE FROM `grid_map_flo2d_initial_cond` " \
"WHERE `grid_id` like %s ESCAPE '$'"
row_count = cursor.execute(sql_statement, "{}$_%".format(flo2d_model))
connection.commit()
return row_count
except Exception as exception:
connection.rollback()
error_message = "Deletion of {} initial conditions failed.".format(flo2d_model)
logger.error(error_message)
traceback.print_exc()
raise exception
finally:
if connection is not None:
connection.close()
| 41.392857
| 139
| 0.628799
| 1,281
| 10,431
| 4.862607
| 0.120999
| 0.046556
| 0.026489
| 0.025686
| 0.815219
| 0.796597
| 0.762402
| 0.734789
| 0.726762
| 0.703484
| 0
| 0.031344
| 0.269006
| 10,431
| 251
| 140
| 41.557769
| 0.785574
| 0.189915
| 0
| 0.710059
| 0
| 0
| 0.180344
| 0.060518
| 0
| 0
| 0
| 0
| 0
| 1
| 0.035503
| false
| 0
| 0.023669
| 0
| 0.112426
| 0.035503
| 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
|
d8f1d6cdf7cb6bf9e25449f9f4f8c9533bab3e41
| 2,128
|
py
|
Python
|
node/blockchain/tests/test_blockchain_facade/test_get_primary_validator.py
|
thenewboston-developers/Node
|
e71a405f4867786a54dd17ddd97595dd3a630018
|
[
"MIT"
] | 18
|
2021-11-30T04:02:13.000Z
|
2022-03-24T12:33:57.000Z
|
node/blockchain/tests/test_blockchain_facade/test_get_primary_validator.py
|
thenewboston-developers/Node
|
e71a405f4867786a54dd17ddd97595dd3a630018
|
[
"MIT"
] | 1
|
2022-02-04T17:07:38.000Z
|
2022-02-04T17:07:38.000Z
|
node/blockchain/tests/test_blockchain_facade/test_get_primary_validator.py
|
thenewboston-developers/Node
|
e71a405f4867786a54dd17ddd97595dd3a630018
|
[
"MIT"
] | 5
|
2022-01-31T05:28:13.000Z
|
2022-03-08T17:25:31.000Z
|
import pytest
from node.blockchain.facade import BlockchainFacade
from node.blockchain.models import AccountState, Schedule
@pytest.mark.django_db
def test_get_primary_validator_empty_schedule():
assert not Schedule.objects.exists()
assert BlockchainFacade.get_instance().get_primary_validator() is None
@pytest.mark.usefixtures('base_blockchain')
def test_get_primary_validator_basic(primary_validator_node):
assert Schedule.objects.all().count() == 1
schedule = Schedule.objects.get_or_none()
assert schedule
assert schedule._id == 0
assert schedule.node_identifier == primary_validator_node.identifier
facade = BlockchainFacade.get_instance()
assert facade.get_next_block_number() == 1
assert facade.get_primary_validator() == primary_validator_node
@pytest.mark.usefixtures('base_blockchain')
def test_get_primary_validator_exactly_next_block(primary_validator_node, regular_node):
assert Schedule.objects.all().count() == 1
schedule = Schedule.objects.get_or_none()
assert schedule
assert schedule._id == 0
assert schedule.node_identifier == primary_validator_node.identifier
facade = BlockchainFacade.get_instance()
assert facade.get_next_block_number() == 1
Schedule.objects.create(_id=1, node_identifier=regular_node.identifier)
AccountState.objects.create(_id=regular_node.identifier, node=regular_node.dict())
assert facade.get_primary_validator() == regular_node
@pytest.mark.usefixtures('base_blockchain')
def test_get_primary_validator_with_queue(primary_validator_node, regular_node):
assert Schedule.objects.all().count() == 1
schedule = Schedule.objects.get_or_none()
assert schedule
assert schedule._id == 0
assert schedule.node_identifier == primary_validator_node.identifier
facade = BlockchainFacade.get_instance()
assert facade.get_next_block_number() == 1
Schedule.objects.create(_id=2, node_identifier=regular_node.identifier)
AccountState.objects.create(_id=regular_node.identifier, node=regular_node.dict())
assert facade.get_primary_validator() == primary_validator_node
| 40.150943
| 88
| 0.788064
| 265
| 2,128
| 5.996226
| 0.173585
| 0.161108
| 0.095658
| 0.042794
| 0.82253
| 0.806167
| 0.806167
| 0.806167
| 0.764003
| 0.764003
| 0
| 0.005873
| 0.119831
| 2,128
| 52
| 89
| 40.923077
| 0.842499
| 0
| 0
| 0.682927
| 0
| 0
| 0.021147
| 0
| 0
| 0
| 0
| 0
| 0.487805
| 1
| 0.097561
| false
| 0
| 0.073171
| 0
| 0.170732
| 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
2b4c216c3d7e84c2cbd9f2f6eb187ef1eae9f2cc
| 1,037
|
py
|
Python
|
elmo/moon_tracker/utils.py
|
stephenswat/eve_lunar_mining_organiser
|
2f6e84b0a9fc60588ca9bdc2ffd074be7fbf0b12
|
[
"MIT"
] | 1
|
2017-09-20T09:15:14.000Z
|
2017-09-20T09:15:14.000Z
|
elmo/moon_tracker/utils.py
|
stephenswat/eve_lunar_mining_organiser
|
2f6e84b0a9fc60588ca9bdc2ffd074be7fbf0b12
|
[
"MIT"
] | 2
|
2021-08-19T13:26:04.000Z
|
2021-08-19T13:26:08.000Z
|
elmo/moon_tracker/utils.py
|
stephenswat/eve_lunar_mining_organiser
|
2f6e84b0a9fc60588ca9bdc2ffd074be7fbf0b12
|
[
"MIT"
] | 2
|
2017-10-09T20:15:03.000Z
|
2018-02-03T15:54:53.000Z
|
def user_can_view_scans(user, moon):
return (
user_can_delete_scans(user, moon) or
user.has_perm('eve_sde.sys_can_view_scans', moon.planet.system) or
user.has_perm('eve_sde.con_can_view_scans', moon.planet.system.constellation) or
user.has_perm('eve_sde.reg_can_view_scans', moon.planet.system.constellation.region)
)
def user_can_add_scans(user, moon):
return (
user_can_delete_scans(user, moon) or
user.has_perm('eve_sde.sys_can_add_scans', moon.planet.system) or
user.has_perm('eve_sde.con_can_add_scans', moon.planet.system.constellation) or
user.has_perm('eve_sde.reg_can_add_scans', moon.planet.system.constellation.region)
)
def user_can_delete_scans(user, moon):
return (
user.has_perm('eve_sde.sys_can_delete_scans', moon.planet.system) or
user.has_perm('eve_sde.con_can_delete_scans', moon.planet.system.constellation) or
user.has_perm('eve_sde.reg_can_delete_scans', moon.planet.system.constellation.region)
)
| 41.48
| 94
| 0.731919
| 161
| 1,037
| 4.341615
| 0.130435
| 0.090129
| 0.141631
| 0.180258
| 0.97568
| 0.967096
| 0.912732
| 0.793991
| 0.793991
| 0.65093
| 0
| 0
| 0.158149
| 1,037
| 24
| 95
| 43.208333
| 0.800687
| 0
| 0
| 0.25
| 0
| 0
| 0.228544
| 0.228544
| 0
| 0
| 0
| 0
| 0
| 1
| 0.15
| false
| 0
| 0
| 0.15
| 0.3
| 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
| 1
| 0
| 0
|
0
| 8
|
995f0dd6c7a3cfc36cc04611593446bf657cdb17
| 3,090
|
py
|
Python
|
genetic_neural_network.py
|
JehunYoo/SnakeRL
|
3635e2e5bcd6d3147cafa90e57471dbc5587d49a
|
[
"MIT"
] | null | null | null |
genetic_neural_network.py
|
JehunYoo/SnakeRL
|
3635e2e5bcd6d3147cafa90e57471dbc5587d49a
|
[
"MIT"
] | null | null | null |
genetic_neural_network.py
|
JehunYoo/SnakeRL
|
3635e2e5bcd6d3147cafa90e57471dbc5587d49a
|
[
"MIT"
] | null | null | null |
<<<<<<< HEAD
import numpy as np
import scipy
class GeneticNeuralNetwork():
def __init__(self, inodes, hnodes, onodes=4, activation='relu', eta=0.01,
classifier={'activation' : 'softmax'}):
'''
inodes : int
hnodes : list (the number of each ith hidden nodes)
onodes : int (default 4)
activation : string
eta : float (learning rate)
classifier : dict
'''
assert type(inodes) is int, 'inodes must be int type'
assert type(hnodes) is list and ([True] * len(hnodes) == [type(val) is int for val in hnodes]),\
'hnodes must be list of integer'
assert type(onodes) is int, 'onodes must be int type'
self.inodes = inodes
self.hnodes = hnodes
self.onodes = onodes
self.eta = eta
self.weight = np.array([], dtype=np.float64)
if activation=='relu':
self.activation = lambda x: np.maximum(0, x)
elif activation=='sigmoid':
self.activation = lambda x: scipy.special.expit(x)
else :
assert False, 'invalid activation'
if classifier['activation'] == 'softmax':
self.activation_clf = lambda x: scipy.special.softmax(x)
else:
self.activation_clf = self.activation
def compile(self):
pass
def fit(self):
pass
def predict(self):
pass
def crossover(self):
pass
def mutation(self):
pass
=======
import numpy as np
import scipy
class GeneticNeuralNetwork():
def __init__(self, inodes, hnodes, onodes=4, activation='relu', eta=0.01,
classifier={'activation' : 'softmax'}):
'''
inodes : int
hnodes : list (the number of each ith hidden nodes)
onodes : int (default 4)
activation : string
eta : float (learning rate)
classifier : dict
'''
assert type(inodes) is int, 'inodes must be int type'
assert type(hnodes) is list and ([True] * len(hnodes) == [type(val) is int for val in hnodes]),\
'hnodes must be list of integer'
assert type(onodes) is int, 'onodes must be int type'
self.inodes = inodes
self.hnodes = hnodes
self.onodes = onodes
self.eta = eta
self.weight = np.array([], dtype=np.float64)
if activation=='relu':
self.activation = lambda x: np.maximum(0, x)
elif activation=='sigmoid':
self.activation = lambda x: scipy.special.expit(x)
else :
assert False, 'invalid activation'
if classifier['activation'] == 'softmax':
self.activation_clf = lambda x: scipy.special.softmax(x)
else:
self.activation_clf = self.activation
def compile(self):
pass
def fit(self):
pass
def predict(self):
pass
def crossover(self):
pass
def mutation(self):
pass
>>>>>>> ff35870c235c677bd6e367cfedf2974cac4a6e8a
| 28.611111
| 104
| 0.560518
| 350
| 3,090
| 4.914286
| 0.205714
| 0.081395
| 0.051163
| 0.030233
| 0.974419
| 0.974419
| 0.974419
| 0.974419
| 0.974419
| 0.974419
| 0
| 0.018438
| 0.33301
| 3,090
| 107
| 105
| 28.878505
| 0.816109
| 0
| 0
| 0.957746
| 0
| 0
| 0.107843
| 0
| 0
| 0
| 0
| 0
| 0.112676
| 0
| null | null | 0.140845
| 0.056338
| 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
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 9
|
99879864fecabb2fe0b38f62c96683e631a50408
| 17,268
|
py
|
Python
|
tagupy/design/generator/_dsd_ref.py
|
algebra-club/TaguPy
|
1ff5a792f7c78cfb6741cf27659215fef287a1c1
|
[
"MIT"
] | 1
|
2021-08-21T07:36:24.000Z
|
2021-08-21T07:36:24.000Z
|
tagupy/design/generator/_dsd_ref.py
|
algebra-club/TaguPy
|
1ff5a792f7c78cfb6741cf27659215fef287a1c1
|
[
"MIT"
] | 29
|
2021-08-15T18:12:58.000Z
|
2021-09-12T14:48:17.000Z
|
tagupy/design/generator/_dsd_ref.py
|
algebra-club/TaguPy
|
1ff5a792f7c78cfb6741cf27659215fef287a1c1
|
[
"MIT"
] | null | null | null |
import numpy as np
from typing import Dict, List
_gen_vec = {
4: [0, -1, 1],
6: [0, -1, 1, 1, -1],
8: [0, 1, 1, -1, 1, -1, -1],
10: [[0, 1, -1, -1, 1], [-1, 1, 1, 1, 1]],
12: [0, 1, -1, 1, 1, 1, -1, -1, -1, 1, -1],
14: [0, 1, -1, 1, 1, -1, -1, -1, -1, 1, 1, -1, 1],
16: [],
18: [0, -1, -1, 1, -1, 1, 1, 1, -1, -1, 1, 1, 1, -1, 1, -1, -1],
20: [0, -1, 1, 1, -1, -1, -1, -1, 1, -1, 1, -1, 1, 1, 1, 1, -1, -1, 1],
22: [[0, 1, -1, -1, -1, -1, -1, 1, 1, -1, -1], [0, 1, -1, 1, -1, -1, 1, -1, 1, 1, 1]],
24: [0, 1, 1, 1, 1, -1, 1, -1, 1, 1, -1, -1, 1, 1, -1, -1, 1, -1, 1, -1, -1, -1, -1],
26: [[0, 1, -1, -1, 1, 1, 1, 1, 1, 1, -1, -1, 1], [-1, -1, 1, -1, 1, 1, 1, -1, 1, 1, 1, -1, 1]],
28: [[0, 1, 1, 1, -1, -1, -1], [1, -1, 1, -1, 1, -1, -1], [1, 1, -1, 1, 1, -1, -1], [1, 1, 1, -1, 1, 1, 1]],
30: [0, 1, -1, -1, 1, 1, 1, 1, -1, 1, -1, -1, -1, 1, -1, -1, 1, -1, -1, -1, 1, -1, 1, 1, 1, 1, -1, -1, 1],
32: [0, 1, 1, -1, 1, 1, -1, 1, 1, 1, 1, -1, -1, -1, 1, -1, 1, -1, 1, 1, 1, -1, -1, -1, -1, 1, -1, -1, 1, -1, -1],
34: [[0, -1, 1, 1, -1, -1, 1, 1, -1, -1, -1, -1, -1, 1, -1, 1, -1], [0, -1, -1, -1, -1, -1, -1, 1, -1, 1, 1, -1, 1, -1, -1, 1, 1]],
36: [[0, 1, 1, 1, -1, 1, -1, -1, -1], [-1, -1, -1, 1, -1, 1, 1, -1, 1], [1, 1, -1, -1, -1, 1, -1, -1, -1], [1, 1, 1, 1, 1, -1, 1, 1, -1]],
38: [0, -1, 1, -1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, 1, -1, 1, 1, 1, 1, -1, 1, 1, 1, -1, -1, -1, -1, 1, -1, 1, 1, -1, -1, 1, -1],
40: [],
42: [0, -1, -1, 1, -1, -1, 1, 1, -1, -1, -1, 1, 1, 1, 1, 1, -1, 1, -1, 1, -1, -1, 1, -1, 1, -1, 1, 1, 1, 1, 1, -1, -1, -1, 1, 1, -1, -1, 1, -1, -1],
44: [0, 1, -1, -1, 1, -1, 1, -1, -1, 1, 1, 1, -1, 1, 1, 1, 1, 1, -1, -1, -1, 1, -1, 1, 1, 1, -1, -1, -1, -1, -1, 1, -1, -1, -1, 1, 1, -1, 1, -1, 1, 1, -1],
46: [],
48: [0, 1, 1, 1, 1, -1, 1, 1, 1, 1, -1, -1, 1, -1, 1, -1, 1, 1, 1, -1, -1, 1, -1, -1, 1, 1, -1, 1, 1, -1, -1, -1, 1, -1, 1, -1, 1, 1, -1, -1, -1, -1, 1, -1, -1, -1, -1],
50: [[0, 1, 1, -1, 1, -1, 1, -1, -1, 1, 1, -1, -1, -1, -1, 1, 1, -1, -1, 1, -1, 1, -1, 1, 1], [1, 1, -1, -1, -1, -1, -1, 1, 1, 1, 1, -1, 1, 1, 1, -1, 1, 1, -1, 1, 1, 1, -1, 1, 1]],
}
def _cmateq5(sum_fac: int, gen_vec: Dict[int, List[int]]) -> np.ndarray:
'''
create a conference matrix of sum_fac: 4, 6, 8, 12, 14, 18, 20, 24, 30, 32, 38, 42, 44, 48
Parameters
----------
sum_fac: int
sum of the number of factors(n_factor) and the number of fake factor
gen_vec: dict[int, List[int]]
list of vectors used for generating conference matrix
returns
-------
cmat: np.ndarray(sum_fac * sum_fac if sum_fac is even)
conference matrix
Note
----
conference matrices are constructed as:
([0, ones(1, sum_fac - 1)],
[ones(sum_fac - 1, 0), S])
S is a circulant (0, ±1)-matrix of order(sum_fac - 1), which is generated by gen_vec
'''
v = gen_vec[sum_fac]
s = np.concatenate([np.roll(v, i).reshape(1, -1) for i in range(len(v))], axis=0)
one_vec = np.array([1 for i in range(sum_fac-1)]).reshape(-1, 1)
temp0 = np.concatenate([np.array([0]), -one_vec.reshape(-1)]).reshape(1, -1)
temp1 = np.concatenate([one_vec, s], axis=1)
c_mat = np.concatenate([temp0, temp1], axis=0)
return c_mat
def _cmateq2(sum_fac: int, gen_vec: Dict[int, List[List[int]]]) -> np.ndarray:
'''
create a conference matrix of sum_fac: 10, 22, 26, 34, 50
Parameters
----------
sum_fac: int
sum of the number of factors(n_factor) and the number of fake factor
gen_vec: dict[int, List[int]]
list of vectors used for generating conference matrix
returns
-------
cmat: np.ndarray(sum_fac * sum_fac if sum_fac is even)
conference matrix
Note
----
If A is a (0, ±1)-matrix of order m and B a ±1-matrix of the same order such that AB = BA and AA′ + BB′ = (2m − 1)I_m×m,
then the following conference matrix of order 2m can be constructed:
([A, B],
[B.T, -B.T])
A and B are two circulant matrices generated by gen_vec
Only for sum_fac = 22, alternative conference matrix is used based on the paper.
See below for details.
NGUYEN, N. & STYLIANOU, S. (2013).
Constructing Definitive Screening Designs Using Cyclic Generators.
Journal of Statistical Theory and Practice.
DOI: 10.1080/15598608.2013.781891
'''
v0 = gen_vec[sum_fac][0]
v1 = gen_vec[sum_fac][1]
a = np.concatenate([np.roll(v0, i).reshape(1, -1) for i in range(len(v0))], axis=0)
b = np.concatenate([np.roll(v1, i).reshape(1, -1) for i in range(len(v1))], axis=0)
c_mat = np.block([
[a, b],
[b.T, -a.T]
])
return c_mat
def _dsddb(sum_fac: int, gen_vec: Dict[int, List[int]]) -> np.ndarray:
'''
create a conference matrix of sum_fac: 16, 40
Parameters
----------
sum_fac: int
sum of the number of factors(n_factor) and the number of fake factor
gen_vec: dict[int, List[int]]
list of vectors used for generating conference matrix
returns
-------
cmat: np.ndarray(sum_fac * sum_fac if sum_fac is even)
conference matrix
Note
----
If A is a (0, ±1)-matrix of order 1/2(sum_fac) and B a ±1-matrix of the same order such that
AB = BA and AA′ + BB′ = (sum_fac − 1)I_1/2(sum_fac),1/2(sum_fac),
then the following conference matrix of order sum_fac can be constructed:
([A, B],
[B.T, -B.T])
A is the conference matrix of the order 1/2(sum_fac) constructed by the gen_vec and B = A + I
'''
half_fac = int(sum_fac/2)
a = _cmateq5(half_fac, gen_vec)
b = a + np.eye(int(sum_fac/2))
b = b.astype(int)
c_mat = np.block([
[a, b],
[b.T, -a.T]
])
return c_mat
def _dsdeq3(sum_fac: int, gen_vec: Dict[int, List[int]]) -> np.ndarray:
'''
create a conference matrix of sum_fac: 28, 36
Parameters
----------
sum_fac: int
sum of the number of factors(n_factor) and the number of fake factor
gen_vec: dict[int, List[int]]
list of vectors used for generating conference matrix
returns
-------
cmat: np.ndarray(sum_fac * sum_fac if sum_fac is even)
conference matrix
Note
----
If A is a circulant (0,±1)-matrix of order 1/4(sum_fac) and B,C,D are circulant ±1-matrices of the same order such that
AA′ + BB′ + CC′ + DD′ = (sum_fac − 1)I_1/4(sum_fac),1/4(sum_fac)
then the following conference matrix of order sum_fac can be constructed:
([A, BR, CR, DR],
[-BR, A, (D.T)R, -(C.T)R],
[-CR, -(D.T)R, A, (B.T)R],
[-DR, (C.T)R, -(B.T)R, A])
A, B, C, and D are four circulant matrices generated by gen_vec
'''
a = np.concatenate([np.roll(gen_vec[sum_fac][0], i).reshape(1, -1) for i in range(len(gen_vec[sum_fac][0]))], axis=0)
b = np.concatenate([np.roll(gen_vec[sum_fac][1], i).reshape(1, -1) for i in range(len(gen_vec[sum_fac][1]))], axis=0)
c = np.concatenate([np.roll(gen_vec[sum_fac][2], i).reshape(1, -1) for i in range(len(gen_vec[sum_fac][2]))], axis=0)
d = np.concatenate([np.roll(gen_vec[sum_fac][3], i).reshape(1, -1) for i in range(len(gen_vec[sum_fac][3]))], axis=0)
r = np.eye(len(gen_vec[sum_fac][0]), dtype=int)[::-1]
c_mat = np.block([
[a, b @ r, c @ r, d @ r],
[-b @ r, a, d.T @ r, -c.T @ r],
[-c @ r, -d.T @ r, a, b.T @ r],
[-d @ r, c.T @ r, -b.T @ r, a]
])
return c_mat
def _dsd46():
cmat = np.array([
[ 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[ 1, 0, 1, 1, -1, 1, -1, -1, -1, 1, 1, 1, -1, -1, 1, 1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, -1, 1, -1, -1, -1, 1, 1, 1, -1, -1, -1, 1, -1, 1, -1, 1, 1, 1, 1, -1],
[ 1, 1, 0, 1, -1, -1, 1, 1, -1, -1, -1, 1, 1, 1, -1, 1, 1, 1, -1, -1, -1, 1, -1, -1, 1, -1, -1, 1, -1, -1, -1, -1, -1, -1, 1, 1, 1, 1, 1, -1, 1, -1, 1, -1, 1, 1],
[ 1, 1, 1, 0, 1, -1, -1, -1, 1, -1, 1, -1, 1, 1, 1, -1, -1, 1, 1, -1, -1, 1, -1, -1, 1, -1, -1, 1, 1, 1, 1, -1, -1, -1, -1, -1, -1, -1, 1, 1, 1, 1, -1, 1, -1, 1],
[ 1, -1, -1, 1, 0, 1, 1, -1, 1, -1, -1, 1, 1, 1, -1, 1, 1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, -1, -1, -1, -1, 1, 1, 1, -1, -1, -1, -1, 1, 1, 1, 1, -1, 1, -1, 1],
[ 1, 1, -1, -1, 1, 0, 1, -1, -1, 1, 1, -1, 1, 1, 1, -1, -1, 1, 1, 1, -1, -1, 1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, -1, 1, 1, 1, 1, -1, 1, -1, 1, 1, 1, 1, -1],
[ 1, -1, 1, -1, 1, 1, 0, 1, -1, -1, 1, 1, -1, -1, 1, 1, 1, -1, 1, 1, -1, -1, 1, -1, -1, 1, -1, -1, 1, 1, 1, -1, -1, -1, -1, -1, -1, 1, 1, -1, 1, -1, 1, -1, 1, 1],
[ 1, -1, 1, -1, -1, -1, 1, 0, 1, 1, 1, -1, 1, 1, 1, -1, -1, 1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, -1, -1, -1, 1, 1, 1, -1, -1, -1, 1, 1, -1, 1, -1, 1, -1, 1, 1],
[ 1, -1, -1, 1, 1, -1, -1, 1, 0, 1, 1, 1, -1, -1, 1, 1, 1, -1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, 1, 1, 1, -1, 1, 1, 1, 1, -1, 1, -1, 1],
[ 1, 1, -1, -1, -1, 1, -1, 1, 1, 0, -1, 1, 1, 1, -1, 1, 1, 1, -1, -1, 1, -1, -1, 1, -1, -1, 1, -1, 1, 1, 1, -1, -1, -1, -1, -1, -1, 1, -1, 1, -1, 1, 1, 1, 1, -1],
[ 1, 1, -1, 1, -1, 1, 1, 1, 1, -1, 0, 1, 1, -1, 1, -1, -1, -1, 1, 1, 1, -1, -1, 1, 1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, -1, 1, -1, -1, -1, 1, 1, 1, -1, -1, -1],
[ 1, 1, 1, -1, 1, -1, 1, -1, 1, 1, 1, 0, 1, -1, -1, 1, 1, -1, -1, -1, 1, 1, 1, -1, 1, 1, 1, -1, -1, -1, 1, -1, -1, 1, -1, -1, 1, -1, -1, -1, -1, -1, -1, 1, 1, 1],
[ 1, -1, 1, 1, 1, 1, -1, 1, -1, 1, 1, 1, 0, 1, -1, -1, -1, 1, -1, 1, -1, 1, 1, 1, -1, -1, 1, 1, -1, -1, 1, -1, -1, 1, -1, -1, 1, 1, 1, 1, -1, -1, -1, -1, -1, -1],
[ 1, -1, 1, 1, 1, 1, -1, 1, -1, 1, -1, -1, 1, 0, 1, 1, -1, 1, -1, -1, 1, 1, 1, -1, 1, 1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, -1, -1, -1, -1, 1, 1, 1, -1, -1, -1],
[ 1, 1, -1, 1, -1, 1, 1, 1, 1, -1, 1, -1, -1, 1, 0, 1, -1, -1, 1, 1, -1, 1, 1, 1, -1, -1, 1, 1, 1, -1, -1, 1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, -1, 1, 1, 1],
[ 1, 1, 1, -1, 1, -1, 1, -1, 1, 1, -1, 1, -1, 1, 1, 0, 1, -1, -1, 1, 1, -1, -1, 1, 1, 1, -1, 1, 1, -1, -1, 1, -1, -1, 1, -1, -1, 1, 1, 1, -1, -1, -1, -1, -1, -1],
[ 1, 1, 1, -1, 1, -1, 1, -1, 1, 1, -1, 1, -1, -1, -1, 1, 0, 1, 1, 1, -1, 1, 1, 1, -1, -1, 1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, -1, -1, -1, 1, 1, 1, -1, -1, -1],
[ 1, -1, 1, 1, 1, 1, -1, 1, -1, 1, -1, -1, 1, 1, -1, -1, 1, 0, 1, 1, 1, -1, -1, 1, 1, 1, -1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, 1, 1, 1],
[ 1, 1, -1, 1, -1, 1, 1, 1, 1, -1, 1, -1, -1, -1, 1, -1, 1, 1, 0, -1, 1, 1, 1, -1, 1, 1, 1, -1, -1, 1, -1, -1, 1, -1, -1, 1, -1, 1, 1, 1, -1, -1, -1, -1, -1, -1],
[ 1, -1, -1, -1, 1, 1, 1, -1, -1, -1, 1, -1, 1, -1, 1, 1, 1, 1, -1, 0, 1, 1, -1, 1, -1, -1, -1, 1, 1, 1, -1, -1, 1, 1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, -1, 1],
[ 1, -1, -1, -1, -1, -1, -1, 1, 1, 1, 1, 1, -1, 1, -1, 1, -1, 1, 1, 1, 0, 1, -1, -1, 1, 1, -1, -1, -1, 1, 1, 1, -1, 1, 1, 1, -1, -1, -1, 1, -1, -1, 1, -1, -1, 1],
[ 1, 1, 1, 1, -1, -1, -1, -1, -1, -1, -1, 1, 1, 1, 1, -1, 1, -1, 1, 1, 1, 0, 1, -1, -1, -1, 1, -1, 1, -1, 1, 1, 1, -1, -1, 1, 1, -1, -1, 1, -1, -1, 1, -1, -1, 1],
[ 1, -1, -1, -1, 1, 1, 1, -1, -1, -1, -1, 1, 1, 1, 1, -1, 1, -1, 1, -1, -1, 1, 0, 1, 1, -1, 1, -1, -1, 1, 1, 1, -1, 1, 1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, -1],
[ 1, -1, -1, -1, -1, -1, -1, 1, 1, 1, 1, -1, 1, -1, 1, 1, 1, 1, -1, 1, -1, -1, 1, 0, 1, -1, -1, 1, 1, -1, 1, 1, 1, -1, -1, 1, 1, 1, -1, -1, 1, -1, -1, 1, -1, -1],
[ 1, 1, 1, 1, -1, -1, -1, -1, -1, -1, 1, 1, -1, 1, -1, 1, -1, 1, 1, -1, 1, -1, 1, 1, 0, 1, -1, -1, 1, 1, -1, -1, 1, 1, 1, -1, 1, 1, -1, -1, 1, -1, -1, 1, -1, -1],
[ 1, -1, -1, -1, 1, 1, 1, -1, -1, -1, 1, 1, -1, 1, -1, 1, -1, 1, 1, -1, 1, -1, -1, -1, 1, 0, 1, 1, 1, -1, 1, 1, 1, -1, -1, 1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1],
[ 1, -1, -1, -1, -1, -1, -1, 1, 1, 1, -1, 1, 1, 1, 1, -1, 1, -1, 1, -1, -1, 1, 1, -1, -1, 1, 0, 1, 1, 1, -1, -1, 1, 1, 1, -1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1],
[ 1, 1, 1, 1, -1, -1, -1, -1, -1, -1, 1, -1, 1, -1, 1, 1, 1, 1, -1, 1, -1, -1, -1, 1, -1, 1, 1, 0, -1, 1, 1, 1, -1, 1, 1, 1, -1, -1, 1, -1, -1, 1, -1, -1, 1, -1],
[ 1, -1, -1, 1, -1, -1, 1, -1, -1, 1, -1, -1, -1, 1, 1, 1, -1, -1, -1, 1, -1, 1, -1, 1, 1, 1, 1, -1, 0, 1, 1, -1, 1, -1, -1, -1, 1, 1, 1, -1, -1, 1, 1, 1, -1, 1],
[ 1, -1, -1, 1, -1, -1, 1, -1, -1, 1, -1, -1, -1, -1, -1, -1, 1, 1, 1, 1, 1, -1, 1, -1, 1, -1, 1, 1, 1, 0, 1, -1, -1, 1, 1, -1, -1, -1, 1, 1, 1, -1, 1, 1, 1, -1],
[ 1, -1, -1, 1, -1, -1, 1, -1, -1, 1, 1, 1, 1, -1, -1, -1, -1, -1, -1, -1, 1, 1, 1, 1, -1, 1, -1, 1, 1, 1, 0, 1, -1, -1, -1, 1, -1, 1, -1, 1, 1, 1, -1, -1, 1, 1],
[ 1, 1, -1, -1, 1, -1, -1, 1, -1, -1, -1, -1, -1, 1, 1, 1, -1, -1, -1, -1, 1, 1, 1, 1, -1, 1, -1, 1, -1, -1, 1, 0, 1, 1, -1, 1, -1, -1, 1, 1, 1, -1, 1, 1, 1, -1],
[ 1, 1, -1, -1, 1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, -1, 1, 1, 1, 1, -1, 1, -1, 1, 1, 1, 1, -1, 1, -1, -1, 1, 0, 1, -1, -1, 1, 1, -1, 1, 1, 1, -1, -1, 1, 1],
[ 1, 1, -1, -1, 1, -1, -1, 1, -1, -1, 1, 1, 1, -1, -1, -1, -1, -1, -1, 1, 1, -1, 1, -1, 1, -1, 1, 1, -1, 1, -1, 1, 1, 0, 1, -1, -1, 1, 1, -1, -1, 1, 1, 1, -1, 1],
[ 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, -1, -1, -1, 1, 1, 1, -1, -1, -1, 1, 1, -1, 1, -1, 1, -1, 1, 1, -1, 1, -1, -1, -1, 1, 0, 1, 1, 1, -1, 1, 1, 1, -1, -1, 1, 1],
[ 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, 1, 1, 1, -1, 1, 1, 1, 1, -1, 1, -1, 1, -1, -1, 1, 1, -1, -1, 1, 0, 1, 1, 1, -1, -1, 1, 1, 1, -1, 1],
[ 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, 1, 1, 1, -1, -1, -1, -1, -1, -1, 1, -1, 1, -1, 1, 1, 1, 1, -1, 1, -1, -1, -1, 1, -1, 1, 1, 0, -1, 1, 1, 1, -1, 1, 1, 1, -1],
[ 1, 1, 1, -1, -1, 1, 1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, -1, 1, -1, -1, -1, 1, 1, 1, -1, -1, -1, 1, -1, 1, -1, 1, 1, 1, 1, -1, 0, 1, 1, -1, 1, -1, -1, -1, 1],
[ 1, -1, 1, 1, 1, -1, 1, 1, 1, -1, -1, -1, 1, -1, -1, 1, -1, -1, 1, -1, -1, -1, -1, -1, -1, 1, 1, 1, 1, 1, -1, 1, -1, 1, -1, 1, 1, 1, 0, 1, -1, -1, 1, 1, -1, -1],
[ 1, 1, -1, 1, 1, 1, -1, -1, 1, 1, -1, -1, 1, -1, -1, 1, -1, -1, 1, 1, 1, 1, -1, -1, -1, -1, -1, -1, -1, 1, 1, 1, 1, -1, 1, -1, 1, 1, 1, 0, 1, -1, -1, -1, 1, -1],
[ 1, -1, 1, 1, 1, -1, 1, 1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, -1, -1, -1, -1, 1, 1, 1, -1, -1, -1, -1, 1, 1, 1, 1, -1, 1, -1, 1, -1, -1, 1, 0, 1, 1, -1, 1, -1],
[ 1, 1, -1, 1, 1, 1, -1, -1, 1, 1, 1, -1, -1, 1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, -1, 1, 1, 1, 1, -1, 1, -1, 1, 1, 1, 1, -1, 1, -1, -1, 1, 0, 1, -1, -1, 1],
[ 1, 1, 1, -1, -1, 1, 1, 1, -1, 1, 1, -1, -1, 1, -1, -1, 1, -1, -1, 1, 1, 1, -1, -1, -1, -1, -1, -1, 1, 1, -1, 1, -1, 1, -1, 1, 1, -1, 1, -1, 1, 1, 0, 1, -1, -1],
[ 1, 1, -1, 1, 1, 1, -1, -1, 1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, -1, -1, -1, 1, 1, 1, -1, -1, -1, 1, 1, -1, 1, -1, 1, -1, 1, 1, -1, 1, -1, -1, -1, 1, 0, 1, 1],
[ 1, 1, 1, -1, -1, 1, 1, 1, -1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, 1, 1, 1, -1, 1, 1, 1, 1, -1, 1, -1, 1, -1, -1, 1, 1, -1, -1, 1, 0, 1],
[ 1, -1, 1, 1, 1, -1, 1, 1, 1, -1, -1, 1, -1, -1, 1, -1, -1, 1, -1, 1, 1, 1, -1, -1, -1, -1, -1, -1, 1, -1, 1, -1, 1, 1, 1, 1, -1, 1, -1, -1, -1, 1, -1, 1, 1, 0]
])
return cmat
def _get_dsd(n_factor: int, c_mat: np.ndarray) -> np.ndarray:
'''
create a definitive screening design from conference matrix
Parameters
----------
n_factor: int
number of factors used in the experiment
c_mat: np.ndarray
conference matrix
Returns
-------
d_mat(: np.ndarray(2(n_factor+n_fake) + 1) * n_factor) if n_factor+n_fake is even)
experiment design of dsd
n_fake = len(c_mat) - n_factor
Note
----
The design matrix for a DSD can be written as
([C],
[C],
[zeros(1, len(C))]
)
'''
zero_vec = np.zeros((1, c_mat.shape[1]), dtype=int)
d_mat = np.concatenate([c_mat, -c_mat, zero_vec], axis=0)[:, :n_factor]
return d_mat
| 64.432836
| 195
| 0.369527
| 3,756
| 17,268
| 1.669329
| 0.044196
| 0.803828
| 1.1689
| 1.515789
| 0.774482
| 0.759011
| 0.739872
| 0.717225
| 0.693301
| 0.673365
| 0
| 0.251711
| 0.339935
| 17,268
| 267
| 196
| 64.674157
| 0.296719
| 0.196606
| 0
| 0.121951
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.04878
| false
| 0
| 0.01626
| 0
| 0.113821
| 0
| 0
| 0
| 1
| null | 1
| 1
| 1
| 0
| 1
| 1
| 1
| 0
| 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
|
99977f0abbea58b01cf937083f08cd153e43d73f
| 5,574
|
py
|
Python
|
api/alembic/versions/004ac48ffe18_indices.py
|
bcgov/wps
|
71df0de72de9cd656dc9ebf8461ffe47cfb155f6
|
[
"Apache-2.0"
] | 19
|
2020-01-31T21:51:31.000Z
|
2022-01-07T14:40:03.000Z
|
api/alembic/versions/004ac48ffe18_indices.py
|
bcgov/wps
|
71df0de72de9cd656dc9ebf8461ffe47cfb155f6
|
[
"Apache-2.0"
] | 1,680
|
2020-01-24T23:25:08.000Z
|
2022-03-31T23:50:27.000Z
|
api/alembic/versions/004ac48ffe18_indices.py
|
bcgov/wps
|
71df0de72de9cd656dc9ebf8461ffe47cfb155f6
|
[
"Apache-2.0"
] | 6
|
2020-04-28T22:41:08.000Z
|
2021-05-05T18:16:06.000Z
|
"""indices
Revision ID: 004ac48ffe18
Revises: 81c96876355a
Create Date: 2021-03-29 18:24:10.485482
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = '004ac48ffe18'
down_revision = '81c96876355a'
branch_labels = None
depends_on = None
def upgrade():
# ### commands auto generated by Alembic ###
op.create_index(op.f('ix_hourly_actuals_rh_valid'), 'hourly_actuals', ['rh_valid'], unique=False)
op.create_index(op.f('ix_hourly_actuals_station_code'), 'hourly_actuals', ['station_code'], unique=False)
op.create_index(op.f('ix_hourly_actuals_temp_valid'), 'hourly_actuals', ['temp_valid'], unique=False)
op.create_index(op.f('ix_hourly_actuals_weather_date'), 'hourly_actuals', ['weather_date'], unique=False)
op.create_index(op.f('ix_model_run_grid_subset_predictions_prediction_model_grid_subset_id'),
'model_run_grid_subset_predictions', ['prediction_model_grid_subset_id'], unique=False)
op.create_index(op.f('ix_model_run_grid_subset_predictions_prediction_model_run_timestamp_id'),
'model_run_grid_subset_predictions', ['prediction_model_run_timestamp_id'], unique=False)
op.create_index(op.f('ix_model_run_grid_subset_predictions_prediction_timestamp'),
'model_run_grid_subset_predictions', ['prediction_timestamp'], unique=False)
op.create_index(op.f('ix_noon_forecasts_created_at'), 'noon_forecasts', ['created_at'], unique=False)
op.create_index(op.f('ix_noon_forecasts_station_code'), 'noon_forecasts', ['station_code'], unique=False)
op.create_index(op.f('ix_noon_forecasts_weather_date'), 'noon_forecasts', ['weather_date'], unique=False)
op.create_index(op.f('ix_prediction_model_grid_subsets_prediction_model_id'),
'prediction_model_grid_subsets', ['prediction_model_id'], unique=False)
op.create_index(op.f('ix_prediction_model_run_timestamps_prediction_model_id'),
'prediction_model_run_timestamps', ['prediction_model_id'], unique=False)
op.create_index(op.f('ix_prediction_model_run_timestamps_prediction_run_timestamp'),
'prediction_model_run_timestamps', ['prediction_run_timestamp'], unique=False)
op.create_index(op.f('ix_prediction_models_abbreviation'),
'prediction_models', ['abbreviation'], unique=False)
op.drop_constraint('processed_model_run_files_url_key', 'processed_model_run_urls', type_='unique')
op.create_index(op.f('ix_processed_model_run_urls_url'), 'processed_model_run_urls', ['url'], unique=True)
op.create_index(op.f('ix_weather_station_model_predictions_prediction_model_run_timestamp_id'),
'weather_station_model_predictions', ['prediction_model_run_timestamp_id'], unique=False)
op.create_index(op.f('ix_weather_station_model_predictions_prediction_timestamp'),
'weather_station_model_predictions', ['prediction_timestamp'], unique=False)
op.create_index(op.f('ix_weather_station_model_predictions_station_code'),
'weather_station_model_predictions', ['station_code'], unique=False)
# ### end Alembic commands ###
def downgrade():
# ### commands auto generated by Alembic ###
op.drop_index(op.f('ix_weather_station_model_predictions_station_code'),
table_name='weather_station_model_predictions')
op.drop_index(op.f('ix_weather_station_model_predictions_prediction_timestamp'),
table_name='weather_station_model_predictions')
op.drop_index(op.f('ix_weather_station_model_predictions_prediction_model_run_timestamp_id'),
table_name='weather_station_model_predictions')
op.drop_index(op.f('ix_processed_model_run_urls_url'), table_name='processed_model_run_urls')
op.create_unique_constraint('processed_model_run_files_url_key', 'processed_model_run_urls', ['url'])
op.drop_index(op.f('ix_prediction_models_abbreviation'), table_name='prediction_models')
op.drop_index(op.f('ix_prediction_model_run_timestamps_prediction_run_timestamp'),
table_name='prediction_model_run_timestamps')
op.drop_index(op.f('ix_prediction_model_run_timestamps_prediction_model_id'),
table_name='prediction_model_run_timestamps')
op.drop_index(op.f('ix_prediction_model_grid_subsets_prediction_model_id'),
table_name='prediction_model_grid_subsets')
op.drop_index(op.f('ix_noon_forecasts_weather_date'), table_name='noon_forecasts')
op.drop_index(op.f('ix_noon_forecasts_station_code'), table_name='noon_forecasts')
op.drop_index(op.f('ix_noon_forecasts_created_at'), table_name='noon_forecasts')
op.drop_index(op.f('ix_model_run_grid_subset_predictions_prediction_timestamp'),
table_name='model_run_grid_subset_predictions')
op.drop_index(op.f('ix_model_run_grid_subset_predictions_prediction_model_run_timestamp_id'),
table_name='model_run_grid_subset_predictions')
op.drop_index(op.f('ix_model_run_grid_subset_predictions_prediction_model_grid_subset_id'),
table_name='model_run_grid_subset_predictions')
op.drop_index(op.f('ix_hourly_actuals_weather_date'), table_name='hourly_actuals')
op.drop_index(op.f('ix_hourly_actuals_temp_valid'), table_name='hourly_actuals')
op.drop_index(op.f('ix_hourly_actuals_station_code'), table_name='hourly_actuals')
op.drop_index(op.f('ix_hourly_actuals_rh_valid'), table_name='hourly_actuals')
# ### end Alembic commands ###
| 66.357143
| 110
| 0.758701
| 751
| 5,574
| 5.10253
| 0.10253
| 0.065762
| 0.075157
| 0.093946
| 0.85856
| 0.845772
| 0.81237
| 0.760438
| 0.705376
| 0.672756
| 0
| 0.011127
| 0.129351
| 5,574
| 83
| 111
| 67.156627
| 0.778488
| 0.045748
| 0
| 0.123077
| 0
| 0
| 0.550738
| 0.466679
| 0
| 0
| 0
| 0
| 0
| 1
| 0.030769
| false
| 0
| 0.030769
| 0
| 0.061538
| 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
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
99a5a6a0c42b6294fcc61e31dd4f994cc5f6e08e
| 8,725
|
py
|
Python
|
src/repoAnalysis.py
|
jasper-xian/github-collab-analyses
|
a07e00952eda5d0d85e9a4faccc7d238bb0e5191
|
[
"MIT"
] | null | null | null |
src/repoAnalysis.py
|
jasper-xian/github-collab-analyses
|
a07e00952eda5d0d85e9a4faccc7d238bb0e5191
|
[
"MIT"
] | null | null | null |
src/repoAnalysis.py
|
jasper-xian/github-collab-analyses
|
a07e00952eda5d0d85e9a4faccc7d238bb0e5191
|
[
"MIT"
] | null | null | null |
import github
from fileAuthorScore import fileAuthorScore
from github import Github
import requests
import json
import cryptocode
import pickle
import concurrent.futures
class GitAnalysis:
def __init__(self):
self.fileDict = {}
self.g = None
self.repoName = ""
def clearCurrentFileName(self):
self.currentFileName = ""
def changeCurrentFileName(self, name):
self.currentFileName = name
def setRepoName(self, repoName):
self.repoName = repoName
def setG(self, token):
self.g = Github(token)
def authorsPerFileCommits(self):
repo = self.g.get_repo(self.repoName)
commits = repo.get_commits()
with concurrent.futures.ThreadPoolExecutor(max_workers=commits.totalCount) as executor:
list(map(lambda x: executor.submit(self.addCommit, x), commits))
def authorsPerFilePulls(self):
repo = self.g.get_repo(self.repoName)
pulls = repo.get_pulls("all")
with concurrent.futures.ThreadPoolExecutor(max_workers=pulls.totalCount) as executor:
list(map(lambda x: executor.submit(self.addPull, x), pulls))
def addCommit(self, commit):
files = commit.files
author = 0
if commit.author is None:
if commit.commit.author is None:
return
else:
author = "*" + commit.commit.author.name
else:
author = commit.author.login
if commit.get_pulls().totalCount != 0:
for file in files:
fileName = file.filename
if fileName in self.fileDict.keys():
if author in self.fileDict[fileName].keys():
self.fileDict[fileName][author].addAdditions(file.additions * 0.2)
self.fileDict[fileName][author].addDeletions(file.deletions * 0.2)
self.fileDict[fileName][author].addChanges(file.changes * 0.2)
else:
self.fileDict[fileName][author] = fileAuthorScore(file.additions * 0.2, file.deletions * 0.2, file.changes * 0.2)
else:
self.fileDict[fileName] = {}
self.fileDict[fileName][author] = fileAuthorScore(file.additions * 0.2, file.deletions * 0.2, file.changes * 0.2)
else:
for file in files:
fileName = file.filename
if fileName in self.fileDict.keys():
if author in self.fileDict[fileName].keys():
self.fileDict[fileName][author].addAdditions(file.additions)
self.fileDict[fileName][author].addDeletions(file.deletions)
self.fileDict[fileName][author].addChanges(file.changes)
else:
self.fileDict[fileName][author] = fileAuthorScore(file.additions, file.deletions, file.changes)
else:
self.fileDict[fileName] = {}
self.fileDict[fileName][author] = fileAuthorScore(file.additions, file.deletions, file.changes)
if file.status == "added" or file.status == "renamed":
self.fileDict[fileName][author].changeIsOriginalAuthor(True)
return True
def addPull(self, pull):
files = pull.get_files()
user = 0
if pull.user is None:
return
else:
user = pull.user.login
for file in files:
fileName = file.filename
if fileName in self.fileDict.keys():
if user in self.fileDict[fileName].keys():
self.fileDict[fileName][user].addAdditions(file.additions * 0.6)
self.fileDict[fileName][user].addDeletions(file.deletions * 0.6)
self.fileDict[fileName][user].addChanges(file.changes * 0.6)
else:
self.fileDict[fileName][user] = fileAuthorScore(file.additions * 0.6, file.deletions * 0.6, file.changes * 0.6)
else:
self.fileDict[fileName] = {}
self.fileDict[fileName][user] = fileAuthorScore(file.additions * 0.6, file.deletions * 0.6, file.changes * 0.6)
if file.status == "added" or file.status == "renamed":
self.fileDict[fileName][user].changeIsOriginalAuthor(True)
return True
# def authorsPerFileCommits(self):
# repo = self.g.get_repo(self.repoName)
# commits = repo.get_commits()
# count = 0
# for commit in commits:
# files = commit.files
# author = 0
# if commit.author is None:
# if commit.commit.author is None:
# continue
# else:
# author = "*" + commit.commit.author.name
# else:
# author = commit.author.login
# if commit.get_pulls().totalCount != 0:
# for file in files:
# fileName = file.filename
# if fileName in self.fileDict.keys():
# if author in self.fileDict[fileName].keys():
# self.fileDict[fileName][author].addAdditions(file.additions * 0.2)
# self.fileDict[fileName][author].addDeletions(file.deletions * 0.2)
# self.fileDict[fileName][author].addChanges(file.changes * 0.2)
# else:
# self.fileDict[fileName][author] = fileAuthorScore(file.additions * 0.2, file.deletions * 0.2, file.changes * 0.2)
# else:
# self.fileDict[fileName] = {}
# self.fileDict[fileName][author] = fileAuthorScore(file.additions * 0.2, file.deletions * 0.2, file.changes * 0.2)
# else:
# for file in files:
# fileName = file.filename
# if fileName in self.fileDict.keys():
# if author in self.fileDict[fileName].keys():
# self.fileDict[fileName][author].addAdditions(file.additions)
# self.fileDict[fileName][author].addDeletions(file.deletions)
# self.fileDict[fileName][author].addChanges(file.changes)
# else:
# self.fileDict[fileName][author] = fileAuthorScore(file.additions, file.deletions, file.changes)
# else:
# self.fileDict[fileName] = {}
# self.fileDict[fileName][author] = fileAuthorScore(file.additions, file.deletions, file.changes)
# if file.status == "added" or file.status == "renamed":
# self.fileDict[fileName][author].changeIsOriginalAuthor(True)
# #count = count + 1
# if count > 100:
# break
# def authorsPerFilePulls(self):
# repo = self.g.get_repo(self.repoName)
# pulls = repo.get_pulls("all")
# for pull in pulls:
# files = pull.get_files()
# user = 0
# if pull.user is None:
# continue
# else:
# user = pull.user.login
# for file in files:
# fileName = file.filename
# if fileName in self.fileDict.keys():
# if user in self.fileDict[fileName].keys():
# self.fileDict[fileName][user].addAdditions(file.additions * 0.6)
# self.fileDict[fileName][user].addDeletions(file.deletions * 0.6)
# self.fileDict[fileName][user].addChanges(file.changes * 0.6)
# else:
# self.fileDict[fileName][user] = fileAuthorScore(file.additions * 0.6, file.deletions * 0.6, file.changes * 0.6)
# else:
# self.fileDict[fileName] = {}
# self.fileDict[fileName][user] = fileAuthorScore(file.additions * 0.6, file.deletions * 0.6, file.changes * 0.6)
# if file.status == "added" or file.status == "renamed":
# self.fileDict[fileName][user].changeIsOriginalAuthor(True)
def isGitHubAuthor(self, login):
contributors = self.g.get_repo(self.repoName).get_contributors()
for contributor in contributors:
if contributor.login == login:
return True
return False
def clearFileDict(self):
self.fileDict = {}
def pickledFileDict(self):
return pickle.dumps(self.fileDict)
| 47.162162
| 143
| 0.543725
| 865
| 8,725
| 5.461272
| 0.10289
| 0.139712
| 0.19475
| 0.121084
| 0.837638
| 0.832769
| 0.806943
| 0.806943
| 0.806943
| 0.806943
| 0
| 0.014615
| 0.349112
| 8,725
| 185
| 144
| 47.162162
| 0.817221
| 0.401948
| 0
| 0.401961
| 0
| 0
| 0.005439
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.117647
| false
| 0
| 0.078431
| 0.009804
| 0.27451
| 0
| 0
| 0
| 0
| null | 0
| 1
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
41d33b481868cbb1b76beaa263932f648eaeb744
| 213
|
py
|
Python
|
cs/hmac/__init__.py
|
splunk-soar-connectors/crowdstrike
|
cea9d92522ff14b14146d85be2cee5a90c85823f
|
[
"Apache-2.0"
] | 1
|
2022-02-13T19:59:11.000Z
|
2022-02-13T19:59:11.000Z
|
cs/hmac/__init__.py
|
splunk-soar-connectors/crowdstrike
|
cea9d92522ff14b14146d85be2cee5a90c85823f
|
[
"Apache-2.0"
] | null | null | null |
cs/hmac/__init__.py
|
splunk-soar-connectors/crowdstrike
|
cea9d92522ff14b14146d85be2cee5a90c85823f
|
[
"Apache-2.0"
] | null | null | null |
try:
from client import get, post, put, delete, head, patch, Auth
except:
from .client import get, post, put, delete, head, patch, Auth
__all__ = ['get', 'post', 'put', 'delete', 'head', 'patch', 'Auth']
| 30.428571
| 67
| 0.633803
| 30
| 213
| 4.366667
| 0.433333
| 0.160305
| 0.229008
| 0.366412
| 0.908397
| 0.908397
| 0.908397
| 0.687023
| 0.687023
| 0.687023
| 0
| 0
| 0.187793
| 213
| 6
| 68
| 35.5
| 0.757225
| 0
| 0
| 0
| 0
| 0
| 0.13615
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.4
| 0
| 0.4
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 1
| 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
| 8
|
41fdb288ef495be2ff3607479135350b8cbf345c
| 39
|
py
|
Python
|
max_dump/__init__.py
|
mki/max_dump
|
99c5b180d9891349087f7a9d381b3aed1c78a5c3
|
[
"MIT"
] | 3
|
2019-07-01T05:31:04.000Z
|
2019-12-16T10:32:38.000Z
|
max_dump/__init__.py
|
mki/max_dump
|
99c5b180d9891349087f7a9d381b3aed1c78a5c3
|
[
"MIT"
] | null | null | null |
max_dump/__init__.py
|
mki/max_dump
|
99c5b180d9891349087f7a9d381b3aed1c78a5c3
|
[
"MIT"
] | 2
|
2019-12-12T04:00:18.000Z
|
2019-12-13T01:20:19.000Z
|
from .dump_cameras import dump_cameras
| 19.5
| 38
| 0.871795
| 6
| 39
| 5.333333
| 0.666667
| 0.6875
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.102564
| 39
| 1
| 39
| 39
| 0.914286
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
5124e51ca292f29faf5e444361d737a29e40fa79
| 22
|
py
|
Python
|
flipper/utils.py
|
yukinarit/flipper
|
8c4c0ae94ff2113a6723658b951cbab2f4eafb1f
|
[
"Unlicense"
] | 1
|
2020-10-24T14:17:41.000Z
|
2020-10-24T14:17:41.000Z
|
flipper/utils.py
|
yukinarit/flipper
|
8c4c0ae94ff2113a6723658b951cbab2f4eafb1f
|
[
"Unlicense"
] | null | null | null |
flipper/utils.py
|
yukinarit/flipper
|
8c4c0ae94ff2113a6723658b951cbab2f4eafb1f
|
[
"Unlicense"
] | null | null | null |
def print():
pass
| 7.333333
| 12
| 0.545455
| 3
| 22
| 4
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.318182
| 22
| 2
| 13
| 11
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| true
| 0.5
| 0
| 0
| 0.5
| 0.5
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 1
|
0
| 7
|
51495acadeb50b6e2121cc123900265aa0dffcdc
| 11,136
|
py
|
Python
|
py3canvas/tests/outcome_groups.py
|
tylerclair/py3canvas
|
7485d458606b65200f0ffa5bbe597a9d0bee189f
|
[
"MIT"
] | null | null | null |
py3canvas/tests/outcome_groups.py
|
tylerclair/py3canvas
|
7485d458606b65200f0ffa5bbe597a9d0bee189f
|
[
"MIT"
] | null | null | null |
py3canvas/tests/outcome_groups.py
|
tylerclair/py3canvas
|
7485d458606b65200f0ffa5bbe597a9d0bee189f
|
[
"MIT"
] | null | null | null |
"""OutcomeGroups API Tests for Version 1.0.
This is a testing template for the generated OutcomeGroupsAPI Class.
"""
import unittest
import requests
import secrets
from py3canvas.apis.outcome_groups import OutcomeGroupsAPI
from py3canvas.apis.outcome_groups import Outcomegroup
from py3canvas.apis.outcome_groups import Outcomelink
class TestOutcomeGroupsAPI(unittest.TestCase):
"""Tests for the OutcomeGroupsAPI."""
def setUp(self):
self.client = OutcomeGroupsAPI(secrets.instance_address, secrets.access_token)
def test_redirect_to_root_outcome_group_for_context_global(self):
"""Integration test for the OutcomeGroupsAPI.redirect_to_root_outcome_group_for_context_global method."""
r = self.client.redirect_to_root_outcome_group_for_context_global()
def test_redirect_to_root_outcome_group_for_context_accounts(self):
"""Integration test for the OutcomeGroupsAPI.redirect_to_root_outcome_group_for_context_accounts method."""
account_id = None # Change me!!
r = self.client.redirect_to_root_outcome_group_for_context_accounts(account_id)
def test_redirect_to_root_outcome_group_for_context_courses(self):
"""Integration test for the OutcomeGroupsAPI.redirect_to_root_outcome_group_for_context_courses method."""
course_id = None # Change me!!
r = self.client.redirect_to_root_outcome_group_for_context_courses(course_id)
def test_get_all_outcome_groups_for_context_accounts(self):
"""Integration test for the OutcomeGroupsAPI.get_all_outcome_groups_for_context_accounts method."""
account_id = None # Change me!!
r = self.client.get_all_outcome_groups_for_context_accounts(account_id)
def test_get_all_outcome_groups_for_context_courses(self):
"""Integration test for the OutcomeGroupsAPI.get_all_outcome_groups_for_context_courses method."""
course_id = None # Change me!!
r = self.client.get_all_outcome_groups_for_context_courses(course_id)
def test_get_all_outcome_links_for_context_accounts(self):
"""Integration test for the OutcomeGroupsAPI.get_all_outcome_links_for_context_accounts method."""
account_id = None # Change me!!
r = self.client.get_all_outcome_links_for_context_accounts(
account_id, outcome_group_style=None, outcome_style=None
)
def test_get_all_outcome_links_for_context_courses(self):
"""Integration test for the OutcomeGroupsAPI.get_all_outcome_links_for_context_courses method."""
course_id = None # Change me!!
r = self.client.get_all_outcome_links_for_context_courses(
course_id, outcome_group_style=None, outcome_style=None
)
def test_show_outcome_group_global(self):
"""Integration test for the OutcomeGroupsAPI.show_outcome_group_global method."""
id = None # Change me!!
r = self.client.show_outcome_group_global(id)
def test_show_outcome_group_accounts(self):
"""Integration test for the OutcomeGroupsAPI.show_outcome_group_accounts method."""
account_id = None # Change me!!
id = None # Change me!!
r = self.client.show_outcome_group_accounts(account_id, id)
def test_show_outcome_group_courses(self):
"""Integration test for the OutcomeGroupsAPI.show_outcome_group_courses method."""
course_id = None # Change me!!
id = None # Change me!!
r = self.client.show_outcome_group_courses(course_id, id)
def test_update_outcome_group_global(self):
"""Integration test for the OutcomeGroupsAPI.update_outcome_group_global method."""
# This method utilises the PUT request method and will make changes to the Canvas instance. This needs consideration.
pass
def test_update_outcome_group_accounts(self):
"""Integration test for the OutcomeGroupsAPI.update_outcome_group_accounts method."""
# This method utilises the PUT request method and will make changes to the Canvas instance. This needs consideration.
pass
def test_update_outcome_group_courses(self):
"""Integration test for the OutcomeGroupsAPI.update_outcome_group_courses method."""
# This method utilises the PUT request method and will make changes to the Canvas instance. This needs consideration.
pass
def test_delete_outcome_group_global(self):
"""Integration test for the OutcomeGroupsAPI.delete_outcome_group_global method."""
id = None # Change me!!
r = self.client.delete_outcome_group_global(id)
def test_delete_outcome_group_accounts(self):
"""Integration test for the OutcomeGroupsAPI.delete_outcome_group_accounts method."""
account_id = None # Change me!!
id = None # Change me!!
r = self.client.delete_outcome_group_accounts(account_id, id)
def test_delete_outcome_group_courses(self):
"""Integration test for the OutcomeGroupsAPI.delete_outcome_group_courses method."""
course_id = None # Change me!!
id = None # Change me!!
r = self.client.delete_outcome_group_courses(course_id, id)
def test_list_linked_outcomes_global(self):
"""Integration test for the OutcomeGroupsAPI.list_linked_outcomes_global method."""
id = None # Change me!!
r = self.client.list_linked_outcomes_global(id, outcome_style=None)
def test_list_linked_outcomes_accounts(self):
"""Integration test for the OutcomeGroupsAPI.list_linked_outcomes_accounts method."""
account_id = None # Change me!!
id = None # Change me!!
r = self.client.list_linked_outcomes_accounts(
account_id, id, outcome_style=None
)
def test_list_linked_outcomes_courses(self):
"""Integration test for the OutcomeGroupsAPI.list_linked_outcomes_courses method."""
course_id = None # Change me!!
id = None # Change me!!
r = self.client.list_linked_outcomes_courses(course_id, id, outcome_style=None)
def test_create_link_outcome_global(self):
"""Integration test for the OutcomeGroupsAPI.create_link_outcome_global method."""
# This method utilises the POST request method and will make changes to the Canvas instance. This needs consideration.
pass
def test_create_link_outcome_global_outcome_id(self):
"""Integration test for the OutcomeGroupsAPI.create_link_outcome_global_outcome_id method."""
# This method utilises the PUT request method and will make changes to the Canvas instance. This needs consideration.
pass
def test_create_link_outcome_accounts(self):
"""Integration test for the OutcomeGroupsAPI.create_link_outcome_accounts method."""
# This method utilises the POST request method and will make changes to the Canvas instance. This needs consideration.
pass
def test_create_link_outcome_accounts_outcome_id(self):
"""Integration test for the OutcomeGroupsAPI.create_link_outcome_accounts_outcome_id method."""
# This method utilises the PUT request method and will make changes to the Canvas instance. This needs consideration.
pass
def test_create_link_outcome_courses(self):
"""Integration test for the OutcomeGroupsAPI.create_link_outcome_courses method."""
# This method utilises the POST request method and will make changes to the Canvas instance. This needs consideration.
pass
def test_create_link_outcome_courses_outcome_id(self):
"""Integration test for the OutcomeGroupsAPI.create_link_outcome_courses_outcome_id method."""
# This method utilises the PUT request method and will make changes to the Canvas instance. This needs consideration.
pass
def test_unlink_outcome_global(self):
"""Integration test for the OutcomeGroupsAPI.unlink_outcome_global method."""
id = None # Change me!!
outcome_id = None # Change me!!
r = self.client.unlink_outcome_global(id, outcome_id)
def test_unlink_outcome_accounts(self):
"""Integration test for the OutcomeGroupsAPI.unlink_outcome_accounts method."""
account_id = None # Change me!!
id = None # Change me!!
outcome_id = None # Change me!!
r = self.client.unlink_outcome_accounts(account_id, id, outcome_id)
def test_unlink_outcome_courses(self):
"""Integration test for the OutcomeGroupsAPI.unlink_outcome_courses method."""
course_id = None # Change me!!
id = None # Change me!!
outcome_id = None # Change me!!
r = self.client.unlink_outcome_courses(course_id, id, outcome_id)
def test_list_subgroups_global(self):
"""Integration test for the OutcomeGroupsAPI.list_subgroups_global method."""
id = None # Change me!!
r = self.client.list_subgroups_global(id)
def test_list_subgroups_accounts(self):
"""Integration test for the OutcomeGroupsAPI.list_subgroups_accounts method."""
account_id = None # Change me!!
id = None # Change me!!
r = self.client.list_subgroups_accounts(account_id, id)
def test_list_subgroups_courses(self):
"""Integration test for the OutcomeGroupsAPI.list_subgroups_courses method."""
course_id = None # Change me!!
id = None # Change me!!
r = self.client.list_subgroups_courses(course_id, id)
def test_create_subgroup_global(self):
"""Integration test for the OutcomeGroupsAPI.create_subgroup_global method."""
# This method utilises the POST request method and will make changes to the Canvas instance. This needs consideration.
pass
def test_create_subgroup_accounts(self):
"""Integration test for the OutcomeGroupsAPI.create_subgroup_accounts method."""
# This method utilises the POST request method and will make changes to the Canvas instance. This needs consideration.
pass
def test_create_subgroup_courses(self):
"""Integration test for the OutcomeGroupsAPI.create_subgroup_courses method."""
# This method utilises the POST request method and will make changes to the Canvas instance. This needs consideration.
pass
def test_import_outcome_group_global(self):
"""Integration test for the OutcomeGroupsAPI.import_outcome_group_global method."""
# This method utilises the POST request method and will make changes to the Canvas instance. This needs consideration.
pass
def test_import_outcome_group_accounts(self):
"""Integration test for the OutcomeGroupsAPI.import_outcome_group_accounts method."""
# This method utilises the POST request method and will make changes to the Canvas instance. This needs consideration.
pass
def test_import_outcome_group_courses(self):
"""Integration test for the OutcomeGroupsAPI.import_outcome_group_courses method."""
# This method utilises the POST request method and will make changes to the Canvas instance. This needs consideration.
pass
| 45.82716
| 126
| 0.730154
| 1,423
| 11,136
| 5.406887
| 0.057625
| 0.063946
| 0.108656
| 0.105797
| 0.95308
| 0.933455
| 0.899922
| 0.884845
| 0.783858
| 0.621653
| 0
| 0.000562
| 0.201509
| 11,136
| 242
| 127
| 46.016529
| 0.86471
| 0.477281
| 0
| 0.398374
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.308943
| false
| 0.121951
| 0.073171
| 0
| 0.390244
| 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
| 1
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 9
|
514e0ddb6d640d2271cde5f580e233e706cafa44
| 176
|
py
|
Python
|
gfdx/analysis/__init__.py
|
streamlit-badge-bot/gfdx
|
7dadc5240cd3be40aff458e227c02bfd3c5ecc12
|
[
"MIT"
] | null | null | null |
gfdx/analysis/__init__.py
|
streamlit-badge-bot/gfdx
|
7dadc5240cd3be40aff458e227c02bfd3c5ecc12
|
[
"MIT"
] | null | null | null |
gfdx/analysis/__init__.py
|
streamlit-badge-bot/gfdx
|
7dadc5240cd3be40aff458e227c02bfd3c5ecc12
|
[
"MIT"
] | null | null | null |
from . import analysis_potential_nutrient_intake
from . import foundational_documents
from . import gfdx_redcap_algorithm
from . import gfdx_redcap_who
from . import monitoring
| 35.2
| 48
| 0.863636
| 23
| 176
| 6.26087
| 0.565217
| 0.347222
| 0.194444
| 0.277778
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.107955
| 176
| 5
| 49
| 35.2
| 0.917197
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 8
|
5aa881a7bc23e3fd575dc2d08e0691f37bfa9b84
| 42
|
py
|
Python
|
src/networks/__init__.py
|
claudius-kienle/self-supervised-depth-denoising
|
4dffb30e8ef5022ef665825d26f45f67bf712cfd
|
[
"MIT"
] | 2
|
2021-12-02T15:06:28.000Z
|
2021-12-03T09:48:32.000Z
|
src/networks/__init__.py
|
claudius-kienle/self-supervised-depth-denoising
|
4dffb30e8ef5022ef665825d26f45f67bf712cfd
|
[
"MIT"
] | 23
|
2022-02-24T09:17:03.000Z
|
2022-03-21T16:57:58.000Z
|
src/networks/__init__.py
|
alr-internship/self-supervised-depth-denoising
|
4dffb30e8ef5022ef665825d26f45f67bf712cfd
|
[
"MIT"
] | null | null | null |
from . import UNet
from . import LSTMUNet
| 14
| 22
| 0.761905
| 6
| 42
| 5.333333
| 0.666667
| 0.625
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.190476
| 42
| 2
| 23
| 21
| 0.941176
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
5ac34a87645091063036eed20f608783dcdcc2b0
| 167
|
py
|
Python
|
djangae/contrib/gauth/sql.py
|
ikedaosushi/djangae
|
5fd2f8d70699fbbf155740effe42a36b205a6540
|
[
"BSD-3-Clause"
] | null | null | null |
djangae/contrib/gauth/sql.py
|
ikedaosushi/djangae
|
5fd2f8d70699fbbf155740effe42a36b205a6540
|
[
"BSD-3-Clause"
] | null | null | null |
djangae/contrib/gauth/sql.py
|
ikedaosushi/djangae
|
5fd2f8d70699fbbf155740effe42a36b205a6540
|
[
"BSD-3-Clause"
] | null | null | null |
import warnings
warnings.warn(
"djangae.contrib.gauth.sql is deprecated, please use djangae.contrib.gauth_sql instead"
)
from djangae.contrib.gauth_sql import *
| 20.875
| 91
| 0.790419
| 23
| 167
| 5.652174
| 0.565217
| 0.323077
| 0.438462
| 0.507692
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.125749
| 167
| 7
| 92
| 23.857143
| 0.890411
| 0
| 0
| 0
| 0
| 0
| 0.508982
| 0.299401
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.4
| 0
| 0.4
| 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
|
5ac5ae904109ff40c0da14378fc8031e575f4e95
| 225
|
py
|
Python
|
tests/test_utils.py
|
sasikala-binary/python-deriv-api
|
b40fca1b5ba06a47579f87258f41a50de9fb55fc
|
[
"MIT"
] | 2
|
2022-01-23T13:31:22.000Z
|
2022-03-04T23:26:41.000Z
|
tests/test_utils.py
|
sasikala-binary/python-deriv-api
|
b40fca1b5ba06a47579f87258f41a50de9fb55fc
|
[
"MIT"
] | 1
|
2021-12-20T14:55:03.000Z
|
2021-12-22T03:00:53.000Z
|
tests/test_utils.py
|
sasikala-binary/python-deriv-api
|
b40fca1b5ba06a47579f87258f41a50de9fb55fc
|
[
"MIT"
] | 4
|
2021-12-10T05:18:44.000Z
|
2022-03-07T20:06:11.000Z
|
from deriv_api.utils import dict_to_cache_key
import pickle
def test_dict_to_cache_key():
assert(pickle.loads(dict_to_cache_key({"hello": "world", "subscribe": 1, "passthrough": 1, "req_id": 1})) == {"hello": "world"})
| 32.142857
| 132
| 0.715556
| 35
| 225
| 4.257143
| 0.6
| 0.120805
| 0.221477
| 0.281879
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.015
| 0.111111
| 225
| 6
| 133
| 37.5
| 0.73
| 0
| 0
| 0
| 0
| 0
| 0.204444
| 0
| 0
| 0
| 0
| 0
| 0.25
| 1
| 0.25
| true
| 0.25
| 0.5
| 0
| 0.75
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 0
| 0
| 0
|
0
| 7
|
5acca2bf3aa586de9d30b3feab67bfd874aa29da
| 39
|
py
|
Python
|
EC2/backup_ec2/tests/test_todo.py
|
kyhau/aws-lambdas
|
be2f4de47f20dbee2157245895832d59a1e19c00
|
[
"Unlicense"
] | null | null | null |
EC2/backup_ec2/tests/test_todo.py
|
kyhau/aws-lambdas
|
be2f4de47f20dbee2157245895832d59a1e19c00
|
[
"Unlicense"
] | 1
|
2020-09-25T09:14:42.000Z
|
2020-09-28T09:13:43.000Z
|
EC2/backup_ec2/tests/test_todo.py
|
kyhau/aws-lambdas
|
be2f4de47f20dbee2157245895832d59a1e19c00
|
[
"Unlicense"
] | 2
|
2018-04-22T17:46:51.000Z
|
2021-09-25T05:28:31.000Z
|
def test_nothing():
assert 2+2==2*2
| 19.5
| 19
| 0.641026
| 8
| 39
| 3
| 0.625
| 0.25
| 0.25
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.125
| 0.179487
| 39
| 2
| 20
| 19.5
| 0.625
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.5
| 1
| 0.5
| true
| 0
| 0
| 0
| 0.5
| 0
| 1
| 1
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
5aef40b287e53d7f08ce0a9a3eebef068758c828
| 358
|
py
|
Python
|
test_get_speakable_weather.py
|
jluszcz/JakeSky
|
10a794817ff49773a57e6b900f74becf20613554
|
[
"MIT"
] | 1
|
2018-01-11T16:36:57.000Z
|
2018-01-11T16:36:57.000Z
|
test_get_speakable_weather.py
|
jluszcz/JakeSky
|
10a794817ff49773a57e6b900f74becf20613554
|
[
"MIT"
] | 3
|
2021-03-25T21:41:57.000Z
|
2021-06-01T21:49:14.000Z
|
test_get_speakable_weather.py
|
jluszcz/JakeSky
|
10a794817ff49773a57e6b900f74becf20613554
|
[
"MIT"
] | null | null | null |
import jakesky
def test_get_speakable_weather_summary():
assert 'Drizzling' == jakesky.get_speakable_weather_summary('Drizzle')
assert 'Raining' == jakesky.get_speakable_weather_summary('Raining')
def test_get_speakable_weather():
assert '65 and Sunny' == jakesky.get_speakable_weather(jakesky.Weather('2021-06-05T00:00:00Z', 'Sunny', 65.45))
| 35.8
| 115
| 0.77095
| 47
| 358
| 5.553191
| 0.446809
| 0.229885
| 0.363985
| 0.298851
| 0.452107
| 0
| 0
| 0
| 0
| 0
| 0
| 0.062305
| 0.103352
| 358
| 9
| 116
| 39.777778
| 0.750779
| 0
| 0
| 0
| 0
| 0
| 0.187151
| 0
| 0
| 0
| 0
| 0
| 0.5
| 1
| 0.333333
| true
| 0
| 0.166667
| 0
| 0.5
| 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
| 1
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
5af0d2d65f9b829cadd51ff719c535a50adeddd2
| 199
|
py
|
Python
|
QFTSampler/transformers/BaseTransformer.py
|
IntenF/QFTSampler
|
5324e1a11ed77bfc67aaef0902da4b32543e96cc
|
[
"MIT"
] | 2
|
2021-03-19T14:15:16.000Z
|
2022-02-13T14:34:52.000Z
|
QFTSampler/transformers/BaseTransformer.py
|
IntenF/QFTSampler
|
5324e1a11ed77bfc67aaef0902da4b32543e96cc
|
[
"MIT"
] | null | null | null |
QFTSampler/transformers/BaseTransformer.py
|
IntenF/QFTSampler
|
5324e1a11ed77bfc67aaef0902da4b32543e96cc
|
[
"MIT"
] | 1
|
2021-03-31T17:38:03.000Z
|
2021-03-31T17:38:03.000Z
|
class BaseTransformer:
def __init__(me):
pass
def phi(me):
raise NotImplementedError()
def update(me):
raise NotImplementedError()
def clear(me):
pass
| 19.9
| 35
| 0.592965
| 20
| 199
| 5.7
| 0.55
| 0.105263
| 0.45614
| 0.508772
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.321608
| 199
| 9
| 36
| 22.111111
| 0.844444
| 0
| 0
| 0.444444
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.444444
| false
| 0.222222
| 0
| 0
| 0.555556
| 0
| 1
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 1
| 0
|
0
| 7
|
51cae440dc5bffecd46bcbcfeca2bc1e0d03377f
| 12,487
|
py
|
Python
|
lom/_numba/posterior_score_fcts.py
|
TammoR/LogicalFactorisationMachines
|
55bd94001f2852ea61f69cbb07a0cbdb41231028
|
[
"Apache-2.0"
] | 19
|
2018-05-16T00:51:52.000Z
|
2022-02-02T10:04:13.000Z
|
lom/_numba/posterior_score_fcts.py
|
TammoR/LogicalOperatorMachines
|
55bd94001f2852ea61f69cbb07a0cbdb41231028
|
[
"Apache-2.0"
] | 1
|
2018-07-20T01:46:25.000Z
|
2019-01-10T14:44:42.000Z
|
lom/_numba/posterior_score_fcts.py
|
TammoR/LogicalOperatorMachines
|
55bd94001f2852ea61f69cbb07a0cbdb41231028
|
[
"Apache-2.0"
] | 6
|
2018-05-16T03:05:41.000Z
|
2020-10-08T06:34:07.000Z
|
#!/usr/bin/env python
"""
Posterior score functions for logical operator machines
"""
import numpy as np
from numba import jit
from numba.types import int64
# OR-AND
@jit('int16(int8[:], int8[:,:], int8[:], int16)', nopython=True, nogil=True)
def posterior_score_OR_AND_2D(Z_n, U, X_n, l):
"""
Return count of correct/incorrect explanations
caused by setting Z[n,l] to 1, respecting
explaining away dependencies
TODO: should this be given a signature?
"""
D, L = U.shape
score = 0
for d in range(D):
if U[d, l] != 1: # AND
continue
alrdy_active = False
for l_prime in range(L):
if (Z_n[l_prime] == 1) and\
(U[d, l_prime] == 1) and\
(l_prime != l):
alrdy_active = True # OR
break
if alrdy_active is False:
score += X_n[d]
return score
@jit('int64(int8[:], int8[:,:], int8[:,:], int8[:,:], int16)',
nopython=True, nogil=True)
def posterior_score_OR_AND_3D(Z_n, U, V, X_n, l):
"""
Return count of correct/incorrect explanations
caused by setting Z[n,l] to 1, respecting
explaining away dependencies
TODO: should this be given a signature?
"""
D, L = U.shape
M, _ = V.shape
score = int64(0)
for d in range(D):
for m in range(M):
if (U[d, l] != 1) or (V[m, l] != 1): # AND
continue
alrdy_active = False
for l_prime in range(L):
if (Z_n[l_prime] == 1) and\
(U[d, l_prime] == 1) and\
(V[m, l_prime] == 1) and\
(l_prime != l):
alrdy_active = True # OR
break
if alrdy_active is False:
score += X_n[d, m]
return score
# XOR-AND
@jit('int16(int8[:], int8[:,:], int8[:], int16)', nopython=True, nogil=True)
def posterior_score_XOR_AND_2D(Z_n, U, X_n, l):
"""
Return count of correct/incorrect explanations
caused by setting Z[n,l] to 1, respecting
explaining away dependencies
TODO: should this be given a signature?
"""
D, L = U.shape
score = 0
for d in range(D):
if U[d, l] != 1: # AND
continue
# compute deltaXOR-AND
num_active = np.int8(0)
for l_prime in range(L):
if (Z_n[l_prime] == 1) and\
(U[d, l_prime] == 1) and\
(l_prime != l):
num_active += 1
if num_active > 1:
break
if num_active == 0:
score += X_n[d]
elif num_active == 1:
score -= X_n[d]
return score
@jit('int64(int8[:], int8[:,:], int8[:,:], int8[:,:], int16)', nopython=True, nogil=True)
def posterior_score_XOR_AND_3D(Z_n, U, V, X_n, l):
"""
Return count of correct/incorrect explanations
caused by setting Z[n,l] to 1, respecting
explaining away dependencies
TODO: should this be given a signature?
"""
D, L = U.shape
M, _ = V.shape
score = int64(0)
for d in range(D):
for m in range(M):
if (U[d, l] != 1) or (V[m, l] != 1): # AND
continue
# compute deltaXOR-AND
num_active = np.int8(0)
for l_prime in range(L):
if (Z_n[l_prime] == 1) and\
(U[d, l_prime] == 1) and\
(V[m, l_prime] == 1) and\
(l_prime != l):
num_active += 1
if num_active > 1:
break
if num_active == 0:
score += X_n[d, m]
elif num_active == 1:
score -= X_n[d, m]
return score
# XOR-NAND
@jit('int16(int8[:], int8[:,:], int8[:], int16)', nopython=True, nogil=True)
def posterior_score_XOR_NAND_2D(Z_n, U, X_n, l):
"""
Return count of correct/incorrect explanations
caused by setting Z[n,l] to 1, respecting
explaining away dependencies
TODO: should this be given a signature?
"""
D, L = U.shape
score = 0
for d in range(D):
if U[d, l] != 1: # AND
continue
# compute deltaXOR-NAND
num_active = np.int8(0)
for l_prime in range(L):
if ((Z_n[l_prime] != 1) or (U[d, l_prime] != 1)) and\
(l_prime != l):
num_active += 1
if num_active > 1:
break
if num_active == 0:
score += X_n[d]
elif num_active == 1:
score -= X_n[d]
return -score
@jit('int64(int8[:], int8[:,:], int8[:,:], int8[:,:], int16)', nopython=True, nogil=True)
def posterior_score_XOR_NAND_3D(Z_n, U, V, X_n, l):
D, L = U.shape
M, _ = V.shape
score = int64(0)
for d in range(D):
for m in range(M):
if U[d, l] != 1 or V[m, l] != 1: # AND
continue
# compute deltaXOR-NAND
num_active = np.int8(0)
for l_prime in range(L):
if ((Z_n[l_prime] != 1) or
(U[d, l_prime] != 1) or
(V[m, l_prime] != 1)) and\
(l_prime != l):
num_active += 1
if num_active > 1:
break
if num_active == 0:
score += X_n[d, m]
elif num_active == 1:
score -= X_n[d, m]
return -score
raise NotImplementedError
# OR-NAND
@jit('int16(int8[:], int8[:,:], int8[:], int16)', nopython=True, nogil=True)
def posterior_score_OR_NAND_2D(Z_n, U, X_n, l):
"""
Return count of correct/incorrect explanations
caused by setting Z[n,l] to 1, respecting
explaining away dependencies
TODO: needs testing!
"""
D, L = U.shape
score = 0
for d in range(D):
if U[d, l] == -1: # NAND
continue
alrdy_active = False
for l_prime in range(L):
if ((Z_n[l_prime] == -1) or (U[d, l_prime] == -1)) and\
(l_prime != l):
alrdy_active = True # OR
break
if alrdy_active is False:
score += X_n[d]
return -score
@jit('int64(int8[:], int8[:,:], int8[:,:], int8[:,:], int16)', nopython=True, nogil=True)
def posterior_score_OR_NAND_3D(Z_n, U, V, X_n, l):
M, _ = V.shape
D, L = U.shape
score = int64(0)
for d in range(D):
for m in range(M):
if (U[d, l] == -1) or (V[m, l] == -1): # NAND
continue
alrdy_active = False
for l_prime in range(L):
if ((Z_n[l_prime] == -1) or
(U[d, l_prime] == -1) or
(V[m, l_prime] == -1)) and\
(l_prime != l):
alrdy_active = True # OR
break
if alrdy_active is False:
score += X_n[d, m]
return -score
# OR-XOR
@jit('int16(int8[:], int8[:,:], int8[:], int16)', nopython=True, nogil=True)
def posterior_score_OR_XOR_2D(Z_n, U, X_n, l):
"""
Return count of correct/incorrect explanations
caused by setting Z[n,l] to 1, respecting
explaining away dependencies
TODO: needs testing!
"""
D, L = U.shape
score = 0
for d in range(D):
explained_away = False
for l_prime in range(L):
if (Z_n[l_prime] != U[d, l_prime]) and (l_prime != l):
explained_away = True
break
if explained_away is False:
score += X_n[d] * U[d, l]
return -score
@jit('int64(int8[:], int8[:,:], int8[:,:], int8[:,:], int16)', nopython=True, nogil=True)
def posterior_score_OR_XOR_3D(Z_n, U, V, X_n, l):
D, L = U.shape
M, _ = V.shape
score = int64(0)
for d in range(D):
for m in range(M):
if U[d, l] == 1 and V[m, l] == 1: # XOR cant be changed by z_nl
continue
explained_away = False
for l_prime in range(L):
if (Z_n[l_prime] + U[d, l_prime] + V[m, l_prime] == -1) and\
(l_prime != l):
explained_away = True
break
if explained_away is False:
score += X_n[d, m] * U[d, l] * V[m, l] # very elegant ;)
return score
# NAND-XOR
@jit('int16(int8[:], int8[:,:], int8[:], int16)', nopython=True, nogil=True)
def posterior_score_NAND_XOR_2D(Z_n, U, X_n, l):
D, L = U.shape
score = 0
for d in range(D):
explained_away = False
for l_prime in range(L):
if (Z_n[l_prime] == U[d, l_prime]) and (l_prime != l):
explained_away = True
break
if explained_away is False:
score += X_n[d] * U[d, l]
return score
@jit('int64(int8[:], int8[:,:], int8[:,:], int8[:,:], int16)', nopython=True, nogil=True)
def posterior_score_NAND_XOR_3D(Z_n, U, V, X_n, l):
M, _ = V.shape
D, L = U.shape
score = int64(0)
for d in range(D):
for m in range(M):
if U[d, l] == 1 and V[m, l] == 1: # XOR cant be changed by z_nl
continue
explained_away = False
for l_prime in range(L):
if (Z_n[l_prime] + U[d, l_prime] + V[m, l_prime] != -1) and\
(l_prime != l):
explained_away = True
break
if explained_away is False:
score += X_n[d, m] * U[d, l] * V[m, l]
return -score
# XOR-XOR
@jit('int16(int8[:], int8[:,:], int8[:], int16)', nopython=True, nogil=True)
def posterior_score_XOR_XOR_2D(Z_n, U, X_n, l):
D, L = U.shape
score = 0
for d in range(D):
num_active = np.int8(0)
for l_prime in range(L):
if (Z_n[l_prime] != U[d, l_prime]) and (l_prime != l):
num_active += 1
if num_active > 1:
break
if num_active == 0:
score -= X_n[d] * U[d, l]
elif num_active == 1:
score += X_n[d] * U[d, l]
return score
@jit('int64(int8[:], int8[:,:], int8[:,:], int8[:,:], int16)', nopython=True, nogil=True)
def posterior_score_XOR_XOR_3D(Z_n, U, V, X_n, l):
M, _ = V.shape
D, L = U.shape
score = int64(0)
for d in range(D):
for m in range(M):
if U[d, l] == 1 and V[m, l] == 1: # XOR cant be changed by z_nl
continue
num_active = np.int8(0)
for l_prime in range(L):
if (Z_n[l_prime] + U[d, l_prime] + V[m, l_prime] == -1) and\
(l_prime != l):
num_active += 1
if num_active > 1:
break
if num_active == 0:
score += X_n[d, m] * U[d, l] * V[m, l]
elif num_active == 1:
score -= X_n[d, m] * U[d, l] * V[m, l]
return score
# XOR-NXOR
@jit('int16(int8[:], int8[:,:], int8[:], int16)', nopython=True, nogil=True)
def posterior_score_XOR_NXOR_2D(Z_n, U, X_n, l):
D, L = U.shape
score = 0
for d in range(D):
num_active = np.int8(0)
for l_prime in range(L):
if (U[d, l_prime] == Z_n[l_prime]) and (l_prime != l):
num_active += 1
if num_active > 1:
break
if num_active == 0:
score += X_n[d] * U[d, l]
elif num_active == 1:
score -= X_n[d] * U[d, l]
return score
@jit('int64(int8[:], int8[:,:], int8[:,:], int8[:,:], int16)', nopython=True, nogil=True)
def posterior_score_XOR_NXOR_3D(Z_n, U, V, X_n, l):
M, _ = V.shape
D, L = U.shape
score = int64(0)
for d in range(D):
for m in range(M):
if U[d, l] == 1 and V[m, l] == 1: # NXOR cant be changed by z_nl
continue
num_active = np.int8(0)
for l_prime in range(L):
if (U[d, l_prime] + Z_n[l_prime] + V[m, l_prime] != -1) and\
(l_prime != l):
num_active += 1
if num_active > 1:
break
if num_active == 0:
score -= X_n[d, m] * U[d, l] * V[m, l]
elif num_active == 1:
score += X_n[d, m] * U[d, l] * V[m, l]
return score
| 27.204793
| 89
| 0.476816
| 1,821
| 12,487
| 3.113674
| 0.048874
| 0.07619
| 0.021164
| 0.033862
| 0.967196
| 0.967196
| 0.967196
| 0.967196
| 0.966138
| 0.966138
| 0
| 0.035026
| 0.38496
| 12,487
| 458
| 90
| 27.264192
| 0.703255
| 0.117722
| 0
| 0.876254
| 0
| 0
| 0.070429
| 0
| 0
| 0
| 0
| 0.015284
| 0
| 1
| 0.053512
| false
| 0
| 0.010033
| 0
| 0.117057
| 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
cfe9d68eba7ade9416dbbb76c0477e9b7e0dbcba
| 1,923
|
py
|
Python
|
tests/errors/values_not_allowed_error.py
|
caputomarcos/mongorest
|
57d6b28d75e18afed5cef7160522958153b5be15
|
[
"BSD-3-Clause"
] | 16
|
2015-04-18T02:51:09.000Z
|
2020-12-15T18:05:16.000Z
|
tests/errors/values_not_allowed_error.py
|
caputomarcos/mongorest
|
57d6b28d75e18afed5cef7160522958153b5be15
|
[
"BSD-3-Clause"
] | 8
|
2015-11-24T23:06:03.000Z
|
2016-07-21T17:57:59.000Z
|
tests/errors/values_not_allowed_error.py
|
caputomarcos/mongorest
|
57d6b28d75e18afed5cef7160522958153b5be15
|
[
"BSD-3-Clause"
] | 2
|
2015-12-04T13:45:32.000Z
|
2016-06-11T13:44:53.000Z
|
# -*- encoding: UTF-8 -*-
from __future__ import absolute_import, unicode_literals
from mongorest.errors import ValuesNotAllowedError
from mongorest.testcase import TestCase
class TestValuesNotAllowedError(TestCase):
def test_values_not_allowed_error_sets_correct_fields_if_list(self):
self.assertEqual(
ValuesNotAllowedError('collection', 'field', ['values']),
{
'error_code': 31,
'error_type': 'ValuesNotAllowedError',
'error_message': 'Values: values; are not allowed for field '
'\'field\' on collection \'collection\'.',
'collection': 'collection',
'field': 'field',
'values': 'values'
}
)
def test_values_not_allowed_error_sets_correct_fields_if_json_string(self):
self.assertEqual(
ValuesNotAllowedError('collection', 'field', '[\'values\']'),
{
'error_code': 31,
'error_type': 'ValuesNotAllowedError',
'error_message': 'Values: values; are not allowed for field '
'\'field\' on collection \'collection\'.',
'collection': 'collection',
'field': 'field',
'values': 'values'
}
)
def test_values_not_allowed_error_sets_correct_fields_if_string(self):
self.assertEqual(
ValuesNotAllowedError('collection', 'field', 'values'),
{
'error_code': 31,
'error_type': 'ValuesNotAllowedError',
'error_message': 'Values: values; are not allowed for field '
'\'field\' on collection \'collection\'.',
'collection': 'collection',
'field': 'field',
'values': 'values'
}
)
| 37.705882
| 79
| 0.534581
| 153
| 1,923
| 6.437909
| 0.254902
| 0.182741
| 0.182741
| 0.048731
| 0.813198
| 0.813198
| 0.813198
| 0.813198
| 0.813198
| 0.813198
| 0
| 0.005618
| 0.352054
| 1,923
| 50
| 80
| 38.46
| 0.784912
| 0.01196
| 0
| 0.55814
| 0
| 0
| 0.278188
| 0.033193
| 0
| 0
| 0
| 0
| 0.069767
| 1
| 0.069767
| false
| 0
| 0.069767
| 0
| 0.162791
| 0
| 0
| 0
| 0
| null | 0
| 1
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
321cbda9991956b3e819c4e9866e769b8380062e
| 248
|
py
|
Python
|
bitio/src/microbit/microbits.py
|
hungjuchen/Atmosmakers
|
4e8e64fba3d7a31840f69a5aa3823247aa5dca02
|
[
"MIT"
] | 85
|
2017-06-09T20:53:46.000Z
|
2022-03-09T21:35:05.000Z
|
bitio/src/microbit/microbits.py
|
hungjuchen/Atmosmakers
|
4e8e64fba3d7a31840f69a5aa3823247aa5dca02
|
[
"MIT"
] | 34
|
2017-06-09T20:52:05.000Z
|
2021-02-19T19:49:45.000Z
|
bitio/src/microbit/microbits.py
|
hungjuchen/Atmosmakers
|
4e8e64fba3d7a31840f69a5aa3823247aa5dca02
|
[
"MIT"
] | 32
|
2017-06-09T10:15:19.000Z
|
2021-11-20T09:08:08.000Z
|
# microbits.py - provide non auto connect to 1 or more microbits
print("microbits imported")
#TODO: The idea of this is to have a microbit factory, that won't auto connect
#to a single microbit, and will allow multiple to be discovered and used.
| 35.428571
| 78
| 0.766129
| 43
| 248
| 4.418605
| 0.767442
| 0.115789
| 0.136842
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.004902
| 0.177419
| 248
| 6
| 79
| 41.333333
| 0.926471
| 0.850806
| 0
| 0
| 0
| 0
| 0.545455
| 0
| 0
| 0
| 0
| 0.166667
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 1
|
0
| 7
|
5c82deab0e930410243bb45349cd9d730edc3406
| 144
|
py
|
Python
|
SSD/ssd_model/__init__.py
|
erum-omdena/ai-challenge-mars
|
50fbfe1f478093aaba92e8c267548f64846c7846
|
[
"MIT"
] | 3
|
2021-04-25T16:02:47.000Z
|
2021-04-26T07:30:07.000Z
|
SSD/ssd_model/__init__.py
|
erum-omdena/ai-challenge-mars
|
50fbfe1f478093aaba92e8c267548f64846c7846
|
[
"MIT"
] | 39
|
2019-06-03T18:10:45.000Z
|
2022-02-10T11:11:51.000Z
|
SSD/ssd_model/__init__.py
|
erum-omdena/ai-challenge-mars
|
50fbfe1f478093aaba92e8c267548f64846c7846
|
[
"MIT"
] | 12
|
2019-06-01T11:21:27.000Z
|
2021-12-29T15:18:42.000Z
|
from ssd_model import data_processor
from ssd_model import data_processor_utils
from ssd_model import model
from ssd_model import model_utils
| 36
| 43
| 0.875
| 24
| 144
| 4.916667
| 0.291667
| 0.237288
| 0.40678
| 0.610169
| 0.915254
| 0.525424
| 0
| 0
| 0
| 0
| 0
| 0
| 0.125
| 144
| 4
| 44
| 36
| 0.936508
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 8
|
5c88c2aa7fcd3336f8d63e2a779b630f0dc7454d
| 2,550
|
py
|
Python
|
trec2014/python/cuttsum/judgements.py
|
kedz/cuttsum
|
992c21192af03fd2ef863f5ab7d10752f75580fa
|
[
"Apache-2.0"
] | 6
|
2015-09-10T02:22:21.000Z
|
2021-10-01T16:36:46.000Z
|
trec2014/python/cuttsum/judgements.py
|
kedz/cuttsum
|
992c21192af03fd2ef863f5ab7d10752f75580fa
|
[
"Apache-2.0"
] | null | null | null |
trec2014/python/cuttsum/judgements.py
|
kedz/cuttsum
|
992c21192af03fd2ef863f5ab7d10752f75580fa
|
[
"Apache-2.0"
] | 2
|
2018-04-04T10:44:32.000Z
|
2021-10-01T16:37:26.000Z
|
from datetime import datetime
from pkg_resources import resource_stream, resource_filename
import gzip
import pandas as pd
import os
#from pkg_resources import resource_filename
#def this_is_a_test():
# print "High"
def convert_to_datetime(x):
return datetime.utcfromtimestamp(int(x))
def get_2014_nuggets():
nuggets_tsv = resource_filename(
u'cuttsum', os.path.join(u'2014-data', u'nuggets.tsv.gz'))
with gzip.open(nuggets_tsv, u'r') as f:
df = pd.io.parsers.read_csv(
f, sep='\t', quoting=3, header=0,
converters={u'timestamp': convert_to_datetime},
names=[u'query id', u'nugget id', u'timestamp',
u'important', u'length', 'text'])
return df
def get_2013_nuggets():
nuggets_tsv = resource_filename(
u'cuttsum', os.path.join(u'2013-data', u'nuggets.tsv.gz'))
with gzip.open(nuggets_tsv, u'r') as f:
df = pd.io.parsers.read_csv(
f, sep='\t', quoting=3, header=0,
converters={u'timestamp': convert_to_datetime},
names=[u'query id', u'nugget id', u'timestamp',
u'important', u'length', 'text'])
return df
def get_2013_matches():
matches_tsv = resource_filename(
u'cuttsum', os.path.join(u'2013-data', u'matches.tsv.gz'))
with gzip.open(matches_tsv, u'r') as f:
df = pd.io.parsers.read_csv(
f, sep='\t', quoting=3, header=0,
dtype={u'match start': int, u'match end': int},
names=[u'query id', u'update id', u'nugget id',
u'match start', u'match end', 'auto p'])
return df
def get_2014_matches():
matches_tsv = resource_filename(
u'cuttsum', os.path.join(u'2014-data', u'matches.tsv.gz'))
with gzip.open(matches_tsv, u'r') as f:
df = pd.io.parsers.read_csv(
f, sep='\t', quoting=3, header=0,
dtype={u'match start': int, u'match end': int},
names=[u'query id', u'update id', u'nugget id',
u'match start', u'match end', 'auto p'])
return df
def get_mturk_matches():
matches_tsv = resource_filename(
u'cuttsum', os.path.join(u'2015-data', u'mturk-matches.tsv.gz'))
with gzip.open(matches_tsv, u'r') as f:
df = pd.io.parsers.read_csv(
f, sep='\t', quoting=3, header=0,
dtype={u'match start': int, u'match end': int},
names=[u'query id', u'update id', u'nugget id',
u'match start', u'match end', 'auto p'])
return df
| 36.956522
| 76
| 0.591765
| 385
| 2,550
| 3.805195
| 0.184416
| 0.026621
| 0.045051
| 0.068259
| 0.862799
| 0.821843
| 0.821843
| 0.821843
| 0.821843
| 0.821843
| 0
| 0.024287
| 0.257255
| 2,550
| 68
| 77
| 37.5
| 0.749208
| 0.031373
| 0
| 0.701754
| 0
| 0
| 0.200649
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.105263
| false
| 0
| 0.122807
| 0.017544
| 0.333333
| 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
|
5c9294612bef69498f10a1cc04234531da78b9a8
| 149
|
py
|
Python
|
plotly_express/__init__.py
|
gaofp/plotly_express
|
df55408739c88bf8a2249d79f2d1887d68b72af9
|
[
"MIT"
] | null | null | null |
plotly_express/__init__.py
|
gaofp/plotly_express
|
df55408739c88bf8a2249d79f2d1887d68b72af9
|
[
"MIT"
] | null | null | null |
plotly_express/__init__.py
|
gaofp/plotly_express
|
df55408739c88bf8a2249d79f2d1887d68b72af9
|
[
"MIT"
] | null | null | null |
"""
`plotly_express` is now an alias to `plotly.express`
"""
__version__ = "0.4.0"
from plotly.express import *
from plotly.express import line_3d
| 16.555556
| 52
| 0.724832
| 23
| 149
| 4.434783
| 0.608696
| 0.509804
| 0.333333
| 0.45098
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.031496
| 0.147651
| 149
| 8
| 53
| 18.625
| 0.771654
| 0.348993
| 0
| 0
| 0
| 0
| 0.05618
| 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
|
5ca35f2a8738cdf6000f49a46fef7fde9d283f16
| 24,794
|
py
|
Python
|
src/ebay_rest/api/buy_marketplace_insights/api/item_sales_api.py
|
gbm001/ebay_rest
|
077d3478423ccd80ff35e0361821d6a11180bc54
|
[
"MIT"
] | null | null | null |
src/ebay_rest/api/buy_marketplace_insights/api/item_sales_api.py
|
gbm001/ebay_rest
|
077d3478423ccd80ff35e0361821d6a11180bc54
|
[
"MIT"
] | null | null | null |
src/ebay_rest/api/buy_marketplace_insights/api/item_sales_api.py
|
gbm001/ebay_rest
|
077d3478423ccd80ff35e0361821d6a11180bc54
|
[
"MIT"
] | null | null | null |
# coding: utf-8
"""
Marketplace Insights API
<a href=\"https://developer.ebay.com/api-docs/static/versioning.html#Limited\" target=\"_blank\"> <img src=\"/cms/img/docs/partners-api.svg\" class=\"legend-icon partners-icon\" title=\"Limited Release\" alt=\"Limited Release\" />(Limited Release)</a> The Marketplace Insights API provides the ability to search for sold items on eBay by keyword, GTIN, category, and product and returns the of sales history of those items. # noqa: E501
OpenAPI spec version: v1_beta.2.2
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 ...buy_marketplace_insights.api_client import ApiClient
class ItemSalesApi(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 search(self, **kwargs): # noqa: E501
"""search # noqa: E501
(Limited Release) This method searches for sold eBay items by various URI query parameters and retrieves the sales history of the items for the last 90 days. You can search by keyword, category, eBay product ID (ePID), or GTIN, or a combination of these. This method also supports the following: Filtering by the value of one or multiple fields, such as listing format, item condition, price range, location, and more. For the fields supported by this method, see the filter parameter. Retrieving the refinements (metadata) of an item , such as item aspects (color, brand), condition, category, etc. using the fieldgroups parameter. Filtering by item aspects and other refinements using the aspect_filter parameter. Creating aspects histograms, which enables shoppers to drill down in each refinement narrowing the search results. For details and examples of these capabilities, see Browse API in the Buying Integration Guide. Pagination and sort controls There are pagination controls (limit and offset fields) and sort query parameters that control/sort the data that is returned. By default, the results are sorted by "Best Match". For more information about Best Match, see the eBay help page Best Match. URLs for this method Production URL: https://api.ebay.com/buy/marketplace_insights/v1_beta/item_sales/search? Sandbox URL: https://api.sandbox.ebay.com/buy/marketplace_insights/v1_beta/item_sales/search? Request headers You will want to use the X-EBAY-C-ENDUSERCTX request header with this method. If you are an eBay Network Partner you must use affiliateCampaignId=ePNCampaignId,affiliateReferenceId=referenceId in the header in order to be paid for selling eBay items on your site . For details see, Request headers in the Buy APIs Overview. URL Encoding for Parameters Query parameter values need to be URL encoded. For details, see URL encoding query parameter values. Restrictions For a list of supported sites and other restrictions, see API Restrictions. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.search(async_req=True)
>>> result = thread.get()
:param async_req bool
:param str aspect_filter: This field lets you filter by item aspects. The aspect name/value pairs and category, which is required, is used to limit the results to specific aspects of the item. For example, in a clothing category one aspect pair would be Color/Red. The results are returned in the refinement container. For example, the method below uses the category ID for Women's Clothing. This will return only sold items for a woman's red or blue shirt. /buy/marketplace_insights/v1_beta/item_sales/search?q=shirt&category_ids=15724&aspect_filter=categoryId:15724,Color:{Red|Blue} To get a list of the aspects pairs and the category, which is returned in the dominantCategoryId field, set fieldgroups to ASPECT_REFINEMENTS. /buy/marketplace_insights/v1_beta/item_sales/search?q=shirt&category_ids=15724&fieldgroups=ASPECT_REFINEMENTS Format: aspectName:{value1|value2} Required: The category ID is required twice; once as a URI parameter and as part of the aspect_filter parameter. For implementation help, refer to eBay API documentation at https://developer.ebay.com/api-docs/buy/marketplace_insights/types/gct:AspectFilter
:param str category_ids: The category ID is required and is used to limit the results. For example, if you search for 'shirt' the result set will be very large. But if you also include the category ID 137084, the results will be limited to 'Men's Athletic Apparel'. For example: /buy/marketplace-insights/v1_beta/item_sales/search?q=shirt&category_ids=137084 The list of eBay category IDs is not published and category IDs are not the same across all the eBay marketplaces. You can use the following techniques to find a category by site: For the US marketplace, use the Category Changes page. Use the Taxonomy API. For details see Get Categories for Buy APIs. Usage: This field can have one category ID or a comma separated list of IDs. You can use category_ids by itself or use it with any combination of the gtin, epid, and q fields, which gives you additional control over the result set. Restrictions: Partners will be given a list of categories they can use. To use a top-level (L1) category, you must also include the q, or gtin, or epid query parameter. Maximum number of categories: 4
:param str epid: The ePID is the eBay product identifier of a product from the eBay product catalog. This field limits the results to only items in the specified ePID. /buy/marketplace-insights/v1_beta/item_sales/search?epid=241986085&category_ids=168058 You can use the product_summary/search method in the Catalog API to search for the ePID of the product. Required: At least 1 category_ids Maximum: 1 epid Optional: Any combination of epid, gtin, or q
:param str fieldgroups: This field lets you control what is to be returned in the response and accepts a comma separated list of values. The default is MATCHING_ITEMS, which returns the items that match the keyword or category specified. The other values return data that can be used to create histograms. For code examples see, aspect_filter. Valid Values: ASPECT_REFINEMENTS - This returns the aspectDistributions container, which has the dominantCategoryId, matchCount, and refinementHref for the various aspects of the items found. For example, if you searched for 'Mustang', some of the aspect would be Model Year, Exterior Color, Vehicle Mileage, etc. Note: ASPECT_REFINEMENTS are category specific. BUYING_OPTION_REFINEMENTS - This returns the buyingOptionDistributions container, which has the matchCount and refinementHref for AUCTION and FIXED_PRICE (Buy It Now) items. Note: Classified items are not supported. CATEGORY_REFINEMENTS - This returns the categoryDistributions container, which has the categories that the item is in. CONDITION_REFINEMENTS - This returns the conditionDistributions container, such as NEW, USED, etc. Within these groups are multiple states of the condition. For example, New can be New without tag, New in box, New without box, etc. MATCHING_ITEMS - This is meant to be used with one or more of the refinement values above. You use this to return the specified refinements and all the matching items. FULL - This returns all the refinement containers and all the matching items. Code so that your app gracefully handles any future changes to this list. Default: MATCHING_ITEMS
:param str filter: This field supports multiple field filters that can be used to limit/customize the result set. The following lists the supported filters. For details and examples for all the filters, see Buy API Field Filters. buyingOptions conditionIds conditions itemLocationCountry lastSoldDate price priceCurrency The following example filters the result set by price. Note: To filter by price, price and priceCurrency must always be used together. /buy/marketplace-insights/v1_beta/item_sales/search?q=iphone&category_ids=15724&filter=price:[50..500],priceCurrency:USD For implementation help, refer to eBay API documentation at https://developer.ebay.com/api-docs/buy/marketplace_insights/types/cos:FilterField
:param str gtin: This field lets you search by the Global Trade Item Number of the item as defined by https://www.gtin.info. This can be a UPC (Universal Product Code), EAN (European Article Number), or an ISBN (International Standard Book Number) value. /buy/marketplace-insights/v1_beta/item_sales/search?gtin=241986085&category_ids=9355 Required: At least 1 category_ids Maximum: 1 gtin Optional: Any combination of epid, gtin, or q
:param str limit: The number of items, from the result set, returned in a single page. Default: 50 Maximum number of items per page (limit): 200 Maximum number of items in a result set: 10,000
:param str offset: Specifies the number of items to skip in the result set. This is used with the limit field to control the pagination of the output. If offset is 0 and limit is 10, the method will retrieve items 1-10 from the list of items returned, if offset is 10 and limit is 10, the method will retrieve items 11 thru 20 from the list of items returned. Valid Values: 0-10,000 (inclusive) Default: 0 Maximum number of items returned: 10,000
:param str q: A string consisting of one or more keywords that are used to search for items on eBay. The keywords are handled as follows: If the keywords are separated by a comma, it is treated as an AND. In the following example, the query returns items that have iphone AND ipad. /buy/marketplace-insights/v1_beta/item_sales/search?q=iphone,ipad&category_ids=15724 If the keywords are separated by a space, it is treated as an OR. In the following examples, the query returns items that have iphone OR ipad. /buy/marketplace-insights/v1_beta/item_sales/search?q=iphone&category_ids=15724 ipad /buy/marketplace-insights/v1_beta/item_sales/search?q=iphone, ipad&category_ids=15724 Restriction: The * wildcard character is not allowed in this field. Required: At least 1 category_ids Optional: Any combination of epid, gtin, or q
:param str sort: This field specifies the order and the field name to use to sort the items. To sort in descending order use - before the field name. Currently, you can only sort by price (in ascending or descending order). If no sort parameter is submitted, the result set is sorted by "Best Match". The following are examples of using the sort query parameter. Sort Result &sort=price Sorts by price in ascending order (lowest price first) &sort=-price Sorts by price in descending order (highest price first) Default: ascending For implementation help, refer to eBay API documentation at https://developer.ebay.com/api-docs/buy/marketplace_insights/types/cos:SortField
:return: SalesHistoryPagedCollection
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.search_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.search_with_http_info(**kwargs) # noqa: E501
return data
def search_with_http_info(self, **kwargs): # noqa: E501
"""search # noqa: E501
(Limited Release) This method searches for sold eBay items by various URI query parameters and retrieves the sales history of the items for the last 90 days. You can search by keyword, category, eBay product ID (ePID), or GTIN, or a combination of these. This method also supports the following: Filtering by the value of one or multiple fields, such as listing format, item condition, price range, location, and more. For the fields supported by this method, see the filter parameter. Retrieving the refinements (metadata) of an item , such as item aspects (color, brand), condition, category, etc. using the fieldgroups parameter. Filtering by item aspects and other refinements using the aspect_filter parameter. Creating aspects histograms, which enables shoppers to drill down in each refinement narrowing the search results. For details and examples of these capabilities, see Browse API in the Buying Integration Guide. Pagination and sort controls There are pagination controls (limit and offset fields) and sort query parameters that control/sort the data that is returned. By default, the results are sorted by "Best Match". For more information about Best Match, see the eBay help page Best Match. URLs for this method Production URL: https://api.ebay.com/buy/marketplace_insights/v1_beta/item_sales/search? Sandbox URL: https://api.sandbox.ebay.com/buy/marketplace_insights/v1_beta/item_sales/search? Request headers You will want to use the X-EBAY-C-ENDUSERCTX request header with this method. If you are an eBay Network Partner you must use affiliateCampaignId=ePNCampaignId,affiliateReferenceId=referenceId in the header in order to be paid for selling eBay items on your site . For details see, Request headers in the Buy APIs Overview. URL Encoding for Parameters Query parameter values need to be URL encoded. For details, see URL encoding query parameter values. Restrictions For a list of supported sites and other restrictions, see API Restrictions. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.search_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:param str aspect_filter: This field lets you filter by item aspects. The aspect name/value pairs and category, which is required, is used to limit the results to specific aspects of the item. For example, in a clothing category one aspect pair would be Color/Red. The results are returned in the refinement container. For example, the method below uses the category ID for Women's Clothing. This will return only sold items for a woman's red or blue shirt. /buy/marketplace_insights/v1_beta/item_sales/search?q=shirt&category_ids=15724&aspect_filter=categoryId:15724,Color:{Red|Blue} To get a list of the aspects pairs and the category, which is returned in the dominantCategoryId field, set fieldgroups to ASPECT_REFINEMENTS. /buy/marketplace_insights/v1_beta/item_sales/search?q=shirt&category_ids=15724&fieldgroups=ASPECT_REFINEMENTS Format: aspectName:{value1|value2} Required: The category ID is required twice; once as a URI parameter and as part of the aspect_filter parameter. For implementation help, refer to eBay API documentation at https://developer.ebay.com/api-docs/buy/marketplace_insights/types/gct:AspectFilter
:param str category_ids: The category ID is required and is used to limit the results. For example, if you search for 'shirt' the result set will be very large. But if you also include the category ID 137084, the results will be limited to 'Men's Athletic Apparel'. For example: /buy/marketplace-insights/v1_beta/item_sales/search?q=shirt&category_ids=137084 The list of eBay category IDs is not published and category IDs are not the same across all the eBay marketplaces. You can use the following techniques to find a category by site: For the US marketplace, use the Category Changes page. Use the Taxonomy API. For details see Get Categories for Buy APIs. Usage: This field can have one category ID or a comma separated list of IDs. You can use category_ids by itself or use it with any combination of the gtin, epid, and q fields, which gives you additional control over the result set. Restrictions: Partners will be given a list of categories they can use. To use a top-level (L1) category, you must also include the q, or gtin, or epid query parameter. Maximum number of categories: 4
:param str epid: The ePID is the eBay product identifier of a product from the eBay product catalog. This field limits the results to only items in the specified ePID. /buy/marketplace-insights/v1_beta/item_sales/search?epid=241986085&category_ids=168058 You can use the product_summary/search method in the Catalog API to search for the ePID of the product. Required: At least 1 category_ids Maximum: 1 epid Optional: Any combination of epid, gtin, or q
:param str fieldgroups: This field lets you control what is to be returned in the response and accepts a comma separated list of values. The default is MATCHING_ITEMS, which returns the items that match the keyword or category specified. The other values return data that can be used to create histograms. For code examples see, aspect_filter. Valid Values: ASPECT_REFINEMENTS - This returns the aspectDistributions container, which has the dominantCategoryId, matchCount, and refinementHref for the various aspects of the items found. For example, if you searched for 'Mustang', some of the aspect would be Model Year, Exterior Color, Vehicle Mileage, etc. Note: ASPECT_REFINEMENTS are category specific. BUYING_OPTION_REFINEMENTS - This returns the buyingOptionDistributions container, which has the matchCount and refinementHref for AUCTION and FIXED_PRICE (Buy It Now) items. Note: Classified items are not supported. CATEGORY_REFINEMENTS - This returns the categoryDistributions container, which has the categories that the item is in. CONDITION_REFINEMENTS - This returns the conditionDistributions container, such as NEW, USED, etc. Within these groups are multiple states of the condition. For example, New can be New without tag, New in box, New without box, etc. MATCHING_ITEMS - This is meant to be used with one or more of the refinement values above. You use this to return the specified refinements and all the matching items. FULL - This returns all the refinement containers and all the matching items. Code so that your app gracefully handles any future changes to this list. Default: MATCHING_ITEMS
:param str filter: This field supports multiple field filters that can be used to limit/customize the result set. The following lists the supported filters. For details and examples for all the filters, see Buy API Field Filters. buyingOptions conditionIds conditions itemLocationCountry lastSoldDate price priceCurrency The following example filters the result set by price. Note: To filter by price, price and priceCurrency must always be used together. /buy/marketplace-insights/v1_beta/item_sales/search?q=iphone&category_ids=15724&filter=price:[50..500],priceCurrency:USD For implementation help, refer to eBay API documentation at https://developer.ebay.com/api-docs/buy/marketplace_insights/types/cos:FilterField
:param str gtin: This field lets you search by the Global Trade Item Number of the item as defined by https://www.gtin.info. This can be a UPC (Universal Product Code), EAN (European Article Number), or an ISBN (International Standard Book Number) value. /buy/marketplace-insights/v1_beta/item_sales/search?gtin=241986085&category_ids=9355 Required: At least 1 category_ids Maximum: 1 gtin Optional: Any combination of epid, gtin, or q
:param str limit: The number of items, from the result set, returned in a single page. Default: 50 Maximum number of items per page (limit): 200 Maximum number of items in a result set: 10,000
:param str offset: Specifies the number of items to skip in the result set. This is used with the limit field to control the pagination of the output. If offset is 0 and limit is 10, the method will retrieve items 1-10 from the list of items returned, if offset is 10 and limit is 10, the method will retrieve items 11 thru 20 from the list of items returned. Valid Values: 0-10,000 (inclusive) Default: 0 Maximum number of items returned: 10,000
:param str q: A string consisting of one or more keywords that are used to search for items on eBay. The keywords are handled as follows: If the keywords are separated by a comma, it is treated as an AND. In the following example, the query returns items that have iphone AND ipad. /buy/marketplace-insights/v1_beta/item_sales/search?q=iphone,ipad&category_ids=15724 If the keywords are separated by a space, it is treated as an OR. In the following examples, the query returns items that have iphone OR ipad. /buy/marketplace-insights/v1_beta/item_sales/search?q=iphone&category_ids=15724 ipad /buy/marketplace-insights/v1_beta/item_sales/search?q=iphone, ipad&category_ids=15724 Restriction: The * wildcard character is not allowed in this field. Required: At least 1 category_ids Optional: Any combination of epid, gtin, or q
:param str sort: This field specifies the order and the field name to use to sort the items. To sort in descending order use - before the field name. Currently, you can only sort by price (in ascending or descending order). If no sort parameter is submitted, the result set is sorted by "Best Match". The following are examples of using the sort query parameter. Sort Result &sort=price Sorts by price in ascending order (lowest price first) &sort=-price Sorts by price in descending order (highest price first) Default: ascending For implementation help, refer to eBay API documentation at https://developer.ebay.com/api-docs/buy/marketplace_insights/types/cos:SortField
:return: SalesHistoryPagedCollection
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['aspect_filter', 'category_ids', 'epid', 'fieldgroups', 'filter', 'gtin', 'limit', 'offset', 'q', 'sort'] # 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 search" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
if 'aspect_filter' in params:
query_params.append(('aspect_filter', params['aspect_filter'])) # noqa: E501
if 'category_ids' in params:
query_params.append(('category_ids', params['category_ids'])) # noqa: E501
if 'epid' in params:
query_params.append(('epid', params['epid'])) # noqa: E501
if 'fieldgroups' in params:
query_params.append(('fieldgroups', params['fieldgroups'])) # noqa: E501
if 'filter' in params:
query_params.append(('filter', params['filter'])) # noqa: E501
if 'gtin' in params:
query_params.append(('gtin', params['gtin'])) # noqa: E501
if 'limit' in params:
query_params.append(('limit', params['limit'])) # noqa: E501
if 'offset' in params:
query_params.append(('offset', params['offset'])) # noqa: E501
if 'q' in params:
query_params.append(('q', params['q'])) # noqa: E501
if 'sort' in params:
query_params.append(('sort', params['sort'])) # 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 = ['api_auth'] # noqa: E501
return self.api_client.call_api(
'/item_sales/search', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='SalesHistoryPagedCollection', # 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)
| 154
| 2,002
| 0.760547
| 3,827
| 24,794
| 4.863339
| 0.124902
| 0.021277
| 0.034279
| 0.028369
| 0.886525
| 0.875779
| 0.870943
| 0.86686
| 0.862992
| 0.862992
| 0
| 0.017335
| 0.176373
| 24,794
| 160
| 2,003
| 154.9625
| 0.89408
| 0.850165
| 0
| 0
| 0
| 0
| 0.171662
| 0.028156
| 0
| 0
| 0
| 0
| 0
| 1
| 0.039474
| false
| 0
| 0.052632
| 0
| 0.144737
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 9
|
5cbc9522d9a5a86115abcf2c7bf63837e436bb7f
| 1,354
|
py
|
Python
|
tests/test_415.py
|
sungho-joo/leetcode2github
|
ce7730ef40f6051df23681dd3c0e1e657abba620
|
[
"MIT"
] | null | null | null |
tests/test_415.py
|
sungho-joo/leetcode2github
|
ce7730ef40f6051df23681dd3c0e1e657abba620
|
[
"MIT"
] | null | null | null |
tests/test_415.py
|
sungho-joo/leetcode2github
|
ce7730ef40f6051df23681dd3c0e1e657abba620
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python
import pytest
"""
Test 415. Add Strings
"""
@pytest.fixture(scope="session")
def init_variables_415():
from src.leetcode_415_add_strings import Solution
solution = Solution()
def _init_variables_415():
return solution
yield _init_variables_415
class TestClass415:
def test_solution_0(self, init_variables_415):
assert init_variables_415().addStrings("11", "123") == "134"
def test_solution_1(self, init_variables_415):
assert init_variables_415().addStrings("456", "77") == "533"
def test_solution_2(self, init_variables_415):
assert init_variables_415().addStrings("0", "0") == "0"
#!/usr/bin/env python
import pytest
"""
Test 415. Add Strings
"""
@pytest.fixture(scope="session")
def init_variables_415():
from src.leetcode_415_add_strings import Solution
solution = Solution()
def _init_variables_415():
return solution
yield _init_variables_415
class TestClass415:
def test_solution_0(self, init_variables_415):
assert init_variables_415().addStrings("11", "123") == "134"
def test_solution_1(self, init_variables_415):
assert init_variables_415().addStrings("456", "77") == "533"
def test_solution_2(self, init_variables_415):
assert init_variables_415().addStrings("0", "0") == "0"
| 21.492063
| 68
| 0.692762
| 176
| 1,354
| 5
| 0.204545
| 0.265909
| 0.327273
| 0.136364
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0.104882
| 0.183161
| 1,354
| 62
| 69
| 21.83871
| 0.690778
| 0.029542
| 0
| 1
| 0
| 0
| 0.041467
| 0
| 0
| 0
| 0
| 0
| 0.2
| 1
| 0.333333
| false
| 0
| 0.133333
| 0.066667
| 0.6
| 0
| 0
| 0
| 0
| null | 1
| 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
| 1
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 11
|
5cbe959a079d0f720bf726d3eb9cb46a854cd58f
| 44
|
py
|
Python
|
generator/__init__.py
|
crabmandable/cereal-pack
|
22674cbb0369df786df858e247f50ab9edcfe8b2
|
[
"MIT"
] | null | null | null |
generator/__init__.py
|
crabmandable/cereal-pack
|
22674cbb0369df786df858e247f50ab9edcfe8b2
|
[
"MIT"
] | null | null | null |
generator/__init__.py
|
crabmandable/cereal-pack
|
22674cbb0369df786df858e247f50ab9edcfe8b2
|
[
"MIT"
] | null | null | null |
from . import generate
from . import parser
| 14.666667
| 22
| 0.772727
| 6
| 44
| 5.666667
| 0.666667
| 0.588235
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.181818
| 44
| 2
| 23
| 22
| 0.944444
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 8
|
5ce53ef9d963f60e66cc61b7eb187ab29d0b9545
| 46
|
py
|
Python
|
python/testData/inspections/PyUnresolvedReferencesInspection/UnusedImportsInPackage/a.py
|
jnthn/intellij-community
|
8fa7c8a3ace62400c838e0d5926a7be106aa8557
|
[
"Apache-2.0"
] | 2
|
2019-04-28T07:48:50.000Z
|
2020-12-11T14:18:08.000Z
|
python/testData/inspections/PyUnresolvedReferencesInspection/UnusedImportsInPackage/a.py
|
Cyril-lamirand/intellij-community
|
60ab6c61b82fc761dd68363eca7d9d69663cfa39
|
[
"Apache-2.0"
] | 173
|
2018-07-05T13:59:39.000Z
|
2018-08-09T01:12:03.000Z
|
python/testData/inspections/PyUnresolvedReferencesInspection/UnusedImportsInPackage/a.py
|
Cyril-lamirand/intellij-community
|
60ab6c61b82fc761dd68363eca7d9d69663cfa39
|
[
"Apache-2.0"
] | 2
|
2020-03-15T08:57:37.000Z
|
2020-04-07T04:48:14.000Z
|
def g(x):
return x
def h(x):
return x
| 9.2
| 12
| 0.521739
| 10
| 46
| 2.4
| 0.5
| 0.583333
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.347826
| 46
| 5
| 13
| 9.2
| 0.8
| 0
| 0
| 0.5
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| false
| 0
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| 0.5
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| 1
| 0
| null | 1
| 1
| 0
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| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
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| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 8
|
7a426c37b275a5ddb3527f83269cd1955f361070
| 1,753
|
py
|
Python
|
python/euler/8.py
|
1m0r74l17y/random-files
|
46dd697b4748ba355f647f02d3625ebd3d2b3014
|
[
"MIT"
] | null | null | null |
python/euler/8.py
|
1m0r74l17y/random-files
|
46dd697b4748ba355f647f02d3625ebd3d2b3014
|
[
"MIT"
] | null | null | null |
python/euler/8.py
|
1m0r74l17y/random-files
|
46dd697b4748ba355f647f02d3625ebd3d2b3014
|
[
"MIT"
] | null | null | null |
'''
Find the greatest product of five consecutive digits in the 1000-digit number
'''
import time
start = time.time()
num = '7316717653133062491922511967442657474235534919493496983520312774506326239578318016984801869478851843858615607891129494954595017379583319528532088055111254069874715852386305071569329096329522744304355766896648950445244523161731856403098711121722383113622298934233803081353362766142828064444866452387493035890729629049156044077239071381051585930796086670172427121883998797908792274921901699720888093776657273330010533678812202354218097512545405947522435258490771167055601360483958644670632441572215539753697817977846174064955149290862569321978468622482839722413756570560574902614079729686524145351004748216637048440319989000889524345065854122758866688116427171479924442928230863465674813919123162824586178664583591245665294765456828489128831426076900422421902267105562632111110937054421750694165896040807198403850962455444362981230987879927244284909188845801561660979191338754992005240636899125607176060588611646710940507754100225698315520005593572972571636269561882670428252483600823257530420752963450'
biggest = 0
i = 0
while i < len(num) - 12:
one = int(num[i])
two = int(num[i+1])
thr = int(num[i+2])
fou = int(num[i+3])
fiv = int(num[i+4])
six = int(num[i+5])
sev = int(num[i+6])
eig = int(num[i+7])
nin = int(num[i+8])
ten = int(num[i+9])
ele = int(num[i+10])
twe = int(num[i+11])
thi = int(num[i+12])
product = one*two*thr*fou*fiv*six*sev*eig*nin*ten*ele*twe*thi
if product > biggest:
biggest = product
i = i + 1
print(biggest)
elapsed = (time.time() - start)
print("This code took: " + str(elapsed) + " seconds")
| 51.558824
| 1,009
| 0.801483
| 128
| 1,753
| 10.976563
| 0.4375
| 0.055516
| 0.064769
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.664504
| 0.120936
| 1,753
| 33
| 1,010
| 53.121212
| 0.247242
| 0.043925
| 0
| 0
| 0
| 0
| 0.6263
| 0.611621
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.038462
| 0
| 0.038462
| 0.076923
| 0
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
8fe7e8b720f671a78d7a8b3cca47f8294fc488d5
| 113
|
py
|
Python
|
handler.py
|
cfk1996/teacher-tree
|
dc03a464a96f7fbd8dc8b289507045a14ba73c4e
|
[
"MIT"
] | null | null | null |
handler.py
|
cfk1996/teacher-tree
|
dc03a464a96f7fbd8dc8b289507045a14ba73c4e
|
[
"MIT"
] | null | null | null |
handler.py
|
cfk1996/teacher-tree
|
dc03a464a96f7fbd8dc8b289507045a14ba73c4e
|
[
"MIT"
] | null | null | null |
# -*-coding: utf-8 -*-
'''请求基类
'''
import tornado.web
class BaseHandler(tornado.web.RequestHandler):
pass
| 11.3
| 46
| 0.654867
| 13
| 113
| 5.692308
| 0.846154
| 0.27027
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.010526
| 0.159292
| 113
| 9
| 47
| 12.555556
| 0.768421
| 0.230089
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.333333
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 7
|
89166028cb6c2fee5c3c10de82c27e1c407ffb14
| 1,874
|
py
|
Python
|
recipes/stages/_base_/data/pipelines/selfsl.py
|
openvinotoolkit/model_preparation_algorithm
|
8d36bf5944837b7a3d22fc2c3a4cb93423619fc2
|
[
"Apache-2.0"
] | null | null | null |
recipes/stages/_base_/data/pipelines/selfsl.py
|
openvinotoolkit/model_preparation_algorithm
|
8d36bf5944837b7a3d22fc2c3a4cb93423619fc2
|
[
"Apache-2.0"
] | null | null | null |
recipes/stages/_base_/data/pipelines/selfsl.py
|
openvinotoolkit/model_preparation_algorithm
|
8d36bf5944837b7a3d22fc2c3a4cb93423619fc2
|
[
"Apache-2.0"
] | null | null | null |
__img_norm_cfg = dict(mean=None, std=None)
__resize_target_size = -1
train_pipeline_v0 = [
dict(type='RandomResizedCrop', size=__resize_target_size),
dict(type='RandomHorizontalFlip'),
dict(
type='RandomAppliedTrans',
transforms=[
dict(
type='ColorJitter',
brightness=0.4,
contrast=0.4,
saturation=0.2,
hue=0.1)
],
p=0.8),
dict(type='RandomGrayscale', p=0.2),
dict(
type='RandomAppliedTrans',
transforms=[
dict(
type='GaussianBlur',
sigma_min=0.1,
sigma_max=2.0)
],
p=1.),
dict(type='RandomAppliedTrans',
transforms=[dict(type='Solarization')],
p=0.),
dict(type='ToNumpy'),
dict(type='Normalize', **__img_norm_cfg),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
]
train_pipeline_v1 = [
dict(type='RandomResizedCrop', size=__resize_target_size),
dict(type='RandomHorizontalFlip'),
dict(
type='RandomAppliedTrans',
transforms=[
dict(
type='ColorJitter',
brightness=0.4,
contrast=0.4,
saturation=0.2,
hue=0.1)
],
p=0.8),
dict(type='RandomGrayscale', p=0.2),
dict(
type='RandomAppliedTrans',
transforms=[
dict(
type='GaussianBlur',
sigma_min=0.1,
sigma_max=2.0)
],
p=0.1),
dict(type='RandomAppliedTrans',
transforms=[dict(type='Solarization')],
p=0.2),
dict(type='ToNumpy'),
dict(type='Normalize', **__img_norm_cfg),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
]
| 27.558824
| 62
| 0.511206
| 186
| 1,874
| 4.978495
| 0.22043
| 0.224622
| 0.168467
| 0.233261
| 0.916847
| 0.915767
| 0.915767
| 0.915767
| 0.915767
| 0.915767
| 0
| 0.033118
| 0.339381
| 1,874
| 68
| 63
| 27.558824
| 0.714863
| 0
| 0
| 0.848485
| 0
| 0
| 0.1952
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 1
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 9
|
64e6d49ae27896259a23034583b18c70b9c9f837
| 144
|
py
|
Python
|
nagios_registration/tests.py
|
k24dizzle/nagios_registration
|
be18dbadd2c08def81e795e4afe2fe2cf41775cf
|
[
"Apache-2.0"
] | 1
|
2021-04-27T02:04:10.000Z
|
2021-04-27T02:04:10.000Z
|
nagios_registration/tests.py
|
k24dizzle/nagios_registration
|
be18dbadd2c08def81e795e4afe2fe2cf41775cf
|
[
"Apache-2.0"
] | null | null | null |
nagios_registration/tests.py
|
k24dizzle/nagios_registration
|
be18dbadd2c08def81e795e4afe2fe2cf41775cf
|
[
"Apache-2.0"
] | null | null | null |
from django.test import TestCase
from nagios_registration.test.file_output import TestFile
from nagios_registration.test.views import TestViews
| 36
| 57
| 0.881944
| 20
| 144
| 6.2
| 0.6
| 0.16129
| 0.354839
| 0.419355
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.083333
| 144
| 3
| 58
| 48
| 0.939394
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
8f315544f58bfdc707f17efd003ad48ecf38364a
| 48
|
py
|
Python
|
Florence/BoundaryCondition/__init__.py
|
jdlaubrie/florence
|
830dca4a34be00d6e53cbec3007c10d438b27f57
|
[
"MIT"
] | 65
|
2017-08-04T10:21:13.000Z
|
2022-02-21T21:45:09.000Z
|
Florence/BoundaryCondition/__init__.py
|
jdlaubrie/florence
|
830dca4a34be00d6e53cbec3007c10d438b27f57
|
[
"MIT"
] | 6
|
2018-06-03T02:29:20.000Z
|
2022-01-18T02:30:22.000Z
|
Florence/BoundaryCondition/__init__.py
|
jdlaubrie/florence
|
830dca4a34be00d6e53cbec3007c10d438b27f57
|
[
"MIT"
] | 10
|
2018-05-30T09:44:10.000Z
|
2021-05-18T08:06:51.000Z
|
from .BoundaryCondition import BoundaryCondition
| 48
| 48
| 0.916667
| 4
| 48
| 11
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.0625
| 48
| 1
| 48
| 48
| 0.977778
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
8f3812b48a1ad7271b831278d39fd2844b33b701
| 3,968
|
py
|
Python
|
apps/snippet/api/mock/mock_snippet_views.py
|
zavanton123/coderators
|
55f860689ad48409bb4a1460c10e33694fed1b8a
|
[
"MIT"
] | null | null | null |
apps/snippet/api/mock/mock_snippet_views.py
|
zavanton123/coderators
|
55f860689ad48409bb4a1460c10e33694fed1b8a
|
[
"MIT"
] | null | null | null |
apps/snippet/api/mock/mock_snippet_views.py
|
zavanton123/coderators
|
55f860689ad48409bb4a1460c10e33694fed1b8a
|
[
"MIT"
] | null | null | null |
from rest_framework import status
from rest_framework.response import Response
from rest_framework.views import APIView
class MockCategoriesApiView(APIView):
def get(self, request, *args, **kwargs):
cat1 = {
'name': 'Some-category',
'slug': 'some-slug',
'created-at': 'some-time',
'updated-at': 'some-time'
}
cat2 = {
'name': 'Some-category',
'slug': 'some-slug',
'created-at': 'some-time',
'updated-at': 'some-time'
}
data = [cat1, cat2]
return Response(data)
def post(self, request, *args, **kwargs):
return Response(status=status.HTTP_201_CREATED)
class MockCategoryApiView(APIView):
def get(self, request, *args, **kwargs):
data = {
'name': 'Some-category',
'slug': 'some-slug',
'created-at': 'some-time',
'updated-at': 'some-time'
}
return Response(data)
def put(self, request, *args, **kwargs):
return Response(status=status.HTTP_204_NO_CONTENT)
def patch(self, request, *args, **kwargs):
return Response(status=status.HTTP_204_NO_CONTENT)
def delete(self, request, *args, **kwargs):
return Response(status=status.HTTP_204_NO_CONTENT)
class MockTagsApiView(APIView):
def get(self, request, *args, **kwargs):
tag1 = {
'name': 'Some-tag',
'slug': 'some-slug',
'created-at': 'some-time',
'updated-at': 'some-time'
}
tag2 = {
'name': 'Some-tag',
'slug': 'some-slug',
'created-at': 'some-time',
'updated-at': 'some-time'
}
data = [tag1, tag2]
return Response(data)
def post(self, request, *args, **kwargs):
return Response(status=status.HTTP_201_CREATED)
class MockTagApiView(APIView):
def get(self, request, *args, **kwargs):
data = {
'name': 'Some-tag',
'slug': 'some-slug',
'created-at': 'some-time',
'updated-at': 'some-time'
}
return Response(data)
def put(self, request, *args, **kwargs):
return Response(status=status.HTTP_204_NO_CONTENT)
def patch(self, request, *args, **kwargs):
return Response(status=status.HTTP_204_NO_CONTENT)
def delete(self, request, *args, **kwargs):
return Response(status=status.HTTP_204_NO_CONTENT)
class MockSnippetsApiView(APIView):
def get(self, request, *args, **kwargs):
snippet1 = {
'title': 'Some Title',
'content': 'Some Content',
'category': 'Some Category',
'tags': [
'Tag One',
'Tag Two',
],
'author': 'https://127.0.0.1:9999?api/users/123',
'published_at': 'some-time',
'updated_at': 'some-time'
}
snippet2 = snippet1
data = [snippet1, snippet2]
return Response(data)
def post(self, request, *args, **kwargs):
return Response(status=status.HTTP_201_CREATED)
class MockSnippetApiView(APIView):
def get(self, request, *args, **kwargs):
snippet1 = {
'title': 'Some Title',
'content': 'Some Content',
'category': 'Some Category',
'tags': [
'Tag One',
'Tag Two',
],
'author': 'https://127.0.0.1:9999?api/users/123',
'published_at': 'some-time',
'updated_at': 'some-time'
}
return Response(snippet1)
def put(self, request, *args, **kwargs):
return Response(status=status.HTTP_204_NO_CONTENT)
def patch(self, request, *args, **kwargs):
return Response(status=status.HTTP_204_NO_CONTENT)
def delete(self, request, *args, **kwargs):
return Response(status=status.HTTP_204_NO_CONTENT)
| 29.61194
| 61
| 0.545363
| 421
| 3,968
| 5.045131
| 0.144893
| 0.09322
| 0.127119
| 0.177966
| 0.863465
| 0.863465
| 0.863465
| 0.83145
| 0.83145
| 0.824859
| 0
| 0.028154
| 0.310736
| 3,968
| 133
| 62
| 29.834586
| 0.748446
| 0
| 0
| 0.761468
| 0
| 0
| 0.177167
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.165138
| false
| 0
| 0.027523
| 0.110092
| 0.412844
| 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
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
|
0
| 9
|
8f6f85951312c6e8a73447fd819c053b16dbf640
| 3,631
|
py
|
Python
|
spider.py
|
dlb-shy/baidu_hs
|
830d58879ddfb9014ff7783b0a97e019e22b1538
|
[
"Apache-2.0"
] | null | null | null |
spider.py
|
dlb-shy/baidu_hs
|
830d58879ddfb9014ff7783b0a97e019e22b1538
|
[
"Apache-2.0"
] | null | null | null |
spider.py
|
dlb-shy/baidu_hs
|
830d58879ddfb9014ff7783b0a97e019e22b1538
|
[
"Apache-2.0"
] | null | null | null |
#
# # 笔记详情
# import requests
#
# headers = {
# 'Host': 'www.xiaohongshu.com',
# 'asid': '202109064127e42cb0a201ebc4f9c00c',
# 'x-sign': 'X880f26f7d10297673687b8dd698c7831',
# 'x-b3-traceid': 'bf5b291fb5699ce9',
# 'referer': 'https://smartapps.cn/KuRdr9OR39BqyAGIg7mYK7Bytityu0Vi/2.35.16/page-frame.html',
# 'user-agent': 'Mozilla/5.0 (Linux; Android 6.0; Redmi Note 4 Build/MRA58K; wv) AppleWebKit/537.36 (KHTML, like Gecko) Version/4.0 Chrome/76.0.3809.89 Mobile Safari/537.36 T7/12.23 swan/2.35.0 swan-baiduboxapp/12.23.0.11 baiduboxapp/12.23.0.11 (Baidu; P1 6.0)',
# 'x-bd-traceid': '03e47248d5104788a0a6d18015eacf28',
# }
#
# response = requests.get('https://www.xiaohongshu.com/fe_api/burdock/baidu/v2/note/61331521000000002103405c', headers=headers)
#
#
# # 评论
# import requests
#
# headers = {
# 'Host': 'www.xiaohongshu.com',
# 'asid': '202109064127e42cb0a201ebc4f9c00c',
# 'x-sign': 'Xb080d995f49c0e3f4c81f6316548007b',
# 'x-b3-traceid': '9a09cf31a1029930',
# 'referer': 'https://smartapps.cn/KuRdr9OR39BqyAGIg7mYK7Bytityu0Vi/2.35.16/page-frame.html',
# 'user-agent': 'Mozilla/5.0 (Linux; Android 6.0; Redmi Note 4 Build/MRA58K; wv) AppleWebKit/537.36 (KHTML, like Gecko) Version/4.0 Chrome/76.0.3809.89 Mobile Safari/537.36 T7/12.23 swan/2.35.0 swan-baiduboxapp/12.23.0.11 baiduboxapp/12.23.0.11 (Baidu; P1 6.0)',
# 'x-bd-traceid': 'd14f5b1407a74b7c8cb7020c9aefe637',
# }
#
# params = (
# ('endId', ''),
# ('hot', 'no'),
# ('pageSize', '2'),
# )
#
# response = requests.get('https://www.xiaohongshu.com/fe_api/burdock/baidu/v2/notes/61331521000000002103405c/comments', headers=headers, params=params)
#
# #NB. Original query string below. It seems impossible to parse and
# #reproduce query strings 100% accurately so the one below is given
# #in case the reproduced version is not "correct".
# # response = requests.get('https://www.xiaohongshu.com/fe_api/burdock/baidu/v2/notes/61331521000000002103405c/comments?endId=&hot=no&pageSize=2', headers=headers)
#
# # 用户详情
# import requests
#
# headers = {
# 'Host': 'www.xiaohongshu.com',
# 'asid': '202109064127e42cb0a201ebc4f9c00c',
# 'x-sign': 'Xb4850ed626be6848c03fe05d5d21a544',
# 'x-b3-traceid': '5011521b164a1228',
# 'referer': 'https://smartapps.cn/KuRdr9OR39BqyAGIg7mYK7Bytityu0Vi/2.35.16/page-frame.html',
# 'user-agent': 'Mozilla/5.0 (Linux; Android 6.0; Redmi Note 4 Build/MRA58K; wv) AppleWebKit/537.36 (KHTML, like Gecko) Version/4.0 Chrome/76.0.3809.89 Mobile Safari/537.36 T7/12.23 swan/2.35.0 swan-baiduboxapp/12.23.0.11 baiduboxapp/12.23.0.11 (Baidu; P1 6.0)',
# 'x-bd-traceid': 'caf5a17134a3434ba3a9a8b06e7a41a1',
# }
#
# response = requests.get('https://www.xiaohongshu.com/fe_api/burdock/baidu/v2/user/6004ddc5000000000101e58d', headers=headers)
#
# 笔记列表
import requests
headers = {
'Host': 'www.xiaohongshu.com',
'asid': '202109064127e42cb0a201ebc4f9c00c',
'x-sign': 'X8647f6658a6fd79a7a20ea6efe24b9e4',
'x-b3-traceid': 'bc66794e90ad8627',
'referer': 'https://smartapps.cn/KuRdr9OR39BqyAGIg7mYK7Bytityu0Vi/2.35.16/page-frame.html',
'user-agent': 'Mozilla/5.0 (Linux; Android 6.0; Redmi Note 4 Build/MRA58K; wv) AppleWebKit/537.36 (KHTML, like Gecko) Version/4.0 Chrome/76.0.3809.89 Mobile Safari/537.36 T7/12.23 swan/2.35.0 swan-baiduboxapp/12.23.0.11 baiduboxapp/12.23.0.11 (Baidu; P1 6.0)',
'x-bd-traceid': 'c87537dcdc314c849b32e1a28eb4cd50',
}
response = requests.get('https://www.xiaohongshu.com/fe_api/burdock/baidu/v2/user/6004ddc5000000000101e58d/notes?page=1&pageSize=10', headers=headers)
print(response.text)
| 44.82716
| 266
| 0.707519
| 481
| 3,631
| 5.330561
| 0.24948
| 0.018721
| 0.059672
| 0.049922
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| 0.721529
| 0.721529
| 0.721529
| 0.721529
| 0.721529
| 0
| 0.214576
| 0.119526
| 3,631
| 80
| 267
| 45.3875
| 0.587426
| 0.744148
| 0
| 0
| 0
| 0.166667
| 0.714452
| 0.170163
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.083333
| 0
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| 0.083333
| 0
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| null | 0
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|
0
| 7
|
71236f24d0ee0f9b56a272051dad27a0399904f8
| 15,360
|
py
|
Python
|
csdl/examples/ex_sum.py
|
LSDOlab/csdl
|
04c2c5764f6ca9b865ec87ecfeaf6f22ecacc5a3
|
[
"MIT"
] | null | null | null |
csdl/examples/ex_sum.py
|
LSDOlab/csdl
|
04c2c5764f6ca9b865ec87ecfeaf6f22ecacc5a3
|
[
"MIT"
] | null | null | null |
csdl/examples/ex_sum.py
|
LSDOlab/csdl
|
04c2c5764f6ca9b865ec87ecfeaf6f22ecacc5a3
|
[
"MIT"
] | 1
|
2021-10-04T19:40:32.000Z
|
2021-10-04T19:40:32.000Z
|
from csdl import Model
import csdl
import numpy as np
class ExampleSingleVector(Model):
"""
:param var: v1
:param var: single_vector_sum
"""
def define(self):
n = 3
# Declare a vector of length 3 as input
v1 = self.declare_variable('v1', val=np.arange(n))
# Output the sum of all the elements of the vector v1
self.register_output('single_vector_sum', csdl.sum(v1))
class ExampleSingleTensor(Model):
"""
:param var: T1
:param var: single_tensor_sum
"""
def define(self):
n = 3
m = 4
p = 5
q = 6
# Declare a tensor of shape 3x6x7x10 as input
T1 = self.declare_variable('T1',
val=np.arange(n * m * p * q).reshape(
(n, m, p, q)))
# Output the sum of all the elements of the matrix M1
self.register_output('single_tensor_sum', csdl.sum(T1))
class ExampleSingleMatrix(Model):
"""
:param var: M1
:param var: single_matrix_sum
"""
def define(self):
n = 3
m = 6
# Declare a matrix of shape 3x6 as input
M1 = self.declare_variable('M1',
val=np.arange(n * m).reshape((n, m)))
# Output the sum of all the elements of the tensor T1
self.register_output('single_matrix_sum', csdl.sum(M1))
class ExampleMultipleVector(Model):
"""
:param var: v1
:param var: v2
:param var: multiple_vector_sum
"""
def define(self):
n = 3
# Declare a vector of length 3 as input
v1 = self.declare_variable('v1', val=np.arange(n))
# Declare another vector of length 3 as input
v2 = self.declare_variable('v2', val=np.arange(n, 2 * n))
# Output the elementwise sum of vectors v1 and v2
self.register_output('multiple_vector_sum', csdl.sum(v1, v2))
class ExampleMultipleMatrix(Model):
"""
:param var: M1
:param var: M2
:param var: multiple_matrix_sum
"""
def define(self):
n = 3
m = 6
# Declare a matrix of shape 3x6 as input
M1 = self.declare_variable('M1',
val=np.arange(n * m).reshape((n, m)))
# Declare another matrix of shape 3x6 as input
M2 = self.declare_variable('M2',
val=np.arange(n * m,
2 * n * m).reshape(
(n, m)))
# Output the elementwise sum of matrices M1 and M2
self.register_output('multiple_matrix_sum', csdl.sum(M1, M2))
class ExampleMultipleTensor(Model):
"""
:param var: T1
:param var: T2
:param var: multiple_tensor_sum
"""
def define(self):
n = 3
m = 6
p = 7
q = 10
# Declare a tensor of shape 3x6x7x10 as input
T1 = self.declare_variable('T1',
val=np.arange(n * m * p * q).reshape(
(n, m, p, q)))
# Declare another tensor of shape 3x6x7x10 as input
T2 = self.declare_variable('T2',
val=np.arange(n * m * p * q, 2 * n *
m * p * q).reshape(
(n, m, p, q)))
# Output the elementwise sum of tensors T1 and T2
self.register_output('multiple_tensor_sum', csdl.sum(T1, T2))
class ExampleSingleMatrixAlong0(Model):
"""
:param var: M1
:param var: single_matrix_sum_along_0
"""
def define(self):
n = 3
m = 6
# Declare a matrix of shape 3x6 as input
M1 = self.declare_variable('M1',
val=np.arange(n * m).reshape((n, m)))
# Output the axiswise sum of matrix M1 along the columns
self.register_output('single_matrix_sum_along_0',
csdl.sum(M1, axes=(0, )))
class ExampleSingleMatrixAlong1(Model):
"""
:param var: M1
:param var: single_matrix_sum_along_1
"""
def define(self):
n = 3
m = 6
# Declare a matrix of shape 3x6 as input
M1 = self.declare_variable('M1',
val=np.arange(n * m).reshape((n, m)))
# Output the axiswise sum of matrix M1 along the columns
self.register_output('single_matrix_sum_along_1',
csdl.sum(M1, axes=(1, )))
class ExampleMultipleMatrixAlong0(Model):
"""
:param var: M1
:param var: M2
:param var: multiple_matrix_sum_along_0
"""
def define(self):
n = 3
m = 6
# Declare a matrix of shape 3x6 as input
M1 = self.declare_variable('M1',
val=np.arange(n * m).reshape((n, m)))
# Declare another matrix of shape 3x6 as input
M2 = self.declare_variable('M2',
val=np.arange(n * m,
2 * n * m).reshape(
(n, m)))
# Output the elementwise sum of the axiswise sum of matrices M1 ad M2 along the columns
self.register_output('multiple_matrix_sum_along_0',
csdl.sum(M1, M2, axes=(0, )))
class ExampleMultipleMatrixAlong1(Model):
"""
:param var: M1
:param var: M2
:param var: multiple_matrix_sum_along_1
"""
def define(self):
n = 3
m = 6
# Declare a matrix of shape 3x6 as input
M1 = self.declare_variable('M1',
val=np.arange(n * m).reshape((n, m)))
# Declare another matrix of shape 3x6 as input
M2 = self.declare_variable('M2',
val=np.arange(n * m,
2 * n * m).reshape(
(n, m)))
# Output the elementwise sum of the axiswise sum of matrices M1 ad M2 along the columns
self.register_output('multiple_matrix_sum_along_1',
csdl.sum(M1, M2, axes=(1, )))
class ExampleConcatenate(Model):
"""
:param var: single_vector_sum_1a
:param var: single_vector_sum_1b
:param var: single_vector_sum_2
:param var: single_vector_sum_3
:param var: sum_vector
"""
def define(self):
n = 5
# Declare a vector of length 3 as input
v1 = self.declare_variable('v1', val=np.arange(n))
v2 = self.declare_variable('v2', val=np.arange(n - 1))
v3 = self.declare_variable('v3', val=np.zeros(n))
# Output the sum of all the elements of the vector v1
single_vector_sum_1a = csdl.sum(v1, axes=(0, ))
single_vector_sum_1b = csdl.sum(v1)
self.register_output('single_vector_sum_1a',
single_vector_sum_1a)
self.register_output('single_vector_sum_1b',
single_vector_sum_1b)
single_vector_sum_2 = self.register_output(
'single_vector_sum_2', csdl.sum(v2, axes=(0, )))
single_vector_sum_3 = csdl.sum(v3)
self.register_output('single_vector_sum_3', single_vector_sum_3)
sum_vector = self.create_output(name='sum_vector', shape=(3, ))
sum_vector[0] = single_vector_sum_1a
sum_vector[1] = single_vector_sum_2
sum_vector[2] = single_vector_sum_3
class ExampleSingleVectorRandom(Model):
"""
:param var: v1
:param var: single_vector_sum
"""
def define(self):
n = 3
np.random.seed(0)
# Declare a vector of length 3 as input
v1 = self.declare_variable('v1', val=np.random.rand(n))
# Output the sum of all the elements of the vector v1
self.register_output('single_vector_sum', csdl.sum(v1))
class ExampleSingleTensorRandom(Model):
"""
:param var: T1
:param var: single_tensor_sum
"""
def define(self):
n = 3
m = 4
p = 5
q = 6
np.random.seed(0)
# Declare a tensor of shape 3x6x7x10 as input
T1 = self.declare_variable(
'T1',
val=np.random.rand(n * m * p * q).reshape((n, m, p, q)))
# Output the sum of all the elements of the matrix M1
self.register_output('single_tensor_sum', csdl.sum(T1))
class ExampleSingleMatrixRandom(Model):
"""
:param var: M1
:param var: single_matrix_sum
"""
def define(self):
n = 3
m = 6
np.random.seed(0)
# Declare a matrix of shape 3x6 as input
M1 = self.declare_variable('M1',
val=np.random.rand(n * m).reshape(
(n, m)))
# Output the sum of all the elements of the tensor T1
self.register_output('single_matrix_sum', csdl.sum(M1))
class ExampleMultipleVectorRandom(Model):
"""
:param var: v1
:param var: v2
:param var: multiple_vector_sum
"""
def define(self):
n = 3
np.random.seed(0)
# Declare a vector of length 3 as input
v1 = self.declare_variable('v1', val=np.random.rand(n))
# Declare another vector of length 3 as input
v2 = self.declare_variable('v2', val=np.random.rand(n))
# Output the elementwise sum of vectors v1 and v2
self.register_output('multiple_vector_sum', csdl.sum(v1, v2))
class ExampleMultipleMatrixRandom(Model):
"""
:param var: M1
:param var: M2
:param var: multiple_matrix_sum
"""
def define(self):
n = 3
m = 6
np.random.seed(0)
# Declare a matrix of shape 3x6 as input
M1 = self.declare_variable('M1',
val=np.random.rand(n * m).reshape(
(n, m)))
# Declare another matrix of shape 3x6 as input
M2 = self.declare_variable('M2',
val=np.random.rand(n * m).reshape(
(n, m)))
# Output the elementwise sum of matrices M1 and M2
self.register_output('multiple_matrix_sum', csdl.sum(M1, M2))
class ExampleMultipleTensorRandom(Model):
"""
:param var: T1
:param var: T2
:param var: multiple_tensor_sum
"""
def define(self):
n = 3
m = 6
p = 7
q = 10
np.random.seed(0)
# Declare a tensor of shape 3x6x7x10 as input
T1 = self.declare_variable(
'T1',
val=np.random.rand(n * m * p * q).reshape((n, m, p, q)))
# Declare another tensor of shape 3x6x7x10 as input
T2 = self.declare_variable(
'T2',
val=np.random.rand(n * m * p * q).reshape((n, m, p, q)))
# Output the elementwise sum of tensors T1 and T2
self.register_output('multiple_tensor_sum', csdl.sum(T1, T2))
class ExampleSingleMatrixAlong0Random(Model):
"""
:param var: M1
:param var: single_matrix_sum_along_0
"""
def define(self):
n = 3
m = 6
np.random.seed(0)
# Declare a matrix of shape 3x6 as input
M1 = self.declare_variable('M1',
val=np.random.rand(n * m).reshape(
(n, m)))
# Output the axiswise sum of matrix M1 along the columns
self.register_output('single_matrix_sum_along_0',
csdl.sum(M1, axes=(0, )))
class ExampleSingleMatrixAlong1Random(Model):
"""
:param var: M1
:param var: single_matrix_sum_along_1
"""
def define(self):
n = 3
m = 6
np.random.seed(0)
# Declare a matrix of shape 3x6 as input
M1 = self.declare_variable('M1',
val=np.random.rand(n * m).reshape(
(n, m)))
# Output the axiswise sum of matrix M1 along the columns
self.register_output('single_matrix_sum_along_1',
csdl.sum(M1, axes=(1, )))
class ExampleMultipleMatrixAlong0Random(Model):
"""
:param var: M1
:param var: M2
:param var: multiple_matrix_sum_along_0
"""
def define(self):
n = 3
m = 6
np.random.seed(0)
# Declare a matrix of shape 3x6 as input
M1 = self.declare_variable('M1',
val=np.random.rand(n * m).reshape(
(n, m)))
# Declare another matrix of shape 3x6 as input
M2 = self.declare_variable('M2',
val=np.random.rand(n * m).reshape(
(n, m)))
# Output the elementwise sum of the axiswise sum of matrices M1 ad M2 along the columns
self.register_output('multiple_matrix_sum_along_0',
csdl.sum(M1, M2, axes=(0, )))
class ExampleMultipleMatrixAlong1Random(Model):
"""
:param var: M1
:param var: M2
:param var: multiple_matrix_sum_along_1
"""
def define(self):
n = 3
m = 6
np.random.seed(0)
# Declare a matrix of shape 3x6 as input
M1 = self.declare_variable('M1',
val=np.random.rand(n * m).reshape(
(n, m)))
# Declare another matrix of shape 3x6 as input
M2 = self.declare_variable('M2',
val=np.random.rand(n * m).reshape(
(n, m)))
# Output the elementwise sum of the axiswise sum of matrices M1 ad M2 along the columns
self.register_output('multiple_matrix_sum_along_1',
csdl.sum(M1, M2, axes=(1, )))
class ExampleConcatenateRandom(Model):
"""
:param var: single_vector_sum_1a
:param var: single_vector_sum_1b
:param var: single_vector_sum_2
:param var: single_vector_sum_3
:param var: sum_vector
"""
def define(self):
n = 5
np.random.seed(0)
# Declare a vector of length 3 as input
v1 = self.declare_variable('v1', val=np.random.rand(n))
v2 = self.declare_variable('v2', val=np.random.rand(n - 1))
v3 = self.declare_variable('v3', val=np.zeros(n))
# Output the sum of all the elements of the vector v1
single_vector_sum_1a = csdl.sum(v1, axes=(0, ))
single_vector_sum_1b = csdl.sum(v1)
self.register_output('single_vector_sum_1a',
single_vector_sum_1a)
self.register_output('single_vector_sum_1b',
single_vector_sum_1b)
single_vector_sum_2 = self.register_output(
'single_vector_sum_2', csdl.sum(v2, axes=(0, )))
single_vector_sum_3 = csdl.sum(v3)
self.register_output('single_vector_sum_3', single_vector_sum_3)
sum_vector = self.create_output(name='sum_vector', shape=(3, ))
sum_vector[0] = single_vector_sum_1a
sum_vector[1] = single_vector_sum_2
sum_vector[2] = single_vector_sum_3
| 29.369025
| 95
| 0.534701
| 1,949
| 15,360
| 4.061057
| 0.049769
| 0.060644
| 0.075805
| 0.038913
| 0.923437
| 0.923437
| 0.923437
| 0.922173
| 0.922173
| 0.916993
| 0
| 0.040263
| 0.366146
| 15,360
| 522
| 96
| 29.425287
| 0.772699
| 0.263021
| 0
| 0.859504
| 0
| 0
| 0.062315
| 0.019288
| 0
| 0
| 0
| 0
| 0
| 1
| 0.090909
| false
| 0
| 0.012397
| 0
| 0.194215
| 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
|
712527dbf0029ef4b6775dad7205dbb4a7d5e0c9
| 11,494
|
py
|
Python
|
S9/EVA4/Models/Cifar10.py
|
VijayPrakashReddy-k/EVA
|
fd78ff8bda4227aebd0f5db14865d3c5a47b19b0
|
[
"MIT"
] | null | null | null |
S9/EVA4/Models/Cifar10.py
|
VijayPrakashReddy-k/EVA
|
fd78ff8bda4227aebd0f5db14865d3c5a47b19b0
|
[
"MIT"
] | null | null | null |
S9/EVA4/Models/Cifar10.py
|
VijayPrakashReddy-k/EVA
|
fd78ff8bda4227aebd0f5db14865d3c5a47b19b0
|
[
"MIT"
] | null | null | null |
import torch
import torch.nn as nn
import torch.nn.functional as F
from Net import Net
class Cifar10_net1(Net):
def __init__(self, name="Model", dropout_value=0):
super(Cifar10_net1, self).__init__(name)
# Input Convolution: C0
self.conv1 = self.create_conv2d(3, 32, dropout=dropout_value) # IN 32x32x3, OUT 32x32x32, RF = 3
self.conv2 = self.create_conv2d(32, 32, dropout=dropout_value) # IN 32x32x32, OUT 32x32x32, RF = 5
self.conv3 = self.create_conv2d(32, 32, dropout=dropout_value) # IN 32x32x32, OUT 32x32x32, RF = 7
# Transition 1
self.pool1 = nn.MaxPool2d(2, 2) # IN 32x32x32 OUT 16x16x32, RF = 8, jump = 2
self.conv4 = self.create_conv2d(32, 64, dropout=dropout_value) # IN 16x16x32, OUT 16x16x64, RF = 12
self.conv5 = self.create_conv2d(64, 64, dropout=dropout_value) # IN 16x16x64, OUT 16x16x64, RF = 16
# Transition 2
self.pool2 = nn.MaxPool2d(2, 2) # IN 16x16x64 OUT 8x8x64, RF = 18, jump = 4
self.dconv1 = self.create_conv2d(64, 128, dilation=2, padding=2) # IN 8x8x64, OUT 8x8x128
self.conv6 = self.create_conv2d(64, 128, dropout=dropout_value) # IN 8x8x64, OUT 8x8x128, RF = 26
self.conv7 = self.create_conv2d(128, 128, dropout=dropout_value) # IN 8x8x128, OUT 8x8x128, RF = 34
# Transition 3
self.pool3 = nn.MaxPool2d(2, 2) # IN 8x8x128 OUT 4x4x128, RF = 38, jump = 8
self.conv8 = self.create_depthwise_conv2d(128, 256, dropout=dropout_value) # IN 4x4x128, OUT 4x4x256, RF = 54
self.conv9 = self.create_depthwise_conv2d(256, 256, dropout=dropout_value) # IN 4x4x256, OUT 4x4x256, RF = 70
# GAP + FC
self.gap = nn.AvgPool2d(kernel_size=(4,4))
self.conv10 = self.create_conv2d(256, 10, kernel_size=(1,1), padding=0, bn=False, relu=False) # IN: 256 OUT:10
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
x = self.pool1(x)
x = self.conv4(x)
x = self.conv5(x)
x = self.pool2(x)
x2 = self.dconv1(x)
x = self.conv6(x)
x = self.conv7(x)
x = torch.add(x, x2)
x = self.pool3(x)
x = self.conv8(x)
x = self.conv9(x)
x = self.gap(x)
x = self.conv10(x)
x = x.view(-1, 10)
return F.log_softmax(x, dim=-1)
class Cifar10_net2(Net):
def __init__(self, name="Model", dropout_value=0):
super(Cifar10_net2, self).__init__(name)
# Input Convolution: C0
self.conv1 = self.create_conv2d(3, 16, dropout=dropout_value) # IN 32x32x3, OUT 32x32x16, RF = 3
self.conv2 = self.create_conv2d(16, 16, dropout=dropout_value) # IN 32x32x16, OUT 32x32x16, RF = 5
self.conv3 = self.create_conv2d(16, 16, dropout=dropout_value) # IN 32x32x16, OUT 32x32x16, RF = 7
# Transition 1
self.pool1 = nn.MaxPool2d(2, 2) # IN 32x32x32 OUT 16x16x32, RF = 8, jump = 2
self.conv4 = self.create_conv2d(16, 32, dropout=dropout_value) # IN 16x16x16, OUT 16x16x32, RF = 12
self.conv5 = self.create_conv2d(32, 32, dropout=dropout_value) # IN 16x16x32, OUT 16x16x32, RF = 16
# Transition 2
self.pool2 = nn.MaxPool2d(2, 2) # IN 16x16x64 OUT 8x8x64, RF = 18, jump = 4
self.dconv1 = self.create_conv2d(32, 64, dilation=2, padding=2) # IN 8x8x32, OUT 8x8x64
self.conv6 = self.create_conv2d(32, 64, dropout=dropout_value) # IN 8x8x32, OUT 8x8x64, RF = 26
self.conv7 = self.create_conv2d(64, 64, dropout=dropout_value) # IN 8x8x64, OUT 8x8x64, RF = 34
# Transition 3
self.pool3 = nn.MaxPool2d(2, 2) # IN 8x8x128 OUT 4x4x128, RF = 38, jump = 8
#self.dconv2 = self.create_conv2d(64, 128, dilation=2, padding=2) # IN 8x8x64, OUT 8x8x128
self.conv8 = self.create_depthwise_conv2d(64, 128, dropout=dropout_value) # IN 4x4x64, OUT 4x4x128, RF = 54
self.conv9 = self.create_depthwise_conv2d(128, 128, dropout=dropout_value) # IN 4x4x128, OUT 4x4x128, RF = 70
# GAP + FC
self.gap = nn.AvgPool2d(kernel_size=(4,4))
self.conv10 = self.create_conv2d(128, 10, kernel_size=(1,1), padding=0, bn=False, relu=False) # IN: 256 OUT:10
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
x = self.pool1(x)
x = self.conv4(x)
x = self.conv5(x)
x = self.pool2(x)
x2 = self.dconv1(x)
x = self.conv6(x)
x = self.conv7(x)
x = torch.add(x, x2)
x = self.pool3(x)
x = self.conv8(x)
x = self.conv9(x)
x = self.gap(x)
x = self.conv10(x)
x = x.view(-1, 10)
return F.log_softmax(x, dim=-1)
class Cifar10_net3(Net):
def __init__(self, name="Cfar10Net3", dropout_value=0):
super(Cifar10_net3, self).__init__(name)
# Input Convolution: C0
self.conv1 = self.create_depthwise_conv2d(3, 16, dropout=dropout_value) # IN 32x32x3, OUT 32x32x16, RF = 3
self.conv2 = self.create_depthwise_conv2d(16, 16, dropout=dropout_value) # IN 32x32x16, OUT 32x32x16, RF = 5
self.conv3 = self.create_depthwise_conv2d(16, 16, dropout=dropout_value) # IN 32x32x16, OUT 32x32x16, RF = 7
# Transition 1
self.pool1 = nn.MaxPool2d(2, 2) # IN 32x32x32 OUT 16x16x32, RF = 8, jump = 2
self.conv4 = self.create_depthwise_conv2d(16, 32, dropout=dropout_value) # IN 16x16x16, OUT 16x16x32, RF = 12
self.conv5 = self.create_depthwise_conv2d(32, 32, dropout=dropout_value) # IN 16x16x32, OUT 16x16x32, RF = 16
# Transition 2
self.pool2 = nn.MaxPool2d(2, 2) # IN 16x16x64 OUT 8x8x64, RF = 18, jump = 4
self.dconv1 = self.create_depthwise_conv2d(32, 64, dilation=2, padding=2) # IN 8x8x32, OUT 8x8x64
self.conv6 = self.create_depthwise_conv2d(32, 64, dropout=dropout_value) # IN 8x8x32, OUT 8x8x64, RF = 26
self.conv7 = self.create_depthwise_conv2d(64, 64, dropout=dropout_value) # IN 8x8x64, OUT 8x8x64, RF = 34
# Transition 3
self.pool3 = nn.MaxPool2d(2, 2) # IN 8x8x128 OUT 4x4x128, RF = 38, jump = 8
#self.dconv2 = self.create_conv2d(64, 128, dilation=2, padding=2) # IN 8x8x64, OUT 8x8x128
self.conv8 = self.create_depthwise_conv2d(64, 128, dropout=dropout_value) # IN 4x4x64, OUT 4x4x128, RF = 54
self.conv9 = self.create_depthwise_conv2d(128, 128, dropout=dropout_value) # IN 4x4x128, OUT 4x4x128, RF = 70
# GAP + FC
self.gap = nn.AvgPool2d(kernel_size=(4,4))
self.conv10 = self.create_conv2d(128, 10, kernel_size=(1,1), padding=0, bn=False, relu=False) # IN: 256 OUT:10
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
x = self.pool1(x)
x = self.conv4(x)
x = self.conv5(x)
x = self.pool2(x)
x2 = self.dconv1(x)
x = self.conv6(x)
x = self.conv7(x)
x = torch.add(x, x2)
x = self.pool3(x)
x = self.conv8(x)
x = self.conv9(x)
x = self.gap(x)
x = self.conv10(x)
x = x.view(-1, 10)
return F.log_softmax(x, dim=-1)
class Cifar10_net4(Net):
def __init__(self, name="Cfar10Net4", dropout_value=0):
super(Cifar10_net4, self).__init__(name)
# Input Convolution: C0
self.conv1 = self.create_conv2d(3, 16, dropout=dropout_value) # IN 32x32x3, OUT 32x32x16, RF = 3
self.conv2 = self.create_conv2d(16, 16, dropout=dropout_value, dilation=2, padding=2) # IN 32x32x16, OUT 32x32x16, RF = 7
# Transition 1
self.pool1 = nn.MaxPool2d(2, 2) # IN 32x32x32 OUT 16x16x32, RF = 8, jump = 2
self.conv4 = self.create_conv2d(16, 32, dropout=dropout_value) # IN 16x16x16, OUT 16x16x32, RF = 12
self.conv5 = self.create_conv2d(32, 32, dropout=dropout_value) # IN 16x16x32, OUT 16x16x32, RF = 16
# Transition 2
self.pool2 = nn.MaxPool2d(2, 2) # IN 16x16x64 OUT 8x8x64, RF = 18, jump = 4
self.conv6 = self.create_conv2d(32, 64, dropout=dropout_value) # IN 8x8x32, OUT 8x8x64, RF = 26
self.conv7 = self.create_conv2d(64, 64, dropout=dropout_value) # IN 8x8x64, OUT 8x8x64, RF = 34
# Transition 3
self.pool3 = nn.MaxPool2d(2, 2) # IN 8x8x128 OUT 4x4x128, RF = 38, jump = 8
#self.dconv2 = self.create_conv2d(64, 128, dilation=2, padding=2) # IN 8x8x64, OUT 8x8x128
self.conv8 = self.create_depthwise_conv2d(64, 128, dropout=dropout_value) # IN 4x4x64, OUT 4x4x128, RF = 54
self.conv9 = self.create_depthwise_conv2d(128, 128, dropout=dropout_value) # IN 4x4x128, OUT 4x4x128, RF = 70
# GAP + FC
self.gap = nn.AvgPool2d(kernel_size=(4,4))
self.conv10 = self.create_conv2d(128, 10, kernel_size=(1,1), padding=0, bn=False, relu=False) # IN: 256 OUT:10
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = self.pool1(x)
x = self.conv4(x)
x = self.conv5(x)
x = self.pool2(x)
x = self.conv6(x)
x = self.conv7(x)
x = self.pool3(x)
x = self.conv8(x)
x = self.conv9(x)
x = self.gap(x)
x = self.conv10(x)
x = x.view(-1, 10)
return F.log_softmax(x, dim=-1)
class Cifar10_net5(Net):
def __init__(self, name="Cfar10Net5", dropout_value=0):
super(Cifar10_net5, self).__init__(name)
# Input Convolution: C0
self.conv1 = self.create_conv2d(3, 16, dropout=dropout_value) # IN 32x32x3, OUT 32x32x16, RF = 3
self.conv2 = self.create_conv2d(16, 16, dropout=dropout_value, dilation=2, padding=2) # IN 32x32x16, OUT 32x32x16, RF = 7
# Transition 1
self.pool1 = nn.MaxPool2d(2, 2) # IN 32x32x32 OUT 16x16x32, RF = 8, jump = 2
self.conv4 = self.create_conv2d(16, 32, dropout=dropout_value, dilation=2, padding=2) # IN 16x16x16, OUT 16x16x32, RF = 16
#self.conv5 = self.create_conv2d(32, 32, dropout=dropout_value) # IN 16x16x32, OUT 16x16x32, RF = 16
# Transition 2
self.pool2 = nn.MaxPool2d(2, 2) # IN 16x16x64 OUT 8x8x64, RF = 18, jump = 4
self.conv6 = self.create_conv2d(32, 64, dropout=dropout_value, dilation=2, padding=2) # IN 8x8x32, OUT 8x8x64, RF = 34
#self.conv7 = self.create_conv2d(64, 64, dropout=dropout_value) # IN 8x8x64, OUT 8x8x64, RF = 34
# Transition 3
self.pool3 = nn.MaxPool2d(2, 2) # IN 8x8x128 OUT 4x4x128, RF = 38, jump = 8
#self.dconv2 = self.create_conv2d(64, 128, dilation=2, padding=2) # IN 8x8x64, OUT 8x8x128
self.conv8 = self.create_depthwise_conv2d(64, 128, dropout=dropout_value) # IN 4x4x64, OUT 4x4x128, RF = 70
self.conv9 = self.create_depthwise_conv2d(128, 128, dropout=dropout_value) # IN 4x4x128, OUT 4x4x128, RF = 86
# GAP + FC
self.gap = nn.AvgPool2d(kernel_size=(4,4))
self.conv10 = self.create_conv2d(128, 10, kernel_size=(1,1), padding=0, bn=False, relu=False) # IN: 256 OUT:10
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = self.pool1(x)
x = self.conv4(x)
#x = self.conv5(x)
x = self.pool2(x)
x = self.conv6(x)
#x = self.conv7(x)
x = self.pool3(x)
x = self.conv8(x)
x = self.conv9(x)
x = self.gap(x)
x = self.conv10(x)
x = x.view(-1, 10)
return F.log_softmax(x, dim=-1)
| 41.197133
| 130
| 0.61345
| 1,723
| 11,494
| 3.98433
| 0.061521
| 0.022724
| 0.05681
| 0.119301
| 0.962418
| 0.929497
| 0.910561
| 0.892644
| 0.879388
| 0.869774
| 0
| 0.169689
| 0.26118
| 11,494
| 279
| 131
| 41.197133
| 0.638719
| 0.266574
| 0
| 0.765714
| 0
| 0
| 0.004804
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.057143
| false
| 0
| 0.022857
| 0
| 0.137143
| 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
|
714391328b292cb722ebb5e04f807e4e9895ffce
| 120
|
py
|
Python
|
batchout/indexes/__init__.py
|
ilia-khaustov/batchout
|
e916a1b0bfac771e6c96d0ff2478dc3f44804a94
|
[
"MIT"
] | 8
|
2019-11-05T06:54:30.000Z
|
2021-12-14T14:52:24.000Z
|
batchout/indexes/__init__.py
|
ilia-khaustov/batchout
|
e916a1b0bfac771e6c96d0ff2478dc3f44804a94
|
[
"MIT"
] | null | null | null |
batchout/indexes/__init__.py
|
ilia-khaustov/batchout
|
e916a1b0bfac771e6c96d0ff2478dc3f44804a94
|
[
"MIT"
] | 1
|
2020-05-05T09:31:14.000Z
|
2020-05-05T09:31:14.000Z
|
from batchout.indexes.base import Index
from batchout.indexes.scalar import IndexForList, IndexForObject, IndexFromList
| 40
| 79
| 0.866667
| 14
| 120
| 7.428571
| 0.714286
| 0.230769
| 0.365385
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.083333
| 120
| 2
| 80
| 60
| 0.945455
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
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| 1
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| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
856ac39882c7a22a209ea6377e3381a418887f14
| 22,043
|
py
|
Python
|
backend/api/v1/routers/poem.py
|
B3zaleel/Cartedepoezii
|
217050d5ea1203a11a5ba9a74b3d497b5120cb9a
|
[
"MIT"
] | 4
|
2022-03-19T09:25:14.000Z
|
2022-03-31T21:51:30.000Z
|
backend/api/v1/routers/poem.py
|
B3zaleel/Cartedepoezii
|
217050d5ea1203a11a5ba9a74b3d497b5120cb9a
|
[
"MIT"
] | 2
|
2022-03-24T01:02:13.000Z
|
2022-03-26T09:50:09.000Z
|
backend/api/v1/routers/poem.py
|
B3zaleel/Cartedepoezii
|
217050d5ea1203a11a5ba9a74b3d497b5120cb9a
|
[
"MIT"
] | null | null | null |
#!/usr/bin/python3
'''The poem router's module.
'''
import json
import re
import uuid
from datetime import datetime
from fastapi import APIRouter
from sqlalchemy import and_
from ..form_types import (
PoemAddForm,
PoemUpdateForm,
PoemLikeForm,
PoemDeleteForm
)
from ..database import (
get_session,
User,
Comment,
Poem,
PoemLike,
UserFollowing
)
from ..utils.token_handlers import AuthToken
from ..utils.pagination import extract_page
router = APIRouter(prefix='/api/v1')
@router.get('/poem')
async def get_poem(id: str, token: str):
'''Retrieves information about a given poem.
'''
response = {
'success': False,
'message': 'Failed to find poem.'
}
auth_token = AuthToken.decode(token)
user_id = auth_token.user_id if auth_token is not None else None
db_session = get_session()
try:
poem = db_session.query(Poem).filter(
Poem.id == id
).first()
if poem:
# get the relevant information related to the poem
user = db_session.query(User).filter(
User.id == poem.user_id
).first()
if not user:
return response
comments = db_session.query(Comment).filter(and_(
Comment.poem_id == id,
Comment.comment_id == None
)).all()
comments_count = len(comments) if comments else 0
likes = db_session.query(PoemLike).filter(
PoemLike.poem_id == id
).all()
likes_count = len(likes) if likes else 0
is_liked_by_user = False
if user_id:
# check current users reaction on this poem
poem_interaction = db_session.query(PoemLike).filter(and_(
PoemLike.poem_id == id,
PoemLike.user_id == user_id
)).first()
if poem_interaction:
is_liked_by_user = True
response = {
'success': True,
'data': {
'id': poem.id,
'user': {
'id': user.id,
'name': user.name,
'profilePhotoId': user.profile_photo_id
},
'title': poem.title,
'publishedOn': poem.created_on.isoformat(),
'verses': json.JSONDecoder().decode(poem.text),
'commentsCount': comments_count,
'likesCount': likes_count,
'isLiked': is_liked_by_user
}
}
finally:
db_session.close()
return response
@router.post('/poem')
async def add_poem(body: PoemAddForm):
'''Creates a new poem.
'''
response = {
'success': False,
'message': 'Failed to add poem.'
}
# validate body data
auth_token = AuthToken.decode(body.authToken)
if auth_token is None or auth_token.user_id != body.userId:
response['message'] = 'Invalid authentication token.'
return response
if len(body.title) > 256:
response['message'] = 'Title is too long.'
return response
if len(body.verses) < 1:
response['message'] = 'Verses is too short.'
return response
if not all(list(map(lambda x: len(x.strip()) > 1, body.verses))):
response['message'] = 'Verses is too short.'
return response
db_session = get_session()
try:
gen_id = str(uuid.uuid4())
cur_time = datetime.utcnow()
verses_txt = json.JSONEncoder().encode(body.verses)
poem = Poem(
id=gen_id,
created_on=cur_time,
updated_on=cur_time,
user_id=body.userId,
title=body.title,
text=verses_txt
)
db_session.add(poem)
db_session.commit()
response = {
'success': True,
'data': {
'id': gen_id,
'createdOn': cur_time.isoformat(),
'repliesCount': 0,
'likesCount': 0
}
}
except Exception as ex:
print(ex.args[0])
db_session.rollback()
finally:
db_session.close()
return response
@router.put('/poem')
async def update_poem(body: PoemUpdateForm):
'''Edits an existing poem.
'''
response = {
'success': False,
'message': 'Failed to update poem.'
}
# validate body data
auth_token = AuthToken.decode(body.authToken)
if auth_token is None or auth_token.user_id != body.userId:
response['message'] = 'Invalid authentication token.'
return response
if len(body.title) > 256:
response['message'] = 'Title is too long.'
return response
if len(body.verses) < 1:
response['message'] = 'Verses is too short.'
return response
if not all(list(map(lambda x: len(x.strip()) > 1, body.verses))):
response['message'] = 'Verses is too short.'
return response
db_session = get_session()
try:
cur_time = datetime.utcnow()
verses_txt = json.JSONEncoder().encode(body.verses)
db_session.query(Poem).filter(Poem.id == body.poemId).update(
{
Poem.title: body.title,
Poem.updated_on: cur_time,
Poem.text: verses_txt
},
synchronize_session=False
)
db_session.commit()
response = {
'success': True,
'data': {}
}
except Exception as ex:
print(ex.args[0])
db_session.rollback()
finally:
db_session.close()
return response
@router.delete('/poem')
async def remove_poem(body: PoemDeleteForm):
'''Deletes a poem.
'''
response = {
'success': False,
'message': 'Failed to remove poem.'
}
auth_token = AuthToken.decode(body.authToken)
if auth_token is None or auth_token.user_id != body.userId:
response['message'] = 'Invalid authentication token.'
return response
db_session = get_session()
try:
poem = db_session.query(Poem).filter(and_(
Poem.id == body.poemId,
Poem.user_id == body.userId,
)).first()
if poem:
db_session.query(PoemLike).filter(
PoemLike.poem_id == body.poemId,
).delete(
synchronize_session=False
)
db_session.query(Comment).filter(
Comment.poem_id == body.poemId,
).delete(
synchronize_session=False
)
db_session.query(Poem).filter(and_(
Poem.id == body.poemId,
Poem.user_id == body.userId,
)).delete(
synchronize_session=False
)
db_session.commit()
response = {
'success': True,
'data': {}
}
finally:
db_session.close()
return response
@router.put('/like-poem')
async def like_poem(body: PoemLikeForm):
'''Toggles a user's reaction on a poem.
'''
response = {
'success': False,
'message': 'Failed to like poem.'
}
auth_token = AuthToken.decode(body.authToken)
if auth_token is None or auth_token.user_id != body.userId:
response['message'] = 'Invalid authentication token.'
return response
db_session = get_session()
try:
cur_usr_fav = db_session.query(PoemLike).filter(and_(
PoemLike.user_id == auth_token.user_id,
PoemLike.poem_id == body.poemId,
)).first()
if cur_usr_fav:
# dislike poem
db_session.query(PoemLike).filter(and_(
PoemLike.user_id == auth_token.user_id,
PoemLike.poem_id == body.poemId,
)).delete(
synchronize_session=False
)
db_session.commit()
response = {
'success': True,
'data': {'status': False}
}
else:
# like poem
new_favourite = PoemLike(
id=str(uuid.uuid4()),
created_on=datetime.utcnow(),
user_id=body.userId,
poem_id=body.poemId,
)
db_session.add(new_favourite)
db_session.commit()
response = {
'success': True,
'data': {'status': True}
}
except Exception as ex:
print(ex.args[0])
db_session.rollback()
finally:
db_session.close()
return response
@router.get('/poems-user-created')
async def get_created_poems(userId, token='', span='', after='', before=''):
'''Retrieves poems created by the current user.
'''
response = {
'success': False,
'message': 'Failed to find poems created by the user.'
}
if not userId:
return response
auth_token = AuthToken.decode(token)
user_id = auth_token.user_id if auth_token is not None else None
db_session = get_session()
try:
# sanitize span
span = span.strip()
if span and re.fullmatch(r'\d+', span) is None:
response = {
'success': False,
'message': 'Invalid span type.'
}
db_session.close()
return response
span = int(span if span else '12')
poems_created = db_session.query(Poem).filter(
Poem.user_id == userId
).all()
user_poems = []
if poems_created:
user = db_session.query(User).filter(
User.id == userId
).first()
if not user:
return response
for poem in poems_created:
# retrieve information related to the current poem
comments = db_session.query(Comment).filter(and_(
Comment.poem_id == poem.id,
Comment.comment_id == None
)).all()
comments_count = len(comments) if comments else 0
likes = db_session.query(PoemLike).filter(
PoemLike.poem_id == poem.id
).all()
likes_count = len(likes) if likes else 0
is_liked_by_user = False
if user_id:
poem_interaction = db_session.query(PoemLike).filter(and_(
PoemLike.poem_id == poem.id,
PoemLike.user_id == user_id
)).first()
if poem_interaction:
is_liked_by_user = True
obj = {
'id': poem.id,
'user': {
'id': user.id,
'name': user.name,
'profilePhotoId': user.profile_photo_id,
},
'title': poem.title,
'publishedOn': poem.created_on.isoformat(),
'verses': json.JSONDecoder().decode(poem.text),
'commentsCount': comments_count,
'likesCount': likes_count,
'isLiked': is_liked_by_user
}
user_poems.append(obj)
user_poems.sort(
key=lambda x: datetime.fromisoformat(x['publishedOn']),
reverse=True
)
response = {
'success': True,
'data': extract_page(
user_poems,
span,
after,
before,
True,
lambda x: x['id']
)
}
finally:
db_session.close()
return response
@router.get('/poems-user-likes')
async def get_liked_poems(userId, token='', span='', after='', before=''):
'''Retrieves poems liked by a given user.
'''
response = {
'success': False,
'message': 'Failed to find poems liked by the user.'
}
if not userId:
return response
auth_token = AuthToken.decode(token)
user_id = auth_token.user_id if auth_token is not None else None
db_session = get_session()
try:
# sanitize span
span = span.strip()
if span and re.fullmatch(r'\d+', span) is None:
response = {
'success': False,
'message': 'Invalid span type.'
}
db_session.close()
return response
span = int(span if span else '12')
likes = db_session.query(PoemLike).filter(
PoemLike.user_id == userId
).all()
user_poems_liked = []
for poem_like in likes:
# retrieve information related to the current reaction
poem = db_session.query(Poem).filter(
Poem.id == poem_like.poem_id
).first()
user = db_session.query(User).filter(
User.id == poem.user_id
).first()
comments = db_session.query(Comment).filter(and_(
Comment.poem_id == poem.id,
Comment.comment_id == None
)).all()
comments_count = len(comments) if comments else 0
likes = db_session.query(PoemLike).filter(
PoemLike.poem_id == poem.id
).all()
likes_count = len(likes) if likes else 0
is_liked_by_user = False
if user_id != userId:
poem_interaction = db_session.query(PoemLike).filter(and_(
PoemLike.poem_id == poem.id,
PoemLike.user_id == user_id
)).first()
if poem_interaction:
is_liked_by_user = True
else:
is_liked_by_user = True
obj = {
'id': poem.id,
'user': {
'id': user.id,
'name': user.name,
'profilePhotoId': user.profile_photo_id,
},
'title': poem.title,
'publishedOn': poem.created_on.isoformat(),
'verses': json.JSONDecoder().decode(poem.text),
'commentsCount': comments_count,
'likesCount': likes_count,
'isLiked': is_liked_by_user
}
user_poems_liked.append(obj)
user_poems_liked.sort(
key=lambda x: datetime.fromisoformat(x['publishedOn'])
)
response = {
'success': True,
'data': extract_page(
user_poems_liked,
span,
after,
before,
True,
lambda x: x['id']
)
}
finally:
db_session.close()
return response
@router.get('/poems-channel')
async def get_channel_poems(token, span='', after='', before=''):
'''Retrieves poems for a user's timeline or home section.
'''
response = {
'success': False,
'message': 'Failed to find poems for the channel.'
}
auth_token = AuthToken.decode(token)
if auth_token is None:
response['message'] = 'Invalid authentication token.'
return response
user_id = auth_token.user_id
db_session = get_session()
try:
# sanitize span
span = span.strip()
if span and re.fullmatch(r'\d+', span) is None:
response = {
'success': False,
'message': 'Invalid span type.'
}
db_session.close()
return response
span = int(span if span else '12')
followings = db_session.query(UserFollowing).filter(
UserFollowing.follower_id == user_id
).all()
max_size = 2**32 - 1
followings_count = len(followings) + 1 if followings else 1
poems_per_following = max_size // followings_count
poem_users_ids = [user_id]
if followings:
poem_users_ids.extend(list(map(lambda x: x.following_id, followings)))
users_poems = []
for id in poem_users_ids:
# fetch poems_per_following poems for each following
poems = db_session.query(Poem).filter(
Poem.user_id == id
).limit(poems_per_following).all()
user = db_session.query(User).filter(
User.id == id
).first()
for poem in poems:
# retrieve information related to the current poem
comments = db_session.query(Comment).filter(and_(
Comment.poem_id == poem.id,
Comment.comment_id == None
)).all()
comments_count = len(comments) if comments else 0
likes = db_session.query(PoemLike).filter(
PoemLike.poem_id == poem.id
).all()
likes_count = len(likes) if likes else 0
is_liked_by_user = False
if user_id:
poem_interaction = db_session.query(PoemLike).filter(and_(
PoemLike.poem_id == poem.id,
PoemLike.user_id == user_id
)).first()
if poem_interaction:
is_liked_by_user = True
obj = {
'id': poem.id,
'user': {
'id': user.id,
'name': user.name,
'profilePhotoId': user.profile_photo_id
},
'title': poem.title,
'publishedOn': poem.created_on.isoformat(),
'verses': json.JSONDecoder().decode(poem.text),
'commentsCount': comments_count,
'likesCount': likes_count,
'isLiked': is_liked_by_user
}
users_poems.append(obj)
# stable sort based on creation time
users_poems.sort(
key=lambda x: datetime.fromisoformat(x['publishedOn']),
reverse=True
)
response = {
'success': True,
'data': extract_page(
users_poems,
span,
after,
before,
True,
lambda x: x['id']
)
}
finally:
db_session.close()
return response
@router.get('/poems-explore')
async def get_exploratory_poems(token, span='', after='', before=''):
'''Retrieves poems a user can explore.
'''
response = {
'success': False,
'message': 'Failed to find poems for the user.'
}
auth_token = AuthToken.decode(token)
if auth_token is None:
response['message'] = 'Invalid authentication token.'
return response
user_id = auth_token.user_id if auth_token is not None else None
db_session = get_session()
try:
# sanitize span
span = span.strip()
if span and re.fullmatch(r'\d+', span) is None:
response = {
'success': False,
'message': 'Invalid span type.'
}
db_session.close()
return response
span = int(span if span else '12')
followings = db_session.query(UserFollowing).filter(
UserFollowing.follower_id == user_id
).all()
max_poems_count = 48
poem_users_ids = [user_id]
if followings:
poem_users_ids.extend(
list(map(lambda x: x.following_id, followings))
)
explore_poems = []
# fetch max_poems_count for the user from people
# the user isn't following
poems = db_session.query(Poem).filter(
Poem.user_id.notin_(poem_users_ids)
).limit(max_poems_count).all()
for poem in poems:
# retrieve information related to the current poem
user = db_session.query(User).filter(
User.id == poem.user_id
).first()
comments = db_session.query(Comment).filter(and_(
Comment.poem_id == poem.id,
Comment.comment_id == None
)).all()
comments_count = len(comments) if comments else 0
likes = db_session.query(PoemLike).filter(
PoemLike.poem_id == poem.id
).all()
likes_count = len(likes) if likes else 0
is_liked_by_user = False
if user_id:
poem_interaction = db_session.query(PoemLike).filter(and_(
PoemLike.poem_id == poem.id,
PoemLike.user_id == user_id
)).first()
if poem_interaction:
is_liked_by_user = True
obj = {
'id': poem.id,
'user': {
'id': user.id,
'name': user.name,
'profilePhotoId': user.profile_photo_id
},
'title': poem.title,
'publishedOn': poem.created_on.isoformat(),
'verses': json.JSONDecoder().decode(poem.text),
'commentsCount': comments_count,
'likesCount': likes_count,
'isLiked': is_liked_by_user
}
explore_poems.append(obj)
explore_poems.sort(
key=lambda x: x['likesCount'],
reverse=True
)
response = {
'success': True,
'data': extract_page(
explore_poems,
span,
after,
before,
True,
lambda x: x['id']
)
}
finally:
db_session.close()
return response
| 33.24736
| 82
| 0.508007
| 2,248
| 22,043
| 4.805605
| 0.092527
| 0.055818
| 0.045358
| 0.019254
| 0.819124
| 0.813478
| 0.800611
| 0.768305
| 0.714709
| 0.705822
| 0
| 0.003354
| 0.391326
| 22,043
| 662
| 83
| 33.297583
| 0.801819
| 0.027628
| 0
| 0.704319
| 0
| 0
| 0.076125
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.016611
| 0
| 0.064784
| 0.004983
| 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
|
8589d8733519ab58d88b816f23405403882994ef
| 108
|
py
|
Python
|
sites/one_drive/__init__.py
|
GeorgOhneH/ethz-document-fetcher
|
42921e5d71698a269eb54cf9d3979e4a7d88a9cf
|
[
"MIT"
] | 15
|
2020-03-17T15:43:46.000Z
|
2022-01-08T04:23:49.000Z
|
sites/one_drive/__init__.py
|
GeorgOhneH/ethz-document-fetcher
|
42921e5d71698a269eb54cf9d3979e4a7d88a9cf
|
[
"MIT"
] | 5
|
2020-03-12T10:05:27.000Z
|
2021-03-03T16:01:47.000Z
|
sites/one_drive/__init__.py
|
GeorgOhneH/ethz-document-fetcher
|
42921e5d71698a269eb54cf9d3979e4a7d88a9cf
|
[
"MIT"
] | 2
|
2020-03-17T17:09:20.000Z
|
2020-12-28T22:59:17.000Z
|
from sites.one_drive.producer import producer, get_folder_name
from .get_website_url import get_website_url
| 36
| 62
| 0.87963
| 18
| 108
| 4.888889
| 0.611111
| 0.227273
| 0.295455
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.083333
| 108
| 2
| 63
| 54
| 0.888889
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
85bc65c7d281b0d77a307384a41b5d7c31af1c21
| 137
|
py
|
Python
|
historia/pops/__init__.py
|
eranimo/historia
|
5e0b047d4bcdd534f48f8b9bf19d425b0b31a3fd
|
[
"MIT"
] | 6
|
2016-04-26T18:39:36.000Z
|
2021-09-01T09:13:38.000Z
|
historia/pops/__init__.py
|
eranimo/historia
|
5e0b047d4bcdd534f48f8b9bf19d425b0b31a3fd
|
[
"MIT"
] | null | null | null |
historia/pops/__init__.py
|
eranimo/historia
|
5e0b047d4bcdd534f48f8b9bf19d425b0b31a3fd
|
[
"MIT"
] | 4
|
2016-04-10T23:47:23.000Z
|
2021-08-15T11:40:28.000Z
|
from historia.pops.enums import PopJob, PopClass
from historia.pops.models import Inventory, Pop
from historia.pops.pop_service import *
| 34.25
| 48
| 0.832117
| 20
| 137
| 5.65
| 0.55
| 0.318584
| 0.424779
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.10219
| 137
| 3
| 49
| 45.666667
| 0.918699
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
85bcf6935f35e48f9fd70b86e7f823623c63d66c
| 9,574
|
py
|
Python
|
prototyping/Python/test_SweepLine.py
|
pyvain/WebSight
|
d9b0201ef472c46020bb2fd75af6f867eaa66312
|
[
"MIT"
] | null | null | null |
prototyping/Python/test_SweepLine.py
|
pyvain/WebSight
|
d9b0201ef472c46020bb2fd75af6f867eaa66312
|
[
"MIT"
] | null | null | null |
prototyping/Python/test_SweepLine.py
|
pyvain/WebSight
|
d9b0201ef472c46020bb2fd75af6f867eaa66312
|
[
"MIT"
] | null | null | null |
import unittest
from SweepLine import SweepLine
from ComparableSegment import ComparableSegment
class TestSweepLine(unittest.TestCase):
# __init__
def test__init__empty(self):
line = SweepLine()
self.assertTrue(line.isEmpty())
# addSegment
def test__addSegment__first(self):
line = SweepLine()
s1 = ComparableSegment(0, 0, 2, 2)
line.addSegment(s1)
self.assertEqual(line.aboveSegments(s1), [])
self.assertEqual(line.belowSegments(s1), [])
def test__addSegment__equal(self):
line = SweepLine()
s1 = ComparableSegment(0, 0, 2, 2)
s2 = ComparableSegment(1, 1, 2, 2)
line.addSegment(s1)
line.addSegment(s2)
self.assertEqual(line.aboveSegments(s1), [])
self.assertEqual(line.belowSegments(s1), [])
self.assertEqual(line.aboveSegments(s2), [])
self.assertEqual(line.belowSegments(s2), [])
def test__addSegment__above_different_y(self):
line = SweepLine()
s1 = ComparableSegment(0, 0, 2, 2)
s2 = ComparableSegment(1, 2, 2, 2)
line.addSegment(s1)
line.addSegment(s2)
self.assertEqual(line.aboveSegments(s2), [])
self.assertEqual(line.belowSegments(s2), [s1])
self.assertEqual(line.aboveSegments(s1), [s2])
self.assertEqual(line.belowSegments(s1), [])
def test__addSegment__above_same_y_different_gradient(self):
line = SweepLine()
s1 = ComparableSegment(0, 0, 2, 2)
s2 = ComparableSegment(1, 1, 2, 3)
line.addSegment(s1)
line.addSegment(s2)
self.assertEqual(line.aboveSegments(s2), [])
self.assertEqual(line.belowSegments(s2), [s1])
self.assertEqual(line.aboveSegments(s1), [s2])
self.assertEqual(line.belowSegments(s1), [])
def test__addSegment__below_different_y(self):
line = SweepLine()
s1 = ComparableSegment(0, 0, 2, 2)
s2 = ComparableSegment(1, 0, 2, 2)
line.addSegment(s1)
line.addSegment(s2)
self.assertEqual(line.aboveSegments(s1), [])
self.assertEqual(line.belowSegments(s1), [s2])
self.assertEqual(line.aboveSegments(s2), [s1])
self.assertEqual(line.belowSegments(s2), [])
def test__addSegment__below_same_y_different_gradient(self):
line = SweepLine()
s1 = ComparableSegment(0, 0, 2, 2)
s2 = ComparableSegment(1, 1, 2, 1)
line.addSegment(s1)
line.addSegment(s2)
self.assertEqual(line.aboveSegments(s1), [])
self.assertEqual(line.belowSegments(s1), [s2])
self.assertEqual(line.aboveSegments(s2), [s1])
self.assertEqual(line.belowSegments(s2), [])
# remove
def test__remove__single(self):
line = SweepLine()
s1 = ComparableSegment(0, 0, 2, 2)
line.addSegment(s1)
line.removeSegment(s1)
self.assertTrue(line.isEmpty)
def test__remove__multiple_not_equals(self):
line = SweepLine()
s1 = ComparableSegment(1, 2, 2, 2)
s2 = ComparableSegment(0, 0, 2, 2)
s3 = ComparableSegment(1, 0, 2, 2)
line.addSegment(s2)
line.addSegment(s1)
line.addSegment(s3)
self.assertEqual(line.aboveSegments(s1), [])
self.assertEqual(line.belowSegments(s1), [s2])
self.assertEqual(line.aboveSegments(s2), [s1])
self.assertEqual(line.belowSegments(s2), [s3])
self.assertEqual(line.aboveSegments(s3), [s2])
self.assertEqual(line.belowSegments(s3), [])
line.removeSegment(s2)
self.assertEqual(line.aboveSegments(s1), [])
self.assertEqual(line.belowSegments(s1), [s3])
self.assertEqual(line.aboveSegments(s3), [s1])
self.assertEqual(line.belowSegments(s3), [])
def test__remove__multiple_equals(self):
line = SweepLine()
s1 = ComparableSegment(0, 0, 3, 3)
s2 = ComparableSegment(0, 0, 2, 2)
s3 = ComparableSegment(1, 1, 3, 3)
line.addSegment(s1)
line.addSegment(s2)
line.addSegment(s3)
self.assertEqual(len(line.l), 3)
self.assertTrue(s1 in line.l)
self.assertTrue(s2 in line.l)
self.assertTrue(s3 in line.l)
line.removeSegment(s2)
self.assertEqual(len(line.l), 2)
self.assertTrue(s1 in line.l)
self.assertFalse(s2 in line.l)
self.assertTrue(s3 in line.l)
# sameLevelAs
def test__sameLevelAs__one_on_one(self):
line = SweepLine()
s1 = ComparableSegment(0, 0, 2, 2)
line.addSegment(s1)
self.assertEqual(line.sameLevelAs(s1), [s1])
# sameLevelAs
def test__sameLevelAs__one_on_several(self):
line = SweepLine()
s1 = ComparableSegment(0, 0, 1, 1)
s2 = ComparableSegment(0, 0, 1, 2)
s3 = ComparableSegment(0, 0, 1, 3)
line.addSegment(s1)
line.addSegment(s2)
line.addSegment(s3)
self.assertEqual(line.sameLevelAs(s2), [s2])
# sameLevelAs
def test__sameLevelAs__all_on_several(self):
line = SweepLine()
s1 = ComparableSegment(0, 0, 1, 1)
s2 = ComparableSegment(0, 0, 2, 2)
s3 = ComparableSegment(0, 0, 3, 3)
line.addSegment(s1)
line.addSegment(s2)
line.addSegment(s3)
res = line.sameLevelAs(s2)
self.assertEqual(len(res), 3)
self.assertTrue(s1 in res and s2 in res and s3 in res)
# sameLevelAs
def test__sameLevelAs__several_on_several(self):
line = SweepLine()
s1 = ComparableSegment(0, 0, 1, 1)
s2 = ComparableSegment(0, 0, 2, 2)
s3 = ComparableSegment(0, 0, 3, 3)
s4 = ComparableSegment(0, 0, 3, 2)
s5 = ComparableSegment(0, 0, 2, 3)
line.addSegment(s4)
line.addSegment(s1)
line.addSegment(s2)
line.addSegment(s3)
line.addSegment(s5)
res = line.sameLevelAs(s2)
self.assertEqual(len(res), 3)
self.assertTrue(s1 in res and s2 in res and s3 in res)
# betweenY
def test__betweenY__empty(self):
line = SweepLine()
self.assertEqual(line.betweenY(0, 1, 0), [])
def test__betweenY__all_in(self):
line = SweepLine()
s1 = ComparableSegment(0, 0, 2, 2)
s2 = ComparableSegment(0, 1, 2, 3)
s3 = ComparableSegment(0, 2, 2, 4)
line.addSegment(s1)
line.addSegment(s2)
line.addSegment(s3)
self.assertEqual(line.betweenY(1, 3, 1), [s1, s2, s3])
def test_betweenY__few_in(self):
line = SweepLine()
s1 = ComparableSegment(0, 0, 1, 0)
s2 = ComparableSegment(0, 1, 1, 1)
s3 = ComparableSegment(0, 2, 1, 2)
s4 = ComparableSegment(0, 3, 1, 3)
s5 = ComparableSegment(0, 4, 1, 4)
line.addSegment(s1)
line.addSegment(s2)
line.addSegment(s3)
line.addSegment(s4)
line.addSegment(s5)
self.assertEqual(line.betweenY(1, 3, 0), [s2, s3, s4])
def test_betweenY__none_in(self):
line = SweepLine()
s1 = ComparableSegment(0, 0, 1, 0)
s2 = ComparableSegment(0, 1, 1, 1)
s3 = ComparableSegment(0, 2, 1, 2)
line.addSegment(s1)
line.addSegment(s2)
line.addSegment(s3)
self.assertEqual(line.betweenY(1.5, 1.75, 0), [])
# revertOrder
def test__revertOrder_nothing_in_between(self):
line = SweepLine()
s1 = ComparableSegment(0, 0, 1, 1)
s2 = ComparableSegment(0, 1, 1, 0)
line.addSegment(s1)
line.addSegment(s2)
self.assertEqual(line.aboveSegments(s2), [])
self.assertEqual(line.belowSegments(s2), [s1])
self.assertEqual(line.aboveSegments(s1), [s2])
self.assertEqual(line.belowSegments(s1), [])
line.revertOrder(0.5, [s1, s2])
self.assertEqual(line.aboveSegments(s1), [])
self.assertEqual(line.belowSegments(s1), [s2])
self.assertEqual(line.aboveSegments(s2), [s1])
self.assertEqual(line.belowSegments(s2), [])
def test__revertOrder__2_segments(self):
line = SweepLine()
s1 = ComparableSegment(0, 0, 1, 1)
s2 = ComparableSegment(0, 1, 1, 0)
line.addSegment(s1)
line.addSegment(s2)
self.assertEqual(line.aboveSegments(s2), [])
self.assertEqual(line.belowSegments(s2), [s1])
self.assertEqual(line.aboveSegments(s1), [s2])
self.assertEqual(line.belowSegments(s1), [])
line.revertOrder(0.5, [s1, s2])
self.assertEqual(line.aboveSegments(s1), [])
self.assertEqual(line.belowSegments(s1), [s2])
self.assertEqual(line.aboveSegments(s2), [s1])
self.assertEqual(line.belowSegments(s2), [])
def test__revertOrder__3_segments(self):
line = SweepLine()
s1 = ComparableSegment(0, 0, 1, 1)
s2 = ComparableSegment(0, 0.5, 1, 0.5)
s3 = ComparableSegment(0, 1, 1, 0)
line.addSegment(s1)
line.addSegment(s2)
line.addSegment(s3)
self.assertEqual(line.aboveSegments(s3), [])
self.assertEqual(line.belowSegments(s3), [s2])
self.assertEqual(line.aboveSegments(s2), [s3])
self.assertEqual(line.belowSegments(s2), [s1])
self.assertEqual(line.aboveSegments(s1), [s2])
self.assertEqual(line.belowSegments(s1), [])
line.revertOrder(0.5, [s1, s2, s3])
self.assertEqual(line.aboveSegments(s1), [])
self.assertEqual(line.belowSegments(s1), [s2])
self.assertEqual(line.aboveSegments(s2), [s1])
self.assertEqual(line.belowSegments(s2), [s3])
self.assertEqual(line.aboveSegments(s3), [s2])
self.assertEqual(line.belowSegments(s3), [])
def test__revertOrder__4_segments(self):
line = SweepLine()
s1 = ComparableSegment(0, 0, 1, 1)
s2 = ComparableSegment(0, 0.25, 1, 0.75)
s3 = ComparableSegment(0, 0.75, 1, 0.25)
s4 = ComparableSegment(0, 1, 1, 0)
line.addSegment(s1)
line.addSegment(s2)
line.addSegment(s3)
line.addSegment(s4)
self.assertEqual(line.aboveSegments(s4), [])
self.assertEqual(line.belowSegments(s4), [s3])
self.assertEqual(line.aboveSegments(s3), [s4])
self.assertEqual(line.belowSegments(s3), [s2])
self.assertEqual(line.aboveSegments(s2), [s3])
self.assertEqual(line.belowSegments(s2), [s1])
self.assertEqual(line.aboveSegments(s1), [s2])
self.assertEqual(line.belowSegments(s1), [])
line.revertOrder(0.5, [s1, s2, s3, s4])
self.assertEqual(line.aboveSegments(s1), [])
self.assertEqual(line.belowSegments(s1), [s2])
self.assertEqual(line.aboveSegments(s2), [s1])
self.assertEqual(line.belowSegments(s2), [s3])
self.assertEqual(line.aboveSegments(s3), [s2])
self.assertEqual(line.belowSegments(s3), [s4])
self.assertEqual(line.aboveSegments(s4), [s3])
self.assertEqual(line.belowSegments(s4), [])
if __name__ == '__main__':
unittest.main()
| 32.127517
| 61
| 0.709212
| 1,293
| 9,574
| 5.152359
| 0.052591
| 0.193636
| 0.233864
| 0.182528
| 0.880216
| 0.837887
| 0.794356
| 0.765836
| 0.758931
| 0.746022
| 0
| 0.061301
| 0.134427
| 9,574
| 298
| 62
| 32.127517
| 0.742609
| 0.009923
| 0
| 0.714286
| 0
| 0
| 0.000845
| 0
| 0
| 0
| 0
| 0
| 0.370656
| 1
| 0.084942
| false
| 0
| 0.011583
| 0
| 0.100386
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
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| null | 0
| 0
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| 0
| 0
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| 0
| 0
| 0
| 0
|
0
| 9
|
a42eb437c5e11ef3452517a9c0c39c2a9c406dbc
| 100
|
py
|
Python
|
psych_metric/__init__.py
|
prijatelj/bayesian_eval_ground_truth-free
|
c0e569c78d63beb79f5e1e727c322293c3584323
|
[
"MIT"
] | 1
|
2021-12-26T05:55:46.000Z
|
2021-12-26T05:55:46.000Z
|
psych_metric/__init__.py
|
prijatelj/bayesian_eval_ground_truth-free
|
c0e569c78d63beb79f5e1e727c322293c3584323
|
[
"MIT"
] | null | null | null |
psych_metric/__init__.py
|
prijatelj/bayesian_eval_ground_truth-free
|
c0e569c78d63beb79f5e1e727c322293c3584323
|
[
"MIT"
] | null | null | null |
from psych_metric import datasets
from psych_metric import distrib
from psych_metric import metrics
| 25
| 33
| 0.88
| 15
| 100
| 5.666667
| 0.466667
| 0.317647
| 0.529412
| 0.741176
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.12
| 100
| 3
| 34
| 33.333333
| 0.965909
| 0
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| true
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| null | 0
| 0
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| 0
| 0
| 0
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| 0
| 1
| 0
| 1
| 0
|
0
| 8
|
a45efaa6e9bca6ad4edcaf7a50a7ea5054045bb9
| 36,330
|
py
|
Python
|
models/inv.py
|
shuxiang/MT-WMS
|
38ef18baed6d9eddb88d43da2eeed55988410daf
|
[
"Apache-2.0"
] | 1
|
2022-03-11T05:42:25.000Z
|
2022-03-11T05:42:25.000Z
|
models/inv.py
|
shuxiang/MT-WMS
|
38ef18baed6d9eddb88d43da2eeed55988410daf
|
[
"Apache-2.0"
] | null | null | null |
models/inv.py
|
shuxiang/MT-WMS
|
38ef18baed6d9eddb88d43da2eeed55988410daf
|
[
"Apache-2.0"
] | null | null | null |
#coding=utf8
__all__ = ['Inv', 'InvRfid', 'InvRfidTrans', 'InvTrans',
'Category', 'Good', 'GoodMap',
'InvAdjust', 'InvMove', 'InvCount', 'InvWarn']
import os.path
import json
from sqlalchemy.sql import text
from uuid import uuid4
from datetime import datetime, timedelta
from sqlalchemy import Index, UniqueConstraint
from sqlalchemy import func, or_, and_
from werkzeug.utils import cached_property
from utils.upload import get_oss_image, save_inv_qrcode, save_inv_barcode
from utils.flask_tools import json_dump
from extensions.database import db
import settings
class Inv(db.Model):
__tablename__ = 'inv'
__table_args__ = (
Index("ix_inv_sku", "sku", "location_code", "company_code"),
Index("ix_inv_barcode", "barcode", "location_code", "company_code"),
Index("ix_inv_tenant", "company_code", 'warehouse_code', "owner_code",),
)
id = db.Column(db.Integer, primary_key=True, autoincrement=True)
# # 可用库存, 不可用库存, 限制库存
# state = db.Column(db.Enum('Y', 'N', 'L'), default='Y')
owner_code = db.Column(db.String(50))
company_code = db.Column(db.String(50))
warehouse_code = db.Column(db.String(50))
# 库位信息
location_code = db.Column(db.String(50))
area_code = db.Column(db.String(50))
workarea_code = db.Column(db.String(50))
# 货品信息
category_code = db.Column(db.String(50), default='')
sku = db.Column(db.String(50), server_default='')
name = db.Column(db.String(200), default='')
name_en = db.Column(db.String(200), default='')
barcode = db.Column(db.String(50), server_default='')
brand = db.Column(db.String(20), server_default='')
# qty = qty_alloc + qty_able;
qty = db.Column(db.Integer, server_default='0', default=0)
qty_alloc = db.Column(db.Integer, server_default='0', default=0)
qty_able = db.Column(db.Integer, server_default='0', default=0)
# 冻结数量
qty_freeze = db.Column(db.Integer, server_default='0', default=0)
stockin_date = db.Column(db.Date, default=db.func.current_date())
partner_name = db.Column(db.String(50), server_default='')
# 批次属性
supplier_code = db.Column(db.String(50), server_default='')
# 库存类型(ZP=正品;CC=残次;JS=机损;XS= 箱损;ZT=在途库存;DJ=冻结;)
quality_type = db.Column(db.Enum('ZP', 'CC', 'DJ', 'ZT', 'JS', 'XS'), server_default='ZP')
product_date = db.Column(db.Date)
expire_date = db.Column(db.Date)
batch_code = db.Column(db.String(50), server_default='')
virtual_warehouse = db.Column(db.String(50), server_default='')
spec = db.Column(db.String(50), server_default='')
# 款色码
style = db.Column(db.String(50), server_default='')
color = db.Column(db.String(50), server_default='')
size = db.Column(db.String(50), server_default='')
# 单位
unit = db.Column(db.String(20), server_default='')
weight_unit = db.Column(db.String(10), server_default='')
# 容器
lpn = db.Column(db.String(50), server_default='', default='')
# 出库单PICK关联的stockout_id
refid = db.Column(db.Integer, default=0)
refin_order_code = db.Column(db.String(50), server_default='', default='')
# 分裂库存模式, split by `order_code` 的时候有效
price = db.Column(db.Float(asdecimal=True, precision='15,4'), server_default='0.00', default=0)
remark = db.Column(db.String(200), server_default='', default='')
create_time = db.Column(db.DateTime, default=db.default_datetime())
update_time = db.Column(db.DateTime, default=db.default_datetime(), onupdate=db.default_datetime())
@property
def location(self):
Location = db.M('Location')
return Location.filter(and_(
Location.code==self.location_code,
Location.company_code==self.company_code,
Location.warehouse_code==self.warehouse_code)).first()
@property
def company(self):
Company = db.M('Company')
return Company.query.filter_by(code=self.company_code).first()
@property
def warehouse(self):
Warehouse = db.M('Warehouse')
return Warehouse.query.filter(and_(
Warehouse.code==self.warehouse_code,
Warehouse.company_code==self.company_code)).first()
@property
def owner(self):
Partner = db.M('Partner')
return Partner.query.filter(and_(
Partner.code==self.owner_code,
Partner.company_code==self.company_code)).first()
@property
def area(self):
Area = db.M('Area')
return Area.query.filter(and_(
Area.code==self.area_code,
Area.company_code==self.company_code,
Area.warehouse_code==self.warehouse_code)).first()
@property
def workarea(self):
Workarea = db.M('Workarea')
return Workarea.query.filter(and_(
Workarea.code==self.workarea_code,
Workarea.company_code==self.company_code,
Workarea.warehouse_code==self.warehouse_code)).first()
@property
def category(self):
Category = db.M('Category')
return Category.query.filter(and_(
Category.code==self.category_code,
Category.owner_code==self.owner_code,
Category.company_code==self.company_code)).first()
@property
def good(self):
if getattr(self, '_good', None) is None:
Good = db.M('Good')
self._good = Good.query.filter(and_(
Good.code==self.sku,
Good.company_code==self.company_code,
Good.owner_code==self.owner_code)).first()
return self._good
class InvRfid(db.Model):
__tablename__ = 'inv_rfid'
__table_args__ = (
Index("ix_invrfid_sku", "sku", "location_code", "company_code"),
Index("ix_invrfid_rfid", "rfid", "location_code", "company_code"),
Index("ix_invrfid_tenant", "company_code", 'warehouse_code', "owner_code",),
)
id = db.Column(db.Integer, primary_key=True, autoincrement=True)
owner_code = db.Column(db.String(50))
company_code = db.Column(db.String(50))
warehouse_code = db.Column(db.String(50))
inv_id = db.Column(db.Integer)
qty = db.Column(db.Integer, server_default='0', default=0)
rfid = db.Column(db.String(255), server_default='', default='')
# 非标件, 可能按重量来算
_weight = db.Column(db.Float(asdecimal=True, precision='15,4'), name='weight', server_default='0.00', default=0.00)
_gross_weight = db.Column(db.Float(asdecimal=True, precision='15,4'), name='gross_weight', server_default='0.00', default=0.00)
# 非标件, 内部数量
_qty_inner = db.Column(db.Integer, name='qty_inner', server_default='1', default=1)
# 上游传入的/系统自动产生的, 用户生成的, 用户导入的
source = db.Column(db.String(50), server_default='erp')
printed = db.Column(db.Boolean, default=False, server_default='0')
# 在用 on / 废弃 off
state = db.Column(db.String(10), default='on', server_default='on')
# 库位信息
location_code = db.Column(db.String(50))
area_code = db.Column(db.String(50))
workarea_code = db.Column(db.String(50))
# 货品信息
category_code = db.Column(db.String(50), default='')
sku = db.Column(db.String(50), server_default='')
name = db.Column(db.String(200), default='')
name_en = db.Column(db.String(200), default='')
barcode = db.Column(db.String(50), server_default='')
brand = db.Column(db.String(20), server_default='')
partner_name = db.Column(db.String(50), server_default='')
# 批次属性
supplier_code = db.Column(db.String(50), server_default='')
quality_type = db.Column(db.Enum('ZP', 'CC', 'DJ', 'ZT', 'JS', 'XS'), server_default='ZP')
product_date = db.Column(db.Date)
expire_date = db.Column(db.Date)
batch_code = db.Column(db.String(50), server_default='')
virtual_warehouse = db.Column(db.String(50), server_default='')
spec = db.Column(db.String(50), server_default='')
# 款色码
style = db.Column(db.String(50), server_default='')
color = db.Column(db.String(50), server_default='')
size = db.Column(db.String(50), server_default='')
# 单位
unit = db.Column(db.String(20), server_default='')
weight_unit = db.Column(db.String(10), server_default='')
# 容器
lpn = db.Column(db.String(50), server_default='', default='')
# 入库信息----------
stockin_order_code = db.Column(db.String(50)) # erp_order_code
stockin_date = db.Column(db.DateTime, default=db.func.current_timestamp())
# 系统操作人信息
in_user_code = db.Column(db.String(20))
in_user_name = db.Column(db.String(20))
# 仓库操作人信息/领料人信息
in_w_user_code = db.Column(db.String(20))
in_w_user_name = db.Column(db.String(20))
# end 入库信息----------
# 出库信息 ----------
stockout_order_code = db.Column(db.String(50)) # erp_order_code
stockout_date = db.Column(db.DateTime)
# 系统操作人信息
out_user_code = db.Column(db.String(20))
out_user_name = db.Column(db.String(20))
# 仓库操作人信息/领料人信息
out_w_user_code = db.Column(db.String(20))
out_w_user_name = db.Column(db.String(20))
# end 出库信息 ----------
remark = db.Column(db.String(200), server_default='', default='')
create_time = db.Column(db.DateTime, default=db.default_datetime())
update_time = db.Column(db.DateTime, default=db.default_datetime(), onupdate=db.default_datetime())
__dump_prop__ = ('weight', 'gross_weight', 'qty_inner', )
@property
def inv(self):
return Inv.query.filter(Inv.id==self.inv_id).first()
@property
def weight(self):
return self._weight
@property
def gross_weight(self):
return self._gross_weight
@property
def qty_inner(self):
return self._qty_inner
@weight.setter
def weight(self, v):
self._weight = v
@gross_weight.setter
def gross_weight(self, v):
self._gross_weight = v
@qty_inner.setter
def qty_inner(self, v):
self._qty_inner = v
def get_barcode(self, company_id):
if not os.path.exists(os.path.join(settings.UPLOAD_DIR, 'barcode', company_id, self.barcode)):
_, path = save_inv_barcode(settings.UPLOAD_DIR, company_id, self.barcode)
else:
path = '/static/upload/barcode/%s/%s.png'%(company_id, self.barcode)
return path
def get_qrcode(self, company_id):
if not os.path.exists(os.path.join(settings.UPLOAD_DIR, 'qrcode', company_id, self.rfid)):
_, path = save_inv_qrcode(settings.UPLOAD_DIR, company_id, self.rfid)
else:
path = '/static/upload/qrcode/%s/%s.png'%(company_id, self.rfid)
return path
# 唯一码流水只记录进出库信息
class InvRfidTrans(db.Model):
__tablename__ = 'inv_rfid_trans'
__table_args__ = (
Index("ix_invrfid_rfid", "company_code", 'warehouse_code', "owner_code", "rfid"),
Index("ix_invrfid_tenant", "company_code", 'warehouse_code', "owner_code",),
)
id = db.Column(db.Integer, primary_key=True, autoincrement=True)
owner_code = db.Column(db.String(50))
company_code = db.Column(db.String(50))
warehouse_code = db.Column(db.String(50))
rfid = db.Column(db.String(255), server_default='', default='')
sku = db.Column(db.String(50), server_default='')
name = db.Column(db.String(200), server_default='')
barcode = db.Column(db.String(50), server_default='')
# 系统操作人信息
user_code = db.Column(db.String(20))
user_name = db.Column(db.String(20))
# 仓库操作人信息/领料人信息
w_user_code = db.Column(db.String(20))
w_user_name = db.Column(db.String(20))
xtype = db.Column(db.String(20), default='in') # in/out
order_type = db.Column(db.String(20), default='produce') # in.xtype/out.order_type
order_code = db.Column(db.String(50), default='')
remark = db.Column(db.String(200), server_default='', default='')
create_time = db.Column(db.DateTime, default=db.default_datetime())
update_time = db.Column(db.DateTime, default=db.default_datetime(), onupdate=db.default_datetime())
# 库存流水
class InvTrans(db.Model):
__tablename__ = 'inv_trans'
__table_args__ = (Index("ix_inv_trans_sku", "sku", "location_code", "company_code",),
Index("ix_inv_trans_tenant", "company_code", 'warehouse_code', "owner_code",),
)
id = db.Column(db.Integer, primary_key=True, autoincrement=True)
owner_code = db.Column(db.String(50))
company_code = db.Column(db.String(50))
warehouse_code = db.Column(db.String(50))
# 库位信息
location_code = db.Column(db.String(50))
area_code = db.Column(db.String(50))
# 货品信息
category_code = db.Column(db.String(50), server_default='')
sku = db.Column(db.String(50), server_default='')
name = db.Column(db.String(200), server_default='')
barcode = db.Column(db.String(50), server_default='')
before_qty = db.Column(db.Integer, server_default='0', default=0)
change_qty = db.Column(db.Integer, server_default='0', default=0)
after_qty = db.Column(db.Integer, server_default='0', default=0)
price = db.Column(db.Float(asdecimal=True, precision='15,4'), server_default='0.00', default=0)
qty_able = db.Column(db.Integer, server_default='0', default=0)
# 入库单， 出库单， 移库单， 调整单， 转换单
# stockin stockout inv_move inv_adjust inv_transfer
xtype = db.Column(db.Enum('stockin', 'stockout', 'inv_move', 'inv_adjust', 'inv_transfer'), server_default='stockout')
# 操作过程
xtype_opt = db.Column(db.Enum('alloc', 'pick', 'cancel', 'in', 'out'), default='in')
# 操作信息
order_code = db.Column(db.String(50))
erp_order_code = db.Column(db.String(50))
# 系统操作人信息
user_code = db.Column(db.String(20), default='')
user_name = db.Column(db.String(20), default='')
# 外键
inventory_id = db.Column(db.Integer)
remark = db.Column(db.String(200), server_default='')
create_time = db.Column(db.DateTime, default=db.default_datetime())
update_time = db.Column(db.DateTime, default=db.default_datetime(), onupdate=db.default_datetime())
# 货类数据库结构定义
class Category(db.Model):
__tablename__ = 'inv_category'
__table_args__ = (Index("ix_inv_category_code", 'code', "company_code",),
Index("ix_inv_category_tenant", 'owner_code', "company_code",),
)
id = db.Column(db.Integer, primary_key=True)
code = db.Column(db.String(50), default='')
name = db.Column(db.String(50), default='')
owner_code = db.Column(db.String(50))
company_code = db.Column(db.String(50))
remark = db.Column(db.String(200), server_default='')
create_time = db.Column(db.DateTime, default=db.default_datetime())
update_time = db.Column(db.DateTime, default=db.default_datetime(), onupdate=db.default_datetime())
@property
def company(self):
Company = db.M('Company')
return Company.query.filter_by(code=self.company_code).first()
@property
def owner(self):
Partner = db.M('Partner')
return Partner.query.filter(and_(
Partner.code==self.owner_code,
Partner.company_code==self.company_code)).first()
# 货品数据结构定义
class Good(db.Model):
__tablename__ = 'inv_good'
__table_args__ = (Index("ix_inv_good_code", "code", "company_code",),
Index("ix_inv_good_tenant", 'owner_code', "company_code",),
)
id = db.Column(db.Integer, primary_key=True)
code = db.Column(db.String(50), default='')
name = db.Column(db.String(200), default='')
name_en = db.Column(db.String(200), default='')
barcode = db.Column(db.String(50), default='')
middle_code = db.Column(db.String(50), default='')
# 需要生产的, 会生成 生产单, 不需要生产的, 会生成 采购单
is_produce = db.Column(db.Boolean, default=False)
# is_main
has_subs = db.Column(db.Boolean, default=False)
# 规格
spec = db.Column(db.String(100), default='', server_default='')
# 上架/下架 on/down, 删除delete
state = db.Column(db.String(100), default='on', server_default='on')
# 长、款、高、体积（奇门发来的是double类型，这里需要string转换）
length = db.Column(db.String(50), default='')
width = db.Column(db.String(50), default='')
height = db.Column(db.String(50), default='')
volume = db.Column(db.String(50), default='')
# 净重
weight = db.Column(db.String(50), default='0', server_default='0')
# 毛重
gross_weight = db.Column(db.String(50), default='0', server_default='0')
# 重量单位 kg/g
weight_unit = db.Column(db.String(10), default='')
index = db.Column(db.Integer, default=0, server_default='0')
# 预警: 最高库存, 最低库存
min_qty = db.Column(db.Integer, default=0, server_default='0')
max_qty = db.Column(db.Integer, default=0, server_default='0')
# 价格，先选择零售价
price = db.Column(db.Float(asdecimal=True, precision='15,4'), server_default='0.00', default=0)
# 生产成本
cost_price = db.Column(db.Float(asdecimal=True, precision='15,4'), server_default='0.00', default=0)
# 最近一次价格
last_in_price = db.Column(db.Float(asdecimal=True, precision='15,4'), server_default='0.00', default=0)
last_out_price = db.Column(db.Float(asdecimal=True, precision='15,4'), server_default='0.00', default=0)
# 来源 erp, import, other:上游公司码
source = db.Column(db.String(50), server_default='erp')
# laoa fields;
# app这边的good id
appid = db.Column(db.String(50), server_default='')
# 质保期限
quality_month = db.Column(db.Integer, server_default='0', default=0)
# 开模分摊费
model_price = db.Column(db.Float(asdecimal=True, precision='15,4'), server_default='0.00', default=0)
# 运费
express_price = db.Column(db.Float(asdecimal=True, precision='15,4'), server_default='0.00', default=0)
# 分级经销商价
lv1_price = db.Column(db.Float(asdecimal=True, precision='15,4'), server_default='0.00', default=0)
lv2_price = db.Column(db.Float(asdecimal=True, precision='15,4'), server_default='0.00', default=0)
lv3_price = db.Column(db.Float(asdecimal=True, precision='15,4'), server_default='0.00', default=0)
# 是否同步
is_sync = db.Column(db.String(1), server_default='0')
# 图片
image_url = db.Column(db.String(255), server_default='')
# images
images = db.Column(db.String(1500), server_default='')
ad_images = db.Column(db.String(1500), server_default='')
# 默认存放的库区/库位
area_code = db.Column(db.String(50), server_default='')
location_code = db.Column(db.String(50), server_default='')
# 款色码
style = db.Column(db.String(50), server_default='')
color = db.Column(db.String(50), server_default='')
size = db.Column(db.String(50), server_default='')
unit = db.Column(db.String(20), server_default='')
# 产品参数
args = db.Column(db.String(500), server_default='')
# qimen 类型 ZC=正常商品;FX=分销商品;ZH=组合商品;ZP=赠品;BC=包材;HC=耗材;FL=辅料;XN=虚拟品;FS=附属品;CC=残次品; OTHER=其它;
item_type = db.Column(db.String(20), server_default='ZC', default='ZC')
brand = db.Column(db.String(20), server_default='')
category_code = db.Column(db.String(50), default='')
# 是否使用保质期管理 on/off
is_shelf_life = db.Column(db.String(20), server_default='off', default='off')
owner_code = db.Column(db.String(50))
company_code = db.Column(db.String(50))
custom_uuid = db.Column(db.String(50), server_default='')
version = db.Column(db.Integer)
remark = db.Column(db.String(200), server_default='')
create_time = db.Column(db.DateTime, default=db.default_datetime())
update_time = db.Column(db.DateTime, default=db.default_datetime(), onupdate=db.default_datetime())
__dump_prop__ = ('sku', 'is_main', 'images_list', 'ad_images_list', 'args_list',)
@property
def sku(self):
return self.code
@cached_property
def company_id(self):
return db.M('Company').query.filter_by(code=self.company_code).first().id
@property
def images_list(self):
if self.images:
if self.image_url:
return [img for img in self.images.split(',') if img]
return [img for img in self.images.split(',') if img]
return []
@property
def ad_images_list(self):
if self.ad_images:
return [img for img in self.ad_images.split(',') if img]
return []
@property
def args_list(self):
return [a for a in self.args.split('\n') if a]
@property
def company(self):
Company = db.M('Company')
return Company.query.filter_by(code=self.company_code).first()
@property
def owner(self):
Partner = db.M('Partner')
return Partner.query.filter(and_(
Partner.code==self.owner_code,
Partner.company_code==self.company_code)).first()
@property
def category(self):
Category = db.M('Category')
return Category.query.filter(and_(
Category.code==self.category_code,
Category.owner_code==self.owner_code,
Category.company_code==self.company_code)).first()
# 是否主件
@property
def is_main(self):
return self.has_subs
# self.has_subs = GoodMap.query.filter(GoodMap.code==self.code, GoodMap.owner_code==self.owner_code, GoodMap.company_code==self.company_code).count() > 0
# return self.has_subs
# 需要生产
@property
def need_produce(self):
return self.is_produce or self.is_main
# 是否配件
@property
def is_sub(self):
return GoodMap.query.filter(GoodMap.subcode==self.code, GoodMap.owner_code==self.owner_code, GoodMap.company_code==self.company_code).count()
# 配件列表
@property
def sub_goods(self):
gm = GoodMap.query.filter(GoodMap.code==self.code, GoodMap.owner_code==self.owner_code, GoodMap.company_code==self.company_code).first()
if gm:
return gm.sub_goods
return []
@property
def JSON(self):
big = None
if self.custom_uuid:
big = db.M('Big').query.filter_by(code='JSON', subcode='inv_good__json', uuid=self.custom_uuid).first()
return json.loads(big.blob) if big else {}
@JSON.setter
def JSON(self, obj):
val = json_dump(obj)
if self.custom_uuid:
db.M('Big').query.filter_by(code='JSON', subcode='inv_good__json', uuid=self.custom_uuid).update({'blob':val})
else:
uuid = str(uuid4())
big = db.M('Big')(company_code=self.company_code, code='JSON', subcode='inv_good__json', blob=val, uuid=uuid)
db.session.add(big)
self.custom_uuid = uuid
# 计算配件成本价-- 子件
def calc_cost_price(self):
return self.cost_price
def calc_main_cost_price(self):
gm = GoodMap.query.o_query.filter_by(code=self.code).first()
if gm:
self.cost_price = gm.main_cost_price
return self.cost_price
def get_barcode(self, company_id):
if not os.path.exists(os.path.join(settings.UPLOAD_DIR, 'barcode', company_id, self.barcode)):
_, path = save_inv_barcode(settings.UPLOAD_DIR, company_id, self.barcode)
else:
path = '/static/upload/barcode/%s/%s.png'%(company_id, self.barcode)
return path
def get_qrcode(self, company_id):
if not os.path.exists(os.path.join(settings.UPLOAD_DIR, 'qrcode', company_id, self.sku)):
_, path = save_inv_qrcode(settings.UPLOAD_DIR, company_id, self.sku)
else:
path = '/static/upload/qrcode/%s/%s.png'%(company_id, self.sku)
return path
class GoodMap(db.Model):
__tablename__ = 'inv_good_map'
__table_args__ = (Index("ix_inv_good_map_code", "code", 'subcode', "company_code",),
Index("ix_inv_good_map_tenant", 'owner_code', "company_code",),
)
# 导入时要删除主配件关系，再新增新的
id = db.Column(db.Integer, primary_key=True)
code = db.Column(db.String(50), default='')
name = db.Column(db.String(200), default='')
name_en = db.Column(db.String(200), default='')
barcode = db.Column(db.String(50), default='')
subcode = db.Column(db.String(50), default='')
subname = db.Column(db.String(200), default='')
subbarcode = db.Column(db.String(50), default='')
qty = db.Column(db.Integer, server_default='1', default=1)
owner_code = db.Column(db.String(50))
company_code = db.Column(db.String(50))
remark = db.Column(db.String(200), server_default='')
create_time = db.Column(db.DateTime, default=db.default_datetime())
update_time = db.Column(db.DateTime, default=db.default_datetime(), onupdate=db.default_datetime())
@property
def company(self):
Company = db.M('Company')
return Company.query.filter_by(code=self.company_code).first()
@property
def owner(self):
Partner = db.M('Partner')
return Partner.query.filter(and_(
Partner.code==self.owner_code,
Partner.company_code==self.company_code)).first()
@property
def good(self):
return Good.query.filter_by(code=self.code, owner_code=self.owner_code, company_code=self.company_code).first()
@property
def sub_good(self):
return Good.query.filter_by(code=self.subcode, owner_code=self.owner_code, company_code=self.company_code).first()
@property
def sub_goods(self):
return Good.query.filter(
Good.owner_code==self.owner_code,
Good.company_code==self.company_code,
Good.code==GoodMap.subcode,
GoodMap.code==self.code,
GoodMap.owner_code==self.owner_code,
GoodMap.company_code==self.company_code).all()
@property
def main_cost_price(self):
o = GoodMap.query.with_entities(func.sum(Good.cost_price*GoodMap.qty).label('cost_price')).filter(
Good.owner_code==self.owner_code,
Good.company_code==self.company_code,
Good.code==GoodMap.subcode,
GoodMap.code==self.code,
GoodMap.owner_code==self.owner_code,
GoodMap.company_code==self.company_code).first()
return float(o.cost_price or 0) if o else 0
@property
def map_goods(self):
return GoodMap.query.filter_by(code=self.code, owner_code=self.owner_code, company_code=self.company_code).all()
class InvAdjust(db.Model):
__tablename__ = 'inv_adjust'
__table_args__ = (
Index("ix_inv_adjust_series", "company_code", "series_code"),
Index("ix_inv_adjust_tenant", "company_code", 'warehouse_code', "owner_code",),
)
id = db.Column(db.Integer, primary_key=True)
code = db.Column(db.String(50), default='')
count_code = db.Column(db.String(50), default='')
owner_code = db.Column(db.String(50))
warehouse_code = db.Column(db.String(50))
company_code = db.Column(db.String(50))
location_code = db.Column(db.String(50))
sku = db.Column(db.String(50), server_default='')
name = db.Column(db.String(200), default='')
barcode = db.Column(db.String(50), server_default='')
qty_before = db.Column(db.Integer, default=0) # 调整前数量
qty_after = db.Column(db.Integer, default=0) # 调整后数量
qty_diff = db.Column(db.Integer, default=0) # 前后差值; qty_after - qty_before; qty_real - qty
# 一系列可以下发多个调整单
series_code = db.Column(db.String(50), default='')
# 盘点单号
count_series_code = db.Column(db.String(50), default='')
stockin_date = db.Column(db.Date)
source = db.Column(db.String(50), server_default='erp')
partner_name = db.Column(db.String(50), server_default='')
# 批次属性
supplier_code = db.Column(db.String(50), server_default='')
quality_type = db.Column(db.Enum('ZP', 'CC', 'DJ', 'ZT', 'JS', 'XS'), server_default='ZP')
product_date = db.Column(db.Date)
expire_date = db.Column(db.Date)
batch_code = db.Column(db.String(50), server_default='')
virtual_warehouse = db.Column(db.String(50), server_default='')
spec = db.Column(db.String(50), server_default='')
# 款色码
style = db.Column(db.String(50), server_default='')
color = db.Column(db.String(50), server_default='')
size = db.Column(db.String(50), server_default='')
# 容器
lpn = db.Column(db.String(50), server_default='', default='')
state = db.Column(db.Enum('create', 'done', 'cancel'), server_default='create')
user_code = db.Column(db.String(20), default='')
user_name = db.Column(db.String(20), default='')
remark = db.Column(db.String(200), server_default='', default='')
create_time = db.Column(db.DateTime, default=db.default_datetime())
update_time = db.Column(db.DateTime, default=db.default_datetime(), onupdate=db.default_datetime())
@property
def inv_count(self):
InvCount = db.M('InvCount')
return InvCount.query.filter(and_(
InvCount.code==self.count_code,
InvCount.company_code==self.company_code,
InvCount.owner_code==self.owner_code,
InvCount.warehouse_code==self.warehouse_code)).first()
class InvCount(db.Model):
__tablename__ = 'inv_count'
__table_args__ = (
Index("ix_inv_count_series", "company_code", "series_code"),
Index("ix_inv_count_tenant", "company_code", 'warehouse_code', "owner_code",),
)
id = db.Column(db.Integer, primary_key=True)
code = db.Column(db.String(50), default='')
owner_code = db.Column(db.String(50))
warehouse_code = db.Column(db.String(50))
company_code = db.Column(db.String(50))
location_code = db.Column(db.String(50))
sku = db.Column(db.String(50), server_default='')
name = db.Column(db.String(200), default='')
barcode = db.Column(db.String(50), server_default='')
qty = db.Column(db.Integer, default=0) # 系统数量
qty_real = db.Column(db.Integer, default=0) # 盘点数量
qty_alloc = db.Column(db.Integer, default=0) # 锁定数量
# 一系列可以下发多个盘点单
series_code = db.Column(db.String(50), default='')
adjust_series_code = db.Column(db.String(50), default='')
stockin_date = db.Column(db.Date)
source = db.Column(db.String(50), server_default='erp')
partner_name = db.Column(db.String(50), server_default='')
# 批次属性
supplier_code = db.Column(db.String(50), server_default='')
quality_type = db.Column(db.Enum('ZP', 'CC', 'DJ', 'ZT', 'JS', 'XS'), server_default='ZP')
product_date = db.Column(db.Date)
expire_date = db.Column(db.Date)
batch_code = db.Column(db.String(50), server_default='')
virtual_warehouse = db.Column(db.String(50), server_default='')
spec = db.Column(db.String(50), server_default='')
# 款色码
style = db.Column(db.String(50), server_default='')
color = db.Column(db.String(50), server_default='')
size = db.Column(db.String(50), server_default='')
# 容器
lpn = db.Column(db.String(50), server_default='', default='')
state = db.Column(db.Enum('create', 'done', 'cancel'), server_default='create')
user_code = db.Column(db.String(20), default='')
user_name = db.Column(db.String(20), default='')
remark = db.Column(db.String(200), server_default='', default='')
create_time = db.Column(db.DateTime, default=db.default_datetime())
update_time = db.Column(db.DateTime, default=db.default_datetime(), onupdate=db.default_datetime())
class InvMove(db.Model):
__tablename__ = 'inv_move'
__table_args__ = (
Index("ix_inv_move_series", "company_code", "series_code"),
Index("ix_inv_move_tenant", "company_code", 'warehouse_code', "owner_code",),
)
id = db.Column(db.Integer, primary_key=True)
code = db.Column(db.String(50), default='')
# 入库单转移库单
stockin_order_code = db.Column(db.String(50), default='', server_default='')
owner_code = db.Column(db.String(50))
warehouse_code = db.Column(db.String(50))
dest_warehouse_code = db.Column(db.String(50))
company_code = db.Column(db.String(50))
location_code = db.Column(db.String(50))
dest_location_code = db.Column(db.String(50))
sku = db.Column(db.String(50), server_default='')
name = db.Column(db.String(200), default='')
barcode = db.Column(db.String(50), server_default='')
qty = db.Column(db.Integer, default=0)
# 实际移库数量
qty_real = db.Column(db.Integer, default=0, server_default='0')
# 一系列可以下发多个移库单
series_code = db.Column(db.String(50), default='')
# 系统生成的移库单（可以移库，也可以拣货）system，还是用户生成的移库单 user, 捕获移库 replenish, 上架 onshelf
move_type = db.Column(db.String(20), default='user')
stockin_date = db.Column(db.Date)
# 'erp', 'custom', 'import'
source = db.Column(db.String(50), server_default='erp')
partner_name = db.Column(db.String(50), server_default='')
# 批次属性
supplier_code = db.Column(db.String(50), server_default='')
quality_type = db.Column(db.Enum('ZP', 'CC', 'DJ', 'ZT', 'JS', 'XS'), server_default='ZP')
product_date = db.Column(db.Date)
expire_date = db.Column(db.Date)
batch_code = db.Column(db.String(50), server_default='')
virtual_warehouse = db.Column(db.String(50), server_default='')
spec = db.Column(db.String(50), server_default='')
# 款色码
style = db.Column(db.String(50), server_default='')
color = db.Column(db.String(50), server_default='')
size = db.Column(db.String(50), server_default='')
# 容器
lpn = db.Column(db.String(50), server_default='', default='')
dest_lpn = db.Column(db.String(50), server_default='', default='')
state = db.Column(db.Enum('create', 'done', 'doing', 'cancel'), server_default='create')
user_code = db.Column(db.String(20), default='')
user_name = db.Column(db.String(20), default='')
remark = db.Column(db.String(200), server_default='', default='')
create_time = db.Column(db.DateTime, default=db.default_datetime())
update_time = db.Column(db.DateTime, default=db.default_datetime(), onupdate=db.default_datetime())
# 库位库存预警
class InvWarn(db.Model):
__tablename__ = 'inv_warn'
__table_args__ = (
Index("ix_inv_warn_tenant", "company_code", 'warehouse_code', "owner_code",),
)
id = db.Column(db.Integer, primary_key=True)
owner_code = db.Column(db.String(50))
warehouse_code = db.Column(db.String(50))
company_code = db.Column(db.String(50))
location_code = db.Column(db.String(50))
sku = db.Column(db.String(50), server_default='')
name = db.Column(db.String(200), default='')
barcode = db.Column(db.String(50), server_default='')
min_qty = db.Column(db.Integer, default=0) # 预警数量
max_qty = db.Column(db.Integer, default=0) # 最高补货数量
# 批次属性
supplier_code = db.Column(db.String(50), server_default='')
spec = db.Column(db.String(50), server_default='')
remark = db.Column(db.String(200), server_default='', default='')
create_time = db.Column(db.DateTime, default=db.default_datetime())
update_time = db.Column(db.DateTime, default=db.default_datetime(), onupdate=db.default_datetime())
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| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
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| 0
| 1
| 0
|
0
| 7
|
2d35203f6c49c02d33f6d813348ecea7459d1525
| 43,299
|
py
|
Python
|
tb_rest_client/api/api_ce/tenant_profile_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_ce/tenant_profile_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_ce/tenant_profile_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 open-source IoT platform REST API documentation. # noqa: E501
OpenAPI spec version: 3.3.3-SNAPSHOT
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 TenantProfileControllerApi(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_tenant_profile_using_delete(self, tenant_profile_id, **kwargs): # noqa: E501
"""Delete Tenant Profile (deleteTenantProfile) # noqa: E501
Deletes the tenant profile. Referencing non-existing tenant profile Id will cause an error. Referencing profile that is used by the tenants will cause an error. Available for users with 'SYS_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.delete_tenant_profile_using_delete(tenant_profile_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str tenant_profile_id: A string value representing the tenant profile 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_tenant_profile_using_delete_with_http_info(tenant_profile_id, **kwargs) # noqa: E501
else:
(data) = self.delete_tenant_profile_using_delete_with_http_info(tenant_profile_id, **kwargs) # noqa: E501
return data
def delete_tenant_profile_using_delete_with_http_info(self, tenant_profile_id, **kwargs): # noqa: E501
"""Delete Tenant Profile (deleteTenantProfile) # noqa: E501
Deletes the tenant profile. Referencing non-existing tenant profile Id will cause an error. Referencing profile that is used by the tenants will cause an error. Available for users with 'SYS_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.delete_tenant_profile_using_delete_with_http_info(tenant_profile_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str tenant_profile_id: A string value representing the tenant profile id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required)
:return: None
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['tenant_profile_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_tenant_profile_using_delete" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'tenant_profile_id' is set
if ('tenant_profile_id' not in params or
params['tenant_profile_id'] is None):
raise ValueError("Missing the required parameter `tenant_profile_id` when calling `delete_tenant_profile_using_delete`") # noqa: E501
collection_formats = {}
path_params = {}
if 'tenant_profile_id' in params:
path_params['tenantProfileId'] = params['tenant_profile_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/tenantProfile/{tenantProfileId}', '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 get_default_tenant_profile_info_using_get(self, **kwargs): # noqa: E501
"""Get default Tenant Profile Info (getDefaultTenantProfileInfo) # noqa: E501
Fetch the default Tenant Profile Info object based. Tenant Profile Info is a lightweight object that contains only id and name of the profile. Available for users with 'SYS_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_default_tenant_profile_info_using_get(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: EntityInfo
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_default_tenant_profile_info_using_get_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.get_default_tenant_profile_info_using_get_with_http_info(**kwargs) # noqa: E501
return data
def get_default_tenant_profile_info_using_get_with_http_info(self, **kwargs): # noqa: E501
"""Get default Tenant Profile Info (getDefaultTenantProfileInfo) # noqa: E501
Fetch the default Tenant Profile Info object based. Tenant Profile Info is a lightweight object that contains only id and name of the profile. Available for users with 'SYS_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_default_tenant_profile_info_using_get_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: EntityInfo
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_default_tenant_profile_info_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/tenantProfileInfo/default', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='EntityInfo', # 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_profile_by_id_using_get(self, tenant_profile_id, **kwargs): # noqa: E501
"""Get Tenant Profile (getTenantProfileById) # noqa: E501
Fetch the Tenant Profile object based on the provided Tenant Profile Id. Available for users with 'SYS_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_profile_by_id_using_get(tenant_profile_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str tenant_profile_id: A string value representing the tenant profile id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required)
:return: TenantProfile
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_profile_by_id_using_get_with_http_info(tenant_profile_id, **kwargs) # noqa: E501
else:
(data) = self.get_tenant_profile_by_id_using_get_with_http_info(tenant_profile_id, **kwargs) # noqa: E501
return data
def get_tenant_profile_by_id_using_get_with_http_info(self, tenant_profile_id, **kwargs): # noqa: E501
"""Get Tenant Profile (getTenantProfileById) # noqa: E501
Fetch the Tenant Profile object based on the provided Tenant Profile Id. Available for users with 'SYS_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_profile_by_id_using_get_with_http_info(tenant_profile_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str tenant_profile_id: A string value representing the tenant profile id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required)
:return: TenantProfile
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['tenant_profile_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_tenant_profile_by_id_using_get" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'tenant_profile_id' is set
if ('tenant_profile_id' not in params or
params['tenant_profile_id'] is None):
raise ValueError("Missing the required parameter `tenant_profile_id` when calling `get_tenant_profile_by_id_using_get`") # noqa: E501
collection_formats = {}
path_params = {}
if 'tenant_profile_id' in params:
path_params['tenantProfileId'] = params['tenant_profile_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/tenantProfile/{tenantProfileId}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='TenantProfile', # 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_profile_info_by_id_using_get(self, tenant_profile_id, **kwargs): # noqa: E501
"""Get Tenant Profile Info (getTenantProfileInfoById) # noqa: E501
Fetch the Tenant Profile Info object based on the provided Tenant Profile Id. Tenant Profile Info is a lightweight object that contains only id and name of the profile. Available for users with 'SYS_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_profile_info_by_id_using_get(tenant_profile_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str tenant_profile_id: A string value representing the tenant profile id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required)
:return: EntityInfo
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_profile_info_by_id_using_get_with_http_info(tenant_profile_id, **kwargs) # noqa: E501
else:
(data) = self.get_tenant_profile_info_by_id_using_get_with_http_info(tenant_profile_id, **kwargs) # noqa: E501
return data
def get_tenant_profile_info_by_id_using_get_with_http_info(self, tenant_profile_id, **kwargs): # noqa: E501
"""Get Tenant Profile Info (getTenantProfileInfoById) # noqa: E501
Fetch the Tenant Profile Info object based on the provided Tenant Profile Id. Tenant Profile Info is a lightweight object that contains only id and name of the profile. Available for users with 'SYS_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_profile_info_by_id_using_get_with_http_info(tenant_profile_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str tenant_profile_id: A string value representing the tenant profile id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required)
:return: EntityInfo
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['tenant_profile_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_tenant_profile_info_by_id_using_get" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'tenant_profile_id' is set
if ('tenant_profile_id' not in params or
params['tenant_profile_id'] is None):
raise ValueError("Missing the required parameter `tenant_profile_id` when calling `get_tenant_profile_info_by_id_using_get`") # noqa: E501
collection_formats = {}
path_params = {}
if 'tenant_profile_id' in params:
path_params['tenantProfileId'] = params['tenant_profile_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/tenantProfileInfo/{tenantProfileId}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='EntityInfo', # 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_profile_infos_using_get(self, page_size, page, **kwargs): # noqa: E501
"""Get Tenant Profiles Info (getTenantProfileInfos) # noqa: E501
Returns a page of tenant profile info objects registered in the platform. Tenant Profile Info is a lightweight object that contains only id and name of the profile. 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 'SYS_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_profile_infos_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 text_search: The case insensitive 'startsWith' filter based on the tenant profile name.
:param str sort_property: Property of entity to sort by
:param str sort_order: Sort order. ASC (ASCENDING) or DESC (DESCENDING)
:return: PageDataEntityInfo
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_profile_infos_using_get_with_http_info(page_size, page, **kwargs) # noqa: E501
else:
(data) = self.get_tenant_profile_infos_using_get_with_http_info(page_size, page, **kwargs) # noqa: E501
return data
def get_tenant_profile_infos_using_get_with_http_info(self, page_size, page, **kwargs): # noqa: E501
"""Get Tenant Profiles Info (getTenantProfileInfos) # noqa: E501
Returns a page of tenant profile info objects registered in the platform. Tenant Profile Info is a lightweight object that contains only id and name of the profile. 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 'SYS_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_profile_infos_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 text_search: The case insensitive 'startsWith' filter based on the tenant profile name.
:param str sort_property: Property of entity to sort by
:param str sort_order: Sort order. ASC (ASCENDING) or DESC (DESCENDING)
:return: PageDataEntityInfo
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['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_tenant_profile_infos_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_profile_infos_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_profile_infos_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 '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/tenantProfileInfos{?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='PageDataEntityInfo', # 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_profiles_using_get(self, page_size, page, **kwargs): # noqa: E501
"""Get Tenant Profiles (getTenantProfiles) # noqa: E501
Returns a page of tenant profiles registered in the platform. 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 'SYS_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_profiles_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 text_search: The case insensitive 'startsWith' filter based on the tenant profile name.
:param str sort_property: Property of entity to sort by
:param str sort_order: Sort order. ASC (ASCENDING) or DESC (DESCENDING)
:return: PageDataTenantProfile
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_profiles_using_get_with_http_info(page_size, page, **kwargs) # noqa: E501
else:
(data) = self.get_tenant_profiles_using_get_with_http_info(page_size, page, **kwargs) # noqa: E501
return data
def get_tenant_profiles_using_get_with_http_info(self, page_size, page, **kwargs): # noqa: E501
"""Get Tenant Profiles (getTenantProfiles) # noqa: E501
Returns a page of tenant profiles registered in the platform. 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 'SYS_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_profiles_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 text_search: The case insensitive 'startsWith' filter based on the tenant profile name.
:param str sort_property: Property of entity to sort by
:param str sort_order: Sort order. ASC (ASCENDING) or DESC (DESCENDING)
:return: PageDataTenantProfile
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['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_tenant_profiles_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_profiles_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_profiles_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 '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/tenantProfiles{?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='PageDataTenantProfile', # 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_tenant_profile_using_post(self, **kwargs): # noqa: E501
"""Create Or update Tenant Profile (saveTenantProfile) # noqa: E501
Create or update the Tenant Profile. When creating tenant profile, platform generates Tenant Profile Id as [time-based UUID](https://en.wikipedia.org/wiki/Universally_unique_identifier#Version_1_(date-time_and_MAC_address)). The newly created Tenant Profile Id will be present in the response. Specify existing Tenant Profile Id id to update the Tenant Profile. Referencing non-existing Tenant Profile Id will cause 'Not Found' error. Update of the tenant profile configuration will cause immediate recalculation of API limits for all affected Tenants. The **'profileData'** object is the part of Tenant Profile that defines API limits and Rate limits. You have an ability to define maximum number of devices ('maxDevice'), assets ('maxAssets') and other entities. You may also define maximum number of messages to be processed per month ('maxTransportMessages', 'maxREExecutions', etc). The '*RateLimit' defines the rate limits using simple syntax. For example, '1000:1,20000:60' means up to 1000 events per second but no more than 20000 event per minute. Let's review the example of tenant profile data below: ```json { \"name\": \"Default\", \"description\": \"Default tenant profile\", \"isolatedTbCore\": false, \"isolatedTbRuleEngine\": false, \"profileData\": { \"configuration\": { \"type\": \"DEFAULT\", \"maxDevices\": 0, \"maxAssets\": 0, \"maxCustomers\": 0, \"maxUsers\": 0, \"maxDashboards\": 0, \"maxRuleChains\": 0, \"maxResourcesInBytes\": 0, \"maxOtaPackagesInBytes\": 0, \"transportTenantMsgRateLimit\": \"1000:1,20000:60\", \"transportTenantTelemetryMsgRateLimit\": \"1000:1,20000:60\", \"transportTenantTelemetryDataPointsRateLimit\": \"1000:1,20000:60\", \"transportDeviceMsgRateLimit\": \"20:1,600:60\", \"transportDeviceTelemetryMsgRateLimit\": \"20:1,600:60\", \"transportDeviceTelemetryDataPointsRateLimit\": \"20:1,600:60\", \"maxTransportMessages\": 10000000, \"maxTransportDataPoints\": 10000000, \"maxREExecutions\": 4000000, \"maxJSExecutions\": 5000000, \"maxDPStorageDays\": 0, \"maxRuleNodeExecutionsPerMessage\": 50, \"maxEmails\": 0, \"maxSms\": 0, \"maxCreatedAlarms\": 1000, \"defaultStorageTtlDays\": 0, \"alarmsTtlDays\": 0, \"rpcTtlDays\": 0, \"warnThreshold\": 0 } }, \"default\": true } ``` Available for users with 'SYS_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.save_tenant_profile_using_post(async_req=True)
>>> result = thread.get()
:param async_req bool
:param TenantProfile body:
:return: TenantProfile
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.save_tenant_profile_using_post_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.save_tenant_profile_using_post_with_http_info(**kwargs) # noqa: E501
return data
def save_tenant_profile_using_post_with_http_info(self, **kwargs): # noqa: E501
"""Create Or update Tenant Profile (saveTenantProfile) # noqa: E501
Create or update the Tenant Profile. When creating tenant profile, platform generates Tenant Profile Id as [time-based UUID](https://en.wikipedia.org/wiki/Universally_unique_identifier#Version_1_(date-time_and_MAC_address)). The newly created Tenant Profile Id will be present in the response. Specify existing Tenant Profile Id id to update the Tenant Profile. Referencing non-existing Tenant Profile Id will cause 'Not Found' error. Update of the tenant profile configuration will cause immediate recalculation of API limits for all affected Tenants. The **'profileData'** object is the part of Tenant Profile that defines API limits and Rate limits. You have an ability to define maximum number of devices ('maxDevice'), assets ('maxAssets') and other entities. You may also define maximum number of messages to be processed per month ('maxTransportMessages', 'maxREExecutions', etc). The '*RateLimit' defines the rate limits using simple syntax. For example, '1000:1,20000:60' means up to 1000 events per second but no more than 20000 event per minute. Let's review the example of tenant profile data below: ```json { \"name\": \"Default\", \"description\": \"Default tenant profile\", \"isolatedTbCore\": false, \"isolatedTbRuleEngine\": false, \"profileData\": { \"configuration\": { \"type\": \"DEFAULT\", \"maxDevices\": 0, \"maxAssets\": 0, \"maxCustomers\": 0, \"maxUsers\": 0, \"maxDashboards\": 0, \"maxRuleChains\": 0, \"maxResourcesInBytes\": 0, \"maxOtaPackagesInBytes\": 0, \"transportTenantMsgRateLimit\": \"1000:1,20000:60\", \"transportTenantTelemetryMsgRateLimit\": \"1000:1,20000:60\", \"transportTenantTelemetryDataPointsRateLimit\": \"1000:1,20000:60\", \"transportDeviceMsgRateLimit\": \"20:1,600:60\", \"transportDeviceTelemetryMsgRateLimit\": \"20:1,600:60\", \"transportDeviceTelemetryDataPointsRateLimit\": \"20:1,600:60\", \"maxTransportMessages\": 10000000, \"maxTransportDataPoints\": 10000000, \"maxREExecutions\": 4000000, \"maxJSExecutions\": 5000000, \"maxDPStorageDays\": 0, \"maxRuleNodeExecutionsPerMessage\": 50, \"maxEmails\": 0, \"maxSms\": 0, \"maxCreatedAlarms\": 1000, \"defaultStorageTtlDays\": 0, \"alarmsTtlDays\": 0, \"rpcTtlDays\": 0, \"warnThreshold\": 0 } }, \"default\": true } ``` Available for users with 'SYS_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.save_tenant_profile_using_post_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:param TenantProfile body:
:return: TenantProfile
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 save_tenant_profile_using_post" % 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/tenantProfile', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='TenantProfile', # 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 set_default_tenant_profile_using_post(self, tenant_profile_id, **kwargs): # noqa: E501
"""Make tenant profile default (setDefaultTenantProfile) # noqa: E501
Makes specified tenant profile to be default. Referencing non-existing tenant profile Id will cause an error. Available for users with 'SYS_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.set_default_tenant_profile_using_post(tenant_profile_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str tenant_profile_id: A string value representing the tenant profile id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required)
:return: TenantProfile
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.set_default_tenant_profile_using_post_with_http_info(tenant_profile_id, **kwargs) # noqa: E501
else:
(data) = self.set_default_tenant_profile_using_post_with_http_info(tenant_profile_id, **kwargs) # noqa: E501
return data
def set_default_tenant_profile_using_post_with_http_info(self, tenant_profile_id, **kwargs): # noqa: E501
"""Make tenant profile default (setDefaultTenantProfile) # noqa: E501
Makes specified tenant profile to be default. Referencing non-existing tenant profile Id will cause an error. Available for users with 'SYS_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.set_default_tenant_profile_using_post_with_http_info(tenant_profile_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str tenant_profile_id: A string value representing the tenant profile id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required)
:return: TenantProfile
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['tenant_profile_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 set_default_tenant_profile_using_post" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'tenant_profile_id' is set
if ('tenant_profile_id' not in params or
params['tenant_profile_id'] is None):
raise ValueError("Missing the required parameter `tenant_profile_id` when calling `set_default_tenant_profile_using_post`") # noqa: E501
collection_formats = {}
path_params = {}
if 'tenant_profile_id' in params:
path_params['tenantProfileId'] = params['tenant_profile_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/tenantProfile/{tenantProfileId}/default', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='TenantProfile', # 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)
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| 2,502
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0
| 8
|
7436d72769d5af35535008d2a3fd4dd68372e08b
| 8,681
|
py
|
Python
|
2-cannab/code/zoo/models.py
|
remtav/SpaceNet7_Multi-Temporal_Solutions
|
ee535c61fc22bffa45331519239c6d1b044b1514
|
[
"Apache-2.0"
] | 38
|
2021-02-18T07:04:54.000Z
|
2022-03-22T15:31:06.000Z
|
2-cannab/code/zoo/models.py
|
remtav/SpaceNet7_Multi-Temporal_Solutions
|
ee535c61fc22bffa45331519239c6d1b044b1514
|
[
"Apache-2.0"
] | 2
|
2021-02-22T18:53:19.000Z
|
2021-06-22T20:28:06.000Z
|
2-cannab/code/zoo/models.py
|
remtav/SpaceNet7_Multi-Temporal_Solutions
|
ee535c61fc22bffa45331519239c6d1b044b1514
|
[
"Apache-2.0"
] | 15
|
2021-02-25T17:25:40.000Z
|
2022-01-31T16:59:32.000Z
|
import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
from efficientnet_pytorch import EfficientNet
from efficientnet_pytorch.utils import Conv2dStaticSamePadding
class ConvRelu(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3):
super(ConvRelu, self).__init__()
self.layer = nn.Sequential(
nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, padding=1),
nn.ReLU(inplace=True)
)
def forward(self, x):
return self.layer(x)
class EfficientNet_Unet(nn.Module):
def __init__(self, name='efficientnet-b0', pretrained=True, **kwargs):
super(EfficientNet_Unet, self).__init__()
enc_sizes = {
'efficientnet-b0': [16, 24, 40, 112, 1280],
'efficientnet-b1': [16, 24, 40, 112, 1280],
'efficientnet-b2': [16, 24, 48, 120, 1408],
'efficientnet-b3': [24, 32, 48, 136, 1536],
'efficientnet-b4': [24, 32, 56, 160, 1792],
'efficientnet-b5': [24, 40, 64, 176, 2048],
'efficientnet-b6': [32, 40, 72, 200, 2304],
'efficientnet-b7': [32, 48, 80, 224, 2560],
'efficientnet-b8': [32, 56, 88, 248, 2816]
}
encoder_filters = enc_sizes[name]
decoder_filters = np.asarray([48, 64, 128, 160, 320])
self.conv6 = ConvRelu(encoder_filters[-1], decoder_filters[-1])
self.conv6_2 = ConvRelu(decoder_filters[-1] + encoder_filters[-2], decoder_filters[-1])
self.conv7 = ConvRelu(decoder_filters[-1], decoder_filters[-2])
self.conv7_2 = ConvRelu(decoder_filters[-2] + encoder_filters[-3], decoder_filters[-2])
self.conv8 = ConvRelu(decoder_filters[-2], decoder_filters[-3])
self.conv8_2 = ConvRelu(decoder_filters[-3] + encoder_filters[-4], decoder_filters[-3])
self.conv9 = ConvRelu(decoder_filters[-3], decoder_filters[-4])
self.conv9_2 = ConvRelu(decoder_filters[-4] + encoder_filters[-5], decoder_filters[-4])
self.conv10 = ConvRelu(decoder_filters[-4], decoder_filters[-5])
self.res = nn.Conv2d(decoder_filters[-5], 3, 1, stride=1, padding=0)
self._initialize_weights()
if pretrained:
self.encoder = EfficientNet.from_pretrained(name)
else:
self.encoder = EfficientNet.from_name(name)
def extract_features(self, inp):
out = []
# Stem
x = self.encoder._swish(self.encoder._bn0(self.encoder._conv_stem(inp)))
# Blocks
for idx, block in enumerate(self.encoder._blocks):
drop_connect_rate = self.encoder._global_params.drop_connect_rate
if drop_connect_rate:
drop_connect_rate *= float(idx) / len(self.encoder._blocks)
y = block(x, drop_connect_rate=drop_connect_rate)
if y.size()[-1] != x.size()[-1]:
out.append(x)
x = y
# Head
x = self.encoder._swish(self.encoder._bn1(self.encoder._conv_head(x)))
out.append(x)
return out
def forward(self, x):
batch_size, C, H, W = x.shape
enc1, enc2, enc3, enc4, enc5 = self.extract_features(x)
dec6 = self.conv6(F.interpolate(enc5, scale_factor=2))
dec6 = self.conv6_2(torch.cat([dec6, enc4
], 1))
dec7 = self.conv7(F.interpolate(dec6, scale_factor=2))
dec7 = self.conv7_2(torch.cat([dec7, enc3
], 1))
dec8 = self.conv8(F.interpolate(dec7, scale_factor=2))
dec8 = self.conv8_2(torch.cat([dec8, enc2
], 1))
dec9 = self.conv9(F.interpolate(dec8, scale_factor=2))
dec9 = self.conv9_2(torch.cat([dec9,
enc1
], 1))
dec10 = self.conv10(F.interpolate(dec9, scale_factor=2))
return self.res(dec10)
def _initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d) or isinstance(m, nn.Linear):
m.weight.data = nn.init.kaiming_normal_(m.weight.data)
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
class EfficientNet_Unet_Double(nn.Module):
def __init__(self, name='efficientnet-b0', pretrained=True, **kwargs):
super(EfficientNet_Unet_Double, self).__init__()
enc_sizes = {
'efficientnet-b0': [16, 24, 40, 112, 1280],
'efficientnet-b1': [16, 24, 40, 112, 1280],
'efficientnet-b2': [16, 24, 48, 120, 1408],
'efficientnet-b3': [24, 32, 48, 136, 1536],
'efficientnet-b4': [24, 32, 56, 160, 1792],
'efficientnet-b5': [24, 40, 64, 176, 2048],
'efficientnet-b6': [32, 40, 72, 200, 2304],
'efficientnet-b7': [32, 48, 80, 224, 2560],
'efficientnet-b8': [32, 56, 88, 248, 2816]
}
encoder_filters = enc_sizes[name]
decoder_filters = np.asarray([48, 64, 128, 160, 320])
self.conv6 = ConvRelu(encoder_filters[-1], decoder_filters[-1])
self.conv6_2 = ConvRelu(decoder_filters[-1] + encoder_filters[-2], decoder_filters[-1])
self.conv7 = ConvRelu(decoder_filters[-1], decoder_filters[-2])
self.conv7_2 = ConvRelu(decoder_filters[-2] + encoder_filters[-3], decoder_filters[-2])
self.conv8 = ConvRelu(decoder_filters[-2], decoder_filters[-3])
self.conv8_2 = ConvRelu(decoder_filters[-3] + encoder_filters[-4], decoder_filters[-3])
self.conv9 = ConvRelu(decoder_filters[-3], decoder_filters[-4])
self.conv9_2 = ConvRelu(decoder_filters[-4] + encoder_filters[-5], decoder_filters[-4])
self.conv10 = ConvRelu(decoder_filters[-4], decoder_filters[-5])
self.res = nn.Conv2d(decoder_filters[-5] * 2, 7, 1, stride=1, padding=0)
self._initialize_weights()
if pretrained:
self.encoder = EfficientNet.from_pretrained(name)
else:
self.encoder = EfficientNet.from_name(name)
def extract_features(self, inp):
out = []
# Stem
x = self.encoder._swish(self.encoder._bn0(self.encoder._conv_stem(inp)))
# Blocks
for idx, block in enumerate(self.encoder._blocks):
drop_connect_rate = self.encoder._global_params.drop_connect_rate
if drop_connect_rate:
drop_connect_rate *= float(idx) / len(self.encoder._blocks)
y = block(x, drop_connect_rate=drop_connect_rate)
if y.size()[-1] != x.size()[-1]:
out.append(x)
x = y
# Head
x = self.encoder._swish(self.encoder._bn1(self.encoder._conv_head(x)))
out.append(x)
return out
def forward1(self, x):
batch_size, C, H, W = x.shape
enc1, enc2, enc3, enc4, enc5 = self.extract_features(x)
dec6 = self.conv6(F.interpolate(enc5, scale_factor=2))
dec6 = self.conv6_2(torch.cat([dec6, enc4
], 1))
dec7 = self.conv7(F.interpolate(dec6, scale_factor=2))
dec7 = self.conv7_2(torch.cat([dec7, enc3
], 1))
dec8 = self.conv8(F.interpolate(dec7, scale_factor=2))
dec8 = self.conv8_2(torch.cat([dec8, enc2
], 1))
dec9 = self.conv9(F.interpolate(dec8, scale_factor=2))
dec9 = self.conv9_2(torch.cat([dec9,
enc1
], 1))
dec10 = self.conv10(F.interpolate(dec9, scale_factor=2))
return dec10
def forward(self, x):
batch_size, C, H, W = x.shape
dec10_0 = self.forward1(x[:, :3, :, :])
dec10_1 = self.forward1(x[:, 3:, :, :])
dec10 = torch.cat([dec10_0, dec10_1], 1)
return self.res(dec10)
def _initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d) or isinstance(m, nn.Linear):
m.weight.data = nn.init.kaiming_normal_(m.weight.data)
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
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| 111
| 0.569635
| 1,071
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| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
74434f7716b57e0bdb42a4996e3cd13dbf97a76f
| 229
|
py
|
Python
|
blog/views.py
|
AndrewJBateman/python-django-site
|
dd1aba6d36cfa10b6786d630263bf6fad23473f1
|
[
"CNRI-Python"
] | null | null | null |
blog/views.py
|
AndrewJBateman/python-django-site
|
dd1aba6d36cfa10b6786d630263bf6fad23473f1
|
[
"CNRI-Python"
] | null | null | null |
blog/views.py
|
AndrewJBateman/python-django-site
|
dd1aba6d36cfa10b6786d630263bf6fad23473f1
|
[
"CNRI-Python"
] | null | null | null |
from django.shortcuts import render
from django.http import HttpResponse
# Create your views here.
def home(request):
return HttpResponse('<h1>Helooooooo</h1>')
def members(request):
return HttpResponse('<h1>Members</h1>')
| 22.9
| 44
| 0.759825
| 30
| 229
| 5.8
| 0.6
| 0.114943
| 0.287356
| 0.310345
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.019802
| 0.117904
| 229
| 9
| 45
| 25.444444
| 0.841584
| 0.100437
| 0
| 0
| 0
| 0
| 0.171569
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0.333333
| 0.333333
| 1
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 1
| 1
| 0
|
0
| 7
|
ae027aa3890bd9cd164f065b77d31697f4d4da9e
| 36
|
py
|
Python
|
fake_camera/__init__.py
|
fjolublar/fake_camera
|
05686f729f4514f4e7cf339de9b8b6f794c2fe44
|
[
"MIT"
] | null | null | null |
fake_camera/__init__.py
|
fjolublar/fake_camera
|
05686f729f4514f4e7cf339de9b8b6f794c2fe44
|
[
"MIT"
] | 1
|
2021-04-07T10:39:57.000Z
|
2021-04-07T10:42:23.000Z
|
fake_camera/__init__.py
|
fjolublar/fake_camera
|
05686f729f4514f4e7cf339de9b8b6f794c2fe44
|
[
"MIT"
] | null | null | null |
from .fake_camera import Fake_Camera
| 36
| 36
| 0.888889
| 6
| 36
| 5
| 0.666667
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.083333
| 36
| 1
| 36
| 36
| 0.909091
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
bb087ea7cb483328cfb8cdd8d03fa9e5b3031b34
| 16,012
|
py
|
Python
|
sorting/quicksort.py
|
travisariggs/Algorithms
|
0864880e6b193954a670073244bfd5de523b4e72
|
[
"MIT"
] | null | null | null |
sorting/quicksort.py
|
travisariggs/Algorithms
|
0864880e6b193954a670073244bfd5de523b4e72
|
[
"MIT"
] | null | null | null |
sorting/quicksort.py
|
travisariggs/Algorithms
|
0864880e6b193954a670073244bfd5de523b4e72
|
[
"MIT"
] | null | null | null |
"""
Quicksort Algorithm
by Travis Riggs
This module implements the quicksort algorithm.
"""
import copy
import random
def quick_sort_simple_first(aList, startIndex=0, endIndex=None,
comparisons=False):
"""Sort a list from least to greatest using quicksort
Returns a sorted list
If 'comparisons' is set to True, it returns the sorted list and the
number of comparisons
It chooses the first element in the list as the pivot.
"""
if endIndex is None:
endIndex = len(aList)
# Base Case
if endIndex - startIndex <= 1:
if comparisons:
return aList, 0
else:
return aList
# Select the first element as the pivot
pivot = aList[startIndex]
# Partition the list between elements greater than and less than
# the pivot element
p = startIndex + 1 # Partition index
i = startIndex + 1 # Element index
for elem in aList[startIndex+1:endIndex]:
# Is this element less than our pivot?
if elem < pivot:
# Swap this element with the lowest item in the upper
# partition. But only do that if we've created an upper
# partition.
if i != p:
aList[i] = aList[p]
aList[p] = elem
# Move the partition index up to make room for the new
# value.
p += 1
# Track the index of the next list element
i += 1
# Move the pivot element between the partitions
aList[startIndex] = aList[p-1]
aList[p-1] = pivot
## DEBUG
#print(aList, aList[startIndex:endIndex], startIndex, endIndex)
## DEBUG
#import ipdb; ipdb.set_trace()
if comparisons:
compares = len(aList[startIndex:endIndex]) - 1
# Rescursively call quick_sort on the upper and lower partitions
aList, lowerCompares = quick_sort_simple_first(aList,
startIndex,
p-1,
True)
aList, upperCompares = quick_sort_simple_first(aList,
p,
endIndex,
True)
totalCompares = compares + lowerCompares + upperCompares
return aList, totalCompares
else:
# Rescursively call quick_sort on the upper and lower partitions
aList = quick_sort_simple_first(aList, startIndex, p-1)
aList = quick_sort_simple_first(aList, p, endIndex)
# Return the sorted list
return aList
def quick_sort_simple_last(aList, startIndex=0, endIndex=None,
comparisons=False):
"""Sort a list from least to greatest using quicksort
Returns a sorted list
If 'comparisons' is set to True, it returns the sorted list and the
number of comparisons
It chooses the last element in the list as the pivot.
"""
if endIndex is None:
endIndex = len(aList)
# Base Case
if endIndex - startIndex <= 1:
if comparisons:
return aList, 0
else:
return aList
# Select the last element as the pivot
pivot = aList[endIndex-1]
# Switch the last element with the first
aList[endIndex-1] = aList[startIndex]
aList[startIndex] = pivot
# Partition the list between elements greater than and less than
# the pivot element
p = startIndex + 1 # Partition index
i = startIndex + 1 # Element index
for elem in aList[startIndex+1:endIndex]:
# Is this element less than our pivot?
if elem < pivot:
# Swap this element with the lowest item in the upper
# partition. But only do that if we've created an upper
# partition.
if i != p:
aList[i] = aList[p]
aList[p] = elem
# Move the partition index up to make room for the new
# value.
p += 1
# Track the index of the next list element
i += 1
# Move the pivot element between the partitions
aList[startIndex] = aList[p-1]
aList[p-1] = pivot
## DEBUG
#print(aList, aList[startIndex:endIndex], startIndex, endIndex)
## DEBUG
#import ipdb; ipdb.set_trace()
if comparisons:
compares = len(aList[startIndex:endIndex]) - 1
# Rescursively call quick_sort on the upper and lower partitions
aList, lowerCompares = quick_sort_simple_last(aList,
startIndex,
p-1,
True)
aList, upperCompares = quick_sort_simple_last(aList,
p,
endIndex,
True)
totalCompares = compares + lowerCompares + upperCompares
return aList, totalCompares
else:
# Rescursively call quick_sort on the upper and lower partitions
aList = quick_sort_simple_last(aList, startIndex, p-1)
aList = quick_sort_simple_last(aList, p, endIndex)
# Return the sorted list
return aList
def quick_sort_median(aList, startIndex=0, endIndex=None,
comparisons=False):
"""Sort a list from least to greatest using quicksort
Returns a sorted list
If 'comparisons' is set to True, it returns the sorted list and the
number of comparisons
It chooses the median of the first, middle and last element in the
list as the pivot.
"""
if endIndex is None:
endIndex = len(aList)
# Base Case
if endIndex - startIndex <= 1:
if comparisons:
return aList, 0
else:
return aList
## DEBUG
#import ipdb; ipdb.set_trace()
#print(aList[startIndex:endIndex])
# Find the median of the first, middle and last elements
first = aList[startIndex]
if (endIndex - startIndex) % 2 == 0:
middle = aList[startIndex + int((endIndex-startIndex)/2)-1]
else:
middle = aList[startIndex + int((endIndex-startIndex)/2)]
last = aList[endIndex-1]
# Is the first element the median of the three?
if middle < first < last or last < first < middle:
pivot = first
# Is the middle element the median of the three?
elif first < middle < last or last < middle < first:
pivot = middle
# Swap the middle with the first
if (endIndex - startIndex) % 2 == 0:
aList[startIndex + int((endIndex-startIndex)/2)-1] = aList[startIndex]
else:
aList[startIndex + int((endIndex-startIndex)/2)] = aList[startIndex]
aList[startIndex] = pivot
# The last element must be the median of the three...
else:
pivot = last
# Switch the last element with the first
aList[endIndex-1] = aList[startIndex]
aList[startIndex] = pivot
## DEBUG
#print(aList, aList[startIndex:endIndex], first, middle, last, pivot)
# Partition the list between elements greater than and less than
# the pivot element
p = startIndex + 1 # Partition index
i = startIndex + 1 # Element index
for elem in aList[startIndex+1:endIndex]:
# Is this element less than our pivot?
if elem < pivot:
# Swap this element with the lowest item in the upper
# partition. But only do that if we've created an upper
# partition.
if i != p:
aList[i] = aList[p]
aList[p] = elem
# Move the partition index up to make room for the new
# value.
p += 1
# Track the index of the next list element
i += 1
# Move the pivot element between the partitions
aList[startIndex] = aList[p-1]
aList[p-1] = pivot
## DEBUG
#import ipdb; ipdb.set_trace()
if comparisons:
compares = len(aList[startIndex:endIndex]) - 1
# Rescursively call quick_sort on the upper and lower partitions
aList, lowerCompares = quick_sort_median(aList,
startIndex,
p-1,
True)
aList, upperCompares = quick_sort_median(aList,
p,
endIndex,
True)
totalCompares = compares + lowerCompares + upperCompares
return aList, totalCompares
else:
# Rescursively call quick_sort on the upper and lower partitions
aList = quick_sort_median(aList, startIndex, p-1)
aList = quick_sort_median(aList, p, endIndex)
# Return the sorted list
return aList
def quick_sort_random(aList, startIndex=0, endIndex=None,
comparisons=False):
"""Sort a list from least to greatest using quicksort
Returns a sorted list
If 'comparisons' is set to True, it returns the sorted list and the
number of comparisons
It chooses a randomized pivot element
"""
if endIndex is None:
endIndex = len(aList)
# Base Case
if endIndex - startIndex <= 1:
if comparisons:
return aList, 0
else:
return aList
## DEBUG
#import ipdb; ipdb.set_trace()
#print(aList[startIndex:endIndex])
# Select a random element for the pivot
pivotInd = random.randint(startIndex, endIndex-1)
pivot = aList[pivotInd]
# Switch the pivot element with the first
aList[pivotInd] = aList[startIndex]
aList[startIndex] = pivot
## DEBUG
#print(aList, aList[startIndex:endIndex], first, middle, last, pivot)
# Partition the list between elements greater than and less than
# the pivot element
p = startIndex + 1 # Partition index
i = startIndex + 1 # Element index
for elem in aList[startIndex+1:endIndex]:
# Is this element less than our pivot?
if elem < pivot:
# Swap this element with the lowest item in the upper
# partition. But only do that if we've created an upper
# partition.
if i != p:
aList[i] = aList[p]
aList[p] = elem
# Move the partition index up to make room for the new
# value.
p += 1
# Track the index of the next list element
i += 1
# Move the pivot element between the partitions
aList[startIndex] = aList[p-1]
aList[p-1] = pivot
## DEBUG
#import ipdb; ipdb.set_trace()
if comparisons:
compares = len(aList[startIndex:endIndex]) - 1
# Rescursively call quick_sort on the upper and lower partitions
aList, lowerCompares = quick_sort_median(aList,
startIndex,
p-1,
True)
aList, upperCompares = quick_sort_median(aList,
p,
endIndex,
True)
totalCompares = compares + lowerCompares + upperCompares
return aList, totalCompares
else:
# Rescursively call quick_sort on the upper and lower partitions
aList = quick_sort_median(aList, startIndex, p-1)
aList = quick_sort_median(aList, p, endIndex)
# Return the sorted list
return aList
def quick_sort_ideal(aList, startIndex=0, endIndex=None,
comparisons=False):
"""Sort a list from least to greatest using an idealized quicksort
This is not intended for actual use. It is only used to find the
best possible sort for analysis of other variations of quick sort.
Returns a sorted list
If 'comparisons' is set to True, it returns the sorted list and the
number of comparisons
It chooses the perfect pivot element each time: the median.
"""
if endIndex is None:
endIndex = len(aList)
# Base Case
if endIndex - startIndex <= 1:
if comparisons:
return aList, 0
else:
return aList
## DEBUG
#import ipdb; ipdb.set_trace()
#print(aList[startIndex:endIndex])
temp = copy.copy(aList[startIndex:endIndex])
temp.sort()
if len(temp) % 2 == 0:
median = temp[int(len(temp)/2)-1]
else:
median = temp[int(len(temp)/2)]
# Find the median value's index
pivotInd = aList.index(median)
pivot = aList[pivotInd]
# Switch the pivot element with the first
aList[pivotInd] = aList[startIndex]
aList[startIndex] = pivot
## DEBUG
#print(aList, aList[startIndex:endIndex], pivot)
# Partition the list between elements greater than and less than
# the pivot element
p = startIndex + 1 # Partition index
i = startIndex + 1 # Element index
for elem in aList[startIndex+1:endIndex]:
# Is this element less than our pivot?
if elem < pivot:
# Swap this element with the lowest item in the upper
# partition. But only do that if we've created an upper
# partition.
if i != p:
aList[i] = aList[p]
aList[p] = elem
# Move the partition index up to make room for the new
# value.
p += 1
# Track the index of the next list element
i += 1
# Move the pivot element between the partitions
aList[startIndex] = aList[p-1]
aList[p-1] = pivot
## DEBUG
#import ipdb; ipdb.set_trace()
if comparisons:
compares = len(aList[startIndex:endIndex]) - 1
# Rescursively call quick_sort on the upper and lower partitions
aList, lowerCompares = quick_sort_ideal(aList,
startIndex,
p-1,
True)
aList, upperCompares = quick_sort_ideal(aList,
p,
endIndex,
True)
totalCompares = compares + lowerCompares + upperCompares
return aList, totalCompares
else:
# Rescursively call quick_sort on the upper and lower partitions
aList = quick_sort_ideal(aList, startIndex, p-1)
aList = quick_sort_ideal(aList, p, endIndex)
# Return the sorted list
return aList
if __name__ == '__main__':
a = [4, 2, 234, 9, 1, 10, 2300, 3]
b = [1, 2, 3, 4, 9, 10, 234, 2300]
result, comparisons = quick_sort_simple_first(a, comparisons=True)
print(result, comparisons)
a = [4, 2, 234, 9, 1, 10, 2300, 3]
b = [1, 2, 3, 4, 9, 10, 234, 2300]
result, comparisons = quick_sort_simple_last(a, comparisons=True)
print(result, comparisons)
a = [4, 2, 234, 9, 1, 10, 2300, 3]
b = [1, 2, 3, 4, 9, 10, 234, 2300]
result, comparisons = quick_sort_median(a, comparisons=True)
print(result, comparisons)
a = [4, 2, 234, 9, 1, 10, 2300, 3]
b = [1, 2, 3, 4, 9, 10, 234, 2300]
result, comparisons = quick_sort_random(a, comparisons=True)
print(result, comparisons)
a = [4, 2, 234, 9, 1, 10, 2300, 3]
b = [1, 2, 3, 4, 9, 10, 234, 2300]
result, comparisons = quick_sort_ideal(a, comparisons=True)
print(result, comparisons)
| 28.695341
| 82
| 0.556832
| 1,861
| 16,012
| 4.738313
| 0.075766
| 0.09526
| 0.036516
| 0.028351
| 0.911885
| 0.899524
| 0.869698
| 0.848832
| 0.831935
| 0.821501
| 0
| 0.022422
| 0.373283
| 16,012
| 557
| 83
| 28.746858
| 0.856303
| 0.342431
| 0
| 0.8125
| 0
| 0
| 0.000782
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.020833
| false
| 0
| 0.008333
| 0
| 0.1125
| 0.020833
| 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
|
bb457b46936be0b55b310ed6152e1926baff42ad
| 99,061
|
py
|
Python
|
optimizer.py
|
shermanluo/personalDriving
|
5e80bb7248f8d91703050acde43b71291b8fa704
|
[
"MIT"
] | 1
|
2019-08-31T15:13:58.000Z
|
2019-08-31T15:13:58.000Z
|
optimizer.py
|
shermanluo/personalDriving
|
5e80bb7248f8d91703050acde43b71291b8fa704
|
[
"MIT"
] | null | null | null |
optimizer.py
|
shermanluo/personalDriving
|
5e80bb7248f8d91703050acde43b71291b8fa704
|
[
"MIT"
] | 1
|
2019-08-30T21:08:11.000Z
|
2019-08-30T21:08:11.000Z
|
import itertools
import pdb
import time
import numpy as np
import numpy.linalg as nl
import theano as th
import theano.tensor as tt
import theano.tensor.slinalg as ts
import scipy.optimize
import scipy.io
import config
import constants
import opt_timeup
import time_profile
import utils
from utils import shape, jacobian, hessian, grad
import pdb as pdb
import ilqgames.python.CarExample as unicycle
#pdb.set_trace()
#import ilqgames.python.two_player_unicycle_4d_example as unicycle
class Maximizer(object):
def __init__(self, f, vs, g={}, pre=None, gen=None, method='bfgs', eps=1, iters=100000, debug=False, inf_ignore=np.inf):
self.inf_ignore = inf_ignore
self.debug = debug
self.iters = iters
self.eps = eps
self.method = method
def one_gen():
yield
self.gen = gen
if self.gen is None:
self.gen = one_gen
self.pre = pre
self.f = f
self.vs = vs
self.sz = [shape(v)[0] for v in self.vs]
for i in range(1,len(self.sz)):
self.sz[i] += self.sz[i-1]
self.sz = [(0 if i==0 else self.sz[i-1], self.sz[i]) for i in range(len(self.sz))]
if isinstance(g, dict):
self.df = tt.concatenate([g[v] if v in g else grad(f, v) for v in self.vs])
else:
self.df = g
self.new_vs = [tt.vector() for v in self.vs]
self.func = th.function(self.new_vs, [-self.f, -self.df], givens=zip(self.vs, self.new_vs))
def f_and_df(x0):
if self.debug:
print x0
s = None
N = 0
for _ in self.gen():
if self.pre:
for v, (a, b) in zip(self.vs, self.sz):
v.set_value(x0[a:b])
self.pre()
res = self.func(*[x0[a:b] for a, b in self.sz])
if np.isnan(res[0]).any() or np.isnan(res[1]).any() or (np.abs(res[0])>self.inf_ignore).any() or (np.abs(res[1])>self.inf_ignore).any():
continue
if s is None:
s = res
N = 1
else:
s[0] += res[0]
s[1] += res[1]
N += 1
s[0]/=N
s[1]/=N
return s
self.f_and_df = f_and_df
def argmax(self, vals={}, bounds={}):
if not isinstance(bounds, dict):
bounds = {v: bounds for v in self.vs}
B = []
for v, (a, b) in zip(self.vs, self.sz):
if v in bounds:
B += bounds[v]
else:
B += [(None, None)]*(b-a)
x0 = np.hstack([np.asarray(vals[v]) if v in vals else v.get_value() for v in self.vs])
if self.method=='bfgs':
opt = scipy.optimize.fmin_l_bfgs_b(self.f_and_df, x0=x0, bounds=B)[0]
elif self.method=='gd':
opt = x0
for _ in range(self.iters):
opt -= self.f_and_df(opt)[1]*self.eps
else:
opt = scipy.optimize.minimize(self.f_and_df, x0=x0, method=self.method, jac=True).x
return opt
def maximize(self, *args, **vargs):
return self.argmax(*args, **vargs)
class IteratedBestResponseMaximizer(object):
def __init__(self, r_h, traj_h, r_r, traj_r, use_timeup=True,
use_second_order=False, update_with_curr_plan_fn=None,
init_plan_scheme='prev_opt',
# num_optimizations_r=1, get_init_plan_r_fn=None,
# num_optimizations_h=1, get_init_plan_h_fn=None,
init_grads=True):
"""
Arguments:
- r_h: the human tactical reward.
- traj_h: the human trajectory.
- r_r: the robot tactical reward.
- traj_r: the robot trajectory.
- update_with_curr_plan_fn: function to update any necessary information
based on the current plan. This is only necessary for the
HierarchicalMaximizer, not the NestedMaximizer.
- init_plan_scheme: string specifying the plan initialization scheme.
- num_optimizations_r: number of times to optimize the robot reward (the
best result of these optimizations will be chosen).
- get_init_plan_r_fn: function to return a function that initializes
the robot's plan for optimization, based on the current optimization
iteration.
- num_optimizations_h: number of times to optimize the human reward (the
best result of these optimizations will be chosen).
- get_init_plan_h_fn: function to return a function that initializes
the human's plan for optimization, based on the current optimization
iteration.
- init_grads: if True, initialize the gradients. This argument can be
set to False if another function is meant to initialize the gradients.
"""
# ---------------------------------------------------------------------------------------------------
# Basics.
self.r_h = r_h
self.r_r = r_r
self.traj_h = traj_h
self.traj_r = traj_r
self.plan_h = traj_h.u_th # human plan (controls)
self.plan_r = traj_r.u_th # robot plan (controls)
# (start, end) indices for each control in the plan when it's represented
# as a flattened array. Ex: [(0, 2), (2, 4), (4, 6), (6, 8), (8, 10)]
self.control_indices_h = traj_h.control_indices
self.control_indices_r = traj_r.control_indices
# maximum time for optimization
if use_timeup:
self.timeup = config.OPT_TIMEOUT
else:
self.timeup = float('inf')
self.use_second_order = use_second_order
if update_with_curr_plan_fn is None: # no functionality necessary here
update_with_curr_plan_fn = lambda: None
self.update_with_curr_plan_fn = update_with_curr_plan_fn
self.create_get_init_plans_fn(init_plan_scheme)
self.maximizer_inner_iters_r = 0 # number of iterations of maximizer_inner
self.maximizer_inner_iters_h = 0
if init_grads: # initialize the gradients
self.init_grads()
# self.nested = self.optimizer = NestedMaximizer(
# r_h, traj_h, r_r, traj_r,
# use_second_order=True,
# init_plan_scheme=init_plan_scheme)
def create_get_init_plans_fn(self, init_plan_scheme):
"""Create the functions that return the plan initialization functions
for the robot and the human, depending on the optimization iteration.
Also set the number of optimization loops for the robot and human.
Arguments:
- init_plan_scheme: string specifying the plan initialization scheme.
"""
assert init_plan_scheme in constants.INIT_PLAN_SCHEMES_OPTIONS
self.get_init_plan_r_fn = eval('self.get_init_plan_r_fn_' + init_plan_scheme)
self.get_init_plan_h_fn = eval('self.get_init_plan_h_fn_' + init_plan_scheme)
self.num_optimizations_r = constants.INIT_PLAN_SCHEME_TO_NUM_OPTS_R[init_plan_scheme]
self.num_optimizations_h = constants.INIT_PLAN_SCHEME_TO_NUM_OPTS_H[init_plan_scheme]
def get_init_plan_r_fn_maintain_speed_lsr_and_prev_opt(self, iter):
# TODO: comment this
v = self.traj_r.x0[3]
acc = constants.FRICTION * v ** 2
init_plan_r_fn_list = [
lambda: np.hstack([constants.CAR_CONTROL_BOUNDS[0][0], acc] for _ in range(self.traj_r.horizon)),
lambda: np.hstack([0., acc] for _ in range(self.traj_r.horizon)),
lambda: np.hstack([constants.CAR_CONTROL_BOUNDS[0][1], acc] for _ in range(self.traj_r.horizon)),
lambda: np.hstack([v.get_value() for v in self.plan_r[:-1]] + [self.traj_r.default_control])
]
return init_plan_r_fn_list[iter]
def get_init_plan_h_fn_maintain_speed_lsr_and_prev_opt(self, iter):
# TODO: comment this
v = self.traj_h.x0[3]
acc = constants.FRICTION * v ** 2
init_plan_h_fn_list = [
lambda: np.hstack([constants.CAR_CONTROL_BOUNDS[0][0], acc] for _ in range(self.traj_h.horizon)),
lambda: np.hstack([0., acc] for _ in range(self.traj_h.horizon)),
lambda: np.hstack([constants.CAR_CONTROL_BOUNDS[0][1], acc] for _ in range(self.traj_h.horizon)),
lambda: np.hstack([v.get_value() for v in self.plan_h[:-1]] + [self.traj_h.default_control])
]
return init_plan_h_fn_list[iter]
def get_init_plan_r_fn_maintain_speed_lsr(self, iter):
# TODO: comment this
v = self.traj_r.x0[3]
acc = constants.FRICTION * v ** 2
init_plan_r_fn_list = [
lambda: np.hstack([constants.CAR_CONTROL_BOUNDS[0][0], acc] for _ in range(self.traj_r.horizon)),
lambda: np.hstack([0., acc] for _ in range(self.traj_r.horizon)),
lambda: np.hstack([constants.CAR_CONTROL_BOUNDS[0][1], acc] for _ in range(self.traj_r.horizon))
]
return init_plan_r_fn_list[iter]
def get_init_plan_h_fn_maintain_speed_lsr(self, iter):
# TODO: comment this
v = self.traj_h.x0[3]
acc = constants.FRICTION * v ** 2
init_plan_h_fn_list = [
lambda: np.hstack([constants.CAR_CONTROL_BOUNDS[0][0], acc] for _ in range(self.traj_h.horizon)),
lambda: np.hstack([0., acc] for _ in range(self.traj_h.horizon)),
lambda: np.hstack([constants.CAR_CONTROL_BOUNDS[0][1], acc] for _ in range(self.traj_h.horizon))
]
return init_plan_h_fn_list[iter]
def get_init_plan_r_fn_lsr(self, iter):
# TODO: comment this
init_plan_r_fn_list = [
lambda: np.hstack([constants.CAR_CONTROL_BOUNDS[0][0], 0.] for _ in range(self.traj_r.horizon)),
lambda: np.hstack([0., 0.] for _ in range(self.traj_r.horizon)),
lambda: np.hstack([constants.CAR_CONTROL_BOUNDS[0][1], 0.] for _ in range(self.traj_r.horizon))
]
return init_plan_r_fn_list[iter]
def get_init_plan_h_fn_lsr(self, iter):
# TODO: comment this
init_plan_h_fn_list = [
lambda: np.hstack([constants.CAR_CONTROL_BOUNDS[0][0], 0.] for _ in range(self.traj_h.horizon)),
lambda: np.hstack([0., 0.] for _ in range(self.traj_h.horizon)),
lambda: np.hstack([constants.CAR_CONTROL_BOUNDS[0][1], 0.] for _ in range(self.traj_h.horizon))
]
return init_plan_h_fn_list[iter]
def get_init_plan_r_fn_max_speed_prev_steer(self, iter):
# TODO: comment this
return lambda: np.hstack([[v.get_value()[0], constants.CAR_CONTROL_BOUNDS[1][1]] for v in self.plan_r[:-1]] + [
self.traj_r.default_control[0], constants.CAR_CONTROL_BOUNDS[1][1]])
def get_init_plan_h_fn_max_speed_prev_steer(self, iter):
# TODO: comment this
return lambda: np.hstack([[v.get_value()[0], constants.CAR_CONTROL_BOUNDS[1][1]] for v in self.plan_h[:-1]] + [
self.traj_h.default_control[0], constants.CAR_CONTROL_BOUNDS[1][1]])
def get_init_plan_r_fn_maintain_speed_prev_steer(self, iter):
# TODO: comment this
v = self.traj_r.x0[3]
acc = constants.FRICTION * v ** 2
return lambda: np.hstack(
[[v.get_value()[0], acc] for v in self.plan_r[:-1]] + [self.traj_r.default_control[0], acc])
def get_init_plan_h_fn_maintain_speed_prev_steer(self, iter):
# TODO: comment this
v = self.traj_h.x0[3]
acc = constants.FRICTION * v ** 2
return lambda: np.hstack(
[[v.get_value()[0], acc] for v in self.plan_h[:-1]] + [self.traj_h.default_control[0], acc])
def get_init_plan_r_fn_prev_opt(self, iter):
# TODO: comment this
"""Initialize the robot plan using the default way."""
return lambda: np.hstack([v.get_value() for v in self.plan_r[:-1]] + [self.traj_r.default_control])
def get_init_plan_h_fn_prev_opt(self, iter):
# TODO: comment this
"""Initialize the human's plan using the default way."""
return lambda: np.hstack([v.get_value() for v in self.plan_h[:-1]] + [self.traj_h.default_control])
def get_my_init(self, iter):
return lambda: np.hstack((0, -2 * 0.0878) for _ in range(self.traj_h.horizon))
def get_my_init2(self, iter):
return lambda: np.hstack((0, -8 * 0.0878) for _ in range(self.traj_h.horizon))
def init_grads(self):
"""Initialize the gradients based on the rewards.
Precondition: the rewards (self.r_h and self.r_r) have already been
initialized.
"""
# gradient of human reward wrt human controls
self.dr_h = grad(self.r_h, self.plan_h)
# negative human reward and its derivative
self.func1 = th.function([], [-self.r_h, -self.dr_h])
def r_h_and_dr_h(plan_h_0):
"""Evaluate negative human reward and its derivative.
- plan_h_0: initial value for human plan."""
start_time = time.time()
# set plan_h to the given (initial) plan_h_0
for v, (a, b) in zip(self.plan_h, self.control_indices_h):
v.set_value(plan_h_0[a:b])
# do any necessary updates based on the current plan
# (this is necessary for the HierarchicalMaximizer, not the Nested Maximizer)
self.update_with_curr_plan_fn()
func1_val = self.func1() # negative human reward and its derivative
end_time = time.time()
time_profile.inner_loop_time_profile.update(start_time, end_time)
return func1_val
self.r_h_and_dr_h = r_h_and_dr_h
# ------------------------------------------------------------------------------------------
# Robot's reward and its derivative.
# ------------------------------------------------------------------------------------------
# OPTION 2: Partial derivative computation. FAST
# (Only direct effect of robot action given current human action)
# Below is the simplified derivative that neglects the second-order
# effect through human (and therefore avoids the heavy Hessian
# inversion)
self.dr_r = grad(self.r_r, self.plan_r)
# ------------------------------------------------------------------------------------------
# negative robot reward and its derivative
self.func2 = th.function([], [-self.r_r, -self.dr_r])
def r_r_and_dr_r(plan_r_0):
"""Get optimal human response, and return negative robot reward
and its derivative.
- plan_r_0: initial value for robot plan."""
# set self.plan_r to the given (initial) plan_r_0
for v, (a, b) in zip(self.plan_r, self.control_indices_r):
v.set_value(plan_r_0[a:b])
start_time = time.time()
func2_val = self.func2() # negative robot reward and its derivative
end_time = time.time()
time_profile.func2_time_profile.update(start_time, end_time)
return func2_val
self.r_r_and_dr_r = r_r_and_dr_r
def maximize_h(self, bounds={}, maxiter=config.NESTEDMAX_MAXITER_INNER):
"""Get optimal human response (controls).
Arguments:
- bounds: control bounds for the human.
- maxiter: maximum number of iterations.
"""
start_time = time.time()
bounds = constants.CAR_CONTROL_BOUNDS
if not isinstance(bounds, dict): # convert bounds to dictionary
bounds = {v: bounds for v in self.plan_h}
B = [] # list of bounds for each control in the plan
for v, (a, b) in zip(self.plan_h, self.control_indices_h):
if v in bounds:
B += bounds[v]
else:
B += [(None, None)] * (b - a)
# TODO: can we replace the .get_value() approach with using the numpy
# version because at this point the Theano and numpy plans are the same?
# plan_h_0 = np.hstack(self.traj_h.u) # initial robot plan (numpy version)
opt_h_list = []
for i in range(self.num_optimizations_h):
self.init_plan_h = self.get_init_plan_h_fn(i)
plan_h_0 = self.init_plan_h()
opt_h = scipy.optimize.fmin_l_bfgs_b(self.r_h_and_dr_h, x0=plan_h_0,
bounds=B)
opt_h_list.append(opt_h)
plan_h_0 = np.hstack([v.get_value() for v in self.plan_h])
opt_h = scipy.optimize.fmin_l_bfgs_b(self.r_h_and_dr_h, x0=plan_h_0,
bounds=B)
opt_h_list.append(opt_h)
best_opt_h = min(opt_h_list, key=lambda opt: opt[1])
opt_plan_h= best_opt_h[0] # optimal robot control
for v, (a, b) in zip(self.plan_h, self.control_indices_h):
v.set_value(opt_plan_h[a:b])
# do any necessary updates based on the current plan
# (this is necessary for the HierarchicalMaximizer, not the Nested Maximizer)
self.update_with_curr_plan_fn()
# increment the counter for the number of iterations of maximizer_inner
self.maximizer_inner_iters_h += 1
end_time = time.time()
time_profile.maximize_inner_time_profile.update(start_time, end_time)
return opt_h
def maximize_r(self, bounds={}, maxiter=config.NESTEDMAX_MAXITER_INNER):
"""Get optimal human response (controls).
Arguments:
- bounds: control bounds for the human.
- maxiter: maximum number of iterations.
"""
start_time = time.time()
bounds = constants.CAR_CONTROL_BOUNDS
if not isinstance(bounds, dict): # convert bounds to dictionary
bounds = {v: bounds for v in self.plan_r}
B = [] # list of bounds for each control in the plan
for v, (a, b) in zip(self.plan_r, self.control_indices_r):
if v in bounds:
B += bounds[v]
else:
B += [(None, None)] * (b - a)
opt_r_list = []
for i in range(self.num_optimizations_r):
self.init_plan_r = self.get_init_plan_r_fn(i)
plan_r_0 = self.init_plan_r()
opt_r = scipy.optimize.fmin_l_bfgs_b(self.r_r_and_dr_r, x0=plan_r_0,
bounds=B)
opt_r_list.append(opt_r)
plan_r_0 = np.hstack([v.get_value() for v in self.plan_r])
opt_r = scipy.optimize.fmin_l_bfgs_b(self.r_r_and_dr_r, x0=plan_r_0,
bounds=B)
opt_r_list.append(opt_r)
best_opt_r = min(opt_r_list, key=lambda opt: opt[1])
opt_plan_r = best_opt_r[0] # optimal robot control
for v, (a, b) in zip(self.plan_r, self.control_indices_r):
v.set_value(opt_plan_r[a:b])
# do any necessary updates based on the current plan
# (this is necessary for the HierarchicalMaximizer, not the Nested Maximizer)
self.update_with_curr_plan_fn()
# increment the counter for the number of iterations of maximizer_inner
self.maximizer_inner_iters_r += 1
end_time = time.time()
time_profile.maximize_inner_time_profile.update(start_time, end_time)
return opt_r
def maximize(self, bounds={}, bounds_inner={},
maxiter_inner=config.NESTEDMAX_MAXITER_INNER):
# Get optimal robot plan (controls) and human response using nested
# optimization.
start_time = time.time()
#for r in range(self.num_optimizations_r):
# for h in range(self.num_optimizations_h):
#self.init_plan_r = self.get_my_init2(0)
#self.init_plan_h = self.get_my_init(0)
#
# #pdb.set_trace()
# opt_plan_r, opt_plan_h = self.nested.maximize(bounds=bounds, bounds_inner=bounds)
# for v, (a, b) in zip(self.plan_r, self.control_indices_r):
# v.set_value(opt_plan_r[a:b])
opt_plan_h = np.hstack((0, -8 * 0.0878) for _ in range(self.traj_h.horizon))
for v, (a, b) in zip(self.plan_h, self.control_indices_h):
v.set_value(opt_plan_h[a:b])
#pdb.set_trace()
for i in range(5):
opt_r = self.maximize_r(bounds=bounds)
opt_h = self.maximize_h(bounds=bounds)
#print([x.get_value() for x in self.plan_r])
#print([x.get_value() for x in self.plan_h])
#pdb.set_trace()
# time profile of HierarchicalMaximizer
maximize_end_time = time.time()
time_profile.maximizer_time_profile.update(start_time, maximize_end_time)
return opt_r[0], opt_h[0]
class NestedMaximizer(object):
def __init__(self, r_h, traj_h, r_r, traj_r, use_timeup=True,
use_second_order=False, update_with_curr_plan_fn=None,
init_plan_scheme='prev_opt',
# num_optimizations_r=1, get_init_plan_r_fn=None,
# num_optimizations_h=1, get_init_plan_h_fn=None,
init_grads=True):
"""
Arguments:
- r_h: the human tactical reward.
- traj_h: the human trajectory.
- r_r: the robot tactical reward.
- traj_r: the robot trajectory.
- update_with_curr_plan_fn: function to update any necessary information
based on the current plan. This is only necessary for the
HierarchicalMaximizer, not the NestedMaximizer.
- init_plan_scheme: string specifying the plan initialization scheme.
- num_optimizations_r: number of times to optimize the robot reward (the
best result of these optimizations will be chosen).
- get_init_plan_r_fn: function to return a function that initializes
the robot's plan for optimization, based on the current optimization
iteration.
- num_optimizations_h: number of times to optimize the human reward (the
best result of these optimizations will be chosen).
- get_init_plan_h_fn: function to return a function that initializes
the human's plan for optimization, based on the current optimization
iteration.
- init_grads: if True, initialize the gradients. This argument can be
set to False if another function is meant to initialize the gradients.
"""
# ---------------------------------------------------------------------------------------------------
# Basics.
self.r_h = r_h
self.r_r = r_r
self.traj_h = traj_h
self.traj_r = traj_r
self.plan_h = traj_h.u_th # human plan (controls)
self.plan_r = traj_r.u_th # robot plan (controls)
# (start, end) indices for each control in the plan when it's represented
# as a flattened array. Ex: [(0, 2), (2, 4), (4, 6), (6, 8), (8, 10)]
self.control_indices_h = traj_h.control_indices
self.control_indices_r = traj_r.control_indices
# maximum time for optimization
if use_timeup:
self.timeup = config.OPT_TIMEOUT
else:
self.timeup = float('inf')
self.use_second_order = use_second_order
if update_with_curr_plan_fn is None: # no functionality necessary here
update_with_curr_plan_fn = lambda: None
self.update_with_curr_plan_fn = update_with_curr_plan_fn
# self.num_optimizations_r = num_optimizations_r
# if get_init_plan_r_fn is None:
# self.get_init_plan_r_fn = self.get_init_plan_r_fn_default
# else:
# self.get_init_plan_r_fn = get_init_plan_r_fn
# self.num_optimizations_h = num_optimizations_h
# if get_init_plan_h_fn is None:
# self.get_init_plan_h_fn = self.get_init_plan_h_fn_default
# else:
# self.get_init_plan_h_fn = get_init_plan_h_fn
self.create_get_init_plans_fn(init_plan_scheme)
self.maximizer_inner_iters = 0 # number of iterations of maximizer_inner
if init_grads: # initialize the gradients
self.init_grads()
def create_get_init_plans_fn(self, init_plan_scheme):
"""Create the functions that return the plan initialization functions
for the robot and the human, depending on the optimization iteration.
Also set the number of optimization loops for the robot and human.
Arguments:
- init_plan_scheme: string specifying the plan initialization scheme.
"""
assert init_plan_scheme in constants.INIT_PLAN_SCHEMES_OPTIONS
self.get_init_plan_r_fn = eval('self.get_init_plan_r_fn_' + init_plan_scheme)
self.get_init_plan_h_fn = eval('self.get_init_plan_h_fn_' + init_plan_scheme)
self.num_optimizations_r = constants.INIT_PLAN_SCHEME_TO_NUM_OPTS_R[init_plan_scheme]
self.num_optimizations_h = constants.INIT_PLAN_SCHEME_TO_NUM_OPTS_H[init_plan_scheme]
def get_init_plan_r_fn_maintain_speed_lsr_and_prev_opt(self, iter):
# TODO: comment this
v = self.traj_r.x0[3]
acc = constants.FRICTION * v**2
init_plan_r_fn_list = [
lambda: np.hstack([constants.CAR_CONTROL_BOUNDS[0][0], acc] for _ in range(self.traj_r.horizon)),
lambda: np.hstack([0., acc] for _ in range(self.traj_r.horizon)),
lambda: np.hstack([constants.CAR_CONTROL_BOUNDS[0][1], acc] for _ in range(self.traj_r.horizon)),
lambda: np.hstack([v.get_value() for v in self.plan_r[:-1]] + [self.traj_r.default_control])
]
return init_plan_r_fn_list[iter]
def get_init_plan_h_fn_maintain_speed_lsr_and_prev_opt(self, iter):
# TODO: comment this
v = self.traj_h.x0[3]
acc = constants.FRICTION * v**2
init_plan_h_fn_list = [
lambda: np.hstack([constants.CAR_CONTROL_BOUNDS[0][0], acc] for _ in range(self.traj_h.horizon)),
lambda: np.hstack([0., acc] for _ in range(self.traj_h.horizon)),
lambda: np.hstack([constants.CAR_CONTROL_BOUNDS[0][1], acc] for _ in range(self.traj_h.horizon)),
lambda: np.hstack([v.get_value() for v in self.plan_h[:-1]] + [self.traj_h.default_control])
]
return init_plan_h_fn_list[iter]
def get_init_plan_r_fn_maintain_speed_lsr(self, iter):
# TODO: comment this
v = self.traj_r.x0[3]
acc = constants.FRICTION * v**2
init_plan_r_fn_list = [
lambda: np.hstack([constants.CAR_CONTROL_BOUNDS[0][0], acc] for _ in range(self.traj_r.horizon)),
lambda: np.hstack([0., acc] for _ in range(self.traj_r.horizon)),
lambda: np.hstack([constants.CAR_CONTROL_BOUNDS[0][1], acc] for _ in range(self.traj_r.horizon))
]
return init_plan_r_fn_list[iter]
def get_init_plan_h_fn_maintain_speed_lsr(self, iter):
# TODO: comment this
v = self.traj_h.x0[3]
acc = constants.FRICTION * v**2
init_plan_h_fn_list = [
lambda: np.hstack([constants.CAR_CONTROL_BOUNDS[0][0], acc] for _ in range(self.traj_h.horizon)),
lambda: np.hstack([0., acc] for _ in range(self.traj_h.horizon)),
lambda: np.hstack([constants.CAR_CONTROL_BOUNDS[0][1], acc] for _ in range(self.traj_h.horizon))
]
return init_plan_h_fn_list[iter]
def get_init_plan_r_fn_lsr(self, iter):
# TODO: comment this
init_plan_r_fn_list = [
lambda: np.hstack([constants.CAR_CONTROL_BOUNDS[0][0], 0.] for _ in range(self.traj_r.horizon)),
lambda: np.hstack([0., 0.] for _ in range(self.traj_r.horizon)),
lambda: np.hstack([constants.CAR_CONTROL_BOUNDS[0][1], 0.] for _ in range(self.traj_r.horizon))
]
return init_plan_r_fn_list[iter]
def get_init_plan_h_fn_lsr(self, iter):
# TODO: comment this
init_plan_h_fn_list = [
lambda: np.hstack([constants.CAR_CONTROL_BOUNDS[0][0], 0.] for _ in range(self.traj_h.horizon)),
lambda: np.hstack([0., 0.] for _ in range(self.traj_h.horizon)),
lambda: np.hstack([constants.CAR_CONTROL_BOUNDS[0][1], 0.] for _ in range(self.traj_h.horizon))
]
return init_plan_h_fn_list[iter]
def get_init_plan_r_fn_max_speed_prev_steer(self, iter):
# TODO: comment this
return lambda: np.hstack([[v.get_value()[0], constants.CAR_CONTROL_BOUNDS[1][1]] for v in self.plan_r[:-1]] + [self.traj_r.default_control[0], constants.CAR_CONTROL_BOUNDS[1][1]])
def get_init_plan_h_fn_max_speed_prev_steer(self, iter):
# TODO: comment this
return lambda: np.hstack([[v.get_value()[0], constants.CAR_CONTROL_BOUNDS[1][1]] for v in self.plan_h[:-1]] + [self.traj_h.default_control[0], constants.CAR_CONTROL_BOUNDS[1][1]])
def get_init_plan_r_fn_maintain_speed_prev_steer(self, iter):
# TODO: comment this
v = self.traj_r.x0[3]
acc = constants.FRICTION * v**2
return lambda: np.hstack([[v.get_value()[0], acc] for v in self.plan_r[:-1]] + [self.traj_r.default_control[0], acc])
def get_init_plan_h_fn_maintain_speed_prev_steer(self, iter):
# TODO: comment this
v = self.traj_h.x0[3]
acc = constants.FRICTION * v**2
return lambda: np.hstack([[v.get_value()[0], acc] for v in self.plan_h[:-1]] + [self.traj_h.default_control[0], acc])
def get_init_plan_r_fn_prev_opt(self, iter):
# TODO: comment this
"""Initialize the robot plan using the default way."""
return lambda: np.hstack([v.get_value() for v in self.plan_r[:-1]] + [self.traj_r.default_control])
def get_init_plan_h_fn_prev_opt(self, iter):
# TODO: comment this
"""Initialize the human's plan using the default way."""
return lambda: np.hstack([v.get_value() for v in self.plan_h[:-1]] + [self.traj_h.default_control])
def init_grads(self):
"""Initialize the gradients based on the rewards.
Precondition: the rewards (self.r_h and self.r_r) have already been
initialized.
"""
# gradient of human reward wrt human controls
self.dr_h = grad(self.r_h, self.plan_h)
# negative human reward and its derivative
self.func1 = th.function([], [-self.r_h, -self.dr_h])
def r_h_and_dr_h(plan_h_0):
"""Evaluate negative human reward and its derivative.
- plan_h_0: initial value for human plan."""
start_time = time.time()
# set plan_h to the given (initial) plan_h_0
for v, (a, b) in zip(self.plan_h, self.control_indices_h):
v.set_value(plan_h_0[a:b])
# do any necessary updates based on the current plan
# (this is necessary for the HierarchicalMaximizer, not the Nested Maximizer)
self.update_with_curr_plan_fn()
func1_val = self.func1() # negative human reward and its derivative
end_time = time.time()
time_profile.inner_loop_time_profile.update(start_time, end_time)
return func1_val
self.r_h_and_dr_h = r_h_and_dr_h
# ------------------------------------------------------------------------------------------
# Robot's reward and its derivative.
# ------------------------------------------------------------------------------------------
if self.use_second_order:
# OPTION 1: Full derivative computation with Hessian inversion.
# SLOW, DEPRECATED
# jacobian of (d human reward / d robot actions) w.r.t. human actions
J = jacobian(grad(self.r_h, self.plan_r), self.plan_h)
# hessian of human reward w.r.t. human actions
H = hessian(self.r_h, self.plan_h)
# d robot reward / d human actions
g = grad(self.r_r, self.plan_h)
# Below is the most time-consuming step (the solve(H,g))
self.dr_r = -tt.dot(J, ts.solve(H, g))+grad(self.r_r, self.plan_r)
# ------------------------------------------------------------------------------------------
else:
# OPTION 2: Partial derivative computation. FAST
# (Only direct effect of robot action given current human action)
# Below is the simplified derivative that neglects the second-order
# effect through human (and therefore avoids the heavy Hessian
# inversion)
self.dr_r = grad(self.r_r, self.plan_r)
# ------------------------------------------------------------------------------------------
# negative robot reward and its derivative
self.func2 = th.function([], [-self.r_r, -self.dr_r])
def r_r_and_dr_r(plan_r_0):
"""Get optimal human response, and return negative robot reward
and its derivative.
- plan_r_0: initial value for robot plan."""
# set self.plan_r to the given (initial) plan_r_0
for v, (a, b) in zip(self.plan_r, self.control_indices_r):
v.set_value(plan_r_0[a:b])
self.maximize_inner() # get optimal human response
start_time = time.time()
func2_val = self.func2() # negative robot reward and its derivative
end_time = time.time()
time_profile.func2_time_profile.update(start_time, end_time)
return func2_val
self.r_r_and_dr_r = r_r_and_dr_r
def maximize_inner(self, bounds={}, maxiter=config.NESTEDMAX_MAXITER_INNER):
"""Get optimal human response (controls).
Arguments:
- bounds: control bounds for the human.
- maxiter: maximum number of iterations.
"""
start_time = time.time()
bounds = constants.HIERARCHICAL_HUMAN_CONTROL_BOUNDS
if not isinstance(bounds, dict): # convert bounds to dictionary
bounds = {v: bounds for v in self.plan_h}
B = [] # list of bounds for each control in the plan
for v, (a, b) in zip(self.plan_h, self.control_indices_h):
if v in bounds:
B += bounds[v]
else:
B += [(None, None)]*(b-a)
# TODO: can we replace the .get_value() approach with using the numpy
# version because at this point the Theano and numpy plans are the same?
# plan_h_0 = np.hstack(self.traj_h.u) # initial robot plan (numpy version)
if self.maximizer_inner_iters == 0:
# initialize the robot's plan using the defined plan initialization scheme
plan_h_0 = self.init_plan_h()
else:
# initialize human plan to previous optimal value
plan_h_0 = np.hstack([v.get_value() for v in self.plan_h])
# optimal human response, value, etc.
opt_h = scipy.optimize.fmin_l_bfgs_b(self.r_h_and_dr_h, x0=plan_h_0,
bounds=B)
opt_plan_h = opt_h[0] # optimal human response
for v, (a, b) in zip(self.plan_h, self.control_indices_h):
v.set_value(opt_plan_h[a:b])
# do any necessary updates based on the current plan
# (this is necessary for the HierarchicalMaximizer, not the Nested Maximizer)
self.update_with_curr_plan_fn()
# increment the counter for the number of iterations of maximizer_inner
self.maximizer_inner_iters += 1
end_time = time.time()
time_profile.maximize_inner_time_profile.update(start_time, end_time)
return opt_h
def maximize(self, bounds={}, bounds_inner={},
maxiter_inner=config.NESTEDMAX_MAXITER_INNER):
# Get optimal robot plan (controls) and human response using nested
# optimization.
start_time = time.time()
if not isinstance(bounds, dict): # convert bounds to dictionary
bounds = {v: bounds for v in self.plan_r}
B = [] # list of bounds for each control in the plan
for v, (a, b) in zip(self.plan_r, self.control_indices_r):
if v in bounds:
B += bounds[v]
else:
B += [(None, None)]*(b-a)
opt_r_list = [] # list of optimization results
for i in range(self.num_optimizations_r):
# TODO: can we replace the .get_value() approach with using the numpy
# version because at this point the Theano and numpy plans are the same?
# plan_r_0 = np.hstack(self.traj_r.u) # initial robot plan (numpy version)
plan_r_0 = self.get_init_plan_r_fn(i)() # initialize the robot's plan
# plan_r_0 = np.hstack([v.get_value() for v in self.plan_r])
for j in range(self.num_optimizations_h):
# debugging
# print('robot optimization iter:', i)
# print('human optimization iter:', j)
# reset number of maximizer_inner iterations
self.maximizer_inner_iters = 0
# get the human's plan initialization function
self.init_plan_h = self.get_init_plan_h_fn(j)
# optimal robot control, value, etc.
opt_r = opt_timeup.fmin_l_bfgs_b_timeup(self.r_r_and_dr_r,
x0=plan_r_0, bounds=B, t0=start_time, timeup=self.timeup)
opt_r_list.append(opt_r)
# opt_plan_r = opt_r[0] # optimal robot control
# get the best plan based on its value
best_opt_r = min(opt_r_list, key=lambda opt: opt[1])
opt_plan_r = best_opt_r[0] # optimal robot control
#pdb.set_trace()
# debugging
# print('opt_r_list:', opt_r_list)
# print('best_opt_r:', best_opt_r)
# TODO: remove?
for v, (a, b) in zip(self.plan_r, self.control_indices_r):
v.set_value(opt_plan_r[a:b])
# optimal human response, value, etc. to optimal robot control
opt_h = self.maximize_inner(bounds=bounds_inner, maxiter=maxiter_inner)
print([x.get_value() for x in self.plan_r])
print([x.get_value() for x in self.plan_h])
# time profile of HierarchicalMaximizer
maximize_end_time = time.time()
time_profile.maximizer_time_profile.update(start_time, maximize_end_time)
return opt_r[0], opt_h[0]
# class NestedMaximizer(object):
# def __init__(self, r_h, traj_h, r_r, traj_r, use_timeup=True,
# use_second_order=False, update_with_curr_plan_fn=None,
# init_plan_scheme='prev_opt',
# # num_optimizations_r=1, get_init_plan_r_fn=None,
# # num_optimizations_h=1, get_init_plan_h_fn=None,
# init_grads=True):
# """
# Arguments:
# - r_h: the human tactical reward.
# - traj_h: the human trajectory.
# - r_r: the robot tactical reward.
# - traj_r: the robot trajectory.
# - update_with_curr_plan_fn: function to update any necessary information
# based on the current plan. This is only necessary for the
# HierarchicalMaximizer, not the NestedMaximizer.
# - init_plan_scheme: string specifying the plan initialization scheme.
# - num_optimizations_r: number of times to optimize the robot reward (the
# best result of these optimizations will be chosen).
# - get_init_plan_r_fn: function to return a function that initializes
# the robot's plan for optimization, based on the current optimization
# iteration.
# - num_optimizations_h: number of times to optimize the human reward (the
# best result of these optimizations will be chosen).
# - get_init_plan_h_fn: function to return a function that initializes
# the human's plan for optimization, based on the current optimization
# iteration.
# - init_grads: if True, initialize the gradients. This argument can be
# set to False if another function is meant to initialize the gradients.
# """
#
# # ---------------------------------------------------------------------------------------------------
# # Basics.
#
# self.r_h = r_h
# self.r_r = r_r
# self.traj_h = traj_h
# self.traj_r = traj_r
# self.plan_h = traj_h.u_th # human plan (controls)
# self.plan_r = traj_r.u_th # robot plan (controls)
# # (start, end) indices for each control in the plan when it's represented
# # as a flattened array. Ex: [(0, 2), (2, 4), (4, 6), (6, 8), (8, 10)]
# self.control_indices_h = traj_h.control_indices
# self.control_indices_r = traj_r.control_indices
# # maximum time for optimization
# if use_timeup:
# self.timeup = config.OPT_TIMEOUT
# else:
# self.timeup = float('inf')
# self.use_second_order = use_second_order
# if update_with_curr_plan_fn is None: # no functionality necessary here
# update_with_curr_plan_fn = lambda: None
# self.update_with_curr_plan_fn = update_with_curr_plan_fn
#
# # self.num_optimizations_r = num_optimizations_r
# # if get_init_plan_r_fn is None:
# # self.get_init_plan_r_fn = self.get_init_plan_r_fn_default
# # else:
# # self.get_init_plan_r_fn = get_init_plan_r_fn
# # self.num_optimizations_h = num_optimizations_h
# # if get_init_plan_h_fn is None:
# # self.get_init_plan_h_fn = self.get_init_plan_h_fn_default
# # else:
# # self.get_init_plan_h_fn = get_init_plan_h_fn
# self.create_get_init_plans_fn(init_plan_scheme)
#
# self.maximizer_inner_iters = 0 # number of iterations of maximizer_inner
#
# if init_grads: # initialize the gradients
# self.init_grads()
#
# def create_get_init_plans_fn(self, init_plan_scheme):
# """Create the functions that return the plan initialization functions
# for the robot and the human, depending on the optimization iteration.
# Also set the number of optimization loops for the robot and human.
# Arguments:
# - init_plan_scheme: string specifying the plan initialization scheme.
# """
# assert init_plan_scheme in constants.INIT_PLAN_SCHEMES_OPTIONS
# self.get_init_plan_r_fn = eval('self.get_init_plan_r_fn_' + init_plan_scheme)
# self.get_init_plan_h_fn = eval('self.get_init_plan_h_fn_' + init_plan_scheme)
# self.num_optimizations_r = constants.INIT_PLAN_SCHEME_TO_NUM_OPTS_R[init_plan_scheme]
# self.num_optimizations_h = constants.INIT_PLAN_SCHEME_TO_NUM_OPTS_H[init_plan_scheme]
#
# def get_init_plan_r_fn_maintain_speed_lsr_and_prev_opt(self, iter):
# # TODO: comment this
# v = self.traj_r.x0[3]
# acc = constants.FRICTION * v ** 2
# init_plan_r_fn_list = [
# lambda: np.hstack([constants.CAR_CONTROL_BOUNDS[0][0], acc] for _ in range(self.traj_r.horizon)),
# lambda: np.hstack([0., acc] for _ in range(self.traj_r.horizon)),
# lambda: np.hstack([constants.CAR_CONTROL_BOUNDS[0][1], acc] for _ in range(self.traj_r.horizon)),
# lambda: np.hstack([v.get_value() for v in self.plan_r[:-1]] + [self.traj_r.default_control])
# ]
# return init_plan_r_fn_list[iter]
#
# def get_init_plan_h_fn_maintain_speed_lsr_and_prev_opt(self, iter):
# # TODO: comment this
# v = self.traj_h.x0[3]
# acc = constants.FRICTION * v ** 2
# init_plan_h_fn_list = [
# lambda: np.hstack([constants.CAR_CONTROL_BOUNDS[0][0], acc] for _ in range(self.traj_h.horizon)),
# lambda: np.hstack([0., acc] for _ in range(self.traj_h.horizon)),
# lambda: np.hstack([constants.CAR_CONTROL_BOUNDS[0][1], acc] for _ in range(self.traj_h.horizon)),
# lambda: np.hstack([v.get_value() for v in self.plan_h[:-1]] + [self.traj_h.default_control])
# ]
# return init_plan_h_fn_list[iter]
#
# def get_init_plan_r_fn_maintain_speed_lsr(self, iter):
# # TODO: comment this
# v = self.traj_r.x0[3]
# acc = constants.FRICTION * v ** 2
# init_plan_r_fn_list = [
# lambda: np.hstack([constants.CAR_CONTROL_BOUNDS[0][0], acc] for _ in range(self.traj_r.horizon)),
# lambda: np.hstack([0., acc] for _ in range(self.traj_r.horizon)),
# lambda: np.hstack([constants.CAR_CONTROL_BOUNDS[0][1], acc] for _ in range(self.traj_r.horizon))
# ]
# return init_plan_r_fn_list[iter]
#
# def get_init_plan_h_fn_maintain_speed_lsr(self, iter):
# # TODO: comment this
# v = self.traj_h.x0[3]
# acc = constants.FRICTION * v ** 2
# init_plan_h_fn_list = [
# lambda: np.hstack([constants.CAR_CONTROL_BOUNDS[0][0], acc] for _ in range(self.traj_h.horizon)),
# lambda: np.hstack([0., acc] for _ in range(self.traj_h.horizon)),
# lambda: np.hstack([constants.CAR_CONTROL_BOUNDS[0][1], acc] for _ in range(self.traj_h.horizon))
# ]
# return init_plan_h_fn_list[iter]
#
# def get_init_plan_r_fn_lsr(self, iter):
# # TODO: comment this
# init_plan_r_fn_list = [
# lambda: np.hstack([constants.CAR_CONTROL_BOUNDS[0][0], 0.] for _ in range(self.traj_r.horizon)),
# lambda: np.hstack([0., 0.] for _ in range(self.traj_r.horizon)),
# lambda: np.hstack([constants.CAR_CONTROL_BOUNDS[0][1], 0.] for _ in range(self.traj_r.horizon))
# ]
# return init_plan_r_fn_list[iter]
#
# def get_init_plan_h_fn_lsr(self, iter):
# # TODO: comment this
# init_plan_h_fn_list = [
# lambda: np.hstack([constants.CAR_CONTROL_BOUNDS[0][0], 0.] for _ in range(self.traj_h.horizon)),
# lambda: np.hstack([0., 0.] for _ in range(self.traj_h.horizon)),
# lambda: np.hstack([constants.CAR_CONTROL_BOUNDS[0][1], 0.] for _ in range(self.traj_h.horizon))
# ]
# return init_plan_h_fn_list[iter]
#
# def get_init_plan_r_fn_max_speed_prev_steer(self, iter):
# # TODO: comment this
# return lambda: np.hstack([[v.get_value()[0], constants.CAR_CONTROL_BOUNDS[1][1]] for v in self.plan_r[:-1]] + [
# self.traj_r.default_control[0], constants.CAR_CONTROL_BOUNDS[1][1]])
#
# def get_init_plan_h_fn_max_speed_prev_steer(self, iter):
# # TODO: comment this
# return lambda: np.hstack([[v.get_value()[0], constants.CAR_CONTROL_BOUNDS[1][1]] for v in self.plan_h[:-1]] + [
# self.traj_h.default_control[0], constants.CAR_CONTROL_BOUNDS[1][1]])
#
# def get_init_plan_r_fn_maintain_speed_prev_steer(self, iter):
# # TODO: comment this
# v = self.traj_r.x0[3]
# acc = constants.FRICTION * v ** 2
# return lambda: np.hstack(
# [[v.get_value()[0], acc] for v in self.plan_r[:-1]] + [self.traj_r.default_control[0], acc])
#
# def get_init_plan_h_fn_maintain_speed_prev_steer(self, iter):
# # TODO: comment this
# v = self.traj_h.x0[3]
# acc = constants.FRICTION * v ** 2
# return lambda: np.hstack(
# [[v.get_value()[0], acc] for v in self.plan_h[:-1]] + [self.traj_h.default_control[0], acc])
#
# def get_init_plan_r_fn_prev_opt(self, iter):
# # TODO: comment this
# """Initialize the robot plan using the default way."""
# return lambda: np.hstack([v.get_value() for v in self.plan_r[:-1]] + [self.traj_r.default_control])
#
# def get_init_plan_h_fn_prev_opt(self, iter):
# # TODO: comment this
# """Initialize the human's plan using the default way."""
# return lambda: np.hstack([v.get_value() for v in self.plan_h[:-1]] + [self.traj_h.default_control])
#
# def init_grads(self):
# """Initialize the gradients based on the rewards.
# Precondition: the rewards (self.r_h and self.r_r) have already been
# initialized.
# """
# # gradient of human reward wrt human controls
# self.dr_h = grad(self.r_h, self.plan_h)
# # negative human reward and its derivative
# self.func1 = th.function([], [-self.r_h, -self.dr_h])
#
# def r_h_and_dr_h(plan_h_0):
# """Evaluate negative human reward and its derivative.
# - plan_h_0: initial value for human plan."""
# start_time = time.time()
# # set plan_h to the given (initial) plan_h_0
# for v, (a, b) in zip(self.plan_h, self.control_indices_h):
# v.set_value(plan_h_0[a:b])
# # do any necessary updates based on the current plan
# # (this is necessary for the HierarchicalMaximizer, not the Nested Maximizer)
# self.update_with_curr_plan_fn()
# func1_val = self.func1() # negative human reward and its derivative
# end_time = time.time()
# time_profile.inner_loop_time_profile.update(start_time, end_time)
# return func1_val
#
# self.r_h_and_dr_h = r_h_and_dr_h
#
# # ------------------------------------------------------------------------------------------
# # Robot's reward and its derivative.
#
# # ------------------------------------------------------------------------------------------
# if self.use_second_order:
# # OPTION 1: Full derivative computation with Hessian inversion.
# # SLOW, DEPRECATED
# # jacobian of (d human reward / d robot actions) w.r.t. human actions
# J = jacobian(grad(self.r_h, self.plan_r), self.plan_h)
# # hessian of human reward w.r.t. human actions
# H = hessian(self.r_h, self.plan_h)
# # d robot reward / d human actions
# g = grad(self.r_r, self.plan_h)
# # Below is the most time-consuming step (the solve(H,g))
# self.dr_r = -tt.dot(J, ts.solve(H, g)) + grad(self.r_r, self.plan_r)
# # ------------------------------------------------------------------------------------------
# else:
# # OPTION 2: Partial derivative computation. FAST
# # (Only direct effect of robot action given current human action)
# # Below is the simplified derivative that neglects the second-order
# # effect through human (and therefore avoids the heavy Hessian
# # inversion)
# self.dr_r = grad(self.r_r, self.plan_r)
# # ------------------------------------------------------------------------------------------
#
# # negative robot reward and its derivative
# self.func2 = th.function([], [-self.r_r, -self.dr_r])
#
# def r_r_and_dr_r(plan_r_0):
# """Get optimal human response, and return negative robot reward
# and its derivative.
# - plan_r_0: initial value for robot plan."""
# # set self.plan_r to the given (initial) plan_r_0
# for v, (a, b) in zip(self.plan_r, self.control_indices_r):
# v.set_value(plan_r_0[a:b])
# self.maximize_inner() # get optimal human response
# start_time = time.time()
# func2_val = self.func2() # negative robot reward and its derivative
# end_time = time.time()
# time_profile.func2_time_profile.update(start_time, end_time)
# return func2_val
#
# self.r_r_and_dr_r = r_r_and_dr_r
#
# def maximize_inner(self, bounds={}, maxiter=config.NESTEDMAX_MAXITER_INNER):
# """Get optimal human response (controls).
# Arguments:
# - bounds: control bounds for the human.
# - maxiter: maximum number of iterations.
# """
# start_time = time.time()
#
# bounds = constants.HIERARCHICAL_HUMAN_CONTROL_BOUNDS
# if not isinstance(bounds, dict): # convert bounds to dictionary
# bounds = {v: bounds for v in self.plan_h}
# B = [] # list of bounds for each control in the plan
# for v, (a, b) in zip(self.plan_h, self.control_indices_h):
# if v in bounds:
# B += bounds[v]
# else:
# B += [(None, None)] * (b - a)
#
# # optimal human response, value, etc.
# opt_plan_h = np.hstack((-2*0.13/3, 0) for _ in range(self.traj_h.horizon))
#
# for v, (a, b) in zip(self.plan_h, self.control_indices_h):
# v.set_value(opt_plan_h[a:b])
#
# # do any necessary updates based on the current plan
# # (this is necessary for the HierarchicalMaximizer, not the Nested Maximizer)
# self.update_with_curr_plan_fn()
#
# # increment the counter for the number of iterations of maximizer_inner
# self.maximizer_inner_iters += 1
# end_time = time.time()
# time_profile.maximize_inner_time_profile.update(start_time, end_time)
# return opt_plan_h
#
# def maximize(self, bounds={}, bounds_inner={},
# maxiter_inner=config.NESTEDMAX_MAXITER_INNER):
# # Get optimal robot plan (controls) and human response using nested
# # optimization.
# start_time = time.time()
# if not isinstance(bounds, dict): # convert bounds to dictionary
# bounds = {v: bounds for v in self.plan_r}
# B = [] # list of bounds for each control in the plan
# for v, (a, b) in zip(self.plan_r, self.control_indices_r):
# if v in bounds:
# B += bounds[v]
# else:
# B += [(None, None)] * (b - a)
#
# opt_r_list = [] # list of optimization results
# for i in range(self.num_optimizations_r):
# # TODO: can we replace the .get_value() approach with using the numpy
# # version because at this point the Theano and numpy plans are the same?
# # plan_r_0 = np.hstack(self.traj_r.u) # initial robot plan (numpy version)
# plan_r_0 = self.get_init_plan_r_fn(i)() # initialize the robot's plan
# # plan_r_0 = np.hstack([v.get_value() for v in self.plan_r])
# for j in range(self.num_optimizations_h):
# # debugging
# # print('robot optimization iter:', i)
# # print('human optimization iter:', j)
#
# # reset number of maximizer_inner iterations
# self.maximizer_inner_iters = 0
# # get the human's plan initialization function
# self.init_plan_h = self.get_init_plan_h_fn(j)
# # optimal robot control, value, etc.
# opt_r = opt_timeup.fmin_l_bfgs_b_timeup(self.r_r_and_dr_r,
# x0=plan_r_0, bounds=B, t0=start_time, timeup=self.timeup)
# opt_r_list.append(opt_r)
# # opt_plan_r = opt_r[0] # optimal robot control
#
# # get the best plan based on its value
# best_opt_r = min(opt_r_list, key=lambda opt: opt[1])
# opt_plan_r = best_opt_r[0] # optimal robot control
#
# # debugging
# # print('opt_r_list:', opt_r_list)
# # print('best_opt_r:', best_opt_r)
#
# # TODO: remove?
# for v, (a, b) in zip(self.plan_r, self.control_indices_r):
# v.set_value(opt_plan_r[a:b])
#
# # optimal human response, value, etc. to optimal robot control
# opt_h = self.maximize_inner(bounds=bounds_inner, maxiter=maxiter_inner)
#
# print([x.get_value() for x in self.plan_r])
# print([x.get_value() for x in self.plan_h])
# # time profile of HierarchicalMaximizer
# maximize_end_time = time.time()
# time_profile.maximizer_time_profile.update(start_time, maximize_end_time)
# return opt_r[0], opt_h
class HierarchicalMaximizer(NestedMaximizer):
# The following class maximizes the hierarchical game between the robot (leader)
# and the human (follower). The tactic reward is given analytically as inputs
# while the terminal rewards given as the value functions of the strategic level
# is loaded into the class as grids. To get the value from the value functions,
# grid interpltion is used (see code below for details).
def __init__(self, r_h, traj_h, r_r, traj_r, mat_name, n,
proj, traj_truck=None, use_timeup=True, use_second_order=False,
init_plan_scheme='prev_opt',
disc_grid=None, step_grid=None, vH_grid=None, vR_grid=None):
"""
Arguments
- r_h: the human tactical reward.
- traj_h: the human trajectory.
- r_r: the robot tactical reward.
- traj_r: the robot trajectory.
- mat_name: the matlab file with the value function grids.
- n: the number of dimensions of the strategic level.
- proj: function specifying the projection from the tactical level to the strategic level.
- traj_truck: the truck trajectory. If None, the truck is not used in the
strategic value.
"""
# ---------------------------------------------------------------------------------------------------
# Basics.
NestedMaximizer.__init__(self, r_h, traj_h, r_r, traj_r,
use_timeup=use_timeup,
use_second_order=use_second_order,
update_with_curr_plan_fn=self.update_corners,
init_plan_scheme=init_plan_scheme,
init_grads=False)
self.n = n # dimension of strategic state
self.x_tact_h = traj_h.x_th[-1] # final human tactical state
self.x_tact_r = traj_r.x_th[-1] # final robot tactical state
if traj_truck is not None:
self.x_tact_truck = traj_truck.x_th[-1]
# ---------------------------------------------------------------------------------------------------
# Shared varables.
# Grid interpolation is done with the grid corners of the current grid
# cell the state is in. These grid corners are shared variables which
# are updated as the state is updated. Since the grid step lengths
# are given, only the lower corners (self.cell_corners below) are needed
# as shared variables.
self.cell_corners = th.shared(np.zeros(self.n)) # corners of each box in grid
# corners for human value function
self.vH_corners = th.shared(np.zeros([2 for i in range(n)]))
# corners for robot value function
self.vR_corners = th.shared(np.zeros([2 for i in range(n)]))
# ---------------------------------------------------------------------------------------------------
# Load grid data.
if (disc_grid is None or step_grid is None or vH_grid is None or
vR_grid is None):
self.disc_grid, self.step_grid, self.vH_grid, self.vR_grid = (
utils.load_grid_data(mat_name, n=self.n))
else:
self.disc_grid, self.step_grid, self.vH_grid, self.vR_grid = (
disc_grid, step_grid, vH_grid, vR_grid)
# ---------------------------------------------------------------------------------------------------
# Value functions.
# strategic state (project using Theano)
if traj_truck is not None:
self.x_strat = proj(self.x_tact_r, self.x_tact_h, self.x_tact_truck)
else:
self.x_strat = proj(self.x_tact_r, self.x_tact_h)
self.x_strat_func = th.function([], self.x_strat)
def value_function(value_corners):
return HierarchicalMaximizer.value_function_fn(self.x_strat,
self.cell_corners, value_corners, self.step_grid, self.n)
self.vR = value_function(self.vR_corners) # robot value function
self.vH = value_function(self.vH_corners) # human value function
# ---------------------------------------------------------------------------------------------------
# Human's reward and its derivative.
# add strategic value function
self.r_h += config.STRATEGIC_VALUE_SCALE * self.vH
# ------------------------------------------------------------------------------------------
# Robot's reward and its derivative.
# add strategic value function
self.r_r += config.STRATEGIC_VALUE_SCALE * self.vR
# initialize the gradients
NestedMaximizer.init_grads(self)
@staticmethod
def value_function_fn(x_strat, cell_corners, value_corners, step_grid, n):
# Strategic value function computed using multilinear grid
# interpolation.
start_time = time.time()
sumterms = []
volume = step_grid.prod()
for i in itertools.product(range(2), repeat=n):
partial_volume = [((-1)**(i[j]+1) *
(x_strat[j] - cell_corners[j]) +
(1-i[j]) * step_grid[j]) for j in range(n)]
partial_volume = np.asarray(partial_volume).prod() # convert to array to use prod.
sumterm = value_corners[i]*partial_volume/volume
sumterms.append(sumterm)
sum_val = sum(sumterms)
end_time = time.time()
time_profile.value_function_time_profile.update(start_time, end_time)
return sum_val
def update_corners(self):
# Update the corner values of the strategic cell grid by determining
# which grid cell the current strategic state belongs to.
cell_corners, vR_corners, vH_corners = (HierarchicalMaximizer
.update_corners_fn(self.x_strat_func(), self.n,
self.disc_grid, self.vH_grid, self.vR_grid))
self.cell_corners.set_value(cell_corners)
self.vR_corners.set_value(vR_corners)
self.vH_corners.set_value(vH_corners)
@staticmethod
def update_corners_fn(x_strat, n, disc_grid, vH_grid, vR_grid):
# Update the corner values of the strategic cell grid by determining
# which grid cell the current strategic state belongs to.
start_time = time.time()
# outside (length n) has outside[i] True if the value of the strategi state
# at that index is outside the strategic domain. Then either project back
# onto the strategic grid, or set the value function = 0
# (i.e. just consider tactical reward)
outside = []
inds = []
cell_corners_new = []
for i in range(n):
if x_strat[i] < disc_grid[i][0]:
ind = 0
# only set value=0 if not projecting onto grid
outside.append(True)
elif x_strat[i] > disc_grid[i][-1]:
ind = len(disc_grid[i]) - 1 # was - 2
# only set value=0 if not projecting onto grid
outside.append(True)
else:
ind = np.where(disc_grid[i] <= x_strat[i])[0][-1]
outside.append(False) # inside the strategid domain
inds.append(ind)
cell_corners_new.append(disc_grid[i][ind])
# debugging
# if outside:
# print 'OUTSIDE grid interpolation!'
# else:
# print 'INSIDE grid interpolation'
cell_corners_new = np.array(cell_corners_new)
# self.cell_corners.set_value(cell_corners_new)
vH_corners_new = np.zeros([2 for i in range(n)])
vR_corners_new = np.zeros([2 for i in range(n)])
if not any(outside) or config.PROJECT_ONTO_STRAT_GRID:
# iterate through unit vectors representing the corners of the grid
# with dimension n
for i in itertools.product(range(2), repeat=n):
# gp_ind = tuple([sum(pair) for pair in zip(inds, list(i))]) # tuple to just be compatible with below.
gp_ind = [] # list of indices of the grid cell's corners
for j, (ind, direction, dimension) in enumerate(zip(inds, list(i), vH_grid.shape)):
# ind: index of grid cell "smaller" than the strategic state
# direction: vector specifying the grid cell corner
# dimension: length of the grid in this dimension
if outside[j]:
# if this state variable of the strategic state is outside
# of the strategic grid, project it back onto the grid
# by setting its index to be either 0 or dimension - 1
# (this is set above)
gp_ind.append(ind)
else:
# clip to dimension - 1 to avoid index out of bounds error
gp_ind.append(min(ind + direction, dimension - 1))
gp_ind = tuple(gp_ind) # tuple to use for indexing into value grids
try:
vH_corners_new[i] = vH_grid[gp_ind]
vR_corners_new[i] = vR_grid[gp_ind]
except Exception as e:
print e
pdb.set_trace()
# self.vH_corners.set_value(vH_corners_new)
# self.vR_corners.set_value(vR_corners_new)
end_time = time.time()
time_profile.update_corners_time_profile.update(start_time, end_time)
return cell_corners_new, vR_corners_new, vH_corners_new
class ILQRMaximizer():
def __init__(self, r_h, traj_h, r_r, traj, dyn):
self.r_h = r_h
self.traj_h = traj_h
self.r_r = r_r
self.traj = traj
self.dyn = dyn
def maximize(self, bounds={}):
#return unicycle.run()
return unicycle.run(self.traj.x0, None, None, self.dyn, self.r_r, self.r_h)
class PredictReactMaximizer(NestedMaximizer):
def init_grads(self):
"""Initialize the gradients based on the rewards.
Precondition: the rewards (self.r_h and self.r_r) have already been
initialized.
"""
# gradient of human reward wrt human controls
self.dr_h = grad(self.r_h, self.plan_h)
# negative human reward and its derivative
self.func1 = th.function([], [-self.r_h, -self.dr_h])
def r_h_and_dr_h(plan_h_0):
"""Evaluate negative human reward and its derivative.
- plan_h_0: initial value for human plan."""
start_time = time.time()
# set plan_h to the given (initial) plan_h_0
for v, (a, b) in zip(self.plan_h, self.control_indices_h):
v.set_value(plan_h_0[a:b])
# do any necessary updates based on the current plan
# (this is necessary when using a strategic value)
self.update_with_curr_plan_fn()
func1_val = self.func1() # negative human reward and its derivative
end_time = time.time()
time_profile.inner_loop_time_profile.update(start_time, end_time)
return func1_val
self.r_h_and_dr_h = r_h_and_dr_h
# ------------------------------------------------------------------------------------------
# Robot's reward and its derivative.
# ------------------------------------------------------------------------------------------
if self.use_second_order:
# OPTION 1: Full derivative computation with Hessian inversion.
# SLOW, DEPRECATED
# jacobian of (d human reward / d robot actions) w.r.t. human actions
J = jacobian(grad(self.r_h, self.plan_r), self.plan_h)
# hessian of human reward w.r.t. human actions
H = hessian(self.r_h, self.plan_h)
# d robot reward / d human actions
g = grad(self.r_r, self.plan_h)
# Below is the most time-consuming step (the solve(H,g))
self.dr_r = -tt.dot(J, ts.solve(H, g))+grad(self.r_r, self.plan_r)
# ------------------------------------------------------------------------------------------
else:
# OPTION 2: Partial derivative computation. FAST
# (Only direct effect of robot action given current human action)
# Below is the simplified derivative that neglects the second-order
# effect through human (and therefore avoids the heavy Hessian
# inversion)
self.dr_r = grad(self.r_r, self.plan_r)
# ------------------------------------------------------------------------------------------
# negative robot reward and its derivative
self.func2 = th.function([], [-self.r_r, -self.dr_r])
def r_r_and_dr_r(plan_r_0):
"""Get optimal human response, and return negative robot reward
and its derivative.
- plan_r_0: initial value for robot plan."""
start_time = time.time()
# set self.plan_r to the given (initial) plan_r_0
for v, (a, b) in zip(self.plan_r, self.control_indices_r):
v.set_value(plan_r_0[a:b])
# do any necessary updates based on the current plan
# (this is necessary when using a strategic value)
self.update_with_curr_plan_fn()
func2_val = self.func2() # negative robot reward and its derivative
end_time = time.time()
time_profile.func2_time_profile.update(start_time, end_time)
return func2_val
self.r_r_and_dr_r = r_r_and_dr_r
def maximize(self, bounds={}):
"""Optimize the robot's and human's plans with respect to their rewards
using the predict-then-react scheme:
1) "Predict" the human's plan by optimizing the human's plan w.r.t. its
reward.
2) Optimize the robot's plan w.r.t. its reward by treating the human as
a moving obstacle following the "predicted" human plan.
"""
# Get optimal robot plan (controls) and human response using nested
# optimization.
# start_time = time.time()
# bounds_list = [robot_bounds, human_bounds]
# plan_list = [self.plan_r, self.plan_h]
# control_indices_list = [self.control_indices_r, self.control_indices_h]
# opt_bounds_list = [] # bounds to pass into optimization
# for i, (bounds, plan, control_indices) in enumerate(zip(
# bounds_list, plan_list, control_indices_list)):
# if not isinstance(bounds, dict): # convert bounds to dictionary
# bounds = {v: bounds for v in plan}
# B = [] # list of bounds for each control in the plan
# for v, (a, b) in zip(plan, control_indices):
# if v in bounds:
# B += bounds[v]
# else:
# B += [(None, None)]*(b-a)
# opt_bounds_list.append(B)
# opt_robot_bounds, opt_human_bounds = opt_bounds_list
start_time = time.time()
if not isinstance(bounds, dict): # convert bounds to dictionary
bounds = {v: bounds for v in self.plan_r}
B = [] # list of bounds for each control in the plan
for v, (a, b) in zip(self.plan_r, self.control_indices_r):
if v in bounds:
B += bounds[v]
else:
B += [(None, None)]*(b-a)
opt_robot_bounds = B
opt_human_bounds = B
opt_r_list = [] # list of robot optimization results
opt_h_list = [] # list of human optimization results
for i in range(self.num_optimizations_r):
# TODO: can we replace the .get_value() approach with using the numpy
# version because at this point the Theano and numpy plans are the same?
# plan_r_0 = np.hstack(self.traj_r.u) # initial robot plan (numpy version)
plan_r_0 = self.get_init_plan_r_fn(i)() # initialize the robot's plan
# plan_r_0 = np.hstack([v.get_value() for v in self.plan_r])
for j in range(self.num_optimizations_h):
# debugging
# print('robot optimization iter:', i)
# print('human optimization iter:', j)
# get the human's plan initialization function
plan_h_0 = self.get_init_plan_h_fn(j)()
# optimal human control, value, etc.
opt_h = opt_timeup.fmin_l_bfgs_b_timeup(self.r_h_and_dr_h,
x0=plan_h_0, bounds=opt_human_bounds, t0=start_time, timeup=self.timeup)
opt_h_list.append(opt_h)
opt_plan_h = opt_h[0] # optimal human control
# Set the robot's belief of the human plan to the predicted/planned
# human plan
for v, (a, b) in zip(self.plan_h, self.control_indices_h):
v.set_value(opt_plan_h[a:b])
# optimal robot control, value, etc.
opt_r = opt_timeup.fmin_l_bfgs_b_timeup(self.r_r_and_dr_r,
x0=plan_r_0, bounds=opt_robot_bounds, t0=start_time, timeup=self.timeup)
opt_r_list.append(opt_r)
# get the best plan based on its value
opt_r_vals = [opt[1] for opt in opt_r_list] # list of values for robot
best_opt_r_idx = np.argmin(opt_r_vals)
opt_r = opt_r_list[best_opt_r_idx]
opt_plan_r = opt_r[0] # optimal robot control
opt_h = opt_h_list[best_opt_r_idx]
opt_plan_h = opt_h[0] # human control corresponding to optimal robot control
# debugging
# print('opt_r_list:', opt_r_list)
# print('opt_r:', opt_r)
# Set optimal plans in Theano
for v, (a, b) in zip(self.plan_r, self.control_indices_r):
v.set_value(opt_plan_r[a:b])
for v, (a, b) in zip(self.plan_h, self.control_indices_h):
v.set_value(opt_plan_h[a:b])
# time profile of HierarchicalMaximizer
maximize_end_time = time.time()
time_profile.maximizer_time_profile.update(start_time, maximize_end_time)
return opt_r, opt_h
# TODO: make this more modular so there isn't so much copied code
class PredictReactHierarchicalMaximizer(PredictReactMaximizer):
def __init__(self, r_h, traj_h, r_r, traj_r, mat_name, n,
proj, traj_truck=None, use_timeup=True, use_second_order=False,
init_plan_scheme='prev_opt',
disc_grid=None, step_grid=None, vH_grid=None, vR_grid=None):
"""
Arguments
- r_h: the human tactical reward.
- traj_h: the human trajectory.
- r_r: the robot tactical reward.
- traj_r: the robot trajectory.
- mat_name: the matlab file with the value function grids.
- n: the number of dimensions of the strategic level.
- proj: function specifying the projection from the tactical level to the strategic level.
- traj_truck: the truck trajectory. If None, the truck is not used in the
strategic value.
"""
# ---------------------------------------------------------------------------------------------------
# Basics.
PredictReactMaximizer.__init__(self, r_h, traj_h, r_r, traj_r,
use_timeup=use_timeup,
use_second_order=use_second_order,
update_with_curr_plan_fn=self.update_corners,
init_plan_scheme=init_plan_scheme,
init_grads=False)
self.n = n # dimension of strategic state
self.x_tact_h = traj_h.x_th[-1] # final human tactical state
self.x_tact_r = traj_r.x_th[-1] # final robot tactical state
if traj_truck is not None:
self.x_tact_truck = traj_truck.x_th[-1]
# ---------------------------------------------------------------------------------------------------
# Shared varables.
# Grid interpolation is done with the grid corners of the current grid
# cell the state is in. These grid corners are shared variables which
# are updated as the state is updated. Since the grid step lengths
# are given, only the lower corners (self.cell_corners below) are needed
# as shared variables.
self.cell_corners = th.shared(np.zeros(self.n)) # corners of each box in grid
# corners for human value function
self.vH_corners = th.shared(np.zeros([2 for i in range(n)]))
# corners for robot value function
self.vR_corners = th.shared(np.zeros([2 for i in range(n)]))
# ---------------------------------------------------------------------------------------------------
# Load grid data.
if (disc_grid is None or step_grid is None or vH_grid is None or
vR_grid is None):
self.disc_grid, self.step_grid, self.vH_grid, self.vR_grid = (
utils.load_grid_data(mat_name, n=self.n))
else:
self.disc_grid, self.step_grid, self.vH_grid, self.vR_grid = (
disc_grid, step_grid, vH_grid, vR_grid)
# ---------------------------------------------------------------------------------------------------
# Value functions.
# strategic state (project using Theano)
if traj_truck is not None:
self.x_strat = proj(self.x_tact_r, self.x_tact_h, self.x_tact_truck)
else:
self.x_strat = proj(self.x_tact_r, self.x_tact_h)
self.x_strat_func = th.function([], self.x_strat)
def value_function(value_corners):
return HierarchicalMaximizer.value_function_fn(self.x_strat,
self.cell_corners, value_corners, self.step_grid, self.n)
self.vR = value_function(self.vR_corners) # robot value function
self.vH = value_function(self.vH_corners) # human value function
# ---------------------------------------------------------------------------------------------------
# Human's reward and its derivative.
# add strategic value function
self.r_h += config.STRATEGIC_VALUE_SCALE * self.vH
# ------------------------------------------------------------------------------------------
# Robot's reward and its derivative.
# add strategic value function
self.r_r += config.STRATEGIC_VALUE_SCALE * self.vR
# initialize the gradients
PredictReactMaximizer.init_grads(self)
@staticmethod
def value_function_fn(x_strat, cell_corners, value_corners, step_grid, n):
# Strategic value function computed using multilinear grid
# interpolation.
start_time = time.time()
sumterms = []
volume = step_grid.prod()
for i in itertools.product(range(2), repeat=n):
partial_volume = [((-1)**(i[j]+1) *
(x_strat[j] - cell_corners[j]) +
(1-i[j]) * step_grid[j]) for j in range(n)]
partial_volume = np.asarray(partial_volume).prod() # convert to array to use prod.
sumterm = value_corners[i]*partial_volume/volume
sumterms.append(sumterm)
sum_val = sum(sumterms)
end_time = time.time()
time_profile.value_function_time_profile.update(start_time, end_time)
return sum_val
def update_corners(self):
# Update the corner values of the strategic cell grid by determining
# which grid cell the current strategic state belongs to.
cell_corners, vR_corners, vH_corners = (HierarchicalMaximizer
.update_corners_fn(self.x_strat_func(), self.n,
self.disc_grid, self.vH_grid, self.vR_grid))
self.cell_corners.set_value(cell_corners)
self.vR_corners.set_value(vR_corners)
self.vH_corners.set_value(vH_corners)
@staticmethod
def update_corners_fn(x_strat, n, disc_grid, vH_grid, vR_grid):
# Update the corner values of the strategic cell grid by determining
# which grid cell the current strategic state belongs to.
start_time = time.time()
# outside (length n) has outside[i] True if the value of the strategi state
# at that index is outside the strategic domain. Then either project back
# onto the strategic grid, or set the value function = 0
# (i.e. just consider tactical reward)
outside = []
inds = []
cell_corners_new = []
for i in range(n):
if x_strat[i] < disc_grid[i][0]:
ind = 0
# only set value=0 if not projecting onto grid
outside.append(True)
elif x_strat[i] > disc_grid[i][-1]:
ind = len(disc_grid[i]) - 1 # was - 2
# only set value=0 if not projecting onto grid
outside.append(True)
else:
ind = np.where(disc_grid[i] <= x_strat[i])[0][-1]
outside.append(False) # inside the strategid domain
inds.append(ind)
cell_corners_new.append(disc_grid[i][ind])
# debugging
# if outside:
# print 'OUTSIDE grid interpolation!'
# else:
# print 'INSIDE grid interpolation'
cell_corners_new = np.array(cell_corners_new)
# self.cell_corners.set_value(cell_corners_new)
vH_corners_new = np.zeros([2 for i in range(n)])
vR_corners_new = np.zeros([2 for i in range(n)])
if not any(outside) or config.PROJECT_ONTO_STRAT_GRID:
# iterate through unit vectors representing the corners of the grid
# with dimension n
for i in itertools.product(range(2), repeat=n):
# gp_ind = tuple([sum(pair) for pair in zip(inds, list(i))]) # tuple to just be compatible with below.
gp_ind = [] # list of indices of the grid cell's corners
for j, (ind, direction, dimension) in enumerate(zip(inds, list(i), vH_grid.shape)):
# ind: index of grid cell "smaller" than the strategic state
# direction: vector specifying the grid cell corner
# dimension: length of the grid in this dimension
if outside[j]:
# if this state variable of the strategic state is outside
# of the strategic grid, project it back onto the grid
# by setting its index to be either 0 or dimension - 1
# (this is set above)
gp_ind.append(ind)
else:
# clip to dimension - 1 to avoid index out of bounds error
gp_ind.append(min(ind + direction, dimension - 1))
gp_ind = tuple(gp_ind) # tuple to use for indexing into value grids
try:
vH_corners_new[i] = vH_grid[gp_ind]
vR_corners_new[i] = vR_grid[gp_ind]
except Exception as e:
print e
pdb.set_trace()
# self.vH_corners.set_value(vH_corners_new)
# self.vR_corners.set_value(vR_corners_new)
end_time = time.time()
time_profile.update_corners_time_profile.update(start_time, end_time)
return cell_corners_new, vR_corners_new, vH_corners_new
# Old HierarchicalMaximizer: duplicated a lot of code from the NestedMaximizer
# class HierarchicalMaximizer(object):
# # The following class maximizes the hierarchical game between the robot (leader)
# # and the human (follower). The tactic reward is given analytically as inputs
# # while the terminal rewards given as the value functions of the strategic level
# # is loaded into the class as grids. To get the value from the value functions,
# # grid interpltion is used (see code below for details).
# def __init__(self, r_h, traj_h, r_r, traj_r, mat_name, n,
# proj, use_timeup=True, use_second_order=False,
# disc_grid=None, step_grid=None, vH_grid=None, vR_grid=None):
# # The input parameters are:
# # - r_h: the human tactical reward.
# # - traj_h: the human trajectory.
# # - r_r: the robot tactical reward.
# # - traj_r: the robot trajectory.
# # - mat_name: the matlab file with the value function grids.
# # - n: the number of dimensions of the strategic level.
# # - proj: function specifying the projection from the tactical level to the strategic level.
# # ---------------------------------------------------------------------------------------------------
# # Basics.
# self.n = n # dimension of strategic state
# self.r_h = r_h
# self.r_r = r_r
# self.x_tact_h = traj_h.x_th[-1] # final human tactical state
# self.x_tact_r = traj_r.x_th[-1] # final robot tactical state
# self.plan_h = traj_h.u_th # human plan (controls)
# self.plan_r = traj_r.u_th # robot plan (controls)
# # (start, end) indices for each control in the plan when it's represented
# # as a flattened array. Ex: [(0, 2), (2, 4), (4, 6), (6, 8), (8, 10)]
# self.control_indices_h = traj_h.control_indices
# self.control_indices_r = traj_r.control_indices
# # maximum time for optimization
# if use_timeup:
# self.timeup = config.OPT_TIMEOUT
# else:
# self.timeup = float('inf')
# # ---------------------------------------------------------------------------------------------------
# # Shared varables.
# # Grid interpolation is done with the grid corners of the current grid
# # cell the state is in. These grid corners are shared variables which
# # are updated as the state is updated. Since the grid step lengths
# # are given, only the lower corners (self.cell_corners below) are needed
# # as shared variables.
# self.cell_corners = th.shared(np.zeros(self.n)) # corners of each box in grid
# # corners for human value function
# self.vH_corners = th.shared(np.zeros([2 for i in range(n)]))
# # corners for robot value function
# self.vR_corners = th.shared(np.zeros([2 for i in range(n)]))
# # ---------------------------------------------------------------------------------------------------
# # Load grid data.
# if (disc_grid is None or step_grid is None or vH_grid is None or
# vR_grid is None):
# self.disc_grid, self.step_grid, self.vH_grid, self.vR_grid = (
# utils.load_grid_data(mat_name, n=self.n))
# else:
# self.disc_grid, self.step_grid, self.vH_grid, self.vR_grid = (
# disc_grid, step_grid, vH_grid, vR_grid)
# # ---------------------------------------------------------------------------------------------------
# # Value functions.
# # strategic state (project using Theano)
# self.x_strat = proj(self.x_tact_r, self.x_tact_h)
# self.x_strat_func = th.function([], self.x_strat)
# def value_function(value_corners):
# return HierarchicalMaximizer.value_function_fn(self.x_strat,
# self.cell_corners, value_corners, self.step_grid, self.n)
# self.vR = value_function(self.vR_corners) # robot value function
# self.vH = value_function(self.vH_corners) # human value function
# # ---------------------------------------------------------------------------------------------------
# # Human's reward and its derivative.
# # add strategic value function
# self.r_h += config.STRATEGIC_VALUE_SCALE * self.vH
# # gradient of human reward wrt human controls
# self.dr_h = grad(self.r_h, self.plan_h)
# # negative human reward and its derivative
# self.func1 = th.function([], [-self.r_h, -self.dr_h])
# def r_h_and_dr_h(plan_h_0):
# """Evaluate negative human reward and its derivative.
# - plan_h_0: initial value for human plan."""
# start_time = time.time()
# # set plan_h to the given (initial) plan_h_0
# for v, (a, b) in zip(self.plan_h, self.control_indices_h):
# v.set_value(plan_h_0[a:b])
# # update strategic value corners according to current grid cell
# self.update_corners()
# func1_val = self.func1() # negative human reward and its derivative
# end_time = time.time()
# time_profile.inner_loop_time_profile.update(start_time, end_time)
# return func1_val
# self.r_h_and_dr_h = r_h_and_dr_h
# # ------------------------------------------------------------------------------------------
# # Robot's reward and its derivative.
# # add strategic value function
# self.r_r += config.STRATEGIC_VALUE_SCALE * self.vR
# # ------------------------------------------------------------------------------------------
# if use_second_order:
# # OPTION 1: Full derivative computation with Hessian inversion.
# # SLOW, DEPRECATED
# # jacobian of (d human reward / d robot actions) w.r.t. human actions
# J = jacobian(grad(self.r_h, self.plan_r), self.plan_h)
# # hessian of human reward w.r.t. human actions
# H = hessian(self.r_h, self.plan_h)
# # d robot reward / d human actions
# g = grad(self.r_r, self.plan_h)
# # Below is the most time-consuming step (the solve(H,g))
# self.dr_r = -tt.dot(J, ts.solve(H, g))+grad(self.r_r, self.plan_r)
# # ------------------------------------------------------------------------------------------
# else:
# # OPTION 2: Partial derivative computation. FAST
# # (Only direct effect of robot action given current human action)
# # Below is the simplified derivative that neglects the second-order
# # effect through human (and therefore avoids the heavy Hessian
# # inversion)
# self.dr_r = grad(self.r_r, self.plan_r)
# # ------------------------------------------------------------------------------------------
# # negative robot reward and its derivative
# self.func2 = th.function([], [-self.r_r, -self.dr_r])
# def r_r_and_dr_r(plan_r_0):
# """Get optimal human response, and return negative robot reward
# and its derivative.
# - plan_r_0: initial value for robot plan."""
# # set self.plan_r to the given (initial) plan_r_0
# for v, (a, b) in zip(self.plan_r, self.control_indices_r):
# v.set_value(plan_r_0[a:b])
# self.maximize_inner() # get optimal human response
# start_time = time.time()
# func2_val = self.func2() # negative robot reward and its derivative
# end_time = time.time()
# time_profile.func2_time_profile.update(start_time, end_time)
# return func2_val
# self.r_r_and_dr_r = r_r_and_dr_r
# @staticmethod
# def value_function_fn(x_strat, cell_corners, value_corners, step_grid, n):
# # Strategic value function computed using multilinear grid
# # interpolation.
# start_time = time.time()
# sumterms = []
# volume = step_grid.prod()
# for i in itertools.product(range(2), repeat=n):
# partial_volume = [((-1)**(i[j]+1) *
# (x_strat[j] - cell_corners[j]) +
# (1-i[j]) * step_grid[j]) for j in range(n)]
# partial_volume = np.asarray(partial_volume).prod() # convert to array to use prod.
# sumterm = value_corners[i]*partial_volume/volume
# sumterms.append(sumterm)
# sum_val = sum(sumterms)
# end_time = time.time()
# time_profile.value_function_time_profile.update(start_time, end_time)
# return sum_val
# def update_corners(self):
# # Update the corner values of the strategic cell grid by determining
# # which grid cell the current strategic state belongs to.
# cell_corners, vR_corners, vH_corners = (HierarchicalMaximizer
# .update_corners_fn(self.x_strat_func(), self.n,
# self.disc_grid, self.vH_grid, self.vR_grid))
# self.cell_corners.set_value(cell_corners)
# self.vR_corners.set_value(vR_corners)
# self.vH_corners.set_value(vH_corners)
# @staticmethod
# def update_corners_fn(x_strat, n, disc_grid, vH_grid, vR_grid):
# # Update the corner values of the strategic cell grid by determining
# # which grid cell the current strategic state belongs to.
# start_time = time.time()
# # outside is True if outside the strategic domain. Then value function = 0
# # (i.e. just consider tactical reward)
# outside = False
# inds = []
# cell_corners_new = []
# for i in range(n):
# if disc_grid[i][0] > x_strat[i]:
# ind = 0
# outside = True
# elif disc_grid[i][-1] < x_strat[i]:
# ind = len(disc_grid[i])-2
# outside = True
# else:
# ind = np.where(disc_grid[i] <= x_strat[i])[0][-1]
# inds.append(ind)
# cell_corners_new.append(disc_grid[i][ind])
# # debugging
# # if outside:
# # print 'OUTSIDE grid interpolation!'
# # else:
# # print 'INSIDE grid interpolation'
# cell_corners_new = np.array(cell_corners_new)
# # self.cell_corners.set_value(cell_corners_new)
# vH_corners_new = np.zeros([2 for i in range(n)])
# vR_corners_new = np.zeros([2 for i in range(n)])
# if not outside:
# # iterate through unit vectors representing the corners of the grid
# # with dimension n
# for i in itertools.product(range(2), repeat=n):
# # gp_ind = tuple([sum(pair) for pair in zip(inds, list(i))]) # tuple to just be compatible with below.
# gp_ind = [] # list of indices of the grid cell's corners
# for ind, direction, dimension in zip(inds, list(i), vH_grid.shape):
# # ind: index of grid cell "smaller" than the strategic state
# # direction: vector specifying the grid cell corner
# # dimension: length of the grid in this dimension
# # clip to dimension - 1 to avoid index out of bounds error
# gp_ind.append(min(ind + direction, dimension - 1))
# gp_ind = tuple(gp_ind) # tuple to use for indexing into value grids
# try:
# vH_corners_new[i] = vH_grid[gp_ind]
# vR_corners_new[i] = vR_grid[gp_ind]
# except Exception as e:
# print e
# pdb.set_trace()
# # self.vH_corners.set_value(vH_corners_new)
# # self.vR_corners.set_value(vR_corners_new)
# end_time = time.time()
# time_profile.update_corners_time_profile.update(start_time, end_time)
# return cell_corners_new, vR_corners_new, vH_corners_new
# def maximize_inner(self, bounds={}, maxiter=config.NESTEDMAX_MAXITER_INNER):
# """Get optimal human response (controls).
# Arguments:
# - bounds: control bounds for the human.
# - maxiter: maximum number of iterations.
# """
# start_time = time.time()
# # initialize human plan to previous optimal value
# plan_h_0 = np.hstack([v.get_value() for v in self.plan_h])
# bounds = constants.HIERARCHICAL_HUMAN_CONTROL_BOUNDS
# if not isinstance(bounds, dict): # convert bounds to dictionary
# bounds = {v: bounds for v in self.plan_h}
# B = [] # list of bounds for each control in the plan
# for v, (a, b) in zip(self.plan_h, self.control_indices_h):
# if v in bounds:
# B += bounds[v]
# else:
# B += [(None, None)]*(b-a)
# # optimal human response, value, etc.
# opt_h = scipy.optimize.fmin_l_bfgs_b(self.r_h_and_dr_h, x0=plan_h_0,
# bounds=B)
# opt_plan_h = opt_h[0] # optimal human response
# for v, (a, b) in zip(self.plan_h, self.control_indices_h):
# v.set_value(opt_plan_h[a:b])
# # update strategic value corners according to current grid cell
# self.update_corners()
# end_time = time.time()
# time_profile.maximize_inner_time_profile.update(start_time, end_time)
# return opt_h
# def maximize(self, bounds={}, bounds_inner={},
# maxiter_inner=config.NESTEDMAX_MAXITER_INNER):
# # Get optimal robot plan (controls) and human response using nested
# # optimization.
# start_time = time.time()
# if not isinstance(bounds, dict): # convert bounds to dictionary
# bounds = {v: bounds for v in self.plan_r}
# B = [] # list of bounds for each control in the plan
# for v, (a, b) in zip(self.plan_r, self.control_indices_r):
# if v in bounds:
# B += bounds[v]
# else:
# B += [(None, None)]*(b-a)
# # TODO: can we replace the .get_value() approach with using the numpy
# # version because at this point the Theano and numpy plans are the same?
# # plan_r_0 = np.hstack(self.traj_r.u) # initial robot plan (numpy version)
# plan_r_0 = np.hstack([v.get_value() for v in self.plan_r])
# # optimal robot control, value, etc.
# opt_r = opt_timeup.fmin_l_bfgs_b_timeup(self.r_r_and_dr_r,
# x0=plan_r_0, bounds=B, t0=start_time, timeup=self.timeup)
# opt_plan_r = opt_r[0] # optimal robot control
# # TODO: remove?
# for v, (a, b) in zip(self.plan_r, self.control_indices_r):
# v.set_value(opt_plan_r[a:b])
# # time profile of HierarchicalMaximizer
# maximize_end_time = time.time()
# time_profile.maximizer_time_profile.update(start_time, maximize_end_time)
# # optimal human response, value, etc. to optimal robot control
# opt_h = self.maximize_inner(bounds=bounds_inner, maxiter=maxiter_inner)
# return opt_r, opt_h
| 50.005553
| 187
| 0.585286
| 13,543
| 99,061
| 4.034335
| 0.034261
| 0.016655
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| 0.941779
| 0.937057
| 0.932354
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| 0.922269
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| 0
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| 99,061
| 1,980
| 188
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| 0.003419
| 0.002279
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| 0.002625
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| null | null | 0
| 0.023622
| null | null | 0.006562
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0
| 8
|
246fd5db15818c7d7844a8460d9e88bae7dd0aaf
| 5,356
|
py
|
Python
|
day-1/captcha.py
|
michaelze/advent-of-code-2017
|
241d7a1e06f55b31b41ea61f6fe49839a7d14985
|
[
"MIT"
] | null | null | null |
day-1/captcha.py
|
michaelze/advent-of-code-2017
|
241d7a1e06f55b31b41ea61f6fe49839a7d14985
|
[
"MIT"
] | null | null | null |
day-1/captcha.py
|
michaelze/advent-of-code-2017
|
241d7a1e06f55b31b41ea61f6fe49839a7d14985
|
[
"MIT"
] | null | null | null |
import unittest
def captcha(input, stepSize = 1):
if callable(stepSize):
useStepSize = stepSize(input)
else:
useStepSize = stepSize
inputLength = len(input)
sum = 0
for index in range(0, inputLength):
compIndex = index + useStepSize
if compIndex >= inputLength:
compIndex = useStepSize - (inputLength - index)
if input[index] == input[compIndex]:
sum = sum + int(input[index])
return sum
class TestCaptcha(unittest.TestCase):
def testCaptcha(self):
self.assertEqual(captcha('1122'), 3)
self.assertEqual(captcha('1111'), 4)
self.assertEqual(captcha('1234'), 0)
self.assertEqual(captcha('91212129'), 9)
self.assertEqual(captcha('6592822488931338589815525425236818285229555616392928433262436847386544514648645288129834834862363847542262953164877694234514375164927616649264122487182321437459646851966649732474925353281699895326824852555747127547527163197544539468632369858413232684269835288817735678173986264554586412678364433327621627496939956645283712453265255261565511586373551439198276373843771249563722914847255524452675842558622845416218195374459386785618255129831539984559644185369543662821311686162137672168266152494656448824719791398797359326412235723234585539515385352426579831251943911197862994974133738196775618715739412713224837531544346114877971977411275354168752719858889347588136787894798476123335894514342411742111135337286449968879251481449757294167363867119927811513529711239534914119292833111624483472466781475951494348516125474142532923858941279569675445694654355314925386833175795464912974865287564866767924677333599828829875283753669783176288899797691713766199641716546284841387455733132519649365113182432238477673375234793394595435816924453585513973119548841577126141962776649294322189695375451743747581241922657947182232454611837512564776273929815169367899818698892234618847815155578736875295629917247977658723868641411493551796998791839776335793682643551875947346347344695869874564432566956882395424267187552799458352121248147371938943799995158617871393289534789214852747976587432857675156884837634687257363975437535621197887877326295229195663235129213398178282549432599455965759999159247295857366485345759516622427833518837458236123723353817444545271644684925297477149298484753858863551357266259935298184325926848958828192317538375317946457985874965434486829387647425222952585293626473351211161684297351932771462665621764392833122236577353669215833721772482863775629244619639234636853267934895783891823877845198326665728659328729472456175285229681244974389248235457688922179237895954959228638193933854787917647154837695422429184757725387589969781672596568421191236374563718951738499591454571728641951699981615249635314789251239677393251756396'), 1029)
calcStepSize = lambda i: int(len(i) / 2)
self.assertEqual(captcha('1212', calcStepSize), 6)
self.assertEqual(captcha('1221', calcStepSize), 0)
self.assertEqual(captcha('123425', calcStepSize), 4)
self.assertEqual(captcha('123123', calcStepSize), 12)
self.assertEqual(captcha('12131415', calcStepSize), 4)
self.assertEqual(captcha('6592822488931338589815525425236818285229555616392928433262436847386544514648645288129834834862363847542262953164877694234514375164927616649264122487182321437459646851966649732474925353281699895326824852555747127547527163197544539468632369858413232684269835288817735678173986264554586412678364433327621627496939956645283712453265255261565511586373551439198276373843771249563722914847255524452675842558622845416218195374459386785618255129831539984559644185369543662821311686162137672168266152494656448824719791398797359326412235723234585539515385352426579831251943911197862994974133738196775618715739412713224837531544346114877971977411275354168752719858889347588136787894798476123335894514342411742111135337286449968879251481449757294167363867119927811513529711239534914119292833111624483472466781475951494348516125474142532923858941279569675445694654355314925386833175795464912974865287564866767924677333599828829875283753669783176288899797691713766199641716546284841387455733132519649365113182432238477673375234793394595435816924453585513973119548841577126141962776649294322189695375451743747581241922657947182232454611837512564776273929815169367899818698892234618847815155578736875295629917247977658723868641411493551796998791839776335793682643551875947346347344695869874564432566956882395424267187552799458352121248147371938943799995158617871393289534789214852747976587432857675156884837634687257363975437535621197887877326295229195663235129213398178282549432599455965759999159247295857366485345759516622427833518837458236123723353817444545271644684925297477149298484753858863551357266259935298184325926848958828192317538375317946457985874965434486829387647425222952585293626473351211161684297351932771462665621764392833122236577353669215833721772482863775629244619639234636853267934895783891823877845198326665728659328729472456175285229681244974389248235457688922179237895954959228638193933854787917647154837695422429184757725387589969781672596568421191236374563718951738499591454571728641951699981615249635314789251239677393251756396', calcStepSize), 1220)
if __name__ == '__main__':
unittest.main()
| 137.333333
| 2,096
| 0.906087
| 129
| 5,356
| 37.55814
| 0.387597
| 0.034056
| 0.049948
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| 0.82705
| 0.06404
| 5,356
| 38
| 2,097
| 140.947368
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| 0.776984
| 0.766454
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| 0.354839
| 1
| 0.064516
| false
| 0
| 0.032258
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| null | 0
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| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
707ccbba777c1fc9288acace107ac44e73fb8301
| 66
|
py
|
Python
|
stackapi/__init__.py
|
cgrtrifork/stackapi
|
146c2c5a201aa51dc8218a6e03d3e903b1d2c36d
|
[
"MIT"
] | 56
|
2016-02-25T20:00:49.000Z
|
2022-03-07T23:27:18.000Z
|
stackapi/__init__.py
|
cgrtrifork/stackapi
|
146c2c5a201aa51dc8218a6e03d3e903b1d2c36d
|
[
"MIT"
] | 42
|
2016-02-24T20:14:03.000Z
|
2021-12-05T21:36:29.000Z
|
stackapi/__init__.py
|
AWegnerGitHub/StackAPI
|
602d9ce6de4b8a3e0462576365536c99a5a14c71
|
[
"MIT"
] | 19
|
2016-03-10T17:24:43.000Z
|
2022-01-31T18:22:29.000Z
|
from .stackapi import StackAPI
from .stackapi import StackAPIError
| 33
| 35
| 0.863636
| 8
| 66
| 7.125
| 0.5
| 0.421053
| 0.631579
| 0
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| 0
| 0
| 0
| 0
| 0
| 0.106061
| 66
| 2
| 35
| 33
| 0.966102
| 0
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| 0
| true
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| null | 1
| 1
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| 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
|
562b4d6f4bd52a8f5b6c0f19685a6a5b23752271
| 44
|
py
|
Python
|
exercise/newfile42.py
|
LeeBeral/python
|
9f0d360d69ee5245e3ef13a9dc9fc666374587a4
|
[
"MIT"
] | null | null | null |
exercise/newfile42.py
|
LeeBeral/python
|
9f0d360d69ee5245e3ef13a9dc9fc666374587a4
|
[
"MIT"
] | null | null | null |
exercise/newfile42.py
|
LeeBeral/python
|
9f0d360d69ee5245e3ef13a9dc9fc666374587a4
|
[
"MIT"
] | null | null | null |
a,b = 1,2
print(a,b)
a,b = b,a
print(a,b)
| 11
| 11
| 0.5
| 14
| 44
| 1.571429
| 0.357143
| 0.363636
| 0.636364
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.058824
| 0.227273
| 44
| 4
| 12
| 11
| 0.588235
| 0
| 0
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 0.5
| 1
| 1
| 1
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 8
|
564221225d9998566dce280423a3f022b29b041c
| 207
|
py
|
Python
|
src/test/test_sample.py
|
howaboutudance/pytomltemplate
|
41f375f75be378f3aa7749b03ca7819ab482c611
|
[
"Apache-2.0"
] | null | null | null |
src/test/test_sample.py
|
howaboutudance/pytomltemplate
|
41f375f75be378f3aa7749b03ca7819ab482c611
|
[
"Apache-2.0"
] | 2
|
2021-12-11T22:15:59.000Z
|
2021-12-11T22:31:40.000Z
|
src/test/test_sample.py
|
howaboutudance/pytomltemplate
|
41f375f75be378f3aa7749b03ca7819ab482c611
|
[
"Apache-2.0"
] | null | null | null |
import pytest
from sample_module import sample
def test_helloworld():
assert sample.helloworld() == "Hello World"
def test_helloworld_name():
assert sample.helloworld(name="Flargen") == "Hello Flargen"
| 25.875
| 61
| 0.768116
| 26
| 207
| 5.961538
| 0.5
| 0.090323
| 0.219355
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.120773
| 207
| 8
| 61
| 25.875
| 0.851648
| 0
| 0
| 0
| 0
| 0
| 0.149038
| 0
| 0
| 0
| 0
| 0
| 0.333333
| 1
| 0.333333
| true
| 0
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
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